diff --git a/LICENSE b/LICENSE index a297b751..13b9621c 100644 --- a/LICENSE +++ b/LICENSE @@ -1,6 +1,6 @@ MIT License -Copyright 2025 Ashgro, Inc. +Copyright 2025 Timaeus Research, Inc. Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: diff --git a/Makefile b/Makefile index 3441d326..f51eb7d8 100644 --- a/Makefile +++ b/Makefile @@ -1,22 +1,22 @@ .PHONY: docs docs-auto test -VENV_NAME := .venv +# Try to find uv, otherwise fall back to system paths +UV := $(shell command -v uv 2>/dev/null || echo ~/.local/bin/uv) +VENV_NAME := ../../.venv PYTHON := $(VENV_NAME)/bin/python -PIP := $(VENV_NAME)/bin/pip docs-prep: - pip install devinterp[docs] - cd docs && python generate_docs.py && cd .. + cd docs && $(UV) run python generate_docs.py && cd .. docs: make docs-prep - make -C docs html + make -C docs html SPHINXBUILD="$(UV) run sphinx-build" # sphinx-apidoc -o docs ./src/devinterp ./src/devinterp/mechinterp --force # sphinx-build -b html -E -a docs docs/_build/html docs-auto: make docs-prep - sphinx-autobuild docs docs/_build/html + $(UV) run sphinx-autobuild docs docs/_build/html publish-docs: diff --git a/README.md b/README.md index 754ce1a4..6ad286e6 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ # DevInterp -[![PyPI version](https://badge.fury.io/py/devinterp.svg)](https://badge.fury.io/py/devinterp) ![Python version](https://img.shields.io/pypi/pyversions/devinterp) ![Contributors](https://img.shields.io/github/contributors/timaeus-research/devinterp) [![Docs](https://img.shields.io/badge/Read_the_Docs!-white?style=flat&logo=Read-the-Docs&logoColor=black&link=https%3A%2F%2Ftimaeus-research.github.io%2Fdevinterp%2F)](https://devinterp.timaeus.co/) +[![PyPI version](https://badge.fury.io/py/devinterp.svg)](https://badge.fury.io/py/devinterp) ![Python version](https://img.shields.io/pypi/pyversions/devinterp) ![Contributors](https://img.shields.io/github/contributors/timaeus-research/devinterp) [![Docs](https://img.shields.io/badge/Read_the_Docs!-white?style=flat&logo=Read-the-Docs&logoColor=black)](https://devinterp.timaeus.co/) ## A Python Library for Developmental Interpretability Research @@ -9,72 +9,195 @@ DevInterp is a python library for conducting research on developmental interpret [Read more about developmental interpretability](https://www.lesswrong.com/posts/TjaeCWvLZtEDAS5Ex/towards-developmental-interpretability). +## Features -> :warning: This library is still in early development. Don't expect things to work on a first attempt. We are actively working on improving the library and adding new features. +- **SGLD Sampling** with per-token loss storage to xarray/Zarr +- **Local Learning Coefficient (LLC)** estimation from sampling results +- **Susceptibilities** measuring first-order posterior response to data perturbations, localized on model components +- **Bayesian Influence Functions (BIF)** as posterior correlations (or covariances) between per-sample losses +- **Weight restrictions** for sampling over parameter subsets (e.g., individual attention heads) ## Installation - To install `devinterp`, simply run `pip install devinterp`. (Note: This has PyTorch as a dependency.) +`devinterp` is distributed through PyPI. Install with [uv](https://docs.astral.sh/uv/): -### Minimal Example +```bash +uv add devinterp +``` + +## Example + +See [`examples/quickstart.py`](examples/quickstart.py) for a runnable script that computes LLC and susceptibilities on Qwen2.5-0.5B. + +## Quick Start + +### Compute the Local Learning Coefficient ```python +from devinterp.slt.llc import llc + +result = llc( + model=model, + dataset=dataset, # HuggingFace Dataset with "input_ids" + observables={"train": dataset}, + lr=0.001, + n_beta=30, + num_chains=4, + num_draws=200, +) + +print(result["llc_mean"]) # scalar LLC +print(result["llc_per_chain"]) # (num_chains,) per-chain LLC +print(result["loss_trace"]) # (num_chains, num_steps) per-step loss, num_steps = num_draws * num_steps_bw_draws + num_burnin_steps +``` -from devinterp.slt.sampler import sample, LLCEstimator -from devinterp.optim import SGLD -from devinterp.utils import default_nbeta +### Sample with Observables + +```python +from devinterp.slt.sampling import sample + +tree = sample( + model=model, + dataset=train_data, + observables={ + "train": train_data, + "code": (code_data, 5), # (dataset, batches_per_draw) + }, + lr=0.001, + n_beta=30, + num_chains=4, + num_draws=200, +) +# tree is an xr.DataTree backed by Zarr with full per-token loss traces +``` + +### Compute Susceptibilities + +```python +from devinterp.slt.susceptibilities import susceptibilities +from devinterp.slt.weight_restrictions import create_param_masks + +result = susceptibilities( + model=model, + dataset=train_data, + observables={"train": train_data, "code": code_data}, + weight_restrictions={ + "full": None, + "l0h0": create_param_masks(model, "l0h0"), + "l0h1": create_param_masks(model, "l0h1"), + }, + sampling_task="train", + lr=0.001, + n_beta=30, +) +# result is a DataTree with /susceptibilities and /context subtrees +``` -# Assuming you have a PyTorch Model assigned to model, and DataLoader assigned to trainloader -llc_estimator = LLCEstimator(..., nbeta=default_nbeta(trainloader)) -sample(model, trainloader, ..., callbacks = [llc_estimator]) +`create_param_masks` supports 85+ HuggingFace model types and TransformerLens. +Restriction patterns: `"full"`, `"l0"`, `"l0h1"`, `"l0g0"` (GQA group), `"l0 attn"`, `"l0 mlp"`, `"embed"`, `"unembed"`. -llc_mean = llc_estimator.get_results()["llc/mean"] +### Compute BIF +```python +from devinterp.slt.bif import bif + +result = bif( + model=model, + dataset=train_data, + observables={"train": train_data, "code": code_data}, + lr=0.001, + n_beta=30, + num_chains=4, + num_draws=200, + correlation_method="token", # or "sequence" +) +# result["influences"] contains pairwise correlation matrix ``` -## Advanced Usage +## Architecture -To see DevInterp in action, check out our example notebooks: +Each analysis has two entry points: -- [Introduction](https://www.github.com/timaeus-research/devinterp/blob/main/examples/introduction.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/timaeus-research/devinterp/blob/main/examples/introduction.ipynb) -- [Normal Crossing Demo](https://www.github.com/timaeus-research/devinterp/blob/main/examples/normal_crossing.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/timaeus-research/devinterp/blob/main/examples/normal_crossing.ipynb) -- [Grokking Demo](https://www.github.com/timaeus-research/devinterp/blob/main/examples/grokking.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/timaeus-research/devinterp/blob/main/examples/grokking.ipynb) +- **High-level** (`llc()`, `bif()`, `susceptibilities()`): runs sampling and post-processing in one call +- **Low-level** (`compute_llc()`, `compute_bif()`): takes a pre-computed `xr.DataTree` from `sample()`, useful when you want to run sampling once and compute multiple analyses. `compute_susceptibilities()` takes a `dict[str, xr.DataTree]` (one tree per weight restriction), since susceptibilities require a separate sampling run for each restriction. -For more advanced usage, see [the Diagnostics notebook](https://www.github.com/timaeus-research/devinterp/blob/main/examples/diagnostics.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/timaeus-research/devinterp/blob/main/examples/diagnostics.ipynb) and for a quick guide on picking hyperparameters, see the above [Grokking Demo](https://www.github.com/timaeus-research/devinterp/blob/main/examples/grokking.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/timaeus-research/devinterp/blob/main/examples/grokking.ipynb) or the [the Calibration notebook.](https://www.github.com/timaeus-research/devinterp/blob/main/examples/sgld_calibration.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/timaeus-research/devinterp/blob/main/examples/sgld_calibration.ipynb). Documentation can be [found here](https://devinterp.timaeus.co/). [![Docs](https://img.shields.io/badge/Read_the_Docs!-white?style=flat&logo=Read-the-Docs&logoColor=black&link=https%3A%2F%2Ftimaeus-research.github.io%2Fdevinterp%2F)](https://devinterp.timaeus.co/) +The sampling pipeline stores full per-token losses to Zarr via `sample()`, and post-processing functions operate on the resulting `xr.DataTree`. -For papers that either inspired or used the DevInterp package, [click here](https://devinterp.com/publications). +## Model Requirements -## Known Issues +The current API assumes **autoregressive language models** with fixed-length tokenized sequences: -- LLC Estimation is currently more of an art than a science. It will take some time and pain to get it work reliably. +- Model must accept `input_ids` and return logits (HuggingFace models, TransformerLens HookedTransformer, or any model returning a tensor or object with `.logits`) +- Dataset must be a HuggingFace `Dataset` with an `"input_ids"` column of uniform-length sequences +- Loss defaults to next-token cross-entropy -If you run into issues not mentioned here, please first check the github issues, then ask in [the DevInterp Discord](https://discord.gg/UwjWKCZZYR), and only then make a new github issue. +For non-standard losses, pass `loss_fn=...` to `sample()`, `bif()`, `llc()`, or `susceptibilities()`. The function takes `(model, input_ids)` and must return per-token loss of shape `(batch, seq_len-1)`. For more exotic control, `sample_single_chain()` in `devinterp.slt.sampler` accepts a custom `evaluate` callable. -## Contributing +## Migrating from v1 -See [CONTRIBUTING.md](./CONTRIBUTING.md) for guidelines on how to contribute. +The v2 API replaces the callback-based sampling with a data-centric pipeline. Key changes: + +```python +# v1 (old) +from devinterp.slt.sampler import estimate_learning_coeff_with_summary +from devinterp.optim import SGLD + +result = estimate_learning_coeff_with_summary( + model, loader, + sampling_method=SGLD, + sampling_method_kwargs={"lr": 0.001, "nbeta": 30}, + num_chains=4, num_draws=200, +) +llc = result["llc/mean"] + +# v2 (new) +from devinterp.slt.llc import llc + +result = llc( + model=model, + dataset=dataset, # HF Dataset, not DataLoader + observables={"train": dataset}, + lr=0.001, n_beta=30, + num_chains=4, num_draws=200, +) +llc_value = float(result["llc_mean"]) +``` + +**What changed:** +- `estimate_learning_coeff` / `LLCEstimator` / `SamplerCallback` → `llc()` and `compute_llc()` +- `DataLoader` → HuggingFace `Dataset` with `"input_ids"` column +- `sampling_method_kwargs={"nbeta": ...}` → `n_beta=...` as a direct parameter +- Results are `xr.Dataset` / `xr.DataTree`, not dicts with string keys +- New capabilities: `susceptibilities()`, `bif()`, observables, weight restrictions, per-token loss storage + +## Hyperparameter selection + +All sampling is sensitive to hyperparameters. See our [Sampling Hyperparameter Guide](https://timaeus.co/research/2026-04-21-sampling-guide). + + +## Further Reading + +- [You're Measuring Model Complexity Wrong](https://www.lesswrong.com/posts/6g8cAftfQufLmFDYT/you-re-measuring-model-complexity-wrong) - Introduction to LLC and phase transitions (2024) +- [Structural Inference with Susceptibilities](https://arxiv.org/abs/2504.18274) (2025) +- [Towards Spectroscopy: Susceptibility Clusters in Language Models](https://arxiv.org/abs/2601.12703) (2026) +- [The Local Learning Coefficient: A Singularity-Aware Complexity Measure](https://arxiv.org/pdf/2308.12108) (2023) +- [Algebraic Geometry and Statistical Learning Theory](https://www.cambridge.org/core/books/algebraic-geometry-and-statistical-learning-theory/9C8FD1BDC817E2FC79117C7F41544A3A#fndtn-information) Watanabe (2009) ## Credits & Citations -This package was created by [Timaeus](https://timaeus.co). The main contributors to this package are Stan van Wingerden, Jesse Hoogland, George Wang, and William Zhou. Zach Furman, Matthew Farrugia-Roberts, Rohan Hitchcock, and Edmund Lau also made valuable contributions or provided useful advice. +This package was created by [Timaeus](https://timaeus.co). Most of the sampling, LLC, susceptibility, and BIF implementations were developed internally; this package is a port of that joint work. If this package was useful in your work, please cite it as: ```BibTeX -@misc{devinterpcode, - title = {DevInterp}, - author = {van Wingerden, Stan and Hoogland, Jesse and Wang, George and Zhou, William}, - year = {2024}, +@misc{devinterp2026, + title = {DevInterp}, + author = {Snell, William and Wind, Johan Sokrates and Snikkers, Billy + and Fraser, Sandy and Newgas, Adam and Hoogland, Jesse + and Wang, George and Gordon, Andrew and Zhou, William + and van Wingerden, Stan}, + year = {2026}, + version = {2.0}, howpublished = {\url{https://github.com/timaeus-research/devinterp}}, } ``` - -## Optional Dependencies - -DevInterp offers additional visualization functionalities that are not included in the base installation. To enable these features, install the package with the `vis` extra: - -```sh -pip install devinterp[vis] -``` - -This will install `plotly`, which is required for the visualization utilities provided in `vis_utils.py`. diff --git a/docs/_static/custom.css b/docs/_static/custom.css new file mode 100644 index 00000000..49ccc2a1 --- /dev/null +++ b/docs/_static/custom.css @@ -0,0 +1,10 @@ +.rst-content figure { + margin-block: 2em; +} + +.rst-content figure figcaption, +.rst-content figure .caption-text { + font-size: 0.9rem; + margin-inline: 12px; + text-align: left; +} diff --git a/docs/conf.py b/docs/conf.py index fec92d9f..381ad3e4 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -1,84 +1,81 @@ -# Configuration file for the Sphinx documentation builder. -# -# For the full list of built-in configuration values, see the documentation: -# https://www.sphinx-doc.org/en/master/usage/configuration.html - -# -- Project information ----------------------------------------------------- -# https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information +"""Sphinx configuration for devinterp documentation.""" import os import subprocess import sys -sys.path.insert(0, os.path.abspath("../")) +sys.path.insert(0, os.path.abspath("../src")) + +# -- Project information ----------------------------------------------------- project = "devinterp" -copyright = "2024, Van Wingerden et al." -author = "Van Wingerden et al." -release = "1.0.0" +copyright = "2024-2026, Timaeus" +author = "Timaeus" # -- General configuration --------------------------------------------------- -# https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration - -extensions = [] - -templates_path = ["_templates"] -exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"] extensions = [ "sphinx.ext.autodoc", "sphinx.ext.viewcode", "sphinx.ext.mathjax", "sphinx.ext.napoleon", - "sphinx_math_dollar", "sphinx.ext.autosectionlabel", "sphinx.ext.githubpages", + "sphinx_math_dollar", + "myst_parser", ] -autosectionlabel_prefix_document = True -mathjax_config = { - "tex2jax": { - "inlineMath": [["\\(", "\\)"]], - "displayMath": [["\\[", "\\]"]], - }, + +templates_path = ["_templates"] +exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"] + +source_suffix = { + ".rst": "restructuredtext", + ".md": "markdown", } +autosectionlabel_prefix_document = True + mathjax3_config = { "tex": { "inlineMath": [["\\(", "\\)"]], "displayMath": [["\\[", "\\]"]], - } + }, } -# -- Options for HTML output ------------------------------------------------- -# https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output -html_theme = "sphinx_rtd_theme" -html_sidebars = { - "**": [ - "about.html", - "navigation.html", - "relations.html", - "searchbox.html", - "donate.html", - ] -} rst_prolog = """ .. role:: python(code) :language: python :class: highlight - + .. role:: bash(code) :language: bash :class: highlight """ + +# -- Options for HTML output ------------------------------------------------- + +html_theme = "sphinx_rtd_theme" html_static_path = ["_static"] +html_css_files = ["custom.css"] + +# -- Autodoc ----------------------------------------------------------------- + +autodoc_default_options = { + "members": True, + "undoc-members": True, + "private-members": True, + "special-members": True, + "inherited-members": True, + "show-inheritance": True, +} + +autosummary_generate = True def run_apidoc(_): current_dir = os.path.abspath(os.path.dirname(__file__)) - module_dir = os.path.join( - current_dir, "..", "your_module" - ) # Adjust the path to your module - output_dir = os.path.join(current_dir, "") # Adjust the path to your output + module_dir = os.path.join(current_dir, "..", "src", "devinterp") + output_dir = os.path.join(current_dir, "source") subprocess.call(["sphinx-apidoc", "-o", output_dir, module_dir, "--force"]) @@ -91,40 +88,3 @@ def skip(app, what, name, obj, would_skip, options): def setup(app): app.connect("autodoc-skip-member", skip) app.connect("builder-inited", run_apidoc) - - app.add_js_file("custom.js") - - -extensions = [ - "myst_parser", - "sphinx.ext.autosectionlabel", - "sphinx.ext.autodoc", - "sphinx.ext.napoleon", - # "autodocsumm", -] - -templates_path = ["_templates"] -exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"] - -source_suffix = { - ".rst": "restructuredtext", - ".md": "markdown", -} - -# -- Options for HTML output ------------------------------------------------- -# https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output - -html_theme = "alabaster" -html_static_path = ["_static"] - - -autodoc_default_options = { - "members": True, - "undoc-members": True, - "private-members": True, - "special-members": True, - "inherited-members": True, - "show-inheritance": True, -} - -autosummary_generate = True diff --git a/docs/figures/num_steps_bw_draws-1.svg b/docs/figures/num_steps_bw_draws-1.svg new file mode 100644 index 00000000..67efa73e --- /dev/null +++ b/docs/figures/num_steps_bw_draws-1.svg @@ -0,0 +1,58 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/docs/figures/num_steps_bw_draws-3.svg b/docs/figures/num_steps_bw_draws-3.svg new file mode 100644 index 00000000..5c112b44 --- /dev/null +++ b/docs/figures/num_steps_bw_draws-3.svg @@ -0,0 +1,67 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/docs/figures/sample-macro.svg b/docs/figures/sample-macro.svg new file mode 100644 index 00000000..c18b31aa --- /dev/null +++ b/docs/figures/sample-macro.svg @@ -0,0 +1,45 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/docs/figures/sample-nano.svg b/docs/figures/sample-nano.svg new file mode 100644 index 00000000..84c50a4a --- /dev/null +++ b/docs/figures/sample-nano.svg @@ -0,0 +1,63 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/docs/index.rst b/docs/index.rst index db2bb9a6..9da7bf89 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -1,100 +1,222 @@ -.. devinterp documentation master file, created by - sphinx-quickstart on Fri Jan 19 13:45:44 2024. - You can adapt this file completely to your liking, but it should at least - contain the root `toctree` directive. - -Welcome to devinterp's documentation! +Welcome to DevInterp's documentation! ===================================== - -DevInterp is a python library for conducting research on developmental interpretability, which is a novel AI safety research agenda rooted in Singular Learning Theory (SLT). DevInterp proposes tools for detecting, locating, and ultimately *controlling* the development of structure over training. +DevInterp is a Python library for conducting research on developmental interpretability, +a novel AI safety research agenda rooted in Singular Learning Theory (SLT). DevInterp +proposes tools for detecting, locating, and ultimately *controlling* the development of +structure over training. Read more about `developmental interpretability here `_! -For an overview of papers that either used or inspired this package, `click here `_. +For questions, `join the DevInterp discord `_! -For questions, it's easiest to `join the DevInterp discord `_! - -.. warning:: This library is still in early development. Don't expect things to work on a first attempt. We are actively working on improving the library and adding new features. +.. warning:: This library is under active development. The API may change between releases. Installation -===================================== +============ -To install :code:`devinterp`, simply run :bash:`pip install devinterp`. +``devinterp`` is distributed through PyPI. Install with `uv `_: -**Requirements**: Python 3.8 or higher. +.. code-block:: bash + + uv add devinterp + +**Requirements**: Python 3.10 or higher. -Minimal Example -===================================== + +Quick Start +=========== + +Compute the Local Learning Coefficient +--------------------------------------- .. code-block:: python - from devinterp.slt import sample, LLCEstimator - from devinterp.optim import SGLD + from devinterp.slt.llc import llc - # Assuming you have a PyTorch Module and DataLoader - llc_estimator = LLCEstimator(..., nbeta=optimal_nbeta(trainloader)) - sample(model, trainloader, ..., callbacks = [llc_estimator]) - - llc_mean = llc_estimator.get_results()["llc/mean"] + result = llc( + model=model, + dataset=dataset, # HuggingFace Dataset with "input_ids" + observables={"train": dataset}, + lr=0.001, + n_beta=30, + num_chains=4, + num_draws=200, + ) + print(result["llc_mean"]) # scalar LLC + print(result["llc_per_chain"]) # (num_chains,) per-chain LLC + print(result["loss_trace"]) # (num_chains, num_steps) per-step loss, + # num_steps = num_draws * num_steps_bw_draws + num_burnin_steps -Examples -===================================== -To see DevInterp in action, check out our example notebooks: +Sample with Observables +----------------------- -.. |colab1| image:: https://colab.research.google.com/assets/colab-badge.svg - :target: https://colab.research.google.com/github/timaeus-research/devinterp/blob/main/examples/introduction.ipynb -.. |colab2| image:: https://colab.research.google.com/assets/colab-badge.svg - :target: https://colab.research.google.com/github/timaeus-research/devinterp/blob/main/examples/normal_crossing.ipynb -.. |colab3| image:: https://colab.research.google.com/assets/colab-badge.svg - :target: https://colab.research.google.com/github/timaeus-research/devinterp/blob/main/examples/tms.ipynb -.. |colab4| image:: https://colab.research.google.com/assets/colab-badge.svg - :target: https://colab.research.google.com/github/timaeus-research/devinterp/blob/main/examples/mnist.ipynb -.. |colab5| image:: https://colab.research.google.com/assets/colab-badge.svg - :target: https://colab.research.google.com/github/timaeus-research/devinterp/blob/main/examples/diagnostics.ipynb -.. |colab6| image:: https://colab.research.google.com/assets/colab-badge.svg - :target: https://colab.research.google.com/github/timaeus-research/devinterp/blob/main/examples/sgld_calibration.ipynb +.. code-block:: python -- `Introduction `_ |colab1| -- `Normal Crossing degeneracy quantification `_ |colab2| -- `Toy Models of Superposition phase transition detection `_ |colab3| -- `MNIST eSGD vs SGD comparison `_ |colab4| + from devinterp.slt.sampling import sample -For more advanced usage, see `the Diagnostics notebook `_ |colab5| and for a quick guide on picking hyperparameters, see `the calibration notebook. `_ |colab6| + tree = sample( + model=model, + dataset=train_data, + observables={ + "train": train_data, + "code": (code_data, 5), # (dataset, batches_per_draw) + }, + lr=0.001, + n_beta=30, + num_chains=4, + num_draws=200, + ) + # tree is an xr.DataTree backed by Zarr with full per-token loss traces -Known Issues -===================================== +Concepts +======== + +Posterior Sampling with SGLD +---------------------------- + +The core workflow: + +1. Start at a checkpoint :math:`\hat{w}^*` +2. Take SGLD steps (SGD + noise) using one dataset for gradients +3. Evaluate losses on multiple datasets (observables) at each draw +4. Store the full per-token loss chains as Zarr datasets +5. Compute observables (LLC, susceptibilities, BIF) from these chains + +The SGLD noise allows exploring low-loss directions while staying near the original +checkpoint. This samples from the local posterior distribution around the checkpoint. + +Local Learning Coefficient (LLC) +-------------------------------- + +The **LLC** measures model complexity by counting "effective parameters" in a region of +weight space: + +.. math:: + + \hat{\lambda}(\hat{w}^*) = n\beta \cdot (\bar{L}_n - L_n(\hat{w}^*)) + +Unlike parameter count or Hessian rank, LLC accounts for **singularities** -- regions where +multiple parameter configurations produce identical outputs. This makes it suitable for +neural networks. + +**Why LLC matters:** + +- **Detect phase transitions** during training (sudden capability changes) +- **Predict generalization** via the Free Energy formula +- **Compare checkpoints** across training + +Susceptibilities +---------------- + +**Susceptibilities** measure how a model component responds to distribution shifts. For +example, how does an attention head's behavior change when shifting from general text toward +code or math? + +This is computed by sampling with different **weight restrictions** (parameter subsets) and +measuring the covariance between sampling loss and observable loss. -- LLC Estimation is currently more of an art than a science. It will take some time and pain to get it work reliably. +See `Structural Inference: Interpreting Small Language Models with Susceptibilities +`_ (Baker et al., 2025) for details. + +Bayesian Influence Functions (BIF) +---------------------------------- + +**BIF** computes pairwise correlations between observable loss traces across sequences from +SGLD sampling results. This reveals which sequences influence each other's loss under +posterior sampling, providing a measure of functional similarity. + + +Architecture +============ + +Each analysis has two entry points: + +- **High-level** (``llc()``, ``bif()``, ``susceptibilities()``): runs sampling and + post-processing in one call +- **Low-level** (``compute_llc()``, ``compute_bif()``): takes a pre-computed + ``xr.DataTree`` from ``sample()``, useful when you want to run sampling once and + compute multiple analyses. ``compute_susceptibilities()`` takes a + ``dict[str, xr.DataTree]`` (one tree per weight restriction), since susceptibilities + require a separate sampling run for each restriction. + +The sampling pipeline stores full per-token losses to Zarr via ``sample()``, and +post-processing functions operate on the resulting ``xr.DataTree``. + + +Model Requirements +================== + +The current API assumes **autoregressive language models** with fixed-length tokenized +sequences: + +- Model must accept ``input_ids`` and return logits (HuggingFace models, TransformerLens + ``HookedTransformer``, or any model returning a tensor or object with ``.logits``) +- Dataset must be a HuggingFace ``Dataset`` with an ``"input_ids"`` column of + uniform-length sequences +- Loss is next-token cross-entropy + +For non-standard models, ``sample_single_chain()`` in ``devinterp.slt.sampler`` accepts a +custom ``evaluate`` callable. + + +Hyperparameter selection +======================== + +All sampling is sensitive to hyperparameters. See our `Sampling Hyperparameter Guide +`_. + +Further Reading +=============== + +- `You're Measuring Model Complexity Wrong `_ - Introduction to LLC and phase transitions (2024) +- `Structural Inference with Susceptibilities `_ (2025) +- `Towards Spectroscopy: Susceptibility Clusters in Language Models `_ (2026) +- `The Local Learning Coefficient: A Singularity-Aware Complexity Measure `_ (2023) +- `Algebraic Geometry and Statistical Learning Theory `_ Watanabe (2009) -If you run into issues not mentioned here, please first `check the GitHub issues `_, then `ask in the DevInterp discord `_, and only then make a new github issue. Credits & Citations -===================================== +=================== -This package was created by `Timaeus `_. The main contributors to this package are Stan van Wingerden, Jesse Hoogland, and George Wang. Zach Furman, Matthew Farrugia-Roberts, William Zhou, Rohan Hitchcock and Edmund Lau also made valuable contributions or provided useful advice. +This package was created by `Timaeus `_. Most of the sampling, LLC, +susceptibility, and BIF implementations were developed internally; this package is a port +of that joint work. If this package was useful in your work, please cite it as: -.. code-block:: - - @misc{devinterp2024, - title = {DevInterp}, - author = {Stan van Wingerden, Jesse Hoogland, and George Wang}, - year = {2024}, - howpublished = {\url{https://github.com/timaeus-research/devinterp}}, +.. code-block:: bibtex + + @misc{devinterp2026, + title = {DevInterp}, + author = {Snell, William and Wind, Johan Sokrates and Snikkers, Billy + and Fraser, Sandy and Newgas, Adam and Hoogland, Jesse + and Wang, George and Gordon, Andrew and Zhou, William + and van Wingerden, Stan}, + year = {2026}, + version = {2.0}, + howpublished = {\url{https://github.com/timaeus-research/devinterp}}, } -Table of Contents -===================================== + +Guides +====== + +.. toctree:: + :maxdepth: 2 + + sampling + output_formats + + +API Reference +============= .. toctree:: - :maxdepth: 4 + :maxdepth: 2 - self - SLT Observables + Configs, Observables, Postprocessing Sampling Methods - Utils & Visualisation Methods - \ No newline at end of file + Utilities diff --git a/docs/output_formats.rst b/docs/output_formats.rst new file mode 100644 index 00000000..dd65be1b --- /dev/null +++ b/docs/output_formats.rst @@ -0,0 +1,201 @@ +.. _output-formats: + +Output Formats +============== + +All devinterp functions return xarray objects backed by Zarr. This page +documents the exact structure of each output. + +Working with Zarr Output +------------------------ + +``sample()`` writes results to a ``.zarr`` directory (by default in a temp +directory, or at ``output_path`` if specified). The returned ``xr.DataTree`` +is lazy-loaded — data is read from disk on access. + +.. code-block:: python + + # Save to a specific path + tree = sample(..., output_path="my_experiment.zarr") + + # Reopen later + import xarray as xr + tree = xr.open_datatree("my_experiment.zarr", engine="zarr", consolidated=False) + + # Access data + loss = tree.dataset["sampling_loss"] # lazy xr.DataArray + loss.values # loads into memory as numpy array + + # Post-process from saved results + from devinterp.slt.llc import compute_llc + result = compute_llc(tree) + + +``sample()`` → ``xr.DataTree`` +------------------------------ + +The root dataset contains all arrays at the top level. + +.. list-table:: + :header-rows: 1 + :widths: 30 25 45 + + * - Variable + - Dimensions + - Description + * - ``init_loss`` + - (chain, token_pos) + - Mean per-token loss before sampling, averaged over ``num_init_loss_batches`` batches + * - ``sampling_loss`` + - (chain, draw, batch, token_pos) + - Per-token loss on the sampling dataset at each draw + * - ``loss_{obs}`` + - (chain, draw, batch_{obs}, token_pos) + - Per-token loss on observable ``obs`` at each draw + * - ``input_ids_{obs}`` + - (batch_{obs}, token) + - Fixed input IDs for observable ``obs`` (same across all draws) + * - ``n_beta`` + - scalar + - Inverse temperature used for sampling + +When ``save_metrics=True``, additional per-step SGLD diagnostics are included: + +.. list-table:: + :header-rows: 1 + :widths: 30 25 45 + + * - Variable + - Dimensions + - Description + * - ``metrics_scaled_grad`` + - (chain, step) + - L2 norm of the scaled gradient component + * - ``metrics_unscaled_grad`` + - (chain, step) + - L2 norm of the unscaled (raw) gradient + * - ``metrics_localization`` + - (chain, step) + - L2 norm of the localization force + * - ``metrics_weight_decay`` + - (chain, step) + - L2 norm of the weight decay component + * - ``metrics_noise`` + - (chain, step) + - L2 norm of the injected noise + * - ``metrics_distance`` + - (chain, step) + - L2 distance from initial parameters + * - ``metrics_dot_grad_prior`` + - (chain, step) + - Dot product of gradient and prior components + * - ``metrics_dot_grad_noise`` + - (chain, step) + - Dot product of gradient and noise + * - ``metrics_dot_prior_noise`` + - (chain, step) + - Dot product of prior and noise + +Where ``step = num_burnin_steps + num_draws * num_steps_bw_draws``. + +Metadata is stored in ``tree.attrs["metadata"]`` as a nested dict containing +the ``SamplerConfig``, observable specs, and other configuration. + + +``compute_llc()`` → ``xr.Dataset`` +----------------------------------- + +.. list-table:: + :header-rows: 1 + :widths: 25 20 55 + + * - Variable + - Dimensions + - Description + * - ``llc_mean`` + - scalar + - Mean LLC across chains: ``mean(llc_per_chain)`` + * - ``llc_std`` + - scalar + - Std of LLC across chains + * - ``llc_per_chain`` + - (chain,) + - LLC per chain: ``n_beta * (mean_loss_per_chain - init_loss)`` + * - ``llc_scalar`` + - scalar + - LLC with all dims reduced at once (for bitwise parity with aether) + * - ``loss_trace`` + - (chain, draw) + - Mean loss per chain per draw (averaged over batch and token_pos) + * - ``init_loss`` + - scalar + - Mean init loss (averaged over all dims) + + +``compute_bif()`` → ``xr.Dataset`` +------------------------------------ + +With ``correlation_method="token"`` (default): + +.. list-table:: + :header-rows: 1 + :widths: 25 35 40 + + * - Variable + - Dimensions + - Description + * - ``influences`` + - (batch_1, batch_2, target_position, target_position_T) + - Token-wise pairwise correlation matrix between all sequence pairs. + With ``average_tokenwise_bif=True``, collapses to (batch_1, batch_2). + * - ``input_ids`` + - (batch, position) + - Concatenated input IDs from all observables + +With ``correlation_method="sequence"``: + +.. list-table:: + :header-rows: 1 + :widths: 25 25 50 + + * - Variable + - Dimensions + - Description + * - ``influences`` + - (batch_1, batch_2) + - Sequence-level pairwise correlation (loss averaged over tokens first) + * - ``input_ids`` + - (batch, position) + - Concatenated input IDs from all observables + +Coordinates ``batch_1`` and ``batch_2`` are integer indices into the +concatenated observable sequences. ``target_position`` is 1-indexed. + + +``compute_susceptibilities()`` → ``xr.DataTree`` +-------------------------------------------------- + +.. code-block:: text + + /susceptibilities + sus (sus_flat, wr) — susceptibility values + Coordinates: + wr (wr,) — weight restriction names (excluding "full") + dataset_id (sus_flat,) — which observable each entry belongs to + batch (sus_flat,) — batch index within that observable + target_position (sus_flat,) — token position (1-indexed) + Attributes: + dataset_id_to_name — list mapping dataset_id integers to names + + /context + input_ids (ctx_flat,) — flattened input token IDs + Coordinates: + dataset_id (ctx_flat,) — which observable + batch (ctx_flat,) — batch index + position (ctx_flat,) — token position (0-indexed, includes BOS) + Attributes: + dataset_id_to_name — same mapping as /susceptibilities + +The ``sus_flat`` dimension flattens ``(batch, target_position)`` across all +observables. Use the ``dataset_id``, ``batch``, and ``target_position`` +coordinates to reconstruct the original structure. diff --git a/docs/sampling.rst b/docs/sampling.rst new file mode 100644 index 00000000..3a7244f5 --- /dev/null +++ b/docs/sampling.rst @@ -0,0 +1,140 @@ +.. _sampling: + +Sampling +======== + +DevInterp uses Stochastic Gradient Langevin Dynamics (SGLD) to sample from the posterior +distribution around model parameters. This is the foundation for computing LLC, +susceptibilities, and BIF. + +.. figure:: figures/sample-macro.svg + :class: dark-invert dark-screen + :alt: A curved dashed line represents a model training trajectory with checkpoints. At one checkpoint, a box zooms in to show the sample action: multiple MCMC chains orbit the checkpoint, producing draws along dashed paths. + + Sampling runs at a checkpoint along a model training trajectory. Training is separate + from and precedes sampling. + +How Sampling Works +------------------ + +The ``sample()`` function: + +1. Initializes the model at a checkpoint +2. Runs multiple independent SGLD chains +3. At each draw, evaluates per-token loss on the sampling dataset and all observables +4. Writes everything to a Zarr store, returned as an ``xr.DataTree`` + +.. code-block:: python + + from devinterp.slt.sampling import sample + + tree = sample( + model=model, + dataset=train_data, # Used for SGLD gradients + observables={ + "train": train_data, # Evaluate loss on training data + "code": (code_data, 5), # (dataset, batches_per_draw) + }, + lr=0.001, + n_beta=30, + num_chains=4, + num_draws=200, + batch_size=32, + ) + +Chains, Steps, and Draws +------------------------- + +.. figure:: figures/sample-nano.svg + :class: dark-invert dark-screen + :alt: Detailed view of sampling structure. Multiple chains radiate from a central checkpoint. Each chain begins with a burn-in phase, then produces draws spaced by num_steps_bw_draws. + + Each chain is an independent SGLD trajectory, initialized from the training checkpoint. + Chains evolve through mini-batch gradients and injected Gaussian noise to explore the + local loss landscape. + +Within each chain: + +- **Steps** are individual SGLD updates that move through parameter space. Each step uses + ``gradient_accumulation_steps * batch_size * seq_len`` tokens per chain. +- **Burn-in steps** (``num_burnin_steps``) are taken before the first draw to let the + chain reach equilibrium. +- **Draws** occur every ``num_steps_bw_draws`` steps, where we compute observables. + +The total number of SGLD steps per chain is: +``num_burnin_steps + num_draws * num_steps_bw_draws``. + +For example, with ``num_burnin_steps=0`` and ``num_steps_bw_draws=1``: + +.. figure:: figures/num_steps_bw_draws-1.svg + :class: dark-invert dark-screen + + With one step between draws, observables are evaluated after every step. + +With ``num_burnin_steps=3`` and ``num_steps_bw_draws=3``: + +.. figure:: figures/num_steps_bw_draws-3.svg + :class: dark-invert dark-screen + + With three steps between draws, observables are evaluated after every third step. + Burn-in steps delay the first draw. + +Key Parameters +-------------- + +**Sampling parameters:** + +- ``lr``: SGLD learning rate. Controls step size. +- ``n_beta``: Inverse temperature. Higher values stay closer to the MAP estimate. +- ``num_chains``: Number of independent chains. More chains give better statistics. +- ``num_draws``: Number of draws per chain. More draws give more samples. +- ``num_burnin_steps``: Steps before first draw. Allows the chain to reach equilibrium. +- ``num_steps_bw_draws``: Steps between draws. Reduces autocorrelation between samples. +- ``batch_size``: Batch size for SGLD gradient computation. + +**Observable parameters:** + +- ``observables``: Dict mapping names to datasets (or ``(dataset, batches_per_draw)`` + tuples). Each observable is evaluated at every draw. +- ``batches_per_draw``: Default number of batches per observable evaluation (default: 3). + +**Optimizer hyperparameters:** + +- ``sampling_method``: Which SGLD variant to use. ``"sgmcmc_sgld"`` (default) is + plain SGLD with a localization term; ``"rmsprop_sgld"`` adds RMSprop-style + preconditioning. +- ``sampling_method_kwargs``: Extra kwargs forwarded to the chosen method + (e.g. rmsprop's ``alpha`` / ``eps`` / ``add_grad_correction``). +- ``rmsprop_eps`` / ``rmsprop_alpha``: Convenience aliases for the two most + common rmsprop knobs; only valid when ``sampling_method='rmsprop_sgld'``. + Equivalent to ``sampling_method_kwargs={"eps": ..., "alpha": ...}``. +- ``localization``: Strength :math:`\gamma` of the pull toward initial parameters + (Lau et al. 2023). ``0`` disables. +- ``noise_level``: SGLD noise std :math:`\sigma`. Default ``1.0``; changing + this breaks the posterior-sampling guarantee. +- ``llc_weight_decay``: L2 regularization :math:`\lambda`, applied as a Gaussian + prior centered at zero. +- ``bounding_box_size``: If set, restricts sampling to a box of this radius + around the initial parameters. ``None`` disables. +- ``init_noise``: If set, add Gaussian noise with this std to parameters once + before sampling starts. + +**Weight restrictions:** + +- ``param_masks``: Dict mapping parameter names to mask tensors (or ``None`` for + unrestricted). Only parameters in the dict are optimized; all others are frozen. + +Output Format +------------- + +``sample()`` returns an ``xr.DataTree`` backed by Zarr containing: + +- ``init_loss`` *(chain, token_pos)*: Initial loss before sampling +- ``sampling_loss`` *(chain, draw, batch, token_pos)*: Per-token loss at each draw +- ``loss_{obs}`` *(chain, draw, batch_{obs}, token_pos)*: Per-token observable losses +- ``input_ids_{obs}`` *(batch_{obs}, token)*: Fixed input IDs for each observable +- ``n_beta``: Scalar inverse temperature +- ``metrics_*`` *(chain, step)*: SGLD diagnostics (when ``save_metrics=True``) + +This DataTree is the input to post-processing functions like ``compute_llc()``, +``compute_bif()``, and ``compute_susceptibilities()``. diff --git a/docs/source/devinterp.backends.default.rst b/docs/source/devinterp.backends.default.rst deleted file mode 100644 index 28882e59..00000000 --- a/docs/source/devinterp.backends.default.rst +++ /dev/null @@ -1,18 +0,0 @@ -devinterp.backends.default package -================================== - -Subpackages ------------ - -.. toctree:: - :maxdepth: 4 - - devinterp.backends.default.slt - -Module contents ---------------- - -.. automodule:: devinterp.backends.default - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/devinterp.backends.default.slt.rst b/docs/source/devinterp.backends.default.slt.rst deleted file mode 100644 index 3f20450b..00000000 --- a/docs/source/devinterp.backends.default.slt.rst +++ /dev/null @@ -1,21 +0,0 @@ -devinterp.backends.default.slt package -====================================== - -Submodules ----------- - -devinterp.backends.default.slt.sampler module ---------------------------------------------- - -.. automodule:: devinterp.backends.default.slt.sampler - :members: - :undoc-members: - :show-inheritance: - -Module contents ---------------- - -.. automodule:: devinterp.backends.default.slt - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/devinterp.backends.rst b/docs/source/devinterp.backends.rst deleted file mode 100644 index 6eb158b8..00000000 --- a/docs/source/devinterp.backends.rst +++ /dev/null @@ -1,19 +0,0 @@ -devinterp.backends package -========================== - -Subpackages ------------ - -.. toctree:: - :maxdepth: 4 - - devinterp.backends.default - devinterp.backends.tpu - -Module contents ---------------- - -.. automodule:: devinterp.backends - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/devinterp.backends.tpu.rst b/docs/source/devinterp.backends.tpu.rst deleted file mode 100644 index a0fbb14f..00000000 --- a/docs/source/devinterp.backends.tpu.rst +++ /dev/null @@ -1,18 +0,0 @@ -devinterp.backends.tpu package -============================== - -Subpackages ------------ - -.. toctree:: - :maxdepth: 4 - - devinterp.backends.tpu.slt - -Module contents ---------------- - -.. automodule:: devinterp.backends.tpu - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/devinterp.backends.tpu.slt.rst b/docs/source/devinterp.backends.tpu.slt.rst deleted file mode 100644 index 563f5d4b..00000000 --- a/docs/source/devinterp.backends.tpu.slt.rst +++ /dev/null @@ -1,21 +0,0 @@ -devinterp.backends.tpu.slt package -================================== - -Submodules ----------- - -devinterp.backends.tpu.slt.sampler module ------------------------------------------ - -.. automodule:: devinterp.backends.tpu.slt.sampler - :members: - :undoc-members: - :show-inheritance: - -Module contents ---------------- - -.. automodule:: devinterp.backends.tpu.slt - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/devinterp.mechinterp.rst b/docs/source/devinterp.mechinterp.rst deleted file mode 100644 index 8c37c354..00000000 --- a/docs/source/devinterp.mechinterp.rst +++ /dev/null @@ -1,29 +0,0 @@ -devinterp.mechinterp package -============================ - -Submodules ----------- - -devinterp.mechinterp.activations module ---------------------------------------- - -.. automodule:: devinterp.mechinterp.activations - :members: - :undoc-members: - :show-inheritance: - -devinterp.mechinterp.hooks module ---------------------------------- - -.. automodule:: devinterp.mechinterp.hooks - :members: - :undoc-members: - :show-inheritance: - -Module contents ---------------- - -.. automodule:: devinterp.mechinterp - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/devinterp.optim.rst b/docs/source/devinterp.optim.rst index 808229d4..19156f1a 100644 --- a/docs/source/devinterp.optim.rst +++ b/docs/source/devinterp.optim.rst @@ -4,6 +4,14 @@ devinterp.optim package Submodules ---------- +devinterp.optim.metrics module +------------------------------ + +.. automodule:: devinterp.optim.metrics + :members: + :undoc-members: + :show-inheritance: + devinterp.optim.preconditioner module ------------------------------------- @@ -44,6 +52,14 @@ devinterp.optim.sgnht module :undoc-members: :show-inheritance: +devinterp.optim.sketch module +----------------------------- + +.. automodule:: devinterp.optim.sketch + :members: + :undoc-members: + :show-inheritance: + devinterp.optim.utils module ---------------------------- diff --git a/docs/source/devinterp.rst b/docs/source/devinterp.rst index 0a20bcbf..d7cac1fd 100644 --- a/docs/source/devinterp.rst +++ b/docs/source/devinterp.rst @@ -7,22 +7,12 @@ Subpackages .. toctree:: :maxdepth: 4 - devinterp.backends - devinterp.mechinterp devinterp.optim devinterp.slt Submodules ---------- -devinterp.test\_utils module ----------------------------- - -.. automodule:: devinterp.test_utils - :members: - :undoc-members: - :show-inheritance: - devinterp.utils module ---------------------- @@ -31,14 +21,6 @@ devinterp.utils module :undoc-members: :show-inheritance: -devinterp.vis\_utils module ---------------------------- - -.. automodule:: devinterp.vis_utils - :members: - :undoc-members: - :show-inheritance: - Module contents --------------- diff --git a/docs/source/devinterp.slt.rst b/docs/source/devinterp.slt.rst index 50b7e140..3696c81a 100644 --- a/docs/source/devinterp.slt.rst +++ b/docs/source/devinterp.slt.rst @@ -4,26 +4,26 @@ devinterp.slt package Submodules ---------- -devinterp.slt.callback module ------------------------------ +devinterp.slt.bif module +------------------------ -.. automodule:: devinterp.slt.callback +.. automodule:: devinterp.slt.bif :members: :undoc-members: :show-inheritance: -devinterp.slt.cov module ------------------------- +devinterp.slt.config module +--------------------------- -.. automodule:: devinterp.slt.cov +.. automodule:: devinterp.slt.config :members: :undoc-members: :show-inheritance: -devinterp.slt.gradient module ------------------------------ +devinterp.slt.covariance module +------------------------------- -.. automodule:: devinterp.slt.gradient +.. automodule:: devinterp.slt.covariance :members: :undoc-members: :show-inheritance: @@ -36,50 +36,66 @@ devinterp.slt.llc module :undoc-members: :show-inheritance: -devinterp.slt.loss module -------------------------- +devinterp.slt.lm\_loss module +----------------------------- -.. automodule:: devinterp.slt.loss +.. automodule:: devinterp.slt.lm_loss :members: :undoc-members: :show-inheritance: -devinterp.slt.mala module -------------------------- +devinterp.slt.observables module +-------------------------------- -.. automodule:: devinterp.slt.mala +.. automodule:: devinterp.slt.observables :members: :undoc-members: :show-inheritance: -devinterp.slt.norms module --------------------------- +devinterp.slt.sampler module +---------------------------- -.. automodule:: devinterp.slt.norms +.. automodule:: devinterp.slt.sampler :members: :undoc-members: :show-inheritance: -devinterp.slt.sampler module ----------------------------- +devinterp.slt.sampling module +----------------------------- -.. automodule:: devinterp.slt.sampler +.. automodule:: devinterp.slt.sampling :members: :undoc-members: :show-inheritance: -devinterp.slt.trace module --------------------------- +devinterp.slt.susceptibilities module +------------------------------------- + +.. automodule:: devinterp.slt.susceptibilities + :members: + :undoc-members: + :show-inheritance: + +devinterp.slt.weight\_restrictions module +----------------------------------------- + +.. automodule:: devinterp.slt.weight_restrictions + :members: + :undoc-members: + :show-inheritance: + +devinterp.slt.writing module +---------------------------- -.. automodule:: devinterp.slt.trace +.. automodule:: devinterp.slt.writing :members: :undoc-members: :show-inheritance: -devinterp.slt.wbic module -------------------------- +devinterp.slt.zarr\_schema module +--------------------------------- -.. automodule:: devinterp.slt.wbic +.. automodule:: devinterp.slt.zarr_schema :members: :undoc-members: :show-inheritance: diff --git a/docs/source/devinterp.utils.rst b/docs/source/devinterp.utils.rst new file mode 100644 index 00000000..8aa74929 --- /dev/null +++ b/docs/source/devinterp.utils.rst @@ -0,0 +1,7 @@ +devinterp.utils module +====================== + +.. automodule:: devinterp.utils + :members: + :show-inheritance: + :undoc-members: diff --git a/examples/diagnostics.ipynb b/examples/diagnostics.ipynb deleted file mode 100644 index 727fe46b..00000000 --- a/examples/diagnostics.ipynb +++ /dev/null @@ -1,1389 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "48d0ad1e-0e17-4727-9205-90ef694636b0", - "metadata": {}, - "source": [ - "# Sampler Diagnostics Demo (Normal Crossing)" - ] - }, - { - "cell_type": "markdown", - "id": "a7ff06fe-37ca-41cf-845e-ca9f886d8720", - "metadata": {}, - "source": [ - "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/timaeus-research/devinterp/blob/main/examples/diagnostics.ipynb)\n", - "\n", - "Note: GradientNorm and NoiseNorm are currently incorrectly implemented, see [this issue](https://github.com/timaeus-research/devinterp/issues/90).\n", - "\n", - "\n", - "This notebook demonstrates the usage of various diagnostic tools for the sampling and RLCT estimation process. As an example, we'll use normal crossings for each diagnostic. This is a polynomial model characterized by $f(x) = w_1^a w_2^b x$ for some $(a, b)$, where $w_1$ and $w_2$ are weights to be learned. The data is generated with gaussian noise around the origin, so the model achieves its lowest loss when $w_1=0$ or $w_2 =0$.\n", - "\n", - "We'll also be using the SGLD optimizer." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "5a648589-14f4-4b4a-b73f-7203318f2d58", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Requirement already satisfied: pip in /home/paperspace/devinterp/.venv/lib/python3.9/site-packages (24.2)\n", - "Note: you may need to restart the kernel to use updated packages.\n", - "Requirement already satisfied: torch in /home/paperspace/devinterp/.venv/lib/python3.9/site-packages (2.0.1)\n", - "Requirement already satisfied: matplotlib in /home/paperspace/devinterp/.venv/lib/python3.9/site-packages (3.8.3)\n", - "Requirement already satisfied: seaborn in /home/paperspace/devinterp/.venv/lib/python3.9/site-packages (0.12.2)\n", - "Requirement already satisfied: devinterp in /home/paperspace/devinterp/.venv/lib/python3.9/site-packages (0.1.0)\n", - 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"Requirement already satisfied: six>=1.5 in /home/paperspace/devinterp/.venv/lib/python3.9/site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n", - "Requirement already satisfied: MarkupSafe>=2.0 in /home/paperspace/devinterp/.venv/lib/python3.9/site-packages (from jinja2->torch) (2.1.3)\n", - "Requirement already satisfied: mpmath>=0.19 in /home/paperspace/devinterp/.venv/lib/python3.9/site-packages (from sympy->torch) (1.3.0)\n", - "Note: you may need to restart the kernel to use updated packages.\n" - ] - } - ], - "source": [ - "%pip install --upgrade pip\n", - "%pip install torch matplotlib seaborn devinterp" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "e75ab1df-f3e5-4552-b7ba-acbe3ffe0a1a", - "metadata": {}, - "outputs": [], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "import torch\n", - "import torch.nn as nn\n", - "from torch.utils.data import DataLoader, TensorDataset\n", - "\n", - "from devinterp.optim.sgld import SGLD\n", - "from devinterp.utils import plot_trace, default_nbeta, get_init_loss_multi_batch\n", - "from devinterp.slt.sampler import sample, OnlineLLCEstimator, LLCEstimator\n", - "\n", - "import warnings\n", - "\n", - "\n", - "warnings.filterwarnings(\"ignore\")" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "78dddc4d-367e-44d1-97fc-03beb642e3a1", - "metadata": {}, - "outputs": [], - "source": [ - "from devinterp.utils import evaluate_mse\n", - "\n", - "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", - "\n", - "# plotting\n", - "sns.set_style(\"whitegrid\")\n", - "CMAP = sns.color_palette(\"muted\", as_cmap=True)\n", - "PRIMARY, SECONDARY, TERTIARY = CMAP[:3]\n", - "plt.rcParams[\"figure.figsize\"] = (\n", - " 12,\n", - " 9,\n", - ") # note: this cell may need to be re-run after creating a plot to take effect\n", - "\n", - "# constants\n", - "SIGMA = 0.25\n", - "NUM_TRAIN_SAMPLES = 1000\n", - "BATCH_SIZE = NUM_TRAIN_SAMPLES\n", - "LR = 0.0005\n", - "\n", - "NUM_CHAINS = 20\n", - "NUM_DRAWS = 2000" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "830c3348-6dd7-4c20-b217-6b1d16ae83d7", - "metadata": {}, - "outputs": [], - "source": [ - "# Set up RLCT estimation\n", - "class PolyModel(nn.Module):\n", - " def __init__(self, powers):\n", - " super(PolyModel, self).__init__()\n", - " self.weights = nn.Parameter(\n", - " torch.tensor(\n", - " [1.0, 0.3], dtype=torch.float32, requires_grad=True, device=DEVICE\n", - " )\n", - " )\n", - " self.powers = powers\n", - "\n", - " def forward(self, x):\n", - " multiplied = torch.prod(self.weights**self.powers)\n", - " x = x * multiplied\n", - " return x\n", - "\n", - "\n", - "def generate_dataset_for_seed(seed=0):\n", - " x = torch.normal(0, 2, size=(NUM_TRAIN_SAMPLES,))\n", - " y = torch.normal(0, SIGMA, size=(NUM_TRAIN_SAMPLES,))\n", - " train_data = TensorDataset(x, y)\n", - " train_loader = DataLoader(train_data, batch_size=BATCH_SIZE, shuffle=True)\n", - " return train_loader, train_data\n", - "\n", - "\n", - "def run_callbacks(\n", - " train_loader,\n", - " train_data,\n", - " model,\n", - " callbacks=None,\n", - " device=DEVICE,\n", - "):\n", - " nbeta = default_nbeta(train_loader)\n", - " optim_kwargs = {\n", - " \"lr\": 0.0005,\n", - " \"localization\": 1.0,\n", - " \"save_noise\": True,\n", - " \"nbeta\": nbeta,\n", - " }\n", - " if callbacks is None:\n", - " init_loss = get_init_loss_multi_batch(\n", - " dataloader=train_loader,\n", - " model=model,\n", - " device=DEVICE,\n", - " n_batches=16,\n", - " evaluate=evaluate_mse,\n", - " )\n", - " llc_estimator = LLCEstimator(\n", - " num_chains=NUM_CHAINS,\n", - " num_draws=NUM_DRAWS,\n", - " nbeta=nbeta,\n", - " device=DEVICE,\n", - " init_loss=init_loss,\n", - " )\n", - " callbacks = [llc_estimator]\n", - "\n", - " sample(\n", - " model=model,\n", - " loader=train_loader,\n", - " evaluate=evaluate_mse,\n", - " optimizer_kwargs=optim_kwargs,\n", - " sampling_method=SGLD,\n", - " num_chains=NUM_CHAINS,\n", - " num_draws=NUM_DRAWS,\n", - " callbacks=callbacks,\n", - " device=device,\n", - " )\n", - "\n", - " results = {}\n", - "\n", - " for callback in callbacks:\n", - " if hasattr(callback, \"get_results\"):\n", - " results.update(callback.get_results())\n", - "\n", - " return results\n", - "\n", - "\n", - "def get_rlct(\n", - " train_loader,\n", - " train_data,\n", - " weights=[0.0, 0.0],\n", - " lr=LR,\n", - " powers=torch.tensor([1, 2]).to(DEVICE),\n", - " device=DEVICE,\n", - "):\n", - " model = PolyModel(powers).to(DEVICE)\n", - " model.weights = nn.Parameter(\n", - " torch.tensor(weights, dtype=torch.float32, requires_grad=True, device=DEVICE)\n", - " )\n", - " return run_callbacks(\n", - " train_loader=train_loader,\n", - " train_data=train_data,\n", - " model=model,\n", - " device=device,\n", - " )[\"llc/mean\"]" - ] - }, - { - "cell_type": "markdown", - "id": "c0809e61-0a07-46e2-a89a-bf3ed9c76577", - "metadata": {}, - "source": [ - "## Testing RLCT estimation\n", - "\n", - "Let's start with estimating some RLCTs at known values for a simple normal crossing with $(a, b) = (1, 2)$." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "00f8f5f1-4699-47b4-9a5e-8dba737de9e6", - "metadata": {}, - "outputs": [], - "source": [ - "train_loader, train_data = generate_dataset_for_seed(seed=0)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "cbb105d5-98ca-4770-876c-3726d3ef3345", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "If you're setting a nbeta in optimizer_kwargs, please also make sure to set it in the callbacks.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Chain 0: 0%| | 0/2000 [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "wbic_trace = results[\"wbic/trace\"]\n", - "plot_trace(wbic_trace, \"WBIC\")" - ] - }, - { - "cell_type": "markdown", - "id": "5154b2f2-6797-4294-93c4-d79dfdb8f94d", - "metadata": {}, - "source": [ - "## Other diagnostics\n", - "\n", - "There are various diagnostic tools implemented as `SamplerCallback`s. Some are regular `SamplerCallback`s that can be passed to `sample` on their own, and others are \"derivative\" callbacks that run diagnostics on a particular `SamplerCallback` instance.\n", - "\n", - "Regular implementations:\n", - "- `OnlineWBICEstimator`: estimates WBIC (seen above)\n", - "- `MalaAcceptanceRate`: track the MALA acceptance 'probability' of the SGLD step. (Note: this does not change the SGLD algorithm, it only calculates a hypothetical acceptance rate.)\n", - "- `WeightNorm`: track the L2 norm of model weights during sampling\n", - "- `GradientNorm`: track the L2 norm of gradients during sampling\n", - "- `NoiseNorm`: track the L2 norm of SGLD noise term during sampling\n", - "- `GradientDistribution`: view a histogram/heatmap of gradient values at each SGLD step for specific named model parameters -- useful e.g. for checking that gradients haven't exploded or collapsed\n", - "\n", - "Derivative implementations:\n", - "- `OnlineTraceStatistics`: compute the mean/std of the trace of another `SamplerCallback` across draws and across chains\n", - "- `OnlineLossStatistics`: computes various loss statistics for `OnlineLLCEstimator`\n", - "\n", - "Additionally, since derivative callbacks depend on a base callback, they must be positioned later in the list of callbacks so that they're called after the base callback. A helper function `validate_callbacks` checks whether a list of callbacks satisfies this condition." - ] - }, - { - "cell_type": "markdown", - "id": "726dd735", - "metadata": {}, - "source": [ - "### MalaAcceptanceRate example" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "3860c266", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "If you're setting a nbeta in optimizer_kwargs, please also make sure to set it in the callbacks.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Chain 0: 9%|▉ | 185/2000 [00:00<00:02, 614.93it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Chain 0: 100%|██████████| 2000/2000 [00:03<00:00, 596.74it/s]\n", - "Chain 1: 100%|██████████| 2000/2000 [00:03<00:00, 607.79it/s]\n", - "Chain 2: 100%|██████████| 2000/2000 [00:03<00:00, 615.07it/s]\n", - "Chain 3: 100%|██████████| 2000/2000 [00:03<00:00, 623.34it/s]\n", - "Chain 4: 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2000/2000 [00:03<00:00, 608.48it/s]\n", - "Chain 19: 100%|██████████| 2000/2000 [00:03<00:00, 641.58it/s]\n" - ] - } - ], - "source": [ - "from devinterp.slt.mala import MalaAcceptanceRate\n", - "\n", - "train_loader, train_data = generate_dataset_for_seed(seed=0)\n", - "\n", - "mala_accept_rate = mala_estimator = MalaAcceptanceRate(\n", - " num_chains=NUM_CHAINS,\n", - " num_draws=NUM_DRAWS,\n", - " nbeta=default_nbeta(train_loader),\n", - " learning_rate=LR,\n", - " device=DEVICE,\n", - ")\n", - "\n", - "\n", - "callbacks = [mala_accept_rate]\n", - "\n", - "nbeta = default_nbeta(train_loader)\n", - "results = run_callbacks(train_loader, train_data, model=model, callbacks=callbacks)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "95e150e1", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average MALA acceptance rate over draws and chains is 0.9970715641975403\n" - ] - } - ], - "source": [ - "from numpy import mean\n", - "\n", - "mala_trace = results[\"mala_accept/trace\"]\n", - "\n", - "plot_trace(mala_trace, \"MALA acceptance rate\")\n", - "print(f\"Average MALA acceptance rate over draws and chains is {mean(mala_trace)}\")" - ] - }, - { - "cell_type": "markdown", - "id": "41964db4", - "metadata": {}, - "source": [ - "From this mean and plot, we should conclude that our acceptance rate could be a bit lower. Ideally we'd aim for $\\sim0.9$, but this is not always achievable." - ] - }, - { - "cell_type": "markdown", - "id": "c34cf213-a337-41ae-8ae3-a541190200e3", - "metadata": {}, - "source": [ - "### WeightNorm, GradientNorm, NoiseNorm examples" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "b2b3d904-3795-4354-a273-ea9a3461eafe", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "If you're setting a nbeta in optimizer_kwargs, please also make sure to set it in the callbacks.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Chain 0: 0%| | 0/2000 [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_trace(grad_trace, \"gradient norm\")" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "668cd03a-fe4d-47bb-a0e6-ae90587333d4", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# note: the weight norm starts near 0 in this graph because the weights are initialized to [0, 0]\n", - "plot_trace(weight_trace, \"weight_norm\")" - ] - }, - { - "cell_type": "markdown", - "id": "fb279db3-42cb-436a-8047-a545851bb3d0", - "metadata": {}, - "source": [ - "### GradientDistribution" - ] - }, - { - "cell_type": "markdown", - "id": "2ebfba12-be24-4109-a025-bdc2befb4e30", - "metadata": {}, - "source": [ - "`GradientDistribution` shows the histogram of gradient values at each SGLD time step, with a darker color indicating a bin with a higher count. Below, we can see that some gradient values are relatively large, but most cluster around 0 (the darker blue line). In this case, gradients don't seem to be exploding or collapsing during sampling." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "d8716fc9-40a0-4183-a86c-d8a589e5d22e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "If you're setting a nbeta in optimizer_kwargs, please also make sure to set it in the callbacks.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Chain 0: 2%|▏ | 48/2000 [00:00<00:04, 475.08it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Chain 0: 100%|██████████| 2000/2000 [00:02<00:00, 691.41it/s]\n", - "Chain 1: 100%|██████████| 2000/2000 [00:02<00:00, 757.88it/s]\n", - "Chain 2: 100%|██████████| 2000/2000 [00:02<00:00, 742.94it/s]\n", - "Chain 3: 100%|██████████| 2000/2000 [00:02<00:00, 743.81it/s]\n", - "Chain 4: 100%|██████████| 2000/2000 [00:02<00:00, 748.98it/s]\n", - "Chain 5: 100%|██████████| 2000/2000 [00:02<00:00, 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], - "source": [ - "from devinterp.slt.gradient import GradientDistribution\n", - "\n", - "grad_dist = GradientDistribution(\n", - " num_chains=NUM_CHAINS, num_draws=NUM_DRAWS, min_bins=40, device=DEVICE\n", - ")\n", - "callbacks = [grad_dist]\n", - "results = run_callbacks(train_loader, train_data, model=model, callbacks=callbacks)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "f3a6bbbb-1e90-424f-b1e4-fdca1d600c5c", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "grad_dist.plot(\"weights\")" - ] - }, - { - "cell_type": "markdown", - "id": "4896006b-7f5e-4d3a-b460-0ee90c0bc8f5", - "metadata": {}, - "source": [ - "### OnlineLossStatistics, OnlineTraceStatistics" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "0bc6527a-f386-4d23-b7f0-96c251f7d9ad", - "metadata": {}, - "outputs": [], - "source": [ - "from devinterp.slt.callback import validate_callbacks\n", - "from devinterp.slt.loss import OnlineLossStatistics\n", - "from devinterp.slt.norms import GradientNorm, NoiseNorm, WeightNorm\n", - "from devinterp.slt.trace import OnlineTraceStatistics\n", - "\n", - "# llc estimator for OnlineLossStatistics\n", - "llc_estimator = OnlineLLCEstimator(\n", - " num_chains=NUM_CHAINS,\n", - " num_draws=NUM_DRAWS,\n", - " nbeta=len(train_data),\n", - " init_loss=get_init_loss_multi_batch(\n", - " dataloader=train_loader,\n", - " model=model,\n", - " device=DEVICE,\n", - " n_batches=16,\n", - " evaluate=evaluate_mse,\n", - " ),\n", - ")\n", - "loss_statistics = OnlineLossStatistics(base_callback=llc_estimator)\n", - "\n", - "# weight norm for OnlineTraceStatistics\n", - "weight_norm = weight_norm = WeightNorm(\n", - " num_chains=NUM_CHAINS, num_draws=NUM_DRAWS, device=DEVICE\n", - ")\n", - "trace_statistics = OnlineTraceStatistics(\n", - " base_callback=weight_norm, attribute=\"weight_norms\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "d47e2934-4010-4f21-9686-d76375fe0274", - "metadata": {}, - "outputs": [ - { - "ename": "ValueError", - "evalue": "Derivative callback must be called after base callback .", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[21], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# validate_callbacks throws an error if you try to pass a derivative callback before its base callback\u001b[39;00m\n\u001b[1;32m 2\u001b[0m callbacks \u001b[38;5;241m=\u001b[39m [loss_statistics, llc_estimator]\n\u001b[0;32m----> 3\u001b[0m \u001b[43mvalidate_callbacks\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/devinterp/src/devinterp/slt/callback.py:62\u001b[0m, in \u001b[0;36mvalidate_callbacks\u001b[0;34m(callbacks)\u001b[0m\n\u001b[1;32m 60\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m other_callback \u001b[38;5;129;01mis\u001b[39;00m base_callback:\n\u001b[1;32m 61\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m j \u001b[38;5;241m>\u001b[39m i:\n\u001b[0;32m---> 62\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 63\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDerivative callback \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mcallback\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m must be called after base callback \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mbase_callback\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 64\u001b[0m )\n\u001b[1;32m 65\u001b[0m base_callback_exists \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m 66\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m base_callback_exists:\n", - "\u001b[0;31mValueError\u001b[0m: Derivative callback must be called after base callback ." - ] - } - ], - "source": [ - "# validate_callbacks throws an error if you try to pass a derivative callback before its base callback\n", - "callbacks = [loss_statistics, llc_estimator]\n", - "validate_callbacks(callbacks)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "dc0da417-948e-4270-9c7f-c8a4c62f948d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# it passes True if the callbacks meet the ordering condition\n", - "callbacks = [llc_estimator, loss_statistics, weight_norm, trace_statistics]\n", - "validate_callbacks(callbacks)" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "1edad109-c2c3-48ff-941d-091ae2765eae", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "If you're setting a nbeta in optimizer_kwargs, please also make sure to set it in the callbacks.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Chain 0: 100%|██████████| 2000/2000 [00:03<00:00, 560.50it/s]\n", - "Chain 1: 100%|██████████| 2000/2000 [00:03<00:00, 548.20it/s]\n", - "Chain 2: 100%|██████████| 2000/2000 [00:03<00:00, 532.24it/s]\n", - "Chain 3: 100%|██████████| 2000/2000 [00:03<00:00, 540.36it/s]\n", - "Chain 4: 100%|██████████| 2000/2000 [00:03<00:00, 550.41it/s]\n", - "Chain 5: 100%|██████████| 2000/2000 [00:03<00:00, 546.00it/s]\n", - "Chain 6: 100%|██████████| 2000/2000 [00:03<00:00, 559.36it/s]\n", - "Chain 7: 100%|██████████| 2000/2000 [00:03<00:00, 529.64it/s]\n", - "Chain 8: 100%|██████████| 2000/2000 [00:03<00:00, 537.31it/s]\n", - "Chain 9: 100%|██████████| 2000/2000 [00:03<00:00, 539.81it/s]\n", - "Chain 10: 100%|██████████| 2000/2000 [00:03<00:00, 556.02it/s]\n", - "Chain 11: 100%|██████████| 2000/2000 [00:03<00:00, 551.79it/s]\n", - "Chain 12: 100%|██████████| 2000/2000 [00:03<00:00, 547.96it/s]\n", - "Chain 13: 100%|██████████| 2000/2000 [00:03<00:00, 555.84it/s]\n", - "Chain 14: 100%|██████████| 2000/2000 [00:03<00:00, 534.83it/s]\n", - "Chain 15: 100%|██████████| 2000/2000 [00:03<00:00, 544.25it/s]\n", - "Chain 16: 100%|██████████| 2000/2000 [00:03<00:00, 547.65it/s]\n", - "Chain 17: 100%|██████████| 2000/2000 [00:03<00:00, 534.60it/s]\n", - "Chain 18: 100%|██████████| 2000/2000 [00:03<00:00, 557.90it/s]\n", - "Chain 19: 100%|██████████| 2000/2000 [00:03<00:00, 535.85it/s]\n" - ] - } - ], - "source": [ - "results = run_callbacks(train_loader, train_data, model=model, callbacks=callbacks)" - ] - }, - { - "cell_type": "markdown", - "id": "bfd5380b-6356-4338-bc22-ccdc6b0c0506", - "metadata": {}, - "source": [ - "**OnlineTraceStatistics**\n", - "\n", - "`OnlineTraceStatistics` is a general purpose 'derivative' `SamplerCallback` that takes any base statistic that's computed as a trace and computes the mean and standard deviation of that statistic in two ways: across *draws* and across *chains*.\n", - "\n", - "The black dotted lines and gray overlays in the previous charts are a visualization of what it looks like to compute the mean and std across *draws*.\n", - "\n", - "The mean and std statistics computed across a *chain* are the cumulative mean and std of that single chain at a given draw step. For example, the final mean and std computed for a chain would be the mean and std of the base statistic computed at all draw steps of that chain." - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "536ad9ab-ae71-4313-9e77-5e44ce7d97fd", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The shapes match the shape of the trace.\n", - "(20, 2000)\n", - "(20, 2000)\n", - "\n", - "The mean of all draw steps across a given chain i is the ith index below.\n", - "[0.50411123 0.32196382 0.59018016 0.3667175 0.4143017 0.37731305\n", - " 0.47246945 0.36382496 0.6432762 0.58859307 0.28380862 0.41569713\n", - " 0.33385512 0.4882355 0.39871842 0.49782977 0.37063482 0.43182674\n", - " 0.32041517 0.4569179 ]\n", - "\n", - "The std of all draw steps across chain 5:\n", - "0.17924201\n" - ] - } - ], - "source": [ - "# Mean and std for each chain of the computed weight norms\n", - "means = results[\"weight_norms/chain/mean\"]\n", - "stds = results[\"weight_norms/chain/std\"]\n", - "\n", - "print(\"The shapes match the shape of the trace.\")\n", - "print(means.shape)\n", - "print(stds.shape)\n", - "\n", - "print(\"\\nThe mean of all draw steps across a given chain i is the ith index below.\")\n", - "final_means = means[:, -1]\n", - "print(final_means)\n", - "\n", - "print(\"\\nThe std of all draw steps across chain 5:\")\n", - "std = stds[5, -1]\n", - "print(std)" - ] - }, - { - "cell_type": "markdown", - "id": "5c7b0511-8d19-4d7f-aed8-b40ef0c55636", - "metadata": { - "jp-MarkdownHeadingCollapsed": true - }, - "source": [ - "**OnlineLossStatistics**\n", - "\n", - "Since we're using minibatches instead of being in the limit of infinite training data, there's noise introduced by the random selection of minibatch. We can estimate/visualize the noise by looking at a histogram of the initial losses of each chain in our sampler, since these are losses from different minibatches on a fixed weight. Note that we get this by calling a method of `OnlineLossStatistics` rather than referring to `results`.\n", - "\n", - "Note that these values may be so close together that it causes a rendering issue in `plt.hist`, but we can see from the values that the minibatch noise is very small." - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "2a29db13-9647-4a75-9370-6ac5394a2eaa", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "tensor([0.0635, 0.0635, 0.0635, 0.0635, 0.0635, 0.0635, 0.0635, 0.0635, 0.0635,\n", - " 0.0635, 0.0635, 0.0635, 0.0635, 0.0635, 0.0635, 0.0635, 0.0635, 0.0635,\n", - " 0.0635, 0.0635])" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "init_losses = loss_statistics.loss_hist_by_draw(draw=0, bins=10)\n", - "init_losses" - ] - }, - { - "cell_type": "markdown", - "id": "86f720b2-a775-45d8-9f72-9dff1901f2a2", - "metadata": {}, - "source": [ - "`OnlineLossStatistics` also provides a few ways to check the \"health\" of your loss chains. For example, your chains should ideally not be achieving loss values lower than your initial loss. You can see the loss values directly by plotting the loss trace, or you can check the cumulative percent of negative steps relative to the initial loss through a few statistics computed by `OnlineLossStatistics`, one of which is `\"loss/percent_neg_steps\"`." - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "6956571d-fba8-4793-8d40-7923403a9665", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "loss_trace = results[\"loss/trace\"]\n", - "plot_trace(loss_trace, \"loss\")" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "de3fca75-1bce-4708-a1a5-526997be8e23", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "cum_perc_neg_steps = results[\"loss/percent_neg_steps\"]\n", - "plot_trace(cum_perc_neg_steps, \"cumulative % negative\")" - ] - }, - { - "cell_type": "markdown", - "id": "eb789d3c-6954-4375-b97e-e0be5d204933", - "metadata": {}, - "source": [ - "Recall that there is still some minibatch noise, so we can also check how negative losses are relative to the initial loss while also thresholding by the estimated minibatch noise. Most negative draws end up within the threshold, and any chains that still end up consistently negative can be pruned from estimations." - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "e9a6f049-b77b-49a9-b63e-02766a0bdd69", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "thresholded_neg_steps = results[\"loss/percent_thresholded_neg_steps\"]\n", - "plot_trace(thresholded_neg_steps, \"cumulative % of thresholded negative\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.18" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/examples/dlns.ipynb b/examples/dlns.ipynb deleted file mode 100644 index 61b2b79c..00000000 --- a/examples/dlns.ipynb +++ /dev/null @@ -1,2397 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Deep Linear Networks\n", - "\n", - "Warning: This notebook is currently functional, but does not show actually reproduce the results it's attempting to. Use only as inspiration, not as gospel. For more well-calibrated LLC estimates of a simple linear network, see tests/slt/rrr_test.py.\n", - "\n", - "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/timaeus-research/devinterp/blob/main/examples/dlns.ipynb)\n", - "\n", - "Here, we repeat the experiments by [Jacot et al. (2022)](https://arxiv.org/abs/2106.15933), then [estimate SLT-derived invariants like the learning coefficient](https://github.com/edmundlth/scalable_learning_coefficient_with_sgld/blob/v1.0/experiment.py).\n", - "\n", - "Currently, this only looks at the learning task behind figure 3 (not the MC loss behind figure 2).\n", - "\n", - "A **deep linear network** (DLN) of length $L$ is a neural network with $L$ layers of widths $n_0, \\dots, n_L$, that computes the transformation:\n", - "\n", - "$$\n", - "\\begin{align}\n", - "f: \\mathbb{R}^{n_0} &\\to \\mathbb{R}^{n_L} \\\\\n", - "x &\\mapsto W_L \\cdots W_1 x =: A_\\theta x,\n", - "\\end{align}\n", - "$$\n", - "\n", - "Parametrized by $\\theta \\in \\mathbb{R}^P$, where $P = \\sum_{l=1}^L n_{l-1} n_l$ is the number of parameters.\n", - "\n", - "For convenience, we consider **rectangular networks**, or $(L, w)$-DLNs, with constant hidden width $w$ across all layers: $n_1 = \\dots = n_{L-1} = w$.\n", - "\n", - "## Hyperparameters\n", - "\n", - "- $L$ is the number of layers\n", - "- $N=n_0$ is the input dimension\n", - "- $M=n_L$ is the output dimension\n", - "- $r$ is the rank of the \"true\" matrix / teacher $A^*$\n", - "- $w$ or $H$ is the hidden width (for rectangular networks).\n", - "- $\\sigma$ is the teacher's output noise. By default, we use $\\sigma=0$.\n", - "\n", - "\n", - "# Set-up\n", - "\n", - "- For the definition of the model `DLN`, a `torch.nn.Module`, see `devinterp.zoo.dlns.model`.\n", - "- For the definition of the dataset `DLNDataset`, a `torch.utils.data.Dataset`, see `devinterp.zoo.dlns.dataset`." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Requirement already satisfied: pip in /home/paperspace/devinterp/.venv/lib/python3.9/site-packages (24.2)\n", - "Note: you may need to restart the kernel to use updated packages.\n", - "Requirement already satisfied: devinterp in /home/paperspace/devinterp/.venv/lib/python3.9/site-packages (0.1.0)\n", - "Requirement already satisfied: einops>=0.6.1 in /home/paperspace/devinterp/.venv/lib/python3.9/site-packages (from devinterp) (0.6.1)\n", - "Requirement already satisfied: matplotlib>=3.7.4 in 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} - ], - "source": [ - "%pip install --upgrade pip\n", - "%pip install devinterp\n", - "%pip install seaborn pydantic pandas jupyter ipywidgets" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Imports" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(device(type='cuda', index=0), 1)" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import logging\n", - "import os\n", - "import warnings\n", - "from typing import Callable, Dict, List, Optional, Union\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import pandas as pd\n", - "import seaborn as sns\n", - "import torch\n", - "from torch import nn, optim\n", - "from torch.nn import functional as F\n", - "from torch.utils.data import Dataset\n", - "from tqdm.notebook import tqdm\n", - "\n", - "from devinterp.optim.sgld import SGLD\n", - "from devinterp.slt.sampler import estimate_learning_coeff\n", - "\n", - "warnings.filterwarnings(\"ignore\")\n", - "\n", - "\n", - "class DLN(nn.Module):\n", - " \"\"\"\n", - " A deep linear network with `L` layers with dimensions `dims`.\n", - "\n", - " Weights are initialized with variance `init_variance`.\n", - " \"\"\"\n", - "\n", - " def __init__(self, dims: List[int], init_variance: float = 1.0):\n", - " super().__init__()\n", - " self.dims = dims\n", - " self.L = len(dims) - 1\n", - " self.init_variance = init_variance\n", - " self.linears = nn.ModuleList(\n", - " [nn.Linear(d1, d2, bias=False) for d1, d2 in zip(dims[:-1], dims[1:])]\n", - " )\n", - "\n", - " # Initialize weights and biases\n", - " for l in range(self.L):\n", - " self.linears[l].weight.data.normal_(\n", - " 0, self.init_variance\n", - " ) # Note: this is not normalized by the input dimension\n", - "\n", - " def forward(self, x):\n", - " for linear in self.linears:\n", - " x = linear(x)\n", - " return x\n", - "\n", - " def __repr__(self):\n", - " return f\"DLN({self.dims})\"\n", - "\n", - " @classmethod\n", - " def make_rectangular(\n", - " cls, input_dim: int, output_dim: int, L: int, w: int, gamma: float\n", - " ):\n", - " \"\"\"\n", - " Make a rectangular DLN with `L` layers and constant hidden width `w`.\n", - "\n", - " The input dimension is `input_dim` and the output dimension is `output_dim`.\n", - "\n", - " The weights are initialized from a normal distribution with variance`w ** (-gamma)`.\n", - " \"\"\"\n", - " init_variance = w ** (-gamma)\n", - " return cls(\n", - " [input_dim] + [w] * (L - 1) + [output_dim], init_variance=init_variance\n", - " )\n", - "\n", - " def to_matrix(self):\n", - " \"\"\"Return the collapsed matrix representation of the DLN.\"\"\"\n", - " return self.forward(torch.eye(self.dims[0], device=self.device)).T\n", - "\n", - " @classmethod\n", - " def from_matrix(cls, A: torch.Tensor, L=1):\n", - " if L != 1:\n", - " raise NotImplementedError(\"Only L=1 is supported for now.\")\n", - "\n", - " output_dim, input_dim = A.shape\n", - " instance = cls([input_dim, output_dim])\n", - " instance.linears[0].weight.data.copy_(A)\n", - "\n", - " return instance\n", - "\n", - " def rank(self, **kwargs):\n", - " \"\"\"Return the rank of the DLN.\"\"\"\n", - " return torch.linalg.matrix_rank(self.to_matrix().to(\"cpu\"), **kwargs)\n", - "\n", - " def ranks(self, **kwargs):\n", - " \"\"\"Return the ranks of the individual layers of the DLN.\"\"\"\n", - " return [\n", - " torch.linalg.matrix_rank(l.weight.data.to(\"cpu\"), **kwargs)\n", - " for l in self.linears\n", - " ]\n", - "\n", - " def norm(self, p: Union[int, float, str] = 2):\n", - " \"\"\"Return the nuclear norm of the DLN.\"\"\"\n", - " return torch.norm(self.to_matrix().to(\"cpu\"), p=p)\n", - "\n", - " def norms(self, p: Union[int, float, str] = 2):\n", - " \"\"\"Return the nuclear norms of the individual layers of the DLN.\"\"\"\n", - " return [torch.norm(l.weight.data.to(\"cpu\"), p=p) for l in self.linears]\n", - "\n", - " def grad_norm(self, p=2, reduction=\"sum\"):\n", - " \"\"\"Return the norm of the gradient of the DLN.\n", - "\n", - " If `reduction` is \"sum\", return the sum of the norms over all layers.\n", - " If `reduction` is \"none\", return a list of the norms of the individual layers.\n", - "\n", - " \"\"\"\n", - " grad_norm = torch.zeros(self.L + 1, device=self.device)\n", - "\n", - " if p != 2:\n", - " raise NotImplementedError(\"Only p=2 is implemented.\")\n", - "\n", - " grad_norms = [torch.sum(linear.weight.grad**p) for linear in self.linears]\n", - "\n", - " if reduction == \"sum\":\n", - " return sum(grad_norms)\n", - " elif reduction != \"none\":\n", - " raise ValueError(f\"Unknown reduction {reduction}\")\n", - "\n", - " return (grad_norm ** (1 / p)).to(\"cpu\")\n", - "\n", - " @property\n", - " def device(self):\n", - " return next(self.parameters()).device\n", - "\n", - "\n", - "class DLNDataset(Dataset):\n", - " teacher: DLN\n", - "\n", - " def __init__(\n", - " self,\n", - " teacher: Union[DLN, torch.Tensor],\n", - " num_samples: int = 100,\n", - " noise_level: float = 0.0,\n", - " seed: int = 0,\n", - " device: torch.device = torch.device(\"cpu\"),\n", - " ):\n", - " torch.manual_seed(seed)\n", - "\n", - " if isinstance(teacher, torch.Tensor):\n", - " teacher = DLN.from_matrix(teacher)\n", - "\n", - " self.teacher = teacher.to(device=device)\n", - " self.num_features = teacher.to_matrix().shape[0]\n", - " self.num_samples = num_samples\n", - " self.noise_level = noise_level\n", - "\n", - " inputs = torch.rand(self.num_samples, self.num_features, device=device).detach()\n", - "\n", - " num_outputs = self.teacher.to_matrix().shape[1]\n", - " labels = (teacher(inputs) + noise_level * torch.rand(num_outputs)).detach()\n", - " self.data = torch.utils.data.TensorDataset(inputs, labels)\n", - "\n", - " def __len__(self):\n", - " return self.num_samples\n", - "\n", - " def __getitem__(self, idx):\n", - " return self.data[idx]\n", - "\n", - " def __repr__(self):\n", - " return f\"DLNDataset(teacher={self.teacher}, num_samples={self.num_samples}, noise_level={self.noise_level})\"\n", - "\n", - " @classmethod\n", - " def generate_split(\n", - " cls,\n", - " teacher: Union[DLN, torch.Tensor],\n", - " num_samples: int = 100,\n", - " noise_level: float = 0.0,\n", - " seed: int = 0,\n", - " device: torch.device = torch.device(\"cpu\"),\n", - " ):\n", - " if isinstance(teacher, torch.Tensor):\n", - " teacher = DLN.from_matrix(teacher)\n", - "\n", - " train_data = cls(\n", - " teacher,\n", - " num_samples=num_samples,\n", - " noise_level=noise_level,\n", - " seed=seed,\n", - " device=device,\n", - " )\n", - " test_data = cls(\n", - " teacher,\n", - " num_samples=num_samples,\n", - " noise_level=noise_level,\n", - " seed=seed + 1,\n", - " device=device,\n", - " )\n", - "\n", - " return train_data, test_data\n", - "\n", - "\n", - "logging.basicConfig(level=logging.INFO)\n", - "\n", - "sns.set_palette(\"deep\")\n", - "sns.set_style(\"whitegrid\")\n", - "\n", - "PRIMARY, SECONDARY, TERTIARY = sns.color_palette(\"deep\")[:3]\n", - "PRIMARY_LIGHT, SECONDARY_LIGHT, TERTIARY_LIGHT = sns.color_palette(\"muted\")[:3]\n", - "\n", - "DEVICE = os.environ.get(\n", - " \"DEVICE\",\n", - " (\n", - " \"cuda:0\"\n", - " if torch.cuda.is_available()\n", - " else \"mps\"\n", - " if torch.backends.mps.is_available()\n", - " else \"cpu\"\n", - " ),\n", - ")\n", - "DEVICE = torch.device(DEVICE)\n", - "NUM_CORES = int(os.environ.get(\"NUM_CORES\", 1))\n", - "DEVICE, NUM_CORES" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "from pydantic import BaseModel\n", - "from dataclasses import dataclass\n", - "\n", - "from devinterp.utils import evaluate_mse, default_nbeta\n", - "\n", - "\n", - "@dataclass\n", - "class Learner:\n", - " config: \"RectangularDLNConfig\"\n", - " model: nn.Module\n", - " dataset: torch.utils.data.Dataset\n", - " loader: torch.utils.data.DataLoader\n", - " optimizer: torch.optim.Optimizer\n", - " evals: Callable[[nn.Module], Dict[str, float]]\n", - "\n", - "\n", - "class RectangularDLNConfig(BaseModel):\n", - " teacher_matrix: torch.Tensor\n", - " gamma: float = 1.1\n", - " w: int = 100\n", - " L: int = 4\n", - " seed: int = 0\n", - " noise_level: float = 1.0\n", - " num_training_samples: int = 1024\n", - " batch_size: int = 128\n", - " num_steps: int = 10_000\n", - " device: str = \"cpu\"\n", - " lr: float = 1e-3\n", - " momentum: float = 0.9\n", - " weight_decay: float = 1e-3\n", - "\n", - " class Config:\n", - " arbitrary_types_allowed = True\n", - "\n", - " def create_teacher(self):\n", - " return DLN.from_matrix(self.teacher_matrix, L=1)\n", - "\n", - " def create_student(self):\n", - " return DLN.make_rectangular(\n", - " input_dim=self.input_dim,\n", - " output_dim=self.output_dim,\n", - " L=self.L,\n", - " w=self.w,\n", - " gamma=self.gamma,\n", - " )\n", - "\n", - " def create_data(self, teacher: DLN):\n", - " return DLNDataset.generate_split(\n", - " teacher, self.num_training_samples, self.noise_level, self.seed\n", - " )\n", - "\n", - " def create_learner(self, **kwargs):\n", - " teacher = self.create_teacher()\n", - " student = self.create_student()\n", - " trainset, testset = self.create_data(teacher)\n", - " trainloader = torch.utils.data.DataLoader(\n", - " trainset, batch_size=self.batch_size, shuffle=True\n", - " )\n", - " evals = make_evals(teacher_matrix, trainset, testset, self.device, **kwargs)\n", - " optimizer = optim.SGD(\n", - " student.parameters(),\n", - " lr=self.lr,\n", - " momentum=self.momentum,\n", - " weight_decay=self.weight_decay,\n", - " )\n", - "\n", - " learner = Learner(self, student, trainset, trainloader, optimizer, evals)\n", - " return learner\n", - "\n", - " @property\n", - " def input_dim(self):\n", - " return self.teacher_matrix.shape[1]\n", - "\n", - " @property\n", - " def output_dim(self):\n", - " return self.teacher_matrix.shape[0]\n", - "\n", - " def model_dump(self, *args, **kwargs):\n", - " dump = super().model_dump(*args, **kwargs)\n", - " dump[\"teacher_matrix\"] = self.teacher_matrix.tolist()\n", - "\n", - " return dump\n", - "\n", - "\n", - "def make_evals(\n", - " teacher_matrix: torch.Tensor,\n", - " trainset: DLNDataset,\n", - " testset: DLNDataset,\n", - " device: str,\n", - " num_draws: int = 10,\n", - " num_chains: int = 10,\n", - " num_burnin_steps: int = 0,\n", - " num_steps_bw_draws: int = 1,\n", - " num_cores: int = NUM_CORES,\n", - " repeats=5,\n", - " **kwargs,\n", - "):\n", - " teacher_matrix = teacher_matrix.to(device)\n", - "\n", - " trainloader = torch.utils.data.DataLoader(trainset, batch_size=128, shuffle=True)\n", - " testloader = torch.utils.data.DataLoader(testset, batch_size=128, shuffle=False)\n", - "\n", - " def eval_mse(model, loader):\n", - " loss = 0\n", - " count = 0\n", - "\n", - " for x, y in loader:\n", - " x, y = x.to(device), y.to(device)\n", - " loss += F.mse_loss(model(x), y, reduction=\"sum\").item()\n", - " count += len(x)\n", - "\n", - " return loss / count\n", - "\n", - " def eval_progress(model: DLN):\n", - " # Divide the first singular value by the first singular value of the teacher, and so on, then sum.\n", - " # This needs a new name.\n", - " singular_values = model.to_matrix().to(\"cpu\").svd().S\n", - " teacher_singular_values = teacher_matrix.to(\"cpu\").svd().S\n", - " missing_singular_values = teacher_singular_values == 0\n", - " teacher_singular_values[missing_singular_values] = 1\n", - " progress = singular_values / teacher_singular_values\n", - " # Get rid of division by zero problems\n", - " progress[progress == np.inf] = 0\n", - " progress[progress == -np.inf] = 0\n", - " progress[missing_singular_values] = 0\n", - "\n", - " return torch.sum(progress).item()\n", - "\n", - " def eval_matrix_properties(model: DLN):\n", - " return {\n", - " \"rank\": model.rank(atol=1e-1).item(),\n", - " \"ranks\": [e.item() for e in model.ranks(atol=1e-1)],\n", - " \"grad_norm\": model.grad_norm().item(),\n", - " \"nuc_norm\": model.norm(p=\"nuc\").item(),\n", - " \"nuc_norms\": [e.item() for e in model.norms(p=\"nuc\")],\n", - " }\n", - "\n", - " def eval_rlct(model: DLN):\n", - " model.to(\"cpu\")\n", - " optimizer_kwargs = dict(\n", - " lr=1e-4, localization=1.0, nbeta=default_nbeta(len(trainset))\n", - " )\n", - " optimizer_kwargs.update(kwargs)\n", - " rlct = estimate_learning_coeff(\n", - " model,\n", - " loader=trainloader,\n", - " evaluate=evaluate_mse,\n", - " sampling_method=SGLD,\n", - " optimizer_kwargs=optimizer_kwargs,\n", - " num_draws=num_draws,\n", - " num_chains=num_chains,\n", - " num_burnin_steps=num_burnin_steps,\n", - " num_steps_bw_draws=num_steps_bw_draws,\n", - " cores=num_cores,\n", - " device=torch.device(device),\n", - " verbose=False,\n", - " )\n", - " model.to(device)\n", - " return rlct\n", - "\n", - " def eval_rlct_repeated(model):\n", - " results = {f\"rlct/{i}\": eval_rlct(model) for i in range(repeats)}\n", - " rlcts = list(results.values())\n", - " results[\"rlct/mean\"] = np.mean(rlcts)\n", - " results[\"rlct/std\"] = np.std(rlcts)\n", - "\n", - " return results\n", - "\n", - " def evals(model):\n", - " return {\n", - " \"mse/train\": eval_mse(model, trainloader),\n", - " \"mse/test\": eval_mse(model, testloader),\n", - " \"progress\": eval_progress(model),\n", - " **eval_matrix_properties(model),\n", - " **eval_rlct_repeated(model),\n", - " }\n", - "\n", - " return evals\n", - "\n", - "\n", - "teacher_matrix = 10.0 * torch.Tensor(np.diag([1, 2, 3, 4, 5])).detach()\n", - "config = RectangularDLNConfig(\n", - " teacher_matrix=teacher_matrix,\n", - " num_training_samples=1024,\n", - " batch_size=128,\n", - " num_steps=10_000,\n", - " w=100,\n", - " L=4,\n", - " gamma=1.0,\n", - " noise_level=0.0,\n", - " device=str(DEVICE),\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "def train(learner):\n", - " learner.model.to(learner.config.device)\n", - " learner.model.train()\n", - "\n", - " evals = []\n", - "\n", - " num_steps = learner.config.num_steps\n", - " logging_steps = set(np.linspace(0, num_steps, 50).astype(int)) | set(\n", - " np.logspace(0, num_steps, 50).astype(int)\n", - " )\n", - " print(logging_steps)\n", - "\n", - " def log(step):\n", - " learner.model.eval()\n", - " evals.append({\"step\": step, **learner.evals(learner.model)})\n", - " # print(yaml.dump(evals[-1]))\n", - " learner.model.train()\n", - "\n", - " step = -1\n", - " epoch = -1\n", - "\n", - " pbar = tqdm(\n", - " total=learner.config.num_steps,\n", - " desc=\"Training...\",\n", - " )\n", - "\n", - " while step < learner.config.num_steps:\n", - " torch.manual_seed(step)\n", - " epoch += 1\n", - "\n", - " for x, y in learner.loader:\n", - " step += 1\n", - " x, y = x.to(learner.config.device), y.to(learner.config.device)\n", - " learner.optimizer.zero_grad()\n", - " y_hat = learner.model(x)\n", - " loss = F.mse_loss(y_hat, y)\n", - " loss.backward()\n", - " learner.optimizer.step()\n", - "\n", - " if step in logging_steps:\n", - " log(step=step)\n", - "\n", - " pbar.update(1)\n", - "\n", - " if pbar:\n", - " pbar.close()\n", - "\n", - " log(step=step)\n", - "\n", - " evals_df = pd.DataFrame(evals)\n", - " evals_df.sort_values(\"step\", inplace=True)\n", - "\n", - " return evals_df" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{0, 1, 6530, 5510, 4489, 3469, 2448, 10000, 8979, 1428, 7959, 408, 6938, 5918, 4897, 3877, 2857, 9387, 1836, 8367, 816, 7346, 6326, 5306, 4285, 3265, 9795, 2244, 8775, 1224, 7755, 204, 6734, 5714, 4693, 3673, 2653, 9183, 1632, 8163, 612, 7142, 6122, -9223372036854775808, 5102, 4081, 3061, 9591, 2040, 8571, 1020, 7551}\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "6033dab8407047b484fc5cfb019a3807", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Training...: 0%| | 0/10000 [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def plot_loss_vs_learning_coeff(\n", - " df, figsize=(8, 6), title=None, ax: Optional[plt.Axes] = None, xlog=False, std=False\n", - "):\n", - " if not ax:\n", - " fig, ax = plt.subplots(figsize=figsize)\n", - "\n", - " ax.set_title(title if title else \"Loss vs. Learning Coefficient\")\n", - "\n", - " # Train error\n", - " ax.plot(df.step, df[\"mse/test\"], label=\"Test error\", color=PRIMARY)\n", - " ax.plot(\n", - " df.step, df[\"mse/train\"], label=\"Train error\", color=PRIMARY_LIGHT, alpha=0.5\n", - " )\n", - " ax.set_yscale(\"log\")\n", - " ax.set_ylabel(\"MSE\", color=PRIMARY)\n", - " ax.tick_params(axis=\"y\", labelcolor=PRIMARY)\n", - " ax.legend(loc=\"lower right\")\n", - "\n", - " # Learning coefficients\n", - " axb = ax.twinx()\n", - " rlcts = np.clip(df[\"rlct/mean\"].to_numpy(), 0, None)\n", - " axb.plot(df.step, rlcts, label=\"RLCTs\", color=SECONDARY)\n", - " axb.set_ylabel(r\"Local Learning Coefficient, $\\hat \\lambda$\", color=SECONDARY)\n", - " axb.tick_params(axis=\"y\", labelcolor=SECONDARY)\n", - "\n", - " ax.set_xlabel(\"Step\")\n", - "\n", - " if xlog:\n", - " ax.set_xscale(\"log\")\n", - "\n", - " if std:\n", - " axb.fill_between(\n", - " df.step,\n", - " df[\"rlct/mean\"] - df[\"rlct/std\"],\n", - " df[\"rlct/mean\"] + df[\"rlct/std\"],\n", - " color=SECONDARY,\n", - " alpha=0.3,\n", - " label=r\"Std $\\hat\\lambda$\",\n", - " )\n", - "\n", - "\n", - "def plot_all(df, xlog=False, figsize=(8, 6), title=None):\n", - " L = len(df.ranks[0])\n", - "\n", - " # Figure 1: Loss and RLCTs\n", - " fig, axes = plt.subplots(2, 1, figsize=figsize)\n", - " ax, ax2 = axes\n", - "\n", - " plot_loss_vs_learning_coeff(df, ax=ax, title=title, xlog=xlog)\n", - "\n", - " # Figure 2: Nuclear Norms\n", - " ax2.set_title(title if title else \"Nuclear Norms\")\n", - " ax2.set_xlabel(\"Step\")\n", - " if xlog:\n", - " ax2.set_xscale(\"log\")\n", - "\n", - " # Nuclear Norms\n", - " for l in range(L):\n", - " ax2.plot(df.step, [e[l] for e in df.nuc_norms], label=f\"Nuclear Norm {l}\")\n", - "\n", - " ax2.set_ylabel(\"Nuclear Norms\")\n", - " ax2.legend(loc=\"lower right\")\n", - "\n", - " plt.tight_layout()\n", - " plt.show()\n", - "\n", - "\n", - "plot_loss_vs_learning_coeff(df, xlog=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{0, 1, 6530, 5510, 4489, 3469, 2448, 10000, 8979, 1428, 7959, 408, 6938, 5918, 4897, 3877, 2857, 9387, 1836, 8367, 816, 7346, 6326, 5306, 4285, 3265, 9795, 2244, 8775, 1224, 7755, 204, 6734, 5714, 4693, 3673, 2653, 9183, 1632, 8163, 612, 7142, 6122, -9223372036854775808, 5102, 4081, 3061, 9591, 2040, 8571, 1020, 7551}\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "54108951d791431a92c0857abc432d3c", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Training...: 0%| | 0/10000 [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{0, 1, 6530, 5510, 4489, 3469, 2448, 10000, 8979, 1428, 7959, 408, 6938, 5918, 4897, 3877, 2857, 9387, 1836, 8367, 816, 7346, 6326, 5306, 4285, 3265, 9795, 2244, 8775, 1224, 7755, 204, 6734, 5714, 4693, 3673, 2653, 9183, 1632, 8163, 612, 7142, 6122, -9223372036854775808, 5102, 4081, 3061, 9591, 2040, 8571, 1020, 7551}\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "5474755ace724414907cb24b28fce82f", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Training...: 0%| | 0/10000 [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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", 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", 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "for df in dfs:\n", - " plot_loss_vs_learning_coeff(df, std=True)\n", - " plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Experiments" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Defining all the teacher matrices\n", - "\n", - "\n", - "def run_experiment(teacher_matrix: torch.Tensor, seed=None, **kwargs):\n", - " if seed:\n", - " torch.manual_seed(seed)\n", - "\n", - " config = RectangularDLNConfig(teacher_matrix=teacher_matrix, **kwargs)\n", - " learner = config.create_learner()\n", - " df = train(learner)\n", - " return df\n", - "\n", - "\n", - "# Set up the teacher matrices\n", - "\n", - "rk5_matrix = torch.Tensor(10 * np.diag(np.arange(1, 6)))\n", - "\n", - "rk4_matrix = rk5_matrix.clone()\n", - "rk4_matrix[-1, -1] = 0\n", - "\n", - "rk2_matrix = rk4_matrix.clone()\n", - "rk2_matrix[-2, -2] = 0\n", - "rk2_matrix[-3, -3] = 0\n", - "\n", - "default_settings = dict(\n", - " num_training_samples=1024,\n", - " batch_size=128,\n", - " num_steps=10_000,\n", - " w=100,\n", - " L=4,\n", - " gamma=1.0,\n", - " noise_level=0.0,\n", - " device=str(DEVICE),\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{0, 1, 6530, 5510, 4489, 3469, 2448, 10000, 8979, 1428, 7959, 408, 6938, 5918, 4897, 3877, 2857, 9387, 1836, 8367, 816, 7346, 6326, 5306, 4285, 3265, 9795, 2244, 8775, 1224, 7755, 204, 6734, 5714, 4693, 3673, 2653, 9183, 1632, 8163, 612, 7142, 6122, -9223372036854775808, 5102, 4081, 3061, 9591, 2040, 8571, 1020, 7551}\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "cf7649ef26554ee8a4e71a19dd7637e9", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Training...: 0%| | 0/10000 [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{0, 1, 6530, 5510, 4489, 3469, 2448, 10000, 8979, 1428, 7959, 408, 6938, 5918, 4897, 3877, 2857, 9387, 1836, 8367, 816, 7346, 6326, 5306, 4285, 3265, 9795, 2244, 8775, 1224, 7755, 204, 6734, 5714, 4693, 3673, 2653, 9183, 1632, 8163, 612, 7142, 6122, -9223372036854775808, 5102, 4081, 3061, 9591, 2040, 8571, 1020, 7551}\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "445dc7b679bd4f01bf5a02afc989121c", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Training...: 0%| | 0/10000 [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{0, 1, 6530, 5510, 4489, 3469, 2448, 10000, 8979, 1428, 7959, 408, 6938, 5918, 4897, 3877, 2857, 9387, 1836, 8367, 816, 7346, 6326, 5306, 4285, 3265, 9795, 2244, 8775, 1224, 7755, 204, 6734, 5714, 4693, 3673, 2653, 9183, 1632, 8163, 612, 7142, 6122, -9223372036854775808, 5102, 4081, 3061, 9591, 2040, 8571, 1020, 7551}\n" - 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stepmse/trainmse/testprogressrankranksgrad_normnuc_normnuc_normsrlct/0rlct/1rlct/2rlct/3rlct/4rlct/meanrlct/stdrnoise_level
001793.9443511795.0024410.0000030[3, 39, 39, 2]0.0015700.000094[0.495727002620697, 8.405984878540039, 8.38990...11.7986224.14195225.7784333.165637-3.4683458.2832609.99879650.0
111793.9443661795.0024410.0000030[3, 39, 39, 2]0.0014900.000094[0.49572277069091797, 8.405965805053711, 8.389...-26.440283-7.3732013.04173616.50750543.3888135.82491423.44416150.0
22041793.9155431794.9738310.0000090[3, 39, 38, 3]0.0083000.000404[0.5216946005821228, 8.41380786895752, 8.39311...-28.127401-8.59450129.221821-38.5695500.706212-9.07268423.64517750.0
3408444.907421460.6836700.5376041[2, 39, 38, 3]12996.93945326.880136[2.788567543029785, 10.468053817749023, 10.435...-591.672302-595.342224-592.287476-589.571899-591.082642-591.9913091.90248950.0
4612303.016163321.2277720.8596811[2, 39, 38, 2]76.90752442.983959[3.2394349575042725, 10.633877754211426, 10.60...3.2714740.6996715.6419284.6162627.0237694.2506212.15998950.0
.........................................................
47938749.12545349.0974186.4545613[4, 35, 34, 4]6.771225118.613503[7.847429275512695, 14.443892478942871, 14.410...2.9341423.7093473.2523234.3923684.1400053.6856370.540183210.0
48959149.11949949.0420786.4547483[4, 35, 34, 4]57.930408118.622581[7.88250732421875, 14.464000701904297, 14.4317...2.5421394.8513542.7989582.7983014.7682153.5517931.031742210.0
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312 rows × 18 columns

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" - ], - "text/plain": [ - " step mse/train mse/test progress rank ranks \\\n", - "0 0 1793.944351 1795.002441 0.000003 0 [3, 39, 39, 2] \n", - "1 1 1793.944366 1795.002441 0.000003 0 [3, 39, 39, 2] \n", - "2 204 1793.915543 1794.973831 0.000009 0 [3, 39, 38, 3] \n", - "3 408 444.907421 460.683670 0.537604 1 [2, 39, 38, 3] \n", - "4 612 303.016163 321.227772 0.859681 1 [2, 39, 38, 2] \n", - ".. ... ... ... ... ... ... \n", - "47 9387 49.125453 49.097418 6.454561 3 [4, 35, 34, 4] \n", - "48 9591 49.119499 49.042078 6.454748 3 [4, 35, 34, 4] \n", - "49 9795 49.085578 49.020588 6.454556 3 [4, 35, 34, 4] \n", - "50 10000 48.963258 49.001051 6.454456 3 [4, 35, 34, 4] \n", - "51 10007 48.953617 48.936526 6.453847 3 [4, 35, 34, 4] \n", - "\n", - " grad_norm nuc_norm \\\n", - "0 0.001570 0.000094 \n", - "1 0.001490 0.000094 \n", - "2 0.008300 0.000404 \n", - "3 12996.939453 26.880136 \n", - "4 76.907524 42.983959 \n", - ".. ... ... \n", - "47 6.771225 118.613503 \n", - "48 57.930408 118.622581 \n", - "49 1.057504 118.625137 \n", - "50 5.737196 118.657356 \n", - "51 9.501528 118.649910 \n", - "\n", - " nuc_norms rlct/0 rlct/1 \\\n", - "0 [0.495727002620697, 8.405984878540039, 8.38990... 11.798622 4.141952 \n", - "1 [0.49572277069091797, 8.405965805053711, 8.389... -26.440283 -7.373201 \n", - "2 [0.5216946005821228, 8.41380786895752, 8.39311... -28.127401 -8.594501 \n", - "3 [2.788567543029785, 10.468053817749023, 10.435... -591.672302 -595.342224 \n", - "4 [3.2394349575042725, 10.633877754211426, 10.60... 3.271474 0.699671 \n", - ".. ... ... ... \n", - "47 [7.847429275512695, 14.443892478942871, 14.410... 2.934142 3.709347 \n", - "48 [7.88250732421875, 14.464000701904297, 14.4317... 2.542139 4.851354 \n", - "49 [7.936989784240723, 14.502958297729492, 14.471... 3.820201 4.054455 \n", - "50 [8.0411376953125, 14.590907096862793, 14.56081... 2.385313 3.325741 \n", - "51 [8.04632568359375, 14.595489501953125, 14.5654... 3.292753 3.698418 \n", - "\n", - " rlct/2 rlct/3 rlct/4 rlct/mean rlct/std r noise_level \n", - "0 25.778433 3.165637 -3.468345 8.283260 9.998796 5 0.0 \n", - "1 3.041736 16.507505 43.388813 5.824914 23.444161 5 0.0 \n", - "2 29.221821 -38.569550 0.706212 -9.072684 23.645177 5 0.0 \n", - "3 -592.287476 -589.571899 -591.082642 -591.991309 1.902489 5 0.0 \n", - "4 5.641928 4.616262 7.023769 4.250621 2.159989 5 0.0 \n", - ".. ... ... ... ... ... .. ... \n", - "47 3.252323 4.392368 4.140005 3.685637 0.540183 2 10.0 \n", - "48 2.798958 2.798301 4.768215 3.551793 1.031742 2 10.0 \n", - "49 2.616412 5.147030 4.176042 3.962828 0.811094 2 10.0 \n", - "50 5.047893 2.866841 4.976895 3.720536 1.096157 2 10.0 \n", - "51 5.259744 4.514405 2.820934 3.917251 0.871628 2 10.0 \n", - "\n", - "[312 rows x 18 columns]" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "results = {}\n", - "SEED = 0\n", - "\n", - "for rk, teacher_matrix in zip([5, 4, 2], [rk5_matrix, rk4_matrix, rk2_matrix]):\n", - " for noise_level in [0.0, 10.0]:\n", - " name = f\"rk{rk}_L4_w100_noise{noise_level}\"\n", - " results[name] = run_experiment(rk5_matrix, seed=SEED, **default_settings)\n", - " plot_all(\n", - " results[name], xlog=False, title=f\"r={rk}, L=4, w=100, noise={noise_level}\"\n", - " )\n", - "\n", - "df = None\n", - "\n", - "for rk, teacher_matrix in zip([5, 4, 2], [rk5_matrix, rk4_matrix, rk2_matrix]):\n", - " for noise_level in [0.0, 10.0]:\n", - " _df = pd.DataFrame(results[f\"rk{rk}_L4_w100_noise{noise_level}\"])\n", - " _df[\"r\"] = rk\n", - " _df[\"noise_level\"] = noise_level\n", - "\n", - " df = pd.concat([df, _df]) if df is not None else _df\n", - "\n", - "df" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Recreate figure 5" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "ename": "", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[1;31mnotebook controller is DISPOSED. \n", - "\u001b[1;31mView Jupyter log for further details." - ] - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "\n", - "def plot_grid(\n", - " df,\n", - " x_axis: str,\n", - " z_axis: str,\n", - " metrics: List[str],\n", - " title: str,\n", - " logscale: bool = True,\n", - " inset=False,\n", - " figsize=(10, 6),\n", - "):\n", - " xs = df[x_axis].unique()\n", - " zs = df[z_axis].unique()\n", - "\n", - " # Define the colors for each w value\n", - " colors = [PRIMARY, SECONDARY, TERTIARY]\n", - "\n", - " # Create a figure with 3 subplots (one for each gamma)\n", - " fig, axes = plt.subplots(len(metrics), 3, figsize=figsize)\n", - " fig.suptitle(title)\n", - "\n", - " fig.tight_layout()\n", - "\n", - " # Iterate through the unique gammas\n", - " for i, x in enumerate(xs):\n", - " for j, metric in enumerate(metrics):\n", - " axes[j, 0].set_ylabel(metric)\n", - " axes[-1, i].set_xlabel(\"# of steps\")\n", - "\n", - " ax = axes[j, i]\n", - " # Add an inset focusing on the first 2000 steps\n", - " ax_inset = ax.inset_axes([0.65, 0.7, 0.3, 0.25])\n", - "\n", - " for k, z in enumerate(zs):\n", - " data = df[(df[x_axis] == x) & (df[z_axis] == z)]\n", - " color = colors[k]\n", - "\n", - " # Plot the training error against the number of steps\n", - " ax.plot(data.step, data[metric], color=color, label=f\"{z_axis}={z}\")\n", - "\n", - " inset_data = data.loc[data.step < 2000]\n", - " ax_inset.plot(inset_data.step, inset_data[metric], color=color)\n", - "\n", - " ax_inset.yaxis.set_visible(False)\n", - " ax_inset.xaxis.set_visible(False)\n", - "\n", - " if logscale:\n", - " ax_inset.set_yscale(\"log\")\n", - " ax.set_yscale(\"log\")\n", - " # ax_inset.set_xscale('log')\n", - " # ax.set_xscale('log')\n", - "\n", - " if not inset:\n", - " ax_inset.remove()\n", - "\n", - " ax.set_title(f\"{x_axis}={x}\")\n", - " ax.legend(loc=\"lower left\")\n", - "\n", - " plt.show()\n", - "\n", - "\n", - "plot_grid(\n", - " df,\n", - " \"r\",\n", - " \"noise_level\",\n", - " [\"mse/train\", \"rlct/mean\", \"nuc_norm\"],\n", - " \"Rank in [5, 4, 2], Noise Level in [0., 10.]\",\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "\n", - "def plot_grid(\n", - " df,\n", - " x_axis: str,\n", - " z_axis: str,\n", - " metrics: List[str],\n", - " title: str,\n", - " logscale: bool = True,\n", - " inset=False,\n", - " figsize=(10, 6),\n", - "):\n", - " xs = df[x_axis].unique()\n", - " zs = df[z_axis].unique()\n", - "\n", - " # Define the colors for each w value\n", - " colors = [PRIMARY, SECONDARY, TERTIARY]\n", - "\n", - " # Create a figure with 3 subplots (one for each gamma)\n", - " fig, axes = plt.subplots(len(metrics), 3, figsize=figsize)\n", - " fig.suptitle(title)\n", - "\n", - " fig.tight_layout()\n", - "\n", - " # Iterate through the unique gammas\n", - " for i, x in enumerate(xs):\n", - " for j, metric in enumerate(metrics):\n", - " axes[j, 0].set_ylabel(metric)\n", - " axes[-1, i].set_xlabel(\"# of steps\")\n", - "\n", - " ax = axes[j, i]\n", - " # Add an inset focusing on the first 2000 steps\n", - " ax_inset = ax.inset_axes([0.65, 0.7, 0.3, 0.25])\n", - "\n", - " for k, z in enumerate(zs):\n", - " data = df[(df[x_axis] == x) & (df[z_axis] == z)]\n", - " color = colors[k]\n", - "\n", - " # Plot the training error against the number of steps\n", - " ax.plot(data.step, data[metric], color=color, label=f\"{z_axis}={z}\")\n", - "\n", - " inset_data = data.loc[data.step < 2000]\n", - " ax_inset.plot(inset_data.step, inset_data[metric], color=color)\n", - "\n", - " ax_inset.yaxis.set_visible(False)\n", - " ax_inset.xaxis.set_visible(False)\n", - "\n", - " if logscale:\n", - " ax_inset.set_yscale(\"log\")\n", - " ax.set_yscale(\"log\")\n", - " # ax_inset.set_xscale('log')\n", - " # ax.set_xscale('log')\n", - "\n", - " if not inset:\n", - " ax_inset.remove()\n", - "\n", - " ax.set_title(f\"{x_axis}={x}\")\n", - " ax.legend(loc=\"lower left\")\n", - "\n", - " plt.show()\n", - "\n", - "\n", - "plot_grid(\n", - " df,\n", - " \"r\",\n", - " \"noise_level\",\n", - " [\"mse/train\", \"rlct/mean\", \"nuc_norm\"],\n", - " \"Rank in [5, 4, 2], Noise Level in [0., 10.]\",\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{0, 1, 6530, 5510, 4489, 3469, 2448, 10000, 8979, 1428, 7959, 408, 6938, 5918, 4897, 3877, 2857, 9387, 1836, 8367, 816, 7346, 6326, 5306, 4285, 3265, 9795, 2244, 8775, 1224, 7755, 204, 6734, 5714, 4693, 3673, 2653, 9183, 1632, 8163, 612, 7142, 6122, -9223372036854775808, 5102, 4081, 3061, 9591, 2040, 8571, 1020, 7551}\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "62840490968145f6aa262c9707ef4f5d", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Training...: 0%| | 0/10000 [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{0, 1, 6530, 5510, 4489, 3469, 2448, 10000, 8979, 1428, 7959, 408, 6938, 5918, 4897, 3877, 2857, 9387, 1836, 8367, 816, 7346, 6326, 5306, 4285, 3265, 9795, 2244, 8775, 1224, 7755, 204, 6734, 5714, 4693, 3673, 2653, 9183, 1632, 8163, 612, 7142, 6122, -9223372036854775808, 5102, 4081, 3061, 9591, 2040, 8571, 1020, 7551}\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "5000a9584f9f4240a6bd15fba9971555", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Training...: 0%| | 0/10000 [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{0, 1, 6530, 5510, 4489, 3469, 2448, 10000, 8979, 1428, 7959, 408, 6938, 5918, 4897, 3877, 2857, 9387, 1836, 8367, 816, 7346, 6326, 5306, 4285, 3265, 9795, 2244, 8775, 1224, 7755, 204, 6734, 5714, 4693, 3673, 2653, 9183, 1632, 8163, 612, 7142, 6122, -9223372036854775808, 5102, 4081, 3061, 9591, 2040, 8571, 1020, 7551}\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "c6ed01415921472abaa91da0a112719f", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Training...: 0%| | 0/10000 [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{0, 1, 6530, 5510, 4489, 3469, 2448, 10000, 8979, 1428, 7959, 408, 6938, 5918, 4897, 3877, 2857, 9387, 1836, 8367, 816, 7346, 6326, 5306, 4285, 3265, 9795, 2244, 8775, 1224, 7755, 204, 6734, 5714, 4693, 3673, 2653, 9183, 1632, 8163, 612, 7142, 6122, -9223372036854775808, 5102, 4081, 3061, 9591, 2040, 8571, 1020, 7551}\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "bdf6cbedf68949ffac9c89e58edd8fb7", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Training...: 0%| | 0/10000 [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{0, 1, 6530, 5510, 4489, 3469, 2448, 10000, 8979, 1428, 7959, 408, 6938, 5918, 4897, 3877, 2857, 9387, 1836, 8367, 816, 7346, 6326, 5306, 4285, 3265, 9795, 2244, 8775, 1224, 7755, 204, 6734, 5714, 4693, 3673, 2653, 9183, 1632, 8163, 612, 7142, 6122, -9223372036854775808, 5102, 4081, 3061, 9591, 2040, 8571, 1020, 7551}\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "ac3b421478f049ffb142d748cb407957", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Training...: 0%| | 0/10000 [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{0, 1, 6530, 5510, 4489, 3469, 2448, 10000, 8979, 1428, 7959, 408, 6938, 5918, 4897, 3877, 2857, 9387, 1836, 8367, 816, 7346, 6326, 5306, 4285, 3265, 9795, 2244, 8775, 1224, 7755, 204, 6734, 5714, 4693, 3673, 2653, 9183, 1632, 8163, 612, 7142, 6122, -9223372036854775808, 5102, 4081, 3061, 9591, 2040, 8571, 1020, 7551}\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "e6c868aff4da429a98930877c62e1b4d", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Training...: 0%| | 0/10000 [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig5_df = None\n", - "\n", - "fig5_settings = dict(\n", - " num_training_samples=1024,\n", - " batch_size=128,\n", - " num_steps=10_000,\n", - " L=4,\n", - " noise_level=0.0,\n", - " device=str(DEVICE),\n", - ")\n", - "\n", - "for gamma in [0.75, 1.0, 1.5]:\n", - " # for w in [10, 100, 1000]:\n", - " for w in [10, 100]:\n", - " results = run_experiment(\n", - " rk5_matrix, seed=SEED, w=w, gamma=gamma, **fig5_settings\n", - " )\n", - " _df = pd.DataFrame(results)\n", - " _df[\"w\"] = w\n", - " _df[\"gamma\"] = gamma\n", - " fig5_df = pd.concat([fig5_df, _df]) if fig5_df is not None else _df\n", - " plot_all(results, xlog=False, title=f\"r=5, L=4, w={w}, noise=0, gamma={gamma}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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stepmse/trainmse/testprogressrankranksgrad_normnuc_normnuc_normsrlct/0rlct/1rlct/2rlct/3rlct/4rlct/meanrlct/stdwgamma
001791.7377621792.7927091.535396e-021[5, 9, 8, 5]3.465538e+012.910638e-01[2.7768821716308594, 4.9453816413879395, 4.337...7.601440-1.90456720.066181-1.741379-8.8435113.0356339.992253100.75
111791.4247281792.4864651.547082e-021[5, 9, 8, 5]3.279961e+012.923573e-01[2.777719497680664, 4.9458770751953125, 4.3397...-30.221968-12.378678-1.54574410.94403337.5169180.86291222.770842100.75
2204205.225048219.1667195.288178e+003[5, 9, 8, 5]2.763649e+017.588062e+01[6.866804122924805, 8.36233139038086, 8.073616...0.098753-0.9993000.9877774.4339121.6647451.2371771.830954100.75
340845.95342845.8257786.456892e+004[5, 9, 8, 5]3.577122e+001.195385e+02[9.123072624206543, 10.259856224060059, 10.058...5.2118593.6670383.2877193.5809154.1989483.9892960.678447100.75
46129.1089369.3469906.494348e+005[5, 9, 8, 5]1.588845e+001.399085e+02[10.47614574432373, 11.478628158569336, 11.376...4.9704134.1339584.4082114.7801714.6406984.5866900.291347100.75
.........................................................
4793871793.9435581795.0015565.590545e-100[0, 0, 0, 0]1.138192e-091.050557e-08[0.04441055282950401, 0.7701930403709412, 0.76...22.771879-4.430894-0.7896991.8510295.2165654.9237769.4682201001.50
4895911793.9435121795.0015565.617849e-100[0, 0, 0, 0]1.284186e-091.056772e-08[0.04434099420905113, 0.7686431407928467, 0.76...13.9604891.09933017.18288227.155029-14.9384958.89184714.5367451001.50
4997951793.9435421795.0015565.645333e-100[0, 0, 0, 0]1.067729e-091.063012e-08[0.04427191615104675, 0.7670963406562805, 0.76...-35.16399829.569128-6.97702417.976690-9.283003-0.77564122.6532401001.50
50100001793.9435121795.0015565.673199e-100[0, 0, 0, 0]1.284707e-091.069321e-08[0.04420298710465431, 0.765545666217804, 0.759...1.9143886.7753522.1815940.05514934.6375629.11280912.9528141001.50
51100071793.9435421795.0015565.674137e-100[0, 0, 0, 0]1.302487e-091.069534e-08[0.04420063644647598, 0.7654927968978882, 0.75...-5.06835219.944534-6.5822965.582393-7.7923251.21679110.5002841001.50
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312 rows × 18 columns

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" - ], - "text/plain": [ - " step mse/train mse/test progress rank ranks \\\n", - "0 0 1791.737762 1792.792709 1.535396e-02 1 [5, 9, 8, 5] \n", - "1 1 1791.424728 1792.486465 1.547082e-02 1 [5, 9, 8, 5] \n", - "2 204 205.225048 219.166719 5.288178e+00 3 [5, 9, 8, 5] \n", - "3 408 45.953428 45.825778 6.456892e+00 4 [5, 9, 8, 5] \n", - "4 612 9.108936 9.346990 6.494348e+00 5 [5, 9, 8, 5] \n", - ".. ... ... ... ... ... ... \n", - "47 9387 1793.943558 1795.001556 5.590545e-10 0 [0, 0, 0, 0] \n", - "48 9591 1793.943512 1795.001556 5.617849e-10 0 [0, 0, 0, 0] \n", - "49 9795 1793.943542 1795.001556 5.645333e-10 0 [0, 0, 0, 0] \n", - "50 10000 1793.943512 1795.001556 5.673199e-10 0 [0, 0, 0, 0] \n", - "51 10007 1793.943542 1795.001556 5.674137e-10 0 [0, 0, 0, 0] \n", - "\n", - " grad_norm nuc_norm \\\n", - "0 3.465538e+01 2.910638e-01 \n", - "1 3.279961e+01 2.923573e-01 \n", - "2 2.763649e+01 7.588062e+01 \n", - "3 3.577122e+00 1.195385e+02 \n", - "4 1.588845e+00 1.399085e+02 \n", - ".. ... ... \n", - "47 1.138192e-09 1.050557e-08 \n", - "48 1.284186e-09 1.056772e-08 \n", - "49 1.067729e-09 1.063012e-08 \n", - "50 1.284707e-09 1.069321e-08 \n", - "51 1.302487e-09 1.069534e-08 \n", - "\n", - " nuc_norms rlct/0 rlct/1 \\\n", - "0 [2.7768821716308594, 4.9453816413879395, 4.337... 7.601440 -1.904567 \n", - "1 [2.777719497680664, 4.9458770751953125, 4.3397... -30.221968 -12.378678 \n", - "2 [6.866804122924805, 8.36233139038086, 8.073616... 0.098753 -0.999300 \n", - "3 [9.123072624206543, 10.259856224060059, 10.058... 5.211859 3.667038 \n", - "4 [10.47614574432373, 11.478628158569336, 11.376... 4.970413 4.133958 \n", - ".. ... ... ... \n", - "47 [0.04441055282950401, 0.7701930403709412, 0.76... 22.771879 -4.430894 \n", - "48 [0.04434099420905113, 0.7686431407928467, 0.76... 13.960489 1.099330 \n", - "49 [0.04427191615104675, 0.7670963406562805, 0.76... -35.163998 29.569128 \n", - "50 [0.04420298710465431, 0.765545666217804, 0.759... 1.914388 6.775352 \n", - "51 [0.04420063644647598, 0.7654927968978882, 0.75... -5.068352 19.944534 \n", - "\n", - " rlct/2 rlct/3 rlct/4 rlct/mean rlct/std w gamma \n", - "0 20.066181 -1.741379 -8.843511 3.035633 9.992253 10 0.75 \n", - "1 -1.545744 10.944033 37.516918 0.862912 22.770842 10 0.75 \n", - "2 0.987777 4.433912 1.664745 1.237177 1.830954 10 0.75 \n", - "3 3.287719 3.580915 4.198948 3.989296 0.678447 10 0.75 \n", - "4 4.408211 4.780171 4.640698 4.586690 0.291347 10 0.75 \n", - ".. ... ... ... ... ... ... ... \n", - "47 -0.789699 1.851029 5.216565 4.923776 9.468220 100 1.50 \n", - "48 17.182882 27.155029 -14.938495 8.891847 14.536745 100 1.50 \n", - "49 -6.977024 17.976690 -9.283003 -0.775641 22.653240 100 1.50 \n", - "50 2.181594 0.055149 34.637562 9.112809 12.952814 100 1.50 \n", - "51 -6.582296 5.582393 -7.792325 1.216791 10.500284 100 1.50 \n", - "\n", - "[312 rows x 18 columns]" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "plot_grid(\n", - " fig5_df,\n", - " \"gamma\",\n", - " \"w\",\n", - " [\"mse/train\", \"rlct/mean\", \"nuc_norm\"],\n", - " \"Gamma in [0.75, 1.0, 1.5], w in [10, 100, 1000]\",\n", - ")\n", - "fig5_df" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": ".venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.18" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/examples/epsilon_beta.ipynb b/examples/epsilon_beta.ipynb deleted file mode 100644 index 7709f36c..00000000 --- a/examples/epsilon_beta.ipynb +++ /dev/null @@ -1,17443 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Epsilon-Beta Visualizations\n", - "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/timaeus-research/devinterp/blob/main/examples/epsilon_beta.ipynb)\n", - "\n", - "This notebook aims to visualize $\\hat{\\lambda}_n^\\beta$ for various values of $\\beta$ (inverse temperature) and $\\epsilon$ (step size). \n", - "\n", - "Roughly (per Adrian Xu's master thesis):\n", - "- $\\beta$ can be tuned via graphing $\\hat{\\lambda}_n^\\beta$ for a sweep of $\\beta$, and using $\\beta$ in a range around the critical points on the graph.\n", - "- $\\epsilon$ should be the greatest possible value that doesn't cause excessive numerical instability or cause the SGLD chains to fail to converge. An MALA proposal acceptance rate (see `sgld_calibration.ipynb`) between 0.9 and 0.95 is roughly optimal." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Set-up" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Requirement already satisfied: pip in /home/paperspace/devinterp/.venv/lib/python3.9/site-packages (24.2)\n", - "Note: you may need to restart the kernel to use updated packages.\n", - "Requirement already satisfied: transformers in /home/paperspace/devinterp/.venv/lib/python3.9/site-packages (4.30.2)\n", - "Requirement already satisfied: torch in /home/paperspace/devinterp/.venv/lib/python3.9/site-packages (2.0.1)\n", - "Requirement already satisfied: torchvision in /home/paperspace/devinterp/.venv/lib/python3.9/site-packages (0.15.2)\n", - "Requirement already satisfied: filelock in /home/paperspace/devinterp/.venv/lib/python3.9/site-packages (from transformers) (3.12.2)\n", - 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"Requirement already satisfied: MarkupSafe>=2.0 in /home/paperspace/devinterp/.venv/lib/python3.9/site-packages (from jinja2->torch>=2.0.1->devinterp) (2.1.3)\n", - "Requirement already satisfied: mpmath>=0.19 in /home/paperspace/devinterp/.venv/lib/python3.9/site-packages (from sympy->torch>=2.0.1->devinterp) (1.3.0)\n", - "Note: you may need to restart the kernel to use updated packages.\n" - ] - } - ], - "source": [ - "%pip install --upgrade pip\n", - "%pip install transformers torch torchvision\n", - "%pip install devinterp[vis]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The epsilon-beta sweep analyzer is fairly flexible. To sweep, all you need is:\n", - "- A callable function (typically a built-in DevInterp function) that returns local learning coefficient traces.\n", - "- Epsilon and beta ranges.\n", - "\n", - "To start, we'll visualize an epsilon-beta LLC sweep for a pretrained MNIST classifier." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Sweep LLC given a model" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import torch\n", - "import torchvision\n", - "from transformers import AutoModelForImageClassification\n", - "\n", - "from devinterp.optim import SGLD\n", - "from devinterp.slt.sampler import estimate_learning_coeff_with_summary\n", - "from devinterp.vis_utils import EpsilonBetaAnalyzer\n", - "\n", - "# Local import:\n", - "# import sys\n", - "# sys.path.append(\"../src/devinterp\")\n", - "# from vis_utils import EpsilonBetaAnalyzer\n", - "\n", - "\n", - "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", - "\n", - "\n", - "def evaluate(model, data):\n", - " inputs, outputs = data\n", - "\n", - " return torch.nn.functional.cross_entropy(model(inputs).logits, outputs), {\n", - " \"outputs\": outputs\n", - " }\n", - "\n", - "\n", - "# Load a pretrained MNIST classifier\n", - "model = AutoModelForImageClassification.from_pretrained(\"fxmarty/resnet-tiny-mnist\")\n", - "\n", - "data = torchvision.datasets.MNIST(\n", - " root=\"../data\",\n", - " download=True,\n", - " transform=torchvision.transforms.ToTensor(),\n", - ")\n", - "loader = torch.utils.data.DataLoader(data, batch_size=256, shuffle=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Define the LLC estimator function.\n", - "Note: The local learning coefficient estimator function expected by EpsilonBetaAnalyzer must have the following signature:\n", - "```python\n", - "def estimator(epsilon: float, beta: float, **kwargs) -> dict\n", - "```\n", - "- Where kwargs correspond to `llc_estimator_kwargs` that are passed in when EpsilonBetaVisualizer.configure_sweep() is called.\n", - "- The return value must be a dict with a `\"llc/trace\"` key corresponding to a numpy array of shape `(num_chains, num_draws)`\n", - "- Additional keys can represent other values of interest (e.g. acceptance rates, true LLC.)\n", - "\n", - "See below for an example that uses DevInterp's `estimate_learning_coeff_with_summary` to estimate the local learning coefficient for an arbitrary python model." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "import typing\n", - "from typing import Type\n", - "\n", - "import numpy as np\n", - "import torch\n", - "\n", - "\n", - "def estimate_llc_given_model(\n", - " model: torch.nn.Module,\n", - " loader: torch.utils.data.DataLoader,\n", - " evaluate: typing.Callable,\n", - " epsilon: float,\n", - " beta: float,\n", - " sampling_method: Type[torch.optim.Optimizer] = SGLD,\n", - " localization: float = 100.0,\n", - " num_chains: int = 5,\n", - " num_draws: int = 300,\n", - " num_burnin_steps: int = 0,\n", - " num_steps_bw_draws: int = 1,\n", - " device: torch.device = torch.device(\"cpu\"),\n", - " online: bool = True,\n", - " verbose: bool = False,\n", - "):\n", - " sweep_stats = estimate_learning_coeff_with_summary(\n", - " model,\n", - " loader=loader,\n", - " evaluate=evaluate,\n", - " sampling_method=sampling_method,\n", - " optimizer_kwargs=dict(lr=epsilon, localization=localization, nbeta=beta),\n", - " num_chains=num_chains, # How many independent chains to run\n", - " num_draws=num_draws, # How many samples to draw per chain\n", - " num_burnin_steps=num_burnin_steps, # How many samples to discard at the beginning of each chain\n", - " num_steps_bw_draws=num_steps_bw_draws, # How many steps to take between each sample\n", - " device=device,\n", - " online=online,\n", - " verbose=verbose,\n", - " )\n", - "\n", - " sweep_stats[\"llc/trace\"] = np.array(sweep_stats[\"llc/trace\"])\n", - " return sweep_stats" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Running the analyzer:\n", - "Methods:\n", - "- configure_sweep: Sets up the config for the following sweep.\n", - "- sweep: Runs a sweep over beta (inverse temperature) and epsilon (learning rate), using the provided llc_estimator function to calculate the loss traces.\n", - "- plot: Plots $\\hat{\\lambda}$ over epsilon and beta with various options." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 0%| | 0/64 [00:00\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - 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init_lossllc/meansllc/stdsllc/traceloss/traceepsilonbeta
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"title": { - "standoff": 15 - }, - "zerolinecolor": "#283442", - "zerolinewidth": 2 - } - } - }, - "title": { - "text": "Local learning coefficient vs. epsilon and beta" - } - } - }, - "text/html": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Want to plot slices over a particular subset of the data?\n", - "analyzer.plot(template=\"plotly_dark\", num_last_steps_to_average=50, slider=\"epsilon\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Visualizing an Epsilon-Beta Sweep for a Deep Linear Network (DLN)\n", - "\n", - "Credit for DLN code goes to Edmundlth. [Source notebook](https://colab.research.google.com/github/edmundlth/validating_lambdahat/blob/dev/DLN_lambdahat.ipynb)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### DLN Setup:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Collecting git+https://github.com/deepmind/dm-haiku\n", - " Cloning https://github.com/deepmind/dm-haiku to /tmp/pip-req-build-jp2d77c9\n", - " Running command git clone --filter=blob:none --quiet https://github.com/deepmind/dm-haiku /tmp/pip-req-build-jp2d77c9\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Resolved https://github.com/deepmind/dm-haiku to commit 54a1ebaa1c8387cd34114f4e0901e628bec92504\n", - " Preparing metadata (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25hRequirement already satisfied: jax in /home/paperspace/devinterp/.venv/lib/python3.9/site-packages (0.4.30)\n", - "Requirement already satisfied: optax in /home/paperspace/devinterp/.venv/lib/python3.9/site-packages (0.2.3)\n", - "Requirement already satisfied: absl-py>=0.7.1 in /home/paperspace/devinterp/.venv/lib/python3.9/site-packages (from dm-haiku==0.0.13.dev0) (2.1.0)\n", - "Requirement already satisfied: jmp>=0.0.2 in /home/paperspace/devinterp/.venv/lib/python3.9/site-packages (from dm-haiku==0.0.13.dev0) (0.0.4)\n", - "Requirement already satisfied: numpy>=1.18.0 in /home/paperspace/devinterp/.venv/lib/python3.9/site-packages (from dm-haiku==0.0.13.dev0) (1.26.4)\n", - "Requirement already satisfied: tabulate>=0.8.9 in /home/paperspace/devinterp/.venv/lib/python3.9/site-packages (from dm-haiku==0.0.13.dev0) (0.9.0)\n", - "Requirement already satisfied: jaxlib<=0.4.30,>=0.4.27 in /home/paperspace/devinterp/.venv/lib/python3.9/site-packages (from jax) (0.4.30)\n", - "Requirement already satisfied: ml-dtypes>=0.2.0 in /home/paperspace/devinterp/.venv/lib/python3.9/site-packages (from jax) (0.4.0)\n", - "Requirement already satisfied: opt-einsum in /home/paperspace/devinterp/.venv/lib/python3.9/site-packages (from jax) (3.3.0)\n", - "Requirement already satisfied: scipy>=1.9 in /home/paperspace/devinterp/.venv/lib/python3.9/site-packages (from jax) (1.11.3)\n", - "Requirement already satisfied: importlib-metadata>=4.6 in /home/paperspace/devinterp/.venv/lib/python3.9/site-packages (from jax) (6.8.0)\n", - "Requirement already satisfied: chex>=0.1.86 in /home/paperspace/devinterp/.venv/lib/python3.9/site-packages (from optax) (0.1.86)\n", - "Requirement already satisfied: etils[epy] in /home/paperspace/devinterp/.venv/lib/python3.9/site-packages (from optax) (1.5.2)\n", - "Requirement already satisfied: typing-extensions>=4.2.0 in /home/paperspace/devinterp/.venv/lib/python3.9/site-packages (from chex>=0.1.86->optax) (4.7.1)\n", - "Requirement already satisfied: toolz>=0.9.0 in /home/paperspace/devinterp/.venv/lib/python3.9/site-packages (from chex>=0.1.86->optax) (0.12.1)\n", - "Requirement already satisfied: zipp>=0.5 in /home/paperspace/devinterp/.venv/lib/python3.9/site-packages (from importlib-metadata>=4.6->jax) (3.16.2)\n", - "Note: you may need to restart the kernel to use updated packages.\n" - ] - } - ], - "source": [ - "%pip install git+https://github.com/deepmind/dm-haiku jax optax" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "import haiku as hk\n", - "import jax\n", - "import jax.numpy as jnp\n", - "import jax.tree_util as jtree\n", - "\n", - "import optax\n", - "from typing import Sequence, NamedTuple\n", - "\n", - "\n", - "import itertools" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# Define the DLN model\n", - "class DeepLinearNetwork(hk.Module):\n", - " def __init__(self, layer_widths: Sequence[int], name: str = None, with_bias=False):\n", - " super().__init__(name=name)\n", - " self.layer_widths = layer_widths\n", - " self.with_bias = with_bias\n", - "\n", - " def __call__(self, x):\n", - " for width in self.layer_widths:\n", - " x = hk.Linear(width, with_bias=self.with_bias)(x)\n", - " return x\n", - "\n", - "\n", - "# Function to initialize and apply the DLN model\n", - "def forward_fn(x, layer_widths):\n", - " net = DeepLinearNetwork(layer_widths)\n", - " return net(x)\n", - "\n", - "\n", - "# Create a Haiku-transformed version of the model\n", - "def create_model(layer_widths):\n", - " model = hk.without_apply_rng(hk.transform(lambda x: forward_fn(x, layer_widths)))\n", - " return model\n", - "\n", - "\n", - "def generate_training_data(true_param, model, input_dim, num_samples):\n", - " # Generate random inputs\n", - " inputs = np.random.uniform(-10, 10, size=(num_samples, input_dim))\n", - "\n", - " # Apply the true model to generate outputs\n", - " true_outputs = model.apply(true_param, inputs)\n", - "\n", - " return inputs, true_outputs\n", - "\n", - "\n", - "def mse_loss(param, model, inputs, targets):\n", - " predictions = model.apply(param, inputs)\n", - " return jnp.mean((predictions - targets) ** 2)\n", - "\n", - "\n", - "def create_minibatches(inputs, targets, batch_size, shuffle=True):\n", - " assert len(inputs) == len(targets)\n", - " if shuffle:\n", - " indices = np.random.permutation(len(inputs))\n", - " else:\n", - " indices = np.arange(len(inputs))\n", - "\n", - " for start_idx in range(0, len(inputs) - batch_size + 1, batch_size):\n", - " excerpt = indices[start_idx : start_idx + batch_size]\n", - " yield inputs[excerpt], targets[excerpt]" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "class SGLDConfig(NamedTuple):\n", - " epsilon: float\n", - " gamma: float\n", - " num_steps: int\n", - "\n", - "\n", - "def generate_rngkey_tree(key_or_seed, tree_or_treedef):\n", - " rngseq = hk.PRNGSequence(key_or_seed)\n", - " return jtree.tree_map(lambda _: next(rngseq), tree_or_treedef)\n", - "\n", - "\n", - "def optim_sgld(epsilon, rngkey_or_seed):\n", - " @jax.jit\n", - " def sgld_delta(g, rngkey):\n", - " eta = jax.random.normal(rngkey, shape=g.shape) * jnp.sqrt(epsilon)\n", - " return -epsilon * g / 2 + eta\n", - "\n", - " def init_fn(_):\n", - " return rngkey_or_seed\n", - "\n", - " @jax.jit\n", - " def update_fn(grads, state):\n", - " rngkey, new_rngkey = jax.random.split(state)\n", - " rngkey_tree = generate_rngkey_tree(rngkey, grads)\n", - " updates = jax.tree_map(sgld_delta, grads, rngkey_tree)\n", - " return updates, new_rngkey\n", - "\n", - " return optax.GradientTransformation(init_fn, update_fn)\n", - "\n", - "\n", - "def create_local_logposterior(\n", - " avgnegloglikelihood_fn, num_training_data, w_init, gamma, itemp\n", - "):\n", - " def helper(x, y):\n", - " return jnp.sum((x - y) ** 2)\n", - "\n", - " def _logprior_fn(w):\n", - " sqnorm = jax.tree_util.tree_map(helper, w, w_init)\n", - " return jax.tree_util.tree_reduce(lambda a, b: a + b, sqnorm)\n", - "\n", - " def logprob(w, x, y):\n", - " loglike = -num_training_data * avgnegloglikelihood_fn(w, x, y)\n", - " logprior = -gamma / 2 * _logprior_fn(w)\n", - " return itemp * loglike + logprior\n", - "\n", - " return logprob" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "def true_dln_learning_coefficient(true_rank, layer_widths, input_dim, verbose=False):\n", - " M_list = np.array([input_dim] + list(layer_widths)) - true_rank\n", - " indices = brute_force_search_subset(M_list, early_return=verbose)\n", - " M_subset = M_list[indices]\n", - " if verbose:\n", - " print(f\"M_list: {M_list}, indices: {indices}, M_subset: {M_subset}\")\n", - " M_subset_sum = np.sum(M_subset)\n", - " ell = len(M_subset) - 1\n", - " M = np.ceil(M_subset_sum / ell)\n", - " a = M_subset_sum - (M - 1) * ell\n", - " output_dim = layer_widths[-1]\n", - "\n", - " term1 = (-(true_rank**2) + true_rank * (output_dim + input_dim)) / 2\n", - " term2 = a * (ell - a) / (4 * ell)\n", - " term3 = -ell * (ell - 1) / 4 * (M_subset_sum / ell) ** 2\n", - " term4 = (\n", - " 1\n", - " / 2\n", - " * np.sum(\n", - " [\n", - " M_subset[i] * M_subset[j]\n", - " for i in range(ell + 1)\n", - " for j in range(i + 1, ell + 1)\n", - " ]\n", - " )\n", - " )\n", - " learning_coefficient = term1 + term2 + term3 + term4\n", - " return learning_coefficient\n", - "\n", - "\n", - "def _condition(indices, intlist, verbose=False):\n", - " intlist = np.array(intlist)\n", - " ell = len(indices) - 1\n", - " subset = intlist[indices]\n", - " complement = intlist[[i for i in range(len(intlist)) if i not in indices]]\n", - " has_complement = len(complement) > 0\n", - " # print(indices, subset, complement)\n", - " if has_complement and not (np.max(subset) < np.min(complement)):\n", - " if verbose:\n", - " print(\n", - " f\"max(subset) = {np.max(subset)}, min(complement) = {np.min(complement)}\"\n", - " )\n", - " return False\n", - " if not (np.sum(subset) >= ell * np.max(subset)):\n", - " if verbose:\n", - " print(\n", - " f\"sum(subset) = {sum(subset)}, ell * max(subset) = {ell * np.max(subset)}\"\n", - " )\n", - " return False\n", - " if has_complement and not (np.sum(subset) < ell * np.min(complement)):\n", - " if verbose:\n", - " print(\n", - " f\"sum(subset) = {sum(subset)}, ell * min(complement) = {ell * np.min(complement)}\"\n", - " )\n", - " return False\n", - " return True\n", - "\n", - "\n", - "def generate_indices_subsets(length):\n", - " indices = list(range(length))\n", - " for size in range(1, length + 1):\n", - " for subset in itertools.combinations(indices, size):\n", - " subset = np.array(subset)\n", - " yield subset\n", - "\n", - "\n", - "def brute_force_search_subset(intlist, early_return=False):\n", - " candidates = []\n", - " for indices in generate_indices_subsets(len(intlist)):\n", - " if _condition(indices, intlist):\n", - " if early_return:\n", - " return indices\n", - " candidates.append(indices)\n", - " if len(candidates) == 0:\n", - " raise RuntimeError(\"No candidates\")\n", - " if len(candidates) > 1:\n", - " print(\"More than one candidate\")\n", - " return candidates[0]\n", - "\n", - "\n", - "def to_float_or_list(x):\n", - " if isinstance(x, (float, int)):\n", - " return float(x)\n", - " elif isinstance(x, (list, tuple)):\n", - " return [float(el) for el in x]\n", - " elif hasattr(x, \"tolist\"): # For JAX or numpy arrays\n", - " return x.tolist()\n", - " else:\n", - " raise ValueError(f\"Unsupported type {type(x)}\")\n", - "\n", - "\n", - "def to_json_friendly_tree(tree):\n", - " return jtree.tree_map(to_float_or_list, tree)\n", - "\n", - "\n", - "def reduce_matrix_rank(matrix, reduction):\n", - " \"\"\"\n", - " Reduce the rank of the matrix by 'reduction' amount.\n", - "\n", - " :param matrix: Input matrix.\n", - " :param reduction: The amount by which the rank should be reduced.\n", - " :return: A matrix similar to the input but with reduced rank.\n", - " \"\"\"\n", - " U, S, Vh = np.linalg.svd(matrix, full_matrices=False)\n", - "\n", - " # Reduce the number of non-zero singular values by 'reduction'\n", - " new_rank = max(len(S) - reduction, 0)\n", - " S[new_rank:] = 0\n", - "\n", - " # Reconstruct the matrix with the reduced number of singular values\n", - " reduced_matrix = np.dot(U * S, Vh)\n", - " return reduced_matrix\n", - "\n", - "\n", - "def rand_reduce_matrix_rank(matrix):\n", - " r = np.linalg.matrix_rank(matrix)\n", - " reduction = np.random.randint(0, max(1, r))\n", - " return reduce_matrix_rank(matrix, reduction)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Randomly generate a DLN, calculate its true lambda, and sweep over epsilon and beta" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - } - ], - "source": [ - "num_training_data = 10000 # Number of training samples\n", - "\n", - "num_epochs = 100\n", - "learning_rate = 2e-3\n", - "optimizer = optax.sgd(learning_rate)\n", - "rngkey = jax.random.PRNGKey(42)\n", - "batch_size = 200\n", - "\n", - "# Generate a random network\n", - "num_layer = np.random.randint(2, 8)\n", - "layer_widths = list(np.random.randint(5, 30, size=num_layer))\n", - "input_dim = np.random.randint(5, 20)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "True lambda: 53.0\n" - ] - } - ], - "source": [ - "def generate_random_DLN(rngkey, input_dim, layer_widths):\n", - " # Random true parameters\n", - " model = create_model(layer_widths)\n", - " dummy_input = jnp.zeros((1, input_dim))\n", - " rngkey, subkey = jax.random.split(rngkey)\n", - " # true_param = model.init(rngkey, dummy_input)\n", - " # true_param = jtree.tree_map(lambda x: x * 0.0, model.init(rngkey, dummy_input)) # zero true parameter\n", - " true_param = jtree.tree_map(\n", - " lambda x: rand_reduce_matrix_rank(x) if np.random.rand() > 0.5 else x,\n", - " model.init(rngkey, dummy_input),\n", - " ) # randomly reduce rank of random matrices\n", - " x_train, y_train = generate_training_data(\n", - " true_param, model, input_dim, num_training_data\n", - " )\n", - " loss_fn = jax.jit(\n", - " lambda param, inputs, targets: mse_loss(param, model, inputs, targets)\n", - " )\n", - "\n", - " # Training the network\n", - " rngkey, _ = jax.random.split(rngkey)\n", - " param = model.init(rngkey, jnp.zeros((1, input_dim)))\n", - " opt_state = optimizer.init(param)\n", - " grad_fn = jax.jit(jax.grad(loss_fn, argnums=0))\n", - "\n", - " for _ in range(num_epochs):\n", - " for x_batch, y_batch in create_minibatches(\n", - " x_train, y_train, batch_size=batch_size\n", - " ):\n", - " grads = grad_fn(param, x_batch, y_batch)\n", - " updates, opt_state = optimizer.update(grads, opt_state)\n", - " param = optax.apply_updates(param, updates)\n", - "\n", - " # Calculate true lambda for the generated network.\n", - " true_matrix = jnp.linalg.multi_dot(\n", - " [\n", - " true_param[f\"deep_linear_network/linear{loc}\"][\"w\"]\n", - " for loc in [\"\"] + [f\"_{i}\" for i in range(1, len(layer_widths))]\n", - " ]\n", - " )\n", - " true_rank = jnp.linalg.matrix_rank(true_matrix)\n", - " true_lambda = true_dln_learning_coefficient(\n", - " true_rank, layer_widths, input_dim, verbose=False\n", - " )\n", - " return model, true_param, true_lambda, x_train, y_train\n", - "\n", - "\n", - "model, true_param, true_lambda, x_train, y_train = generate_random_DLN(\n", - " rngkey, input_dim, layer_widths\n", - ")\n", - "print(\"True lambda:\", true_lambda)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Define the LLC estimator function:" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "def estimate_llc_given_dln(\n", - " model: hk.Transformed,\n", - " true_param: hk.Params,\n", - 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" nlls = []\n", - " opt_state = sgldoptim.init(param_init)\n", - " param = param_init\n", - "\n", - " t = 0\n", - " while t < sgld_config.num_steps:\n", - " for x_batch, y_batch in create_minibatches(\n", - " x_train, y_train, batch_size=batch_size\n", - " ):\n", - " nll, grads = sgld_grad_fn(param, x_batch, y_batch)\n", - " nlls.append(float(nll))\n", - " updates, opt_state = sgldoptim.update(grads, opt_state)\n", - " param = optax.apply_updates(param, updates)\n", - " samples.append(param)\n", - " t += 1\n", - "\n", - " init_loss = loss_fn(param_init, x_train, y_train)\n", - " loss_trace = [loss_fn(p, x_train, y_train) for p in samples]\n", - " lambdahat = (np.mean(loss_trace) - init_loss) * num_training_data * beta\n", - " llc_trace = [(i - init_loss) * num_training_data * beta for i in loss_trace]\n", - "\n", - " sweep_stats = {\n", - " \"lambdahat\": lambdahat,\n", - " \"llc/trace\": llc_trace,\n", - " \"true_lambda\": true_lambda.item(),\n", - " }\n", - "\n", - " sweep_stats[\"llc/trace\"] = np.array(sweep_stats[\"llc/trace\"])\n", - " return sweep_stats" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Use EpsilonBetaAnalyzer to sweep and plot:" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 64/64 [04:51<00:00, 4.56s/it]\n" - ] - } - ], - "source": [ - "import warnings\n", - "\n", - "warnings.filterwarnings(\"ignore\")\n", - "\n", - "DLNanalyzer = EpsilonBetaAnalyzer()\n", - "DLNanalyzer.configure_sweep(\n", - " llc_estimator=estimate_llc_given_dln,\n", - " llc_estimator_kwargs=dict(\n", - " model=model,\n", - " true_param=true_param,\n", - " true_lambda=true_lambda,\n", - " x_train=x_train,\n", - " y_train=y_train,\n", - " gamma=10.0,\n", - " num_steps=1000,\n", - " ),\n", - " min_epsilon=1e-6,\n", - " max_epsilon=1e-2,\n", - " epsilon_samples=8,\n", - " min_beta=1e-5,\n", - " max_beta=1e3,\n", - " beta_samples=8,\n", - ")\n", - "DLNanalyzer.sweep()" - ] - 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# To plot a convergence metric on the colorbar:\n", - "DLNanalyzer.plot(\n", - " true_lambda=true_lambda,\n", - " num_last_steps_to_average=50,\n", - " color=\"llc/std_over_mean\",\n", - " template=\"seaborn\",\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Sweep over randomly-generated DLNs with different true lambdas:" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 64/64 [05:10<00:00, 4.86s/it]\n", - "100%|██████████| 64/64 [03:15<00:00, 3.06s/it]\n", - "100%|██████████| 64/64 [03:04<00:00, 2.88s/it]\n", - "100%|██████████| 64/64 [02:42<00:00, 2.54s/it]\n", - "100%|██████████| 64/64 [02:47<00:00, 2.62s/it]\n", - "100%|██████████| 64/64 [04:31<00:00, 4.24s/it]\n", - "100%|██████████| 64/64 [04:52<00:00, 4.58s/it]\n", - "100%|██████████| 64/64 [03:59<00:00, 3.74s/it]\n" - ] - } - ], - "source": [ - "import warnings\n", - "\n", - "warnings.filterwarnings(\"ignore\")\n", - "\n", - "DLNanalyzer = EpsilonBetaAnalyzer()\n", - "\n", - "for _ in range(8):\n", - " # Generate a random network\n", - " num_layer = np.random.randint(2, 8)\n", - " layer_widths = list(np.random.randint(5, 30, size=num_layer))\n", - " input_dim = np.random.randint(5, 20)\n", - " rngkey = jax.random.PRNGKey(np.random.randint(0, 10000))\n", - "\n", - " model, true_param, true_lambda, x_train, y_train = generate_random_DLN(\n", - " rngkey, input_dim, layer_widths\n", - " )\n", - " DLNanalyzer.configure_sweep(\n", - " llc_estimator=estimate_llc_given_dln,\n", - " llc_estimator_kwargs=dict(\n", - " model=model,\n", - " true_param=true_param,\n", - " true_lambda=true_lambda,\n", - " x_train=x_train,\n", - " y_train=y_train,\n", - " gamma=10.0,\n", - " num_steps=1000,\n", - " ),\n", - " min_epsilon=1e-6,\n", - " max_epsilon=1e-2,\n", - " epsilon_samples=8,\n", - " min_beta=1e-5,\n", - " max_beta=1e3,\n", - " beta_samples=8,\n", - " )\n", - " DLNanalyzer.sweep(add_to_existing=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ - { - "hovertemplate": "epsilon=%{x}
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"gridcolor": "#506784", - "gridwidth": 2, - "linecolor": "#506784", - "showbackground": true, - "ticks": "", - "zerolinecolor": "#C8D4E3" - } - }, - "shapedefaults": { - "line": { - "color": "#f2f5fa" - } - }, - "sliderdefaults": { - "bgcolor": "#C8D4E3", - "bordercolor": "rgb(17,17,17)", - "borderwidth": 1, - "tickwidth": 0 - }, - "ternary": { - "aaxis": { - "gridcolor": "#506784", - "linecolor": "#506784", - "ticks": "" - }, - "baxis": { - "gridcolor": "#506784", - "linecolor": "#506784", - "ticks": "" - }, - "bgcolor": "rgb(17,17,17)", - "caxis": { - "gridcolor": "#506784", - "linecolor": "#506784", - "ticks": "" - } - }, - "title": { - "x": 0.05 - }, - "updatemenudefaults": { - "bgcolor": "#506784", - "borderwidth": 0 - }, - "xaxis": { - "automargin": true, - "gridcolor": "#283442", - "linecolor": "#506784", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "#283442", - "zerolinewidth": 2 - }, - "yaxis": { - "automargin": true, - "gridcolor": "#283442", - "linecolor": "#506784", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "#283442", - "zerolinewidth": 2 - } - } - }, - "title": { - "text": "Local learning coefficient vs. epsilon and beta" - } - } - }, - "text/html": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Let's add a slider to the plot to visualize the epsilon-beta sweep for each true lambda.\n", - "DLNanalyzer.plot(\n", - " true_lambda=\"true_lambda\",\n", - " slider=\"true_lambda\",\n", - " slider_plane=True,\n", - " template=\"plotly_dark\",\n", - " color=\"log_lambda_delta\",\n", - ") # other valid values include log_delta_lambda and llc/std_over_mean" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "# Save plot as an interactive HTML file\n", - "DLNanalyzer.save_fig(\"dln_llc_sweep.html\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "devinterp", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.18" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/examples/grokking.ipynb b/examples/grokking.ipynb deleted file mode 100644 index a4b00f25..00000000 --- a/examples/grokking.ipynb +++ /dev/null @@ -1,1036 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "5wwbrhXCeP9Q" - }, - "source": [ - "# Grokking\n", - "\n", - "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/timaeus-research/devinterp/blob/main/examples/grokking.ipynb)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "9S3pemU_eP9W" - }, - "source": [ - "This notebook aims to show how LLC estimation is calibrated in a simple modular addition grokking example, showing a moderately interesting result at the end.\n", - "\n", - "We'll starting off with some standard grokking code, adapted loosely from Nina Panickssery and Dmitry Vaintrob's [modular addition learning coefficient post](https://www.alignmentforum.org/posts/4v3hMuKfsGatLXPgt/investigating-the-learning-coefficient-of-modular-addition) and [github code repo](https://github.com/nrimsky/devinterp). (Thank you for your help!)" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "suJsw_j5eP9Y", - "outputId": "17013fd2-6930-4305-af12-2a21e2feb98c" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Collecting devinterp\n", - " Downloading devinterp-1.2.0-py3-none-any.whl.metadata (6.5 kB)\n", - "Requirement already satisfied: nbformat in /usr/local/lib/python3.10/dist-packages (5.10.4)\n", - "Requirement already satisfied: einops>=0.6.1 in /usr/local/lib/python3.10/dist-packages (from devinterp) (0.8.0)\n", - "Requirement already satisfied: matplotlib>=3.7.5 in /usr/local/lib/python3.10/dist-packages (from devinterp) (3.8.0)\n", - "Requirement already satisfied: numpy>=1.23.5 in /usr/local/lib/python3.10/dist-packages (from devinterp) (1.26.4)\n", - "Requirement already satisfied: pandas>=1.5.3 in /usr/local/lib/python3.10/dist-packages (from devinterp) 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import estimate_learning_coeff_with_summary\n", - "from devinterp.utils import evaluate_ce\n", - "\n", - "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "id": "qvF8FyrLeP9g" - }, - "outputs": [], - "source": [ - "@dataclass\n", - "class ExperimentParams:\n", - " p: int = 53\n", - " n_batches: int = 25000\n", - " n_save_model_checkpoints: int = 100\n", - " print_times: int = 100\n", - " lr: float = 0.005\n", - " batch_size: int = 128\n", - " hidden_size: int = 48\n", - " embed_dim: int = 12\n", - " train_frac: float = 0.4\n", - " # the shown grokking / llc curve behavior is robust to change of seed from my experiments, but not all seeds show grokking withying the first 100 checkpoints, NB!\n", - " random_seed: int = 0\n", - " device: str = DEVICE\n", - " weight_decay: float = 0.0002\n", - "\n", - "\n", - "class MLP(nn.Module):\n", - " def __init__(self, params):\n", - " super().__init__()\n", - " self.embedding = nn.Embedding(params.p, params.embed_dim)\n", - " self.linear1r = nn.Linear(params.embed_dim, params.hidden_size, bias=True)\n", - " self.linear1l = nn.Linear(params.embed_dim, params.hidden_size, bias=True)\n", - " self.linear2 = nn.Linear(params.hidden_size, params.p, bias=False)\n", - " self.act = nn.GELU()\n", - " self.vocab_size = params.p\n", - "\n", - " def forward(self, x):\n", - " x1 = self.embedding(x[..., 0])\n", - " x2 = self.embedding(x[..., 1])\n", - " x1 = self.linear1l(x1)\n", - " x2 = self.linear1r(x2)\n", - " x = x1 + x2\n", - " x = self.act(x)\n", - " x = self.linear2(x)\n", - " return x\n", - "\n", - "\n", - "def test(model, dataset, device):\n", - " n_correct = 0\n", - " total_loss = 0\n", - " model.eval()\n", - " loss_fn = nn.CrossEntropyLoss()\n", - " with torch.no_grad():\n", - " for x, y in dataset:\n", - " x, y = x.to(device), y.to(device)\n", - " out = model(x)\n", - " loss = loss_fn(out, y)\n", - " total_loss += loss.item()\n", - " pred = torch.argmax(out)\n", - " if pred == y:\n", - " n_correct += 1\n", - " return n_correct / len(dataset), total_loss / len(dataset)\n", - "\n", - "\n", - "def train(train_dataset, test_dataset, params, verbose=True):\n", - " all_models = []\n", - " model = MLP(params).to(params.device)\n", - " optimizer = torch.optim.Adam(\n", - " model.parameters(), weight_decay=params.weight_decay, lr=params.lr\n", - " )\n", - " loss_fn = torch.nn.CrossEntropyLoss()\n", - "\n", - " train_loader = DataLoader(train_dataset, batch_size=params.batch_size, shuffle=True)\n", - "\n", - " print_every = params.n_batches // params.print_times\n", - " checkpoint_every = None\n", - " if params.n_save_model_checkpoints > 0:\n", - " checkpoint_every = params.n_batches // params.n_save_model_checkpoints\n", - "\n", - " loss_data = []\n", - " if verbose:\n", - " pbar = tqdm(total=params.n_batches, desc=\"Training\")\n", - " for i in range(params.n_batches):\n", - " # Sample random batch of data\n", - " batch = next(iter(train_loader))\n", - " X, Y = batch\n", - " X, Y = X.to(params.device), Y.to(params.device)\n", - " # Gradient update\n", - " optimizer.zero_grad()\n", - " out = model(X)\n", - " loss = loss_fn(out, Y)\n", - " loss.backward()\n", - " optimizer.step()\n", - "\n", - " if checkpoint_every and (i + 1) % checkpoint_every == 0:\n", - " all_models += [deepcopy(model)]\n", - "\n", - " if (i + 1) % print_every == 0:\n", - " val_acc, val_loss = test(model, test_dataset, params.device)\n", - " train_acc, train_loss = test(model, train_dataset, params.device)\n", - " loss_data.append(\n", - " {\n", - " \"batch\": i + 1,\n", - " \"train_loss\": train_loss,\n", - " \"train_acc\": train_acc,\n", - " \"val_loss\": val_loss,\n", - " \"val_acc\": val_acc,\n", - " }\n", - " )\n", - " if verbose:\n", - " pbar.set_postfix(\n", - " {\n", - " \"train_loss\": f\"{train_loss:.4f}\",\n", - " \"train_acc\": f\"{train_acc:.4f}\",\n", - " \"val_loss\": f\"{val_loss:.4f}\",\n", - " \"val_acc\": f\"{val_acc:.4f}\",\n", - " }\n", - " )\n", - " pbar.update(print_every)\n", - " if verbose:\n", - " pbar.close()\n", - " df = pd.DataFrame(loss_data)\n", - " train_acc, train_loss = test(model, train_dataset, params.device)\n", - " val_acc, val_loss = test(model, test_dataset, params.device)\n", - " if verbose:\n", - " print(f\"Final Train Acc: {train_acc:.4f} | Final Train Loss: {train_loss:.4f}\")\n", - " print(f\"Final Val Acc: {val_acc:.4f} | Final Val Loss: {val_loss:.4f}\")\n", - " return all_models, df\n", - "\n", - "\n", - "def deterministic_shuffle(lst, seed):\n", - " random.seed(seed)\n", - " random.shuffle(lst)\n", - " return lst\n", - "\n", - "\n", - "def get_all_pairs(p):\n", - " pairs = []\n", - " for i in range(p):\n", - " for j in range(p):\n", - " pairs.append((i, j))\n", - " return set(pairs)\n", - "\n", - "\n", - "def make_dataset(p):\n", - " data = []\n", - " pairs = get_all_pairs(p)\n", - " for a, b in pairs:\n", - " data.append((torch.tensor([a, b]), torch.tensor((a + b) % p)))\n", - " return data\n", - "\n", - "\n", - "def train_test_split(dataset, train_split_proportion, seed):\n", - " l = len(dataset)\n", - " train_len = int(train_split_proportion * l)\n", - " idx = list(range(l))\n", - " idx = deterministic_shuffle(idx, seed)\n", - " train_idx = idx[:train_len]\n", - " test_idx = idx[train_len:]\n", - " return [dataset[i] for i in train_idx], [dataset[i] for i in test_idx]" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "4xVu2IN7eP9o", - "outputId": "da84ba29-4b2a-46c7-d4ea-9f5c4f338366" - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Training: 100%|██████████| 25000/25000 [02:52<00:00, 144.75it/s, train_loss=0.0137, train_acc=1.0000, val_loss=0.0395, val_acc=1.0000]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Final Train Acc: 1.0000 | Final Train Loss: 0.0395\n", - "Final Val Acc: 1.0000 | Final Val Loss: 0.0395\n" - ] - } - ], - "source": [ - "params = ExperimentParams()\n", - "torch.manual_seed(params.random_seed)\n", - "\n", - "dataset = make_dataset(params.p)\n", - "train_data, test_data = train_test_split(dataset, params.train_frac, params.random_seed)\n", - "\n", - "all_checkpointed_models, df = train(\n", - " train_dataset=train_data, test_dataset=test_data, params=params\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 490 - }, - "id": "U4NIjv3oeP9s", - "outputId": "957d56b3-e487-4665-f713-6e307e96830f" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'Train & test correct answer % for modular addition with p=53')" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(df[\"val_acc\"], label=\"test\")\n", - "plt.plot(df[\"train_acc\"], label=\"train\")\n", - "plt.legend()\n", - "plt.ylabel(\"Correct answer %\")\n", - "plt.xlabel(\"Checkpoint\")\n", - "plt.title(f\"Train & test correct answer % for modular addition with p={params.p}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "dII4gLo6eP9u" - }, - "source": [ - "From this plot, we see the classic grokking behavior: although the train accuracy is perfect after a few iterations, it takes many more examples for the test accuracy to meaningfully improve. (Note that this is not the same statement as train loss being perfect, see below plot.)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 490 - }, - "id": "LJ25wEvreP9w", - "outputId": "06fcfff0-59f3-49b9-eec0-c129c7a6c0f4" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'Train & test loss for modular addition with p=53')" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(df[\"val_loss\"], label=\"test\")\n", - "plt.plot(df[\"train_loss\"], label=\"train\")\n", - "plt.legend()\n", - "plt.ylabel(\"Loss\")\n", - "plt.xlabel(\"Checkpoint\")\n", - "plt.title(f\"Train & test loss for modular addition with p={params.p}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "1WvYf-UZeP92" - }, - "source": [ - "## LLC estimation hyperparameter tuning" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "bP4h1UJEeP94" - }, - "source": [ - "In order to get LLC estimates for this simple grokking model over training, we first need to choose hyperparameters. The most important ones to calibrate are epsilon (the SGLD learning rate / step size) and n\\*beta (the effective inverse temperature). Let's run a quick sweep over a wide range of epsilon and n\\*beta, and look for a range of values within this which shows little change in LLC change in LLC values when we change epsilon and nbeta. We can use `devinterp.vis_utils.EpsilonBetaAnalyzer` for this." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "id": "MCGWHjNkeP96" - }, - "outputs": [], - "source": [ - "import typing\n", - "from typing import Type\n", - "\n", - "import numpy as np\n", - "\n", - "\n", - "def estimate_llc_given_model(\n", - " model: torch.nn.Module,\n", - " loader: torch.utils.data.DataLoader,\n", - " evaluate: typing.Callable,\n", - " epsilon: float,\n", - " beta: float,\n", - " sampling_method: Type[torch.optim.Optimizer] = SGLD,\n", - " localization: float = 5.0,\n", - " num_chains: int = 2,\n", - " num_draws: int = 500,\n", - " num_burnin_steps: int = 0,\n", - " num_steps_bw_draws: int = 1,\n", - " device: torch.device = DEVICE,\n", - " online: bool = True,\n", - " verbose: bool = False,\n", - "):\n", - " sweep_stats = estimate_learning_coeff_with_summary(\n", - " model,\n", - " loader=loader,\n", - " evaluate=evaluate,\n", - " sampling_method=sampling_method,\n", - " optimizer_kwargs=dict(lr=epsilon, localization=localization, nbeta=beta),\n", - " num_chains=num_chains, # How many independent chains to run\n", - " num_draws=num_draws, # How many samples to draw per chain\n", - " num_burnin_steps=num_burnin_steps, # How many samples to discard at the beginning of each chain\n", - " num_steps_bw_draws=num_steps_bw_draws, # How many steps to take between each sample\n", - " device=device,\n", - " online=online,\n", - " verbose=verbose,\n", - " )\n", - "\n", - " sweep_stats[\"llc/trace\"] = np.array(sweep_stats[\"llc/trace\"])\n", - " return sweep_stats" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "8NnCyNGueP99", - "outputId": "388e75e3-dadb-4c9d-9fed-88bac739fa88" - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 25/25 [01:48<00:00, 4.33s/it]\n" - ] - } - ], - "source": [ - "from devinterp.vis_utils import EpsilonBetaAnalyzer\n", - "\n", - "loader = DataLoader(train_data, shuffle=True, batch_size=params.batch_size)\n", - "analyzer = EpsilonBetaAnalyzer()\n", - "analyzer.configure_sweep(\n", - " llc_estimator=estimate_llc_given_model,\n", - " llc_estimator_kwargs=dict(\n", - " model=all_checkpointed_models[-1],\n", - " evaluate=evaluate_ce,\n", - " device=DEVICE,\n", - " loader=loader,\n", - " ),\n", - " min_epsilon=3e-5,\n", - " max_epsilon=3e-1,\n", - " epsilon_samples=5,\n", - " min_beta=None,\n", - " max_beta=None,\n", - " beta_samples=5,\n", - " dataloader=loader,\n", - ")\n", - "analyzer.sweep()" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 542 - }, - "id": "TTP1vX3EeP9_", - "outputId": "5a42fc43-901c-4001-9b49-6af99cd5eb2a" - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "
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\n", - "\n", - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "analyzer.plot(div_out_beta=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "DlGfy4ZAeP-C" - }, - "source": [ - "From this, we can see that the effective sampled loss for low-ish nbetas (<100) shows very little dependence on the exact choice of nbeta. So let's a point in this flat region (~1), and a high-but-still-in-the-flat-region epsilon (0.03), so we don't need to run many draws, but still have little dependence of our samples on epsilon.\n", - "\n", - "Let's check that the loss chain for these hyperparams looks decent, and then run LLC estimation on all trained checkpoints if it does." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "id": "9vVC0yMSeP-E" - }, - "outputs": [], - "source": [ - "lr = 3e-3\n", - "gamma = 5\n", - "nbeta = 2.0\n", - "num_draws = 500\n", - "num_chains = 2" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "_bBq6sUXeP-H", - "outputId": "e1dd4c1a-ce63-48e2-c7f8-2b0549c91c09" - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/lib/python3.10/dist-packages/devinterp/slt/sampler.py:118: UserWarning:\n", - "\n", - "Using passed in nbeta. Make sure callbacks are also initialized with the same nbeta.\n", - "\n", - "/usr/local/lib/python3.10/dist-packages/devinterp/backends/default/slt/sampler.py:232: UserWarning:\n", - "\n", - "You are taking more draws than burn-in steps, your LLC estimates will likely be underestimates. Please check LLC chain convergence.\n", - "\n", - "/usr/local/lib/python3.10/dist-packages/devinterp/backends/default/slt/sampler.py:236: UserWarning:\n", - "\n", - "You are taking more sample batches than there are dataloader batches available, this removes some randomness from sampling but is probably fine. (All sample batches beyond the number dataloader batches are cycled from the start, f.e. 9 samples from [A, B, C] would be [B, A, C, B, A, C, B, A, C].)\n", - "\n", - "/usr/local/lib/python3.10/dist-packages/devinterp/backends/default/slt/sampler.py:277: UserWarning:\n", - "\n", - "If you're setting a nbeta or temperature in optimizer_kwargs, please also make sure to set it in the callbacks.\n", - "\n", - "/usr/local/lib/python3.10/dist-packages/devinterp/backends/default/slt/sampler.py:54: UserWarning:\n", - "\n", - "You are taking more sample batches than there are dataloader batches available, this removes some randomness from sampling but is probably fine. (All sample batches beyond the number dataloader batches are cycled from the start, f.e. 9 samples from [A, B, C] would be [B, A, C, B, A, C, B, A, C].)\n", - "\n", - "Chain 0: 100%|██████████| 1500/1500 [00:03<00:00, 414.11it/s]\n", - "Chain 1: 100%|██████████| 1500/1500 [00:03<00:00, 413.31it/s]\n", - "Chain 2: 100%|██████████| 1500/1500 [00:04<00:00, 358.68it/s]\n" - ] - } - ], - "source": [ - "learning_coeff_stats = estimate_learning_coeff_with_summary(\n", - " all_checkpointed_models[-1],\n", - " loader=DataLoader(train_data, batch_size=params.batch_size, shuffle=True),\n", - " evaluate=evaluate_ce,\n", - " sampling_method=SGLD,\n", - " optimizer_kwargs=dict(lr=0.03, nbeta=2.0, localization=5.0),\n", - " num_chains=3,\n", - " num_draws=1500,\n", - " device=DEVICE,\n", - " online=True,\n", - ")\n", - "trace = learning_coeff_stats[\"loss/trace\"]" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 490 - }, - "id": "U7A9q8PVeP-I", - "outputId": "0e29f3c9-1b49-4a74-d4ee-93d187cda33d" - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from devinterp.utils import plot_trace\n", - "\n", - "plot_trace(\n", - " trace,\n", - " \"Loss\",\n", - " x_axis=\"Step\",\n", - " title=f\"Loss Trace, avg LLC = {sum(learning_coeff_stats['llc/means']) / len(learning_coeff_stats['llc/means']):.2f}\",\n", - " plot_mean=False,\n", - " plot_std=False,\n", - " fig_size=(12, 9),\n", - " true_lc=None,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "cj1Jr9kIeP-K" - }, - "source": [ - "This looks good! The loss flattens out nicely, and well within the num_draws we chose. Looks like we can get away with using 500 draws, as the loss trace has well flattened out by then." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "WJfjqhXdeP-L", - "outputId": "017daa08-db49-4f39-ea1f-2da1e9a4aa86" - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Chain 0: 100%|██████████| 500/500 [00:01<00:00, 421.32it/s]\n", - "/usr/local/lib/python3.10/dist-packages/devinterp/slt/llc.py:71: UserWarning:\n", - "\n", - "std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1823.)\n", - "\n", - "Chain 0: 100%|██████████| 500/500 [00:01<00:00, 421.31it/s]\n", - "Chain 0: 100%|██████████| 500/500 [00:01<00:00, 432.16it/s]\n", - "Chain 0: 100%|██████████| 500/500 [00:01<00:00, 426.92it/s]\n", - "Chain 0: 100%|██████████| 500/500 [00:01<00:00, 431.96it/s]\n", - "Chain 0: 100%|██████████| 500/500 [00:01<00:00, 426.20it/s]\n", - "Chain 0: 100%|██████████| 500/500 [00:01<00:00, 344.14it/s]\n", - "Chain 0: 100%|██████████| 500/500 [00:01<00:00, 373.41it/s]\n", - "Chain 0: 100%|██████████| 500/500 [00:01<00:00, 431.63it/s]\n", - "Chain 0: 100%|██████████| 500/500 [00:01<00:00, 427.79it/s]\n", - "Chain 0: 100%|██████████| 500/500 [00:01<00:00, 429.96it/s]\n", - "Chain 0: 100%|██████████| 500/500 [00:01<00:00, 425.36it/s]\n", - "Chain 0: 100%|██████████| 500/500 [00:01<00:00, 430.17it/s]\n", - "Chain 0: 100%|██████████| 500/500 [00:01<00:00, 429.84it/s]\n", - "Chain 0: 100%|██████████| 500/500 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sampling_method=SGLD,\n", - " optimizer_kwargs=dict(lr=lr, nbeta=nbeta, localization=gamma),\n", - " num_chains=1,\n", - " num_draws=num_draws,\n", - " device=DEVICE,\n", - " online=False,\n", - " )\n", - " for model_checkpoint in all_checkpointed_models\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 945 - }, - "id": "r0pc1PKCeP-M", - "outputId": "2a0c0a32-5edc-4e66-ac18-fc472fb92f6b" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax1 = plt.subplots()\n", - "plt.title(\n", - " f\"Lambdahat vs acc for modular addition p={params.p}, train_frac={params.train_frac}, nβ={nbeta:.1f}, ε={lr}, γ={gamma}, num_draws={num_draws}, num_chains={num_chains}\"\n", - ")\n", - "\n", - "ax2 = ax1.twinx()\n", - "ax1.plot(df[\"val_acc\"], label=\"test acc\")\n", - "ax1.plot(df[\"train_acc\"], label=\"train acc\")\n", - "ax2.plot([llc[\"llc/mean\"] for llc in llcs], color=\"g\", label=\"Lambdahat\")\n", - "ax1.set_xlabel(\"Checkpoint no.\")\n", - "fig.legend(loc=\"center right\")\n", - "\n", - "fig.show()\n", - "\n", - "fig, ax1 = plt.subplots()\n", - "plt.title(\n", - " f\"Lambdahat vs loss for modular addition, p={params.p}, train_frac={params.train_frac}, nβ={nbeta:.1f}, ε={lr}, γ={gamma}, num_draws={num_draws}, num_chains={num_chains}\"\n", - ")\n", - "ax2 = ax1.twinx()\n", - "ax1.plot(df[\"val_loss\"], label=\"test loss\")\n", - "ax1.plot(df[\"train_loss\"], label=\"train loss\")\n", - "ax2.plot([llc[\"llc/mean\"] for llc in llcs], color=\"g\", label=\"Lambdahat\")\n", - "ax1.set_xlabel(\"Checkpoint no.\")\n", - "fig.legend(loc=\"center right\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "ZhnimJrWeP-Q" - }, - "source": [ - "That's interesting!\n", - "\n", - "In the first plot, we see that the LLC first increases during memorization and then decreases ~smoothly afterward, flattening out after the model is done grokking. This is basically what we would expect from a simple reading of phase transitions in the free energy formula.\n", - "\n", - "From the second plot, we see that the LLC, which was measured only on the train set, tracks the test loss pretty well. That was a big surprise for me when I made this notebook, and I don't know what it means.\n", - "\n", - "Anyway, I hope this notebook clarifies how one can use the devinterp library and LLC estimation more generally to gain insight in the development of structure in neural networks. Thanks for reading!" - ] - } - ], - "metadata": { - "accelerator": "GPU", - "colab": { - "gpuType": "T4", - "provenance": [] - }, - "kernelspec": { - "display_name": "Python 3", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.18" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/examples/introduction.ipynb b/examples/introduction.ipynb deleted file mode 100644 index b40b6aac..00000000 --- a/examples/introduction.ipynb +++ /dev/null @@ -1,462 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Intro to Developmental Interpretability\n", - "\n", - "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/timaeus-research/devinterp/blob/main/examples/introduction.ipynb)\n", - "\n", - "Developmental interpretability (\"devinterp\") studies how structure develops over the course of training. \n", - "\n", - "## Aim\n", - "\n", - "Our aim is to develop tools for detecting, understanding, and preventing dangerous transitions during training — to capabilities, values, behaviors. \n", - "\n", - "This library is where we'll put those tools. **It's very much a work in progress** (and we welcome contributions)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Set-up" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Requirement already satisfied: pip in /home/svwin/devinterp/.venv/lib/python3.11/site-packages (24.3.1)\n", - "Note: you may need to restart the kernel to use updated packages.\n", - "Collecting transformers\n", - " Using cached transformers-4.48.1-py3-none-any.whl.metadata (44 kB)\n", - "Requirement already satisfied: torch in /home/svwin/devinterp/.venv/lib/python3.11/site-packages (2.5.1)\n", - 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"Note: you may need to restart the kernel to use updated packages.\n" - ] - } - ], - "source": [ - "%pip install --upgrade pip\n", - "%pip install transformers torch torchvision" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Methods\n", - "\n", - "## Local Learning Coefficients\n", - "\n", - "The first method we have have online is local learning coefficient estimation ([Lau et al. 2023](https://arxiv.org/abs/2308.12108)). \n", - "\n", - "For an in-depth explaination, see [this post](https://www.lesswrong.com/posts/6g8cAftfQufLmFDYT/you-re-counting-your-parameters-wrong). The short version is that: \n", - "- The (local) learning coefficient $\\hat\\lambda$ is the \"correct\" measure of model complexity. Besides the loss, it's the most principled high-level way to compare models.\n", - "- We can cheaply estimate the learning coefficient associated to a choice of weights $\\hat w^*$ by using the following formula:\n", - "\n", - "$$\n", - "\\hat\\lambda(\\hat w^*)=\\frac{\\mathbb{E}_{w|\\hat w^*, \\gamma}^{\\beta^*}\\left[n L_n(w)\\right] - n L_n(\\hat w^*)}{\\log n}.\n", - "$$\n", - "\n", - "where, $n$ is the number of (training) samples, $L_n(w)$ is the negative log likelihood (i.e., your objective function), and $\\mathbb{E}_w^{\\beta^*}\\left[n L_n(w)\\right]$ is a \"local average\" of $nL_n(w)$ over a neighborhood of $\\hat w^*$. The parameters $\\gamma$ and $\\beta^*$ control the local averaging.\n", - "\n", - "The difficulty is in performing the local average: we need to sample enough points and they need to be both diverse / spread out enough and close enough to the starting weight. To make sure they're spread out enough we use an optimizer like Stochastic Gradient Langevin Dynamics (SGLD), which is SGD plus Gaussian noise.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import torch\n", - "\n", - "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", - "# if you want to use your TPU (note that torch_xla is not by default installed, install using `pip install devinterp[tpu]`)\n", - "# import os\n", - "# os.environ[\"USE_TPU_BACKEND\"] = \"1\"\n", - "# import torch_xla.core.xla_model as xm\n", - "# DEVICE = xm.xla_device()" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/svwin/devinterp/.venv/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n" - ] - } - ], - "source": [ - "import warnings\n", - "\n", - "import torch\n", - "import torchvision\n", - "from torch.nn import functional as F\n", - "from transformers import AutoModelForImageClassification\n", - "\n", - "from devinterp.optim import SGLD\n", - "from devinterp.slt.sampler import estimate_learning_coeff_with_summary\n", - "from devinterp.utils import plot_trace, default_nbeta\n", - "\n", - "warnings.filterwarnings(\"ignore\")\n", - "\n", - "\n", - "def evaluate(model, data):\n", - " inputs, outputs = data\n", - "\n", - " return F.cross_entropy(model(inputs).logits, outputs), {\n", - " \"logits\": model(inputs).logits\n", - " } # transformers doesn't output a vector. TODO FIX NON TPU" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n", - "Failed to download (trying next):\n", - "\n", - "\n", - "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-images-idx3-ubyte.gz\n", - "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-images-idx3-ubyte.gz to ../data/MNIST/raw/train-images-idx3-ubyte.gz\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 9.91M/9.91M [00:00<00:00, 40.9MB/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Extracting ../data/MNIST/raw/train-images-idx3-ubyte.gz to ../data/MNIST/raw\n", - "\n", - "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n", - "Failed to download (trying next):\n", - "\n", - "\n", - "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-labels-idx1-ubyte.gz\n", - 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] - } - ], - "source": [ - "# Load a pretrained MNIST classifier\n", - "model = AutoModelForImageClassification.from_pretrained(\"fxmarty/resnet-tiny-mnist\").to(\n", - " DEVICE\n", - ")\n", - "data = torchvision.datasets.MNIST(\n", - " root=\"../data\",\n", - " download=True,\n", - " transform=torchvision.transforms.Compose(\n", - " [\n", - " torchvision.transforms.ToTensor(),\n", - " ]\n", - " ),\n", - ")\n", - "loader = torch.utils.data.DataLoader(data, batch_size=256, shuffle=True)\n", - "\n", - "learning_coeff_stats = estimate_learning_coeff_with_summary(\n", - " model,\n", - " loader=loader,\n", - " evaluate=evaluate,\n", - " sampling_method=SGLD,\n", - " optimizer_kwargs=dict(lr=4e-4, localization=100.0, nbeta=default_nbeta(loader)),\n", - " num_chains=3, # How many independent chains to run\n", - " num_draws=100, # How many samples to draw per chain\n", - " num_burnin_steps=0, # How many samples to discard at the beginning of each chain\n", - " num_steps_bw_draws=1, # How many steps to take between each sample\n", - " device=DEVICE,\n", - " online=True,\n", - ")\n", - "\n", - "# Optionally, specify cores and gpu_idxs to parallelize sampling across multiple devices.\n", - "# E.g cores=4, gpu_idxs=[0, 1] will run 2 chains on each of the 2 GPUs." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "dict_keys(['init_loss', 'llc/means', 'llc/stds', 'llc/trace', 'loss/trace'])\n" - ] - } - ], - "source": [ - "trace = learning_coeff_stats[\"loss/trace\"]\n", - "print(learning_coeff_stats.keys())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Diagnostics\n", - "\n", - "Below you'll see what's actually happening when you run `local_learning_coefficients`.\n", - "\n", - "We sample 10 different chains, with the same starting positions but different batch schedules and noise realizations at each step. For each of these chains, we take 200 steps using SGLD. We observe the loss at each of these points. At the end, we average the loss across chains, compare it to the initial loss, and apply a correction that depends on the dataset size to get the local learning coefficient. \n", - "\n", - "For a healthy chain, the Loss Trace should increase rapidly at first and then level off." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_trace(\n", - " trace,\n", - " \"Loss\",\n", - " x_axis=\"Step\",\n", - " title=f\"Loss Trace, avg LLC = {sum(learning_coeff_stats['llc/means']) / len(learning_coeff_stats['llc/means']):.2f}\",\n", - " plot_mean=False,\n", - " plot_std=False,\n", - " fig_size=(12, 9),\n", - " true_lc=None,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To get a better understanding of the sampling methods, check out:\n", - "- [`normal_crossing.ipynb`](../examples/normal_crossing.ipynb) showing both SGLD and SGNHT get the right answers in known simple cases.\n", - "- [`mnist.ipynb`](../examples/mnist.ipynb) showing how we can use LLC estimation to assess relative LLCs of MNIST models trained with different optimizers.\n", - "- [`sgld_calibration.ipynb`](../examples/sgld_calibration.ipynb) shows how to gain confidence in using SGLD-based LLC estimation to a model with unknown LLC.\n", - "- [`diagnostics.ipynb`](../examples/diagnostics.ipynb) shows how to use callbacks to diagnose if your sampling is going well.\n", - "- [`epsilon_beta.ipynb`](../examples/epsilon_beta.ipynb) shows how to use use a callback to calibrate SGLD hyperparameters.\n", - "\n", - "For a small demo of how to use the library to study grokking, see [`grokking.ipynb`](../examples/grokking.ipynb)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Project ideas\n", - "\n", - "What can you do with this? We wrote up [a bunch of ideas](https://devinterp.com/projects), most of which are focused on learning coefficient estimation, and we'd recommend starting there. There's a lot of low-hanging fruit. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Other Techniques\n", - "\n", - "There are plenty of other techniques that would fit naturally under the umbrella of \"developmental interpretability.\" If you're interested in looking beyond learning coefficient estimation, here are some ideas (we welcome PRs to introduce these methods to the library!):\n", - "\n", - "- **Dimensionality reduction techniques**: Run PCA / t-SNE / UMAP / etc. over the weights / [tokens](https://transformer-circuits.pub/2022/in-context-learning-and-induction-heads/index.html#per-token-loss-intro) / activations over time. For larger models, you'll have to restrict your attention to a subset of the weights and activations for this to be tractable\n", - "- **Progress measures**. If you have an understanding of some structure at the end of training, you can roll that understanding backwards to track how that structure develops over time. \n", - "- **Probes**. Similarly, you can train a linear probe from activations onto features, then roll that probe back to previous checkpoints to measure how those features are learned. \n", - "- **Gradients**. Just look at the gradients! \n", - "- **Evals**. You can measure performance on a targeted benchmarks to track when the model learns the associated capabilities. \n", - "- **Covariance estimators**. That's a secret for now. More coming soon!" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": ".venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.11" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/examples/mnist.ipynb b/examples/mnist.ipynb deleted file mode 100644 index 7316b23a..00000000 --- a/examples/mnist.ipynb +++ /dev/null @@ -1,1070 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# RLCT Estimation of MNIST\n", - "\n", - "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/timaeus-research/devinterp/blob/main/examples/mnist.ipynb)\n", - "\n", - "This Jupyter Notebook aims to reproduce the results of Lau et al. (2023) by measuring the Real Log Canonical Threshold (RLCT) for a small 2-layer ReLU model (about 1M parameters) trained on the MNIST dataset. It uses both Stochastic Gradient Nose-Hoover Thermostat (SGNHT) and Stochastic Gradient Langevin Dynamics (SGLD) as sampling methods.\n", - "\n", - "## Main Steps:\n", - "\n", - "1. **Data Preparation**: Load the MNIST dataset for training and testing.\n", - "2. **Model Training**: Train a multi-layer perceptron model using stochastic gradient descent.\n", - "3. **Model Evaluation**: Evaluate the model's performance on a test set.\n", - "4. **RLCT Estimation**: Use SGNHT and SGLD samplers to estimate RLCT.\n", - "5. **Plotting**: Visualize train and test losses, and RLCT estimates." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Requirement already satisfied: pip in /home/paperspace/devinterp/.venv/lib/python3.9/site-packages (24.2)\n", - "Note: you may need to restart the kernel to use updated packages.\n", - "Requirement already satisfied: transformers in /home/paperspace/devinterp/.venv/lib/python3.9/site-packages (4.30.2)\n", - 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"remote: Total 89 (delta 0), reused 0 (delta 0), pack-reused 89 (from 1)\u001b[K\n", - "Unpacking objects: 100% (89/89), 25.31 KiB | 1.41 MiB/s, done.\n", - "/home/paperspace/devinterp/examples/entropy-sgd\n", - "/home/paperspace/devinterp/examples\n" - ] - } - ], - "source": [ - "%pip install --upgrade pip\n", - "%pip install transformers torch torchvision seaborn\n", - "%pip install devinterp\n", - "\n", - "!git clone https://github.com/ucla-vision/entropy-sgd.git\n", - "%cd entropy-sgd\n", - "from python.optim import EntropySGD\n", - "\n", - "%cd .." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import copy\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "import torch\n", - "import torch.nn as nn\n", - "import torch.nn.functional as F\n", - "import torch.optim as optim\n", - "from torch.utils.data import DataLoader\n", - "from torchvision import datasets, transforms\n", - "from tqdm import tqdm\n", - "\n", - "from devinterp.optim.sgld import SGLD\n", - "from devinterp.optim.sgnht import SGNHT\n", - "\n", - "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", - "\n", - "PRIMARY, SECONDARY, TERTIARY, QUATERNARY = sns.color_palette(\"muted\")[:4]\n", - "PRIMARY_LIGHT, SECONDARY_LIGHT, TERTIARY_LIGHT, QUATERNARY_LIGHT = sns.color_palette(\n", - " \"pastel\"\n", - ")[:4]" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "def emtpy_func():\n", - " return (), ()\n", - "\n", - "\n", - "def train_one_epoch(model, train_loader, optimizer, criterion, model_key):\n", - " model.train()\n", - " train_loss = 0\n", - " for data, target in tqdm(train_loader):\n", - " optimizer.zero_grad()\n", - " output = model(data.to(DEVICE))\n", - " loss = criterion(output, target.to(DEVICE))\n", - " train_loss += loss.item()\n", - " loss.backward()\n", - " if model_key == \"sgd\":\n", - " optimizer.step()\n", - " else:\n", - " optimizer.step(emtpy_func, model, criterion)\n", - " return train_loss / len(train_loader)\n", - "\n", - "\n", - "def evaluate(model, test_loader, criterion):\n", - " model.eval()\n", - " test_loss = 0\n", - " with torch.no_grad():\n", - " for data, target in test_loader:\n", - " output = model(data.to(DEVICE))\n", - " loss = criterion(output, target.to(DEVICE))\n", - " test_loss += loss.item()\n", - " return test_loss / len(test_loader)\n", - "\n", - "\n", - "# Define the neural network\n", - "class Net(nn.Module):\n", - " def __init__(\n", - " self,\n", - " hidden_layer_sizes=[1024, 1024],\n", - " input_dim=28 * 28,\n", - " output_dim=10,\n", - " activation=F.relu,\n", - " with_bias=True,\n", - " ):\n", - " super(Net, self).__init__()\n", - " self.input_dim = input_dim\n", - " self.layer_sizes = [input_dim] + hidden_layer_sizes + [output_dim]\n", - " self.activation = activation\n", - " self.with_bias = with_bias\n", - " self.layers = nn.ModuleList()\n", - " for i in range(len(self.layer_sizes) - 1):\n", - " dim_in, dim_out = self.layer_sizes[i : i + 2]\n", - " self.layers.append(nn.Linear(dim_in, dim_out, bias=self.with_bias).float())\n", - "\n", - " def forward(self, x):\n", - " x = x.view(-1, self.input_dim)\n", - " for layer in self.layers[:-1]:\n", - " x = self.activation(layer(x))\n", - " x = self.layers[-1](x)\n", - " return x" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Constants\n", - "DEVICE = \"cpu\"\n", - "BATCH_SIZE = 512\n", - "LR = 0.05\n", - "MOMENTUM = 0.9\n", - "N_EPOCHS = 10\n", - "DATA_PATH = \"../data\"\n", - "\n", - "\n", - "# Load MNIST dataset\n", - "def load_mnist_data(train, batch_size, shuffle):\n", - " dataset = datasets.MNIST(\n", - " DATA_PATH, train=train, transform=transforms.ToTensor(), download=True\n", - " )\n", - " return DataLoader(dataset, batch_size=batch_size, shuffle=shuffle)\n", - "\n", - "\n", - "train_loader = load_mnist_data(train=True, batch_size=BATCH_SIZE, shuffle=True)\n", - "test_loader = load_mnist_data(train=False, batch_size=BATCH_SIZE, shuffle=False)\n", - "\n", - "model_esgd = Net().to(DEVICE)\n", - "model_sgd = Net().to(DEVICE)\n", - "optimizer_esgd = EntropySGD(\n", - " model_esgd.parameters(), config=dict(lr=LR, momentum=MOMENTUM, L=5)\n", - ")\n", - "optimizer_sgd = optim.SGD(\n", - " model_sgd.parameters(), lr=LR, momentum=MOMENTUM, nesterov=True\n", - ")\n", - "\n", - "criterion = nn.CrossEntropyLoss()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 0%| | 0/118 [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "sns.set_style(\"whitegrid\")\n", - "\n", - "fig, ax1 = plt.subplots(figsize=(10, 6))\n", - "ax1.set_xlabel(\"Epoch\")\n", - "ax1.set_ylabel(\"Loss\", color=PRIMARY)\n", - "ax1.plot(train_losses[\"sgd\"], label=\"Train Loss, sgd\", color=PRIMARY)\n", - "ax1.plot(test_losses[\"sgd\"], label=\"Test Loss, sgd\", color=PRIMARY_LIGHT)\n", - "\n", - "ax1.plot(train_losses[\"esgd\"], label=\"Train Loss, esgd\", color=SECONDARY)\n", - "ax1.plot(test_losses[\"esgd\"], label=\"Test Loss, esgd\", color=SECONDARY_LIGHT)\n", - "ax1.tick_params(axis=\"y\", labelcolor=PRIMARY)\n", - "ax1.legend(loc=\"upper left\")\n", - "\n", - "ax2 = ax1.twinx()\n", - "ax2.set_ylabel(r\"Local Learning Coefficient, $\\hat \\lambda$\", color=SECONDARY)\n", - "ax2.plot(rlct_sgd[\"sgnht\"], label=\"SGNHT, sgd\", color=TERTIARY)\n", - "ax2.plot(rlct_sgd[\"sgld\"], label=\"SGLD, sgd\", color=TERTIARY_LIGHT)\n", - "\n", - "ax2.plot(rlct_esgd[\"sgnht\"], label=\"SGNHT, esgd\", color=QUATERNARY)\n", - "ax2.plot(rlct_esgd[\"sgld\"], label=\"SGLD, esgd\", color=QUATERNARY_LIGHT)\n", - "ax2.tick_params(axis=\"y\", labelcolor=SECONDARY)\n", - "ax2.legend(loc=\"center right\")\n", - "\n", - "fig.tight_layout()\n", - "plt.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": ".venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.18" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/examples/normal_crossing.ipynb b/examples/normal_crossing.ipynb deleted file mode 100644 index 197b04ee..00000000 --- a/examples/normal_crossing.ipynb +++ /dev/null @@ -1,833 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Normal Crossing RLCT Estimation\n", - "\n", - "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/timaeus-research/devinterp/blob/main/examples/normal_crossing.ipynb)\n", - "\n", - "This notebook estimates the Local Learning Coefficient (LLC) of normal crossings using two algorithms: SGNHT (Stochastic Gradient Nose-Hoover Thermostat) and SGLD (Stochastic Gradient Langevin Dynamics). We recreate some key results from [Lau et al. (2023)](https://arxiv.org/pdf/2308.12108.pdf), namely the ordinality check, trajectory plot, and the table of theoretical vs empirical learning coefficients. \n", - "\n", - "We use two different models in this notebook, one for testing LLC ordinality and one for doing visualisations and checking estimated LLCs against known values. For the ordinality check, we have a model $y = (w_1 - 1) *(w_1^2 + w_2^2)^2$ which we use to predict gaussian noise, so the best loss is reached when $w_1 = 1$ or $w_1 = w_2 = 0 $, the latter being more degenerate. The second model used for the comparison vs theory is a polynomial model characterized by $w_1^a * w_2^b$ for some $(a, b)$, where $w_1$ and $w_2$ are weights to be learned. The data is again gaussian noise, so the model achieves its lowest loss when $w_1=0$ or $w_2 =0$.\n", - "\n", - "For a not-yet-finished version of the MNIST plot from this paper, see the MNIST notebook." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Collecting pyro-ppl\n", - " Downloading pyro_ppl-1.9.1-py3-none-any.whl.metadata (7.8 kB)\n", - "Requirement already satisfied: seaborn in /home/svwin/devinterp/.venv/lib/python3.8/site-packages (0.13.2)\n", - "Requirement already satisfied: numpy>=1.7 in /home/svwin/devinterp/.venv/lib/python3.8/site-packages (from pyro-ppl) (1.24.4)\n", - "Collecting opt-einsum>=2.3.2 (from pyro-ppl)\n", - " Downloading opt_einsum-3.3.0-py3-none-any.whl.metadata (6.5 kB)\n", - "Collecting pyro-api>=0.1.1 (from pyro-ppl)\n", - " Downloading pyro_api-0.1.2-py3-none-any.whl.metadata (2.5 kB)\n", - "Requirement already satisfied: torch>=2.0 in /home/svwin/devinterp/.venv/lib/python3.8/site-packages (from pyro-ppl) (2.4.1)\n", - 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"Installing collected packages: pyro-api, opt-einsum, pyro-ppl\n", - "Successfully installed opt-einsum-3.3.0 pyro-api-0.1.2 pyro-ppl-1.9.1\n", - "Note: you may need to restart the kernel to use updated packages.\n" - ] - } - ], - "source": [ - "%pip install pyro-ppl seaborn devinterp" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import torch\n", - "\n", - "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", - "# if you want to use your TPU (note that torch_xla is not by default installed, install using `pip install devinterp[tpu]`)\n", - "# import os\n", - "# os.environ[\"USE_TPU_BACKEND\"] = \"1\"\n", - "# import torch_xla.core.xla_model as xm\n", - "# DEVICE = xm.xla_device()" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/svwin/devinterp/.venv/lib/python3.8/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n" - ] - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import pandas as pd\n", - "import pyro\n", - "import pyro.distributions as dist\n", - "import seaborn as sns\n", - "import torch\n", - "import torch.nn as nn\n", - "from matplotlib.colors import LinearSegmentedColormap\n", - "from pyro.infer import MCMC, NUTS\n", - "from torch.utils.data import DataLoader, TensorDataset\n", - "\n", - "from devinterp.optim.sgld import SGLD\n", - "from devinterp.optim.sgnht import SGNHT\n", - "from devinterp.slt.llc import LLCEstimator, SamplerCallback\n", - "from devinterp.slt.sampler import estimate_learning_coeff_with_summary, sample\n", - "from devinterp.utils import get_init_loss_multi_batch\n", - "\n", - "# DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", - "sns.set_style(\"whitegrid\")\n", - "\n", - "# plotting\n", - "CMAP = sns.color_palette(\"muted\", as_cmap=True)\n", - "PRIMARY, SECONDARY, TERTIARY = CMAP[:3]" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "from devinterp.utils import evaluate_mse\n", - "\n", - "# constants\n", - "SIGMA = 0.25\n", - "NUM_TRAIN_SAMPLES = 1000\n", - "BATCH_SIZE = NUM_TRAIN_SAMPLES\n", - "EVALUATE = evaluate_mse" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# some necessary functions\n", - "class PolyModel(nn.Module):\n", - " def __init__(self, powers):\n", - " super(PolyModel, self).__init__()\n", - " self.weights = nn.Parameter(\n", - " torch.tensor([0.0, 0.0], dtype=torch.float32, requires_grad=True).to(DEVICE)\n", - " )\n", - " self.powers = powers\n", - "\n", - " def forward(self, x):\n", - " multiplied = torch.prod(self.weights**self.powers)\n", - " x = x * multiplied\n", - " return x\n", - "\n", - "\n", - "class LinePlusDotModel(nn.Module):\n", - " def __init__(self):\n", - " super(LinePlusDotModel, self).__init__()\n", - " self.weights = nn.Parameter(\n", - " torch.tensor([0.0, 0.0], dtype=torch.float32, requires_grad=True).to(DEVICE)\n", - " )\n", - "\n", - " def forward(self, x):\n", - " return (\n", - " x\n", - " * (self.weights[0] - 1)\n", - " * ((self.weights[0] ** 2 + self.weights[1] ** 2) ** 2)\n", - " )\n", - "\n", - "\n", - "def generate_dataset_for_seed(seed=0):\n", - " torch.manual_seed(seed)\n", - " np.random.seed(seed)\n", - " x = torch.normal(0, 2, size=(NUM_TRAIN_SAMPLES,))\n", - " y = SIGMA * torch.normal(0, 1, size=(NUM_TRAIN_SAMPLES,))\n", - " train_data = TensorDataset(x, y)\n", - " train_loader = DataLoader(train_data, batch_size=BATCH_SIZE, shuffle=True)\n", - " return train_loader, train_data, x, y" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Benchmarking\n", - "\n", - "First, let's estimate some RLCTs of a simple degenerate line and point, where the point is more degenerate than the line. We first estimate the RLCTs at the point, and then at the line. This is a reproduction of Figure 1 from [Lau et al. (2023)](https://arxiv.org/pdf/2308.12108.pdf)." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/svwin/devinterp/src/devinterp/backends/default/slt/sampler.py:216: UserWarning: You are taking more draws than burn-in steps, your LLC estimates will likely be underestimates. Please check LLC chain convergence.\n", - " warnings.warn(\n", - "/home/svwin/devinterp/src/devinterp/backends/default/slt/sampler.py:220: UserWarning: You are taking more sample batches than there are dataloader batches available, this removes some randomness from sampling but is probably fine. (All sample batches beyond the number dataloader batches are cycled from the start, f.e. 9 samples from [A, B, C] would be [B, A, C, B, A, C, B, A, C].)\n", - " warnings.warn(\n", - "/home/svwin/devinterp/src/devinterp/backends/default/slt/sampler.py:239: UserWarning: If you're setting a nbeta or temperature in optimizer_kwargs, please also make sure to set it in the callbacks.\n", - " warnings.warn(\n", - "/home/svwin/devinterp/src/devinterp/backends/default/slt/sampler.py:53: UserWarning: You are taking more sample batches than there are dataloader batches available, this removes some randomness from sampling but is probably fine. (All sample batches beyond the number dataloader batches are cycled from the start, f.e. 9 samples from [A, B, C] would be [B, A, C, B, A, C, B, A, C].)\n", - " warnings.warn(\n", - "Chain 0: 100%|██████████| 5000/5000 [00:03<00:00, 1539.29it/s]\n", - "/home/svwin/devinterp/src/devinterp/backends/default/slt/sampler.py:53: UserWarning: You are taking more sample batches than there are dataloader batches available, this removes some randomness from sampling but is probably fine. (All sample batches beyond the number dataloader batches are cycled from the start, f.e. 9 samples from [A, B, C] would be [B, A, C, B, A, C, B, A, C].)\n", - " warnings.warn(\n", - "Chain 1: 100%|██████████| 5000/5000 [00:03<00:00, 1565.90it/s]\n", - "Chain 2: 100%|██████████| 5000/5000 [00:03<00:00, 1570.95it/s]\n", - "Chain 3: 100%|██████████| 5000/5000 [00:03<00:00, 1570.16it/s]\n", - "Chain 4: 100%|██████████| 5000/5000 [00:03<00:00, 1569.93it/s]\n", - "/home/svwin/devinterp/src/devinterp/backends/default/slt/sampler.py:216: UserWarning: You are taking more draws than burn-in steps, your LLC estimates will likely be underestimates. Please check LLC chain convergence.\n", - " warnings.warn(\n", - "/home/svwin/devinterp/src/devinterp/backends/default/slt/sampler.py:220: UserWarning: You are taking more sample batches than there are dataloader batches available, this removes some randomness from sampling but is probably fine. (All sample batches beyond the number dataloader batches are cycled from the start, f.e. 9 samples from [A, B, C] would be [B, A, C, B, A, C, B, A, C].)\n", - " warnings.warn(\n", - "/home/svwin/devinterp/src/devinterp/backends/default/slt/sampler.py:239: UserWarning: If you're setting a nbeta or temperature in optimizer_kwargs, please also make sure to set it in the callbacks.\n", - " warnings.warn(\n", - "Chain 0: 100%|██████████| 5000/5000 [00:02<00:00, 2043.49it/s]\n", - "Chain 1: 100%|██████████| 5000/5000 [00:02<00:00, 2041.53it/s]\n", - "Chain 2: 100%|██████████| 5000/5000 [00:02<00:00, 2037.82it/s]\n", - "Chain 3: 100%|██████████| 5000/5000 [00:02<00:00, 2036.79it/s]\n", - "Chain 4: 100%|██████████| 5000/5000 [00:02<00:00, 2033.40it/s]\n", - "Chain 0: 100%|██████████| 5000/5000 [00:03<00:00, 1561.85it/s]\n", - "Chain 1: 100%|██████████| 5000/5000 [00:03<00:00, 1565.91it/s]\n", - "Chain 2: 100%|██████████| 5000/5000 [00:03<00:00, 1566.39it/s]\n", - "Chain 3: 100%|██████████| 5000/5000 [00:03<00:00, 1570.48it/s]\n", - "Chain 4: 100%|██████████| 5000/5000 [00:03<00:00, 1572.46it/s]\n", - "Chain 0: 100%|██████████| 5000/5000 [00:02<00:00, 2039.88it/s]\n", - "Chain 1: 100%|██████████| 5000/5000 [00:02<00:00, 2045.51it/s]\n", - "Chain 2: 100%|██████████| 5000/5000 [00:02<00:00, 2039.15it/s]\n", - "Chain 3: 100%|██████████| 5000/5000 [00:02<00:00, 2035.71it/s]\n", - "Chain 4: 100%|██████████| 5000/5000 [00:02<00:00, 2042.70it/s]\n" - ] - } - ], - "source": [ - "from devinterp.utils import default_nbeta\n", - "\n", - "train_loader, train_data, x, y = generate_dataset_for_seed(0)\n", - "model = LinePlusDotModel().to(DEVICE)\n", - "lr = 0.0001\n", - "num_chains = 5\n", - "num_draws = 5000\n", - "nbeta = default_nbeta(train_loader)\n", - "sgld_llcs = []\n", - "sgnht_llcs = []\n", - "sample_points = [[0.0, 0.0], [1.0, 0.3]]\n", - "actual_rlcts = [0.25, 0.5]\n", - "for sample_point in sample_points:\n", - " model.weights = nn.Parameter(\n", - " torch.tensor(sample_point, dtype=torch.float32, requires_grad=True)\n", - " )\n", - " init_loss = get_init_loss_multi_batch(\n", - " train_loader, num_chains, model, EVALUATE, DEVICE\n", - " )\n", - " sgnht_llc = LLCEstimator(\n", - " num_chains=num_chains, num_draws=num_draws, nbeta=nbeta, init_loss=init_loss\n", - " )\n", - " sgld_llc = LLCEstimator(\n", - " num_chains=num_chains, num_draws=num_draws, nbeta=nbeta, init_loss=init_loss\n", - " )\n", - " sample(\n", - " model,\n", - " train_loader,\n", - " evaluate=EVALUATE,\n", - " optimizer_kwargs=dict(lr=lr, bounding_box_size=0.5, nbeta=nbeta),\n", - " sampling_method=SGNHT,\n", - " num_chains=num_chains,\n", - " num_draws=num_draws,\n", - " device=DEVICE,\n", - " callbacks=[sgnht_llc],\n", - " )\n", - " sample(\n", - " model,\n", - " train_loader,\n", - " evaluate=EVALUATE,\n", - " optimizer_kwargs=dict(lr=lr, localization=0.2, nbeta=nbeta), # TODO FIX\n", - " sampling_method=SGLD,\n", - " num_chains=num_chains,\n", - " num_draws=num_draws,\n", - " device=DEVICE,\n", - " callbacks=[sgld_llc],\n", - " )\n", - " sgld_llcs += [sgld_llc.get_results()]\n", - " sgnht_llcs += [sgnht_llc.get_results()]" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.14887431263923645\n", - "[0.2005019336938858, 0.07028903812170029, 0.1147444099187851, 0.15498630702495575, 0.20384985208511353]\n", - "0.654302716255188\n", - "[0.56605064868927, 0.4899264872074127, 0.5089262127876282, 0.8386633396148682, 0.8679468631744385]\n", - "0.20462393760681152\n", - "[0.202658012509346, 0.205875962972641, 0.18717604875564575, 0.22053281962871552, 0.20687688887119293]\n", - "0.8179630041122437\n", - "[1.257343053817749, 0.9880695939064026, 0.8615443706512451, 0.404544860124588, 0.5783130526542664]\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, axes = plt.subplots(1, 2, figsize=(14, 3))\n", - "fig.patch.set_facecolor(\"white\")\n", - "for i, (sampler_name, rlct_estimates) in enumerate(\n", - " zip([\"SGLD\", \"SGNHT\"], [sgld_llcs, sgnht_llcs])\n", - "):\n", - " axes[i].set_xlim([-0.5, 1.5])\n", - " axes[i].set_title(sampler_name)\n", - " axes[i].set_xlabel(r\"$\\hat\\lambda$\")\n", - " for j, (rlct_estimates, sample_point) in enumerate(\n", - " zip(rlct_estimates, sample_points)\n", - " ):\n", - " print(rlct_estimates[\"llc/mean\"])\n", - " print([rlct_estimates[f\"llc-chain/{k}\"] for k in range(num_chains)])\n", - " axes[i].axvline(actual_rlcts[j], color=\"black\", linestyle=\"--\")\n", - " axes[i].hist(\n", - " [rlct_estimates[f\"llc-chain/{i}\"] for i in range(num_chains)],\n", - " alpha=0.6,\n", - " bins=40,\n", - " range=(-0.5, 1.5),\n", - " label=rf\"$w_1={sample_point[0]:.1f}, w_2={sample_point[1]:.1f}$\",\n", - " color=SECONDARY if j else PRIMARY,\n", - " )\n", - " axes[i].legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Trajectory Plots\n", - "\n", - "Let's see how well our different sampling methods explore the local loss landscape. We start out sampling at $w_1 = 0.5, w_2 = 0.01$, and plot the resulting trajectories after 10k steps." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# plotting\n", - "def hex_to_rgb(hex_color):\n", - " hex_color = hex_color.lstrip(\"#\")\n", - " return tuple(int(hex_color[i : i + 2], 16) / 255.0 for i in (0, 2, 4))\n", - "\n", - "\n", - "lighter_factor = 0.9 # Between 0 and 1, higher values make it closer to white\n", - "lighter_SECONDARY = tuple(\n", - " [x + (1 - x) * lighter_factor for x in hex_to_rgb(SECONDARY)[:3]] + [1.0]\n", - ")\n", - "\n", - "colors = [SECONDARY, lighter_SECONDARY]\n", - "n_bins = 20 # Number of bins\n", - "contour_cmap = LinearSegmentedColormap.from_list(\"custom_cmap\", colors, N=n_bins)\n", - "\n", - "powers = torch.tensor([1, 3]).to(DEVICE)\n", - "model = PolyModel(powers).to(DEVICE)\n", - "\n", - "\n", - "def plot_trajectories(trajectories, names, device=DEVICE):\n", - " fig, axes = plt.subplots(1, len(trajectories), figsize=(15, 5))\n", - " w1_range = np.linspace(-2, 2, 21)\n", - " w2_range = np.linspace(-2, 2, 21)\n", - " w1_vals, w2_vals = np.meshgrid(w1_range, w2_range)\n", - " Z = np.zeros_like(w1_vals, dtype=float)\n", - "\n", - " for i in range(w1_vals.shape[0]):\n", - " for j in range(w1_vals.shape[1]):\n", - " w1 = w1_vals[i, j]\n", - " w2 = w2_vals[i, j]\n", - " model.weights = nn.Parameter(\n", - " torch.tensor([w1, w2], dtype=torch.float32).to(device)\n", - " )\n", - " Z[i, j] = (\n", - " model.to(device)(torch.tensor(1.0).to(device)).item() ** 2\n", - " ) # MSE, so square this\n", - "\n", - " custom_levels = np.linspace(Z.min(), Z.max() * 0.04, n_bins)\n", - "\n", - " for i, trajectory in enumerate(trajectories):\n", - " axes[i].contourf(\n", - " w1_vals, w2_vals, Z, levels=custom_levels, cmap=contour_cmap, alpha=0.8\n", - " )\n", - " weights = trajectory[\"ws/trace\"][0]\n", - " draws_array = np.array(\n", - " [\n", - " d\n", - " for d in weights\n", - " if w1_range[0] <= d[0] <= w1_range[-1]\n", - " and w2_range[0] <= d[1] <= w2_range[-1]\n", - " ]\n", - " )\n", - " sns.scatterplot(\n", - " x=draws_array[:, 0],\n", - " y=draws_array[:, 1],\n", - " marker=\"x\",\n", - " ax=axes[i],\n", - " s=10,\n", - " color=PRIMARY,\n", - " )\n", - " axes[i].axhline(0, linestyle=\"--\", color=\"gray\")\n", - " axes[i].axvline(0, linestyle=\"--\", color=\"gray\")\n", - " axes[i].set_xlabel(r\"$w_{1}$\")\n", - " axes[i].set_ylabel(r\"$w_{2}$\")\n", - " axes[i].set_title(names[i])\n", - " axes[i].grid(False)\n", - " plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "train_loader, train_data, x, y = generate_dataset_for_seed(0)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "class WeightCallback(SamplerCallback):\n", - " def __init__(self, num_chains: int, num_draws: int, model, device=\"cpu\"):\n", - " self.num_chains = num_chains\n", - " self.num_draws = num_draws\n", - " self.ws = np.zeros(\n", - " (num_chains, num_draws, len(model.weights)), dtype=np.float32\n", - " )\n", - " self.device = device\n", - "\n", - " def update(self, chain: int, draw: int, model):\n", - " self.ws[chain, draw] = model.weights.cpu().detach()\n", - "\n", - " def get_results(self):\n", - " return {\n", - " \"ws/trace\": self.ws,\n", - " }\n", - "\n", - " def __call__(self, chain: int, draw: int, model, **kwargs):\n", - " self.update(chain, draw, model)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Chain 0: 0%| | 0/10000 [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_trajectories(\n", - " [sgld_weights.get_results(), sgnht_weights.get_results(), trace_mcmc],\n", - " names=[\"SGLD\", \"SGNHT\", \"MCMC\"],\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Benchmarking\n", - "\n", - "Finally, let's check if we get the expected lambdahat values for a few different crossings. This is similar to Table 1 from [Lau et al. (2023)](https://arxiv.org/pdf/2308.12108.pdf)." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/svwin/devinterp/src/devinterp/slt/sampler.py:50: UserWarning: Using passed in nbeta. Make sure callbacks are also initialized with the same nbeta.\n", - " warnings.warn(\n", - "/home/svwin/devinterp/src/devinterp/backends/default/slt/sampler.py:216: UserWarning: You are taking more draws than burn-in steps, your LLC estimates will likely be underestimates. Please check LLC chain convergence.\n", - " warnings.warn(\n", - "/home/svwin/devinterp/src/devinterp/backends/default/slt/sampler.py:220: UserWarning: You are taking more sample batches than there are dataloader batches available, this removes some randomness from sampling but is probably fine. (All sample batches beyond the number dataloader batches are cycled from the start, f.e. 9 samples from [A, B, C] would be [B, A, C, B, A, C, B, A, C].)\n", - " warnings.warn(\n", - "/home/svwin/devinterp/src/devinterp/backends/default/slt/sampler.py:239: UserWarning: If you're setting a nbeta or temperature in optimizer_kwargs, please also make sure to set it in the callbacks.\n", - " warnings.warn(\n", - "/home/svwin/devinterp/src/devinterp/backends/default/slt/sampler.py:53: UserWarning: You are taking more sample batches than there are dataloader batches available, this removes some randomness from sampling but is probably fine. (All sample batches beyond the number dataloader batches are cycled from the start, f.e. 9 samples from [A, B, C] would be [B, A, C, B, A, C, B, A, C].)\n", - " warnings.warn(\n", - "Chain 0: 0%| | 0/5000 [00:00 50% (1/2). +l0h0_mask = create_param_masks(model, "l0h0") +print("\n--- l0h0 (single attention head) ---") +preview_weight_restriction(model, l0h0_mask) + +# --- Susceptibilities with weight restrictions --- + +result = susceptibilities( + model=model, + dataset=ds, + observables={"train": (ds, 2), "probe": (probe, 2)}, + weight_restrictions={ + "full": None, + "l0h0": l0h0_mask, + "l0h1": create_param_masks(model, "l0h1"), + }, + sampling_task="train", + lr=1e-4, + n_beta=n_beta, + num_chains=2, + num_draws=50, + batch_size=BATCH_SIZE, + num_init_loss_batches=4, +) +sus = result["susceptibilities"].dataset +print(f"Susceptibilities shape: {dict(sus.dims)}") +print(sus["sus"]) + +# --- Manual weight restrictions --- +# For unsupported architectures, build masks directly. +# A mask is just {param_name: bool_tensor | None} where None means unrestricted. +# Example: restrict to the first MLP layer's gate projection + +manual_masks = {} +for name, param in model.named_parameters(): + if "model.layers.0.mlp.gate_proj" in name: + manual_masks[name] = None # optimize this entire param + elif "model.layers.0.mlp.up_proj" in name: + # partially mask: only first half of neurons + mask = torch.zeros_like(param, dtype=torch.bool) + mask[: param.shape[0] // 2] = True + manual_masks[name] = mask + +print("\nManual mask:") +preview_weight_restriction(model, manual_masks) diff --git a/examples/sgld_calibration.ipynb b/examples/sgld_calibration.ipynb deleted file mode 100644 index 873ce107..00000000 --- a/examples/sgld_calibration.ipynb +++ /dev/null @@ -1,1067 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "38551abb-5509-4306-8463-e61d5e29eff3", - "metadata": {}, - "source": [ - "# Calibration of SGLD hyperparameters for MNIST" - ] - }, - { - "cell_type": "markdown", - "id": "b54bdb93-e084-4cfb-b629-3acc541a524e", - "metadata": {}, - "source": [ - "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/timaeus-research/devinterp/blob/main/examples/sgld_calibration.ipynb)\n", - "\n", - "**NOTE: this notebook is outdated, check out [`epsilon_beta.ipynb`](../examples/epsilon_beta.ipynb)[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/timaeus-research/devinterp/blob/main/examples/epsilon_beta.ipynb) for a more up-to-date method of calibrating hyperparameters $\\epsilon$ and $\\beta$. (Note that `num_draws` can still be calibrated using the heuristics in this notebook.)**\n", - "\n", - "**This notebook takes a while to run, and therefore is not intended to be run as part of the demo.**\n", - "\n", - "This notebook walks through the process for calibrating hyperparameters for Stochastic Gradient Langevin Dynamics (SGLD) based LLC (or $\\hat\\lambda$) estimation. The model we'll use is a small 2-layer ReLU network (~1M params) trained on the MNIST dataset.\n", - "\n", - "Note that in this case, the theoretical value of the LLC is not known. The intent of this notebook is to demonstrate that, even when we don't have access to the ground truth $\\lambda$, there are diagnostic processes that can allow us to be more confident that $\\hat\\lambda$ preserves desirable properties of $\\lambda$, such as maintaining relative ordering of degeneracy. This also reflects most real-world situations where we're interested in $\\hat\\lambda$ estimations.\n", - "\n", - "Finally, we should emphasize that this is more an art than a science. There is still a great deal of uncertainty and many unknown unknowns. The process of LLC estimation is a **work in progress**, and this notebook only reflects the best state of our knowledge at the time of the creation of this notebook.\n", - "\n", - "This notebook is split into 5 sections:\n", - "1. Data preparation\n", - "2. Training model checkpoints\n", - "3. Running calibration sweep\n", - "4. Plotting LLC estimations over time\n", - "5. Selecting $\\epsilon$ and $\\gamma$" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "68bd3efa-f196-44c6-b4a1-4a086b995db0", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Requirement already satisfied: pip in /home/paperspace/devinterp/.venv/lib/python3.9/site-packages (24.2)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Note: you may need to restart the kernel to use updated packages.\n", - "Requirement already satisfied: torch in /home/paperspace/devinterp/.venv/lib/python3.9/site-packages (2.0.1)\n", - "Requirement already satisfied: torchvision in /home/paperspace/devinterp/.venv/lib/python3.9/site-packages (0.15.2)\n", - "Requirement already satisfied: filelock in /home/paperspace/devinterp/.venv/lib/python3.9/site-packages (from torch) (3.12.2)\n", - "Requirement already satisfied: typing-extensions in /home/paperspace/devinterp/.venv/lib/python3.9/site-packages (from torch) (4.7.1)\n", - 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"Requirement already satisfied: mpmath>=0.19 in /home/paperspace/devinterp/.venv/lib/python3.9/site-packages (from sympy->torch>=2.0.1->devinterp) (1.3.0)\n", - "Note: you may need to restart the kernel to use updated packages.\n" - ] - } - ], - "source": [ - "%pip install --upgrade pip\n", - "%pip install torch torchvision\n", - "%pip install devinterp" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "5303693e-4e53-48ed-987c-9ac71e40786f", - "metadata": {}, - "outputs": [], - "source": [ - "import copy\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import torch\n", - "import torch.nn as nn\n", - "import torch.nn.functional as F\n", - "import torch.optim as optim\n", - "from torch.utils.data import DataLoader\n", - "from torchvision import datasets, transforms\n", - "from tqdm import tqdm\n", - "\n", - "from devinterp.optim.sgld import SGLD\n", - "from devinterp.slt.mala import MalaAcceptanceRate\n", - "from devinterp.slt.sampler import estimate_learning_coeff_with_summary\n", - "from devinterp.utils import evaluate_ce, default_nbeta\n", - "\n", - "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", - "\n", - "NUM_CHAINS = 8\n", - "NUM_DRAWS = 2000\n", - "\n", - "plt.rcParams[\"figure.figsize\"] = (\n", - " 15,\n", - " 12,\n", - ") # note: this cell may need to be re-run after creating a plot to take effect" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "ea5b8c3e-6c5e-4e32-ab97-57af3561c4aa", - "metadata": {}, - "outputs": [], - "source": [ - "# Define the neural network\n", - "class MNIST(nn.Module):\n", - " def __init__(\n", - " self,\n", - " hidden_layer_sizes=[1024, 1024],\n", - " input_dim=28 * 28,\n", - " output_dim=10,\n", - " activation=F.relu,\n", - " with_bias=True,\n", - " ):\n", - " super(MNIST, self).__init__()\n", - " self.input_dim = input_dim\n", - " self.layer_sizes = [input_dim] + hidden_layer_sizes + [output_dim]\n", - " self.activation = activation\n", - " self.with_bias = with_bias\n", - " self.layers = nn.ModuleList()\n", - " for i in range(len(self.layer_sizes) - 1):\n", - " dim_in, dim_out = self.layer_sizes[i : i + 2]\n", - " self.layers.append(nn.Linear(dim_in, dim_out, bias=self.with_bias).float())\n", - "\n", - " def forward(self, x):\n", - " x = x.view(-1, self.input_dim)\n", - " for layer in self.layers[:-1]:\n", - " x = self.activation(layer(x))\n", - " x = self.layers[-1](x)\n", - " return x\n", - "\n", - "\n", - "# Train/test utils\n", - "def train_one_epoch(model, train_loader, optimizer, criterion):\n", - " model.train()\n", - " train_loss = 0\n", - " for data, target in tqdm(train_loader):\n", - " optimizer.zero_grad()\n", - " output = model(data.to(DEVICE))\n", - " loss = criterion(output, target.to(DEVICE))\n", - " train_loss += loss.item()\n", - " loss.backward()\n", - " optimizer.step()\n", - " return train_loss / len(train_loader)\n", - "\n", - "\n", - "def evaluate(model, test_loader, criterion):\n", - " model.eval()\n", - " test_loss = 0\n", - " with torch.no_grad():\n", - " for data, target in test_loader:\n", - " output = model(data.to(DEVICE))\n", - " loss = criterion(output, target.to(DEVICE))\n", - " test_loss += loss.item()\n", - " return test_loss / len(test_loader)" - ] - }, - { - "cell_type": "markdown", - "id": "466a6d56-b10e-4605-bf94-a4d9edf628b7", - "metadata": {}, - "source": [ - "### 1. Data preparation" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "4d8ad0ad-8c28-4d1b-8a65-70237afc09dd", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "60000\n" - ] - } - ], - "source": [ - "# Load MNIST data\n", - "batch_size = 512\n", - "train_data = datasets.MNIST(\n", - " \"../data\", train=True, transform=transforms.ToTensor(), download=True\n", - ")\n", - "train_loader = DataLoader(train_data, batch_size=batch_size, shuffle=True)\n", - "print(len(train_data))\n", - "# Load test data\n", - "test_data = datasets.MNIST(\"../data\", train=False, transform=transforms.ToTensor())\n", - "test_loader = DataLoader(test_data, batch_size=batch_size, shuffle=False)\n", - "# Initialize model, loss, optimizer and sgld sampler\n", - "model = MNIST().to(DEVICE)\n", - "criterion = nn.CrossEntropyLoss()\n", - "lr = 0.05\n", - "optimizer = optim.SGD(model.parameters(), lr=lr, momentum=0.9, nesterov=True)\n", - "n_epochs = 15" - ] - }, - { - "cell_type": "markdown", - "id": "a176d767-987b-46f9-ab08-40076bf089e5", - "metadata": {}, - "source": [ - "### 2. Training model checkpoints" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "6b186841-3ee3-413b-9e98-effac262419b", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 118/118 [00:08<00:00, 13.67it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1, Train Loss: 0.6961577100268865, Test Loss: 0.2755667187273502\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 118/118 [00:08<00:00, 13.72it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 2, Train Loss: 0.2325787971711765, Test Loss: 0.18936889730393885\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 118/118 [00:08<00:00, 13.78it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 3, Train Loss: 0.1642560908981299, Test Loss: 0.1447964208200574\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 118/118 [00:08<00:00, 13.84it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 4, Train Loss: 0.12386205574592292, Test Loss: 0.11548007428646087\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 118/118 [00:08<00:00, 13.77it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 5, Train Loss: 0.09761389427013316, Test Loss: 0.10598343191668391\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 118/118 [00:08<00:00, 14.11it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 6, Train Loss: 0.07895224992880377, Test Loss: 0.09333303300663828\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 118/118 [00:08<00:00, 14.07it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 7, Train Loss: 0.06505178098203772, Test Loss: 0.0757683047093451\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 118/118 [00:08<00:00, 13.90it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 8, Train Loss: 0.053109435680306565, Test Loss: 0.07709892550483347\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 118/118 [00:08<00:00, 13.60it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 9, Train Loss: 0.046335753400699564, Test Loss: 0.06573324371129274\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 118/118 [00:08<00:00, 13.53it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 10, Train Loss: 0.03854203184719308, Test Loss: 0.07850201493129134\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 118/118 [00:08<00:00, 13.71it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 11, Train Loss: 0.03294009649021141, Test Loss: 0.06791895171627402\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 118/118 [00:08<00:00, 14.15it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 12, Train Loss: 0.028329799969885813, Test Loss: 0.06756850690580904\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 118/118 [00:08<00:00, 13.63it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 13, Train Loss: 0.023483506269689838, Test Loss: 0.07217952976934612\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 118/118 [00:08<00:00, 13.52it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 14, Train Loss: 0.020007405201194144, Test Loss: 0.06043984862044453\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 118/118 [00:08<00:00, 13.82it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 15, Train Loss: 0.016724266253916893, Test Loss: 0.05954055767506361\n" - ] - } - ], - "source": [ - "# train model\n", - "train_losses = []\n", - "test_losses = []\n", - "checkpoints = []\n", - "for epoch in range(n_epochs):\n", - " train_loss = train_one_epoch(model, train_loader, optimizer, criterion)\n", - " test_loss = evaluate(model, test_loader, criterion)\n", - " train_losses.append(train_loss)\n", - " test_losses.append(test_loss)\n", - " checkpoints += [copy.deepcopy(model)]\n", - " print(f\"Epoch {epoch + 1}, Train Loss: {train_loss}, Test Loss: {test_loss}\")" - ] - }, - { - "cell_type": "markdown", - "id": "6460d0a4-08e2-4e6c-9964-9100217d4b1a", - "metadata": {}, - "source": [ - "Plotting the train and test loss shows that training has not entirely converged, but still we should be able to get reasonable LLC estimates if we tune our hyperparameters correctly." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "5a4512d7-2a67-41e4-8f66-3a19f966c542", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "epochs = list(range(n_epochs))\n", - "plt.plot(epochs, train_losses, label=\"Train\")\n", - "plt.plot(epochs, test_losses, label=\"Test\")\n", - "plt.xlabel(\"Training epochs\")\n", - "plt.ylabel(\"Loss\")\n", - "plt.title(\"Training and test loss for MNIST model\")\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "534f438e-955e-442f-8529-f30667b65d01", - "metadata": {}, - "source": [ - "### 3. Running calibration sweep\n", - "\n", - "The main idea is to sweep across orders of magnitude of learning rate (epsilon) and localization (gamma). We look at the mean of LLC estimations over time by looking at the average estimated LLC for each chain at a given time step. A reasonable starting point for sweeping is to use `[1e-5, 1e-4, 1e-3]` for epsilon values and `[1, 10, 100]` for gamma values." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "23babf15-c873-4d0b-8eb3-bd4ceff0ac78", - "metadata": {}, - "outputs": [], - "source": [ - "EPSILONS = [1e-5, 1e-4, 1e-3]\n", - "GAMMAS = [1, 10, 100]\n", - "plt.rcParams[\"figure.figsize\"] = (15, 12)\n", - "\n", - "\n", - "def estimate_llcs_sweeper(model, epsilons, gammas):\n", - " results = {}\n", - " for epsilon in epsilons:\n", - " for gamma in gammas:\n", - " optim_kwargs = dict(lr=epsilon, noise_level=1.0, localization=gamma)\n", - " pair = (epsilon, gamma)\n", - " results[pair] = estimate_learning_coeff_with_summary(\n", - " model=model,\n", - " loader=train_loader,\n", - " evaluate=evaluate_ce,\n", - " sampling_method=SGLD,\n", - " optimizer_kwargs=optim_kwargs,\n", - " num_chains=NUM_CHAINS,\n", - " num_draws=NUM_DRAWS,\n", - " device=DEVICE,\n", - " online=True,\n", - " )\n", - " return results\n", - "\n", - "\n", - "def plot_single_graph(result, title=\"\"):\n", - " llc_color = \"teal\"\n", - " fig, axs = plt.subplots(1, 1)\n", - " # plot loss traces\n", - " loss_traces = result[\"loss/trace\"]\n", - " for trace in loss_traces:\n", - " init_loss = trace[0]\n", - " zeroed_trace = trace - init_loss\n", - " sgld_steps = list(range(len(trace)))\n", - " axs.plot(sgld_steps, zeroed_trace)\n", - "\n", - " # plot llcs\n", - " means = result[\"llc/means\"]\n", - " stds = result[\"llc/stds\"]\n", - " sgld_steps = list(range(len(means)))\n", - " axs2 = axs.twinx()\n", - " axs2.plot(\n", - " sgld_steps,\n", - " means,\n", - " color=llc_color,\n", - " linestyle=\"--\",\n", - " linewidth=2,\n", - " label=\"llc\",\n", - " zorder=3,\n", - " )\n", - " axs2.fill_between(\n", - " sgld_steps, means - stds, means + stds, color=llc_color, alpha=0.3, zorder=2\n", - " )\n", - "\n", - " # center zero, assume zero is in the range of both y axes already\n", - " y1_min, y1_max = axs.get_ylim()\n", - " y2_min, y2_max = axs2.get_ylim()\n", - " y1_zero_ratio = abs(y1_min) / (abs(y1_min) + abs(y1_max))\n", - " y2_zero_ratio = abs(y2_min) / (abs(y2_min) + abs(y2_max))\n", - " percent_to_add = abs(y1_zero_ratio - y2_zero_ratio)\n", - " y1_amt_to_add = (y1_max - y1_min) * percent_to_add\n", - " y2_amt_to_add = (y2_max - y2_min) * percent_to_add\n", - " if y1_zero_ratio < y2_zero_ratio:\n", - " # add to bottom of y1 and top of y2\n", - " y1_min -= y1_amt_to_add\n", - " y2_max += y2_amt_to_add\n", - " elif y2_zero_ratio < y1_zero_ratio:\n", - " # add to bottom of y2 and top of y1\n", - " y2_min -= y2_amt_to_add\n", - " y1_max += y1_amt_to_add\n", - " axs.set_ylim(y1_min, y1_max)\n", - " axs2.set_ylim(y2_min, y2_max)\n", - " axs.set_xlabel(\"SGLD time step\")\n", - " axs.set_ylabel(\"loss\")\n", - " axs2.set_ylabel(\"llc\", color=llc_color)\n", - " axs2.tick_params(axis=\"y\", labelcolor=llc_color)\n", - " axs.axhline(color=\"black\", linestyle=\":\")\n", - " fig.suptitle(title, fontsize=16)\n", - " plt.tight_layout()\n", - " plt.show()\n", - "\n", - "\n", - "def plot_sweep_single_model(results, epsilons, gammas, **kwargs):\n", - " llc_color = \"teal\"\n", - " fig, axs = plt.subplots(len(epsilons), len(gammas))\n", - "\n", - " for i, epsilon in enumerate(epsilons):\n", - " for j, gamma in enumerate(gammas):\n", - " result = results[(epsilon, gamma)]\n", - " # plot loss traces\n", - " loss_traces = result[\"loss/trace\"]\n", - " for trace in loss_traces:\n", - " init_loss = trace[0]\n", - " zeroed_trace = trace - init_loss\n", - " sgld_steps = list(range(len(trace)))\n", - " axs[i, j].plot(sgld_steps, zeroed_trace)\n", - "\n", - " # plot llcs\n", - " means = result[\"llc/means\"]\n", - " stds = result[\"llc/stds\"]\n", - " sgld_steps = list(range(len(means)))\n", - " axs2 = axs[i, j].twinx()\n", - " axs2.plot(\n", - " sgld_steps,\n", - " means,\n", - " color=llc_color,\n", - " linestyle=\"--\",\n", - " linewidth=2,\n", - " label=\"llc\",\n", - " zorder=3,\n", - " )\n", - " axs2.fill_between(\n", - " sgld_steps,\n", - " means - stds,\n", - " means + stds,\n", - " color=llc_color,\n", - " alpha=0.3,\n", - " zorder=2,\n", - " )\n", - "\n", - " # center zero, assume zero is in the range of both y axes already\n", - " y1_min, y1_max = axs[i, j].get_ylim()\n", - " y2_min, y2_max = axs2.get_ylim()\n", - " y1_zero_ratio = abs(y1_min) / (abs(y1_min) + abs(y1_max))\n", - " y2_zero_ratio = abs(y2_min) / (abs(y2_min) + abs(y2_max))\n", - " percent_to_add = abs(y1_zero_ratio - y2_zero_ratio)\n", - " y1_amt_to_add = (y1_max - y1_min) * percent_to_add\n", - " y2_amt_to_add = (y2_max - y2_min) * percent_to_add\n", - " if y1_zero_ratio < y2_zero_ratio:\n", - " # add to bottom of y1 and top of y2\n", - " y1_min -= y1_amt_to_add\n", - " y2_max += y2_amt_to_add\n", - " elif y2_zero_ratio < y1_zero_ratio:\n", - " # add to bottom of y2 and top of y1\n", - " y2_min -= y2_amt_to_add\n", - " y1_max += y1_amt_to_add\n", - " axs[i, j].set_ylim(y1_min, y1_max)\n", - " axs2.set_ylim(y2_min, y2_max)\n", - "\n", - " axs[i, j].set_title(f\"$\\epsilon$ = {epsilon} : $\\gamma$ = {gamma}\")\n", - " # only show x axis label on last row\n", - " if i == len(epsilons) - 1:\n", - " axs[i, j].set_xlabel(\"SGLD time step\")\n", - " axs[i, j].set_ylabel(\"loss\")\n", - " axs2.set_ylabel(\"llc\", color=llc_color)\n", - " axs2.tick_params(axis=\"y\", labelcolor=llc_color)\n", - " if kwargs[\"title\"]:\n", - " fig.suptitle(kwargs[\"title\"], fontsize=16)\n", - " plt.tight_layout()\n", - " plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "049c47a1-461b-481a-b847-d750ff109298", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Using passed in nbeta. Make sure callbacks are also initialized with the same nbeta.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/paperspace/devinterp/src/devinterp/backends/default/slt/sampler.py:208: UserWarning: You are taking more draws than burn-in steps, your LLC estimates will likely be underestimates. Please check LLC chain convergence.\n", - " warnings.warn(\n", - "/home/paperspace/devinterp/src/devinterp/backends/default/slt/sampler.py:212: UserWarning: You are taking more sample batches than there are dataloader batches available, this removes some randomness from sampling but is probably fine. (All sample batches beyond the number dataloader batches are cycled from the start, f.e. 9 samples from [A, B, C] would be [B, A, C, B, A, C, B, A, C].)\n", - " warnings.warn(\n", - "/home/paperspace/devinterp/src/devinterp/backends/default/slt/sampler.py:52: UserWarning: You are taking more sample batches than there are dataloader batches available, this removes some randomness from sampling but is probably fine. (All sample batches beyond the number dataloader batches are cycled from the start, f.e. 9 samples from [A, B, C] would be [B, A, C, B, A, C, B, A, C].)\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "If you're setting a nbeta in optimizer_kwargs, please also make sure to set it in the callbacks.\n", - "epsilon 0.001, gamma 100.0, mala rate: 0.05232007056474686\n", - "Using passed in nbeta. Make sure callbacks are also initialized with the same nbeta.\n", - "If you're setting a nbeta in optimizer_kwargs, please also make sure to set it in the callbacks.\n", - "epsilon 0.001, gamma 1.0, mala rate: 0.03671271726489067\n", - "Using passed in nbeta. Make sure callbacks are also initialized with the same nbeta.\n", - "If you're setting a nbeta in optimizer_kwargs, please also make sure to set it in the callbacks.\n", - "epsilon 0.0001, gamma 100.0, mala rate: 0.3139558434486389\n", - "Using passed in nbeta. Make sure callbacks are also initialized with the same nbeta.\n", - "If you're setting a nbeta in optimizer_kwargs, please also make sure to set it in the callbacks.\n", - "epsilon 0.0001, gamma 1.0, mala rate: 0.4310963451862335\n", - "Using passed in nbeta. Make sure callbacks are also initialized with the same nbeta.\n", - "If you're setting a nbeta in optimizer_kwargs, please also make sure to set it in the callbacks.\n", - "epsilon 1e-05, gamma 100.0, mala rate: 0.5909321308135986\n", - "Using passed in nbeta. Make sure callbacks are also initialized with the same nbeta.\n", - "If you're setting a nbeta in optimizer_kwargs, please also make sure to set it in the callbacks.\n", - "epsilon 1e-05, gamma 1.0, mala rate: 0.6140137910842896\n" - ] - } - ], - "source": [ - "EPSILONS = [1e-3, 1e-4, 1e-5]\n", - "GAMMAS = [100.0, 1.0]\n", - "NUM_CHAINS = 1\n", - "NUM_DRAWS = 500\n", - "results = {}\n", - "\n", - "\n", - "def estimate_mala_sweeper(model):\n", - " for epsilon in EPSILONS:\n", - " for gamma in GAMMAS:\n", - " mala_estimator = MalaAcceptanceRate(\n", - " num_chains=NUM_CHAINS,\n", - " num_draws=NUM_DRAWS,\n", - " nbeta=default_nbeta(train_loader),\n", - " learning_rate=epsilon,\n", - " device=DEVICE,\n", - " )\n", - "\n", - " result = estimate_learning_coeff_with_summary(\n", - " model,\n", - " train_loader,\n", - " evaluate=evaluate_ce,\n", - " optimizer_kwargs=dict(\n", - " lr=epsilon,\n", - " localization=gamma,\n", - " nbeta=default_nbeta(train_loader),\n", - " ),\n", - " sampling_method=SGLD,\n", - " num_chains=NUM_CHAINS,\n", - " num_draws=NUM_DRAWS,\n", - " callbacks=[mala_estimator],\n", - " verbose=False,\n", - " online=True,\n", - " )\n", - " mala_acceptance_rate_mean = mala_estimator.get_results()[\"mala_accept/mean\"]\n", - " results[(epsilon, gamma)] = result\n", - " print(\n", - " f\"epsilon {epsilon}, gamma {gamma}, mala rate: {mala_acceptance_rate_mean}\"\n", - " )\n", - "\n", - "\n", - "estimate_mala_sweeper(model)" - ] - }, - { - "cell_type": "markdown", - "id": "9d7e0ef8", - "metadata": {}, - "source": [ - "Judging by this, $\\epsilon = 1e^{-2}$ is out (did not converge), and a $\\gamma$ of $1$ is too low. A higher MALA acceptance prob would be better (ideally we'd aim for $.9$) but that might not be possible for this model. The higher learning rate is generally preferred, but we have to be careful not to get a thermalization peak at the start of sampling. Let's take a look at the loss curves next to check if our sampling works as expected." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "b4c7a36f", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_sweep_single_model(\n", - " results,\n", - " EPSILONS,\n", - " GAMMAS,\n", - " title=\"Calibration sweep of MNIST model for lr ($\\\\epsilon$) and localization ($\\\\gamma$)\",\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "6bce759a-fe07-4273-9f64-f5422de47375", - "metadata": {}, - "source": [ - "In these graphs, the LLC estimation mean is shown as a teal dotted line, and the standard deviation is shown as a shaded teal area around the mean. The remaining lines are the loss trace for each estimation. These are plotted with a shared $y=0$ line and have different scales of y axis (which can be seen on the left and right sides of each subplot).\n" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "201acbb4-016a-4cd3-97dd-9e47fdc2c1ea", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "epsilon = 1e-4\n", - "gamma = 100\n", - "result = results[(epsilon, gamma)]\n", - "plot_single_graph(result)" - ] - }, - { - "cell_type": "markdown", - "id": "91d50f18-8b82-4775-aedb-f11889cf80f7", - "metadata": {}, - "source": [ - "This trace clearly has not converged yet, but looks healthy thus far (no big spikes, no flatlining)." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "d9629a46-15cc-4840-aaad-4b9d4f17913a", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "epsilon = 1e-3\n", - "gamma = 100\n", - "result = results[(epsilon, gamma)]\n", - "plot_single_graph(result)" - ] - }, - { - "cell_type": "markdown", - "id": "d780a7c4-51e3-445a-92ee-ef9fcfd3d80a", - "metadata": {}, - "source": [ - "In this case, there are big spikes in the loss trace. In some models, this shape of LLC curve might happen because the SGLD sampling process is actually continuing to train the model, however in that case, we'd expect the loss traces to drop below the initial loss. In this example, the losses still remain positive after the initial spike, so it doesn't appear that SGLD is turning into continued training. It is currently unclear whether a spike (or in general, a not-monotonically-increasing curve) is a reason to invalidate a set of parameters, but it may be safest to consider it a yellow flag and pick a better set of parameters if possible.\n", - "\n", - "**In general, the loss traces are a useful diagnostic tool and should be the second thing to check (after MALA prob) when there's odd behavior in your LLC estimation.**" - ] - }, - { - "cell_type": "markdown", - "id": "cf876f0b-ab5f-4601-8b21-c2127882b09f", - "metadata": {}, - "source": [ - "Let's try running more samples on the $\\epsilon=1e^{-4},\\ \\gamma=100$ case to see if it flattens out." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "b644f97a-5f0c-4546-92f3-7877f5935eec", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "nbeta not set - using default nbeta.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "If you're setting a nbeta in optimizer_kwargs, please also make sure to set it in the callbacks.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Chain 0: 100%|██████████| 5000/5000 [00:34<00:00, 144.07it/s]\n", - "Chain 1: 100%|██████████| 5000/5000 [00:34<00:00, 143.02it/s]\n", - "Chain 2: 100%|██████████| 5000/5000 [00:34<00:00, 144.15it/s]\n", - "Chain 3: 100%|██████████| 5000/5000 [00:35<00:00, 142.30it/s]\n", - "Chain 4: 100%|██████████| 5000/5000 [00:34<00:00, 143.30it/s]\n", - "Chain 5: 100%|██████████| 5000/5000 [00:34<00:00, 143.19it/s]\n" - ] - } - ], - "source": [ - "optim_kwargs = dict(lr=1e-4, localization=100)\n", - "result = estimate_learning_coeff_with_summary(\n", - " model=checkpoints[-1],\n", - " loader=train_loader,\n", - " evaluate=evaluate_ce,\n", - " sampling_method=SGLD,\n", - " optimizer_kwargs=optim_kwargs,\n", - " num_chains=6,\n", - " num_draws=5000,\n", - " device=DEVICE,\n", - " online=True,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "b5467341-627c-4c74-a76b-fb176441e3db", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_single_graph(result)" - ] - }, - { - "cell_type": "markdown", - "id": "1b0fc954-8101-46b9-9d7d-a30f7dd93884", - "metadata": {}, - "source": [ - "### 5. Heuristics for selecting $\\epsilon$ and $\\gamma$" - ] - }, - { - "cell_type": "markdown", - "id": "53579538-da4d-490f-b1d8-b8bbf0119e65", - "metadata": {}, - "source": [ - "There is currently no One True Set of hyperparameters. Instead, the goal of calibration is to identify hyperparameters that maintain certain desirable properties. Some general such heuristics:\n", - "- Optimal traces should converge.\n", - "- Absolute $\\hat\\lambda$ estimates are not as important as relative $\\hat\\lambda$ estimates.\n", - "- The most important feature is that the relative ordering of estimates is maintained if you have different models to compare (e.g. different checkpoints).\n", - "- The MALA acceptance criterion should be as close as possible to $.9$ (but don't worry if you can't reach that). MALA acceptance probs of $0$ and $1$ are indications of unhealthy chains.\n", - "- The graph should not have negative dips (or not dip negative for very long).\n", - "- You can also examine the loss traces or other diagnostics to check that they look healthy (f.e. No Starting Peak), such as in the examples above.\n", - " - A good set of loss traces should stay above the initial loss value (or at least not drop below it too far or for too many steps) and should not diverge away from the initial loss value.\n", - "\n", - "If you don't get good results on the first sweep, you might recenter your sweep and try again." - ] - }, - { - "cell_type": "markdown", - "id": "20177ce1-96f0-4b7c-ae5b-60aa579e0457", - "metadata": {}, - "source": [ - "### 6. Selecting $\\epsilon$ and $\\gamma$ in this MNIST example" - ] - }, - { - "cell_type": "markdown", - "id": "7c6b07e8-83be-4a14-a66e-7ae27d5ea902", - "metadata": {}, - "source": [ - "- $\\epsilon=1e^{-2}$ is definitely a no-go, since the values quickly diverge to NaN.\n", - "- $\\epsilon=1e^{-3}$ causes a big spike in the initial LLC estimation. This should be avoided.\n", - "- With enough draws, the LLC estimation for $\\epsilon=1e^{-4}, \\ \\gamma=1$ looks like it will converge nicely. The loss traces are basically flattened out as well, which is another indication that the LLC estimation should continue to converge without issue. In this first sweep, this would be my recommendation for hyperparameters.\n", - "- If more refinement is needed (e.g. it's necessary for LLC estimation to converge in a fewer number of draws), then another option would be to sweep with more granular values (say, a half order of magnitude) around $\\epsilon=1e^{-4}, \\ \\gamma=1$" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.18" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/examples/tms.ipynb b/examples/tms.ipynb deleted file mode 100644 index e5370b7c..00000000 --- a/examples/tms.ipynb +++ /dev/null @@ -1,9395 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Toy Models of Superposition\n", - "\n", - "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/timaeus-research/devinterp/blob/main/examples/tms.ipynb)\n", - "\n", - "Let's run through an example using Anthropic's toy models of superposition. \n", - "\n", - "This example is mostly to test that our SGLD estimator is working as expected and to figure out how to integrate this in an SGD setting.\n", - "\n", - "Credits: [Chen et al. (2023)](https://arxiv.org/abs/2310.06301)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Set-up\n", - "### Imports" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Requirement already satisfied: pip in /home/paperspace/devinterp/.venv/lib/python3.9/site-packages (24.2)\n", - "Note: you may need to restart the kernel to use updated packages.\n", - "Requirement already satisfied: devinterp in /home/paperspace/devinterp/.venv/lib/python3.9/site-packages (0.1.0)\n", - "Requirement already satisfied: scipy in /home/paperspace/devinterp/.venv/lib/python3.9/site-packages (1.11.3)\n", - "Requirement already satisfied: pyyaml in /home/paperspace/devinterp/.venv/lib/python3.9/site-packages (6.0)\n", - "Requirement already satisfied: pandas in /home/paperspace/devinterp/.venv/lib/python3.9/site-packages (1.5.3)\n", - "Requirement already satisfied: einops>=0.6.1 in /home/paperspace/devinterp/.venv/lib/python3.9/site-packages (from devinterp) (0.6.1)\n", - 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"Note: you may need to restart the kernel to use updated packages.\n" - ] - } - ], - "source": [ - "%pip install --upgrade pip\n", - "%pip install devinterp scipy pyyaml pandas" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import itertools\n", - "import os\n", - "from typing import Callable, Iterable, Optional\n", - "\n", - "import numpy as np\n", - "import pandas as pd\n", - "import torch\n", - "import torch.nn as nn\n", - "import torch.optim as optim\n", - "from matplotlib import pyplot as plt\n", - "from scipy.spatial import ConvexHull\n", - "from torch.nn import functional as F\n", - "from torch.utils.data import DataLoader, TensorDataset\n", - "from tqdm import tqdm\n", - "\n", - "from devinterp.optim.sgld import SGLD\n", - "from devinterp.slt.sampler import estimate_learning_coeff_with_summary\n", - "from devinterp.utils import evaluate_mse\n", - "\n", - "\n", - "class ToyAutoencoder(nn.Module):\n", - " \"\"\"\n", - " Basic Network class for linear transformation with non-linear activations\n", - " \"\"\"\n", - "\n", - " def __init__(\n", - " self,\n", - " input_dim: int,\n", - " hidden_dim: int,\n", - " tied: bool = True,\n", - " final_bias: bool = False,\n", - " hidden_bias: bool = False,\n", - " nonlinearity: Callable = F.relu,\n", - " unit_weights: bool = False,\n", - " standard_magnitude: bool = False,\n", - " initial_scale_factor: float = 1.0,\n", - " initial_bias: Optional[torch.Tensor] = None,\n", - " initial_embed: Optional[torch.Tensor] = None,\n", - " ):\n", - " super().__init__()\n", - "\n", - " # Set the dimensions and parameters\n", - " self.input_dim = input_dim\n", - " self.hidden_dim = hidden_dim\n", - " self.nonlinearity = nonlinearity\n", - " self.tied = tied\n", - " self.final_bias = final_bias\n", - " self.unit_weights = unit_weights\n", - " self.standard_magnitude = standard_magnitude\n", - "\n", - " # Define the input layer (embedding)\n", - " self.embedding = nn.Linear(self.input_dim, self.hidden_dim, bias=hidden_bias)\n", - "\n", - " # Set initial embeddings if provided\n", - " if initial_embed is not None:\n", - " self.embedding.weight.data = initial_embed\n", - "\n", - " # Define the output layer (unembedding)\n", - " self.unembedding = nn.Linear(self.hidden_dim, self.input_dim, bias=final_bias)\n", - "\n", - " # Set initial bias if provided\n", - " if initial_bias is not None:\n", - " self.unembedding.bias.data = initial_bias\n", - "\n", - " # If standard magnitude is set, normalize weights and maintain average norm\n", - " if self.standard_magnitude:\n", - " avg_norm = torch.norm(self.embedding.weight.data, p=2, dim=0).mean()\n", - " self.embedding.weight.data = (\n", - " F.normalize(self.embedding.weight.data, p=2, dim=0) * avg_norm\n", - " )\n", - "\n", - " # If unit weights is set, normalize weights\n", - " if self.unit_weights:\n", - " self.embedding.weight.data = F.normalize(\n", - " self.embedding.weight.data, p=2, dim=0\n", - " )\n", - "\n", - " # Tie the weights of embedding and unembedding layers\n", - " if tied:\n", - " self.unembedding.weight = torch.nn.Parameter(\n", - " self.embedding.weight.transpose(0, 1)\n", - " )\n", - "\n", - " def forward(self, x: torch.Tensor):\n", - " \"\"\"\n", - " Forward pass through the network\n", - " \"\"\"\n", - " # Apply the same steps for weights as done during initialization\n", - " if self.unit_weights:\n", - " self.embedding.weight.data = F.normalize(\n", - " self.embedding.weight.data, p=2, dim=0\n", - " )\n", - "\n", - " if self.standard_magnitude:\n", - " avg_norm = torch.norm(self.embedding.weight.data, p=2, dim=0).mean()\n", - " self.embedding.weight.data = (\n", - " F.normalize(self.embedding.weight.data, p=2, dim=0) * avg_norm\n", - " )\n", - "\n", - " if self.tied:\n", - " self.unembedding.weight.data = self.embedding.weight.data.transpose(0, 1)\n", - "\n", - " x = self.embedding(x)\n", - " x = self.unembedding(x)\n", - " x = self.nonlinearity(x)\n", - "\n", - " return x\n", - "\n", - "\n", - "\"\"\"\n", - "Adapted from [TMS-zoo](https://github.com/JakeMendel/TMS-zoo)\n", - "\"\"\"\n", - "\n", - "from abc import ABC\n", - "from typing import Union\n", - "\n", - "import torch\n", - "from torch.utils.data import Dataset\n", - "\n", - "\n", - "class SyntheticDataset(Dataset, ABC):\n", - " num_samples: int\n", - " num_features: int\n", - " sparsity: Union[float, int]\n", - " # importance: Optional[float]\n", - "\n", - " def __init__(\n", - " self,\n", - " num_samples,\n", - " num_features,\n", - " sparsity,\n", - " # importance=None\n", - " ):\n", - " \"\"\"\n", - " Initialize the object.\n", - "\n", - " Args:\n", - " num_samples: The number of samples to generate.\n", - " num_features: The dimension of the feature vector.\n", - " sparsity: (float) the probability that a given feature is zero or (int) the number of features that are set to one.\n", - " importance: The importance of the features. If None, then the features are weighted uniformly.\n", - " Otherwise, the features are weighted by `importance ** (1 + i)`, where `i` is the index of the feature.\n", - " \"\"\"\n", - " self.num_samples = num_samples # The number of samples in the dataset\n", - " self.num_features = (\n", - " num_features # The size of the feature vector for each sample\n", - " )\n", - " self.sparsity = sparsity\n", - " # self.importance = importance\n", - " self.data = self.generate_data() # Generate the synthetic data\n", - "\n", - " def generate_values(self):\n", - " raise NotImplementedError\n", - "\n", - " def generate_mask(self):\n", - " \"\"\"\n", - " Generate a sparse mask for the given dataset.\n", - "\n", - " If ``sparsity`` is a float, then the mask is generated by sampling from a Bernoulli distribution with parameter ``1 - sparsity``.\n", - " If ``sparsity`` is an integer, then the mask is generated by sampling exactly ``sparsity`` indices without replacement.\n", - "\n", - " Args:\n", - " dataset: The dataset to generate the mask for.\n", - "\n", - " Returns:\n", - " A sparse mask for the given dataset.\n", - " \"\"\"\n", - "\n", - " if isinstance(self.sparsity, float):\n", - " return torch.bernoulli(\n", - " torch.ones((self.num_samples, self.num_features)) * (1 - self.sparsity)\n", - " )\n", - " elif isinstance(self.sparsity, int):\n", - " mask = torch.zeros((self.num_samples, self.num_features))\n", - " for i in range(self.num_samples):\n", - " indices = torch.randperm(self.num_features)[: self.sparsity]\n", - " mask[i, indices] = 1\n", - "\n", - " return mask\n", - "\n", - " else:\n", - " raise ValueError(\n", - " f\"Sparsity must be a float or an integer. Received {type(self.sparsity)}.\"\n", - " )\n", - "\n", - " def generate_data(self):\n", - " return self.generate_mask() * self.generate_values()\n", - "\n", - " def __len__(self):\n", - " return self.num_samples\n", - "\n", - " def __getitem__(self, idx):\n", - " return self.data[idx]\n", - "\n", - "\n", - "class SyntheticUniformValued(SyntheticDataset):\n", - " \"\"\"\n", - " This class creates a synthetic dataset where each sample is a vector which has indices which are zero with probability sparsity and uniform between 0 and 1 otherwise\n", - " \"\"\"\n", - "\n", - " def generate_values(self):\n", - " return torch.rand((self.num_samples, self.num_features))\n", - "\n", - "\n", - "class SyntheticBinaryValued(SyntheticDataset):\n", - " \"\"\"\n", - " This class creates a synthetic dataset where each sample is a vector which has indices which are zero with probability ``sparsity`` and 1 otherwise\n", - " \"\"\"\n", - "\n", - " def generate_values(self):\n", - " return 1.0" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Environmental variables" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "torch.manual_seed(1)\n", - "\n", - "DEVICE = os.environ.get(\n", - " \"DEVICE\",\n", - " (\n", - " \"cuda:0\"\n", - " if torch.cuda.is_available()\n", - " else \"mps\"\n", - " if torch.backends.mps.is_available()\n", - " else \"cpu\"\n", - " ),\n", - ")\n", - "DEVICE = torch.device(DEVICE)\n", - "NUM_CORES = int(os.environ.get(\"NUM_CORES\", 1))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### K-gon Plotting Utils" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "def generate_2d_kgon_vertices(k, rot=0.0, pad_to=None, force_length=0.9):\n", - " \"\"\"Set the weights of a 2D k-gon to be the vertices of a regular k-gon.\"\"\"\n", - " # Angles for the vertices\n", - " theta = np.linspace(0, 2 * np.pi, k, endpoint=False) + rot\n", - "\n", - " # Generate the vertices\n", - " x = np.cos(theta)\n", - " y = np.sin(theta)\n", - " result = np.vstack((x, y))\n", - "\n", - " if pad_to is not None and k < pad_to:\n", - " num_pad = pad_to - k\n", - " result = np.hstack([result, np.zeros((2, num_pad))])\n", - "\n", - " return result * force_length\n", - "\n", - "\n", - "def generate_init_param(\n", - " m,\n", - " n,\n", - " init_kgon,\n", - " prior_std=1.0,\n", - " no_bias=True,\n", - " init_zerobias=True,\n", - " seed=0,\n", - " force_negb=False,\n", - " noise=0.01,\n", - "):\n", - " np.random.seed(seed)\n", - "\n", - " if init_kgon is None or m != 2:\n", - " init_W = np.random.normal(size=(m, n)) * prior_std\n", - " else:\n", - " assert init_kgon <= n\n", - " rand_angle = np.random.uniform(0, 2 * np.pi, size=(1,))\n", - " noise = np.random.normal(size=(m, n)) * noise\n", - " init_W = generate_2d_kgon_vertices(init_kgon, rot=rand_angle, pad_to=n) + noise\n", - "\n", - " if no_bias:\n", - " param = {\"W\": init_W}\n", - " else:\n", - " init_b = np.random.normal(size=(n, 1)) * prior_std\n", - " if force_negb:\n", - " init_b = -np.abs(init_b)\n", - " if init_zerobias:\n", - " init_b = init_b * 0\n", - " param = {\"W\": init_W, \"b\": init_b}\n", - " return param" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "def plot_polygon(\n", - " W: torch.Tensor,\n", - " b=None,\n", - " ax=None,\n", - " ax_bias=None,\n", - " ax_wnorm=None,\n", - " hull_alpha=0.3,\n", - " dW=None,\n", - " dW_scale=0.3,\n", - " orderb=True,\n", - "):\n", - " \"\"\"Credits: Edmund Lau\"\"\"\n", - " if ax is None:\n", - " if W.shape[0] == 2:\n", - " fig, ax = plt.subplots(1, 1)\n", - " elif W.shape[0] == 3:\n", - " fig = plt.figure()\n", - " ax = fig.add_subplot(111, projection=\"3d\")\n", - "\n", - " if W.shape[0] == 2: # 2D case\n", - " # Compute the norms of the columns\n", - " norms = np.linalg.norm(W, axis=0)\n", - "\n", - " # Normalize a copy of the vectors for angle calculations\n", - " W_normalized = W / norms\n", - "\n", - " # Compute angles from the x-axis for each vector\n", - " angles = np.arctan2(W_normalized[1, :], W_normalized[0, :])\n", - "\n", - " # Sort the columns of W by angles\n", - " order = np.argsort(angles)\n", - " W_sorted = W[:, order]\n", - "\n", - " # Plot the origin\n", - " ax.scatter(0, 0, color=\"red\")\n", - "\n", - " # Plot the vectors\n", - " for i in range(W_sorted.shape[1]):\n", - " ax.quiver(\n", - " 0,\n", - " 0,\n", - " W_sorted[0, i],\n", - " W_sorted[1, i],\n", - " angles=\"xy\",\n", - " scale_units=\"xy\",\n", - " scale=1,\n", - " width=0.003,\n", - " )\n", - " if dW is not None:\n", - " dW = -dW_scale * dW / np.max(np.linalg.norm(dW, axis=0))\n", - " for col in range(W.shape[1]):\n", - " ax.quiver(\n", - " W[0, col],\n", - " W[1, col],\n", - " dW[0, col],\n", - " dW[1, col],\n", - " angles=\"xy\",\n", - " scale_units=\"xy\",\n", - " scale=1,\n", - " color=\"r\",\n", - " width=0.005,\n", - " )\n", - "\n", - " # Connect the vectors to form a polygon\n", - " polygon = np.column_stack((W_sorted, W_sorted[:, 0]))\n", - " ax.plot(polygon[0, :], polygon[1, :], alpha=0.5)\n", - "\n", - " # Plot the convex hull\n", - " hull = ConvexHull(W.T)\n", - " vs = list(hull.vertices) + [hull.vertices[0]]\n", - " ax.plot(W[0, vs], W[1, vs], \"r--\", alpha=hull_alpha)\n", - "\n", - " # Set the aspect ratio of the plot to equal to ensure that angles are displayed correctly\n", - " ax.set_aspect(\"equal\", adjustable=\"box\")\n", - "\n", - " elif W.shape[0] == 3: # 3D case\n", - " # Plot the origin\n", - " ax.scatter([0], [0], [0], color=\"red\")\n", - "\n", - " # Plot the vectors\n", - " for i in range(W.shape[1]):\n", - " ax.plot([0, W[0, i]], [0, W[1, i]], [0, W[2, i]], \"b-\")\n", - "\n", - " # Plot the convex hull\n", - " hull = ConvexHull(W.T)\n", - " for s in hull.simplices:\n", - " s = np.append(s, s[0]) # Here we cycle back to the first coordinate\n", - " ax.plot(W[0, s], W[1, s], W[2, s], \"r--\", alpha=hull_alpha)\n", - " else:\n", - " raise ValueError(\"W must have either 2 or 3 rows\")\n", - "\n", - " if b is not None and ax_bias is not None:\n", - " b_plot = np.ravel(b)\n", - " if orderb:\n", - " b_plot = b_plot[order]\n", - " bar_colors = [\"r\" if val < 0 else \"b\" for val in b_plot]\n", - " yticks = np.array(range(1, len(b_plot) + 1))\n", - " ax_bias.barh(\n", - " yticks - 0.4,\n", - " np.abs(b_plot),\n", - " height=0.4,\n", - " color=bar_colors,\n", - " align=\"edge\",\n", - " )\n", - " ax_bias.set_yticks(yticks)\n", - " ax_bias.yaxis.tick_right()\n", - " ax_bias.tick_params(axis=\"y\", labelsize=\"x-small\")\n", - " ax_bias.tick_params(axis=\"x\", labelsize=\"x-small\")\n", - "\n", - " if ax_wnorm is not None:\n", - " yticks = np.array(range(1, W.shape[1] + 1))\n", - " wnorms = np.linalg.norm(W, axis=0)\n", - " if orderb:\n", - " wnorms = wnorms[order]\n", - " ax_wnorm.barh(\n", - " yticks, width=wnorms, height=0.4, color=\"black\", alpha=0.9, align=\"edge\"\n", - " )\n", - " return ax\n", - "\n", - "\n", - "def plot_polygons(Ws, axes=None):\n", - " if axes is None:\n", - " fig, axes = plt.subplots(1, len(Ws), figsize=(15, 4))\n", - "\n", - " for ax, W in zip(axes, Ws):\n", - " plot_polygon(W, ax=ax)\n", - "\n", - "\n", - "def plot_losses_and_polygons(steps, losses, highlights, Ws):\n", - " fig = plt.figure(figsize=(15, 6))\n", - "\n", - " gs = fig.add_gridspec(2, len(Ws))\n", - " ax_losses = fig.add_subplot(gs[1, :])\n", - " ax_polygons = []\n", - "\n", - " max_x, min_x = max([np.max(W[0]) for W in Ws]), min([np.min(W[0]) for W in Ws])\n", - " max_y, min_y = max([np.max(W[1]) for W in Ws]), min([np.min(W[1]) for W in Ws])\n", - "\n", - " for i in range(len(Ws)):\n", - " ax = fig.add_subplot(gs[0, i], adjustable=\"box\")\n", - " ax.set_aspect(\"equal\")\n", - " ax_polygons.append(ax)\n", - " ax.set_xlim(min_x, max_x)\n", - " ax.set_ylim(min_y, max_y)\n", - "\n", - " ax_losses.plot(steps, losses)\n", - " ax_losses.set_xlabel(\"Step\")\n", - " ax_losses.set_ylabel(\"Loss\")\n", - " ax_losses.set_xscale(\"log\")\n", - " ax_losses.set_yscale(\"log\")\n", - "\n", - " for i, step in enumerate(highlights):\n", - " ax_losses.axvline(step, color=\"gray\", linestyle=\"--\")\n", - "\n", - " plot_polygons(Ws, ax_polygons)\n", - "\n", - " plt.suptitle(\"Loss and Weight snapshots\")\n", - " plt.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Training loop" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "def create_and_train(\n", - " m: int,\n", - " n: int,\n", - " num_samples: int,\n", - " batch_size: Optional[int] = 1,\n", - " num_epochs: int = 100,\n", - " lr: float = 0.001,\n", - " log_ivl: Iterable[int] = [],\n", - " device=DEVICE,\n", - " momentum=0.9,\n", - " weight_decay=0.0,\n", - " init_kgon=None,\n", - " no_bias=False,\n", - " init_zerobias=False,\n", - " prior_std=10.0,\n", - " seed=0,\n", - "):\n", - " model = ToyAutoencoder(m, n, final_bias=True)\n", - "\n", - " init_weights = generate_init_param(\n", - " n,\n", - " m,\n", - " init_kgon,\n", - " no_bias=no_bias,\n", - " init_zerobias=init_zerobias,\n", - " prior_std=prior_std,\n", - " seed=seed,\n", - " )\n", - " model.embedding.weight.data = torch.from_numpy(init_weights[\"W\"]).float()\n", - "\n", - " if \"b\" in init_weights:\n", - " model.unembedding.bias.data = torch.from_numpy(\n", - " init_weights[\"b\"].flatten()\n", - " ).float()\n", - "\n", - " dataset = SyntheticBinaryValued(num_samples, m, 1)\n", - " batch_size = batch_size\n", - "\n", - " dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True)\n", - " optimizer = optim.SGD(\n", - " model.parameters(), lr=lr, momentum=momentum, weight_decay=weight_decay\n", - " )\n", - " criterion = nn.MSELoss()\n", - "\n", - " logs = pd.DataFrame([{\"loss\": None, \"acc\": None, \"step\": step} for step in log_ivl])\n", - "\n", - " model.to(device)\n", - " weights = []\n", - "\n", - " def log(step):\n", - " loss = 0.0\n", - " acc = 0.0\n", - " length = 0\n", - "\n", - " with torch.no_grad():\n", - " for batch in dataloader:\n", - " batch = batch.to(device)\n", - " outputs = model(batch)\n", - " loss += criterion(outputs, batch).item()\n", - " acc += (outputs.round() == batch).float().sum().item()\n", - " length += len(batch)\n", - "\n", - " loss /= length\n", - " acc /= length\n", - "\n", - " logs.loc[logs[\"step\"] == step, [\"loss\", \"acc\"]] = [loss, acc]\n", - " weights.append(\n", - " {k: v.cpu().detach().clone().numpy() for k, v in model.state_dict().items()}\n", - " )\n", - "\n", - " step = 0\n", - " log(step)\n", - "\n", - " for epoch in tqdm(range(num_epochs), desc=\"Training\"):\n", - " for batch in dataloader:\n", - " batch = batch.to(device)\n", - "\n", - " # Zero the gradients\n", - " optimizer.zero_grad()\n", - "\n", - " # Forward pass\n", - " outputs = model(batch)\n", - " loss = criterion(outputs, batch)\n", - "\n", - " # Backward pass and optimize\n", - " loss.backward()\n", - " optimizer.step()\n", - "\n", - " step += 1\n", - "\n", - " if step in log_ivl:\n", - " log(step)\n", - "\n", - " return logs, weights" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Training" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Training: 100%|██████████| 20000/20000 [04:04<00:00, 81.74it/s]\n" - ] - }, - { - "data": { - "image/png": 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25LuDAwAAAAA6GkL0VpLMITp/0gEAwLF5q2r0zIfb9MT7W1V2uEaSNKB7horHDdYVp/dRCmE60Cq4LgWQ6OhzQMtwDsWG4xZ99pt8RwZR8/v9WrhwoRYuXCi/3291OQAAxB1neop+eMEQvf/TC3TXxcPULTNV2w8e1l0LP9UFDy7R35fvkM8ftLpMoMPjuhRAoqPPAS3DORQbjlv0CNEBAABaqHOaQz84f7Deu2uc/u9/hiu7c6p2Hjqiu/+5TuMeWKLnPtquan/A6jIBAAAAADEgRAcAAGglmWkO3XruyXrvrgv0s2/lqUdWmnaXHdH/t2i9zvv9Ej394TbCdAAAAADoYAjRAQAAWlmnVLtuPvskvXfXON132anq5UxXibdK9/7rM13y8Ptase2Q1SUCAAAAAKJEiA4AANBG0lPsmnLWQL171/n65aQRyu6cqi37KnT13GX6v5fWyXOkxuoSAQAAAADHQYgOAADQxtIcdt1w5gC9PeM8fWdMP0nSXz/eoYseelevr9sr0zQtrhAAAAAAEAkhOgAAQDvpkpGq3141Un+bfqYGZWdqX3m1vv/8ak1/ZpX2lB2xujwAAAAAQDMMMwlufbriiiu0ZMkSXXjhhVq4cOEJLev1euVyueTxeOR0OtuowvgUCAS0bt06SdJpp50mu91ucUUAACSOqpqA3O9s0aNLvpQ/aCoz1a67Lh6u7545QHabYXV5QFzhuhRAoqPPAS3DORQbjlv02W9ShOhLlixReXm5nn76aUJ0AAAQVzaVlGvmPz/V6h1lkqSCfl3026tO0/BeXHcAAAAAQFuKNvtNiuFczj//fGVlZVldBgAAQBPDemVp4W1n6ZeTRqhzmkNrdpbpWw+/r9lvbFRVTcDq8gAAAAAg6Vkeoi9dulSXXnqpcnNzZRiGFi1a1GQet9utgQMHKj09XUVFRVq+fHn7F5qEgsGgNm/erM2bNysYDFpdDgAACctmM8JfPPrNvBz5g6bc73ypi+cs1YdbDlhdHmA5rksBJDr6HNAynEOx4bhFz/IQvbKyUvn5+XK73c2+v2DBAs2YMUP33nuvVq9erfz8fE2YMEH79u0Lz1NQUKARI0Y0+dmzZ0977UZC8vv9+tvf/qa//e1v8vv9VpcDAEDC6+VK12M3jtbc745SjjNN2w4e1nVPfKw7X1yrryt9VpcHWIbrUgCJjj4HtAznUGw4btFzWF3AxIkTNXHixIjvP/TQQ5o+fbqmTZsmSZo7d65effVVzZs3T3fffbckac2aNa1WT3V1taqrq8OvvV5vq60bAAAgGheP6KWzBnfX7P9s0nMfb9eLq3bpvxv36eeX5umy/NBf7wEAAAAA2ofld6Ifi8/n06pVqzR+/PjwNJvNpvHjx2vZsmVtss1Zs2bJ5XKFf/r169cm2wEAADgWZ3qKfjlphBbeNlZDenbWwUqffvT3NZr61ArtPHTY6vIAAAAAIGnEdYh+4MABBQIB5eTkNJqek5OjkpKSqNczfvx4XX311XrttdfUt2/fYwbwM2fOlMfjCf/s3Lkz5voBAABaatSAbnr1/52jH180VKl2m97dvF/f/MNSPb70K/kDjFsIAAAAAG3N8uFc2sPbb78d9bxpaWlKS0trw2oAAABOTKrDptsvHKL/GdlbM/+5Tsu3HtKvX/tcL6/drd9eOVIj+risLhEAAAAAElZc34menZ0tu92u0tLSRtNLS0vVq1evNt222+1WXl6exowZ06bbAQAAiNbJPTrr79PP1O+uOk3OdIfW7/bqcvcH+s1rn+uwjy8CAgAAAIC2ENchempqqkaNGqXFixeHpwWDQS1evFhjx45t020XFxdrw4YNWrFiRZtuBwAA4ETYbIa+Paa/3v7xefrWyN4KBE09tvQrffMPS/Xu5v1WlwcAAAAACcfy4VwqKiq0ZcuW8OutW7dqzZo16tatm/r3768ZM2ZoypQpGj16tAoLCzVnzhxVVlZq2rRpFladHOx2uyZOnBh+DgAA4kfPrHQ9ct0ZuvKMUv1s0Wfa9fURTZm3XJMKcvXLSSOUlZ5idYlAq+G6FECio88BLcM5FBuOW/QM0zRNKwtYsmSJxo0b12T6lClTNH/+fEnSI488otmzZ6ukpEQFBQV6+OGHVVRU1C71eb1euVwueTweOZ3OdtkmAADAiais9uvBNzdr/odbFTSlk3tk6rEbR+vkHp2tLg0AAAAA4la02a/lIXq8crvdcrvdCgQC2rx5MyE6AACIe6u2f63i51erxFulrDSH/vDtAo3Py7G6LAAAAACIS4TorSSZ70QPBoPasWOHJKl///6y2eJ6CH0AACBpf3m1ip9freXbDkmS/nf8UN1+wWDZbIbFlQGx47oUQKKjzwEtwzkUG45b9Nlv8h0ZRM3v9+vpp5/W008/Lb/fb3U5AAAgCj2y0vTcLUW6cewASdIf3t6s7z23SuVVNRZXBsSO61IAiY4+B7QM51BsOG7RI0QHAABIMKkOm+6/fIR+P3mkUu02vbWhVJPcH+jL/RVWlwYAAAAAHQ4hegRut1t5eXkaM2aM1aUAAADE5JrR/fTCbWPVy5muL/dXatIjH+jtDaVWlwUAAAAAHQohegTFxcXasGGDVqxYYXUpAAAAMSvo10X/vv1sFQ7spvJqv255ZqX++PYXCgb5WhwAAAAAiAYhOgAAQIJjnHQAAAAAiB0hOgAAQBJgnHQAAAAAiA0hegSMiQ4AABIR46QDAAAAwIkxTNNkQMxj8Hq9crlc8ng8cjqdVpfTrgKBgD766CNJ0plnnim73W5xRQAAoLXsL69W8fOrtXzbIUnS/44fqtsvGCybzbC4MqAprksBJDr6HNAynEOx4bhFn/0Soh9HMofoAAAgsfn8Qf3q1Q16Ztl2SdJFeTl66Jp8ZaWnWFwZAAAAALS9aLNfhnMBAABIUoyTDgAAAADHR4iOiILBoHbv3q3du3crGAxaXQ4AAGgjjJOOeMd1KYBER58DWoZzKDYct+gRokfAF4tKfr9fTzzxhJ544gn5/X6rywEAAG2ooF8X/fv2s1U4sJvKq/265ZmV+uPbXygYZOQ/WI/rUgCJjj4HtAznUGw4btEjRI+guLhYGzZs0IoVK6wuBQAAoF30yErTc7cU6caxAyRJf3h7s7733CqVV9VYXBkAAAAAWIcQHQAAAGGMkw4AAAAAjRGiAwAAoAnGSQcAAACAEEJ0AAAANItx0gEAAACAEB0AAADHwDjpAAAAAJIdIToAAACOqblx0q9/4mN5jhCkAwAAAEh8DqsLiFdut1tut1uBQMDqUixjt9t13nnnhZ8DAIDkds3ofhqak6VpTy3Xp7s8uuHJj/XszUVydUqxujQkOK5LASQ6+hzQMpxDseG4Rc8wTZNBLY/B6/XK5XLJ4/HI6XRaXQ4AAIDlPt/r1XWPf6SvD9doZF8XQToAAACADina7JfhXAAAAHBCTunt1F+nn6muGSn6dJdHNz7J0C4AAAAAEhchOiIyTVP79u3Tvn37xB8sAACAhk7p7dTzt4SC9LUE6WhjXJcCSHT0OaBlOIdiw3GLHiE6IqqpqdGjjz6qRx99VDU1/E8xAABoLC/3qCB93nKCdLQJrksBJDr6HNAynEOx4bhFjxAdAAAAMasL0rtkpGjtzjLdOG+5vFVcgAMAAABIHDGF6Dt37tSuXbvCr5cvX6477rhDjz32WKsVBgAAgI4hL9epvzYI0m94kiAdAAAAQOKIKUS/7rrr9M4770iSSkpKdNFFF2n58uW65557dP/997dqgQAAAIh/BOkAAAAAElVMIfr69etVWFgoSXrhhRc0YsQIffjhh3r++ec1f/781qzPMm63W3l5eRozZozVpQAAAHQIoaFdiuqHdiFIBwAAAJAAYgrRa2pqlJaWJkl6++23ddlll0mShg8frr1797ZedRYqLi7Whg0btGLFCqtLAQAA6DBOzXWFg/Q1BOkAAAAAEkBMIfqpp56quXPn6r333tNbb72liy++WJK0Z88ede/evVULBAAAQMdyaq5Lz91cH6RP4ctGAQAAAHRgjlgW+t3vfqcrrrhCs2fP1pQpU5Sfny9J+te//hUe5gUdn91u19ixY8PPAQAAojWiTyhIv/6Jj/XJjlCQ/sxNhcpKT7G6NHRAXJcCSHT0OaBlOIdiw3GLnmGaphnLgoFAQF6vV127dg1P27ZtmzIyMtSzZ89WK9BqXq9XLpdLHo9HTqfT6nIAAAA6lPW7Pbr+iY/lOVKj0/t3IUgHAAAAEDeizX5jGs7lyJEjqq6uDgfo27dv15w5c7Rp06aECtABAADQMiP6hMZId3VK0Sc7ynTjvOUqZ2gXAAAAAB1ITCH65ZdfrmeeeUaSVFZWpqKiIj344IOaNGmSHn300VYtENYxTVNlZWUqKytTjH+wAAAA0CRIn0KQjhPEdSmAREefA1qGcyg2HLfoxRSir169Wuecc44kaeHChcrJydH27dv1zDPP6OGHH27VAmGdmpoa/fGPf9Qf//hH1dTwP7oAACB2DYP01QTpOEFclwJIdPQ5oGU4h2LDcYteTCH64cOHlZWVJUl68803deWVV8pms+nMM8/U9u3bW7VAAAAAJIajg/SpT60gSAcAAAAQ92IK0QcPHqxFixZp586deuONN/TNb35TkrRv3z6+fBMAAAARNQzSV23/WlOfWqGKar/VZQEAAABARDGF6D//+c/1k5/8RAMHDlRhYaHGjh0rKXRX+umnn96qBQIAACCx1AXpznSHVm3/WlPmLSdIBwAAABC3YgrRJ0+erB07dmjlypV64403wtMvvPBC/eEPf2i14gAAAJCYQkH6mQTpAAAAAOJeTCG6JPXq1Uunn3669uzZo127dkmSCgsLNXz48FYrzkput1t5eXkaM2aM1aUAAAAkpNP6Ng7SpxKkAwAAAIhDMYXowWBQ999/v1wulwYMGKABAwaoS5cu+uUvf6lgMNjaNVqiuLhYGzZs0IoVK6wuBQAAIGGd1tel52qHdllJkA4AAAAgDjliWeiee+7Rk08+qd/+9rf6xje+IUl6//339Ytf/EJVVVX69a9/3apFwho2m02jR48OPwcAAGgLI/t20XO3FOm7T3ysldu/1rSnluupaYXqnBbTpSoSENelABIdfQ5oGc6h2HDcomeYpmme6EK5ubmaO3euLrvsskbTX375Zf3gBz/Q7t27W61Aq3m9XrlcLnk8HjmdTqvLAQAASFif7irT9U98rPIqv8YM7EqQDgAAAKBNRZv9xvQrhkOHDjU79vnw4cN16NChWFYJAACAJDeybxc9f0uRstIdWrEtdEd6JUO7AAAAALBYTCF6fn6+HnnkkSbTH3nkEY0cObLFRSE+mKapyspKVVZWKoY/WAAAADhhRwfpUwnSIa5LASQ++hzQMpxDseG4RS+m4VzeffddXXLJJerfv7/Gjh0rSVq2bJl27typ1157Teecc06rF2qVZB7OxefzadasWZKkmTNnKjU11eKKAABAsli7s0zffTI0tMs5Q7L11NQxctgZpzFZcV0KINHR54CW4RyKDcetjYdzOe+887R582ZdccUVKisrU1lZma688kp99tlnevbZZ2MuGgAAAJCk/H5d9OzNReqUYtd7XxzQL/79GXfHAAAAALBEzN/UlJubq1//+teNpq1du1ZPPvmkHnvssRYXBgAAgORW0K+L5nynQLc9t0rPfbRDg7I766azT7K6LAAAAABJhr+JBQAAQNyacGovzZwY+kL7X766QYs/L7W4IgAAAADJhhAdAAAAcW36OYN0bWE/maZ0+98+0YY9XqtLAgAAAJBECNEBAAAQ1wzD0P2Xj9A3BnfXYV9ANz+9Qvu8VVaXBQAAACBJnNCY6FdeeeUx3y8rK2tJLQAAAECzUuw2/fm6Ubry0Q/05f5K3fz0Si343pnKSI35K34AAAAAICon9H8dLpfruO/feOONLSoI8cNmsyk/Pz/8HAAAwEqujBTNmzpGk9wfaN1uj2YsWKs/X3+GbDbD6tLQxrguBZDo6HNAy3AOxYbjFj3DNE3T6iLa0s6dO3XDDTdo3759cjgc+tnPfqarr7466uW9Xq9cLpc8Ho+cTmcbVgoAAIBorNh2SNc//rF8gaBuO+9k3V37xaMAAAAAcCKizX4T/lcMDodDc+bM0YYNG/Tmm2/qjjvuUGVlpdVlAQAAIEZjBnbT7yePlCTNffdLLVixw+KKAAAAACSyhA/Re/furYKCAklSr169lJ2drUOHDllbVAdhmqZ8Pp98Pp8S/A8WAABABzPp9D76fxcOkSTd89J6ffjlAYsrQlviuhRAoqPPAS3DORQbjlv0LA/Rly5dqksvvVS5ubkyDEOLFi1qMo/b7dbAgQOVnp6uoqIiLV++PKZtrVq1SoFAQP369Wth1cmhpqZGs2bN0qxZs1RTU2N1OQAAAI387/ghujQ/V/6gqdueXaUv91dYXRLaCNelABIdfQ5oGc6h2HDcomd5iF5ZWan8/Hy53e5m31+wYIFmzJihe++9V6tXr1Z+fr4mTJigffv2hecpKCjQiBEjmvzs2bMnPM+hQ4d044036rHHHmvzfQIAAEDbMwxDsyeP1Bn9u8hb5ddN81foUKXP6rIAAAAAJBiH1QVMnDhREydOjPj+Qw89pOnTp2vatGmSpLlz5+rVV1/VvHnzdPfdd0uS1qxZc8xtVFdXa9KkSbr77rt11llntVrtAAAAsFZ6il2P3Thak9wfaPvBw7rt2VV69pZCpTnsVpcGAAAAIEFYfif6sfh8Pq1atUrjx48PT7PZbBo/fryWLVsW1TpM09TUqVN1wQUX6IYbbjju/NXV1fJ6vY1+AAAAEL+yO6dp3tQxykpzaPm2Q5r5z3WM6QgAAACg1cR1iH7gwAEFAgHl5OQ0mp6Tk6OSkpKo1vHBBx9owYIFWrRokQoKClRQUKB169ZFnH/WrFlyuVzhH8ZPBwAAiH9Dc7Lkvv4M2W2G/rl6t9zvbLG6JAAAAAAJwvLhXNra2WefrWAwGPX8M2fO1IwZM8KvvV4vQToAAEAHcO7QHvrFZafqZ4vW64E3N2tgdqa+NTLX6rIAAAAAdHBxHaJnZ2fLbrertLS00fTS0lL16tWrTbaZlpamtLS0Nlk3AAAA2tYNZw7Q1v2VmvfBVv34hbXq06WTTu/f1eqyAAAAAHRgcT2cS2pqqkaNGqXFixeHpwWDQS1evFhjx45t02273W7l5eVpzJgxbbqdeGaz2ZSXl6e8vDzZbHH9TwUAACDsnktO0YXDe6raH9T0Z1Zq56HDVpeEFuK6FECio88BLcM5FBuOW/QM0+JvXaqoqNCWLaExK08//XQ99NBDGjdunLp166b+/ftrwYIFmjJliv7yl7+osLBQc+bM0QsvvKCNGzc2GSu9LXi9XrlcLnk8HjmdzjbfHgAAAFqustqvyXOX6fO9Xg3LydLC749VVnqK1WUBAAAAiCPRZr+Wh+hLlizRuHHjmkyfMmWK5s+fL0l65JFHNHv2bJWUlKigoEAPP/ywioqK2qU+QnQAAICOaU/ZEU1yf6B95dU6b2gPPTlltBx27rABAAAAENJhQvR45Xa75Xa7FQgEtHnzZkJ0AACADmjdLo+u/suHqqoJ6saxA3T/5SOsLgkAAABAnCBEbyXJfCe6z+fTrFmzJEkzZ85UamqqxRUBAACcuP+sL9H3n18l05R+cWmepn7jJKtLwgniuhRAoqPPAS3DORQbjlv02S9/zwoAAICEdvGIXvrpxcMlSfe/skH/3VhqcUUAAAAAOhJCdAAAACS87507SN8e3U9BU7r9r5/o871eq0sCAAAA0EEQokfgdruVl5enMWPGWF0KAAAAWsgwDP1y0giNHdRdlb6Abp6/Qvu8VVaXBQAAAKADIESPoLi4WBs2bNCKFSusLgUAAACtINVh09zvjtKgHpna46nS9GdW6ogvYHVZAAAAAOIcIToAAACShisjRfOmjFGXjBSt3eXRjBfWKBg0rS4LAAAAQBwjRAcAAEBSGZidqcduGK0Uu6HX15fogTc3WV0SAAAAgDjmsLoAxC+bzaYhQ4aEnwMAACSKwpO66bdXjtSPX1yrPy/5UgOzM3XN6H5Wl4UIuC4FkOjoc0DLcA7FhuMWPcM0Tf5+tRlut1tut1uBQECbN2+Wx+OR0+m0uiwAAAC0ogff3KQ//XeLUuyGnrmpSGNP7m51SQAAAADaidfrlcvlOm72S4h+HNEeSAAAAHQ8waCp2//+iV79dK9cnVL00g/O0qAena0uCwAAAEA7iDb75T59AAAAJC2bzdCDV+eroF8XeY7UaNr8FTpYUW11WQAAAADiCCE6IvL5fPrNb36j3/zmN/L5fFaXAwAA0CbSU+x6/MbR6tu1k7YfPKzpz6xUVU3A6rLQANelABIdfQ5oGc6h2HDcokeIjmOqqalRTU2N1WUAAAC0qR5ZaZo/bYyc6Q6t3lGmGS+sUTDIqIfxhOtSAImOPge0DOdQbDhu0SFEj8DtdisvL09jxoyxuhQAAAC0g8E9s/TYjaOVYjf02roS/fY/G60uCQAAAEAcIESPoLi4WBs2bNCKFSusLgUAAADt5MxB3TV7cr4k6bGlX+nZZdusLQgAAACA5QjRAQAAgAYmnd5HP75oqCTp3n99pv9uLLW4IgAAAABWIkQHAAAAjvLDCwbrmtF9FTSlH/71E63b5bG6JAAAAAAWIUQHAAAAjmIYhn59xWk6e3C2DvsCuunpFdpddsTqsgAAAABYwGF1AYhfhmFowIAB4ecAAADJJMVu05+/e4aumbtMG0vKNe2p5Vr4/bPkTE+xurSkw3UpgERHnwNahnMoNhy36BmmaZpWFxGP3G633G63AoGANm/eLI/HI6fTaXVZAAAAaGd7yo5okvsD7Suv1jcGd9dTUwuV6uAPOgEAAICOzuv1yuVyHTf7JUQ/jmgPJAAAABLX+t0eXfOXZTrsC2jyqL6aPXkkd+sAAAAAHVy02S+30AAAAADHMaKPS+7rzpDdZmjhql3603+3WF0SAAAAgHZCiI6IfD6fZs+erdmzZ8vn81ldDgAAgKXGDe+p+y8/VZL00Fub9c/VuyyuKHlwXQog0dHngJbhHIoNxy16fLEojunw4cNWlwAAABA3ri8aoB2HDusv736ln/7jU/Vypeusk7OtLispcF0KINHR54CW4RyKDcctOtyJDgAAAJyAn04YrktG9lZNwNT3nl2lL0rLrS4JAAAAQBsiRAcAAABOgM1m6MGr8zVqQFeVV/k19akV2ldeZXVZAAAAANoIIToAAABwgtJT7Hr8xtEa2D1Du8uO6JanV+qwz291WQAAAADaACE6AAAAEINumamaP61QXTNS9Okuj/7f39YoEDStLgsAAABAKyNEj8DtdisvL09jxoyxuhQAAADEqYHZmXpiymilOmx6+/NS/fKVDTJNgnQAAAAgkRCiR1BcXKwNGzZoxYoVVpdiGcMwlJubq9zcXBmGYXU5AAAAcWnUgG76wzUFkqT5H27TvA+2WVpPIuK6FECio88BLcM5FBuOW/QMk1tljsnr9crlcsnj8cjpdFpdDgAAAOLUX979UrNe3yjDkB69fpQuHtHL6pIAAAAAHEO02S93ogMAAACt4NZzB+n6ov4yTelHf/9En+z42uqSAAAAALQCQnQAAACgFRiGofsuO1XjhvVQtT+oW55eqR0HD1tdFgAAAIAWIkRHRDU1NZozZ47mzJmjmpoaq8sBAACIew67TY9cd4ZOzXXqYKVPU+cvV9lhn9VldXhclwJIdPQ5oGU4h2LDcYseIToiMk1THo9HHo9HDJ0PAAAQncw0h+ZNHaNcV7q+2l+pW59dpWp/wOqyOjSuSwEkOvoc0DKcQ7HhuEWPEB0AAABoZTnOdM2bNkZZaQ4t33pId774qYJB/scEAAAA6IgI0QEAAIA2MLyXU49+d5QcNkP/WrtHD761yeqSAAAAAMSAEB0AAABoI2cPydZvrjxNkuR+50v9ffkOiysCAAAAcKII0QEAAIA2dM3ofvp/FwyWJN2zaL2Wbt5vcUUAAAAATgQhOgAAANDG/veiobri9D4KBE394PnV+mTH11aXBAAAACBKDqsLiFdut1tut1uBQMDqUixjGIZ69OgRfg4AAIDYGIah3151mvZ6juijrw5p8txl+t65g/T/Lhyi9BS71eXFPa5LASQ6+hzQMpxDseG4Rc8wTdO0uoh45vV65XK55PF45HQ6rS4HAAAAHZjnSI3+75/r9Oq6vZKkwT076/eTR+qM/l0trgwAAABIPtFmvwznAgAAALQTV6cUua8/Q3O/e4ayO6dpy74KXfXoh/rVKxt0xJe8fwEJAAAAxDNCdAAAAKCdXTyit97633N15el9ZJrSE+9v1cQ/LtXHXx20ujQAAAAAR2E4l+NI5uFcampq9Pjjj0uSpk+frpSUFIsrAgAASDz/3Viq//vnepV4qyRJN44doJ9ePFyZaXx9UR2uSwEkOvoc0DKcQ7HhuDGcC1qBaZrav3+/9u/fL37XAgAA0DYuGJ6jN2ecq2sL+0mSnlm2Xd/8w1K9/8UBiyuLH1yXAkh09DmgZTiHYsNxix4hOgAAAGAxZ3qKZl05Us/dXKQ+XTppd9kRfffJj3X3Pz6Vt6rG6vIAAACApEaIDgAAAMSJs4dk683/PVdTxg6QJP19xU5986Gl+u/GUosrAwAAAJIXIToAAAAQRzLTHLrv8hFacOuZGtg9QyXeKt00f6VmLFijssM+q8sDAAAAkg4hOgAAABCHigZ11+s/OlfTzzlJNkP65ye7Nf6hpfrP+r1WlwYAAAAkFUJ0AAAAIE51SrXrnkvytPD7Z2lwz846UFGt255breK/rtaBimqrywMAAACSAiE6IjIMQy6XSy6XS4ZhWF0OAABA0jqjf1e9cvvZKh53suw2Q69+ulcXPfSuXl6zW6ZpWl1em+O6FECio88BLcM5FBuOW/QMMxmuulvA6/XK5XLJ4/HI6XRaXQ4AAACS3PrdHv3kxbXaWFIuSbooL0e/mjRCOc50iysDAAAAOpZos1/uRAcAAAA6kBF9XPrXD8/W/44fqhS7obc2lOqih97Viyt3JsVd6QAAAEB7S/gQvaysTKNHj1ZBQYFGjBihxx9/3OqSAAAAgBZJddj0o/FD9O/bz9bIvi55q/y6c+GnmvLUCu0uO2J1eQAAAEBCSfjhXAKBgKqrq5WRkaHKykqNGDFCK1euVPfu3aNaPpmHc6mpqdH8+fMlSVOnTlVKSoq1BQEAAKAJfyCoJ97fqofe2iyfP6jOaQ7N/J/hunZMf9lsiTG2JdelABIdfQ5oGc6h2HDcos9+He1YkyXsdrsyMjIkSdXV1TJNkz9zjZJpmtqzZ0/4OQAAAOKPw27TbeedrPGn5OiuhWu1ekeZ7nlpvV5es0cXn9pLw3tlaWivLGV3TrO61JhxXQog0dHngJbhHIoNxy16lg/nsnTpUl166aXKzc2VYRhatGhRk3ncbrcGDhyo9PR0FRUVafny5Se0jbKyMuXn56tv37668847lZ2d3UrVAwAAAPFhcM/OevG2s/Tzb+UpPcWm5VsP6f5XNui6Jz7W6F+9rdG/ekvXPf6R7vv3Z/r78h36ZMfXqqz2W102AAAAEPcsvxO9srJS+fn5uummm3TllVc2eX/BggWaMWOG5s6dq6KiIs2ZM0cTJkzQpk2b1LNnT0lSQUGB/P6m/wPw5ptvKjc3V126dNHatWtVWlqqK6+8UpMnT1ZOTk6b7xsAAADQnuw2QzedfZLGn5Kjhat3aeNerzaXlmv7ocM6UOHTgYqD+vDLg42W6detk4blZGlYrywN6+XUsJwsDeqRqRS75ffbAAAAAHHB8hB94sSJmjhxYsT3H3roIU2fPl3Tpk2TJM2dO1evvvqq5s2bp7vvvluStGbNmqi2lZOTo/z8fL333nuaPHlys/NUV1eruro6/Nrr9Ua5JwAAAEB86N89QzMuGhp+fdjn15Z9FdpYUq5NJeXaXFqujSXl2l9erZ2HjmjnoSN6+/N94flT7IYGZXeuDdazwiF7ny6dEmacdQAAACBalofox+Lz+bRq1SrNnDkzPM1ms2n8+PFatmxZVOsoLS1VRkaGsrKy5PF4tHTpUn3/+9+POP+sWbN03333tbh2AAAAIF5kpDo0sm8XjezbpdH0Q5W+RqH65tJybS4pV3m1X5tKy7WptFxaWz9/ZqpdQ3KyNPyocL17Bx5vHQAAADieuA7RDxw4oEAg0GTolZycHG3cuDGqdWzfvl233npr+AtFb7/9dp122mkR5585c6ZmzJgRfu31etWvX7/YdgAAAACIY90yUzX25O4ae3L38DTTNLXHU6VNJV5tKqkIPZZW6Mt9Far0BbRmZ5nW7CxrtJ5Uh01pDpvSHPbQY0qD5w6b0lLqn6eHn9tr5zv+cnYzEN7W1gOVSk2tkc2QbIYhm80IPzfqphmhaUbto91mRHwfAAAAOJ64DtFbQ2FhYdTDvUhSWlqa0tK4k6ZORkaG1SUAAACgHRmGoT5dOqlPl066YHj9zSw1gaC2H6wM3bFeUn/n+vZDh+XzB+XzB1WutvmiUocCujo99L8ulzz8nvyyt9q6bQ2DdVttGK/QoxqE83XTjNrw3ZDCoXzoed17zUxruKwar8Nmazqt8Tqamd7cehrN11zNDec1mq3TVrtem02S6n7R0Nx8dbU3Xf5Y86rRthvvg62u3ibrbObzqD1uoXXVv29rsJ8N52v0una+unU1qqfRPA1+6WJr/MsXm2HIbqt/v+65PTzPUcsctTy/vEEk/P830DKcQ7HhuEXHME3TtLqIOoZh6KWXXtKkSZMkhYZzycjI0MKFC8PTJGnKlCkqKyvTyy+/3Ga1uN1uud1uBQIBbd68WR6PR06ns822BwAAAHRER3wBfX3Yp2p/UNX+gKprgo2eVx09zR+sfR1ofpkG71c1mi+omkBQpikFg6aCpqmgKQVNMzTNrJ8GxLOGgbtR+5cSdsOQw27IYbcpxVb7aDeUYreFptsavq6bJ/Q6xW6To5llUmy2BvOE1pHqsCkr3aHMVIc6pzvUOS30k5nmUFa6Q2kOGyE/ACCpeL1euVyu42a/cX0nempqqkaNGqXFixeHQ/RgMKjFixfrhz/8YZtuu7i4WMXFxeEDCQAAAKCpTql2dUrtZHUZjZi1YXqgNmw/OmQ3GwTwde8fPW/4sXZ9pimZUpN51GBaw/fN8LLHmNZo3XXrbThfM+s9aplgsOm08HrU/HbD26hbh1n/nhT6JUXdNoO1CwaPmrdunQ3nbTTdrK9D4TqbWT7CsW6y/FGfmxrtT92269dzdO1H73fQbFp3w1/KmKapQIMa6rYTCNYvX/c80KCG6P59Sn7TVOiTiy8Om6HMtPpwvXN6bcDeIGwPhe92dU5LUWaavVEonxWelqJUh83q3QEAoNVYHqJXVFRoy5Yt4ddbt27VmjVr1K1bN/Xv318zZszQlClTNHr0aBUWFmrOnDmqrKzUtGnTLKwaAAAAQLwyDEP22jt8gfbSMIhvFLabpsxgfdgeNEO/VGg4nz8YlD9oqiYQlD9gyh8MqiZQ/7omUP9+TcCUPxBUTTD06A+YqgkGVeOvX85fO78vEGwwj6nqmoAqfX5VVPlVUR36qawOqKI6NBSTP2jKc6RGniM1LT4e3TNT1btLunq7OinXla7eXTqptytdubWPOc50pdgJ2gEAHYPlIfrKlSs1bty48Ou6L/WcMmWK5s+fr29/+9vav3+/fv7zn6ukpEQFBQX6z3/+0+TLRtH6ampq9Pzzz0uSrr/+eqWkpFhcEQAAAJIR16XoCMK/vJGhlNYbtr9dBIOmKn11gXqNKqoDjYL2iqoaVfoCKq/yq7J2WsPnldV+ldc+HvaFvgj4YKVPByt9Wr/b2+w2DUPqmZWmXnUhu6uTcmtD995d0pXr6qQeWWlJ88sw+hzQMpxDseG4Rc/yEP3888/X8YZl/+EPf9jmw7ccreGY6MnKNE1t3749/BwAAACwAtelQNuy2QxlpacoKz1FUnqL1uUPBOWt8muv54j2llVpr+eI9niqtLes9tFzRCWeKtUETJV6q1Xqrdbanc2vy2EzlONMV+/aO9lDYXvd81DY3j0zNSHGcafPAS3DORQbjlv0LA/R4xVjogMAAAAAcGIcdpu6ZaaqW2aqTs1t/v+lg0FTByt9oYC9Nmjf66nSnrLQ496yIyotr5Y/aGp32RHtLjsibf+62XVlpTl0Uo9MDcrO1KAenTWoR6YGZXfWSdmZ6pTawf4kAAAQtwjRAQAAAABAu7HZDPXISlOPrDSN7Nv8PP5AUPsrqutD9rIq7am7u90bCtr3V1SrvNqvT3d59OkuT5N15LrSGwTr9SF7rquTbEkyTAwAoHUQokfAcC4AAAAAAFjDYbeFxkd3dZLUtdl5qmoC2nHosL7aX6Ev91dq64FKfbW/Ql8dqFTZ4Rrt8VRpj6dK72850Gi5NIdNJ2Vnhu9aH9SjPmB3pjMeMACgKUL0CBjOBQAAAACA+JWeYtfQnCwNzclq8t6hSl8oUN9fqa8ahOvbD1aq2h/UxpJybSwpb7JcdufUBsF6KGQfmJ2p7pmpykxzKNVha49dAwDEGUJ0AAAAAACQUELjsnfT6IHdGk33B4La9fURfXXgqIB9f6X2lVfrQIVPByoOafm2Q82uN9VhU1aaQ53THeqc5lBmmqPR6/BPeoT36pZLdTCkDAB0IIToOKaUFP6UDQAAANbjuhRAa3DYbRqYnamB2Zm6YHjj98qrarTtwGF9dSA0PExduL79YKUqfaGhXn3+oA76fTpY6WtxLZmp9nCo7kw1NNIIfRHq955dKdkcMgxDhiTDkCRDhqHwa6Pude1zhd8zGsxT/1qGZGv03okH+EYMmf+xl4n8ZqTljrW6yMu09naiX9/R2274/tFrabJsM9tpdssnsN/NlX68OppbV9P9bK4A4/jzHGObzW33WMuYAb9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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "NUM_FEATURES = 8\n", - "NUM_HIDDEN_UNITS = 2\n", - "NUM_SAMPLES = 1000\n", - "NUM_EPOCHS = 20000\n", - "INIT_KGON = 2\n", - "NUM_OBSERVATIONS = 50\n", - "\n", - "STEPS = sorted(\n", - " list(set(np.logspace(0, np.log10(NUM_EPOCHS), NUM_OBSERVATIONS).astype(int)))\n", - ")\n", - "PLOT_STEPS = [\n", - " min(STEPS, key=lambda s: abs(s - i)) for i in [0, 200, 2000, 10000, NUM_EPOCHS - 1]\n", - "]\n", - "PLOT_INDICES = [STEPS.index(s) for s in PLOT_STEPS]\n", - "\n", - "logs, weights = create_and_train(\n", - " NUM_FEATURES,\n", - " NUM_HIDDEN_UNITS,\n", - " num_samples=NUM_SAMPLES,\n", - " log_ivl=STEPS,\n", - " batch_size=100,\n", - " lr=0.01,\n", - " num_epochs=NUM_EPOCHS,\n", - " init_kgon=INIT_KGON,\n", - " init_zerobias=False,\n", - " seed=1,\n", - ")\n", - "\n", - "weights_to_plot = [weights[i][\"embedding.weight\"] for i in PLOT_INDICES]\n", - "losses = [logs.loc[logs[\"step\"] == s, \"loss\"].values[0] for s in STEPS]\n", - "plot_losses_and_polygons(STEPS, losses, PLOT_STEPS, weights_to_plot)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Analysis\n", - "\n", - "### Local learning coefficient estimation\n", - "\n", - "See the other notebooks for more details on learning coefficient estimation. " - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Sweeping hyperparameters: 0%| | 0/20 [00:00\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
llcllc/stdbatch_sizelrt_sgldllc_typeloss
0NaNNaN10.000010mean0.080738
1infNaN10.00001000.090292
2infNaN10.00001010.126968
3-infNaN10.00001020.005846
4infNaN10.00001030.090292
........................
119952.227974NaN3000.010009900.100745
119961.434123NaN3000.010009910.085652
119973.626991NaN3000.010009920.127344
119983.012219NaN3000.010009930.115656
119997.614182NaN3000.010009940.203151
\n", - "

12000 rows × 7 columns

\n", - "" - ], - "text/plain": [ - " llc llc/std batch_size lr t_sgld llc_type loss\n", - "0 NaN NaN 1 0.00001 0 mean 0.080738\n", - "1 inf NaN 1 0.00001 0 0 0.090292\n", - "2 inf NaN 1 0.00001 0 1 0.126968\n", - "3 -inf NaN 1 0.00001 0 2 0.005846\n", - "4 inf NaN 1 0.00001 0 3 0.090292\n", - "... ... ... ... ... ... ... ...\n", - "11995 2.227974 NaN 300 0.01000 99 0 0.100745\n", - "11996 1.434123 NaN 300 0.01000 99 1 0.085652\n", - "11997 3.626991 NaN 300 0.01000 99 2 0.127344\n", - "11998 3.012219 NaN 300 0.01000 99 3 0.115656\n", - "11999 7.614182 NaN 300 0.01000 99 4 0.203151\n", - "\n", - "[12000 rows x 7 columns]" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Sweep SGLD hyperparameters\n", - "from devinterp.utils import default_nbeta\n", - "\n", - "NUM_SAMPLES_TEST = 200\n", - "NUM_DRAWS_SGLD = 100\n", - "NUM_CHAINS_SGLD = 5\n", - "\n", - "dataset = SyntheticBinaryValued(NUM_SAMPLES_TEST, NUM_FEATURES, 1)\n", - "dataset_double = TensorDataset(dataset.data, dataset.data)\n", - "model = ToyAutoencoder(NUM_FEATURES, NUM_HIDDEN_UNITS, final_bias=True)\n", - "\n", - "\n", - "def sweep_lambdahat_estimation_hyperparams(\n", - " model,\n", - " dataset,\n", - " device=DEVICE,\n", - " sgld_kwargs=None,\n", - " num_draws=NUM_DRAWS_SGLD,\n", - " num_chains=NUM_CHAINS_SGLD,\n", - "):\n", - " observations = []\n", - " hyperparam_combos = list(\n", - " itertools.product([1, 10, 30, 100, 300], [1e-5, 1e-4, 1e-3, 1e-2])\n", - " )\n", - "\n", - " sgld_kwargs = sgld_kwargs or {}\n", - " sgld_kwargs.setdefault(\"localization\", 1.0)\n", - " sgld_kwargs.setdefault(\"nbeta\", default_nbeta(len(dataset)))\n", - "\n", - " for batch_size, lr in tqdm(\n", - " hyperparam_combos,\n", - " desc=\"Sweeping hyperparameters\",\n", - " ):\n", - " loader = DataLoader(dataset, batch_size=batch_size, shuffle=True)\n", - " model.load_state_dict({k: torch.Tensor(v) for k, v in weights[-1].items()})\n", - "\n", - " observation = estimate_learning_coeff_with_summary(\n", - " model,\n", - " loader,\n", - " evaluate=evaluate_mse,\n", - " device=device,\n", - " sampling_method=SGLD,\n", - " optimizer_kwargs={\"lr\": lr, **sgld_kwargs},\n", - " verbose=False,\n", - " num_draws=num_draws,\n", - " num_chains=num_chains,\n", - " online=True,\n", - " )\n", - "\n", - " for t_sgld in range(num_draws):\n", - " observations.append(\n", - " {\n", - " \"llc\": observation[\"llc/means\"][t_sgld].item(),\n", - " \"llc/std\": observation[\"llc/stds\"][t_sgld].item(),\n", - " \"batch_size\": batch_size,\n", - " \"lr\": lr,\n", - " \"t_sgld\": t_sgld,\n", - " \"llc_type\": \"mean\",\n", - " \"loss\": observation[\"loss/trace\"][:, t_sgld].mean().item(),\n", - " }\n", - " )\n", - "\n", - " for llc_type in range(num_chains):\n", - " observations.append(\n", - " {\n", - " \"llc\": observation[\"llc/trace\"][llc_type, t_sgld].item(),\n", - " \"batch_size\": batch_size,\n", - " \"lr\": lr,\n", - " \"t_sgld\": t_sgld,\n", - " \"llc_type\": str(llc_type),\n", - " \"loss\": observation[\"loss/trace\"][llc_type, t_sgld].item(),\n", - " }\n", - " )\n", - "\n", - " return pd.DataFrame(observations)\n", - "\n", - "\n", - "lambdahat_sweep_df = sweep_lambdahat_estimation_hyperparams(model, dataset_double)\n", - "lambdahat_sweep_df" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Plot SGLD hyperparameter grid sweep\n", - "\n", - "import seaborn as sns\n", - "\n", - "\n", - "def plot_lambdahat_estimation_hyperparams(observations_df):\n", - " def plot_losses(data, **kwargs):\n", - " if kwargs[\"label\"] == \"mean\":\n", - " plt.axhline(\n", - " data.loc[data.t_sgld == 0, \"loss\"].values[0], color=\"grey\", alpha=0.5\n", - " )\n", - " return\n", - "\n", - " _data = data.loc[data.llc_type != \"mean\"]\n", - " sns.lineplot(_data, x=\"t_sgld\", y=\"loss\", **kwargs, alpha=0.5)\n", - "\n", - " def plot_llcs(data, **kwargs):\n", - " if kwargs[\"label\"] != \"mean\":\n", - " return\n", - " _data = data.loc[data.llc_type == \"mean\"]\n", - " ax = plt.twinx()\n", - " kwargs.pop(\"color\")\n", - " color = sns.color_palette(\"dark\")[3]\n", - " ax.plot(_data[\"t_sgld\"], _data[\"llc\"], color=color, **kwargs)\n", - " ax.fill_between(\n", - " _data[\"t_sgld\"],\n", - " _data[\"llc\"] - _data[\"llc/std\"],\n", - " _data[\"llc\"] + _data[\"llc/std\"],\n", - " color=color,\n", - " alpha=0.2,\n", - " )\n", - " ax.set_ylabel(\"llc\", color=color)\n", - " ax.tick_params(axis=\"y\", colors=color)\n", - "\n", - " g = sns.FacetGrid(\n", - " observations_df,\n", - " col=\"lr\",\n", - " row=\"batch_size\",\n", - " hue=\"llc_type\",\n", - " palette=\"viridis\",\n", - " sharey=False,\n", - " )\n", - " g.map_dataframe(plot_losses)\n", - " g.map_dataframe(plot_llcs)\n", - "\n", - " g.fig.set_facecolor(\"white\")\n", - " g.fig.tight_layout()\n", - "\n", - " plt.show()\n", - "\n", - "\n", - "plot_lambdahat_estimation_hyperparams(lambdahat_sweep_df)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Covariance Analysis\n", - "\n", - "The idea behind covariance analysis is to look at the covariance between weights across a bunch of different trajectories and how this changes over time. \n", - "\n", - "Based on literature from developmental biology ([Freedman et al. 2023](https://journals.biologists.com/dev/article/150/11/dev201280/312613/A-dynamical-systems-treatment-of-transcriptomic)), we expect transitions to be associated with discontinuous increases in the maximal eigenvalues of the covariance matrix. Moreover, by looking at the associated eigenvectors, we can get a sense of how the weights are involved in these transitions, that is, \"where the circuits are forming.\"\n", - "\n", - "**A note on tractability:** These models are small enough that we can handle the full covariance matrix, but in general we'll have to consider simplifications like looking at covariances within a specific layer. In the same vein, we will generally consider only a subset of the covariance eigenspectrum, e.g., the largest-K eigenvalues, which can be cheaply estimated using power iteration.\n", - "\n", - "**Versus SGLD estimation:** In principle, we could perform covariance analysis over the weights sampled by SGLD. Unlike SGLD-based sampling for lambdahat estimation, we'll want to include a larger thinning factor because weights at more of risk for autocorrelation. For reasons we'll flesh out in the future, we think it's probably best to do covariance across different trajectories *at the same point in time*. This means drawing only one sample per chain. We'll want to sample more and shorter chains. " - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "from typing import List\n", - "from devinterp.slt.callback import SamplerCallback\n", - "from devinterp.slt.cov import WeightAccessor\n", - "from scipy.sparse.linalg import eigsh\n", - "\n", - "\n", - "class CovarianceAccumulator(SamplerCallback):\n", - " \"\"\"\n", - " A callback to iteratively compute and store the covariance matrix of model weights.\n", - " For use with `estimate`.\n", - "\n", - " Attributes:\n", - " num_weights (int): Total number of weights.\n", - " first_moment (torch.Tensor): First moment of weights.\n", - " second_moment (torch.Tensor): Second moment of weights.\n", - " num_draws (int): Number of draws made to accumulate moments.\n", - " accessors (List[WeightAccessor]): Functions to access model weights.\n", - " num_evals (int): Number of eigenvalues to compute.\n", - " \"\"\"\n", - "\n", - " def __init__(\n", - " self,\n", - " num_draws_per_chain: int,\n", - " num_chains: int,\n", - " num_weights: int,\n", - " accessors: List[WeightAccessor],\n", - " device=\"cpu\",\n", - " num_evals=3,\n", - " ):\n", - " \"\"\"\n", - " Initialize the accumulator.\n", - " \"\"\"\n", - " self.num_weights = num_weights\n", - " self.first_moment = torch.zeros(num_weights, device=device)\n", - " self.second_moment = torch.zeros(num_weights, num_weights, device=device)\n", - " self.num_draws = num_draws_per_chain * num_chains\n", - " self.accessors = accessors\n", - " self.num_evals = num_evals\n", - " self.is_finished = False\n", - "\n", - " def accumulate(self, model: nn.Module):\n", - " \"\"\"Accumulate moments from model weights.\"\"\"\n", - " assert not self.is_finished, \"Cannot accumulate after finalizing.\"\n", - "\n", - " weights = torch.cat(\n", - " [accessor(model).view((-1,)) for accessor in self.accessors]\n", - " )\n", - " self.first_moment += weights / self.num_draws\n", - " self.second_moment += torch.outer(weights, weights) / (self.num_draws - 1)\n", - " # print(self.first_moment.mean(), self.second_moment.mean())\n", - "\n", - " def finalize(self):\n", - " \"\"\"Finalize the moments by dividing by the number of draws.\"\"\"\n", - " self.is_finished = True\n", - "\n", - " def reset(self):\n", - " \"\"\"Reset the accumulator.\"\"\"\n", - " self.first_moment.zero_()\n", - " self.second_moment.zero_()\n", - " self.is_finished = False\n", - "\n", - " def to_matrix(self):\n", - " \"\"\"Convert the moments to a covariance matrix.\"\"\"\n", - " return self.second_moment - (\n", - " self.num_draws / (self.num_draws - 1)\n", - " ) * torch.outer(self.first_moment, self.first_moment)\n", - "\n", - " def to_eigen(self, include_matrix=False):\n", - " \"\"\"Convert the covariance matrix to pairs of eigenvalues and vectors.\"\"\"\n", - " cov = self.to_matrix().detach().cpu().numpy()\n", - " evals, evecs = eigsh(cov, k=self.num_evals, which=\"LM\")\n", - "\n", - " results = {\"evals\": evals, \"evecs\": evecs}\n", - "\n", - " if include_matrix:\n", - " results[\"matrix\"] = cov\n", - "\n", - " return results\n", - "\n", - " def get_results(self):\n", - " return self.to_eigen(include_matrix=True)\n", - "\n", - " def __call__(self, model, **kwargs):\n", - " self.accumulate(model)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Calibrating Covariance Estimation\n", - "\n", - "As mentioned, we're currently interested in using covariance analysis over SGD trajectories rather than local posterior samples. By default, we recommend setting hyperparameters like sampling noise and localization strength to match the original SGD hyperparameters used during training (i.e., set them equal to zero). \n", - "\n", - "First, let's see what difference it makes whether we use SGD or SGLD. We'll sweep over different batch sizes and interpolate between zero injected Gaussian noise (SGD) and standard SGLD (noise = 1.0). We'll use a localization strength of 0.0 for now." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Sweeping hyperparameters: 0%| | 0/6 [00:00\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
stepevaleval_idxllc/meanllc/stdllc-chain/0llc-chain/1llc-chain/2llc-chain/3llc-chain/4...llc-chain/49loss/tracebatch_sizelocalizationlrnoise_levelnbetanum_drawsnum_chainsnum_burnin_steps
01NaN0NaNNaNNaNNaNNaNNaNNaN...NaN[[nan], [nan], [nan], [nan], [nan], [nan], [na...10.00.010.0inf15010
11NaN1NaNNaNNaNNaNNaNNaNNaN...NaN[[nan], [nan], [nan], [nan], [nan], [nan], [na...10.00.010.0inf15010
21NaN2NaNNaNNaNNaNNaNNaNNaN...NaN[[nan], [nan], [nan], [nan], [nan], [nan], [na...10.00.010.0inf15010
32NaN0NaNNaNNaNNaNNaNNaNNaN...NaN[[nan], [nan], [nan], [nan], [nan], [nan], [na...10.00.010.0inf15010
42NaN1NaNNaNNaNNaNNaNNaNNaN...NaN[[nan], [nan], [nan], [nan], [nan], [nan], [na...10.00.010.0inf15010
..................................................................
823163400.42630312.6674251.7625990.9758561.4131824.6319881.7509331.861538...1.642683[[0.10351442], [0.123654], [0.2718855], [0.139...1000.00.011.021.71472415010
824163400.49267922.6674251.7625990.9758561.4131824.6319881.7509331.861538...1.642683[[0.10351442], [0.123654], [0.2718855], [0.139...1000.00.011.021.71472415010
825200000.38958502.6692451.7544440.9648931.4216354.6466711.7842671.843256...1.644831[[0.10291874], [0.12395249], [0.27247086], [0....1000.00.011.021.71472415010
826200000.42663012.6692451.7544440.9648931.4216354.6466711.7842671.843256...1.644831[[0.10291874], [0.12395249], [0.27247086], [0....1000.00.011.021.71472415010
827200000.49276822.6692451.7544440.9648931.4216354.6466711.7842671.843256...1.644831[[0.10291874], [0.12395249], [0.27247086], [0....1000.00.011.021.71472415010
\n", - "

828 rows × 64 columns

\n", - "" - ], - "text/plain": [ - " step eval eval_idx llc/mean llc/std llc-chain/0 llc-chain/1 \\\n", - "0 1 NaN 0 NaN NaN NaN NaN \n", - "1 1 NaN 1 NaN NaN NaN NaN \n", - "2 1 NaN 2 NaN NaN NaN NaN \n", - "3 2 NaN 0 NaN NaN NaN NaN \n", - "4 2 NaN 1 NaN NaN NaN NaN \n", - ".. ... ... ... ... ... ... ... \n", - "823 16340 0.426303 1 2.667425 1.762599 0.975856 1.413182 \n", - "824 16340 0.492679 2 2.667425 1.762599 0.975856 1.413182 \n", - "825 20000 0.389585 0 2.669245 1.754444 0.964893 1.421635 \n", - "826 20000 0.426630 1 2.669245 1.754444 0.964893 1.421635 \n", - "827 20000 0.492768 2 2.669245 1.754444 0.964893 1.421635 \n", - "\n", - " llc-chain/2 llc-chain/3 llc-chain/4 ... llc-chain/49 \\\n", - "0 NaN NaN NaN ... NaN \n", - "1 NaN NaN NaN ... NaN \n", - "2 NaN NaN NaN ... NaN \n", - "3 NaN NaN NaN ... NaN \n", - "4 NaN NaN NaN ... NaN \n", - ".. ... ... ... ... ... \n", - "823 4.631988 1.750933 1.861538 ... 1.642683 \n", - "824 4.631988 1.750933 1.861538 ... 1.642683 \n", - "825 4.646671 1.784267 1.843256 ... 1.644831 \n", - "826 4.646671 1.784267 1.843256 ... 1.644831 \n", - "827 4.646671 1.784267 1.843256 ... 1.644831 \n", - "\n", - " loss/trace batch_size \\\n", - "0 [[nan], [nan], [nan], [nan], [nan], [nan], [na... 1 \n", - "1 [[nan], [nan], [nan], [nan], [nan], [nan], [na... 1 \n", - "2 [[nan], [nan], [nan], [nan], [nan], [nan], [na... 1 \n", - "3 [[nan], [nan], [nan], [nan], [nan], [nan], [na... 1 \n", - "4 [[nan], [nan], [nan], [nan], [nan], [nan], [na... 1 \n", - ".. ... ... \n", - "823 [[0.10351442], [0.123654], [0.2718855], [0.139... 100 \n", - "824 [[0.10351442], [0.123654], [0.2718855], [0.139... 100 \n", - "825 [[0.10291874], [0.12395249], [0.27247086], [0.... 100 \n", - "826 [[0.10291874], [0.12395249], [0.27247086], [0.... 100 \n", - "827 [[0.10291874], [0.12395249], [0.27247086], [0.... 100 \n", - "\n", - " localization lr noise_level nbeta num_draws num_chains \\\n", - "0 0.0 0.01 0.0 inf 1 50 \n", - "1 0.0 0.01 0.0 inf 1 50 \n", - "2 0.0 0.01 0.0 inf 1 50 \n", - "3 0.0 0.01 0.0 inf 1 50 \n", - "4 0.0 0.01 0.0 inf 1 50 \n", - ".. ... ... ... ... ... ... \n", - "823 0.0 0.01 1.0 21.714724 1 50 \n", - "824 0.0 0.01 1.0 21.714724 1 50 \n", - "825 0.0 0.01 1.0 21.714724 1 50 \n", - "826 0.0 0.01 1.0 21.714724 1 50 \n", - "827 0.0 0.01 1.0 21.714724 1 50 \n", - "\n", - " num_burnin_steps \n", - "0 10 \n", - "1 10 \n", - "2 10 \n", - "3 10 \n", - "4 10 \n", - ".. ... \n", - "823 10 \n", - "824 10 \n", - "825 10 \n", - "826 10 \n", - "827 10 \n", - "\n", - "[828 rows x 64 columns]" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Sweep covariance estimation hyperparameters\n", - "\n", - "NUM_DRAWS_COV = 1\n", - "NUM_BURNIN_COV = 10\n", - "NUM_CHAINS_COV = 50\n", - "NUM_EIGVALS_COV = 3\n", - "\n", - "\n", - "def sweep_covariance_estimation_hyperparams(\n", - " model,\n", - " dataset,\n", - " steps,\n", - " checkpoints,\n", - " grid: dict,\n", - " device=DEVICE,\n", - " sgld_kwargs=None,\n", - " sampling_kwargs=None,\n", - "):\n", - " observations = []\n", - " accessors = {\n", - " \"weight\": lambda model: model.embedding.weight,\n", - " \"bias\": lambda model: model.unembedding.bias,\n", - " }\n", - " num_weights = sum([v(model).numel() for v in accessors.values()])\n", - " hyperparam_combos = list(itertools.product(*grid.values()))\n", - "\n", - " sgld_kwargs = sgld_kwargs or {}\n", - " sgld_kwargs.setdefault(\"localization\", 0.0)\n", - " sgld_kwargs.setdefault(\"lr\", 0.01)\n", - " sgld_kwargs.setdefault(\"noise_level\", 1.0)\n", - "\n", - " loader_kwargs = {\"batch_size\": 100}\n", - "\n", - " sampling_kwargs = sampling_kwargs or {}\n", - " sampling_kwargs.setdefault(\"num_draws\", NUM_DRAWS_COV)\n", - " sampling_kwargs.setdefault(\"num_chains\", NUM_CHAINS_COV)\n", - " sampling_kwargs.setdefault(\"num_burnin_steps\", NUM_BURNIN_COV)\n", - " sampling_kwargs.setdefault(\"num_evals\", NUM_EIGVALS_COV)\n", - "\n", - " for i, hyperparam_values in tqdm(\n", - " enumerate(hyperparam_combos),\n", - " total=len(hyperparam_combos),\n", - " desc=\"Sweeping hyperparameters\",\n", - " ):\n", - " torch.manual_seed(i)\n", - " hyperparams = dict(zip(grid.keys(), hyperparam_values))\n", - "\n", - " _sgld_kwargs = sgld_kwargs.copy()\n", - " _loader_kwargs = loader_kwargs.copy()\n", - " _sampling_kwargs = sampling_kwargs.copy()\n", - "\n", - " for key in hyperparams:\n", - " if key in _sgld_kwargs:\n", - " _sgld_kwargs[key] = hyperparams[key]\n", - " elif key in _loader_kwargs:\n", - " _loader_kwargs[key] = hyperparams[key]\n", - " elif key in _sampling_kwargs:\n", - " _sampling_kwargs[key] = hyperparams[key]\n", - " else:\n", - " raise ValueError(f\"Unknown hyperparameter {key}\")\n", - "\n", - " loader = DataLoader(dataset, shuffle=True, **_loader_kwargs)\n", - "\n", - " callbacks = [\n", - " CovarianceAccumulator(\n", - " _sampling_kwargs[\"num_draws\"],\n", - " _sampling_kwargs[\"num_chains\"],\n", - " num_weights,\n", - " list(accessors.values()),\n", - " device=device,\n", - " num_evals=_sampling_kwargs.pop(\"num_evals\"),\n", - " ),\n", - " ]\n", - "\n", - " for step, checkpoint in tqdm(\n", - " zip(steps, checkpoints), desc=\"Sweeping checkpoints\", total=len(steps)\n", - " ):\n", - " model.load_state_dict({k: torch.Tensor(v) for k, v in checkpoint.items()})\n", - "\n", - " observation = estimate_learning_coeff_with_summary(\n", - " model,\n", - " loader,\n", - " evaluate=evaluate_mse,\n", - " device=device, # type: ignore\n", - " sampling_method=SGLD,\n", - " optimizer_kwargs=_sgld_kwargs,\n", - " verbose=False,\n", - " callbacks=callbacks,\n", - " seed=i * _sampling_kwargs[\"num_chains\"],\n", - " **_sampling_kwargs,\n", - " )\n", - "\n", - " evals, _, cov_matrix = (\n", - " observation.pop(\"evals\"),\n", - " observation.pop(\"evecs\"),\n", - " observation.pop(\"matrix\"),\n", - " )\n", - "\n", - " for j, eval in enumerate(evals):\n", - " observations.append(\n", - " {\n", - " \"step\": step,\n", - " \"eval\": eval,\n", - " \"eval_idx\": j,\n", - " **observation,\n", - " **_loader_kwargs,\n", - " **_sgld_kwargs,\n", - " **_sampling_kwargs,\n", - " }\n", - " )\n", - "\n", - " for callback in callbacks:\n", - " callback.reset()\n", - "\n", - " observations_df = pd.DataFrame(observations)\n", - " return observations_df\n", - "\n", - "\n", - "model = ToyAutoencoder(NUM_FEATURES, NUM_HIDDEN_UNITS, final_bias=True)\n", - "covariance_estimation_sweep_df = sweep_covariance_estimation_hyperparams(\n", - " model,\n", - " dataset_double,\n", - " STEPS,\n", - " weights,\n", - " grid={\"batch_size\": [1, 10, 100], \"noise_level\": [0.0, 1.0]},\n", - ")\n", - "covariance_estimation_sweep_df" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's plot the results using a seaborn `FacetGrid`. Each subgraph shows the values of the three largest covariance eigenvalues over training time (with indices in increasing order). Each subgraph corresponds to a unique combination of batch size and noise level. The left column represents SGD trajectories. The right column represents SGLD trajectories (without localization). \n", - "\n", - "We see that indeed the SGLD-based covariance analysis seems to miss the last transition (while this shows up as a bump in the SGD-based analysis). " - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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GDBmC8ePHY+rUqXXqFFV6HxTvdx8fn3Knl/ySjY+Px7vvvovff/+9zJdv6R8L1ZGWlob8/Hy0b9++zHsdOnSAyWTC/fv30bFjxwrjL/0ZVkYikZivay05f+l5p0+fjtmzZyMuLg5+fn7YuXMn9Ho9nn766TLLbNu2rcVrjuPQpk0b83V8xclGWFhYhXHl5OSYtwOo+Pjn8Xhlrolu164dAFhcr/jnn39i2bJliIyMtLjmr7xxVStaV02WUdtjqjb7prS7d++CMVbmcyhWeoQKb29vix+bFRGLxfj444+xYMECuLu7o2/fvhg7diymT58ODw8Pc7lBgwaZf1ScPHkSPXv2RM+ePeHk5ISTJ0/C3d0dV65cwdSpUy1ivnnzZpljsVhxHVDTbfPy8irTcazk8dG3b98qt5uQilCiSSqlUCjg5eWFa9eu1Wi+6gz4PW7cOMhkMvz000/o378/fvrpJ/B4PEyaNMlcJjo6GsOHD0dAQADWrFkDHx8fiEQi7N27F2vXri3TalGyNaUyy5cvxzvvvINnnnkGH3zwAZycnMDj8TB37txyW0L4fH65yynZGluV4uUuXLiwwlbX0gl66fnd3NwsWsxKKv7y4TgOu3btwt9//40//vgDf/31F5555hmsXr0af//9d60HH69oH1S1b4xGI0aMGIHMzEy8/vrrCAgIgJ2dHRISEjBjxowqW56spS6fYUXzlvbUU09h3rx52LZtG95880388MMP6NmzZ7kJcVWK98vKlSvRrVu3csuU/iyre/yX5+TJk3j00UcxePBgfPHFF/D09IRQKMSWLVuwffv2MuXLW1dNl1HbY6o2+6Y0k8kEjuOwb9++ctdXl307d+5cjBs3Drt378Zff/2Fd955BytWrMCRI0fQvXt3AMDAgQOxefNm3Lt3DydPnsSgQYPAcRwGDhyIkydPwsvLCyaTyXz2pDjmzp07Y82aNeWutzhBr+m2EVKfKNEkVRo7diy+/PJLnD17Fv369au0rJ+fH0wmE+7evYsOHTqYp6ekpCA7Oxt+fn7maXZ2dhg7dix27tyJNWvWYMeOHRg0aBC8vLzMZf744w9otVr8/vvvFq0fpU/91NSuXbswdOhQfP311xbTs7Oz4eLiUuPltW7dGgBw7do1hISElFumuEVJKBRWWKaqdRw6dAgDBgyo1pde37590bdvX3z44YfYvn07pk2bhh9//BHPPfdcg9755+rVq7hz5w6+/fZbTJ8+3Tz94MGDZcpWNy5XV1fIZDLcvn27zHu3bt0Cj8cr0yrWEJycnDBmzBhs27YN06ZNw+nTp8u00BcrbpUrxhhDVFQUunTpAuC/Y0qhUNTqeCnJZDLh3r175lYqALhz5w4AmE9d//zzz5BIJPjrr78sWr63bNlS7fVYYxnVUZN9U9Ex1bp1azDG0LJlS4v9Ys0YFyxYgAULFuDu3bvo1q0bVq9ejR9++AHAf5ffHDx4EOfPn8fixYsBAIMHD8aGDRvMrYxBQUEWy7xy5QqGDx9e6b+Vmm5bYmJimeGwSh8fhNQWXaNJqvTaa6/Bzs4Ozz33HFJSUsq8Hx0dbR7C55FHHgGAMl+uxb/Ax4wZYzF98uTJSExMxFdffYUrV65g8uTJFu8X/xov2eqUk5NT5y8uPp9fpiVr586dlV4jWZkePXqgZcuWWLduXZk7kBSvx83NDUOGDMGmTZuQlJRUZhlVDbXz5JNPwmg04oMPPijznsFgMK83KyurzLYVt/oUn8osHv+vIe6WUt5nyBgrM+wTAPMXXVVx8fl8jBw5Er/99pvFqd+UlBRs374dAwcONF/20dCefvpp3LhxA4sWLQKfz8dTTz1VbrnvvvvO4pKUXbt2ISkpCaNHjwYABAUFoXXr1li1alW5g8XXZGgmAPj888/Nzxlj+PzzzyEUCjF8+HAAhfuU4ziLy0hiY2Oxe/fuaq/DGsuojprsm4qOqQkTJoDP5+O9994r8++FMYaMjIxaxZafnw+NRmMxrXXr1rC3t7e4lKBly5bw9vbG2rVrodfrMWDAAACFCWh0dDR27dqFvn37Woxp++STTyIhIQGbN28us96CggLk5eXVatsMBoN5KDgA0Ol02LRpE1xdXS0SXUJqg1o0SZVat26N7du3Y/LkyejQoYPFnYHOnDmDnTt3mseo7Nq1K8LCwvDll18iOzsbwcHB+Oeff/Dtt99i/PjxGDp0qMWyH3nkEdjb22PhwoXg8/mYOHGixfsjR46ESCTCuHHj8MILL0CtVmPz5s1wc3MrN1mrrrFjx+L999/HzJkz0b9/f1y9ehXbtm2rcGzPqvB4PGzYsAHjxo1Dt27dMHPmTHh6euLWrVu4fv06/vrrLwBAREQEBg4ciM6dO2PWrFlo1aoVUlJScPbsWTx48KDMOJ4lBQcH44UXXsCKFSsQGRmJkSNHQigU4u7du9i5cyc+/fRTPPHEE/j222/xxRdf4PHHH0fr1q2Rm5uLzZs3Q6FQmH8ISKVSBAYGYseOHWjXrh2cnJzQqVOnSq8xra2AgAC0bt0aCxcuREJCAhQKBX7++edyr40s/lJ75ZVXEBoaWmmitmzZMhw8eBADBw7Eyy+/DIFAgE2bNkGr1eKTTz6x+nZU15gxY+Ds7IydO3di9OjRFV5T6+TkhIEDB2LmzJlISUnBunXr0KZNG8yaNQtA4TH11VdfYfTo0ejYsSNmzpwJb29vJCQk4OjRo1AoFPjjjz+qFZNEIsH+/fsRFhaGPn36YN++fdizZw/efPNN8yUXY8aMwZo1azBq1ChMnToVqampiIiIQJs2bfDvv/9We9vruozqqMm+KT6m3nrrLTz11FMQCoUYN24cWrdujWXLluGNN95AbGwsxo8fD3t7e8TExODXX3/F888/j4ULF9Y4tjt37mD48OF48sknERgYCIFAgF9//RUpKSlljuVBgwbhxx9/ROfOnc3Xk/bo0QN2dna4c+eOxfWZQOGPmJ9++gkvvvgijh49igEDBsBoNOLWrVv46aefzGMI13TbvLy88PHHHyM2Nhbt2rXDjh07EBkZiS+//JLupkbqrgF7uJOH3J07d9isWbOYv78/E4lEzN7eng0YMIB99tlnFsP16PV69t5777GWLVsyoVDIfHx82BtvvGFRpqRp06aZh58pz++//866dOnCJBIJ8/f3Zx9//DH75ptvygxZ4ufnx8aMGVPuMsob3mjBggXM09OTSaVSNmDAAHb27FkWHBzMgoODzeWKh6LZuXOnxfJiYmIYALZlyxaL6adOnWIjRoxg9vb2zM7OjnXp0oV99tlnFmWio6PZ9OnTmYeHBxMKhczb25uNHTuW7dq1q9zYS/vyyy9ZUFAQk0qlzN7ennXu3Jm99tprLDExkTHG2KVLl9iUKVOYr68vE4vFzM3NjY0dO5ZduHDBYjlnzpxhQUFBTCQSWQwdVNHwRuHh4eXug9LDuJS3z27cuMFCQkKYXC5nLi4ubNasWezKlStl9qHBYGBz5sxhrq6ujOM4izhKxljs0qVLLDQ0lMnlciaTydjQoUPZmTNnLMoUD29Uerin4jiLx1qtSFhYGLOzsyszvbz9VOzll19mANj27dvLvFe83v/7v/9jb7zxBnNzc2NSqZSNGTPGYqinYpcvX2YTJkxgzs7OTCwWMz8/P/bkk0+yw4cPl4ml5FBFpeOPjo42j8Ho7u7OlixZUmaIr6+//pq1bduWicViFhAQwLZs2VLt48Eay6jJMVXdfcMYYx988AHz9vZmPB6vTL3x888/s4EDBzI7OztmZ2fHAgICWHh4OLt9+7a5THBwMOvYsWO521taeno6Cw8PZwEBAczOzo4plUrWp08f9tNPP5UpGxERwQCwl156yWJ6SEgIA1BmOxgrHC7q448/Zh07dmRisZg5OjqyoKAg9t5777GcnByLsjXZtgsXLrB+/foxiUTC/Pz8yoxTTEht0b3OCSHEyubNm4evv/4aycnJZW5TeOzYMQwdOhQ7d+7EE088Ue+x0L3aSWWGDBmC9PT0Gnf4JKS66BpNQgixIo1Ggx9++AETJ06ke2ETQpo9ukaTEEKsIDU1FYcOHcKuXbuQkZGBV1991dYhEUKIzVGiSQghVnDjxg1MmzYNbm5uWL9+fYXjOxJCSHNC12gSQgghhJB6QddoEkIIIYSQekGJJiGEEEIIqReUaBJCCCGEkHpBiSYhhBBCCKkXlGgSQgghhJB6QYkmIYQQQgipF5RoEkIIIYSQekGJJiGEEEIIqReUaBJCCCGEkHpBiSYhhBBCCKkXlGgSQgghhJB6QYkmIYQQQgipF5RoEkIIIYSQekGJJiGEEEIIqReUaBJCCCGEkHpBiSYhhBBCCKkXlGgSQgghhJB6QYkmIYQQQgipF5Ro1sCQIUMwd+7cBl9vbGwsOI5DZGSk1Zd97NgxcByH7Oxsqy+7sZsxYwaWLl1a7+vx9/fHunXr6n09W7duhYODQ72vp6TmfPyQsqiObFqojqy75nz8FKNEs4E1toOuf//+SEpKglKptHUoFfrwww/Rv39/yGSyCiuJ+Ph4jBkzBjKZDG5ubli0aBEMBkPDBlqB8+fP4/nnn7d1GA8tjUaD8PBwODs7Qy6XY+LEiUhJSal0HsYY3n33XXh6ekIqlSIkJAR3795toIhJXVAdWXNURzZvX375JYYMGQKFQlGjfzsRERHw9/eHRCJBnz598M8//9RLfJRoNnMikQgeHh7gOM7WoVRIp9Nh0qRJeOmll8p932g0YsyYMdDpdDhz5gy+/fZbbN26Fe+++24DR1o+V1dXyGQyW4fx0Jo3bx7++OMP7Ny5E8ePH0diYiImTJhQ6TyffPIJ1q9fj40bN+LcuXOws7NDaGgoNBpNA0VNmgqqI+sf1ZF1k5+fj1GjRuHNN9+s9jw7duzA/PnzsWTJEly6dAldu3ZFaGgoUlNTrR8gI9UWHBzMwsPDWXh4OFMoFMzZ2Zm9/fbbzGQymct89913LCgoiMnlcubu7s6mTJnCUlJSGGOMxcTEMAAWj7CwMMYYY0ajkX388cesdevWTCQSMR8fH7Zs2TKL+X7++Wc2ZMgQJpVKWZcuXdiZM2eqFXdsbCwbO3Ysc3BwYDKZjAUGBrI9e/Ywxhg7evQoA8CysrLM21g6RgAsJiaGMcZYVlYWe/bZZ5mLiwuzt7dnQ4cOZZGRkVbYu1XbsmULUyqVZabv3buX8Xg8lpycbJ62YcMGplAomFarrXB5YWFhbMmSJRW+X939vmvXLhYYGMhEIhHz8/Njq1atsnjfz8+PrV27ljHGmMlkYkuWLGE+Pj5MJBIxT09PNmfOHHNZjUbDFixYwLy8vJhMJmO9e/dmR48erXinlFDe/tm9ezfr3r07E4vFrGXLlmzp0qVMr9czxhibMmUKe/LJJy3K63Q65uzszL799lvGWOFxuXz5cubv788kEgnr0qUL27lzp7l86ePH2rKzs5lQKLRY582bNxkAdvbs2XLnMZlMzMPDg61cudJiOWKxmP3f//1fvcRJClEdSXUk1ZENW0eWVJN19e7dm4WHh5tfG41G5uXlxVasWGH1uCjRrIHg4GAml8vZq6++ym7dusV++OEHJpPJ2Jdffmku8/XXX7O9e/ey6OhodvbsWdavXz82evRoxhhjBoOB/fzzzwwAu337NktKSmLZ2dmMMcZee+015ujoyLZu3cqioqLYyZMn2ebNmxlj//1jDggIYH/++Se7ffs2e+KJJ5ifn5/5H0RlxowZw0aMGMH+/fdfFh0dzf744w92/PhxxljZAzMjI4MlJSWZHxMmTGDt27dn+fn5jDHGQkJC2Lhx49j58+fZnTt32IIFC5izszPLyMiocP2BgYHMzs6uwseoUaOqtf8rqkTfeecd1rVrV4tp9+7dYwDYpUuXKlxedSvRyvb7hQsXGI/HY++//z67ffs227JlC5NKpWzLli3m5ZSsRHfu3MkUCgXbu3cvi4uLY+fOnbM4fp577jnWv39/duLECRYVFcVWrlzJxGIxu3PnTo33z4kTJ5hCoWBbt25l0dHR7MCBA8zf358tXbqUMcbYn3/+yaRSKcvNzTXP88cffzCpVMpUKhVjjLFly5axgIAAtn//fhYdHc22bNnCxGIxO3bsGGOsehXbqFGjKv38AwMDK5z38OHD5S7f19eXrVmzptx5oqOjGQB2+fJli+mDBw9mr7zySoXrInVHdSTVkVRHNmwdWVJ1E02tVsv4fD779ddfLaZPnz6dPfroo9VaV01QolkDwcHBrEOHDha/zl9//XXWoUOHCuc5f/48A2A+UMs7EFQqFROLxeZKs7Tif8xfffWVedr169cZAHbz5s0q4+7cubP5H05plR2Ya9asYQ4ODuz27duMMcZOnjzJFAoF02g0FuVat27NNm3aVOH6Y2Nj2d27dyt8PHjwoMptYKziSnTWrFls5MiRFtPy8vIYALZ3794Kl1fdSrSy/T516lQ2YsQIi/kWLVpkUTGUrERXr17N2rVrx3Q6XZn1xcXFMT6fzxISEiymDx8+nL3xxhsVxlms9P4ZPnw4W758uUWZ77//nnl6ejLGGNPr9czFxYV999135venTJnCJk+ezBgrbDmQyWRlWieeffZZNmXKFMZY9Sq2Bw8eVPr5x8bGVjjvtm3bmEgkKjO9V69e7LXXXit3ntOnTzMALDEx0WL6pEmTyrROEOuiOpLqSKojG7aOLKm6iWZCQgIDUCbuRYsWsd69e1drXTUhqPvJ9+alb9++Ftfq9OvXD6tXr4bRaASfz8fFixexdOlSXLlyBVlZWTCZTAAKL8QODAwsd5k3b96EVqvF8OHDK113ly5dzM89PT0BAKmpqQgICKh0vldeeQUvvfQSDhw4gJCQEEycONFiWeXZt28fFi9ejD/++APt2rUDAFy5cgVqtRrOzs4WZQsKChAdHV3hsvz8/CpdV2NX2X6/efMmHnvsMYvyAwYMwLp168zHREmTJk3CunXr0KpVK4waNQqPPPIIxo0bB4FAgKtXr8JoNJr3dzGtVltmn1fHlStXcPr0aXz44YfmaUajERqNBvn5+ZDJZHjyySexbds2PP3008jLy8Nvv/2GH3/8EQAQFRWF/Px8jBgxwmK5Op0O3bt3r3Yc3t7eNY6dPLyojqQ6EqA6kurI/1CiaUV5eXkIDQ1FaGgotm3bBldXV8THxyM0NBQ6na7C+aRSabWWLxQKzc+LK/LiSroyzz33HEJDQ7Fnzx4cOHAAK1aswOrVqzFnzpxyy9+4cQNPPfUUPvroI4wcOdI8Xa1Ww9PTE8eOHSszT2VDRnTs2BFxcXEVvj9o0CDs27evyu2oiIeHR5necsW9kj08PGq93GK13e/l8fHxwe3bt3Ho0CEcPHgQL7/8MlauXInjx49DrVabv4hLV75yubzG61Kr1XjvvffK7TgjkUgAANOmTUNwcDBSU1Nx8OBBSKVSjBo1yjw/AOzZs6dMRSgWi6sdx+jRo3Hy5MkK3/fz88P169fLfc/DwwM6nQ7Z2dkWx1hKSkqFn23x9JSUFPOXXvHrbt26VTtuYn1UR5aP6sj/UB1ZVmV1ZG24uLiAz+eXGb2jsnq1LijRrKFz585ZvP7777/Rtm1b8Pl83Lp1CxkZGfjoo4/g4+MDALhw4YJFeZFIBKDwV1Oxtm3bQiqV4vDhw3juuefqJW4fHx+8+OKLePHFF/HGG29g8+bN5Vai6enpGDduHCZOnIh58+ZZvNejRw8kJydDIBDA39+/2uveu3cv9Hp9he9X90ukIv369cOHH36I1NRUuLm5AQAOHjwIhUJRYQuJtXTo0AGnT5+2mHb69Gm0a9euTEVYTCqVYty4cRg3bhzCw8MREBCAq1evonv37jAajUhNTcWgQYPqHFuPHj1w+/ZttGnTpsIy/fv3h4+PD3bs2IF9+/Zh0qRJ5i+NwMBAiMVixMfHIzg4uNZxfPXVVygoKKjw/ZJfUqUFBQVBKBTi8OHDmDhxIgDg9u3biI+PR79+/cqdp2XLlvDw8MDhw4fNiaVKpcK5c+cq7JVLrIfqSKojS6I6smp1qSNrQyQSISgoCIcPH8b48eMBFP4wOHz4MGbPnm3VdQGUaNZYfHw85s+fjxdeeAGXLl3CZ599htWrVwMAfH19IRKJ8Nlnn+HFF1/EtWvX8MEHH1jM7+fnB47j8Oeff+KRRx6BVCqFXC7H66+/jtdeew0ikQgDBgxAWloarl+/jmeffbbOMc+dOxejR49Gu3btkJWVhaNHj6JDhw7llp04cSJkMhmWLl2K5ORk83RXV1eEhISgX79+GD9+PD755BO0a9cOiYmJ2LNnDx5//HH07Nmz3GXW9bRQfHw8MjMzER8fD6PRaB6UuU2bNpDL5Rg5ciQCAwPx9NNP45NPPkFycjLefvtthIeH1+hXZW0sWLAAvXr1wgcffIDJkyfj7Nmz+Pzzz/HFF1+UW37r1q0wGo3o06cPZDIZfvjhB0ilUvj5+cHZ2RnTpk3D9OnTsXr1anTv3h1paWk4fPgwunTpgjFjxtQotnfffRdjx46Fr68vnnjiCfB4PFy5cgXXrl3DsmXLzOWmTp2KjRs34s6dOzh69Kh5ur29PRYuXIh58+bBZDJh4MCByMnJwenTp6FQKBAWFlatOOpyWkipVOLZZ5/F/Pnz4eTkBIVCgTlz5qBfv37o27evuVxAQABWrFiBxx9/HBzHYe7cuVi2bBnatm2Lli1b4p133oGXl5e5UiX1h+pIqiNLojqyanU9dZ6cnIzk5GRERUUBAK5evQp7e3v4+vrCyckJADB8+HA8/vjj5kRy/vz5CAsLQ8+ePdG7d2+sW7cOeXl5mDlzZp1iKZfVr/pswoKDg9nLL7/MXnzxRaZQKJijoyN78803LS583759O/P392disZj169eP/f7772V6wL7//vvMw8ODcRxnMXTHsmXLmJ+fHxMKhczX19d8kXLxBdcll5GVlcUAVGtYh9mzZ7PWrVszsVjMXF1d2dNPP83S09MZY2UvHkY5w3agxNAdKpWKzZkzh3l5eTGhUMh8fHzYtGnTWHx8fK33a1XCwsLKjanktsfGxrLRo0czqVTKXFxc2IIFC6rsbVrdC92r2u/FQ3cUf24lh9VhzPJC919//ZX16dOHKRQKZmdnx/r27csOHTpkLqvT6di7777L/P39mVAoZJ6enuzxxx9n//77b5X7qbyOAPv372f9+/dnUqmUKRQK1rt3b4senIwxduPGDQaA+fn5WRzLjBUONbJu3TrWvn17JhQKmaurKwsNDa2wR259KCgoYC+//DJzdHRkMpmMPf744ywpKcmiDACLXqwmk4m98847zN3dnYnFYjZ8+HBzhw1Sf6iOpDqS6siGryOXLFlS7udfumd/6c/ys88+Y76+vkwkErHevXuzv//+u17i4xhjzPrpKyGN34wZM+Dv798gt1gjhJCHDdWRxBrozkCEEEIIIaReUKLZBIwePRpyubzcx/Lly20dHrES+pwJqR36t9M80OfcONGp8yYgISGhwh5rTk5O5ouBiaXdu3fDwcEBQ4YMsXUo1UKfMyG1Q/92aofqSGINlGgSQgghhJB6QafOCSGEEEJIvaBEswqMMahUKlDDLyGkqXv88cfh6OiIJ554olrlqX4khFSFEs0q5ObmQqlUIjc319ahEEJIvXr11Vfx3XffVbs81Y+EkKpQokkIIQQAMGTIENjb29s6DEJIE0KJJiGE1MHSpUvBcZzFIyAgwKrrOHHiBMaNGwcvLy9wHIfdu3eXWy4iIgL+/v6QSCTo06cP/vnnH6vGQQghNUWJJiGE1FHHjh2RlJRkfpw6darCsqdPn4Zery8z/caNG0hJSSl3nry8PHTt2hUREREVLnfHjh2YP38+lixZgkuXLqFr164IDQ1FamqquUy3bt3QqVOnMo/ExMQabC0hhFSfwNYBEELIw04gEMDDw6PKciaTCeHh4Wjbti1+/PFH8Pl8AMDt27cxbNgwzJ8/H6+99lqZ+UaPHo3Ro0dXuuw1a9Zg1qxZmDlzJgBg48aN2LNnD7755hssXrwYABAZGVnDLSOEkLqhFk1CCKmju3fvwsvLC61atcK0adMQHx9fbjkej4e9e/fi8uXLmD59OkwmE6KjozFs2DCMHz++3CSzOnQ6HS5evIiQkBCLdYWEhODs2bO1WmZlIiIiEBgYiF69ell92YSQpoUSTUIIqYM+ffpg69at2L9/PzZs2ICYmBgMGjSowp7YXl5eOHLkCE6dOoWpU6di2LBhCAkJwYYNG2odQ3p6OoxGI9zd3S2mu7u7Izk5udrLCQkJwaRJk7B37160aNGiwiQ1PDwcN27cwPnz52sdMyGkeaBT54QQUgclT2l36dIFffr0gZ+fH3766Sc8++yz5c7j6+uL77//HsHBwWjVqhW+/vprcBzXUCFX6NChQ7YOgRDSxFCLJiGEWJGDgwPatWuHqKioCsukpKTg+eefx7hx45Cfn4958+bVaZ0uLi7g8/llOhOlpKRU69pRQgipL9SiaSU6kw5zLy8Ax/HAgQOP41D4Hw8chxLPOfBQNAwKeOAA83Mex4HPCSDkBBDwiv8KIeAEEPKEJaYLwef44PP4EHAC80PIE0AusIdSqIBCqIBSqICEL7H1riGkWVGr1YiOjsbTTz9d7vvp6ekYPnw4OnTogJ07d+LOnTsYMmQIxGIxVq1aVat1ikQiBAUF4fDhwxg/fjyAwo5Hhw8fxuzZs2u7KVa1P/kAfk/409xya/n/HAonV/4eB4DH8cADDzyODx7HFT0veg0OfI4PHscr+xc8CHhCiHgiiHkiiIoeYr4YEp4EUr4UUr4EMr4UdgI57AR2sBPIwOf4DbiXCGl6KNG0EsYY8oz5tg6jDDFPDBexM1zFrnATu8JN7AYPqTu8JF5wEjk2itN1hDzMFi5ciHHjxsHPzw+JiYlYsmQJ+Hw+pkyZUqasyWTC6NGj4efnhx07dkAgECAwMBAHDx7EsGHD4O3tXW7rplqttmghjYmJQWRkJJycnODr6wsAmD9/PsLCwtCzZ0/07t0b69atQ15enrkXuq3pTXrkGfNsHUaNiXgiSHiSooRUDDFfAilfAieRE5xFTnARO8NZ5Ax3iRschA7Nrk5ljCFdl4H7+Q+Qrc+GSq+CSp8LlUGFfEM+DMwAIzPCYCr8W97NSosbXP57zf33/xyvxCtLJjAADIyxomcmc0z//Qeg6H2AgcfxIOKJIOQJIeaJIeaJoBAq4ChyhJPQEY4iR7hL3OAidrHqfmrOONbEb1KbnZ2NkJAQGAwGGAwGvPrqq5g1a1a151epVFAqlcjJyYFCoaiwnImZkKxJKTywSx7kzARTyWnFr8uUYzAxEwzMAAMzQG8ywMD0RX8NMJj00DMDDMWvmQEGZoSx6LWRGaE16ZBb9A88R6+CzqSrdNskPDHciipHpVABpVBZ+FykgI/UBx4S92ZXaRJSU0899RROnDiBjIwMuLq6YuDAgfjwww/RunXrcssfPHgQgwYNgkRiebbh8uXLcHV1RYsWLcrMc+zYMQwdOrTM9LCwMGzdutX8+vPPP8fKlSuRnJyMbt26Yf369ejTp0/dNrAS1a0fARTVTYUdpAq/doq/+lGcDlT8XtHbJpjMdaiJmWCCCSZmgpEVTzfByIwwMQYTM8IIU+FfZipKdvTQMT10Rh20Jh10Jh20Jg0KjBpojBoUGAuQb8xHniEP+caCGu8PGV8GL6kXvKWe8JB4FP64l7jBTezaZM4u5RnycUN1A1HqaMTnxyMuL75RNrLUlYvIGQGK9uhgH4AOigA4i51tHdJDq8knmkajEVqtFjKZDHl5eejUqRMuXLgAZ+fqHTQ1qUgbG41Rg2x9DtK0aUjVpCFNm4YUbSqSCpKRqk2FkRkrnd9BqEQHRQd0UAQgUNEBrvQLjxBSwsNcP1bFyIzIM+RDY9RAY9JAZ9RCY9JCY9Qi35iHDG0mMnQZyNBlIF2biXRtOkxFLWrlUQoV8JZ6oYW0BVrIWqCF1BstpN4Q88UNuFU1Z2ImxObF4WrONVzNuYZo9b0y28nn+PCWesFF5Ax7oQIKoT0UAgXsBHaFl3bx+IWXe3F88MrpGmJuezT/oGDmqYX/K5umMKDw4jPuvxbQ4tfF6+CKLmED/msRNcEEXdGPDJ1JD23R92SWLgtZumxk6rOQokkp8/3oKHRES7k/Wtm1hL+dH3ykPpDyJRDyhOBx9dfdpbhBCij8LAq3obDRqni/mRgDg8lc1mTRwFW0N83LYXASOUHIE9ZbzKU1+USzpMzMTPTo0QMXLlyAi0v1kqamWpEaTAakalORps1Ajj7H/MjWZSNLn424vDjomcFinvb27TDMbQh6OgZBwKOrLghp7ppq/VgbepMeSZpkJBYkIrEgCSmaVKRoU5GqSa3wkgEOHFpIvdHGvg3aylujjbwN3MSuNj+TZDAZcFN1CxezLuFSdiRy9DkW73tKPNBBEQB/O3/4yXzhLfVq0MSlvmmNWtxR38VN1W3cUt1CTF5spT8ihJwQIp4QfE5g7pNR3DPjvxP4hQlhyb/F7fYmxsq01gPlJ9jW8GGn99BCVvbMSX2xeaJ54sQJrFy5EhcvXkRSUhJ+/fVX88XsxSIiIsyng7p27YrPPvsMvXv3rvY6srOzERwcjLt372LlypUIDw+v9rzNtSLVmfSIVkfjpuoWbqhuIkodbT7oFQIFBrsOxCCXAfCQUo9WQpqr5lo/1lSeIR/JmmQkFCTgfn4CHhQ8QEJBAnL0qjJllUIF2tm3Qwf79ghQBMBL4lnviafGqEFsXhxi8mIRrY7GddUNi0sHJDwJOioD0VnZEZ2UnZrd2S2NUYO4/HjEqGNwLy8WMXmxSNWmVj2jjRR2SC5sZeUVdUIufF6YAL8T+CZayLwbLh5bJ5r79u3D6dOnERQUhAkTJpRJNHfs2IHp06dj48aN6NOnD9atW4edO3fi9u3bcHNzA1B4/16DwVBm2QcOHICXl5f5dUpKCiZMmIBffvmlzMDGFaGKtFCmLhPHUk/geNoJZJf4ddtG3hoDXfqjt1Nv2AlkNoyQENLQqH6sm2xdNqLU0birjkKUOhqxeXEwlDqTZC+wR4B9O7SWt0YreUv4y/zqdLpda9Qirujaytj8WMTmxSGhILFM65lSqEB3h+4IcuyODoqAJtViaQ0mVvIUfOE1v8Wn21mJFkvOPMrMfyfxedx/LZ7FHZ6KR1DgOJ45IfyvHEos47/l8YpGuSmeVjKpbExsnmiWxHFcmUSzT58+6NWrFz7//HMAhb02fXx8MGfOHPP9e2vi5ZdfxrBhw/DEE0+U+75Wq4VWqzW/VqlU8PHxoYq0iMFkwOXsSJxMO4V/c66ZKychJ0Af5z4Y7/1os/u1S0hzRYmmdelMesTmxeKm6hZu5d5GlDq6TKdODhy8pd7wlnpBIbSHvcAecoEcMoGssLOTSW9OfgqMBcgz5EFtzIPaoIZKr0KKJrXcU7JOIke0tGuJlnb+CLBvj9byVvV67SFpPhr1hXbF9+994403zNNqev/elJQUyGQy2NvbIycnBydOnMBLL71UYfkVK1bgvffeq3PsTZWAJ0Avp57o5dQT2bpsnMn4G6fTz+BBQQJOpZ/G2Yy/McxtCMZ5jYFSqLR1uIQQ8tAQ8YRoZ98W7ezb4jGMg96kR0xeLG7n3kFMXizuqWOQpc/Cg4IHeFDwoNbrcRAqzddX+tv5o5WdPxxEDtbbEEJKaNSJZmX3771161a1lhEXF4fnn3++sMcVY5gzZw46d+5cYfk33ngD8+fPN78ubtEkZTmIHPCI5yiM9ghFtDoaPyfsxg3VTRxMOYwTaacw0iMEj3iMgoxOqRNCSI0JSySexbJ0WUXXCKYhV5+LXEMucg1qFBgKIOAJIOaJIOSJIOIJIeVLIRfIIRfYFQ5Az7eDt9SLkkrSoBp1omkNvXv3RmRkZLXLi8ViiMWNe7iJxobjOLSxb4PXAxbies4N7HzwC2LyYvBH4h6cTj+Dee1eha+MknVCCKkrR1HhoOKEPCwa9QUYtrx/b0REBAIDA9GrV696XU9T01EZiCWBb2FOm3C4i92QqcvChzc+wrWc67YOjRBCCCENrFEnmiXv31us+P69/fr1q9d1h4eH48aNGzh//ny9rqcp4jgOPZ16YEnHtxFg3x4akwZr7nyKE2mnbB0aIYQQQhqQzRNNtVqNyMhI8+nt4nv4xsfHAyi8f+/mzZvx7bff4ubNm3jppZca1f17ScXsBHZY2H4e+jn3gZEZ8XXMFvz64Dc0ooEOCCGEEFKPbH6N5oULFyzu4VvcEaf4Hr6TJ09GWloa3n33XfP9e/fv31/tcTCJbQl5Qjzf6jm4iFzwR9Ie7E78HZn6LDzjH9Yox/sihBBCiPU0qnE0G5OIiAhERETAaDTizp07NE6cFRxNPY7vYn+ACSY823ImBrsOtHVIhJA6oHE0CSFVoUSzClSRWteexL346cHPkPKl+LDT+3AWO9k6JEJILVH9SAipis2v0STNy2jPUWht1woFxgJ8HbOFrtckhBBCmjBKNEmD4nE8zGr1LIScENdVN3A07bitQyKEEEJIPaFEswI0jmb98ZR64EmfiQCAH+N/QqomzcYREUIIIaQ+0DWaVaBrkOqHiZnw8a1VuJV7G+3t22FxwCLwOPrdQ8jDhOpHQkhV6Jud2ASP4+HZljMh5olxO/cODqYcrnomQgghhDxUKNEkNuMmccUU3ycBAD8/+BXZumzbBkQIIYQQq6JEswJ0jWbDGOIajNZ2raA1abE74Xdbh0MIIYQQK6JEswJ0r/OGwXEcJvtOAgAcTzuJxIJEG0dECCGEEGuhRJPYXHv7dujh0A0mmLDz/s+2DocQQgghVkKJJmkUJvk8AR54uJQdidu5d2wdDiGEEEKsgBJN0ih4ST0R7DoIQOHYmjTqFiGEEPLwo0SzAtQZqOGN934MYp4Y9/JicD7rgq3DIYQQQkgdUaJZAeoM1PAcREqM9gwFAOy8/wsMJoONIyKEEEJIXVCiSRqV0R6hUAoVSNWm0n3QCSGEkIccJZqkUZHwJXjMaxwAYG/SPmrVJIQQQh5ilGiSRmeQ6yA4CJXI1GXhdMZZW4dDCCGEkFqiRJM0OiKeEKM8Cq/V3JO4F0ZmtHFEhBBCCKkNSjRJozTULRh2fDukaFPxTyb1QCeEEEIeRpRoVoCGN7ItCV+CUI8RAIA/E/fAxEw2jogQQgghNUWJZgVoeCPbC3EfBglPggcFCYjM/tfW4RBCCCGkhijRJI2WncAOw92HAQD+SPyT7hZECCGEPGQo0SSN2iiPERDxRLiXF4Mbqpu2DocQQgghNUCJJmnUFEKF+R7ofybtsXE0hBBCCKkJSjRJozfaIxQcONxQ3UKaNs3W4RBCCCGkmijRJI2es9gZgYoOAIBT6WdsHA0hhBBCqosSTfJQGOgyAABwOv0MDXVECCGEPCQo0awAjaPZuAQ5doeEJ0GaNh13cu/aOhxCCCGEVAMlmhWgcTQbFzFfjN7OhUn/qfTTNo6GEEIIIdVBiSZ5aAx06Q8AOJ95AVqj1sbREEIIIaQqlGiSh0Y7eVu4il2hMWlxIeuircMhhBBCSBUo0SQPDY7jzK2a1PucEEIIafwo0XwIGEwmqHU6pOfnIyFXBaOp+fa6HlCUaN5U3UKGNsPG0RBCCCGkMgJbB0AKpajVWHLsMGKys1Bg0ENjMEBjMEBrMEBfKrHs5eWNHU88ZaNIbctV7IIA+/a4lXsbpzPO4lGvsbYOiRBCCCEVoBbNRsBoMuHV/Xtw4F4U7mZm4IFKhfT8fKh1ujJJJgBcS02xQZSNh/n0edoZMMZsHA0hhBBCKkItmo3A5+f/xj+JD2AnFGJd6Bg4SaWQCIWQCASQ8AWQCASQCgVIUedh6Hdf2zpcm+vl1BPfx21HijYF0epotLFvY+uQCCGEEFIOSjRt7NyD+/jsn78BAB8MHYHhrVpXWJbHcQ0VVqMm4UvQw7E7zmb8jX8yL1CiSQghhDRSdOrchrIKCjDvr70wMYaJHTpifEAHW4f00OjlFAQAOJ91kU6fE0IIIY0UJZo2whjD64f+QnKeGq0cHbE0eJitQ3qodFZ2gpgnRqYuE/fyYmwdDiGEEELKQYlmBer7Xuff/XsZh2KiIeLxsX7UWNiJRPWynqZKxBOhq0MXAKDB2wkhhJBGihLNCtTnvc5vpKVixckTAIA3Bg1GoKub1dfRHJhPn2fS6XNCCCGkMaJEs4Hl6XR4Zf+f0JmMCGnZGtO7dLd1SA+tLsrOEHJCpGnTEJ9/39bhEEIIIaQUSjQb2NLjR3AvKwsednJ8HBIKjnqS15qEL0EXh84AgPNZF2wcDSGEEEJKo0SzAe2+dRM/37wOHsdh3agxcJRKbR3SQ6+nY+Hp8wt0+pwQQghpdCjRbCCx2Vl45+hBAMDsXn3R27uFjSNqGro5dIGAEyBJk4yEgkRbh0MIIYSQEijRbAA6oxGv7t+DPL0evb1aYHbvvrYOqcmQCWTopOwIgE6fE0IIIY0NJZoNYNWZk7iamgIHiQRrQx+BgEe73Zp6OvYAAFzIvGTjSAghhBBSEmU89exYbAy+ulw4zuMnIaHwtLe3cURNT3fHbuBzfDwoeIDkgmRbh0MIIYSQIpRo1qMUtRoLD+wDAIR17Y6QVra9J3d6fj5e2vM7/n7QtIYCkgvk6GAfAKDwlpSEEEIIaRwo0awnRpMJ8w/sQ6amAB1cXLF4wGBbh4SD96LwV/RdvHv0UJProV1y8HZCCCGENA6UaNaTjRfP4+yDeMiEQnw2eizEAoGtQ4LBZAIARGVl4t/UFBtHY109HHuAA4e4/Dio9Lm2DocQQgghoESzXlxMSsC6v08DAJYGD0MrRycbR1TWrzev2zoEq1II7eEmLryVZ1x+nI2jIYQQQghAiabV5Wg0eHX/HhgZw6PtAzCxQ0dbh1SuP+7cgs5otHUYVuVn5wsAiM+Lt3EkhBBCCAGaUaKZn58PPz8/LFy4sN7WwRjDG4cPIDE3F35KB3wwJKTR3mIyS6PBsdh7tg7DqvxkhYlmXD4lmoQQQkhj0GwSzQ8//BB9+9bvQOn/d+1f7I++CyGPh09HjYG9WFyv66urX27eaPB1HoyOwuF70fWy7OJEM5ZaNAkhhJBGoVkkmnfv3sWtW7cwevToelvHnYx0fHDiGABgUf9B6OLuUW/rqqvWRdeMHo29h8yC/AZbr0qrxQt7fsOsP3dDY9BbffnFp85TtCkoMBZYffmEEEIIqRmbJ5onTpzAuHHj4OXlBY7jsHv37jJlIiIi4O/vD4lEgj59+uCff/6p0ToWLlyIFStWWCni8nnZKzC6TVsE+7XEM92D6nVdddXO2QWdXN2gN5nw553bDbbefL3O/FxnNFl9+QqhAo5CRwDA/fwHVl8+IYQQQmrG5olmXl4eunbtioiIiHLf37FjB+bPn48lS5bg0qVL6Nq1K0JDQ5Gammou061bN3Tq1KnMIzExEb/99hvatWuHdu3a1et2yEUirAl9BBvHPApeI70us6QJRZ2Ufm5ivc+LWzXj6PQ5IYQQYnM2H9xx9OjRlZ7SXrNmDWbNmoWZM2cCADZu3Ig9e/bgm2++weLFiwEAkZGRFc7/999/48cff8TOnTuhVquh1+uhUCjw7rvvllteq9VCq9WaX6tUqhptT2MYL7M6xrULwPJTx3E1NQUJuSp42ytsHZJV+Ml8EZl9hYY4IoQQQhoBm7doVkan0+HixYsICQkxT+PxeAgJCcHZs2ertYwVK1bg/v37iI2NxapVqzBr1qwKk8zi8kql0vzw8fGp83Y0Rs4yGZRFnZXydLoqSj88fKnnOSGEENJoNOpEMz09HUajEe7u7hbT3d3dkZycXC/rfOONN5CTk2N+3L/ftO4L3tQVnzpPKEiEwWSwcTSEEEJI8/ZwnOe1khkzZlRZRiwWQ9zIhyWytRNxsbieloIXg3o3unFCXUTOsOPLkGfMR0JBAvzs/GwdEiGEENJsNeoWTRcXF/D5fKSkWN6XOyUlBR4e9Tt8UEREBAIDA9GrV696Xc/DaMZvP2PlmVM4Fhdj61DK4DiOTp8TQgghjUSjTjRFIhGCgoJw+PBh8zSTyYTDhw+jX79+9bru8PBw3LhxA+fPn6/X9TzMknJzbR1CuajnOSGEENI42PzUuVqtRlRUlPl1TEwMIiMj4eTkBF9fX8yfPx9hYWHo2bMnevfujXXr1iEvL8/cC52Q0qhFkxBCCGkcbJ5oXrhwAUOHDjW/nj9/PgAgLCwMW7duxeTJk5GWloZ3330XycnJ6NatG/bv31+mg5C1RUREICIiAkajsV7XQ6yv+FaU9/Pvw8RM4HGNuuGeEEIIabJsnmgOGTIEjLFKy8yePRuzZ89uoIgKhYeHIzw8HCqVCkqlskHXTerGU+oBISeExqRFqiYVHtLGeztQQgghpCmjph7S5PA5PnxkLQAAsTRwOyGEEGIzlGiSJsmPrtMkhBBCbI4SzQrQ8EYPt+LxM6nnOSGEEGI7lGhWgIY3ergVt2jG58dXeQ0wIYQQQuoHJZqkSWohawEeeMg1qJGlz7J1OIQQQkizVO1e546OjtW+3WBmZmatAyLEGkQ8IbyknnhQkIC4vHg4iZxsHRIhhBDS7FQ70Vy3bl09htH40DiaDz9fmW9hopkfj+6O3WwdDiGEENLsVDvRDAsLq884Gp3GOI5mNRuUSRE/O1+cyThLPc8JIYQQG6nzgO0ajQY6nc5imkKhqOtiCakzX5kPACAhP9HGkRBCCCHNU606A+Xl5WH27Nlwc3ODnZ0dHB0dLR6ENAYeksI7AqVp02AwGWwcDSGEENL81CrRfO2113DkyBFs2LABYrEYX331Fd577z14eXnhu+++s3aMhNSKo9ABEp4YJpiQqk2zdTiEEEJIs1OrRPOPP/7AF198gYkTJ0IgEGDQoEF4++23sXz5cmzbts3aMRJSKxzHwb2oVTNZk2zjaAghhJDmp1aJZmZmJlq1agWg8HrM4uGMBg4ciBMnTlgvOhuiOwM1DR4SdwBAEiWahBBCSIOrVaLZqlUrxMTEAAACAgLw008/AShs6XRwcLBacLZEdwZqGjyLWzQLKNEkhBBCGlqtEs2ZM2fiypUrAIDFixcjIiICEokE8+bNw6JFi6waICF14SEtTDSpRZMQQghpeLUa3mjevHnm5yEhIbh16xYuXryINm3aoEuXLlYLjpC68qRrNAkhhBCbqVWief/+ffj4+Jhf+/n5wc/Pz2pBEWItxddo5hrUUBvUkAvkNo6IEEIIaT5qderc398fwcHB2Lx5M7KysqwdEyFWI+FL4CgsHNs1WZNi42gIIYSQ5qVWieaFCxfQu3dvvP/++/D09MT48eOxa9cuaLVaa8dnM9TrvOnwlBa2alKHIEIIIaRh1SrR7N69O1auXIn4+Hjs27cPrq6ueP755+Hu7o5nnnnG2jHaRGPudc5sHcBDpngsTeoQRAghhDSsWiWaxTiOw9ChQ7F582YcOnQILVu2xLfffmut2AixCuoQRAghhNhGnRLNBw8e4JNPPkG3bt3Qu3dvyOVyREREWCs2QqzCg1o0CSGEEJuoVa/zTZs2Yfv27Th16hQ6dOiAadOm4bfffqOe56RRKm7RTNWkwsRM4HF1+n1FCCGEkGqqVaK5bNkyTJkyBevXr0fXrl2tHRMhVuUidoaQE0DPDEjXZsBN4mrrkAghhJBmoVZNO/Hx8Rg3bhxWrlyJ/v37IyEhAQDw/fff49SpU1YNkJC64nE8uEncANB1moQQQkhDqlWi+csvvyA0NBRSqRSXLl0yD2uUk5OD5cuXWzVAQqyBOgQRQgghDa/Wp843btyI6dOn48cffzRPHzBgAJYtW2a14GwpIiICERERMBqNtg6FWAENcUSIbf1y8zr+79q/5tccxxX+BcCh6Dln+RwW0wvf43EcOK7wL5+zfM3jCs9gCHg88HkcBEXPC18X/hXx+RDxBRDxC59L+AJIhELIBEJIhULIRSIoxGLYi8VQiMQQ8fnmWAkhNVerRPP27dsYPHhwmelKpRLZ2dl1jalRCA8PR3h4OFQqFZRKpa3DIXXkWXQrSmrRJMQ2ktRqXExKtHUYNcbnOEiFQkgFQsiEQkgEAshFIrjbyeFpbw9PuT085PbwVSrRytEJMqHQ1iET0qjUKtH08PBAVFQU/P39LaafOnUKrVq1skZchFhV8anzpAK6DSUhtjC6TVu0dXICA8CK7jrBwMzPUeI5AzPfmIKxUs8ZYGQmmBgDYwxGxmAqejAwGEwMJmaCwWSC0cQK/zIT9CYTDEYTdEYDdEYjdEYj9CYjNAYD8vUGaAx65Ov1yNProNJqkavVggEwMga1Tge1Tlet7fSU26O1oxP8HRzgq3SAr1IJH6UDfBVK2IlEVtmXhDxMapVozpo1C6+++iq++eYbcByHxMREnD17FgsXLsQ777xj7RgJqTMPaWGimaXPgsaogYQvsXFEhDQ+jz/+OI4dO4bhw4dj165dVl12K0cntHJ0suoy65OJMah1WuTr9SgwGKDR65Fv0KNAb0CuTotktRpJuSokq9VIzFUhLicbGQUFSFLnIkmdi1P348os00/pgI6ubujo5oaOru7o6OoGZ5nMBltHSMOpVaK5ePFimEwmDB8+HPn5+Rg8eDDEYjEWLlyIOXPmWDtGQupMLpDDXiBHrkGNZE0K/O1ozFdCSnv11VfxzDPP0B3eAPA4DgqxBApx9X+UZhUU4F52JqIyMxGfk437OTmIy8nGfVUOsjUaxOVkIy4nG3uj7pjnaevkjAE+vhjg64c+3j6QU6snaWJqlWhyHIe33noLixYtQlRUFNRqNQIDAyGXy60dHyFW4yHxQK46CsmaZEo0CSnHkCFDcOzYMVuH8dBylEoRJPVGkKd3mfcyC/JxPS0VN9JScT01FdfTUhGTnYW7mRm4m5mBrVcuQ8Djoau7B0Jbt8WYtu3haW9vg60gxLrqdIsUkUiEwMBA8+0nCWnMPKnnOalnH330ETiOw9y5c6263BMnTmDcuHHw8vICx3HYvXt3ueUiIiLg7+8PiUSCPn364J9//rFqHKT2nKQyDPL1xwtBvbF+9Fgcnv4MLj3/MiIeGYcpnbrAT+kAg8mEi0mJWH7qOAZs+RKTd/2IH/6NREZ+vq3DJ6TWatWiSUjdsaqLWJlHcc9z6hBE6sH58+exadMmdOnSpdJyp0+fRu/evSEs1Tv5xo0bcHZ2hru7e5l58vLy0LVrVzzzzDOYMGFCucvdsWMH5s+fj40bN6JPnz5Yt24dQkNDcfv2bbi5Fd6woFu3bjAYDGXmPXDgALy8vKq7qcRKHCRSjG7TDqPbtAMA3M/JwdHYe9hz9zbOJyaYH+8dP4LhLVtjaueuGOjrBx4Nt0QeIpRokmajuEMQtWgSa1Or1Zg2bRo2b95c6VjCJpMJ4eHhaNu2LX788Ufw+XwAhUPGDRs2DPPnz8drr71WZr7Ro0dj9OjRlcawZs0azJo1CzNnzgQAbNy4EXv27ME333yDxYsXAwAiIyNruYWkIfgolZjetTumd+2OxFwV9t69gz/u3MLV1BQcuBeFA/ei4KNQYnLHzpjUsRNcZXa2DpmQKtXp1DlpGhq+bdE2ik+dp2iSwVhz2WrSEMLDwzFmzBiEhIRUWo7H42Hv3r24fPkypk+fDpPJhOjoaAwbNgzjx48vN8msDp1Oh4sXL1qsn8fjISQkBGfPnq3VMisTERGBwMBA9OrVy+rLJoW87BV4rkdP/PbU/7B/WhjCunaHvUiM+6ocrDp7CgO++RKz9/6BM/fjqT4jjRq1aDZrzev0i5vYDTzwoDFpkaXPhpPI0dYhkSbgxx9/xKVLl3D+/Plqlffy8sKRI0cwaNAgTJ06FWfPnkVISAg2bNhQ6xjS09NhNBrLnHZ3d3fHrVu3qr2ckJAQXLlyBXl5eWjRogV27tyJfv36lSlHN7RoWO2cXbAkeBhe6z8Ie+7exv9d+xeXk5OwN+oO9kbdQUsHR0zp1AWPBwTScEmk0aFEkzQbAp4ArmIXpGhTkaxJpkST1Nn9+/fx6quv4uDBg5BIqj8Mjq+vL77//nsEBwejVatW+PrrrxvFbQ4PHTpk6xBIJaRCIZ4I7IQnAjvhZloqtl/7F7/duomY7CwsP3Ucn5w5iWH+rfBEYEcE+7WEsOjSDEJsiU6dV4BODVXtYTxZU9whKFWTauNISFNw8eJFpKamokePHhAIBBAIBDh+/DjWr18PgUAAo9FY7nwpKSl4/vnnMW7cOOTn52PevHl1isPFxQV8Ph8pKZYd3VJSUuDh4VGnZZPGqYOrGz4YGoKzz76A5cNGoKu7BwwmEw7ci8Lzf/6G/t98iSXHDuNYbAw0Bn2Dx6c1GJBVUIC0/Dyk5qmRlJuLBJUKibkqpOapkZGfjxyNBmqdDkaTqcHjIw2HWjQrQKeGmiZnsTMAIF2XYeNISFMwfPhwXL161WLazJkzERAQgNdff93c2aek9PR0DB8+HB06dMDOnTtx584dDBkyBGKxGKtWrapVHCKRCEFBQTh8+DDGjx8PoLDj0eHDhzF79uxaLZM8HOxEIjzVqQue6tQFdzLSsevGNfx66yYyCvLx/b+R+P7fSEgEAvRr4Ysh/i0R5OmFNk7OENWytdPEGB6ochCbnY3EXBUSclVIUKmQpM5FVkEBcrRa5Gg10JQzukFlxHwBZEIBZEIR7EQiKMViKMRiKMQSKMViOEllcJbJ4CyVwlkmg4vUDu5yO0gEdG/5xo4STdKsuIgKE80MLSWapO7s7e3RqVMni2l2dnZwdnYuMx0oTP5Gjx4NPz8/7NixAwKBAIGBgTh48CCGDRsGb2/vcls31Wo1oqKizK9jYmIQGRkJJycn+Pr6AgDmz5+PsLAw9OzZE71798a6deuQl5dn7oVOmr52zi54c9AQLOo/CCfj43A4JhrHYu8hSa3G0dh7OBp7DwAg4vHRzsUFHV3d0N7ZBS4yGRwkUjhKJHCQSmE0mZCl0SC7oABZGg3S8tW4k5GBOxnpiMrMQEENk0gex4HPcYWXhzDAUHSv+pK0RgO0RgOyNJoaLVsplsBdLoe7nV3RXzlcZSWe29nBVWZX68Sa1B0lmqRZoRZNYks8Hg/Lly/HoEGDICpxq8GuXbvi0KFDcHV1LXe+CxcuYOjQoebX8+fPBwCEhYVh69atAIDJkycjLS0N7777LpKTk9GtWzfs37+/3HE5SdMm5PMxrGUrDGvZCowx3M5Ix7HYGJyMj8P1tBSotFpcS03BtdTajSks4vPhr3SAl0IBb/vCh5e9PZxlMnMLpFIsgZ1I9F+CWQpjDEbGoDcaUWDQI1//30Ot00Gl1UCl1UKl1SJbo0GWpgAZ+fnIKMhHen4+0vLzoDEYkKPVIEerwZ2M9EpjdpJI4SaXw1NuDx+FAj5KB/Pf1o5OlIjWI0o0SbNCLZqkvlV1C8cRI0aUO7179+4VzjNkyJBqDWEze/ZsOlVOLHAchwAXVwS4uOLFnr3BGMMDlQrX0lJwPTUV97IykaUpbLnMKihAlqYAfI4HJ6kEDhIpHCRSOMukaO3ohHbOLmjv7AJfpQMEvLp18eA4DgKOg4DHg1QohJO0ZvMzxpCr0yJZrUZKnhopajVS8wqvB00x/1UjLS8PepMJmZoCZGoKcCs9rcyyhDwe2ju7oKObOzq5uaO7hyc6uLg2ig56TQElmqRZKW7RzNJlw8iM4HP0K5YQ0nxwHAcfpRI+SqX5jkQlMcYeigSL4zgoxBIoxBK0c3apsJyJMWRrCgqTT7Uaiepc3M/JwX1VNu6rVIjNzips4U1LxbW0VOy4XnjNtadcjqEtWyOkZWv0a+EDsYDSpdqiPUeaFaVQCQEngIEZkKXLgou44gqKEEKam4chyawJHsfBSSqDk1SGDi5lL01hjCEhV4WrqYUtvFdTk3EhMQFJajW2X72C7VevQCYUoq+3D/r7+KK/jy/aO7s0uf1UnyjRJM0Kj+PBSeSEVG0q0rUZlGgSQkgzxnEcWiiUaKH4r4VXazDg7IP7OBQTjSP3opGcp8aR2Hs4UtSZykUmQ5Cnd+E1qaLC3vFKiQQSvgACPg8CHg8CjgcBnwc+xwPHFSa8PHCFf3lcUQcpHriijlJ8jgOPx4OA48DnFS2Dx4OAx4eIX/hXWDTtYUtyKdF8iHDN7E4+9cVFXJhoZlCHIEIIIaWIBQIM8W+JIf4twYYMx420VJy+H48z9+NxPvEB0vPz8Vf0XZvFJ+LxIeDzzMkqjytsRBGUSFCFPB4EfD7EfD7EfAHEgsK/EoEArw0YhBaKhhu2kRJN0uw4FXUISqcOQYQQQirBcRw6urmjo5s7ng/qBa3BgMjkJNxIT4NKq0GOVotcrRY5Gg20RgMMJhP0JhOMRX8ZYzAVPYp72ptKPIzMBJOpeLoJBhMzz2swGWEspxOgzmSEzlT+zSCqY3bvvnXZJTVGiSZpdsw9z6lFkxBCSA2IBQL0aeGDPi18GmR9pqIhoAwmEwwmE3Qmo/m1sTh5NRUmrMaiMnrzXyN0BmPhGKXFf40GuNvJGyT2YpRokmbHPJYmtWgSQghpxHgcB7FAALGtA6kDutc5aXZcxNSiSQghhDQESjRJs+NsHrQ9s1qDYBNCCCGkdprFqXN/f38oFArweDw4Ojri6NGjtg6J2JCzyAkcOOiZHiqDCkphw/W+I4QQQpqTZpFoAsCZM2cglzfsBbCNDbXeFRLwBFAKlcjWZyNdm0GJJiGEEFJP6NQ5aZaKr9PM1GXaOBJCCCGk6bJ5onnixAmMGzcOXl5e4DgOu3fvLlMmIiIC/v7+kEgk6NOnD/75558arYPjOAQHB6NXr17Ytm2blSInDzNnGkuTEEIIqXc2P3Wel5eHrl274plnnsGECRPKvL9jxw7Mnz8fGzduRJ8+fbBu3TqEhobi9u3bcHNzAwB069YNBoOhzLwHDhyAl5cXTp06BW9vbyQlJSEkJASdO3dGly5d6n3bSONFPc8JIYSQ+mfzRHP06NEYPXp0he+vWbMGs2bNwsyZMwEAGzduxJ49e/DNN99g8eLFAIDIyMhK1+Ht7Q0A8PT0xCOPPIJLly5VmGhqtVpotVrza5VKVZPNeSg0jhtZ2jYKatEkhBBC6p/NT51XRqfT4eLFiwgJCTFP4/F4CAkJwdmzZ6u1jLy8POTm5gIA1Go1jhw5go4dO1ZYfsWKFVAqleaHj0/DjP5PGha1aBJCCCH1r1Enmunp6TAajXB3d7eY7u7ujuTk5GotIyUlBQMHDkTXrl3Rt29fTJ8+Hb169aqw/BtvvIGcnBzz4/79+3XaBtI4UYsmIYQQUv9sfuq8vrVq1QpXrlypdnmxWAyx+GG+2ROpjuIWzXxjPgqMBZDypTaOiBBCCGl6GnWLpouLC/h8PlJSUiymp6SkwMPDo17XHRERgcDAwEpbP8nDS8KXwI5vB4BaNQkhhJD60qgTTZFIhKCgIBw+fNg8zWQy4fDhw+jXr1+9rjs8PBw3btzA+fPn63U9xHbM12lSokkIIYTUC5ufOler1YiKijK/jomJQWRkJJycnODr64v58+cjLCwMPXv2RO/evbFu3Trk5eWZe6ETUlvOYmfE5ccjnToEEUIIIfXC5onmhQsXMHToUPPr+fPnAwDCwsKwdetWTJ48GWlpaXj33XeRnJyMbt26Yf/+/WU6CBFSUy4iatEkhBBC6pPNE80hQ4ZUeQ/u2bNnY/bs2Q0UUaGIiAhERETAaDQ26HpJw3EuOnVOLZqEEEJI/WjU12jaEl2j2fRRiyYhhBBSvyjRJM0WtWgSQggh9YsSzQrQ8EZNX3GLZo4+BzqT3sbREEIIIU0PJZoVoFPnTZ9cIIeIJwIAZOoybRwNIYQQ0vRQokmaLY7j6DpNQgghpB5RovkQqqKTPqmB4us0M+g6TUIIIcTqKNEkzVpxiybdhpIQQgixPko0K0CdgZoHatEkhBBC6g8lmhWgzkDNA7VoEkIIIfWHEk3SrJnH0tSm2zgSQgghpOmhRJM0a14STwCFg7YXGAtsHA0hhBDStFCiSZo1uVAOB6EDACChING2wRBCCCFNDCWaFaDOQPWt8YzR5CNrAQC4n//AxpEQQgghTQslmhVoTp2BWDMfmNNHSokmIYQQUh8o0WzGOM7WETQOLYpaNB8UUKJJCCGEWBMlmqTWmkpLaMlT501lmwghhJDGgBJN0ux5SjzA5/jIN+YjU5dl63AIIYSQJoMSTdLsCXlCeEo8ANDpc0IIIcSaKNEkBEAL6hBECCGEWB0lmhVolMMbUeedekNDHBFCCCHWR4lmBZrT8Ebkv0STTp0TQggh1kOJJiH479R5kiYZepPextEQQgghTQMlmoQAcBI5QsaXwciMSNIk2zocQgghpEmgRJMQABzH0XWahBBCiJVRoklIkeLT5w8o0SSEEEKsghJNQoqYWzSpQxAhhBBiFZRoElKETp0TQggh1kWJZgUa5TiapF55S70AANn6bOTqc20cDSGEEPLwo0SzAjSOZvMj5UvhKnYFADwoSLBxNIQQQsjDjxJNQkrwkXoDoNPnhBBCiDVQoklICT4yHwDUIYgQQgixBko0CSmBOgQRQggh1kOJJiElFI+lmVCQABMz2TgaQggh5OFGiSYhJbhL3CDkhNCZdEjVptk6HEIIIeShRokmISXwOB5ayIo7BN23cTSEEELIw40STUJKaVHU8/xQyhGo9CobR0MIIYT8Z+vWrXBwcKhW2aVLl6Jbt26VlpkxYwbGjx9f57gqQokmIaUMdh0EISfArdzbeOvqElzJvmrrkAghhJAaW7hwIQ4fPmzTGCjRJKSUdvZtsaTjO2gh9YbKoMKaO+vwQ9x26Ex6W4dWIyZmQqYuCzn6HDDGbB0OIYSQBiaXy+Hs7GzTGAQ2XTshjZSPrAWWdHwbP93fhYMph3Ew5TDOpP8NmUAGASeAkCcAnxOAB85iPj7Hh0wgg4wvg51ABju+HeQCOeyFctgL7GEvtIdcIIeEJ4aYLwaf49cqPoPJgAJjAXL0KmTps5Cly0a2PhuZ2kykadORpk1Hhi4DBmYAAMj4MnhKPOAp9YSP1BtD3YZAzBfXeT8RQgixZDKZ8PHHH+PLL79EcnIy2rVrh3feeQcTJkyAr68v3nrrLbz00kvm8pcvX0ZQUBBiYmLg5+eHNWvWYMuWLbh37x6cnJwwbtw4fPLJJ5DL5TWOZenSpdi9ezciIyMBAEajEYsWLcI333wDPp+PZ5991qIhIi0tDZ07d8Yrr7yCN998EwBw5swZDBkyBPv27cPw4cNrHAMlmhWIiIhAREQEjEajrUMhNiLiifA/v6norOyEr+5tgcqgQp4xz6rrEHACiHgiSPhiSHgSiPkSSPliiHhiGJkRepMeOpMOOpMOWpMOGmMBNEYN9EUJZFV44IGBId+Yj+i8e4jOuwcAuKG6hfntX7XqthBCiLUwxmAyFT9MMJpY4TTGwExFfxkDY4CJMaDor6m4nOm/9wGAofAJn8eDWCSASMiHSCSAUMAHn2/dk7srVqzADz/8gI0bN6Jt27Y4ceIE/ve//+Gvv/7ClClTsH37dotEc9u2bRgwYAD8/PwAADweD+vXr0fLli1x7949vPzyy3jttdfwxRdf1Dm21atXY+vWrfjmm2/QoUMHrF69Gr/++iuGDRsGAHB1dcU333yD8ePHY+TIkWjfvj2efvppzJ49u1ZJJkCJZoXCw8MRHh4OlUoFpVJp63CIDXV16IJVXT9CoiYJRpMBBmaEgRlgMBnMlVcxAzMgz5CPfGM+8gx5yDPmQ21QI1efi1xDLnL1aqgNaphgMpc3GA3IN+bXKjY7vh0cRQ5wFDnCQaiEo8gRrmIXuIpd4SJ2gZPIEUZmQoomBcmaZDwoSMAfiXtwJedfXMm+iq4Oneu8fxqT+/kPzIPuE0Lql9FoQnKaCvGJmXiQlI1sVT5Uag1UuRqo1AVQ52uh0xqg1Ruh0xmg0xvMCSNKJIDm5JChMIksSiwbAscBXu4OaOnjjJY+Lmjp64KAVu7w8XIEx3FVL6AUrVaL5cuX49ChQ+jXrx8AoFWrVjh16hQ2bdqE1157DatXr0Z8fDx8fX1hMpnw448/4u233zYvY+7cuebn/v7+WLZsGV588UWrJJrr1q3DG2+8gQkTJgAANm7ciL/++suizCOPPIJZs2Zh2rRp6NmzJ+zs7LBixYpar5MSTUKqQcwXo6Wdv1WWxRiDgRmgNWmhNeqK/mqhMWmgNWpRUPRXwPEh5Ikg4gkh4okg4okg5Usg5Ush4Usg4Uuqdeqdz/HhI2sBH1kL9EJPaIwa7E8+gP+L/xEdFR0g4DWNauBU2ml8FbMFE1qMx6NeY20dDiFNjlarx/l/43Di3F3cuJuMhORs6A22PevHcQDHceBQ2BLI8TjwiqdxHIpzRa7oMiejyQSdzmBOZBkDEpKzkZCcjVPno83L9XBVoE/3lujTzR9BnX1hJ6vepUZRUVHIz8/HiBEjLKbrdDp0794d3bp1Q4cOHbB9+3YsXrwYx48fR2pqKiZNmmQue+jQIaxYsQK3bt2CSqWCwWCARqNBfn4+ZDJZrfdVTk4OkpKS0KdPH/M0gUCAnj17lrmOf9WqVejUqRN27tyJixcvQiyu/aVWTeMbhpCHCMdxEHJCCHlCyG3wL/Axr3E4nX4WSZpkHEo9glEeIxs+CCs7lnocW2O/BwNDpjYTjLFatUaQ+nP49C3sO3rd/Lr0x1PR51V6epn5UGYC+HweBHye+a9AwIeAzwOPx4HHcUXJCAdwAI/jwOPxwOMAHp8HPo8HkYgPkVAAsVAAoZAPoZAPkZAPoaDwuVgkhEIugVIhhVwmBo/XdI+1/AIdTl+IxvFzd3HucgwKNJadIkVCPlp4OsLH0xHOjnZQyCVQ2EuhkEsgtxMXnqYWCYpOVwsgKD5Nzf332fF4HHi8wsSw+DPi84s+Fx4HftFfjuPA53HmJLIu+91gNEGvNyCvQIf4B5m4dz8dMfczcC8uDbfvpSA5TYXfDlzBbweugM/noZWvC1r7uaCVryva+LnC18sREonQvF3Fp9/VajUAYM+ePfD29rZYp1gsBmMMU6dOxbbt27FgwSJ8/8MPGDFyJMQSOXJyC3DvXgzGjh2LGTOexYLX3oSjgyPOnfsb814NR0JyBhyURuSqNWAMSM9S/7fwoksHDAYjjEYGg7Hwb0ZWHrQ6A27fS0FubuFwfTpd1ZdeRUdHIzExESaTCbGxsejcufZnvyjRJKSZkQlkeKLFBGyJ/Ra/JfyO/s59oRAqbB1WrR1KOYLv47YBAEa4D8c03ymUZDZCCcnZ+PtyjK3DsDo+j4PCXgqlvRQOSikcFDI4KmVQFiVc9nJJYVJqL4WLox2cHeUQCmvXCbChGAxG/HMlFn8dv4lT56OgLZGYuLnYY3DvtujTzR9+LZzg7qKw+jWODUHA50HAF0EqEcHFUY4enX3N7xVodIi8/gDnImPw9+VYPEjKwt2YVNyNSa1wefyiHzYGvQY8ngAvL/4SXq16F14qUHQ5gKnomlFNXh6uXb2GXqELcO3U/6F19wkYNf0zAEB6wr/Q6Qy4muqHa19eBgDcv3UIAPD0q1shEEmREnceeQVajH9uY5XbGX/jCjKTMvHsou8BAEKJAoeOHEdISOE1mQaDARcvXkSPHj3M8+h0Ovzvf//D5MmT0b59ezz33HO4evUq3NzcariXC1GiSUgzNNh1II6kHkNcfhx+frAbM1tOb5D1ao1a3M69A0+pJ1zFLnVeXuElADsAAKM8RuIpnycpyWyk+ge1hovjf71mS1/fXO4IXKz0y/8mlCxf8rQfYwxGowkGo8n812A0wWAwwWQygTEUdRJh5i9+k8lk7nhiMJqg0xug0xmg1RmgN5ig1xuhNxQ99EZodXrk5GqQX6CD0cSQlZOPrJx84EH19oWTgwwuTvZwdZLD1VkOdxcFXJ3kcHO2h4uzHM4OdpBJRQ16LGt1Bly+fh+nzkfh2Nk7yFYVmN9r4emIof3aIbhPW7Rv7d7k/41JJSL0C2qFfkGtAABJqTm4G5OKe/HpiIpLw724dCSmZsNgMJnnMRYdb4AAXm2DcefSbuj1Rtg7+8Oo10CVGQuBQAI3v56Q2DnB3tkPUZd+ApgJTp4d/1u33AWMGZEScwbOXh2hSo9BcszfAACBoDCZLW7J5Zdq0eXxeODzOQj4/KLEl0OaVAg+nw9XJzk4jkO7zsOxacNn6Nu7OwICArBmzRpkZ2dbLOett95CTk4O1q9fD7lcjr179+KZZ57Bn3/+Wav9SYkmIc0Qj+Phf35T8OHNj3A87QSGuQXDz86v3taXq8/FodQjOJxyBLkGNThw6KTsiKFuwejm0NXiWlODyYAMXSa0Ji0AgDETGAAjMxZ2sCrqZPWgIAFHU48BAMZ6PoInWkxo8l+AD7M2/q5o4+9q6zCsSqc3QJWrQZYqH9k5BchW5SNbVWDRKSZXrUFungbZqnykZ+ZBbzAiMzsfmdn5uHMvpcJlCwV8OCplcFDK4KiUws3ZvvDhYg83ZwW83JXwdFPW+vQxYwwPkrIReeM+zly4h/P/xkKj/a/l0lEpQ8jAAIwc3AEBrT2a9b8tT7fCfT24T1uL6cbiHyVFnZ2MxsK6ymR6Fl9t3oTvv/0GV//9BQqlEl26dMO8+S9h4MCB4PF4+PprERbMexVTp07D198sBJ/3XxK5dq0PVq5cicTbBzB48GC8uekzTJ8+HX9uCYeDgwO2bt2KuXP34/jOBVXGvnRpKnbvTsCvm18EABgMz2LhwoUICwsDj8fDM888g8cffxw5OTkAgGPHjmHdunU4evQoFIrCM13ff/89unbtig0bNlj0lq8ujtFIzpUq7nWek5Nj3um2kpCrwqAtmyHmC3AzvOZD03x35TKWHj+CR9q0w+ePjEPvrzYgPT8fe6dOR4BL9b8AWq1fDQB4f8hw/K9Lt2rPl6zORf9vvgQARL4QDoVYUqP4ifV9EbUJ5zL/gZ/MD4NcB8CObwc7gR3s+FLkGQuQpctEli4bWfos5BvyYS+0h0KogFKohEKgQL4xH0maJCQVJCNJk4RMXRacRU7mMTs9JB6IzYvDyfRT0Jl0AAB7gT1yDbnmGByEDuis7IhsfQ5SNKlI16abe+VXx3jvRzHe69Fm/UVoK42pfnwYMMaQrSpAWkYu0jLVSE0v/JuWkYvUjFykZaiRlplb5jrIikjEAvi1cEZLH2f4eTvDXi6BXCaGnUxUdO0oDwbDf62x+QU6RMWm4WZUMm5FJ0Odp7VYnquTHP17tsag3m3Qs4vff9dTElIH1KJJSDM22ecJXM6ORFx+HOLi4qyyzCRNMpI0yUB2pMV0f5kfHvEcjZ5OPZCuzcDxtBM4kXYK2fpsnEw/bVFWyAkhE0hR2F2g8D8+x4NMIINcYAdZUUIcqAhAX+c+IORhwHEcHJWF13C2a+VeYTmNVo/snHxkqQpPyWdm5SM1MxdpGblISc9FanouElOyodEacDs6BbejK24ZrYxIyEe7Vu7o270l+ge1QtuWbvSDjVgdJZqENGPOYme80iYc57MuIr/olHThOKB5kPKlcBQ6wlHkCCeRI6R8KdQGNbL1OVDpVcjR50DCF8NT4gVPqQe8JJ5wEjkhQ5dRmGwWtXLK+FKEuA9HgH1785eYu8QNT/o8gQne43Ex6zISChLgInaGm9gN7hI3OAgd6AuPNFsSsRAebkp4uFU8hrPBaEJiSjZi7mcg5n46HiRlQZ2nhTpfi7x8XVFrJYNAUNhbXiDgQyzkw9/HGe1be6BDGw+08nFp9B2TSOU6duxYYSPBpk2bMG3atAaOqCxKNAlp5jo7dEJnh05WW56n1AOdlB2rLghAwBOgj3MvAL2stn5CmgMBnwdfLyf4ejkhuNS1g6T52Lt3L/T68i+1cHevuNW8IVGiSQghhBDyECq+bWVjRlf6EkIIIYSQekGJJiGEEEIIqReUaBJCCCGEkHpBiSYhhBBCCKkXlGgSQgghhJB6QYkmIYQQQgipFzS8URWK79CpUqlsHAmQm6uCSaOBgcevVTwFajVMGg10+flQqVQwFhTApNEgNzcXKpG42ssxaTQAgHy1ukZx5KpzzfPm5KgAia5mG0BIPbC3t6fB4WupMdWPhJDa27x5M9avX4+UlBR06tQJK1euRFBQkFXqR7rXeRUePHgAHx8fW4dBCKkndJ/u2qP6kZCmzRr1I7VoVsHLywv37983Z/W9evXC+fPnAaDM88OHD8PHxwf379+v9QdTcpk1LVPe9KqmNebtqe42NeXtqWibiv+qVKo6bVN1tqeyco1te6q7TSXL2Nvb12o9pPL6EbDN8Ur//mq+PcXPbV1H0neYbbZn2LBh6NGjB1atWgUA6NmzJ9RqNZ5//nmr1I+UaFaBx+OhRYsW5td8Pt/8AVf0XKFQ1PogKLmcmpYpb3pV0xrz9lQn/qa+PRVtR+kytd2m6mxPZeUa2/ZUFmtNy5CqVVY/lnzdkMcr/fur+faUfk7fYXXfHsYYNFo9+AIxhCKJZflS00q+ruy5SCwFjy+ESCwts8ySJGJhhae7S8eq0+kQGRmJt99+2zxdIBBgxIgRuHz5slUuK6JEs4bCw8OrfG6t5de0THnTq5rWmLenovdKT2vK21P6dfHzhtyeyso1tu2p7rKsuT7yn4o+a/r3V/14alLOWttTk1iqQt9hhTRaPUZMWw+ngOkYMW29xXulp5V8XdnzCS9uQb/HlmPCi1sqjefgtlcglYiqFWt6ejqMRqPFfdHDw8MRFxeHW7duVbqe6qJrNK1IpVJBqVQ2mWu+aHsav6a2TU1te4ilpvb50vY0brbcngKNrkyC2VAqSzRLS0xMhLe3N86cOYN+/fqZp7/22ms4fvw4zp07V+d4qEXTisRiMZYsWQKxuPo9uBsz2p7Gr6ltU1PbHmKpqX2+tD2Nmy23RyIW4uC2V6y6TK1Wh9WrV2HBgoUQiytOJCViYbWX6eLiAj6fj5SUFIvpKSkp8PDwqHWsJVGLJiGEEEJIM9WnTx/07t0bn332GQDAZDLB19cXs2fPxuLFi+u8fGrRJIQQQghppubPn4+wsDD07NkTvXv3xrp165CXl4eZM2daZfmUaBJCCCGENFOTJ09GWloa3n33XSQnJ6Nbt27Yv3+/RQehuqBT54QQQgghpF7Qvc4JIYQQQki9oESTEEIIIYTUC0o0CSGEEEJIvaBEkxBCCCGE1AtKNAkhhBBCSL2gRJMQQgghhNQLSjQJIYQQQki9oESTEEIIIYTUC0o0CSGEEEJIvaBEkxBCCCGE1AtKNCsxZMgQzJ07t8HXGxsbC47jEBkZafVlHzt2DBzHITs72+rLbgr8/f1x7Nixel1HfX6+pc2YMQPjx4+v9/WUtHTpUnTr1q1B10lsg+rI5ofqyLprbHXkiRMnMG7cOHh5eYHjOOzevduqy6dEs541tkqrf//+SEpKglKptHUoFfrwww/Rv39/yGQyODg4lFsmPj4eY8aMgUwmg5ubGxYtWgSDwWBR5tixY+jRowfEYjHatGmDrVu31n/w1eDj44OkpCR06tTJ1qE8tKrz+ZeWmZmJadOmQaFQwMHBAc8++yzUanUDRUwqQnVkzVEdSaryyiuvICgoCGKxuMqkNi8vD127dsXatWsBAE8//TTkcjkmTpyIlJSUOsdCiWYzIxKJ4OHhAY7jbB1KhXQ6HSZNmoSXXnqp3PeNRiPGjBkDnU6HM2fO4Ntvv8XWrVvx7rvvmsvExMRgzJgxGDp0KCIjIzF37lw899xz+OuvvxpqMyrE5/Ph4eEBgUBg61AeStX5/Mszbdo0XL9+HQcPHsSff/6JEydO4Pnnn2+gqMnDgupIqiObimeeeQaTJ0+ustzo0aOxbNkyc0v1okWLcPz4cSQmJmLChAl1D4SRCgUHB7Pw8HAWHh7OFAoFc3Z2Zm+//TYzmUzmMt999x0LCgpicrmcubu7sylTprCUlBTGGGMxMTEMgMUjLCyMMcaY0WhkH3/8MWvdujUTiUTMx8eHLVu2zGK+n3/+mQ0ZMoRJpVLWpUsXdubMmWrFHRsby8aOHcscHByYTCZjgYGBbM+ePYwxxo4ePcoAsKysLPM2lo4RAIuJiWGMMZaVlcWeffZZ5uLiwuzt7dnQoUNZZGSkFfZu1bZs2cKUSmWZ6Xv37mU8Ho8lJyebp23YsIEpFAqm1WoZY4y99tprrGPHjhbzTZ48mYWGhla6Tj8/P3b06NEqY9q/fz8LCAhgdnZ2LDQ0lCUmJprLGI1G9t577zFvb28mEolY165d2b59+8zvF3++ly9fZowxlpmZyaZOncpcXFyYRCJhbdq0Yd988425fHx8PJs0aRJTKpXM0dGRPfroo+bPpyphYWHsscces4ht+fLlzN/fn0kkEtalSxe2c+dO83ve3t7siy++sFjGpUuXGMdxLDY2ljFW9TGxZMkS1rVr12rFVxvV+fxLu3HjBgPAzp8/b562b98+xnEcS0hIqLdYmzqqI6mOrCim5l5HPvPss8zF05PZOzmx4JAQ9vfFiyxPp2N5Oh17c8kS1iUoyPza2o+S//6qWx9nZ2czoVDIALBff/2VMcbYzZs3GQB29uzZau3LitDPhSp8++23ePbZZ/HPP//gwoULeP755+Hr64tZs2YBAPR6PT744AO0b98eqampmD9/PmbMmIG9e/fCx8cHP//8MyZOnIjbt29DoVBAKpUCAN544w1s3rwZa9euxcCBA5GUlIRbt25ZrPutt97CqlWr0LZtW7z11luYMmUKoqKiqvyVFx4eDp1OhxMnTsDOzg43btyAXC4vt+wvv/wCnU5nMe/169fh7u4OAJg0aRKkUin27dsHpVKJTZs2Yfjw4bhz5w6cnJzKXWbHjh0RFxdXYXyDBg3Cvn37Kt2Gypw9exadO3c2xwgAoaGheOmll3D9+nV0794dZ8+eRUhIiMV8oaGhVrmeLD8/H6tWrcL3338PHo+H//3vf1i4cCG2bdsGAPj000+xevVqbNq0Cd27d8c333yDRx99FNevX0fbtm3LLO+dd97BjRs3sG/fPri4uCAqKgoFBQUACo+v0NBQ9OvXDydPnoRAIMCyZcswatQo/PvvvxCJRDWKfcWKFfjhhx+wceNGtG3bFidOnMD//vc/uLq6Ijg4GFOmTMH27dstWkq2bduGAQMGwM/PD0DtjonSKjoei/3vf//Dxo0by32vOp9/efM4ODigZ8+e5mkhISHg8Xg4d+4cHn/88WrFTcqiOpLqyNKojpwEsZ0d7BcvgD2AeABTTh0FTh0tnMHJHpg+BZ02rK/5zq2Gay+9AplQWKN5Ll68CL1ebzEtICAAvr6+OHv2LPr27VvreCjRrIKPjw/Wrl0LjuPQvn17XL16FWvXrjVXos8884y5bKtWrbB+/Xr06tULarUacrncXNG4ubmZr6XJzc3Fp59+is8//xxhYWEAgNatW2PgwIEW6164cCHGjBkDAHjvvffQsWNHREVFISAgoNKY4+PjMXHiRHTu3NkcV0VKVoRr167FkSNHcO7cOUilUpw6dQr//PMPUlNTIRaLAQCrVq3C7t27sWvXrgpPO+7du7fMAVtS8RdJbSUnJ1tUoADMr5OTkysto1KpUFBQUKcY9Ho9Nm7ciNatWwMAZs+ejffff9/8/qpVq/D666/jqaeeAgB8/PHHOHr0KNatW4eIiIgyy4uPj0f37t3NSZC/v7/5vR07dsBkMuGrr74yn8rbsmULHBwccOzYMYwcObLacWu1WixfvhyHDh1Cv379ABQeG6dOncKmTZsQHByMadOmYfXq1YiPj4evry9MJhN+/PFHvP322wBQ62OitKou8lcoFBW+V53Pv7x53NzcLKYJBAI4OTlVOA+pHqojqY4sjerIfxCbkICgbzbVYK/ZVnJyMkQikcWPKqDwmKhrHUmJZhX69u1rca1Ov379sHr1ahiNRvD5fFy8eBFLly7FlStXkJWVBZPJBKDwH0ZgYGC5y7x58ya0Wi2GDx9e6bq7dOlifu7p6QkASE1NrbISfeWVV/DSSy/hwIEDCAkJwcSJEy2WVZ59+/Zh8eLF+OOPP9CuXTsAwJUrV6BWq+Hs7GxRtqCgANHR0RUuq/hXXVMlk8nMFShQ+NmkpqYCAFQqFRITEzFgwACLeQYMGIArV66Uu7yXXnoJEydOxKVLlzBy5EiMHz8e/fv3B1D4GURFRcHe3t5iHo1GU+lnUJ6oqCjk5+djxIgRFtN1Op25FbBbt27o0KEDtm/fjsWLF+P48eNITU3FpEmTzPHU5pgorU2bNjWKnTReVEdSHVka1ZFq+Hh4ACVaFTWaArz66qv44INl+PDDD/Hnn3/i7NmzNYqvuqSN7NrWxhXNQyYvLw+hoaEIDQ3Ftm3b4Orqivj4eISGhpb5VVBSdX8pCkscpMUVeXElXZnnnnsOoaGh2LNnDw4cOIAVK1Zg9erVmDNnTrnlb9y4gaeeegofffSRxa8/tVoNT0/PcoeyqKinI1D/p4U8PDzwzz//WEwr7hnn4eFh/lu6t1xKSorFqbnaEpY6JcFxHBhjtV7e6NGjERcXh7179+LgwYMYPnw4wsPDsWrVKqjVagQFBZlPOZXk6upao/UU97Des2cPvL29Ld4rbo0BCjvNFFei27dvx6hRo8xfpLU9Jkqry6nz6nz+5c1T/EVXzGAwIDMzs8J5SN1RHVk+qiNrpinVkTKhEEIAnMFQ6entutSRteHh4VHuv8mUlJQ615GUaFbh3LlzFq///vtvtG3bFnw+H7du3UJGRgY++ugj+Pj4AAAuXLhgUb74+hCj0Wie1rZtW0ilUhw+fBjPPfdcvcTt4+ODF198ES+++KL5WqfyKtH09HSMGzcOEydOxLx58yze69GjB5KTkyEQCCxOVVSlvk8L9evXDx9++CFSU1PNp0MPHjwIhUJhbiHp168f9u7dazHfwYMHzadD6otCoYCXlxdOnz6N4OBg8/TTp0+jd+/eFc7n6uqKsLAwhIWFYdCgQVi0aBFWrVqFHj16YMeOHXBzc6v0dHJ1BAYGQiwWIz4+3iK20qZOnYq3334bFy9exK5duywqs9oeE6XV5dR5dT7/8ubJzs7GxYsXERQUBAA4cuQITCYT+vTpU7uNIACojqQ6smaojqyeutSRNaVWqyEUCiEQCGAwGBATE4PIyEhkZWUhPj6+7sdEnboSNXHBwcFMLpezefPmsVu3brHt27czOzs7tnHjRsYYY6mpqUwkErFFixax6Oho9ttvv7F27dpZ9JZ78OAB4ziObd26laWmprLc3FzGGGNLly5ljo6O7Ntvv2VRUVHs7Nmz7KuvvmKMle1xx1hhLzYAlfb2K/bqq6+y/fv3s3v37rGLFy+yPn36sCeffJIxVrZH5eDBg1mnTp1YXFwcS0pKMj8MBgMzmUxs4MCBrGvXruyvv/5iMTEx7PTp0+zNN9+06L1rbXFxcezy5cvsvffeY3K5nF2+fJldvnzZvO8MBgPr1KkTGzlyJIuMjGT79+9nrq6u7I033jAv4969e0wmk7FFixaxmzdvsoiICMbn89n+/fsrXXd1e1SW9Ouvv7KS/5TWrl3LFAoF+/HHH9mtW7fY66+/zoRCIbtz5w5jrOzn+84777Ddu3ezu3fvsmvXrrGxY8ey3r17M8YYy8vLY23btmVDhgxhJ06cYPfu3WNHjx5lc+bMYffv369yX5buUfnWW28xZ2dntnXrVhYVFcUuXrzI1q9fz7Zu3Wox34ABA1jXrl2Zvb09y8/PN0+vzjFR373Oq/P5nzt3jrVv3549ePDAPG3UqFGse/fu7Ny5c+zUqVOsbdu2bMqUKfUWZ3NAdSTVkaVRHWn7OpIxxu7evcsuX77MXnjhBdauXTvzMVI86sCDBw9Y+/bt2blz58zHfOmHq6sr69evX51joUSzEsHBwezll19mL774IlMoFMzR0ZG9+eabFkMHbN++nfn7+zOxWMz69evHfv/99zIV4Pvvv888PDwYx3EWQ3csW7aM+fn5MaFQyHx9fdny5csZY3WvRGfPns1at27NxGIxc3V1ZU8//TRLT09njJWtRMs7uFBi6A6VSsXmzJnDvLy8mFAoZD4+PmzatGksPj6+1vu1KmFhYeXGVHLbY2Nj2ejRo5lUKmUuLi5swYIFTK/XWyzn6NGjrFu3bkwkErFWrVqxLVu2VLlua1SiRqORLV26lHl7ezOhUFjl0B0ffPAB69ChA5NKpczJyYk99thj7N69e+bySUlJbPr06czFxYWJxWLWqlUrNmvWLJaTk1Pl9pSuRE0mE1u3bh1r3749EwqFzNXVlYWGhrLjx49bzPfFF18wAGz69OllllnVMdEQlWhVn3/xcV5yiJOMjAw2ZcoUJpfLmUKhYDNnzjR/MZPaoTqS6sjSqI5sHHVkVcNyFe/jkp9lQUEBe/nll5mjoyOTyWTs8ccfZ0lJSXWOhWOsDhdOENLE+Pv7Y+vWrRgyZIitQyGEkEaH6khSU3RnIEIIIYQQUi8o0XwIjR49GnK5vNzH8uXLbR0eaUAVHQdyuRwnT560dXiE2ATVkaQY1ZG2R73OH0JfffWV+a4IpVX3ziykfHPnzq1TT8GGVlnPxNLDcxDSXFAdWX+ojiQ1RddoEkIIIYSQekGnzgkhhBBCSL2gRJMQQgghhNQLSjSrwBiDSqWq0+2zCCGkKaL6kRBSFUo0q5CbmwulUonc3Fxbh0IIIY0K1Y+EkKpQokkIIYQQQuoFJZoViIiIQGBgIHr16mXrUAghhBBCrG7FihXo1asX7O3t4ebmhvHjx+P27dtWXQcNb1QFlUoFpVKJnJwcKBQKW4dDCCGNBtWPhDzcRo0ahaeeegq9evWCwWDAm2++iWvXruHGjRuws7Ozyjoo0awCVaSEEFI+qh8JaVrS0tLg5uaG48ePY/DgwVZZJt0ZiBBCCCHEihhj0Jl0Nlm3iCcCx3G1mjcnJweAde+gRYkmIYQQQogV6Uw6PH/xZZus+8ugLyDmi2s8n8lkwty5czFgwAB06tTJavFQokkIIYQQ0syFh4fj2rVrOHXqlFWXS4lmM8EYQ7ImBXfVUXATu6K1vBWEPKGtwyKENBP31DG4q46CiCeEkBNCyBNCyBNByBNAyAkh4omKpgktyoh4IvA4GiCFPFxEPBG+DPrCZuuuqdmzZ+PPP//EiRMn0KJFC6vGQ4lmE6Y1anFDdRP/5lzF1ZxrSNOmm98TckK0kbdGO/u28JJ6wlPiCU+pR60OUEIIqcp11Q3sevBLreblc3wIOQHEfAkchA5wFDnAUegAR5EjHEo8dxQ6wE5gV+vr0wixFo7janX6uqExxjBnzhz8+uuvOHbsGFq2bGn1dVCi2YgwxpBnyEOOQQWVXoUcfQ5y9Crk6FVQ6XOKpqkg4UvgKfEoTBClnvCSeMFJ5AiO45CiScGV7Ku4kv0vbuXehoEZzMsXcAL42/kjTZuKHL0KN3Nv4WbuLfP7HDi4iJ3hKfFEX+fe6O/cjypsQohVeEk90depD/RMD51JB71JX/hgevNzXYnXJesuIzPCyIzQmLTI0ecgLj+uwvWIeCL4ynzR2q4lWstboZW8JVxELlSXEVKO8PBwbN++Hb/99hvs7e2RnJwMAFAqlZBKpVZZBw1vVAVrD9+hM+mQqklDmjYNqdo0pGpSzc/TtenQl6hca0LCE8NOYIcMXabFdBeRC7o4dEIXZWd0UARAwpeAMYYkTTJuqm4hLj8OiQVJSCxIQp4xz2Le/s59Eeb/NCR8Sa23lxDSdNXn8EYmZrJIRHUmPTTGAmTps5Gly0aWLgvZpZ7nGtTlLkshUKCVvCXa27dDD4du8JB6WDVWQh5WFf0A27JlC2bMmGGddVCiWbmaVqSMMeQacpGqSUWqNh2p2qJEUpOGVG0qsvU5VS7Dji+DQqiEUqiAQqiAUqiAUqiEQqiAQmCPfGMBkgqSkKgpTBBTtakwMiOAwlNM7e3boouyC7o6dIanxLNav+QL41YjsSAR11U38GfiXphggpfEE+FtXkILmXfVO4sQ0qw0tnE09SY90rTpiMmLQbQ6Bvfy7iE+/765fizmJfFEkGMP9HDsjpZ2/tTaSUg9okSzCtWtSHP0OVh5aw3StGnQmLSVLlPGl8JV7AY3sQvcJG5wE7vCVewKN4krHIQONe6kYzAZkFp0Otzfzg9Sft2bu2/n3sEXUZuQrc+GiCdCmP/TGOjSv87LJYQ0HY0t0SyPzqRHfH48otX38G/2VdzMvWWReLqKXTHW8xEMdOkPAY+uJiPE2ijRrEJ1K1KDyYDnLrwIBgYOHBxFjnATu5oTyZLJpB3/4bhYXaVXYWP0ZlxX3QAA9HLqiaf9pkIpVNo4MkJIY/AwJJql5Rny8W/OVVzMuoSr2VfNDQMuImeM9XoEg1wGUsJJiBVRolmBiIgIREREwGg04s6dO9WqSG+obsJR6AgXsXOTGTrIxEz4PfFP/JbwB0wwwY5vh2l+T1FHIULIQ5lolqQ1anE07Tj2Ju1HTtFlTS4iZ0z3/x+6OnSxcXSENA2UaFbhYa9IrSU2Lw5fx2xBfP59AEBnZSdMajERfna+No6MEGIrTaV+1Jl0OJZ6AnuS9iFbnw2gsDPkNN8pkAvltg2OkIccJZpVaCoVqTUYTAbsS/4LuxN+Nw890tOxBx7zfhS+Mh8bR0cIaWhNrX7UGrX4JWE3/ko+CAYGe4E9JrQYjwHO/R6KMREJaYwo0axCU6tIrSG5IBm/JvyOc5n/gKHw8OnlGISJLSbAk4YNIaTZaKr1Y7T6Hr6K2YLEgkQAhSOBBLsNRojbMDiLnW0cHSEPF0o0q9BUK1JrSChIxO6E33E+8wLY/7d33/FRlWnj/z9nenovpNJLKKEjIj2IqCxgV1axfPFZN7iuPK6r3/3+dN1HZYu67GoedVkLuroirmVVVimK9G7oLZBKSCN9JpnJlN8fk0QwBEIyyZlMrvfrNSRz5sw5102SO1fuiguTxsR/D/olA4MGqB2aEKIL+HL92OBs4JuSjawrXt+8q5qCwpiw0cyOnSX1nBBtJInmZfhyReopBZYCVub8gxO1JzFoDPxywMMMDUlROywhRCfrCfWj0+Uks/IA64rXcaT6h53U7ky8jet6zVYxMiG6B0k0L6MnVKSeYHPa+OvJDA5WHUKv6Hh4QLrM2hTCx/W0+rHAUsCas1+z9dw2FBR+OeBhRoalqh2WEF5No3YAwjcYNAYeGbCE0aEjaXDZ+cvJV9hTvlftsIQQwmMS/BNY3Pd+pkVNxYWLV0+9ToGlQO2whPBqkmgKj9Fr9KT3f4gJ4eNwuBxkZL3G9nM71Q5LCCE8RlEU7k6+i8FBg6h3WvnziZepbqhROywhvJYkmsKjdBodP+v3INdETsKJkzdOv8k56zm1wxJCCI/RaXQ83P/nRBujKbOV8XJWBnanXe2whPBKkmgKj9MoGh7ocy+DggbS4LLzyZnP1A5JCCE8KlAfyKMDH8ZP68eJmpO8k/sPtUMSwitJoik6hUbRcHvirQBsKdsm45iEED4nzi+O9H4/Q0Hhu9LNfF+RqXZIQngdSTRFp+kX2JexYWNw4WJ1wcdqhyOEEB43PHQYcxqXOXo7510sdovKEQnhXSTRFJ3qloSb0KAhs3I/J2pOqh2OEEJ43IL4ecQYY6hsqOSD/NVqhyOEV5FEU3SqXn6xTImaDMCq/I+QZVuFEL7GoDFwf59FAHxXuokj1UdVjkgI7yGJpuh08+PnYtAYyKrN4vvKTLXDEUK0YsGCBYSFhXHLLbeoHUq3Mzh4EDOipwHwZvZKrA6rugEJ4SV6TKJpsVhITk7mscceUzuUHifMEMbsmFkArM7/Fw6XQ+WIhBAX88gjj/DOO++oHUa3dVviLYQbwim1lrK64F9qhyOEV+gxieZzzz3HVVddpXYYPdb1va4jQBtAYf1ZtpZtUzscIcRFTJs2jaCgILXD6Lb8tH7c2/seANYVb2BX+W6VIxJCfT0i0Tx58iTHjh1jzpw5aofSY/nr/PlJ3A0AfFzwGTZng8oRCeE5Z86c4ac//SkRERH4+fkxfPhw9uzZ47Hrb9q0iblz5xIXF4eiKHz66acXPS8jI4PevXtjMpmYMGECu3bt8lgMom1SQ4dzfa/rAPj76bcorCtUOSIh1KV6otmWCrSjledjjz3GsmXLPBSxb3M4nfx/367ns+OeH8w+I2YG4YYwKhoq2FK21ePXF0INFRUVTJo0Cb1ez3/+8x+OHDnCiy++SFhY2EXP37p1Kw0NLf/QOnLkCMXFxRd9j9lsJjU1lYyMjFbjWLVqFUuXLuXpp59m3759pKamMnv2bEpKSprPGTlyJMOGDWvxKCyUZMiTbkm4iSFBg7E6rfz1ZAZ1jjq1QxJCNaonmperQDtaeX722WcMHDiQgQMHdlWRurUvTx7nvYP7efTrNR6/tkGj5/pYd6vyl4VrZMs24RP+8Ic/kJiYyFtvvcX48ePp06cP1157Lf369WtxrtPpJD09nbvuuguH44exysePH2fGjBmsXLnyoveYM2cOzz77LAsWLGg1jpdeeonFixdz3333kZKSwmuvvYa/vz9vvvlm8zmZmZkcOnSoxSMuLq4D/wPix7SKlp/3/y/C9GGcrS/i76ffkhU3RI+leqJ5uQq0o5Xnjh07+OCDD+jduzePPfYYK1as4He/+12r8VitVqqrqy949CTn6jr3L++p0ZMJ0QdTZjvHtnM7OvVeQnSFf//734wdO5Zbb72V6OhoRo0axYoVKy56rkajYc2aNXz//ffcc889OJ1OTp06xYwZM5g/fz6PP/54u2Kw2Wzs3buXtLS0C+6VlpbG9u3b23VN0THB+mCWDHgIraJlT8Ve/lP0tdohCaEK1RPNS/FE5bls2TLy8/PJycnhhRdeYPHixTz11FOXPD8kJKT5kZiY2OFy+LoGh4M7PlrF77d8d9lzDRoD18W6d9H4ovBLnC5nZ4cnRKc6ffo0r776KgMGDODrr7/moYce4he/+EWrrZNxcXF88803bNmyhbvuuosZM2aQlpbGq6++2u4YysrKcDgcxMTEXHA8JiaGoqKiNl8nLS2NW2+9lTVr1pCQkNBqPZuRkUFKSgrjxo1rd8w9Qf/AfixMuhOAD/M/kk0rRI/k1YmmpyrPK/Hkk09SVVXV/MjPz++U+/iS9dmn2FVYwN/2tW3yw4zoaQRoAyi2lrBTZmWKbs7pdDJ69Gief/55Ro0axYMPPsjixYt57bXXWn1PUlIS7777LqtWrUKn0/HGG2+gKEoXRn1x69evp7S0FIvFQkFBARMnTrzoeenp6Rw5coTdu+Xn93JmRE/j6oircOHiH7n/lD+uRY/j1Ymmp91777288MILlzzHaDQSHBx8wUNcWoPjytbFNGlNzI51r6v5eeEXUvGKbq1Xr16kpKRccGzIkCHk5eW1+p7i4mIefPBB5s6di8Vi4dFHH+1QDJGRkWi12haTiYqLi4mNje3QtUXHKIrCnUl34Kf1I9eSy/ZzO9UOSYgu5dWJplSevmtWzEz8tX6cqStkX8X3aocjRLtNmjSJ48ePX3DsxIkTJCcnX/T8srIyZs6cyZAhQ/j444/ZsGEDq1at6tBmEgaDgTFjxrBhw4bmY06nkw0bNrTaKim6TrA+iBt7XQ/ARwUfY3PaVI5IiK7j1YmmmpWnjEHqXP46f9JiZgLw78IvZEam6LYeffRRduzYwfPPP09WVhbvv/8+f/vb30hPT29xrtPpZM6cOSQnJzd3m6ekpLBu3Treeust/vznP1/0HrW1tWRmZpKZmQlAdnY2mZmZF7SaLl26lBUrVrBy5UqOHj3KQw89hNls5r777uuUcosrc21sGuGGcMpt5awrWq92OEJ0GZ3aAdTW1pKVldX8vKkCDQ8PJykpiaVLl7Jo0SLGjh3L+PHjWb58eZdUnunp6aSnp1NdXU1ISEin3qunujYmja+L1pFryeNA1UFSQ0eoHZIQV2zcuHF88sknPPnkk/zud7+jT58+LF++nIULF7Y4V6PR8PzzzzN58mQMBkPz8dTUVNavX09UVNRF77Fnzx6mT5/e/Hzp0qUALFq0iLfffhuA22+/ndLSUp566imKiooYOXIkX331VYsx7kIdBo2BWxIW8LfTb/D52TVMjppMsF52YRK+T/VE83IVqFSevitIH8SM6Gn8p+hrvihcI4mm6LZuvPFGbrzxxjadO2vWrIseHzVqVKvvmTZtWpta/ZcsWcKSJUvaFIfoehMjrmr+4/rfhZ/z0+S71A5JiE6netd5UwX640fTX+ngrjxzc3OxWq3s3LmTCRMmdHpc0nXeNa6NdS9ddbI2C7PdrHI0QgjReTSKhjuSbgPgm5KNFNV1zuopQngT1RNNbyXLd3SNcEM4vUyxuHBxXNaYE0L4uJTgIaSGDMfhcrC64F9qhyNEp5NEU6huUNAgAI7XHL/MmUII0f3dlngrCgp7KvbJIu7C50miKVQ3KMi9D/3xmhMqRyKEEJ0vwT+eKVHXAO4d0oTwZZJoCtUNDnYnmjnmXOocnbvXuhBCeIMbes1BQWF/1UEK686qHY4QnUYSzVbIZKCuE24IJ8oYhQsXJ2qyLv8GIYTo5mJMMYwMTQVgXbGsqyl8lySarZDJQF1rsHSfCyF6mKateLeUbaPWXqtyNEJ0Dkk0hVf4YZymTAgSQvQMg4MGkeSfiM1pY2PJJrXDEaJTSKIpvMLgYPfM82xzDlaHVeVohBCi8ymKwuwYd6vm+uJvsDvtKkckhOdJoim8QqQhknBDOA6Xg6zaU2qHI4QQXWJCxHhC9MFUNFSwu2Kv2uEI4XGSaLZCJgN1LUVRZJymEKLH0Wv0zIyeAcDXRevatNWoEN2JJJqtkMlAXa9pnOYxGacphOhBpkdPRa/oyDZnS4+O8DmSaAqvMbhxh6DTtaexORtUjkYIIbpGsD6YiZETAfi6aK3K0QjhWZJoCq8RY4ohRB9Cg8vO6drTaocjhBBd5tqYNAD2VOyjzFqmcjRCeI4kmsJrKIoi21EKIXqkRP8Ehgan4MLF+uJv1A5HCI/RqR2AEOcbHDSQXeW7OVZznHnMVTscIYSHnDx3jmPnSjFpdZj0Okw6HSadHj+d+3OjToefTo9Jp0On6ZltILNiZnK4+giby7Zyc8IC9Bq92iEJ0WGSaLYiIyODjIwMHA6H2qH0KIMax2lm1Z7C7rSj08i3qBC+YN3pLF7YvqVN5+o0msZE1P3w0+kx6nQEGQzEBgYRFxREXFAwcYGNH4OCCDAYOrkEnS81dAThhnDKbeXsKd/LxMir1A5JiA6T3+KtSE9PJz09nerqakJCQtQOp8eI94sjSBdIjb2WbHMOA4L6qx2SEMID4oKCmJiQSF2DnXqHnXq7nfqGhh8+t/+wWLnd6aTWZqPWZmvz9UOMJuKCgogPCmZodDSjY+NIjY0l2GjqjOJ0Co2iYWrUZD458xnfln4niabwCZJoCq+iKAoDgwayt2IfR6qPSqIphI+YPziF+YNTWn3d5XJhbUw6WySjdvfzaquVszU1FNZUU1hTQ2Gt+/Nqq5Uqaz1V1nqOlpWyPvuHJYL6h4Uzqlcco2J7MTEhieTQ0C4obftNiZrMZ2c+53jNCc7UFRLvF6d2SEJ0iCSawuuMCBnO3op97Crfw7x4GacpRE+gKAomnR6TTk/oFTZC1litnK2tobCmhpzKCvYXF/H92ULyqqvIqignq6Kc1UcOATAuLp5bU4Yxp/9Ar+xuDzeEMTIslX0V37Ox5DsWJt+pdkhCdIgkmsLrjAsfw7u571FQV0C+pYBE/wS1QxJCeLEgo5Ego5GBEZFAn+bjZRYL+4vOsq+okL2Fhew5e4bdhe7HM999ww0DBnHnsBGkxvZSL/iLmB41lX0V37OlbBu3Jt6MQeN9CbEQbSWJpvA6AboARoQOZ1/F92w/t4NE/1vUDkkI0Q1F+vszs28/ZvbtB0BRbQ0fHz3C6iOHyK2q5MMjh/jwyCGm9e7DYxOvISUqWuWI3YaFDCXSEEmZrYxd53ZzTdQktUMSot165hoSwutdHeEeBL/93E6cLqfK0QghfEFsYBA/HzeBb+65nw9uvp0Fg1PQKgobc7K58Z/v8ov/fMHpinK1w0SjaJgWPQWAb0u/UzkaITpGEs1WZGRkkJKSwrhx49QOpUdKDU3FT+tHua2cEzUn1Q5HCOFDFEVhfHwCL147h7V338fcgYMB+OLkca57byV/3bmdBpWXtpsceQ1aRUtW7SnyLPmqxiJER0ii2Yr09HSOHDnC7t271Q6lRzJo9IwLGwPAtnM7VI5GCOGr+oSG8ZfrbuDLO+9mWu8+2J1Olu/cxs2r/8nJc+dUiyvUEMLo0JEAbCyRVk3RfUmiKbxW0xpyu8v30OBsUDkaIYQvGxIVzRtzF/Dn2dcTbDRyqKSYuR+8yx+2bqKotkaVmKZHTwNga9l26h31qsQgREdJoim81uCgQYTqQ7E4LByoOqh2OEIIH6coCvMGDeHrhfcyNbkPNoeD1/fuZsrbf2fp12s4VFLcpfEMCR5MjDGaemc9O87t6tJ7C+EpkmgKr6VRNFwVMR6A7WXSfS6E6BoxgYG8+ZMFvH7DPMbFxWN3Ovn0+FF+8sE/uPNfq9hw+hQul6vT43BPCpoKwMbSTZ1+PyE6gySawqs1zT7PrNyPxW5RORohRE+hKAqz+vVn1S138OntC/nJoMHoNBp2nilg8Ref8tdd27skjmsiJ6FVtGSbs8k153bJPYXwJEk0hVdL8k8iztSLBpedPRX71A5HCNEDjYiJZfnsG/hu0f/hvpGjAfjLzu18ldX5K2IE64MYG+a+57fSqim6IUk0hVdTFKV5UtB2mX0uhFBRr6Ag/r8p07m3Mdl8bN1/OFZW2un3bZoUtL1sh0wKEt2OJJrC610VPgGAY9XHqW2oVTkaIURP93+vmcrEhCQsDQ381xefUVFX16n3Gxw0iBhjjEwKEt2SJJqtkAXbvUe0KYpEvwScOMmsOqB2OEKIHk6n0fDKnBtJDA4hv7qKJf/5Aruz83YwUxSleaegjbJTkOhmJNFshSzY7l3GNI5R2ivjNIUQXiDMz4/Xb5yHv17P9oI8lm3p3ATwmshJ6BQd2eYccmRSkOhGJNEU3cLosFEAHKo6jNVhVTkaIYSAwZFRvHjtHADeytzH9vy8TrtXsD6o+Q9uWepIdCeSaIpuIck/kUhDBDanjcPVR9QORwghAJjdbwALh6cC8H+/WUe9vfN2MZveuKamTAoS3YkkmuICXbEIcXsoitLcqrm34nuVoxFCiB/86urJxAQEkltVyV93dt7qGDIpSHRHkmiKbqMp0cys3I/D5VA5GiGEcAs2GvndtJkArNi3m6OlJZ1yH5kUJLojSTRFtzEwaAAB2gBq7bWcrMlSOxwhhGg2q19/5vQfiMPl4okNazttFvrk8yYFnajp/AXjhegoSTRFhymK0iX30SpaRoW5x0JJ97kQwtv8duoMgo1GDpYU81bm3k65R5A+iEmRVwPwReGaTrmHEJ4kiaboVppmXe6r+N5rx5MKIXqmqIAAnpzk7tp+YdsW9hed7ZT73NDrOhQU9lcdINfceTPdhfAESTRFtzI0OAWDxkCZrYz8ugK1wxFCiAvcNnQ41/UbQIPTSfp/Pu+UXYNiTDFMCB8PwJdnpVVTeDdJNEW3YtQaGRYyFJDF24UQ3kdRFP6QNpveoWEU1tSwdO0anJ3Q+3JDnHv9zl3leyiqK/L49YXwFEk0RbczJtQ9+3yfjNMUQnihIKOR/71+Liadju9yc3hll+eXPEryT2RkaCouXHxZ9JXHry+Ep0iiKbqdkaGpKCjkWfIptZaqHY4QQrQwODKKZ6enAfCXndvYnJvj8XvM7XU9AFvLtnHOWu7x6wvhCZJotiIjI4OUlBTGjRundijiRwL1gQwKGgjA9xX7VY5GCCEu7qYhQ7lz2AhcwC+//pKzNTUevX7/oP4MDhqEw+Xgq6KvPXptITxFEs1WpKenc+TIEXbv3q12KOIiRoW6lznKrJREUwjhvZ6aMp1hUdFU1NezbIvnF1mfG3cD4N7/vLrBs4msEJ4giabolkY2JprHao5T5/D8rE4hhPAEo07HH2ZdhwJ8cfI4+4s9O3FnaHAKfQJ6Y3Pa+FfBJx69thCeIImm6JZi/WKJMcbgcDk4XHVE7XCEEKJVQyKjWDA4BYA/bNnk0TWAFUXhjsTbAPe2lMeqj3vs2kJ4giSaotsaGToCkO5zIYT3e3TiJAxaLTvO5LMxN9uj1x4cPIipUe6F4t/KWYnN2eDR6wvREZJoim5rZON2lPsrD+J0dc6+wkII4QnxQcHcm+pemu0PWzfj8PBe6Lcn3kqIPoSi+mI+L/zCo9cWoiMk0RTd1sDAAfhp/ai2V5NtzlE7HCGEuKSHxk4gxGjixLkyPjnm2SE/ATp/7kleCMCXZ/9DvkV2ThPeoc2JZlhYGOHh4W16CNEVdBpd8y5B+ysPqByNEEJcWojJxM/HubeO/POOrdTbPdvFPTZ8DGPCRuNwOXgz+23p6RFeQdfWE5cvX96JYQjRPiNDU9ldvofMyv3clDBf7XCEEOKS7hkxipX7v6ewpoa3M7/nZ2PHe/T6dyffxZHqo5w2Z7OueAOzY2d59PpCXKk2J5qLFi3qzDiEaJcRIcNRUMi15FFuqyDcEKZ2SEII0SqjTsd/X3UN/73uP7y6Zxe3Dx1OmJ+fx64fZgjj9sRbeDvnXVblrybBL56hISkeu74QV6rDYzTr6+uprq6+4CFEVwnWB9EvsC8g3edCiO5h3uAhDImMosZm5Y3v93r8+lOjpjA+fBwOl4O/nnyFHHOux+8hRFu1K9E0m80sWbKE6OhoAgICCAsLu+AhRFcaKbsECeERCxYsICwsjFtuuUXtUHyaRlF4ePxEAFYdPoDVbvfw9TU82PcBhgQNpt5p5cXjyymuL/HoPYRoqzZ3nZ/v8ccf59tvv+XVV1/l7rvvJiMjgzNnzvD666/z+9//3tMxCnFJqaEj+KjgY45UH8XmtGHQGNQOSYhu6ZFHHuH+++9n5cqVaofi89L69iMmIJBicy1rT2cxd+Bgj15fr9HziwHpLDv2R/Is+bxw/M/8v5QnCNGHePQ+AC6XizxLPkX1RZRYSyltfFQ1uHs4FZTGMxW0ihadokWn0aFTdOg1evRNHzV6DBoDWkWLBg2KoqCgoCgKekXfeK7+vHN/eI9B0WPUmjBpTfhp3B91mnalOMLD2vVV+Pzzz3nnnXeYNm0a9913H5MnT6Z///4kJyfz3nvvsXDhQk/HKUSrEv0SCDeEU24r52j1MVIbF3IXQlyZadOmsXHjRrXD6BF0Gg23Dx3OX3dt570D+z2eaAL46/z574G/5H+OLKPEWsKLx//Ck0N+hZ+242NCXS4Xp83Z7Crfze7yPZyzlXsgYs/SK3rCDWGEG8KJMIYTbggnwS+BocFDCNQHqh1ej9GuRLO8vJy+fd3j4oKDgykvd3+DXXPNNTz00EOei06INlAUhZGhI/imZCOZlfsl0RRd6re//S3PPPPMBccGDRrEsWPHPHaPTZs28ac//Ym9e/dy9uxZPvnkE+bPn9/ivIyMDP70pz9RVFREamoqL7/8MuPHe3ZWs/CcO4YNJ2P3DnYVFnDiXBkDIyI9fo9QQyi/GvQozx5dRq4ll2cOP8eDfR+gb2Cfdl2v0lbJ+pJv2F62gzLbuebjJo2RJP8kooxRRJkiiTZGEaoPRVGU5i03XbhwuJw4XHYanHbsLvejwdngfrgasDltOFwOXC4XLlw4XS5cOGlw2pvPaXC6z2v6aHM10OC0Ue+wUu+op8HlXjaqwdVAsbWEYmsJ1PxQBgWFPgG9GR4yjOEhw+gX2BeNIsuKd5Z2JZp9+/YlOzubpKQkBg8ezIcffsj48eP5/PPPCQ0N9XCIQlzeyNDUxkTzAPe4XCiKcvk3CeEhQ4cOZf369c3PdbrWq9atW7cyfvx49Hr9BcePHDlCREQEMTExLd5jNptJTU3l/vvv56abbrrodVetWsXSpUt57bXXmDBhAsuXL2f27NkcP36c6OhoAEaOHIn9IuMB165dS1xcXJvKKjwnNjCImX37sfZUFu8f3M9vp83snPv4xfLfAx/lzyf/ytn6s/zPkeeZG3cDP4m7sc3dywWWM3xV9DXbz+3E7nJ/Dxk1RkaFjmR8xFiGhwzzmmFLDpeDekc9ZruFcls552zl7o/Wc5yszaKg7gynzdmcNmfzWeHnJPglMD9+LmPCRkvC2QnalWjed9997N+/n6lTp/LEE08wd+5cXnnlFRoaGnjppZc8HWOH9e7dm+DgYDQaDWFhYXz77bdqhyQ8bEjwYLSKlnJbOWW2c0QZPd8yIERrdDodsbGxlz3P6XSSnp7OgAED+OCDD9BqtQAcP36cGTNmsHTpUh5//PEW75szZw5z5sy55LVfeuklFi9ezH333QfAa6+9xpdffsmbb77JE088AUBmZuYVlkx0toXDU1l7KouPjx3hV1dPJsDQOclan8DePD/sd7yT+w92lu/ms8LPyaw8wIN9HyDBP/6i76lz1HGg8iCby7ZysOpQ8/H+gf25NmYmI0NTMWqNnRJvR2gVLQG6AAJ0AUSbolq8Xm4r51DVYQ5WHeZA5QEK6gp4JetVEvzimRc/l7FhYyTh9KB2JZqPPvpo8+dpaWkcO3aMvXv30r9/f0aM8M5uy23bthEYKGMyfJVBYyDeL448Sz55ljxJNEWXOnnyJHFxcZhMJiZOnMiyZctISkpqcZ5Go2HNmjVMmTKFe+65h3fffZfs7GxmzJjB/PnzL5pktoXNZmPv3r08+eSTF9wrLS2N7du3t7tcrcnIyCAjIwOHw+Hxa/c0kxKTSQ4JJbeqks9PHOOOYZ33OzRQH8jP+/+MMedGszLnH+Racvl/h54m1hRLsn8SyQFJJPonUFJfwr6KTI7WHMPhcn+NFRTGhI1mTuy19A/q32kxdoVwQzhToiYzJWoytfZa1hatZ23xegrqzpCR9RoJfgncnXwXg4MHqR2qT2hXopmfn09iYmLz8+TkZJKTkz0WlBDtkeyfRJ4ln1xzHmPCRqsdjughJkyYwNtvv82gQYM4e/YszzzzDJMnT+bQoUMEBQW1OD8uLo5vvvmGyZMnc9ddd7F9+3bS0tJ49dVX2x1DWVkZDoejRbd7TEzMFY0VTUtLY//+/ZjNZhISEli9ejUTJ05scV56ejrp6elUV1cTEuL5Wcw9iUZRWDg8lee3fMd7B/dz+9DhnT70Z0LEeAYFDeTtnHf5vjKTs/VnOVt/lh3lO1uc28sUy+iwUUyNmkKMKbpT41JDoC6QmxLmMzt2VmPCuY6CugKWHfsjV0dcxe2JtxFqkO/xjmhX23Dv3r2ZOnUqK1asoKKiokMBbNq0iblz5xIXF4eiKHz66actzsnIyKB3796YTCYmTJjArl27rugeiqIwdepUxo0bx3vvvdeheIX3SvZ3/7GTa8lTORLRk8yZM4dbb72VESNGMHv2bNasWUNlZSUffvhhq+9JSkri3XffZdWqVeh0Ot544w2vGFe8fv16SktLsVgsFBQUXDTJFJ5385ChGLRaDpeWcKC4qEvuGWoI5ZcDH+YvI19i6cBfckvCTYwLG0OMMYb+gf25LeFmlg1/lt+PeI7bEm/xySTzfAG6ABYkzONPqb9netRUFBS2ndvBkwd/w7riDbJvfAe0q0Vzz549vP/++/zud7/j4Ycf5rrrruOnP/0pc+fOxWi8svEalxvk7okB7lu2bCE+Pp6zZ8+SlpbG8OHDvbaLX7RfUoC7lT1PEk2hotDQUAYOHEhWVlar5xQXF/Pggw8yd+5cdu/ezaOPPsrLL7/c7ntGRkai1WopLi5ucZ+2jB0V6grz8+OGAYP45NgR/nFwP6mxvbrs3qGGEEINw0kNHd5l9/RmgbpA7u1zD5OjrmkeXvCP3Pf5onAN48PHcVXEePoG9PGKPwy7i3YlmqNGjWLUqFH88Y9/ZOPGjbz//vs8+OCDOJ1ObrrpJt588802X+tyg9w9McA9Pt490LlXr15cf/317Nu3r9VE02q1YrVam5/LlprdR5K/O9Est1VQ01BDkL5lt6UQna22tpZTp05x9913X/T1srIyZs6cyZAhQ1i9ejUnTpxg2rRpGI1GXnjhhXbd02AwMGbMGDZs2NC87JHT6WTDhg0sWbKkvUURXWjh8FQ+OXaEL04c5zeTpxJq8tz+5+LK9Qvsy2+H/j++LdnIvwo+pbKhkrXF61hbvI4oYySjQkcSrA/GpDFh0hrdC8QrOvfi9ErTIvU/LFXfxAW4cJ63fJMTJ073R5frvM8dOFzuzx04Go85cbgcOBvf736vC9d513A0ndP0fpqu88PHe3r/lMgunMfQoWXzFUVh+vTpTJ8+nYceeogHHniAlStXXlGieSmeGOBuNptxOp0EBQVRW1vLN998w2233dbq+cuWLWuxJp7oHvy0fsQYoym2lpBnyWdoSIraIYke4LHHHmPu3LkkJydTWFjI008/jVar5c4772xxrtPpZM6cOSQnJzd3m6ekpLBu3TpmzJhBfHz8BZMtm9TW1l7QQpqdnU1mZibh4eHNk46WLl3KokWLGDt2LOPHj2f58uWYzebmP9KFdxsV24shkVEcLSvlX0eP8MCoMWqH1ONpFA0zY2YwJWoyh6oOs7N8F/sqMim1lrG2eP3lL+Cl6hz1XXq/DiWaBQUFvP/++7z//vscOnSIiRMnkpGR4anYPDLAvbi4mAULFgDgcDhYvHgx48aNa/X8J598kqVLlzY/r66uvmDik/BuSf6JFFtLyLXkSaIpukRBQQF33nkn586dIyoqimuuuYYdO3YQFdVyWRWNRsPzzz/P5MmTMZy3jE1qairr16+/6HvAPVxp+vTpzc+b6qhFixbx9ttvA3D77bdTWlrKU089RVFRESNHjuSrr7666LqcwvsoisKdw0bw1MYNfHb8qCSaXkSv0TMqbCSjwkZidVjJrDxAVu0p6h111DnrqXe4Hw6Xg+al6V2uxs9b0igaNI1bayooaBRN80cNGjSKBq2iRato0DRux6lVtGgUxf28+TwFpfHjhe/TXvRz93U0hBlCu+z/DtqZaL7++uu8//77bNmyhSFDhrBw4UI+++wzr5x53rdvX/bv39/m841G4xWPMxXeIzkgmd0Ve2WcpugyH3zwwRWdP2vWrIseHzVqVKvvmTZtWvPuKpeyZMkS6Srvxq4fMJBnvvuGQyXFZFdW0Cc0TO2QxI8YtUYmRIxjQkTrDVbiQu2adf7ss88yYcIE9u3bx6FDh3jyySc7JcmUAe7iSiX5u7sRc82SaAohupdwP38mJbp/l3554rjK0QjhGe1KNPPy8pg7dy5/+tOfuPrqqzlz5gwA7777Llu2bPFYcOcPcG/SNMC9s5fdyMjIICUl5ZLd7ML7JDcmmmfri7A6rJc5WwghvMuNA92LhH9xou3rnwrhzdqVaH788cfMnj0bPz8/9u3b1zxLu6qqiueff/6KrlVbW0tmZmbzzPGmQe55ee4WqaVLl7JixQpWrlzJ0aNHeeihh7pkgHt6ejpHjhxh9+7dnXof4VmhhhBC9MG4cFFQd0btcIQQ4opc268/Bo2WE+XnOH6uTO1whOiwdnedv/baa6xYsQK9Xt98fNKkSezbt++KrrVnz57m5ZLAnViOGjWKp556CnAPcH/hhRd46qmnGDlyJJmZmTLAXVxSc/e5jNMUQnQzwUYTU3v3BqT7XPiGdiWax48fZ8qUKS2Oh4SEUFlZeUXXahrk/uNH00xKcA9wz83NxWq1snPnTiZMmNCesEUPkSzjNIUQ3dgNA9zd55+fONamSWBCeLN2JZqxsbEX3fViy5Yt9O3bt8NBeQMZo9l9SYumEKI7m9mnHyadjtyqSg6VlqgdjhAd0q5Ec/HixTzyyCPs3LkTRVEoLCzkvffe47HHHuOhhx7ydIyqkDGa3VdygDvRLLAU4HA5VI5GCCGuTIDBwMw+7kYbmRQkurt2raP5xBNP4HQ6mTlzJhaLhSlTpmA0Gnnsscd4+OGHPR2jEFck2hiFSWOk3mnlbF0RCf7xaockhBBX5MaBg/ny5Am+OHGcX0+agkb21hbdVLtaNBVF4Te/+Q3l5eUcOnSIHTt2UFpayv/8z/94Oj4hrphG0ZDYuO+5dJ8LIbqjacl9CNQbOFtbw76zhWqHI0S7tSvRbGIwGEhJSWH8+PEEBgZ6KiavIGM0u7emCUGyQ5AQojsy6nTM6tcfkO5z0b11KNH0ZTJGs3trGqcpiaYQortqWrx9TdYJHE6nytEI0T6SaAqfdP4SR7I8iBCiO5qUmEyoyUSZxcKOM/lqhyNEu0iiKXxSnF8cWkWL2WHhnO2c2uEIIcQVM2i1zO43AICvs06qHI0Q7SOJpvBJeo2eeL84APIs0hIghOieZvV1j9PckH1KemdEtySJZitkMlD3lyQ7BAkhurmrExPx0+k4W1vLYVm8XXRDkmi2QiYDdX/JssSREKKbM+n0TE7uDcD606fUDUaIdpBEU/isprU0C+rOqByJEEK0X1qffoC7+1yI7kYSTeGz4vx6AVBmLcPmtKkcjRBCtM/03n3RKAqHS0s4U1OtdjhCXBFJNIXPCtYFE6D1x4WLovpitcMRQoh2ifD3Z3Qv9+TGDdJ9LroZSTSFz1IUhV6NM8/P1p1VORohhGg/6T4X3ZUkmq2QWee+oan7vFASTSFEN5bW151o7ijIp9pqVTkaIdpOEs1WyKxz39DLFAtAYb0kmkKI7qtvWDh9QsNocDrZnJujdjhCtJkkmsKnSYumEMJXzGps1Vwv3eeiG5FEU/i0OJN7jGZxfRFOl1PlaIQQov1mNiaa3+acpsHhUDkaIdpGEk3h0yKNEegVPQ0uO6XWUrXDEUKIdhsdG0e4yY9qq5U9hbI+sOgeJNEUPk2jaOjl1zhOU7rPhRDdmFajYXqfvoB0n4vuQxJN4fN6mdzjNM/WF6kciRBCdEzT7PP1p0/hcrlUjkaIy5NEsxWyvJHv+GFCUKHKkQghRMdck5iMQaslv7qKE+Xn1A5HiMuSRLMVsryR72hq0ZSucyFEdxdgMDApMRmA9aezVI5GiMuTRFP4vKYWzbP1Z6WrSQjR7V3b2H2+9pQkmsL7SaIpfF6sKQYFBYujjqqGKrXDEUKIDpnRtx8KcLCkmLM1NWqHI8QlSaIpfJ5eoyfaGAXIDkFCiO4vyj+AUbHunhqZfS68nSSaokfoJTsECSF8yKx+/QEZpym8nySaokeQrSiFEL5kVl93ormjIJ9qq1XlaIRonSSaokeIa15LU5Y4EkJ0f33DwukXFk6D08l3udlqhyNEqyTRFD3CD13nsmi7EMI3NC3evk66z4UXk0SzFbJgu29patGsbKjEYreoHI0QQnRcU/f5xpxsbA6HytEIcXGSaLZCFmz3Lf46f0L1IYBsRSmE8A0jY3sR6e9Prc3GzoJ8tcMR4qIk0RQ9hmxFKYTwJRpFIa1P4+Lt0n0uvJQkmqLH6GWKA2QtTSGE72ha5mjD6VM4Zecz4YUk0RQ9hixxJITwNVcnJOGv11NkruVgSbHa4QjRgiSaosdo3vNcEk0hhI8w6nRMTe4DwL+PH1U5GiFakkRT9Bi9TLEAlFhLsTkbVI5GCCE84+YhQwH45NgRrHa7ytEIcSFJNEWPEaoPxU/rhwsXJfXSxSSE8A1Tk3vTKzCQyvp6vj51Uu1whLiAJJqix1AUpXk9zYK6MypHI4QQnqHVaLglZRgAqw4fVDkaIS4kiaboUXoH9AbgVO1pdQMRQggPum3ocBRge0E+2ZUVaocjRDNJNEWPMiDIvRTIyVpZc04I4Tvig4KZ0jgpSFo1hTeRRFP0KAMD3YlmrjmPeke9ytEIIYTn3DFsOAD/OnJYtqQUXkMSTdGjRBgjCDeE48TJaXO22uEIIYTHzOjdl0h/f87VWdiQfapL711VX09BdRXZlRWcOFfG4ZJijpSWUGKuxeF0dmkswrvo1A7AW2VkZJCRkYFD/ir0OQMC+7OzfBcnak6SEjxE7XCEEMIj9Fott6YM49U9u1h16CBz+g/s1PtV1NWxJusE/z5+lN2FrU+w1CgKEX7+RAcEEBMQSExgILGBgcQEBNIrMIjEkBDig4LRa7WdGq9QhySarUhPTyc9PZ3q6mpCQkLUDkd40MCgAews38XJGhmnKYTwLbelDOfVPbvYnJdDQXUVCcGe//21KTeHdw98z6bcHBrOa6006XToNVoMWg16jRa7y0l5XR1Ol4tSi5lSi5nDpSUXvaZWUYgPCiY5NJSkkFASg0NICA4hKSSExOAQQkwmj5ejJ3I4nSiKgkZRuuyekmiKHmdA4zjNrNpTOF1ONIqMIBFC+Ibk0FCuTkxiW34eHx4+xNKJkzx27bqGBp7b8h3vH9zffCwlMop5g4dww4BBxAUFt3iPw+nkXJ2FErOZYnMtJWYzRbU1FNfWUmw2c7a2hryqSurtdvKqq8irrgJyW1wn2GgkKSSUpOAQkkJCSQ4JITk0jN6hoUQHBHZp4qQml8tFjc1KVb0Vc4MNS0MDloYG6hoaqLLWU1FfR3md+1FRV0e11Uq1zUq1tZ7qeiu1DTa+ued+eoeGdVnMkmiKHifRPwGTxkS9s558SwHJAUlqh+QT9lVksjLnHRb3fYBhIUPVDkeIHuuOocPZlp/HuwcyuTVlGIke6JU7WlbKL7/6kpPl5wC4e8RIfjp8JAMiIi75Pq1GQ3RAINEBgQwj5qLnuFwuSsxmcqsqya6sIL+qivzqKgqqq8irquJcnYVqq5VDJcUcush+7iadjuSQUJJDQ+kdEkpyaBjJja2iwUYjAQYDOo13NCi4XC5sDgdWh516u526BjsV9XWcq7NwzmKhvK6Oyvo6amw2aqxWamxWaqxWKq31VNbVU2Wtx+FydSiGaqvVQ6VpG0k0RY+jUTT0D+zHoerDnKzNkkTTA1wuF/8q+JjKhiq+KloriaYQKprdbwAjYmI5UFxE+pp/8+Gtd2DS6dt1LZfLxTsHvmfZlk3YHA6i/AN44drrmJzU22PxKopCTKB77Ob4+IQWr1saGsivriKvstLd6llVSW5lJblVlRRUV1Fvt3P8XBnHz5W1eg+TTkegwUC4yY9eQUH0Cgxq/hgdEEiUvz9RAYGE+/ldtHXU7nRSbK7lbE0NhTXVlJjNzUlgU1JoddhpcDqxO5w4XE5sDoc7mbQ3UNdgp97eQJ3djrODiSKAn06Hv96Av17f/AgyGAn38yPMz49wP3/CTCZCTSaCjEaCjSaCjUaCDUZCu3gYgiSaokcaENSfQ9WHOVFzkrSYGWqH0+1l1Z5q3m3pSPVRLHYL/jp/laMSomfSa7VkXD+Xef/8B4dKS3hq4wb+MHM2yhV2L5+uKOepjRvYlp8HwPTefflj2mwi/Lv2Z9tfr2dQRCSDIiJbvNbgcFBQU01OZUVzAprT+LGwpgarw733e73d3YJYZrFworFV9mK0ikKgwYhGAQWl8f/MRUV9vUcSxPMpuBPgpsQwws+fiMZEMchgJMhoJMhgILAxOQw1mQgz+RFqMmHUdZ/0rftEKoQHDQwaAMjC7Z7yTcnG5s8dLgf7Kw8wMfIq9QISooeLDwrmL3NuYNGn/+KjI4cZHRvHHcNGtOm9Vrud1/bu4tXdu7A5HRi1On49aTKLUkddcbLa2fRaLX1Cw+jTyphDm8OB2WbD3GCjxmajzGLmbE0NZ2trOFtTQ1FtLSUWM6VmM+V1FhwuF1XWi6+xrNdoiA0MIi7I3QoaYjQSaDASZDQQZDBi0unQabToNRp0jROi/HQ6/PR6TDodfjo9fnodJp0Oo1aHQav1uv/PziCJpriAZ/9e8179AvqiQUO5rZxz1nNEGC89zshXnKg5yVs573B74i2MDE31yDVrG2rZXb4bgGHBQzlUfZi9Ffsk0RRCZZMSk/nviZP407Yt/HbjNwyJiiY1JvaS79mSl8tTGzeQ07iN5ZSk3jwzbSbJoaFdELHnGbRaDI2thG5RrZ5rdzo5Z7FQY7PicoELF06XCxcQ4edHpH9Aj5l05EmSaIoO644/dkatkST/RHIsuZyozWJiD0g0nS4nK3P+QWFdIVtKt3os0dxStpUGl51k/yRuSbiJQ0cOs7/qIDanDYPG4JF7CCHa52djxrO/qIi1p7P4+Zf/5p8330ZSSGiL8/YXF/Hiti1syXfP+I7yD+CpKdO5fsDAHtHqBqDTaNxjRQlUOxSfIomm6LEGBg0gx5LLyZosJkZMUDucTrfz3C4K6goAKKpvOXOzPVwuF9+WfgfA9Ohp9A5IJtwQTrmtnENVRxgdNtIj9xFCtI+iKPxx1nWcWPUeOZUVTFv5BkMio5jZpx8z+/TFqNOxfMc21p52DyPSaTQsHJ7Ko1dNIthoVDl64Qsk0RQ91oCg/qwtXs/J2pNqh9Lp7E47H5/5rPl5sbUEl8vV4ZaKYzXHKaovxqQxcVXEeBRFYUzYKNYVb2BvxT5JNEWz73aeZN3moyiARqMACoriToQu+Nh4nMZFpd3fou6PGkVBp9Oi02nQ67TotBq0532u02nR67QY9FoMBh1Ggw6DXofR6P7cz6gnwN9IYIARo6Hn/PoLNhr5+9z5PLlhLXsKz3C0rJSjZaW8sntH8zkaRWHB4BR+MX6iR5ZDEqJJz/lJE+JHmhZuz7cU+Pws6c1lWyixlhCsC6bWXovNaaOioZJwQ8cW7W2aBDQx8ir8tO4xUGPCRrOueAOZlftxuBxoFdlWTkBuwTk2bj+hdhjN9DotAf4GggP9CAkyERLsR0iQH6HB/kSEBRARFkB4aAAhQX4EB5oICjR16+S0b1g4q265g3MWC9/lZrMh+zSbc3OobbBxXb8BPHrVpMuuiSlEe3TfnxohOijMEEaUMZJSaxmnak8zPHSY2iF1CpvTxqdnPgfgJ3E3sq54PcXWEorrizuUaFY1VLG3Yh8AM6KnNR8fGDSAQF0gtfZaTtScZEjw4ObXrA4rW89tJyV4CLGmiy/eLHzT+JG9CfA3QuPkCqfTPfXQ5XI1T7xwOd2vNR2j8TwXLnC5d5mx2504HE4a7A7sDid2uwO7/YfnDQ0ObA12bDYHVlsDtgYHNpsdq81OXX0D5jr3RI8Gu4PK6joqq+vaXAaDQUdQgJHQYH96J0TQv3cU/ZIj6d87mqjwwG4xljHC35+bhgzlpiFDsTkc1DU0yPaOolNJoil6tAGB/Sm1lnGyNstnE831xd9Q2VBJhCGcadFTOFh1kGJrCUX1xRckgVdqc+lWHC4H/QL6kuSf2Hxcq2gZFZrK5rKt7K3Y13wPu9POy1n/y8GqQ/hp/XhkwJIO3V90L4P7xTK436VnPHcFp9OFpc5GrcVKrdlKjbmequo6KmvqqK6po7zSQnmlmXMVZs5VmqmprafGXI/T6cJms3POZudchZlTuaVs2Hqs+bqhwX6MGJJA6pB4RgyJp39yNHq9d7fmG7RaDFrvjlF0f5Joih5tYNAAtp3bwYka3xynWeeo44vCNQDMj5+HXqMnxhQDVQcp7sCEIKvDyrclTZOAprZ4fUzY6MZE83sWJt2JCxcrTr/JwapDzXH96fhLLO77QLsmYlnsFv5y8hX0Gj2/HPAwOo1UZaJtNBqFwAD3OM1LrHRzgabktMZcT3VtPecqajmdV8ap3DJO5ZaSe6acyuo6Nu08yaad7rpEp9PQJzGSAX2iGdgnmoF9YxjcLwaDXr5XRc8i3/GiR+vfOE4z25yN0+VEo3jHfrgWuwWT1tTheL46uxazw0wvUy8mRU4EcCeatH/mebmtgr+ceJkyWxkB2gDGh49rcc7QkKEYNUbKbeVkm3PYWraNHeU70SpalvT/OdvKtrG7Yi+vnfobFbYK5sS2fdcSu9POK1mvcqzmOADrS77huthr21UWIdri/OS0V3QIEMPVY/o1v25rsHP8VDEHjp1h/5ECDh4vpKa2npPZJZzMLmFN43lGg47UIQmMGZHEmGFJDOgTjVbrHXWOEJ2lRySa2dnZ3H///RQXF6PVatmxYwcBAQFqhyW8QC9TLHpFR73TSqm1jBhTtNohcbT6GH849gIL4ucxL35uu69jc9r4qmgtADcnzG+elNNUxva0aJ6qPc1fT75CZUMVQbpAfjFgCUZtyyVQDBo9I0KGNSaTKyi2FqOgsLjvA4wOG8nI0BH8M28Va4vXsyp/NeW2cu5KuuOyibXL5eKd3H9wuPoICgouXHx65t9MjJhAiF5mygp1GPQ6hg+OZ/jgeBbOH4/L5aKotJoT2SWcPF3MiewSjmYVUVFlYdf+HHbtzwEgKNDEVaP6MHl8f64a1Qd/P1l3VvieHpFo3nvvvTz77LNMnjyZ8vJyjLI2mGik0+iI94snx5JLniXPKxLNXeW7ceFiT8XeDiWa2eYc6p31hOhDGBM2uvl4rMk9Tq7EWtpqK251QzVWpxV/rT9+Wj80iobtZTt4I/stGlx2Evzi+eXAh4kytt73OCZsNLsr9lJsdSe0C5PvbO4m1ygaFibfSYQhnH/mf8i64g1oFS13Jt1+yTKtKfqK70o3o6DwiwFL+Hfh52Sbc/gw/18s7nv/Ff8fCdEZFEWhV3QIvaJDmDrBvd2ty+UiO7+MPQfy2Hswj8wj+dTU1rNu81HWbT6KXqdl9PBEJo/rz9SrBhIW4rurYIiexecTzcOHD6PX65k8eTIA4eHhKkckvE1SQFJjopnPuPCxaodDVu0pAM7UFdLgbECv0bfvOjXuBZgHBPa7IJmMMISjU3TYXXbO2c61SBbzLPk8degZ90xf3Osa+mlNWBzu2bkjQ1P5Wb/FzcsZtSY1dAR6RUeDy868uLnMipnZ4pzres0mQBfA37Pf4quitYQbwpkdO+ui19tdvocP8z8CYGHSnYwOG0mIPpjfHXmOLWVbmR41hf5B/dv4vyNE11IUhb5JUfRNiuK2G8dgdzg5cvIsW3ZlsWlXFgVnK9j5fQ47v8/hz298w9Wj+3Ld9KFcPbqv108qEuJSVB8csmnTJubOnUtcXByKovDpp5+2OCcjI4PevXtjMpmYMGECu3btavP1T548SWBgIHPnzmX06NE8//zzHoxe+IKmGdN5lnyVI3FPksm3uHfvcbgcnKkrbPe1mhLWpnGoTTSKhujG5PJi4zT3Vx7AhQulcXNRF67mJPOGXtfzyIAll00yAfx1/jw8IJ0H+tzLgvh5rZ43Oeoabk24GYB/5q1iV+O+6U1cLhf7KjJ5/dTfAZgVM5NZse6ktV9gX66JnATAu7nv43Q5LxuXEN5Ap9UwYnA8P79nKv98+X7+8Zf7+K+FkxnYNwaHw8nm3Vn85o+fMe//vMqf/76BvDPlaocsRLuo3qJpNptJTU3l/vvv56abbmrx+qpVq1i6dCmvvfYaEyZMYPny5cyePZvjx48THe3u5hw5ciR2u73Fe9euXYvdbmfz5s1kZmYSHR3Nddddx7hx45g16+KtJqLnSfZPAiDPkqdyJHC6Nru5JREg15JH74DkK76Oy+XiZHOi2a/F67GmGArrz1JcX8LwHw1tbEpQ70i8lZkxM6hz1GGxWzBoDYQbrqxHIDV0RJvOu6HXHMptFWwo+YbXT/2dYF0wg4IGcqj6MJ8UfMYp82nA3Zp6V9IdF7z3tsSb2VuxjxxLLptKNzPtIrPghfBmiqLQOyGC3gkR3H3TBE7nlfHVd4dZu+koZeW1/Os/3/PxV98zdcIAFi4Yz5D+vdQOWYg2Uz3RnDNnDnPmzGn19ZdeeonFixdz3333AfDaa6/x5Zdf8uabb/LEE08AkJmZ2er74+PjGTt2LImJ7lar66+/nszMTEk0RbNE/wTAPZu6pqGGIH2QarGcrM264HmuOReiJl/xdUqsJdTYa9ApOpIvkqj+MPO86ILjLpeLU+clqHqNHr1GT7A++IpjuBKKovDT5DupbKhkb8U+/nLyFeL84shq/P8waAzMjJ7Ogvh5LcaUhuhDWBA/j/fzPmB1wceMDR9DoC6wU+P1VQsWLGDjxo3MnDmTjz76SO1weqy+SZH8/O6p/Nddk9lzIJePv8pk655TbNxxko07TjJmeBKLbrmK0cOS1A5ViMtSvev8Umw2G3v37iUtLa35mEajIS0tje3bt7fpGuPGjaOkpISKigqcTiebNm1iyJAhrZ5vtVqprq6+4CF8m5/Wj2iju3Vc7e7zptbEpu0xc9vZytp0nd4ByRguMsazKdEsri+54HiJtZQae22rCWpn0igaftZvMf0D+2NxWMiqzUKv6JkdM4s/jfg9dyTddtEZ7gAzo6cT5xdHrb2Wjws+u+g54vIeeeQR3nnnHbXDEI20Wg0TRvXhD08u4J0/38ucaUPRajXsPZjHL57+kNff24zDIcNFhHfz6kSzrKwMh8NBTMyFW9XFxMRQVFTUyrsupNPpeP7555kyZQojRoxgwIAB3Hjjja2ev2zZMkJCQpofTS2hwrd5wzhNp8vZnCCmxcxojqc94w4v1W0ONG//+OMljprun+yf1O5JSB1h0Bh4dODDTAgfx7Uxs/hT6jLuSr6DUMOlly7SaXTcnXwXekVPSCe3vvqyadOmERSkXou+aF3fpEh+8/AcVmX8H+amuYekvPvxTv772X9RUWVROTohWufViaanzJkzh4MHD3Lo0CFeeumlS5775JNPUlVV1fzIz1d/gojofD8kmuqN0yysK6TOUYdJY2Rs2BgMGgM2p61F93ZbnLpMotnUollqLcPutJ/3PvdYyH6tvK8rBOoC+Xn/n7Ew+Q7CrmAv9pTgIbw08k8dWhKqo37/+9+jKAq//OUvPXrdtkyahI5NnBTdQ2xUML9+6Fp+++iN+Jn07DmQywOPv8uRk2fVDk2Ii/LqRDMyMhKtVktx8YWtLsXFxcTGds6euUajkeDg4Asewvd5Q4tmUytk38C+6DQ6khonKeWYryz5PX/memuJZpg+FIPGgBMnZday5uM/zFTve8Xxe4NgFcfX7t69m9dff50RIy49AWrr1q00NDS0OH7kyJEWdV2TpkmTGRkZrV63aeLk008/zb59+0hNTWX27NmUlPwwPGLkyJEMGzasxaOwsP2rGwh1pF0zmNeXLSQxLoySshrS/98HF+y9LoS38OpE02AwMGbMGDZs2NB8zOl0smHDBiZOnNip987IyCAlJYVx41puryd8T1NSV1h3FpuzZRLQFZomvjSNz2yaDZ9ryb2i6zTNXI80RLTaIqgoSnP3eVHjgupWh5X8xkS7tQRVXFxtbS0LFy5kxYoVhIW13grrdDpJT0/nrrvuwuFwNB8/fvw4M2bMYOXKlRd935w5c3j22WdZsGBBq9c+f+JkSkoKr732Gv7+/rz55pvN52RmZnLo0KEWj7i4uHaUWqitb1Ikf//DT5kyYQANdge//fMXfLHhoNphCXEB1RPN2tpaMjMzm2eOZ2dnk5mZSV6euxVn6dKlrFixgpUrV3L06FEeeughzGZz8yz0zpKens6RI0fYvXv35U8W3V64IYwAbQBOnJypO6NKDCdr3K2JTd3WvQMaE80rbNFsbf3MH4tpnADVtJZmtjkHJ05C9aFXvIxRT5eens4NN9xwwcTFi9FoNKxZs4bvv/+ee+65B6fTyalTp5gxYwbz58/n8ccfb9f9PTFxUnRPAf5Gnn3sJ8y7NhWXC37/v1+z+st9aoclRDPVlzfas2cP06dPb36+dOlSABYtWsTbb7/N7bffTmlpKU899RRFRUWMHDmSr776qsUEISE6QlEUkgMSOVJ9jHxLPn0Cel/xNcx2C6drT6NRNGgVLTqNDq2ipbqhmuL6YooaH2a7mXt6/5R+53VPVzfUNG/V2NRt/UOLZh4ulwtFUdoUR9MSSZdrlYz50YSgprUq+wf2bfO9BHzwwQfs27evzX+UxsXF8c033zB58mTuuusutm/fTlpaGq+++mq7Y7jUxMljx9renZqWlsb+/fsxm80kJCSwevXqi/YeZWRkkJGRcUGrrFCPRqPw2INpmIw6Vn2+l7+8+Q119TbuufkqtUMTQv1Ec9q0abhcrkues2TJEpYsWdJFEYmeKsk/iSPVx9o1TtPlcvHSib80d39fzhvZb/G7oU+j07h/BJtaIeP84gjQBQAQ7xePVtFicVgos5Vdcl/xJk6Xs3lCT/+gtiaaJRfEoOZEoO4mPz+fRx55hHXr1mEymdr8vqSkJN59912mTp1K3759eeONN7wiuV+/fn2bzktPTyc9PZ3q6mpCQi69IoDoGoqisGTRNPz9DLz14Xb+9v4WGuwOHrh9UpfHYnc4OXaqiKzsEiqqLFRW11FRZWmeHW806DAYdBgNOkxGHcGBfoSG+BEa7E9osB+hwX4EB/oREuRHgL/BK342RPupnmh6K/mLvedpmhDUnrUrD1QdJKs2C52iI9YUi8Nlx+6yY3c6CNAFEGuKIcYUQ7QxitUF/+JMXSHflHzLtY37ev8wPvOHJE+n0ZHgF0+uJY8cc26bEs2z9UVYHBYMGgOJfgmXPDf2vEXbf7xQu2ibvXv3UlJSwujRo5uPORwONm3axCuvvILVakWrbblPdXFxMQ8++CBz585l9+7dPProo7z88svtjkONiZPC+yiKwgO3T8LPZOB/3/mOtz7czuB+sUwa2/k/0ydOF7PnQC77DuWz/2gBdfWeGeuu1SgEBZoIDDAR6G8gwN9IoL+RgIDGj/6Gxo9G9Dr3z5qiuP/RKAp6vRaDXotRr8Ng1BEZGkBMVLAkr11IEs1WyF/sPU/ThKA8s3vtyh/vQNMal8vFp2f+DbjXv7wz6fbLvYO3ct7h4zOfMSFiPCH6kPO6uy8cV5kckEyuJY9cSx7jwsdeNpaTNe7r9A3o09xa2pqmRLPcVsHZ+iKqGqrRKtp2bXnZU82cOZODBy+cfHHfffcxePBgfv3rX180ySwrK2PmzJkMGTKE1atXc+LECaZNm4bRaOSFF15oVxznT5ycP38+8MPESekN6nnumjeOkrIaPlqzj+df+Yq3XryH6IjOWZHhXIWZ5W9s4NvtJy44HhxoYujAXkSGBxEW4k9YiLu1UqNRsFrtWG3uR721gcrqOqpq6qisdrd+VlXXUV1bR73VjsPporK6jsrqOo/FHBhgZEDvaAb0cT8mj+tPYMDFN4MQHSeJphCNepli0Sk66p31lFnPEW26fAsiwMGqQ5w2Z2PQGLi+13WXPX9K1GS+LfmOHEsuH+b/i/t630N2bQ7ww4zzJr39k9hE2ycEXUmrZJAuCD+tH3WOOradc08YSfJPxKAxtOleAoKCghg2bNgFxwICAoiIiGhxHNzJ35w5c0hOTmbVqlXodDpSUlJYt24dM2bMID4+nkcffbTF+2pra8nK+mFYRtOkyfDwcJKS3H8gLV26lEWLFjF27FjGjx/P8uXLu2TipPBOP79nCgeOneHE6WJ+t/xLlv/2NnRaz83/dblcfPnNIV5ZuZFasxWtRmHCqD6MGZ7E6GFJ9EuOQqPpWKuh1WanuqaOqpp6zBYrtRbreR9t1JqtFxx3OFy4cNE0Gs/pdNLQ4MDa4MDWmNiWltdQa7by/eF8vj/sHiYV6G/k1htHc+sNYwgObPsQGNE2kmgK0Uin0RHvF0+uJZc8S16bEs3zWzNnRE8jRH/51m+NouHu3gv5nyPPs6VsK8n+STS4GgjQBjS3MjY5f0JQW5z80RaWl9K0xFG2OYdtZe5EU7rNO5dGo+H5559n8uTJGAw/JPSpqamsX7+eqKiLf89dbtIkIBMnxQUMeh3PPHoj9//qHTKPFLDyo+0eG695pqiSP762lr0H3fXSwL4xPPHQtQzs69nvNaNBR1REEFEebI1taHCQU3COk9klnMwpYVdmDrlnynnrw+18+MVebrl+NLffOIbgID+P3bOnk0SzFTJGs2dK9k9sTDTzGRs+5rLnH6o6zCnzaQwaA3NiL9+a2aR/YD8mR05ic9lW/pm3CoABQf1bjBtK9E9EQaGqoYpKWyWhhtBWr1lrr+VsvXt3kH5tXHA9xuhONM/ZyhvfJ4lmR23cuPGSr8+aNeuix0eNGtXqe9oyaRJk4qS4UGJcGL/6r1n87i9rWPnRDkYNTWT0sKQOXXNXZg7/34v/xmyxYTToeOCOSdx24xiPtpZ2Jr1e29xlDuB0uti44wRvr97O6bwyVn60g4/W7OPBuyYz/9pUtN2kXN5M/gdbIeto9kxJV9CC6HK5+LTQ3Zo5PWrqZffj/rFbE2/GX+uHE/de5hdrTTRqjfQyxbYppqbZ5rGmGILauENOjCn6gufddUcgIcTFXTslhetnDMPpdPHM8i87tC/6p19n8qvn/oXZYmP44Hje+fO93DVvXLdJMi9Go1GYcfUg3n5xEc89Po/+vaMwW2z8+e8b+K8n3+P46Yvv1iXaTlo0hThPUkDbt6I8VH2YrNpT6BU91/eac8X3CtGHsCB+Pu/l/RNovbs7OSCZwvqz5JhzSQ1tfXvDYzXHgSvr/o41/TAjOUQfTKQhss3vFUJ0D48+MIPDxwvJPVPOI7/9kD88uYBe0W3/w9jhcPK/73zHqi/2AnDdtBQe/9m1GPS+k0JoNApTJwzgmrH9+Gzdfl5/bzPHThWz+Nf/4KbrRnHvrVcRGuyvdpgtOBxOrDY7tgY79VY7dfU26uobGh826q126usbqLe5j1mtDdxyw+guLYvvfJcI4QFNSwKV28qpbaglUB940fPcYzM/B2B69LQrbs1sMjNmOt9XZmK2W+jbSmtisn8S28/tIO8SLZoHqw7xddE6AFKCU9p8//NbNPsFyELtQvgiP5OB/3nsJzz6zGpO55Wx+Nf/4LnH55E65NJLoAFY6mz87i9fsmW3e/z34juv4Z6bJ/hsXaHVarjpulFMnTCQV1ZuZN3mo3y0Zh8ff/U9wwbFMWlsP64Z24+k+PB2/x/Y7Q4sjYmgpc6G2WKjxlxPrdnqflis1NU3YKmz/XBOnQ1z82Qo9zGrzY7D4bzi+8+YNEgSTSHU4q/zJ8oYRam1lLy6fFL0Q1qcc7Imiy/OriGrNgu9oueGNsw0b41W0fLrwY9d8pym5YZyWpl5nm3O4ZWT/4vD5WBC+HgmRkxo8/3Pn3wkE4GE8F19kyJZ8cef8sTvP+XE6WJ++dvVPP6zWcyZ3nJ1hCYFZyv4v3/8jNN5ZRj0Wv7vkjmkXTO4C6NWT0RYAE//8gaunz6MV/+xiROnizlw9AwHjp7h1Xc3ERUeSEiwH34mA34mPf5+BjSKgt3hxOFwYnc4sNudzS2LTa2MljobDfbOmfuh12nx93PH42fSYzLp8TcZMBp0+Jn0GI16/Ix6Avy7diknSTRbIZOBeq5k/0RKraV8kLeaEaHD6O2f7O6+rjvLF2e/5ETNSQAUFG5OWHDJCTqe0DRutMxWRk1DzQXjL4vri3nx+HLqnVZSgoewuO/9bV7/EyBAF0CIPoSqhir6B11+proQovuKjggi439u57lXvmLj9hM898pXnMor475bJ7ZIPrbvPc0zf/mSWrOV8FB/nvvVPIYPjlcpcvWMS01mXOrdFJVUsXXvabbuPsW+w3mUltdSWl7boWsb9Fr8TAb3ovONC9I3fXQnjE1Jo6F5sfqAxkXqA/yMGI3u3ZUMei0Gvc5rJy4prrZMZezBmhZsr6qqIjg4WO1wOt0b3+/luc0bATj9i/9u03u+OHGMX3z15RW9x5ttKP6Wd3L/0errWkXLNZFXc33sdcT6dc2uK4/tf4JSayn+Wn/GhY/l6oiriDXF8OzR31NqLSXZP4knhzyOn/bKl+Q4WHmIM3VnmB17rc92h4nO0dPqR1/hdLp4c9VW3v5oBwAGg44p4/tz3bShjB2exD8+2cUbq7bicsHQgb149rGfeHSJoe7ObLFyOq+ssWv7hy5wF6DTatBqNe6POi3+jYliUyujn6kxiTTq0etbbujgi6RFU4gfmRE9jb4BfThtzibXkkuOOZeCujPoFB3To6cyO/Zawg1hXRrTLQkLWJW/mnJbBd+VbuK70k1oFS0Ol4MoYyRLB/6yXUkmwPDQYQwPbb37TAjhWzQahf9z5zX06x3F3/+5ldwz5azfcoz1W47hZ9I3bx85/9pUfnH/dJ+a9OMJAf7GHtm6217y3SPEjyiKQp/A3vQJ7N18zO60A1x2W8fOclXEBMaHj+N4zQm2lW1nd8Ve6hx1BOkCeWzQ0nZPRhJC9FzTJw5i2lUDOX6qmK++O8z6LceorK7DoNeydHEaN84crnaIwgdIoilEG6iVYJ5Po2gYEjyYIcGDudu5kKPVx4j3iyPSKEsSCSHaR1EUBvePZXD/WNLvmcb3h/OJiQwiOSFC7dCEj1D/t6cQ4ooZNIZLrqkphBBXSq/XMn5kb7XDED7GO6coeYGMjAxSUlIYN26c2qEIIYQQQnRLkmi2QragFEIIIYToGEk0hRBCCCFEp5BEUwghhBBCdApJNIUQQgghRKeQRFMIIYQQQnQKWd7oMpp26KyurlY5kq5RV1uLs74eaHuZLe14jxDeIigoSLbebKeeVj8K0dN4on6Uvc4vo6CggMTERLXDEEJ0Etmnu/2kfhTCt3mifpRE8zKcTieFhYXNWf24ceOalzz68ecbNmwgMTGR/Pz8dn9hzr/mlZ5zseOXO+bN5WlrmXy5PK2VqeljdXV1h8rUlvJc6jxvK09by3T+OdKi2X6Xqh9Bne9X+fm78vI0fa52HSm/w7yvPJ6oH6Xr/DI0Gg0JCQnNz7VabfMXuLXPg4OD2/1NcP51rvScix2/3DFvLk9b4vf18rRWjh+f094ytaU8lzrP28pzqViv9BxxeZeqH89/3pXfr/Lzd+Xl+fHn8jvMM+VpS/zdrTztIZOBrlB6evplP/fU9a/0nIsdv9wxby5Pa6/9+Jgvl+fHz5s+78ryXOo8bytPW6/lyfuJH7T2tZafv7bHcyXneao8VxLL5cjvsMu/1p1/h7WHdJ17UHV1NSEhIT4z5kvK4/18rUy+Vh5xIV/7+kp5vJuUxztIi6YHGY1Gnn76aYxGo9qheISUx/v5Wpl8rTziQr729ZXyeDcpj3eQFk0hhBBCCNEppEVTCCGEEEJ0Ckk0hRBCCCFEp5BEUwghhBBCdApJNIUQQgghRKeQRLOLfPHFFwwaNIgBAwbw97//Xe1wPGLBggWEhYVxyy23qB1Kh+Xn5zNt2jRSUlIYMWIEq1evVjukDqmsrGTs2LGMHDmSYcOGsWLFCrVD8giLxUJycjKPPfaY2qEID/O1OlLqR+8mdWTXkVnnXcBut5OSksK3335LSEgIY8aMYdu2bURERKgdWods3LiRmpoaVq5cyUcffaR2OB1y9uxZiouLGTlyJEVFRYwZM4YTJ04QEBCgdmjt4nA4sFqt+Pv7YzabGTZsGHv27On233O/+c1vyMrKIjExkRdeeEHtcISH+GIdKfWjd5M6sutIi2YX2LVrF0OHDiU+Pp7AwEDmzJnD2rVr1Q6rw6ZNm0ZQUJDaYXhEr169GDlyJACxsbFERkZSXl6ublAdoNVq8ff3B8BqteJyuejuf1OePHmSY8eOMWfOHLVDER7mi3Wk1I/eTerIriOJZhts2rSJuXPnEhcXh6IofPrppy3OycjIoHfv3phMJiZMmMCuXbuaXyssLCQ+Pr75eXx8PGfOnOmK0FvV0TJ5G0+WZ+/evTgcDhITEzs56tZ5ojyVlZWkpqaSkJDAr371KyIjI7so+pY8UZ7HHnuMZcuWdVHE4kr4Wh0p9aN3148gdWR3qiMl0WwDs9lMamoqGRkZF3191apVLF26lKeffpp9+/aRmprK7NmzKSkp6eJI287XyuSp8pSXl3PPPffwt7/9rSvCbpUnyhMaGsr+/fvJzs7m/fffp7i4uKvCb6Gj5fnss88YOHAgAwcO7MqwRRtJfdIzyuMt9SN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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def plot_covariance_estimation_hyperparam_sweep(\n", - " observations_df, y=\"eval\", row=\"batch_size\", col=\"noise_level\", hue=\"eval_idx\"\n", - "):\n", - " fig = plt.figure(figsize=(15, 6))\n", - "\n", - " g = sns.FacetGrid(\n", - " observations_df, col=col, row=row, hue=hue, palette=\"viridis\", sharey=False\n", - " )\n", - " g.map_dataframe(sns.lineplot, x=\"step\", y=\"eval\")\n", - " g.add_legend()\n", - " g.set(xscale=\"log\", yscale=\"log\")\n", - " # g.set(xscale=\"log\", yscale=\"linear\")\n", - "\n", - " plt.suptitle(\"Covariance estimation hyperparameter sweep\")\n", - " g.fig.tight_layout()\n", - " plt.show()\n", - "\n", - "\n", - "covariance_estimation_sweep_df = covariance_estimation_sweep_df.dropna()\n", - "plot_covariance_estimation_hyperparam_sweep(covariance_estimation_sweep_df)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Calibrating Burn-in Period\n", - "\n", - "We'll want to use a burn-in period to avoid including the initial transient in our analysis. We'll want to use a burn-in period that's long enough to ensure that the weights have diverged from their initial values (and thus that there is any meaningful covariance to analyze).\n", - "\n", - "To avoid having to repeat the covariance analysis for a bunch of different burn-in periods, we'll follow an online approach where we compute a novel covariance estimate for increasing numbers of sampling time steps." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Sweeping hyperparameters: 0%| | 0/5 [00:00\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
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6898200001.464869192.4314531.3846832.0636624.6824381.9837534.655952...2.3720471.7098415.2258291.9208131.0785843.9409671.9693152.3673945.0051671.326375
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6900 rows × 115 columns

\n", - "" - ], - "text/plain": [ - " step eval eval_idx draw_idx llc/mean llc/std \\\n", - "0 1 -1006.388550 0 0 -73.289040 8.219649 \n", - "1 1 0.410458 1 0 -73.289040 8.219649 \n", - "2 1 0.726697 2 0 -73.289040 8.219649 \n", - "3 1 -961.311157 0 1 -73.289040 8.219649 \n", - "4 1 0.801542 1 1 -73.289040 8.219649 \n", - "... ... ... ... ... ... ... \n", - "6895 20000 1.312034 1 8 2.431453 1.384683 \n", - "6896 20000 1.616121 2 8 2.431453 1.384683 \n", - "6897 20000 -206.976318 0 9 2.431453 1.384683 \n", - "6898 20000 1.464869 1 9 2.431453 1.384683 \n", - "6899 20000 1.561731 2 9 2.431453 1.384683 \n", - "\n", - " llc-chain/0 llc-chain/1 llc-chain/2 llc-chain/3 ... llc-chain/90 \\\n", - "0 -74.140991 -65.367905 -70.472008 -86.891708 ... NaN \n", - "1 -74.140991 -65.367905 -70.472008 -86.891708 ... NaN \n", - "2 -74.140991 -65.367905 -70.472008 -86.891708 ... NaN \n", - "3 -74.140991 -65.367905 -70.472008 -86.891708 ... NaN \n", - "4 -74.140991 -65.367905 -70.472008 -86.891708 ... NaN \n", - "... ... ... ... ... ... ... \n", - "6895 2.063662 4.682438 1.983753 4.655952 ... 2.372047 \n", - "6896 2.063662 4.682438 1.983753 4.655952 ... 2.372047 \n", - "6897 2.063662 4.682438 1.983753 4.655952 ... 2.372047 \n", - "6898 2.063662 4.682438 1.983753 4.655952 ... 2.372047 \n", - "6899 2.063662 4.682438 1.983753 4.655952 ... 2.372047 \n", - "\n", - " llc-chain/91 llc-chain/92 llc-chain/93 llc-chain/94 llc-chain/95 \\\n", - "0 NaN NaN NaN NaN NaN \n", - "1 NaN NaN NaN NaN NaN \n", - "2 NaN NaN NaN NaN NaN \n", - "3 NaN NaN NaN NaN NaN \n", - "4 NaN NaN NaN NaN NaN \n", - "... ... ... ... ... ... \n", - "6895 1.709841 5.225829 1.920813 1.078584 3.940967 \n", - "6896 1.709841 5.225829 1.920813 1.078584 3.940967 \n", - "6897 1.709841 5.225829 1.920813 1.078584 3.940967 \n", - "6898 1.709841 5.225829 1.920813 1.078584 3.940967 \n", - "6899 1.709841 5.225829 1.920813 1.078584 3.940967 \n", - "\n", - " llc-chain/96 llc-chain/97 llc-chain/98 llc-chain/99 \n", - "0 NaN NaN NaN NaN \n", - "1 NaN NaN NaN NaN \n", - "2 NaN NaN NaN NaN \n", - "3 NaN NaN NaN NaN \n", - "4 NaN NaN NaN NaN \n", - "... ... ... ... ... \n", - "6895 1.969315 2.367394 5.005167 1.326375 \n", - "6896 1.969315 2.367394 5.005167 1.326375 \n", - "6897 1.969315 2.367394 5.005167 1.326375 \n", - "6898 1.969315 2.367394 5.005167 1.326375 \n", - "6899 1.969315 2.367394 5.005167 1.326375 \n", - "\n", - "[6900 rows x 115 columns]" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Let's try to understand the dependence of the covariance analysis on the number of burnin steps\n", - "\n", - "NUM_STEPS_BW_DRAWS_COV = 5\n", - "\n", - "\n", - "class OnlineCovarianceAccumulator(SamplerCallback):\n", - " \"\"\"\n", - " A callback to iteratively compute and store the covariance matrix of model weights.\n", - " For use with `estimate`.\n", - "\n", - " Attributes:\n", - " num_weights (int): Total number of weights.\n", - " first_moment (torch.Tensor): First moment of weights.\n", - " second_moment (torch.Tensor): Second moment of weights.\n", - " num_draws (int): Number of draws made to accumulate moments.\n", - " accessors (List[WeightAccessor]): Functions to access model weights.\n", - " num_evals (int): Number of eigenvalues to compute.\n", - " \"\"\"\n", - "\n", - " def __init__(\n", - " self,\n", - " num_draws_per_chain: int,\n", - " num_chains: int,\n", - " num_weights: int,\n", - " accessors: List[WeightAccessor],\n", - " device=\"cpu\",\n", - " num_evals=3,\n", - " ):\n", - " \"\"\"\n", - " Initialize the accumulator.\n", - " \"\"\"\n", - " self.num_weights = num_weights\n", - " self.first_moment = torch.zeros(num_draws_per_chain, num_weights, device=device)\n", - " self.second_moment = torch.zeros(\n", - " num_draws_per_chain, num_weights, num_weights, device=device\n", - " )\n", - " self.num_draws_per_chain = num_draws_per_chain\n", - " self.num_chains = num_chains\n", - " self.accessors = accessors\n", - " self.num_evals = num_evals\n", - " self.is_finished = False\n", - "\n", - " def accumulate(self, model: nn.Module, draw: int):\n", - " \"\"\"Accumulate moments from model weights.\"\"\"\n", - " assert not self.is_finished, \"Cannot accumulate after finalizing.\"\n", - "\n", - " weights = torch.cat(\n", - " [accessor(model).view((-1,)) for accessor in self.accessors]\n", - " )\n", - " self.first_moment[draw] += weights / self.num_chains\n", - " self.second_moment[draw] += torch.outer(weights, weights) / (\n", - " self.num_chains - 1\n", - " )\n", - " # print(self.first_moment.mean(), self.second_moment.mean())\n", - "\n", - " def finalize(self):\n", - " \"\"\"Finalize the moments by dividing by the number of draws.\"\"\"\n", - " self.is_finished = True\n", - "\n", - " def reset(self):\n", - " \"\"\"Reset the accumulator.\"\"\"\n", - " self.first_moment.zero_()\n", - " self.second_moment.zero_()\n", - " self.is_finished = False\n", - "\n", - " def to_matrix(self):\n", - " \"\"\"Convert the moments to a covariance matrix.\"\"\"\n", - " covariance = self.second_moment\n", - "\n", - " for d in range(self.num_draws_per_chain):\n", - " first_moment = self.first_moment[d]\n", - " covariance[d] -= (self.num_chains / (self.num_chains - 1)) * torch.outer(\n", - " first_moment, first_moment\n", - " )\n", - "\n", - " return covariance\n", - "\n", - " def to_eigen(self, include_matrix=False):\n", - " \"\"\"Convert the covariance matrix to pairs of eigenvalues and vectors.\"\"\"\n", - " cov = self.to_matrix().detach().cpu().numpy()\n", - "\n", - " evals = np.zeros((self.num_evals, self.num_draws_per_chain))\n", - " evecs = np.zeros((self.num_evals, self.num_draws_per_chain, self.num_weights))\n", - "\n", - " for d in range(self.num_draws_per_chain):\n", - " _evals, _evecs = eigsh(cov[d], k=self.num_evals, which=\"LM\")\n", - " evals[:, d], evecs[:, d, :] = _evals, _evecs.T\n", - "\n", - " results = {\"evals\": evals, \"evecs\": evecs}\n", - "\n", - " if include_matrix:\n", - " results[\"matrix\"] = cov\n", - "\n", - " return results\n", - "\n", - " def get_results(self):\n", - " return self.to_eigen(include_matrix=True)\n", - "\n", - " def __call__(self, model, draw, **kwargs):\n", - " self.accumulate(model, draw)\n", - "\n", - "\n", - "def sweep_online_covariance_estimation_hyperparams(\n", - " model,\n", - " dataset,\n", - " steps,\n", - " checkpoints,\n", - " grid: dict,\n", - " device=DEVICE,\n", - " sgld_kwargs=None,\n", - " loader_kwargs=None,\n", - " sampling_kwargs=None,\n", - "):\n", - " observations = []\n", - " accessors = {\n", - " \"weight\": lambda model: model.embedding.weight,\n", - " \"bias\": lambda model: model.unembedding.bias,\n", - " }\n", - " num_weights = sum([v(model).numel() for v in accessors.values()])\n", - " hyperparam_combos = list(itertools.product(*grid.values()))\n", - "\n", - " sgld_kwargs = sgld_kwargs or {}\n", - " sgld_kwargs.setdefault(\"localization\", 0.0)\n", - " sgld_kwargs.setdefault(\"lr\", 0.01)\n", - "\n", - " loader_kwargs = loader_kwargs or {\"batch_size\": 100}\n", - "\n", - " sampling_kwargs = sampling_kwargs or {}\n", - " sampling_kwargs.setdefault(\"num_draws\", NUM_DRAWS_COV)\n", - " sampling_kwargs.setdefault(\"num_chains\", NUM_CHAINS_COV)\n", - " sampling_kwargs.setdefault(\"num_burnin_steps\", NUM_BURNIN_COV)\n", - " sampling_kwargs.setdefault(\"num_steps_bw_draws\", NUM_STEPS_BW_DRAWS_COV)\n", - "\n", - " for i, hyperparam_values in tqdm(\n", - " enumerate(hyperparam_combos),\n", - " total=len(hyperparam_combos),\n", - " desc=\"Sweeping hyperparameters\",\n", - " ):\n", - " torch.manual_seed(i)\n", - " hyperparams = dict(zip(grid.keys(), hyperparam_values))\n", - "\n", - " _sgld_kwargs = sgld_kwargs.copy()\n", - " _loader_kwargs = loader_kwargs.copy()\n", - " _sampling_kwargs = sampling_kwargs.copy()\n", - "\n", - " for key in hyperparams:\n", - " if key in _sgld_kwargs:\n", - " _sgld_kwargs[key] = hyperparams[key]\n", - " elif key in _loader_kwargs:\n", - " _loader_kwargs[key] = hyperparams[key]\n", - " elif key in _sampling_kwargs:\n", - " _sampling_kwargs[key] = hyperparams[key]\n", - " else:\n", - " raise ValueError(f\"Unknown hyperparameter {key}\")\n", - "\n", - " loader = DataLoader(dataset, shuffle=True, **_loader_kwargs)\n", - "\n", - " callbacks = [\n", - " OnlineCovarianceAccumulator(\n", - " _sampling_kwargs[\"num_draws\"],\n", - " _sampling_kwargs[\"num_chains\"],\n", - " num_weights,\n", - " list(accessors.values()),\n", - " device=device,\n", - " ),\n", - " ]\n", - "\n", - " for step, checkpoint in tqdm(\n", - " zip(steps, checkpoints), desc=\"Sweeping checkpoints\", total=len(steps)\n", - " ):\n", - " model.load_state_dict({k: torch.Tensor(v) for k, v in checkpoint.items()})\n", - "\n", - " observation = estimate_learning_coeff_with_summary(\n", - " model,\n", - " loader,\n", - " evaluate=evaluate_mse,\n", - " device=device, # type: ignore\n", - " sampling_method=SGLD,\n", - " optimizer_kwargs=_sgld_kwargs,\n", - " verbose=False,\n", - " callbacks=callbacks,\n", - " seed=i * _sampling_kwargs[\"num_chains\"],\n", - " **_sampling_kwargs,\n", - " )\n", - "\n", - " evals, _, _ = (\n", - " observation.pop(\"evals\"),\n", - " observation.pop(\"evecs\"),\n", - " observation.pop(\"matrix\"),\n", - " )\n", - "\n", - " for draw in range(_sampling_kwargs[\"num_draws\"]):\n", - " for j, eval in enumerate(evals[:, draw]):\n", - " observations.append(\n", - " {\n", - " \"step\": step,\n", - " \"eval\": eval,\n", - " \"eval_idx\": j,\n", - " \"draw_idx\": draw,\n", - " **observation,\n", - " **_loader_kwargs,\n", - " **_sgld_kwargs,\n", - " **_sampling_kwargs,\n", - " }\n", - " )\n", - "\n", - " for callback in callbacks:\n", - " callback.reset()\n", - "\n", - " observations_df = pd.DataFrame(observations)\n", - " return observations_df\n", - "\n", - "\n", - "model = ToyAutoencoder(NUM_FEATURES, NUM_HIDDEN_UNITS, final_bias=True)\n", - "online_covariance_estimation_sweep_df = sweep_online_covariance_estimation_hyperparams(\n", - " model,\n", - " dataset_double,\n", - " STEPS,\n", - " weights,\n", - " loader_kwargs={\"batch_size\": 10},\n", - " sampling_kwargs={\"num_burnin_steps\": 0, \"num_steps_bw_draws\": 5},\n", - " grid={\"num_draws\": [10], \"num_chains\": [5, 10, 20, 50, 100]},\n", - ")\n", - "online_covariance_estimation_sweep_df" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "online_covariance_estimation_sweep_df = online_covariance_estimation_sweep_df.dropna()\n", - "plot_covariance_estimation_hyperparam_sweep(\n", - " online_covariance_estimation_sweep_df, col=\"draw_idx\", row=\"num_chains\"\n", - ")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "devinterp-2EHw3yLJ-py3.11", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.18" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/pyproject.toml b/pyproject.toml index 2ec2a8fe..d9c784e6 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -3,44 +3,48 @@ requires = ["setuptools>=61.0.0", "wheel"] [project] name = "devinterp" -version = "1.3.2" +version = "2.0.0" description = "A library for doing research on developmental interpretability" -# license = "LICENSE" +license = "MIT" +license-files = ["LICENSE"] readme = "README.md" -requires-python = ">=3.8" -dynamic = ["dependencies"] +requires-python = ">=3.10" +dependencies = [ + "datasets>=2.14.0", + "numpy>=1.23.5", + "pandas>=1.5.3", + "pydantic>=2.0.0", + "torch>=2.0.1", + "tqdm>=4.65.0", + "transformers", + "xarray>=2024.1.0", + "zarr>=3.0.0", +] classifiers = [ - "Programming Language :: Python :: 3.8", - "Programming Language :: Python :: 3.9", "Programming Language :: Python :: 3.10", "Programming Language :: Python :: 3.11", - # "License :: OSI Approved :: MIT License", "Operating System :: OS Independent", ] -[tool.setuptools.dynamic] -dependencies = {file = ["requirements.txt"]} - -[project.optional-dependencies] -docs = ["sphinx", "sphinx-math-dollar", "sphinx-rtd-theme", "sphinx-autobuild", "myst_parser"] -vis = ["plotly>=5.20.0"] - [dependency-groups] dev = [ "pytest==7.4.3", "pytest-cov>=6.0.0", "pytest-xdist>=3.6.1", - "transformers", - "datasets", - "transformer_lens", "syrupy" ] +docs = [ + "sphinx", + "sphinx-rtd-theme", + "sphinx-math-dollar", + "sphinx-autobuild", + "myst_parser", +] [project.urls] "Homepage" = "https://github.com/timaeus-research/devinterp" "Bug Tracker" = "https://github.com/timaeus-research/devinterp/issues" -[tool.pyright] -venv = "../../.venv" -venvPath = "." +[tool.setuptools] +package-dir = {"" = "src"} diff --git a/requirements.txt b/requirements.txt deleted file mode 100644 index 71c085b3..00000000 --- a/requirements.txt +++ /dev/null @@ -1,7 +0,0 @@ -einops>=0.6.1 -matplotlib>=3.7.5 -numpy>=1.23.5 -pandas>=1.5.3 -torch>=2.0.1 -tqdm>=4.65.0 -cloudpickle>=3.0.0 \ No newline at end of file diff --git a/src/devinterp/backends/__init__.py b/src/devinterp/backends/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/src/devinterp/backends/default/__init__.py b/src/devinterp/backends/default/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/src/devinterp/backends/default/slt/__init__.py b/src/devinterp/backends/default/slt/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/src/devinterp/backends/default/slt/sampler.py b/src/devinterp/backends/default/slt/sampler.py deleted file mode 100644 index e09f488a..00000000 --- a/src/devinterp/backends/default/slt/sampler.py +++ /dev/null @@ -1,408 +0,0 @@ -import os -import warnings -from copy import deepcopy -from typing import Dict, List, Literal, Optional, Type, Union - -import cloudpickle -import numpy as np -import torch -from devinterp.optim.sgld import SGLD -from devinterp.slt.callback import SamplerCallback, validate_callbacks -from devinterp.slt.llc import LLCEstimator, OnlineLLCEstimator -from devinterp.slt.mala import MalaAcceptanceRate -from devinterp.slt.norms import NoiseNorm -from devinterp.utils import ( - EvaluateFn, - cycle, - default_nbeta, - get_init_loss_multi_batch, - prepare_input, - split_results, -) -from torch import nn -from torch.multiprocessing import cpu_count, get_context -from torch.utils.data import DataLoader -from tqdm import tqdm - - -def sample_single_chain( - ref_model: nn.Module, - loader: DataLoader, - evaluate: EvaluateFn, - sampling_method_kwargs: Dict, - num_draws=100, - num_burnin_steps=0, - num_steps_bw_draws=1, - gradient_accumulation_steps=1, - sampling_method: Type[torch.optim.Optimizer] = SGLD, - chain: int = 0, - seed: Optional[int] = None, - verbose: bool = True, - device: torch.device = torch.device("cpu"), - optimize_over_per_model_param: Optional[dict] = None, - callbacks: List[SamplerCallback] = [], - dtype: torch.dtype = ( - torch.bfloat16 - if os.environ.get("BF16") - else torch.float16 - if os.environ.get("FP16") - else torch.float32 - ), - **kwargs, -): - if gradient_accumulation_steps > 1: - assert isinstance(gradient_accumulation_steps, int), ( - "gradient_accumulation_steps must be an integer." - ) - num_steps_bw_draws *= gradient_accumulation_steps - num_burnin_steps *= gradient_accumulation_steps - - if num_draws > len(loader): - warnings.warn( - "You are taking more sample batches than there are dataloader batches available, this removes some randomness from sampling but is probably fine. (All sample batches beyond the number dataloader batches are cycled from the start, f.e. 9 samples from [A, B, C] would be [B, A, C, B, A, C, B, A, C].)" - ) - - # Initialize new model and optimizer for this chain - model = deepcopy(ref_model).to(device) - - if "temperature" in sampling_method_kwargs: - assert "nbeta" not in sampling_method_kwargs, ( - "Set either nbeta or temperature in sampling_method_kwargs, not both" - ) - sampling_method_kwargs["nbeta"] = sampling_method_kwargs.pop("temperature") - - assert "nbeta" in sampling_method_kwargs, "Set nbeta in sampling_method_kwargs" - - optimizer_metrics = sampling_method_kwargs.get("metrics", []) - if any(isinstance(callback, MalaAcceptanceRate) for callback in callbacks): - optimizer_metrics.extend(["dws", "localization_loss"]) - - if any(isinstance(callback, NoiseNorm) for callback in callbacks): - optimizer_metrics.extend(["noise"]) - - sampling_method_kwargs["metrics"] = optimizer_metrics - sampling_method_kwargs.setdefault( - "nbeta", - default_nbeta(loader, gradient_accumulation_steps=gradient_accumulation_steps), - ) - - if optimize_over_per_model_param: - param_groups = [] - for name, parameter in model.named_parameters(): - param_groups.append( - { - "params": parameter, - "optimize_over": optimize_over_per_model_param[name].to(device), - } - ) - optimizer = sampling_method( - param_groups, - **sampling_method_kwargs, - ) - else: - optimizer = sampling_method(model.parameters(), **sampling_method_kwargs) - - if seed is not None: - torch.manual_seed(seed) - - num_steps = num_draws * num_steps_bw_draws + num_burnin_steps - - cumulative_loss = 0 - with tqdm( - desc=f"Chain {chain}", - total=num_steps // gradient_accumulation_steps, - disable=not verbose, - ) as pbar: - model.train() - for i, data in zip(range(num_steps), cycle(loader)): - model.train() - data = prepare_input(data, device) - with torch.autocast( - device_type=device.type, - dtype=dtype, - enabled=dtype != torch.float32, - ): - results = evaluate(model, data) - loss, results = split_results(results) - - loss /= gradient_accumulation_steps - cumulative_loss += loss - loss.backward() - - # i+1 instead of i so that the gradient accumulates to an entire batch first - # otherwise the first draw happens after batch_size/gradient_accumulation_steps samples instead of batch_size samples - if (i + 1) % gradient_accumulation_steps == 0: - optimizer.step() - - if ( - i >= num_burnin_steps - and (i + 1 - num_burnin_steps) % num_steps_bw_draws == 0 - ): - draw = ( - i - num_burnin_steps - ) // num_steps_bw_draws # required for locals() - loss = cumulative_loss - - with torch.no_grad(): - for callback in callbacks: - callback(**locals()) # Cursed. This is the way - - if (i + 1) % gradient_accumulation_steps == 0: - optimizer.zero_grad() - cumulative_loss = 0 - pbar.update(1) - - -def _sample_single_chain(kwargs): - pickled_args = ["evaluate", "loader"] - evaluate = cloudpickle.loads(kwargs["evaluate"]) - loader = cloudpickle.loads(kwargs["loader"]) - kwargs = {k: v for k, v in kwargs.items() if k not in pickled_args} - return sample_single_chain(**kwargs, evaluate=evaluate, loader=loader) - - -def get_args(chain_idx: int, seeds: List[int], device, callbacks, shared_kwargs): - if isinstance(device, list): - instance_device = device[chain_idx % len(device)] - else: - instance_device = device - return dict( - chain=chain_idx, - seed=seeds[chain_idx], - device=instance_device, - callbacks=callbacks, - **shared_kwargs, - ) - - -def sample( - model: torch.nn.Module, - loader: DataLoader, - callbacks: List[SamplerCallback], - evaluate: Optional[EvaluateFn] = None, - sampling_method: Type[torch.optim.Optimizer] = SGLD, - sampling_method_kwargs: Optional[ - Dict[str, Union[float, Literal["adaptive"]]] - ] = None, - num_draws: int = 100, - num_chains: int = 10, - num_burnin_steps: int = 0, - num_steps_bw_draws: int = 1, - init_loss: float = None, - gradient_accumulation_steps: int = 1, - cores: Union[int, List[Union[str, torch.device]]] = 1, - seed: Optional[Union[int, List[int]]] = None, - device: Union[torch.device, str] = torch.device("cpu"), - verbose: bool = True, - optimize_over_per_model_param: Optional[Dict[str, List[bool]]] = None, - gpu_idxs: Optional[List[int]] = None, - batch_size: bool = 1, - dtype: torch.dtype = ( - torch.bfloat16 - if os.environ.get("BF16") - else torch.float16 - if os.environ.get("FP16") - else torch.float32 - ), - **kwargs, -): - """ - Sample model weights using a given sampling_method, supporting multiple chains/cores, - and calculate the observables (loss, llc, etc.) for each callback passed along. - The :python:`update`, :python:`finalize` and :python:`sample` methods of each :func:`~devinterp.slt.callback.SamplerCallback` are called - during sampling, after sampling, and at :python:`sampler_callback_object.get_results()` respectively. - - After calling this function, the stats of interest live in the callback object. - - :param model: The neural network model. - :type model: torch.nn.Module - :param loader: DataLoader for input data. - :type loader: DataLoader - :param evaluate: Maps a model and batch of data to an object with a loss attribute. - :type evaluate: EvaluateFn - :param callbacks: list of callbacks, each of type SamplerCallback - :type callbacks: list[SamplerCallback] - :param sampling_method: Sampling method to use (a PyTorch optimizer under the hood). Default is SGLD - :type sampling_method: torch.optim.Optimizer, optional - :param sampling_method_kwargs: Keyword arguments for the PyTorch optimizer (used as sampler here). Default is None (using standard SGLD parameters as defined in the SGLD class) - :type sampling_method_kwargs: dict, optional - :param num_draws: Number of samples to draw. Default is 100 - :type num_draws: int, optional - :param num_chains: Number of chains to run. Default is 10 - :type num_chains: int, optional - :param num_burnin_steps: Number of burn-in steps before sampling. Default is 0 - :type num_burnin_steps: int, optional - :param num_steps_bw_draws: Number of steps between each draw. Default is 1 - :type num_steps_bw_draws: int, optional - :param init_loss: Initial loss for use in `LLCEstimator` and `OnlineLLCEstimator` - :type init_loss: float, optional - :param cores: Number or list of cores for parallel execution. Default is 1 - :type cores: int, optional - :param seed: Random seed(s) for sampling. Each chain gets a different (deterministic) seed if this is passed. Default is None - :type seed: int, optional - :param device: Device to perform computations on, e.g., 'cpu' or 'cuda'. Default is 'cpu' - :type device: str or torch.device, optional - :param verbose: whether to print sample chain progress. Default is True - :type verbose: bool, optional - :param optimize_over_per_model_param: Dictionary of booleans indicating whether to optimize over each parameter of the model. \ - Keys are parameter names, and values are boolean tensors that match the shape of the parameter. \ - A value of True (or 1) indicates that this particular element of the parameter should be optimized over. \ - None by default, which means that we optimize over all parameters. - :type optimize_over_per_model_param: dict, optional - :param dtype: Data type for sampling. Default is bfloat16 if BF16 is True, float16 if FP16 is True, or float32 otherwise. - :type dtype: torch.dtype - :raises ValueError: if derivative callbacks (f.e. :func:`~devinterp.slt.loss.OnlineLossStatistics`) are passed before base callbacks (f.e. :func:`~devinterp.slt.llc.OnlineLLCEstimator`) - :raises Warning: if num_burnin_steps < num_draws - :raises Warning: if num_draws > len(loader) - :raises Warning: if using seeded runs - - :returns: None (access LLCs or other observables through `callback_object.get_results()`) - """ - if num_burnin_steps < num_draws: - warnings.warn( - "You are taking more draws than burn-in steps, your LLC estimates will likely be underestimates. Please check LLC chain convergence." - ) - if num_draws > len(loader): - warnings.warn( - "You are taking more sample batches than there are dataloader batches available, " - "this removes some randomness from sampling but is probably fine. (All sample batches " - "beyond the number dataloader batches are cycled from the start, f.e. 9 samples from [A, B, C] would be [B, A, C, B, A, C, B, A, C].)" - ) - if not init_loss: - if seed is not None and not isinstance(seed, int): - init_loss_seed = seed[0] - else: - init_loss_seed = seed - init_loss = get_init_loss_multi_batch( - loader, - num_chains * gradient_accumulation_steps, - model, - evaluate, - device, - init_loss_seed, - ) - # alternative: init_loss = get_init_loss_full_batch(loader, model, evaluate, device) - # alternative: init_loss = get_init_loss_one_batch(loader, model, evaluate, device) - for callback in callbacks: - if isinstance(callback, (OnlineLLCEstimator, LLCEstimator)): - setattr(callback, "init_loss", init_loss) - - # Temperature consistency warning - if sampling_method_kwargs is not None and ( - "nbeta" in sampling_method_kwargs or "temperature" in sampling_method_kwargs - ): - if "nbeta" in sampling_method_kwargs: - assert not any( - getattr(callback, "temperature", None) is not None - for callback in callbacks - ), ( - "If you're setting nbeta in sampling_method_kwargs, don't set temperature in the callbacks." - ) - if "temperature" in sampling_method_kwargs: - assert not any( - ( - getattr(callback, "nbeta", None) is not None - and getattr(callback, "temperature") is None - ) - for callback in callbacks - ), ( - "If you're setting temperature in sampling_method_kwargs, don't set nbeta in the callbacks." - ) - warnings.warn( - "If you're setting a nbeta or temperature in sampling_method_kwargs, please also make sure to set it in the callbacks." - ) - - if cores is None: - cores = min(cpu_count(), 4) - - if isinstance(device, str): - device = torch.device(device) - - if device.type == "cuda": - if gpu_idxs is not None: - assert cores >= len(gpu_idxs), ( - "Number of cores must be greater than number of devices." - ) - else: - assert gpu_idxs is None, ( - "Multi-GPU sampling is only supported for CUDA devices. Check your device parameter." - ) - - if seed is not None: - warnings.warn( - "You are using seeded runs, for full reproducibility check https://pytorch.org/docs/stable/notes/randomness.html" - ) - if isinstance(seed, int): - seeds = np.random.SeedSequence(seed).generate_state(num_chains) - elif len(seed) != num_chains: - raise ValueError("Length of seed list must match number of chains") - else: - seeds = seed - else: - seeds = [None] * num_chains - - if evaluate is None: - # NOTE: If you'd like to update evaluate, please update _sample_single_chain as well. - def evaluate(model, data): - return model(data) - - validate_callbacks(callbacks) - - shared_kwargs = dict( - ref_model=model, - loader=cloudpickle.dumps(loader), - evaluate=cloudpickle.dumps(evaluate), - num_draws=num_draws, - num_burnin_steps=num_burnin_steps, - num_steps_bw_draws=num_steps_bw_draws, - init_loss=init_loss, - gradient_accumulation_steps=gradient_accumulation_steps, - sampling_method=sampling_method, - sampling_method_kwargs=sampling_method_kwargs, - verbose=verbose, - optimize_over_per_model_param=optimize_over_per_model_param, - dtype=dtype, - ) - - if cores > 1: - # mp.spawn( - # _sample_single_chain_mp, - # args=(seeds, shared_kwargs), - # nprocs=cores, - # join=True, - # start_method="spawn", - # ) - - if gpu_idxs is not None: - device = [torch.device(f"cuda:{i}") for i in gpu_idxs] - ctx = get_context("spawn") - with ctx.Pool(cores) as pool: - pool.map( - _sample_single_chain, - [ - get_args(i, seeds, device, callbacks, shared_kwargs) - for i in range(num_chains) - ], - ) - else: - for i in range(num_chains): - _sample_single_chain(get_args(i, seeds, device, callbacks, shared_kwargs)) - - for callback in callbacks: - if hasattr(callback, "finalize"): - callback.finalize() - - results = {} - - if isinstance(callbacks, dict): - for name, callback in callbacks.items(): - if name == "": - results.update(callback.get_results()) - else: - results[name] = callback.get_results() - else: - for callback in callbacks: - if hasattr(callback, "get_results"): - results.update(callback.get_results()) - - return results diff --git a/src/devinterp/backends/tpu/__init__.py b/src/devinterp/backends/tpu/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/src/devinterp/backends/tpu/slt/__init__.py b/src/devinterp/backends/tpu/slt/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/src/devinterp/backends/tpu/slt/sampler.py b/src/devinterp/backends/tpu/slt/sampler.py deleted file mode 100644 index e4c17289..00000000 --- a/src/devinterp/backends/tpu/slt/sampler.py +++ /dev/null @@ -1,469 +0,0 @@ -import os -import warnings -from copy import deepcopy -from itertools import cycle -from typing import Any, Callable, Dict, List, Literal, Optional, Tuple, Type, Union - -import numpy as np -import torch -import torch_xla.core.xla_model as xm -import torch_xla.runtime as xr -from devinterp.optim.sgld import SGLD -from devinterp.slt.callback import SamplerCallback, validate_callbacks -from devinterp.slt.llc import LLCEstimator, OnlineLLCEstimator -from devinterp.slt.mala import MalaAcceptanceRate -from devinterp.utils import ( - USE_TPU_BACKEND, - get_init_loss_multi_batch, - prepare_input, - set_seed, -) -from torch import nn -from torch.multiprocessing import cpu_count, get_context -from torch.utils.data import DataLoader -from torch_xla.amp import autocast -from tqdm import trange - - -def mark_step_if_xla(device): - if USE_TPU_BACKEND and str(device).startswith( - "xla:" - ): # not ideal, but allows CPU process while TPU is running - xm.mark_step() - - -def sample_single_chain( - ref_model: nn.Module, - loader: torch.utils.data.DataLoader, - evaluate: Callable[[nn.Module, torch.Tensor], Tuple[torch.Tensor, Dict[str, Any]]], - sampling_method_kwargs: Dict, - num_draws=100, - num_burnin_steps=0, - num_steps_bw_draws=1, - gradient_accumulation_steps: int = 1, - sampling_method: Type[torch.optim.Optimizer] = SGLD, - scheduler_cls: Optional[Type[torch.optim.lr_scheduler._LRScheduler]] = None, - scheduler_kwargs: Optional[Dict] = None, - chain: int = 0, - seed: Optional[int] = None, - verbose=True, - device: Union[str, torch.device] = torch.device("xla"), - callbacks: List[Callable] = [], - optimize_over_per_model_param: Optional[Dict[str, torch.Tensor]] = None, - init_noise: Optional[float] = None, - use_alternate_batching=False, # See George's alternate SGLD sampling method - dtype: Optional[torch.dtype] = ( - torch.bfloat16 - if os.environ.get("BF16") - else torch.float16 - if os.environ.get("FP16") - else torch.float32 - ), - **kwargs, -): - """ - Base function to sample a single chain. This function is called by the `sample` function on both single and multi-core setups. - """ - - # == Model == - model = deepcopy(ref_model).to(device) - ref_model.to("cpu") - - if seed is not None: - set_seed(seed, device=device) - - # == Optimizer == - - # Temperature consistency warning - if sampling_method_kwargs is not None and ( - "nbeta" in sampling_method_kwargs or "temperature" in sampling_method_kwargs - ): - if "nbeta" in sampling_method_kwargs: - assert not any( - getattr(callback, "temperature", None) is not None - for callback in callbacks - ), ( - "If you're setting nbeta in sampling_method_kwargs, don't set temperature in the callbacks." - ) - if "temperature" in sampling_method_kwargs: - assert not any( - ( - getattr(callback, "nbeta", None) is not None - and getattr(callback, "temperature") is None - ) - for callback in callbacks - ), ( - "If you're setting temperature in sampling_method_kwargs, don't set nbeta in the callbacks." - ) - warnings.warn( - "If you're setting a nbeta or temperature in sampling_method_kwargs, please also make sure to set it in the callbacks." - ) - - optimizer_metrics = sampling_method_kwargs.get("metrics", []) - if any(isinstance(callback, MalaAcceptanceRate) for callback in callbacks): - optimizer_metrics.extend(["dws", "localization_loss"]) - - sampling_method_kwargs["metrics"] = optimizer_metrics - - param_groups = [] - - for name, parameter in model.named_parameters(): - if optimize_over_per_model_param: - if name in optimize_over_per_model_param: - param_groups.append( - { - "params": parameter, - "optimize_over": optimize_over_per_model_param[name].to(device), - } - ) - else: - param_groups.append(parameter) - - optimizer = sampling_method( - param_groups, - **sampling_method_kwargs, - ) - - # == (Optional) Init Noise == - if init_noise: - for pg in optimizer.param_groups: - params = pg["params"] - for parameter in params: - optimize_over = pg.get("optimize_over", 1.0) or 1.0 - noise_term = ( - torch.randn_like(parameter.data) * init_noise * optimize_over - ) - parameter.data.add_(noise_term) - - # == (Optional) Scheduler == - scheduler = None - - if scheduler_cls is not None: - scheduler_kwargs = scheduler_kwargs or {} - scheduler = scheduler_cls(optimizer, **scheduler_kwargs) - - # == Sampling == - num_steps = num_draws * num_steps_bw_draws + num_burnin_steps - - if use_alternate_batching: - # We take one very large batch and sample SGLD on the fixed batch. - cycler = cycle(loader) - feed = [] - for step in range(gradient_accumulation_steps): - data = next(cycler) - feed.append(data) - feed = zip(range(num_steps * gradient_accumulation_steps), cycle(feed)) - else: - feed = zip(range(num_steps * gradient_accumulation_steps), cycle(loader)) - loader = cycle(loader) - - model.train() - no_grad = not any(map(lambda pg: pg["nbeta"] >= 0, optimizer.param_groups)) - - mark_step_if_xla(device) - - # The nested loop structure is present to prevent XLA from recompiling the computation graph at each iteration, - # which is what happens if we have an if statement that changes control flow at each iteration. - - # Will: If my modifications break on TPUs, let me know. - with trange( - 0, num_steps, desc=f"[{device}] Chain {chain}", disable=not verbose - ) as pbar: - for i in pbar: - # optimizer.zero_grad() - loss, results = None, {} - - # Note: The effective batch size is gradient_accumulation_steps * batch_size - # To implement George's alternate SGLD sampling method, we set gradient_accumulation_steps to, say, 100 - # and batch_size to 32 to sample from 1 effective batch of size 3.2k - - for j in range(gradient_accumulation_steps): - data = next(loader) - with autocast( - device=xm.xla_device(), - dtype=dtype, - enabled=dtype != torch.float32, - ): - _loss, _results = evaluate(model, prepare_input(data, device)) - _mean_loss = _loss.mean() / gradient_accumulation_steps - - if not no_grad: - _mean_loss.backward() - - if j == 0: - # First gradient accumulation iteration: create the loss object - loss = _loss.detach() / gradient_accumulation_steps - for k, v in _results.items(): - if torch.is_tensor(v): - results[k] = v.detach() / gradient_accumulation_steps - - else: - # Later gradient accumulation iterations: accumulate the loss - loss += _loss.detach() / gradient_accumulation_steps - - for k, v in _results.items(): - if torch.is_tensor(v): - results[k] += v.detach() / gradient_accumulation_steps - - pbar.set_postfix({"gradient_accumulation_steps": j}) - - mark_step_if_xla(device) - - # Check loss is not nan or inf - # if torch.isnan(loss).any() or torch.isinf(loss).any(): - # raise ChainHealthError( - # f"Chain {chain} failed: Loss is NaN or Inf" - # ) - - optimizer.step() - - if scheduler is not None: - scheduler.step() - - mark_step_if_xla(device) - - if ( - i >= num_burnin_steps - and (i - num_burnin_steps) % num_steps_bw_draws == 0 - ): - draw = ( - i - num_burnin_steps - ) // num_steps_bw_draws # required for locals() - - loss = loss.detach() - - for k, v in results.items(): - if torch.is_tensor(v): - results[k] = v.detach() - - with torch.no_grad(): - for callback in callbacks: - callback( - loss=loss, - draw=draw, - chain=chain, - model=model, - optimizer=optimizer, - **results, - ) - - mark_step_if_xla(device) - optimizer.zero_grad() - - # except ChainHealthError as e: - # warnings.warn(f"Chain failed: {e}") - - # if scheduler: - # del scheduler - - -def _sample_single_chain(kwargs): - return sample_single_chain(**kwargs) - - -def _sample_single_chain_xla(kwargs): - device = kwargs.pop("device") - ordinal = xr.global_ordinal() - num_chains = kwargs["num_chains"] - seeds = kwargs.pop("seeds") - - if ordinal > num_chains: - return - - for chain in range(ordinal, num_chains, xr.world_size()): - seed = seeds[chain] - - for callback in kwargs["callbacks"]: - if hasattr(callback, "to"): - callback.to(device, chain=chain) - - sample_single_chain(**kwargs, chain=chain, seed=seed, device=device) - - -def sample( - model: torch.nn.Module, - loader: DataLoader, - callbacks: Union[List[SamplerCallback], Dict[str, SamplerCallback]], - evaluate: Callable = lambda model, data: model(data), - sampling_method: Type[torch.optim.Optimizer] = SGLD, - sampling_method_kwargs: Optional[ - Dict[str, Union[float, Literal["adaptive"]]] - ] = None, - scheduler_cls: Optional[Type[torch.optim.lr_scheduler._LRScheduler]] = None, - scheduler_kwargs: Optional[Dict[str, Any]] = None, - num_draws: int = 100, - num_chains: int = 10, - num_burnin_steps: int = 0, - num_steps_bw_draws: int = 1, - init_loss=None, - gradient_accumulation_steps: int = 1, - cores: int = 1, - seed: Optional[Union[int, List[int]]] = None, - device: Union[torch.device, str] = torch.device("cpu"), - verbose: bool = True, - optimize_over_per_model_param: Optional[Dict[str, torch.Tensor]] = None, - batch_size: int = 32, - init_noise: Optional[float] = None, - shuffle: bool = True, - use_alternate_batching=False, # See George's alternate SGLD sampling method - dtype: Optional[torch.dtype] = ( - torch.bfloat16 - if os.environ.get("BF16") - else torch.float16 - if os.environ.get("FP16") - else torch.float32 - ), - **kwargs, # NOTE: This is an important catch-all for any additional arguments that may be passed to the function. Please don't remove it. -): - """ - Sample model weights using a given sampling_method, supporting multiple chains/cores, - and calculate the observables (loss, llc, etc.) for each callback passed along. - The :python:`update`, :python:`finalize` and :python:`sample` methods of each :func:`~devinterp.slt.callback.SamplerCallback` are called - during sampling, after sampling, and at :python:`sampler_callback_object.get_results()` respectively. - - After calling this function, the stats of interest live in the callback object. - - :param model: The neural network model. - :type model: torch.nn.Module - :param loader: DataLoader for input data. - :type loader: DataLoader - :param criterion: Loss function. - :type criterion: Callable - :param callbacks: list of callbacks, each of type SamplerCallback - :type callbacks: list[SamplerCallback] - :param sampling_method: Sampling method to use (a PyTorch optimizer under the hood). Default is SGLD - :type sampling_method: torch.optim.Optimizer, optional - :param sampling_method_kwargs: Keyword arguments for the PyTorch optimizer (used as sampler here). Default is None (using standard SGLD parameters as defined in the SGLD class) - :type sampling_method_kwargs: dict, optional - :param num_draws: Number of samples to draw. Default is 100 - :type num_draws: int, optional - :param num_chains: Number of chains to run. Default is 10 - :type num_chains: int, optional - :param num_burnin_steps: Number of burn-in steps before sampling. Default is 0 - :type num_burnin_steps: int, optional - :param num_steps_bw_draws: Number of steps between each draw. Default is 1 - :type num_steps_bw_draws: int, optional - :param cores: Number of cores for parallel execution. Default is 1 - :type cores: int, optional - :param seed: Random seed(s) for sampling. Each chain gets a different (deterministic) seed if this is passed. Default is None - :type seed: int, optional - :param device: Device to perform computations on, e.g., 'cpu' or 'cuda'. Default is 'cpu' - :type device: str or torch.device, optional - :param verbose: whether to print sample chain progress. Default is True - :type verbose: bool, optional - - :raises ValueError: if derivative callbacks (f.e. :func:`~devinterp.slt.loss.OnlineLossStatistics`) are passed before base callbacks (f.e. :func:`~devinterp.slt.llc.OnlineLLCEstimator`) - :raises Warning: if num_burnin_steps < num_draws - :raises Warning: if num_draws > len(loader) - :raises Warning: if using seeded runs - - :returns: None (access LLCs or other observables through `callback_object.get_results()`) - """ - if num_burnin_steps < num_draws: - warnings.warn( - "You are taking more draws than burn-in steps, your LLC estimates will likely be underestimates. Please check LLC chain convergence." - ) - - callbacks_list = ( - callbacks if isinstance(callbacks, list) else list(callbacks.values()) - ) - - if not init_loss: - init_loss = get_init_loss_multi_batch( - loader, num_chains, model, evaluate, device, seed - ) - # alternative: init_loss = get_init_loss_full_batch(loader, model, evaluate, device) - # alternative: init_loss = get_init_loss_one_batch(loader, model, evaluate, device) - for callback in callbacks: - if isinstance(callback, (OnlineLLCEstimator, LLCEstimator)): - setattr(callback, "init_loss", init_loss) - if cores is None: - cores = min(4, cpu_count()) - - if seed is not None: - warnings.warn( - "You are using seeded runs, for full reproducibility check https://pytorch.org/docs/stable/notes/randomness.html" - ) - if isinstance(seed, int): - seeds = np.random.SeedSequence(seed).generate_state(num_chains) - elif len(seed) != num_chains: - raise ValueError("Length of seed list must match number of chains") - else: - seeds = seed - else: - seeds = [None] * num_chains - - validate_callbacks(callbacks_list) - - # shared_kwargs is passed into sample_single_chain for each chain. - # Args differ slightly based on chain index. - shared_kwargs = dict( - ref_model=model, - evaluate=evaluate, - num_draws=num_draws, - num_burnin_steps=num_burnin_steps, - num_steps_bw_draws=num_steps_bw_draws, - sampling_method=sampling_method, - sampling_method_kwargs=sampling_method_kwargs, - scheduler_cls=scheduler_cls, - scheduler_kwargs=scheduler_kwargs, - verbose=verbose, - callbacks=callbacks_list, - loader=loader, - device=device, - num_chains=num_chains, - seeds=seeds, - batch_size=batch_size, - gradient_accumulation_steps=gradient_accumulation_steps, - optimize_over_per_model_param=optimize_over_per_model_param, - init_noise=init_noise, - shuffle=shuffle, - use_alternate_batching=use_alternate_batching, - dtype=dtype, - ) - - def get_args(i): - return dict( - chain=i, - seed=seeds[i], - **shared_kwargs, - ) - - if "xla" in str(device): - if num_chains % xr.world_size() != 0: - raise ValueError( - f"Number of chains ({num_chains}) must be divisible by number of TPU cores ({xr.world_size()})" - ) - - if seed is None: - warnings.warn("No seed provided. Each process will be identical.") - - _sample_single_chain_xla(shared_kwargs) - # xm.wait_device_ops() - - elif cores > 1: - ctx = get_context("spawn") - with ctx.Pool(cores) as pool: - pool.map(_sample_single_chain, [get_args(i) for i in range(num_chains)]) - else: - for i in range(num_chains): - _sample_single_chain(get_args(i)) - - for callback in callbacks_list: - if hasattr(callback, "finalize"): - callback.finalize() - - results = {} - - if isinstance(callbacks, dict): - for name, callback in callbacks.items(): - if name == "": - results.update(callback.get_results()) - else: - results[name] = callback.get_results() - else: - for callback in callbacks: - if hasattr(callback, "get_results"): - results.update(callback.get_results()) - - return results diff --git a/src/devinterp/mechinterp/__init__.py b/src/devinterp/mechinterp/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/src/devinterp/mechinterp/activations.py b/src/devinterp/mechinterp/activations.py deleted file mode 100644 index 015f1acd..00000000 --- a/src/devinterp/mechinterp/activations.py +++ /dev/null @@ -1,77 +0,0 @@ -from contextlib import contextmanager -from typing import Callable - -import torch - -Transform = Callable[[torch.Tensor], torch.Tensor] - - -class ActivationProbe: - """ - A utility class to extract the activation value of a specific layer or neuron within a neural network. - - The location of the target is defined using a string that can specify the layer, channel, and spatial coordinates (y, x). The format allows flexibility in defining the location: - - - 'layer1.0.conv1': Targets the entire layer. - - 'layer1.0.conv1.3': Targets channel 3 in the specified layer. - - 'layer1.0.conv1.3.2.2': Targets channel 3, y-coordinate 2, and x-coordinate 2 in the specified layer. - - 'layer1.0.conv1.*': Targets all neurons in the specified layer. - - The class provides methods to register a forward hook into a PyTorch model to capture the activation of the specified target during model inference. - - Attributes: - model: The PyTorch model from which to extract the activation. - layer_location (List[str]): List of strings specifying the layer hierarchy. - neuron_location (List[int]): List of integers specifying the channel, y, and x coordinates. - activation: The value of the activation at the specified location. - - Example: - model = ResNet18() - extractor = ActivationProbe(model, 'layer1.0.conv1.3') - handle = extractor.register_hook() - output = model(input_tensor) - print(extractor.activation) # Prints the activation value - - The wildcard '*' in neuron_location means that all neurons in the specified layer will be targeted. - For example, 'layer1.0.conv1.*' will capture activations for all neurons in the 'layer1.0.conv1' layer. - """ - - def __init__(self, model, location): - self.activation = None - self.model = model - location = location.split(".") - - self.layer_location = [] - self.neuron_location = [] - - # Get the target layer - self.layer = model - for part in location: - if hasattr(self.layer, part): - self.layer_location.append(part) - self.layer = getattr(self.layer, part) - else: - if part == "*": - self.neuron_location.append(...) - else: - self.neuron_location.append(int(part)) - - def hook_fn(self, module, input, output): - self.activation = output - - if self.neuron_location: - # Assumes first index is over batch - self.activation = self.activation[(..., *self.neuron_location)] - - def register_hook(self): - self.handle = self.layer.register_forward_hook(self.hook_fn) - return self.handle - - def unregister_hook(self): - self.handle.remove() - - @contextmanager - def watch(self): - handle = self.register_hook() - yield - handle.remove() diff --git a/src/devinterp/mechinterp/hooks.py b/src/devinterp/mechinterp/hooks.py deleted file mode 100644 index 3dd5b4a2..00000000 --- a/src/devinterp/mechinterp/hooks.py +++ /dev/null @@ -1,241 +0,0 @@ -import textwrap -from typing import Iterator, List, Tuple - -from torch import nn -from torch.nn.modules.module import Module - - -def prepend_dict(d: dict, prefix: str, delimiter="."): - """Utility function to prepend a prefix to the keys of a dictionary.""" - - def get_key(key): - if key == "": - return prefix - - return f"{prefix}{delimiter}{key}" - - return {get_key(k): v for k, v in d.items()} - - -def hook(module: nn.Module, *paths: str): - """Recursively wraps PyTorch modules in Hooked, HookedList, or HookedSequential classes. - - This exposes a `run_with_cache()` method inspired by TransformerLens's HookedTransformer. - Unlike TransformerLens, this works with any PyTorch module right out of the box. - - Args: - module (nn.Module): The module to be wrapped. - paths (str): Paths to the children modules to be wrapped and whose activations will be returned. - If empty, all children will be wrapped and all internal activations will be returned. - - Examples: - - Returns: - Hooked, HookedList, or HookedSequential: Wrapped module. - """ - if len(paths) == 0: # Default to hooking all children - return _hook(module) - - module = Hooked(module) - for path in paths: - components = path.split(".") - _hook_recursive(module, components) - - return module - - -def _hook(module: nn.Module): - """Recursively wraps PyTorch modules in Hooked, HookedList, or HookedSequential classes.""" - if isinstance(module, (Hooked, HookedList, HookedSequential)): - for n, c in module.named_children(): - setattr(module, n, _hook(c)) - return module - if isinstance(module, nn.ModuleList): - return HookedList([_hook(c) for c in module]) - elif isinstance(module, nn.Sequential): - return HookedSequential(*[_hook(c) for c in module.children()]) - else: - module = Hooked(module) - - for n, c in module.named_children(): - setattr(module, n, _hook(c)) - - return module - - -def _hook_recursive(module: nn.Module, components: List[str]): - # Base case - if len(components) == 0: - if isinstance(module, (Hooked, HookedList, HookedSequential)): - return module - elif isinstance(module, nn.ModuleList): - return HookedList(module) - elif isinstance(module, nn.Sequential): - return HookedSequential(*module.children()) - else: - return Hooked(module) - - # Recursive case - head, *tail = components - - if not isinstance(module, (Hooked, HookedList, HookedSequential)): - if isinstance(module, (nn.ModuleList, nn.Sequential)): - children = list(module.children()) - - if head == "*": # Allow wildcards to hook all children - children = [_hook_recursive(c, tail) for c in children] - else: - head = int(head) - children[head] = _hook_recursive(children[head], tail) - - if isinstance(module, nn.ModuleList): - module = HookedList(children) - elif isinstance(module, nn.Sequential): - module = HookedSequential(*children) - - return module - else: - module = Hooked(module) - - # If the current module is already Hooked, we may still need to further hook its children. - next_module = _hook_recursive(getattr(module, head), tail) - setattr(module, head, next_module) - - return module - - -class Hooked(nn.Module): - def __init__(self, module: nn.Module): - """Wraps a PyTorch module to cache its output during forward pass. - - Attributes: - _module (nn.Module): Original module. - _forward (Callable): Original forward method. - output: Cached output of the last forward pass. - """ - super().__init__() - self._module = module - self._forward = type(module).forward - self.output = None - - def collect_cache(self): - """Collects cached outputs from the current module and its immediate children. - - Returns: - dict: Nested dictionary of cached outputs. - """ - cache = {"": self.output} - - for n, c in self.named_children(): - if isinstance(c, (Hooked, HookedList, HookedSequential)): - cache.update(prepend_dict(c.collect_cache(), n)) - - return cache - - def forward(self, *args, **kwargs): - """Runs the forward pass and caches the output.""" - self.output = self._forward(self, *args, **kwargs) - return self.output - - def run_with_cache(self, *args, **kwargs): - """Runs the forward pass and collects cached outputs. - - Inspired by TransformerLens's HookedTransformer. - - Returns: - tuple: Output of forward pass and the cache. - """ - output = self.forward(*args, **kwargs) - cache = self.collect_cache() - del cache[""] - - return output, cache - - def __getattr__(self, name): - try: - return super().__getattr__(name) - except AttributeError: - return getattr(self._module, name) - - def state_dict(self, destination=None, prefix="", keep_vars=False): - """Returns the state dictionary of the original module.""" - return self._module.state_dict(destination, prefix, keep_vars) - - def load_state_dict(self, state_dict, strict=True): - """Loads the state dictionary into the original module.""" - return self._module.load_state_dict(state_dict, strict) - - def named_children(self) -> Iterator[Tuple[str, Module]]: - """Returns an iterator over immediate children modules, yielding both the name of the module as well as the module itself.""" - for n, c_original in self._module.named_children(): - if hasattr(self, n): - yield n, getattr(self, n) - else: - yield n, c_original - - def __repr__(self): - """Returns a string representation of the module.""" - child_str_list = [] - children = list(self.named_children()) - - for n, c in children: - if n != "_module": - child_repr = repr(c) - child_str_list.append(f"{n}: {child_repr}") - - child_str = ",\n".join(child_str_list) - - indented_child_str = textwrap.indent(child_str, " ") - mod_str = self._module.__class__.__name__ - - if len(children) == 0: - return f"Hooked({repr(self._module)})" - - return f"Hooked({mod_str}(\n{indented_child_str}\n))" - - -class HookedList(nn.ModuleList): - """Extension of PyTorch's ModuleList to support caching of outputs.""" - - def collect_cache(self): - """Collects cached outputs from the list of modules. - - Returns: - dict: Nested dictionary of cached outputs. - """ - cache = {} - - for i, c in enumerate(self): - if isinstance(c, (Hooked, HookedList, HookedSequential)): - cache.update(prepend_dict(c.collect_cache(), str(i))) - - return cache - - -class HookedSequential(nn.Sequential): - """Extension of PyTorch's Sequential to support caching of outputs.""" - - def collect_cache(self): - """Collects cached outputs from the sequence of modules. - - Returns: - dict: Nested dictionary of cached outputs. - """ - cache = {} - - for i, c in enumerate(self): - if isinstance(c, (Hooked, HookedList, HookedSequential)): - cache.update(prepend_dict(c.collect_cache(), str(i))) - - return cache - - def run_with_cache(self, *args, **kwargs): - """Runs the forward pass and collects cached outputs. - - Inspired by TransformerLens's HookedTransformer. - - Returns: - tuple: Output of forward pass and the cache. - """ - output = self.forward(*args, **kwargs) - return output, self.collect_cache() diff --git a/src/devinterp/optim/metrics.py b/src/devinterp/optim/metrics.py new file mode 100644 index 00000000..e8dd53e2 --- /dev/null +++ b/src/devinterp/optim/metrics.py @@ -0,0 +1,182 @@ +from collections.abc import Iterable +from dataclasses import dataclass, field +from typing import ClassVar + +import torch + + +def _zeros() -> torch.Tensor: + return torch.zeros(1, dtype=torch.float32) + + +@dataclass +class Metrics: + """Norms and dot products of SGMCMC parameter update components. + + Each step, w += dw where dw = -(scaled_grad + prior) + noise. + + Norm fields store L2 norms of the post-preconditioned update components + (i.e. actual magnitudes applied to parameters, not raw gradients). + + scaled_grad: (ε/2) · nβ · G · ∇L — preconditioned gradient + unscaled_grad: (ε/2) · nβ · ∇L — raw gradient (no preconditioner) + localization: (ε/2) · G · γ(w - w₀) — pull toward initial params + weight_decay: (ε/2) · G · λw — L2 regularization + noise: √ε · √G · η — stochastic exploration + distance: w - w₀ — raw displacement from init + + Dot product fields store inner products between the three main component + vectors (scaled_grad, combined prior, noise). + + dot_grad_prior: ⟨scaled_grad, localization + weight_decay⟩ + dot_grad_noise: ⟨scaled_grad, noise⟩ + dot_prior_noise: ⟨localization + weight_decay, noise⟩ + + Cosine similarities can be derived: cos = dot / (norm_a * norm_b). + + Lifecycle (one Metrics per optimizer param group, on the group's device): + + 1. __init__: group["metrics"] = Metrics().to(device) + 2. step(), start: group["metrics"].zero_() + 3. step(), per-p: group["metrics"].add_sum_squared_(...) + group["metrics"].add_dot_products_(...) + 4. step(), end: group["metrics"].sqrt_norms_() + 5. get_metrics(): combine per-group metrics on CPU + """ + + # TODO: Could these be annotations in the `field` declarations? + NORM_FIELDS: ClassVar[tuple[str, ...]] = ( + "scaled_grad", + "unscaled_grad", + "localization", + "weight_decay", + "noise", + "distance", + ) + + DOT_FIELDS: ClassVar[tuple[str, ...]] = ( + "dot_grad_prior", + "dot_grad_noise", + "dot_prior_noise", + ) + + scaled_grad: torch.Tensor = field(default_factory=_zeros) + unscaled_grad: torch.Tensor = field(default_factory=_zeros) + localization: torch.Tensor = field(default_factory=_zeros) + weight_decay: torch.Tensor = field(default_factory=_zeros) + noise: torch.Tensor = field(default_factory=_zeros) + distance: torch.Tensor = field(default_factory=_zeros) + + dot_grad_prior: torch.Tensor = field(default_factory=_zeros) + dot_grad_noise: torch.Tensor = field(default_factory=_zeros) + dot_prior_noise: torch.Tensor = field(default_factory=_zeros) + + numel: int = 0 + + @property + def prior(self) -> torch.Tensor: + """Combined prior norm: ||[localization; weight_decay]||₂.""" + return (self.localization.square() + self.weight_decay.square()).sqrt() + + def to(self, device: str | torch.device | int) -> "Metrics": + """Return a copy of these metrics on the specified device.""" + return Metrics( + scaled_grad=self.scaled_grad.to(device), + unscaled_grad=self.unscaled_grad.to(device), + localization=self.localization.to(device), + weight_decay=self.weight_decay.to(device), + noise=self.noise.to(device), + distance=self.distance.to(device), + dot_grad_prior=self.dot_grad_prior.to(device), + dot_grad_noise=self.dot_grad_noise.to(device), + dot_prior_noise=self.dot_prior_noise.to(device), + numel=self.numel, + ) + + def zero_(self) -> None: + """Reset all metrics to zero in-place.""" + self.scaled_grad.zero_() + self.unscaled_grad.zero_() + self.localization.zero_() + self.weight_decay.zero_() + self.noise.zero_() + self.distance.zero_() + self.dot_grad_prior.zero_() + self.dot_grad_noise.zero_() + self.dot_prior_noise.zero_() + self.numel = 0 + + def add_sum_squared_( + self, + scaled_grad: torch.Tensor, + unscaled_grad: torch.Tensor, + localization: torch.Tensor, + weight_decay: torch.Tensor, + noise: torch.Tensor, + distance: torch.Tensor, + ) -> None: + """Accumulate sum-of-squares for each norm component in-place. + + Casts to float32 before squaring to avoid precision loss with bf16/fp16 + inputs (where squaring can overflow or underflow in the input dtype). + """ + self.scaled_grad += scaled_grad.float().square().sum() + self.unscaled_grad += unscaled_grad.float().square().sum() + self.localization += localization.float().square().sum() + self.weight_decay += weight_decay.float().square().sum() + self.noise += noise.float().square().sum() + self.distance += distance.float().square().sum() + + def add_dot_products_( + self, + scaled_grad: torch.Tensor, + prior: torch.Tensor, + noise: torch.Tensor, + ) -> None: + """Accumulate dot products between the three main component vectors. + + Args: + scaled_grad: The preconditioned gradient vector. + prior: Combined prior vector (localization + weight_decay). + noise: The noise vector. + """ + sg = scaled_grad.float() + p = prior.float() + n = noise.float() + self.dot_grad_prior += (sg * p).sum() + self.dot_grad_noise += (sg * n).sum() + self.dot_prior_noise += (p * n).sum() + + def sqrt_norms_(self) -> None: + """Convert norm fields from sum-of-squares to L2 norms in-place.""" + self.scaled_grad = self.scaled_grad.sqrt() + self.unscaled_grad = self.unscaled_grad.sqrt() + self.localization = self.localization.sqrt() + self.weight_decay = self.weight_decay.sqrt() + self.noise = self.noise.sqrt() + self.distance = self.distance.sqrt() + + @staticmethod + def aggregate(group_metrics: Iterable["Metrics"]) -> "Metrics": + """Combine per-group metrics into a single Metrics on CPU. + + Norms: re-square to get sum-of-squares, accumulate, then sqrt: + ||[a; b]|| = sqrt(||a||^2 + ||b||^2). + + Dot products: additive across disjoint parameter sets. + """ + result = Metrics() + for m in group_metrics: + m_cpu = m.to("cpu") + result.scaled_grad += m_cpu.scaled_grad.square() + result.unscaled_grad += m_cpu.unscaled_grad.square() + result.localization += m_cpu.localization.square() + result.weight_decay += m_cpu.weight_decay.square() + result.noise += m_cpu.noise.square() + result.distance += m_cpu.distance.square() + result.dot_grad_prior += m_cpu.dot_grad_prior + result.dot_grad_noise += m_cpu.dot_grad_noise + result.dot_prior_noise += m_cpu.dot_prior_noise + result.numel += m_cpu.numel + result.sqrt_norms_() + return result diff --git a/src/devinterp/optim/preconditioner.py b/src/devinterp/optim/preconditioner.py index 76b1014f..8e2689a2 100644 --- a/src/devinterp/optim/preconditioner.py +++ b/src/devinterp/optim/preconditioner.py @@ -1,6 +1,6 @@ from abc import ABC, abstractmethod from functools import reduce -from typing import List, NamedTuple, Optional, Union +from typing import NamedTuple, Optional, Union import torch @@ -72,7 +72,7 @@ class MaskPreconditioner(Preconditioner): Stores one mask per parameter in the parameter group. """ - def __init__(self, masks: List[Union[torch.Tensor, float]]): + def __init__(self, masks: list[Union[torch.Tensor, float]]): """ Args: masks: List of masks, one per parameter in the parameter group. @@ -106,7 +106,7 @@ def __repr__(self) -> str: class CompositePreconditioner(Preconditioner): """Combines multiple preconditioners by multiplying their coefficients""" - def __new__(cls, preconditioners: List[Preconditioner]): + def __new__(cls, preconditioners: list[Preconditioner]): # Filter out identity preconditioners non_identity_preconditioners = [ p for p in preconditioners if not isinstance(p, IdentityPreconditioner) @@ -125,7 +125,7 @@ def __new__(cls, preconditioners: List[Preconditioner]): return instance - def __init__(self, preconditioners: List[Preconditioner]): + def __init__(self, preconditioners: list[Preconditioner]): self.preconditioners = preconditioners def get_coefficients( @@ -208,7 +208,9 @@ def get_coefficients( grad_correction = -thermostat * momentum # Update thermostat based on kinetic energy - kinetic_energy = torch.sum(momentum * momentum) / momentum.numel() + kinetic_energy = ( + torch.sum(momentum * momentum, dtype=torch.float32) / momentum.numel() + ) state["thermostat"] += kinetic_energy - 1.0 return PreconditionerCoefs( diff --git a/src/devinterp/optim/prior.py b/src/devinterp/optim/prior.py index 68568414..3b284c3e 100644 --- a/src/devinterp/optim/prior.py +++ b/src/devinterp/optim/prior.py @@ -1,7 +1,8 @@ from abc import ABC, abstractmethod from collections import defaultdict +from collections.abc import Sequence from numbers import Real -from typing import Any, Dict, Iterable, Iterator, List, Literal, Optional, Union +from typing import Any, Iterable, Literal, Optional, Union import numpy as np import torch @@ -10,10 +11,12 @@ class Prior(ABC): """Abstract base class for parameter priors""" + key: str + @abstractmethod def initialize( - self, params: Iterator[torch.Tensor] - ) -> Dict[torch.Tensor, Dict[str, Any]]: + self, params: Sequence[torch.Tensor] + ) -> dict[torch.Tensor, dict[str, Any]]: """Initialize prior for parameters Args: @@ -28,7 +31,7 @@ def initialize( def grad( self, param: torch.Tensor, - state: Dict[str, Any], + state: dict[str, Any], ) -> torch.Tensor: """Compute gradient of the prior @@ -51,8 +54,6 @@ def __init__( center: Optional[ Union[Literal["initial"], Iterable[torch.Tensor], Real] ] = "initial", - key: str = "prior_center", - **kwargs, # Accept additional kwargs for consistency ): """ Args: @@ -62,11 +63,11 @@ def __init__( - 'initial': centered at initial parameter values (localization) - iterable of tensors: centered at provided parameter values (must match model parameter shapes) - key: Key to use in state dictionary - **kwargs: Additional keyword arguments (ignored by GaussianPrior) """ self.localization = localization - self.key = key + self.key = ( + "prior_center" # Mutable - will be modified if passed to a CompositePrior + ) if isinstance(center, (str, type(None))): self.center = center @@ -77,8 +78,8 @@ def __init__( self.center = list(center) def initialize( - self, params: Iterator[torch.Tensor] - ) -> Dict[torch.Tensor, Dict[str, Any]]: + self, params: Sequence[torch.Tensor] + ) -> dict[torch.Tensor, dict[str, Any]]: """Initialize centers for all parameters Args: @@ -87,17 +88,16 @@ def initialize( Returns: State dictionary containing prior centers """ - params_list = list(params) state = defaultdict(dict) if isinstance(self.center, list): # Validate and use provided centers - if len(self.center) != len(params_list): + if len(self.center) != len(params): raise ValueError( f"Number of centers ({len(self.center)}) does not match " - f"number of parameters ({len(params_list)})" + f"number of parameters ({len(params)})" ) - for c, p in zip(self.center, params_list): + for c, p in zip(self.center, params): if c.shape != p.shape: raise ValueError( f"Center shape {c.shape} does not match " @@ -107,11 +107,11 @@ def initialize( elif self.center == "initial": # Use initial parameter values as centers - for p in params_list: + for p in params: state[p][self.key] = p.detach().clone() else: # None case - zero-centered - for p in params_list: + for p in params: state[p][self.key] = None return state @@ -119,7 +119,7 @@ def initialize( def grad( self, param: torch.Tensor, - state: Dict[str, Any], + state: dict[str, Any], ) -> torch.Tensor: """Compute gradient of the prior. If state is provided, the prior center is looked up in the state dictionary using the instance key. @@ -138,27 +138,6 @@ def grad( else: return self.localization * (param - center) - def distance_sq( - self, - param: torch.Tensor, - state: Dict[str, Any], - scale: Optional[Union[float, torch.Tensor]] = 1.0, - ) -> torch.Tensor: - """Compute squared distance from prior center. If state is provided, the - prior center is looked up in the state dictionary using the instance key. - - Args: - param: Parameter tensor - state: State dictionary - scale: Scale factor - """ - center = state.get(self.key) - - if center is None: - return ((scale * param) ** 2).sum() - else: - return ((scale * (param - center)) ** 2).sum() - def __repr__(self) -> str: return f"GaussianPrior(localization={self.localization}, center={self.center})" @@ -166,12 +145,14 @@ def __repr__(self) -> str: class UniformPrior(Prior): """Uniform prior.""" - def __init__(self, box_size: float = np.inf, **kwargs): + def __init__(self, box_size: float = np.inf): """ Args: box_size: Size of the box constraint - **kwargs: Additional keyword arguments (ignored by UniformPrior) """ + # Required by the Prior ABC but unused: initialize() returns {} and + # grad() returns zeros, so the key is never looked up in any state dict. + self.key = "uniform_prior_center" self.box_size = box_size if box_size != np.inf: @@ -180,65 +161,31 @@ def __init__(self, box_size: float = np.inf, **kwargs): ) def initialize( - self, params: Iterator[torch.Tensor] - ) -> Dict[torch.Tensor, Dict[str, Any]]: + self, params: Sequence[torch.Tensor] + ) -> dict[torch.Tensor, dict[str, Any]]: return {} - def grad(self, param: torch.Tensor, state: Dict[str, Any]) -> torch.Tensor: + def grad(self, param: torch.Tensor, state: dict[str, Any]) -> torch.Tensor: return torch.zeros_like(param) - def distance_sq( - self, - param: torch.Tensor, - state: Dict[str, Any] = {}, - scale: Optional[Union[float, torch.Tensor]] = 1.0, - ) -> torch.Tensor: - return 0.0 - class CompositePrior(Prior): """Combines multiple priors, summing their contributions. - The last prior in the list takes precedence for distance_sq and as a default for getattr. - """ - def __new__(cls, priors: List[Prior], **kwargs): - """ - Args: - priors: List of prior instances - **kwargs: Additional keyword arguments (passed to child priors) - """ - non_uniform_priors = [p for p in priors if not isinstance(p, UniformPrior)] - - if not non_uniform_priors: - return UniformPrior(**kwargs) - - if len(non_uniform_priors) == 1: - instance = non_uniform_priors[0] - else: - instance = super().__new__(cls) - instance.priors = non_uniform_priors - - return instance - - def __init__(self, priors: List[Prior], **kwargs): - """ - Args: - priors: List of prior instances - **kwargs: Additional keyword arguments (passed to child priors) - """ - # Skip initialization if this is actually a uniform or single prior - if not hasattr(self, "priors"): - return - - self.priors = priors + Always wraps even a single non-uniform prior (no short-circuit). + Callers that need to decompose sub-priors (e.g. SGMCMC._update_metrics) + can iterate ``self.priors`` uniformly. + """ - for i, prior in enumerate(priors): + def __init__(self, priors: list[Prior]): + self.key = "composite_prior" + self.priors = [p for p in priors if not isinstance(p, UniformPrior)] + for i, prior in enumerate(self.priors): prior.key = f"{prior.key}_{i}" def initialize( - self, params: Iterator[torch.Tensor] - ) -> Dict[torch.Tensor, Dict[str, Any]]: - params = list(params) # Convert iterator to list for reuse + self, params: Sequence[torch.Tensor] + ) -> dict[torch.Tensor, dict[str, Any]]: combined_state = defaultdict(dict) for prior in self.priors: @@ -249,22 +196,11 @@ def initialize( return combined_state - def grad(self, param: torch.Tensor, state: Dict[str, Any]) -> torch.Tensor: - return sum(prior.grad(param, state) for prior in self.priors) - - def distance_sq( - self, - param: torch.Tensor, - state: Dict[str, Any], - scale: Optional[Union[float, torch.Tensor]] = 1.0, - ) -> torch.Tensor: - """Compute squared distance from prior center. The last prior in the list - takes precedence (i.e. is used for distance_sq). - """ - return self.priors[-1].distance_sq(param, state, scale) - - def __getattr__(self, name: str) -> Any: - return getattr(self.priors[-1], name) + def grad(self, param: torch.Tensor, state: dict[str, Any]) -> torch.Tensor: + result = torch.zeros_like(param) + for prior in self.priors: + result += prior.grad(param, state) + return result def __repr__(self) -> str: return f"CompositePrior({self.priors})" diff --git a/src/devinterp/optim/sgld.py b/src/devinterp/optim/sgld.py index b03180c7..fbb3a432 100644 --- a/src/devinterp/optim/sgld.py +++ b/src/devinterp/optim/sgld.py @@ -1,9 +1,26 @@ import warnings -from collections import defaultdict -from typing import Callable, List, Optional, Union +from typing import Callable, Iterable, Iterator, NamedTuple, Optional, Union import torch +from .metrics import Metrics +from .sketch import CountSketch, SketchBuffer + + +class _ComponentVectors(NamedTuple): + """Post-masked component vectors from a single SGLD parameter update.""" + + # Duplicated from SGMCMC deliberately: SGLD is deprecated and will be + # removed, so coupling it to the replacement via a shared type would make + # that cleanup harder. Also the semantics differ slightly in the way masks + # are applied. + + scaled_grad: torch.Tensor + unscaled_grad: torch.Tensor + localization: torch.Tensor + weight_decay: torch.Tensor + noise: torch.Tensor + class SGLD(torch.optim.Optimizer): r""" @@ -18,13 +35,15 @@ class SGLD(torch.optim.Optimizer): The equation for the update is as follows: - $$\Delta w_t = \frac{\epsilon}{2}\left(\frac{\beta n}{m} \sum_{i=1}^m \nabla \log p\left(y_{l_i} \mid x_{l_i}, w_t\right)+\gamma\left(w_0-w_t\right) - \lambda w_t\right) + N(0, \epsilon\sigma^2)$$ + .. math:: + + \Delta w_t = \frac{\epsilon}{2}\left(\frac{\beta n}{m} \sum_{i=1}^m \nabla \log p\left(y_{l_i} \mid x_{l_i}, w_t\right)+\gamma\left(w_0-w_t\right) - \lambda w_t\right) + N(0, \epsilon\sigma^2) - where $w_t$ is the weight at time $t$, $\epsilon$ is the learning rate, - $(\beta n)$ is the inverse temperature (we're in the tempered Bayes paradigm), - $n$ is the number of training samples, $m$ is the batch size, $\gamma$ is - the localization strength, $\lambda$ is the weight decay strength, - and $\sigma$ is the noise term. + where :math:`w_t` is the weight at time :math:`t`, :math:`\epsilon` is the learning rate, + :math:`(\beta n)` is the inverse temperature (we're in the tempered Bayes paradigm), + :math:`n` is the number of training samples, :math:`m` is the batch size, :math:`\gamma` is + the localization strength, :math:`\lambda` is the weight decay strength, + and :math:`\sigma` is the noise term. Example: >>> optimizer = SGLD(model.parameters(), lr=0.1, nbeta=utils.default_nbeta(dataloader)) @@ -37,99 +56,52 @@ class SGLD(torch.optim.Optimizer): :target: https://colab.research.google.com/github/timaeus-research/devinterp/blob/main/examples/sgld_calibration.ipynb Note: - - :python:`localization` is unique to this class and serves to guide the weights towards their original values. This is useful for estimating quantities over the local posterior. - - :python:`noise_level` is not intended to be changed, except when testing! Doing so will raise a warning. - - Although this class is a subclass of :python:`torch.optim.Optimizer`, this is a bit of a misnomer in this case. It's not used for optimizing in LLC estimation, but rather for sampling from the posterior distribution around a point. + - ``localization`` is unique to this class and serves to guide the weights towards their original values. This is useful for estimating quantities over the local posterior. + - ``noise_level`` is not intended to be changed, except when testing! Doing so will raise a warning. + - Although this class is a subclass of ``torch.optim.Optimizer``, this is a bit of a misnomer in this case. It's not used for optimizing in LLC estimation, but rather for sampling from the posterior distribution around a point. - Hyperparameter optimization is more of an art than a science. Check out `the calibration notebook `_ |colab6| for how to go about it in a simple case. - :param params: Iterable of parameters to optimize or dicts defining parameter groups. Either :python:`model.parameters()` or something more fancy, just like other :python:`torch.optim.Optimizer` classes. + :param params: Iterable of parameters to optimize or dicts defining parameter groups. Either ``model.parameters()`` or something more fancy, just like other ``torch.optim.Optimizer`` classes. :type params: Iterable - :param lr: Learning rate $\epsilon$. Default is 0.01 + :param lr: Learning rate :math:`\epsilon`. Default is 0.01 :type lr: float, optional - :param noise_level: Amount of Gaussian noise $\sigma$ introduced into gradient updates. Don't change this unless you know very well what you're doing! Default is 1 + :param noise_level: Amount of Gaussian noise :math:`\sigma` introduced into gradient updates. Don't change this unless you know very well what you're doing! Default is 1 :type noise_level: float, optional - :param weight_decay: L2 regularization term $\lambda$, applied as weight decay. Default is 0 + :param weight_decay: L2 regularization term :math:`\lambda`, applied as weight decay. Default is 0 :type weight_decay: float, optional - :param localization: Strength of the force $\gamma$ pulling weights back to their initial values. Default is 0 + :param localization: Strength of the force :math:`\gamma` pulling weights back to their initial values. Default is 0 :type localization: float, optional :param nbeta: Inverse reparameterized temperature (otherwise known as n*beta or ~beta), float (default: 1., set to utils.default_nbeta(dataloader)=len(batch_size)/np.log(len(batch_size))) - :type nbeta: int, optional + :type nbeta: float or Callable, optional :param bounding_box_size: the size of the bounding box enclosing our trajectory in parameter space. Default is None, in which case no bounding box is used. :type bounding_box_size: float, optional - :param save_noise: Whether to store the per-parameter noise during optimization. Default is False - :type save_noise: bool, optional - :param save_mala_vars: Whether to store variables for calculating Metropolis-Adjusted Langevin Algorithm (MALA) metrics. - :type save_mala_vars: bool, optional - :param optimize_over: A boolean tensor of the same shape as the parameters. Used to implement weight restrictions. - Think of it as a boolean mask that restricts the set of parameters that can be updated. Default is None (no restrictions). - :type optimize_over: torch.Tensor, optional - :param noise_norm: Boolean flag to track the norm of the noise. Default is False - :type noise_norm: bool, optional - :param grad_norm: Boolean flag to track the norm of the gradient. Default is False - :type grad_norm: bool, optional - :param weight_norm: Boolean flag to track the norm of the weights. Default is False - :type weight_norm: bool, optional - :param distance: Boolean flag to track the distance between the current weights and the initial weights. Default is False - :type distance: bool, optional - - - :raises Warning: if :python:`noise_level` is set to anything other than 1 - :raises Warning: if :python:`nbeta` is set to 1 + :param save_metrics: Whether to track metrics (scaled_grad, localization, weight_decay, noise norms) during optimization. Use :meth:`get_metrics` to retrieve. Default is False + :type save_metrics: bool, optional + + :raises Warning: if ``noise_level`` is set to anything other than 1 + :raises Warning: if ``nbeta`` is set to 1 """ def __init__( self, - params, - lr=0.01, - noise_level=1.0, - weight_decay=0.0, - localization=0.0, - nbeta: Union[Callable, float] = 1.0, - bounding_box_size=None, - save_noise=False, - save_mala_vars=False, - optimize_over=None, - noise_norm=False, - grad_norm=False, - weight_norm=False, - distance=False, - temperature: Optional[float] = None, - metrics: Optional[List[str]] = None, + params: Iterable[torch.nn.Parameter], + *, + lr: float = 0.01, + noise_level: float = 1.0, + weight_decay: float = 0.0, + localization: float = 0.0, + nbeta: Union[Callable[[], float], float] = 1.0, + bounding_box_size: Optional[float] = None, + save_metrics: bool = False, + sketch_dim: Optional[int] = None, + sketch_seed: int = 0, ): warnings.warn( "SGLD has been deprecated. Please use SGMCMC.sgld instead.", DeprecationWarning, ) - if metrics: - valid_metrics = { - "noise", - "dws", - "localization_loss", - "noise_norm", - "grad_norm", - "weight_norm", - "distance", - } - invalid_metrics = set(metrics) - valid_metrics - if invalid_metrics: - raise ValueError( - f"Invalid metrics: {invalid_metrics}. Valid metrics are: {valid_metrics}" - ) - - save_noise = save_noise or "noise" in metrics - save_mala_vars = ( - save_mala_vars or "dws" in metrics or "localization_loss" in metrics - ) - noise_norm = noise_norm or "noise_norm" in metrics - grad_norm = grad_norm or "grad_norm" in metrics - weight_norm = weight_norm or "weight_norm" in metrics - distance = distance or "distance" in metrics - - if temperature is not None: - nbeta = temperature - warnings.warn( - "Temperature is deprecated. Please use nbeta in your yaml file instead." - ) + self.save_metrics = save_metrics + self.save_sketches = sketch_dim is not None if noise_level != 1.0: warnings.warn( @@ -146,51 +118,72 @@ def __init__( localization=localization, nbeta=nbeta, bounding_box_size=bounding_box_size, - optimize_over=optimize_over, - noise_norm=noise_norm, - grad_norm=grad_norm, - weight_norm=weight_norm, - distance=distance, ) # In torch.optim.Optimizer, the parameters are stored in a list of dictionaries. # defaults holds the default values for the optimizer parameters. super(SGLD, self).__init__(params, defaults) - self.save_noise = save_noise - self.save_mala_vars = save_mala_vars - self.noise = None # Save the initial parameters if the localization term is set for group in self.param_groups: group["num_el"] = 0 - if group["localization"] != 0 or group["bounding_box_size"] != 0: + # Validate mask shape if present + if group.get("mask") is not None: + for p in group["params"]: + mask = group["mask"] + if isinstance(mask, torch.Tensor) and mask.shape != p.shape: + raise ValueError( + f"Mask shape {mask.shape} does not match parameter shape {p.shape}. " + "Scalar masks are not supported." + ) + + store_initial = ( + group["localization"] != 0 + or group["bounding_box_size"] != 0 + or self.save_metrics + or self.save_sketches + ) + if store_initial: for p in group["params"]: param_state = self.state[p] param_state["initial_param"] = p.data.clone().detach() group["num_el"] += p.numel() - for hp in ["noise_norm", "grad_norm", "distance", "weight_norm"]: - if group[hp] is not False: - group[hp] = torch.tensor(0.0).to(p.device) + if self.save_metrics: + device = next(iter(group["params"])).device + group["metrics"] = Metrics().to(device) - def step(self, noise_generator: Optional[torch.Generator] = None): + if self.save_sketches: + assert sketch_dim is not None + total_numel = sum( + p.numel() for group in self.param_groups for p in group["params"] + ) + device = next(iter(self.param_groups[0]["params"])).device + self._sketch = CountSketch.create(total_numel, sketch_dim, sketch_seed).to( + device + ) + self._sketch_buf = SketchBuffer.create(sketch_dim, device) + else: + self._sketch = None + self._sketch_buf = None + + def step(self, noise_generator: Optional[torch.Generator] = None) -> None: """ Perform a single SGLD optimization step. """ - if self.save_noise: - self.noise = defaultdict(list) + with torch.no_grad(): + _need_decomposition = self.save_metrics or self.save_sketches + param_offset = 0 - if self.save_mala_vars: - self.dws = [] - self.localization_loss = 0.0 + if self.save_sketches: + assert self._sketch_buf is not None + self._sketch_buf.zero_() - with torch.no_grad(): for group_idx, group in enumerate(self.param_groups): - for hp in ["noise_norm", "grad_norm", "distance", "weight_norm"]: - # Zero iteration-level metrics that haven't been disabled. - if group[hp] is not False: - group[hp] *= 0.0 + # Metrics lifecycle: zero → accumulate per-param → sqrt (see Metrics docstring) + if self.save_metrics: + group["metrics"].zero_() for p in group["params"]: param_state = self.state[p] @@ -214,20 +207,6 @@ def step(self, noise_generator: Optional[torch.Generator] = None): if group["localization"] != 0: dw.add_(initial_param_distance, alpha=group["localization"]) - if self.save_mala_vars: - if group["optimize_over"] is not None: - initial_param_distance = ( - initial_param_distance * group["optimize_over"] - ) - # localization_loss = (p.data - initial_param)^2 * group["optimize_over"]^2 * group["localization"] / 2 - # ^ boolean - distance = (initial_param_distance.detach() ** 2).sum() - self.localization_loss += distance * group["localization"] / 2 - self.dws.append(dw.clone()) - - if group["distance"] is not False: - group["distance"] += distance - # Add Gaussian noise noise = torch.normal( mean=0.0, @@ -236,14 +215,22 @@ def step(self, noise_generator: Optional[torch.Generator] = None): device=dw.device, generator=noise_generator, ) - if self.save_noise: - # Noise saved here is the unscaled noise. - self.noise[group_idx].append(noise) - if group["optimize_over"] is not None: + if group.get("mask") is not None: # Restrict the noise and gradient to the subset of parameters we're optimizing over. - dw = dw * group["optimize_over"] - noise = noise * group["optimize_over"] + dw = dw * group["mask"] + noise = noise * group["mask"] + + if _need_decomposition: + components = self._decompose_update( + group, p, dw, initial_param_distance, noise + ) + if self.save_metrics: + self._accumulate_metrics( + group, p, components, initial_param_distance + ) + if self.save_sketches: + self._scatter_sketches(components, param_offset) # Update parameters p.data.add_(dw, alpha=-0.5 * group["lr"]) @@ -251,23 +238,8 @@ def step(self, noise_generator: Optional[torch.Generator] = None): noise, alpha=group["lr"] ** 0.5 ) # Scale noise by sqrt(lr) - # Track the size of the changes & relative contributions - if group["grad_norm"] is not False and p.grad is not None: - group["grad_norm"] += ( - ((p.grad.data * group["nbeta"] * 0.5 * group["lr"]) ** 2) - .sum() - .detach() - ) - - if group["noise_norm"] is not False: - group["noise_norm"] += (noise**2).sum().detach() - - if group["distance"] is not False and not self.save_mala_vars: - group["distance"] += ( - ((initial_param_distance * group["localization"]) ** 2) - .sum() - .detach() - ) + if self.save_sketches: + param_offset += p.numel() # Rebound if exceeded bounding box size if group["bounding_box_size"]: @@ -279,21 +251,132 @@ def step(self, noise_generator: Optional[torch.Generator] = None): + group["bounding_box_size"], ) - if group["weight_norm"] is not False: - group["weight_norm"] += (p.data**2).sum().detach() + if self.save_metrics: + # All params accumulated; convert sum-of-squares to L2 norms + group["metrics"].sqrt_norms_() - for hp in ["noise_norm", "grad_norm", "distance", "weight_norm"]: - if group[hp] is not False: - group[hp] = (group[hp] ** 0.5).detach() - - def __getattr__(self, name): - """ - Return iteration-level metrics if requested. + def _decompose_update( + self, + group: dict, + p: torch.Tensor, + dw: torch.Tensor, + initial_param_distance: torch.Tensor, + noise: torch.Tensor, + ) -> _ComponentVectors: + """Decompose an SGLD parameter update into post-masked component vectors. + + Shared by both metrics accumulation and sketch scattering. SGLD has no + preconditioner, so unscaled_grad == scaled_grad. Debug assertions verify + the decomposition matches the actual update. """ + _lr = group["lr"] + _mask = group.get("mask") + _half_lr = 0.5 * _lr + + # Multiplication order must match step() to avoid bfloat16 associativity errors: + # step() builds dw as: (p.grad * nbeta) + (p.data * wd) + (dist * loc) + raw_grad = p.grad.data if p.grad is not None else torch.zeros_like(p.data) + _scaled_grad = _half_lr * raw_grad.mul(group["nbeta"]) + _noise_vec = noise * (_lr**0.5) + _loc = _half_lr * initial_param_distance.mul(group["localization"]) + _wd = _half_lr * p.data.mul(group["weight_decay"]) + + if _mask is not None: + _scaled_grad = _scaled_grad * _mask + _loc = _loc * _mask + _wd = _wd * _mask + _noise_vec = _noise_vec * _mask + + # Sanity-check: reconstructed components must sum to the actual update. + # step() builds dw via in-place add_ (which may use FMA on GPU), + # then we multiply by half_lr once. Here we multiply half_lr into + # each component separately and sum — distributing the scalar + # breaks IEEE 754 associativity. The rounding gap scales with the + # component magnitudes and the dtype's epsilon (significant for + # bfloat16 models where eps ≈ 0.004). Using the sum of absolute + # values as the scale handles catastrophic cancellation, where + # large components nearly cancel leaving a small residual whose + # relative error would otherwise look enormous. + if __debug__: + _step_eps = torch.finfo(dw.dtype).eps + _decomp_scale = float((_scaled_grad.abs() + _loc.abs() + _wd.abs()).max()) + _decomp_atol = max(_decomp_scale * _step_eps * 16, 1e-12) + torch.testing.assert_close( + (_scaled_grad + _loc + _wd).float(), + (_half_lr * dw).float(), + atol=_decomp_atol, + rtol=0, + msg=lambda s: f"Decomposition components don't match gradient update:\n{s}", + ) + + return _ComponentVectors( + scaled_grad=_scaled_grad, + unscaled_grad=_scaled_grad, + localization=_loc, + weight_decay=_wd, + noise=_noise_vec, + ) + + def _accumulate_metrics( + self, + group: dict, + p: torch.Tensor, + components: _ComponentVectors, + initial_param_distance: torch.Tensor, + ) -> None: + """Accumulate decomposed component vectors into per-group metrics.""" + _mask = group.get("mask") + if _mask is not None: + initial_param_distance = initial_param_distance * _mask + numel = p[_mask.bool()].numel() + else: + numel = p.numel() + + group["metrics"].add_sum_squared_( + scaled_grad=components.scaled_grad, + unscaled_grad=components.unscaled_grad, + localization=components.localization, + weight_decay=components.weight_decay, + noise=components.noise, + distance=initial_param_distance, + ) + group["metrics"].add_dot_products_( + scaled_grad=components.scaled_grad, + prior=components.localization + components.weight_decay, + noise=components.noise, + ) + group["metrics"].numel += numel + + def _scatter_sketches(self, components: _ComponentVectors, offset: int) -> None: + """Scatter decomposed component vectors into the sketch buffer.""" + buf = self._sketch_buf + sketch = self._sketch + assert buf is not None and sketch is not None + sketch.scatter_into_(buf.scaled_grad, components.scaled_grad, offset) + sketch.scatter_into_(buf.unscaled_grad, components.unscaled_grad, offset) + sketch.scatter_into_(buf.localization, components.localization, offset) + sketch.scatter_into_(buf.weight_decay, components.weight_decay, offset) + sketch.scatter_into_(buf.noise, components.noise, offset) + + def get_sketches(self) -> SketchBuffer: + """Return a CPU snapshot of the current sketch buffer.""" + if not self.save_sketches: + raise RuntimeError("Sketches not enabled.") + assert self._sketch_buf is not None + return SketchBuffer( + **{ + q: getattr(self._sketch_buf, q).detach().cpu().clone() + for q in SketchBuffer.QUANTITIES + } + ) + + def iter_group_metrics(self) -> Iterator[Metrics]: + """Yield metrics for each param group.""" + if not self.save_metrics: + raise RuntimeError("Metrics not enabled. Set save_metrics=True.") for group in self.param_groups: - # If the metric key is missing or set to False, raise AttributeError - if name not in group or group[name] is False: - raise AttributeError(f"'SGLD' object has no tracked metric '{name}'") - return group[name] + yield group["metrics"] - raise AttributeError(f"'SGLD' object has no attribute '{name}'") + def get_metrics(self) -> Metrics: + """Aggregate metrics across all param groups into a single CPU Metrics.""" + return Metrics.aggregate(self.iter_group_metrics()) diff --git a/src/devinterp/optim/sgmcmc.py b/src/devinterp/optim/sgmcmc.py index c37e909e..597e960e 100644 --- a/src/devinterp/optim/sgmcmc.py +++ b/src/devinterp/optim/sgmcmc.py @@ -1,22 +1,39 @@ import warnings -from typing import Dict, Iterable, Iterator, List, Literal, Optional, Union +from typing import Iterable, Iterator, Literal, NamedTuple, Optional, Union import torch +from torch.optim import Optimizer + +from devinterp.optim.metrics import Metrics from devinterp.optim.preconditioner import ( CompositePreconditioner, IdentityPreconditioner, MaskPreconditioner, NHTPreconditioning, Preconditioner, + PreconditionerCoefs, RMSpropPreconditioner, ) from devinterp.optim.prior import CompositePrior, GaussianPrior, Prior -from devinterp.optim.utils import OptimizerMetric -from torch.optim import Optimizer +from devinterp.optim.sketch import CountSketch, SketchBuffer SamplingMethodLiteral = Literal["sgld", "rmsprop_sgld", "sgnht"] +class _ComponentVectors(NamedTuple): + """Post-preconditioned component vectors from a single parameter update.""" + + # Structurally identical to the copy in sgld.py. Kept separate because + # SGLD is deprecated — sharing a private type would couple the old + # module to this one and complicate its eventual removal. + + scaled_grad: torch.Tensor + unscaled_grad: torch.Tensor + localization: torch.Tensor + weight_decay: torch.Tensor + noise: torch.Tensor + + class SGMCMC(Optimizer): """Unified Stochastic Gradient Markov Chain Monte Carlo (SGMCMC) optimizer. @@ -44,12 +61,13 @@ class SGMCMC(Optimizer): :param noise_level: Standard deviation σ of the noise (default: 1.0) :param nbeta: Inverse temperature nβ (default: 1.0) :param prior: Prior distribution specification. Can be: + In SGMCMC, weight_decay and localization should be implemented + as a prior. See the `rmsprop_sgld()` method for an example. - GaussianPrior instance - string specifying center type - iterable of tensor centers - float specifying precision (default: None) - :param prior_kwargs: Additional keyword arguments for prior initialization :param preconditioner: Preconditioner specification. Can be: - "identity" for no preconditioning - "rmsprop" for RMSprop-style preconditioning @@ -57,32 +75,18 @@ class SGMCMC(Optimizer): (default: None, equivalent to "identity") :param preconditioner_kwargs: Additional keyword arguments for preconditioner :param bounding_box_size: Size of bounding box around initial parameters - :param optimize_over: Boolean mask for restricting updatable parameters - :param metrics: List of metrics to track during training - :param weight_decay: Weight decay factor applied separately from other updates (like AdamW). - For preconditioned weight decay (like Adam), use a GaussianPrior centered at zero instead. - (default: 0.0) + :param mask: Boolean mask for restricting updatable parameters + :param save_metrics: Whether to track metrics during training (default: False) :type params: Iterable :type lr: float :type noise_level: float :type nbeta: float :type prior: Optional[Union[Prior, Literal["initial"], Iterable[torch.Tensor], float]] - :type prior_kwargs: Optional[Dict] :type preconditioner: Optional[Union[Preconditioner, str]] - :type preconditioner_kwargs: Optional[Dict] + :type preconditioner_kwargs: Optional[dict] :type bounding_box_size: Optional[float] - :type optimize_over: Optional[torch.Tensor] - :type metrics: Optional[List[OptimizerMetric]] - :type weight_decay: float - - Valid metrics options: - * noise_norm - * grad_norm - * weight_norm - * distance - * noise - * dws - * localization_loss + :type mask: Optional[torch.Tensor] + :type save_metrics: bool Example:: @@ -122,7 +126,7 @@ class SGMCMC(Optimizer): * Use the factory methods (sgld, rmsprop_sgld, sgnht) for easier initialization * nbeta should typically be set using devinterp.utils.default_nbeta() rather than manually * The prior helps explore the local posterior by pulling toward initialization - * Tracked metrics can be accessed as attributes, e.g. optimizer.grad_norm + * Use save_metrics=True and call get_metrics() to access tracked metrics References: * Welling & Teh (2011) - Original SGLD paper @@ -134,27 +138,23 @@ class SGMCMC(Optimizer): def __init__( self, params, + *, lr: float = 0.01, noise_level: float = 1.0, nbeta: float = 1.0, prior: Optional[ Union[Prior, Literal["initial"], Iterable[torch.Tensor], float] ] = None, - localization: Optional[float] = None, preconditioner: Optional[ Union[Preconditioner, Literal["identity", "rmsprop"]] ] = "identity", - preconditioner_kwargs: Optional[Dict] = None, + preconditioner_kwargs: Optional[dict] = None, bounding_box_size: Optional[float] = None, - optimize_over: Optional[torch.Tensor] = None, - metrics: Optional[List[OptimizerMetric]] = None, - weight_decay: float = 0.0, + mask: Optional[torch.Tensor] = None, + save_metrics: bool = False, + sketch_dim: Optional[int] = None, + sketch_seed: int = 0, ): - """ - The localization parameter controls how strongly parameters are pulled back toward their - initialization point (or other specified center). If provided directly, it will override - prior_kwargs["localization"]. - """ # Handle single parameter case if isinstance(params, (torch.Tensor, dict)): params = [params] @@ -164,17 +164,36 @@ def __init__( lr=lr, noise_level=noise_level, nbeta=nbeta, - weight_decay=weight_decay, prior=prior, - localization=localization, preconditioner=preconditioner, preconditioner_kwargs=preconditioner_kwargs or {}, bounding_box_size=bounding_box_size, - optimize_over=optimize_over, - metrics=metrics or [], + mask=mask, ) super().__init__(params, defaults) + self.save_metrics = save_metrics + + if sketch_dim is not None: + self._total_numel = sum( + p.numel() for group in self.param_groups for p in group["params"] + ) + device = next(iter(self.param_groups[0]["params"])).device + self._sketch = CountSketch.create( + self._total_numel, sketch_dim, sketch_seed + ).to(device) + # Single buffer shared across all param groups. Sketch accumulation + # is purely additive (linear), so per-group buffers are unnecessary. + # N.B. With FSDP each rank only sees a parameter shard; this buffer + # would hold only the local contribution and would need an all-reduce + # across ranks before consumption. See the FSDP guard in sampler.py. + self._sketch_buf = SketchBuffer.create(sketch_dim, device) + self.save_sketches = True + else: + self._sketch = None + self._sketch_buf = None + self.save_sketches = False + # Initialize each parameter group for group in self.param_groups: self._init_group(group) @@ -184,48 +203,18 @@ def _init_group(self, group: dict) -> None: Prior initialization supports several formats: 1. Prior object: Use any existing Prior instance directly + - localization and weight_decay should be implemented via a Prior. To see how + to do this, look at e.g. `SGMCMC.sgld()`. 2. String ("initial"): Creates GaussianPrior centered at parameter initialization 3. Tensor centers: Creates GaussianPrior centered at provided tensor values 4. Number (float/int): Creates GaussianPrior with specified localization strength - The localization parameter controls how strongly parameters are pulled toward their center: - - If prior is None but localization > 0, defaults to "initial" center - - Default localization is 0.0 (no pull) - - Additional prior parameters can be passed via prior_kwargs - Args: group: Parameter group dictionary containing optimizer settings """ - - # Validate metrics - valid_metrics = { - "noise_norm", - "grad_norm", - "weight_norm", - "distance", - "noise", - "dws", - "localization_loss", - } - if not set(group["metrics"]).issubset(valid_metrics): - raise ValueError( - f"Invalid metrics {group['metrics']}. Choose from: {valid_metrics}" - ) - - # Initialize metrics directly in the group - group["metrics"] = { - metric: ( - [] - if metric in {"noise", "dws"} - else torch.zeros((), device=self.device) - ) - for metric in group["metrics"] - } - # Initialize prior prior = group["prior"] - prior_kwargs = group.pop("prior_kwargs", {}) - localization = group.get("localization", prior_kwargs.get("localization", 0.0)) + localization = group.get("localization", 0.0) if prior is not None or localization: prior = prior if prior is not None else "initial" @@ -234,17 +223,11 @@ def _init_group(self, group: dict) -> None: if isinstance(prior, Prior): group["prior"] = prior elif isinstance(prior, str): - group["prior"] = GaussianPrior( - localization=localization, center=prior, **prior_kwargs - ) + group["prior"] = GaussianPrior(localization=localization, center=prior) elif isinstance(prior, Iterable) and not isinstance(prior, str): - group["prior"] = GaussianPrior( - localization=localization, center=prior, **prior_kwargs - ) + group["prior"] = GaussianPrior(localization=localization, center=prior) elif isinstance(prior, (int, float)): - group["prior"] = GaussianPrior( - localization=float(prior), **prior_kwargs - ) + group["prior"] = GaussianPrior(localization=float(prior)) else: raise ValueError(f"Unsupported prior type: {type(prior)}") @@ -261,19 +244,26 @@ def _init_group(self, group: dict) -> None: else: raise ValueError(f"Unsupported preconditioner type: {preconditioner}") - optimize_over = group.pop("optimize_over", None) - if optimize_over is not None: - # Convert optimize_over to masks (1.0 where True, 0.0 where False) - if isinstance(optimize_over, torch.Tensor): - optimize_over = [optimize_over] + mask = group.get("mask", None) + if mask is not None: + # Convert mask to masks (1.0 where True, 0.0 where False) + if isinstance(mask, torch.Tensor): + mask = [mask] - def _process_mask(m): + def _process_mask(m, p): if not isinstance(m, torch.Tensor): m = torch.tensor(m).to(self.device) + # Validate mask shape matches parameter shape + if m.shape != p.shape: + raise ValueError( + f"Mask shape {m.shape} does not match parameter shape {p.shape}. " + ) + return m.float() - masks = [_process_mask(mask) for mask in optimize_over] + params = list(group["params"]) + masks = [_process_mask(_mask, p) for _mask, p in zip(mask, params)] mask_preconditioner = MaskPreconditioner(masks=masks) if group["preconditioner"] is not None: @@ -287,9 +277,10 @@ def _process_mask(m): # Initialize prior state if needed if group["prior"] is not None: - pstates = group["prior"].initialize(iter(group["params"])) + pstates = group["prior"].initialize(list(group["params"])) # Initialize states for each parameter in the group + store_initial = group["bounding_box_size"] is not None or self.save_metrics for i, p in enumerate(group["params"]): pstate = pstates.get(p, {}) @@ -297,34 +288,154 @@ def _process_mask(m): **self.state[p], **pstate, "param_idx": i, - "initial_param": ( - p.data.clone().detach() - if group["bounding_box_size"] is not None - else None - ), + "initial_param": (p.data.clone().detach() if store_initial else None), } - def reset_metrics(self): - """Reset all tracked metrics at the start of each step.""" - for group in self.param_groups: - for metric, value in group["metrics"].items(): - if isinstance(value, torch.Tensor): - value.zero_() - else: # List metrics (noise, dws) - value.clear() + # Initialize per-group metrics + if self.save_metrics: + device = next(iter(group["params"])).device + group["metrics"] = Metrics().to(device) + + def _decompose_update( + self, + group: dict, + p: torch.Tensor, + loc_grad: Optional[torch.Tensor], + wd_grad: Optional[torch.Tensor], + noise: torch.Tensor, + preconditioning: PreconditionerCoefs, + d_p: torch.Tensor, + ) -> _ComponentVectors: + """Decompose a parameter update into its post-preconditioned component vectors. + + Shared by both metrics accumulation and sketch scattering. Debug + assertions verify the decomposition matches the actual update. + + Args: + loc_grad: Pre-computed localization prior gradient, or None. + wd_grad: Pre-computed weight_decay prior gradient, or None. + d_p: The actual deterministic update vector (for debug assertion). + """ + _lr = group["lr"] + _precond = preconditioning.overall_coef + _half_lr = 0.5 * _lr - def zero_grad(self, *args, **kwargs): - """Zero out gradients""" - self.reset_metrics() - super().zero_grad(*args, **kwargs) + _grad_pre = preconditioning.grad_coef * p.grad.mul(group["nbeta"]) + _loc_pre = loc_grad if loc_grad is not None else torch.zeros_like(p) + _wd_pre = wd_grad if wd_grad is not None else torch.zeros_like(p) + + _scaled_grad = _half_lr * (_precond * _grad_pre) + _unscaled_grad = _half_lr * p.grad.mul(group["nbeta"]) + + _prior_scale = _half_lr * preconditioning.prior_coef * _precond + _loc = _prior_scale * _loc_pre + _wd = _prior_scale * _wd_pre + + # noise already includes overall_coef from step() + _noise = (group["lr"] ** 0.5) * preconditioning.noise_coef * noise + + if __debug__: + _combined_prior = ( + _half_lr * preconditioning.prior_coef * _precond * (_loc_pre + _wd_pre) + ) + + # (1) grad + prior must reconstruct the full step update. + # step() computes d_p at _grad_pre's precision before + # overall_coef promotion, so rounding error is at that scale. + _step_eps = torch.finfo(_grad_pre.dtype).eps + _decomp_scale = float((_scaled_grad.abs() + _combined_prior.abs()).max()) + _decomp_atol = max(_decomp_scale * _step_eps * 16, 1e-12) + torch.testing.assert_close( + (_scaled_grad + _combined_prior).float(), + (_half_lr * d_p).float(), + atol=_decomp_atol, + rtol=0, + msg=lambda s: f"Decomposition components don't match gradient update:\n{s}", + ) + + # (2) loc + wd must match the combined prior. + # _loc_pre + _wd_pre sums at input precision regardless + # of preconditioner promotion, so use p.grad.dtype. + _input_eps = torch.finfo(p.grad.dtype).eps + _dist_scale = float((_loc.abs() + _wd.abs()).max()) + _dist_atol = max(_dist_scale * _input_eps * 16, 1e-12) + torch.testing.assert_close( + (_loc + _wd).float(), + _combined_prior.float(), + atol=_dist_atol, + rtol=0, + msg=lambda s: f"Prior distribution error exceeds bound:\n{s}", + ) + + return _ComponentVectors( + scaled_grad=_scaled_grad, + unscaled_grad=_unscaled_grad, + localization=_loc, + weight_decay=_wd, + noise=_noise, + ) + + def _accumulate_metrics( + self, + group: dict, + components: _ComponentVectors, + distance: torch.Tensor, + preconditioning: PreconditionerCoefs, + p: torch.Tensor, + ) -> None: + """Accumulate decomposed component vectors into per-group metrics.""" + group["metrics"].add_sum_squared_( + scaled_grad=components.scaled_grad, + unscaled_grad=components.unscaled_grad, + localization=components.localization, + weight_decay=components.weight_decay, + noise=components.noise, + distance=distance, + ) + group["metrics"].add_dot_products_( + scaled_grad=components.scaled_grad, + prior=components.localization + components.weight_decay, + noise=components.noise, + ) + _precond = preconditioning.overall_coef + if isinstance(_precond, torch.Tensor): + group["metrics"].numel += int(_precond.count_nonzero()) + else: + group["metrics"].numel += p.numel() + + def _scatter_sketches(self, components: _ComponentVectors, offset: int) -> None: + """Scatter decomposed component vectors into the sketch buffer.""" + buf = self._sketch_buf + sketch = self._sketch + assert buf is not None and sketch is not None + sketch.scatter_into_(buf.scaled_grad, components.scaled_grad, offset) + sketch.scatter_into_(buf.unscaled_grad, components.unscaled_grad, offset) + sketch.scatter_into_(buf.localization, components.localization, offset) + sketch.scatter_into_(buf.weight_decay, components.weight_decay, offset) + sketch.scatter_into_(buf.noise, components.noise, offset) + + def get_sketches(self) -> SketchBuffer: + """Return a CPU snapshot of the current sketch buffer.""" + if not self.save_sketches: + raise RuntimeError("Sketches not enabled.") + assert self._sketch_buf is not None + return SketchBuffer( + **{ + q: getattr(self._sketch_buf, q).detach().cpu().clone() + for q in SketchBuffer.QUANTITIES + } + ) @torch.no_grad() - def step(self, closure=None): + def step( + self, closure=None, noise_generator: Optional[torch.Generator] = None + ) -> None: """Perform a single optimization step. Args: closure (callable, optional): A closure that reevaluates the model and returns the loss. + noise_generator (torch.Generator, optional): Generator for reproducible noise. Returns: Optional[float]: The loss value if closure is provided, else None. @@ -334,13 +445,28 @@ def step(self, closure=None): with torch.enable_grad(): loss = closure() + _need_decomposition = self.save_metrics or self.save_sketches + param_offset = 0 + + if self.save_sketches: + assert self._sketch_buf is not None + self._sketch_buf.zero_() + for group_idx, group in enumerate(self.param_groups): + # Metrics lifecycle: zero → accumulate per-param → sqrt (see Metrics docstring) + if self.save_metrics: + group["metrics"].zero_() + prior = group["prior"] preconditioner = group["preconditioner"] params = group["params"] for i, p in enumerate(params): if p.grad is None: + if self.save_sketches: + # Advance past this param's region in the sketch's + # hash/sign index space so subsequent params stay aligned. + param_offset += p.numel() continue state = self.state[p] @@ -351,26 +477,40 @@ def step(self, closure=None): # Gradient computation d_p = preconditioning.grad_coef * p.grad.mul(group["nbeta"]) - # Prior contribution + # Prior contribution — decompose into sub-priors when + # tracking metrics or sketches so we can record localization + # and weight_decay separately. Must happen before p.data + # is modified below. + loc_grad = None + wd_grad = None if prior is not None: - prior_grad = prior.grad(p.data, state) + if _need_decomposition: + loc_grad = torch.zeros_like(p) + wd_grad = torch.zeros_like(p) + sub_priors = ( + prior.priors + if isinstance(prior, CompositePrior) + else [prior] + ) + for sub_prior in sub_priors: + sub_grad = sub_prior.grad(p.data, state) + if ( + isinstance(sub_prior, GaussianPrior) + and sub_prior.center is None + ): + wd_grad += sub_grad + else: + loc_grad += sub_grad + prior_grad = loc_grad + wd_grad + else: + prior_grad = prior.grad(p.data, state) d_p.add_(preconditioning.prior_coef * prior_grad) - if ( - "distance" in self.tracked_metrics - or "localization_loss" in self.tracked_metrics - ): - group["metrics"]["distance"] += prior.distance_sq(p.data, state) - - # if ( - # isinstance(preconditioning.overall_coef, torch.Tensor) - # and not preconditioning.overall_coef.any() - # ) or ( - # not isinstance(preconditioning.overall_coef, torch.Tensor) - # and not preconditioning.overall_coef - # ): - # continue d_p = preconditioning.overall_coef * d_p + + if self.save_metrics: + _distance = p.data - state["initial_param"] + p.data.add_(d_p, alpha=-0.5 * group["lr"]) # Noise addition @@ -379,6 +519,7 @@ def step(self, closure=None): std=group["noise_level"], size=d_p.size(), device=d_p.device, + generator=noise_generator, ) noise = preconditioning.overall_coef * noise @@ -387,9 +528,6 @@ def step(self, closure=None): preconditioning.noise_coef * noise, alpha=group["lr"] ** 0.5, ) - # Apply weight decay separately from other updates - if group["weight_decay"] != 0: - d_p.add_(group["weight_decay"] * p.data) # Bounding box enforcement if group["bounding_box_size"] is not None: @@ -400,53 +538,23 @@ def step(self, closure=None): max=initial_param + group["bounding_box_size"], ) - # Track metrics - metrics = group["metrics"] - if "dws" in metrics: - metrics["dws"].append(d_p.clone()) - - if "grad_norm" in metrics and p.grad is not None: - metrics["grad_norm"] += ( - (p.grad.data * group["nbeta"] * 0.5 * group["lr"]) ** 2 - ).sum() - - if "weight_norm" in metrics: - metrics["weight_norm"] += (p.data**2).sum() + if _need_decomposition: + components = self._decompose_update( + group, p, loc_grad, wd_grad, noise, preconditioning, d_p + ) + if self.save_metrics: + self._accumulate_metrics( + group, components, _distance, preconditioning, p + ) + if self.save_sketches: + self._scatter_sketches(components, param_offset) - if "noise" in metrics: - metrics["noise"].append(noise) + if self.save_sketches: + param_offset += p.numel() - if "noise_norm" in metrics: - metrics["noise_norm"] += (noise**2).sum() - - # Finalize scalar metrics - for group in self.param_groups: - metrics = group["metrics"] - - for metric in { - "grad_norm", - "weight_norm", - "noise_norm", - "distance", - } & metrics.keys(): - metrics[metric] = torch.sqrt(metrics[metric]) - - if "localization_loss" in metrics and group["prior"] is not None: - localization = 0.0 - if ( - isinstance(group["prior"], GaussianPrior) - and group["prior"].center is not None - ): - localization = group["prior"].localization - elif ( - isinstance(group["prior"], CompositePrior) - and isinstance(group["prior"].priors[-1], GaussianPrior) - and group["prior"].priors[-1].center is not None - ): - localization = group["prior"].priors[-1].localization - metrics["localization_loss"] = ( - metrics["distance"] ** 2 * localization / 2 - ) + if self.save_metrics: + # All params accumulated; convert sum-of-squares to L2 norms + group["metrics"].sqrt_norms_() return loss @@ -456,6 +564,17 @@ def get_params(self) -> Iterator[torch.Tensor]: for p in group["params"]: yield p + def iter_group_metrics(self) -> Iterator[Metrics]: + """Yield metrics for each param group.""" + if not self.save_metrics: + raise RuntimeError("Metrics not enabled. Set save_metrics=True.") + for group in self.param_groups: + yield group["metrics"] + + def get_metrics(self) -> Metrics: + """Aggregate metrics across all param groups into a single CPU Metrics.""" + return Metrics.aggregate(self.iter_group_metrics()) + @classmethod def sgld( cls, @@ -466,9 +585,10 @@ def sgld( localization=0.0, nbeta=1.0, bounding_box_size=None, - optimize_over=None, - metrics: Optional[List[OptimizerMetric]] = None, - prior_kwargs: Optional[Dict] = None, + mask=None, + save_metrics: bool = False, + sketch_dim: Optional[int] = None, + sketch_seed: int = 0, ): """Factory method to create an SGMCMC instance that implements Stochastic Gradient Langevin Dynamics (SGLD) with a localization term (Lau et al. 2023). @@ -498,6 +618,7 @@ def sgld( This allows SGMCMC to be used as a drop-in replacement for SGLD. + :param params: Iterable of parameters to optimize :param lr: Learning rate (default: 0.01) :param noise_level: Standard deviation of noise (default: 1.0) @@ -509,9 +630,8 @@ def sgld( (default: 0.0) :param nbeta: Inverse temperature (default: 1.0) :param bounding_box_size: Size of bounding box around initial parameters (default: None) - :param optimize_over: Boolean mask for restricting updatable parameters (default: None) - :param metrics: List of metrics to track during training (default: None) - :param prior_kwargs: Additional keyword arguments for prior initialization. + :param mask: Boolean mask for restricting updatable parameters (default: None) + :param save_metrics: Whether to track metrics during training (default: False) :return: SGMCMC optimizer instance """ if noise_level != 1.0: @@ -522,20 +642,11 @@ def sgld( warnings.warn( "nbeta set to 1, LLC estimates will be off unless you know what you're doing. Use utils.default_nbeta(dataloader) instead" ) - if prior_kwargs is None: - prior_kwargs = {} - # Updated prior initialization to handle both weight decay and localization priors = [] if weight_decay > 0: - priors.append( - GaussianPrior(localization=weight_decay, center=None, **prior_kwargs) - ) + priors.append(GaussianPrior(localization=weight_decay, center=None)) if localization > 0: - priors.append( - GaussianPrior( - localization=localization, center="initial", **prior_kwargs - ) - ) + priors.append(GaussianPrior(localization=localization, center="initial")) prior = CompositePrior(priors) @@ -546,8 +657,10 @@ def sgld( nbeta=nbeta, prior=prior, bounding_box_size=bounding_box_size, - optimize_over=optimize_over, - metrics=metrics, + mask=mask, + save_metrics=save_metrics, + sketch_dim=sketch_dim, + sketch_seed=sketch_seed, ) return instance @@ -560,8 +673,9 @@ def sgnht( diffusion_factor=0.01, nbeta=1.0, bounding_box_size=None, - metrics: Optional[List[OptimizerMetric]] = None, - prior_kwargs: Optional[Dict] = None, + save_metrics: bool = False, + sketch_dim: Optional[int] = None, + sketch_seed: int = 0, ): """Factory method to create an SGMCMC instance that matches SGNHT's interface. @@ -572,16 +686,13 @@ def sgnht( :param diffusion_factor: Diffusion factor (default: 0.01) :param nbeta: Inverse temperature (default: 1.0) :param bounding_box_size: Size of bounding box around initial parameters (default: None) - :param metrics: List of metrics to track during training (default: None) - :param prior_kwargs: Additional keyword arguments for prior initialization. + :param save_metrics: Whether to track metrics (default: False) :return: SGMCMC optimizer instance """ if nbeta == 1.0: warnings.warn( "nbeta set to 1, LLC estimates will be off unless you know what you're doing. Use utils.default_nbeta(dataloader) instead" ) - if prior_kwargs is None: - prior_kwargs = {} # Create NHT preconditioner preconditioner = NHTPreconditioning(diffusion_factor=diffusion_factor) @@ -592,7 +703,9 @@ def sgnht( nbeta=nbeta, preconditioner=preconditioner, bounding_box_size=bounding_box_size, - metrics=metrics, # Pass metrics here + save_metrics=save_metrics, + sketch_dim=sketch_dim, + sketch_seed=sketch_seed, ) return instance @@ -610,9 +723,10 @@ def rmsprop_sgld( eps=0.1, add_grad_correction=False, bounding_box_size=None, - optimize_over=None, - metrics: Optional[List[OptimizerMetric]] = None, - prior_kwargs: Optional[Dict] = None, + mask=None, + save_metrics: bool = False, + sketch_dim: Optional[int] = None, + sketch_seed: int = 0, ): """Factory method to create an SGMCMC instance that wraps RMSprop's adaptive preconditioning with SGLD to perform Bayesian sampling of neural network weights. @@ -655,9 +769,8 @@ def rmsprop_sgld( :param eps: RMSprop stability constant (default: 0.1) :param add_grad_correction: Whether to add gradient correction term (default: False) :param bounding_box_size: Size of bounding box around initial parameters (default: None) - :param optimize_over: Boolean mask for restricting updatable parameters (default: None) - :param metrics: List of metrics to track during training (default: None) - :param prior_kwargs: Additional keyword arguments for prior initialization. + :param mask: Boolean mask for restricting updatable parameters (default: None) + :param save_metrics: Whether to track metrics during training (default: False) :return: SGMCMC optimizer instance """ if noise_level != 1.0: @@ -668,22 +781,12 @@ def rmsprop_sgld( warnings.warn( "nbeta set to 1, LLC estimates will be off unless you know what you're doing. Use utils.default_nbeta(dataloader) instead" ) - if prior_kwargs is None: - prior_kwargs = {} - # Updated prior initialization to handle both weight decay and localization priors = [] - prior = None if weight_decay > 0: - priors.append( - GaussianPrior(localization=weight_decay, center=None, **prior_kwargs) - ) + priors.append(GaussianPrior(localization=weight_decay, center=None)) if localization > 0: - priors.append( - GaussianPrior( - localization=localization, center="initial", **prior_kwargs - ) - ) + priors.append(GaussianPrior(localization=localization, center="initial")) prior = CompositePrior(priors) @@ -703,8 +806,10 @@ def rmsprop_sgld( preconditioner="rmsprop", preconditioner_kwargs=preconditioner_kwargs, bounding_box_size=bounding_box_size, - optimize_over=optimize_over, - metrics=metrics, + mask=mask, + save_metrics=save_metrics, + sketch_dim=sketch_dim, + sketch_seed=sketch_seed, ) return instance @@ -725,36 +830,3 @@ def get_method(cls, method: SamplingMethodLiteral): @property def device(self): return next(self.get_params()).device - - @property - def metrics(self): - if len(self.param_groups) > 1: - warnings.warn( - "metrics is only available for single-parameter groups. Returning first group's metrics." - ) - - return self.param_groups[0]["metrics"] - - @property - def tracked_metrics(self): - if len(self.param_groups) > 1: - warnings.warn( - "tracked_metrics is only available for single-parameter groups. Returning first group's tracked metrics." - ) - - return set(self.param_groups[0]["metrics"].keys()) - - def __getattr__(self, name): - """Return metrics if there's only one param group""" - - # For other attributes, check if they exist in param_groups - if len(self.param_groups) != 1: - warnings.warn( - "metrics is only available for single-parameter groups. Returning first group's metrics." - ) - if name in self.param_groups[0]: - return self.param_groups[0][name] - elif name in self.param_groups[0]["metrics"]: - return self.param_groups[0]["metrics"][name] - else: - raise AttributeError(f"'SGMCMC' object has no attribute '{name}'") diff --git a/src/devinterp/optim/sgnht.py b/src/devinterp/optim/sgnht.py index d75020db..0b6baab0 100644 --- a/src/devinterp/optim/sgnht.py +++ b/src/devinterp/optim/sgnht.py @@ -1,5 +1,5 @@ import warnings -from typing import List, Optional +from typing import Optional import numpy as np import torch @@ -15,35 +15,40 @@ class SGNHT(torch.optim.Optimizer): The equations for the update are as follows: - $$\Delta w_t = \epsilon\left(\frac{\beta n}{m} \sum_{i=1}^m \nabla \log p\left(y_{l_i} \mid x_{l_i}, w_t\right) - \xi_t w_t \right) + \sqrt{2A} N(0, \epsilon)$$ - $$\Delta\xi_{t} = \epsilon \left( \frac{1}{n} \|w_t\|^2 - 1 \right)$$ + .. math:: - where $w_t$ is the weight at time $t$, $\epsilon$ is the learning rate, - $(\beta n)$ is the inverse temperature (we're in the tempered Bayes paradigm), - $n$ is the number of samples, $m$ is the batch size, - $\xi_t$ is the thermostat variable at time $t$, $A$ is the diffusion factor, - and $N(0, A)$ represents Gaussian noise with mean 0 and variance $A$. + \Delta w_t = \epsilon\left(\frac{\beta n}{m} \sum_{i=1}^m \nabla \log p\left(y_{l_i} \mid x_{l_i}, w_t\right) - \xi_t w_t \right) + \sqrt{2A} N(0, \epsilon) + + .. math:: + + \Delta\xi_{t} = \epsilon \left( \frac{1}{n} \|w_t\|^2 - 1 \right) + + where :math:`w_t` is the weight at time :math:`t`, :math:`\epsilon` is the learning rate, + :math:`(\beta n)` is the inverse temperature (we're in the tempered Bayes paradigm), + :math:`n` is the number of samples, :math:`m` is the batch size, + :math:`\xi_t` is the thermostat variable at time :math:`t`, :math:`A` is the diffusion factor, + and :math:`N(0, A)` represents Gaussian noise with mean 0 and variance :math:`A`. Note: - - :python:`diffusion_factor` is unique to this class, and functions as a way to allow for random parameter changes while keeping them from blowing up by guiding parameters back to a slowly-changing thermostat value using a friction term. - - This class does not have an explicit localization term like :func:`~devinterp.optim.sgld.SGLD` does. If you want to constrain your sampling, use :python:`bounding_box_size` - - Although this class is a subclass of :python:`torch.optim.Optimizer`, this is a bit of a misnomer in this case. It's not used for optimizing in LLC estimation, but rather for sampling from the posterior distribution around a point. + - ``diffusion_factor`` is unique to this class, and functions as a way to allow for random parameter changes while keeping them from blowing up by guiding parameters back to a slowly-changing thermostat value using a friction term. + - This class does not have an explicit localization term like :func:`~devinterp.optim.sgld.SGLD` does. If you want to constrain your sampling, use ``bounding_box_size``. + - Although this class is a subclass of ``torch.optim.Optimizer``, this is a bit of a misnomer in this case. It's not used for optimizing in LLC estimation, but rather for sampling from the posterior distribution around a point. - :param params: Iterable of parameters to optimize or dicts defining parameter groups. Either :python:`model.parameters()` or something more fancy, just like other :python:`torch.optim.Optimizer` classes. + :param params: Iterable of parameters to optimize or dicts defining parameter groups. Either ``model.parameters()`` or something more fancy, just like other ``torch.optim.Optimizer`` classes. :type params: Iterable - :param lr: Learning rate $\epsilon$. Default is 0.01 + :param lr: Learning rate :math:`\epsilon`. Default is 0.01 :type lr: float, optional - :param diffusion_factor: The diffusion factor $A$ of the thermostat. Default is 0.01 + :param diffusion_factor: The diffusion factor :math:`A` of the thermostat. Default is 0.01 :type diffusion_factor: float, optional :param bounding_box_size: the size of the bounding box enclosing our trajectory. Default is None :type bounding_box_size: float, optional :param nbeta: Effective Inverse Temperature, float (default: 1., set to utils.default_nbeta(dataloader)=len(batch_size)/np.log(len(batch_size))) :type nbeta: int, optional - :raises Warning: if :python:`nbeta` is set to 1 - :raises Warning: if :python:`NoiseNorm` callback is used - :raises Warning: if :python:`MALA` callback is used + :raises Warning: if ``nbeta`` is set to 1 + :raises Warning: if ``NoiseNorm`` callback is used + :raises Warning: if ``MALA`` callback is used """ def __init__( @@ -55,7 +60,7 @@ def __init__( save_noise=False, save_mala_vars=False, nbeta=1.0, - metrics: Optional[List[str]] = None, + metrics: Optional[list[str]] = None, ): warnings.warn( "SGNHT has been deprecated. Please use SGMCMC.sgnht instead.", @@ -116,7 +121,7 @@ def __init__( def step(self, closure=None): with torch.no_grad(): for group in self.param_groups: - if group.get("optimize_over") is not None: + if group.get("mask") is not None: raise NotImplementedError group_energy_sum = 0.0 group_energy_size = 0 diff --git a/src/devinterp/optim/sketch.py b/src/devinterp/optim/sketch.py new file mode 100644 index 00000000..e4f66c39 --- /dev/null +++ b/src/devinterp/optim/sketch.py @@ -0,0 +1,155 @@ +from __future__ import annotations + +from dataclasses import dataclass +from typing import ClassVar + +import torch + +SKETCH_QUANTITIES: tuple[str, ...] = ( + "scaled_grad", + "unscaled_grad", + "localization", + "weight_decay", + "noise", +) + + +@dataclass +class CountSketch: + """Count sketch projection for dimensionality reduction. + + Projects d-dimensional vectors to k-dimensional sketches while preserving + inner products in expectation: E[] = . + + The sketch is defined by a hash function h: [d] -> [k] (mapping each input + coordinate to an output bucket) and a sign function s: [d] -> {-1, +1} + (random per coordinate). The sketch of vector v is: + + S(v)[j] = sum_{i : h(i) = j} s(i) * v[i] + + Both h and s are generated deterministically from a seed. Two sketch vectors + are only comparable when produced by the same CountSketch instance (same + seed, same input_dim). When used with an optimizer, input_dim is the total + trainable parameter count, so different weight restrictions yield + incomparable sketches even with the same seed. + + This single-row construction is equivalent to what Weinberger et al. call + "feature hashing". The inner product preservation property is proved there. + + References: + - Weinberger, Dasgupta, Langford, Smola & Attenberg, "Feature Hashing + for Large Scale Multitask Learning" (ICML 2009), + https://doi.org/10.1145/1553374.1553516 + - Charikar, Chen & Farach-Colton, "Finding Frequent Items in Data + Streams" (ICALP 2002), https://doi.org/10.1007/3-540-45465-9_59 + - Larsen, Pagh & Tetek, "CountSketches, Feature Hashing and the Median + of Three" (ICML 2021), https://doi.org/10.48550/arXiv.2102.02193 + """ + + hash_indices: torch.Tensor + hash_signs: torch.Tensor + _output_dim: int + + @property + def device(self) -> torch.device: + if self.hash_indices.device != self.hash_signs.device: + raise RuntimeError( + f"Inconsistent devices: hash_indices on {self.hash_indices.device}, " + f"hash_signs on {self.hash_signs.device}" + ) + return self.hash_indices.device + + @property + def input_dim(self) -> int: + return self.hash_indices.shape[0] + + @property + def output_dim(self) -> int: + return self._output_dim + + @classmethod + def create(cls, input_dim: int, output_dim: int, seed: int = 0) -> CountSketch: + gen = torch.Generator().manual_seed(seed) + hash_indices = torch.randint(0, output_dim, (input_dim,), generator=gen) + hash_signs = (2 * torch.randint(0, 2, (input_dim,), generator=gen) - 1).float() + return cls( + hash_indices=hash_indices, + hash_signs=hash_signs, + _output_dim=output_dim, + ) + + def to(self, device: torch.device | str | int) -> CountSketch: + if self.device == torch.device(device): + return self + return CountSketch( + hash_indices=self.hash_indices.to(device), + hash_signs=self.hash_signs.to(device), + _output_dim=self._output_dim, + ) + + def sketch(self, v: torch.Tensor) -> torch.Tensor: + """Apply the full sketch to a flat vector.""" + result = torch.zeros(self._output_dim, dtype=torch.float32, device=v.device) + self.scatter_into_(result, v, 0) + return result + + def scatter_into_(self, result: torch.Tensor, v: torch.Tensor, offset: int) -> None: + """Accumulate one parameter's contribution into a running sketch buffer. + + Exploits linearity: sketching cat(p1, p2, ...) is equivalent to + accumulating each pi at its offset into the same buffer. + """ + n = v.numel() + v_flat = v.detach().reshape(-1).float() + idx = self.hash_indices[offset : offset + n] + signs = self.hash_signs[offset : offset + n] + result.scatter_add_(0, idx, signs * v_flat) + + +@dataclass +class SketchBuffer: + """Per-step accumulation buffers for count sketch metrics. + + One buffer per tracked quantity. Lifecycle: + zero_() at step start -> accumulate per-param via scatter_into_ -> read. + """ + + QUANTITIES: ClassVar[tuple[str, ...]] = SKETCH_QUANTITIES + + scaled_grad: torch.Tensor + unscaled_grad: torch.Tensor + localization: torch.Tensor + weight_decay: torch.Tensor + noise: torch.Tensor + + @classmethod + def create( + cls, output_dim: int, device: torch.device | str = "cpu" + ) -> SketchBuffer: + def _z() -> torch.Tensor: + return torch.zeros(output_dim, dtype=torch.float32, device=device) + + return cls( + scaled_grad=_z(), + unscaled_grad=_z(), + localization=_z(), + weight_decay=_z(), + noise=_z(), + ) + + @property + def device(self) -> torch.device: + devices = {q: getattr(self, q).device for q in self.QUANTITIES} + unique = set(devices.values()) + if len(unique) != 1: + raise RuntimeError(f"Inconsistent devices across buffers: {devices}") + return next(iter(unique)) + + def zero_(self) -> None: + for q in self.QUANTITIES: + getattr(self, q).zero_() + + def to(self, device: torch.device | str | int) -> SketchBuffer: + if self.device == torch.device(device): + return self + return SketchBuffer(**{q: getattr(self, q).to(device) for q in self.QUANTITIES}) diff --git a/src/devinterp/slt/_model_structures.py b/src/devinterp/slt/_model_structures.py new file mode 100644 index 00000000..fb9f8e62 --- /dev/null +++ b/src/devinterp/slt/_model_structures.py @@ -0,0 +1,2254 @@ +"""Generated model structure data for weight restrictions.""" + +HEAD_STRUCTURES: dict[str, dict | list[dict]] = { + "hooked_transformer": [ + { # MHA (W_K/W_V have n_heads as dim 0) + "layer_prefixes": ["blocks"], + "embed": ["embed.W_E"], + "unembed": ["unembed.W_U", "unembed.b_U"], + "attn": [ + "attn.W_Q", + "attn.W_K", + "attn.W_V", + "attn.W_O", + "attn.b_Q", + "attn.b_K", + "attn.b_V", + "attn.b_O", + ], + "mlp": ["mlp.W_in", "mlp.W_out", "mlp.W_gate", "mlp.b_in", "mlp.b_out"], + "head_params": { + "attn.W_Q": {"slice_dim": 0}, + "attn.W_K": {"slice_dim": 0}, + "attn.W_V": {"slice_dim": 0}, + "attn.W_O": {"slice_dim": 0}, + "attn.b_Q": {"slice_dim": 0}, + "attn.b_K": {"slice_dim": 0}, + "attn.b_V": {"slice_dim": 0}, + }, + }, + { # GQA (TL stores KV as _W_K/_W_V with n_kv heads) + "layer_prefixes": ["blocks"], + "embed": ["embed.W_E"], + "unembed": ["unembed.W_U", "unembed.b_U"], + "attn": [ + "attn.W_Q", + "attn._W_K", + "attn._W_V", + "attn.W_O", + "attn.b_Q", + "attn._b_K", + "attn._b_V", + "attn.b_O", + ], + "mlp": ["mlp.W_in", "mlp.W_out", "mlp.W_gate", "mlp.b_in", "mlp.b_out"], + "head_params": { + "attn.W_Q": {"slice_dim": 0}, + "attn._W_K": {"slice_dim": 0}, + "attn._W_V": {"slice_dim": 0}, + "attn.W_O": {"slice_dim": 0}, + "attn.b_Q": {"slice_dim": 0}, + "attn._b_K": {"slice_dim": 0}, + "attn._b_V": {"slice_dim": 0}, + }, + }, + ], + "afmoe": { + "layer_prefixes": ["model.layers"], + "embed": ["model.embed_tokens.weight"], + "unembed": ["lm_head.weight"], + "attn": [ + "self_attn.gate_proj.weight", + "self_attn.k_proj.weight", + "self_attn.o_proj.weight", + 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+ "self_attn.q_proj.weight": {"slice_dim": 0}, + "self_attn.v_proj.weight": {"slice_dim": 0}, + }, + }, + "mistral": { + "layer_prefixes": ["model.layers"], + "embed": ["model.embed_tokens.weight"], + "unembed": ["lm_head.weight"], + "attn": [ + "self_attn.k_proj.weight", + "self_attn.o_proj.weight", + "self_attn.q_proj.weight", + "self_attn.v_proj.weight", + ], + "mlp": ["mlp.down_proj.weight", "mlp.gate_proj.weight", "mlp.up_proj.weight"], + "head_params": { + "self_attn.k_proj.weight": {"slice_dim": 0}, + "self_attn.o_proj.weight": {"slice_dim": 1}, + "self_attn.q_proj.weight": {"slice_dim": 0}, + "self_attn.v_proj.weight": {"slice_dim": 0}, + }, + }, + "mixtral": [ + { # transformers >= 5.5 (fused expert weights) + "layer_prefixes": ["model.layers"], + "embed": ["model.embed_tokens.weight"], + "unembed": ["lm_head.weight"], + "attn": [ + "self_attn.k_proj.weight", + "self_attn.o_proj.weight", + "self_attn.q_proj.weight", + "self_attn.v_proj.weight", + ], + "mlp": [ + "mlp.experts.down_proj", + "mlp.experts.gate_up_proj", + "mlp.gate.weight", + ], + "head_params": { + "self_attn.k_proj.weight": {"slice_dim": 0}, + "self_attn.o_proj.weight": {"slice_dim": 1}, + "self_attn.q_proj.weight": {"slice_dim": 0}, + "self_attn.v_proj.weight": {"slice_dim": 0}, + }, + }, + { # transformers < 5.5 (per-expert indexed weights, block_sparse_moe prefix) + "layer_prefixes": ["model.layers"], + "embed": ["model.embed_tokens.weight"], + "unembed": ["lm_head.weight"], + "attn": [ + "self_attn.k_proj.weight", + "self_attn.o_proj.weight", + "self_attn.q_proj.weight", + "self_attn.v_proj.weight", + ], + "mlp": [ + "block_sparse_moe.gate.weight", + "block_sparse_moe.experts.0.w1.weight", + "block_sparse_moe.experts.0.w2.weight", + "block_sparse_moe.experts.0.w3.weight", + ], + "head_params": { + "self_attn.k_proj.weight": {"slice_dim": 0}, + "self_attn.o_proj.weight": {"slice_dim": 1}, + "self_attn.q_proj.weight": {"slice_dim": 0}, + "self_attn.v_proj.weight": {"slice_dim": 0}, + }, + }, + ], + "mpt": { + "layer_prefixes": ["transformer.blocks"], + "embed": ["transformer.wte.weight"], + "unembed": ["lm_head.weight"], + "attn": ["attn.Wqkv.weight", "attn.out_proj.weight"], + "mlp": ["ffn.down_proj.weight", "ffn.up_proj.weight"], + "head_params": { + "attn.Wqkv.weight": {"fused": "concat", "slice_dim": 0}, + "attn.out_proj.weight": {"slice_dim": 1}, + }, + }, + "mvp": { + "layer_prefixes": ["model.decoder.layers"], + "embed": ["model.decoder.embed_tokens.weight"], + "unembed": ["lm_head.weight"], + "attn": [ + "self_attn.k_proj.bias", + "self_attn.k_proj.weight", + "self_attn.out_proj.bias", + "self_attn.out_proj.weight", + "self_attn.q_proj.bias", + "self_attn.q_proj.weight", + "self_attn.v_proj.bias", + "self_attn.v_proj.weight", + ], + "mlp": ["fc1.bias", "fc1.weight", "fc2.bias", "fc2.weight"], + "head_params": { + "self_attn.k_proj.weight": {"slice_dim": 0}, + "self_attn.out_proj.weight": {"slice_dim": 1}, + "self_attn.q_proj.bias": {"slice_dim": 0}, + "self_attn.q_proj.weight": {"slice_dim": 0}, + "self_attn.v_proj.bias": {"slice_dim": 0}, + "self_attn.v_proj.weight": {"slice_dim": 0}, + }, + }, + "nanochat": { + "layer_prefixes": ["model.layers"], + "embed": ["model.embed_tokens.weight"], + "unembed": ["lm_head.weight"], + "attn": [ + "self_attn.k_proj.weight", + "self_attn.o_proj.weight", + "self_attn.q_proj.weight", + "self_attn.v_proj.weight", + ], + "mlp": ["mlp.fc1.weight", "mlp.fc2.weight"], + "head_params": { + "self_attn.k_proj.weight": {"slice_dim": 0}, + "self_attn.o_proj.weight": {"slice_dim": 1}, + "self_attn.q_proj.weight": {"slice_dim": 0}, + "self_attn.v_proj.weight": {"slice_dim": 0}, + }, + }, + "nemotron": { + "layer_prefixes": ["model.layers"], + "embed": ["model.embed_tokens.weight"], + "unembed": ["lm_head.weight"], + "attn": [ + "self_attn.k_proj.weight", + "self_attn.o_proj.weight", + "self_attn.q_proj.weight", + "self_attn.v_proj.weight", + ], + "mlp": ["mlp.down_proj.weight", "mlp.up_proj.weight"], + "head_params": { + "self_attn.k_proj.weight": {"slice_dim": 0}, + "self_attn.o_proj.weight": {"slice_dim": 1}, + "self_attn.q_proj.weight": {"slice_dim": 0}, + "self_attn.v_proj.weight": {"slice_dim": 0}, + }, + }, + "olmo": { + "layer_prefixes": ["model.layers"], + "embed": ["model.embed_tokens.weight"], + "unembed": ["lm_head.weight"], + "attn": [ + "self_attn.k_proj.weight", + "self_attn.o_proj.weight", + "self_attn.q_proj.weight", + "self_attn.v_proj.weight", + ], + "mlp": ["mlp.down_proj.weight", "mlp.gate_proj.weight", "mlp.up_proj.weight"], + "head_params": { + "self_attn.k_proj.weight": {"slice_dim": 0}, + "self_attn.o_proj.weight": {"slice_dim": 1}, + "self_attn.q_proj.weight": {"slice_dim": 0}, + "self_attn.v_proj.weight": {"slice_dim": 0}, + }, + }, + "openai-gpt": { + "layer_prefixes": ["transformer.h"], + "embed": ["transformer.tokens_embed.weight"], + "unembed": ["lm_head.weight"], + "attn": [ + "attn.c_attn.bias", + "attn.c_attn.weight", + "attn.c_proj.bias", + "attn.c_proj.weight", + ], + "mlp": [ + "mlp.c_fc.bias", + "mlp.c_fc.weight", + "mlp.c_proj.bias", + "mlp.c_proj.weight", + ], + "head_params": { + "attn.c_attn.bias": {"fused": "concat", "slice_dim": 0}, + "attn.c_attn.weight": {"fused": "concat", "slice_dim": 1}, + "attn.c_proj.weight": {"slice_dim": 0}, + }, + }, + "opt": { + "layer_prefixes": ["model.decoder.layers"], + "embed": ["model.decoder.embed_tokens.weight"], + "unembed": ["lm_head.weight"], + "attn": [ + "self_attn.k_proj.bias", + "self_attn.k_proj.weight", + "self_attn.out_proj.bias", + "self_attn.out_proj.weight", + "self_attn.q_proj.bias", + "self_attn.q_proj.weight", + "self_attn.v_proj.bias", + "self_attn.v_proj.weight", + ], + "mlp": ["fc1.bias", "fc1.weight", "fc2.bias", "fc2.weight"], + "head_params": { + "self_attn.k_proj.weight": {"slice_dim": 0}, + "self_attn.out_proj.weight": {"slice_dim": 1}, + "self_attn.q_proj.bias": {"slice_dim": 0}, + "self_attn.q_proj.weight": {"slice_dim": 0}, + "self_attn.v_proj.bias": {"slice_dim": 0}, + "self_attn.v_proj.weight": {"slice_dim": 0}, + }, + }, + "pegasus": { + "layer_prefixes": ["model.decoder.layers"], + "embed": ["model.decoder.embed_tokens.weight"], + "unembed": ["lm_head.weight"], + "attn": [ + "self_attn.k_proj.bias", + "self_attn.k_proj.weight", + "self_attn.out_proj.bias", + "self_attn.out_proj.weight", + "self_attn.q_proj.bias", + "self_attn.q_proj.weight", + "self_attn.v_proj.bias", + "self_attn.v_proj.weight", + ], + "mlp": ["fc1.bias", "fc1.weight", "fc2.bias", "fc2.weight"], + "head_params": { + "self_attn.k_proj.weight": {"slice_dim": 0}, + "self_attn.out_proj.weight": {"slice_dim": 1}, + "self_attn.q_proj.bias": {"slice_dim": 0}, + "self_attn.q_proj.weight": {"slice_dim": 0}, + "self_attn.v_proj.bias": {"slice_dim": 0}, + "self_attn.v_proj.weight": {"slice_dim": 0}, + }, + }, + "persimmon": { + "layer_prefixes": ["model.layers"], + "embed": ["model.embed_tokens.weight"], + "unembed": ["lm_head.weight"], + "attn": [ + "self_attn.dense.bias", + "self_attn.dense.weight", + "self_attn.query_key_value.bias", + "self_attn.query_key_value.weight", + ], + "mlp": [ + "mlp.dense_4h_to_h.bias", + "mlp.dense_4h_to_h.weight", + "mlp.dense_h_to_4h.bias", + "mlp.dense_h_to_4h.weight", + ], + "head_params": { + "self_attn.dense.weight": {"slice_dim": 1}, + "self_attn.query_key_value.bias": {"fused": "interleaved", "slice_dim": 0}, + "self_attn.query_key_value.weight": { + "fused": "interleaved", + "slice_dim": 0, + }, + }, + }, + "phi": { + "layer_prefixes": ["model.layers"], + "embed": ["model.embed_tokens.weight"], + "unembed": ["lm_head.weight", "lm_head.bias"], + "attn": [ + "self_attn.dense.bias", + "self_attn.dense.weight", + "self_attn.k_proj.bias", + "self_attn.k_proj.weight", + "self_attn.q_proj.bias", + "self_attn.q_proj.weight", + "self_attn.v_proj.bias", + "self_attn.v_proj.weight", + ], + "mlp": ["mlp.fc1.bias", "mlp.fc1.weight", "mlp.fc2.bias", "mlp.fc2.weight"], + "head_params": { + "self_attn.dense.weight": {"slice_dim": 1}, + "self_attn.k_proj.bias": {"slice_dim": 0}, + "self_attn.k_proj.weight": {"slice_dim": 0}, + "self_attn.q_proj.bias": {"slice_dim": 0}, + "self_attn.q_proj.weight": {"slice_dim": 0}, + "self_attn.v_proj.bias": {"slice_dim": 0}, + "self_attn.v_proj.weight": {"slice_dim": 0}, + }, + }, + "phi3": { + "layer_prefixes": ["model.layers"], + "embed": ["model.embed_tokens.weight"], + "unembed": ["lm_head.weight"], + "attn": ["self_attn.o_proj.weight", "self_attn.qkv_proj.weight"], + "mlp": ["mlp.down_proj.weight", "mlp.gate_up_proj.weight"], + "head_params": { + "self_attn.o_proj.weight": {"slice_dim": 1}, + "self_attn.qkv_proj.weight": {"fused": "concat", "slice_dim": 0}, + }, + }, + "phimoe": { + "layer_prefixes": ["model.layers"], + "embed": ["model.embed_tokens.weight"], + "unembed": ["lm_head.weight"], + "attn": [ + "self_attn.k_proj.weight", + "self_attn.o_proj.weight", + "self_attn.q_proj.weight", + "self_attn.v_proj.weight", + ], + "mlp": [ + "mlp.experts.down_proj", + "mlp.experts.gate_up_proj", + "mlp.router.weight", + ], + "head_params": { + "self_attn.k_proj.weight": {"slice_dim": 0}, + "self_attn.o_proj.weight": {"slice_dim": 1}, + "self_attn.q_proj.weight": {"slice_dim": 0}, + "self_attn.v_proj.weight": {"slice_dim": 0}, + }, + }, + "plbart": { + "layer_prefixes": ["model.decoder.layers"], + "embed": ["model.decoder.embed_tokens.weight"], + "unembed": ["lm_head.weight"], + "attn": [ + "self_attn.k_proj.bias", + "self_attn.k_proj.weight", + "self_attn.out_proj.bias", + "self_attn.out_proj.weight", + "self_attn.q_proj.bias", + "self_attn.q_proj.weight", + "self_attn.v_proj.bias", + "self_attn.v_proj.weight", + ], + "mlp": ["fc1.bias", "fc1.weight", "fc2.bias", "fc2.weight"], + "head_params": { + "self_attn.k_proj.weight": {"slice_dim": 0}, + "self_attn.out_proj.weight": {"slice_dim": 1}, + "self_attn.q_proj.bias": {"slice_dim": 0}, + "self_attn.q_proj.weight": {"slice_dim": 0}, + "self_attn.v_proj.bias": {"slice_dim": 0}, + "self_attn.v_proj.weight": {"slice_dim": 0}, + }, + }, + "qwen2": { + "layer_prefixes": ["model.layers"], + "embed": ["model.embed_tokens.weight"], + "unembed": ["lm_head.weight"], + "attn": [ + "self_attn.k_proj.bias", + "self_attn.k_proj.weight", + "self_attn.o_proj.weight", + "self_attn.q_proj.bias", + "self_attn.q_proj.weight", + "self_attn.v_proj.bias", + "self_attn.v_proj.weight", + ], + "mlp": ["mlp.down_proj.weight", "mlp.gate_proj.weight", "mlp.up_proj.weight"], + "head_params": { + "self_attn.k_proj.bias": {"slice_dim": 0}, + "self_attn.k_proj.weight": {"slice_dim": 0}, + "self_attn.o_proj.weight": {"slice_dim": 1}, + "self_attn.q_proj.bias": {"slice_dim": 0}, + "self_attn.q_proj.weight": {"slice_dim": 0}, + "self_attn.v_proj.bias": {"slice_dim": 0}, + "self_attn.v_proj.weight": {"slice_dim": 0}, + }, + }, + "qwen2_moe": [ + { # transformers >= 5.5 (fused expert weights) + "layer_prefixes": ["model.layers"], + "embed": ["model.embed_tokens.weight"], + "unembed": ["lm_head.weight"], + "attn": [ + "self_attn.k_proj.bias", + "self_attn.k_proj.weight", + "self_attn.o_proj.weight", + "self_attn.q_proj.bias", + "self_attn.q_proj.weight", + "self_attn.v_proj.bias", + "self_attn.v_proj.weight", + ], + "mlp": [ + "mlp.experts.down_proj", + "mlp.experts.gate_up_proj", + "mlp.gate.weight", + "mlp.shared_expert.down_proj.weight", + "mlp.shared_expert.gate_proj.weight", + "mlp.shared_expert.up_proj.weight", + "mlp.shared_expert_gate.weight", + ], + "head_params": { + "self_attn.k_proj.bias": {"slice_dim": 0}, + "self_attn.k_proj.weight": {"slice_dim": 0}, + "self_attn.o_proj.weight": {"slice_dim": 1}, + "self_attn.q_proj.bias": {"slice_dim": 0}, + "self_attn.q_proj.weight": {"slice_dim": 0}, + "self_attn.v_proj.bias": {"slice_dim": 0}, + "self_attn.v_proj.weight": {"slice_dim": 0}, + }, + }, + { # transformers < 5.5 (per-expert indexed weights) + "layer_prefixes": ["model.layers"], + "embed": ["model.embed_tokens.weight"], + "unembed": ["lm_head.weight"], + "attn": [ + "self_attn.k_proj.bias", + "self_attn.k_proj.weight", + "self_attn.o_proj.weight", + "self_attn.q_proj.bias", + "self_attn.q_proj.weight", + "self_attn.v_proj.bias", + "self_attn.v_proj.weight", + ], + "mlp": [ + "mlp.gate.weight", + "mlp.experts.0.gate_proj.weight", + "mlp.experts.0.up_proj.weight", + "mlp.experts.0.down_proj.weight", + "mlp.shared_expert.gate_proj.weight", + "mlp.shared_expert.up_proj.weight", + "mlp.shared_expert.down_proj.weight", + "mlp.shared_expert_gate.weight", + ], + "head_params": { + "self_attn.k_proj.bias": {"slice_dim": 0}, + "self_attn.k_proj.weight": {"slice_dim": 0}, + "self_attn.o_proj.weight": {"slice_dim": 1}, + "self_attn.q_proj.bias": {"slice_dim": 0}, + "self_attn.q_proj.weight": {"slice_dim": 0}, + "self_attn.v_proj.bias": {"slice_dim": 0}, + "self_attn.v_proj.weight": {"slice_dim": 0}, + }, + }, + ], + "qwen3": { + "layer_prefixes": ["model.layers"], + "embed": ["model.embed_tokens.weight"], + "unembed": ["lm_head.weight"], + "attn": [ + "self_attn.k_proj.weight", + "self_attn.o_proj.weight", + "self_attn.q_proj.weight", + "self_attn.v_proj.weight", + ], + "mlp": ["mlp.down_proj.weight", "mlp.gate_proj.weight", "mlp.up_proj.weight"], + "head_params": { + "self_attn.k_proj.weight": {"slice_dim": 0}, + "self_attn.o_proj.weight": {"slice_dim": 1}, + "self_attn.q_proj.weight": {"slice_dim": 0}, + "self_attn.v_proj.weight": {"slice_dim": 0}, + }, + }, + "qwen3_moe": { + "layer_prefixes": ["model.layers"], + "embed": ["model.embed_tokens.weight"], + "unembed": ["lm_head.weight"], + "attn": [ + "self_attn.k_proj.weight", + "self_attn.o_proj.weight", + "self_attn.q_proj.weight", + "self_attn.v_proj.weight", + ], + "mlp": ["mlp.experts.down_proj", "mlp.experts.gate_up_proj", "mlp.gate.weight"], + "head_params": { + "self_attn.k_proj.weight": {"slice_dim": 0}, + "self_attn.o_proj.weight": {"slice_dim": 1}, + "self_attn.q_proj.weight": {"slice_dim": 0}, + "self_attn.v_proj.weight": {"slice_dim": 0}, + }, + }, + "rembert": { + "layer_prefixes": ["rembert.encoder.layer"], + "embed": ["rembert.embeddings.word_embeddings.weight"], + "unembed": ["cls.predictions.decoder.weight", "cls.predictions.decoder.bias"], + "attn": [ + "attention.output.dense.bias", + "attention.output.dense.weight", + "attention.self.key.bias", + "attention.self.key.weight", + "attention.self.query.bias", + "attention.self.query.weight", + "attention.self.value.bias", + "attention.self.value.weight", + ], + "mlp": [ + "intermediate.dense.bias", + "intermediate.dense.weight", + "output.dense.bias", + "output.dense.weight", + ], + "head_params": { + "attention.output.dense.weight": {"slice_dim": 1}, + "attention.self.key.weight": {"slice_dim": 0}, + "attention.self.query.bias": {"slice_dim": 0}, + "attention.self.query.weight": {"slice_dim": 0}, + "attention.self.value.bias": {"slice_dim": 0}, + "attention.self.value.weight": {"slice_dim": 0}, + }, + }, + "roberta": { + "layer_prefixes": ["roberta.encoder.layer"], + "embed": ["roberta.embeddings.word_embeddings.weight"], + "unembed": ["lm_head.decoder.weight", "lm_head.decoder.bias"], + "attn": [ + "attention.output.dense.bias", + "attention.output.dense.weight", + "attention.self.key.bias", + "attention.self.key.weight", + "attention.self.query.bias", + "attention.self.query.weight", + "attention.self.value.bias", + "attention.self.value.weight", + ], + "mlp": [ + "intermediate.dense.bias", + "intermediate.dense.weight", + "output.dense.bias", + "output.dense.weight", + ], + "head_params": { + "attention.output.dense.weight": {"slice_dim": 1}, + "attention.self.key.weight": {"slice_dim": 0}, + "attention.self.query.bias": {"slice_dim": 0}, + "attention.self.query.weight": {"slice_dim": 0}, + "attention.self.value.bias": {"slice_dim": 0}, + "attention.self.value.weight": {"slice_dim": 0}, + }, + }, + "roberta-prelayernorm": { + "layer_prefixes": ["roberta_prelayernorm.encoder.layer"], + "embed": ["roberta_prelayernorm.embeddings.word_embeddings.weight"], + 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Intended for parity tests only. + +`make_tiny(model_type)` produces an `AutoConfig` sized small enough for +fast test iteration (4 attention heads x 8 dim = 32 hidden, vocab=32, +max_position=64, 1 layer, etc). Used by parity tests that need to +instantiate every supported architecture. + +Only supports model types present in HEAD_STRUCTURES (_model_structures.py). + +`_ATTR_TABLE` is a list of `(value, [attr_names])` pairs. For each +config, we set whichever of those attrs exist to the given value -- this +handles HuggingFace's inconsistent naming across model families +(e.g. `num_hidden_layers` vs `n_layer` vs `num_layers` all mean the same +thing). Example: `(1, ["num_hidden_layers", "n_layer", ...])` sets +any of those attrs to 1, making a 1-layer model. +""" + +from transformers import AutoConfig + +from devinterp.slt._model_structures import HEAD_STRUCTURES + +NUM_HEADS = 4 +HEAD_DIM = 8 +HIDDEN = NUM_HEADS * HEAD_DIM # 32 + +# {value: [attr_names]} — set each attr to value if it exists on the config. +_ATTR_TABLE: list[tuple[object, list[str]]] = [ + ( + 1, + [ + "num_hidden_layers", + "n_layer", + "num_layers", + "decoder_layers", + "encoder_layers", + "num_decoder_layers", + "num_encoder_layers", + "num_experts_per_tok", + ], + ), + ( + NUM_HEADS, + [ + "num_attention_heads", + "n_head", + "decoder_attention_heads", + "encoder_attention_heads", + "num_decoder_attention_heads", + "num_encoder_attention_heads", + ], + ), + ( + HIDDEN, + ["hidden_size", "d_model", "n_embd", "embed_dim", "dim", "word_embed_proj_dim"], + ), + ( + HIDDEN * 4, + [ + "intermediate_size", + "n_inner", + "ffn_dim", + "d_inner", + "encoder_ffn_dim", + "decoder_ffn_dim", + ], + ), + (32, ["vocab_size"]), + (2, ["num_experts", "num_local_experts", "moe_num_experts"]), + ( + 64, + [ + "max_position_embeddings", + "n_positions", + "max_seq_len", + "max_source_positions", + "max_target_positions", + "original_max_position_embeddings", + ], + ), +] + + +def _set_if_exists( + config: AutoConfig, attr: str, value: object, even_if_none: bool = False +) -> None: + if even_if_none: + if not hasattr(config, attr): + return + elif getattr(config, attr, None) is None: + return + try: + setattr(config, attr, value) + except AttributeError: + pass # read-only property + + +def make_tiny(model_type: str, gqa: bool = False) -> AutoConfig: + if model_type not in HEAD_STRUCTURES or model_type == "hooked_transformer": + raise ValueError(f"{model_type} is not a supported HuggingFace model type") + + config = AutoConfig.for_model(model_type) + + for value, attrs in _ATTR_TABLE: + for attr in attrs: + _set_if_exists(config, attr, value) + + # These attrs may default to None but must be set for model construction + for attr in ["head_dim", "d_head", "kv_channels", "rotary_dim", "dim_head"]: + _set_if_exists(config, attr, HEAD_DIM, even_if_none=True) + _set_if_exists(config, "num_key_value_heads", NUM_HEADS, even_if_none=True) + + # Catch variant vocab/hidden sizes (e.g. vocab_size_per_layer_input) + for attr in dir(config): + if attr.startswith("_"): + continue + try: + if "vocab_size" in attr: + setattr(config, attr, 32) + elif "hidden_size" in attr: + setattr(config, attr, HIDDEN) + except (AttributeError, TypeError): + pass + + config.is_decoder = True + config.attn_implementation = "eager" + config.pad_token_id = 0 + dec_start = getattr(config, "decoder_start_token_id", None) + if dec_start is not None and dec_start >= config.vocab_size: + config.decoder_start_token_id = 1 + + # GQA: use fewer KV heads than Q heads + GQA_N_KV = 2 + if gqa and getattr(config, "num_key_value_heads", None) is not None: + config.num_key_value_heads = GQA_N_KV + + return config diff --git a/src/devinterp/slt/bif.py b/src/devinterp/slt/bif.py new file mode 100644 index 00000000..8b9f45de --- /dev/null +++ b/src/devinterp/slt/bif.py @@ -0,0 +1,357 @@ +"""Bayesian Influence Function (BIF) computation. + +Computes pairwise correlations between observable loss traces across +sequences from SGLD sampling results. + +Two entry points: +- bif(): high-level, takes model + dataset, runs sampling + BIF +- compute_bif(): low-level, takes pre-computed sampling DataTree +""" + +from __future__ import annotations + +from collections.abc import Iterator +from typing import Literal, Sequence + +import numpy as np +import torch +import xarray as xr +from datasets import Dataset +from tqdm import tqdm + +from devinterp.slt.covariance import ( + batch_corrcoef, + batch_cov, + xr_corrcoef_with_torch_backend, +) +from devinterp.slt.lm_loss import LossFn +from devinterp.slt.sampler import ParamMasks +from devinterp.slt.sampling import ObservableSpec, sample + +CorrelationMethod = Literal["token", "sequence"] +ChainReductionMethod = Literal["stack", "mean"] + + +def bif( + model: torch.nn.Module, + dataset: Dataset, + observables: dict[str, ObservableSpec], + *, + lr: float, + n_beta: float, + param_masks: ParamMasks | None = None, + correlation_method: CorrelationMethod = "token", + reduce_chain_dimension_method: ChainReductionMethod = "stack", + average_tokenwise_bif: bool = False, + compute_covariance: bool = False, + bif_batch_size: int = 32, + bif_device: str | torch.device | None = None, + loss_fn: LossFn | None = None, + **kwargs, +) -> xr.Dataset: + """Sample and compute BIF in one call. + + Args: + model: PyTorch model. + dataset: HuggingFace Dataset with "input_ids" column. + observables: Dict mapping names to datasets (or (dataset, batches_per_draw) tuples). + lr: SGLD learning rate. + n_beta: SGLD inverse temperature. + param_masks: Which parameters to optimize. None for full model. + correlation_method: "token" or "sequence". + reduce_chain_dimension_method: "stack" (recommended) or "mean". + average_tokenwise_bif: Average token-wise BIF to scalar per pair. + compute_covariance: Compute covariance instead of correlation. + bif_batch_size: Batch size for BIF block processing. + bif_device: Torch device for BIF computation. None for auto-detect. + loss_fn: Optional custom per-token loss `(model, input_ids) -> (batch, seq-1)`. + Defaults to cross-entropy on the model's logits. + **kwargs: Additional arguments passed to sample() (num_chains, num_draws, + batch_size, output_path, etc.) + + Returns: + xr.Dataset with "influences" and "input_ids" variables. + """ + samples = sample( + model=model, + dataset=dataset, + observables=observables, + param_masks=param_masks, + lr=lr, + n_beta=n_beta, + loss_fn=loss_fn, + **kwargs, + ) + return compute_bif( + samples, + correlation_method=correlation_method, + reduce_chain_dimension_method=reduce_chain_dimension_method, + average_tokenwise_bif=average_tokenwise_bif, + compute_covariance=compute_covariance, + batch_size=bif_batch_size, + device=bif_device, + ) + + +def compute_bif( + samples: xr.DataTree, + *, + correlation_method: CorrelationMethod = "token", + reduce_chain_dimension_method: ChainReductionMethod = "stack", + loss_keys: Literal["all"] | list[str] = "all", + batch_index_range_1: Literal["all"] | Sequence[int] = "all", + batch_index_range_2: Literal["all"] | Sequence[int] = "all", + average_tokenwise_bif: bool = False, + compute_covariance: bool = False, + batch_size: int = 32, + device: str | torch.device | None = None, +) -> xr.Dataset: + """Compute BIF from a sampling DataTree. + + Args: + samples: DataTree output from sample(). + correlation_method: "token" for token-wise, "sequence" for sequence-level. + reduce_chain_dimension_method: "stack" (recommended) or "mean". + loss_keys: Which observables to include. "all" auto-discovers. + batch_index_range_1: Batch indices for first operand. + batch_index_range_2: Batch indices for second operand. + average_tokenwise_bif: Average token-wise BIF to scalar per pair. + compute_covariance: Compute covariance instead of correlation. + batch_size: Batch size for block processing. + device: Torch device. None for auto-detect. + + Returns: + xr.Dataset with "influences" and "input_ids" variables. + """ + if device is None: + device = "cuda" if torch.cuda.is_available() else "cpu" + + ds = samples.dataset + resolved_keys = _resolve_loss_keys(ds, loss_keys) + + losses = _concatenate_observables(ds, resolved_keys, var_prefix="loss") + losses = _adapt_to_correlation_method(losses, correlation_method) + losses = _reduce_chains(losses, reduce_chain_dimension_method) + + losses_1 = ( + losses + if batch_index_range_1 == "all" + else losses.sel(batch=list(batch_index_range_1)) + ) + losses_2 = ( + losses + if batch_index_range_2 == "all" + else losses.sel(batch=list(batch_index_range_2)) + ) + + bif_dataset = _compute_correlations( + losses_1, + losses_2, + correlation_method=correlation_method, + average_tokenwise_bif=average_tokenwise_bif, + compute_covariance=compute_covariance, + batch_size=batch_size, + device=device, + ) + + # Attach input_ids + input_ids = _concatenate_observables(ds, resolved_keys, var_prefix="input_ids") + # Remove chain/draw dims (input_ids are fixed across draws) + while input_ids.ndim > 2: + input_ids = input_ids.isel({input_ids.dims[0]: 0}) + bif_dataset["input_ids"] = input_ids + + # Drop scalar coordinates from sampling metadata + scalar_coords = [c for c in bif_dataset.coords if bif_dataset[c].ndim == 0] + if scalar_coords: + bif_dataset = bif_dataset.drop_vars(scalar_coords) + + return bif_dataset + + +# ─── Data preparation ──────────────────────────────────────────────────────── + + +def _resolve_loss_keys( + ds: xr.Dataset, loss_keys: Literal["all"] | list[str] +) -> list[str]: + all_ids = [ + str(v)[len("loss_") :] + for v in ds.data_vars + if str(v).startswith("loss_") and str(v) != "sampling_loss" + ] + if loss_keys == "all": + return sorted(all_ids) + missing = [k for k in loss_keys if k not in all_ids] + if missing: + raise KeyError(f"Loss key(s) {missing} not found. Available: {all_ids}") + return list(loss_keys) + + +def _normalize_dims(arr: xr.DataArray, obs_id: str) -> xr.DataArray: + """Rename disambiguated dims back to standard names.""" + rename = {} + for dim in arr.dims: + d = str(dim) + if d == f"batch_{obs_id}": + rename[d] = "batch" + if "token_pos" in arr.dims: + rename["token_pos"] = "target_position" + if "token" in arr.dims: + rename["token"] = "position" + return arr.rename(rename) if rename else arr + + +def _concatenate_observables( + ds: xr.Dataset, keys: list[str], var_prefix: str +) -> xr.DataArray: + """Concatenate observable arrays along batch, normalizing dim names.""" + arrays = [_normalize_dims(ds[f"{var_prefix}_{key}"], key) for key in keys] + return xr.concat(arrays, dim="batch").load() + + +def _adapt_to_correlation_method( + losses: xr.DataArray, method: CorrelationMethod +) -> xr.DataArray: + if method == "sequence": + losses = losses.mean(dim="target_position", keep_attrs=True) + expected = ("chain", "draw", "batch") + elif method == "token": + expected = ("chain", "draw", "batch", "target_position") + else: + raise ValueError(f"Unknown correlation method: {method}") + + assert losses.dims == expected, f"Expected dims {expected}, got {losses.dims}" + + # Ensure integer coords + updates = { + dim: xr.DataArray(np.arange(losses.sizes[dim]), dims=(dim,)) + for dim in expected + if dim not in losses.coords or losses[dim].dims != (dim,) + } + return losses.assign_coords(updates) if updates else losses + + +def _reduce_chains(losses: xr.DataArray, method: ChainReductionMethod) -> xr.DataArray: + if method == "stack": + return losses.stack(chain_draw=("chain", "draw")) + elif method == "mean": + losses = losses.mean(dim="chain", keep_attrs=True).rename( + {"draw": "chain_draw"} + ) + dims = ( + ["batch", "target_position", "chain_draw"] + if "target_position" in losses.dims + else ["batch", "chain_draw"] + ) + return losses.transpose(*dims) + else: + raise ValueError(f"Unknown method: {method}") + + +# ─── Correlation computation ──────────────────────────────────────────────── + + +def _compute_correlations( + losses_1: xr.DataArray, + losses_2: xr.DataArray, + *, + correlation_method: CorrelationMethod, + average_tokenwise_bif: bool, + compute_covariance: bool, + batch_size: int, + device: str | torch.device, +) -> xr.Dataset: + coords_1 = np.asarray(losses_1.coords["batch"].values) + coords_2 = np.asarray(losses_2.coords["batch"].values) + + if correlation_method == "token": + n_tokens = losses_1.sizes["target_position"] + correlations = _tokenwise_bif( + losses_1, + losses_2, + batch_size=batch_size, + average=average_tokenwise_bif, + covariance=compute_covariance, + device=device, + ) + + dims = ["batch_1", "batch_2"] + coords: dict[str, np.ndarray] = {"batch_1": coords_1, "batch_2": coords_2} + if not average_tokenwise_bif: + dims += ["target_position", "target_position_T"] + coords["target_position"] = np.arange(1, n_tokens + 1) + coords["target_position_T"] = np.arange(1, n_tokens + 1) + + return xr.DataArray( + correlations, name="influences", dims=dims, coords=coords + ).to_dataset() + + elif correlation_method == "sequence": + n1 = losses_1.sizes["batch"] + result = xr_corrcoef_with_torch_backend( + losses_1, losses_2, device=device, compute_covariance=compute_covariance + ) + result = result.isel( + batch=slice(0, n1), batch_T=slice(n1, n1 + losses_2.sizes["batch"]) + ) + result = result.rename( + {"correlation": "influences", "batch": "batch_1", "batch_T": "batch_2"} + ) + return result.assign_coords( + batch_1=("batch_1", coords_1), batch_2=("batch_2", coords_2) + ) + + else: + raise ValueError(f"Unknown correlation method: {correlation_method}") + + +@torch.no_grad() +def _tokenwise_bif( + losses_1: xr.DataArray, + losses_2: xr.DataArray, + *, + batch_size: int, + average: bool, + covariance: bool, + device: str | torch.device, +) -> np.ndarray: + n1, n2 = losses_1.sizes["batch"], losses_2.sizes["batch"] + n_tokens = losses_1.sizes["target_position"] + + shape = (n1, n2) if average else (n1, n2, n_tokens, n_tokens) + result = np.empty(shape, dtype=np.float32) + + for s1, s2, block in tqdm( + _iter_blocks(losses_1, losses_2, batch_size, covariance, device), + desc="Computing token-wise BIF", + unit="batch", + ): + if average: + block = block.mean(dim=(-1, -2)) + result[s1, s2, ...] = block.cpu().numpy() + + return result + + +def _iter_blocks( + losses_1: xr.DataArray, + losses_2: xr.DataArray, + batch_size: int, + covariance: bool, + device: str | torch.device, +) -> Iterator[tuple[slice, slice, torch.Tensor]]: + n1, n2 = losses_1.sizes["batch"], losses_2.sizes["batch"] + n_tokens = losses_1.sizes["target_position"] + corr_fn = batch_cov if covariance else batch_corrcoef + + for i in range(0, n1, batch_size): + s1 = slice(i, i + batch_size) + t1 = torch.as_tensor(losses_1.isel(batch=s1).data, device=device) + for j in range(0, n2, batch_size): + s2 = slice(j, j + batch_size) + t2 = torch.as_tensor(losses_2.isel(batch=s2).data, device=device) + block = corr_fn(t1, t2)[:, :, :n_tokens, n_tokens:] + yield s1, s2, block + del t2, block + del t1 diff --git a/src/devinterp/slt/callback.py b/src/devinterp/slt/callback.py deleted file mode 100644 index dd534bec..00000000 --- a/src/devinterp/slt/callback.py +++ /dev/null @@ -1,70 +0,0 @@ -import warnings -from typing import Callable, List, Union - -import torch - - -class SamplerCallback: - """ - Base class for creating callbacks used in :func:`devinterp.slt.sampler.get_results()`. - Each instantiated callback gets its :python:`__call__` called every sample, and :python:`finalize` called at the end of sample (if it exists). - Each callback method can access parameters in :python:`locals()`, so there's no need to pass variables along explicitly. - - :param device: Device to perform computations on, e.g., 'cpu' or 'cuda'. - :raises NotImplementedError: if :python: `__call__` :python: `sample` are not overwritten. - - Note: - - :python:`mps` devices might not work for all callbacks. - """ - - def __init__(self, device: Union[torch.device, str] = "cpu"): - self.device = device - - def share_memory_(self): - if self.device == "mps": - warnings.warn("Cannot share memory with MPS device.") - return self - - for attr in dir(self): - if attr.startswith("_"): - continue - - attr = getattr(self, attr) - if hasattr(attr, "share_memory_"): - attr.share_memory_() - - return self - - def __call__(self, *args, **kwargs): - """Gets called at every (non burn-in) sample draw. Can access any variable in :python:`locals()` when called. - Should be used for calucalting stats at every sample draw, for example running average chain loss. - :raises NotImplementedError: if not overwritten for inherited classes. - """ - raise NotImplementedError - - def finalize(self, *args, **kwargs): - """Gets called at the end of sampling. Can access any variable in :python:`locals()` when called. Should be used for calucalting stats over chains, for example average chain loss.""" - pass - - def get_results(self, *args, **kwargs): - """Does not get called automatically, but functions as an interface to easily access stats calculated by the callback.""" - raise NotImplementedError - - -def validate_callbacks(callbacks: List[Callable]): - for i, callback in enumerate(callbacks): - if isinstance(callback, SamplerCallback) and hasattr(callback, "base_callback"): - base_callback = callback.base_callback - base_callback_exists = False - for j, other_callback in enumerate(callbacks): - if other_callback is base_callback: - if j > i: - raise ValueError( - f"Derivative callback {callback} must be called after base callback {base_callback}." - ) - base_callback_exists = True - if not base_callback_exists: - raise ValueError( - f"Base callback {base_callback} of derivative callback {callback} was not passed." - ) - return True diff --git a/src/devinterp/slt/config.py b/src/devinterp/slt/config.py new file mode 100644 index 00000000..3b54f3ec --- /dev/null +++ b/src/devinterp/slt/config.py @@ -0,0 +1,69 @@ +"""Configuration for SGLD sampling.""" + +from __future__ import annotations + +from typing import Any, Literal + +from pydantic import ( + BaseModel, + ConfigDict, + Field, + NonNegativeFloat, + NonNegativeInt, + PositiveInt, +) + +from devinterp.optim import SGMCMC + +EpochMode = Literal["once", "cycle"] + +SamplingMethodLiteral = Literal[ + "sgmcmc_sgld", + "rmsprop_sgld", +] + +SAMPLING_METHODS = { + "sgmcmc_sgld": SGMCMC.sgld, + "rmsprop_sgld": SGMCMC.rmsprop_sgld, +} + + +class SamplerConfig(BaseModel): + """Configuration for SGLD sampling. + + Validates types and value ranges for all sampler parameters. + """ + + model_config = ConfigDict(extra="forbid") + + lr: NonNegativeFloat + n_beta: NonNegativeFloat + + batch_size: PositiveInt = 32 + num_chains: PositiveInt = 4 + num_draws: PositiveInt = 200 + num_burnin_steps: NonNegativeInt = 0 + num_steps_bw_draws: PositiveInt = 1 + gradient_accumulation_steps: PositiveInt = 1 + num_init_loss_batches: PositiveInt = 32 + + init_seed: int = 100 + + # Optimizer parameters + localization: NonNegativeFloat = 0.0 + noise_level: NonNegativeFloat = 1.0 + llc_weight_decay: NonNegativeFloat = 0.0 + bounding_box_size: NonNegativeFloat | None = None + + sampling_method: SamplingMethodLiteral = "sgmcmc_sgld" + sampling_method_kwargs: dict[str, Any] = Field(default_factory=dict) + + init_noise: NonNegativeFloat | None = None + save_metrics: bool = False + shuffle: bool = True + epoch_mode: EpochMode = "cycle" + match_sampling_input_ids_across_chains: bool = True + + @property + def num_sgld_steps(self) -> int: + return self.num_draws * self.num_steps_bw_draws + self.num_burnin_steps diff --git a/src/devinterp/slt/cov.py b/src/devinterp/slt/cov.py deleted file mode 100644 index b04fa8c7..00000000 --- a/src/devinterp/slt/cov.py +++ /dev/null @@ -1,359 +0,0 @@ -from typing import Callable, Dict, List, Tuple, Union - -import numpy as np -import torch -from torch import nn -from torch.linalg import eigh - -from devinterp.slt.callback import SamplerCallback - -WeightAccessor = Callable[[nn.Module], torch.Tensor] - - -class CovarianceAccumulator(SamplerCallback): - """ - A callback to iteratively compute and store the covariance matrix of model weights. - For passing along to :func:`devinterp.slt.sampler.sample`. - - :param num_weights: Total number of weights. - :type num_weights: int - :param accessors: Functions to access model weights. - :type accessors: List[Callable[[nn.Module], torch.Tensor]] - :param device: Device to perform computations on, e.g., 'cpu' or 'cuda'. Default is 'cpu' - :type device: str | torch.device, optional - :param num_evals: Number of eigenvalues to compute. Default is 3 - :type num_evals: int, optional - - """ - - def __init__( - self, - num_weights: int, - accessors: List[WeightAccessor], - device: Union[torch.device, str] = "cpu", - num_evals: int = 3, - ): - self.num_weights = num_weights - self.first_moment = torch.zeros(num_weights, device=device) - self.second_moment = torch.zeros(num_weights, num_weights, device=device) - self.num_draws = 0 - self.accessors = accessors - self.num_evals = num_evals - self.is_finished = False - - def accumulate(self, model: nn.Module): - # Accumulate moments from model weights. - assert not self.is_finished, "Cannot accumulate after finalizing." - - weights = torch.cat( - [accessor(model).view((-1,)) for accessor in self.accessors] - ) - self.first_moment += weights - self.second_moment += torch.outer(weights, weights) - self.num_draws += 1 - - def finalize(self): - self.first_moment /= self.num_draws - self.second_moment /= self.num_draws - self.is_finished = True - - def reset(self): - self.first_moment.zero_() - self.second_moment.zero_() - self.num_draws = 0 - self.is_finished = False - - def to_matrix(self): - # Convert the moments to a covariance matrix. - return self.second_moment - torch.outer(self.first_moment, self.first_moment) - - def to_eigen(self, include_matrix=False): - # Convert the covariance matrix to pairs of eigenvalues and vectors. - cov = self.to_matrix().detach().cpu() - all_evals, all_evecs = eigh(cov) - evals = all_evals[-self.num_evals :].numpy() - evecs = all_evecs[-self.num_evals :].numpy() - - results = {"evals": evals, "evecs": evecs} - - if include_matrix: - results["matrix"] = cov.numpy() - - return results - - def get_results(self): - """ - :returns: A dict :python:`{"evals": eigenvalues_of_cov_matrix, "evecs": eigenvectors_of_cov_matrix, "matrix": cov_matrix}`. (Only after running :python:`devinterp.slt.sampler.sample(..., [covariance_accumulator_instance], ...)`) - """ - return self.to_eigen(include_matrix=True) - - def __call__(self, model, **kwargs): - self.accumulate(model) - - -AttentionHeadWeightsAccessor = Callable[[nn.Module], Tuple[torch.Tensor, ...]] - - -class WithinHeadCovarianceAccumulator: - """ - A CovarianceAccumulator to compute covariance within attention heads. - For use with :func:`devinterp.slt.sampler.sample`. - - :param num_heads: The number of attention heads. - :type num_heads: int - :param num_weights_per_head: The number of weights per attention head. - :type num_weights_per_head: int - :param accessors: Functions to access attention head weights. - :type accessors: List[Callable[[nn.Module], Tuple[torch.Tensor, ...]]] - :param device: Device to perform computations on, e.g., 'cpu' or 'cuda'. Default is 'cpu' - :type device: str | torch.device, optional - :param num_evals: number of eigenvectors / eigenvalues to compute. Default is 3 - :type num_evals: int, optional - - """ - - def __init__( - self, - num_heads: int, - num_weights_per_head: int, - accessors: List[AttentionHeadWeightsAccessor], - device: Union[torch.device, str] = "cpu", - num_evals: int = 3, - ): - self.num_layers = len(accessors) - self.num_heads = num_heads - self.num_weights_per_head = num_weights_per_head - - self.first_moment = torch.zeros( - self.num_layers, num_heads, self.num_weights_per_head, device=device - ) - self.second_moment = torch.zeros( - self.num_layers, - num_heads, - self.num_weights_per_head, - self.num_weights_per_head, - device=device, - ) - self.num_draws = 0 - self.accessors = accessors - self.num_evals = num_evals - self.is_finished = False - - @property - def num_weights_per_layer(self): - return self.num_heads * self.num_weights_per_head - - @property - def num_weights(self): - return self.num_layers * self.num_weights_per_layer - - def accumulate(self, model: nn.Module): - # Accumulate moments from model weights. - assert not self.is_finished, "Cannot accumulate after finalizing." - - for l, accessor in enumerate(self.accessors): - heads = accessor(model) - - for h, _head in enumerate(heads): - head = _head.flatten() - self.first_moment[l, h] += head - self.second_moment[l, h] += torch.outer(head, head) - - self.num_draws += 1 - - def finalize(self): - self.first_moment /= self.num_draws - self.second_moment /= self.num_draws - self.is_finished = True - - def reset(self): - self.first_moment.zero_() - self.second_moment.zero_() - self.num_draws = 0 - self.is_finished = False - - def to_matrix(self): - # Convert the moments to a covariance matrix. - covariance = self.second_moment - - for l in range(self.num_layers): - for h in range(self.num_heads): - first_moment_head = self.first_moment[l, h] - covariance[l, h] -= torch.outer(first_moment_head, first_moment_head) - - return covariance - - def to_eigen(self, include_matrix=False): - # Convert the covariance matrix to pairs of eigenvalues and vectors. - cov = self.to_matrix().detach().cpu() - results = {} - - evals = np.zeros((self.num_evals, self.num_layers, self.num_heads)) - evecs = np.zeros( - (self.num_evals, self.num_layers, self.num_heads, self.num_weights_per_head) - ) - - for l in range(self.num_layers): - for h in range(self.num_heads): - head_cov = cov[l, h] - all_head_evals, all_head_evecs = eigh(head_cov) - head_evals = all_head_evals[-self.num_evals :].numpy() - head_evecs = all_head_evecs[-self.num_evals :].numpy() - - for i in range(self.num_evals): - evecs[i, l, h, :] = head_evecs[:, i].reshape( - (self.num_weights_per_head,) - ) - evals[i, l, h] = head_evals[i] - - results.update({"evecs": evecs, "evals": evals}) - - if include_matrix: - results["matrix"] = cov - - return results - - def get_results(self): - """ - :returns: A dict :python:`{"evals": array_of_eigenvalues_of_cov_matrix_layer_idx_head_idx, "evecs": array_of_eigenvectors_of_cov_matrix_layer_idx_head_idx, "matrix": array_of_cov_matrices_layer_idx_head_idx}`. (Only after running :python:`devinterp.slt.sampler.sample(..., [covariance_accumulator_instance], ...)`). - """ - return self.to_eigen(include_matrix=True) - - def __call__(self, model): - self.accumulate(model) - - -LayerWeightsAccessor = Callable[[nn.Module], torch.Tensor] - - -class BetweenLayerCovarianceAccumulator: - """ - A CovarianceAccumulator to compute covariance between arbitrary layers. For use with :func:`devinterp.slt.sampler.sample`. - - :param model: The model to compute covariances on. - :type model: torch.nn.Module - :param pairs: Named pairs of layers to compute covariances on. - :type pairs: Dict[str, Tuple[str, str]] - :param device: Device to perform computations on, e.g., 'cpu' or 'cuda'. Default is 'cpu' - :type device: str | torch.device, optional - :param num_evals: number of eigenvectors / eigenvalues to compute. Default is 3 - :type num_evals: int - :param accessors: Functions to access attention head weights. - :type accessors: Callable[[nn.Module], torch.Tensor] - - """ - - def __init__( - self, - model, - pairs: Dict[str, Tuple[str, str]], - device: Union[torch.device, str] = "cpu", - num_evals: int = 3, - **accessors: LayerWeightsAccessor, - ): - self.num_layers = len(accessors) - self.accessors = accessors - self.pairs = pairs - self.num_weights_per_layer = { - name: len(accessor(model).flatten()) for name, accessor in accessors.items() - } - self.first_moments = { - name: torch.zeros(num_weights, device=device) - for name, num_weights in self.num_weights_per_layer.items() - } - self.second_moments = { - pair_name: torch.zeros( - self.num_weights_per_layer[name1], - self.num_weights_per_layer[name2], - device=device, - ) - for pair_name, (name1, name2) in pairs.items() - } - self.num_draws = 0 - self.num_evals = num_evals - self.is_finished = False - self.device = device - - @property - def num_weights(self): - return sum(self.num_weights_per_layer.values()) - - def accumulate(self, model: nn.Module): - # Accumulate moments from model weights. - assert not self.is_finished, "Cannot accumulate after finalizing." - weights = { - name: accessor(model).flatten() for name, accessor in self.accessors.items() - } - - for name, w in weights.items(): - self.first_moments[name] += w - - for pair_name, (name1, name2) in self.pairs.items(): - self.second_moments[pair_name] += torch.outer( - weights[name1], weights[name2] - ) - - self.num_draws += 1 - - def finalize(self): - for name in self.accessors: - self.first_moments[name] /= self.num_draws - - for name in self.pairs: - self.second_moments[name] /= self.num_draws - - self.is_finished = True - - def reset(self): - for name in self.accessors: - self.first_moments[name].zero_() - - for name in self.pairs: - self.second_moments[name].zero_() - - self.num_draws = 0 - self.is_finished = False - - def to_matrices(self): - # Convert the moments to a covariance matrix. - covariances = {} - - for name, (layer1, layer2) in self.pairs.items(): - first_moment1 = self.first_moments[layer1] - first_moment2 = self.first_moments[layer2] - covariances[name] = self.second_moments[name] - torch.outer( - first_moment1, first_moment2 - ) - - return covariances - - def to_eigens(self, include_matrix=False): - # Convert the covariance matrix to pairs of eigenvalues and vectors. - covariances = {k: v.detach().cpu() for k, v in self.to_matrices().items()} - results = {} - - for name, cov in covariances.items(): - # TODO: U, s, Vt = svds(cov, k=self.num_evals, which='LM') - all_evals, all_evecs = eigh(cov) - evals = all_evals[-self.num_evals :].numpy() - evecs = all_evecs[-self.num_evals :].numpy() - - # Reverse the order of the eigenvalues and vectors - evals = evals[::-1] - evecs = evecs[:, ::-1] - - results.update({name: {"evecs": evecs, "evals": evals}}) - - if include_matrix: - results[name]["matrix"] = cov.numpy() - - return results - - def get_results(self): - """ - :returns: A dict with named_pairs keys, with corresponding values :python:`{"evals": eigenvalues_of_cov_matrix, "evecs": eigenvectors_of_cov_matrix, "matrix": cov_matrix}`. (Only after running :python:`devinterp.slt.sampler.sample(..., [covariance_accumulator_instance], ...)`). - """ - return self.to_eigens(include_matrix=True) - - def __call__(self, model): - self.accumulate(model) diff --git a/src/devinterp/slt/covariance.py b/src/devinterp/slt/covariance.py new file mode 100644 index 00000000..dd947c5c --- /dev/null +++ b/src/devinterp/slt/covariance.py @@ -0,0 +1,103 @@ +"""Batched covariance and correlation computation. +Port of aether's covariance_utils.py — torch-based batched correlation +for BIF computation. +""" + +from __future__ import annotations + +import numpy as np +import torch +import xarray as xr + + +def batch_cov(batched_a: torch.Tensor, batched_b: torch.Tensor) -> torch.Tensor: + """Batched covariance between all pairs from batched_a and batched_b. + + Args: + batched_a: shape (n_a, series_a, observations) + batched_b: shape (n_b, series_b, observations) + + Returns: + shape (n_a, n_b, series_a + series_b, series_a + series_b) + """ + assert batched_a.dim() == 3 and batched_b.dim() == 3 + assert batched_a.shape[2] == batched_b.shape[2] + + n_a, series_a, n_obs = batched_a.shape + n_b, series_b, _ = batched_b.shape + + a_centered = batched_a - batched_a.mean(dim=2, keepdim=True) + b_centered = batched_b - batched_b.mean(dim=2, keepdim=True) + + a_broadcast = a_centered[:, None, :, :].expand(n_a, n_b, series_a, n_obs) + b_broadcast = b_centered[None, :, :, :].expand(n_a, n_b, series_b, n_obs) + combined = torch.cat([a_broadcast, b_broadcast], dim=2) + + return combined @ combined.transpose(-1, -2) / (n_obs - 1) + + +def batch_corrcoef(batched_a: torch.Tensor, batched_b: torch.Tensor) -> torch.Tensor: + """Batched Pearson correlation between all pairs from batched_a and batched_b. + + Args: + batched_a: shape (n_a, series_a, observations), float32 or float64 + batched_b: shape (n_b, series_b, observations), float32 or float64 + + Returns: + shape (n_a, n_b, series_a + series_b, series_a + series_b) + """ + assert batched_a.dtype in (torch.float32, torch.float64) + assert batched_b.dtype in (torch.float32, torch.float64) + + cov = batch_cov(batched_a, batched_b) + + diag = torch.diagonal(cov, dim1=-2, dim2=-1) + std = torch.sqrt(diag) + cov /= std.unsqueeze(-1) * std.unsqueeze(-2) + + eye = torch.eye(cov.shape[-1], dtype=cov.dtype, device=cov.device) + cov *= 1 - eye + cov += eye + + return cov + + +def xr_corrcoef_with_torch_backend( + seq1: xr.DataArray, + seq2: xr.DataArray, + *, + device: str | torch.device, + compute_covariance: bool = False, +) -> xr.Dataset: + """Full Pearson correlation matrix between rows of seq1 and seq2 using torch. + + Args: + seq1: 2-D DataArray (e.g. batch, chain_draw) + seq2: 2-D DataArray with same dims as seq1 + device: torch device for computation + compute_covariance: if True, compute covariance instead of correlation + + Returns: + Dataset with "correlation" variable of shape (dim_1, dim_1_T) + """ + assert seq1.ndim == 2 and seq2.ndim == 2 + assert seq1.dims == seq2.dims + + dim_1 = str(seq1.dims[0]) + concatenated = xr.concat([seq1, seq2], dim=dim_1) + + data = torch.as_tensor(concatenated.data, device=device) + if compute_covariance: + matrix = torch.cov(data).cpu().numpy() + else: + matrix = torch.corrcoef(data).cpu().numpy() + + seq_coord = concatenated[dim_1] + dim_2 = f"{dim_1}_T" + return xr.Dataset( + data_vars={"correlation": ((dim_1, dim_2), matrix)}, + coords={ + dim_1: np.asarray(seq_coord), + dim_2: np.asarray(seq_coord), + }, + ) diff --git a/src/devinterp/slt/gradient.py b/src/devinterp/slt/gradient.py deleted file mode 100644 index dea55784..00000000 --- a/src/devinterp/slt/gradient.py +++ /dev/null @@ -1,227 +0,0 @@ -import math -from typing import List, Union - -import matplotlib.pyplot as plt -import matplotlib.ticker as ticker -import torch -import torch.nn as nn -from matplotlib.collections import PatchCollection - -from devinterp.slt.callback import SamplerCallback - - -class GradientDistribution(SamplerCallback): - """ - Callback for plotting the distribution of gradients as a function of draws. Does some magic to automatically adjust bins as more draws are taken. - For use with :func:`devinterp.slt.sampler.sample`. - - :param num_draws: Number of samples to draw (should be identical to :python:`num_draws` passed to :python:`devinterp.slt.sampler.sample`) - :type num_draws: int - :param num_chains: Number of chains to run (should be identical to :python:`num_chains` passed to :python:`devinterp.slt.sampler.sample`) - :param num_chains: int - :param min_bins: Minimum number of bins for histogram approximation. Default is 20 - :type min_bins: int, optional - :param param_names: List of parameter names to track. If None, all parameters are tracked. Default is None - :type param_names: List[str], optional - :param device: Device to perform computations on, e.g., 'cpu' or 'cuda'. - :type device: : str | torch.device, optional - - Raises: - ValueError: If gradients are not computed before calling this callback. - """ - - def __init__( - self, - num_chains: int, - num_draws: int, - min_bins: int = 20, - param_names: List[str] = None, - device: Union[torch.device, str] = "cpu", - ): - self.num_chains = num_chains - self.num_draws = num_draws - self.grad_dists = {} - - self.min_bins = min_bins - self.param_names = param_names - - self.bin_size = torch.tensor(0.0).to(device) - self.num_bins = torch.tensor(0).to(device) - self.min_grad = torch.tensor(0.0).to(device) - - self.device = device - - @property - def max_grad(self): - return self.min_grad + self.bin_size * self.num_bins - - def __call__(self, chain: int, draw: int, model: nn.Module, **kwargs): - self.update(chain, draw, model) - - # Updates the gradient histograms for each parameter. - def update(self, chain: int, draw: int, model: nn.Module): - for param_name, param in model.named_parameters(): - if self.param_names is not None and param_name not in self.param_names: - continue - if param.grad is None: - raise ValueError( - "GradientDistribution callback requires gradients to be computed first" - ) - self._update_param_bins( - chain, draw, param_name, param.grad.detach().flatten() - ) - - def _update_param_bins( - self, chain: int, draw: int, param_name: str, grads: torch.Tensor - ): - # init bins, estimating a good bin size using the first draw of the first params - if self.num_bins == 0: - self._init_bins(grads) - if param_name not in self.grad_dists: - self.grad_dists[param_name] = torch.zeros( - (self.num_chains, self.num_draws, self.num_bins), dtype=torch.int64 - ).to(self.device) - max_grad = grads.max() - min_grad = grads.min() - # extend bins as necessary to include new min/max grad values - if min_grad < self.min_grad: - self._extend_bins(min_grad, self.max_grad) - if max_grad > self.max_grad: - self._extend_bins(self.min_grad, max_grad) - # merge bins as necessary so the num of bins is at most 2 * min_bins - self._merge_bins() - # update bins with new grads - for grad in grads: - bin_idx = math.floor((grad - self.min_grad) / self.bin_size) - # correct bin idx for the max grad value - bin_idx = bin_idx - 1 if bin_idx == self.num_bins else bin_idx - self.grad_dists[param_name][chain, draw, bin_idx] += 1 - - def _init_bins(self, grads: torch.Tensor): - _, bin_ends = torch.histogram(grads.to("cpu"), bins=self.min_bins) - self.bin_size = bin_ends[1] - bin_ends[0] - self.num_bins = self.min_bins - self.min_grad = bin_ends[0] - - def _extend_bins(self, new_min: torch.Tensor, new_max: torch.Tensor): - # extend bins to cover new_min and new_max - min_diff = abs(new_min - self.min_grad) - num_new_min_bins = math.ceil(min_diff / self.bin_size) - max_diff = abs(new_max - self.max_grad) - num_new_max_bins = math.ceil(max_diff / self.bin_size) - if num_new_min_bins: - for param_name in self.grad_dists: - zeros = torch.zeros( - (self.num_chains, self.num_draws, num_new_min_bins), - dtype=torch.int64, - ).to(self.device) - self.grad_dists[param_name] = torch.cat( - [zeros, self.grad_dists[param_name]], dim=2 - ) - if num_new_max_bins: - for param_name in self.grad_dists: - zeros = torch.zeros( - (self.num_chains, self.num_draws, num_new_max_bins), - dtype=torch.int64, - ).to(self.device) - self.grad_dists[param_name] = torch.cat( - [self.grad_dists[param_name], zeros], dim=2 - ) - self.min_grad -= num_new_min_bins * self.bin_size - self.num_bins += num_new_min_bins + num_new_max_bins - - def _merge_bins(self): - while self.num_bins > 2 * self.min_bins: - new_bin_count = math.ceil(self.num_bins / 2) - for param_name in self.grad_dists: - new_grad_dists = torch.zeros( - (self.num_chains, self.num_draws, new_bin_count), dtype=torch.int64 - ).to(self.device) - for i in range(self.num_bins): - new_grad_dists[:, :, i // 2] += self.grad_dists[param_name][:, :, i] - self.grad_dists[param_name] = new_grad_dists - self.num_bins = new_bin_count - self.bin_size *= 2 - - def get_results(self): - """ - :returns: A dict :python:`{"gradient/distributions": grad_dists}`. (Only after running :python:`devinterp.slt.sampler.sample(..., [gradient_dist_instance], ...)`) - """ - return { - "gradient/distributions": self.grad_dists, - } - - def plot(self, param_name: str, color="blue", plot_zero=True, chain: int = None): - """Plots the gradient distribution for a specific parameter. - - :param param_name: the name of the parameter plot gradients for. - :type param_name: str - :param color: The color to plot gradient bins in. Default is blue - :type color: str, optional - :param plot_zero: Whether to plot the line through y=0. Default is True - :type plot_zero: bool, optional - :param chain: The model to compute covariances on. - :type chain: int, optional - - :returns: None, but shows the denisty gradient bins over sampling steps. - """ - grad_dist = self.grad_dists[param_name] - if chain is not None: - max_count = grad_dist[chain].max() - else: - max_count = grad_dist.sum(axis=0).max() - - def get_color_alpha(count): - if count == 0: - return torch.tensor(0).to(self.device) - min_alpha = 0.35 - max_alpha = 0.85 - return (count / max_count) * (max_alpha - min_alpha) + min_alpha - - def build_rect(count, bin_min, bin_max, draw): - alpha = get_color_alpha(count) - pos = (draw, bin_min) - height = bin_max - bin_min - width = 1 - return plt.Rectangle( - pos, - width, - height, - color=color, - alpha=alpha.cpu().numpy().item(), - linewidth=0, - ) - - _, ax = plt.subplots() - patches = [] - for draw in range(self.num_draws): - for pos in range(self.num_bins): - bin_min = self.min_grad + pos * self.bin_size - bin_max = bin_min + self.bin_size - if chain is None: - count = grad_dist[:, draw, pos].sum() - else: - count = grad_dist[chain, draw, pos] - if count != 0: - rect = build_rect(count, bin_min, bin_max, draw) - patches.append(rect) - patches = PatchCollection(patches, match_original=True) - ax.add_collection(patches) - - # note that these y min/max values are relative to *all* gradients, not just the ones for this param - y_min = self.min_grad - y_max = self.max_grad - # ensure the 0 line is visible - y_min = y_min if y_min < 0 else -y_max - y_max = y_max if y_max > 0 else -y_min - plt.ylim(y_min, y_max) - - plt.xlim(0, self.num_draws) - plt.gca().xaxis.set_major_locator(ticker.MaxNLocator(integer=True)) - if plot_zero: - plt.axhline(color="black", linestyle=":", linewidth=1) - - plt.xlabel("Sampler steps") - plt.ylabel("gradient distribution") - plt.title(f"Distribution of {param_name} gradients at each sampler step") - plt.show() diff --git a/src/devinterp/slt/llc.py b/src/devinterp/slt/llc.py index 7c8e82a8..69a14850 100644 --- a/src/devinterp/slt/llc.py +++ b/src/devinterp/slt/llc.py @@ -1,229 +1,107 @@ -import os -import warnings -from typing import Union - -import torch -from devinterp.slt.callback import SamplerCallback -from devinterp.utils import TPU_TYPE, USE_TPU_BACKEND - - -class LLCEstimator(SamplerCallback): - r""" - Callback for estimating the Local Learning Coefficient (LLC) in a rolling fashion during a sampling process. - It calculates the LLC based on the average loss across draws for each chain: - - $$LLC = \textrm{n \beta} * (\textrm{avg_loss} - \textrm{init_loss})$$ - - For use with :func:`devinterp.slt.sampler.sample`. +"""Local Learning Coefficient (LLC) computation from sampling results. +Computes LLC from the stored per-draw losses, without needing callbacks. - :param num_draws: Number of samples to draw (should be identical to :python:`num_draws` passed to :python:`devinterp.slt.sampler.sample`) - :type num_draws: int - :param num_chains: Number of chains to run (should be identical to :python:`num_chains` passed to :python:`devinterp.slt.sampler.sample`) - :type num_chains: int - :param nbeta: Effective Inverse Temperature, float (default: 1., set by sample() to utils.default_nbeta(dataloader)=len(batch_size)/np.log(len(batch_size))) - :type nbeta: int - :param device: Device to perform computations on, e.g., 'cpu' or 'cuda'. Supports GPUs and TPUs. - To use TPUs, be sure to pass in torch_xla.core.xla_model.xla_device() as the device and set the USE_TPU_BACKEND environment flag to "1". Default is 'cpu' - :type device: str | torch.device, optional - """ - - def __init__( - self, - num_chains: int, - num_draws: int, - init_loss: torch.Tensor, - device: Union[torch.device, str] = "cpu", - eval_field: str = "loss", - nbeta: float = None, - temperature: float = None, - ): - self.num_chains = num_chains - self.num_draws = num_draws - self.losses = torch.zeros((num_chains, num_draws), dtype=torch.float32).to( - device - ) - self.init_loss = init_loss - - assert nbeta is not None or temperature is not None, ( - "Please provide a value for nbeta." - ) - if nbeta is None and temperature is not None: - nbeta = temperature - warnings.warn("Temperature is deprecated. Please use nbeta instead.") - self.nbeta = torch.tensor(nbeta, dtype=torch.float32).to(device) - self.temperature = temperature - - self.device = device - self.eval_field = eval_field - - def update(self, chain: int, draw: int, loss: torch.tensor): - if torch.isnan(loss).any(): - raise RuntimeError(f"NaN detected in loss at chain {chain}, draw {draw}") - self.losses[chain, draw] = loss.to(self.device) - - def finalize(self): - if os.environ.get("USE_SPMD", "0") == "1" and not str(self.device).startswith( - "cpu:" - ): - if str(self.device).startswith("cuda") and torch.cuda.device_count() > 1: - if torch.distributed.is_initialized(): - torch.distributed.barrier() - torch.distributed.all_reduce( - self.losses, op=torch.distributed.ReduceOp.AVG - ) - else: - pass - - elif USE_TPU_BACKEND and str(self.device).startswith("xla:"): - import torch_xla.core.xla_model as xm - - if TPU_TYPE == "v4": - self.losses = xm.all_reduce(xm.REDUCE_SUM, self.losses) - elif TPU_TYPE == "v2/v3": - self.losses = self.losses.cpu() - if torch.distributed.is_initialized(): - torch.distributed.all_reduce(self.losses) - else: - warnings.warn( - "torch.distributed has not been initialized. If running on TPU v2/v3, and you want to run chains in parallel, you need to initialize torch.distributed after calling xmp.spawn() as follows:" - ">>> import torch_xla.runtime as xr" - ">>> store = torch.distributed.TCPStore('127.0.0.1', 12345, 4, xr.global_ordinal() == 0)" - ">>> torch.distributed.init_process_group(backend='gloo', store=store, rank=xr.global_ordinal()//2, world_size=xr.world_size()//2)" - ) - - else: - raise NotImplementedError(f"TPU type {TPU_TYPE} not supported") - elif str( - self.device - ).startswith( - "cuda" - ): # if we've ran on multi-GPU, we should do a reduce as well. see above for how this would work - try: - torch.distributed.all_reduce(self.losses) - except ValueError: - pass - avg_losses = self.losses.mean(axis=1) - # bypass automatic bfloat16 issues - if os.environ.get("XLA_USE_BF16", "0") == "1" and str(self.device).startswith( - "xla:" - ): - self.llc_per_chain = self.nbeta.to(device="cpu", dtype=torch.float32) * ( - avg_losses.to(device="cpu", dtype=torch.float32) - - self.init_loss.to(device="cpu", dtype=torch.float32) - ) - elif ( - str(self.device).startswith("cuda") - and os.environ.get("USE_SPMD", "0") == "1" - ): - self.llc_per_chain = self.nbeta.to(device="cpu", dtype=torch.float32) * ( - avg_losses.to(device="cpu", dtype=torch.float32) - - self.init_loss.to(device="cpu", dtype=torch.float32) - ) - else: - self.llc_per_chain = self.nbeta * (avg_losses - self.init_loss) - self.llc_mean = self.llc_per_chain.mean() - self.llc_std = self.llc_per_chain.std() - - def get_results(self): - """ - :returns: A dict :python:`{"llc/mean": llc_mean, "llc/std": llc_std, "llc-chain/{i}": llc_trace_per_chain, "loss/trace": loss_trace_per_chain}`. (Only after running :python:`devinterp.slt.sampler.sample(..., [llc_estimator_instance], ...)`). - """ - - init_loss = ( - self.init_loss.item() - if isinstance(self.init_loss, torch.Tensor) - else self.init_loss - ) - - return { - "init_loss": init_loss, - "llc/mean": self.llc_mean.cpu().numpy().item(), - "llc/std": self.llc_std.cpu().numpy().item(), - **{ - f"llc-chain/{i}": self.llc_per_chain[i].cpu().numpy().item() - for i in range(self.num_chains) - }, - "loss/trace": self.losses.cpu().numpy(), - } + LLC = n_beta * (mean_sampling_loss - init_loss) - def __call__(self, chain: int, draw: int, **kwargs): - self.update(chain, draw, kwargs[self.eval_field]) +Two entry points: +- llc(): high-level, takes model + dataset, runs sampling + LLC +- compute_llc(): low-level, takes pre-computed sampling DataTree +""" +from __future__ import annotations -class OnlineLLCEstimator(SamplerCallback): +import torch +import xarray as xr +from datasets import Dataset + +from devinterp.slt.lm_loss import LossFn +from devinterp.slt.sampler import ParamMasks +from devinterp.slt.sampling import ObservableSpec, sample + + +def llc( + model: torch.nn.Module, + dataset: Dataset, + observables: dict[str, ObservableSpec], + *, + lr: float, + n_beta: float, + param_masks: ParamMasks | None = None, + loss_fn: LossFn | None = None, + **kwargs, +) -> xr.Dataset: + """Sample and compute LLC in one call. + + Args: + model: PyTorch model. + dataset: HuggingFace Dataset with "input_ids" column. + observables: Dict mapping names to datasets (or (dataset, batches_per_draw) tuples). + lr: SGLD learning rate. + n_beta: SGLD inverse temperature. + param_masks: Which parameters to optimize. None for full model. + loss_fn: Optional custom per-token loss `(model, input_ids) -> (batch, seq-1)`. + Defaults to cross-entropy on the model's logits. + **kwargs: Additional arguments passed to sample() (num_chains, num_draws, + batch_size, output_path, etc.) + + Returns: + xr.Dataset with llc_mean, llc_std, llc_per_chain, loss_trace, init_loss. """ - Callback for estimating the Local Learning Coefficient (LLC) in an online fashion during a sampling process. - It calculates LLCs using the same formula as :func:`devinterp.slt.llc.LLCEstimator`, but continuously and including means and std across draws (as opposed to just across chains). - For use with :func:`devinterp.slt.sampler.sample`. - - :param num_draws: Number of samples to draw (should be identical to :python:`num_draws` passed to :python:`devinterp.slt.sampler.sample`) - :type num_draws: int - :param num_chains: Number of chains to run (should be identical to :python:`num_chains` passed to :python:`devinterp.slt.sampler.sample`) - :type num_chains: int - :param nbeta: Effective Inverse Temperature, float (default: 1., set by sample() to utils.default_nbeta(dataloader)=len(batch_size)/np.log(len(batch_size))) - :type nbeta: int - :param device: Device to perform computations on, e.g., 'cpu' or 'cuda'. Supports GPUs and TPUs. \ - To use TPUs, be sure to pass in torch_xla.core.xla_model.xla_device() as the device and set the USE_TPU_BACKEND environment flag to "1". Default is 'cpu' - :type device: str | torch.device, optional + samples = sample( + model=model, + dataset=dataset, + observables=observables, + param_masks=param_masks, + lr=lr, + n_beta=n_beta, + loss_fn=loss_fn, + **kwargs, + ) + return compute_llc(samples) + + +def compute_llc(samples: xr.DataTree) -> xr.Dataset: + """Compute LLC from a sampling DataTree. + + Matches aether's `calculate` action with `function: llc`: averages + `sampling_loss_micro` over every step (including burn-in) and every + micro-batch, then subtracts mean `init_loss` and scales by `n_beta`. + + Args: + samples: DataTree output from sample(), containing sampling_loss_micro, + init_loss, and n_beta. + + Returns: + xr.Dataset with: + llc_mean: scalar, mean LLC across chains + llc_std: scalar, std LLC across chains + llc_per_chain: (chain,) LLC per chain + llc_scalar: scalar LLC matching aether's calculate action + loss_trace: (chain, step) mean loss per chain per step + init_loss: scalar, mean init loss """ - - def __init__( - self, - num_chains: int, - num_draws: int, - init_loss, - device="cpu", - eval_field="loss", - nbeta: float = None, - temperature: float = None, # Temperature is deprecated - ): - self.num_chains = num_chains - self.num_draws = num_draws - self.init_loss = init_loss - - self.losses = torch.zeros((num_chains, num_draws), dtype=torch.float32).to( - device - ) - self.llcs = torch.zeros((num_chains, num_draws), dtype=torch.float32).to(device) - - self.losses = torch.zeros((num_chains, num_draws)).to(device) - self.llcs = torch.zeros((num_chains, num_draws)).to(device) - assert nbeta is not None or temperature is not None, ( - "Please provide a value for nbeta." - ) - if nbeta is None and temperature is not None: - nbeta = temperature - warnings.warn("Temperature is deprecated. Please use nbeta instead.") - self.nbeta = torch.tensor(nbeta, dtype=torch.float32).to(device) - self.temperature = temperature - - self.device = device - self.eval_field = eval_field - - def update(self, chain: int, draw: int, loss: torch.tensor): - if torch.isnan(loss).any(): - raise RuntimeError(f"NaN detected in loss at chain {chain}, draw {draw}") - loss = loss.to(self.device) - self.losses[chain, draw] = loss - self.llcs[chain, draw] = self.nbeta * (loss - self.init_loss) - - def finalize(self): - # TODO - self.llc_means = self.llcs.mean(dim=0) - self.llc_stds = self.llcs.std(dim=0) - - def get_results(self): - """ - :returns: A dict :python:`{"llc/means": llc_means, "llc/stds": llc_stds, "llc/trace": llc_trace_per_chain, "loss/trace": loss_trace_per_chain}`. (Only after running :python:`devinterp.slt.sampler.sample(..., [llc_estimator_instance], ...)`). - """ - return { - "init_loss": self.init_loss, - "llc/means": self.llc_means.cpu().numpy(), - "llc/stds": self.llc_stds.cpu().numpy(), - "llc/trace": self.llcs.cpu().numpy(), - "loss/trace": self.losses.cpu().numpy(), + ds = samples.dataset + n_beta = float(ds.metadata["config"]["sampler"]["n_beta"]) + init_loss_mean = float(ds.init_loss.values.mean()) + + # Per-chain per-step mean loss (average over batch_sampling and token_pos) + loss_trace = ds.sampling_loss_micro.mean(dim=["batch_sampling", "token_pos"]) + + # LLC per chain = n_beta * (mean_loss_per_chain - init_loss) + llc_per_chain = n_beta * (loss_trace.mean(dim="step") - init_loss_mean) # (chain,) + + # Scalar LLC matching aether's calculate action reduction order + # (all dims reduced at once for bitwise parity) + llc_scalar = n_beta * (float(ds.sampling_loss_micro.mean()) - init_loss_mean) + + return xr.Dataset( + { + "llc_mean": llc_per_chain.mean(), + "llc_std": llc_per_chain.std(), + "llc_per_chain": llc_per_chain, + "llc_scalar": llc_scalar, + "loss_trace": loss_trace, + "init_loss": init_loss_mean, } - - def __call__(self, chain: int, draw: int, **kwargs): - self.update(chain, draw, kwargs[self.eval_field]) + ) diff --git a/src/devinterp/slt/lm_loss.py b/src/devinterp/slt/lm_loss.py new file mode 100644 index 00000000..81b82c84 --- /dev/null +++ b/src/devinterp/slt/lm_loss.py @@ -0,0 +1,72 @@ +"""Model-agnostic loss computation for language models.""" + +from __future__ import annotations + +from collections.abc import Callable +from typing import Any + +import torch +from transformers import PreTrainedModel + + +class NonFiniteLogitsError(ValueError): + """Raised when logits contain NaNs or Infs.""" + + +def lm_forward_logits(model: torch.nn.Module, input_ids: torch.Tensor) -> torch.Tensor: + """Run a forward pass and return logits. Handles HF and TransformerLens models.""" + # TransformerLens HookedTransformer + if hasattr(model, "cfg") and hasattr(model.cfg, "n_heads"): + return model(input_ids, return_type="logits") + # Any HuggingFace causal LM: skip KV-cache allocation and output wrapping. + if isinstance(model, PreTrainedModel): + return model(input_ids, return_dict=False, use_cache=False)[0] + # Fallback for plain nn.Modules: return either .logits or a raw tensor. + output = model(input_ids) + if hasattr(output, "logits"): + return output.logits + return output + + +def lm_cross_entropy_loss( + logits: torch.Tensor, input_ids: torch.Tensor +) -> torch.Tensor: + """Per-token cross entropy loss. Returns shape (batch, seq-1).""" + log_probs = torch.nn.functional.log_softmax(logits, dim=-1) + shift_log_probs = log_probs[..., :-1, :] + shift_input_ids = input_ids[..., 1:, None] + return -shift_log_probs.gather(dim=-1, index=shift_input_ids)[..., 0] + + +def compute_per_token_loss( + model: torch.nn.Module, input_ids: torch.Tensor +) -> torch.Tensor: + """Compute per-token cross-entropy loss. Returns shape (batch, seq-1).""" + logits = lm_forward_logits(model, input_ids) + if not torch.isfinite(logits).all(): + raise NonFiniteLogitsError("Non-finite values detected in model logits.") + return lm_cross_entropy_loss(logits, input_ids) + + +EvaluateFn = Callable[ + [torch.nn.Module, dict[str, Any]], tuple[torch.Tensor, dict[str, Any]] +] + +LossFn = Callable[[torch.nn.Module, torch.Tensor], torch.Tensor] +"""(model, input_ids) -> per-token loss tensor of shape (batch, seq-1).""" + + +def make_evaluate_fn(loss_fn: LossFn | None = None) -> EvaluateFn: + """Create an evaluation function returning unreduced per-token loss. + + Returns (loss, {}) matching the (loss, results) protocol expected by sample_single_chain. + If loss_fn is None, uses compute_per_token_loss (cross-entropy on the model's logits). + """ + fn = loss_fn if loss_fn is not None else compute_per_token_loss + + def evaluate( + model: torch.nn.Module, batch: dict[str, Any] + ) -> tuple[torch.Tensor, dict[str, Any]]: + return fn(model, batch["input_ids"]), {} + + return evaluate diff --git a/src/devinterp/slt/loss.py b/src/devinterp/slt/loss.py deleted file mode 100644 index 9e071f76..00000000 --- a/src/devinterp/slt/loss.py +++ /dev/null @@ -1,105 +0,0 @@ -import matplotlib.pyplot as plt -import torch - -from devinterp.slt.callback import SamplerCallback -from devinterp.slt.llc import OnlineLLCEstimator - - -class OnlineLossStatistics(SamplerCallback): - """ - Derivative callback that computes various loss statistics for :func:`~devinterp.slt.llc.OnlineLLCEstimator`. Must - be called after the base :func:`~devinterp.slt.llc.OnlineLLCEstimator` has been called at each draw. - - .. |colab5| image:: https://colab.research.google.com/assets/colab-badge.svg - :target: https://colab.research.google.com/github/timaeus-research/devinterp/blob/main/examples/diagnostics.ipynb - - See `the diagnostics notebook `_ |colab5| for examples on how to use this to diagnose your sample health. - - :param base_callback: Base callback that computes original loss metric. - :type base_callback: :func:`~devinterp.slt.llc.OnlineLLCEstimator` - - Note: - - Requires losses to be computed first, so call using f.e. :python:`devinterp.slt.sampler.sample(..., [llc_estimator_instance, ..., online_loss_stats_instance], ...)` - """ - - def __init__(self, base_callback: OnlineLLCEstimator): - self.base_callback = base_callback - - self.num_chains = base_callback.num_chains - self.num_draws = base_callback.num_draws - - # relative loss is loss - init loss - # percentage of draws with negative relative loss - self.percent_neg_steps = torch.zeros( - (self.num_chains, self.num_draws), dtype=torch.float32 - ) - - # percentage of draw with negative (cumulative) mean relative loss - self.percent_mean_neg_steps = torch.zeros( - (self.num_chains, self.num_draws), dtype=torch.float32 - ) - - # percentage of draws with relative loss < -estimated_noise - self.percent_thresholded_neg_steps = torch.zeros( - (self.num_chains, self.num_draws), dtype=torch.float32 - ) - - # measured by num of std devs of init losses - self.z_scores = torch.zeros( - (self.num_chains, self.num_draws), dtype=torch.float32 - ) - - def __call__(self, chain: int, draw: int, loss: float, **kwargs): - self.update(chain, draw, loss) - - def update(self, chain: int, draw: int, loss: float): - init_loss = self.base_callback.init_loss - estimated_noise = self.est_minibatch_noise - t = draw + 1 - losses = self.base_callback.losses[chain, :t] - relative_losses = losses - init_loss - - self.percent_neg_steps[chain, draw] = (relative_losses < 0).sum() / (t) - - prev_percent = self.percent_mean_neg_steps[chain, draw - 1] if draw > 0 else 0 - self.percent_mean_neg_steps[chain, draw] = ( - (t - 1) * prev_percent + (relative_losses.mean() < 0) - ) / t - - self.percent_thresholded_neg_steps[chain, draw] = ( - relative_losses < -estimated_noise - ).sum() / t - - # only compute if estimated noise is nonzero; this might not happen if e.g. using same random seed for all chains - if estimated_noise > 0: - self.z_scores[chain, draw] = (loss - init_loss) / estimated_noise - else: - self.z_scores[chain, draw] = float("nan") - - def get_results(self): - """ - :returns: A dict :python:`{"loss/percent_neg_steps": percent_neg_steps, "loss/percent_mean_neg_steps": percent_mean_neg_steps, "loss/percent_thresholded_neg_steps": percent_thresholded_neg_steps, "loss/z_scores": z_scores}`. (Only after running :python:`devinterp.slt.sampler.sample(..., [llc_estimator_instance, online_loss_stats_instance], ...)`) - """ - return { - "loss/percent_neg_steps": self.percent_neg_steps.cpu().numpy(), - "loss/percent_mean_neg_steps": self.percent_mean_neg_steps.cpu().numpy(), - "loss/percent_thresholded_neg_steps": self.percent_thresholded_neg_steps.cpu().numpy(), - "loss/z_scores": self.z_scores.cpu().numpy(), - } - - @property - def est_minibatch_noise(self): - init_losses = self.base_callback.losses[:, 0] - return init_losses.std() - - def loss_hist_by_draw(self, draw: int = 0, bins: int = 10): - """Plots a histogram of chain losses for a given draw index. - - :param draw: Draw index to plot histogram for. Default is 0 - :type draw: int, optional - :param bins: number of histogram bins. Default is 10 - :type bins: int, optional - """ - losses_at_draw = self.base_callback.losses[:, draw] - plt.hist(losses_at_draw, bins=bins) - return losses_at_draw diff --git a/src/devinterp/slt/mala.py b/src/devinterp/slt/mala.py deleted file mode 100644 index 20d7843b..00000000 --- a/src/devinterp/slt/mala.py +++ /dev/null @@ -1,155 +0,0 @@ -import warnings -from typing import List, Optional, Union - -import numpy as np -import torch -import torch.nn as nn -from torch import Tensor - -from devinterp.slt.callback import SamplerCallback - - -def mala_acceptance_probability( - prev_params: Union[Tensor, List[Tensor]], - prev_grads: Union[Tensor, List[Tensor]], - prev_loss: Tensor, - current_params: Union[Tensor, List[Tensor]], - current_grads: Union[Tensor, List[Tensor]], - current_loss: Tensor, - learning_rate: float, -) -> float: - """ - Calculate the acceptance probability for a MALA transition. Parameters and - gradients can either all be given as a tensor (all of the same shape) or - all as lists of tensors (eg the parameters of a Module). - - Args: - prev_params: The previous point in parameter space. - prev_grads: Gradient of the prev point in parameter space. - prev_loss: Loss of the previous point in parameter space. - current_params: The current point in parameter space. - current_grads: Gradient of the current point in parameter space. - current_loss: Loss of the current point in parameter space. - learning_rate (float): Learning rate of the model. - - Returns: - float: Acceptance probability for the proposed transition. - """ - if current_loss is np.array: - current_loss = torch.tensor(current_loss) - - if torch.isnan(current_loss): - return np.nan - - # convert tensors to lists with one element - if not isinstance(prev_params, list): - prev_params = [prev_params] - if not isinstance(prev_grads, list): - prev_grads = [prev_grads] - if not isinstance(current_params, list): - current_params = [current_params] - if not isinstance(current_grads, list): - current_grads = [current_grads] - - log_q_current_to_prev = 0 - log_q_prev_to_current = 0 - for current_point, current_grad, prev_point, prev_grad in zip( - current_params, - current_grads, - prev_params, - prev_grads, - ): - # Compute the log of the proposal probabilities (using the Gaussian proposal distribution) - log_q_current_to_prev += -torch.sum( - (prev_point - current_point - (learning_rate * 0.5 * -current_grad)) ** 2 - ) / (2 * learning_rate) - log_q_prev_to_current += -torch.sum( - (current_point - prev_point - (learning_rate * 0.5 * -prev_grad)) ** 2 - ) / (2 * learning_rate) - - acceptance_log_prob = ( - log_q_current_to_prev - log_q_prev_to_current + prev_loss - current_loss - ) - - return min(1.0, torch.exp(acceptance_log_prob)) - - -class MalaAcceptanceRate(SamplerCallback): - """ - Callback for computing MALA acceptance rate. - - Attributes: - num_draws (int): Number of samples to draw. (should be identical to param passed to sample()) - num_chains (int): Number of chains to run. (should be identical to param passed to sample()) - nbeta (float): Effective Inverse Temperature used to calculate the LLC. - learning_rate (int): Learning rate of the model. - device (Union[torch.device, str]): Device to perform computations on, e.g., 'cpu' or 'cuda'. - """ - - def __init__( - self, - num_chains: int, - num_draws: int, - learning_rate: float, - device: Union[torch.device, str] = "cpu", - nbeta: float = None, - temperature: Optional[float] = None, - ): - self.num_chains = num_chains - self.num_draws = num_draws - self.learning_rate = learning_rate - if nbeta is None: - assert temperature is not None, "Please provide a value for nbeta." - self.nbeta = temperature - warnings.warn("Temperature is deprecated. Please use nbeta instead.") - else: - self.nbeta = nbeta - - self.mala_acceptance_rate = torch.zeros( - (num_chains, num_draws - 1), dtype=torch.float32 - ).to(device) - self.device = device - self.current_params = [] - self.current_grads = [] - self.prev_params = [] - self.prev_grads = [] - self.prev_mala_loss = 0.0 - - def __call__( - self, chain: int, draw: int, model: nn.Module, loss: float, optimizer, **kwargs - ): - self.update(chain, draw, model, loss, optimizer) - - def update(self, chain: int, draw: int, model: nn.Module, loss: float, optimizer): - # we need the grads & loss from the pass, but the current params are from after the step - # (so we update those only after the calculation) - self.current_grads = optimizer.dws - # mala acceptance loss is different from pytorch supplied loss - mala_loss = (loss * self.nbeta) + optimizer.localization_loss - if draw > 1: - self.mala_acceptance_rate[chain, draw - 1] = mala_acceptance_probability( - self.prev_params, - self.prev_grads, - self.prev_mala_loss, - self.current_params, - self.current_grads, - mala_loss, - self.learning_rate, - ) - # move new -> old, then update new after - self.prev_params = self.current_params - self.prev_grads = self.current_grads - self.prev_mala_loss = mala_loss - # params update only at the end, as decribed - self.current_params = [ - param.clone().detach() - for param in model.parameters() - if param.requires_grad - ] - - def get_results(self): - return { - "mala_accept/trace": self.mala_acceptance_rate.cpu().numpy(), - "mala_accept/mean": np.mean(self.mala_acceptance_rate.cpu().numpy()), - "mala_accept/std": np.std(self.mala_acceptance_rate.cpu().numpy()), - } diff --git a/src/devinterp/slt/norms.py b/src/devinterp/slt/norms.py deleted file mode 100644 index 9d536589..00000000 --- a/src/devinterp/slt/norms.py +++ /dev/null @@ -1,160 +0,0 @@ -from typing import Union - -import torch -import torch.nn as nn -from torch.optim.optimizer import Optimizer - -from devinterp.optim.sgld import SGLD -from devinterp.slt.callback import SamplerCallback - - -class WeightNorm(SamplerCallback): - """ - Callback for computing the norm of the weights over the sampling process. - - :param num_draws: Number of samples to draw (should be identical to :python:`num_draws` passed to :python:`devinterp.slt.sampler.sample`) - :type num_draws: int - :param num_chains: Number of chains to run (should be identical to :python:`num_chains` passed to :python:`devinterp.slt.sampler.sample`) - :type num_chains: int - :param p_norm: Order of the norm to be computed (e.g., 2 for Euclidean norm). Default is 2 - :type p_norm: int, optional - :param device: Device to perform computations on, e.g., 'cpu' or 'cuda'. Default is 'cpu' - :type device: str | torch.device, optional - """ - - def __init__( - self, - num_chains: int, - num_draws: int, - p_norm: int = 2, - device: Union[torch.device, str] = "cpu", - ): - self.num_chains = num_chains - self.num_draws = num_draws - self.weight_norms = torch.zeros( - (num_chains, num_draws), dtype=torch.float32 - ).to(device) - self.p_norm = p_norm - self.device = device - - def __call__(self, chain: int, draw: int, model: nn.Module, **kwargs): - self.update(chain, draw, model) - - def update(self, chain: int, draw: int, model: nn.Module): - total_norm = torch.tensor(0.0).to(self.device) - for param in model.parameters(): - total_norm += torch.square( - torch.linalg.vector_norm(param, ord=self.p_norm) - ).to(self.device) - total_norm = torch.pow(total_norm, 1 / self.p_norm) - self.weight_norms[chain, draw] = total_norm - - def get_results(self): - """:returns: A dict :python:`{"weight_norm/trace": weight_norms}`. (Only after running :python:`devinterp.slt.sampler.sample(..., [weight_norm_instance], ...)`)""" - return { - "weight_norm/trace": self.weight_norms.cpu().numpy(), - } - - -class GradientNorm(SamplerCallback): - """ - Callback for computing the norm of the gradients of the optimizer / sampler. - - :param num_draws: Number of samples to draw (should be identical to :python:`num_draws` passed to :python:`devinterp.slt.sampler.sample`) - :type num_draws: int - :param num_chains: Number of chains to run (should be identical to :python:`num_chains` passed to :python:`devinterp.slt.sampler.sample`) - :type num_chains: int - :param p_norm: Order of the norm to be computed (e.g., 2 for Euclidean norm). Default is 2 - :type p_norm: int, optional - :param device: Device to perform computations on, e.g., 'cpu' or 'cuda'. Default is 'cpu' - :type device: str | torch.device, optional - """ - - def __init__( - self, - num_chains: int, - num_draws: int, - p_norm: int = 2, - device: Union[torch.device, str] = "cpu", - ): - self.num_chains = num_chains - self.num_draws = num_draws - self.gradient_norms = torch.zeros( - (num_chains, num_draws), dtype=torch.float32 - ).to(device) - self.p_norm = p_norm - self.device = device - - def __call__(self, chain: int, draw: int, optimizer: Optimizer, **kwargs): - self.update(chain, draw, optimizer.param_groups) - - def update(self, chain: int, draw: int, param_groups: dict): - total_norm = torch.tensor(0.0).to(self.device) - for group in param_groups: - for param in group["params"]: - total_norm += torch.square( - torch.linalg.vector_norm( - param.grad * 0.5 * group["lr"], ord=self.p_norm - ) - ).to(self.device) - total_norm = torch.pow(total_norm, 1 / self.p_norm) - self.gradient_norms[chain, draw] = total_norm - - def get_results(self): - """:returns: A dict :python:`{"gradient_norm/trace": gradient_norms}`. (Only after running :python:`devinterp.slt.sampler.sample(..., [grad_norm_instance], ...)`)""" - return { - "gradient_norm/trace": self.gradient_norms.cpu().numpy(), - } - - -class NoiseNorm(SamplerCallback): - """ - Callback for computing the norm of the noise added in the optimizer / sampler. - - :param num_draws: Number of samples to draw (should be identical to :python:`num_draws` passed to :python:`devinterp.slt.sampler.sample`) - :type num_draws: int - :param num_chains: Number of chains to run (should be identical to :python:`num_chains` passed to :python:`devinterp.slt.sampler.sample`) - :type num_chains: int - :param p_norm: Order of the norm to be computed (e.g., 2 for Euclidean norm). Default is 2 - :type p_norm: int, optional - :param device: Device to perform computations on, e.g., 'cpu' or 'cuda'. Default is 'cpu' - :type device: str | torch.device, optional - """ - - def __init__( - self, - num_chains: int, - num_draws: int, - p_norm: int = 2, - device: Union[torch.device, str] = "cpu", - ): - self.num_chains = num_chains - self.num_draws = num_draws - self.noise_norms = torch.zeros((num_chains, num_draws), dtype=torch.float32).to( - device - ) - self.p_norm = p_norm - self.device = device - - def __call__(self, chain: int, draw: int, optimizer: SGLD, **kwargs): - self.update(chain, draw, optimizer) - - def update(self, chain: int, draw: int, optimizer: SGLD): - total_norm = torch.tensor(0.0).to(self.device) - - for group_idx, group_noises in optimizer.noise.items(): - for noise in group_noises: - total_norm += torch.square( - torch.linalg.vector_norm( - noise * optimizer.param_groups[group_idx]["lr"] ** 0.5, - ord=self.p_norm, - ) - ).to(self.device) - total_norm = torch.pow(total_norm, 1 / self.p_norm) - self.noise_norms[chain, draw] = total_norm - - def get_results(self): - """:returns: A dict :python:`{"noise_norm/trace": noise_norms}`. (Only after running :python:`devinterp.slt.sampler.sample(..., [noise_norm_instance], ...)`)""" - return { - "noise_norm/trace": self.noise_norms.cpu().numpy(), - } diff --git a/src/devinterp/slt/observables.py b/src/devinterp/slt/observables.py new file mode 100644 index 00000000..63369f9d --- /dev/null +++ b/src/devinterp/slt/observables.py @@ -0,0 +1,102 @@ +"""Observable: evaluates probe datasets during SGLD sampling. + +Each observable wraps a dataset and computes per-token losses at each draw. +Input IDs are fixed (same sequences every draw via DeterministicShuffledSampler). +""" + +from __future__ import annotations + +import random +from collections.abc import Iterator +import torch +from datasets import Dataset +from torch.utils.data import DataLoader, Sampler + +from devinterp.slt.lm_loss import LossFn, compute_per_token_loss + + +def to_obs_id(task_name: str) -> str: + """Convert a task name to a valid identifier for use in variable names.""" + result = "".join(c if c.isalnum() or c == "_" else "_" for c in task_name) + if result and result[0].isdigit(): + result = "_" + result + return result or "_" + + +class DeterministicShuffledSampler(Sampler): + """A sampler that returns a fixed shuffled order of indices, deterministic from seed.""" + + def __init__(self, data_source: Dataset, num_samples: int, seed: int = 42): + self.data_source = data_source + self.num_samples = num_samples + assert len(data_source) >= num_samples, ( + f"Dataset size ({len(data_source)}) must be >= num_samples ({num_samples})" + ) + self.indices = random.Random(seed).sample(range(len(data_source)), num_samples) + + def __iter__(self) -> Iterator[int]: + return iter(self.indices) + + def __len__(self) -> int: + return self.num_samples + + +class Observable: + """Evaluates a probe dataset at each SGLD draw. + + On construction, loads fixed input_ids (same sequences every draw). + At each draw, compute_loss(model) returns per-token losses. + + Attributes: + obs_id: Identifier derived from task_name (e.g. "pile_github"). + input_ids: Fixed input_ids tensor, shape (n_samples, ctx_len+1). + n_samples: batch_size * batches_per_draw. + context_length: Number of predicted positions (ctx_len). + """ + + def __init__( + self, + *, + dataset: Dataset, + task_name: str, + batches_per_draw: int, + batch_size: int, + context_length: int, + device: torch.device, + seed: int = 1337, + loss_fn: LossFn | None = None, + ): + self.obs_id = to_obs_id(task_name) + self.batch_size = batch_size + self.batches_per_draw = batches_per_draw + self.n_samples = batch_size * batches_per_draw + self.context_length = context_length + self.device = device + self.loss_fn = loss_fn if loss_fn is not None else compute_per_token_loss + + sampler = DeterministicShuffledSampler(dataset, self.n_samples, seed=seed) + loader = DataLoader( + dataset, batch_size=batch_size, sampler=sampler, drop_last=True + ) + + # Load fixed input_ids + self.input_ids = torch.zeros( + self.n_samples, context_length + 1, dtype=torch.int64, device=device + ) + for i, batch in enumerate(loader): + s, e = i * batch_size, (i + 1) * batch_size + self.input_ids[s:e] = batch["input_ids"] + + def compute_loss(self, model: torch.nn.Module) -> torch.Tensor: + """Compute per-token loss for the model on this observable's data. + + Returns shape (n_samples, context_length). + """ + assert not torch.is_grad_enabled() + loss = torch.zeros( + self.n_samples, self.context_length, dtype=torch.float32, device=self.device + ) + for i in range(self.batches_per_draw): + s, e = i * self.batch_size, (i + 1) * self.batch_size + loss[s:e] = self.loss_fn(model, self.input_ids[s:e]) + return loss diff --git a/src/devinterp/slt/sampler.py b/src/devinterp/slt/sampler.py index 0e26677d..75d5f567 100644 --- a/src/devinterp/slt/sampler.py +++ b/src/devinterp/slt/sampler.py @@ -1,326 +1,245 @@ -import os +"""SGLD sampler: runs a single SGLD chain with callbacks. + +The inner loop for SGLD sampling. Called by sample() in sampling.py +once per chain. +""" + +from __future__ import annotations + +import gc +import random import warnings -from typing import Callable, Dict, List, Literal, Optional, Type, Union +from collections.abc import Iterator +from copy import deepcopy +from itertools import cycle +from typing import Any, Callable, Protocol +import numpy as np import torch -from devinterp.optim.sgld import SGLD -from devinterp.slt.llc import LLCEstimator, OnlineLLCEstimator -from devinterp.utils import ( - USE_TPU_BACKEND, - EvaluateFn, - default_nbeta, - get_init_loss_multi_batch, -) +from devinterp.optim import SGLD +from devinterp.slt.config import EpochMode +from devinterp.slt.lm_loss import NonFiniteLogitsError +from torch import nn from torch.utils.data import DataLoader +from tqdm import trange -if USE_TPU_BACKEND: - from devinterp.backends.tpu.slt.sampler import sample -else: - from devinterp.backends.default.slt.sampler import sample - - -def estimate_learning_coeff_with_summary( - model: torch.nn.Module, - loader: DataLoader, - callbacks: List[Callable] = [], - evaluate: Optional[EvaluateFn] = None, - sampling_method: Type[torch.optim.Optimizer] = SGLD, - sampling_method_kwargs: Optional[ - Dict[str, Union[float, Literal["adaptive"]]] - ] = None, - num_draws: int = 100, - num_chains: int = 10, - num_burnin_steps: int = 0, - num_steps_bw_draws: int = 1, - init_loss: float = None, - gradient_accumulation_steps: int = 1, - cores: Union[int, List[Union[str, torch.device]]] = 1, - seed: Optional[Union[int, List[int]]] = None, - device: Union[torch.device, str] = torch.device("cpu"), - gpu_idxs: Optional[List[int]] = None, - verbose: bool = True, - optimize_over_per_model_param: Optional[Dict[str, torch.Tensor]] = None, - online: bool = False, - dtype: Optional[torch.dtype] = None, -) -> dict: - """ - Estimates the local learning coefficient and returns a dictionary of results. - - - :param model: PyTorch model to sample from. - :type model: torch.nn.Module - :param loader: PyTorch DataLoader to sample from. - :type loader: torch.utils.data.DataLoader - :param callbacks: Additional callbacks to use during sampling. Since this function will automatically add the LLC estimator callback, \ - please do not include it in this list. An example of an additional callback is `devinterp.slt.mala.MalaAcceptanceRate`, which uses the Metropolis-Adjusted Langevin Algorithm's acceptance step to compute an acceptance rate. - :type callbacks: list of callable - :type evaluate: callable - :param evaluate: Function to evaluate the model. Should take in a model and a batch of data and return a Transformer-like dictionary of metrics. \ - An example of an evaluate function is: - ```python - def evaluate(model, data): - inputs, outputs = data - return F.cross_entropy(model(inputs).logits, outputs), { - "logits": model(inputs).logits - } - ``` - - :param sampling_method: PyTorch optimizer to use for sampling. Default is SGLD. Currently implemented alternatives include SGNHT. - :type sampling_method: torch.optim.Optimizer - :param sampling_method_kwargs: Dictionary of keyword arguments to pass to the optimizer. \ - For SGLD, this includes nbeta. - :type sampling_method_kwargs: dict - :param num_draws: Number of draws to sample per chain. - :type num_draws: int - :param num_chains: Number of chains to sample from. - :type num_chains: int - :param num_burnin_steps: Number of burn-in steps to use. - :type num_burnin_steps: int - :param num_steps_bw_draws: Number of steps between each draw. - :type num_steps_bw_draws: int - :param init_loss: Initial loss to use for the LLC estimator. If None, the initial loss will be computed using the first batch of data. - :type init_loss: float - :param gradient_accumulation_steps: Number of gradient accumulation steps to use per backward pass. Note that the effective batch size is batch_size * gradient_accumulation_steps. - :type gradient_accumulation_steps: int - :param cores: Number of cores to use for parallel sampling. Can be either an integer (will use cores starting from device 0) or a list of cores. - :type cores: int or list of torch.device or str - :param seed: Seed for reproducibility. If a list of seeds is provided, each chain will be seeded with the corresponding seed. - Otherwise, this parameter will be used as an offset that will be added to the chain index to seed each chain. - :type seed: int or list of int - :param device: Device to run the sampling on. Can be a torch.device or a string (e.g. "cuda:0"). Supports GPUs and TPUs. To use TPUs: - - ``` python - import os - os.environ["USE_TPU_BACKEND"] = "1" - import torch_xla.core.xla_model as xm - DEVICE = xm.xla_device() - # If you are using a TPU, make sure to set the environment variable `USE_TPU_BACKEND` to `1` before importing `devinterp`. - ``` - - :type device: torch.device or str - :param gpu_idxs: List of GPU indices to use. If None, the device will be used as is. Provide a list of indices \ - and set cores greater than the length of the list to use multiple GPUs. - :param verbose: Whether to display progress. - :type verbose: bool - :param optimize_over_per_model_param: Dictionary of booleans indicating whether to optimize over each parameter of the model. \ - Keys are parameter names, and values are boolean tensors that match the shape of the parameter. \ - A value of True (or 1) indicates that this particular element of the parameter should be optimized over. \ - None by default, which means that we optimize over all parameters. - :type optimize_over_per_model_param: dict of str -> torch.Tensor[bool] - :param online: Whether to use the online version of the LLC estimator. - :type online: bool - :param dtype: Data type for sampling. Default is bfloat16 if BF16 is True, float16 if FP16 is True, or float32 otherwise. - :type dtype: torch.dtype, optional - :returns: A dictionary containing the local learning coefficient and loss traces. - """ +# param name -> mask tensor (or None for unrestricted). +# Only params in the dict are optimized; all others are frozen. +ParamMasks = dict[str, torch.Tensor | None] - model.to(device) - # Temperature consistency warning - if ( - "nbeta" in sampling_method_kwargs - and "temperature" not in sampling_method_kwargs - ): - warnings.warn( - "Using passed in nbeta. Make sure callbacks are also initialized with the same nbeta." - ) - elif ( - "nbeta" not in sampling_method_kwargs - and "temperature" in sampling_method_kwargs - ): - warnings.warn( - "Temperature is deprecated, please switch to using nbeta here and in callbacks." - ) - warnings.warn( - "Using passed in temperature. Make sure callbacks are also initialized with the same temperature." - ) - sampling_method_kwargs["nbeta"] = sampling_method_kwargs.pop("temperature") - elif "nbeta" in sampling_method_kwargs and "temperature" in sampling_method_kwargs: - raise ValueError( - "Found temperature and nbeta in sampling_method_kwargs. Temperature is deprecated, please switch to using nbeta only (also in callbacks)." - ) - else: - warnings.warn("nbeta not set - using default nbeta.") +class ChainHealthError(Exception): + pass - sampling_method_kwargs.setdefault( - "nbeta", - default_nbeta( - dataloader=loader, gradient_accumulation_steps=gradient_accumulation_steps - ), - ) - if not init_loss: - init_loss = get_init_loss_multi_batch( - loader, num_chains * gradient_accumulation_steps, model, evaluate, device - ) - # alternative: init_loss = get_init_loss_full_batch(loader, model, evaluate, device) - # alternative: init_loss = get_init_loss_one_batch(loader, model, evaluate, device) - if online: - llc_estimator = OnlineLLCEstimator( - num_chains, - num_draws, - nbeta=sampling_method_kwargs["nbeta"], - device=device, - init_loss=init_loss, - ) - else: - llc_estimator = LLCEstimator( - num_chains, - num_draws, - nbeta=sampling_method_kwargs["nbeta"], - device=device, - init_loss=init_loss, - ) - callbacks = [llc_estimator, *callbacks] +class StepCallback(Protocol): + def __call__( + self, + chain: int, + step: int, + optimizer: torch.optim.Optimizer, + ) -> None: ... - sample( - model=model, - loader=loader, - evaluate=evaluate, - sampling_method=sampling_method, - sampling_method_kwargs=sampling_method_kwargs, - num_draws=num_draws, - num_chains=num_chains, - num_burnin_steps=num_burnin_steps, - num_steps_bw_draws=num_steps_bw_draws, - gradient_accumulation_steps=gradient_accumulation_steps, - cores=cores, - seed=seed, - device=device, - verbose=verbose, - callbacks=callbacks, - optimize_over_per_model_param=optimize_over_per_model_param, - gpu_idxs=gpu_idxs, - dtype=dtype, - init_loss=init_loss, - ) - results = {} +class MicroCallback(Protocol): + """Called once per micro-batch (inside the gradient accumulation loop).""" + + def __call__( + self, + loss: torch.Tensor, + input_ids: torch.Tensor, + chain: int, + step: int, + micro_step: int, + ) -> None: ... - for callback in callbacks: - if hasattr(callback, "get_results"): - results.update(callback.get_results()) - return results +def calculate_num_steps( + *, + num_draws: int, + num_steps_bw_draws: int, + num_burnin_steps: int, +) -> int: + return num_draws * num_steps_bw_draws + num_burnin_steps -def estimate_learning_coeff( - model: torch.nn.Module, - loader: DataLoader, - callbacks: List[Callable] = [], - evaluate: Optional[EvaluateFn] = None, - sampling_method: Type[torch.optim.Optimizer] = SGLD, - sampling_method_kwargs: Optional[ - Dict[str, Union[float, Literal["adaptive"]]] - ] = None, +def set_seed(seed: int, device: str | None = None) -> None: + np.random.seed(seed) + torch.manual_seed(seed) + random.seed(seed) + if device and str(device).startswith("cuda"): + torch.cuda.manual_seed_all(seed) + + +def _gc_and_empty_cache() -> None: + gc.collect() + if torch.cuda.is_available(): + torch.cuda.empty_cache() + + +def is_transformer_lens_model(model: torch.nn.Module) -> bool: + return hasattr(model, "cfg") and hasattr(model.cfg, "n_heads") + + +def _make_feed( + loader: DataLoader, epoch_mode: EpochMode, num_batches_needed: int +) -> Iterator: + """Create a batch iterator, cycling if needed. + + Raises RuntimeError (not StopIteration) if data runs out, to avoid + silently terminating the sampling loop. + """ + if epoch_mode == "cycle": + # itertools.cycle caches the first pass and replays it — matches aether's + # behavior, which means shuffle=True + cycle replays the first-epoch shuffle + # rather than reshuffling each epoch. + yield from cycle(loader) + elif epoch_mode == "once": + if len(loader) < num_batches_needed: + raise ValueError( + f"Dataset too small: need {num_batches_needed} batches, have {len(loader)}. " + f"Use epoch_mode='cycle' or a larger dataset." + ) + yield from loader + raise RuntimeError( + "Data loader exhausted during sampling (epoch_mode='once'). " + "Use epoch_mode='cycle' or a larger dataset." + ) + else: + raise ValueError(f"Unknown epoch_mode: {epoch_mode!r}") + + +def sample_single_chain( + ref_model: nn.Module, + dataset: torch.utils.data.Dataset, + evaluate: Callable[[nn.Module, torch.Tensor], tuple[torch.Tensor, dict[str, Any]]], + param_masks: ParamMasks, num_draws: int = 100, - num_chains: int = 10, num_burnin_steps: int = 0, num_steps_bw_draws: int = 1, - init_loss: float = None, gradient_accumulation_steps: int = 1, - cores: Union[int, List[Union[str, torch.device]]] = 1, - seed: Optional[Union[int, List[int]]] = None, - device: Union[torch.device, str] = torch.device("cpu"), - verbose: bool = True, - optimize_over_per_model_param: Optional[Dict[str, List[bool]]] = None, - dtype: Optional[torch.dtype] = ( - torch.bfloat16 - if os.environ.get("BF16") - else torch.float16 - if os.environ.get("FP16") - else torch.float32 - ), -) -> float: - """ - Estimates the local learning coefficient and returns a dictionary of results. - - - :param model: PyTorch model to sample from. - :type model: torch.nn.Module - :param loader: PyTorch DataLoader to sample from. - :type loader: torch.utils.data.DataLoader - :param callbacks: Additional callbacks to use during sampling. Since this function will automatically add the LLC estimator callback, \ - please do not include it in this list. An example of an additional callback is `devinterp.slt.mala.MalaAcceptanceRate`, which uses the Metropolis-Adjusted Langevin Algorithm's acceptance step to compute an acceptance rate. - :type callbacks: list of callable - :type evaluate: callable - :param evaluate: Function to evaluate the model. Should take in a model and a batch of data and return a Transformer-like dictionary of metrics. \ - An example of an evaluate function is: - ```python - def evaluate(model, data): - inputs, outputs = data - return F.cross_entropy(model(inputs).logits, outputs), { - "logits": model(inputs).logits - } - ``` - - :param sampling_method: PyTorch optimizer to use for sampling. Default is SGLD. Currently implemented alternatives include SGNHT. - :type sampling_method: torch.optim.Optimizer - :param sampling_method_kwargs: Dictionary of keyword arguments to pass to the optimizer. \ - For SGLD, this includes nbeta. - :type sampling_method_kwargs: dict - :param num_draws: Number of draws to sample per chain. - :type num_draws: int - :param num_chains: Number of chains to sample from. - :type num_chains: int - :param num_burnin_steps: Number of burn-in steps to use. - :type num_burnin_steps: int - :param num_steps_bw_draws: Number of steps between each draw. - :type num_steps_bw_draws: int - :param init_loss: Initial loss to use for the LLC estimator. If None, the initial loss will be computed using the first batch of data. - :type init_loss: float - :param gradient_accumulation_steps: Number of gradient accumulation steps to use per backward pass. Note that the effective batch size is batch_size * gradient_accumulation_steps. - :type gradient_accumulation_steps: int - :param cores: Number of cores to use for parallel sampling. Can be either an integer (will use cores starting from device 0) or a list of cores. - :type cores: int or list of torch.device or str - :param seed: Seed for reproducibility. If a list of seeds is provided, each chain will be seeded with the corresponding seed. - Otherwise, this parameter will be used as an offset that will be added to the chain index to seed each chain. - :type seed: int or list of int - :param device: Device to run the sampling on. Can be a torch.device or a string (e.g. "cuda:0"). Supports GPUs and TPUs. To use TPUs: - - ``` python - import os - os.environ["USE_TPU_BACKEND"] = "1" - import torch_xla.core.xla_model as xm - DEVICE = xm.xla_device() - # If you are using a TPU, make sure to set the environment variable `USE_TPU_BACKEND` to `1` before importing `devinterp`. - ``` - - :type device: torch.device or str - :param gpu_idxs: List of GPU indices to use. If None, the device will be used as is. Provide a list of indices \ - and set cores greater than the length of the list to use multiple GPUs. - :param verbose: Whether to display progress. - :type verbose: bool - :param optimize_over_per_model_param: Dictionary of booleans indicating whether to optimize over each parameter of the model. \ - Keys are parameter names, and values are boolean tensors that match the shape of the parameter. \ - A value of True (or 1) indicates that this particular element of the parameter should be optimized over. \ - None by default, which means that we optimize over all parameters. - :type optimize_over_per_model_param: dict of str -> torch.Tensor[bool] - :param online: Whether to use the online version of the LLC estimator. - :type online: bool - :param dtype: Data type for sampling. Default is bfloat16 if BF16 is True, float16 if FP16 is True, or float32 otherwise. - :type dtype: torch.dtype, optional - :returns: A single float representing the local learning coefficient. - """ + sampling_method: type[torch.optim.Optimizer] = SGLD, + sampling_method_kwargs: dict[str, Any] | None = None, + chain: int = 0, + seed: int | None = None, + dataloader_seed: int | None = None, + device: str = "cpu", + callbacks: list[Callable] | None = None, + step_callback: StepCallback | None = None, + micro_callback: MicroCallback | None = None, + batch_size: int = 32, + init_noise: float | None = None, + shuffle: bool = True, + epoch_mode: EpochMode = "cycle", +) -> None: + """Sample a single SGLD chain.""" + dataloader_rng = torch.Generator(device="cpu") + if dataloader_seed is not None: + dataloader_rng.manual_seed(dataloader_seed) + + loader = DataLoader( + dataset, + batch_size=batch_size, + shuffle=shuffle, + generator=dataloader_rng, + drop_last=True, + ) + + model = deepcopy(ref_model) + if is_transformer_lens_model(model): + model = model.to(device, print_details=False) # pyright: ignore[reportCallIssue] + else: + model = model.to(device) + + if seed is not None: + set_seed(seed, device=device) + + sampling_method_kwargs = sampling_method_kwargs or {} + + name_to_param = dict(model.named_parameters()) + for name, param in name_to_param.items(): + if name not in param_masks: + param.requires_grad_(False) + + opt_params = [ + { + "params": name_to_param[name], + "mask": mask.to(device) if mask is not None else None, + } + for name, mask in param_masks.items() + ] + optimizer = sampling_method( + opt_params, + **sampling_method_kwargs, + ) - return estimate_learning_coeff_with_summary( - model=model, - loader=loader, - evaluate=evaluate, - sampling_method=sampling_method, - sampling_method_kwargs=sampling_method_kwargs, + if init_noise: + for name, mask in param_masks.items(): + p = name_to_param[name] + m = mask.to(p.device) if mask is not None else 1.0 + p.data.add_(torch.randn_like(p.data) * init_noise * m) + + num_steps = calculate_num_steps( num_draws=num_draws, - num_chains=num_chains, - num_burnin_steps=num_burnin_steps, num_steps_bw_draws=num_steps_bw_draws, - gradient_accumulation_steps=gradient_accumulation_steps, - cores=1, - seed=seed, - device=device, - verbose=verbose, - callbacks=callbacks, - online=False, - init_loss=init_loss, - optimize_over_per_model_param=optimize_over_per_model_param, - dtype=dtype, - )["llc/mean"] + num_burnin_steps=num_burnin_steps, + ) + + total_batches = num_steps * gradient_accumulation_steps + feed = _make_feed(loader, epoch_mode, total_batches) + + model.train() + _gc_and_empty_cache() + + try: + with trange(0, num_steps, desc=f"Chain {chain}", leave=True) as pbar: + for i in pbar: + loss = torch.zeros((), device=device, dtype=torch.float32) + for j in range(gradient_accumulation_steps): + data = next(feed) + input_ids = data["input_ids"] + data["input_ids"] = input_ids.to(device) + _loss, _ = evaluate(model, data) + + if micro_callback is not None: + micro_callback( + loss=_loss.detach(), + input_ids=input_ids, + chain=chain, + step=i, + micro_step=j, + ) + + _loss = _loss.float() + (_loss.mean() / gradient_accumulation_steps).backward() + loss = loss + _loss.detach() / gradient_accumulation_steps + + if torch.isnan(loss).any() or torch.isinf(loss).any(): + raise ChainHealthError(f"Chain {chain} failed: Loss is NaN or Inf") + + optimizer.step() + + if step_callback is not None: + step_callback(chain, i, optimizer) + + optimizer.zero_grad(set_to_none=True) + + draw, steps_since_draw = divmod( + i - num_burnin_steps, num_steps_bw_draws + ) + if draw >= 0 and steps_since_draw == 0 and callbacks: + with torch.no_grad(): + for callback in callbacks: + callback( + loss=loss.detach(), + draw=draw, + chain=chain, + model=model, + optimizer=optimizer, + ) + + except (ChainHealthError, NonFiniteLogitsError) as e: + warnings.warn(f"Chain failed: {e}") + + del model, optimizer + _gc_and_empty_cache() diff --git a/src/devinterp/slt/sampling.py b/src/devinterp/slt/sampling.py new file mode 100644 index 00000000..d11deb19 --- /dev/null +++ b/src/devinterp/slt/sampling.py @@ -0,0 +1,632 @@ +"""SGLD sampling with observables, writing results to zarr. + +Provides sample() as the main entry point. Internally uses +sample_single_chain from sampler.py for the SGLD inner loop. +""" + +from __future__ import annotations + +import logging +import tempfile +import time +import warnings +from pathlib import Path +from typing import Any, cast + +import torch +import xarray as xr +import zarr +from datasets import Dataset +from torch.utils.data import DataLoader, Dataset as TorchDataset +from zarr.storage import LocalStore + +from devinterp.optim.metrics import Metrics +from devinterp.slt.config import ( + SAMPLING_METHODS, + SamplerConfig, + SamplingMethodLiteral, +) +from devinterp.slt.lm_loss import LossFn, compute_per_token_loss, make_evaluate_fn +from devinterp.slt.observables import Observable +from devinterp.slt.sampler import ( + ParamMasks, + _make_feed, + is_transformer_lens_model, + set_seed, + calculate_num_steps, + sample_single_chain, +) +from devinterp.slt.writing import ZarrWriter +from devinterp.slt.zarr_schema import DataArraySpec, ZarrSchema + +SAMPLES_LOSS_DTYPE_STR = "float32" +ZARR_MAX_WRITE_THREADS = 4 + +logger = logging.getLogger(__name__) + +# Type for observable specification: dataset alone (uses default batches_per_draw) +# or (dataset, batches_per_draw) tuple for explicit control. +ObservableSpec = Dataset | tuple[Dataset, int] + + +def sample( + model: torch.nn.Module, + dataset: Dataset, + observables: dict[str, ObservableSpec], + *, + lr: float, + n_beta: float, + param_masks: ParamMasks | None = None, + num_chains: int = 4, + num_draws: int = 200, + batch_size: int = 32, + num_burnin_steps: int = 0, + num_steps_bw_draws: int = 1, + num_init_loss_batches: int = 32, + init_seed: int = 100, + batches_per_draw: int = 3, + obs_seed: int = 1337, + gradient_accumulation_steps: int = 1, + localization: float = 0.0, + noise_level: float = 1.0, + llc_weight_decay: float = 0.0, + bounding_box_size: float | None = None, + sampling_method: SamplingMethodLiteral = "sgmcmc_sgld", + sampling_method_kwargs: dict[str, Any] | None = None, + rmsprop_eps: float | None = None, + rmsprop_alpha: float | None = None, + shuffle: bool = True, + match_sampling_input_ids_across_chains: bool = True, + init_noise: float | None = None, + device: str | None = None, + save_metrics: bool = False, + output_path: str | Path | None = None, + loss_fn: LossFn | None = None, +) -> xr.DataTree: + """Run SGLD sampling with observables. + + Args: + model: PyTorch model. + dataset: HuggingFace Dataset with "input_ids" column, used for SGLD sampling. + observables: Dict mapping observable names to datasets (or (dataset, batches_per_draw) + tuples). Each dataset must have an "input_ids" column. + lr: SGLD learning rate. + n_beta: SGLD inverse temperature. + param_masks: Which parameters to optimize. None means all parameters (full model). + Otherwise a dict mapping param names to mask tensors (or None for unrestricted). + num_chains: Number of SGLD chains. + num_draws: Number of draws per chain. + batch_size: Batch size for sampling and observables. + num_burnin_steps: SGLD burn-in steps before drawing. + num_steps_bw_draws: Steps between draws. + num_init_loss_batches: Batches for init_loss computation. + init_seed: Random seed. + batches_per_draw: Default batches_per_draw for observables (used when + an observable is specified as just a dataset, not a tuple). + obs_seed: Seed for deterministic observable sampling. + gradient_accumulation_steps: Number of micro-batches per optimizer step. + Effective batch size is batch_size * gradient_accumulation_steps. + localization: Strength of the pull toward initial parameters (gamma in + Lau et al. 2023). 0 disables localization. + noise_level: Standard deviation of SGLD noise. Defaults to 1.0; + changing this breaks the SGLD posterior-sampling guarantee. + llc_weight_decay: L2 regularization strength (lambda). Applied as a + Gaussian prior centered at zero. + bounding_box_size: If set, restricts sampling to a box of this radius + around the initial parameters. None disables. + sampling_method: Which SGLD variant to use. "sgmcmc_sgld" is the + default; "rmsprop_sgld" adds RMSprop-style preconditioning. + sampling_method_kwargs: Extra kwargs forwarded to the sampling-method + constructor (e.g. rmsprop's "alpha" / "eps", or + "add_grad_correction"). Use `rmsprop_eps` / `rmsprop_alpha` as + convenience aliases for the two most common rmsprop knobs. + rmsprop_eps: RMSprop stability constant. Only valid when + sampling_method='rmsprop_sgld'. Shorthand for + sampling_method_kwargs={"eps": ...}. + rmsprop_alpha: RMSprop moving-average coefficient. Only valid when + sampling_method='rmsprop_sgld'. Shorthand for + sampling_method_kwargs={"alpha": ...}. + shuffle: Whether to shuffle the sampling dataset. Default True. + match_sampling_input_ids_across_chains: If True, every chain sees the + same input_ids in the same order (only the SGLD noise differs + across chains). If False, each chain gets an independently-seeded + shuffle. + init_noise: If set, add Gaussian noise with this std to parameters before sampling. + device: Compute device. None for auto-detect. + save_metrics: If True, save per-step SGLD diagnostics (gradient norms, + noise norms, distance from init, etc.) for tuning sampling parameters. + output_path: Path for output zarr. None for a temp directory. + loss_fn: Optional custom per-token loss `(model, input_ids) -> (batch, seq-1)`. + Defaults to cross-entropy on the model's logits. + + Returns: + Lazy-loaded DataTree of sampling results. + """ + start = time.perf_counter() + + if device is None: + device = "cuda" if torch.cuda.is_available() else "cpu" + + if param_masks is None: + param_masks = {name: None for name, _ in model.named_parameters()} + + context_length = len(dataset[0]["input_ids"]) - 1 + if context_length < 1: + raise ValueError( + f"Sequences must have length >= 2 for next-token loss " + f"(got {context_length + 1})." + ) + ds = cast(TorchDataset, dataset) + + sampling_method_kwargs = dict(sampling_method_kwargs or {}) + for name, value in (("eps", rmsprop_eps), ("alpha", rmsprop_alpha)): + if value is None: + continue + if sampling_method != "rmsprop_sgld": + raise ValueError( + f"rmsprop_{name} can only be set when sampling_method='rmsprop_sgld', " + f"got sampling_method={sampling_method!r}" + ) + if name in sampling_method_kwargs: + raise ValueError( + f"rmsprop_{name} is set both as a top-level argument and in " + f"sampling_method_kwargs[{name!r}]; specify only one." + ) + sampling_method_kwargs[name] = value + + # Build observable objects + obs_list: list[Observable] = [] + for name, spec in observables.items(): + if isinstance(spec, tuple): + obs_ds, bpd = spec + else: + obs_ds, bpd = spec, batches_per_draw + obs_list.append( + Observable( + dataset=obs_ds, + task_name=name, + batches_per_draw=bpd, + batch_size=batch_size, + context_length=context_length, + device=torch.device(device), + seed=obs_seed, + loss_fn=loss_fn, + ) + ) + + # Build config + config = SamplerConfig( + lr=lr, + n_beta=n_beta, + num_chains=num_chains, + num_draws=num_draws, + batch_size=batch_size, + num_burnin_steps=num_burnin_steps, + num_steps_bw_draws=num_steps_bw_draws, + num_init_loss_batches=num_init_loss_batches, + init_seed=init_seed, + gradient_accumulation_steps=gradient_accumulation_steps, + localization=localization, + noise_level=noise_level, + llc_weight_decay=llc_weight_decay, + bounding_box_size=bounding_box_size, + sampling_method=sampling_method, + sampling_method_kwargs=sampling_method_kwargs, + shuffle=shuffle, + match_sampling_input_ids_across_chains=match_sampling_input_ids_across_chains, + init_noise=init_noise, + save_metrics=save_metrics, + ) + + # Cache: if output_path exists, validate and return early on match. + if output_path is not None and Path(output_path).exists(): + cached = _check_cache(output_path, config) + logger.info("sample() using cached output at %s", output_path) + return cached + + # Warn about non-persisted big runs + total_work = ( + num_chains * (num_draws * num_steps_bw_draws + num_burnin_steps) * batch_size + ) + if output_path is None and total_work > 1000: + warnings.warn( + f"Sampling without output_path set -- {total_work} effective " + "samples will be written to a temp directory and lost when " + "the process exits. Pass output_path='/path/to/samples.zarr' " + "to save them.", + stacklevel=2, + ) + + # Compute numel for metrics if needed + numel: int | None = None + if save_metrics: + name_to_param = dict(model.named_parameters()) + numel = sum( + int(mask.count_nonzero()) + if mask is not None + else name_to_param[name].numel() + for name, mask in param_masks.items() + ) + + # Build zarr schema and store + chain_buffer_size = min(50, num_draws) + schema = _build_sampling_schema( + config=config, + context_length=context_length, + chain_buffer_size=chain_buffer_size, + observables=obs_list, + numel=numel, + ) + + if output_path is None: + output_path = Path(tempfile.mkdtemp()) / "samples.zarr" + store = LocalStore(output_path) + _, arrays = schema.create_hierarchy(store) + + # Resolve sampling method + sampling_method_cls = SAMPLING_METHODS.get(config.sampling_method) + if sampling_method_cls is None: + raise ValueError(f"Unknown sampling method {config.sampling_method}") + + sampling_method_kwargs = dict( + nbeta=n_beta, + lr=lr, + localization=config.localization, + noise_level=config.noise_level, + weight_decay=config.llc_weight_decay, + bounding_box_size=config.bounding_box_size, + save_metrics=save_metrics, + **config.sampling_method_kwargs, + ) + + # Callbacks for zarr writing + def on_draw(*, loss, draw, chain, model, **_): + writer.push("sampling_loss", loss, chain=chain, draw=draw) + for obs in obs_list: + assert not torch.is_grad_enabled() + obs_loss = obs.compute_loss(model) + writer.push(f"loss_{obs.obs_id}", obs_loss, chain=chain, draw=draw) + writer.flush_full_buffers() + + # Buffer grad_accum micro-batches per (chain, step) and write once full, + # since the zarr writer expects a full row per push. + micro_loss_buf: torch.Tensor | None = None + micro_ids_buf: torch.Tensor | None = None + micro_total = gradient_accumulation_steps * batch_size + + def on_micro( + loss: torch.Tensor, + input_ids: torch.Tensor, + chain: int, + step: int, + micro_step: int, + ) -> None: + nonlocal micro_loss_buf, micro_ids_buf + if micro_step == 0: + micro_loss_buf = torch.empty(micro_total, context_length, dtype=loss.dtype) + micro_ids_buf = torch.empty( + micro_total, context_length + 1, dtype=input_ids.dtype + ) + s = slice(micro_step * batch_size, (micro_step + 1) * batch_size) + assert micro_loss_buf is not None and micro_ids_buf is not None + micro_loss_buf[s] = loss.cpu() + micro_ids_buf[s] = input_ids + if micro_step == gradient_accumulation_steps - 1: + writer.push("sampling_loss_micro", micro_loss_buf, chain=chain, step=step) + writer.push( + "sampling_input_ids_micro", micro_ids_buf, chain=chain, step=step + ) + writer.flush_full_buffers() + + def on_step(chain: int, step: int, optimizer: torch.optim.Optimizer) -> None: + metrics = optimizer.get_metrics() + for field_name in Metrics.NORM_FIELDS + Metrics.DOT_FIELDS: + value = getattr(metrics, field_name).squeeze() + writer.push(f"metrics_{field_name}", value, chain=chain, step=step) + writer.flush_full_buffers() + + step_callback = on_step if save_metrics else None + + # Seed and chain setup + dataloader_seed = ( + init_seed if config.match_sampling_input_ids_across_chains else None + ) + + with ZarrWriter.open( + arrays, chain_buffer_size, torch.device(device), ZARR_MAX_WRITE_THREADS + ) as writer: + # Write fixed observable input_ids + for obs in obs_list: + writer.write(f"input_ids_{obs.obs_id}", obs.input_ids) + + # Compute and write init loss + _write_init_loss(writer, config, model, param_masks, ds, device, loss_fn) + + # Run SGLD chains + for chain_idx in range(num_chains): + sample_single_chain( + ref_model=model, + dataset=ds, + evaluate=make_evaluate_fn(loss_fn), + param_masks=param_masks, + num_draws=num_draws, + num_burnin_steps=num_burnin_steps, + num_steps_bw_draws=num_steps_bw_draws, + gradient_accumulation_steps=config.gradient_accumulation_steps, + sampling_method=sampling_method_cls, + sampling_method_kwargs=sampling_method_kwargs, + chain=chain_idx, + seed=init_seed + chain_idx, + dataloader_seed=dataloader_seed + if dataloader_seed is not None + else init_seed + chain_idx, + device=device, + callbacks=[on_draw], + step_callback=step_callback, + micro_callback=on_micro, + batch_size=batch_size, + init_noise=init_noise, + shuffle=config.shuffle, + epoch_mode=config.epoch_mode, + ) + + # Mark as completed so future cache checks can trust the output. + zarr.open_group(str(output_path)).attrs["completed"] = 1 + + logger.info("sample() total time: %.2f seconds", time.perf_counter() - start) + return xr.open_datatree(str(output_path), engine="zarr", consolidated=False) + + +def _check_cache(output_path: str | Path, config: SamplerConfig) -> xr.DataTree: + """Validate an existing sample output against the current config. + + Raises RuntimeError with a clear "delete and retry" message if the file + is unreadable, incomplete, or was produced with different sampler args. + Otherwise returns the loaded DataTree. + """ + path_str = str(output_path) + try: + existing = xr.open_datatree(path_str, engine="zarr", consolidated=False) + except Exception as e: + raise RuntimeError( + f"Output path '{output_path}' exists but couldn't be opened as zarr:\n" + f" {e!r}\n" + f"Delete and retry: rm -rf '{output_path}'" + ) from e + + if existing.attrs.get("completed") != 1: + raise RuntimeError( + f"Output path '{output_path}' exists but sampling was incomplete " + f"(no 'completed' flag — likely interrupted).\n" + f"Delete and retry: rm -rf '{output_path}'" + ) + + stored_sampler = existing.metadata["config"]["sampler"] + expected_sampler = { + **config.model_dump(), + "scheduler_type": "constant", + "scheduler_kwargs": None, + "cores": 1, + "online": False, + } + if stored_sampler != expected_sampler: + diffs = [] + all_keys = set(stored_sampler) | set(expected_sampler) + for key in sorted(all_keys): + s = stored_sampler.get(key) + e = expected_sampler.get(key) + if s != e: + diffs.append(f" {key}: stored={s!r}, current={e!r}") + raise RuntimeError( + f"Output path '{output_path}' has a different sampler config:\n" + + "\n".join(diffs) + + f"\nDelete and retry: rm -rf '{output_path}'" + ) + + return existing + + +# ─── Internal helpers ──────────────────────────────────────────────────────── + + +def _build_sampling_schema( + *, + config: SamplerConfig, + context_length: int, + chain_buffer_size: int, + observables: list[Observable], + numel: int | None = None, +) -> ZarrSchema: + """Build a ZarrSchema for the sampling pipeline.""" + num_chains = config.num_chains + num_draws = config.num_draws + + chain_chunk_size = 1 + draw_chunk_size = chain_buffer_size + chain_draw_chunks = (chain_chunk_size, draw_chunk_size) + + num_steps = calculate_num_steps( + num_draws=num_draws, + num_steps_bw_draws=config.num_steps_bw_draws, + num_burnin_steps=config.num_burnin_steps, + ) + + arrays_meta: dict[str, DataArraySpec] = {} + + arrays_meta["init_loss"] = DataArraySpec( + dtype_str=SAMPLES_LOSS_DTYPE_STR, + dims=("chain", "token_pos"), + shape=(num_chains, context_length), + chunks=(1, context_length), + ) + + arrays_meta["sampling_loss"] = DataArraySpec( + dtype_str=SAMPLES_LOSS_DTYPE_STR, + dims=("chain", "draw", "batch", "token_pos"), + shape=(num_chains, num_draws, config.batch_size, context_length), + chunks=chain_draw_chunks + (config.batch_size, context_length), + ) + + micro_batch = config.gradient_accumulation_steps * config.batch_size + arrays_meta["sampling_loss_micro"] = DataArraySpec( + dtype_str=SAMPLES_LOSS_DTYPE_STR, + dims=("chain", "step", "batch_sampling", "token_pos"), + shape=(num_chains, num_steps, micro_batch, context_length), + chunks=chain_draw_chunks + (micro_batch, context_length), + ) + arrays_meta["sampling_input_ids_micro"] = DataArraySpec( + dtype_str="int64", + dims=("chain", "step", "batch_sampling", "token"), + shape=(num_chains, num_steps, micro_batch, context_length + 1), + chunks=chain_draw_chunks + (micro_batch, context_length + 1), + ) + + for obs in observables: + batch_dim = f"batch_{obs.obs_id}" + + arrays_meta[f"loss_{obs.obs_id}"] = DataArraySpec( + dtype_str=SAMPLES_LOSS_DTYPE_STR, + dims=("chain", "draw", batch_dim, "token_pos"), + shape=(num_chains, num_draws, obs.n_samples, context_length), + chunks=chain_draw_chunks + (obs.n_samples, context_length), + ) + + arrays_meta[f"input_ids_{obs.obs_id}"] = DataArraySpec( + dtype_str="int64", + dims=(batch_dim, "token"), + shape=(obs.n_samples, context_length + 1), + ) + + group_attrs: dict[str, dict[str, Any]] = { + "": { + "metadata": { + "config": { + "sampler": { + **config.model_dump(), + # Fields we removed but can provide defaults for + "scheduler_type": "constant", + "scheduler_kwargs": None, + "cores": 1, + "online": False, + }, + "observables": [ + { + "type": "ObserveDifferentDataset", + "obs_id": obs.obs_id, + "batches_per_draw": obs.batches_per_draw, + } + for obs in observables + ], + "chain_buffer_size": chain_buffer_size, + "shared_observable_dims": ["token", "token_pos"], + "tokenizer_context_length": context_length + 1, + }, + }, + } + } + + if config.save_metrics: + step_chunk_size = chain_buffer_size + chain_step_chunks = (chain_chunk_size, step_chunk_size) + for field_name in Metrics.NORM_FIELDS + Metrics.DOT_FIELDS: + arrays_meta[f"metrics_{field_name}"] = DataArraySpec( + dtype_str="float32", + dims=("chain", "step"), + shape=(num_chains, num_steps), + chunks=chain_step_chunks, + ) + assert numel is not None, "numel must be provided when save_metrics=True" + group_attrs[""]["metrics_numel"] = numel + + return ZarrSchema( + arrays_meta=arrays_meta, + group_attrs=group_attrs, + ) + + +def _write_init_loss( + writer: ZarrWriter, + config: SamplerConfig, + model: torch.nn.Module, + param_masks: ParamMasks, + dataset: TorchDataset, + device: str, + loss_fn: LossFn | None, +) -> None: + """Compute and write the initial (pre-sampling) loss for each chain.""" + num_batches = config.gradient_accumulation_steps * config.num_init_loss_batches + fn = loss_fn if loss_fn is not None else compute_per_token_loss + + # Remember original device so we can restore after init_loss computation. + # nn.Module.to() is in-place — without this, the caller's model would be + # silently moved to the compute device. + orig_device = next(model.parameters()).device + was_training = model.training + + if is_transformer_lens_model(model): + model_on_device = model.to(device, print_details=False) # pyright: ignore[reportCallIssue] + else: + model_on_device = model.to(device) + model_on_device.train() + + torch.manual_seed(config.init_seed) + + name_to_param = dict(model_on_device.named_parameters()) + orig_data: dict[str, torch.Tensor] = {} + if config.init_noise: + # init_loss is computed at the unrestricted-noise state (noise on all + # params, ignoring weight_restrictions) to match aether's behavior. + # The sampling chain itself still applies masked init_noise. + orig_data = {name: p.data.clone() for name, p in name_to_param.items()} + + for chain_idx in range(config.num_chains): + set_seed(config.init_seed + chain_idx, device=device) + + dataloader_rng = torch.Generator(device="cpu") + dataloader_rng.manual_seed( + config.init_seed + if config.match_sampling_input_ids_across_chains + else config.init_seed + chain_idx + ) + loader = DataLoader( + dataset, + batch_size=config.batch_size, + shuffle=config.shuffle, + generator=dataloader_rng, + drop_last=True, + ) + + if config.init_noise: + for parameter in name_to_param.values(): + parameter.data.add_( + torch.randn_like(parameter.data) * config.init_noise + ) + + feed = _make_feed(loader, config.epoch_mode, num_batches) + accumulated_loss = 0.0 + with torch.no_grad(): + for i in range(num_batches): + batch = next(feed) + input_ids = batch["input_ids"].to(device) + loss = fn(model_on_device, input_ids) + if chain_idx == 0 and i == 0: + expected = input_ids.shape[:-1] + (input_ids.shape[-1] - 1,) + if tuple(loss.shape) != tuple(expected): + raise ValueError( + f"loss_fn must return per-token loss of shape " + f"{tuple(expected)}, got {tuple(loss.shape)}" + ) + accumulated_loss += loss.detach().float() / num_batches + + writer.write("init_loss", accumulated_loss.mean(dim=0), chain=chain_idx) + + if config.init_noise: + for name, parameter in name_to_param.items(): + parameter.data.copy_(orig_data[name]) + + # Restore model to its original device and training mode + if is_transformer_lens_model(model): + model.to(orig_device, print_details=False) # pyright: ignore[reportCallIssue] + else: + model.to(orig_device) + model.train(was_training) diff --git a/src/devinterp/slt/susceptibilities.py b/src/devinterp/slt/susceptibilities.py new file mode 100644 index 00000000..0dbf26f8 --- /dev/null +++ b/src/devinterp/slt/susceptibilities.py @@ -0,0 +1,303 @@ +"""Susceptibility computation. + +Computes per-token susceptibilities from SGLD sampling results, +as described in appendix C.4 of https://arxiv.org/pdf/2504.18274. + +Two entry points: +- susceptibilities(): high-level, takes model + datasets, runs sampling + post-processing +- compute_susceptibilities(): low-level, takes pre-computed sampling DataTrees +""" + +from __future__ import annotations + +from collections import defaultdict +from pathlib import Path + +import numpy as np +import torch +import xarray as xr +from datasets import Dataset + +from devinterp.slt.lm_loss import LossFn +from devinterp.slt.observables import to_obs_id +from devinterp.slt.sampler import ParamMasks +from devinterp.slt.sampling import ObservableSpec, sample + + +def susceptibilities( + model: torch.nn.Module, + dataset: Dataset, + observables: dict[str, ObservableSpec], + weight_restrictions: dict[str, ParamMasks | None], + *, + sampling_task: str, + lr: float, + n_beta: float, + loss_fn: LossFn | None = None, + include_sampling_task: bool = False, + **kwargs, +) -> xr.DataTree: + """Sample multiple weight restrictions and compute susceptibilities. + + Args: + model: PyTorch model. + dataset: HuggingFace Dataset with "input_ids" column. + observables: Dict mapping names to datasets (or (dataset, batches_per_draw) tuples). + weight_restrictions: Dict mapping WR names to param masks. + Must include "full" (use None for full model). + sampling_task: Name of the sampling dataset (must be in observables). + lr: SGLD learning rate. + n_beta: SGLD inverse temperature. + loss_fn: Optional custom per-token loss `(model, input_ids) -> (batch, seq-1)`. + Defaults to cross-entropy on the model's logits. + include_sampling_task: If True, compute susceptibilities for the + sampling_task observable too (per-token variation within that task + is still informative). Default False. + **kwargs: Additional arguments passed to sample() (num_chains, num_draws, + batch_size, output_path, etc.). If output_path is provided, each + weight restriction's samples are saved to a separate zarr with the + WR name appended (e.g. "samples_full.zarr", "samples_l0h1.zarr"). + + Returns: + DataTree with /susceptibilities and /context subtrees. + """ + if "full" not in weight_restrictions: + raise ValueError( + "weight_restrictions must include a 'full' key (use None for the full model)." + ) + if sampling_task not in observables: + raise ValueError( + f"sampling_task '{sampling_task}' not found in observables. " + f"Available: {list(observables)}" + ) + if not include_sampling_task: + non_sampling = [name for name in observables if name != sampling_task] + if not non_sampling: + raise ValueError( + "Need at least one observable besides sampling_task " + "(or set include_sampling_task=True)." + ) + + # If output_path is provided, append WR name so each sample() writes to a + # unique zarr (otherwise they'd all overwrite the same path). + base_output_path = kwargs.pop("output_path", None) + + wr_map: dict[str, xr.DataTree] = {} + for wr_name, masks in weight_restrictions.items(): + if masks is None: + masks = {name: None for name, _ in model.named_parameters()} + wr_kwargs = dict(kwargs) + if base_output_path is not None: + base = Path(str(base_output_path)) + wr_kwargs["output_path"] = ( + base.parent / f"{base.stem}_{wr_name}{base.suffix}" + ) + wr_map[wr_name] = sample( + model=model, + dataset=dataset, + observables=observables, + param_masks=masks, + lr=lr, + n_beta=n_beta, + loss_fn=loss_fn, + **wr_kwargs, + ) + return compute_susceptibilities( + wr_map, sampling_task, include_sampling_task=include_sampling_task + ) + + +def compute_susceptibilities( + wr_map: dict[str, xr.DataTree], + sampling_task: str, + observable_names: list[str] | None = None, + include_sampling_task: bool = False, +) -> xr.DataTree: + """Compute per-token susceptibilities from sampling results. + + Args: + wr_map: Dict mapping weight restriction names to DataTrees. + Must include a "full" key for the unrestricted model. + Each DataTree is the output of sample(). + sampling_task: Name of the sampling/pretraining dataset task + (e.g. "pile10k"). Must appear as an observable. + observable_names: Which observables to compute susceptibilities for. + If None, uses all observables (optionally including sampling_task, + see include_sampling_task). + include_sampling_task: If True and observable_names is None, include + sampling_task in the discovered observables. Default False. + + Returns: + DataTree with /susceptibilities and /context subtrees. + """ + full = wr_map["full"].dataset + sampling_id = to_obs_id(sampling_task) + + # Discover observable ids from the DataTree + all_obs_ids = [ + str(v)[len("loss_") :] + for v in full.data_vars + if str(v).startswith("loss_") and str(v) != "sampling_loss" + ] + if observable_names is None: + obs_ids = ( + all_obs_ids + if include_sampling_task + else [oid for oid in all_obs_ids if oid != sampling_id] + ) + else: + obs_ids = [to_obs_id(n) for n in observable_names] + + if not obs_ids: + raise ValueError( + f"No probe observables to compute susceptibilities for. " + f"Available loss_* observables: {all_obs_ids!r}; " + f"sampling_task={sampling_id!r}; " + f"include_sampling_task={include_sampling_task}. " + f"Add a probe observable distinct from the sampling task, " + f"or pass include_sampling_task=True." + ) + + # Pre-compute full-WR quantities + full_sampling_mean = float(full[f"loss_{sampling_id}"].values.mean()) + init_loss_mean = float(full["init_loss"].values.mean()) + + full_delta = {} + for obs_id in obs_ids: + # Mean over chain+draw, keeping (batch, target_position) + full_delta[obs_id] = full_sampling_mean - full[f"loss_{obs_id}"].values.mean( + axis=(0, 1) + ) + + # Compute susceptibilities per WR x observable + results: list[tuple[str, str, xr.DataArray, xr.DataArray]] = [] + + for wr_name, wr in wr_map.items(): + if wr_name == "full": + continue + ds = wr.dataset + + # Verify expected layout since we slice positionally below. + for oid in [sampling_id, *obs_ids]: + expected = ("chain", "draw", f"batch_{oid}", "token_pos") + assert ds[f"loss_{oid}"].dims == expected, ( + f"loss_{oid} has dims {ds[f'loss_{oid}'].dims}, expected {expected}" + ) + + # phi = per-(chain, draw) mean pretraining loss minus init_loss_mean + pt_loss = ds[f"loss_{sampling_id}"].values # (chain, draw, batch, token_pos) + pt_mean = pt_loss.mean(axis=(-2, -1)) # (chain, draw) + phi = pt_mean - init_loss_mean + E_phi = float(phi.mean()) + E_phi_wr = float((phi * pt_mean).mean()) + + for obs_id in obs_ids: + probe = ds[f"loss_{obs_id}"].values # (chain, draw, batch, token_pos) + E_phi_probe = (phi[:, :, None, None] * probe).mean(axis=(0, 1)) + + sus = E_phi_wr - E_phi_probe - E_phi * full_delta[obs_id] + + ctx_len = sus.shape[1] + sus_da = xr.DataArray( + sus, + dims=["batch", "target_position"], + coords={"target_position": np.arange(1, ctx_len + 1)}, + ) + + ids = ds[f"input_ids_{obs_id}"].values + while ids.ndim > 2: + ids = ids[0] + ids_da = xr.DataArray( + ids, + dims=["batch", "position"], + coords={"position": np.arange(ids.shape[1])}, + ) + + results.append((wr_name, obs_id, sus_da, ids_da)) + + return _build_output(results) + + +# ─── Output format ────────────────────────────────────────────────────────── +# The output uses the SusceptibilitiesV2 format: susceptibilities are flattened +# into a 2D array (sus_flat, wr) and input_ids into a 1D array (ctx_flat), +# both with coordinate arrays for traceability. This matches aether's format +# so downstream visualization tools work unchanged. + + +def _build_output( + results: list[tuple[str, str, xr.DataArray, xr.DataArray]], +) -> xr.DataTree: + """Build SusceptibilitiesV2 DataTree from (wr, obs, sus, ids) tuples.""" + sus_by_wr: dict[str, dict[str, xr.DataArray]] = defaultdict(dict) + all_ids: dict[str, dict[str, xr.DataArray]] = defaultdict(dict) + wr_order: list[str] = [] + + for wr_name, obs, sus, ids in results: + sus_by_wr[wr_name][obs] = sus + all_ids[wr_name][obs] = ids + if wr_name not in wr_order: + wr_order.append(wr_name) + + obs_order = sorted(sus_by_wr[wr_order[0]].keys()) + + # Validate input_ids consistency across WRs + first_wr = wr_order[0] + deduped_ids: dict[str, xr.DataArray] = {} + for obs in obs_order: + ref = all_ids[first_wr][obs] + deduped_ids[obs] = ref + for wr in wr_order[1:]: + if not np.array_equal(ref.values, all_ids[wr][obs].values): + raise AssertionError( + f"input_ids for {obs} differ between {first_wr} and {wr}" + ) + + # Flatten input_ids → 1D with (dataset_id, batch, position) coords + ids_items = {i: (obs, deduped_ids[obs]) for i, obs in enumerate(obs_order)} + ids_coords = _flat_coords(ids_items, "ctx_flat") + ids_data = np.concatenate([v.values.ravel() for _, v in ids_items.values()]) + + # Flatten susceptibilities → 2D (sus_flat, wr) + sus_items = { + i: (obs, sus_by_wr[wr_order[0]][obs]) for i, obs in enumerate(obs_order) + } + sus_coords = _flat_coords(sus_items, "sus_flat") + sus_data = np.concatenate( + [ + np.stack([sus_by_wr[wr][obs].values.ravel() for wr in wr_order], axis=-1) + for obs in obs_order + ], + axis=0, + ) + + return xr.DataTree.from_dict( + { + "/susceptibilities": xr.Dataset( + {"sus": xr.DataArray(sus_data, dims=["sus_flat", "wr"])}, + coords=sus_coords.merge(xr.Coordinates({"wr": wr_order})), + attrs={"dataset_id_to_name": obs_order}, + ), + "/context": xr.Dataset( + {"input_ids": xr.DataArray(ids_data, dims=["ctx_flat"])}, + coords=ids_coords, + attrs={"dataset_id_to_name": obs_order}, + ), + } + ) + + +def _flat_coords( + items: dict[int, tuple[str, xr.DataArray]], dim_name: str +) -> xr.Coordinates: + """Build flattened coordinates from per-observable DataArrays.""" + coords: dict[str, list] = {"dataset_id": []} + for i, (_name, var) in items.items(): + coords["dataset_id"].append(np.full(var.size, i)) + grids = np.meshgrid(*[var.coords[d].values for d in var.dims], indexing="ij") + for dim, grid in zip(var.dims, grids): + coords.setdefault(str(dim), []).append(grid.ravel()) + return xr.Coordinates( + {k: (dim_name, np.concatenate(v)) for k, v in coords.items()}, + indexes={}, + ) diff --git a/src/devinterp/slt/trace.py b/src/devinterp/slt/trace.py deleted file mode 100644 index 66f237b8..00000000 --- a/src/devinterp/slt/trace.py +++ /dev/null @@ -1,92 +0,0 @@ -from typing import Union - -import torch - -from devinterp.slt.callback import SamplerCallback - - -class OnlineTraceStatistics(SamplerCallback): - """ - Derivative callback that computes mean/std statistics of a specified trace online. Must be called after the base callback has been called at each draw. - - .. |colab5| image:: https://colab.research.google.com/assets/colab-badge.svg - :target: https://colab.research.google.com/github/timaeus-research/devinterp/blob/main/examples/diagnostics.ipynb - - See `the diagnostics notebook `_ |colab5| for examples on how to use this to diagnose your sample health. - - :param base_callback: Base callback that computes some metric. - :type base_callback: :func:`~devinterp.slt.callback.SamplerCallback` - :param attribute: Name of attribute to compute which mean/std statistics of. - :type attribute: str - :param device: Device to perform computations on, e.g., 'cpu' or 'cuda'. Default is 'cpu' - :type device: str | torch.device, optional - - :raises: ValueError if underlying trace does not have the requested :python:`attribute`, :python:`num_chains` or :python:`num_draws`. - - Note: - - Requires base trace stats to be computed first, so call using f.e. :python:`devinterp.slt.sampler.sample(..., [weight_norm_instance, online_trace_stats_instance], ...)` - """ - - def __init__( - self, - base_callback: SamplerCallback, - attribute: str, - device: Union[torch.device, str] = "cpu", - ): - self.base_callback = base_callback - self.attribute = attribute - self.validate_base_callback() - - self.attribute = attribute - - self.num_chains = base_callback.num_chains - self.num_draws = base_callback.num_draws - - self.mean_by_chain = torch.zeros( - (self.num_chains, self.num_draws), dtype=torch.float32 - ).to(device) - self.std_by_chain = torch.zeros( - (self.num_chains, self.num_draws), dtype=torch.float32 - ).to(device) - - self.mean_by_draw = torch.zeros(self.num_draws, dtype=torch.float32).to(device) - self.std_by_draw = torch.zeros(self.num_draws, dtype=torch.float32).to(device) - - def validate_base_callback(self): - if not hasattr(self.base_callback, self.attribute): - raise ValueError(f"Base callback must have attribute {self.attribute}") - if not hasattr(self.base_callback, "num_chains"): - raise ValueError("Base callback must have attribute num_chains") - if not hasattr(self.base_callback, "num_draws"): - raise ValueError("Base callback must have attribute num_draws") - - def get_results(self): - """ - :returns: A dict :python:`"{self.attribute}/chain/mean": mean_attribute_by_chain, "{self.attribute}/chain/std": std_attribute_by_chain, "{self.attribute}/draw/mean": mean_attribute_by_draw, "{self.attribute}/draw/std": std_attribute_by_draw}`. (Only after running :python:`devinterp.slt.sampler.sample(..., [some_thing_to_calc_stats_of, ..., trace_stats_instance], ...)`). - """ - return { - f"{self.attribute}/chain/mean": self.mean_by_chain.cpu().numpy(), - f"{self.attribute}/chain/std": self.std_by_chain.cpu().numpy(), - f"{self.attribute}/draw/mean": self.mean_by_draw.cpu().numpy(), - f"{self.attribute}/draw/std": self.std_by_draw.cpu().numpy(), - } - - def sample_at_draw(self, draw=-1): - """ - :param draw: draw index to return stats at. Default is -1 - :type draw: int, optional - :returns: A dict :python:`"{self.attribute}/chain/mean": mean_attribute_of_draw_by_chain, "{self.attribute}/chain/std": std_attribute_of_draw_by_chain, "{self.attribute}/draw/mean": mean_attribute_of_draw, "{self.attribute}/draw/std": std_attribute_of_draw}`. (Only after running :python:`devinterp.slt.sampler.sample(..., [some_thing_to_calc_stats_of, ..., trace_stats_instance], ...)`). - """ - return { - f"{self.attribute}/chain/mean": self.mean_by_chain[:, draw].cpu().numpy(), - f"{self.attribute}/chain/std": self.std_by_chain[:, draw].cpu().numpy(), - f"{self.attribute}/draw/mean": self.mean_by_draw[draw].cpu().numpy(), - f"{self.attribute}/draw/std": self.std_by_draw[draw].cpu().numpy(), - } - - def __call__(self, draw: int, **kwargs): - attribute = getattr(self.base_callback, self.attribute) - self.mean_by_chain[:, draw] = attribute[:, : draw + 1].mean(axis=1) - self.std_by_chain[:, draw] = attribute[:, : draw + 1].std(axis=1) - self.mean_by_draw[draw] = attribute[:, draw].mean() - self.std_by_draw[draw] = attribute[:, draw].std() diff --git a/src/devinterp/slt/wbic.py b/src/devinterp/slt/wbic.py deleted file mode 100644 index 2d736b12..00000000 --- a/src/devinterp/slt/wbic.py +++ /dev/null @@ -1,69 +0,0 @@ -from typing import Union - -import torch - -from devinterp.slt.callback import SamplerCallback - - -class OnlineWBICEstimator(SamplerCallback): - """ - Callback for estimating the Widely Applicable Bayesian Information Criterion (WBIC) in an online fashion. - The WBIC used here is just $n * (\textrm{average sampled loss})$. (Watanabe, 2013) - - :param num_draws: Number of samples to draw (should be identical to :python:`num_draws` passed to :python:`devinterp.slt.sampler.sample`) - :type num_draws: int - :param num_chains: Number of chains to run (should be identical to :python:`num_chains` passed to :python:`devinterp.slt.sampler.sample`) - :type num_chains: int - :param n: Number of samples used to calculate the wbic. - :param n: int - :param device: Device to perform computations on, e.g., 'cpu' or 'cuda'. Default is 'cpu' - :param device: str | torch.device, optional - """ - - def __init__( - self, - num_chains: int, - num_draws: int, - n: int, - device: Union[torch.device, str] = "cpu", - ): - self.num_chains = num_chains - self.num_draws = num_draws - - self.losses = torch.zeros((num_chains, num_draws), dtype=torch.float32).to( - device - ) - self.wbics = torch.zeros((num_chains, num_draws), dtype=torch.float32).to( - device - ) - - self.n = torch.tensor(n, dtype=torch.float32).to(device) - self.wbic_means = torch.zeros(num_draws, dtype=torch.float32).to(device) - self.wbic_stds = torch.zeros(num_draws, dtype=torch.float32).to(device) - - self.device = device - - def update(self, chain: int, draw: int, loss: float): - self.losses[chain, draw] = loss - mean_loss = self.losses[chain, : draw + 1].mean() - # using the formula that wbic is estimated by n * (avg sampled loss) - self.wbics[chain, draw] = mean_loss * self.n - - def finalize(self): - self.wbic_means = self.wbics.mean(axis=0) - self.wbic_stds = self.wbics.std(axis=0) - - def get_results(self): - """ - :returns: A dict :python:`{"wbic/means": wbic_means, "wbic/stds": wbic_stds, "wbic/trace": wbic_trace_per_chain, "loss/trace": loss_trace_per_chain}`. (Only after running :python:`devinterp.slt.sampler.sample(..., [wbic_estimator_instance], ...)`). - """ - return { - "wbic/means": self.wbic_means.cpu().numpy(), - "wbic/stds": self.wbic_stds.cpu().numpy(), - "wbic/trace": self.wbics.cpu().numpy(), - "loss/trace": self.losses.cpu().numpy(), - } - - def __call__(self, chain: int, draw: int, loss: float, **kwargs): - # The **kwargs is to ignore any additional arguments passed to the callback via **locals() from SGLD. - self.update(chain, draw, loss) diff --git a/src/devinterp/slt/weight_restrictions.py b/src/devinterp/slt/weight_restrictions.py new file mode 100644 index 00000000..6309d6b1 --- /dev/null +++ b/src/devinterp/slt/weight_restrictions.py @@ -0,0 +1,429 @@ +"""Weight restrictions for selecting subsets of model parameters. + +Parses restriction strings like "full", "l0", "l0h1", "l0g0", "l0 attn", +"l0 mlp", "embed", "unembed" and returns ParamMasks suitable for the SGLD sampler. + +Supports HuggingFace models (via auto-detected model_type) and TransformerLens +HookedTransformer models, using the structure data in _model_structures.py. +""" + +from __future__ import annotations + +import re + +import torch + +from devinterp.slt._model_structures import HEAD_STRUCTURES +from devinterp.slt.sampler import ParamMasks, is_transformer_lens_model + + +def create_param_masks( + model: torch.nn.Module, + restriction: str | list[str], +) -> ParamMasks: + """Create parameter masks from a restriction string. + + Args: + model: PyTorch model (HuggingFace or TransformerLens). + restriction: Which parameters to select. Options: + - "full": all parameters, no masks + - "l0": all params in layer 0 + - "l0h1": layer 0, head 1 (with per-element masks) + - "l0g0": layer 0, group 0 (GQA group: Q heads sharing a KV head) + - "l0 attn": layer 0 attention params + - "l0 mlp": layer 0 MLP params + - "embed": embedding params + - "unembed": unembedding params + - list of the above: union (masks are ORed for shared params) + + Returns: + Dict mapping parameter names to mask tensors (or None for unrestricted). + """ + if not isinstance(restriction, (str, list)): + raise TypeError( + f"restriction must be a str or list[str], got {type(restriction).__name__}" + ) + + if isinstance(restriction, list): + if "full" in restriction: + raise ValueError("Cannot combine 'full' with other restrictions.") + masks_list = [create_param_masks(model, r) for r in restriction] + return _merge_param_masks(masks_list) + + restriction = restriction.strip() + if restriction == "full": + return {name: None for name, _ in model.named_parameters()} + + spec = _get_spec(model) + params = dict(model.named_parameters()) + + r = restriction.lower() + + if r == "embed": + return _select_component(params, spec, "embed") + if r == "unembed": + return _select_component(params, spec, "unembed") + + # l0h1, L0H1, l0 h1 + m = re.match(r"l(\d+)\s*h(\d+)$", r) + if m: + layer, head = int(m[1]), int(m[2]) + n_layers = _count_layers(params, spec) + if layer >= n_layers: + raise ValueError( + f"Layer {layer} out of range (model has {n_layers} layers)." + ) + n_heads, _, _ = _get_model_config(model) + if head >= n_heads: + raise ValueError(f"Head {head} out of range (model has {n_heads} heads).") + return _head_masks(model, spec, params, layer, head) + + # l0g0, L0G0, l0 g0 — GQA group (Q heads sharing a KV head) + m = re.match(r"l(\d+)\s*g(\d+)$", r) + if m: + layer, group = int(m[1]), int(m[2]) + n_layers = _count_layers(params, spec) + if layer >= n_layers: + raise ValueError( + f"Layer {layer} out of range (model has {n_layers} layers)." + ) + _, _, n_kv = _get_model_config(model) + if group >= n_kv: + raise ValueError( + f"Group {group} out of range (model has {n_kv} KV heads/groups)." + ) + return _group_masks(model, spec, params, layer, group) + + # l0 attn, l0 mlp, l0 + m = re.match(r"l(\d+)(?:\s+(attn|mlp))?$", r) + if m: + layer = int(m[1]) + component = m[2] + n_layers = _count_layers(params, spec) + if layer >= n_layers: + raise ValueError( + f"Layer {layer} out of range (model has {n_layers} layers)." + ) + return _layer_masks(params, spec, layer, component) + + # Direct state dict key fallback + if restriction in params: + return {restriction: None} + + raise ValueError( + f"Unknown restriction '{restriction}'. " + f"Valid patterns: 'full', 'l0', 'l0h1', 'l0g0', 'l0 attn', 'l0 mlp', 'embed', 'unembed', " + f"or a direct parameter name." + ) + + +def _get_spec(model: torch.nn.Module) -> dict: + """Look up the model's structure spec from HEAD_STRUCTURES. + + If the entry is a list of variants, pick the one that best matches the + model's actual parameters (most layer-scoped entries found). + """ + if is_transformer_lens_model(model): + model_type = "hooked_transformer" + else: + model_type = getattr(getattr(model, "config", None), "model_type", None) + + if model_type is None: + raise ValueError( + f"Cannot detect model type for {model.__class__.__name__}. " + f"Expected model.config.model_type or TransformerLens model." + ) + if model_type not in HEAD_STRUCTURES: + raise ValueError( + f"Model type '{model_type}' not in HEAD_STRUCTURES. " + f"Supported: {', '.join(sorted(HEAD_STRUCTURES))}" + ) + + entry = HEAD_STRUCTURES[model_type] + if isinstance(entry, dict): + return entry + + # List of variants: pick the best match against actual params + params = dict(model.named_parameters()) + best_spec, best_score = entry[0], -1 + for variant in entry: + prefixes = variant.get("layer_prefixes", []) + suffixes = ( + set(variant.get("attn", [])) + | set(variant.get("mlp", [])) + | set(variant.get("head_params", {}).keys()) + ) + score = sum( + 1 + for name in params + if any( + name.startswith(f"{p}.") and name.endswith(s) + for p in prefixes + for s in suffixes + ) + ) + if score > best_score: + best_spec, best_score = variant, score + return best_spec + + +def _count_layers(params: dict[str, torch.nn.Parameter], spec: dict) -> int: + """Count the number of layers by checking which layer prefixes exist.""" + prefix_base = spec["layer_prefixes"][0] + layer = 0 + while any(name.startswith(f"{prefix_base}.{layer}.") for name in params): + layer += 1 + return layer + + +def _get_model_config(model: torch.nn.Module) -> tuple[int, int, int]: + """Extract n_heads, d_model, n_kv from model config.""" + if is_transformer_lens_model(model): + cfg = model.cfg # pyright: ignore[reportAttributeAccessIssue] + n_heads: int = cfg.n_heads + d_model: int = cfg.d_model + n_kv: int = getattr(cfg, "n_key_value_heads", None) or n_heads + else: + cfg = model.config # pyright: ignore[reportAttributeAccessIssue] + n_heads = cfg.num_attention_heads + d_model = cfg.hidden_size + n_kv = getattr(cfg, "num_key_value_heads", None) or n_heads + return n_heads, d_model, n_kv + + +def _select_component( + params: dict[str, torch.nn.Parameter], + spec: dict, + component: str, +) -> ParamMasks: + """Select all params matching a component list (embed, unembed, attn, mlp).""" + keys = spec.get(component, []) + return {name: None for name in params if any(name.endswith(k) for k in keys)} + + +def _layer_masks( + params: dict[str, torch.nn.Parameter], + spec: dict, + layer: int, + component: str | None, +) -> ParamMasks: + """Select all params in a layer, optionally filtered to attn or mlp.""" + prefix = f"{spec['layer_prefixes'][0]}.{layer}." + if component is None: + # Bare layer: select all params in the layer + return {name: None for name in params if name.startswith(prefix)} + + suffixes = spec.get(component, []) + return { + name: None + for name in params + if name.startswith(prefix) and any(name.endswith(s) for s in suffixes) + } + + +def _head_masks( + model: torch.nn.Module, + spec: dict, + params: dict[str, torch.nn.Parameter], + layer: int, + head: int, +) -> ParamMasks: + """Build per-element boolean masks for a single attention head.""" + n_heads, d_model, n_kv = _get_model_config(model) + hd = d_model // n_heads + kv_head = head * n_kv // n_heads + prefix = f"{spec['layer_prefixes'][0]}.{layer}." + + head_params = spec.get("head_params", {}) + + masks: ParamMasks = {} + for full_name, param in params.items(): + if not full_name.startswith(prefix): + continue + short_name = full_name[len(prefix) :] + if short_name not in head_params: + continue + + head_spec = head_params[short_name] + dim = head_spec["slice_dim"] + fused = head_spec.get("fused") + axis_size = param.shape[0] if param.dim() == 1 else param.shape[dim] + + mask = torch.zeros(axis_size, dtype=torch.bool) + + if fused == "interleaved": + # GPT-NeoX layout: (n_heads, qkv=3, head_dim) flattened + mask.view(n_heads, 3, hd)[head] = True + elif fused == "concat": + # GPT-2 / Falcon layout: Q then K then V concatenated + q_size = n_heads * hd + kv_size = (axis_size - q_size) // 2 + q, kv = mask.split([q_size, 2 * kv_size]) + q.view(n_heads, hd)[head] = True + kv.view(2, n_kv, hd)[:, kv_head] = True + elif axis_size in (n_kv * hd, n_kv): + # Separate K/V projection for GQA models + mask.view(n_kv, -1)[kv_head] = True + else: + # Separate Q projection or O projection + mask.view(n_heads, -1)[head] = True + + # Expand 1D mask to match param shape + if param.dim() > 1: + shape = [1] * param.dim() + shape[dim] = -1 + mask = mask.view(*shape).expand_as(param) + + masks[full_name] = mask + + return masks + + +def _group_masks( + model: torch.nn.Module, + spec: dict, + params: dict[str, torch.nn.Parameter], + layer: int, + group: int, +) -> ParamMasks: + """Build per-element boolean masks for a GQA group. + + A group contains n_heads // n_kv consecutive Q heads sharing one KV head. + group index ranges from 0 to n_kv - 1. For MHA models (n_kv == n_heads), + this is equivalent to a single head. + """ + n_heads, d_model, n_kv = _get_model_config(model) + hd = d_model // n_heads + prefix = f"{spec['layer_prefixes'][0]}.{layer}." + + head_params = spec.get("head_params", {}) + + masks: ParamMasks = {} + for full_name, param in params.items(): + if not full_name.startswith(prefix): + continue + short_name = full_name[len(prefix) :] + if short_name not in head_params: + continue + + head_spec = head_params[short_name] + dim = head_spec["slice_dim"] + fused = head_spec.get("fused") + axis_size = param.shape[0] if param.dim() == 1 else param.shape[dim] + + mask = torch.zeros(axis_size, dtype=torch.bool) + + if fused == "interleaved": + # GPT-NeoX: (n_heads, qkv=3, head_dim) — group = contiguous Q heads + shared KV + mask.view(n_kv, -1)[group] = True + elif fused == "concat": + # Q then K then V concatenated + q_size = n_heads * hd + kv_size = (axis_size - q_size) // 2 + q, kv = mask.split([q_size, 2 * kv_size]) + q.view(n_kv, n_heads // n_kv, hd)[group] = True + kv.view(2, n_kv, hd)[:, group] = True + else: + # Separate projections: reshape as (n_kv, ...) and select group + mask.view(n_kv, -1)[group] = True + + # Expand 1D mask to match param shape + if param.dim() > 1: + shape = [1] * param.dim() + shape[dim] = -1 + mask = mask.view(*shape).expand_as(param) + + masks[full_name] = mask + + return masks + + +def _merge_param_masks(masks_list: list[ParamMasks]) -> ParamMasks: + """Merge multiple ParamMasks by ORing masks for shared parameters.""" + if len(masks_list) == 1: + return masks_list[0] + + # Preserve model parameter order: use dict.fromkeys across all inputs + all_names: dict[str, None] = {} + for m in masks_list: + all_names.update(dict.fromkeys(m)) + + merged: ParamMasks = {} + for name in all_names: + entries = [m[name] for m in masks_list if name in m] + if any(e is None for e in entries): + merged[name] = None + elif len(entries) == 1: + merged[name] = entries[0] + else: + merged[name] = entries[0] + for e in entries[1:]: + merged[name] = torch.logical_or(merged[name], e) + + return merged + + +def preview_weight_restriction( + model: torch.nn.Module, + masks: ParamMasks, + *, + plain: bool = False, +) -> None: + """Print a tree of which parameters are selected by a mask dict. + + Shows how a weight restriction maps to actual state_dict keys, with a + per-key selected/total count. Useful for debugging ambiguous selection + patterns (e.g. "does 'unembed' include the layer norm?"). + + Args: + model: PyTorch model. Used to look up parameter sizes for the + "selected of total" counts. + masks: Dict mapping param name to mask tensor (or None for unrestricted). + Typically from `create_param_masks` or user-built. + plain: If True, print just one param name per line (useful for piping). + """ + from collections import defaultdict + + if plain: + for name in sorted(masks.keys()): + print(name) + return + + # ANSI colors (no extra dependency). + CYAN, YELLOW, RESET = "\033[36m", "\033[33m", "\033[0m" + + def _bar(percent: float, width: int = 24) -> str: + filled = int(percent / 100 * width) + return "\u25b0" * filled + "\u25b1" * (width - filled) + + stats: list[tuple[str, int, int]] = [] + for name, mask in masks.items(): + total = model.get_parameter(name).numel() + selected = total if mask is None else int(mask.int().sum().item()) + stats.append((name, selected, total)) + + grouped: dict[str, list[tuple[str, int, int]]] = defaultdict(list) + for name, selected, total in stats: + parts = name.split(".") + prefix = ".".join(parts[:-1]) if len(parts) > 1 else "" + suffix = parts[-1] + grouped[prefix].append((suffix, selected, total)) + + for prefix in sorted(grouped): + items = grouped[prefix] + if prefix: + print(f"\n{CYAN}{prefix}{RESET}") + for i, (suffix, selected, total) in enumerate(items): + is_last = i == len(items) - 1 + branch = ( + "" if not prefix else ("\u2514\u2500" if is_last else "\u251c\u2500") + ) + percent = (selected / total) * 100 if total > 0 else 0.0 + indent = " " if prefix else "" + print( + f"{indent}{CYAN}{branch}{RESET} " + f"{suffix:35} " + f"{YELLOW}{_bar(percent)}{RESET} " + f"{YELLOW}{percent:5.1f}%{RESET} " + f"({selected:,} of {total:,})" + ) diff --git a/src/devinterp/slt/writing.py b/src/devinterp/slt/writing.py new file mode 100644 index 00000000..6786095b --- /dev/null +++ b/src/devinterp/slt/writing.py @@ -0,0 +1,276 @@ +"""Zarr writing infrastructure for streaming sampling results. + +ZarrWriter buffers per-chain data and writes to zarr arrays, optionally +using a thread pool for async I/O. +""" + +from __future__ import annotations + +import logging +from collections.abc import Iterator +from concurrent import futures +from concurrent.futures import Future, ThreadPoolExecutor +from contextlib import contextmanager, nullcontext +from dataclasses import dataclass, field +from typing import assert_never + +import numpy as np +import torch +import zarr +from zarr.core.metadata import ArrayV3Metadata + +logger = logging.getLogger(__name__) + + +# ─── Dtype validation (used by zarr_schema.py) ────────────────────────────── + +SAFE_DTYPE_STRINGS = frozenset( + [ + "float16", + "float32", + "float64", + "int8", + "int16", + "int32", + "int64", + "uint8", + "uint16", + "uint32", + "uint64", + "bool", + "complex64", + "complex128", + ] +) + + +def validate_dtype_str_torch_numpy_compat(dtype_str: str) -> None: + if dtype_str not in SAFE_DTYPE_STRINGS: + raise ValueError( + f"Unsupported dtype: '{dtype_str}'. Use one of: {sorted(SAFE_DTYPE_STRINGS)}" + ) + + +# ─── Zarr dimension helpers ────────────────────────────────────────────────── + + +def _zarr_dims(arr: zarr.Array) -> tuple[str, ...]: + assert isinstance(arr.metadata, ArrayV3Metadata) + dims = arr.metadata.dimension_names or () + assert all(d is not None for d in dims), f"All dimensions must be named, got {dims}" + return dims # type: ignore[return-value] + + +def _indices_from_dims( + arr: zarr.Array, **dim_indices: int | slice +) -> tuple[int | slice, ...]: + dims = _zarr_dims(arr) + indices = [dim_indices.pop(dim, slice(None)) for dim in dims] + if dim_indices: + raise ValueError( + f"Unexpected dimensions: {list(dim_indices)}. Valid: {list(dims)}" + ) + return tuple(indices) + + +# ─── Write execution ──────────────────────────────────────────────────────── + + +def _execute_write( + arr: zarr.Array, + data: np.ndarray | torch.Tensor, + dim_indices: dict[str, int | slice], + *, + executor: ThreadPoolExecutor | None = None, + write_futures: list[Future] | None = None, +) -> None: + indices = _indices_from_dims(arr, **dim_indices) + + match data: + case torch.Tensor(): + np_data = data.numpy(force=True).copy() + case np.ndarray(): + np_data = data + case _ as invalid: + assert_never(invalid) + + if executor is not None: + assert write_futures is not None + write_futures.append(executor.submit(arr.set_basic_selection, indices, np_data)) + else: + arr.set_basic_selection(indices, np_data) + + +# ─── Buffer ────────────────────────────────────────────────────────────────── + + +class _Buffer: + """Accumulates complete rows before flushing to zarr.""" + + def __init__(self, buffer: torch.Tensor): + self.buffer = buffer + self.row_ptr = 0 + self.size = 0 + + @property + def capacity(self) -> int: + return self.buffer.shape[0] + + def is_full(self) -> bool: + return self.size >= self.capacity + + def push(self, data: torch.Tensor, row_index: int) -> None: + if self.is_full(): + raise RuntimeError("Buffer full. Call flush before writing more data.") + assert row_index == self.row_ptr + self.size, ( + f"Row index mismatch: expected {self.row_ptr + self.size}, got {row_index}" + ) + self.buffer[self.size].copy_(data) + self.size += 1 + + def flush(self) -> tuple[np.ndarray, slice] | None: + if self.size == 0: + return None + np_data = self.buffer[: self.size].numpy(force=True).copy() + row_slice = slice(self.row_ptr, self.row_ptr + self.size) + self.row_ptr += self.size + self.size = 0 + return np_data, row_slice + + +# ─── ZarrWriter ────────────────────────────────────────────────────────────── + + +@dataclass +class ZarrWriter: + """Buffered writer for streaming per-chain data to zarr arrays. + + Use ZarrWriter.open() for a context manager that manages the thread pool. + """ + + arrays: dict[str, zarr.Array] + chain_buffer_size: int + buffer_device: torch.device + executor: ThreadPoolExecutor | None = None + _write_futures: list[Future] = field(default_factory=list, init=False) + _chain_buffers: dict[tuple[str, int], _Buffer] = field( + default_factory=dict, init=False + ) + + @classmethod + @contextmanager + def open( + cls, + arrays: dict[str, zarr.Array], + chain_buffer_size: int, + buffer_device: torch.device, + max_write_threads: int = 4, + ) -> Iterator[ZarrWriter]: + pool = ( + ThreadPoolExecutor( + max_workers=max_write_threads, thread_name_prefix="zarr_write" + ) + if max_write_threads > 0 + else nullcontext() + ) + with pool as executor: + if executor is not None: + logger.info("zarr_write thread pool: %d max threads", max_write_threads) + writer = cls( + arrays=arrays, + chain_buffer_size=chain_buffer_size, + buffer_device=buffer_device, + executor=executor, + ) + try: + yield writer + finally: + writer.flush_all_buffers() + writer.wait_for_writes() + + def write( + self, path: str, data: np.ndarray | torch.Tensor, /, **dim_indices: int | slice + ) -> None: + """Immediate write (for scalars, init_loss, fixed observables).""" + _execute_write( + self.arrays[path], + data, + dim_indices, + executor=self.executor, + write_futures=self._write_futures, + ) + + def push( + self, + path: str, + data: torch.Tensor, + /, + *, + chain: int, + **dim_indices: int | slice, + ) -> None: + """Push a complete row into a chain buffer.""" + arr = self.arrays[path] + dims = _zarr_dims(arr) + assert dims[0] == "chain" + row_dim = dims[1] + assert row_dim in dim_indices + row_index = dim_indices[row_dim] + assert isinstance(row_index, int) + + buf = self._get_or_create_buffer(path, chain) + buf.push(data, row_index=row_index) + + def flush_full_buffers(self) -> None: + self._flush_buffers(only_full=True) + + def flush_all_buffers(self) -> None: + self._flush_buffers(only_full=False) + + def wait_for_writes(self) -> None: + if not self._write_futures: + return + + done, notdone = futures.wait( + self._write_futures, return_when=futures.ALL_COMPLETED + ) + exceptions = [f.exception() for f in done if f.exception()] + self._write_futures.clear() + + if exceptions: + if len(exceptions) == 1: + raise exceptions[0] # type: ignore[misc] + raise ExceptionGroup(f"{len(exceptions)} zarr write(s) failed", exceptions) # type: ignore[arg-type] + + logger.info("All %d zarr writes completed successfully", len(done)) + + def _flush_buffers(self, only_full: bool) -> None: + for (path, chain), buf in self._chain_buffers.items(): + if only_full and not buf.is_full(): + continue + result = buf.flush() + if result is not None: + np_data, row_slice = result + arr = self.arrays[path] + dims = _zarr_dims(arr) + _execute_write( + arr, + np_data, + {"chain": chain, dims[1]: row_slice}, + executor=self.executor, + write_futures=self._write_futures, + ) + + def _get_or_create_buffer(self, path: str, chain: int) -> _Buffer: + key = (path, chain) + if key not in self._chain_buffers: + arr = self.arrays[path] + self._chain_buffers[key] = _Buffer( + buffer=torch.zeros( + self.chain_buffer_size, + *arr.shape[2:], + dtype=getattr(torch, str(arr.dtype)), + device=self.buffer_device, + ), + ) + return self._chain_buffers[key] diff --git a/src/devinterp/slt/zarr_schema.py b/src/devinterp/slt/zarr_schema.py new file mode 100644 index 00000000..cdb81b6f --- /dev/null +++ b/src/devinterp/slt/zarr_schema.py @@ -0,0 +1,149 @@ +"""Zarr schema for creating xarray-compatible zarr stores. + +Creates a zarr store with typed arrays and group attributes in one +batch call, producing a store that xr.open_datatree reads back as +an equivalent DataTree. +""" + +from __future__ import annotations + +import logging +from dataclasses import dataclass, field +from typing import Any + +import numpy as np +import torch +import zarr +from zarr.abc.store import Store as ZarrStore +from zarr.core.chunk_grids import RegularChunkGrid +from zarr.core.metadata import ArrayV3Metadata + +from devinterp.slt.writing import validate_dtype_str_torch_numpy_compat + +logger = logging.getLogger(__name__) + + +# ─── Fill values ───────────────────────────────────────────────────────────── + + +def _numpy_fill_value(dtype: np.dtype) -> np.generic: + if dtype.kind == "f": + return dtype.type(np.nan) + elif dtype.kind == "c": + return dtype.type(complex(np.nan, np.nan)) + elif dtype.kind in ("U", "S", "O"): + return dtype.type("") + return dtype.type(0) + + +_XARRAY_FILL_VALUE_NAN_B64 = "AAAAAAAA+H8=" + + +def _build_array_metadata( + *, + shape: tuple[int, ...], + dims: tuple[str, ...], + np_dtype: np.dtype, + chunks: tuple[int, ...] | None = None, + attrs: dict[str, Any] | None = None, +) -> ArrayV3Metadata: + """Build zarr ArrayV3Metadata matching xarray encoding conventions.""" + from zarr.codecs import BytesCodec, ZstdCodec + from zarr.core.chunk_key_encodings import DefaultChunkKeyEncoding + from zarr.core.dtype import get_data_type_from_native_dtype + + if chunks is None: + chunks = shape + fill_attrs = ( + {"_FillValue": _XARRAY_FILL_VALUE_NAN_B64} + if np_dtype.kind in ("f", "c") + else {} + ) + attrs = fill_attrs | (attrs or {}) + return ArrayV3Metadata( + shape=shape, + data_type=get_data_type_from_native_dtype(np_dtype), + chunk_grid=RegularChunkGrid(chunk_shape=chunks), + chunk_key_encoding=DefaultChunkKeyEncoding(separator="/"), + codecs=[BytesCodec(), ZstdCodec(level=0, checksum=False)], + fill_value=_numpy_fill_value(np_dtype), + attributes=attrs or None, + dimension_names=dims if dims else None, + ) + + +# ─── DataArraySpec ─────────────────────────────────────────────────────────── + + +@dataclass +class DataArraySpec: + """Specification for a zarr array: shape, dims, dtype, chunks.""" + + shape: tuple[int, ...] + dims: tuple[str, ...] + dtype_str: str + chunks: tuple[int, ...] = None # type: ignore + attrs: dict[str, Any] = None # type: ignore[assignment] + + def __post_init__(self) -> None: + if self.chunks is None: + self.chunks = self.shape + if self.attrs is None: + self.attrs = {} + validate_dtype_str_torch_numpy_compat(self.dtype_str) + assert len(self.shape) == len(self.dims) + assert len(self.chunks) == len(self.shape) + + @property + def numpy_dtype(self) -> np.dtype: + return np.dtype(self.dtype_str) + + @property + def torch_dtype(self) -> torch.dtype: + attr = getattr(torch, self.dtype_str) + assert isinstance(attr, torch.dtype) + return attr + + def to_metadata(self) -> ArrayV3Metadata: + return _build_array_metadata( + shape=self.shape, + dims=self.dims, + np_dtype=self.numpy_dtype, + chunks=self.chunks, + attrs=self.attrs, + ) + + +# ─── ZarrSchema ────────────────────────────────────────────────────────────── + + +@dataclass +class ZarrSchema: + """Schema for a flat zarr store (arrays at root level with group attributes).""" + + arrays_meta: dict[str, DataArraySpec] = field(default_factory=dict) + group_attrs: dict[str, dict[str, Any]] = field(default_factory=dict) + + def create_hierarchy( + self, store: ZarrStore + ) -> tuple[zarr.Group, dict[str, zarr.Array]]: + """Create the zarr store. Returns (root_group, arrays_dict).""" + root = zarr.open_group(store, mode="w") + + root_attrs = self.group_attrs.get("") + if root_attrs: + root.update_attributes(root_attrs) + + nodes = {path: spec.to_metadata() for path, spec in self.arrays_meta.items()} + created = dict(root.create_hierarchy(nodes, overwrite=True)) + + arrays: dict[str, zarr.Array] = {} + for path in self.arrays_meta: + node = created[path] + assert isinstance(node, zarr.Array), ( + f"Expected Array at {path!r}, got {type(node).__name__}" + ) + arrays[path] = node + + logger.info("Zarr store created: %d arrays.", len(arrays)) + return root, arrays diff --git a/src/devinterp/utils.py b/src/devinterp/utils.py index 77297cfd..a03e0f70 100644 --- a/src/devinterp/utils.py +++ b/src/devinterp/utils.py @@ -1,83 +1,9 @@ -import os import warnings -from functools import partial -from itertools import islice -from typing import Any, Callable, Dict, Mapping, NamedTuple, Optional, Tuple, Union +from typing import Union import numpy as np -import torch -from torch import nn -from torch.nn import functional as F from torch.utils.data import DataLoader -PJRT_DEVICE = os.environ.get("PJRT_DEVICE", None) -USE_TPU_BACKEND = os.environ.get( - "USE_TPU_BACKEND", True if (PJRT_DEVICE == "TPU") else False -) -TPU_TYPE = os.environ.get("TPU_TYPE", None) - - -class Outputs(NamedTuple): - loss: torch.Tensor - # Add more outputs here if needed - - -EvalResults = Union[ - Outputs, Dict[str, torch.Tensor], Tuple[torch.Tensor, ...], torch.Tensor -] - -EvaluateFn = Callable[[nn.Module, torch.Tensor], EvalResults] - - -def plot_trace( - trace, - y_axis, - x_axis="step", - title=None, - plot_mean=True, - plot_std=True, - fig_size=(12, 9), - true_lc=None, -): - import matplotlib.pyplot as plt - - num_chains, num_draws = trace.shape - sgld_step = list(range(num_draws)) - if true_lc: - plt.axhline(y=true_lc, color="r", linestyle="dashed") - # trace - for i in range(num_chains): - draws = trace[i] - plt.plot(sgld_step, draws, linewidth=1, label=f"chain {i}") - - # mean - mean = np.mean(trace, axis=0) - plt.plot( - sgld_step, - mean, - color="black", - linestyle="--", - linewidth=2, - label="mean", - zorder=3, - ) - - # std - std = np.std(trace, axis=0) - plt.fill_between( - sgld_step, mean - std, mean + std, color="gray", alpha=0.3, zorder=2 - ) - - if title is None: - title = f"{y_axis} values over sampling draws" - plt.legend(loc="upper left", bbox_to_anchor=(1, 1)) - plt.title(title) - plt.xlabel(x_axis) - plt.ylabel(y_axis) - plt.figure(figsize=fig_size) - plt.tight_layout() - plt.show() - def default_nbeta( dataloader: Union[DataLoader, int], gradient_accumulation_steps: int = 1 @@ -106,194 +32,100 @@ def default_nbeta( ) -def prepare_input( - data: Union[torch.Tensor, Any], device, is_deepspeed_enabled=False, accelerator=None -) -> Union[torch.Tensor, Any]: - """ - Prepares one `data` before feeding it to the model, be it a tensor or a nested list/dictionary of tensors. - - Adapted from HuggingFace's transformers's Trainer._prepare_input(). - """ - if isinstance(data, Mapping): - return type(data)( - { - k: prepare_input( - v, - device=device, - is_deepspeed_enabled=is_deepspeed_enabled, - accelerator=accelerator, - ) - for k, v in data.items() - } - ) - elif isinstance(data, (tuple, list)): - return type(data)( - prepare_input( - v, - device=device, - is_deepspeed_enabled=is_deepspeed_enabled, - accelerator=accelerator, - ) - for v in data - ) - elif isinstance(data, torch.Tensor): - kwargs = {"device": device} - if is_deepspeed_enabled and ( - torch.is_floating_point(data) or torch.is_complex(data) - ): - # NLP models inputs are int/uint and those get adjusted to the right dtype of the - # embedding. Other models such as wav2vec2's inputs are already float and thus - # may need special handling to match the dtypes of the model - kwargs.update( - {"dtype": accelerator.state.deepspeed_plugin.hf_ds_config.dtype()} - ) - return data.to(**kwargs) - return data - - -def split_results(results: EvalResults) -> Tuple[torch.Tensor, Any]: - if isinstance(results, dict): - loss = results.pop("loss") - elif isinstance(results, tuple): - loss = results[0] - if len(results) > 1: - results = results[1:] - elif isinstance(results, torch.Tensor): - loss = results - results = None - elif hasattr(results, "loss"): - loss = results.loss - else: - raise ValueError("compute_loss must return a dict, tuple, or torch.Tensor") - - return loss, results - - -def get_seeded_dataloader(dataloader, seed): - gen = torch.Generator() - gen.manual_seed(seed) - return torch.utils.data.DataLoader( - dataloader.dataset, batch_size=dataloader.batch_size, generator=gen - ) - - -def get_init_loss_one_batch(dataloader, model, evaluate: EvaluateFn, device, seed=None): - if seed is not None: - dataloader = get_seeded_dataloader(dataloader, seed) - model = model.to(device) - model.train() # to make sure we're using train loss, comparable to train loss of sampler() - - with torch.no_grad(): - data = next(iter(dataloader)) - data = prepare_input(data, device=device) - loss = split_results(evaluate(model, data))[0].detach().item() - - return loss - - -def get_init_loss_multi_batch( - dataloader, n_batches, model, evaluate, device, seed=None +def tokenize_and_concatenate( + dataset, + tokenizer, + streaming: bool = False, + max_length: int = 1024, + column_name: str = "text", + add_bos_token: bool = True, + num_proc: int = 10, ): - if seed is not None: - dataloader = get_seeded_dataloader(dataloader, seed) - - model = model.to(device) - model.train() - loss = 0.0 - n_batches = min(n_batches, len(dataloader)) - - with torch.no_grad(): - for data in islice(dataloader, n_batches): - data = prepare_input(data, device=device) - loss += split_results(evaluate(model, data))[0].detach().item() - - return loss / n_batches - - -def get_init_loss_full_batch(dataloader, model, evaluate, device, seed=None): - if seed is not None: - dataloader = get_seeded_dataloader(dataloader, seed) - - model = model.to(device) - model.train() - loss = 0.0 - - with torch.no_grad(): - for data in dataloader: - data = prepare_input(data, device=device) - loss += split_results(evaluate(model, data))[0].detach().item() - - return loss / len(dataloader) - - -def evaluate(criterion, model, data): - x, y = data - y_pred = model(x) - return criterion(y_pred, y), {"output": y_pred} - - -def make_evaluate(criterion): - return partial(evaluate, criterion) + """Tokenize and concatenate a text dataset into fixed-length sequences. + Based on TransformerLens (MIT License, Copyright 2022 TransformerLensOrg): + https://github.com/TransformerLensOrg/TransformerLens + Core algorithm unchanged, with local additions: input validation + (bos token and max_length checks), numpy reshape in place of einops, and + the output column renamed from "tokens" to "input_ids". -evaluate_ce = partial(evaluate, F.cross_entropy) -evaluate_mse = partial(evaluate, F.mse_loss) - - -def set_seed(seed: int, device: Optional[Union[str, torch.device]] = None): - """ - Sets the seed for the Learner. + Joins all text separated by EOS tokens, tokenizes, then reshapes into + (num_sequences, max_length) chunks. Args: - seed (int): Seed to set. + dataset: HuggingFace text dataset. + tokenizer: HuggingFace tokenizer with bos_token_id and eos_token_id. + streaming: If True, disables parallel tokenization. + max_length: Context window length. + column_name: Name of the text column. + add_bos_token: Prepend BOS to each sequence (reduces usable length by 1). + num_proc: Number of processes for dataset.map(). + + Returns: + Dataset with an "input_ids" column of torch tensors. """ - import random - - # np.random.SeedSequence outputs np.uint32, but random.seed() expects int - seed = int(seed) - torch.manual_seed(seed) - random.seed(seed) - - try: - import numpy as np - - np.random.seed(seed) - except ImportError: - pass - - try: - import torch_xla.core.xla_model as xm - - if device is None: - xm.set_rng_state(seed) - elif "xla" in str(device): - xm.set_rng_state(seed, device=device) + dataset = dataset.select_columns(column_name) - except (ImportError, RuntimeError): - pass + if tokenizer.pad_token is None: + tokenizer.add_special_tokens({"pad_token": ""}) - if torch.cuda.is_available(): - torch.cuda.manual_seed_all(seed) + if add_bos_token and tokenizer.bos_token_id is None: + raise ValueError( + f"add_bos_token=True but tokenizer has no bos_token_id. " + f"Use add_bos_token=False for {type(tokenizer).__name__}." + ) + seq_len = max_length - 1 if add_bos_token else max_length + if seq_len < 1: + raise ValueError( + f"max_length={max_length} is too small" + f"{' with add_bos_token=True (needs at least 2)' if add_bos_token else ' (needs at least 1)'}." + ) + def tokenize_function(examples): + text = examples[column_name] + full_text = tokenizer.eos_token.join(text) + + if not full_text.strip(): + return {"input_ids": np.array([], dtype=np.int64)} + + num_chunks = 20 + chunk_length = (len(full_text) - 1) // num_chunks + 1 + chunks = [ + full_text[i * chunk_length : (i + 1) * chunk_length] + for i in range(num_chunks) + ] + tokens = tokenizer(chunks, return_tensors="np", padding=True)[ + "input_ids" + ].flatten() + tokens = tokens[tokens != tokenizer.pad_token_id] + num_tokens = len(tokens) + + if num_tokens < seq_len: + num_batches = 1 + tokens = tokens[:seq_len] + if len(tokens) < seq_len: + padding = np.full(seq_len - len(tokens), tokenizer.pad_token_id) + tokens = np.concatenate([tokens, padding], axis=0) + else: + num_batches = num_tokens // seq_len + tokens = tokens[: seq_len * num_batches] -def cycle(iterable, limit=None): - """ - Use this function to cycle through a dataloader. Unlike itertools.cycle, this function doesn't cache - values in memory. + tokens = tokens.reshape(num_batches, seq_len) - Note: Be careful with cycling a shuffled interable. The shuffling will be different for each loop dependent on the seed - state, unlike with itertools.cycle. + if add_bos_token: + prefix = np.full((num_batches, 1), tokenizer.bos_token_id) + tokens = np.concatenate([prefix, tokens], axis=1) + return {"input_ids": tokens} - :param iterable: Iterable to cycle through - :param limit: Number of cycles to go through. If None, cycles indefinitely. - """ - index = 0 - if limit is None: - limit = float("inf") - while True: - for x in iterable: - if index >= limit: - return - else: - yield x - index += 1 + tokenized_dataset = dataset.map( + tokenize_function, + batched=True, + num_proc=(num_proc if not streaming else None), + remove_columns=[column_name], + ) + if len(tokenized_dataset) == 0 or "input_ids" not in tokenized_dataset.column_names: + raise ValueError( + "Tokenization produced no sequences. Check that the input text is not empty." + ) + tokenized_dataset.set_format(type="torch", columns=["input_ids"]) + return tokenized_dataset diff --git a/src/devinterp/vis_utils.py b/src/devinterp/vis_utils.py deleted file mode 100644 index f01d9529..00000000 --- a/src/devinterp/vis_utils.py +++ /dev/null @@ -1,467 +0,0 @@ -import warnings -from collections.abc import Sequence -from typing import Callable, Container, List, Optional, Union - -import numpy as np -import pandas as pd -import torch -from devinterp.utils import default_nbeta -from tqdm import tqdm - -try: - import plotly.express as px - import plotly.graph_objects as go -except ImportError: - px = None - go = None - warnings.warn( - "Plotly is not installed. Visualization features will be unavailable. " - "Install with `pip install devinterp[vis]` to enable them." - ) - - -# Sampling config validates input parameters while allowing us to use **kwargs later on -class SweepConfig: - epsilon_range: List[float] - beta_range: List[float] - llc_estimator: Callable - llc_estimator_kwargs: dict - - def __init__(self, epsilon_range, beta_range, llc_estimator, llc_estimator_kwargs): - self.epsilon_range = epsilon_range - self.beta_range = beta_range - self.llc_estimator = llc_estimator - self.llc_estimator_kwargs = llc_estimator_kwargs - - # Pydantic-recognized field for custom settings - class Config: - arbitrary_types_allowed = True # Allows Pydantic to accept pytorch models - - # Build epsilon_range and beta_range given different user input formats for beta and epsilon ranges - @classmethod - def setup( - cls, - llc_estimator, - llc_estimator_kwargs, - min_beta, - max_beta, - beta_samples, - beta_range, - min_epsilon, - max_epsilon, - epsilon_samples, - epsilon_range, - dataloader=None, - ): - if epsilon_range is not None: - assert isinstance(epsilon_range, Sequence), ( - "epsilon_range must be a list-like object (e.g list or numpy array)" - ) - if min_epsilon is not None or max_epsilon is not None: - warnings.warn( - "min_epsilon and max_epsilon will be ignored as epsilon_range is provided" - ) - else: - epsilon_range = np.power( - 10, - np.linspace( - np.log10(min_epsilon), np.log10(max_epsilon), epsilon_samples - ), - ) - - if beta_range is not None: - assert isinstance(beta_range, Sequence), ( - "beta_range must be a list-like object (e.g list or numpy array)" - ) - if min_beta is not None or max_beta is not None: - warnings.warn( - "min_beta and max_beta will be ignored as beta_range is provided" - ) - else: - if dataloader is not None: - # Calculate default beta (inverse temperature) range. - default_beta = default_nbeta(dataloader) - if min_beta is None: - min_beta = 1e-2 * default_beta - if max_beta is None: - max_beta = 1e3 * default_beta - else: - if min_beta is None or max_beta is None: - raise ValueError( - "min_beta and max_beta must be provided if dataloader is not provided." - ) - beta_range = np.power( - 10, np.linspace(np.log10(min_beta), np.log10(max_beta), beta_samples) - ) - - assert min(beta_range) > 0, "All beta values must be greater than 0" - assert min(epsilon_range) > 0, "All epsilon values must be greater than 0" - if max(epsilon_range) > 1e-2: - warnings.warn( - "Epsilon values greater than 1e-2 typically lead to instability in the sampling process. Consider reducing epsilon to between 1e-6 and 1e-2." - ) - - return cls( - epsilon_range=epsilon_range, - beta_range=beta_range, - llc_estimator=llc_estimator, - llc_estimator_kwargs=llc_estimator_kwargs, - ) - - -class EpsilonBetaAnalyzer: - """ - A class for analyzing and visualizing the local learning coefficient (LLC) across different epsilon and beta values. - - Includes methods to configure, run, and visualize sweeps of the local learning coefficient over epsilon and beta. - """ - - def __init__(self): - self.sweep_config = None - self.plotting_config = None - self.sweep_df = None - self.fig = None - - def configure_sweep( - self, - llc_estimator: Callable, - llc_estimator_kwargs: dict, - min_epsilon: Optional[float] = 1e-6, - max_epsilon: Optional[float] = 1e-2, - epsilon_samples: float = 8, - epsilon_range: Optional[List[float]] = None, - min_beta: Optional[float] = None, - max_beta: Optional[float] = None, - beta_samples: float = 8, - beta_range: Optional[List[float]] = None, - dataloader: Optional[torch.utils.data.DataLoader] = None, - ) -> None: - """ - Configure the sampling parameters for the LLC analysis. - :param llc_estimator: Callable function to estimate the local learning coefficient. - Note: The estimator function expected by EpsilonBetaAnalyzer must have the following signature: - def estimator(epsilon: float, beta: float, **kwargs) -> dict - where kwargs are the arguments to estimate_learning_coeff_with_summary - The return value must be a dict with a "llc/trace" key corresponding to a numpy array of shape (num_chains, num_draws) - Additional keys can represent other values of interest (e.g. acceptance rates, true LLC.) - :param llc_estimator_kwargs: Keyword arguments for the llc_estimator function. - :param min_epsilon: Minimum value for epsilon range (if epsilon_range not provided). - :param max_epsilon: Maximum value for epsilon range (if epsilon_range not provided). - :param epsilon_samples: Number of samples in epsilon range (if epsilon_range not provided). - :param epsilon_range: Explicit range of epsilon values to use (overrides min/max_epsilon). - :param min_beta: Minimum value for beta range (if beta_range not provided). - :param max_beta: Maximum value for beta range (if beta_range not provided). - :param beta_samples: Number of samples in beta range (if beta_range not provided). - :param beta_range: Explicit range of beta values to use (overrides min/max_beta). - :param dataloader: Optional dataloader for calculating optimal beta. - """ - - self.sweep_config = SweepConfig.setup( - llc_estimator, - llc_estimator_kwargs, - min_beta, - max_beta, - beta_samples, - beta_range, - min_epsilon, - max_epsilon, - epsilon_samples, - epsilon_range, - dataloader, - ) - - def sweep(self, add_to_existing=False) -> None: - """ - Perform the LLC sweep using the configured parameters. - - This method runs the LLC estimator for each combination of epsilon and beta values - and stores the results in self.sweep_df. - - :param add_to_existing: If True, adds new sweep results to existing ones. If False, replaces existing results. - Useful for sweeping over multiple models or datasets. - """ - assert self.sweep_config is not None, ( - "Sweep configuration is not set. Please call configure_sweep() first." - ) - - epsilon_range = self.sweep_config.epsilon_range - beta_range = self.sweep_config.beta_range - llc_estimator = self.sweep_config.llc_estimator - llc_estimator_kwargs = self.sweep_config.llc_estimator_kwargs - - if "device" in llc_estimator_kwargs: - if torch.cuda.is_available() and ( - llc_estimator_kwargs["device"] == "cpu" - or torch.device(llc_estimator_kwargs["device"]).type == "cpu" - ): - warnings.warn( - "CUDA is available but not being used. Consider setting device='cuda' for faster computation." - ) - - all_sweep_stats = [] - with tqdm(total=len(epsilon_range) * len(beta_range)) as pbar: - for epsilon in epsilon_range: - for beta in beta_range: - try: - sweep_stats = llc_estimator( - epsilon=epsilon, beta=beta, **llc_estimator_kwargs - ) - sweep_stats = dict(sweep_stats, epsilon=epsilon, beta=beta) - all_sweep_stats.append(sweep_stats) - except RuntimeError as e: - warnings.warn( - f"Error encountered for epsilon={epsilon}, beta={beta}. Skipping. Warning: {e}" - ) - pbar.update(1) - - sweep_df = pd.DataFrame(all_sweep_stats) - # If there's only one sweep, there'll only be one trace, so we need to add an extra dimension - sweep_df["llc/trace"] = sweep_df["llc/trace"].apply( - lambda x: x if len(x.shape) == 2 else x[np.newaxis, :] - ) - if add_to_existing: - if self.sweep_df is not None: - self.sweep_df = pd.concat([self.sweep_df, sweep_df], ignore_index=True) - else: - self.sweep_df = sweep_df - else: - self.sweep_df = sweep_df - - def plot( - self, - true_lambda: Union[float, int, str, Container] = None, - num_last_steps_to_average: int = 50, - color: Optional[str] = None, - slider: Optional[str] = None, - slider_plane: Optional[str] = False, - div_out_beta=False, - **kwargs, - ): - """ - Plot the results of the LLC sweep. - - :param true_lambda: True value of lambda for comparison (optional). Can be a scalar, a list, or a string column name of sweep_df. - Will plot a horizontal plane at the true_lambda value. - :param num_last_steps_to_average: Number of last steps to average for final LLC value. - :param color: Column name to use for coloring the scatter points. - :param slider: Column name to use for creating a slider in the plot. - :param slider_plane: If True, adds a plane for each slider value. - :param kwargs: Additional keyword arguments to pass to the plotting function. - Example: range_color=[0, 0.15] to set the color range. - :return: A plotly Figure object containing the LLC sweep visualization. - """ - if px is None or go is None: - raise ImportError( - "Plotting is unavailable because Plotly is not installed. " - "Install with `pip install devinterp[vis]` to enable visualization." - ) - - if div_out_beta: - plot_config = { - "title": "LLC / beta vs. epsilon and beta", - "z": "llc_div_beta", - "log_y": True, - "log_x": True, - "log_z": True, - } - else: - plot_config = { - "title": "LLC vs. epsilon and beta", - "z": "llc/final", - "log_y": True, - "log_x": True, - "log_z": True, - } - - assert self.sweep_df is not None, ( - "No data to plot. Please call get_results() first." - ) - - sweep_df = self.sweep_df.copy() - # Calculate additional statistics - sweep_df["llc/std_over_mean"] = sweep_df["llc/trace"].apply( - lambda x: x[:, -num_last_steps_to_average:].std() - / x[:, -num_last_steps_to_average:].mean() - ) - sweep_df["llc/final"] = sweep_df["llc/trace"].apply( - lambda x: x[:, -num_last_steps_to_average].mean() - ) - if div_out_beta: - sweep_df["llc_div_beta"] = sweep_df["llc/final"] / sweep_df["beta"] - if true_lambda is not None: - if type(true_lambda) in [int, float]: - sweep_df["true_lambda"] = sweep_df["llc/trace"].apply( - lambda x: true_lambda - ) - elif isinstance(true_lambda, str): - sweep_df["true_lambda"] = sweep_df[true_lambda] - else: - sweep_df["true_lambda"] = true_lambda - sweep_df["log_true_lambda"] = sweep_df["true_lambda"].apply( - lambda x: np.log10(np.abs(x)) - ) - sweep_df["log_lambda_hat"] = sweep_df["llc/final"].apply( - lambda x: np.log10(np.abs(x)) - ) - sweep_df["log_delta_lambda"] = ( - sweep_df["log_lambda_hat"] - sweep_df["log_true_lambda"] - ) - sweep_df["lambda_delta"] = sweep_df["llc/final"] - sweep_df["true_lambda"] - sweep_df["log_lambda_delta"] = sweep_df["lambda_delta"].apply( - lambda x: np.log10(np.abs(x)) * np.sign(x) - ) - if color is None: - color = "log_lambda_delta" # default color when true_lambda is provided - plot_config["range_color"] = [-4, 4] - - if color is None: - color = ( - "llc/std_over_mean" # default color when true_lambda is not provided - ) - - if color == "llc/std_over_mean": - plot_config["range_color"] = [0, 0.15] - - # Add any additional kwargs to the plot_config - plot_config.update(kwargs) - - if slider is None: - fig = px.scatter_3d( - sweep_df, x="epsilon", y="beta", color=color, **plot_config - ) - - if true_lambda is not None: - # Grid (to easily plot horizontal planes) - epsilon_range = self.sweep_config.epsilon_range - beta_range = self.sweep_config.beta_range - broadcast_grid = np.ones((len(epsilon_range), len(beta_range))) - # Place a horizontal plane at height true_lambda - plane = go.Surface( - x=epsilon_range, - y=beta_range, - z=true_lambda * broadcast_grid, - opacity=0.4, - surfacecolor=true_lambda * broadcast_grid, - showscale=False, - name=f"RLCT={true_lambda}", - ) - fig.add_trace(plane) - - else: - # Create base figure. - fig = None - - # Determine fixed ranges for axes and colorbar - x_range = [ - sweep_df["epsilon"].min() / 2, - sweep_df["epsilon"].max() / 1.5, - ] # Add some margin - y_range = [sweep_df["beta"].min(), sweep_df["beta"].max()] - z_range = [ - max(1e-2, sweep_df["llc/final"].min()), - sweep_df["llc/final"].max(), - ] - color_range = [sweep_df[color].min(), sweep_df[color].max()] - plot_config["range_color"] = color_range - if color == "llc/std_over_mean": - plot_config["range_color"] = [0, 0.15] - - plot_config["range_z"] = z_range - plot_config["range_x"] = x_range - plot_config["range_y"] = y_range - - unique_slider_vals = sweep_df[slider].unique() - unique_slider_vals = sorted(unique_slider_vals) - # Add traces for each unique slider value - for slider_val in unique_slider_vals: - df_filtered = sweep_df[sweep_df[slider] == slider_val] - plot_ = px.scatter_3d( - df_filtered, x="epsilon", y="beta", color=color, **plot_config - ) - if fig is None: - fig = plot_ - else: - trace = plot_.data[0] - fig.add_trace(trace) - - # Grid (to easily plot horizontal planes) - epsilon_range = self.sweep_config.epsilon_range - beta_range = self.sweep_config.beta_range - broadcast_grid = np.ones((len(epsilon_range), len(beta_range))) - - if slider_plane: - # Place a horizontal plane with height = slider_val - plane = go.Surface( - x=epsilon_range, - y=beta_range, - z=slider_val * broadcast_grid, - opacity=0.4, - surfacecolor=slider_val * broadcast_grid, - showscale=False, - name=f"{slider}={slider_val}", - ) - fig.add_trace(plane) - - # Slider - steps = [] - for i, slider_val in enumerate(unique_slider_vals): - step = dict( - method="update", - args=[ - {"visible": [False] * len(fig.data)}, - { - "title": f"Local learning coefficient vs. epsilon and beta ({slider} = {slider_val})" - }, - ], - label=str(slider_val), - ) - - if slider_plane: - step["args"][0]["visible"][2 * i] = ( - True # Toggle i'th scatter trace to "visible" - ) - step["args"][0]["visible"][2 * i + 1] = ( - True # Toggle i'th plane trace to "visible" - ) - else: - step["args"][0]["visible"][i] = True - steps.append(step) - - sliders = [ - dict( - active=0, - currentvalue={"prefix": f"{slider}: "}, - pad={"t": 50}, - steps=steps, - ) - ] - - # Axes and layout - fig.update_layout( - scene=dict( - xaxis_title="epsilon", - yaxis_title="beta", - zaxis_title="lambdahat / beta" if div_out_beta else "lambdahat", - xaxis_type="log", - yaxis_type="log", - zaxis_type="log", - xaxis_range=np.log10(x_range), - yaxis_range=np.log10(y_range), - zaxis_range=np.log10(z_range), - aspectmode="manual", - aspectratio=dict(x=0.7, y=1.2, z=1), - ), - sliders=sliders, - title="Local learning coefficient vs. epsilon and beta", - ) - - self.fig = fig - return fig - - def save_fig(self, path: str) -> None: - """ - Save the current figure to a file. - :param path: Path to save the figure to. - """ - assert self.fig is not None, "No figure to save. Please call plot() first." - self.fig.write_html(path) diff --git a/tests/conftest.py b/tests/conftest.py index 7b3d90b2..9b8e5ec0 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -1,247 +1,6 @@ -import json - -import numpy as np import pytest -import torch -import torch.nn as nn -from devinterp.slt.llc import LLCEstimator -from devinterp.slt.sampler import sample as _sample - -# --- LLCEstimator runner fixture --- -from devinterp.utils import default_nbeta, evaluate_mse, get_init_loss_multi_batch -from torch.utils.data import DataLoader, TensorDataset - - -# --- Toy model fixtures --- -@pytest.fixture -def Polynomial(): - class _Poly(nn.Module): - def __init__(self, powers=(1, 1)): - super().__init__() - self.powers = torch.tensor(powers, dtype=torch.float32) - self.weights = nn.Parameter(torch.zeros_like(self.powers)) - - def forward(self, x): - return x * torch.prod(self.weights**self.powers) - - return _Poly - - -@pytest.fixture -def LinePlusDot(): - class _LPD(nn.Module): - def __init__(self, dim=2): - super().__init__() - self.weights = nn.Parameter(torch.zeros(dim, dtype=torch.float32)) - - def forward(self, x): - return x * (self.weights[0] - 1) * (self.weights.pow(2).sum().pow(2)) - - return _LPD - - -def update_stored_models(model, m: int, h: int, n: int): - # Do this in a context manager to prevent write race conditions. - with open("shared/devinterp/tests/models.json", "r+") as f: - models = json.load(f) - - models[f"{m}_{h}_{n}"] = { - "fc1": model.fc1.weight.detach().numpy().tolist(), - "fc2": model.fc2.weight.detach().numpy().tolist(), - } - - f.seek(0) - json.dump(models, f) - - f.truncate() - - return models - - -@pytest.fixture -def ReducedRankRegressor(is_snapshot_update): - models = json.load(open("shared/devinterp/tests/models.json")) - - class _RRR(nn.Module): - def __init__(self, m, h, n): - super().__init__() - self.fc1 = nn.Linear(m, h, bias=False) - self.fc2 = nn.Linear(h, n, bias=False) - self.is_cached = False - - key = f"{m}_{h}_{n}" - - if key in models: - self.fc1.weight.data = torch.Tensor(models[key]["fc1"]) - self.fc2.weight.data = torch.Tensor(models[key]["fc2"]) - self.is_cached = True - - def forward(self, x): - return self.fc2(self.fc1(x)) - - def perturb(self): - # Perturb the model by a large-enough amount - # that our tests should fail. - self.fc1.weight.data += torch.randn_like(self.fc1.weight.data) - self.fc2.weight.data += torch.randn_like(self.fc2.weight.data) - - def maybe_train_model(m, h, n, x, y, criterion): - nonlocal models - # We'll retrain the model for every `--snapshot-update`. - if is_snapshot_update: - key = f"{m}_{h}_{n}" - - # Delete the model from the cache if it exists. - if key in models: - del models[key] - - _model = _RRR(m, h, n) - assert not _model.is_cached - - # Train the model. - optimizer = torch.optim.Adam(_model.parameters(), lr=0.01) - for _ in range(5000): - optimizer.zero_grad() - outputs = _model(x) - loss = criterion(outputs, y) - loss.backward() - optimizer.step() - - # Cache the model - models = update_stored_models(_model, m, h, n) - - # Always reload the model from cache so we have reproducible results - # between full/snapshot tests. - model = _RRR(m, h, n) - assert model.is_cached - - return model - - return maybe_train_model - - -@pytest.fixture -def DummyNaNModel(): - class _DN(nn.Module): - def __init__(self): - super().__init__() - self.linear = nn.Linear(2, 1) - self.counter = 0 - with torch.no_grad(): - self.linear.weight.fill_(1.0) - self.linear.bias.fill_(0.0) - - def forward(self, x): - self.counter += 1 - if self.counter > 100: - with torch.no_grad(): - self.linear.weight.fill_(float("inf")) - return self.linear(x) - - return _DN - - -# --- Shared dataset fixtures --- -@pytest.fixture -def generated_normalcrossing_dataset(): - """Shared dataset: normal input with small Gaussian noise.""" - torch.manual_seed(42) - np.random.seed(42) - sigma = 0.25 - num_samples = 1000 - x = torch.normal(0, 2, size=(num_samples,)) - y = sigma * torch.normal(0, 1, size=(num_samples,)) - train_data = TensorDataset(x, y) - - # Add deterministic generator for DataLoader shuffling - generator = torch.Generator() - generator.manual_seed(42) - train_dataloader = DataLoader( - train_data, batch_size=num_samples, shuffle=True, generator=generator - ) - return train_dataloader, train_data, x, y - - -@pytest.fixture -def generated_linedot_normalcrossing_dataset(): - """Shared dataset: single-dim x, noise y for line-plus-dot tests.""" - torch.manual_seed(42) - np.random.seed(42) - sigma = 0.25 - num_samples = 1000 - x = torch.normal(0, 2, size=(num_samples,)) - y = sigma * torch.normal(0, 1, size=(num_samples,)) - train_data = TensorDataset(x, y) - - # Add deterministic generator for DataLoader shuffling - generator = torch.Generator() - generator.manual_seed(42) - train_dataloader = DataLoader( - train_data, batch_size=num_samples, shuffle=True, generator=generator - ) - return train_dataloader, train_data, x, y - - -@pytest.fixture -def run_llc_estimator(): - """ - Returns a callable to run LLCEstimator + sampling and return the estimator. - Usage: - estimator = run_llc_estimator( - model, loader, sampling_method, lr, - nbeta=None, num_chains=1, num_draws=100, seed=42, - sampling_method_kwargs=None, - **llc_init_kwargs - ) - """ - - def _run( - model, - loader, - sampling_method, - lr, - nbeta=None, - num_chains=1, - num_draws=100, - seed=42, - sampling_method_kwargs=None, - **llc_init_kwargs, - ): - if nbeta is None: - nbeta = default_nbeta(loader) - init_loss = get_init_loss_multi_batch( - loader, num_chains, model, evaluate_mse, device="cpu" - ) - estimator = LLCEstimator( - num_chains=num_chains, - num_draws=num_draws, - nbeta=nbeta, - init_loss=init_loss, - **llc_init_kwargs, - ) - torch.manual_seed(seed) - sm_kwargs = {"lr": lr, "nbeta": nbeta} - if sampling_method_kwargs: - sm_kwargs.update(sampling_method_kwargs) - _sample( - model, - loader, - evaluate=evaluate_mse, - sampling_method_kwargs=sm_kwargs, - sampling_method=sampling_method, - num_chains=num_chains, - num_draws=num_draws, - num_burnin_steps=0, - callbacks=[estimator], - verbose=False, - seed=seed, - ) - return estimator - - return _run -# --- Snapshot fixture --- @pytest.fixture def is_snapshot_update(request): return request.config.getoption("--snapshot-update") diff --git a/tests/integration/test_back_comp.py b/tests/integration/test_back_comp.py deleted file mode 100644 index 1a8d5f58..00000000 --- a/tests/integration/test_back_comp.py +++ /dev/null @@ -1,152 +0,0 @@ -from unittest import mock - -import numpy as np -import pytest -import torch -from devinterp.optim.sgld import SGLD -from devinterp.slt.llc import LLCEstimator, OnlineLLCEstimator -from devinterp.slt.sampler import sample -from devinterp.utils import default_nbeta, evaluate_mse, get_init_loss_multi_batch -from torch.utils.data import DataLoader, TensorDataset - - -@pytest.mark.parametrize("sampling_method", [SGLD]) -@pytest.mark.parametrize("estimator", [LLCEstimator, OnlineLLCEstimator]) -def test_pass_in_temperature( - generated_normalcrossing_dataset, sampling_method, estimator, Polynomial -): - seed = 0 - torch.manual_seed(seed) - model = Polynomial() - train_dataloader, train_data, _, _ = generated_normalcrossing_dataset - lr = 0.001 - num_chains = 2 - num_draws = 10 - temperature = nbeta = 10.0 - init_loss = get_init_loss_multi_batch( - train_dataloader, num_chains, model, evaluate_mse, device="cpu" - ) - temp_llc_estimator = estimator( - num_chains=num_chains, - num_draws=num_draws, - temperature=temperature, - init_loss=init_loss, - ) - nbeta_llc_estimator = estimator( - num_chains=num_chains, num_draws=num_draws, nbeta=nbeta, init_loss=init_loss - ) - torch.manual_seed(seed) - sample( - model, - train_dataloader, - evaluate=evaluate_mse, - sampling_method_kwargs=dict(lr=lr, temperature=temperature), - sampling_method=sampling_method, - num_chains=num_chains, - num_draws=num_draws, - callbacks=[temp_llc_estimator], - verbose=False, - seed=seed, - ) - torch.manual_seed(seed) - sample( - model, - train_dataloader, - evaluate=evaluate_mse, - sampling_method_kwargs=dict(lr=lr, nbeta=nbeta), - sampling_method=sampling_method, - num_chains=num_chains, - num_draws=num_draws, - callbacks=[nbeta_llc_estimator], - verbose=False, - seed=seed, - ) - nbeta_llc_estimator = nbeta_llc_estimator.get_results() - temp_llc_estimator = temp_llc_estimator.get_results() - for k, v in nbeta_llc_estimator.items(): - if isinstance(v, torch.Tensor): - assert torch.allequal(v, temp_llc_estimator[k]), ( - f"Evaluation failed for {k}" - ) - elif isinstance(v, np.ndarray): - assert np.equal(v, temp_llc_estimator[k]).all(), ( - f"Evaluation failed for {k}" - ) - else: - assert np.equal(v, temp_llc_estimator[k]), f"Evaluation failed for {k}" - - -@pytest.mark.parametrize("sampling_method", [SGLD]) -@pytest.mark.parametrize("estimator", [LLCEstimator, OnlineLLCEstimator]) -def test_dont_allow_both_temp_and_nbeta( - generated_normalcrossing_dataset, sampling_method, estimator, Polynomial -): - model = Polynomial([2, 2]) - with pytest.raises(AssertionError): - train_dataloader, train_data, _, _ = generated_normalcrossing_dataset - lr = 0.0004 - num_chains = 1 - num_draws = 2 - init_loss = get_init_loss_multi_batch( - train_dataloader, num_chains, model, evaluate_mse, device="cpu" - ) - llc_estimator = estimator( - num_chains=num_chains, - num_draws=num_draws, - nbeta=2.0, - init_loss=init_loss, - ) - sample( - model, - train_dataloader, - evaluate=evaluate_mse, - sampling_method_kwargs=dict( - lr=lr, - temperature=2.0, - ), - sampling_method=sampling_method, - num_chains=num_chains, - num_draws=num_draws, - callbacks=[llc_estimator], - verbose=False, - ) - with pytest.raises(AssertionError): - llc_estimator = estimator( - num_chains=num_chains, - num_draws=num_draws, - temperature=2.0, - init_loss=init_loss, - ) - sample( - model, - train_dataloader, - evaluate=evaluate_mse, - sampling_method_kwargs=dict( - lr=lr, - nbeta=2.0, - ), - sampling_method=sampling_method, - num_chains=num_chains, - num_draws=num_draws, - callbacks=[llc_estimator], - verbose=False, - ) - - -def test_warn_on_default_nbeta(): - with mock.patch("devinterp.utils.warnings") as mock_warn: - _ = default_nbeta( - DataLoader(TensorDataset(torch.randn(100, 10)), batch_size=1), - gradient_accumulation_steps=1, - ) - # Check that a warning was issued - mock_warn.warn.assert_called_with( - "default nbeta is undefined for batch_size * gradient_accumulation_steps == 1, falling back to default value of 1" - ) - with mock.patch("devinterp.utils.warnings") as mock_warn: - _ = default_nbeta(1, gradient_accumulation_steps=1) - - # Check that a warning was issued - mock_warn.warn.assert_called_with( - "default nbeta is undefined for batch_size * gradient_accumulation_steps == 1, falling back to default value of 1" - ) diff --git a/tests/integration/test_caching.py b/tests/integration/test_caching.py new file mode 100644 index 00000000..461b5413 --- /dev/null +++ b/tests/integration/test_caching.py @@ -0,0 +1,77 @@ +"""Integration tests for caching in bif() and susceptibilities().""" + +import pytest +import zarr + +from devinterp.slt.bif import bif +from devinterp.slt.susceptibilities import susceptibilities +from devinterp.slt.weight_restrictions import create_param_masks + +from .test_standalone import _load_dataset, _load_model_and_tokenizer + + +@pytest.fixture(scope="module") +def model_and_data(): + model, tokenizer = _load_model_and_tokenizer() + train = _load_dataset("timaeus/dsir-pile-10k", tokenizer, column="contents") + code = _load_dataset("timaeus/pile-github", tokenizer, column="text") + return model, train, code + + +def _sample_kwargs(model, train, code, output_path, lr=0.001): + return dict( + model=model, + dataset=train, + observables={"train": (train, 2), "code": (code, 2)}, + lr=lr, + n_beta=30, + num_chains=1, + num_draws=2, + batch_size=2, + num_init_loss_batches=1, + init_seed=42, + output_path=output_path, + ) + + +@pytest.mark.gpu +def test_bif_caching(model_and_data, tmp_path): + model, train, code = model_and_data + sample_path = tmp_path / "samples.zarr" + + bif(**_sample_kwargs(model, train, code, sample_path, lr=0.001)) + assert zarr.open_group(str(sample_path)).attrs.get("completed") == 1 + + bif(**_sample_kwargs(model, train, code, sample_path, lr=0.001)) + + with pytest.raises(RuntimeError, match="different sampler config"): + bif(**_sample_kwargs(model, train, code, sample_path, lr=0.005)) + + +@pytest.mark.gpu +def test_susceptibilities_per_wr_cache(model_and_data, tmp_path): + model, train, code = model_and_data + base_path = tmp_path / "sus.zarr" + + susceptibilities( + model=model, + dataset=train, + observables={"train": (train, 2), "code": (code, 2)}, + weight_restrictions={ + "full": None, + "l0h0": create_param_masks(model, "l0h0"), + }, + sampling_task="train", + lr=0.001, + n_beta=30, + num_chains=1, + num_draws=2, + batch_size=2, + num_init_loss_batches=1, + init_seed=42, + output_path=base_path, + ) + + for wr in ["full", "l0h0"]: + wr_path = base_path.parent / f"{base_path.stem}_{wr}{base_path.suffix}" + assert zarr.open_group(str(wr_path)).attrs.get("completed") == 1 diff --git a/tests/integration/test_custom_model.py b/tests/integration/test_custom_model.py new file mode 100644 index 00000000..13a4ca3a --- /dev/null +++ b/tests/integration/test_custom_model.py @@ -0,0 +1,199 @@ +"""Verify sample/bif/llc/susceptibilities work with a custom torch module +(no HuggingFace wrapper, no TransformerLens). Exercises the fallback path +in lm_forward_logits (model returns logits directly).""" + +import numpy as np +import pytest +import torch +import torch.nn as nn +from datasets import Dataset + +from devinterp.slt.bif import bif +from devinterp.slt.llc import llc +from devinterp.slt.sampling import sample +from devinterp.slt.susceptibilities import susceptibilities + + +class TinyTransformer(nn.Module): + def __init__(self, vocab_size, d_model=16, n_heads=2): + super().__init__() + self.embed = nn.Embedding(vocab_size, d_model) + self.attn = nn.MultiheadAttention(d_model, n_heads, batch_first=True) + self.ff = nn.Linear(d_model, vocab_size) + + def forward(self, input_ids): + x = self.embed(input_ids) + x, _ = self.attn(x, x, x) + return self.ff(x) + + +@pytest.fixture +def model_and_data(): + vocab, seq, n = 50, 16, 16 + model = TinyTransformer(vocab) + ids = np.random.RandomState(0).randint(0, vocab, (n, seq)) + ds = Dataset.from_dict({"input_ids": ids.tolist()}) + ds.set_format(type="torch", columns=["input_ids"]) + return model, ds + + +_SAMPLER_KW = dict( + lr=1e-4, + n_beta=1.0, + num_chains=1, + num_draws=2, + batch_size=2, + num_init_loss_batches=1, + init_seed=42, +) + + +def _head_0_mask(model, d_model=16, n_heads=2): + """Mirrors 'l0h0' from the README: restrict updates to attention head 0. + + Params not in the returned dict (embed, ff, out_proj.bias) get + requires_grad_(False) and don't train at all. + """ + head_dim = d_model // n_heads + mask: dict[str, torch.Tensor] = {} + # in_proj_weight: concatenated [Q; K; V] of shape (3*d_model, d_model). + # Head 0 lives in rows [0:head_dim] of each Q/K/V block. + w = torch.zeros_like(model.attn.in_proj_weight) + for offset in (0, d_model, 2 * d_model): + w[offset : offset + head_dim] = 1 + mask["attn.in_proj_weight"] = w + b = torch.zeros_like(model.attn.in_proj_bias) + for offset in (0, d_model, 2 * d_model): + b[offset : offset + head_dim] = 1 + mask["attn.in_proj_bias"] = b + # out_proj.weight: shape (d_model, d_model). Head 0 = first head_dim columns. + w = torch.zeros_like(model.attn.out_proj.weight) + w[:, 0:head_dim] = 1 + mask["attn.out_proj.weight"] = w + return mask + + +def test_sample(model_and_data): + model, ds = model_and_data + result = sample( + model=model, dataset=ds, observables={"self": (ds, 2)}, **_SAMPLER_KW + ) + assert "sampling_loss" in result.dataset + + +def test_bif(model_and_data): + model, ds = model_and_data + result = bif( + model=model, + dataset=ds, + observables={"a": (ds, 2), "b": (ds, 2)}, + **_SAMPLER_KW, + ) + assert "influences" in result + + +def test_llc(model_and_data): + model, ds = model_and_data + result = llc(model=model, dataset=ds, observables={"self": (ds, 2)}, **_SAMPLER_KW) + assert "llc_mean" in result + + +def test_llc_scaling_invariance(model_and_data): + """LLC(loss_fn=2*L, n_beta=N/2) must equal LLC(loss_fn=L, n_beta=N). + + Scaling the loss by 2 doubles the gradient; halving n_beta cancels + (SGLD update uses `grad.mul(nbeta)`), so sampling trajectories are + bit-identical. The LLC arithmetic n_beta*(mean_loss - init_loss) + also cancels. Strong end-to-end test that loss_fn is actually + threaded through sampling, init_loss, and observables. + """ + model, ds = model_and_data + base = llc(model=model, dataset=ds, observables={"self": (ds, 2)}, **_SAMPLER_KW) + + def doubled_ce(m: nn.Module, ids: torch.Tensor) -> torch.Tensor: + logits = m(ids) + shift_logits = logits[..., :-1, :] + shift_ids = ids[..., 1:].unsqueeze(-1) + log_probs = torch.nn.functional.log_softmax(shift_logits, dim=-1) + return -2.0 * log_probs.gather(dim=-1, index=shift_ids).squeeze(-1) + + scaled_kw = {**_SAMPLER_KW, "n_beta": _SAMPLER_KW["n_beta"] * 0.5} + scaled = llc( + model=model, + dataset=ds, + observables={"self": (ds, 2)}, + loss_fn=doubled_ce, + **scaled_kw, + ) + assert float(scaled["llc_mean"]) == float(base["llc_mean"]) + + +def test_susceptibilities(model_and_data): + model, ds = model_and_data + result = susceptibilities( + model=model, + dataset=ds, + observables={"train": (ds, 2), "probe": (ds, 2)}, + weight_restrictions={"full": None, "head0": _head_0_mask(model)}, + sampling_task="train", + **_SAMPLER_KW, + ) + assert "susceptibilities" in result.children + + +@pytest.mark.parametrize("field", ["rmsprop_eps", "rmsprop_alpha"]) +def test_rmsprop_arg_rejected_for_non_rmsprop_sampler(model_and_data, field): + model, ds = model_and_data + with pytest.raises(ValueError, match=r"rmsprop_(eps|alpha) can only be set"): + sample( + model=model, + dataset=ds, + observables={"self": (ds, 2)}, + sampling_method="sgmcmc_sgld", + **{field: 0.5}, + **_SAMPLER_KW, + ) + + +@pytest.mark.parametrize( + "field,kwarg_name", [("rmsprop_eps", "eps"), ("rmsprop_alpha", "alpha")] +) +def test_rmsprop_arg_rejects_duplicate(model_and_data, field, kwarg_name): + model, ds = model_and_data + with pytest.raises(ValueError, match=r"specify only one"): + sample( + model=model, + dataset=ds, + observables={"self": (ds, 2)}, + sampling_method="rmsprop_sgld", + sampling_method_kwargs={kwarg_name: 0.3}, + **{field: 0.3}, + **_SAMPLER_KW, + ) + + +def test_rmsprop_eps_alpha_equivalent_to_kwargs(model_and_data): + """Setting rmsprop_eps/rmsprop_alpha must produce identical samples to + passing the same values via sampling_method_kwargs.""" + model, ds = model_and_data + via_top = sample( + model=model, + dataset=ds, + observables={"self": (ds, 2)}, + sampling_method="rmsprop_sgld", + rmsprop_eps=0.2, + rmsprop_alpha=0.95, + **_SAMPLER_KW, + ) + via_kwargs = sample( + model=model, + dataset=ds, + observables={"self": (ds, 2)}, + sampling_method="rmsprop_sgld", + sampling_method_kwargs={"eps": 0.2, "alpha": 0.95}, + **_SAMPLER_KW, + ) + assert torch.equal( + torch.as_tensor(via_top.dataset["sampling_loss_micro"].values), + torch.as_tensor(via_kwargs.dataset["sampling_loss_micro"].values), + ) diff --git a/tests/integration/test_gpu.py b/tests/integration/test_gpu.py deleted file mode 100644 index d64c3261..00000000 --- a/tests/integration/test_gpu.py +++ /dev/null @@ -1,130 +0,0 @@ -from pprint import pp - -import numpy as np -import pytest -import torch -from datasets import load_dataset -from devinterp.backends.default.slt.sampler import sample -from devinterp.optim.sgld import SGLD -from devinterp.slt.llc import LLCEstimator -from devinterp.utils import USE_TPU_BACKEND, prepare_input, set_seed -from torch.utils.data import DataLoader -from tqdm import tqdm -from transformer_lens.utils import lm_cross_entropy_loss, tokenize_and_concatenate -from transformers import AutoModelForCausalLM, AutoTokenizer - - -def _test_hf(model, dataset, device: str, batch_size=4, seed=42): - assert not USE_TPU_BACKEND, "TPU backend not supported for this test" - assert device in ["cpu"] or device.startswith("cuda"), ( - "Invalid device. Should be cpu or cuda:n. Don't worry about this error if you're not on a GPU device." - ) - set_seed(seed) - - print(f"Testing on {device}") - - loader = DataLoader(dataset, batch_size=batch_size, shuffle=True) - model.to(device) - model.eval() - init_loss = torch.zeros(1).to(device) - - def evaluate(model, batch): - logits = model(batch["tokens"]).logits - return lm_cross_entropy_loss(logits, batch["tokens"]), {"logits": logits} - - with torch.no_grad(): - for i, batch in tqdm(enumerate(loader), total=4): - batch = prepare_input( - batch, device, is_deepspeed_enabled=False, accelerator=None - ) - - init_loss += evaluate(model, batch)[0] - - if i >= 4: - break - - init_loss /= 4 - init_loss = init_loss.detach() - - print("\n\nInit loss", init_loss) - - # model = torch.compile(model) - - nbeta = 20.0 - num_chains = 1 - num_draws = 10 - - llc_estimator = LLCEstimator( - num_chains=num_chains, - num_draws=num_draws, - nbeta=nbeta, - device=device, - init_loss=init_loss, - ) - - # Run the LLC estimation - metrics = sample( - model, - loader, - callbacks=[llc_estimator], - evaluate=evaluate, - sampling_method=SGLD, - sampling_method_kwargs=dict( - lr=0.0002, - weight_decay=0.0, - localization=0.0, - nbeta=nbeta, - ), - num_draws=num_draws, - num_chains=num_chains, - num_burnin_steps=0, - num_steps_bw_draws=1, - seed=seed, - device=device, - verbose=True, - batch_size=batch_size, - init_loss=init_loss, - ) - - return metrics - - -@pytest.mark.skip( - reason="This test is currently failing in CI/CD, and should be replaced with a snapshot test" -) -@pytest.mark.gpu -@pytest.mark.slow -def test_hf(): - # Load the model and tokenizer - model = AutoModelForCausalLM.from_pretrained("roneneldan/TinyStories-1M") - tokenizer = AutoTokenizer.from_pretrained("roneneldan/TinyStories-1M") - - # count_parameters(model) - print(tokenizer) - - # Load the dataset - dataset = load_dataset("roneneldan/TinyStories", split="train") - dataset = tokenize_and_concatenate(dataset, tokenizer) - - # Set up the LLC estimator - metrics_cpu = _test_hf(model, dataset, "cpu") - pp(metrics_cpu) - metrics_cpu.pop("llc/std") # 1 chain only - metrics_cpu.pop("loss/trace") # 1 chain only - - for gpu in range(0, torch.cuda.device_count()): - metrics_gpu = _test_hf(model, dataset, f"cuda:{gpu}") - pp(metrics_gpu) - for k, v in metrics_cpu.items(): - if isinstance(v, torch.Tensor): - assert torch.allclose(v, metrics_gpu[k], rtol=1e-1), ( - f"Evaluation failed for {k}" - ) - elif isinstance(v, np.ndarray): - assert np.isclose(v, metrics_gpu[k], rtol=1e-1).all(), ( - f"Evaluation failed for {k}" - ) - else: - assert np.isclose(v, metrics_gpu[k], rtol=1e-1), ( - f"Evaluation failed for {k}" - ) diff --git a/tests/integration/test_multiprocessing.py b/tests/integration/test_multiprocessing.py deleted file mode 100644 index a080706d..00000000 --- a/tests/integration/test_multiprocessing.py +++ /dev/null @@ -1,128 +0,0 @@ -import os -from pprint import pp - -import numpy as np -import torch -from datasets import load_dataset -from devinterp.optim.sgld import SGLD -from devinterp.slt.llc import LLCEstimator -from devinterp.utils import USE_TPU_BACKEND, prepare_input, set_seed -from torch.utils.data import DataLoader -from tqdm import tqdm -from transformer_lens.utils import lm_cross_entropy_loss, tokenize_and_concatenate -from transformers import AutoModelForCausalLM, AutoTokenizer - - -def _test_hf(model, dataset, device: str, batch_size=8, seed=42, cores=1): - assert not USE_TPU_BACKEND, "TPU backend not supported for this test" - assert device in ["cpu"] or device.startswith("cuda"), ( - "Invalid device. Should be cpu or cuda:n. Don't worry about this error if you're not on a GPU device." - ) - set_seed(seed) - - from devinterp.backends.default.slt.sampler import sample - - print(f"Testing on {device}") - - loader = DataLoader(dataset, batch_size=batch_size, shuffle=True) - model.to(device) - model.eval() - init_loss = torch.zeros(1).to(device) - - def evaluate(model, batch): - logits = model(batch["tokens"]).logits - return lm_cross_entropy_loss(logits, batch["tokens"]), {"logits": logits} - - with torch.no_grad(): - for i, batch in tqdm(enumerate(loader), total=4): - batch = prepare_input( - batch, device, is_deepspeed_enabled=False, accelerator=None - ) - - init_loss += evaluate(model, batch)[0] - - if i >= 4: - break - - init_loss /= 4 - init_loss = init_loss.detach() - - print("\n\nInit loss", init_loss) - - # model = torch.compile(model) - - nbeta = 20.0 - num_chains = 1 - num_draws = 50 - - llc_estimator = LLCEstimator( - num_chains=num_chains, - num_draws=num_draws, - nbeta=nbeta, - device=device, - init_loss=init_loss, - ) - - # Run the LLC estimation - metrics = sample( - model, - loader, - callbacks=[llc_estimator], - evaluate=evaluate, - sampling_method=SGLD, - sampling_method_kwargs=dict( - lr=0.0002, - noise_level=10.0, - weight_decay=0.0, - localization=0.0, - nbeta=nbeta, - ), - num_draws=num_draws, - num_chains=num_chains, - num_burnin_steps=0, - num_steps_bw_draws=1, - seed=seed, - device=device, - verbose=True, - batch_size=batch_size, - init_loss=init_loss, - cores=cores, - ) - - return metrics - - -# although it does pass, this test currently takes >1hr to complete. Deactivating it for now -def inactive_test_hf(): - assert os.cpu_count() >= 2, "Multiprocessing requires at least 2 CPU cores." - - # Load the model and tokenizer - model = AutoModelForCausalLM.from_pretrained("roneneldan/TinyStories-1M") - tokenizer = AutoTokenizer.from_pretrained("roneneldan/TinyStories-1M") - - # count_parameters(model) - print(tokenizer) - - # Load the dataset - dataset = load_dataset("roneneldan/TinyStories", split="train") - dataset = tokenize_and_concatenate(dataset, tokenizer) - - # Set up the LLC estimator - metrics_cpu = _test_hf(model, dataset, "cpu", cores=1) - pp(metrics_cpu) - metrics_cpu.pop("llc/std") # 1 chain only - metrics_cpu.pop("loss/trace") # 1 chain only - - metrics_mp = _test_hf(model, dataset, "cpu", cores=min(4, os.cpu_count())) - pp(metrics_mp) - for k, v in metrics_cpu.items(): - if isinstance(v, torch.Tensor): - assert torch.allclose(v, metrics_mp[k], rtol=1e-2), ( - f"Evaluation failed for {k}" - ) - elif isinstance(v, np.ndarray): - assert np.isclose(v, metrics_mp[k], rtol=1e-2).all(), ( - f"Evaluation failed for {k}" - ) - else: - assert np.isclose(v, metrics_mp[k], rtol=1e-2), f"Evaluation failed for {k}" diff --git a/tests/integration/test_sampling.py b/tests/integration/test_sampling.py deleted file mode 100644 index 853f4e7a..00000000 --- a/tests/integration/test_sampling.py +++ /dev/null @@ -1,206 +0,0 @@ -import warnings - -import numpy as np -import pytest -import torch -from datasets import load_dataset -from devinterp.optim import SGLD -from devinterp.slt.sampler import estimate_learning_coeff_with_summary -from torch.nn import functional as F -from transformers import AutoModelForImageClassification - -warnings.filterwarnings("ignore") - - -def evaluate(model, data): - inputs, outputs = data - - return F.cross_entropy(model(inputs).logits, outputs), { - "logits": model(inputs).logits - } # transformers doesn't output a vector - - -class torchvisionWrapper(torch.utils.data.Dataset): - def __init__(self, dataset): - self.dataset = dataset - - def __len__(self): - return len(self.dataset) - - def __getitem__(self, idx): - item = self.dataset[idx] - return item["pixel_values"], item["label"] - - -@pytest.fixture(scope="module") -def data(): - mnist_dataset = load_dataset("mnist") - - def preprocess(examples): - # Convert images to tensors and normalize - examples["pixel_values"] = [ - torch.tensor(np.array(img)).float().unsqueeze(0) / 255.0 - for img in examples["image"] - ] - return examples - - mnist_dataset = mnist_dataset.map( - preprocess, batched=True, remove_columns=["image"] - ) - mnist_dataset.set_format(type="torch", columns=["pixel_values", "label"]) - - return torchvisionWrapper(mnist_dataset["train"]) - - -def get_stats( - data, - device, - gpu_idxs=None, - cores=1, - chains=2, - seed=None, - num_workers=0, - batch_size=64, - gradient_accumulation_steps=1, - dtype=torch.float32, -): - # Load a pretrained MNIST classifier - model = AutoModelForImageClassification.from_pretrained( - "fxmarty/resnet-tiny-mnist" - ).to(device) - torch.manual_seed(seed) - np.random.seed(seed) - loader = torch.utils.data.DataLoader( - data, - batch_size=batch_size, - shuffle=True, - num_workers=num_workers, - ) - # Set the format of the dataset to PyTorch tensors - - torch.manual_seed(seed) - np.random.seed(seed) - return estimate_learning_coeff_with_summary( - model, - loader=loader, - evaluate=evaluate, - sampling_method=SGLD, - sampling_method_kwargs=dict(lr=4e-4, localization=100.0, nbeta=2.0), - num_chains=chains, # How many independent chains to run - num_draws=10, # How many samples to draw per chain - num_burnin_steps=0, # How many samples to discard at the beginning of each chain - num_steps_bw_draws=1, # How many steps to take between each sample - device=device, - online=True, - cores=cores, # How many cores to use for parallelization - gpu_idxs=gpu_idxs, # Which GPUs to use ([0, 1] for using GPU 0 and 1) - seed=seed, - gradient_accumulation_steps=gradient_accumulation_steps, - init_loss=0.1, - dtype=dtype, - ) - - -def check(s1, s2, atol=1e-3, reverse=False): - """ - Check if two stats are close to each other. - - """ - assert s1.keys() == s2.keys(), ( - f"Expected the same keys in both stats, got {s1.keys()} and {s2.keys()}." - ) - assert s1["llc/trace"].shape == s2["llc/trace"].shape, ( - f"Expected the same shape for llc/trace, got {s1['llc/trace'].shape} and {s2['llc/trace'].shape}." - ) - valid = np.allclose(s1["llc/trace"], s2["llc/trace"], atol=atol) - if reverse: - valid = not valid - assert valid, ( - f"Expected {'different' if reverse else 'close'} llc/trace in both stats, got {s1['llc/trace']} and {s2['llc/trace']}, {np.isclose(s1['llc/trace'], s2['llc/trace'], atol=atol)}." - ) - - -@pytest.fixture(scope="module") -def cpu_default(data): - return get_stats(data, "cpu", seed=100) - - -@pytest.mark.gpu -@pytest.fixture(scope="module") -def gpu_default(data): - return get_stats(data, "cuda", seed=100) - - -def test_cpu_consistent(data, cpu_default): - repeat_stats = get_stats(data, "cpu", seed=100) - check(cpu_default, repeat_stats, 1e-3) - - -def test_cpu_consistent_seeds(data, cpu_default): - diff_seed_stats = get_stats(data, "cpu", seed=101) - check(cpu_default, diff_seed_stats, 0.000001, reverse=True) - - -@pytest.mark.slow -def test_cpu_multicore(data, cpu_default): - multicore_stats = get_stats(data, "cpu", seed=100, cores=2) - check(cpu_default, multicore_stats, 1e-4) - - -def test_grad_accum(data, cpu_default: dict): - grad_accum_stats = get_stats( - data, "cpu", seed=100, gradient_accumulation_steps=4, batch_size=16 - ) - check(cpu_default, grad_accum_stats, 1) - - -@pytest.mark.gpu -def test_gpu_consistent(data, gpu_default): - repeat_stats = get_stats(data, "cuda", seed=100) - check(gpu_default, repeat_stats, 0.2) - - -@pytest.mark.gpu -def test_gpu_multicore(data, gpu_default): - multicore_stats = get_stats(data, "cuda", seed=100, cores=4) - check(gpu_default, multicore_stats, 0.2) - - -@pytest.mark.gpu -def test_gpu_multiworker(data, gpu_default): - multiworker_stats = get_stats(data, "cuda", seed=100, num_workers=4) - check(gpu_default, multiworker_stats, 0.2) - - -@pytest.mark.gpu -def test_multigpu(data, gpu_default): - if torch.cuda.device_count() > 1: - multigpu_stats = get_stats(data, "cuda", seed=100, gpu_idxs=[0, 1], cores=2) - check(gpu_default, multigpu_stats, 0.2) - else: - pytest.skip("Multiple GPUs unavailable.") - - -@pytest.mark.gpu -def test_multigpu_multicore(data, gpu_default): - if torch.cuda.device_count() > 1: - multigpu_multicore_stats = get_stats( - data, "cuda", seed=100, gpu_idxs=[0, 1], cores=4 - ) - check(gpu_default, multigpu_multicore_stats, 0.4) - else: - pytest.skip("Multiple GPUs unavailable.") - - -@pytest.mark.gpu -def test_gpu_grad_accum(data, gpu_default: dict): - grad_accum_stats = get_stats( - data, "cuda", seed=100, cores=4, gradient_accumulation_steps=2, batch_size=128 - ) - check(gpu_default, grad_accum_stats, 1) - - -@pytest.mark.gpu -def test_gpu_bf16(data, gpu_default: dict): - bf16_stats = get_stats(data, "cuda", seed=100, cores=4, dtype=torch.bfloat16) - check(gpu_default, bf16_stats, 0.2) diff --git a/tests/integration/test_standalone.py b/tests/integration/test_standalone.py new file mode 100644 index 00000000..8542c4a0 --- /dev/null +++ b/tests/integration/test_standalone.py @@ -0,0 +1,173 @@ +"""Standalone test: runs the full pipeline with only public dependencies. + +No aether imports. Loads model/tokenizer/datasets directly from HuggingFace. +This validates that devinterp can work independently. +""" + +import pytest +import torch +from datasets import load_dataset +from transformers import AutoModelForCausalLM, AutoTokenizer +from devinterp.utils import tokenize_and_concatenate + +from devinterp.slt.sampling import sample + + +def _load_model_and_tokenizer(name="EleutherAI/pythia-14m"): + model = AutoModelForCausalLM.from_pretrained(name, torch_dtype=torch.bfloat16) + tokenizer = AutoTokenizer.from_pretrained(name) + # Pythia's tokenizer.model_max_length is a sentinel (~1e30) so we fall back + # to model_config.max_position_embeddings (= 2048). + tokenizer.model_max_length = model.config.max_position_embeddings + return model, tokenizer + + +def _load_dataset(hf_path, tokenizer, column="text", limit=200): + ds = load_dataset(hf_path, split="train") + if limit: + ds = ds.select(range(min(limit, len(ds)))) + ds = tokenize_and_concatenate( + ds, + tokenizer, + column_name=column, + add_bos_token=True, + max_length=tokenizer.model_max_length, + ) + return ds + + +@pytest.mark.gpu +def test_standalone_sample(): + """Run sample() with no aether dependencies.""" + model, tokenizer = _load_model_and_tokenizer() + + train_data = _load_dataset("timaeus/dsir-pile-10k", tokenizer, column="contents") + code_data = _load_dataset("timaeus/pile-github", tokenizer, column="text") + + result = sample( + model=model, + dataset=train_data, + observables={ + "train": (train_data, 2), + "code": (code_data, 2), + }, + lr=0.001, + n_beta=30, + num_chains=1, + num_draws=2, + batch_size=2, + num_init_loss_batches=1, + init_seed=42, + ) + + ds = result.dataset + assert "init_loss" in ds + assert "sampling_loss" in ds + assert "loss_train" in ds + assert "loss_code" in ds + assert "input_ids_train" in ds + assert "input_ids_code" in ds + + +@pytest.mark.gpu +def test_standalone_susceptibilities(): + """Run susceptibilities() with no aether dependencies.""" + from devinterp.slt.susceptibilities import susceptibilities + from devinterp.slt.weight_restrictions import create_param_masks + + model, tokenizer = _load_model_and_tokenizer() + + train_data = _load_dataset("timaeus/dsir-pile-10k", tokenizer, column="contents") + code_data = _load_dataset("timaeus/pile-github", tokenizer, column="text") + + observables = { + "train": (train_data, 2), + "code": (code_data, 2), + } + + result = susceptibilities( + model=model, + dataset=train_data, + observables=observables, + weight_restrictions={ + "full": None, + "l0h1": create_param_masks(model, "l0h1"), + }, + sampling_task="train", + lr=0.001, + n_beta=30, + num_chains=1, + num_draws=2, + batch_size=2, + num_init_loss_batches=1, + init_seed=42, + ) + + assert "susceptibilities" in result.children + assert "context" in result.children + sus = result["susceptibilities"].dataset + assert "sus" in sus.data_vars + assert "wr" in sus.coords + assert list(sus.coords["wr"].values) == ["l0h1"] + + +@pytest.mark.gpu +def test_standalone_bif(): + """Run compute_bif() with no aether dependencies.""" + from devinterp.slt.bif import compute_bif + + model, tokenizer = _load_model_and_tokenizer() + + train_data = _load_dataset("timaeus/dsir-pile-10k", tokenizer, column="contents") + code_data = _load_dataset("timaeus/pile-github", tokenizer, column="text") + + samples = sample( + model=model, + dataset=train_data, + observables={ + "train": (train_data, 2), + "code": (code_data, 2), + }, + lr=0.001, + n_beta=30, + num_chains=2, + num_draws=4, + batch_size=2, + num_init_loss_batches=1, + init_seed=42, + ) + + result = compute_bif(samples, correlation_method="token") + assert "influences" in result.data_vars + + +@pytest.mark.gpu +def test_standalone_llc(): + """Run llc() with no aether dependencies.""" + from devinterp.slt.llc import llc + + model, tokenizer = _load_model_and_tokenizer() + + train_data = _load_dataset("timaeus/dsir-pile-10k", tokenizer, column="contents") + + result = llc( + model=model, + dataset=train_data, + observables={"train": (train_data, 2)}, + lr=0.001, + n_beta=30, + num_chains=2, + num_draws=4, + batch_size=2, + num_init_loss_batches=1, + init_seed=42, + ) + + assert "llc_mean" in result + assert "llc_std" in result + assert "llc_per_chain" in result + assert "loss_trace" in result + assert result["llc_per_chain"].shape == (2,) # num_chains + assert result["loss_trace"].shape == (2, 4) # (num_chains, num_draws) + # LLC should be positive (loss increases away from optimum) + assert float(result["llc_mean"]) > 0 diff --git a/tests/integration/test_tpu.py b/tests/integration/test_tpu.py deleted file mode 100644 index 623bac29..00000000 --- a/tests/integration/test_tpu.py +++ /dev/null @@ -1,145 +0,0 @@ -from pprint import pp - -import numpy as np -import pytest -import torch -from datasets import load_dataset -from devinterp.optim.sgld import SGLD -from devinterp.slt.llc import LLCEstimator -from devinterp.utils import USE_TPU_BACKEND, prepare_input, set_seed -from torch.utils.data import DataLoader -from tqdm import tqdm -from transformer_lens.utils import lm_cross_entropy_loss, tokenize_and_concatenate -from transformers import AutoModelForCausalLM, AutoTokenizer - - -def _test_hf(model, dataset, device: str): - assert USE_TPU_BACKEND, ( - "This test is intended to run using TPU, feel free to ignore failure if unavailable" - ) - - set_seed(1) - - if device == "tpu": - import torch_xla.core.xla_model as xm - from devinterp.backends.tpu.slt.sampler import sample - - device = xm.xla_device() - - else: - from devinterp.backends.default.slt.sampler import sample - - print(f"Testing on {device}") - - loader = DataLoader(dataset, batch_size=4, shuffle=False) - model.to(device) - model.eval() - init_loss = torch.zeros(1).to(device) - - def evaluate(model, batch): - logits = model(batch["tokens"]).logits - return lm_cross_entropy_loss(logits, batch["tokens"]), {"logits": logits} - - num_batches = 16 - - with torch.no_grad(): - for i, batch in tqdm(enumerate(loader), total=num_batches): - batch = prepare_input( - batch, device, is_deepspeed_enabled=False, accelerator=None - ) - - init_loss += evaluate(model, batch)[0] - - if i >= num_batches: - break - - init_loss /= num_batches - init_loss = init_loss.detach() - - print("\n\nInit loss", init_loss) - - # model = torch.compile(model) - - nbeta = 20.0 - num_chains = 1 - num_draws = 25 - batch_size = 4 - - llc_estimator = LLCEstimator( - num_chains=num_chains, - num_draws=num_draws, - nbeta=nbeta, - device=device, - init_loss=init_loss, - ) - - # Run the LLC estimation - metrics = sample( - model, - loader, - callbacks=[llc_estimator], - evaluate=evaluate, - sampling_method=SGLD, - sampling_method_kwargs=dict( - lr=0.001, - noise_level=1.0, - weight_decay=0.0, - localization=0.0, - nbeta=nbeta, - ), - num_draws=num_draws, - num_chains=num_chains, - num_burnin_steps=0, - num_steps_bw_draws=1, - gradient_accumulation_steps=4, - seed=42, - device=device, - verbose=True, - batch_size=batch_size, - init_loss=init_loss, - ) - - return metrics - - -@pytest.mark.tpu -@pytest.mark.slow -def test_hf(): - # Load the model and tokenizer - model = AutoModelForCausalLM.from_pretrained("roneneldan/TinyStories-1M") - tokenizer = AutoTokenizer.from_pretrained("roneneldan/TinyStories-1M") - - # count_parameters(model) - print(tokenizer) - - # Load the dataset - dataset = load_dataset("roneneldan/TinyStories", split="train") - dataset = tokenize_and_concatenate(dataset, tokenizer) - - # Set up the LLC estimator - - metrics_tpu = _test_hf(model, dataset, "tpu") - pp(metrics_tpu) - - metrics_cpu = _test_hf(model, dataset, "cpu") - pp(metrics_cpu) - - metrics_cpu.pop("llc/std") # 1 chain only - metrics_cpu.pop("loss/trace") # 1 chain only - - for k, v in metrics_cpu.items(): - if isinstance(v, torch.Tensor): - assert torch.allclose(v, metrics_tpu[k], rtol=3e-2), ( - f"Evaluation failed for {k}" - ) - elif isinstance(v, np.ndarray): - assert np.isclose(v, metrics_tpu[k], rtol=3e-2).all(), ( - f"Evaluation failed for {k}" - ) - else: - try: - is_close = np.isclose(v, metrics_tpu[k], rtol=3e-2) - except RuntimeError: - is_close = True - - assert is_close, f"Evaluation failed for {k}" diff --git a/tests/integration/test_vis_utils.py b/tests/integration/test_vis_utils.py deleted file mode 100644 index aa1a585f..00000000 --- a/tests/integration/test_vis_utils.py +++ /dev/null @@ -1,34 +0,0 @@ -import sys -from unittest import mock - -import pytest - - -# Importing EpsilonBetaAnalyzer within a mock context to simulate Plotly absence -def test_plot_without_plotly(): - with mock.patch.dict( - sys.modules, {"plotly.express": None, "plotly.graph_objects": None} - ): - with mock.patch("devinterp.vis_utils.warnings"): - from devinterp.vis_utils import EpsilonBetaAnalyzer - - analyzer = EpsilonBetaAnalyzer() - with pytest.raises(ImportError): - analyzer.plot() - - -def test_plot_with_plotly(): - # This test assumes Plotly is installed - from devinterp.vis_utils import EpsilonBetaAnalyzer - - analyzer = EpsilonBetaAnalyzer() - - # Mock data for plotting - analyzer.sweep_config = mock.Mock() - analyzer.sweep_config.epsilon_range = [1e-6, 1e-5] - analyzer.sweep_config.beta_range = [1e-2, 1e-1] - analyzer.beta_range = [1e-2, 1e-1] - - # with plotly plotting should raise an AssertionError (no data), which is past the early plotly return - with pytest.raises(AssertionError): - analyzer.plot() diff --git a/tests/models.json b/tests/models.json deleted file mode 100644 index 56f16a63..00000000 --- a/tests/models.json +++ /dev/null @@ -1 +0,0 @@ -{"4_3_8": {"fc1": [[0.13058477640151978, 0.2603178322315216, 0.07086149603128433, -0.2661084234714508], [-0.1588452011346817, -0.05944357067346573, 0.04334805905818939, 0.08745945245027542], [0.05306638777256012, -0.16910728812217712, 0.04627612605690956, 0.12260667234659195]], "fc2": [[-0.12713536620140076, -0.007516740821301937, -0.011074508540332317], [-0.12840858101844788, -0.24186375737190247, -0.3566165268421173], [0.20582033693790436, -0.018925359472632408, 0.1348433941602707], [-0.26652422547340393, -0.15888507664203644, -0.16494037210941315], [-0.13811787962913513, -0.10039176791906357, -0.05715123936533928], [0.0985599234700203, 0.4490949511528015, -0.24888499081134796], [0.1445678025484085, 0.29856258630752563, 0.15785619616508484], [-0.25682833790779114, -0.11722467839717865, -0.5305270552635193]]}, "8_3_4": {"fc1": [[-0.03370917961001396, 0.08595053851604462, 0.01831091195344925, 0.027756284922361374, -0.2176009565591812, 0.05304659903049469, -0.03143400698900223, -0.16821251809597015], [0.062464699149131775, 0.1574568897485733, -0.03328480198979378, -0.005895007401704788, 0.18066802620887756, 0.006586302537471056, -0.154925137758255, 0.03730711340904236], [-0.061966732144355774, -0.2201957404613495, -0.11023040860891342, -0.09611227363348007, -0.1141410693526268, 0.033560335636138916, 0.2126406580209732, 0.04798286035656929]], "fc2": [[0.3571043312549591, 0.44703516364097595, 0.21318890154361725], [0.10992469638586044, -0.2839270234107971, -0.33014217019081116], [0.1396838277578354, 0.5334465503692627, 0.23420463502407074], [-0.1724703460931778, -0.2058853805065155, -0.21934586763381958]]}, "5_3_5": {"fc1": [[-0.012223011814057827, -0.03316054120659828, -0.06839729845523834, -0.19000144302845, -0.07077501714229584], [0.21755027770996094, -0.0005100780981592834, 0.13884936273097992, 0.3359018862247467, -0.03140798956155777], [0.3327566385269165, -0.13295289874076843, 0.1481638103723526, 0.6502866744995117, 0.04740230366587639]], "fc2": [[-0.26332297921180725, 0.19614648818969727, -0.21261419355869293], [-0.05037456378340721, -0.3513050377368927, 0.1426653265953064], [-0.7164738774299622, -0.3162553608417511, -0.014696313068270683], [0.28868672251701355, 0.45780572295188904, -0.27246323227882385], [-0.247099369764328, -0.32337871193885803, 0.17474284768104553]]}, "5_4_5": {"fc1": [[0.2630667984485626, -0.10501153022050858, -0.04538096487522125, -0.18578453361988068, -0.2306794822216034], [-0.08310657739639282, 0.05372590944170952, 0.15168732404708862, 0.28534823656082153, -0.23013417422771454], [0.09412834793329239, 0.09814024716615677, 0.224988654255867, 0.1805080622434616, -0.5008586645126343], [-0.03843969479203224, 0.006145290099084377, -0.23114743828773499, -0.5473492741584778, 0.35272783041000366]], "fc2": [[-0.3367514908313751, -0.5626171827316284, 0.36749550700187683, -0.003542796242982149], [0.14652447402477264, 0.4026350677013397, -0.22727055847644806, 0.11946188658475876], [-0.1247502788901329, 0.34410548210144043, -0.094156913459301, 0.14523951709270477], [0.10138491541147232, 0.3606908321380615, 0.1380409151315689, 0.33559057116508484], [-0.047680873423814774, -0.1143849566578865, -0.12584728002548218, -0.1789838820695877]]}, "3_8_4": {"fc1": [[0.3417707681655884, 0.32514238357543945, 0.42512720823287964], [0.312994122505188, 0.3433730900287628, 0.6019430160522461], [-0.24778826534748077, -0.16265948116779327, 0.06295562535524368], [-0.12090058624744415, -0.4069649577140808, -0.09123721718788147], [-0.2644621729850769, -0.17942720651626587, -0.307197242975235], [0.01782120205461979, 0.14507688581943512, -0.16739334166049957], [0.10188020765781403, -0.33113330602645874, -0.27540266513824463], [0.14054042100906372, 0.37453901767730713, -0.39513781666755676]], "fc2": [[0.03517526388168335, -0.010829880833625793, -0.15717683732509613, -0.34510156512260437, 0.2738081216812134, -0.05011550337076187, 0.1175197958946228, -0.1267358958721161], [0.14495569467544556, -0.2553066611289978, 0.10820447653532028, 0.10771501809358597, -0.1484590470790863, -0.039411839097738266, -0.09331030398607254, 0.12301649153232574], [0.09267669171094894, -0.21842709183692932, -0.10575560480356216, -0.09297948330640793, -0.0859868973493576, 0.29451853036880493, 0.02039327844977379, -0.18633438646793365], [0.0333419069647789, -0.08419255167245865, -0.035236164927482605, -0.108195461332798, 0.02408733405172825, -0.29726704955101013, -0.12225140631198883, -0.0007779286243021488]]}} \ No newline at end of file diff --git a/tests/optim/__snapshots__/rllc_test.ambr b/tests/optim/__snapshots__/rllc_test.ambr deleted file mode 100644 index 208861e6..00000000 --- a/tests/optim/__snapshots__/rllc_test.ambr +++ /dev/null @@ -1,133 +0,0 @@ -# serializer version: 1 -# name: test_restricted_gradient_normalcrossing_between_dims[sample_point0-10-relevant_powers0-SGLD] - 0.0 -# --- -# name: test_restricted_gradient_normalcrossing_between_dims[sample_point0-10-relevant_powers0-sgld] - 0.0 -# --- -# name: test_restricted_gradient_normalcrossing_between_dims[sample_point0-10-relevant_powers1-SGLD] - 0.0 -# --- -# name: test_restricted_gradient_normalcrossing_between_dims[sample_point0-10-relevant_powers1-sgld] - 0.0 -# --- -# name: test_restricted_gradient_normalcrossing_between_dims[sample_point0-100-relevant_powers0-SGLD] - 0.0 -# --- -# name: test_restricted_gradient_normalcrossing_between_dims[sample_point0-100-relevant_powers0-sgld] - 0.0 -# --- -# name: test_restricted_gradient_normalcrossing_between_dims[sample_point0-100-relevant_powers1-SGLD] - 0.0 -# --- -# name: test_restricted_gradient_normalcrossing_between_dims[sample_point0-100-relevant_powers1-sgld] - 0.0 -# --- -# name: test_restricted_gradient_normalcrossing_between_dims[sample_point0-3-relevant_powers0-SGLD] - 0.0 -# --- -# name: test_restricted_gradient_normalcrossing_between_dims[sample_point0-3-relevant_powers0-sgld] - 0.0 -# --- -# name: test_restricted_gradient_normalcrossing_between_dims[sample_point0-3-relevant_powers1-SGLD] - 0.0 -# --- -# name: test_restricted_gradient_normalcrossing_between_dims[sample_point0-3-relevant_powers1-sgld] - 0.0 -# --- -# name: test_restricted_gradient_normalcrossing_between_dims[sample_point1-10-relevant_powers0-SGLD] - 0.0 -# --- -# name: test_restricted_gradient_normalcrossing_between_dims[sample_point1-10-relevant_powers0-sgld] - 0.0 -# --- -# name: test_restricted_gradient_normalcrossing_between_dims[sample_point1-10-relevant_powers1-SGLD] - 0.0 -# --- -# name: test_restricted_gradient_normalcrossing_between_dims[sample_point1-10-relevant_powers1-sgld] - 0.0 -# --- -# name: test_restricted_gradient_normalcrossing_between_dims[sample_point1-100-relevant_powers0-SGLD] - 0.0 -# --- -# name: test_restricted_gradient_normalcrossing_between_dims[sample_point1-100-relevant_powers0-sgld] - 0.0 -# --- -# name: test_restricted_gradient_normalcrossing_between_dims[sample_point1-100-relevant_powers1-SGLD] - 0.0 -# --- -# name: test_restricted_gradient_normalcrossing_between_dims[sample_point1-100-relevant_powers1-sgld] - 0.0 -# --- -# name: test_restricted_gradient_normalcrossing_between_dims[sample_point1-3-relevant_powers0-SGLD] - 0.0 -# --- -# name: test_restricted_gradient_normalcrossing_between_dims[sample_point1-3-relevant_powers0-sgld] - 0.0 -# --- -# name: test_restricted_gradient_normalcrossing_between_dims[sample_point1-3-relevant_powers1-SGLD] - 0.0 -# --- -# name: test_restricted_gradient_normalcrossing_between_dims[sample_point1-3-relevant_powers1-sgld] - 0.0 -# --- -# name: test_rllc_different_from_full_llc_between_dims[relevant_powers0-SGLD] - 990.0466468475643 -# --- -# name: test_rllc_different_from_full_llc_between_dims[relevant_powers0-sgld] - 990.0466468475643 -# --- -# name: test_rllc_full_normalcrossing_between_dims[sample_0.0_0.0_1.0-extra_dim_10-powers_0_2-SGLD] - 9.599380427971482e-05 -# --- -# name: test_rllc_full_normalcrossing_between_dims[sample_0.0_0.0_1.0-extra_dim_10-powers_0_2-sgld] - 9.599380427971482e-05 -# --- -# name: test_rllc_full_normalcrossing_between_dims[sample_0.0_0.0_1.0-extra_dim_100-powers_0_2-SGLD] - 9.599380427971482e-05 -# --- -# name: test_rllc_full_normalcrossing_between_dims[sample_0.0_0.0_1.0-extra_dim_100-powers_0_2-sgld] - 9.599380427971482e-05 -# --- -# name: test_rllc_full_normalcrossing_between_dims[sample_0.0_0.0_1.0-extra_dim_3-powers_0_2-SGLD] - 9.599380427971482e-05 -# --- -# name: test_rllc_full_normalcrossing_between_dims[sample_0.0_0.0_1.0-extra_dim_3-powers_0_2-sgld] - 9.599380427971482e-05 -# --- -# name: test_rllc_normalcrossing_between_powers[sample_point0-powers0-SGLD] - 0.0 -# --- -# name: test_rllc_normalcrossing_between_powers[sample_point0-powers0-sgld] - 0.0 -# --- -# name: test_rllc_normalcrossing_between_powers[sample_point0-powers1-SGLD] - 0.0 -# --- -# name: test_rllc_normalcrossing_between_powers[sample_point0-powers1-sgld] - 0.0 -# --- -# name: test_rllc_normalcrossing_between_powers[sample_point0-powers2-SGLD] - 0.0 -# --- -# name: test_rllc_normalcrossing_between_powers[sample_point0-powers2-sgld] - 0.0 -# --- -# name: test_rllc_normalcrossing_between_powers[sample_point1-powers0-SGLD] - 0.0 -# --- -# name: test_rllc_normalcrossing_between_powers[sample_point1-powers0-sgld] - 0.0 -# --- -# name: test_rllc_normalcrossing_between_powers[sample_point1-powers1-SGLD] - 0.0 -# --- -# name: test_rllc_normalcrossing_between_powers[sample_point1-powers1-sgld] - 0.0 -# --- -# name: test_rllc_normalcrossing_between_powers[sample_point1-powers2-SGLD] - 0.0 -# --- -# name: test_rllc_normalcrossing_between_powers[sample_point1-powers2-sgld] - 0.0 -# --- diff --git a/tests/optim/__snapshots__/sgld_test.ambr b/tests/optim/__snapshots__/sgld_test.ambr index 7696000d..29750f73 100644 --- a/tests/optim/__snapshots__/sgld_test.ambr +++ b/tests/optim/__snapshots__/sgld_test.ambr @@ -1,32 +1,4 @@ # serializer version: 1 -# name: test_SGLD_metrics_tracking[SGLD][test_SGLD_metrics_tracking] - dict({ - 'distance': array(0., dtype=float32), - 'grad_norm': array(0.05733979, dtype=float32), - 'noise_norm': array(0.5136473, dtype=float32), - 'weight_norm': array(1.3351691, dtype=float32), - }) -# --- -# name: test_SGLD_metrics_tracking[sgld][test_SGLD_metrics_tracking] - dict({ - 'distance': array(0., dtype=float32), - 'grad_norm': array(0.05733979, dtype=float32), - 'noise_norm': array(0.5136473, dtype=float32), - 'weight_norm': array(1.3351691, dtype=float32), - }) -# --- -# name: test_SGLD_selective_metrics[SGLD][test_SGLD_selective_metrics] - dict({ - 'grad_norm': array(0.05733979, dtype=float32), - 'weight_norm': array(1.3351691, dtype=float32), - }) -# --- -# name: test_SGLD_selective_metrics[sgld][test_SGLD_selective_metrics] - dict({ - 'grad_norm': array(0.05733979, dtype=float32), - 'weight_norm': array(1.3351691, dtype=float32), - }) -# --- # name: test_SGLD_vs_SGD[SGLD-0.0001][test_SGLD_vs_SGD_0.0001] dict({ 'model1': dict({ diff --git a/tests/optim/__snapshots__/test_sgmcmc.ambr b/tests/optim/__snapshots__/test_sgmcmc.ambr index 737531ac..832b78c1 100644 --- a/tests/optim/__snapshots__/test_sgmcmc.ambr +++ b/tests/optim/__snapshots__/test_sgmcmc.ambr @@ -1090,145 +1090,20 @@ ]), }), 'optimizer_rmsprop': dict({ - 'grad_norm': '0.000082', - 'noise': list([ - list([ - list([ - '-0.44', - '-2.3', - '1.3', - '0.22', - '-1.3', - ]), - list([ - '0.17', - '-0.83', - '-0.81', - '0.43', - '-0.052', - ]), - list([ - '-0.19', - '-0.79', - '-1.3', - '-0.038', - '1.9', - ]), - list([ - '0.34', - '0.13', - '0.74', - '-0.31', - '0.59', - ]), - list([ - '0.46', - '0.25', - '-1.4', - '1.8', - '-0.074', - ]), - ]), - list([ - '-0.026', - '0.51', - '-0.13', - '-0.74', - '0.38', - ]), - list([ - list([ - '0.036', - '1.0', - '0.88', - '-1.3', - '-0.37', - ]), - list([ - '1.0', - '-1.4', - '2.1', - '-1.2', - '0.27', - ]), - list([ - '2.7', - '0.43', - '-0.31', - '-0.097', - '1.5', - ]), - list([ - '-1.5', - '1.9', - '1.4', - '-0.71', - '-1.3', - ]), - list([ - '3.0', - '1.3', - '0.86', - '-1.5', - '1.3', - ]), - ]), - list([ - '-0.25', - '-0.45', - '-1.1', - '-0.38', - '-0.74', - ]), - list([ - list([ - '-1.0', - '1.8', - '-0.6', - '-0.15', - '-0.39', - ]), - list([ - '0.81', - '0.96', - '0.10', - '-2.3', - '1.3', - ]), - list([ - '0.29', - '0.87', - '-0.81', - '-1.4', - '-0.15', - ]), - list([ - '-0.8', - '1.9', - '2.0', - '1.8', - '-0.99', - ]), - list([ - '-1.2', - '-2.3', - '0.61', - '0.035', - '-0.51', - ]), - ]), - list([ - '-1.8', - '-1.3', - '0.21', - '-0.19', - '-0.60', - ]), - ]), - 'noise_norm': '11.', - 'weight_norm': '2.5', + 'localization': '0.00000095', + 'noise': '0.11', + 'numel': 90, + 'prior': '0.00000095', + 'scaled_grad': '0.000082', + 'weight_decay': '0.0', }), 'optimizer_sgld': dict({ + 'localization': '0.00000095', + 'noise': '0.11', + 'numel': 90, + 'prior': '0.00000095', + 'scaled_grad': '0.000082', + 'weight_decay': '0.0', }), }) # --- @@ -1503,145 +1378,20 @@ ]), }), 'optimizer_rmsprop': dict({ - 'grad_norm': '0.00094', - 'noise': list([ - list([ - list([ - '-0.44', - '-2.3', - '1.3', - '0.22', - '-1.3', - ]), - list([ - '0.17', - '-0.83', - '-0.81', - '0.43', - '-0.052', - ]), - list([ - '-0.19', - '-0.79', - '-1.3', - '-0.038', - '1.9', - ]), - list([ - '0.34', - '0.13', - '0.74', - '-0.31', - '0.59', - ]), - list([ - '0.46', - '0.25', - '-1.4', - '1.8', - '-0.074', - ]), - ]), - list([ - '-0.026', - '0.51', - '-0.13', - '-0.74', - '0.38', - ]), - list([ - list([ - '0.036', - '1.0', - '0.88', - '-1.3', - '-0.37', - ]), - list([ - '1.0', - '-1.4', - '2.1', - '-1.2', - '0.27', - ]), - list([ - '2.7', - '0.43', - '-0.31', - '-0.097', - '1.5', - ]), - list([ - '-1.5', - '1.9', - '1.4', - '-0.71', - '-1.3', - ]), - list([ - '3.0', - '1.3', - '0.86', - '-1.5', - '1.3', - ]), - ]), - list([ - '-0.25', - '-0.45', - '-1.1', - '-0.38', - '-0.74', - ]), - list([ - list([ - '-1.0', - '1.8', - '-0.6', - '-0.15', - '-0.39', - ]), - list([ - '0.81', - '0.96', - '0.10', - '-2.3', - '1.3', - ]), - list([ - '0.29', - '0.87', - '-0.81', - '-1.4', - '-0.15', - ]), - list([ - '-0.8', - '1.9', - '2.0', - '1.8', - '-0.99', - ]), - list([ - '-1.2', - '-2.3', - '0.61', - '0.035', - '-0.51', - ]), - ]), - list([ - '-1.8', - '-1.3', - '0.21', - '-0.19', - '-0.60', - ]), - ]), - 'noise_norm': '11.', - 'weight_norm': '2.6', + 'localization': '0.00003', + 'noise': '0.34', + 'numel': 90, + 'prior': '0.00003', + 'scaled_grad': '0.00094', + 'weight_decay': '0.0', }), 'optimizer_sgld': dict({ + 'localization': '0.00003', + 'noise': '0.34', + 'numel': 90, + 'prior': '0.00003', + 'scaled_grad': '0.00094', + 'weight_decay': '0.0', }), }) # --- @@ -1916,145 +1666,20 @@ ]), }), 'optimizer_rmsprop': dict({ - 'grad_norm': '0.011', - 'noise': list([ - list([ - list([ - '-0.44', - '-2.3', - '1.3', - '0.22', - '-1.3', - ]), - list([ - '0.17', - '-0.83', - '-0.81', - '0.43', - '-0.052', - ]), - list([ - '-0.19', - '-0.79', - '-1.3', - '-0.038', - '1.9', - ]), - list([ - '0.34', - '0.13', - '0.74', - '-0.31', - '0.59', - ]), - list([ - '0.46', - '0.25', - '-1.4', - '1.8', - '-0.074', - ]), - ]), - list([ - '-0.026', - '0.51', - '-0.13', - '-0.74', - '0.38', - ]), - list([ - list([ - '0.036', - '1.0', - '0.88', - '-1.3', - '-0.37', - ]), - list([ - '1.0', - '-1.4', - '2.1', - '-1.2', - '0.27', - ]), - list([ - '2.7', - '0.43', - '-0.31', - '-0.097', - '1.5', - ]), - list([ - '-1.5', - '1.9', - '1.4', - '-0.71', - '-1.3', - ]), - list([ - '3.0', - '1.3', - '0.86', - '-1.5', - '1.3', - ]), - ]), - list([ - '-0.25', - '-0.45', - '-1.1', - '-0.38', - '-0.74', - ]), - list([ - list([ - '-1.0', - '1.8', - '-0.6', - '-0.15', - '-0.39', - ]), - list([ - '0.81', - '0.96', - '0.10', - '-2.3', - '1.3', - ]), - list([ - '0.29', - '0.87', - '-0.81', - '-1.4', - '-0.15', - ]), - list([ - '-0.8', - '1.9', - '2.0', - '1.8', - '-0.99', - ]), - list([ - '-1.2', - '-2.3', - '0.61', - '0.035', - '-0.51', - ]), - ]), - list([ - '-1.8', - '-1.3', - '0.21', - '-0.19', - '-0.60', - ]), - ]), - 'noise_norm': '11.', - 'weight_norm': '3.4', + 'localization': '0.00094', + 'noise': '1.1', + 'numel': 90, + 'prior': '0.00094', + 'scaled_grad': '0.011', + 'weight_decay': '0.0', }), 'optimizer_sgld': dict({ + 'localization': '0.00094', + 'noise': '1.1', + 'numel': 90, + 'prior': '0.00094', + 'scaled_grad': '0.011', + 'weight_decay': '0.0', }), }) # --- @@ -2329,217 +1954,116 @@ ]), }), 'optimizer_rmsprop': dict({ - 'grad_norm': '1.3', - 'noise': list([ - list([ - list([ - '-0.44', - '-2.3', - '1.3', - '0.22', - '-1.3', - ]), - list([ - '0.17', - '-0.83', - '-0.81', - '0.43', - '-0.052', - ]), - list([ - '-0.19', - '-0.79', - '-1.3', - '-0.038', - '1.9', - ]), - list([ - '0.34', - '0.13', - '0.74', - '-0.31', - '0.59', - ]), - list([ - '0.46', - '0.25', - '-1.4', - '1.8', - '-0.074', - ]), - ]), - list([ - '-0.026', - '0.51', - '-0.13', - '-0.74', - '0.38', - ]), - list([ - list([ - '0.036', - '1.0', - '0.88', - '-1.3', - '-0.37', - ]), - list([ - '1.0', - '-1.4', - '2.1', - '-1.2', - '0.27', - ]), - list([ - '2.7', - '0.43', - '-0.31', - '-0.097', - '1.5', - ]), - list([ - '-1.5', - '1.9', - '1.4', - '-0.71', - '-1.3', - ]), - list([ - '3.0', - '1.3', - '0.86', - '-1.5', - '1.3', - ]), - ]), - list([ - '-0.25', - '-0.45', - '-1.1', - '-0.38', - '-0.74', - ]), - list([ - list([ - '-1.0', - '1.8', - '-0.6', - '-0.15', - '-0.39', - ]), - list([ - '0.81', - '0.96', - '0.10', - '-2.3', - '1.3', - ]), - list([ - '0.29', - '0.87', - '-0.81', - '-1.4', - '-0.15', - ]), - list([ - '-0.8', - '1.9', - '2.0', - '1.8', - '-0.99', - ]), - list([ - '-1.2', - '-2.3', - '0.61', - '0.035', - '-0.51', - ]), - ]), - list([ - '-1.8', - '-1.3', - '0.21', - '-0.19', - '-0.60', - ]), - ]), - 'noise_norm': '11.', - 'weight_norm': '7.0', + 'localization': '0.028', + 'noise': '3.4', + 'numel': 90, + 'prior': '0.028', + 'scaled_grad': '1.3', + 'weight_decay': '0.0', }), 'optimizer_sgld': dict({ + 'localization': '0.028', + 'noise': '3.4', + 'numel': 90, + 'prior': '0.028', + 'scaled_grad': '1.3', + 'weight_decay': '0.0', }), }) # --- # name: test_SGMCMC_rmsprop_sgld[False-0.1-0.9-0.01][test_SGMCMC_rmsprop_sgld] dict({ 'optimizer': dict({ - 'grad_norm': '0.016', - 'noise_norm': '9.6', - 'weight_norm': '5.3', + 'localization': '0.0', + 'noise': '2.0', + 'numel': 90, + 'prior': '0.0', + 'scaled_grad': '0.050', + 'weight_decay': '0.0', }), }) # --- # name: test_SGMCMC_rmsprop_sgld[False-0.1-0.9-0.1][test_SGMCMC_rmsprop_sgld] dict({ 'optimizer': dict({ - 'grad_norm': '0.99', - 'noise_norm': '9.2', - 'weight_norm': '7.6', + 'localization': '0.0', + 'noise': '2.1', + 'numel': 90, + 'prior': '0.0', + 'scaled_grad': '0.35', + 'weight_decay': '0.0', }), }) # --- # name: test_SGMCMC_rmsprop_sgld[False-0.1-0.99-0.01][test_SGMCMC_rmsprop_sgld] dict({ 'optimizer': dict({ - 'grad_norm': '0.032', - 'noise_norm': '10.', - 'weight_norm': '5.8', + 'localization': '0.0', + 'noise': '2.4', + 'numel': 90, + 'prior': '0.0', + 'scaled_grad': '0.14', + 'weight_decay': '0.0', }), }) # --- # name: test_SGMCMC_rmsprop_sgld[False-0.1-0.99-0.1][test_SGMCMC_rmsprop_sgld] dict({ 'optimizer': dict({ - 'grad_norm': '1.2', - 'noise_norm': '9.0', - 'weight_norm': '9.5', + 'localization': '0.0', + 'noise': '2.0', + 'numel': 90, + 'prior': '0.0', + 'scaled_grad': '0.53', + 'weight_decay': '0.0', }), }) # --- # name: test_SGMCMC_rmsprop_sgld[False-1e-08-0.9-0.01][test_SGMCMC_rmsprop_sgld] dict({ 'optimizer': dict({ - 'grad_norm': '0.27', - 'noise_norm': '9.1', - 'weight_norm': '8.0', + 'localization': '0.0', + 'noise': '0.97', + 'numel': 90, + 'prior': '0.0', + 'scaled_grad': '0.076', + 'weight_decay': '0.0', }), }) # --- # name: test_SGMCMC_rmsprop_sgld[False-1e-08-0.9-0.1][test_SGMCMC_rmsprop_sgld] dict({ 'optimizer': dict({ - 'grad_norm': '2100.', - 'noise_norm': '8.9', - 'weight_norm': '30.', + 'localization': '0.0', + 'noise': '0.43', + 'numel': 90, + 'prior': '0.0', + 'scaled_grad': '0.55', + 'weight_decay': '0.0', }), }) # --- # name: test_SGMCMC_rmsprop_sgld[False-1e-08-0.99-0.01][test_SGMCMC_rmsprop_sgld] dict({ 'optimizer': dict({ - 'grad_norm': '120.', - 'noise_norm': '8.1', - 'weight_norm': '21.', + 'localization': '0.0', + 'noise': '0.25', + 'numel': 90, + 'prior': '0.0', + 'scaled_grad': '0.22', + 'weight_decay': '0.0', }), }) # --- # name: test_SGMCMC_rmsprop_sgld[False-1e-08-0.99-0.1][test_SGMCMC_rmsprop_sgld] dict({ 'optimizer': dict({ - 'grad_norm': '340000.', - 'noise_norm': '8.6', - 'weight_norm': '95.', + 'localization': '0.0', + 'noise': '0.15', + 'numel': 90, + 'prior': '0.0', + 'scaled_grad': '1.7', + 'weight_decay': '0.0', }), }) # --- @@ -2814,281 +2338,20 @@ ]), }), 'optimizer_sgld': dict({ + 'localization': '0.0', + 'noise': '0.089', + 'numel': 90, + 'prior': '0.0', + 'scaled_grad': '0.000080', + 'weight_decay': '0.0', }), 'optimizer_sgmcmc': dict({ - 'distance': '0.0', - 'dws': list([ - list([ - list([ - '0.15', - '-0.029', - '-0.17', - '-0.13', - '-0.028', - ]), - list([ - '-0.15', - '0.070', - '0.16', - '0.056', - '-0.0051', - ]), - list([ - '0.28', - '-0.016', - '-0.35', - '-0.27', - '-0.10', - ]), - list([ - '-0.036', - '0.023', - '0.031', - '0.0095', - '-0.011', - ]), - list([ - '0.15', - '0.013', - '-0.19', - '-0.17', - '-0.076', - ]), - ]), - list([ - '-0.11', - '0.11', - '-0.18', - '0.030', - '-0.089', - ]), - list([ - list([ - '-0.33', - '0.26', - '-0.46', - '-0.066', - '-0.021', - ]), - list([ - '0.11', - '-0.062', - '0.12', - '0.030', - '0.0066', - ]), - list([ - '0.24', - '-0.22', - '0.37', - '0.045', - '0.012', - ]), - list([ - '0.047', - '-0.0078', - '0.027', - '0.018', - '0.0051', - ]), - list([ - '0.32', - '-0.2', - '0.39', - '0.10', - '0.0094', - ]), - ]), - list([ - '0.32', - '-0.06', - '-0.29', - '0.017', - '-0.23', - ]), - list([ - list([ - '0.037', - '-0.072', - '-0.16', - '-0.091', - '-0.023', - ]), - list([ - '-0.22', - '0.11', - '0.20', - '0.076', - '0.15', - ]), - list([ - '-0.26', - '0.087', - '0.12', - '-0.00082', - '0.14', - ]), - list([ - '-0.0026', - '-0.0089', - '-0.033', - '-0.032', - '-0.036', - ]), - list([ - '-0.28', - '0.12', - '0.19', - '0.049', - '0.17', - ]), - ]), - list([ - '0.23', - '-0.37', - '-0.18', - '0.12', - '-0.33', - ]), - ]), - 'grad_norm': '0.000080', - 'localization_loss': '0.0', - 'noise': list([ - list([ - list([ - '0.5', - '-0.43', - '-1.3', - '-0.19', - '0.65', - ]), - list([ - '-1.9', - '0.23', - '0.025', - '0.19', - '-0.93', - ]), - list([ - '-1.6', - '-1.1', - '-0.52', - '0.72', - '1.5', - ]), - list([ - '-1.5', - '-0.79', - '1.0', - '-0.56', - '0.70', - ]), - list([ - '0.71', - '-1.5', - '-0.73', - '0.47', - '0.67', - ]), - ]), - list([ - '0.11', - '2.4', - '0.92', - '-0.64', - '0.71', - ]), - list([ - list([ - '1.1', - '0.21', - '-0.58', - '0.33', - '-0.81', - ]), - list([ - '-1.0', - '-0.49', - '-0.59', - '0.40', - '0.084', - ]), - list([ - '0.4', - '2.0', - '-0.46', - '-0.064', - '-1.4', - ]), - list([ - '0.33', - '-0.98', - '0.078', - '0.19', - '0.41', - ]), - list([ - '-1.6', - '2.3', - '1.0', - '1.4', - '0.63', - ]), - ]), - list([ - '0.36', - '0.82', - '-0.36', - '0.071', - '0.61', - ]), - list([ - list([ - '0.56', - '-1.5', - '-1.6', - '-1.5', - '0.43', - ]), - list([ - '-0.13', - '0.78', - '0.56', - '-0.11', - '-1.0', - ]), - list([ - '-0.22', - '0.8', - '0.91', - '-0.088', - '0.34', - ]), - list([ - '0.97', - '-1.0', - '0.39', - '0.73', - '0.25', - ]), - list([ - '0.077', - '-0.21', - '2.2', - '2.0', - '0.25', - ]), - ]), - list([ - '0.24', - '-0.26', - '-0.46', - '-1.2', - '-1.2', - ]), - ]), - 'noise_norm': '8.9', - 'weight_norm': '2.5', + 'localization': '0.0', + 'noise': '0.089', + 'numel': 90, + 'prior': '0.0', + 'scaled_grad': '0.000080', + 'weight_decay': '0.0', }), }) # --- @@ -3363,281 +2626,20 @@ ]), }), 'optimizer_sgld': dict({ + 'localization': '0.0', + 'noise': '0.28', + 'numel': 90, + 'prior': '0.0', + 'scaled_grad': '0.00085', + 'weight_decay': '0.0', }), 'optimizer_sgmcmc': dict({ - 'distance': '0.0', - 'dws': list([ - list([ - list([ - '0.16', - '-0.032', - '-0.17', - '-0.13', - '-0.026', - ]), - list([ - '-0.17', - '0.077', - '0.19', - '0.072', - '0.0010', - ]), - list([ - '0.30', - '-0.021', - '-0.38', - '-0.29', - '-0.12', - ]), - list([ - '-0.04', - '0.026', - '0.031', - '0.011', - '-0.015', - ]), - list([ - '0.16', - '0.012', - '-0.21', - '-0.18', - '-0.083', - ]), - ]), - list([ - '-0.11', - '0.13', - '-0.19', - '0.035', - '-0.094', - ]), - list([ - list([ - '-0.36', - '0.27', - '-0.51', - '-0.057', - '-0.036', - ]), - list([ - '0.13', - '-0.068', - '0.15', - '0.027', - '0.016', - ]), - list([ - '0.26', - '-0.21', - '0.39', - '0.033', - '0.024', - ]), - list([ - '0.041', - '0.00078', - '0.019', - '0.017', - '0.0051', - ]), - list([ - '0.34', - '-0.18', - '0.40', - '0.09', - '0.022', - ]), - ]), - list([ - '0.35', - '-0.065', - '-0.30', - '0.024', - '-0.22', - ]), - list([ - list([ - '0.035', - '-0.069', - '-0.16', - '-0.094', - '-0.039', - ]), - list([ - '-0.22', - '0.12', - '0.21', - '0.082', - '0.19', - ]), - list([ - '-0.25', - '0.10', - '0.11', - '0.0036', - '0.16', - ]), - list([ - '0.0092', - '-0.0083', - '-0.047', - '-0.036', - '-0.049', - ]), - list([ - '-0.28', - '0.13', - '0.19', - '0.053', - '0.21', - ]), - ]), - list([ - '0.24', - '-0.38', - '-0.17', - '0.14', - '-0.33', - ]), - ]), - 'grad_norm': '0.00085', - 'localization_loss': '0.0', - 'noise': list([ - list([ - list([ - '0.5', - '-0.43', - '-1.3', - '-0.19', - '0.65', - ]), - list([ - '-1.9', - '0.23', - '0.025', - '0.19', - '-0.93', - ]), - list([ - '-1.6', - '-1.1', - '-0.52', - '0.72', - '1.5', - ]), - list([ - '-1.5', - '-0.79', - '1.0', - '-0.56', - '0.70', - ]), - list([ - '0.71', - '-1.5', - '-0.73', - '0.47', - '0.67', - ]), - ]), - list([ - '0.11', - '2.4', - '0.92', - '-0.64', - '0.71', - ]), - list([ - list([ - '1.1', - '0.21', - '-0.58', - '0.33', - '-0.81', - ]), - list([ - '-1.0', - '-0.49', - '-0.59', - '0.40', - '0.084', - ]), - list([ - '0.4', - '2.0', - '-0.46', - '-0.064', - '-1.4', - ]), - list([ - '0.33', - '-0.98', - '0.078', - '0.19', - '0.41', - ]), - list([ - '-1.6', - '2.3', - '1.0', - '1.4', - '0.63', - ]), - ]), - list([ - '0.36', - '0.82', - '-0.36', - '0.071', - '0.61', - ]), - list([ - list([ - '0.56', - '-1.5', - '-1.6', - '-1.5', - '0.43', - ]), - list([ - '-0.13', - '0.78', - '0.56', - '-0.11', - '-1.0', - ]), - list([ - '-0.22', - '0.8', - '0.91', - '-0.088', - '0.34', - ]), - list([ - '0.97', - '-1.0', - '0.39', - '0.73', - '0.25', - ]), - list([ - '0.077', - '-0.21', - '2.2', - '2.0', - '0.25', - ]), - ]), - list([ - '0.24', - '-0.26', - '-0.46', - '-1.2', - '-1.2', - ]), - ]), - 'noise_norm': '8.9', - 'weight_norm': '2.5', + 'localization': '0.0', + 'noise': '0.28', + 'numel': 90, + 'prior': '0.0', + 'scaled_grad': '0.00085', + 'weight_decay': '0.0', }), }) # --- @@ -3912,281 +2914,20 @@ ]), }), 'optimizer_sgld': dict({ + 'localization': '0.0', + 'noise': '0.89', + 'numel': 90, + 'prior': '0.0', + 'scaled_grad': '0.0089', + 'weight_decay': '0.0', }), 'optimizer_sgmcmc': dict({ - 'distance': '0.0', - 'dws': list([ - list([ - list([ - '0.12', - '-0.029', - '-0.12', - '-0.097', - '-0.0059', - ]), - list([ - '-0.2', - '0.085', - '0.23', - '0.081', - '0.011', - ]), - list([ - '0.36', - '-0.029', - '-0.47', - '-0.32', - '-0.15', - ]), - list([ - '-0.018', - '0.028', - '-0.0094', - '-0.0087', - '-0.036', - ]), - list([ - '0.18', - '0.012', - '-0.25', - '-0.19', - '-0.10', - ]), - ]), - list([ - '-0.089', - '0.14', - '-0.23', - '0.025', - '-0.10', - ]), - list([ - list([ - '-0.34', - '0.24', - '-0.57', - '-0.032', - '-0.080', - ]), - list([ - '0.16', - '-0.08', - '0.22', - '0.020', - '0.048', - ]), - list([ - '0.21', - '-0.18', - '0.39', - '0.0081', - '0.053', - ]), - list([ - '0.049', - '0.0043', - '0.028', - '0.018', - '0.0069', - ]), - list([ - '0.30', - '-0.13', - '0.39', - '0.068', - '0.044', - ]), - ]), - list([ - '0.38', - '-0.083', - '-0.3', - '0.023', - '-0.2', - ]), - list([ - list([ - '0.029', - '-0.070', - '-0.13', - '-0.096', - '-0.067', - ]), - list([ - '-0.22', - '0.15', - '0.17', - '0.11', - '0.26', - ]), - list([ - '-0.25', - '0.14', - '0.078', - '0.027', - '0.21', - ]), - list([ - '-0.0085', - '0.0088', - '-0.046', - '-0.042', - '-0.059', - ]), - list([ - '-0.28', - '0.17', - '0.14', - '0.079', - '0.27', - ]), - ]), - list([ - '0.24', - '-0.39', - '-0.16', - '0.14', - '-0.31', - ]), - ]), - 'grad_norm': '0.0089', - 'localization_loss': '0.0', - 'noise': list([ - list([ - list([ - '0.5', - '-0.43', - '-1.3', - '-0.19', - '0.65', - ]), - list([ - '-1.9', - '0.23', - '0.025', - '0.19', - '-0.93', - ]), - list([ - '-1.6', - '-1.1', - '-0.52', - '0.72', - '1.5', - ]), - list([ - '-1.5', - '-0.79', - '1.0', - '-0.56', - '0.70', - ]), - list([ - '0.71', - '-1.5', - '-0.73', - '0.47', - '0.67', - ]), - ]), - list([ - '0.11', - '2.4', - '0.92', - '-0.64', - '0.71', - ]), - list([ - list([ - '1.1', - '0.21', - '-0.58', - '0.33', - '-0.81', - ]), - list([ - '-1.0', - '-0.49', - '-0.59', - '0.40', - '0.084', - ]), - list([ - '0.4', - '2.0', - '-0.46', - '-0.064', - '-1.4', - ]), - list([ - '0.33', - '-0.98', - '0.078', - '0.19', - '0.41', - ]), - list([ - '-1.6', - '2.3', - '1.0', - '1.4', - '0.63', - ]), - ]), - list([ - '0.36', - '0.82', - '-0.36', - '0.071', - '0.61', - ]), - list([ - list([ - '0.56', - '-1.5', - '-1.6', - '-1.5', - '0.43', - ]), - list([ - '-0.13', - '0.78', - '0.56', - '-0.11', - '-1.0', - ]), - list([ - '-0.22', - '0.8', - '0.91', - '-0.088', - '0.34', - ]), - list([ - '0.97', - '-1.0', - '0.39', - '0.73', - '0.25', - ]), - list([ - '0.077', - '-0.21', - '2.2', - '2.0', - '0.25', - ]), - ]), - list([ - '0.24', - '-0.26', - '-0.46', - '-1.2', - '-1.2', - ]), - ]), - 'noise_norm': '8.9', - 'weight_norm': '2.6', + 'localization': '0.0', + 'noise': '0.89', + 'numel': 90, + 'prior': '0.0', + 'scaled_grad': '0.0089', + 'weight_decay': '0.0', }), }) # --- @@ -4461,295 +3202,34 @@ ]), }), 'optimizer_sgld': dict({ + 'localization': '0.0', + 'noise': '2.8', + 'numel': 90, + 'prior': '0.0', + 'scaled_grad': '0.073', + 'weight_decay': '0.0', }), 'optimizer_sgmcmc': dict({ - 'distance': '0.0', - 'dws': list([ - list([ - list([ - '0.079', - '-0.022', - '-0.063', - '-0.069', - '0.0092', - ]), - list([ - '-0.16', - '0.088', - '0.19', - '0.039', - '-0.0020', - ]), - list([ - '0.30', - '-0.0056', - '-0.41', - '-0.29', - '-0.15', - ]), - list([ - '-0.00059', - '0.028', - '-0.037', - '-0.022', - '-0.047', - ]), - list([ - '0.14', - '0.029', - '-0.21', - '-0.17', - '-0.11', - ]), - ]), - list([ - '-0.065', - '0.12', - '-0.18', - '0.016', - '-0.071', - ]), - list([ - list([ - '-0.29', - '0.18', - '-0.46', - '-0.041', - '-0.037', - ]), - list([ - '0.12', - '-0.061', - '0.18', - '0.021', - '0.043', - ]), - list([ - '0.15', - '-0.13', - '0.28', - '0.0043', - '0.023', - ]), - list([ - '0.06', - '0.0042', - '0.041', - '0.026', - '0.0026', - ]), - list([ - '0.24', - '-0.067', - '0.27', - '0.072', - '0.00034', - ]), - ]), - list([ - '0.33', - '-0.053', - '-0.25', - '0.018', - '-0.13', - ]), - list([ - list([ - '-0.014', - '-0.05', - '-0.095', - '-0.078', - '-0.054', - ]), - list([ - '-0.16', - '0.13', - '0.12', - '0.11', - '0.24', - ]), - list([ - '-0.22', - '0.14', - '0.017', - '0.026', - '0.18', - ]), - list([ - '-0.064', - '0.052', - '-0.040', - '-0.029', - '-0.023', - ]), - list([ - '-0.23', - '0.17', - '0.069', - '0.07', - '0.23', - ]), - ]), - list([ - '0.21', - '-0.36', - '-0.12', - '0.091', - '-0.25', - ]), - ]), - 'grad_norm': '0.073', - 'localization_loss': '0.0', - 'noise': list([ - list([ - list([ - '0.5', - '-0.43', - '-1.3', - '-0.19', - '0.65', - ]), - list([ - '-1.9', - '0.23', - '0.025', - '0.19', - '-0.93', - ]), - list([ - '-1.6', - '-1.1', - '-0.52', - '0.72', - '1.5', - ]), - list([ - '-1.5', - '-0.79', - '1.0', - '-0.56', - '0.70', - ]), - list([ - '0.71', - '-1.5', - '-0.73', - '0.47', - '0.67', - ]), - ]), - list([ - '0.11', - '2.4', - '0.92', - '-0.64', - '0.71', - ]), - list([ - list([ - '1.1', - '0.21', - '-0.58', - '0.33', - '-0.81', - ]), - list([ - '-1.0', - '-0.49', - '-0.59', - '0.40', - '0.084', - ]), - list([ - '0.4', - '2.0', - '-0.46', - '-0.064', - '-1.4', - ]), - list([ - '0.33', - '-0.98', - '0.078', - '0.19', - '0.41', - ]), - list([ - '-1.6', - '2.3', - '1.0', - '1.4', - '0.63', - ]), - ]), - list([ - '0.36', - '0.82', - '-0.36', - '0.071', - '0.61', - ]), - list([ - list([ - '0.56', - '-1.5', - '-1.6', - '-1.5', - '0.43', - ]), - list([ - '-0.13', - '0.78', - '0.56', - '-0.11', - '-1.0', - ]), - list([ - '-0.22', - '0.8', - '0.91', - '-0.088', - '0.34', - ]), - list([ - '0.97', - '-1.0', - '0.39', - '0.73', - '0.25', - ]), - list([ - '0.077', - '-0.21', - '2.2', - '2.0', - '0.25', - ]), - ]), - list([ - '0.24', - '-0.26', - '-0.46', - '-1.2', - '-1.2', - ]), - ]), - 'noise_norm': '8.9', - 'weight_norm': '2.7', - }), - }) -# --- -# name: test_SGMCMC_vs_SGLD[0.0-0.1-0.0-10.0-0.0001][test_SGMCMC_vs_SGLD_0.0001_10.0_0.0_0.1_0.0] - dict({ - 'model1': dict({ - '0.bias': list([ - '0.06', - '0.42', - '-0.41', - '-0.27', - '-0.12', - ]), - '0.weight': list([ + 'localization': '0.0', + 'noise': '2.8', + 'numel': 90, + 'prior': '0.0', + 'scaled_grad': '0.073', + 'weight_decay': '0.0', + }), + }) +# --- +# name: test_SGMCMC_vs_SGLD[0.0-0.1-0.0-10.0-0.0001][test_SGMCMC_vs_SGLD_0.0001_10.0_0.0_0.1_0.0] + dict({ + 'model1': dict({ + '0.bias': list([ + '0.06', + '0.42', + '-0.41', + '-0.27', + '-0.12', + ]), + '0.weight': list([ list([ '0.021', '0.25', @@ -5010,281 +3490,20 @@ ]), }), 'optimizer_sgld': dict({ + 'localization': '0.0', + 'noise': '0.089', + 'numel': 90, + 'prior': '0.0', + 'scaled_grad': '0.00080', + 'weight_decay': '0.0', }), 'optimizer_sgmcmc': dict({ - 'distance': '0.0', - 'dws': list([ - list([ - list([ - '1.5', - '-0.29', - '-1.7', - '-1.3', - '-0.27', - ]), - list([ - '-1.5', - '0.70', - '1.6', - '0.56', - '-0.052', - ]), - list([ - '2.8', - '-0.16', - '-3.4', - '-2.7', - '-1.0', - ]), - list([ - '-0.36', - '0.23', - '0.31', - '0.095', - '-0.11', - ]), - list([ - '1.5', - '0.13', - '-1.9', - '-1.7', - '-0.76', - ]), - ]), - list([ - '-1.1', - '1.1', - '-1.8', - '0.30', - '-0.89', - ]), - list([ - list([ - '-3.3', - '2.6', - '-4.6', - '-0.66', - '-0.20', - ]), - list([ - '1.0', - '-0.62', - '1.2', - '0.30', - '0.066', - ]), - list([ - '2.4', - '-2.2', - '3.7', - '0.45', - '0.12', - ]), - list([ - '0.47', - '-0.078', - '0.27', - '0.18', - '0.051', - ]), - list([ - '3.2', - '-2.0', - '3.9', - '1.0', - '0.092', - ]), - ]), - list([ - '3.2', - '-0.6', - '-2.9', - '0.17', - '-2.3', - ]), - list([ - list([ - '0.37', - '-0.72', - '-1.6', - '-0.91', - '-0.22', - ]), - list([ - '-2.2', - '1.1', - '2.0', - '0.76', - '1.5', - ]), - list([ - '-2.6', - '0.87', - '1.2', - '-0.0089', - '1.4', - ]), - list([ - '-0.029', - '-0.088', - '-0.33', - '-0.32', - '-0.36', - ]), - list([ - '-2.8', - '1.2', - '1.9', - '0.49', - '1.7', - ]), - ]), - list([ - '2.3', - '-3.7', - '-1.8', - '1.2', - '-3.3', - ]), - ]), - 'grad_norm': '0.00080', - 'localization_loss': '0.0', - 'noise': list([ - list([ - list([ - '0.5', - '-0.43', - '-1.3', - '-0.19', - '0.65', - ]), - list([ - '-1.9', - '0.23', - '0.025', - '0.19', - '-0.93', - ]), - list([ - '-1.6', - '-1.1', - '-0.52', - '0.72', - '1.5', - ]), - list([ - '-1.5', - '-0.79', - '1.0', - '-0.56', - '0.70', - ]), - list([ - '0.71', - '-1.5', - '-0.73', - '0.47', - '0.67', - ]), - ]), - list([ - '0.11', - '2.4', - '0.92', - '-0.64', - '0.71', - ]), - list([ - list([ - '1.1', - '0.21', - '-0.58', - '0.33', - '-0.81', - ]), - list([ - '-1.0', - '-0.49', - '-0.59', - '0.40', - '0.084', - ]), - list([ - '0.4', - '2.0', - '-0.46', - '-0.064', - '-1.4', - ]), - list([ - '0.33', - '-0.98', - '0.078', - '0.19', - '0.41', - ]), - list([ - '-1.6', - '2.3', - '1.0', - '1.4', - '0.63', - ]), - ]), - list([ - '0.36', - '0.82', - '-0.36', - '0.071', - '0.61', - ]), - list([ - list([ - '0.56', - '-1.5', - '-1.6', - '-1.5', - '0.43', - ]), - list([ - '-0.13', - '0.78', - '0.56', - '-0.11', - '-1.0', - ]), - list([ - '-0.22', - '0.8', - '0.91', - '-0.088', - '0.34', - ]), - list([ - '0.97', - '-1.0', - '0.39', - '0.73', - '0.25', - ]), - list([ - '0.077', - '-0.21', - '2.2', - '2.0', - '0.25', - ]), - ]), - list([ - '0.24', - '-0.26', - '-0.46', - '-1.2', - '-1.2', - ]), - ]), - 'noise_norm': '8.9', - 'weight_norm': '2.5', + 'localization': '0.0', + 'noise': '0.089', + 'numel': 90, + 'prior': '0.0', + 'scaled_grad': '0.00080', + 'weight_decay': '0.0', }), }) # --- @@ -5559,281 +3778,20 @@ ]), }), 'optimizer_sgld': dict({ + 'localization': '0.0', + 'noise': '0.28', + 'numel': 90, + 'prior': '0.0', + 'scaled_grad': '0.0083', + 'weight_decay': '0.0', }), 'optimizer_sgmcmc': dict({ - 'distance': '0.0', - 'dws': list([ - list([ - list([ - '1.6', - '-0.31', - '-1.7', - '-1.3', - '-0.24', - ]), - list([ - '-1.7', - '0.76', - '1.8', - '0.69', - '-0.0033', - ]), - list([ - '3.0', - '-0.19', - '-3.7', - '-2.8', - '-1.1', - ]), - list([ - '-0.4', - '0.26', - '0.30', - '0.12', - '-0.15', - ]), - list([ - '1.5', - '0.13', - '-2.0', - '-1.8', - '-0.82', - ]), - ]), - list([ - '-1.1', - '1.2', - '-1.9', - '0.35', - '-0.92', - ]), - list([ - list([ - '-3.5', - '2.6', - '-5.0', - '-0.57', - '-0.34', - ]), - list([ - '1.3', - '-0.67', - '1.5', - '0.26', - '0.16', - ]), - list([ - '2.5', - '-2.1', - '3.8', - '0.32', - '0.22', - ]), - list([ - '0.41', - '0.0061', - '0.19', - '0.17', - '0.051', - ]), - list([ - '3.3', - '-1.8', - '3.9', - '0.9', - '0.20', - ]), - ]), - list([ - '3.5', - '-0.64', - '-3.0', - '0.23', - '-2.2', - ]), - list([ - list([ - '0.33', - '-0.68', - '-1.6', - '-0.94', - '-0.38', - ]), - list([ - '-2.2', - '1.2', - '2.0', - '0.81', - '1.8', - ]), - list([ - '-2.5', - '0.99', - '1.1', - '0.029', - '1.6', - ]), - list([ - '0.058', - '-0.067', - '-0.45', - '-0.35', - '-0.47', - ]), - list([ - '-2.8', - '1.3', - '1.9', - '0.52', - '2.0', - ]), - ]), - list([ - '2.4', - '-3.8', - '-1.7', - '1.4', - '-3.3', - ]), - ]), - 'grad_norm': '0.0083', - 'localization_loss': '0.0', - 'noise': list([ - list([ - list([ - '0.5', - '-0.43', - '-1.3', - '-0.19', - '0.65', - ]), - list([ - '-1.9', - '0.23', - '0.025', - '0.19', - '-0.93', - ]), - list([ - '-1.6', - '-1.1', - '-0.52', - '0.72', - '1.5', - ]), - list([ - '-1.5', - '-0.79', - '1.0', - '-0.56', - '0.70', - ]), - list([ - '0.71', - '-1.5', - '-0.73', - '0.47', - '0.67', - ]), - ]), - list([ - '0.11', - '2.4', - '0.92', - '-0.64', - '0.71', - ]), - list([ - list([ - '1.1', - '0.21', - '-0.58', - '0.33', - '-0.81', - ]), - list([ - '-1.0', - '-0.49', - '-0.59', - '0.40', - '0.084', - ]), - list([ - '0.4', - '2.0', - '-0.46', - '-0.064', - '-1.4', - ]), - list([ - '0.33', - '-0.98', - '0.078', - '0.19', - '0.41', - ]), - list([ - '-1.6', - '2.3', - '1.0', - '1.4', - '0.63', - ]), - ]), - list([ - '0.36', - '0.82', - '-0.36', - '0.071', - '0.61', - ]), - list([ - list([ - '0.56', - '-1.5', - '-1.6', - '-1.5', - '0.43', - ]), - list([ - '-0.13', - '0.78', - '0.56', - '-0.11', - '-1.0', - ]), - list([ - '-0.22', - '0.8', - '0.91', - '-0.088', - '0.34', - ]), - list([ - '0.97', - '-1.0', - '0.39', - '0.73', - '0.25', - ]), - list([ - '0.077', - '-0.21', - '2.2', - '2.0', - '0.25', - ]), - ]), - list([ - '0.24', - '-0.26', - '-0.46', - '-1.2', - '-1.2', - ]), - ]), - 'noise_norm': '8.9', - 'weight_norm': '2.5', + 'localization': '0.0', + 'noise': '0.28', + 'numel': 90, + 'prior': '0.0', + 'scaled_grad': '0.0083', + 'weight_decay': '0.0', }), }) # --- @@ -6108,281 +4066,20 @@ ]), }), 'optimizer_sgld': dict({ + 'localization': '0.0', + 'noise': '0.89', + 'numel': 90, + 'prior': '0.0', + 'scaled_grad': '0.076', + 'weight_decay': '0.0', }), 'optimizer_sgmcmc': dict({ - 'distance': '0.0', - 'dws': list([ - list([ - list([ - '1.0', - '-0.27', - '-0.92', - '-0.82', - '0.014', - ]), - list([ - '-1.6', - '0.78', - '1.9', - '0.59', - '0.035', - ]), - list([ - '3.0', - '-0.16', - '-3.9', - '-2.8', - '-1.3', - ]), - list([ - '-0.16', - '0.28', - '-0.12', - '-0.091', - '-0.37', - ]), - list([ - '1.5', - '0.16', - '-2.2', - '-1.8', - '-0.94', - ]), - ]), - list([ - '-0.78', - '1.2', - '-1.8', - '0.24', - '-0.88', - ]), - list([ - list([ - '-3.0', - '2.1', - '-4.9', - '-0.33', - '-0.56', - ]), - list([ - '1.4', - '-0.7', - '1.9', - '0.20', - '0.4', - ]), - list([ - '1.7', - '-1.6', - '3.3', - '0.085', - '0.35', - ]), - list([ - '0.49', - '0.035', - '0.29', - '0.18', - '0.066', - ]), - list([ - '2.7', - '-1.1', - '3.3', - '0.67', - '0.27', - ]), - ]), - list([ - '3.4', - '-0.68', - '-2.6', - '0.22', - '-1.7', - ]), - list([ - list([ - '0.13', - '-0.63', - '-1.2', - '-0.94', - '-0.55', - ]), - list([ - '-1.7', - '1.2', - '1.5', - '1.0', - '2.2', - ]), - list([ - '-2.2', - '1.2', - '0.59', - '0.19', - '1.7', - ]), - list([ - '-0.31', - '0.23', - '-0.34', - '-0.35', - '-0.38', - ]), - list([ - '-2.3', - '1.5', - '1.2', - '0.67', - '2.2', - ]), - ]), - list([ - '2.4', - '-3.6', - '-1.4', - '1.1', - '-2.8', - ]), - ]), - 'grad_norm': '0.076', - 'localization_loss': '0.0', - 'noise': list([ - list([ - list([ - '0.5', - '-0.43', - '-1.3', - '-0.19', - '0.65', - ]), - list([ - '-1.9', - '0.23', - '0.025', - '0.19', - '-0.93', - ]), - list([ - '-1.6', - '-1.1', - '-0.52', - '0.72', - '1.5', - ]), - list([ - '-1.5', - '-0.79', - '1.0', - '-0.56', - '0.70', - ]), - list([ - '0.71', - '-1.5', - '-0.73', - '0.47', - '0.67', - ]), - ]), - list([ - '0.11', - '2.4', - '0.92', - '-0.64', - '0.71', - ]), - list([ - list([ - '1.1', - '0.21', - '-0.58', - '0.33', - '-0.81', - ]), - list([ - '-1.0', - '-0.49', - '-0.59', - '0.40', - '0.084', - ]), - list([ - '0.4', - '2.0', - '-0.46', - '-0.064', - '-1.4', - ]), - list([ - '0.33', - '-0.98', - '0.078', - '0.19', - '0.41', - ]), - list([ - '-1.6', - '2.3', - '1.0', - '1.4', - '0.63', - ]), - ]), - list([ - '0.36', - '0.82', - '-0.36', - '0.071', - '0.61', - ]), - list([ - list([ - '0.56', - '-1.5', - '-1.6', - '-1.5', - '0.43', - ]), - list([ - '-0.13', - '0.78', - '0.56', - '-0.11', - '-1.0', - ]), - list([ - '-0.22', - '0.8', - '0.91', - '-0.088', - '0.34', - ]), - list([ - '0.97', - '-1.0', - '0.39', - '0.73', - '0.25', - ]), - list([ - '0.077', - '-0.21', - '2.2', - '2.0', - '0.25', - ]), - ]), - list([ - '0.24', - '-0.26', - '-0.46', - '-1.2', - '-1.2', - ]), - ]), - 'noise_norm': '8.9', - 'weight_norm': '2.6', + 'localization': '0.0', + 'noise': '0.89', + 'numel': 90, + 'prior': '0.0', + 'scaled_grad': '0.076', + 'weight_decay': '0.0', }), }) # --- @@ -6657,281 +4354,20 @@ ]), }), 'optimizer_sgld': dict({ + 'localization': '0.0', + 'noise': '2.8', + 'numel': 90, + 'prior': '0.0', + 'scaled_grad': '0.48', + 'weight_decay': '0.0', }), 'optimizer_sgmcmc': dict({ - 'distance': '0.0', - 'dws': list([ - list([ - list([ - '0.51', - '-0.18', - '-0.23', - '-0.5', - '0.23', - ]), - list([ - '-0.98', - '0.66', - '1.2', - '0.018', - '-0.068', - ]), - list([ - '1.5', - '0.15', - '-2.2', - '-1.6', - '-0.98', - ]), - list([ - '-0.19', - '0.34', - '-0.2', - '-0.092', - '-0.49', - ]), - list([ - '0.7', - '0.43', - '-1.3', - '-1.2', - '-0.88', - ]), - ]), - list([ - '-0.48', - '0.73', - '-0.81', - '0.31', - '-0.26', - ]), - list([ - list([ - '-1.9', - '1.1', - '-2.6', - '-0.36', - '-0.17', - ]), - list([ - '1.1', - '-0.47', - '1.4', - '0.21', - '0.41', - ]), - list([ - '1.0', - '-0.98', - '1.8', - '0.037', - '0.12', - ]), - list([ - '0.53', - '0.042', - '0.32', - '0.24', - '0.00093', - ]), - list([ - '1.7', - '-0.28', - '1.4', - '0.64', - '-0.17', - ]), - ]), - list([ - '2.1', - '-0.25', - '-2.0', - '0.17', - '-0.73', - ]), - list([ - list([ - '-0.29', - '-0.35', - '-0.79', - '-0.77', - '-0.42', - ]), - list([ - '-0.66', - '0.61', - '0.95', - '0.92', - '1.6', - ]), - list([ - '-1.6', - '0.94', - '-0.05', - '0.089', - '1.1', - ]), - list([ - '-0.90', - '0.76', - '-0.33', - '-0.17', - '0.031', - ]), - list([ - '-1.5', - '1.0', - '0.28', - '0.39', - '1.3', - ]), - ]), - list([ - '2.1', - '-3.2', - '-0.76', - '0.54', - '-1.7', - ]), - ]), - 'grad_norm': '0.48', - 'localization_loss': '0.0', - 'noise': list([ - list([ - list([ - '0.5', - '-0.43', - '-1.3', - '-0.19', - '0.65', - ]), - list([ - '-1.9', - '0.23', - '0.025', - '0.19', - '-0.93', - ]), - list([ - '-1.6', - '-1.1', - '-0.52', - '0.72', - '1.5', - ]), - list([ - '-1.5', - '-0.79', - '1.0', - '-0.56', - '0.70', - ]), - list([ - '0.71', - '-1.5', - '-0.73', - '0.47', - '0.67', - ]), - ]), - list([ - '0.11', - '2.4', - '0.92', - '-0.64', - '0.71', - ]), - list([ - list([ - '1.1', - '0.21', - '-0.58', - '0.33', - '-0.81', - ]), - list([ - '-1.0', - '-0.49', - '-0.59', - '0.40', - '0.084', - ]), - list([ - '0.4', - '2.0', - '-0.46', - '-0.064', - '-1.4', - ]), - list([ - '0.33', - '-0.98', - '0.078', - '0.19', - '0.41', - ]), - list([ - '-1.6', - '2.3', - '1.0', - '1.4', - '0.63', - ]), - ]), - list([ - '0.36', - '0.82', - '-0.36', - '0.071', - '0.61', - ]), - list([ - list([ - '0.56', - '-1.5', - '-1.6', - '-1.5', - '0.43', - ]), - list([ - '-0.13', - '0.78', - '0.56', - '-0.11', - '-1.0', - ]), - list([ - '-0.22', - '0.8', - '0.91', - '-0.088', - '0.34', - ]), - list([ - '0.97', - '-1.0', - '0.39', - '0.73', - '0.25', - ]), - list([ - '0.077', - '-0.21', - '2.2', - '2.0', - '0.25', - ]), - ]), - list([ - '0.24', - '-0.26', - '-0.46', - '-1.2', - '-1.2', - ]), - ]), - 'noise_norm': '8.9', - 'weight_norm': '2.6', + 'localization': '0.0', + 'noise': '2.8', + 'numel': 90, + 'prior': '0.0', + 'scaled_grad': '0.48', + 'weight_decay': '0.0', }), }) # --- @@ -7206,281 +4642,20 @@ ]), }), 'optimizer_sgld': dict({ + 'localization': '0.00000049', + 'noise': '0.089', + 'numel': 90, + 'prior': '0.00000049', + 'scaled_grad': '0.000080', + 'weight_decay': '0.0', }), 'optimizer_sgmcmc': dict({ - 'distance': '0.098', - 'dws': list([ - list([ - list([ - '0.16', - '-0.028', - '-0.17', - '-0.13', - '-0.027', - ]), - list([ - '-0.15', - '0.070', - '0.16', - '0.056', - '-0.0058', - ]), - list([ - '0.28', - '-0.016', - '-0.35', - '-0.27', - '-0.11', - ]), - list([ - '-0.036', - '0.021', - '0.030', - '0.010', - '-0.011', - ]), - list([ - '0.15', - '0.013', - '-0.19', - '-0.17', - '-0.078', - ]), - ]), - list([ - '-0.11', - '0.11', - '-0.18', - '0.032', - '-0.09', - ]), - list([ - list([ - '-0.32', - '0.26', - '-0.46', - '-0.067', - '-0.022', - ]), - list([ - '0.11', - '-0.062', - '0.12', - '0.032', - '0.007', - ]), - list([ - '0.24', - '-0.22', - '0.37', - '0.043', - '0.012', - ]), - list([ - '0.048', - '-0.0072', - '0.026', - '0.019', - '0.0059', - ]), - list([ - '0.32', - '-0.20', - '0.39', - '0.10', - '0.0085', - ]), - ]), - list([ - '0.32', - '-0.059', - '-0.29', - '0.016', - '-0.23', - ]), - list([ - list([ - '0.038', - '-0.073', - '-0.16', - '-0.090', - '-0.022', - ]), - list([ - '-0.22', - '0.11', - '0.20', - '0.076', - '0.15', - ]), - list([ - '-0.26', - '0.088', - '0.12', - '-0.0019', - '0.14', - ]), - list([ - '-0.00092', - '-0.0083', - '-0.033', - '-0.030', - '-0.036', - ]), - list([ - '-0.28', - '0.12', - '0.19', - '0.05', - '0.17', - ]), - ]), - list([ - '0.23', - '-0.37', - '-0.18', - '0.13', - '-0.33', - ]), - ]), - 'grad_norm': '0.000080', - 'localization_loss': '0.00048', - 'noise': list([ - list([ - list([ - '0.5', - '-0.43', - '-1.3', - '-0.19', - '0.65', - ]), - list([ - '-1.9', - '0.23', - '0.025', - '0.19', - '-0.93', - ]), - list([ - '-1.6', - '-1.1', - '-0.52', - '0.72', - '1.5', - ]), - list([ - '-1.5', - '-0.79', - '1.0', - '-0.56', - '0.70', - ]), - list([ - '0.71', - '-1.5', - '-0.73', - '0.47', - '0.67', - ]), - ]), - list([ - '0.11', - '2.4', - '0.92', - '-0.64', - '0.71', - ]), - list([ - list([ - '1.1', - '0.21', - '-0.58', - '0.33', - '-0.81', - ]), - list([ - '-1.0', - '-0.49', - '-0.59', - '0.40', - '0.084', - ]), - list([ - '0.4', - '2.0', - '-0.46', - '-0.064', - '-1.4', - ]), - list([ - '0.33', - '-0.98', - '0.078', - '0.19', - '0.41', - ]), - list([ - '-1.6', - '2.3', - '1.0', - '1.4', - '0.63', - ]), - ]), - list([ - '0.36', - '0.82', - '-0.36', - '0.071', - '0.61', - ]), - list([ - list([ - '0.56', - '-1.5', - '-1.6', - '-1.5', - '0.43', - ]), - list([ - '-0.13', - '0.78', - '0.56', - '-0.11', - '-1.0', - ]), - list([ - '-0.22', - '0.8', - '0.91', - '-0.088', - '0.34', - ]), - list([ - '0.97', - '-1.0', - '0.39', - '0.73', - '0.25', - ]), - list([ - '0.077', - '-0.21', - '2.2', - '2.0', - '0.25', - ]), - ]), - list([ - '0.24', - '-0.26', - '-0.46', - '-1.2', - '-1.2', - ]), - ]), - 'noise_norm': '8.9', - 'weight_norm': '2.5', + 'localization': '0.00000049', + 'noise': '0.089', + 'numel': 90, + 'prior': '0.00000049', + 'scaled_grad': '0.000080', + 'weight_decay': '0.0', }), }) # --- @@ -7755,281 +4930,20 @@ ]), }), 'optimizer_sgld': dict({ + 'localization': '0.000015', + 'noise': '0.28', + 'numel': 90, + 'prior': '0.000015', + 'scaled_grad': '0.00085', + 'weight_decay': '0.0', }), 'optimizer_sgmcmc': dict({ - 'distance': '0.31', - 'dws': list([ - list([ - list([ - '0.17', - '-0.027', - '-0.17', - '-0.14', - '-0.024', - ]), - list([ - '-0.18', - '0.077', - '0.18', - '0.07', - '-0.0011', - ]), - list([ - '0.30', - '-0.02', - '-0.39', - '-0.29', - '-0.12', - ]), - list([ - '-0.040', - '0.021', - '0.029', - '0.014', - '-0.017', - ]), - list([ - '0.16', - '0.012', - '-0.21', - '-0.18', - '-0.088', - ]), - ]), - list([ - 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'noise': list([ - list([ - list([ - '0.5', - '-0.43', - '-1.3', - '-0.19', - '0.65', - ]), - list([ - '-1.9', - '0.23', - '0.025', - '0.19', - '-0.93', - ]), - list([ - '-1.6', - '-1.1', - '-0.52', - '0.72', - '1.5', - ]), - list([ - '-1.5', - '-0.79', - '1.0', - '-0.56', - '0.70', - ]), - list([ - '0.71', - '-1.5', - '-0.73', - '0.47', - '0.67', - ]), - ]), - list([ - '0.11', - '2.4', - '0.92', - '-0.64', - '0.71', - ]), - list([ - list([ - '1.1', - '0.21', - '-0.58', - '0.33', - '-0.81', - ]), - list([ - '-1.0', - '-0.49', - '-0.59', - '0.40', - '0.084', - ]), - list([ - '0.4', - '2.0', - '-0.46', - '-0.064', - '-1.4', - ]), - list([ - '0.33', - '-0.98', - '0.078', - '0.19', - '0.41', - ]), - list([ - '-1.6', - '2.3', - '1.0', - '1.4', - '0.63', - ]), - ]), - list([ - '0.36', - '0.82', - '-0.36', - '0.071', - '0.61', - ]), - list([ - list([ - '0.56', - '-1.5', - '-1.6', - '-1.5', - '0.43', - ]), - list([ - '-0.13', - '0.78', - '0.56', - '-0.11', - '-1.0', - ]), - list([ - '-0.22', - '0.8', - '0.91', - '-0.088', - '0.34', - ]), - list([ - '0.97', - '-1.0', - '0.39', - '0.73', - '0.25', - ]), - list([ - '0.077', - '-0.21', - '2.2', - '2.0', - '0.25', - ]), - ]), - list([ - '0.24', - '-0.26', - '-0.46', - '-1.2', - '-1.2', - ]), - ]), - 'noise_norm': '8.9', - 'weight_norm': '2.5', + 'localization': '0.000015', + 'noise': '0.28', + 'numel': 90, + 'prior': '0.000015', + 'scaled_grad': '0.00085', + 'weight_decay': '0.0', }), }) # --- @@ -8304,281 +5218,20 @@ ]), }), 'optimizer_sgld': dict({ + 'localization': '0.00036', + 'noise': '0.89', + 'numel': 90, + 'prior': '0.00036', + 'scaled_grad': '0.0089', + 'weight_decay': '0.0', }), 'optimizer_sgmcmc': dict({ - 'distance': '0.72', - 'dws': list([ - list([ - list([ - '0.13', - '-0.019', - '-0.11', - '-0.11', - '0.00088', - ]), - list([ - '-0.21', - '0.085', - '0.22', - '0.073', - '0.0041', - ]), - list([ - '0.36', - '-0.027', - '-0.48', - '-0.32', - '-0.16', - ]), - list([ - '-0.02', - '0.018', - '-0.013', - '-0.00071', - '-0.042', - ]), - list([ - '0.17', - '0.011', - '-0.25', - '-0.19', - '-0.11', - ]), - ]), - list([ - '-0.079', - '0.13', - '-0.23', - '0.035', - '-0.11', - ]), - list([ - list([ - '-0.33', - '0.24', - '-0.58', - '-0.041', - '-0.089', - ]), - list([ - '0.17', - '-0.078', - '0.22', - '0.030', - '0.051', - ]), - list([ - '0.22', - '-0.18', - '0.39', - '-0.0019', - '0.047', - ]), - list([ - '0.054', - '0.0095', - '0.018', - '0.028', - '0.014', - ]), - list([ - '0.3', - '-0.14', - '0.4', - '0.058', - '0.035', - ]), - ]), - list([ - '0.37', - '-0.073', - '-0.29', - '0.013', - '-0.21', - ]), - list([ - list([ - '0.039', - '-0.075', - '-0.13', - '-0.091', - '-0.065', - ]), - list([ - '-0.22', - '0.14', - '0.18', - '0.11', - '0.26', - ]), - list([ - '-0.24', - '0.15', - '0.068', - '0.017', - '0.21', - ]), - list([ - '0.0015', - '0.015', - '-0.042', - '-0.032', - '-0.058', - ]), - list([ - '-0.29', - '0.18', - '0.14', - '0.089', - '0.26', - ]), - ]), - list([ - '0.25', - '-0.38', - '-0.16', - '0.15', - '-0.31', - ]), - ]), - 'grad_norm': '0.0089', - 'localization_loss': '0.026', - 'noise': list([ - list([ - list([ - '0.5', - '-0.43', - '-1.3', - '-0.19', - '0.65', - ]), - list([ - '-1.9', - '0.23', - '0.025', - '0.19', - '-0.93', - ]), - list([ - '-1.6', - '-1.1', - '-0.52', - '0.72', - '1.5', - ]), - list([ - '-1.5', - '-0.79', - '1.0', - '-0.56', - '0.70', - ]), - list([ - '0.71', - '-1.5', - '-0.73', - '0.47', - '0.67', - ]), - ]), - list([ - '0.11', - '2.4', - '0.92', - '-0.64', - '0.71', - ]), - list([ - list([ - '1.1', - '0.21', - '-0.58', - '0.33', - '-0.81', - ]), - list([ - '-1.0', - '-0.49', - '-0.59', - '0.40', - '0.084', - ]), - list([ - '0.4', - '2.0', - '-0.46', - '-0.064', - '-1.4', - ]), - list([ - '0.33', - '-0.98', - '0.078', - '0.19', - '0.41', - ]), - list([ - '-1.6', - '2.3', - '1.0', - '1.4', - '0.63', - ]), - ]), - list([ - '0.36', - '0.82', - '-0.36', - '0.071', - '0.61', - ]), - list([ - list([ - '0.56', - '-1.5', - '-1.6', - '-1.5', - '0.43', - ]), - list([ - '-0.13', - '0.78', - '0.56', - '-0.11', - '-1.0', - ]), - list([ - '-0.22', - '0.8', - '0.91', - '-0.088', - '0.34', - ]), - list([ - '0.97', - '-1.0', - '0.39', - '0.73', - '0.25', - ]), - list([ - '0.077', - '-0.21', - '2.2', - '2.0', - '0.25', - ]), - ]), - list([ - '0.24', - '-0.26', - '-0.46', - '-1.2', - '-1.2', - ]), - ]), - 'noise_norm': '8.9', - 'weight_norm': '2.6', + 'localization': '0.00036', + 'noise': '0.89', + 'numel': 90, + 'prior': '0.00036', + 'scaled_grad': '0.0089', + 'weight_decay': '0.0', }), }) # --- @@ -8853,281 +5506,20 @@ ]), }), 'optimizer_sgld': dict({ + 'localization': '0.0044', + 'noise': '2.8', + 'numel': 90, + 'prior': '0.0044', + 'scaled_grad': '0.073', + 'weight_decay': '0.0', }), 'optimizer_sgmcmc': dict({ - 'distance': '0.89', - 'dws': list([ - list([ - list([ - '0.089', - '-0.012', - '-0.053', - '-0.079', - '0.019', - ]), - list([ - '-0.17', - '0.086', - '0.18', - '0.029', - '-0.012', - ]), - list([ - '0.29', - '0.0021', - '-0.42', - '-0.28', - '-0.16', - ]), - list([ - '-0.0073', - '0.018', - '-0.047', - '-0.012', - '-0.057', - ]), - list([ - '0.13', - '0.027', - '-0.22', - '-0.16', - '-0.12', - ]), - ]), - list([ - '-0.055', - '0.11', - '-0.18', - '0.026', - '-0.081', - ]), - list([ - list([ - '-0.28', - '0.17', - '-0.47', - '-0.051', - '-0.047', - ]), - list([ - '0.13', - '-0.056', - '0.19', - '0.031', - '0.053', - ]), - list([ - '0.16', - '-0.13', - '0.27', - '-0.0057', - '0.013', - ]), - list([ - '0.07', - '0.014', - '0.031', - '0.036', - '0.013', - ]), - list([ - '0.23', - '-0.077', - '0.28', - '0.062', - '-0.0097', - ]), - ]), - list([ - '0.32', - '-0.043', - '-0.24', - '0.0079', - '-0.14', - ]), - list([ - list([ - '-0.004', - '-0.06', - '-0.10', - '-0.068', - '-0.048', - ]), - list([ - '-0.17', - '0.12', - '0.13', - '0.11', - '0.24', - ]), - list([ - '-0.21', - '0.15', - '0.0069', - '0.016', - '0.17', - ]), - list([ - '-0.054', - '0.062', - '-0.030', - '-0.019', - '-0.022', - ]), - list([ - '-0.24', - '0.18', - '0.059', - '0.08', - '0.22', - ]), - ]), - list([ - '0.22', - '-0.35', - '-0.11', - '0.10', - '-0.25', - ]), - ]), - 'grad_norm': '0.073', - 'localization_loss': '0.039', - 'noise': list([ - list([ - list([ - '0.5', - '-0.43', - '-1.3', - '-0.19', - '0.65', - ]), - list([ - '-1.9', - '0.23', - '0.025', - '0.19', - '-0.93', - ]), - list([ - '-1.6', - '-1.1', - '-0.52', - '0.72', - '1.5', - ]), - list([ - '-1.5', - '-0.79', - '1.0', - '-0.56', - '0.70', - ]), - list([ - '0.71', - '-1.5', - '-0.73', - '0.47', - '0.67', - ]), - ]), - list([ - '0.11', - '2.4', - '0.92', - '-0.64', - '0.71', - ]), - list([ - list([ - '1.1', - '0.21', - '-0.58', - '0.33', - '-0.81', - ]), - list([ - '-1.0', - '-0.49', - '-0.59', - '0.40', - '0.084', - ]), - list([ - '0.4', - '2.0', - '-0.46', - '-0.064', - '-1.4', - ]), - list([ - '0.33', - '-0.98', - '0.078', - '0.19', - '0.41', - ]), - list([ - '-1.6', - '2.3', - '1.0', - '1.4', - '0.63', - ]), - ]), - list([ - '0.36', - '0.82', - '-0.36', - '0.071', - '0.61', - ]), - list([ - list([ - '0.56', - '-1.5', - '-1.6', - '-1.5', - '0.43', - ]), - list([ - '-0.13', - '0.78', - '0.56', - '-0.11', - '-1.0', - ]), - list([ - '-0.22', - '0.8', - '0.91', - '-0.088', - '0.34', - ]), - list([ - '0.97', - '-1.0', - '0.39', - '0.73', - '0.25', - ]), - list([ - '0.077', - '-0.21', - '2.2', - '2.0', - '0.25', - ]), - ]), - list([ - '0.24', - '-0.26', - '-0.46', - '-1.2', - '-1.2', - ]), - ]), - 'noise_norm': '8.9', - 'weight_norm': '2.7', + 'localization': '0.0044', + 'noise': '2.8', + 'numel': 90, + 'prior': '0.0044', + 'scaled_grad': '0.073', + 'weight_decay': '0.0', }), }) # --- @@ -9402,281 +5794,20 @@ ]), }), 'optimizer_sgld': dict({ + 'localization': '0.00000049', + 'noise': '0.089', + 'numel': 90, + 'prior': '0.00000049', + 'scaled_grad': '0.00080', + 'weight_decay': '0.0', }), 'optimizer_sgmcmc': dict({ - 'distance': '0.098', - 'dws': list([ - list([ - list([ - '1.5', - '-0.29', - '-1.7', - '-1.3', - '-0.27', - ]), - list([ - '-1.5', - '0.70', - '1.6', - '0.56', - '-0.053', - ]), - list([ - '2.8', - '-0.16', - '-3.4', - '-2.7', - '-1.0', - ]), - list([ - '-0.36', - '0.22', - '0.31', - '0.096', - '-0.11', - ]), - list([ - '1.5', - '0.13', - '-1.9', - '-1.7', - '-0.76', - ]), - ]), - list([ - '-1.1', - '1.1', - '-1.8', - '0.30', - '-0.89', - ]), - list([ - list([ - '-3.3', - '2.6', - '-4.6', - '-0.67', - '-0.21', - ]), - list([ - '1.1', - '-0.62', - '1.2', - '0.30', - '0.066', - ]), - list([ - '2.4', - '-2.2', - '3.7', - '0.44', - '0.12', - ]), - list([ - '0.47', - '-0.077', - '0.27', - '0.18', - '0.052', - ]), - list([ - '3.2', - '-2.0', - '3.9', - '1.0', - '0.091', - ]), - ]), - list([ - '3.2', - '-0.59', - '-2.9', - '0.17', - '-2.3', - ]), - list([ - list([ - '0.37', - '-0.72', - '-1.6', - '-0.91', - '-0.22', - ]), - list([ - '-2.2', - '1.1', - '2.0', - '0.76', - '1.5', - ]), - list([ - '-2.6', - '0.87', - '1.2', - '-0.010', - '1.4', - ]), - list([ - '-0.027', - '-0.087', - '-0.33', - '-0.31', - '-0.36', - ]), - list([ - '-2.8', - '1.2', - '1.9', - '0.49', - '1.7', - ]), - ]), - list([ - '2.3', - '-3.7', - '-1.8', - '1.2', - '-3.3', - ]), - ]), - 'grad_norm': '0.00080', - 'localization_loss': '0.00048', - 'noise': list([ - list([ - list([ - '0.5', - '-0.43', - '-1.3', - '-0.19', - '0.65', - ]), - list([ - '-1.9', - '0.23', - '0.025', - '0.19', - '-0.93', - ]), - list([ - '-1.6', - '-1.1', - '-0.52', - '0.72', - '1.5', - ]), - list([ - '-1.5', - '-0.79', - '1.0', - '-0.56', - '0.70', - ]), - list([ - '0.71', - '-1.5', - '-0.73', - '0.47', - '0.67', - ]), - ]), - list([ - '0.11', - '2.4', - '0.92', - '-0.64', - '0.71', - ]), - list([ - list([ - '1.1', - '0.21', - '-0.58', - '0.33', - '-0.81', - ]), - list([ - '-1.0', - '-0.49', - '-0.59', - '0.40', - '0.084', - ]), - list([ - '0.4', - '2.0', - '-0.46', - '-0.064', - '-1.4', - ]), - list([ - '0.33', - '-0.98', - '0.078', - '0.19', - '0.41', - ]), - list([ - '-1.6', - '2.3', - '1.0', - '1.4', - '0.63', - ]), - ]), - list([ - '0.36', - '0.82', - '-0.36', - '0.071', - '0.61', - ]), - list([ - list([ - '0.56', - '-1.5', - '-1.6', - '-1.5', - '0.43', - ]), - list([ - '-0.13', - '0.78', - '0.56', - '-0.11', - '-1.0', - ]), - list([ - '-0.22', - '0.8', - '0.91', - '-0.088', - '0.34', - ]), - list([ - '0.97', - '-1.0', - '0.39', - '0.73', - '0.25', - ]), - list([ - '0.077', - '-0.21', - '2.2', - '2.0', - '0.25', - ]), - ]), - list([ - '0.24', - '-0.26', - '-0.46', - '-1.2', - '-1.2', - ]), - ]), - 'noise_norm': '8.9', - 'weight_norm': '2.5', + 'localization': '0.00000049', + 'noise': '0.089', + 'numel': 90, + 'prior': '0.00000049', + 'scaled_grad': '0.00080', + 'weight_decay': '0.0', }), }) # --- @@ -9951,281 +6082,20 @@ ]), }), 'optimizer_sgld': dict({ + 'localization': '0.000015', + 'noise': '0.28', + 'numel': 90, + 'prior': '0.000015', + 'scaled_grad': '0.0083', + 'weight_decay': '0.0', }), 'optimizer_sgmcmc': dict({ - 'distance': '0.31', - 'dws': list([ - list([ - list([ - '1.6', - '-0.31', - '-1.7', - '-1.3', - '-0.24', - ]), - list([ - '-1.7', - '0.76', - '1.8', - '0.69', - '-0.0055', - ]), - list([ - '3.0', - '-0.19', - '-3.7', - '-2.8', - '-1.1', - ]), - list([ - '-0.4', - '0.25', - '0.30', - '0.12', - '-0.15', - ]), - list([ - '1.5', - '0.13', - '-2.1', - '-1.8', - '-0.83', - ]), - ]), - list([ - '-1.1', - '1.2', - '-1.9', - '0.35', - '-0.92', - ]), - list([ - list([ - '-3.5', - '2.6', - '-5.0', - '-0.57', - '-0.34', - ]), - list([ - '1.3', - '-0.67', - '1.5', - '0.27', - '0.16', - ]), - list([ - '2.5', - '-2.1', - '3.8', - '0.32', - '0.22', - ]), - list([ - '0.42', - '0.0077', - '0.19', - '0.18', - '0.053', - ]), - list([ - '3.3', - '-1.8', - '3.9', - '0.89', - '0.2', - ]), - ]), - list([ - '3.5', - '-0.64', - '-3.0', - '0.23', - '-2.2', - ]), - list([ - list([ - '0.33', - '-0.68', - '-1.6', - '-0.94', - '-0.38', - ]), - list([ - '-2.2', - '1.2', - '2.0', - '0.81', - '1.8', - ]), - list([ - '-2.5', - '0.99', - '1.1', - '0.025', - '1.6', - ]), - list([ - '0.064', - '-0.065', - '-0.44', - '-0.35', - '-0.47', - ]), - list([ - '-2.8', - '1.3', - '1.9', - '0.53', - '2.0', - ]), - ]), - list([ - '2.4', - '-3.8', - '-1.7', - '1.4', - '-3.3', - ]), - ]), - 'grad_norm': '0.0083', - 'localization_loss': '0.0048', - 'noise': list([ - list([ - list([ - '0.5', - '-0.43', - '-1.3', - '-0.19', - '0.65', - ]), - list([ - '-1.9', - '0.23', - '0.025', - '0.19', - '-0.93', - ]), - list([ - '-1.6', - '-1.1', - '-0.52', - '0.72', - '1.5', - ]), - list([ - '-1.5', - '-0.79', - '1.0', - '-0.56', - '0.70', - ]), - list([ - '0.71', - '-1.5', - '-0.73', - '0.47', - '0.67', - ]), - ]), - list([ - '0.11', - '2.4', - '0.92', - '-0.64', - '0.71', - ]), - list([ - list([ - '1.1', - '0.21', - '-0.58', - '0.33', - '-0.81', - ]), - list([ - '-1.0', - '-0.49', - '-0.59', - '0.40', - '0.084', - ]), - list([ - '0.4', - '2.0', - '-0.46', - '-0.064', - '-1.4', - ]), - list([ - '0.33', - '-0.98', - '0.078', - '0.19', - '0.41', - ]), - list([ - '-1.6', - '2.3', - '1.0', - '1.4', - '0.63', - ]), - ]), - list([ - '0.36', - '0.82', - '-0.36', - '0.071', - '0.61', - ]), - list([ - list([ - '0.56', - '-1.5', - '-1.6', - '-1.5', - '0.43', - ]), - list([ - '-0.13', - '0.78', - '0.56', - '-0.11', - '-1.0', - ]), - list([ - '-0.22', - '0.8', - '0.91', - '-0.088', - '0.34', - ]), - list([ - '0.97', - '-1.0', - '0.39', - '0.73', - '0.25', - ]), - list([ - '0.077', - '-0.21', - '2.2', - '2.0', - '0.25', - ]), - ]), - list([ - '0.24', - '-0.26', - '-0.46', - '-1.2', - '-1.2', - ]), - ]), - 'noise_norm': '8.9', - 'weight_norm': '2.5', + 'localization': '0.000015', + 'noise': '0.28', + 'numel': 90, + 'prior': '0.000015', + 'scaled_grad': '0.0083', + 'weight_decay': '0.0', }), }) # --- @@ -10500,281 +6370,20 @@ ]), }), 'optimizer_sgld': dict({ + 'localization': '0.00036', + 'noise': '0.89', + 'numel': 90, + 'prior': '0.00036', + 'scaled_grad': '0.076', + 'weight_decay': '0.0', }), 'optimizer_sgmcmc': dict({ - 'distance': '0.72', - 'dws': list([ - list([ - list([ - '1.0', - '-0.26', - '-0.91', - '-0.83', - '0.021', - ]), - list([ - '-1.6', - '0.77', - '1.9', - '0.58', - '0.028', - ]), - list([ - '2.9', - '-0.15', - '-3.9', - '-2.8', - '-1.3', - ]), - list([ - '-0.16', - '0.27', - '-0.12', - '-0.083', - '-0.37', - ]), - list([ - '1.5', - '0.16', - '-2.2', - '-1.7', - '-0.95', - ]), - ]), - list([ - '-0.77', - '1.2', - '-1.8', - '0.25', - '-0.89', - ]), - list([ - list([ - '-3.0', - '2.1', - '-4.9', - '-0.33', - '-0.57', - ]), - list([ - '1.4', - '-0.69', - '1.9', - '0.21', - '0.40', - ]), - list([ - '1.7', - '-1.6', - '3.3', - '0.075', - '0.34', - ]), - list([ - '0.5', - '0.041', - '0.28', - '0.19', - '0.073', - ]), - list([ - '2.7', - '-1.1', - '3.3', - '0.66', - '0.26', - ]), - ]), - list([ - '3.4', - '-0.67', - '-2.6', - '0.21', - '-1.7', - ]), - list([ - list([ - '0.14', - '-0.63', - '-1.2', - '-0.93', - '-0.55', - ]), - list([ - '-1.7', - '1.2', - '1.5', - '1.0', - '2.2', - ]), - list([ - '-2.2', - '1.2', - '0.58', - '0.18', - '1.7', - ]), - list([ - '-0.3', - '0.24', - '-0.33', - '-0.34', - '-0.38', - ]), - list([ - '-2.3', - '1.5', - '1.2', - '0.68', - '2.2', - ]), - ]), - list([ - '2.4', - '-3.6', - '-1.4', - '1.1', - '-2.8', - ]), - ]), - 'grad_norm': '0.076', - 'localization_loss': '0.026', - 'noise': list([ - list([ - list([ - '0.5', - '-0.43', - '-1.3', - '-0.19', - '0.65', - ]), - list([ - '-1.9', - '0.23', - '0.025', - '0.19', - '-0.93', - ]), - list([ - '-1.6', - '-1.1', - '-0.52', - '0.72', - '1.5', - ]), - list([ - '-1.5', - '-0.79', - '1.0', - '-0.56', - '0.70', - ]), - list([ - '0.71', - '-1.5', - '-0.73', - '0.47', - '0.67', - ]), - ]), - list([ - '0.11', - '2.4', - '0.92', - '-0.64', - '0.71', - ]), - list([ - list([ - '1.1', - '0.21', - '-0.58', - '0.33', - '-0.81', - ]), - list([ - '-1.0', - '-0.49', - '-0.59', - '0.40', - '0.084', - ]), - list([ - '0.4', - '2.0', - '-0.46', - '-0.064', - '-1.4', - ]), - list([ - '0.33', - '-0.98', - '0.078', - '0.19', - '0.41', - ]), - list([ - '-1.6', - '2.3', - '1.0', - '1.4', - '0.63', - ]), - ]), - list([ - '0.36', - '0.82', - '-0.36', - '0.071', - '0.61', - ]), - list([ - list([ - '0.56', - '-1.5', - '-1.6', - '-1.5', - '0.43', - ]), - list([ - '-0.13', - '0.78', - '0.56', - '-0.11', - '-1.0', - ]), - list([ - '-0.22', - '0.8', - '0.91', - '-0.088', - '0.34', - ]), - list([ - '0.97', - '-1.0', - '0.39', - '0.73', - '0.25', - ]), - list([ - '0.077', - '-0.21', - '2.2', - '2.0', - '0.25', - ]), - ]), - list([ - '0.24', - '-0.26', - '-0.46', - '-1.2', - '-1.2', - ]), - ]), - 'noise_norm': '8.9', - 'weight_norm': '2.6', + 'localization': '0.00036', + 'noise': '0.89', + 'numel': 90, + 'prior': '0.00036', + 'scaled_grad': '0.076', + 'weight_decay': '0.0', }), }) # --- @@ -11049,301 +6658,40 @@ ]), }), 'optimizer_sgld': dict({ + 'localization': '0.0045', + 'noise': '2.8', + 'numel': 90, + 'prior': '0.0045', + 'scaled_grad': '0.48', + 'weight_decay': '0.0', }), 'optimizer_sgmcmc': dict({ - 'distance': '0.9', - 'dws': list([ - list([ - list([ - '0.52', - '-0.17', - '-0.22', - '-0.51', - '0.24', - ]), - list([ - '-0.99', - '0.65', - '1.2', - '0.0080', - '-0.078', - ]), - list([ - '1.5', - '0.16', - '-2.2', - '-1.6', - '-0.99', - ]), - list([ - '-0.19', - '0.33', - '-0.21', - '-0.082', - '-0.5', - ]), - list([ - '0.69', - '0.43', - '-1.3', - '-1.2', - '-0.89', - ]), - ]), - list([ - '-0.47', - '0.72', - '-0.80', - '0.32', - '-0.27', - ]), - list([ - list([ - '-1.9', - '1.1', - '-2.6', - '-0.37', - '-0.18', - ]), - list([ - '1.1', - '-0.47', - '1.4', - '0.22', - '0.42', - ]), - list([ - '1.0', - '-0.97', - '1.8', - '0.027', - '0.11', - ]), - list([ - '0.54', - '0.052', - '0.31', - '0.25', - '0.011', - ]), - list([ - '1.7', - '-0.29', - '1.4', - '0.63', - '-0.18', - ]), - ]), - list([ - '2.1', - '-0.24', - '-2.0', - '0.16', - '-0.74', - ]), - list([ - list([ - '-0.28', - '-0.36', - '-0.80', - '-0.76', - '-0.42', - ]), - list([ - '-0.66', - '0.6', - '0.96', - '0.91', - '1.6', - ]), - list([ - '-1.5', - '0.95', - '-0.06', - '0.079', - '1.0', - ]), - list([ - '-0.89', - '0.77', - '-0.32', - '-0.16', - '0.033', - ]), - list([ - '-1.5', - '1.0', - '0.27', - '0.4', - '1.3', - ]), - ]), - list([ - '2.1', - '-3.2', - '-0.75', - '0.55', - '-1.7', - ]), - ]), - 'grad_norm': '0.48', - 'localization_loss': '0.040', - 'noise': list([ - list([ - list([ - '0.5', - '-0.43', - '-1.3', - '-0.19', - '0.65', - ]), - list([ - '-1.9', - '0.23', - '0.025', - '0.19', - '-0.93', - ]), - list([ - '-1.6', - '-1.1', - '-0.52', - '0.72', - '1.5', - ]), - list([ - '-1.5', - '-0.79', - '1.0', - '-0.56', - '0.70', - ]), - list([ - '0.71', - '-1.5', - '-0.73', - '0.47', - '0.67', - ]), - ]), - list([ - '0.11', - '2.4', - '0.92', - '-0.64', - '0.71', - ]), - list([ - list([ - '1.1', - '0.21', - '-0.58', - '0.33', - '-0.81', - ]), - list([ - '-1.0', - '-0.49', - '-0.59', - '0.40', - '0.084', - ]), - list([ - '0.4', - '2.0', - '-0.46', - '-0.064', - '-1.4', - ]), - list([ - '0.33', - '-0.98', - '0.078', - '0.19', - '0.41', - ]), - list([ - '-1.6', - '2.3', - '1.0', - '1.4', - '0.63', - ]), - ]), - list([ - '0.36', - '0.82', - '-0.36', - '0.071', - '0.61', - ]), - list([ - list([ - '0.56', - '-1.5', - '-1.6', - '-1.5', - '0.43', - ]), - list([ - '-0.13', - '0.78', - '0.56', - '-0.11', - '-1.0', - ]), - list([ - '-0.22', - '0.8', - '0.91', - '-0.088', - '0.34', - ]), - list([ - '0.97', - '-1.0', - '0.39', - '0.73', - '0.25', - ]), - list([ - '0.077', - '-0.21', - '2.2', - '2.0', - '0.25', - ]), - ]), - list([ - '0.24', - '-0.26', - '-0.46', - '-1.2', - '-1.2', - ]), - ]), - 'noise_norm': '8.9', - 'weight_norm': '2.6', - }), - }) -# --- -# name: test_SGMCMC_vs_SGLD[0.0-None-0.0-1.0-0.0001][test_SGMCMC_vs_SGLD_0.0001_1.0_0.0_None_0.0] - dict({ - 'model1': dict({ - '0.bias': list([ - '0.06', - '0.42', - '-0.41', - '-0.27', - '-0.12', - ]), - '0.weight': list([ - list([ - '0.021', - '0.25', - '-0.37', - '-0.35', - '-0.16', + 'localization': '0.0045', + 'noise': '2.8', + 'numel': 90, + 'prior': '0.0045', + 'scaled_grad': '0.48', + 'weight_decay': '0.0', + }), + }) +# --- +# name: test_SGMCMC_vs_SGLD[0.0-None-0.0-1.0-0.0001][test_SGMCMC_vs_SGLD_0.0001_1.0_0.0_None_0.0] + dict({ + 'model1': dict({ + '0.bias': list([ + '0.06', + '0.42', + '-0.41', + '-0.27', + '-0.12', + ]), + '0.weight': list([ + list([ + '0.021', + '0.25', + '-0.37', + '-0.35', + '-0.16', ]), list([ '0.089', @@ -11598,281 +6946,20 @@ ]), }), 'optimizer_sgld': dict({ + 'localization': '0.0', + 'noise': '0.089', + 'numel': 90, + 'prior': '0.0', + 'scaled_grad': '0.000080', + 'weight_decay': '0.0', }), 'optimizer_sgmcmc': dict({ - 'distance': '0.0', - 'dws': list([ - list([ - list([ - '0.15', - '-0.029', - '-0.17', - '-0.13', - '-0.028', - ]), - list([ - '-0.15', - '0.070', - '0.16', - '0.056', - '-0.0051', - ]), - list([ - '0.28', - '-0.016', - '-0.35', - '-0.27', - '-0.10', - ]), - list([ - '-0.036', - '0.023', - '0.031', - '0.0095', - '-0.011', - ]), - list([ - '0.15', - '0.013', - '-0.19', - '-0.17', - '-0.076', - ]), - ]), - list([ - '-0.11', - '0.11', - '-0.18', - '0.030', - '-0.089', - ]), - list([ - list([ - '-0.33', - '0.26', - '-0.46', - '-0.066', - '-0.021', - ]), - list([ - '0.11', - '-0.062', - '0.12', - '0.030', - '0.0066', - ]), - list([ - '0.24', - '-0.22', - '0.37', - '0.045', - '0.012', - ]), - list([ - '0.047', - '-0.0078', - '0.027', - '0.018', - '0.0051', - ]), - list([ - '0.32', - '-0.2', - '0.39', - '0.10', - '0.0094', - ]), - ]), - list([ - '0.32', - '-0.06', - '-0.29', - '0.017', - '-0.23', - ]), - list([ - list([ - '0.037', - '-0.072', - '-0.16', - '-0.091', - '-0.023', - ]), - list([ - '-0.22', - '0.11', - '0.20', - '0.076', - '0.15', - ]), - list([ - '-0.26', - '0.087', - '0.12', - '-0.00082', - '0.14', - ]), - list([ - '-0.0026', - '-0.0089', - '-0.033', - '-0.032', - '-0.036', - ]), - list([ - '-0.28', - '0.12', - '0.19', - '0.049', - '0.17', - ]), - ]), - list([ - '0.23', - '-0.37', - '-0.18', - '0.12', - '-0.33', - ]), - ]), - 'grad_norm': '0.000080', - 'localization_loss': '0.0', - 'noise': list([ - list([ - list([ - '0.5', - '-0.43', - '-1.3', - '-0.19', - '0.65', - ]), - list([ - '-1.9', - '0.23', - '0.025', - '0.19', - '-0.93', - ]), - list([ - '-1.6', - '-1.1', - '-0.52', - '0.72', - '1.5', - ]), - list([ - '-1.5', - '-0.79', - '1.0', - '-0.56', - '0.70', - ]), - list([ - '0.71', - '-1.5', - '-0.73', - '0.47', - '0.67', - ]), - ]), - list([ - '0.11', - '2.4', - '0.92', - '-0.64', - '0.71', - ]), - list([ - list([ - '1.1', - '0.21', - '-0.58', - '0.33', - '-0.81', - ]), - list([ - '-1.0', - '-0.49', - '-0.59', - '0.40', - '0.084', - ]), - list([ - '0.4', - '2.0', - '-0.46', - '-0.064', - '-1.4', - ]), - list([ - '0.33', - '-0.98', - '0.078', - '0.19', - '0.41', - ]), - list([ - '-1.6', - '2.3', - '1.0', - '1.4', - '0.63', - ]), - ]), - list([ - '0.36', - '0.82', - '-0.36', - '0.071', - '0.61', - ]), - list([ - list([ - '0.56', - '-1.5', - '-1.6', - '-1.5', - '0.43', - ]), - list([ - '-0.13', - '0.78', - '0.56', - '-0.11', - '-1.0', - ]), - list([ - '-0.22', - '0.8', - '0.91', - '-0.088', - '0.34', - ]), - list([ - '0.97', - '-1.0', - '0.39', - '0.73', - '0.25', - ]), - list([ - '0.077', - '-0.21', - '2.2', - '2.0', - '0.25', - ]), - ]), - list([ - '0.24', - '-0.26', - '-0.46', - '-1.2', - '-1.2', - ]), - ]), - 'noise_norm': '8.9', - 'weight_norm': '2.5', + 'localization': '0.0', + 'noise': '0.089', + 'numel': 90, + 'prior': '0.0', + 'scaled_grad': '0.000080', + 'weight_decay': '0.0', }), }) # --- @@ -12147,281 +7234,20 @@ ]), }), 'optimizer_sgld': dict({ + 'localization': '0.0', + 'noise': '0.28', + 'numel': 90, + 'prior': '0.0', + 'scaled_grad': '0.00085', + 'weight_decay': '0.0', }), 'optimizer_sgmcmc': dict({ - 'distance': '0.0', - 'dws': list([ - list([ - list([ - '0.16', - '-0.032', - '-0.17', - '-0.13', - '-0.026', - ]), - list([ - '-0.17', - '0.077', - '0.19', - '0.072', - '0.0010', - ]), - list([ - '0.30', - '-0.021', - '-0.38', - '-0.29', - '-0.12', - ]), - list([ - '-0.04', - '0.026', - '0.031', - '0.011', - '-0.015', - ]), - list([ - '0.16', - '0.012', - '-0.21', - '-0.18', - '-0.083', - ]), - ]), - list([ - '-0.11', - '0.13', - '-0.19', - '0.035', - '-0.094', - ]), - list([ - list([ - '-0.36', - '0.27', - '-0.51', - '-0.057', - '-0.036', - ]), - list([ - '0.13', - '-0.068', - '0.15', - '0.027', - '0.016', - ]), - list([ - '0.26', - '-0.21', - '0.39', - '0.033', - '0.024', - ]), - list([ - '0.041', - '0.00078', - '0.019', - '0.017', - '0.0051', - ]), - list([ - '0.34', - '-0.18', - '0.40', - '0.09', - '0.022', - ]), - ]), - list([ - '0.35', - '-0.065', - '-0.30', - '0.024', - '-0.22', - ]), - list([ - list([ - '0.035', - '-0.069', - '-0.16', - '-0.094', - '-0.039', - ]), - list([ - '-0.22', - '0.12', - '0.21', - '0.082', - '0.19', - ]), - list([ - '-0.25', - '0.10', - '0.11', - '0.0036', - '0.16', - ]), - list([ - '0.0092', - '-0.0083', - '-0.047', - '-0.036', - '-0.049', - ]), - list([ - '-0.28', - '0.13', - '0.19', - '0.053', - '0.21', - ]), - ]), - list([ - '0.24', - '-0.38', - '-0.17', - '0.14', - '-0.33', - ]), - ]), - 'grad_norm': '0.00085', - 'localization_loss': '0.0', - 'noise': list([ - list([ - list([ - '0.5', - '-0.43', - '-1.3', - '-0.19', - '0.65', - ]), - list([ - '-1.9', - '0.23', - '0.025', - '0.19', - '-0.93', - ]), - list([ - '-1.6', - '-1.1', - '-0.52', - '0.72', - '1.5', - ]), - list([ - '-1.5', - '-0.79', - '1.0', - '-0.56', - '0.70', - ]), - list([ - '0.71', - '-1.5', - '-0.73', - '0.47', - '0.67', - ]), - ]), - list([ - '0.11', - '2.4', - '0.92', - '-0.64', - '0.71', - ]), - list([ - list([ - '1.1', - '0.21', - '-0.58', - '0.33', - '-0.81', - ]), - list([ - '-1.0', - '-0.49', - '-0.59', - '0.40', - '0.084', - ]), - list([ - '0.4', - '2.0', - '-0.46', - '-0.064', - '-1.4', - ]), - list([ - '0.33', - '-0.98', - '0.078', - '0.19', - '0.41', - ]), - list([ - '-1.6', - '2.3', - '1.0', - '1.4', - '0.63', - ]), - ]), - list([ - '0.36', - '0.82', - '-0.36', - '0.071', - '0.61', - ]), - list([ - list([ - '0.56', - '-1.5', - '-1.6', - '-1.5', - '0.43', - ]), - list([ - '-0.13', - '0.78', - '0.56', - '-0.11', - '-1.0', - ]), - list([ - '-0.22', - '0.8', - '0.91', - '-0.088', - '0.34', - ]), - list([ - '0.97', - '-1.0', - '0.39', - '0.73', - '0.25', - ]), - list([ - '0.077', - '-0.21', - '2.2', - '2.0', - '0.25', - ]), - ]), - list([ - '0.24', - '-0.26', - '-0.46', - '-1.2', - '-1.2', - ]), - ]), - 'noise_norm': '8.9', - 'weight_norm': '2.5', + 'localization': '0.0', + 'noise': '0.28', + 'numel': 90, + 'prior': '0.0', + 'scaled_grad': '0.00085', + 'weight_decay': '0.0', }), }) # --- @@ -12696,281 +7522,20 @@ ]), }), 'optimizer_sgld': dict({ + 'localization': '0.0', + 'noise': '0.89', + 'numel': 90, + 'prior': '0.0', + 'scaled_grad': '0.010', + 'weight_decay': '0.0', }), 'optimizer_sgmcmc': dict({ - 'distance': '0.0', - 'dws': list([ - list([ - list([ - '0.18', - '-0.040', - '-0.19', - '-0.14', - '-0.024', - ]), - list([ - '-0.25', - '0.092', - '0.30', - '0.12', - '0.037', - ]), - list([ - '0.40', - '-0.035', - '-0.53', - '-0.35', - '-0.17', - ]), - list([ - '-0.036', - '0.037', - '-0.00002', - '0.0047', - '-0.045', - ]), - list([ - '0.20', - '0.010', - '-0.29', - '-0.21', - '-0.12', - ]), - ]), - list([ - '-0.13', - '0.17', - '-0.25', - '0.043', - '-0.12', - ]), - list([ - list([ - '-0.51', - '0.26', - '-0.72', - '-0.017', - '-0.11', - ]), - list([ - '0.20', - '-0.070', - '0.26', - '0.016', - '0.063', - ]), - list([ - '0.30', - '-0.18', - '0.46', - '-0.0051', - '0.068', - ]), - list([ - '0.013', - '0.023', - '-0.022', - '0.016', - '-0.0029', - ]), - list([ - '0.39', - '-0.12', - '0.44', - '0.055', - '0.059', - ]), - ]), - list([ - '0.45', - '-0.076', - '-0.33', - '0.046', - '-0.21', - ]), - list([ - list([ - '0.030', - '-0.051', - '-0.15', - '-0.11', - '-0.097', - ]), - list([ - '-0.23', - '0.15', - '0.22', - '0.10', - '0.31', - ]), - list([ - '-0.24', - '0.15', - '0.1', - '0.013', - '0.23', - ]), - list([ - '0.032', - '-0.015', - '-0.096', - '-0.052', - '-0.11', - ]), - list([ - '-0.33', - '0.21', - '0.2', - '0.063', - '0.35', - ]), - ]), - list([ - '0.28', - '-0.4', - '-0.15', - '0.2', - '-0.32', - ]), - ]), - 'grad_norm': '0.010', - 'localization_loss': '0.0', - 'noise': list([ - list([ - list([ - '0.5', - '-0.43', - '-1.3', - '-0.19', - '0.65', - ]), - list([ - '-1.9', - '0.23', - '0.025', - '0.19', - '-0.93', - ]), - list([ - '-1.6', - '-1.1', - '-0.52', - '0.72', - '1.5', - ]), - list([ - '-1.5', - '-0.79', - '1.0', - '-0.56', - '0.70', - ]), - list([ - '0.71', - '-1.5', - '-0.73', - '0.47', - '0.67', - ]), - ]), - list([ - '0.11', - '2.4', - '0.92', - '-0.64', - '0.71', - ]), - list([ - list([ - '1.1', - '0.21', - '-0.58', - '0.33', - '-0.81', - ]), - list([ - '-1.0', - '-0.49', - '-0.59', - '0.40', - '0.084', - ]), - list([ - '0.4', - '2.0', - '-0.46', - '-0.064', - '-1.4', - ]), - list([ - '0.33', - '-0.98', - '0.078', - '0.19', - '0.41', - ]), - list([ - '-1.6', - '2.3', - '1.0', - '1.4', - '0.63', - ]), - ]), - list([ - '0.36', - '0.82', - '-0.36', - '0.071', - '0.61', - ]), - list([ - list([ - '0.56', - '-1.5', - '-1.6', - '-1.5', - '0.43', - ]), - list([ - '-0.13', - '0.78', - '0.56', - '-0.11', - '-1.0', - ]), - list([ - '-0.22', - '0.8', - '0.91', - '-0.088', - '0.34', - ]), - list([ - '0.97', - '-1.0', - '0.39', - '0.73', - '0.25', - ]), - list([ - '0.077', - '-0.21', - '2.2', - '2.0', - '0.25', - ]), - ]), - list([ - '0.24', - '-0.26', - '-0.46', - '-1.2', - '-1.2', - ]), - ]), - 'noise_norm': '8.9', - 'weight_norm': '2.8', + 'localization': '0.0', + 'noise': '0.89', + 'numel': 90, + 'prior': '0.0', + 'scaled_grad': '0.010', + 'weight_decay': '0.0', }), }) # --- @@ -13245,281 +7810,20 @@ ]), }), 'optimizer_sgld': dict({ + 'localization': '0.0', + 'noise': '2.8', + 'numel': 90, + 'prior': '0.0', + 'scaled_grad': '0.26', + 'weight_decay': '0.0', }), 'optimizer_sgmcmc': dict({ - 'distance': '0.0', - 'dws': list([ - list([ - list([ - '0.11', - '-0.049', - '-0.12', - '-0.048', - '-0.0006', - ]), - list([ - '-0.43', - '0.074', - '0.75', - '0.21', - '0.30', - ]), - list([ - '0.9', - '-0.027', - '-1.6', - '-0.62', - '-0.75', - ]), - list([ - '0.18', - '0.17', - '-0.79', - '-0.13', - '-0.63', - ]), - list([ - '0.55', - '0.044', - '-1.1', - '-0.41', - '-0.6', - ]), - ]), - list([ - '-0.082', - '0.21', - '-0.41', - '0.092', - '-0.20', - ]), - list([ - list([ - '-1.4', - '-0.19', - '-2.6', - '0.11', - '-1.1', - ]), - list([ - '0.46', - '0.16', - '1.1', - '0.067', - '0.52', - ]), - list([ - '0.42', - '0.0051', - '0.84', - '-0.096', - '0.33', - ]), - list([ - '-0.15', - '-0.010', - '-0.41', - '0.027', - '-0.18', - ]), - list([ - '0.36', - '0.036', - '0.26', - '-0.036', - '0.032', - ]), - ]), - list([ - '0.70', - '-0.0035', - '-0.38', - '0.14', - '-0.13', - ]), - list([ - list([ - '0.15', - '-0.069', - '-0.041', - '-0.15', - '-0.38', - ]), - list([ - '-0.16', - '0.27', - '0.32', - '0.15', - '0.63', - ]), - list([ - '-0.27', - '0.35', - '0.063', - '0.0041', - '0.29', - ]), - list([ - '0.18', - '-0.26', - '-0.21', - '-0.099', - '-0.47', - ]), - list([ - '-1.2', - '1.2', - '-0.10', - '0.011', - '0.88', - ]), - ]), - list([ - '0.35', - '-0.44', - '-0.074', - '0.3', - '-0.2', - ]), - ]), - 'grad_norm': '0.26', - 'localization_loss': '0.0', - 'noise': list([ - list([ - list([ - '0.5', - '-0.43', - '-1.3', - '-0.19', - '0.65', - ]), - list([ - '-1.9', - '0.23', - '0.025', - '0.19', - '-0.93', - ]), - list([ - '-1.6', - '-1.1', - '-0.52', - '0.72', - '1.5', - ]), - list([ - '-1.5', - '-0.79', - '1.0', - '-0.56', - '0.70', - ]), - list([ - '0.71', - '-1.5', - '-0.73', - '0.47', - '0.67', - ]), - ]), - list([ - '0.11', - '2.4', - '0.92', - '-0.64', - '0.71', - ]), - list([ - list([ - '1.1', - '0.21', - '-0.58', - '0.33', - '-0.81', - ]), - list([ - '-1.0', - '-0.49', - '-0.59', - '0.40', - '0.084', - ]), - list([ - '0.4', - '2.0', - '-0.46', - '-0.064', - '-1.4', - ]), - list([ - '0.33', - '-0.98', - '0.078', - '0.19', - '0.41', - ]), - list([ - '-1.6', - '2.3', - '1.0', - '1.4', - '0.63', - ]), - ]), - list([ - '0.36', - '0.82', - '-0.36', - '0.071', - '0.61', - ]), - list([ - list([ - '0.56', - '-1.5', - '-1.6', - '-1.5', - '0.43', - ]), - list([ - '-0.13', - '0.78', - '0.56', - '-0.11', - '-1.0', - ]), - list([ - '-0.22', - '0.8', - '0.91', - '-0.088', - '0.34', - ]), - list([ - '0.97', - '-1.0', - '0.39', - '0.73', - '0.25', - ]), - list([ - '0.077', - '-0.21', - '2.2', - '2.0', - '0.25', - ]), - ]), - list([ - '0.24', - '-0.26', - '-0.46', - '-1.2', - '-1.2', - ]), - ]), - 'noise_norm': '8.9', - 'weight_norm': '4.8', + 'localization': '0.0', + 'noise': '2.8', + 'numel': 90, + 'prior': '0.0', + 'scaled_grad': '0.26', + 'weight_decay': '0.0', }), }) # --- @@ -13794,281 +8098,20 @@ ]), }), 'optimizer_sgld': dict({ + 'localization': '0.0', + 'noise': '0.089', + 'numel': 90, + 'prior': '0.0', + 'scaled_grad': '0.00080', + 'weight_decay': '0.0', }), 'optimizer_sgmcmc': dict({ - 'distance': '0.0', - 'dws': list([ - list([ - list([ - '1.5', - '-0.29', - '-1.7', - '-1.3', - '-0.27', - ]), - list([ - '-1.5', - '0.70', - '1.6', - '0.56', - '-0.052', - ]), - list([ - '2.8', - '-0.16', - '-3.4', - '-2.7', - '-1.0', - ]), - list([ - '-0.36', - '0.23', - '0.31', - '0.095', - '-0.11', - ]), - list([ - '1.5', - '0.13', - '-1.9', - '-1.7', - '-0.76', - ]), - ]), - list([ - '-1.1', - '1.1', - '-1.8', - '0.30', - '-0.89', - ]), - list([ - list([ - '-3.3', - '2.6', - '-4.6', - '-0.66', - '-0.20', - ]), - list([ - '1.0', - '-0.62', - '1.2', - '0.30', - '0.066', - ]), - list([ - '2.4', - '-2.2', - '3.7', - '0.45', - '0.12', - ]), - list([ - '0.47', - '-0.078', - '0.27', - '0.18', - '0.051', - ]), - list([ - '3.2', - '-2.0', - '3.9', - '1.0', - '0.092', - ]), - ]), - list([ - '3.2', - '-0.6', - '-2.9', - '0.17', - '-2.3', - ]), - list([ - list([ - '0.37', - '-0.72', - '-1.6', - '-0.91', - '-0.22', - ]), - list([ - '-2.2', - '1.1', - '2.0', - '0.76', - '1.5', - ]), - list([ - '-2.6', - '0.87', - '1.2', - '-0.0089', - '1.4', - ]), - list([ - '-0.029', - '-0.088', - '-0.33', - '-0.32', - '-0.36', - ]), - list([ - '-2.8', - '1.2', - '1.9', - '0.49', - '1.7', - ]), - ]), - list([ - '2.3', - '-3.7', - '-1.8', - '1.2', - '-3.3', - ]), - ]), - 'grad_norm': '0.00080', - 'localization_loss': '0.0', - 'noise': list([ - list([ - list([ - '0.5', - '-0.43', - '-1.3', - '-0.19', - '0.65', - ]), - list([ - '-1.9', - '0.23', - '0.025', - '0.19', - '-0.93', - ]), - list([ - '-1.6', - '-1.1', - '-0.52', - '0.72', - '1.5', - ]), - list([ - '-1.5', - '-0.79', - '1.0', - '-0.56', - '0.70', - ]), - list([ - '0.71', - '-1.5', - '-0.73', - '0.47', - '0.67', - ]), - ]), - list([ - '0.11', - '2.4', - '0.92', - '-0.64', - '0.71', - ]), - list([ - list([ - '1.1', - '0.21', - '-0.58', - '0.33', - '-0.81', - ]), - list([ - '-1.0', - '-0.49', - '-0.59', - '0.40', - '0.084', - ]), - list([ - '0.4', - '2.0', - '-0.46', - '-0.064', - '-1.4', - ]), - list([ - '0.33', - '-0.98', - '0.078', - '0.19', - '0.41', - ]), - list([ - '-1.6', - '2.3', - '1.0', - '1.4', - '0.63', - ]), - ]), - list([ - '0.36', - '0.82', - '-0.36', - '0.071', - '0.61', - ]), - list([ - list([ - '0.56', - '-1.5', - '-1.6', - '-1.5', - '0.43', - ]), - list([ - '-0.13', - '0.78', - '0.56', - '-0.11', - '-1.0', - ]), - list([ - '-0.22', - '0.8', - '0.91', - '-0.088', - '0.34', - ]), - list([ - '0.97', - '-1.0', - '0.39', - '0.73', - '0.25', - ]), - list([ - '0.077', - '-0.21', - '2.2', - '2.0', - '0.25', - ]), - ]), - list([ - '0.24', - '-0.26', - '-0.46', - '-1.2', - '-1.2', - ]), - ]), - 'noise_norm': '8.9', - 'weight_norm': '2.5', + 'localization': '0.0', + 'noise': '0.089', + 'numel': 90, + 'prior': '0.0', + 'scaled_grad': '0.00080', + 'weight_decay': '0.0', }), }) # --- @@ -14343,281 +8386,20 @@ ]), }), 'optimizer_sgld': dict({ + 'localization': '0.0', + 'noise': '0.28', + 'numel': 90, + 'prior': '0.0', + 'scaled_grad': '0.0083', + 'weight_decay': '0.0', }), 'optimizer_sgmcmc': dict({ - 'distance': '0.0', - 'dws': list([ - list([ - list([ - '1.6', - '-0.31', - '-1.7', - '-1.3', - '-0.24', - ]), - list([ - '-1.7', - '0.76', - '1.8', - '0.69', - '-0.0033', - ]), - list([ - '3.0', - '-0.19', - '-3.7', - '-2.8', - '-1.1', - ]), - list([ - '-0.4', - '0.26', - '0.30', - '0.12', - '-0.15', - ]), - list([ - '1.5', - '0.13', - '-2.0', - '-1.8', - '-0.82', - ]), - ]), - list([ - '-1.1', - '1.2', - '-1.9', - '0.35', - '-0.92', - ]), - list([ - list([ - '-3.5', - '2.6', - '-5.0', - '-0.57', - '-0.34', - ]), - list([ - '1.3', - '-0.67', - '1.5', - '0.26', - '0.16', - ]), - list([ - '2.5', - '-2.1', - '3.8', - '0.32', - '0.22', - ]), - list([ - '0.41', - '0.0061', - '0.19', - '0.17', - '0.051', - ]), - list([ - '3.3', - '-1.8', - '3.9', - '0.9', - '0.20', - ]), - ]), - list([ - '3.5', - '-0.64', - '-3.0', - '0.23', - '-2.2', - ]), - list([ - list([ - '0.33', - '-0.68', - '-1.6', - '-0.94', - '-0.38', - ]), - list([ - '-2.2', - '1.2', - '2.0', - '0.81', - '1.8', - ]), - list([ - '-2.5', - '0.99', - '1.1', - '0.029', - '1.6', - ]), - list([ - '0.058', - '-0.067', - '-0.45', - '-0.35', - '-0.47', - ]), - list([ - '-2.8', - '1.3', - '1.9', - '0.52', - '2.0', - ]), - ]), - list([ - '2.4', - '-3.8', - '-1.7', - '1.4', - '-3.3', - ]), - ]), - 'grad_norm': '0.0083', - 'localization_loss': '0.0', - 'noise': list([ - list([ - list([ - '0.5', - '-0.43', - '-1.3', - '-0.19', - '0.65', - ]), - list([ - '-1.9', - '0.23', - '0.025', - '0.19', - '-0.93', - ]), - list([ - '-1.6', - '-1.1', - '-0.52', - '0.72', - '1.5', - ]), - list([ - '-1.5', - '-0.79', - '1.0', - '-0.56', - '0.70', - ]), - list([ - '0.71', - '-1.5', - '-0.73', - '0.47', - '0.67', - ]), - ]), - list([ - '0.11', - '2.4', - '0.92', - '-0.64', - '0.71', - ]), - list([ - list([ - '1.1', - '0.21', - '-0.58', - '0.33', - '-0.81', - ]), - list([ - '-1.0', - '-0.49', - '-0.59', - '0.40', - '0.084', - ]), - list([ - '0.4', - '2.0', - '-0.46', - '-0.064', - '-1.4', - ]), - list([ - '0.33', - '-0.98', - '0.078', - '0.19', - '0.41', - ]), - list([ - '-1.6', - '2.3', - '1.0', - '1.4', - '0.63', - ]), - ]), - list([ - '0.36', - '0.82', - '-0.36', - '0.071', - '0.61', - ]), - list([ - list([ - '0.56', - '-1.5', - '-1.6', - '-1.5', - '0.43', - ]), - list([ - '-0.13', - '0.78', - '0.56', - '-0.11', - '-1.0', - ]), - list([ - '-0.22', - '0.8', - '0.91', - '-0.088', - '0.34', - ]), - list([ - '0.97', - '-1.0', - '0.39', - '0.73', - '0.25', - ]), - list([ - '0.077', - '-0.21', - '2.2', - '2.0', - '0.25', - ]), - ]), - list([ - '0.24', - '-0.26', - '-0.46', - '-1.2', - '-1.2', - ]), - ]), - 'noise_norm': '8.9', - 'weight_norm': '2.5', + 'localization': '0.0', + 'noise': '0.28', + 'numel': 90, + 'prior': '0.0', + 'scaled_grad': '0.0083', + 'weight_decay': '0.0', }), }) # --- @@ -14892,281 +8674,20 @@ ]), }), 'optimizer_sgld': dict({ + 'localization': '0.0', + 'noise': '0.89', + 'numel': 90, + 'prior': '0.0', + 'scaled_grad': '0.085', + 'weight_decay': '0.0', }), 'optimizer_sgmcmc': dict({ - 'distance': '0.0', - 'dws': list([ - list([ - list([ - '1.3', - '-0.33', - '-1.3', - '-1.0', - '-0.092', - ]), - list([ - '-1.9', - '0.81', - '2.3', - '0.83', - '0.18', - ]), - list([ - '3.2', - '-0.19', - '-4.2', - '-2.9', - '-1.4', - ]), - list([ - '-0.39', - '0.36', - '0.039', - '0.08', - '-0.43', - ]), - list([ - '1.7', - '0.16', - '-2.4', - '-1.9', - '-1.0', - ]), - ]), - list([ - '-0.98', - '1.4', - '-1.9', - '0.44', - '-0.96', - ]), - list([ - list([ - '-4.2', - '2.0', - '-5.7', - '-0.19', - '-0.72', - ]), - list([ - '1.8', - '-0.55', - '2.1', - '0.17', - '0.52', - ]), - list([ - '2.4', - '-1.5', - '3.7', - '-0.031', - '0.43', - ]), - list([ - '0.24', - '0.17', - '-0.052', - '0.16', - '0.0075', - ]), - list([ - '3.4', - '-0.93', - '3.6', - '0.56', - '0.38', - ]), - ]), - list([ - '3.8', - '-0.57', - '-2.8', - '0.38', - '-1.8', - ]), - list([ - list([ - '0.052', - '-0.38', - '-1.3', - '-1.0', - '-0.76', - ]), - list([ - '-1.7', - '1.2', - '1.8', - '0.95', - '2.6', - ]), - list([ - '-2.0', - '1.3', - '0.73', - '0.079', - '1.9', - ]), - list([ - '-0.064', - '0.082', - '-0.70', - '-0.44', - '-0.74', - ]), - list([ - '-2.5', - '1.7', - '1.5', - '0.52', - '2.7', - ]), - ]), - list([ - '2.6', - '-3.7', - '-1.3', - '1.6', - '-2.8', - ]), - ]), - 'grad_norm': '0.085', - 'localization_loss': '0.0', - 'noise': list([ - list([ - list([ - '0.5', - '-0.43', - '-1.3', - '-0.19', - '0.65', - ]), - list([ - '-1.9', - '0.23', - '0.025', - '0.19', - '-0.93', - ]), - list([ - '-1.6', - '-1.1', - '-0.52', - '0.72', - '1.5', - ]), - list([ - '-1.5', - '-0.79', - '1.0', - '-0.56', - '0.70', - ]), - list([ - '0.71', - '-1.5', - '-0.73', - '0.47', - '0.67', - ]), - ]), - list([ - '0.11', - '2.4', - '0.92', - '-0.64', - '0.71', - ]), - list([ - list([ - '1.1', - '0.21', - '-0.58', - '0.33', - '-0.81', - ]), - list([ - '-1.0', - '-0.49', - '-0.59', - '0.40', - '0.084', - ]), - list([ - '0.4', - '2.0', - '-0.46', - '-0.064', - '-1.4', - ]), - list([ - '0.33', - '-0.98', - '0.078', - '0.19', - '0.41', - ]), - list([ - '-1.6', - '2.3', - '1.0', - '1.4', - '0.63', - ]), - ]), - list([ - '0.36', - '0.82', - '-0.36', - '0.071', - '0.61', - ]), - list([ - list([ - '0.56', - '-1.5', - '-1.6', - '-1.5', - '0.43', - ]), - list([ - '-0.13', - '0.78', - '0.56', - '-0.11', - '-1.0', - ]), - list([ - '-0.22', - '0.8', - '0.91', - '-0.088', - '0.34', - ]), - list([ - '0.97', - '-1.0', - '0.39', - '0.73', - '0.25', - ]), - list([ - '0.077', - '-0.21', - '2.2', - '2.0', - '0.25', - ]), - ]), - list([ - '0.24', - '-0.26', - '-0.46', - '-1.2', - '-1.2', - ]), - ]), - 'noise_norm': '8.9', - 'weight_norm': '2.8', + 'localization': '0.0', + 'noise': '0.89', + 'numel': 90, + 'prior': '0.0', + 'scaled_grad': '0.085', + 'weight_decay': '0.0', }), }) # --- @@ -15441,281 +8962,20 @@ ]), }), 'optimizer_sgld': dict({ + 'localization': '0.0', + 'noise': '2.8', + 'numel': 90, + 'prior': '0.0', + 'scaled_grad': '0.81', + 'weight_decay': '0.0', }), 'optimizer_sgmcmc': dict({ - 'distance': '0.0', - 'dws': list([ - list([ - list([ - '0.65', - '-0.46', - '-0.19', - '-0.3', - '0.52', - ]), - list([ - '0.91', - '-0.063', - '-0.14', - '-1.4', - '0.44', - ]), - list([ - '-1.2', - '1.1', - '-0.87', - '0.83', - '-2.1', - ]), - list([ - '-2.0', - '2.2', - '-2.3', - '1.3', - '-4.4', - ]), - list([ - '-1.5', - '1.4', - '-0.92', - '0.88', - '-2.5', - ]), - ]), - list([ - '-0.67', - '-0.91', - '1.7', - '3.1', - '2.0', - ]), - list([ - list([ - '2.7', - '0.14', - '-0.44', - '-0.86', - '-1.6', - ]), - list([ - '1.0', - '1.2', - '4.3', - '1.0', - '2.7', - ]), - list([ - '-1.9', - '-0.57', - '-1.3', - '-0.16', - '-0.22', - ]), - list([ - '1.9', - '0.42', - '0.95', - '-0.18', - '-0.16', - ]), - list([ - '2.3', - '0.29', - '0.10', - '-0.33', - '-0.81', - ]), - ]), - list([ - '-2.5', - '1.9', - '-0.17', - '-0.80', - '-1.0', - ]), - list([ - list([ - '-0.037', - '1.5', - '0.78', - '-0.95', - '-0.43', - ]), - list([ - '2.3', - '-0.98', - '1.3', - '1.3', - '1.5', - ]), - list([ - '-0.22', - '1.8', - '-0.18', - '0.31', - '1.2', - ]), - list([ - '-0.74', - '1.1', - '-0.83', - '0.35', - '0.75', - ]), - list([ - '-5.5', - '5.8', - '-3.7', - '-1.6', - '-0.43', - ]), - ]), - list([ - '1.2', - '-2.4', - '-0.83', - '-0.60', - '2.8', - ]), - ]), - 'grad_norm': '0.81', - 'localization_loss': '0.0', - 'noise': list([ - list([ - list([ - '0.5', - '-0.43', - '-1.3', - '-0.19', - '0.65', - ]), - list([ - '-1.9', - '0.23', - '0.025', - '0.19', - '-0.93', - ]), - list([ - '-1.6', - '-1.1', - '-0.52', - '0.72', - '1.5', - ]), - list([ - '-1.5', - '-0.79', - '1.0', - '-0.56', - '0.70', - ]), - list([ - '0.71', - '-1.5', - '-0.73', - '0.47', - '0.67', - ]), - ]), - list([ - '0.11', - '2.4', - '0.92', - '-0.64', - '0.71', - ]), - list([ - list([ - '1.1', - '0.21', - '-0.58', - '0.33', - '-0.81', - ]), - list([ - '-1.0', - '-0.49', - '-0.59', - '0.40', - '0.084', - ]), - list([ - '0.4', - '2.0', - '-0.46', - '-0.064', - '-1.4', - ]), - list([ - '0.33', - '-0.98', - '0.078', - '0.19', - '0.41', - ]), - list([ - '-1.6', - '2.3', - '1.0', - '1.4', - '0.63', - ]), - ]), - list([ - '0.36', - '0.82', - '-0.36', - '0.071', - '0.61', - ]), - list([ - list([ - '0.56', - '-1.5', - '-1.6', - '-1.5', - '0.43', - ]), - list([ - '-0.13', - '0.78', - '0.56', - '-0.11', - '-1.0', - ]), - list([ - '-0.22', - '0.8', - '0.91', - '-0.088', - '0.34', - ]), - list([ - '0.97', - '-1.0', - '0.39', - '0.73', - '0.25', - ]), - list([ - '0.077', - '-0.21', - '2.2', - '2.0', - '0.25', - ]), - ]), - list([ - '0.24', - '-0.26', - '-0.46', - '-1.2', - '-1.2', - ]), - ]), - 'noise_norm': '8.9', - 'weight_norm': '4.7', + 'localization': '0.0', + 'noise': '2.8', + 'numel': 90, + 'prior': '0.0', + 'scaled_grad': '0.81', + 'weight_decay': '0.0', }), }) # --- @@ -15990,281 +9250,20 @@ ]), }), 'optimizer_sgld': dict({ + 'localization': '0.00000049', + 'noise': '0.089', + 'numel': 90, + 'prior': '0.00000049', + 'scaled_grad': '0.000080', + 'weight_decay': '0.0', }), 'optimizer_sgmcmc': dict({ - 'distance': '0.098', - 'dws': list([ - list([ - list([ - '0.16', - '-0.028', - '-0.17', - '-0.13', - '-0.027', - ]), - list([ - '-0.15', - '0.070', - '0.16', - '0.056', - '-0.0058', - ]), - list([ - '0.28', - '-0.016', - '-0.35', - '-0.27', - '-0.11', - ]), - list([ - '-0.036', - '0.021', - '0.030', - '0.010', - '-0.011', - ]), - list([ - '0.15', - '0.013', - '-0.19', - '-0.17', - '-0.078', - ]), - ]), - list([ - '-0.11', - '0.11', - '-0.18', - '0.032', - '-0.09', - ]), - list([ - list([ - '-0.32', - '0.26', - '-0.46', - '-0.067', - '-0.022', - ]), - list([ - '0.11', - '-0.062', - '0.12', - '0.032', - '0.007', - ]), - list([ - '0.24', - '-0.22', - '0.37', - '0.043', - '0.012', - ]), - list([ - '0.048', - '-0.0072', - '0.026', - '0.019', - '0.0059', - ]), - list([ - '0.32', - '-0.20', - '0.39', - '0.10', - '0.0085', - ]), - ]), - list([ - '0.32', - '-0.059', - '-0.29', - '0.016', - '-0.23', - ]), - list([ - list([ - '0.038', - '-0.073', - '-0.16', - '-0.090', - '-0.022', - ]), - list([ - '-0.22', - '0.11', - '0.20', - '0.076', - '0.15', - ]), - list([ - '-0.26', - '0.088', - '0.12', - '-0.0019', - '0.14', - ]), - list([ - '-0.00092', - '-0.0083', - '-0.033', - '-0.030', - '-0.036', - ]), - list([ - '-0.28', - '0.12', - '0.19', - '0.05', - '0.17', - ]), - ]), - list([ - '0.23', - '-0.37', - '-0.18', - '0.13', - '-0.33', - ]), - ]), - 'grad_norm': '0.000080', - 'localization_loss': '0.00048', - 'noise': list([ - list([ - list([ - '0.5', - '-0.43', - '-1.3', - '-0.19', - '0.65', - ]), - list([ - '-1.9', - '0.23', - '0.025', - '0.19', - '-0.93', - ]), - list([ - '-1.6', - '-1.1', - '-0.52', - '0.72', - '1.5', - ]), - list([ - '-1.5', - '-0.79', - '1.0', - '-0.56', - '0.70', - ]), - list([ - '0.71', - '-1.5', - '-0.73', - '0.47', - '0.67', - ]), - ]), - list([ - '0.11', - '2.4', - '0.92', - '-0.64', - '0.71', - ]), - list([ - list([ - '1.1', - '0.21', - '-0.58', - '0.33', - '-0.81', - ]), - list([ - '-1.0', - '-0.49', - '-0.59', - '0.40', - '0.084', - ]), - list([ - '0.4', - '2.0', - '-0.46', - '-0.064', - '-1.4', - ]), - list([ - '0.33', - '-0.98', - '0.078', - '0.19', - '0.41', - ]), - list([ - '-1.6', - '2.3', - '1.0', - '1.4', - '0.63', - ]), - ]), - list([ - '0.36', - '0.82', - '-0.36', - '0.071', - '0.61', - ]), - list([ - list([ - '0.56', - '-1.5', - '-1.6', - '-1.5', - '0.43', - ]), - list([ - '-0.13', - '0.78', - '0.56', - '-0.11', - '-1.0', - ]), - list([ - '-0.22', - '0.8', - '0.91', - '-0.088', - '0.34', - ]), - list([ - '0.97', - '-1.0', - '0.39', - '0.73', - '0.25', - ]), - list([ - '0.077', - '-0.21', - '2.2', - '2.0', - '0.25', - ]), - ]), - list([ - '0.24', - '-0.26', - '-0.46', - '-1.2', - '-1.2', - ]), - ]), - 'noise_norm': '8.9', - 'weight_norm': '2.5', + 'localization': '0.00000049', + 'noise': '0.089', + 'numel': 90, + 'prior': '0.00000049', + 'scaled_grad': '0.000080', + 'weight_decay': '0.0', }), }) # --- @@ -16539,281 +9538,20 @@ ]), }), 'optimizer_sgld': dict({ + 'localization': '0.000015', + 'noise': '0.28', + 'numel': 90, + 'prior': '0.000015', + 'scaled_grad': '0.00085', + 'weight_decay': '0.0', }), 'optimizer_sgmcmc': dict({ - 'distance': '0.31', - 'dws': list([ - list([ - list([ - '0.17', - '-0.027', - '-0.17', - '-0.14', - '-0.024', - ]), - list([ - '-0.18', - '0.077', - '0.18', - '0.07', - '-0.0011', - ]), - list([ - '0.30', - '-0.02', - '-0.39', - '-0.29', - '-0.12', - ]), - list([ - '-0.040', - '0.021', - '0.029', - '0.014', - '-0.017', - ]), - list([ - '0.16', - '0.012', - '-0.21', - '-0.18', - '-0.088', - ]), - ]), - list([ - '-0.11', - '0.13', - '-0.19', - '0.04', - '-0.097', - ]), - list([ - list([ - '-0.36', - '0.26', - '-0.52', - '-0.06', - '-0.039', - ]), - list([ - '0.13', - '-0.068', - '0.15', - '0.032', - '0.017', - ]), - list([ - '0.26', - '-0.21', - '0.39', - '0.028', - '0.022', - ]), - list([ - '0.043', - '0.0024', - '0.015', - '0.022', - '0.0074', - ]), - list([ - '0.34', - '-0.18', - '0.40', - '0.084', - '0.019', - ]), - ]), - list([ - '0.35', - '-0.062', - '-0.3', - '0.019', - '-0.23', - ]), - list([ - list([ - '0.038', - '-0.070', - '-0.16', - '-0.092', - '-0.039', - ]), - list([ - '-0.22', - '0.12', - '0.21', - '0.082', - '0.19', - ]), - list([ - '-0.25', - '0.10', - '0.11', - '-0.0000046', - '0.16', - ]), - list([ - '0.014', - '-0.0063', - '-0.045', - '-0.032', - '-0.049', - ]), - list([ - '-0.29', - '0.14', - '0.19', - '0.057', - '0.21', - ]), - ]), - list([ - '0.25', - '-0.38', - '-0.17', - '0.15', - '-0.33', - ]), - ]), - 'grad_norm': '0.00085', - 'localization_loss': '0.0048', - 'noise': list([ - list([ - list([ - '0.5', - '-0.43', - '-1.3', - '-0.19', - '0.65', - ]), - list([ - '-1.9', - '0.23', - '0.025', - '0.19', - '-0.93', - ]), - list([ - '-1.6', - '-1.1', - '-0.52', - '0.72', - '1.5', - ]), - list([ - '-1.5', - '-0.79', - '1.0', - '-0.56', - '0.70', - ]), - list([ - '0.71', - '-1.5', - '-0.73', - '0.47', - '0.67', - ]), - ]), - list([ - '0.11', - '2.4', - '0.92', - '-0.64', - '0.71', - ]), - list([ - list([ - '1.1', - '0.21', - '-0.58', - '0.33', - '-0.81', - ]), - list([ - '-1.0', - '-0.49', - '-0.59', - '0.40', - '0.084', - ]), - list([ - '0.4', - '2.0', - '-0.46', - '-0.064', - '-1.4', - ]), - list([ - '0.33', - '-0.98', - '0.078', - '0.19', - '0.41', - ]), - list([ - '-1.6', - '2.3', - '1.0', - '1.4', - '0.63', - ]), - ]), - list([ - '0.36', - '0.82', - '-0.36', - '0.071', - '0.61', - ]), - list([ - list([ - '0.56', - '-1.5', - '-1.6', - '-1.5', - '0.43', - ]), - list([ - '-0.13', - '0.78', - '0.56', - '-0.11', - '-1.0', - ]), - list([ - '-0.22', - '0.8', - '0.91', - '-0.088', - '0.34', - ]), - list([ - '0.97', - '-1.0', - '0.39', - '0.73', - '0.25', - ]), - list([ - '0.077', - '-0.21', - '2.2', - '2.0', - '0.25', - ]), - ]), - list([ - '0.24', - '-0.26', - '-0.46', - '-1.2', - '-1.2', - ]), - ]), - 'noise_norm': '8.9', - 'weight_norm': '2.5', + 'localization': '0.000015', + 'noise': '0.28', + 'numel': 90, + 'prior': '0.000015', + 'scaled_grad': '0.00085', + 'weight_decay': '0.0', }), }) # --- @@ -17088,308 +9826,47 @@ ]), }), 'optimizer_sgld': dict({ + 'localization': '0.00049', + 'noise': '0.89', + 'numel': 90, + 'prior': '0.00049', + 'scaled_grad': '0.010', + 'weight_decay': '0.0', }), 'optimizer_sgmcmc': dict({ - 'distance': '0.98', - 'dws': list([ - list([ - list([ - '0.19', - '-0.025', - '-0.18', - '-0.16', - '-0.017', - ]), - list([ - '-0.26', - '0.091', - '0.29', - '0.12', - '0.030', - ]), - list([ - '0.4', - '-0.032', - '-0.54', - '-0.35', - '-0.19', - ]), - list([ - '-0.038', - '0.023', - '-0.004', - '0.013', - '-0.051', - ]), - list([ - '0.2', - '0.0094', - '-0.29', - '-0.21', - '-0.13', - ]), - ]), + 'localization': '0.00049', + 'noise': '0.89', + 'numel': 90, + 'prior': '0.00049', + 'scaled_grad': '0.010', + 'weight_decay': '0.0', + }), + }) +# --- +# name: test_SGMCMC_vs_SGLD[0.0-None-0.1-1.0-0.1][test_SGMCMC_vs_SGLD_0.1_1.0_0.1_None_0.0] + dict({ + 'model1': dict({ + '0.bias': list([ + '0.44', + '0.94', + '-0.10', + '0.0082', + '-0.19', + ]), + '0.weight': list([ list([ - '-0.11', - '0.17', - '-0.25', - '0.058', - '-0.13', + '0.75', + '0.58', + '-0.49', + '-1.0', + '0.25', ]), list([ - list([ - '-0.50', - '0.25', - '-0.73', - '-0.025', - '-0.12', - ]), - list([ - '0.22', - '-0.068', - '0.26', - '0.035', - '0.067', - ]), - list([ - '0.32', - '-0.18', - '0.46', - '-0.018', - '0.062', - ]), - list([ - '0.018', - '0.029', - '-0.036', - '0.030', - '0.0046', - ]), - list([ - '0.38', - '-0.13', - '0.44', - '0.038', - '0.051', - ]), - ]), - list([ - '0.44', - '-0.064', - '-0.32', - '0.033', - '-0.21', - ]), - list([ - list([ - '0.041', - '-0.055', - '-0.15', - '-0.1', - '-0.096', - ]), - list([ - '-0.23', - '0.15', - '0.23', - '0.10', - '0.31', - ]), - list([ - '-0.22', - '0.16', - '0.085', - '0.0018', - '0.23', - ]), - list([ - '0.049', - '-0.0086', - '-0.092', - '-0.040', - '-0.11', - ]), - list([ - '-0.34', - '0.22', - '0.19', - '0.074', - '0.34', - ]), - ]), - list([ - '0.3', - '-0.39', - '-0.15', - '0.21', - '-0.32', - ]), - ]), - 'grad_norm': '0.010', - 'localization_loss': '0.048', - 'noise': list([ - list([ - list([ - '0.5', - '-0.43', - '-1.3', - '-0.19', - '0.65', - ]), - list([ - '-1.9', - '0.23', - '0.025', - '0.19', - '-0.93', - ]), - list([ - '-1.6', - '-1.1', - '-0.52', - '0.72', - '1.5', - ]), - list([ - '-1.5', - '-0.79', - '1.0', - '-0.56', - '0.70', - ]), - list([ - '0.71', - '-1.5', - '-0.73', - '0.47', - '0.67', - ]), - ]), - list([ - '0.11', - '2.4', - '0.92', - '-0.64', - '0.71', - ]), - list([ - list([ - '1.1', - '0.21', - '-0.58', - '0.33', - '-0.81', - ]), - list([ - '-1.0', - '-0.49', - '-0.59', - '0.40', - '0.084', - ]), - list([ - '0.4', - '2.0', - '-0.46', - '-0.064', - '-1.4', - ]), - list([ - '0.33', - '-0.98', - '0.078', - '0.19', - '0.41', - ]), - list([ - '-1.6', - '2.3', - '1.0', - '1.4', - '0.63', - ]), - ]), - list([ - '0.36', - '0.82', - '-0.36', - '0.071', - '0.61', - ]), - list([ - list([ - '0.56', - '-1.5', - '-1.6', - '-1.5', - '0.43', - ]), - list([ - '-0.13', - '0.78', - '0.56', - '-0.11', - '-1.0', - ]), - list([ - '-0.22', - '0.8', - '0.91', - '-0.088', - '0.34', - ]), - list([ - '0.97', - '-1.0', - '0.39', - '0.73', - '0.25', - ]), - list([ - '0.077', - '-0.21', - '2.2', - '2.0', - '0.25', - ]), - ]), - list([ - '0.24', - '-0.26', - '-0.46', - '-1.2', - '-1.2', - ]), - ]), - 'noise_norm': '8.9', - 'weight_norm': '2.8', - }), - }) -# --- -# name: test_SGMCMC_vs_SGLD[0.0-None-0.1-1.0-0.1][test_SGMCMC_vs_SGLD_0.1_1.0_0.1_None_0.0] - dict({ - 'model1': dict({ - '0.bias': list([ - '0.44', - '0.94', - '-0.10', - '0.0082', - '-0.19', - ]), - '0.weight': list([ - list([ - '0.75', - '0.58', - '-0.49', - '-1.0', - '0.25', - ]), - list([ - '-0.84', - '0.043', - '-0.19', - '-0.23', - '-0.41', + '-0.84', + '0.043', + '-0.19', + '-0.23', + '-0.41', ]), list([ '-0.85', @@ -17637,281 +10114,20 @@ ]), }), 'optimizer_sgld': dict({ + 'localization': '0.015', + 'noise': '2.8', + 'numel': 90, + 'prior': '0.015', + 'scaled_grad': '0.26', + 'weight_decay': '0.0', }), 'optimizer_sgmcmc': dict({ - 'distance': '3.1', - 'dws': list([ - list([ - list([ - '0.17', - '-0.0020', - '-0.092', - '-0.11', - '0.021', - ]), - list([ - '-0.47', - '0.072', - '0.70', - '0.18', - '0.28', - ]), - list([ - '0.88', - '-0.019', - '-1.7', - '-0.61', - '-0.82', - ]), - list([ - '0.18', - '0.12', - '-0.81', - '-0.11', - '-0.65', - ]), - list([ - '0.54', - '0.042', - '-1.1', - '-0.4', - '-0.65', - ]), - ]), - list([ - '-0.046', - '0.19', - '-0.41', - '0.14', - '-0.24', - ]), - list([ - list([ - '-1.3', - '-0.22', - '-2.6', - '0.080', - '-1.1', - ]), - list([ - '0.52', - '0.17', - '1.1', - '0.13', - '0.53', - ]), - list([ - '0.47', - '0.0066', - '0.83', - '-0.14', - '0.31', - ]), - list([ - '-0.13', - '0.0065', - '-0.45', - '0.073', - '-0.16', - ]), - list([ - '0.35', - '0.0041', - '0.27', - '-0.091', - '0.0057', - ]), - ]), - list([ - '0.69', - '0.035', - '-0.35', - '0.094', - '-0.16', - ]), - list([ - list([ - '0.18', - '-0.083', - '-0.062', - '-0.13', - '-0.38', - ]), - list([ - '-0.17', - '0.26', - '0.34', - '0.15', - '0.63', - ]), - list([ - '-0.21', - '0.38', - '0.017', - '-0.032', - '0.28', - ]), - list([ - '0.23', - '-0.24', - '-0.20', - '-0.062', - '-0.47', - ]), - list([ - '-1.2', - '1.2', - '-0.12', - '0.043', - '0.86', - ]), - ]), - list([ - '0.41', - '-0.41', - '-0.065', - '0.36', - '-0.2', - ]), - ]), - 'grad_norm': '0.26', - 'localization_loss': '0.48', - 'noise': list([ - list([ - list([ - '0.5', - '-0.43', - '-1.3', - '-0.19', - '0.65', - ]), - list([ - '-1.9', - '0.23', - '0.025', - '0.19', - '-0.93', - ]), - list([ - '-1.6', - '-1.1', - '-0.52', - '0.72', - '1.5', - ]), - list([ - '-1.5', - '-0.79', - '1.0', - '-0.56', - '0.70', - ]), - list([ - '0.71', - '-1.5', - '-0.73', - '0.47', - '0.67', - ]), - ]), - list([ - '0.11', - '2.4', - '0.92', - '-0.64', - '0.71', - ]), - list([ - list([ - '1.1', - '0.21', - '-0.58', - '0.33', - '-0.81', - ]), - list([ - '-1.0', - '-0.49', - '-0.59', - '0.40', - '0.084', - ]), - list([ - '0.4', - '2.0', - '-0.46', - '-0.064', - '-1.4', - ]), - list([ - '0.33', - '-0.98', - '0.078', - '0.19', - '0.41', - ]), - list([ - '-1.6', - '2.3', - '1.0', - '1.4', - '0.63', - ]), - ]), - list([ - '0.36', - '0.82', - '-0.36', - '0.071', - '0.61', - ]), - list([ - list([ - '0.56', - '-1.5', - '-1.6', - '-1.5', - '0.43', - ]), - list([ - '-0.13', - '0.78', - '0.56', - '-0.11', - '-1.0', - ]), - list([ - '-0.22', - '0.8', - '0.91', - '-0.088', - '0.34', - ]), - list([ - '0.97', - '-1.0', - '0.39', - '0.73', - '0.25', - ]), - list([ - '0.077', - '-0.21', - '2.2', - '2.0', - '0.25', - ]), - ]), - list([ - '0.24', - '-0.26', - '-0.46', - '-1.2', - '-1.2', - ]), - ]), - 'noise_norm': '8.9', - 'weight_norm': '4.8', + 'localization': '0.015', + 'noise': '2.8', + 'numel': 90, + 'prior': '0.015', + 'scaled_grad': '0.26', + 'weight_decay': '0.0', }), }) # --- @@ -18186,281 +10402,20 @@ ]), }), 'optimizer_sgld': dict({ + 'localization': '0.00000049', + 'noise': '0.089', + 'numel': 90, + 'prior': '0.00000049', + 'scaled_grad': '0.00080', + 'weight_decay': '0.0', }), 'optimizer_sgmcmc': dict({ - 'distance': '0.098', - 'dws': list([ - list([ - list([ - '1.5', - '-0.29', - '-1.7', - '-1.3', - '-0.27', - ]), - list([ - '-1.5', - '0.70', - '1.6', - '0.56', - '-0.053', - ]), - list([ - '2.8', - '-0.16', - '-3.4', - '-2.7', - '-1.0', - ]), - list([ - '-0.36', - '0.22', - '0.31', - '0.096', - '-0.11', - ]), - list([ - '1.5', - '0.13', - '-1.9', - '-1.7', - '-0.76', - ]), - ]), - list([ - '-1.1', - '1.1', - '-1.8', - '0.30', - '-0.89', - ]), - list([ - list([ - '-3.3', - '2.6', - '-4.6', - '-0.67', - '-0.21', - ]), - list([ - '1.1', - '-0.62', - '1.2', - '0.30', - '0.066', - ]), - list([ - '2.4', - '-2.2', - '3.7', - '0.44', - '0.12', - ]), - list([ - '0.47', - '-0.077', - '0.27', - '0.18', - '0.052', - ]), - list([ - '3.2', - '-2.0', - '3.9', - '1.0', - '0.091', - ]), - ]), - list([ - '3.2', - '-0.59', - '-2.9', - '0.17', - '-2.3', - ]), - list([ - list([ - '0.37', - '-0.72', - '-1.6', - '-0.91', - '-0.22', - ]), - list([ - '-2.2', - '1.1', - '2.0', - '0.76', - '1.5', - ]), - list([ - '-2.6', - '0.87', - '1.2', - '-0.010', - '1.4', - ]), - list([ - '-0.027', - '-0.087', - '-0.33', - '-0.31', - '-0.36', - ]), - list([ - '-2.8', - '1.2', - '1.9', - '0.49', - '1.7', - ]), - ]), - list([ - '2.3', - '-3.7', - '-1.8', - '1.2', - '-3.3', - ]), - ]), - 'grad_norm': '0.00080', - 'localization_loss': '0.00048', - 'noise': list([ - list([ - list([ - '0.5', - '-0.43', - '-1.3', - '-0.19', - '0.65', - ]), - list([ - '-1.9', - '0.23', - '0.025', - '0.19', - '-0.93', - ]), - list([ - '-1.6', - '-1.1', - '-0.52', - '0.72', - '1.5', - ]), - list([ - '-1.5', - '-0.79', - '1.0', - '-0.56', - '0.70', - ]), - list([ - '0.71', - '-1.5', - '-0.73', - '0.47', - '0.67', - ]), - ]), - list([ - '0.11', - '2.4', - '0.92', - '-0.64', - '0.71', - ]), - list([ - list([ - '1.1', - '0.21', - '-0.58', - '0.33', - '-0.81', - ]), - list([ - '-1.0', - '-0.49', - '-0.59', - '0.40', - '0.084', - ]), - list([ - '0.4', - '2.0', - '-0.46', - '-0.064', - '-1.4', - ]), - list([ - '0.33', - '-0.98', - '0.078', - '0.19', - '0.41', - ]), - list([ - '-1.6', - '2.3', - '1.0', - '1.4', - '0.63', - ]), - ]), - list([ - '0.36', - '0.82', - '-0.36', - '0.071', - '0.61', - ]), - list([ - list([ - '0.56', - '-1.5', - '-1.6', - '-1.5', - '0.43', - ]), - list([ - '-0.13', - '0.78', - '0.56', - '-0.11', - '-1.0', - ]), - list([ - '-0.22', - '0.8', - '0.91', - '-0.088', - '0.34', - ]), - list([ - '0.97', - '-1.0', - '0.39', - '0.73', - '0.25', - ]), - list([ - '0.077', - '-0.21', - '2.2', - '2.0', - '0.25', - ]), - ]), - list([ - '0.24', - '-0.26', - '-0.46', - '-1.2', - '-1.2', - ]), - ]), - 'noise_norm': '8.9', - 'weight_norm': '2.5', + 'localization': '0.00000049', + 'noise': '0.089', + 'numel': 90, + 'prior': '0.00000049', + 'scaled_grad': '0.00080', + 'weight_decay': '0.0', }), }) # --- @@ -18735,281 +10690,20 @@ ]), }), 'optimizer_sgld': dict({ + 'localization': '0.000015', + 'noise': '0.28', + 'numel': 90, + 'prior': '0.000015', + 'scaled_grad': '0.0083', + 'weight_decay': '0.0', }), 'optimizer_sgmcmc': dict({ - 'distance': '0.31', - 'dws': list([ - list([ - list([ - '1.6', - '-0.31', - '-1.7', - '-1.3', - '-0.24', - ]), - list([ - '-1.7', - '0.76', - '1.8', - '0.69', - '-0.0055', - ]), - list([ - '3.0', - '-0.19', - '-3.7', - '-2.8', - '-1.1', - ]), - list([ - '-0.4', - '0.25', - '0.30', - '0.12', - '-0.15', - ]), - list([ - '1.5', - '0.13', - '-2.1', - '-1.8', - '-0.83', - ]), - ]), - list([ - '-1.1', - '1.2', - '-1.9', - '0.35', - '-0.92', - ]), - list([ - list([ - '-3.5', - '2.6', - '-5.0', - '-0.57', - '-0.34', - ]), - list([ - '1.3', - '-0.67', - '1.5', - '0.27', - '0.16', - ]), - list([ - '2.5', - '-2.1', - '3.8', - '0.32', - '0.22', - ]), - list([ - '0.42', - '0.0077', - '0.19', - '0.18', - '0.053', - ]), - list([ - '3.3', - '-1.8', - '3.9', - '0.89', - '0.2', - ]), - ]), - list([ - '3.5', - '-0.64', - '-3.0', - '0.23', - '-2.2', - ]), - list([ - list([ - '0.33', - '-0.68', - '-1.6', - '-0.94', - '-0.38', - ]), - list([ - '-2.2', - '1.2', - '2.0', - '0.81', - '1.8', - ]), - list([ - '-2.5', - '0.99', - '1.1', - '0.025', - '1.6', - ]), - list([ - '0.064', - '-0.065', - '-0.44', - '-0.35', - '-0.47', - ]), - list([ - '-2.8', - '1.3', - '1.9', - '0.53', - '2.0', - ]), - ]), - list([ - '2.4', - '-3.8', - '-1.7', - '1.4', - '-3.3', - ]), - ]), - 'grad_norm': '0.0083', - 'localization_loss': '0.0048', - 'noise': list([ - list([ - list([ - '0.5', - '-0.43', - '-1.3', - '-0.19', - '0.65', - ]), - list([ - '-1.9', - '0.23', - '0.025', - '0.19', - '-0.93', - ]), - list([ - '-1.6', - '-1.1', - '-0.52', - '0.72', - '1.5', - ]), - list([ - '-1.5', - '-0.79', - '1.0', - '-0.56', - '0.70', - ]), - list([ - '0.71', - '-1.5', - '-0.73', - '0.47', - '0.67', - ]), - ]), - list([ - '0.11', - '2.4', - '0.92', - '-0.64', - '0.71', - ]), - list([ - list([ - '1.1', - '0.21', - '-0.58', - '0.33', - '-0.81', - ]), - list([ - '-1.0', - '-0.49', - '-0.59', - '0.40', - '0.084', - ]), - list([ - '0.4', - '2.0', - '-0.46', - '-0.064', - '-1.4', - ]), - list([ - '0.33', - '-0.98', - '0.078', - '0.19', - '0.41', - ]), - list([ - '-1.6', - '2.3', - '1.0', - '1.4', - '0.63', - ]), - ]), - list([ - '0.36', - '0.82', - '-0.36', - '0.071', - '0.61', - ]), - list([ - list([ - '0.56', - '-1.5', - '-1.6', - '-1.5', - '0.43', - ]), - list([ - '-0.13', - '0.78', - '0.56', - '-0.11', - '-1.0', - ]), - list([ - '-0.22', - '0.8', - '0.91', - '-0.088', - '0.34', - ]), - list([ - '0.97', - '-1.0', - '0.39', - '0.73', - '0.25', - ]), - list([ - '0.077', - '-0.21', - '2.2', - '2.0', - '0.25', - ]), - ]), - list([ - '0.24', - '-0.26', - '-0.46', - '-1.2', - '-1.2', - ]), - ]), - 'noise_norm': '8.9', - 'weight_norm': '2.5', + 'localization': '0.000015', + 'noise': '0.28', + 'numel': 90, + 'prior': '0.000015', + 'scaled_grad': '0.0083', + 'weight_decay': '0.0', }), }) # --- @@ -19284,281 +10978,20 @@ ]), }), 'optimizer_sgld': dict({ + 'localization': '0.00049', + 'noise': '0.89', + 'numel': 90, + 'prior': '0.00049', + 'scaled_grad': '0.085', + 'weight_decay': '0.0', }), 'optimizer_sgmcmc': dict({ - 'distance': '0.98', - 'dws': list([ - list([ - list([ - '1.3', - '-0.32', - '-1.3', - '-1.1', - '-0.085', - ]), - list([ - '-2.0', - '0.81', - '2.3', - '0.82', - '0.17', - ]), - list([ - '3.1', - '-0.19', - '-4.2', - '-2.9', - '-1.4', - ]), - list([ - '-0.39', - '0.35', - '0.035', - '0.088', - '-0.43', - ]), - list([ - '1.7', - '0.16', - '-2.4', - '-1.9', - '-1.0', - ]), - ]), - list([ - '-0.97', - '1.4', - '-1.9', - '0.45', - '-0.97', - ]), - list([ - list([ - '-4.1', - '2.0', - '-5.7', - '-0.2', - '-0.73', - ]), - list([ - '1.8', - '-0.55', - '2.1', - '0.19', - '0.52', - ]), - list([ - '2.4', - '-1.5', - '3.6', - '-0.045', - '0.42', - ]), - list([ - '0.25', - '0.18', - '-0.067', - '0.17', - '0.015', - ]), - list([ - '3.4', - '-0.94', - '3.6', - '0.54', - '0.38', - ]), - ]), - list([ - '3.8', - '-0.56', - '-2.8', - '0.37', - '-1.8', - ]), - list([ - list([ - '0.063', - '-0.38', - '-1.3', - '-1.0', - '-0.76', - ]), - list([ - '-1.7', - '1.2', - '1.8', - '0.95', - '2.6', - ]), - list([ - '-2.0', - '1.3', - '0.72', - '0.068', - '1.9', - ]), - list([ - '-0.048', - '0.088', - '-0.7', - '-0.43', - '-0.74', - ]), - list([ - '-2.5', - '1.7', - '1.5', - '0.53', - '2.7', - ]), - ]), - list([ - '2.6', - '-3.7', - '-1.3', - '1.6', - '-2.8', - ]), - ]), - 'grad_norm': '0.085', - 'localization_loss': '0.048', - 'noise': list([ - list([ - list([ - '0.5', - '-0.43', - '-1.3', - '-0.19', - '0.65', - ]), - list([ - '-1.9', - '0.23', - '0.025', - '0.19', - '-0.93', - ]), - list([ - '-1.6', - '-1.1', - '-0.52', - '0.72', - '1.5', - ]), - list([ - '-1.5', - '-0.79', - '1.0', - '-0.56', - '0.70', - ]), - list([ - '0.71', - '-1.5', - '-0.73', - '0.47', - '0.67', - ]), - ]), - list([ - '0.11', - '2.4', - '0.92', - '-0.64', - '0.71', - ]), - list([ - list([ - '1.1', - '0.21', - '-0.58', - '0.33', - '-0.81', - ]), - list([ - '-1.0', - '-0.49', - '-0.59', - '0.40', - '0.084', - ]), - list([ - '0.4', - '2.0', - '-0.46', - '-0.064', - '-1.4', - ]), - list([ - '0.33', - '-0.98', - '0.078', - '0.19', - '0.41', - ]), - list([ - '-1.6', - '2.3', - '1.0', - '1.4', - '0.63', - ]), - ]), - list([ - '0.36', - '0.82', - '-0.36', - '0.071', - '0.61', - ]), - list([ - list([ - '0.56', - '-1.5', - '-1.6', - '-1.5', - '0.43', - ]), - list([ - '-0.13', - '0.78', - '0.56', - '-0.11', - '-1.0', - ]), - list([ - '-0.22', - '0.8', - '0.91', - '-0.088', - '0.34', - ]), - list([ - '0.97', - '-1.0', - '0.39', - '0.73', - '0.25', - ]), - list([ - '0.077', - '-0.21', - '2.2', - '2.0', - '0.25', - ]), - ]), - list([ - '0.24', - '-0.26', - '-0.46', - '-1.2', - '-1.2', - ]), - ]), - 'noise_norm': '8.9', - 'weight_norm': '2.8', + 'localization': '0.00049', + 'noise': '0.89', + 'numel': 90, + 'prior': '0.00049', + 'scaled_grad': '0.085', + 'weight_decay': '0.0', }), }) # --- @@ -19833,281 +11266,20 @@ ]), }), 'optimizer_sgld': dict({ + 'localization': '0.016', + 'noise': '2.8', + 'numel': 90, + 'prior': '0.016', + 'scaled_grad': '0.81', + 'weight_decay': '0.0', }), 'optimizer_sgmcmc': dict({ - 'distance': '3.1', - 'dws': list([ - list([ - list([ - '0.70', - '-0.42', - '-0.15', - '-0.36', - '0.55', - ]), - list([ - '0.88', - '-0.068', - '-0.20', - '-1.5', - '0.42', - ]), - list([ - '-1.2', - '1.1', - '-0.89', - '0.84', - '-2.1', - ]), - list([ - '-2.0', - '2.2', - '-2.3', - '1.3', - '-4.4', - ]), - list([ - '-1.5', - '1.4', - '-0.94', - '0.9', - '-2.5', - ]), - ]), - list([ - '-0.63', - '-0.94', - '1.7', - '3.1', - '2.0', - ]), - list([ - list([ - '2.7', - '0.10', - '-0.44', - '-0.88', - '-1.6', - ]), - list([ - '1.1', - '1.2', - '4.3', - '1.1', - '2.7', - ]), - list([ - '-1.9', - '-0.56', - '-1.3', - '-0.21', - '-0.24', - ]), - list([ - '1.9', - '0.44', - '0.90', - '-0.13', - '-0.14', - ]), - list([ - '2.2', - '0.27', - '0.10', - '-0.39', - '-0.84', - ]), - ]), - list([ - '-2.5', - '2.0', - '-0.13', - '-0.85', - '-1.0', - ]), - list([ - list([ - '-0.0054', - '1.5', - '0.77', - '-0.92', - '-0.42', - ]), - list([ - '2.3', - '-1.0', - '1.3', - '1.3', - '1.5', - ]), - list([ - '-0.14', - '1.9', - '-0.23', - '0.28', - '1.2', - ]), - list([ - '-0.69', - '1.1', - '-0.81', - '0.39', - '0.76', - ]), - list([ - '-5.6', - '5.8', - '-3.7', - '-1.6', - '-0.46', - ]), - ]), - list([ - '1.3', - '-2.4', - '-0.81', - '-0.55', - '2.9', - ]), - ]), - 'grad_norm': '0.81', - 'localization_loss': '0.49', - 'noise': list([ - list([ - list([ - '0.5', - '-0.43', - '-1.3', - '-0.19', - '0.65', - ]), - list([ - '-1.9', - '0.23', - '0.025', - '0.19', - '-0.93', - ]), - list([ - '-1.6', - '-1.1', - '-0.52', - '0.72', - '1.5', - ]), - list([ - '-1.5', - '-0.79', - '1.0', - '-0.56', - '0.70', - ]), - list([ - '0.71', - '-1.5', - '-0.73', - '0.47', - '0.67', - ]), - ]), - list([ - '0.11', - '2.4', - '0.92', - '-0.64', - '0.71', - ]), - list([ - list([ - '1.1', - '0.21', - '-0.58', - '0.33', - '-0.81', - ]), - list([ - '-1.0', - '-0.49', - '-0.59', - '0.40', - '0.084', - ]), - list([ - '0.4', - '2.0', - '-0.46', - '-0.064', - '-1.4', - ]), - list([ - '0.33', - '-0.98', - '0.078', - '0.19', - '0.41', - ]), - list([ - '-1.6', - '2.3', - '1.0', - '1.4', - '0.63', - ]), - ]), - list([ - '0.36', - '0.82', - '-0.36', - '0.071', - '0.61', - ]), - list([ - list([ - '0.56', - '-1.5', - '-1.6', - '-1.5', - '0.43', - ]), - list([ - '-0.13', - '0.78', - '0.56', - '-0.11', - '-1.0', - ]), - list([ - '-0.22', - '0.8', - '0.91', - '-0.088', - '0.34', - ]), - list([ - '0.97', - '-1.0', - '0.39', - '0.73', - '0.25', - ]), - list([ - '0.077', - '-0.21', - '2.2', - '2.0', - '0.25', - ]), - ]), - list([ - '0.24', - '-0.26', - '-0.46', - '-1.2', - '-1.2', - ]), - ]), - 'noise_norm': '8.9', - 'weight_norm': '4.7', + 'localization': '0.016', + 'noise': '2.8', + 'numel': 90, + 'prior': '0.016', + 'scaled_grad': '0.81', + 'weight_decay': '0.0', }), }) # --- @@ -20382,281 +11554,20 @@ ]), }), 'optimizer_sgld': dict({ + 'localization': '0.0', + 'noise': '0.089', + 'numel': 90, + 'prior': '0.0000062', + 'scaled_grad': '0.000080', + 'weight_decay': '0.0000062', }), 'optimizer_sgmcmc': dict({ - 'distance': '2.5', - 'dws': list([ - list([ - list([ - '0.16', - '-0.016', - '-0.19', - '-0.15', - '-0.036', - ]), - list([ - '-0.14', - '0.07', - '0.17', - '0.054', - '0.00047', - ]), - list([ - '0.27', - '-0.020', - '-0.37', - '-0.28', - '-0.11', - ]), - list([ - '-0.035', - '0.031', - '0.044', - '-0.0052', - '-0.021', - ]), - list([ - '0.15', - '0.031', - '-0.2', - '-0.15', - '-0.080', - ]), - ]), - list([ - '-0.11', - '0.13', - '-0.2', - '0.017', - '-0.095', - ]), - list([ - list([ - '-0.33', - '0.28', - '-0.48', - '-0.077', - '-0.037', - ]), - list([ - '0.085', - '-0.075', - '0.14', - '0.041', - '0.018', - ]), - list([ - '0.24', - '-0.23', - '0.38', - '0.023', - '-0.0043', - ]), - list([ - '0.036', - '0.0066', - '0.04', - '0.0088', - '0.0047', - ]), - list([ - '0.33', - '-0.18', - '0.4', - '0.10', - '0.024', - ]), - ]), - list([ - '0.31', - '-0.055', - '-0.31', - '0.00080', - '-0.24', - ]), - list([ - list([ - '0.048', - '-0.064', - '-0.17', - '-0.084', - '-0.010', - ]), - list([ - '-0.22', - '0.11', - '0.21', - '0.09', - '0.17', - ]), - list([ - '-0.28', - '0.08', - '0.13', - '0.017', - '0.16', - ]), - list([ - '0.018', - '-0.0041', - '-0.052', - '-0.029', - '-0.050', - ]), - list([ - '-0.3', - '0.14', - '0.21', - '0.027', - '0.17', - ]), - ]), - list([ - '0.23', - '-0.37', - '-0.19', - '0.13', - '-0.34', - ]), - ]), - 'grad_norm': '0.000080', - 'localization_loss': '0.0', - 'noise': list([ - list([ - list([ - '0.5', - '-0.43', - '-1.3', - '-0.19', - '0.65', - ]), - list([ - '-1.9', - '0.23', - '0.025', - '0.19', - '-0.93', - ]), - list([ - '-1.6', - '-1.1', - '-0.52', - '0.72', - '1.5', - ]), - list([ - '-1.5', - '-0.79', - '1.0', - '-0.56', - '0.70', - ]), - list([ - '0.71', - '-1.5', - '-0.73', - '0.47', - '0.67', - ]), - ]), - list([ - '0.11', - '2.4', - '0.92', - '-0.64', - '0.71', - ]), - list([ - list([ - '1.1', - '0.21', - '-0.58', - '0.33', - '-0.81', - ]), - list([ - '-1.0', - '-0.49', - '-0.59', - '0.40', - '0.084', - ]), - list([ - '0.4', - '2.0', - '-0.46', - '-0.064', - '-1.4', - ]), - list([ - '0.33', - '-0.98', - '0.078', - '0.19', - '0.41', - ]), - list([ - '-1.6', - '2.3', - '1.0', - '1.4', - '0.63', - ]), - ]), - list([ - '0.36', - '0.82', - '-0.36', - '0.071', - '0.61', - ]), - list([ - list([ - '0.56', - '-1.5', - '-1.6', - '-1.5', - '0.43', - ]), - list([ - '-0.13', - '0.78', - '0.56', - '-0.11', - '-1.0', - ]), - list([ - '-0.22', - '0.8', - '0.91', - '-0.088', - '0.34', - ]), - list([ - '0.97', - '-1.0', - '0.39', - '0.73', - '0.25', - ]), - list([ - '0.077', - '-0.21', - '2.2', - '2.0', - '0.25', - ]), - ]), - list([ - '0.24', - '-0.26', - '-0.46', - '-1.2', - '-1.2', - ]), - ]), - 'noise_norm': '8.9', - 'weight_norm': '2.5', + 'localization': '0.0', + 'noise': '0.089', + 'numel': 90, + 'prior': '0.0000062', + 'scaled_grad': '0.000080', + 'weight_decay': '0.0000062', }), }) # --- @@ -20931,281 +11842,20 @@ ]), }), 'optimizer_sgld': dict({ + 'localization': '0.0', + 'noise': '0.28', + 'numel': 90, + 'prior': '0.000062', + 'scaled_grad': '0.00085', + 'weight_decay': '0.000062', }), 'optimizer_sgmcmc': dict({ - 'distance': '2.5', - 'dws': list([ - list([ - list([ - '0.16', - '-0.017', - '-0.19', - '-0.15', - '-0.033', - ]), - list([ - '-0.17', - '0.077', - '0.20', - '0.069', - '0.0059', - ]), - list([ - '0.3', - '-0.025', - '-0.41', - '-0.30', - '-0.13', - ]), - list([ - '-0.039', - '0.032', - '0.043', - '-0.0025', - '-0.026', - ]), - list([ - '0.16', - '0.031', - '-0.21', - '-0.16', - '-0.089', - ]), - ]), - list([ - '-0.11', - '0.15', - '-0.22', - '0.023', - '-0.10', - ]), - list([ - list([ - '-0.37', - '0.28', - '-0.53', - '-0.068', - '-0.053', - ]), - list([ - '0.11', - '-0.081', - '0.17', - '0.039', - '0.028', - ]), - list([ - '0.26', - '-0.22', - '0.4', - '0.0096', - '0.0069', - ]), - list([ - '0.031', - '0.016', - '0.03', - '0.0094', - '0.0054', - ]), - list([ - '0.35', - '-0.16', - '0.41', - '0.090', - '0.036', - ]), - ]), - list([ - '0.34', - '-0.059', - '-0.32', - '0.0059', - '-0.24', - ]), - list([ - list([ - '0.047', - '-0.061', - '-0.17', - '-0.087', - '-0.027', - ]), - list([ - '-0.23', - '0.12', - '0.21', - '0.096', - '0.21', - ]), - list([ - '-0.27', - '0.094', - '0.12', - '0.020', - '0.18', - ]), - list([ - '0.032', - '-0.0028', - '-0.066', - '-0.032', - '-0.063', - ]), - list([ - '-0.31', - '0.16', - '0.21', - '0.033', - '0.21', - ]), - ]), - list([ - '0.24', - '-0.38', - '-0.18', - '0.15', - '-0.34', - ]), - ]), - 'grad_norm': '0.00085', - 'localization_loss': '0.0', - 'noise': list([ - list([ - list([ - '0.5', - '-0.43', - '-1.3', - '-0.19', - '0.65', - ]), - list([ - '-1.9', - '0.23', - '0.025', - '0.19', - '-0.93', - ]), - list([ - '-1.6', - '-1.1', - '-0.52', - '0.72', - '1.5', - ]), - list([ - '-1.5', - '-0.79', - '1.0', - '-0.56', - '0.70', - ]), - list([ - '0.71', - '-1.5', - '-0.73', - '0.47', - '0.67', - ]), - ]), - list([ - '0.11', - '2.4', - '0.92', - '-0.64', - '0.71', - ]), - list([ - list([ - '1.1', - '0.21', - '-0.58', - '0.33', - '-0.81', - ]), - list([ - '-1.0', - '-0.49', - '-0.59', - '0.40', - '0.084', - ]), - list([ - '0.4', - '2.0', - '-0.46', - '-0.064', - '-1.4', - ]), - list([ - '0.33', - '-0.98', - '0.078', - '0.19', - '0.41', - ]), - list([ - '-1.6', - '2.3', - '1.0', - '1.4', - '0.63', - ]), - ]), - list([ - '0.36', - '0.82', - '-0.36', - '0.071', - '0.61', - ]), - list([ - list([ - '0.56', - '-1.5', - '-1.6', - '-1.5', - '0.43', - ]), - list([ - '-0.13', - '0.78', - '0.56', - '-0.11', - '-1.0', - ]), - list([ - '-0.22', - '0.8', - '0.91', - '-0.088', - '0.34', - ]), - list([ - '0.97', - '-1.0', - '0.39', - '0.73', - '0.25', - ]), - list([ - '0.077', - '-0.21', - '2.2', - '2.0', - '0.25', - ]), - ]), - list([ - '0.24', - '-0.26', - '-0.46', - '-1.2', - '-1.2', - ]), - ]), - 'noise_norm': '8.9', - 'weight_norm': '2.5', + 'localization': '0.0', + 'noise': '0.28', + 'numel': 90, + 'prior': '0.000062', + 'scaled_grad': '0.00085', + 'weight_decay': '0.000062', }), }) # --- @@ -21480,281 +12130,20 @@ ]), }), 'optimizer_sgld': dict({ + 'localization': '0.0', + 'noise': '0.89', + 'numel': 90, + 'prior': '0.00064', + 'scaled_grad': '0.0088', + 'weight_decay': '0.00064', }), 'optimizer_sgmcmc': dict({ - 'distance': '2.5', - 'dws': list([ - list([ - list([ - '0.12', - '-0.012', - '-0.13', - '-0.12', - '-0.011', - ]), - list([ - '-0.19', - '0.085', - '0.24', - '0.075', - '0.013', - ]), - list([ - '0.35', - '-0.032', - '-0.5', - '-0.34', - '-0.16', - ]), - list([ - '-0.018', - '0.032', - '0.0021', - '-0.02', - '-0.049', - ]), - list([ - '0.18', - '0.03', - '-0.25', - '-0.17', - '-0.11', - ]), - ]), - list([ - '-0.082', - '0.16', - '-0.25', - '0.016', - '-0.11', - ]), - list([ - list([ - '-0.34', - '0.26', - '-0.59', - '-0.047', - '-0.10', - ]), - list([ - '0.14', - '-0.092', - '0.24', - '0.035', - '0.060', - ]), - list([ - '0.21', - '-0.19', - '0.4', - '-0.018', - '0.033', - ]), - list([ - '0.04', - '0.021', - '0.036', - '0.013', - '0.0098', - ]), - list([ - '0.31', - '-0.11', - '0.40', - '0.066', - '0.054', - ]), - ]), - list([ - '0.36', - '-0.073', - '-0.31', - '0.0023', - '-0.21', - ]), - list([ - list([ - '0.044', - '-0.063', - '-0.14', - '-0.087', - '-0.054', - ]), - list([ - '-0.22', - '0.15', - '0.18', - '0.13', - '0.28', - ]), - list([ - '-0.26', - '0.13', - '0.081', - '0.041', - '0.23', - ]), - list([ - '0.016', - '0.017', - '-0.063', - '-0.035', - '-0.072', - ]), - list([ - '-0.31', - '0.2', - '0.16', - '0.062', - '0.27', - ]), - ]), - list([ - '0.24', - '-0.39', - '-0.17', - '0.15', - '-0.33', - ]), - ]), - 'grad_norm': '0.0088', - 'localization_loss': '0.0', - 'noise': list([ - list([ - list([ - '0.5', - '-0.43', - '-1.3', - '-0.19', - '0.65', - ]), - list([ - '-1.9', - '0.23', - '0.025', - '0.19', - '-0.93', - ]), - list([ - '-1.6', - '-1.1', - '-0.52', - '0.72', - '1.5', - ]), - list([ - '-1.5', - '-0.79', - '1.0', - '-0.56', - '0.70', - ]), - list([ - '0.71', - '-1.5', - '-0.73', - '0.47', - '0.67', - ]), - ]), - list([ - '0.11', - '2.4', - '0.92', - '-0.64', - '0.71', - ]), - list([ - list([ - '1.1', - '0.21', - '-0.58', - '0.33', - '-0.81', - ]), - list([ - '-1.0', - '-0.49', - '-0.59', - '0.40', - '0.084', - ]), - list([ - '0.4', - '2.0', - '-0.46', - '-0.064', - '-1.4', - ]), - list([ - '0.33', - '-0.98', - '0.078', - '0.19', - '0.41', - ]), - list([ - '-1.6', - '2.3', - '1.0', - '1.4', - '0.63', - ]), - ]), - list([ - '0.36', - '0.82', - '-0.36', - '0.071', - '0.61', - ]), - list([ - list([ - '0.56', - '-1.5', - '-1.6', - '-1.5', - '0.43', - ]), - list([ - '-0.13', - '0.78', - '0.56', - '-0.11', - '-1.0', - ]), - list([ - '-0.22', - '0.8', - '0.91', - '-0.088', - '0.34', - ]), - list([ - '0.97', - '-1.0', - '0.39', - '0.73', - '0.25', - ]), - list([ - '0.077', - '-0.21', - '2.2', - '2.0', - '0.25', - ]), - ]), - list([ - '0.24', - '-0.26', - '-0.46', - '-1.2', - '-1.2', - ]), - ]), - 'noise_norm': '8.9', - 'weight_norm': '2.6', + 'localization': '0.0', + 'noise': '0.89', + 'numel': 90, + 'prior': '0.00064', + 'scaled_grad': '0.0088', + 'weight_decay': '0.00064', }), }) # --- @@ -22029,281 +12418,20 @@ ]), }), 'optimizer_sgld': dict({ + 'localization': '0.0', + 'noise': '2.8', + 'numel': 90, + 'prior': '0.0064', + 'scaled_grad': '0.073', + 'weight_decay': '0.0064', }), 'optimizer_sgmcmc': dict({ - 'distance': '2.6', - 'dws': list([ - list([ - list([ - '0.084', - '-0.0046', - '-0.076', - '-0.091', - '0.0057', - ]), - list([ - '-0.16', - '0.087', - '0.20', - '0.032', - '-0.0010', - ]), - list([ - '0.29', - '-0.0060', - '-0.44', - '-0.30', - '-0.16', - ]), - list([ - '-0.0031', - '0.031', - '-0.028', - '-0.032', - '-0.062', - ]), - list([ - '0.14', - '0.046', - '-0.22', - '-0.15', - '-0.12', - ]), - ]), - list([ - '-0.057', - '0.13', - '-0.2', - '0.0072', - '-0.081', - ]), - list([ - list([ - '-0.29', - '0.2', - '-0.47', - '-0.057', - '-0.057', - ]), - list([ - '0.11', - '-0.072', - '0.20', - '0.036', - '0.059', - ]), - list([ - '0.15', - '-0.15', - '0.28', - '-0.022', - '0.0023', - ]), - list([ - '0.053', - '0.023', - '0.049', - '0.021', - '0.0067', - ]), - list([ - '0.25', - '-0.049', - '0.28', - '0.07', - '0.010', - ]), - ]), - list([ - '0.31', - '-0.043', - '-0.26', - '-0.0022', - '-0.15', - ]), - list([ - list([ - '0.0012', - '-0.046', - '-0.11', - '-0.066', - '-0.039', - ]), - list([ - '-0.17', - '0.13', - '0.13', - '0.12', - '0.26', - ]), - list([ - '-0.23', - '0.14', - '0.021', - '0.039', - '0.19', - ]), - list([ - '-0.04', - '0.062', - '-0.054', - '-0.022', - '-0.036', - ]), - list([ - '-0.26', - '0.19', - '0.081', - '0.052', - '0.23', - ]), - ]), - list([ - '0.21', - '-0.36', - '-0.12', - '0.10', - '-0.26', - ]), - ]), - 'grad_norm': '0.073', - 'localization_loss': '0.0', - 'noise': list([ - list([ - list([ - '0.5', - '-0.43', - '-1.3', - '-0.19', - '0.65', - ]), - list([ - '-1.9', - '0.23', - '0.025', - '0.19', - '-0.93', - ]), - list([ - '-1.6', - '-1.1', - '-0.52', - '0.72', - '1.5', - ]), - list([ - '-1.5', - '-0.79', - '1.0', - '-0.56', - '0.70', - ]), - list([ - '0.71', - '-1.5', - '-0.73', - '0.47', - '0.67', - ]), - ]), - list([ - '0.11', - '2.4', - '0.92', - '-0.64', - '0.71', - ]), - list([ - list([ - '1.1', - '0.21', - '-0.58', - '0.33', - '-0.81', - ]), - list([ - '-1.0', - '-0.49', - '-0.59', - '0.40', - '0.084', - ]), - list([ - '0.4', - '2.0', - '-0.46', - '-0.064', - '-1.4', - ]), - list([ - '0.33', - '-0.98', - '0.078', - '0.19', - '0.41', - ]), - list([ - '-1.6', - '2.3', - '1.0', - '1.4', - '0.63', - ]), - ]), - list([ - '0.36', - '0.82', - '-0.36', - '0.071', - '0.61', - ]), - list([ - list([ - '0.56', - '-1.5', - '-1.6', - '-1.5', - '0.43', - ]), - list([ - '-0.13', - '0.78', - '0.56', - '-0.11', - '-1.0', - ]), - list([ - '-0.22', - '0.8', - '0.91', - '-0.088', - '0.34', - ]), - list([ - '0.97', - '-1.0', - '0.39', - '0.73', - '0.25', - ]), - list([ - '0.077', - '-0.21', - '2.2', - '2.0', - '0.25', - ]), - ]), - list([ - '0.24', - '-0.26', - '-0.46', - '-1.2', - '-1.2', - ]), - ]), - 'noise_norm': '8.9', - 'weight_norm': '2.7', + 'localization': '0.0', + 'noise': '2.8', + 'numel': 90, + 'prior': '0.0064', + 'scaled_grad': '0.073', + 'weight_decay': '0.0064', }), }) # --- @@ -22578,281 +12706,20 @@ ]), }), 'optimizer_sgld': dict({ + 'localization': '0.0', + 'noise': '0.089', + 'numel': 90, + 'prior': '0.0000062', + 'scaled_grad': '0.00080', + 'weight_decay': '0.0000062', }), 'optimizer_sgmcmc': dict({ - 'distance': '2.5', - 'dws': list([ - list([ - list([ - '1.5', - '-0.28', - '-1.7', - '-1.3', - '-0.28', - ]), - list([ - '-1.5', - '0.70', - '1.6', - '0.56', - '-0.047', - ]), - list([ - '2.8', - '-0.16', - '-3.5', - '-2.7', - '-1.0', - ]), - list([ - '-0.36', - '0.23', - '0.32', - '0.081', - '-0.12', - ]), - list([ - '1.5', - '0.15', - '-1.9', - '-1.7', - '-0.76', - ]), - ]), - list([ - '-1.1', - '1.1', - '-1.8', - '0.29', - '-0.89', - ]), - list([ - list([ - '-3.3', - '2.7', - '-4.6', - '-0.68', - '-0.22', - ]), - list([ - '1.0', - '-0.63', - '1.2', - '0.31', - '0.077', - ]), - list([ - '2.4', - '-2.2', - '3.7', - '0.42', - '0.10', - ]), - list([ - '0.46', - '-0.063', - '0.29', - '0.17', - '0.051', - ]), - list([ - '3.2', - '-2.0', - '3.9', - '1.0', - '0.11', - ]), - ]), - list([ - '3.2', - '-0.59', - '-2.9', - '0.15', - '-2.3', - ]), - list([ - list([ - '0.38', - '-0.71', - '-1.6', - '-0.90', - '-0.21', - ]), - list([ - '-2.2', - '1.1', - '2.0', - '0.77', - '1.5', - ]), - list([ - '-2.6', - '0.86', - '1.2', - '0.0091', - '1.4', - ]), - list([ - '-0.0081', - '-0.083', - '-0.35', - '-0.31', - '-0.37', - ]), - list([ - '-2.8', - '1.2', - '1.9', - '0.46', - '1.7', - ]), - ]), - list([ - '2.3', - '-3.7', - '-1.8', - '1.2', - '-3.3', - ]), - ]), - 'grad_norm': '0.00080', - 'localization_loss': '0.0', - 'noise': list([ - list([ - list([ - '0.5', - '-0.43', - '-1.3', - '-0.19', - '0.65', - ]), - list([ - '-1.9', - '0.23', - '0.025', - '0.19', - '-0.93', - ]), - list([ - '-1.6', - '-1.1', - '-0.52', - '0.72', - '1.5', - ]), - list([ - '-1.5', - '-0.79', - '1.0', - '-0.56', - '0.70', - ]), - list([ - '0.71', - '-1.5', - '-0.73', - '0.47', - '0.67', - ]), - ]), - list([ - '0.11', - '2.4', - '0.92', - '-0.64', - '0.71', - ]), - list([ - list([ - '1.1', - '0.21', - '-0.58', - '0.33', - '-0.81', - ]), - list([ - '-1.0', - '-0.49', - '-0.59', - '0.40', - '0.084', - ]), - list([ - '0.4', - '2.0', - '-0.46', - '-0.064', - '-1.4', - ]), - list([ - '0.33', - '-0.98', - '0.078', - '0.19', - '0.41', - ]), - list([ - '-1.6', - '2.3', - '1.0', - '1.4', - '0.63', - ]), - ]), - list([ - '0.36', - '0.82', - '-0.36', - '0.071', - '0.61', - ]), - list([ - list([ - '0.56', - '-1.5', - '-1.6', - '-1.5', - '0.43', - ]), - list([ - '-0.13', - '0.78', - '0.56', - '-0.11', - '-1.0', - ]), - list([ - '-0.22', - '0.8', - '0.91', - '-0.088', - '0.34', - ]), - list([ - '0.97', - '-1.0', - '0.39', - '0.73', - '0.25', - ]), - list([ - '0.077', - '-0.21', - '2.2', - '2.0', - '0.25', - ]), - ]), - list([ - '0.24', - '-0.26', - '-0.46', - '-1.2', - '-1.2', - ]), - ]), - 'noise_norm': '8.9', - 'weight_norm': '2.5', + 'localization': '0.0', + 'noise': '0.089', + 'numel': 90, + 'prior': '0.0000062', + 'scaled_grad': '0.00080', + 'weight_decay': '0.0000062', }), }) # --- @@ -23127,281 +12994,20 @@ ]), }), 'optimizer_sgld': dict({ + 'localization': '0.0', + 'noise': '0.28', + 'numel': 90, + 'prior': '0.000062', + 'scaled_grad': '0.0083', + 'weight_decay': '0.000062', }), 'optimizer_sgmcmc': dict({ - 'distance': '2.5', - 'dws': list([ - list([ - list([ - '1.6', - '-0.3', - '-1.7', - '-1.3', - '-0.25', - ]), - list([ - '-1.7', - '0.76', - '1.9', - '0.69', - '0.0014', - ]), - list([ - '3.0', - '-0.2', - '-3.8', - '-2.8', - '-1.1', - ]), - list([ - '-0.4', - '0.26', - '0.32', - '0.10', - '-0.16', - ]), - list([ - '1.5', - '0.15', - '-2.1', - '-1.7', - '-0.83', - ]), - ]), - list([ - '-1.1', - '1.3', - '-1.9', - '0.33', - '-0.93', - ]), - list([ - list([ - '-3.6', - '2.6', - '-5.0', - '-0.58', - '-0.36', - ]), - list([ - '1.2', - '-0.68', - '1.5', - '0.28', - '0.17', - ]), - list([ - '2.5', - '-2.1', - '3.8', - '0.30', - '0.20', - ]), - list([ - '0.40', - '0.021', - '0.21', - '0.16', - '0.051', - ]), - list([ - '3.4', - '-1.8', - '3.9', - '0.9', - '0.21', - ]), - ]), - list([ - '3.5', - '-0.63', - '-3.0', - '0.22', - '-2.2', - ]), - list([ - list([ - '0.34', - '-0.67', - '-1.6', - '-0.93', - '-0.37', - ]), - list([ - '-2.2', - '1.2', - '2.1', - '0.83', - '1.9', - ]), - list([ - '-2.5', - '0.98', - '1.1', - '0.046', - '1.6', - ]), - list([ - '0.081', - '-0.062', - '-0.46', - '-0.35', - '-0.48', - ]), - list([ - '-2.8', - '1.3', - '1.9', - '0.50', - '2.0', - ]), - ]), - list([ - '2.4', - '-3.8', - '-1.7', - '1.4', - '-3.3', - ]), - ]), - 'grad_norm': '0.0083', - 'localization_loss': '0.0', - 'noise': list([ - list([ - list([ - '0.5', - '-0.43', - '-1.3', - '-0.19', - '0.65', - ]), - list([ - '-1.9', - '0.23', - '0.025', - '0.19', - '-0.93', - ]), - list([ - '-1.6', - '-1.1', - '-0.52', - '0.72', - '1.5', - ]), - list([ - '-1.5', - '-0.79', - '1.0', - '-0.56', - '0.70', - ]), - list([ - '0.71', - '-1.5', - '-0.73', - '0.47', - '0.67', - ]), - ]), - list([ - '0.11', - '2.4', - '0.92', - '-0.64', - '0.71', - ]), - list([ - list([ - '1.1', - '0.21', - '-0.58', - '0.33', - '-0.81', - ]), - list([ - '-1.0', - '-0.49', - '-0.59', - '0.40', - '0.084', - ]), - list([ - '0.4', - '2.0', - '-0.46', - '-0.064', - '-1.4', - ]), - list([ - '0.33', - '-0.98', - '0.078', - '0.19', - '0.41', - ]), - list([ - '-1.6', - '2.3', - '1.0', - '1.4', - '0.63', - ]), - ]), - list([ - '0.36', - '0.82', - '-0.36', - '0.071', - '0.61', - ]), - list([ - list([ - '0.56', - '-1.5', - '-1.6', - '-1.5', - '0.43', - ]), - list([ - '-0.13', - '0.78', - '0.56', - '-0.11', - '-1.0', - ]), - list([ - '-0.22', - '0.8', - '0.91', - '-0.088', - '0.34', - ]), - list([ - '0.97', - '-1.0', - '0.39', - '0.73', - '0.25', - ]), - list([ - '0.077', - '-0.21', - '2.2', - '2.0', - '0.25', - ]), - ]), - list([ - '0.24', - '-0.26', - '-0.46', - '-1.2', - '-1.2', - ]), - ]), - 'noise_norm': '8.9', - 'weight_norm': '2.5', + 'localization': '0.0', + 'noise': '0.28', + 'numel': 90, + 'prior': '0.000062', + 'scaled_grad': '0.0083', + 'weight_decay': '0.000062', }), }) # --- @@ -23676,281 +13282,20 @@ ]), }), 'optimizer_sgld': dict({ + 'localization': '0.0', + 'noise': '0.89', + 'numel': 90, + 'prior': '0.00063', + 'scaled_grad': '0.076', + 'weight_decay': '0.00063', }), 'optimizer_sgmcmc': dict({ - 'distance': '2.5', - 'dws': list([ - list([ - list([ - '1.0', - '-0.25', - '-0.94', - '-0.84', - '0.0091', - ]), - list([ - '-1.6', - '0.77', - '1.9', - '0.58', - '0.037', - ]), - list([ - '2.9', - '-0.16', - '-3.9', - '-2.8', - '-1.3', - ]), - list([ - '-0.16', - '0.28', - '-0.11', - '-0.10', - '-0.38', - ]), - list([ - '1.6', - '0.18', - '-2.2', - '-1.7', - '-0.95', - ]), - ]), - list([ - '-0.77', - '1.2', - '-1.9', - '0.23', - '-0.89', - ]), - list([ - list([ - '-3.0', - '2.1', - '-4.9', - '-0.34', - '-0.58', - ]), - list([ - '1.4', - '-0.71', - '1.9', - '0.22', - '0.41', - ]), - list([ - '1.7', - '-1.6', - '3.3', - '0.059', - '0.33', - ]), - list([ - '0.48', - '0.052', - '0.3', - '0.18', - '0.069', - ]), - list([ - '2.7', - '-1.1', - '3.3', - '0.67', - '0.28', - ]), - ]), - list([ - '3.3', - '-0.67', - '-2.6', - '0.2', - '-1.7', - ]), - list([ - list([ - '0.14', - '-0.62', - '-1.2', - '-0.93', - '-0.54', - ]), - list([ - '-1.7', - '1.2', - '1.5', - '1.0', - '2.2', - ]), - list([ - '-2.2', - '1.2', - '0.59', - '0.21', - '1.7', - ]), - list([ - '-0.28', - '0.24', - '-0.35', - '-0.34', - '-0.39', - ]), - list([ - '-2.3', - '1.5', - '1.2', - '0.65', - '2.2', - ]), - ]), - list([ - '2.4', - '-3.6', - '-1.4', - '1.2', - '-2.8', - ]), - ]), - 'grad_norm': '0.076', - 'localization_loss': '0.0', - 'noise': list([ - list([ - list([ - '0.5', - '-0.43', - '-1.3', - '-0.19', - '0.65', - ]), - list([ - '-1.9', - '0.23', - '0.025', - '0.19', - '-0.93', - ]), - list([ - '-1.6', - '-1.1', - '-0.52', - '0.72', - '1.5', - ]), - list([ - '-1.5', - '-0.79', - '1.0', - '-0.56', - '0.70', - ]), - list([ - '0.71', - '-1.5', - '-0.73', - '0.47', - '0.67', - ]), - ]), - list([ - '0.11', - '2.4', - '0.92', - '-0.64', - '0.71', - ]), - list([ - list([ - '1.1', - '0.21', - '-0.58', - '0.33', - '-0.81', - ]), - list([ - '-1.0', - '-0.49', - '-0.59', - '0.40', - '0.084', - ]), - list([ - '0.4', - '2.0', - '-0.46', - '-0.064', - '-1.4', - ]), - list([ - '0.33', - '-0.98', - '0.078', - '0.19', - '0.41', - ]), - list([ - '-1.6', - '2.3', - '1.0', - '1.4', - '0.63', - ]), - ]), - list([ - '0.36', - '0.82', - '-0.36', - '0.071', - '0.61', - ]), - list([ - list([ - '0.56', - '-1.5', - '-1.6', - '-1.5', - '0.43', - ]), - list([ - '-0.13', - '0.78', - '0.56', - '-0.11', - '-1.0', - ]), - list([ - '-0.22', - '0.8', - '0.91', - '-0.088', - '0.34', - ]), - list([ - '0.97', - '-1.0', - '0.39', - '0.73', - '0.25', - ]), - list([ - '0.077', - '-0.21', - '2.2', - '2.0', - '0.25', - ]), - ]), - list([ - '0.24', - '-0.26', - '-0.46', - '-1.2', - '-1.2', - ]), - ]), - 'noise_norm': '8.9', - 'weight_norm': '2.6', + 'localization': '0.0', + 'noise': '0.89', + 'numel': 90, + 'prior': '0.00063', + 'scaled_grad': '0.076', + 'weight_decay': '0.00063', }), }) # --- @@ -24225,281 +13570,20 @@ ]), }), 'optimizer_sgld': dict({ + 'localization': '0.0', + 'noise': '2.8', + 'numel': 90, + 'prior': '0.0062', + 'scaled_grad': '0.48', + 'weight_decay': '0.0062', }), 'optimizer_sgmcmc': dict({ - 'distance': '2.5', - 'dws': list([ - list([ - list([ - '0.52', - '-0.16', - '-0.24', - '-0.52', - '0.22', - ]), - list([ - '-0.98', - '0.65', - '1.2', - '0.010', - '-0.069', - ]), - list([ - '1.5', - '0.15', - '-2.2', - '-1.6', - '-0.99', - ]), - list([ - '-0.19', - '0.34', - '-0.19', - '-0.10', - '-0.50', - ]), - list([ - '0.7', - '0.45', - '-1.3', - '-1.2', - '-0.89', - ]), - ]), - list([ - '-0.48', - '0.74', - '-0.82', - '0.3', - '-0.27', - ]), - list([ - list([ - '-1.9', - '1.2', - '-2.6', - '-0.37', - '-0.19', - ]), - list([ - '1.1', - '-0.48', - '1.4', - '0.23', - '0.43', - ]), - list([ - '1.0', - '-0.99', - '1.8', - '0.011', - '0.095', - ]), - list([ - '0.53', - '0.062', - '0.33', - '0.24', - '0.0050', - ]), - list([ - '1.7', - '-0.26', - '1.4', - '0.64', - '-0.16', - ]), - ]), - list([ - '2.1', - '-0.24', - '-2.0', - '0.15', - '-0.75', - ]), - list([ - list([ - '-0.27', - '-0.35', - '-0.81', - '-0.76', - '-0.41', - ]), - list([ - '-0.66', - '0.6', - '0.96', - '0.93', - '1.6', - ]), - list([ - '-1.6', - '0.93', - '-0.046', - '0.10', - '1.1', - ]), - list([ - '-0.88', - '0.77', - '-0.35', - '-0.16', - '0.019', - ]), - list([ - '-1.5', - '1.0', - '0.29', - '0.37', - '1.3', - ]), - ]), - list([ - '2.1', - '-3.2', - '-0.76', - '0.55', - '-1.7', - ]), - ]), - 'grad_norm': '0.48', - 'localization_loss': '0.0', - 'noise': list([ - list([ - list([ - '0.5', - '-0.43', - '-1.3', - '-0.19', - '0.65', - ]), - list([ - '-1.9', - '0.23', - '0.025', - '0.19', - '-0.93', - ]), - list([ - '-1.6', - '-1.1', - '-0.52', - '0.72', - '1.5', - ]), - list([ - '-1.5', - '-0.79', - '1.0', - '-0.56', - '0.70', - ]), - list([ - '0.71', - '-1.5', - '-0.73', - '0.47', - '0.67', - ]), - ]), - list([ - '0.11', - '2.4', - '0.92', - '-0.64', - '0.71', - ]), - list([ - list([ - '1.1', - '0.21', - '-0.58', - '0.33', - '-0.81', - ]), - list([ - '-1.0', - '-0.49', - '-0.59', - '0.40', - '0.084', - ]), - list([ - '0.4', - '2.0', - '-0.46', - '-0.064', - '-1.4', - ]), - list([ - '0.33', - '-0.98', - '0.078', - '0.19', - '0.41', - ]), - list([ - '-1.6', - '2.3', - '1.0', - '1.4', - '0.63', - ]), - ]), - list([ - '0.36', - '0.82', - '-0.36', - '0.071', - '0.61', - ]), - list([ - list([ - '0.56', - '-1.5', - '-1.6', - '-1.5', - '0.43', - ]), - list([ - '-0.13', - '0.78', - '0.56', - '-0.11', - '-1.0', - ]), - list([ - '-0.22', - '0.8', - '0.91', - '-0.088', - '0.34', - ]), - list([ - '0.97', - '-1.0', - '0.39', - '0.73', - '0.25', - ]), - list([ - '0.077', - '-0.21', - '2.2', - '2.0', - '0.25', - ]), - ]), - list([ - '0.24', - '-0.26', - '-0.46', - '-1.2', - '-1.2', - ]), - ]), - 'noise_norm': '8.9', - 'weight_norm': '2.6', + 'localization': '0.0', + 'noise': '2.8', + 'numel': 90, + 'prior': '0.0062', + 'scaled_grad': '0.48', + 'weight_decay': '0.0062', }), }) # --- @@ -24774,281 +13858,20 @@ ]), }), 'optimizer_sgld': dict({ + 'localization': '0.00000049', + 'noise': '0.089', + 'numel': 90, + 'prior': '0.0000062', + 'scaled_grad': '0.000080', + 'weight_decay': '0.0000062', }), 'optimizer_sgmcmc': dict({ - 'distance': '0.098', - 'dws': list([ - list([ - list([ - '0.16', - '-0.015', - '-0.19', - '-0.15', - '-0.035', - ]), - list([ - '-0.14', - '0.07', - '0.17', - '0.053', - '-0.00021', - ]), - list([ - '0.27', - '-0.02', - '-0.37', - '-0.28', - '-0.12', - ]), - list([ - '-0.035', - '0.029', - '0.044', - '-0.0044', - '-0.021', - ]), - list([ - '0.15', - '0.031', - '-0.2', - '-0.15', - '-0.082', - ]), - ]), - list([ - '-0.11', - '0.13', - '-0.2', - '0.018', - '-0.096', - ]), - list([ - list([ - '-0.33', - '0.28', - '-0.48', - '-0.078', - '-0.038', - ]), - list([ - '0.087', - '-0.075', - '0.14', - '0.043', - '0.018', - ]), - list([ - '0.24', - '-0.23', - '0.38', - '0.022', - '-0.0049', - ]), - list([ - '0.036', - '0.0071', - '0.038', - '0.010', - '0.0054', - ]), - list([ - '0.33', - '-0.18', - '0.4', - '0.10', - '0.023', - ]), - ]), - list([ - '0.31', - '-0.054', - '-0.31', - '-0.00057', - '-0.24', - ]), - list([ - list([ - '0.049', - '-0.064', - '-0.17', - '-0.083', - '-0.010', - ]), - list([ - '-0.22', - '0.11', - '0.21', - '0.09', - '0.17', - ]), - list([ - '-0.28', - '0.081', - '0.12', - '0.016', - '0.16', - ]), - list([ - '0.02', - '-0.0035', - '-0.052', - '-0.028', - '-0.050', - ]), - list([ - '-0.3', - '0.14', - '0.21', - '0.028', - '0.17', - ]), - ]), - list([ - '0.23', - '-0.37', - '-0.19', - '0.14', - '-0.34', - ]), - ]), - 'grad_norm': '0.000080', - 'localization_loss': '0.00048', - 'noise': list([ - list([ - list([ - '0.5', - '-0.43', - '-1.3', - '-0.19', - '0.65', - ]), - list([ - '-1.9', - '0.23', - '0.025', - '0.19', - '-0.93', - ]), - list([ - '-1.6', - '-1.1', - '-0.52', - '0.72', - '1.5', - ]), - list([ - '-1.5', - '-0.79', - '1.0', - '-0.56', - '0.70', - ]), - list([ - '0.71', - '-1.5', - '-0.73', - '0.47', - '0.67', - ]), - ]), - list([ - '0.11', - '2.4', - '0.92', - '-0.64', - '0.71', - ]), - list([ - list([ - '1.1', - '0.21', - '-0.58', - '0.33', - '-0.81', - ]), - list([ - '-1.0', - '-0.49', - '-0.59', - '0.40', - '0.084', - ]), - list([ - '0.4', - '2.0', - '-0.46', - '-0.064', - '-1.4', - ]), - list([ - '0.33', - '-0.98', - '0.078', - '0.19', - '0.41', - ]), - list([ - '-1.6', - '2.3', - '1.0', - '1.4', - '0.63', - ]), - ]), - list([ - '0.36', - '0.82', - '-0.36', - '0.071', - '0.61', - ]), - list([ - list([ - '0.56', - '-1.5', - '-1.6', - '-1.5', - '0.43', - ]), - list([ - '-0.13', - '0.78', - '0.56', - '-0.11', - '-1.0', - ]), - list([ - '-0.22', - '0.8', - '0.91', - '-0.088', - '0.34', - ]), - list([ - '0.97', - '-1.0', - '0.39', - '0.73', - '0.25', - ]), - list([ - '0.077', - '-0.21', - '2.2', - '2.0', - '0.25', - ]), - ]), - list([ - '0.24', - '-0.26', - '-0.46', - '-1.2', - '-1.2', - ]), - ]), - 'noise_norm': '8.9', - 'weight_norm': '2.5', + 'localization': '0.00000049', + 'noise': '0.089', + 'numel': 90, + 'prior': '0.0000062', + 'scaled_grad': '0.000080', + 'weight_decay': '0.0000062', }), }) # --- @@ -25323,281 +14146,20 @@ ]), }), 'optimizer_sgld': dict({ + 'localization': '0.000015', + 'noise': '0.28', + 'numel': 90, + 'prior': '0.000064', + 'scaled_grad': '0.00085', + 'weight_decay': '0.000062', }), 'optimizer_sgmcmc': dict({ - 'distance': '0.31', - 'dws': list([ - list([ - list([ - '0.17', - '-0.013', - '-0.19', - '-0.16', - '-0.031', - ]), - list([ - '-0.17', - '0.076', - '0.2', - '0.067', - '0.0037', - ]), - list([ - '0.3', - '-0.024', - '-0.41', - '-0.30', - '-0.14', - ]), - list([ - '-0.04', - '0.028', - '0.042', - '0.000047', - '-0.028', - ]), - list([ - '0.16', - '0.031', - '-0.22', - '-0.16', - '-0.094', - ]), - ]), - list([ - '-0.11', - '0.14', - '-0.22', - '0.028', - '-0.10', - ]), - list([ - list([ - '-0.37', - '0.28', - '-0.53', - '-0.071', - '-0.056', - ]), - list([ - '0.12', - '-0.080', - '0.17', - '0.045', - '0.029', - ]), - list([ - '0.27', - '-0.22', - '0.4', - '0.0054', - '0.0050', - ]), - list([ - '0.032', - '0.017', - '0.025', - '0.014', - '0.0078', - ]), - list([ - '0.35', - '-0.16', - '0.41', - '0.085', - '0.033', - ]), - ]), - list([ - '0.34', - '-0.056', - '-0.31', - '0.0015', - '-0.24', - ]), - list([ - list([ - '0.050', - '-0.062', - '-0.17', - '-0.085', - '-0.026', - ]), - list([ - '-0.23', - '0.12', - '0.22', - '0.096', - '0.21', - ]), - list([ - '-0.26', - '0.098', - '0.11', - '0.017', - '0.18', - ]), - list([ - '0.037', - '-0.00076', - '-0.064', - '-0.028', - '-0.063', - ]), - list([ - '-0.31', - '0.16', - '0.21', - '0.036', - '0.21', - ]), - ]), - list([ - '0.25', - '-0.38', - '-0.18', - '0.16', - '-0.34', - ]), - ]), - 'grad_norm': '0.00085', - 'localization_loss': '0.0048', - 'noise': list([ - list([ - list([ - '0.5', - '-0.43', - '-1.3', - '-0.19', - '0.65', - ]), - list([ - '-1.9', - '0.23', - '0.025', - '0.19', - '-0.93', - ]), - list([ - '-1.6', - '-1.1', - '-0.52', - '0.72', - '1.5', - ]), - list([ - '-1.5', - '-0.79', - '1.0', - '-0.56', - '0.70', - ]), - list([ - '0.71', - '-1.5', - '-0.73', - '0.47', - '0.67', - ]), - ]), - list([ - '0.11', - '2.4', - '0.92', - '-0.64', - '0.71', - ]), - list([ - list([ - '1.1', - '0.21', - '-0.58', - '0.33', - '-0.81', - ]), - list([ - '-1.0', - '-0.49', - '-0.59', - '0.40', - '0.084', - ]), - list([ - '0.4', - '2.0', - '-0.46', - '-0.064', - '-1.4', - ]), - list([ - '0.33', - '-0.98', - '0.078', - '0.19', - '0.41', - ]), - list([ - '-1.6', - '2.3', - '1.0', - '1.4', - '0.63', - ]), - ]), - list([ - '0.36', - '0.82', - '-0.36', - '0.071', - '0.61', - ]), - list([ - list([ - '0.56', - '-1.5', - '-1.6', - '-1.5', - '0.43', - ]), - list([ - '-0.13', - '0.78', - '0.56', - '-0.11', - '-1.0', - ]), - list([ - '-0.22', - '0.8', - '0.91', - '-0.088', - '0.34', - ]), - list([ - '0.97', - '-1.0', - '0.39', - '0.73', - '0.25', - ]), - list([ - '0.077', - '-0.21', - '2.2', - '2.0', - '0.25', - ]), - ]), - list([ - '0.24', - '-0.26', - '-0.46', - '-1.2', - '-1.2', - ]), - ]), - 'noise_norm': '8.9', - 'weight_norm': '2.5', + 'localization': '0.000015', + 'noise': '0.28', + 'numel': 90, + 'prior': '0.000064', + 'scaled_grad': '0.00085', + 'weight_decay': '0.000062', }), }) # --- @@ -25872,281 +14434,20 @@ ]), }), 'optimizer_sgld': dict({ + 'localization': '0.00036', + 'noise': '0.89', + 'numel': 90, + 'prior': '0.00073', + 'scaled_grad': '0.0088', + 'weight_decay': '0.00064', }), 'optimizer_sgmcmc': dict({ - 'distance': '0.72', - 'dws': list([ - list([ - list([ - '0.13', - '-0.0021', - '-0.12', - '-0.13', - '-0.0043', - ]), - list([ - '-0.20', - '0.084', - '0.23', - '0.067', - '0.0066', - ]), - list([ - '0.35', - '-0.03', - '-0.51', - '-0.34', - '-0.17', - ]), - list([ - '-0.020', - '0.022', - '-0.0019', - '-0.012', - '-0.055', - ]), - list([ - '0.18', - '0.029', - '-0.26', - '-0.17', - '-0.12', - ]), - ]), - list([ - '-0.072', - '0.15', - '-0.25', - '0.026', - '-0.12', - ]), - list([ - list([ - '-0.33', - '0.25', - '-0.6', - '-0.055', - '-0.11', - ]), - list([ - '0.15', - '-0.090', - '0.24', - '0.045', - '0.063', - ]), - list([ - '0.22', - '-0.19', - '0.39', - '-0.028', - '0.028', - ]), - list([ - '0.045', - '0.026', - '0.026', - '0.023', - '0.017', - ]), - list([ - '0.31', - '-0.12', - '0.41', - '0.056', - '0.046', - ]), - ]), - list([ - '0.36', - '-0.063', - '-0.30', - '-0.0077', - '-0.22', - ]), - list([ - list([ - '0.054', - '-0.068', - '-0.15', - '-0.081', - '-0.052', - ]), - list([ - '-0.23', - '0.14', - '0.19', - '0.13', - '0.28', - ]), - list([ - '-0.25', - '0.14', - '0.071', - '0.031', - '0.22', - ]), - list([ - '0.026', - '0.023', - '-0.059', - '-0.025', - '-0.072', - ]), - list([ - '-0.32', - '0.20', - '0.16', - '0.072', - '0.26', - ]), - ]), - list([ - '0.25', - '-0.38', - '-0.17', - '0.16', - '-0.33', - ]), - ]), - 'grad_norm': '0.0088', - 'localization_loss': '0.026', - 'noise': list([ - list([ - list([ - '0.5', - '-0.43', - '-1.3', - '-0.19', - '0.65', - ]), - list([ - '-1.9', - '0.23', - '0.025', - '0.19', - '-0.93', - ]), - list([ - '-1.6', - '-1.1', - '-0.52', - '0.72', - '1.5', - ]), - list([ - '-1.5', - '-0.79', - '1.0', - '-0.56', - '0.70', - ]), - list([ - '0.71', - '-1.5', - '-0.73', - '0.47', - '0.67', - ]), - ]), - list([ - '0.11', - '2.4', - '0.92', - '-0.64', - '0.71', - ]), - list([ - list([ - '1.1', - '0.21', - '-0.58', - '0.33', - '-0.81', - ]), - list([ - '-1.0', - '-0.49', - '-0.59', - '0.40', - '0.084', - ]), - list([ - '0.4', - '2.0', - '-0.46', - '-0.064', - '-1.4', - ]), - list([ - '0.33', - '-0.98', - '0.078', - '0.19', - '0.41', - ]), - list([ - '-1.6', - '2.3', - '1.0', - '1.4', - '0.63', - ]), - ]), - list([ - '0.36', - '0.82', - '-0.36', - '0.071', - '0.61', - ]), - list([ - list([ - '0.56', - '-1.5', - '-1.6', - '-1.5', - '0.43', - ]), - list([ - '-0.13', - '0.78', - '0.56', - '-0.11', - '-1.0', - ]), - list([ - '-0.22', - '0.8', - '0.91', - '-0.088', - '0.34', - ]), - list([ - '0.97', - '-1.0', - '0.39', - '0.73', - '0.25', - ]), - list([ - '0.077', - '-0.21', - '2.2', - '2.0', - '0.25', - ]), - ]), - list([ - '0.24', - '-0.26', - '-0.46', - '-1.2', - '-1.2', - ]), - ]), - 'noise_norm': '8.9', - 'weight_norm': '2.6', + 'localization': '0.00036', + 'noise': '0.89', + 'numel': 90, + 'prior': '0.00073', + 'scaled_grad': '0.0088', + 'weight_decay': '0.00064', }), }) # --- @@ -26421,322 +14722,61 @@ ]), }), 'optimizer_sgld': dict({ + 'localization': '0.0044', + 'noise': '2.8', + 'numel': 90, + 'prior': '0.0078', + 'scaled_grad': '0.073', + 'weight_decay': '0.0064', }), 'optimizer_sgmcmc': dict({ - 'distance': '0.89', - 'dws': list([ - list([ - list([ - '0.094', - '0.0054', - '-0.066', - '-0.10', - '0.016', - ]), - list([ - '-0.17', - '0.085', - '0.19', - '0.022', - '-0.011', - ]), - list([ - '0.28', - '0.0017', - '-0.45', - '-0.3', - '-0.17', - ]), - list([ - '-0.0098', - '0.021', - '-0.038', - '-0.022', - '-0.072', - ]), - list([ - '0.13', - '0.044', - '-0.23', - '-0.14', - '-0.13', - ]), - ]), + 'localization': '0.0044', + 'noise': '2.8', + 'numel': 90, + 'prior': '0.0078', + 'scaled_grad': '0.073', + 'weight_decay': '0.0064', + }), + }) +# --- +# name: test_SGMCMC_vs_SGLD[0.05-0.1-0.1-10.0-0.0001][test_SGMCMC_vs_SGLD_0.0001_10.0_0.1_0.1_0.05] + dict({ + 'model1': dict({ + '0.bias': list([ + '0.06', + '0.42', + '-0.41', + '-0.27', + '-0.12', + ]), + '0.weight': list([ list([ - '-0.047', - '0.12', - '-0.2', - '0.017', - '-0.091', + '0.021', + '0.25', + '-0.37', + '-0.35', + '-0.16', ]), list([ - list([ - '-0.28', - '0.19', - '-0.48', - '-0.067', - '-0.067', - ]), - list([ - '0.12', - '-0.066', - '0.21', - '0.046', - '0.069', - ]), - list([ - '0.16', - '-0.14', - '0.27', - '-0.032', - '-0.0077', - ]), - list([ - '0.063', - '0.033', - '0.039', - '0.031', - '0.017', - ]), - list([ - '0.24', - '-0.059', - '0.29', - '0.06', - '0.00020', - ]), + '0.089', + '-0.0071', + '0.34', + '-0.045', + '0.10', ]), list([ - '0.3', - '-0.033', - '-0.25', - '-0.012', '-0.16', + '-0.097', + '-0.44', + '-0.29', + '-0.19', ]), list([ - list([ - '0.011', - '-0.056', - '-0.12', - '-0.056', - '-0.033', - ]), - list([ - '-0.18', - '0.12', - '0.14', - '0.12', - '0.26', - ]), - list([ - '-0.22', - '0.15', - '0.011', - '0.029', - '0.19', - ]), - list([ - '-0.03', - '0.072', - '-0.044', - '-0.012', - '-0.035', - ]), - list([ - '-0.27', - '0.20', - '0.071', - '0.062', - '0.22', - ]), - ]), - list([ - '0.22', - '-0.35', - '-0.12', - '0.11', - '-0.26', - ]), - ]), - 'grad_norm': '0.073', - 'localization_loss': '0.039', - 'noise': list([ - list([ - list([ - '0.5', - '-0.43', - '-1.3', - '-0.19', - '0.65', - ]), - list([ - '-1.9', - '0.23', - '0.025', - '0.19', - '-0.93', - ]), - list([ - '-1.6', - '-1.1', - '-0.52', - '0.72', - '1.5', - ]), - list([ - '-1.5', - '-0.79', - '1.0', - '-0.56', - '0.70', - ]), - list([ - '0.71', - '-1.5', - '-0.73', - '0.47', - '0.67', - ]), - ]), - list([ - '0.11', - '2.4', - '0.92', - '-0.64', - '0.71', - ]), - list([ - list([ - '1.1', - '0.21', - '-0.58', - '0.33', - '-0.81', - ]), - list([ - '-1.0', - '-0.49', - '-0.59', - '0.40', - '0.084', - ]), - list([ - '0.4', - '2.0', - '-0.46', - '-0.064', - '-1.4', - ]), - list([ - '0.33', - '-0.98', - '0.078', - '0.19', - '0.41', - ]), - list([ - '-1.6', - '2.3', - '1.0', - '1.4', - '0.63', - ]), - ]), - list([ - '0.36', - '0.82', - '-0.36', - '0.071', - '0.61', - ]), - list([ - list([ - '0.56', - '-1.5', - '-1.6', - '-1.5', - '0.43', - ]), - list([ - '-0.13', - '0.78', - '0.56', - '-0.11', - '-1.0', - ]), - list([ - '-0.22', - '0.8', - '0.91', - '-0.088', - '0.34', - ]), - list([ - '0.97', - '-1.0', - '0.39', - '0.73', - '0.25', - ]), - list([ - '0.077', - '-0.21', - '2.2', - '2.0', - '0.25', - ]), - ]), - list([ - '0.24', - '-0.26', - '-0.46', - '-1.2', - '-1.2', - ]), - ]), - 'noise_norm': '8.9', - 'weight_norm': '2.7', - }), - }) -# --- -# name: test_SGMCMC_vs_SGLD[0.05-0.1-0.1-10.0-0.0001][test_SGMCMC_vs_SGLD_0.0001_10.0_0.1_0.1_0.05] - dict({ - 'model1': dict({ - '0.bias': list([ - '0.06', - '0.42', - '-0.41', - '-0.27', - '-0.12', - ]), - '0.weight': list([ - list([ - '0.021', - '0.25', - '-0.37', - '-0.35', - '-0.16', - ]), - list([ - '0.089', - '-0.0071', - '0.34', - '-0.045', - '0.10', - ]), - list([ - '-0.16', - '-0.097', - '-0.44', - '-0.29', - '-0.19', - ]), - list([ - '-0.000077', - '0.16', - '0.27', - '-0.30', - '-0.19', + '-0.000077', + '0.16', + '0.27', + '-0.30', + '-0.19', ]), list([ '0.16', @@ -26970,281 +15010,20 @@ ]), }), 'optimizer_sgld': dict({ + 'localization': '0.00000049', + 'noise': '0.089', + 'numel': 90, + 'prior': '0.0000062', + 'scaled_grad': '0.00080', + 'weight_decay': '0.0000062', }), 'optimizer_sgmcmc': dict({ - 'distance': '0.098', - 'dws': list([ - list([ - list([ - '1.5', - '-0.28', - '-1.7', - '-1.3', - '-0.28', - ]), - list([ - '-1.5', - '0.70', - '1.6', - '0.56', - '-0.047', - ]), - list([ - '2.8', - '-0.16', - '-3.5', - '-2.7', - '-1.1', - ]), - list([ - '-0.36', - '0.23', - '0.32', - '0.081', - '-0.12', - ]), - list([ - '1.5', - '0.15', - '-1.9', - '-1.7', - '-0.76', - ]), - ]), - list([ - '-1.1', - '1.1', - '-1.8', - '0.29', - '-0.89', - ]), - list([ - list([ - '-3.3', - '2.7', - '-4.6', - '-0.68', - '-0.22', - ]), - list([ - '1.0', - '-0.63', - '1.2', - '0.31', - '0.077', - ]), - list([ - '2.4', - '-2.2', - '3.7', - '0.42', - '0.10', - ]), - list([ - '0.46', - '-0.063', - '0.28', - '0.17', - '0.051', - ]), - list([ - '3.2', - '-2.0', - '3.9', - '1.0', - '0.11', - ]), - ]), - list([ - '3.2', - '-0.59', - '-2.9', - '0.15', - '-2.3', - ]), - list([ - list([ - '0.38', - '-0.72', - '-1.6', - '-0.90', - '-0.21', - ]), - list([ - '-2.2', - '1.1', - '2.0', - '0.77', - '1.5', - ]), - list([ - '-2.6', - '0.86', - '1.2', - '0.0079', - '1.4', - ]), - list([ - '-0.0065', - '-0.082', - '-0.35', - '-0.31', - '-0.37', - ]), - list([ - '-2.8', - '1.2', - '1.9', - '0.47', - '1.7', - ]), - ]), - list([ - '2.3', - '-3.7', - '-1.8', - '1.2', - '-3.3', - ]), - ]), - 'grad_norm': '0.00080', - 'localization_loss': '0.00048', - 'noise': list([ - list([ - list([ - '0.5', - '-0.43', - '-1.3', - '-0.19', - '0.65', - ]), - list([ - '-1.9', - '0.23', - '0.025', - '0.19', - '-0.93', - ]), - list([ - '-1.6', - '-1.1', - '-0.52', - '0.72', - '1.5', - ]), - list([ - '-1.5', - '-0.79', - '1.0', - '-0.56', - '0.70', - ]), - list([ - '0.71', - '-1.5', - '-0.73', - '0.47', - '0.67', - ]), - ]), - list([ - '0.11', - '2.4', - '0.92', - '-0.64', - '0.71', - ]), - list([ - list([ - '1.1', - '0.21', - '-0.58', - '0.33', - '-0.81', - ]), - list([ - '-1.0', - '-0.49', - '-0.59', - '0.40', - '0.084', - ]), - list([ - '0.4', - '2.0', - '-0.46', - '-0.064', - '-1.4', - ]), - list([ - '0.33', - '-0.98', - '0.078', - '0.19', - '0.41', - ]), - list([ - '-1.6', - '2.3', - '1.0', - '1.4', - '0.63', - ]), - ]), - list([ - '0.36', - '0.82', - '-0.36', - '0.071', - '0.61', - ]), - list([ - list([ - '0.56', - '-1.5', - '-1.6', - '-1.5', - '0.43', - ]), - list([ - '-0.13', - '0.78', - '0.56', - '-0.11', - '-1.0', - ]), - list([ - '-0.22', - '0.8', - '0.91', - '-0.088', - '0.34', - ]), - list([ - '0.97', - '-1.0', - '0.39', - '0.73', - '0.25', - ]), - list([ - '0.077', - '-0.21', - '2.2', - '2.0', - '0.25', - ]), - ]), - list([ - '0.24', - '-0.26', - '-0.46', - '-1.2', - '-1.2', - ]), - ]), - 'noise_norm': '8.9', - 'weight_norm': '2.5', + 'localization': '0.00000049', + 'noise': '0.089', + 'numel': 90, + 'prior': '0.0000062', + 'scaled_grad': '0.00080', + 'weight_decay': '0.0000062', }), }) # --- @@ -27519,281 +15298,20 @@ ]), }), 'optimizer_sgld': dict({ + 'localization': '0.000015', + 'noise': '0.28', + 'numel': 90, + 'prior': '0.000064', + 'scaled_grad': '0.0083', + 'weight_decay': '0.000062', }), 'optimizer_sgmcmc': dict({ - 'distance': '0.31', - 'dws': list([ - list([ - list([ - '1.6', - '-0.29', - '-1.7', - '-1.3', - '-0.25', - ]), - list([ - '-1.7', - '0.76', - '1.8', - '0.68', - '-0.00073', - ]), - list([ - '3.0', - '-0.2', - '-3.8', - '-2.8', - '-1.2', - ]), - list([ - '-0.4', - '0.26', - '0.32', - '0.10', - '-0.16', - ]), - list([ - '1.5', - '0.15', - '-2.1', - '-1.7', - '-0.83', - ]), - ]), - list([ - '-1.1', - '1.3', - '-1.9', - '0.34', - '-0.93', - ]), - list([ - list([ - '-3.6', - '2.6', - '-5.0', - '-0.58', - '-0.36', - ]), - list([ - '1.3', - '-0.68', - '1.5', - '0.28', - '0.17', - ]), - list([ - '2.5', - '-2.1', - '3.8', - '0.3', - '0.20', - ]), - list([ - '0.40', - '0.023', - '0.20', - '0.17', - '0.053', - ]), - list([ - '3.4', - '-1.8', - '3.9', - '0.89', - '0.21', - ]), - ]), - list([ - '3.5', - '-0.63', - '-3.0', - '0.21', - '-2.2', - ]), - list([ - list([ - '0.34', - '-0.67', - '-1.6', - '-0.93', - '-0.36', - ]), - list([ - '-2.2', - '1.2', - '2.1', - '0.83', - '1.9', - ]), - list([ - '-2.5', - '0.98', - '1.1', - '0.042', - '1.6', - ]), - list([ - '0.086', - '-0.06', - '-0.46', - '-0.34', - '-0.48', - ]), - list([ - '-2.8', - '1.3', - '1.9', - '0.51', - '2.0', - ]), - ]), - list([ - '2.4', - '-3.8', - '-1.7', - '1.4', - '-3.3', - ]), - ]), - 'grad_norm': '0.0083', - 'localization_loss': '0.0048', - 'noise': list([ - list([ - list([ - '0.5', - '-0.43', - '-1.3', - '-0.19', - '0.65', - ]), - list([ - '-1.9', - '0.23', - '0.025', - '0.19', - '-0.93', - ]), - list([ - '-1.6', - '-1.1', - '-0.52', - '0.72', - '1.5', - ]), - list([ - '-1.5', - '-0.79', - '1.0', - '-0.56', - '0.70', - ]), - list([ - '0.71', - '-1.5', - '-0.73', - '0.47', - '0.67', - ]), - ]), - list([ - '0.11', - '2.4', - '0.92', - '-0.64', - '0.71', - ]), - list([ - list([ - '1.1', - '0.21', - '-0.58', - '0.33', - '-0.81', - ]), - list([ - '-1.0', - '-0.49', - '-0.59', - '0.40', - '0.084', - ]), - list([ - '0.4', - '2.0', - '-0.46', - '-0.064', - '-1.4', - ]), - list([ - '0.33', - '-0.98', - '0.078', - '0.19', - '0.41', - ]), - list([ - '-1.6', - '2.3', - '1.0', - '1.4', - '0.63', - ]), - ]), - list([ - '0.36', - '0.82', - '-0.36', - '0.071', - '0.61', - ]), - list([ - list([ - '0.56', - '-1.5', - '-1.6', - '-1.5', - '0.43', - ]), - list([ - '-0.13', - '0.78', - '0.56', - '-0.11', - '-1.0', - ]), - list([ - '-0.22', - '0.8', - '0.91', - '-0.088', - '0.34', - ]), - list([ - '0.97', - '-1.0', - '0.39', - '0.73', - '0.25', - ]), - list([ - '0.077', - '-0.21', - '2.2', - '2.0', - '0.25', - ]), - ]), - list([ - '0.24', - '-0.26', - '-0.46', - '-1.2', - '-1.2', - ]), - ]), - 'noise_norm': '8.9', - 'weight_norm': '2.5', + 'localization': '0.000015', + 'noise': '0.28', + 'numel': 90, + 'prior': '0.000064', + 'scaled_grad': '0.0083', + 'weight_decay': '0.000062', }), }) # --- @@ -28068,281 +15586,20 @@ ]), }), 'optimizer_sgld': dict({ + 'localization': '0.00036', + 'noise': '0.89', + 'numel': 90, + 'prior': '0.00073', + 'scaled_grad': '0.076', + 'weight_decay': '0.00063', }), 'optimizer_sgmcmc': dict({ - 'distance': '0.72', - 'dws': list([ - list([ - list([ - '1.0', - '-0.24', - '-0.93', - '-0.85', - '0.016', - ]), - list([ - '-1.6', - '0.77', - '1.9', - '0.57', - '0.031', - ]), - list([ - '2.9', - '-0.16', - '-3.9', - '-2.8', - '-1.3', - ]), - list([ - '-0.16', - '0.27', - '-0.11', - '-0.094', - '-0.38', - ]), - list([ - '1.5', - '0.18', - '-2.2', - '-1.7', - '-0.96', - ]), - ]), - list([ - '-0.76', - '1.2', - '-1.9', - '0.24', - '-0.90', - ]), - list([ - list([ - '-3.0', - '2.1', - '-4.9', - '-0.35', - '-0.59', - ]), - list([ - '1.4', - '-0.71', - '1.9', - '0.23', - '0.41', - ]), - list([ - '1.8', - '-1.6', - '3.3', - '0.049', - '0.32', - ]), - list([ - '0.49', - '0.057', - '0.29', - '0.19', - '0.076', - ]), - list([ - '2.7', - '-1.1', - '3.3', - '0.66', - '0.27', - ]), - ]), - list([ - '3.3', - '-0.66', - '-2.6', - '0.19', - '-1.7', - ]), - list([ - list([ - '0.15', - '-0.63', - '-1.2', - '-0.92', - '-0.53', - ]), - list([ - '-1.7', - '1.2', - '1.5', - '1.0', - '2.2', - ]), - list([ - '-2.2', - '1.2', - '0.58', - '0.2', - '1.7', - ]), - list([ - '-0.27', - '0.25', - '-0.35', - '-0.33', - '-0.39', - ]), - list([ - '-2.3', - '1.5', - '1.2', - '0.66', - '2.2', - ]), - ]), - list([ - '2.4', - '-3.6', - '-1.4', - '1.2', - '-2.8', - ]), - ]), - 'grad_norm': '0.076', - 'localization_loss': '0.026', - 'noise': list([ - list([ - list([ - '0.5', - '-0.43', - '-1.3', - '-0.19', - '0.65', - ]), - list([ - '-1.9', - '0.23', - '0.025', - '0.19', - '-0.93', - ]), - list([ - '-1.6', - '-1.1', - '-0.52', - '0.72', - '1.5', - ]), - list([ - '-1.5', - '-0.79', - '1.0', - '-0.56', - '0.70', - ]), - list([ - '0.71', - '-1.5', - '-0.73', - '0.47', - '0.67', - ]), - ]), - list([ - '0.11', - '2.4', - '0.92', - '-0.64', - '0.71', - ]), - list([ - list([ - '1.1', - '0.21', - '-0.58', - '0.33', - '-0.81', - ]), - list([ - '-1.0', - '-0.49', - '-0.59', - '0.40', - '0.084', - ]), - list([ - '0.4', - '2.0', - '-0.46', - '-0.064', - '-1.4', - ]), - list([ - '0.33', - '-0.98', - '0.078', - '0.19', - '0.41', - ]), - list([ - '-1.6', - '2.3', - '1.0', - '1.4', - '0.63', - ]), - ]), - list([ - '0.36', - '0.82', - '-0.36', - '0.071', - '0.61', - ]), - list([ - list([ - '0.56', - '-1.5', - '-1.6', - '-1.5', - '0.43', - ]), - list([ - '-0.13', - '0.78', - '0.56', - '-0.11', - '-1.0', - ]), - list([ - '-0.22', - '0.8', - '0.91', - '-0.088', - '0.34', - ]), - list([ - '0.97', - '-1.0', - '0.39', - '0.73', - '0.25', - ]), - list([ - '0.077', - '-0.21', - '2.2', - '2.0', - '0.25', - ]), - ]), - list([ - '0.24', - '-0.26', - '-0.46', - '-1.2', - '-1.2', - ]), - ]), - 'noise_norm': '8.9', - 'weight_norm': '2.6', + 'localization': '0.00036', + 'noise': '0.89', + 'numel': 90, + 'prior': '0.00073', + 'scaled_grad': '0.076', + 'weight_decay': '0.00063', }), }) # --- @@ -28617,281 +15874,20 @@ ]), }), 'optimizer_sgld': dict({ + 'localization': '0.0045', + 'noise': '2.8', + 'numel': 90, + 'prior': '0.0077', + 'scaled_grad': '0.48', + 'weight_decay': '0.0062', }), 'optimizer_sgmcmc': dict({ - 'distance': '0.9', - 'dws': list([ - list([ - list([ - '0.53', - '-0.15', - '-0.23', - '-0.53', - '0.23', - ]), - list([ - '-0.99', - '0.65', - '1.2', - '0.0002', - '-0.079', - ]), - list([ - '1.5', - '0.16', - '-2.2', - '-1.6', - '-1.0', - ]), - list([ - '-0.2', - '0.33', - '-0.2', - '-0.091', - '-0.51', - ]), - list([ - '0.69', - '0.45', - '-1.3', - '-1.2', - '-0.90', - ]), - ]), - list([ - '-0.47', - '0.73', - '-0.82', - '0.31', - '-0.28', - ]), - list([ - list([ - '-1.9', - '1.1', - '-2.6', - '-0.38', - '-0.2', - ]), - list([ - '1.1', - '-0.47', - '1.4', - '0.24', - '0.44', - ]), - list([ - '1.0', - '-0.98', - '1.8', - '0.00082', - '0.085', - ]), - list([ - '0.54', - '0.072', - '0.32', - '0.25', - '0.015', - ]), - list([ - '1.7', - '-0.27', - '1.4', - '0.63', - '-0.17', - ]), - ]), - list([ - '2.1', - '-0.23', - '-2.0', - '0.14', - '-0.76', - ]), - list([ - list([ - '-0.26', - '-0.36', - '-0.82', - '-0.75', - '-0.40', - ]), - list([ - '-0.66', - '0.59', - '0.97', - '0.92', - '1.6', - ]), - list([ - '-1.6', - '0.94', - '-0.056', - '0.092', - '1.1', - ]), - list([ - '-0.87', - '0.78', - '-0.34', - '-0.15', - '0.021', - ]), - list([ - '-1.5', - '1.0', - '0.28', - '0.38', - '1.3', - ]), - ]), - list([ - '2.1', - '-3.2', - '-0.75', - '0.56', - '-1.7', - ]), - ]), - 'grad_norm': '0.48', - 'localization_loss': '0.040', - 'noise': list([ - list([ - list([ - '0.5', - '-0.43', - '-1.3', - '-0.19', - '0.65', - ]), - list([ - '-1.9', - '0.23', - '0.025', - '0.19', - '-0.93', - ]), - list([ - '-1.6', - '-1.1', - '-0.52', - '0.72', - '1.5', - ]), - list([ - '-1.5', - '-0.79', - '1.0', - '-0.56', - '0.70', - ]), - list([ - '0.71', - '-1.5', - '-0.73', - '0.47', - '0.67', - ]), - ]), - list([ - '0.11', - '2.4', - '0.92', - '-0.64', - '0.71', - ]), - list([ - list([ - '1.1', - '0.21', - '-0.58', - '0.33', - '-0.81', - ]), - list([ - '-1.0', - '-0.49', - '-0.59', - '0.40', - '0.084', - ]), - list([ - '0.4', - '2.0', - '-0.46', - '-0.064', - '-1.4', - ]), - list([ - '0.33', - '-0.98', - '0.078', - '0.19', - '0.41', - ]), - list([ - '-1.6', - '2.3', - '1.0', - '1.4', - '0.63', - ]), - ]), - list([ - '0.36', - '0.82', - '-0.36', - '0.071', - '0.61', - ]), - list([ - list([ - '0.56', - '-1.5', - '-1.6', - '-1.5', - '0.43', - ]), - list([ - '-0.13', - '0.78', - '0.56', - '-0.11', - '-1.0', - ]), - list([ - '-0.22', - '0.8', - '0.91', - '-0.088', - '0.34', - ]), - list([ - '0.97', - '-1.0', - '0.39', - '0.73', - '0.25', - ]), - list([ - '0.077', - '-0.21', - '2.2', - '2.0', - '0.25', - ]), - ]), - list([ - '0.24', - '-0.26', - '-0.46', - '-1.2', - '-1.2', - ]), - ]), - 'noise_norm': '8.9', - 'weight_norm': '2.6', + 'localization': '0.0045', + 'noise': '2.8', + 'numel': 90, + 'prior': '0.0077', + 'scaled_grad': '0.48', + 'weight_decay': '0.0062', }), }) # --- @@ -29166,281 +16162,20 @@ ]), }), 'optimizer_sgld': dict({ + 'localization': '0.0', + 'noise': '0.089', + 'numel': 90, + 'prior': '0.0000062', + 'scaled_grad': '0.000080', + 'weight_decay': '0.0000062', }), 'optimizer_sgmcmc': dict({ - 'distance': '2.5', - 'dws': list([ - list([ - list([ - '0.16', - '-0.016', - '-0.19', - '-0.15', - '-0.036', - ]), - list([ - '-0.14', - '0.07', - '0.17', - '0.054', - '0.00047', - ]), - list([ - '0.27', - '-0.020', - '-0.37', - '-0.28', - '-0.11', - ]), - list([ - '-0.035', - '0.031', - '0.044', - '-0.0052', - '-0.021', - ]), - list([ - '0.15', - '0.031', - '-0.2', - '-0.15', - '-0.080', - ]), - ]), - list([ - '-0.11', - '0.13', - '-0.2', - '0.017', - '-0.095', - ]), - list([ - list([ - '-0.33', - '0.28', - '-0.48', - '-0.077', - '-0.037', - ]), - list([ - '0.085', - '-0.075', - '0.14', - '0.041', - '0.018', - ]), - list([ - '0.24', - '-0.23', - '0.38', - '0.023', - '-0.0043', - ]), - list([ - '0.036', - '0.0066', - '0.04', - '0.0088', - '0.0047', - ]), - list([ - '0.33', - '-0.18', - '0.4', - '0.10', - '0.024', - ]), - ]), - list([ - '0.31', - '-0.055', - '-0.31', - '0.00080', - '-0.24', - ]), - list([ - list([ - '0.048', - '-0.064', - '-0.17', - '-0.084', - '-0.010', - ]), - list([ - '-0.22', - '0.11', - '0.21', - '0.09', - '0.17', - ]), - list([ - '-0.28', - '0.08', - '0.13', - '0.017', - '0.16', - ]), - list([ - '0.018', - '-0.0041', - '-0.052', - '-0.029', - '-0.050', - ]), - list([ - '-0.3', - '0.14', - '0.21', - '0.027', - '0.17', - ]), - ]), - list([ - '0.23', - '-0.37', - '-0.19', - '0.13', - '-0.34', - ]), - ]), - 'grad_norm': '0.000080', - 'localization_loss': '0.0', - 'noise': list([ - list([ - list([ - '0.5', - '-0.43', - '-1.3', - '-0.19', - '0.65', - ]), - list([ - '-1.9', - '0.23', - '0.025', - '0.19', - '-0.93', - ]), - list([ - '-1.6', - '-1.1', - '-0.52', - '0.72', - '1.5', - ]), - list([ - '-1.5', - '-0.79', - '1.0', - '-0.56', - '0.70', - ]), - list([ - '0.71', - '-1.5', - '-0.73', - '0.47', - '0.67', - ]), - ]), - list([ - '0.11', - '2.4', - '0.92', - '-0.64', - '0.71', - ]), - list([ - list([ - '1.1', - '0.21', - '-0.58', - '0.33', - '-0.81', - ]), - list([ - '-1.0', - '-0.49', - '-0.59', - '0.40', - '0.084', - ]), - list([ - '0.4', - '2.0', - '-0.46', - '-0.064', - '-1.4', - ]), - list([ - '0.33', - '-0.98', - '0.078', - '0.19', - '0.41', - ]), - list([ - '-1.6', - '2.3', - '1.0', - '1.4', - '0.63', - ]), - ]), - list([ - '0.36', - '0.82', - '-0.36', - '0.071', - '0.61', - ]), - list([ - list([ - '0.56', - '-1.5', - '-1.6', - '-1.5', - '0.43', - ]), - list([ - '-0.13', - '0.78', - '0.56', - '-0.11', - '-1.0', - ]), - list([ - '-0.22', - '0.8', - '0.91', - '-0.088', - '0.34', - ]), - list([ - '0.97', - '-1.0', - '0.39', - '0.73', - '0.25', - ]), - list([ - '0.077', - '-0.21', - '2.2', - '2.0', - '0.25', - ]), - ]), - list([ - '0.24', - '-0.26', - '-0.46', - '-1.2', - '-1.2', - ]), - ]), - 'noise_norm': '8.9', - 'weight_norm': '2.5', + 'localization': '0.0', + 'noise': '0.089', + 'numel': 90, + 'prior': '0.0000062', + 'scaled_grad': '0.000080', + 'weight_decay': '0.0000062', }), }) # --- @@ -29715,281 +16450,20 @@ ]), }), 'optimizer_sgld': dict({ + 'localization': '0.0', + 'noise': '0.28', + 'numel': 90, + 'prior': '0.000062', + 'scaled_grad': '0.00085', + 'weight_decay': '0.000062', }), 'optimizer_sgmcmc': dict({ - 'distance': '2.5', - 'dws': list([ - list([ - list([ - '0.16', - '-0.017', - '-0.19', - '-0.15', - '-0.033', - ]), - list([ - '-0.17', - '0.077', - '0.20', - '0.069', - '0.0059', - ]), - list([ - '0.3', - '-0.025', - '-0.41', - '-0.30', - '-0.13', - ]), - list([ - '-0.039', - '0.032', - '0.043', - '-0.0025', - '-0.026', - ]), - list([ - '0.16', - '0.031', - '-0.21', - '-0.16', - '-0.089', - ]), - ]), - list([ - '-0.11', - '0.15', - '-0.22', - '0.023', - '-0.10', - ]), - list([ - list([ - '-0.37', - '0.28', - '-0.53', - '-0.068', - '-0.053', - ]), - list([ - '0.11', - '-0.081', - '0.17', - '0.039', - '0.028', - ]), - list([ - '0.26', - '-0.22', - '0.4', - '0.0096', - '0.0069', - ]), - list([ - '0.031', - '0.016', - '0.03', - '0.0094', - '0.0054', - ]), - list([ - '0.35', - '-0.16', - '0.41', - '0.090', - '0.036', - ]), - ]), - list([ - '0.34', - '-0.059', - '-0.32', - '0.0059', - '-0.24', - ]), - list([ - list([ - '0.047', - '-0.061', - '-0.17', - '-0.087', - '-0.027', - ]), - list([ - '-0.23', - '0.12', - '0.21', - '0.096', - '0.21', - ]), - list([ - '-0.27', - '0.094', - '0.12', - '0.020', - '0.18', - ]), - list([ - '0.032', - '-0.0028', - '-0.066', - '-0.032', - '-0.063', - ]), - list([ - '-0.31', - '0.16', - '0.21', - '0.033', - '0.21', - ]), - ]), - list([ - '0.24', - '-0.38', - '-0.18', - '0.15', - '-0.34', - ]), - ]), - 'grad_norm': '0.00085', - 'localization_loss': '0.0', - 'noise': list([ - list([ - list([ - '0.5', - '-0.43', - '-1.3', - '-0.19', - '0.65', - ]), - list([ - '-1.9', - '0.23', - '0.025', - '0.19', - '-0.93', - ]), - list([ - '-1.6', - '-1.1', - '-0.52', - '0.72', - '1.5', - ]), - list([ - '-1.5', - '-0.79', - '1.0', - '-0.56', - '0.70', - ]), - list([ - '0.71', - '-1.5', - '-0.73', - '0.47', - '0.67', - ]), - ]), - list([ - '0.11', - '2.4', - '0.92', - '-0.64', - '0.71', - ]), - list([ - list([ - '1.1', - '0.21', - '-0.58', - '0.33', - '-0.81', - ]), - list([ - '-1.0', - '-0.49', - '-0.59', - '0.40', - '0.084', - ]), - list([ - '0.4', - '2.0', - '-0.46', - '-0.064', - '-1.4', - ]), - list([ - '0.33', - '-0.98', - '0.078', - '0.19', - '0.41', - ]), - list([ - '-1.6', - '2.3', - '1.0', - '1.4', - '0.63', - ]), - ]), - list([ - '0.36', - '0.82', - '-0.36', - '0.071', - '0.61', - ]), - list([ - list([ - '0.56', - '-1.5', - '-1.6', - '-1.5', - '0.43', - ]), - list([ - '-0.13', - '0.78', - '0.56', - '-0.11', - '-1.0', - ]), - list([ - '-0.22', - '0.8', - '0.91', - '-0.088', - '0.34', - ]), - list([ - '0.97', - '-1.0', - '0.39', - '0.73', - '0.25', - ]), - list([ - '0.077', - '-0.21', - '2.2', - '2.0', - '0.25', - ]), - ]), - list([ - '0.24', - '-0.26', - '-0.46', - '-1.2', - '-1.2', - ]), - ]), - 'noise_norm': '8.9', - 'weight_norm': '2.5', + 'localization': '0.0', + 'noise': '0.28', + 'numel': 90, + 'prior': '0.000062', + 'scaled_grad': '0.00085', + 'weight_decay': '0.000062', }), }) # --- @@ -30264,281 +16738,20 @@ ]), }), 'optimizer_sgld': dict({ + 'localization': '0.0', + 'noise': '0.89', + 'numel': 90, + 'prior': '0.00066', + 'scaled_grad': '0.010', + 'weight_decay': '0.00066', }), 'optimizer_sgmcmc': dict({ - 'distance': '2.6', - 'dws': list([ - list([ - list([ - '0.18', - '-0.021', - '-0.20', - '-0.16', - '-0.029', - ]), - list([ - '-0.25', - '0.091', - '0.31', - '0.12', - '0.039', - ]), - list([ - '0.39', - '-0.038', - '-0.56', - '-0.37', - '-0.19', - ]), - list([ - '-0.036', - '0.039', - '0.011', - '-0.0065', - '-0.058', - ]), - list([ - '0.21', - '0.028', - '-0.29', - '-0.19', - '-0.13', - ]), - ]), - list([ - '-0.12', - '0.19', - '-0.27', - '0.036', - '-0.13', - ]), - list([ - list([ - '-0.52', - '0.27', - '-0.74', - '-0.031', - '-0.13', - ]), - list([ - '0.19', - '-0.082', - '0.28', - '0.035', - '0.076', - ]), - list([ - '0.31', - '-0.19', - '0.46', - '-0.033', - '0.049', - ]), - list([ - '0.0042', - '0.040', - '-0.016', - '0.013', - '0.000089', - ]), - list([ - '0.40', - '-0.10', - '0.45', - '0.049', - '0.07', - ]), - ]), - list([ - '0.43', - '-0.065', - '-0.34', - '0.024', - '-0.22', - ]), - list([ - list([ - '0.046', - '-0.044', - '-0.16', - '-0.096', - '-0.084', - ]), - list([ - '-0.23', - '0.15', - '0.23', - '0.12', - '0.33', - ]), - list([ - '-0.25', - '0.15', - '0.10', - '0.026', - '0.25', - ]), - list([ - '0.060', - '-0.0073', - '-0.11', - '-0.044', - '-0.13', - ]), - list([ - '-0.36', - '0.24', - '0.21', - '0.046', - '0.35', - ]), - ]), - list([ - '0.28', - '-0.4', - '-0.16', - '0.21', - '-0.34', - ]), - ]), - 'grad_norm': '0.010', - 'localization_loss': '0.0', - 'noise': list([ - list([ - list([ - '0.5', - '-0.43', - '-1.3', - '-0.19', - '0.65', - ]), - list([ - '-1.9', - '0.23', - '0.025', - '0.19', - '-0.93', - ]), - list([ - '-1.6', - '-1.1', - '-0.52', - '0.72', - '1.5', - ]), - list([ - '-1.5', - '-0.79', - '1.0', - '-0.56', - '0.70', - ]), - list([ - '0.71', - '-1.5', - '-0.73', - '0.47', - '0.67', - ]), - ]), - list([ - '0.11', - '2.4', - '0.92', - '-0.64', - '0.71', - ]), - list([ - list([ - '1.1', - '0.21', - '-0.58', - '0.33', - '-0.81', - ]), - list([ - '-1.0', - '-0.49', - '-0.59', - '0.40', - '0.084', - ]), - list([ - '0.4', - '2.0', - '-0.46', - '-0.064', - '-1.4', - ]), - list([ - '0.33', - '-0.98', - '0.078', - '0.19', - '0.41', - ]), - list([ - '-1.6', - '2.3', - '1.0', - '1.4', - '0.63', - ]), - ]), - list([ - '0.36', - '0.82', - '-0.36', - '0.071', - '0.61', - ]), - list([ - list([ - '0.56', - '-1.5', - '-1.6', - '-1.5', - '0.43', - ]), - list([ - '-0.13', - '0.78', - '0.56', - '-0.11', - '-1.0', - ]), - list([ - '-0.22', - '0.8', - '0.91', - '-0.088', - '0.34', - ]), - list([ - '0.97', - '-1.0', - '0.39', - '0.73', - '0.25', - ]), - list([ - '0.077', - '-0.21', - '2.2', - '2.0', - '0.25', - ]), - ]), - list([ - '0.24', - '-0.26', - '-0.46', - '-1.2', - '-1.2', - ]), - ]), - 'noise_norm': '8.9', - 'weight_norm': '2.8', + 'localization': '0.0', + 'noise': '0.89', + 'numel': 90, + 'prior': '0.00066', + 'scaled_grad': '0.010', + 'weight_decay': '0.00066', }), }) # --- @@ -30813,281 +17026,20 @@ ]), }), 'optimizer_sgld': dict({ + 'localization': '0.0', + 'noise': '2.8', + 'numel': 90, + 'prior': '0.0098', + 'scaled_grad': '0.26', + 'weight_decay': '0.0098', }), 'optimizer_sgmcmc': dict({ - 'distance': '3.9', - 'dws': list([ - list([ - list([ - '0.14', - '-0.013', - '-0.12', - '-0.096', - '0.0021', - ]), - list([ - '-0.44', - '0.072', - '0.74', - '0.19', - '0.29', - ]), - list([ - '0.87', - '-0.027', - '-1.6', - '-0.62', - '-0.79', - ]), - list([ - '0.18', - '0.15', - '-0.78', - '-0.14', - '-0.65', - ]), - list([ - '0.55', - '0.062', - '-1.1', - '-0.38', - '-0.62', - ]), - ]), - list([ - '-0.061', - '0.22', - '-0.43', - '0.10', - '-0.22', - ]), - list([ - list([ - '-1.3', - '-0.19', - '-2.6', - '0.083', - '-1.1', - ]), - list([ - '0.47', - '0.15', - '1.1', - '0.11', - '0.54', - ]), - list([ - '0.44', - '-0.0051', - '0.83', - '-0.14', - '0.30', - ]), - list([ - '-0.15', - '0.012', - '-0.41', - '0.04', - '-0.17', - ]), - list([ - '0.37', - '0.042', - '0.27', - '-0.061', - '0.032', - ]), - ]), - list([ - '0.68', - '0.020', - '-0.38', - '0.1', - '-0.15', - ]), - list([ - list([ - '0.17', - '-0.066', - '-0.064', - '-0.13', - '-0.37', - ]), - list([ - '-0.17', - '0.26', - '0.33', - '0.16', - '0.65', - ]), - list([ - '-0.26', - '0.35', - '0.048', - '0.0045', - '0.3', - ]), - list([ - '0.22', - '-0.24', - '-0.23', - '-0.078', - '-0.48', - ]), - list([ - '-1.2', - '1.2', - '-0.095', - '0.0042', - '0.87', - ]), - ]), - list([ - '0.38', - '-0.43', - '-0.079', - '0.33', - '-0.21', - ]), - ]), - 'grad_norm': '0.26', - 'localization_loss': '0.0', - 'noise': list([ - list([ - list([ - '0.5', - '-0.43', - '-1.3', - '-0.19', - '0.65', - ]), - list([ - '-1.9', - '0.23', - '0.025', - '0.19', - '-0.93', - ]), - list([ - '-1.6', - '-1.1', - '-0.52', - '0.72', - '1.5', - ]), - list([ - '-1.5', - '-0.79', - '1.0', - '-0.56', - '0.70', - ]), - list([ - '0.71', - '-1.5', - '-0.73', - '0.47', - '0.67', - ]), - ]), - list([ - '0.11', - '2.4', - '0.92', - '-0.64', - '0.71', - ]), - list([ - list([ - '1.1', - '0.21', - '-0.58', - '0.33', - '-0.81', - ]), - list([ - '-1.0', - '-0.49', - '-0.59', - '0.40', - '0.084', - ]), - list([ - '0.4', - '2.0', - '-0.46', - '-0.064', - '-1.4', - ]), - list([ - '0.33', - '-0.98', - '0.078', - '0.19', - '0.41', - ]), - list([ - '-1.6', - '2.3', - '1.0', - '1.4', - '0.63', - ]), - ]), - list([ - '0.36', - '0.82', - '-0.36', - '0.071', - '0.61', - ]), - list([ - list([ - '0.56', - '-1.5', - '-1.6', - '-1.5', - '0.43', - ]), - list([ - '-0.13', - '0.78', - '0.56', - '-0.11', - '-1.0', - ]), - list([ - '-0.22', - '0.8', - '0.91', - '-0.088', - '0.34', - ]), - list([ - '0.97', - '-1.0', - '0.39', - '0.73', - '0.25', - ]), - list([ - '0.077', - '-0.21', - '2.2', - '2.0', - '0.25', - ]), - ]), - list([ - '0.24', - '-0.26', - '-0.46', - '-1.2', - '-1.2', - ]), - ]), - 'noise_norm': '8.9', - 'weight_norm': '4.8', + 'localization': '0.0', + 'noise': '2.8', + 'numel': 90, + 'prior': '0.0098', + 'scaled_grad': '0.26', + 'weight_decay': '0.0098', }), }) # --- @@ -31362,281 +17314,20 @@ ]), }), 'optimizer_sgld': dict({ + 'localization': '0.0', + 'noise': '0.089', + 'numel': 90, + 'prior': '0.0000062', + 'scaled_grad': '0.00080', + 'weight_decay': '0.0000062', }), 'optimizer_sgmcmc': dict({ - 'distance': '2.5', - 'dws': list([ - list([ - list([ - '1.5', - '-0.28', - '-1.7', - '-1.3', - '-0.28', - ]), - list([ - '-1.5', - '0.70', - '1.6', - '0.56', - '-0.047', - ]), - list([ - '2.8', - '-0.16', - '-3.5', - '-2.7', - '-1.0', - ]), - list([ - '-0.36', - '0.23', - '0.32', - '0.081', - '-0.12', - ]), - list([ - '1.5', - '0.15', - '-1.9', - '-1.7', - '-0.76', - ]), - ]), - list([ - '-1.1', - '1.1', - '-1.8', - '0.29', - '-0.89', - ]), - list([ - list([ - '-3.3', - '2.7', - '-4.6', - '-0.68', - '-0.22', - ]), - list([ - '1.0', - '-0.63', - '1.2', - '0.31', - '0.077', - ]), - list([ - '2.4', - '-2.2', - '3.7', - '0.42', - '0.10', - ]), - list([ - '0.46', - '-0.063', - '0.29', - '0.17', - '0.051', - ]), - list([ - '3.2', - '-2.0', - '3.9', - '1.0', - '0.11', - ]), - ]), - list([ - '3.2', - '-0.59', - '-2.9', - '0.15', - '-2.3', - ]), - list([ - list([ - '0.38', - '-0.71', - '-1.6', - '-0.90', - '-0.21', - ]), - list([ - '-2.2', - '1.1', - '2.0', - '0.77', - '1.5', - ]), - list([ - '-2.6', - '0.86', - '1.2', - '0.0091', - '1.4', - ]), - list([ - '-0.0081', - '-0.083', - '-0.35', - '-0.31', - '-0.37', - ]), - list([ - '-2.8', - '1.2', - '1.9', - '0.46', - '1.7', - ]), - ]), - list([ - '2.3', - '-3.7', - '-1.8', - '1.2', - '-3.3', - ]), - ]), - 'grad_norm': '0.00080', - 'localization_loss': '0.0', - 'noise': list([ - list([ - list([ - '0.5', - '-0.43', - '-1.3', - '-0.19', - '0.65', - ]), - list([ - '-1.9', - '0.23', - '0.025', - '0.19', - '-0.93', - ]), - list([ - '-1.6', - '-1.1', - '-0.52', - '0.72', - '1.5', - ]), - list([ - '-1.5', - '-0.79', - '1.0', - '-0.56', - '0.70', - ]), - list([ - '0.71', - '-1.5', - '-0.73', - '0.47', - '0.67', - ]), - ]), - list([ - '0.11', - '2.4', - '0.92', - '-0.64', - '0.71', - ]), - list([ - list([ - '1.1', - '0.21', - '-0.58', - '0.33', - '-0.81', - ]), - list([ - '-1.0', - '-0.49', - '-0.59', - '0.40', - '0.084', - ]), - list([ - '0.4', - '2.0', - '-0.46', - '-0.064', - '-1.4', - ]), - list([ - '0.33', - '-0.98', - '0.078', - '0.19', - '0.41', - ]), - list([ - '-1.6', - '2.3', - '1.0', - '1.4', - '0.63', - ]), - ]), - list([ - '0.36', - '0.82', - '-0.36', - '0.071', - '0.61', - ]), - list([ - list([ - '0.56', - '-1.5', - '-1.6', - '-1.5', - '0.43', - ]), - list([ - '-0.13', - '0.78', - '0.56', - '-0.11', - '-1.0', - ]), - list([ - '-0.22', - '0.8', - '0.91', - '-0.088', - '0.34', - ]), - list([ - '0.97', - '-1.0', - '0.39', - '0.73', - '0.25', - ]), - list([ - '0.077', - '-0.21', - '2.2', - '2.0', - '0.25', - ]), - ]), - list([ - '0.24', - '-0.26', - '-0.46', - '-1.2', - '-1.2', - ]), - ]), - 'noise_norm': '8.9', - 'weight_norm': '2.5', + 'localization': '0.0', + 'noise': '0.089', + 'numel': 90, + 'prior': '0.0000062', + 'scaled_grad': '0.00080', + 'weight_decay': '0.0000062', }), }) # --- @@ -31911,281 +17602,20 @@ ]), }), 'optimizer_sgld': dict({ + 'localization': '0.0', + 'noise': '0.28', + 'numel': 90, + 'prior': '0.000062', + 'scaled_grad': '0.0083', + 'weight_decay': '0.000062', }), 'optimizer_sgmcmc': dict({ - 'distance': '2.5', - 'dws': list([ - list([ - list([ - '1.6', - '-0.3', - '-1.7', - '-1.3', - '-0.25', - ]), - list([ - '-1.7', - '0.76', - '1.9', - '0.69', - '0.0014', - ]), - list([ - '3.0', - '-0.2', - '-3.8', - '-2.8', - '-1.1', - ]), - list([ - '-0.4', - '0.26', - '0.32', - '0.10', - '-0.16', - ]), - list([ - '1.5', - '0.15', - '-2.1', - '-1.7', - '-0.83', - ]), - ]), - list([ - '-1.1', - '1.3', - '-1.9', - '0.33', - '-0.93', - ]), - list([ - list([ - '-3.6', - '2.6', - '-5.0', - '-0.58', - '-0.36', - ]), - list([ - '1.2', - '-0.68', - '1.5', - '0.28', - '0.17', - ]), - list([ - '2.5', - '-2.1', - '3.8', - '0.30', - '0.20', - ]), - list([ - '0.40', - '0.021', - '0.21', - '0.16', - '0.051', - ]), - list([ - '3.4', - '-1.8', - '3.9', - '0.9', - '0.21', - ]), - ]), - list([ - '3.5', - '-0.63', - '-3.0', - '0.22', - '-2.2', - ]), - list([ - list([ - '0.34', - '-0.67', - '-1.6', - '-0.93', - '-0.37', - ]), - list([ - '-2.2', - '1.2', - '2.1', - '0.83', - '1.9', - ]), - list([ - '-2.5', - '0.98', - '1.1', - '0.046', - '1.6', - ]), - list([ - '0.081', - '-0.062', - '-0.46', - '-0.35', - '-0.48', - ]), - list([ - '-2.8', - '1.3', - '1.9', - '0.50', - '2.0', - ]), - ]), - list([ - '2.4', - '-3.8', - '-1.7', - '1.4', - '-3.3', - ]), - ]), - 'grad_norm': '0.0083', - 'localization_loss': '0.0', - 'noise': list([ - list([ - list([ - '0.5', - '-0.43', - '-1.3', - '-0.19', - '0.65', - ]), - list([ - '-1.9', - '0.23', - '0.025', - '0.19', - '-0.93', - ]), - list([ - '-1.6', - '-1.1', - '-0.52', - '0.72', - '1.5', - ]), - list([ - '-1.5', - '-0.79', - '1.0', - '-0.56', - '0.70', - ]), - list([ - '0.71', - '-1.5', - '-0.73', - '0.47', - '0.67', - ]), - ]), - list([ - '0.11', - '2.4', - '0.92', - '-0.64', - '0.71', - ]), - list([ - list([ - '1.1', - '0.21', - '-0.58', - '0.33', - '-0.81', - ]), - list([ - '-1.0', - '-0.49', - '-0.59', - '0.40', - '0.084', - ]), - list([ - '0.4', - '2.0', - '-0.46', - '-0.064', - '-1.4', - ]), - list([ - '0.33', - '-0.98', - '0.078', - '0.19', - '0.41', - ]), - list([ - '-1.6', - '2.3', - '1.0', - '1.4', - '0.63', - ]), - ]), - list([ - '0.36', - '0.82', - '-0.36', - '0.071', - '0.61', - ]), - list([ - list([ - '0.56', - '-1.5', - '-1.6', - '-1.5', - '0.43', - ]), - list([ - '-0.13', - '0.78', - '0.56', - '-0.11', - '-1.0', - ]), - list([ - '-0.22', - '0.8', - '0.91', - '-0.088', - '0.34', - ]), - list([ - '0.97', - '-1.0', - '0.39', - '0.73', - '0.25', - ]), - list([ - '0.077', - '-0.21', - '2.2', - '2.0', - '0.25', - ]), - ]), - list([ - '0.24', - '-0.26', - '-0.46', - '-1.2', - '-1.2', - ]), - ]), - 'noise_norm': '8.9', - 'weight_norm': '2.5', + 'localization': '0.0', + 'noise': '0.28', + 'numel': 90, + 'prior': '0.000062', + 'scaled_grad': '0.0083', + 'weight_decay': '0.000062', }), }) # --- @@ -32460,281 +17890,20 @@ ]), }), 'optimizer_sgld': dict({ + 'localization': '0.0', + 'noise': '0.89', + 'numel': 90, + 'prior': '0.00065', + 'scaled_grad': '0.085', + 'weight_decay': '0.00065', }), 'optimizer_sgmcmc': dict({ - 'distance': '2.6', - 'dws': list([ - list([ - list([ - '1.3', - '-0.31', - '-1.4', - '-1.1', - '-0.096', - ]), - list([ - '-1.9', - '0.81', - '2.3', - '0.82', - '0.18', - ]), - list([ - '3.1', - '-0.19', - '-4.2', - '-2.9', - '-1.4', - ]), - list([ - '-0.39', - '0.37', - '0.05', - '0.069', - '-0.44', - ]), - list([ - '1.7', - '0.17', - '-2.4', - '-1.9', - '-1.0', - ]), - ]), - list([ - '-0.97', - '1.4', - '-2.0', - '0.43', - '-0.97', - ]), - list([ - list([ - '-4.2', - '2.1', - '-5.7', - '-0.20', - '-0.74', - ]), - list([ - '1.8', - '-0.56', - '2.2', - '0.19', - '0.53', - ]), - list([ - '2.4', - '-1.5', - '3.6', - '-0.059', - '0.41', - ]), - list([ - '0.24', - '0.19', - '-0.045', - '0.15', - '0.011', - ]), - list([ - '3.4', - '-0.91', - '3.6', - '0.55', - '0.39', - ]), - ]), - list([ - '3.8', - '-0.56', - '-2.8', - '0.36', - '-1.8', - ]), - list([ - list([ - '0.067', - '-0.37', - '-1.3', - '-1.0', - '-0.75', - ]), - list([ - '-1.7', - '1.2', - '1.8', - '0.97', - '2.6', - ]), - list([ - '-2.0', - '1.3', - '0.73', - '0.092', - '1.9', - ]), - list([ - '-0.038', - '0.091', - '-0.72', - '-0.43', - '-0.76', - ]), - list([ - '-2.5', - '1.7', - '1.5', - '0.51', - '2.7', - ]), - ]), - list([ - '2.6', - '-3.7', - '-1.3', - '1.6', - '-2.8', - ]), - ]), - 'grad_norm': '0.085', - 'localization_loss': '0.0', - 'noise': list([ - list([ - list([ - '0.5', - '-0.43', - '-1.3', - '-0.19', - '0.65', - ]), - list([ - '-1.9', - '0.23', - '0.025', - '0.19', - '-0.93', - ]), - list([ - '-1.6', - '-1.1', - '-0.52', - '0.72', - '1.5', - ]), - list([ - '-1.5', - '-0.79', - '1.0', - '-0.56', - '0.70', - ]), - list([ - '0.71', - '-1.5', - '-0.73', - '0.47', - '0.67', - ]), - ]), - list([ - '0.11', - '2.4', - '0.92', - '-0.64', - '0.71', - ]), - list([ - list([ - '1.1', - '0.21', - '-0.58', - '0.33', - '-0.81', - ]), - list([ - '-1.0', - '-0.49', - '-0.59', - '0.40', - '0.084', - ]), - list([ - '0.4', - '2.0', - '-0.46', - '-0.064', - '-1.4', - ]), - list([ - '0.33', - '-0.98', - '0.078', - '0.19', - '0.41', - ]), - list([ - '-1.6', - '2.3', - '1.0', - '1.4', - '0.63', - ]), - ]), - list([ - '0.36', - '0.82', - '-0.36', - '0.071', - '0.61', - ]), - list([ - list([ - '0.56', - '-1.5', - '-1.6', - '-1.5', - '0.43', - ]), - list([ - '-0.13', - '0.78', - '0.56', - '-0.11', - '-1.0', - ]), - list([ - '-0.22', - '0.8', - '0.91', - '-0.088', - '0.34', - ]), - list([ - '0.97', - '-1.0', - '0.39', - '0.73', - '0.25', - ]), - list([ - '0.077', - '-0.21', - '2.2', - '2.0', - '0.25', - ]), - ]), - list([ - '0.24', - '-0.26', - '-0.46', - '-1.2', - '-1.2', - ]), - ]), - 'noise_norm': '8.9', - 'weight_norm': '2.8', + 'localization': '0.0', + 'noise': '0.89', + 'numel': 90, + 'prior': '0.00065', + 'scaled_grad': '0.085', + 'weight_decay': '0.00065', }), }) # --- @@ -33009,281 +18178,20 @@ ]), }), 'optimizer_sgld': dict({ + 'localization': '0.0', + 'noise': '2.8', + 'numel': 90, + 'prior': '0.0094', + 'scaled_grad': '0.81', + 'weight_decay': '0.0094', }), 'optimizer_sgmcmc': dict({ - 'distance': '3.8', - 'dws': list([ - list([ - list([ - '0.68', - '-0.43', - '-0.2', - '-0.35', - '0.52', - ]), - list([ - '0.89', - '-0.063', - '-0.16', - '-1.4', - '0.43', - ]), - list([ - '-1.2', - '1.1', - '-0.88', - '0.82', - '-2.1', - ]), - list([ - '-2.0', - '2.2', - '-2.3', - '1.3', - '-4.4', - ]), - list([ - '-1.5', - '1.4', - '-0.91', - '0.91', - '-2.5', - ]), - ]), - list([ - '-0.65', - '-0.89', - '1.6', - '3.1', - '2.0', - ]), - list([ - list([ - '2.7', - '0.15', - '-0.42', - '-0.88', - '-1.6', - ]), - list([ - '1.0', - '1.1', - '4.3', - '1.1', - '2.7', - ]), - list([ - '-1.9', - '-0.58', - '-1.3', - '-0.20', - '-0.25', - ]), - list([ - '1.9', - '0.45', - '0.95', - '-0.16', - '-0.14', - ]), - list([ - '2.2', - '0.30', - '0.10', - '-0.36', - '-0.81', - ]), - ]), - list([ - '-2.5', - '2.0', - '-0.17', - '-0.83', - '-1.0', - ]), - list([ - list([ - '-0.0087', - '1.5', - '0.76', - '-0.93', - '-0.41', - ]), - list([ - '2.3', - '-0.99', - '1.3', - '1.3', - '1.5', - ]), - list([ - '-0.19', - '1.8', - '-0.2', - '0.31', - '1.2', - ]), - list([ - '-0.69', - '1.1', - '-0.84', - '0.38', - '0.74', - ]), - list([ - '-5.5', - '5.8', - '-3.7', - '-1.6', - '-0.45', - ]), - ]), - list([ - '1.3', - '-2.4', - '-0.83', - '-0.57', - '2.8', - ]), - ]), - 'grad_norm': '0.81', - 'localization_loss': '0.0', - 'noise': list([ - list([ - list([ - '0.5', - '-0.43', - '-1.3', - '-0.19', - '0.65', - ]), - list([ - '-1.9', - '0.23', - '0.025', - '0.19', - '-0.93', - ]), - list([ - '-1.6', - '-1.1', - '-0.52', - '0.72', - '1.5', - ]), - list([ - '-1.5', - '-0.79', - '1.0', - '-0.56', - '0.70', - ]), - list([ - '0.71', - '-1.5', - '-0.73', - '0.47', - '0.67', - ]), - ]), - list([ - '0.11', - '2.4', - '0.92', - '-0.64', - '0.71', - ]), - list([ - list([ - '1.1', - '0.21', - '-0.58', - '0.33', - '-0.81', - ]), - list([ - '-1.0', - '-0.49', - '-0.59', - '0.40', - '0.084', - ]), - list([ - '0.4', - '2.0', - '-0.46', - '-0.064', - '-1.4', - ]), - list([ - '0.33', - '-0.98', - '0.078', - '0.19', - '0.41', - ]), - list([ - '-1.6', - '2.3', - '1.0', - '1.4', - '0.63', - ]), - ]), - list([ - '0.36', - '0.82', - '-0.36', - '0.071', - '0.61', - ]), - list([ - list([ - '0.56', - '-1.5', - '-1.6', - '-1.5', - '0.43', - ]), - list([ - '-0.13', - '0.78', - '0.56', - '-0.11', - '-1.0', - ]), - list([ - '-0.22', - '0.8', - '0.91', - '-0.088', - '0.34', - ]), - list([ - '0.97', - '-1.0', - '0.39', - '0.73', - '0.25', - ]), - list([ - '0.077', - '-0.21', - '2.2', - '2.0', - '0.25', - ]), - ]), - list([ - '0.24', - '-0.26', - '-0.46', - '-1.2', - '-1.2', - ]), - ]), - 'noise_norm': '8.9', - 'weight_norm': '4.7', + 'localization': '0.0', + 'noise': '2.8', + 'numel': 90, + 'prior': '0.0094', + 'scaled_grad': '0.81', + 'weight_decay': '0.0094', }), }) # --- @@ -33558,281 +18466,20 @@ ]), }), 'optimizer_sgld': dict({ + 'localization': '0.00000049', + 'noise': '0.089', + 'numel': 90, + 'prior': '0.0000062', + 'scaled_grad': '0.000080', + 'weight_decay': '0.0000062', }), 'optimizer_sgmcmc': dict({ - 'distance': '0.098', - 'dws': list([ - list([ - list([ - '0.16', - '-0.015', - '-0.19', - '-0.15', - '-0.035', - ]), - list([ - '-0.14', - '0.07', - '0.17', - '0.053', - '-0.00021', - ]), - list([ - '0.27', - '-0.02', - '-0.37', - '-0.28', - '-0.12', - ]), - list([ - '-0.035', - '0.029', - '0.044', - '-0.0044', - '-0.021', - ]), - list([ - '0.15', - '0.031', - '-0.2', - '-0.15', - '-0.082', - ]), - ]), - list([ - '-0.11', - '0.13', - '-0.2', - '0.018', - '-0.096', - ]), - list([ - list([ - '-0.33', - '0.28', - '-0.48', - '-0.078', - '-0.038', - ]), - list([ - '0.087', - '-0.075', - '0.14', - '0.043', - '0.018', - ]), - list([ - '0.24', - '-0.23', - '0.38', - '0.022', - '-0.0049', - ]), - list([ - '0.036', - '0.0071', - '0.038', - '0.010', - '0.0054', - ]), - list([ - '0.33', - '-0.18', - '0.4', - '0.10', - '0.023', - ]), - ]), - list([ - '0.31', - '-0.054', - '-0.31', - '-0.00057', - '-0.24', - ]), - list([ - list([ - '0.049', - '-0.064', - '-0.17', - '-0.083', - '-0.010', - ]), - list([ - '-0.22', - '0.11', - '0.21', - '0.09', - '0.17', - ]), - list([ - '-0.28', - '0.081', - '0.12', - '0.016', - '0.16', - ]), - list([ - '0.02', - '-0.0035', - '-0.052', - '-0.028', - '-0.050', - ]), - list([ - '-0.3', - '0.14', - '0.21', - '0.028', - '0.17', - ]), - ]), - list([ - '0.23', - '-0.37', - '-0.19', - '0.14', - '-0.34', - ]), - ]), - 'grad_norm': '0.000080', - 'localization_loss': '0.00048', - 'noise': list([ - list([ - list([ - '0.5', - '-0.43', - '-1.3', - '-0.19', - '0.65', - ]), - list([ - '-1.9', - '0.23', - '0.025', - '0.19', - '-0.93', - ]), - list([ - '-1.6', - '-1.1', - '-0.52', - '0.72', - '1.5', - ]), - list([ - '-1.5', - '-0.79', - '1.0', - '-0.56', - '0.70', - ]), - list([ - '0.71', - '-1.5', - '-0.73', - '0.47', - '0.67', - ]), - ]), - list([ - '0.11', - '2.4', - '0.92', - '-0.64', - '0.71', - ]), - list([ - list([ - '1.1', - '0.21', - '-0.58', - '0.33', - '-0.81', - ]), - list([ - '-1.0', - '-0.49', - '-0.59', - '0.40', - '0.084', - ]), - list([ - '0.4', - '2.0', - '-0.46', - '-0.064', - '-1.4', - ]), - list([ - '0.33', - '-0.98', - '0.078', - '0.19', - '0.41', - ]), - list([ - '-1.6', - '2.3', - '1.0', - '1.4', - '0.63', - ]), - ]), - list([ - '0.36', - '0.82', - '-0.36', - '0.071', - '0.61', - ]), - list([ - list([ - '0.56', - '-1.5', - '-1.6', - '-1.5', - '0.43', - ]), - list([ - '-0.13', - '0.78', - '0.56', - '-0.11', - '-1.0', - ]), - list([ - '-0.22', - '0.8', - '0.91', - '-0.088', - '0.34', - ]), - list([ - '0.97', - '-1.0', - '0.39', - '0.73', - '0.25', - ]), - list([ - '0.077', - '-0.21', - '2.2', - '2.0', - '0.25', - ]), - ]), - list([ - '0.24', - '-0.26', - '-0.46', - '-1.2', - '-1.2', - ]), - ]), - 'noise_norm': '8.9', - 'weight_norm': '2.5', + 'localization': '0.00000049', + 'noise': '0.089', + 'numel': 90, + 'prior': '0.0000062', + 'scaled_grad': '0.000080', + 'weight_decay': '0.0000062', }), }) # --- @@ -34107,281 +18754,20 @@ ]), }), 'optimizer_sgld': dict({ + 'localization': '0.000015', + 'noise': '0.28', + 'numel': 90, + 'prior': '0.000064', + 'scaled_grad': '0.00085', + 'weight_decay': '0.000062', }), 'optimizer_sgmcmc': dict({ - 'distance': '0.31', - 'dws': list([ - list([ - list([ - '0.17', - '-0.013', - '-0.19', - '-0.16', - '-0.031', - ]), - list([ - '-0.17', - '0.076', - '0.2', - '0.067', - '0.0037', - ]), - list([ - '0.3', - '-0.024', - '-0.41', - '-0.30', - '-0.14', - ]), - list([ - '-0.04', - '0.028', - '0.042', - '0.000047', - '-0.028', - ]), - list([ - '0.16', - '0.031', - '-0.22', - '-0.16', - '-0.094', - ]), - ]), - list([ - '-0.11', - '0.14', - '-0.22', - '0.028', - '-0.10', - ]), - list([ - list([ - '-0.37', - '0.28', - '-0.53', - '-0.071', - '-0.056', - ]), - list([ - '0.12', - '-0.080', - '0.17', - '0.045', - '0.029', - ]), - list([ - '0.27', - '-0.22', - '0.4', - '0.0054', - '0.0050', - ]), - list([ - '0.032', - '0.017', - '0.025', - '0.014', - '0.0078', - ]), - list([ - '0.35', - '-0.16', - '0.41', - '0.085', - '0.033', - ]), - ]), - list([ - '0.34', - '-0.056', - '-0.31', - '0.0015', - '-0.24', - ]), - list([ - list([ - '0.050', - '-0.062', - '-0.17', - '-0.085', - '-0.026', - ]), - list([ - '-0.23', - '0.12', - '0.22', - '0.096', - '0.21', - ]), - list([ - '-0.26', - '0.098', - '0.11', - '0.017', - '0.18', - ]), - list([ - '0.037', - '-0.00076', - '-0.064', - '-0.028', - '-0.063', - ]), - list([ - '-0.31', - '0.16', - '0.21', - '0.036', - '0.21', - ]), - ]), - list([ - '0.25', - '-0.38', - '-0.18', - '0.16', - '-0.34', - ]), - ]), - 'grad_norm': '0.00085', - 'localization_loss': '0.0048', - 'noise': list([ - list([ - list([ - '0.5', - '-0.43', - '-1.3', - '-0.19', - '0.65', - ]), - list([ - '-1.9', - '0.23', - '0.025', - '0.19', - '-0.93', - ]), - list([ - '-1.6', - '-1.1', - '-0.52', - '0.72', - '1.5', - ]), - list([ - '-1.5', - '-0.79', - '1.0', - '-0.56', - '0.70', - ]), - list([ - '0.71', - '-1.5', - '-0.73', - '0.47', - '0.67', - ]), - ]), - list([ - '0.11', - '2.4', - '0.92', - '-0.64', - '0.71', - ]), - list([ - list([ - '1.1', - '0.21', - '-0.58', - '0.33', - '-0.81', - ]), - list([ - '-1.0', - '-0.49', - '-0.59', - '0.40', - '0.084', - ]), - list([ - '0.4', - '2.0', - '-0.46', - '-0.064', - '-1.4', - ]), - list([ - '0.33', - '-0.98', - '0.078', - '0.19', - '0.41', - ]), - list([ - '-1.6', - '2.3', - '1.0', - '1.4', - '0.63', - ]), - ]), - list([ - '0.36', - '0.82', - '-0.36', - '0.071', - '0.61', - ]), - list([ - list([ - '0.56', - '-1.5', - '-1.6', - '-1.5', - '0.43', - ]), - list([ - '-0.13', - '0.78', - '0.56', - '-0.11', - '-1.0', - ]), - list([ - '-0.22', - '0.8', - '0.91', - '-0.088', - '0.34', - ]), - list([ - '0.97', - '-1.0', - '0.39', - '0.73', - '0.25', - ]), - list([ - '0.077', - '-0.21', - '2.2', - '2.0', - '0.25', - ]), - ]), - list([ - '0.24', - '-0.26', - '-0.46', - '-1.2', - '-1.2', - ]), - ]), - 'noise_norm': '8.9', - 'weight_norm': '2.5', + 'localization': '0.000015', + 'noise': '0.28', + 'numel': 90, + 'prior': '0.000064', + 'scaled_grad': '0.00085', + 'weight_decay': '0.000062', }), }) # --- @@ -34656,281 +19042,20 @@ ]), }), 'optimizer_sgld': dict({ + 'localization': '0.00049', + 'noise': '0.89', + 'numel': 90, + 'prior': '0.00082', + 'scaled_grad': '0.010', + 'weight_decay': '0.00066', }), 'optimizer_sgmcmc': dict({ - 'distance': '0.98', - 'dws': list([ - list([ - list([ - '0.20', - '-0.0059', - '-0.19', - '-0.18', - '-0.023', - ]), - list([ - '-0.26', - '0.091', - '0.3', - '0.11', - '0.032', - ]), - list([ - '0.39', - '-0.035', - '-0.57', - '-0.36', - '-0.21', - ]), - list([ - '-0.039', - '0.025', - '0.0074', - '0.0016', - '-0.064', - ]), - list([ - '0.20', - '0.028', - '-0.30', - '-0.19', - '-0.14', - ]), - ]), - list([ - '-0.11', - '0.18', - '-0.27', - '0.052', - '-0.14', - ]), - list([ - list([ - '-0.50', - '0.27', - '-0.75', - '-0.04', - '-0.14', - ]), - list([ - '0.21', - '-0.081', - '0.28', - '0.054', - '0.079', - ]), - list([ - '0.33', - '-0.19', - '0.46', - '-0.046', - '0.043', - ]), - list([ - '0.0095', - '0.045', - '-0.031', - '0.027', - '0.0075', - ]), - list([ - '0.4', - '-0.11', - '0.45', - '0.032', - '0.062', - ]), - ]), - list([ - '0.43', - '-0.053', - '-0.33', - '0.010', - '-0.23', - ]), - list([ - list([ - '0.056', - '-0.049', - '-0.17', - '-0.090', - '-0.082', - ]), - list([ - '-0.24', - '0.15', - '0.24', - '0.12', - '0.33', - ]), - list([ - '-0.23', - '0.16', - '0.087', - '0.015', - '0.25', - ]), - list([ - '0.076', - '-0.00073', - '-0.11', - '-0.032', - '-0.13', - ]), - list([ - '-0.37', - '0.25', - '0.21', - '0.057', - '0.34', - ]), - ]), - list([ - '0.3', - '-0.39', - '-0.16', - '0.23', - '-0.34', - ]), - ]), - 'grad_norm': '0.010', - 'localization_loss': '0.048', - 'noise': list([ - list([ - list([ - '0.5', - '-0.43', - '-1.3', - '-0.19', - '0.65', - ]), - list([ - '-1.9', - '0.23', - '0.025', - '0.19', - '-0.93', - ]), - list([ - '-1.6', - '-1.1', - '-0.52', - '0.72', - '1.5', - ]), - list([ - '-1.5', - '-0.79', - '1.0', - '-0.56', - '0.70', - ]), - list([ - '0.71', - '-1.5', - '-0.73', - '0.47', - '0.67', - ]), - ]), - list([ - '0.11', - '2.4', - '0.92', - '-0.64', - '0.71', - ]), - list([ - list([ - '1.1', - '0.21', - '-0.58', - '0.33', - '-0.81', - ]), - list([ - '-1.0', - '-0.49', - '-0.59', - '0.40', - '0.084', - ]), - list([ - '0.4', - '2.0', - '-0.46', - '-0.064', - '-1.4', - ]), - list([ - '0.33', - '-0.98', - '0.078', - '0.19', - '0.41', - ]), - list([ - '-1.6', - '2.3', - '1.0', - '1.4', - '0.63', - ]), - ]), - list([ - '0.36', - '0.82', - '-0.36', - '0.071', - '0.61', - ]), - list([ - list([ - '0.56', - '-1.5', - '-1.6', - '-1.5', - '0.43', - ]), - list([ - '-0.13', - '0.78', - '0.56', - '-0.11', - '-1.0', - ]), - list([ - '-0.22', - '0.8', - '0.91', - '-0.088', - '0.34', - ]), - list([ - '0.97', - '-1.0', - '0.39', - '0.73', - '0.25', - ]), - list([ - '0.077', - '-0.21', - '2.2', - '2.0', - '0.25', - ]), - ]), - list([ - '0.24', - '-0.26', - '-0.46', - '-1.2', - '-1.2', - ]), - ]), - 'noise_norm': '8.9', - 'weight_norm': '2.8', + 'localization': '0.00049', + 'noise': '0.89', + 'numel': 90, + 'prior': '0.00082', + 'scaled_grad': '0.010', + 'weight_decay': '0.00066', }), }) # --- @@ -35205,281 +19330,20 @@ ]), }), 'optimizer_sgld': dict({ + 'localization': '0.015', + 'noise': '2.8', + 'numel': 90, + 'prior': '0.018', + 'scaled_grad': '0.26', + 'weight_decay': '0.0098', }), 'optimizer_sgmcmc': dict({ - 'distance': '3.1', - 'dws': list([ - list([ - list([ - '0.2', - '0.034', - '-0.094', - '-0.16', - '0.024', - ]), - list([ - '-0.48', - '0.070', - '0.68', - '0.17', - '0.27', - ]), - list([ - '0.86', - '-0.019', - '-1.7', - '-0.62', - '-0.86', - ]), - list([ - '0.17', - '0.11', - '-0.8', - '-0.11', - '-0.67', - ]), - list([ - '0.53', - '0.059', - '-1.2', - '-0.37', - '-0.67', - ]), - ]), - list([ - '-0.025', - '0.2', - '-0.42', - '0.15', - '-0.26', - ]), - list([ - list([ - '-1.3', - '-0.22', - '-2.6', - '0.057', - '-1.2', - ]), - list([ - '0.52', - '0.16', - '1.1', - '0.16', - '0.55', - ]), - list([ - '0.49', - '-0.0036', - '0.82', - '-0.18', - '0.28', - ]), - list([ - '-0.13', - '0.029', - '-0.46', - '0.085', - '-0.15', - ]), - list([ - '0.35', - '0.010', - '0.29', - '-0.12', - '0.0060', - ]), - ]), - list([ - '0.66', - '0.059', - '-0.36', - '0.056', - '-0.18', - ]), - list([ - list([ - '0.21', - '-0.080', - '-0.085', - '-0.11', - '-0.36', - ]), - list([ - '-0.18', - '0.25', - '0.35', - '0.16', - '0.64', - ]), - list([ - '-0.2', - '0.38', - '0.0021', - '-0.031', - '0.29', - ]), - list([ - '0.27', - '-0.22', - '-0.21', - '-0.042', - '-0.48', - ]), - list([ - '-1.3', - '1.3', - '-0.11', - '0.037', - '0.85', - ]), - ]), - list([ - '0.43', - '-0.40', - '-0.07', - '0.39', - '-0.21', - ]), - ]), - 'grad_norm': '0.26', - 'localization_loss': '0.48', - 'noise': list([ - list([ - list([ - '0.5', - '-0.43', - '-1.3', - '-0.19', - '0.65', - ]), - list([ - '-1.9', - '0.23', - '0.025', - '0.19', - '-0.93', - ]), - list([ - '-1.6', - '-1.1', - '-0.52', - '0.72', - '1.5', - ]), - list([ - '-1.5', - '-0.79', - '1.0', - '-0.56', - '0.70', - ]), - list([ - '0.71', - '-1.5', - '-0.73', - '0.47', - '0.67', - ]), - ]), - list([ - '0.11', - '2.4', - '0.92', - '-0.64', - '0.71', - ]), - list([ - list([ - '1.1', - '0.21', - '-0.58', - '0.33', - '-0.81', - ]), - list([ - '-1.0', - '-0.49', - '-0.59', - '0.40', - '0.084', - ]), - list([ - '0.4', - '2.0', - '-0.46', - '-0.064', - '-1.4', - ]), - list([ - '0.33', - '-0.98', - '0.078', - '0.19', - '0.41', - ]), - list([ - '-1.6', - '2.3', - '1.0', - '1.4', - '0.63', - ]), - ]), - list([ - '0.36', - '0.82', - '-0.36', - '0.071', - '0.61', - ]), - list([ - list([ - '0.56', - '-1.5', - '-1.6', - '-1.5', - '0.43', - ]), - list([ - '-0.13', - '0.78', - '0.56', - '-0.11', - '-1.0', - ]), - list([ - '-0.22', - '0.8', - '0.91', - '-0.088', - '0.34', - ]), - list([ - '0.97', - '-1.0', - '0.39', - '0.73', - '0.25', - ]), - list([ - '0.077', - '-0.21', - '2.2', - '2.0', - '0.25', - ]), - ]), - list([ - '0.24', - '-0.26', - '-0.46', - '-1.2', - '-1.2', - ]), - ]), - 'noise_norm': '8.9', - 'weight_norm': '4.8', + 'localization': '0.015', + 'noise': '2.8', + 'numel': 90, + 'prior': '0.018', + 'scaled_grad': '0.26', + 'weight_decay': '0.0098', }), }) # --- @@ -35754,281 +19618,20 @@ ]), }), 'optimizer_sgld': dict({ + 'localization': '0.00000049', + 'noise': '0.089', + 'numel': 90, + 'prior': '0.0000062', + 'scaled_grad': '0.00080', + 'weight_decay': '0.0000062', }), 'optimizer_sgmcmc': dict({ - 'distance': '0.098', - 'dws': list([ - list([ - list([ - '1.5', - '-0.28', - '-1.7', - '-1.3', - '-0.28', - ]), - list([ - '-1.5', - '0.70', - '1.6', - '0.56', - '-0.047', - ]), - list([ - '2.8', - '-0.16', - '-3.5', - '-2.7', - '-1.1', - ]), - list([ - '-0.36', - '0.23', - '0.32', - '0.081', - '-0.12', - ]), - list([ - '1.5', - '0.15', - '-1.9', - '-1.7', - '-0.76', - ]), - ]), - list([ - '-1.1', - '1.1', - '-1.8', - '0.29', - '-0.89', - ]), - list([ - list([ - '-3.3', - '2.7', - '-4.6', - '-0.68', - '-0.22', - ]), - list([ - '1.0', - '-0.63', - '1.2', - '0.31', - '0.077', - ]), - list([ - '2.4', - '-2.2', - '3.7', - '0.42', - '0.10', - ]), - list([ - '0.46', - '-0.063', - '0.28', - '0.17', - '0.051', - ]), - list([ - '3.2', - '-2.0', - '3.9', - '1.0', - '0.11', - ]), - ]), - list([ - '3.2', - '-0.59', - '-2.9', - '0.15', - '-2.3', - ]), - list([ - list([ - '0.38', - '-0.72', - '-1.6', - '-0.90', - '-0.21', - ]), - list([ - '-2.2', - '1.1', - '2.0', - '0.77', - '1.5', - ]), - list([ - '-2.6', - '0.86', - '1.2', - '0.0079', - '1.4', - ]), - list([ - '-0.0065', - '-0.082', - '-0.35', - '-0.31', - '-0.37', - ]), - list([ - '-2.8', - '1.2', - '1.9', - '0.47', - '1.7', - ]), - ]), - list([ - '2.3', - '-3.7', - '-1.8', - '1.2', - '-3.3', - ]), - ]), - 'grad_norm': '0.00080', - 'localization_loss': '0.00048', - 'noise': list([ - list([ - list([ - '0.5', - '-0.43', - '-1.3', - '-0.19', - '0.65', - ]), - list([ - '-1.9', - '0.23', - '0.025', - '0.19', - '-0.93', - ]), - list([ - '-1.6', - '-1.1', - '-0.52', - '0.72', - '1.5', - ]), - list([ - '-1.5', - '-0.79', - '1.0', - '-0.56', - '0.70', - ]), - list([ - '0.71', - '-1.5', - '-0.73', - '0.47', - '0.67', - ]), - ]), - list([ - '0.11', - '2.4', - '0.92', - '-0.64', - '0.71', - ]), - list([ - list([ - '1.1', - '0.21', - '-0.58', - '0.33', - '-0.81', - ]), - list([ - '-1.0', - '-0.49', - '-0.59', - '0.40', - '0.084', - ]), - list([ - '0.4', - '2.0', - '-0.46', - '-0.064', - '-1.4', - ]), - list([ - '0.33', - '-0.98', - '0.078', - '0.19', - '0.41', - ]), - list([ - '-1.6', - '2.3', - '1.0', - '1.4', - '0.63', - ]), - ]), - list([ - '0.36', - '0.82', - '-0.36', - '0.071', - '0.61', - ]), - list([ - list([ - '0.56', - '-1.5', - '-1.6', - '-1.5', - '0.43', - ]), - list([ - '-0.13', - '0.78', - '0.56', - '-0.11', - '-1.0', - ]), - list([ - '-0.22', - '0.8', - '0.91', - '-0.088', - '0.34', - ]), - list([ - '0.97', - '-1.0', - '0.39', - '0.73', - '0.25', - ]), - list([ - '0.077', - '-0.21', - '2.2', - '2.0', - '0.25', - ]), - ]), - list([ - '0.24', - '-0.26', - '-0.46', - '-1.2', - '-1.2', - ]), - ]), - 'noise_norm': '8.9', - 'weight_norm': '2.5', + 'localization': '0.00000049', + 'noise': '0.089', + 'numel': 90, + 'prior': '0.0000062', + 'scaled_grad': '0.00080', + 'weight_decay': '0.0000062', }), }) # --- @@ -36303,281 +19906,20 @@ ]), }), 'optimizer_sgld': dict({ + 'localization': '0.000015', + 'noise': '0.28', + 'numel': 90, + 'prior': '0.000064', + 'scaled_grad': '0.0083', + 'weight_decay': '0.000062', }), 'optimizer_sgmcmc': dict({ - 'distance': '0.31', - 'dws': list([ - list([ - list([ - '1.6', - '-0.29', - '-1.7', - '-1.3', - '-0.25', - ]), - list([ - '-1.7', - '0.76', - '1.8', - '0.68', - '-0.00073', - ]), - list([ - '3.0', - '-0.2', - '-3.8', - '-2.8', - '-1.2', - ]), - list([ - '-0.4', - '0.26', - '0.32', - '0.10', - '-0.16', - ]), - list([ - '1.5', - '0.15', - '-2.1', - '-1.7', - '-0.83', - ]), - ]), - list([ - '-1.1', - '1.3', - '-1.9', - '0.34', - '-0.93', - ]), - list([ - list([ - '-3.6', - '2.6', - '-5.0', - '-0.58', - '-0.36', - ]), - list([ - '1.3', - '-0.68', - '1.5', - '0.28', - '0.17', - ]), - list([ - '2.5', - '-2.1', - '3.8', - '0.3', - '0.20', - ]), - list([ - '0.40', - '0.023', - '0.20', - '0.17', - '0.053', - ]), - list([ - '3.4', - '-1.8', - '3.9', - '0.89', - '0.21', - ]), - ]), - list([ - '3.5', - '-0.63', - '-3.0', - '0.21', - '-2.2', - ]), - list([ - list([ - '0.34', - '-0.67', - '-1.6', - '-0.93', - '-0.36', - ]), - list([ - '-2.2', - '1.2', - '2.1', - '0.83', - '1.9', - ]), - list([ - '-2.5', - '0.98', - '1.1', - '0.042', - '1.6', - ]), - list([ - '0.086', - '-0.06', - '-0.46', - '-0.34', - '-0.48', - ]), - list([ - '-2.8', - '1.3', - '1.9', - '0.51', - '2.0', - ]), - ]), - list([ - '2.4', - '-3.8', - '-1.7', - '1.4', - '-3.3', - ]), - ]), - 'grad_norm': '0.0083', - 'localization_loss': '0.0048', - 'noise': list([ - list([ - list([ - '0.5', - '-0.43', - '-1.3', - '-0.19', - '0.65', - ]), - list([ - '-1.9', - '0.23', - '0.025', - '0.19', - '-0.93', - ]), - list([ - '-1.6', - '-1.1', - '-0.52', - '0.72', - '1.5', - ]), - list([ - '-1.5', - '-0.79', - '1.0', - '-0.56', - '0.70', - ]), - list([ - '0.71', - '-1.5', - '-0.73', - '0.47', - '0.67', - ]), - ]), - list([ - '0.11', - '2.4', - '0.92', - '-0.64', - '0.71', - ]), - list([ - list([ - '1.1', - '0.21', - '-0.58', - '0.33', - '-0.81', - ]), - list([ - '-1.0', - '-0.49', - '-0.59', - '0.40', - '0.084', - ]), - list([ - '0.4', - '2.0', - '-0.46', - '-0.064', - '-1.4', - ]), - list([ - '0.33', - '-0.98', - '0.078', - '0.19', - '0.41', - ]), - list([ - '-1.6', - '2.3', - '1.0', - '1.4', - '0.63', - ]), - ]), - list([ - '0.36', - '0.82', - '-0.36', - '0.071', - '0.61', - ]), - list([ - list([ - '0.56', - '-1.5', - '-1.6', - '-1.5', - '0.43', - ]), - list([ - '-0.13', - '0.78', - '0.56', - '-0.11', - '-1.0', - ]), - list([ - '-0.22', - '0.8', - '0.91', - '-0.088', - '0.34', - ]), - list([ - '0.97', - '-1.0', - '0.39', - '0.73', - '0.25', - ]), - list([ - '0.077', - '-0.21', - '2.2', - '2.0', - '0.25', - ]), - ]), - list([ - '0.24', - '-0.26', - '-0.46', - '-1.2', - '-1.2', - ]), - ]), - 'noise_norm': '8.9', - 'weight_norm': '2.5', + 'localization': '0.000015', + 'noise': '0.28', + 'numel': 90, + 'prior': '0.000064', + 'scaled_grad': '0.0083', + 'weight_decay': '0.000062', }), }) # --- @@ -36852,281 +20194,20 @@ ]), }), 'optimizer_sgld': dict({ + 'localization': '0.00049', + 'noise': '0.89', + 'numel': 90, + 'prior': '0.00082', + 'scaled_grad': '0.085', + 'weight_decay': '0.00065', }), 'optimizer_sgmcmc': dict({ - 'distance': '0.98', - 'dws': list([ - list([ - list([ - '1.4', - '-0.3', - '-1.3', - '-1.1', - '-0.089', - ]), - list([ - '-2.0', - '0.81', - '2.3', - '0.81', - '0.17', - ]), - list([ - '3.1', - '-0.19', - '-4.2', - '-2.9', - '-1.4', - ]), - list([ - '-0.39', - '0.35', - '0.046', - '0.077', - '-0.45', - ]), - list([ - '1.7', - '0.17', - '-2.4', - '-1.9', - '-1.1', - ]), - ]), - list([ - '-0.96', - '1.4', - '-2.0', - '0.45', - '-0.98', - ]), - list([ - list([ - '-4.1', - '2.1', - '-5.7', - '-0.21', - '-0.75', - ]), - list([ - '1.8', - '-0.56', - '2.2', - '0.21', - '0.53', - ]), - list([ - '2.4', - '-1.5', - '3.6', - '-0.073', - '0.40', - ]), - list([ - '0.24', - '0.19', - '-0.06', - '0.17', - '0.018', - ]), - list([ - '3.4', - '-0.92', - '3.6', - '0.53', - '0.39', - ]), - ]), - list([ - '3.8', - '-0.55', - '-2.8', - '0.35', - '-1.8', - ]), - list([ - list([ - '0.078', - '-0.37', - '-1.3', - '-1.0', - '-0.75', - ]), - list([ - '-1.7', - '1.2', - '1.9', - '0.97', - '2.6', - ]), - list([ - '-2.0', - '1.3', - '0.72', - '0.080', - '1.9', - ]), - list([ - '-0.021', - '0.097', - '-0.71', - '-0.42', - '-0.76', - ]), - list([ - '-2.5', - '1.7', - '1.5', - '0.52', - '2.7', - ]), - ]), - list([ - '2.6', - '-3.7', - '-1.3', - '1.7', - '-2.8', - ]), - ]), - 'grad_norm': '0.085', - 'localization_loss': '0.048', - 'noise': list([ - list([ - list([ - '0.5', - '-0.43', - '-1.3', - '-0.19', - '0.65', - ]), - list([ - '-1.9', - '0.23', - '0.025', - '0.19', - '-0.93', - ]), - list([ - '-1.6', - '-1.1', - '-0.52', - '0.72', - '1.5', - ]), - list([ - '-1.5', - '-0.79', - '1.0', - '-0.56', - '0.70', - ]), - list([ - '0.71', - '-1.5', - '-0.73', - '0.47', - '0.67', - ]), - ]), - list([ - '0.11', - '2.4', - '0.92', - '-0.64', - '0.71', - ]), - list([ - list([ - '1.1', - '0.21', - '-0.58', - '0.33', - '-0.81', - ]), - list([ - '-1.0', - '-0.49', - '-0.59', - '0.40', - '0.084', - ]), - list([ - '0.4', - '2.0', - '-0.46', - '-0.064', - '-1.4', - ]), - list([ - '0.33', - '-0.98', - '0.078', - '0.19', - '0.41', - ]), - list([ - '-1.6', - '2.3', - '1.0', - '1.4', - '0.63', - ]), - ]), - list([ - '0.36', - '0.82', - '-0.36', - '0.071', - '0.61', - ]), - list([ - list([ - '0.56', - '-1.5', - '-1.6', - '-1.5', - '0.43', - ]), - list([ - '-0.13', - '0.78', - '0.56', - '-0.11', - '-1.0', - ]), - list([ - '-0.22', - '0.8', - '0.91', - '-0.088', - '0.34', - ]), - list([ - '0.97', - '-1.0', - '0.39', - '0.73', - '0.25', - ]), - list([ - '0.077', - '-0.21', - '2.2', - '2.0', - '0.25', - ]), - ]), - list([ - '0.24', - '-0.26', - '-0.46', - '-1.2', - '-1.2', - ]), - ]), - 'noise_norm': '8.9', - 'weight_norm': '2.8', + 'localization': '0.00049', + 'noise': '0.89', + 'numel': 90, + 'prior': '0.00082', + 'scaled_grad': '0.085', + 'weight_decay': '0.00065', }), }) # --- @@ -37401,329 +20482,100 @@ ]), }), 'optimizer_sgld': dict({ + 'localization': '0.016', + 'noise': '2.8', + 'numel': 90, + 'prior': '0.018', + 'scaled_grad': '0.81', + 'weight_decay': '0.0094', }), 'optimizer_sgmcmc': dict({ - 'distance': '3.1', - 'dws': list([ - list([ - list([ - '0.73', - '-0.38', - '-0.16', - '-0.41', - '0.54', - ]), - list([ - '0.86', - '-0.068', - '-0.21', - '-1.5', - '0.41', - ]), - list([ - '-1.2', - '1.1', - '-0.9', - '0.84', - '-2.2', - ]), - list([ - '-2.0', - '2.1', - '-2.3', - '1.3', - '-4.4', - ]), - list([ - '-1.5', - '1.4', - '-0.93', - '0.93', - '-2.5', - ]), - ]), - list([ - '-0.61', - '-0.92', - '1.6', - '3.1', - '1.9', - ]), - list([ - list([ - '2.8', - '0.11', - '-0.42', - '-0.90', - '-1.6', - ]), - list([ - '1.1', - '1.2', - '4.3', - '1.1', - '2.7', - ]), - list([ - '-1.9', - '-0.56', - '-1.3', - '-0.25', - '-0.27', - ]), - list([ - '1.9', - '0.46', - '0.9', - '-0.12', - '-0.12', - ]), - list([ - '2.2', - '0.28', - '0.10', - '-0.42', - '-0.84', - ]), - ]), - list([ - '-2.6', - '2.0', - '-0.13', - '-0.88', - '-1.1', - ]), - list([ - list([ - '0.023', - '1.5', - '0.75', - '-0.91', - '-0.41', - ]), - list([ - '2.3', - '-1.0', - '1.3', - '1.3', - '1.5', - ]), - list([ - '-0.12', - '1.9', - '-0.25', - '0.28', - '1.2', - ]), - list([ - '-0.64', - '1.1', - '-0.83', - '0.41', - '0.74', - ]), - list([ - '-5.6', - '5.8', - '-3.7', - '-1.6', - '-0.48', - ]), - ]), - list([ - '1.3', - '-2.3', - '-0.81', - '-0.52', - '2.8', - ]), - ]), - 'grad_norm': '0.81', - 'localization_loss': '0.49', - 'noise': list([ - list([ - list([ - '0.5', - '-0.43', - '-1.3', - '-0.19', - '0.65', - ]), - list([ - '-1.9', - '0.23', - '0.025', - '0.19', - '-0.93', - ]), - list([ - '-1.6', - '-1.1', - '-0.52', - '0.72', - '1.5', - ]), - list([ - '-1.5', - '-0.79', - '1.0', - '-0.56', - '0.70', - ]), - list([ - '0.71', - '-1.5', - '-0.73', - '0.47', - '0.67', - ]), - ]), - list([ - '0.11', - '2.4', - '0.92', - '-0.64', - '0.71', - ]), - list([ - list([ - '1.1', - '0.21', - '-0.58', - '0.33', - '-0.81', - ]), - list([ - '-1.0', - '-0.49', - '-0.59', - '0.40', - '0.084', - ]), - list([ - '0.4', - '2.0', - '-0.46', - '-0.064', - '-1.4', - ]), - list([ - '0.33', - '-0.98', - '0.078', - '0.19', - '0.41', - ]), - list([ - '-1.6', - '2.3', - '1.0', - '1.4', - '0.63', - ]), - ]), - list([ - '0.36', - '0.82', - '-0.36', - '0.071', - '0.61', - ]), - list([ - list([ - '0.56', - '-1.5', - '-1.6', - '-1.5', - '0.43', - ]), - list([ - '-0.13', - '0.78', - '0.56', - '-0.11', - '-1.0', - ]), - list([ - '-0.22', - '0.8', - '0.91', - '-0.088', - '0.34', - ]), - list([ - '0.97', - '-1.0', - '0.39', - '0.73', - '0.25', - ]), - list([ - '0.077', - '-0.21', - '2.2', - '2.0', - '0.25', - ]), - ]), - list([ - '0.24', - '-0.26', - '-0.46', - '-1.2', - '-1.2', - ]), - ]), - 'noise_norm': '8.9', - 'weight_norm': '4.7', + 'localization': '0.016', + 'noise': '2.8', + 'numel': 90, + 'prior': '0.018', + 'scaled_grad': '0.81', + 'weight_decay': '0.0094', }), }) # --- # name: test_SGMCMC_vs_SGLD_param_groups[0.5-0.5][test_SGMCMC_vs_SGLD_param_groups] dict({ 'optimizer_sgld': dict({ + 'localization': '0.0000000011', + 'noise': '0.0', + 'numel': 90, + 'prior': '0.0000000011', + 'scaled_grad': '0.000022', + 'weight_decay': '0.0', }), 'optimizer_sgmcmc': dict({ - 'distance': '0.00009', - 'grad_norm': '0.000022', - 'noise_norm': '0.0', - 'weight_norm': '1.4', + 'localization': '0.0000000011', + 'noise': '0.0', + 'numel': 90, + 'prior': '0.0000000011', + 'scaled_grad': '0.000022', + 'weight_decay': '0.0', }), }) # --- # name: test_SGMCMC_vs_SGLD_param_groups[0.5-3.0][test_SGMCMC_vs_SGLD_param_groups] dict({ 'optimizer_sgld': dict({ + 'localization': '0.000000040', + 'noise': '0.0', + 'numel': 90, + 'prior': '0.000000040', + 'scaled_grad': '0.00013', + 'weight_decay': '0.0', }), 'optimizer_sgmcmc': dict({ - 'distance': '0.00054', - 'grad_norm': '0.00013', - 'noise_norm': '0.0', - 'weight_norm': '1.4', + 'localization': '0.000000040', + 'noise': '0.0', + 'numel': 90, + 'prior': '0.000000040', + 'scaled_grad': '0.00013', + 'weight_decay': '0.0', }), }) # --- # name: test_SGMCMC_vs_SGLD_param_groups[3.0-0.5][test_SGMCMC_vs_SGLD_param_groups] dict({ 'optimizer_sgld': dict({ + 'localization': '0.0000000011', + 'noise': '0.0', + 'numel': 90, + 'prior': '0.0000000011', + 'scaled_grad': '0.000022', + 'weight_decay': '0.0', }), 'optimizer_sgmcmc': dict({ - 'distance': '0.00009', - 'grad_norm': '0.000022', - 'noise_norm': '0.0', - 'weight_norm': '1.4', + 'localization': '0.0000000011', + 'noise': '0.0', + 'numel': 90, + 'prior': '0.0000000011', + 'scaled_grad': '0.000022', + 'weight_decay': '0.0', }), }) # --- # name: test_SGMCMC_vs_SGLD_param_groups[3.0-3.0][test_SGMCMC_vs_SGLD_param_groups] dict({ 'optimizer_sgld': dict({ + 'localization': '0.000000021', + 'noise': '0.0', + 'numel': 90, + 'prior': '0.000000021', + 'scaled_grad': '0.000071', + 'weight_decay': '0.0', }), 'optimizer_sgmcmc': dict({ - 'distance': '0.00028', - 'grad_norm': '0.000071', - 'noise_norm': '0.0', - 'weight_norm': '1.2', + 'localization': '0.000000021', + 'noise': '0.0', + 'numel': 90, + 'prior': '0.000000021', + 'scaled_grad': '0.000071', + 'weight_decay': '0.0', }), }) # --- @@ -39903,1095 +22755,7 @@ }), }) # --- -# name: test_optimize_over[scalar-0.0-0.1-0.0-1.0-0.1][test_optimize_over_0.1_1.0_0.0_0.1_0.0_scalar] - dict({ - 'model1': dict({ - '0.bias': list([ - '0.15', - '0.50', - '-0.31', - '-0.38', - '-0.013', - ]), - '0.weight': list([ - list([ - '-0.0033', - '0.24', - '-0.37', - '-0.33', - '-0.17', - ]), - list([ - '0.12', - '-0.0089', - '0.35', - '-0.04', - '0.12', - ]), - list([ - '-0.14', - '-0.088', - '-0.43', - '-0.3', - '-0.18', - ]), - list([ - '0.017', - '0.18', - '0.27', - '-0.30', - '-0.19', - ]), - list([ - '0.16', - '0.37', - '-0.092', - '0.33', - '-0.072', - ]), - ]), - '1.bias': list([ - '-0.26', - '0.083', - '-0.35', - '-0.31', - '-0.23', - ]), - '1.weight': list([ - list([ - '-0.17', - '0.39', - '-0.29', - '-0.21', - '-0.31', - ]), - list([ - '-0.42', - '-0.26', - '0.38', - '0.2', - '0.22', - ]), - list([ - '0.024', - '-0.23', - '0.076', - '-0.42', - '-0.32', - ]), - list([ - '-0.23', - '0.28', - '0.26', - '-0.2', - '-0.016', - ]), - list([ - '0.29', - '0.44', - '0.18', - '0.060', - '0.3', - ]), - ]), - '2.bias': list([ - '0.025', - '-0.050', - '-0.23', - '0.072', - '-0.36', - ]), - '2.weight': list([ - list([ - '0.20', - '0.18', - '-0.26', - '0.14', - '0.25', - ]), - list([ - '-0.056', - '0.017', - '0.10', - '0.28', - '0.43', - ]), - list([ - '-0.34', - '-0.16', - '0.18', - '0.37', - '0.39', - ]), - list([ - '0.39', - '0.089', - '-0.39', - '0.041', - '-0.28', - ]), - list([ - '-0.42', - '0.4', - '0.34', - '-0.45', - '0.084', - ]), - ]), - }), - 'model2': dict({ - '0.bias': list([ - '0.15', - '0.50', - '-0.31', - '-0.38', - '-0.013', - ]), - '0.weight': list([ - list([ - '-0.0033', - '0.24', - '-0.37', - '-0.33', - '-0.17', - ]), - list([ - '0.12', - '-0.0089', - '0.35', - '-0.04', - '0.12', - ]), - list([ - '-0.14', - '-0.088', - '-0.43', - '-0.3', - '-0.18', - ]), - list([ - '0.017', - '0.18', - '0.27', - '-0.30', - '-0.19', - ]), - list([ - '0.16', - '0.37', - '-0.092', - '0.33', - '-0.072', - ]), - ]), - '1.bias': list([ - '-0.26', - '0.083', - '-0.35', - '-0.31', - '-0.23', - ]), - '1.weight': list([ - list([ - '-0.17', - '0.39', - '-0.29', - '-0.21', - '-0.31', - ]), - list([ - '-0.42', - '-0.26', - '0.38', - '0.2', - '0.22', - ]), - list([ - '0.024', - '-0.23', - '0.076', - '-0.42', - '-0.32', - ]), - list([ - '-0.23', - '0.28', - '0.26', - '-0.2', - '-0.016', - ]), - list([ - '0.29', - '0.44', - '0.18', - '0.060', - '0.3', - ]), - ]), - '2.bias': list([ - '0.025', - '-0.050', - '-0.23', - '0.072', - '-0.36', - ]), - '2.weight': list([ - list([ - '0.20', - '0.18', - '-0.26', - '0.14', - '0.25', - ]), - list([ - '-0.056', - '0.017', - '0.10', - '0.28', - '0.43', - ]), - list([ - '-0.34', - '-0.16', - '0.18', - '0.37', - '0.39', - ]), - list([ - '0.39', - '0.089', - '-0.39', - '0.041', - '-0.28', - ]), - list([ - '-0.42', - '0.4', - '0.34', - '-0.45', - '0.084', - ]), - ]), - }), - }) -# --- -# name: test_optimize_over[scalar-0.0-0.1-0.1-1.0-0.1][test_optimize_over_0.1_1.0_0.1_0.1_0.0_scalar] - dict({ - 'model1': dict({ - '0.bias': list([ - '0.15', - '0.50', - '-0.31', - '-0.38', - '-0.013', - ]), - '0.weight': list([ - list([ - '-0.0033', - '0.24', - '-0.37', - '-0.33', - '-0.17', - ]), - list([ - '0.12', - '-0.0089', - '0.35', - '-0.04', - '0.12', - ]), - list([ - '-0.14', - '-0.088', - '-0.43', - '-0.3', - '-0.18', - ]), - list([ - '0.017', - '0.18', - '0.27', - '-0.30', - '-0.19', - ]), - list([ - '0.16', - '0.37', - '-0.092', - '0.33', - '-0.072', - ]), - ]), - '1.bias': list([ - '-0.26', - '0.083', - '-0.35', - '-0.31', - '-0.23', - ]), - '1.weight': list([ - list([ - '-0.17', - '0.39', - '-0.29', - '-0.21', - '-0.31', - ]), - list([ - '-0.42', - '-0.26', - '0.38', - '0.2', - '0.22', - ]), - list([ - '0.024', - '-0.23', - '0.076', - '-0.42', - '-0.32', - ]), - list([ - '-0.23', - '0.28', - '0.26', - '-0.2', - '-0.016', - ]), - list([ - '0.29', - '0.44', - '0.18', - '0.060', - '0.3', - ]), - ]), - '2.bias': list([ - '0.025', - '-0.051', - '-0.23', - '0.072', - '-0.36', - ]), - '2.weight': list([ - list([ - '0.20', - '0.18', - '-0.26', - '0.14', - '0.25', - ]), - list([ - '-0.056', - '0.017', - '0.10', - '0.28', - '0.43', - ]), - list([ - '-0.34', - '-0.16', - '0.18', - '0.37', - '0.39', - ]), - list([ - '0.39', - '0.089', - '-0.39', - '0.041', - '-0.28', - ]), - list([ - '-0.42', - '0.4', - '0.34', - '-0.45', - '0.084', - ]), - ]), - }), - 'model2': dict({ - '0.bias': list([ - '0.15', - '0.50', - '-0.31', - '-0.38', - '-0.013', - ]), - '0.weight': list([ - list([ - '-0.0033', - '0.24', - '-0.37', - '-0.33', - '-0.17', - ]), - list([ - '0.12', - '-0.0089', - '0.35', - '-0.04', - '0.12', - ]), - list([ - '-0.14', - '-0.088', - '-0.43', - '-0.3', - '-0.18', - ]), - list([ - '0.017', - '0.18', - '0.27', - '-0.30', - '-0.19', - ]), - list([ - '0.16', - '0.37', - '-0.092', - '0.33', - '-0.072', - ]), - ]), - '1.bias': list([ - '-0.26', - '0.083', - '-0.35', - '-0.31', - '-0.23', - ]), - '1.weight': list([ - list([ - '-0.17', - '0.39', - '-0.29', - '-0.21', - '-0.31', - ]), - list([ - '-0.42', - '-0.26', - '0.38', - '0.2', - '0.22', - ]), - list([ - '0.024', - '-0.23', - '0.076', - '-0.42', - '-0.32', - ]), - list([ - '-0.23', - '0.28', - '0.26', - '-0.2', - '-0.016', - ]), - list([ - '0.29', - '0.44', - '0.18', - '0.060', - '0.3', - ]), - ]), - '2.bias': list([ - '0.025', - '-0.051', - '-0.23', - '0.072', - '-0.36', - ]), - '2.weight': list([ - list([ - '0.20', - '0.18', - '-0.26', - '0.14', - '0.25', - ]), - list([ - '-0.056', - '0.017', - '0.10', - '0.28', - '0.43', - ]), - list([ - '-0.34', - '-0.16', - '0.18', - '0.37', - '0.39', - ]), - list([ - '0.39', - '0.089', - '-0.39', - '0.041', - '-0.28', - ]), - list([ - '-0.42', - '0.4', - '0.34', - '-0.45', - '0.084', - ]), - ]), - }), - }) -# --- -# name: test_optimize_over[scalar-0.0-None-0.0-1.0-0.1][test_optimize_over_0.1_1.0_0.0_None_0.0_scalar] - dict({ - 'model1': dict({ - '0.bias': list([ - '0.45', - '0.95', - '-0.11', - '0.011', - '-0.20', - ]), - '0.weight': list([ - list([ - '-0.0033', - '0.24', - '-0.37', - '-0.33', - '-0.17', - ]), - list([ - '0.12', - '-0.0089', - '0.35', - '-0.04', - '0.12', - ]), - list([ - '-0.14', - '-0.088', - '-0.43', - '-0.3', - '-0.18', - ]), - list([ - '0.017', - '0.18', - '0.27', - '-0.30', - '-0.19', - ]), - list([ - '0.16', - '0.37', - '-0.092', - '0.33', - '-0.072', - ]), - ]), - '1.bias': list([ - '-0.26', - '0.083', - '-0.35', - '-0.31', - '-0.23', - ]), - '1.weight': list([ - list([ - '-0.17', - '0.39', - '-0.29', - '-0.21', - '-0.31', - ]), - list([ - '-0.42', - '-0.26', - '0.38', - '0.2', - '0.22', - ]), - list([ - '0.024', - '-0.23', - '0.076', - '-0.42', - '-0.32', - ]), - list([ - '-0.23', - '0.28', - '0.26', - '-0.2', - '-0.016', - ]), - list([ - '0.29', - '0.44', - '0.18', - '0.060', - '0.3', - ]), - ]), - '2.bias': list([ - '0.54', - '0.089', - '-0.22', - '0.37', - '-0.62', - ]), - '2.weight': list([ - list([ - '0.20', - '0.18', - '-0.26', - '0.14', - '0.25', - ]), - list([ - '-0.056', - '0.017', - '0.10', - '0.28', - '0.43', - ]), - list([ - '-0.34', - '-0.16', - '0.18', - '0.37', - '0.39', - ]), - list([ - '0.39', - '0.089', - '-0.39', - '0.041', - '-0.28', - ]), - list([ - '-0.42', - '0.4', - '0.34', - '-0.45', - '0.084', - ]), - ]), - }), - 'model2': dict({ - '0.bias': list([ - '0.45', - '0.95', - '-0.11', - '0.011', - '-0.20', - ]), - '0.weight': list([ - list([ - '-0.0033', - '0.24', - '-0.37', - '-0.33', - '-0.17', - ]), - list([ - '0.12', - '-0.0089', - '0.35', - '-0.04', - '0.12', - ]), - list([ - '-0.14', - '-0.088', - '-0.43', - '-0.3', - '-0.18', - ]), - list([ - '0.017', - '0.18', - '0.27', - '-0.30', - '-0.19', - ]), - list([ - '0.16', - '0.37', - '-0.092', - '0.33', - '-0.072', - ]), - ]), - '1.bias': list([ - '-0.26', - '0.083', - '-0.35', - '-0.31', - '-0.23', - ]), - '1.weight': list([ - list([ - '-0.17', - '0.39', - '-0.29', - '-0.21', - '-0.31', - ]), - list([ - '-0.42', - '-0.26', - '0.38', - '0.2', - '0.22', - ]), - list([ - '0.024', - '-0.23', - '0.076', - '-0.42', - '-0.32', - ]), - list([ - '-0.23', - '0.28', - '0.26', - '-0.2', - '-0.016', - ]), - list([ - '0.29', - '0.44', - '0.18', - '0.060', - '0.3', - ]), - ]), - '2.bias': list([ - '0.54', - '0.089', - '-0.22', - '0.37', - '-0.62', - ]), - '2.weight': list([ - list([ - '0.20', - '0.18', - '-0.26', - '0.14', - '0.25', - ]), - list([ - '-0.056', - '0.017', - '0.10', - '0.28', - '0.43', - ]), - list([ - '-0.34', - '-0.16', - '0.18', - '0.37', - '0.39', - ]), - list([ - '0.39', - '0.089', - '-0.39', - '0.041', - '-0.28', - ]), - list([ - '-0.42', - '0.4', - '0.34', - '-0.45', - '0.084', - ]), - ]), - }), - }) -# --- -# name: test_optimize_over[scalar-0.0-None-0.1-1.0-0.1][test_optimize_over_0.1_1.0_0.1_None_0.0_scalar] - dict({ - 'model1': dict({ - '0.bias': list([ - '0.45', - '0.95', - '-0.11', - '0.0087', - '-0.20', - ]), - '0.weight': list([ - list([ - '-0.0033', - '0.24', - '-0.37', - '-0.33', - '-0.17', - ]), - list([ - '0.12', - '-0.0089', - '0.35', - '-0.04', - '0.12', - ]), - list([ - '-0.14', - '-0.088', - '-0.43', - '-0.3', - '-0.18', - ]), - list([ - '0.017', - '0.18', - '0.27', - '-0.30', - '-0.19', - ]), - list([ - '0.16', - '0.37', - '-0.092', - '0.33', - '-0.072', - ]), - ]), - '1.bias': list([ - '-0.26', - '0.083', - '-0.35', - '-0.31', - '-0.23', - ]), - '1.weight': list([ - list([ - '-0.17', - '0.39', - '-0.29', - '-0.21', - '-0.31', - ]), - list([ - '-0.42', - '-0.26', - '0.38', - '0.2', - '0.22', - ]), - list([ - '0.024', - '-0.23', - '0.076', - '-0.42', - '-0.32', - ]), - list([ - '-0.23', - '0.28', - '0.26', - '-0.2', - '-0.016', - ]), - list([ - '0.29', - '0.44', - '0.18', - '0.060', - '0.3', - ]), - ]), - '2.bias': list([ - '0.54', - '0.088', - '-0.22', - '0.37', - '-0.62', - ]), - '2.weight': list([ - list([ - '0.20', - '0.18', - '-0.26', - '0.14', - '0.25', - ]), - list([ - '-0.056', - '0.017', - '0.10', - '0.28', - '0.43', - ]), - list([ - '-0.34', - '-0.16', - '0.18', - '0.37', - '0.39', - ]), - list([ - '0.39', - '0.089', - '-0.39', - '0.041', - '-0.28', - ]), - list([ - '-0.42', - '0.4', - '0.34', - '-0.45', - '0.084', - ]), - ]), - }), - 'model2': dict({ - '0.bias': list([ - '0.45', - '0.95', - '-0.11', - '0.0087', - '-0.20', - ]), - '0.weight': list([ - list([ - '-0.0033', - '0.24', - '-0.37', - '-0.33', - '-0.17', - ]), - list([ - '0.12', - '-0.0089', - '0.35', - '-0.04', - '0.12', - ]), - list([ - '-0.14', - '-0.088', - '-0.43', - '-0.3', - '-0.18', - ]), - list([ - '0.017', - '0.18', - '0.27', - '-0.30', - '-0.19', - ]), - list([ - '0.16', - '0.37', - '-0.092', - '0.33', - '-0.072', - ]), - ]), - '1.bias': list([ - '-0.26', - '0.083', - '-0.35', - '-0.31', - '-0.23', - ]), - '1.weight': list([ - list([ - '-0.17', - '0.39', - '-0.29', - '-0.21', - '-0.31', - ]), - list([ - '-0.42', - '-0.26', - '0.38', - '0.2', - '0.22', - ]), - list([ - '0.024', - '-0.23', - '0.076', - '-0.42', - '-0.32', - ]), - list([ - '-0.23', - '0.28', - '0.26', - '-0.2', - '-0.016', - ]), - list([ - '0.29', - '0.44', - '0.18', - '0.060', - '0.3', - ]), - ]), - '2.bias': list([ - '0.54', - '0.088', - '-0.22', - '0.37', - '-0.62', - ]), - '2.weight': list([ - list([ - '0.20', - '0.18', - '-0.26', - '0.14', - '0.25', - ]), - list([ - '-0.056', - '0.017', - '0.10', - '0.28', - '0.43', - ]), - list([ - '-0.34', - '-0.16', - '0.18', - '0.37', - '0.39', - ]), - list([ - '0.39', - '0.089', - '-0.39', - '0.041', - '-0.28', - ]), - list([ - '-0.42', - '0.4', - '0.34', - '-0.45', - '0.084', - ]), - ]), - }), - }) -# --- -# name: test_optimize_over[tensor-0.0-0.1-0.0-1.0-0.1][test_optimize_over_0.1_1.0_0.0_0.1_0.0_tensor] +# name: test_optimize_over[0.0-0.1-0.0-1.0-0.1][test_optimize_over_0.1_1.0_0.0_0.1_0.0] dict({ 'model1': dict({ '0.bias': list([ @@ -41263,7 +23027,7 @@ }), }) # --- -# name: test_optimize_over[tensor-0.0-0.1-0.1-1.0-0.1][test_optimize_over_0.1_1.0_0.1_0.1_0.0_tensor] +# name: test_optimize_over[0.0-0.1-0.1-1.0-0.1][test_optimize_over_0.1_1.0_0.1_0.1_0.0] dict({ 'model1': dict({ '0.bias': list([ @@ -41535,7 +23299,7 @@ }), }) # --- -# name: test_optimize_over[tensor-0.0-None-0.0-1.0-0.1][test_optimize_over_0.1_1.0_0.0_None_0.0_tensor] +# name: test_optimize_over[0.0-None-0.0-1.0-0.1][test_optimize_over_0.1_1.0_0.0_None_0.0] dict({ 'model1': dict({ '0.bias': list([ @@ -41807,7 +23571,7 @@ }), }) # --- -# name: test_optimize_over[tensor-0.0-None-0.1-1.0-0.1][test_optimize_over_0.1_1.0_0.1_None_0.0_tensor] +# name: test_optimize_over[0.0-None-0.1-1.0-0.1][test_optimize_over_0.1_1.0_0.1_None_0.0] dict({ 'model1': dict({ '0.bias': list([ diff --git a/tests/optim/rllc_test.py b/tests/optim/rllc_test.py deleted file mode 100644 index 88e289b2..00000000 --- a/tests/optim/rllc_test.py +++ /dev/null @@ -1,584 +0,0 @@ -""" -This test encounters nan loss values during sampling. -Found by adding a feature to throw an error when nan loss values are encountered. -""" - -import numpy as np -import pytest -import torch -import platform -from devinterp.optim.sgld import SGLD -from devinterp.optim.sgmcmc import SGMCMC -from devinterp.slt.llc import LLCEstimator -from devinterp.slt.sampler import sample -from devinterp.utils import evaluate_mse, default_nbeta, get_init_loss_multi_batch -from torch.utils.data import DataLoader, TensorDataset - -# Test configuration constants -TOLERANCE_ATOL = 1e-5 -TOLERANCE_ATOL_FULL = 8e-2 -TOLERANCE_ATOL_DIFFERENCE = 1e-2 -FULL_SAMPLING_DRAWS = 500 -SNAPSHOT_DRAWS = 5 -LEARNING_RATE_FAST = 0.0002 -LEARNING_RATE_SLOW = 0.0001 -LEARNING_RATE_FULL = 0.001 -NUM_CHAINS = 1 -RANDOM_SEED = 42 -NUM_SAMPLES = 1000 - - -def generated_normalcrossing_dataset_seeded(seed): - """Generate synthetic dataset with normal crossing pattern for given seed.""" - torch.manual_seed(seed) - np.random.seed(seed) - sigma = 0.25 - x = torch.normal(0, 2, size=(NUM_SAMPLES,)) - y = sigma * torch.normal(0, 1, size=(NUM_SAMPLES,)) - train_data = TensorDataset(x, y) - - # Add deterministic generator for DataLoader shuffling - generator = torch.Generator() - generator.manual_seed(seed) - train_dataloader = DataLoader( - train_data, batch_size=NUM_SAMPLES, shuffle=True, generator=generator - ) - return train_dataloader, train_data, x, y - - -# Test case configurations -POWERS_BETWEEN = [ - [ - [1, 1, 0], - [1, 1, 10], - ], - [ - [2, 2, 10], - [2, 2, 3], - ], - [ - [3, 3, 6.1], - [3, 3, 1.2], - ], -] - -POWERS_DIMS = [ - [1, 1], - [2, 10], -] - -POWERS_DIMS_FULL = [ - # [1, 1], # For some reason, this consistently fails tests. - [0, 2], -] - -POWERS_DIFFERENCE = [ - [0, 1], -] - -EXTRA_DIM_POWERS = [3, 10, 100] -SAMPLE_POINTS = [[0.0, 0.0, 1.0], [1.0, 1.0, 1.0]] -SAMPLE_POINTS_SINGLE = [[0.0, 0.0, 1.0]] - - -@pytest.mark.parametrize("sampling_method", [SGLD, SGMCMC.sgld]) -@pytest.mark.parametrize("powers", POWERS_BETWEEN) -@pytest.mark.parametrize("sample_point", SAMPLE_POINTS) -def test_rllc_normalcrossing_between_powers( - generated_normalcrossing_dataset, - sampling_method, - powers, - sample_point, - Polynomial, - snapshot, - is_snapshot_update, -): - torch.manual_seed(RANDOM_SEED) - - # Set up models - model1 = Polynomial(powers[0]) - model1.weights = torch.nn.Parameter(torch.tensor(sample_point)) - model2 = Polynomial(powers[1]) - model2.weights = torch.nn.Parameter(torch.tensor(sample_point)) - - train_dataloader, _, _, _ = generated_normalcrossing_dataset - - # Run snapshot test first - llc_mean_1, llc_mean_2 = _do_between_powers_sampling( - model1, model2, train_dataloader, sampling_method, num_draws=SNAPSHOT_DRAWS - ) - - # Run verification test when updating snapshots - if is_snapshot_update: - _test_between_powers_accuracy( - model1, model2, train_dataloader, sampling_method, powers - ) - - # Test against snapshot - difference = abs(llc_mean_1 - llc_mean_2) - assert difference == snapshot - - -@pytest.mark.parametrize("sampling_method", [SGLD, SGMCMC.sgld]) -@pytest.mark.parametrize("relevant_powers", POWERS_DIMS) -@pytest.mark.parametrize("extra_dim_power", EXTRA_DIM_POWERS) -@pytest.mark.parametrize("sample_point", SAMPLE_POINTS) -def test_restricted_gradient_normalcrossing_between_dims( - generated_normalcrossing_dataset, - sampling_method, - relevant_powers, - extra_dim_power, - sample_point, - Polynomial, - snapshot, - is_snapshot_update, -): - torch.manual_seed(RANDOM_SEED) - - # Set up models - model1 = Polynomial(relevant_powers) - model2 = Polynomial(relevant_powers + [extra_dim_power]) - - model1.weights = torch.nn.Parameter(torch.tensor(sample_point[:-1])) - model2.weights = torch.nn.Parameter(torch.tensor(sample_point)) - - train_dataloader, _, _, _ = generated_normalcrossing_dataset - - # Run snapshot test first - llc_mean_2d, llc_mean_3d_restricted = _do_restricted_gradient_sampling( - model1, model2, train_dataloader, sampling_method, num_draws=SNAPSHOT_DRAWS - ) - - # Run verification test when updating snapshots - if is_snapshot_update: - _test_restricted_gradient_accuracy( - model1, - model2, - train_dataloader, - sampling_method, - relevant_powers, - extra_dim_power, - ) - - # Test against snapshot - difference = abs(llc_mean_2d - llc_mean_3d_restricted) - assert difference == snapshot - - -@pytest.mark.skipif( - platform.machine() != "x86_64", - reason=f"Differences in results between ARM and x86_64. Your arch is {platform.machine()}", -) -@pytest.mark.parametrize( - "sampling_method", [SGLD, SGMCMC.sgld], ids=lambda x: x.__name__ -) -@pytest.mark.parametrize( - "relevant_powers", POWERS_DIMS_FULL, ids=lambda x: f"powers_{'_'.join(map(str, x))}" -) -@pytest.mark.parametrize( - "extra_dim_power", EXTRA_DIM_POWERS, ids=lambda x: f"extra_dim_{x}" -) -@pytest.mark.parametrize( - "sample_point", - SAMPLE_POINTS_SINGLE, - ids=lambda x: f"sample_{'_'.join(map(str, x))}", -) -def test_rllc_full_normalcrossing_between_dims( - sampling_method, - relevant_powers, - extra_dim_power, - sample_point, - Polynomial, - snapshot, - is_snapshot_update, -): - seed = 5 - torch.manual_seed(seed) - - # Set up models - model1 = Polynomial(relevant_powers) - model2 = Polynomial(relevant_powers + [extra_dim_power]) - - model1.weights = torch.nn.Parameter(torch.tensor(sample_point[:-1])) - model2.weights = torch.nn.Parameter(torch.tensor(sample_point)) - - train_dataloader, _, _, _ = generated_normalcrossing_dataset_seeded(seed) - - # Run snapshot test first - llc_mean_2d, llc_mean_3d_restricted = _do_full_sampling( - model1, - model2, - train_dataloader, - sampling_method, - seed, - num_draws=SNAPSHOT_DRAWS, - ) - - # Run verification test when updating snapshots - if is_snapshot_update: - _test_full_accuracy( - model1, - model2, - train_dataloader, - sampling_method, - seed, - relevant_powers, - extra_dim_power, - ) - - # Test against snapshot - difference = abs(llc_mean_2d - llc_mean_3d_restricted) - assert difference == snapshot - - -@pytest.mark.parametrize("sampling_method", [SGLD, SGMCMC.sgld]) -@pytest.mark.parametrize("relevant_powers", POWERS_DIFFERENCE) -def test_rllc_different_from_full_llc_between_dims( - generated_normalcrossing_dataset, - sampling_method, - relevant_powers, - Polynomial, - snapshot, - is_snapshot_update, -): - torch.manual_seed(RANDOM_SEED) - - # Set up model - model = Polynomial(relevant_powers) - model.weights = torch.nn.Parameter(torch.tensor([0.3, 1.5])) - - train_dataloader, _, _, _ = generated_normalcrossing_dataset - - # Run snapshot test first - llc_mean, rllc_mean = _do_difference_sampling( - model, train_dataloader, sampling_method, num_draws=SNAPSHOT_DRAWS - ) - - # Run verification test when updating snapshots - if is_snapshot_update: - _test_difference_accuracy( - model, train_dataloader, sampling_method, relevant_powers - ) - - # Test against snapshot - difference = abs(llc_mean - rllc_mean) - assert difference == snapshot - - -def _do_between_powers_sampling( - model1, model2, train_dataloader, sampling_method, num_draws -): - torch.manual_seed(RANDOM_SEED) - - init_loss_1 = get_init_loss_multi_batch( - train_dataloader, NUM_CHAINS, model1, evaluate_mse, device="cpu" - ) - init_loss_2 = get_init_loss_multi_batch( - train_dataloader, NUM_CHAINS, model2, evaluate_mse, device="cpu" - ) - - llc_estimator_1 = LLCEstimator( - num_chains=NUM_CHAINS, - num_draws=num_draws, - nbeta=default_nbeta(train_dataloader), - init_loss=init_loss_1, - ) - llc_estimator_2 = LLCEstimator( - num_chains=NUM_CHAINS, - num_draws=num_draws, - nbeta=default_nbeta(train_dataloader), - init_loss=init_loss_2, - ) - - torch.manual_seed(RANDOM_SEED) - sample( - model1, - train_dataloader, - evaluate=evaluate_mse, - sampling_method_kwargs=dict( - lr=LEARNING_RATE_FAST, - nbeta=default_nbeta(train_dataloader), - ), - sampling_method=sampling_method, - num_chains=NUM_CHAINS, - num_draws=num_draws, - callbacks=[llc_estimator_1], - verbose=False, - seed=RANDOM_SEED, - optimize_over_per_model_param={"weights": torch.tensor([1, 1, 0])}, - ) - - torch.manual_seed(RANDOM_SEED) - sample( - model2, - train_dataloader, - evaluate=evaluate_mse, - sampling_method_kwargs=dict( - lr=LEARNING_RATE_FAST, - nbeta=default_nbeta(train_dataloader), - ), - sampling_method=sampling_method, - num_chains=NUM_CHAINS, - num_draws=num_draws, - callbacks=[llc_estimator_2], - verbose=False, - seed=RANDOM_SEED, - optimize_over_per_model_param={"weights": torch.tensor([1, 1, 0])}, - ) - - llc_mean_1 = llc_estimator_1.get_results()["llc/mean"] - llc_mean_2 = llc_estimator_2.get_results()["llc/mean"] - - return llc_mean_1, llc_mean_2 - - -def _do_restricted_gradient_sampling( - model1, model2, train_dataloader, sampling_method, num_draws -): - torch.manual_seed(RANDOM_SEED) - - init_loss_1 = get_init_loss_multi_batch( - train_dataloader, NUM_CHAINS, model1, evaluate_mse, device="cpu" - ) - init_loss_2 = get_init_loss_multi_batch( - train_dataloader, NUM_CHAINS, model2, evaluate_mse, device="cpu" - ) - - llc_estimator_2d = LLCEstimator( - num_chains=NUM_CHAINS, - num_draws=num_draws, - nbeta=default_nbeta(train_dataloader), - init_loss=init_loss_1, - ) - llc_estimator_3d = LLCEstimator( - num_chains=NUM_CHAINS, - num_draws=num_draws, - nbeta=default_nbeta(train_dataloader), - init_loss=init_loss_2, - ) - - sample( - model1, - train_dataloader, - evaluate=evaluate_mse, - sampling_method_kwargs=dict( - lr=LEARNING_RATE_SLOW, - nbeta=default_nbeta(train_dataloader), - noise_level=0.0, - ), - sampling_method=sampling_method, - num_chains=NUM_CHAINS, - num_draws=num_draws, - callbacks=[llc_estimator_2d], - verbose=False, - seed=RANDOM_SEED, - ) - - sample( - model2, - train_dataloader, - evaluate=evaluate_mse, - sampling_method_kwargs=dict( - lr=LEARNING_RATE_SLOW, - nbeta=default_nbeta(train_dataloader), - noise_level=0.0, - ), - sampling_method=sampling_method, - num_chains=NUM_CHAINS, - num_draws=num_draws, - callbacks=[llc_estimator_3d], - verbose=False, - seed=RANDOM_SEED, - optimize_over_per_model_param={"weights": torch.tensor([1, 1, 0])}, - ) - - llc_mean_2d = llc_estimator_2d.get_results()["llc/mean"] - llc_mean_3d_restricted = llc_estimator_3d.get_results()["llc/mean"] - - return llc_mean_2d, llc_mean_3d_restricted - - -def _do_full_sampling( - model1, model2, train_dataloader, sampling_method, seed, num_draws -): - init_loss_1 = get_init_loss_multi_batch( - train_dataloader, NUM_CHAINS, model1, evaluate_mse, device="cpu" - ) - - llc_estimator_2d = LLCEstimator( - num_chains=NUM_CHAINS, - num_draws=num_draws, - nbeta=default_nbeta(train_dataloader), - init_loss=init_loss_1, - ) - llc_estimator_3d = LLCEstimator( - num_chains=NUM_CHAINS, - num_draws=num_draws, - nbeta=default_nbeta(train_dataloader), - init_loss=init_loss_1, - ) - - torch.manual_seed(seed) - sample( - model1, - train_dataloader, - evaluate=evaluate_mse, - sampling_method_kwargs=dict( - lr=LEARNING_RATE_FULL, nbeta=default_nbeta(train_dataloader) - ), - sampling_method=sampling_method, - num_chains=NUM_CHAINS, - num_draws=num_draws, - callbacks=[llc_estimator_2d], - verbose=False, - seed=seed, - ) - - torch.manual_seed(seed) - sample( - model2, - train_dataloader, - evaluate=evaluate_mse, - sampling_method_kwargs=dict( - lr=LEARNING_RATE_FULL, nbeta=default_nbeta(train_dataloader) - ), - sampling_method=sampling_method, - num_chains=NUM_CHAINS, - num_draws=num_draws, - callbacks=[llc_estimator_3d], - verbose=False, - seed=seed, - optimize_over_per_model_param={"weights": torch.tensor([1, 1, 0])}, - ) - - llc_mean_2d = llc_estimator_2d.get_results()["llc/mean"] - llc_mean_3d_restricted = llc_estimator_3d.get_results()["llc/mean"] - - return llc_mean_2d, llc_mean_3d_restricted - - -def _do_difference_sampling(model, train_dataloader, sampling_method, num_draws): - torch.manual_seed(RANDOM_SEED) - - init_loss = get_init_loss_multi_batch( - train_dataloader, NUM_CHAINS, model, evaluate_mse, device="cpu" - ) - - llc_estimator = LLCEstimator( - num_chains=NUM_CHAINS, - num_draws=num_draws, - nbeta=default_nbeta(train_dataloader), - init_loss=init_loss, - ) - rllc_estimator = LLCEstimator( - num_chains=NUM_CHAINS, - num_draws=num_draws, - nbeta=default_nbeta(train_dataloader), - init_loss=init_loss, - ) - - sample( - model, - train_dataloader, - evaluate=evaluate_mse, - sampling_method_kwargs=dict( - lr=LEARNING_RATE_FULL, nbeta=default_nbeta(train_dataloader) - ), - sampling_method=sampling_method, - num_chains=NUM_CHAINS, - num_draws=num_draws, - callbacks=[llc_estimator], - verbose=False, - seed=RANDOM_SEED, - ) - - sample( - model, - train_dataloader, - evaluate=evaluate_mse, - sampling_method_kwargs=dict( - lr=LEARNING_RATE_FULL, nbeta=default_nbeta(train_dataloader) - ), - sampling_method=sampling_method, - num_chains=NUM_CHAINS, - num_draws=num_draws, - callbacks=[rllc_estimator], - verbose=False, - seed=RANDOM_SEED, - optimize_over_per_model_param={"weights": torch.tensor([1, 0])}, - ) - - llc_mean = llc_estimator.get_results()["llc/mean"] - rllc_mean = rllc_estimator.get_results()["llc/mean"] - - return llc_mean, rllc_mean - - -def _test_between_powers_accuracy( - model1, model2, train_dataloader, sampling_method, powers -): - llc_mean_1, llc_mean_2 = _do_between_powers_sampling( - model1, model2, train_dataloader, sampling_method, num_draws=100 - ) - - error_msg = ( - f"LLC mean {llc_mean_1:.3f}!={llc_mean_2:.3f} for powers {powers} " - f"using {sampling_method}" - ) - assert np.isclose(llc_mean_1, llc_mean_2, atol=TOLERANCE_ATOL), error_msg - - -def _test_restricted_gradient_accuracy( - model1, model2, train_dataloader, sampling_method, relevant_powers, extra_dim_power -): - llc_mean_2d, llc_mean_3d_restricted = _do_restricted_gradient_sampling( - model1, model2, train_dataloader, sampling_method, num_draws=FULL_SAMPLING_DRAWS - ) - - error_msg = ( - f"LLC mean {llc_mean_2d:.3f}!={llc_mean_3d_restricted:.3f} " - f"for powers {relevant_powers + [extra_dim_power]} using {sampling_method}, " - f"{model2.weights}" - ) - assert np.isclose(llc_mean_2d, llc_mean_3d_restricted, atol=TOLERANCE_ATOL), ( - error_msg - ) - - -def _test_full_accuracy( - model1, - model2, - train_dataloader, - sampling_method, - seed, - relevant_powers, - extra_dim_power, -): - llc_mean_2d, llc_mean_3d_restricted = _do_full_sampling( - model1, model2, train_dataloader, sampling_method, seed, num_draws=500 - ) - - error_msg = ( - f"LLC mean {llc_mean_2d:.8f}!={llc_mean_3d_restricted:.8f} " - f"for powers {relevant_powers + [extra_dim_power]} using {sampling_method}, " - f"{model2.weights}" - ) - assert np.isclose(llc_mean_2d, llc_mean_3d_restricted, atol=TOLERANCE_ATOL_FULL), ( - error_msg - ) - - -def _test_difference_accuracy( - model, train_dataloader, sampling_method, relevant_powers -): - llc_mean, rllc_mean = _do_difference_sampling( - model, train_dataloader, sampling_method, num_draws=200 - ) - - error_msg = ( - f"LLC {llc_mean:.3f} too close to RLLC {rllc_mean:.3f} " - f"for powers {relevant_powers} using {sampling_method}" - ) - assert not np.isclose(llc_mean, rllc_mean, atol=TOLERANCE_ATOL_DIFFERENCE), ( - error_msg - ) diff --git a/tests/optim/sgld_test.py b/tests/optim/sgld_test.py index 124b988f..550b8158 100644 --- a/tests/optim/sgld_test.py +++ b/tests/optim/sgld_test.py @@ -58,116 +58,3 @@ def test_SGLD_vs_SGD(lr, sampler_cls, snapshot): "model2": serialize_model_state(model2), } assert state == snapshot(name=f"test_SGLD_vs_SGD_{lr}") - - -@pytest.mark.parametrize("sampler_cls", [SGLD, SGMCMC.sgld]) -def test_SGLD_metrics_tracking(snapshot, sampler_cls): - torch.manual_seed(42) - - # Setup a simple model and data - model = nn.Linear(1, 1) - data = torch.tensor([[1.0]]).reshape(-1, 1) - target = torch.tensor([[2.0]]).reshape(-1, 1) - criterion = nn.MSELoss() - - # Test all available metrics - metrics = [ - "noise_norm", - "grad_norm", - "weight_norm", - "distance", - "noise", - "dws", - "localization_loss", - ] - optimizer = sampler_cls( - model.parameters(), - lr=0.1, - noise_level=1.0, - localization=0.1, - nbeta=1.0, - metrics=metrics, - ) - - # Perform optimization step - optimizer.zero_grad() - output = model(data) - loss = criterion(output, target) - loss.backward() - optimizer.step() - - # Check scalar metrics - assert isinstance(optimizer.noise_norm, torch.Tensor), ( - "noise_norm should be a tensor" - ) - assert isinstance(optimizer.grad_norm, torch.Tensor), "grad_norm should be a tensor" - assert isinstance(optimizer.weight_norm, torch.Tensor), ( - "weight_norm should be a tensor" - ) - assert isinstance(optimizer.distance, torch.Tensor), "distance should be a tensor" - - # Check list-based metrics - # assert isinstance(optimizer.noise, defaultdict), "noise should be a defaultdict" - assert isinstance(optimizer.dws, list), "dws should be a list" - assert len(optimizer.dws) > 0, "dws should not be empty after step" - - # Add snapshot assertion - state = { - "noise_norm": optimizer.noise_norm.detach().cpu().numpy(), - "grad_norm": optimizer.grad_norm.detach().cpu().numpy(), - "weight_norm": optimizer.weight_norm.detach().cpu().numpy(), - "distance": optimizer.distance.detach().cpu().numpy(), - } - assert state == snapshot(name="test_SGLD_metrics_tracking") - - -def test_SGLD_invalid_metrics(): - torch.manual_seed(42) - - model = nn.Linear(1, 1) - - # Test invalid metric name - with pytest.raises(ValueError, match="Invalid metrics"): - SGLD(model.parameters(), metrics=["invalid_metric"]) - with pytest.raises(ValueError, match="Invalid metrics"): - SGMCMC.sgld(model.parameters(), metrics=["invalid_metric"]) - - -@pytest.mark.parametrize("sampler_cls", [SGLD, SGMCMC.sgld]) -def test_SGLD_selective_metrics(snapshot, sampler_cls): - torch.manual_seed(42) - - model = nn.Linear(1, 1) - data = torch.tensor([[1.0]]).reshape(-1, 1) - target = torch.tensor([[2.0]]).reshape(-1, 1) - criterion = nn.MSELoss() - - # Only track grad_norm and weight_norm - optimizer = sampler_cls( - model.parameters(), lr=0.1, metrics=["grad_norm", "weight_norm"] - ) - - # Perform optimization step - optimizer.zero_grad() - output = model(data) - loss = criterion(output, target) - loss.backward() - optimizer.step() - - # Check tracked metrics exist - assert optimizer.grad_norm is not None - assert optimizer.weight_norm is not None - - # Check untracked metrics raise AttributeError - with pytest.raises(AttributeError): - _ = optimizer.noise_norm - - with pytest.raises(AttributeError): - _ = optimizer.distance - - # Add snapshot assertion - state = { - "grad_norm": optimizer.grad_norm.detach().cpu().numpy(), - "weight_norm": optimizer.weight_norm.detach().cpu().numpy(), - } - assert state == snapshot(name="test_SGLD_selective_metrics") diff --git a/tests/optim/test_metrics.py b/tests/optim/test_metrics.py new file mode 100644 index 00000000..de181ab8 --- /dev/null +++ b/tests/optim/test_metrics.py @@ -0,0 +1,172 @@ +import torch +from devinterp.optim.metrics import Metrics + + +def test_add_sum_squared_and_sqrt_norms(): + m = Metrics() + m.add_sum_squared_( + scaled_grad=torch.tensor([3.0, 4.0]), + unscaled_grad=torch.tensor([3.0, 4.0]), + localization=torch.tensor([1.0]), + weight_decay=torch.tensor([2.0]), + noise=torch.tensor([0.0, 5.0]), + distance=torch.tensor([6.0, 8.0]), + ) + m.sqrt_norms_() + + torch.testing.assert_close(m.scaled_grad, torch.tensor([5.0]), atol=0, rtol=0) + torch.testing.assert_close(m.unscaled_grad, torch.tensor([5.0]), atol=0, rtol=0) + torch.testing.assert_close(m.localization, torch.tensor([1.0]), atol=0, rtol=0) + torch.testing.assert_close(m.weight_decay, torch.tensor([2.0]), atol=0, rtol=0) + torch.testing.assert_close(m.noise, torch.tensor([5.0]), atol=0, rtol=0) + torch.testing.assert_close(m.distance, torch.tensor([10.0]), atol=0, rtol=0) + + +def test_accumulates_across_calls(): + m = Metrics() + m.add_sum_squared_( + scaled_grad=torch.tensor([3.0]), + unscaled_grad=torch.tensor([5.0]), + localization=torch.tensor([6.0]), + weight_decay=torch.tensor([1.0]), + noise=torch.tensor([2.0]), + distance=torch.tensor([3.0]), + ) + m.add_sum_squared_( + scaled_grad=torch.tensor([4.0]), + unscaled_grad=torch.tensor([12.0]), + localization=torch.tensor([8.0]), + weight_decay=torch.tensor([1.0]), + noise=torch.tensor([2.0]), + distance=torch.tensor([4.0]), + ) + m.add_dot_products_( + scaled_grad=torch.tensor([1.0, 2.0]), + prior=torch.tensor([3.0, 4.0]), + noise=torch.tensor([5.0, 6.0]), + ) + m.add_dot_products_( + scaled_grad=torch.tensor([1.0]), + prior=torch.tensor([1.0]), + noise=torch.tensor([1.0]), + ) + m.sqrt_norms_() + + # sqrt(3² + 4²) = 5 + torch.testing.assert_close(m.scaled_grad, torch.tensor([5.0]), atol=0, rtol=0) + # sqrt(5² + 12²) = 13 + torch.testing.assert_close(m.unscaled_grad, torch.tensor([13.0]), atol=0, rtol=0) + # sqrt(6² + 8²) = 10 + torch.testing.assert_close(m.localization, torch.tensor([10.0]), atol=0, rtol=0) + # sqrt(1² + 1²) = √2 + torch.testing.assert_close( + m.weight_decay, torch.tensor([2.0]).sqrt(), atol=0, rtol=0 + ) + # sqrt(2² + 2²) = 2√2 + torch.testing.assert_close(m.noise, torch.tensor([8.0]).sqrt(), atol=0, rtol=0) + # sqrt(3² + 4²) = 5 + torch.testing.assert_close(m.distance, torch.tensor([5.0]), atol=0, rtol=0) + # dot_grad_prior: (1*3 + 2*4) + (1*1) = 11 + 1 = 12 + torch.testing.assert_close(m.dot_grad_prior, torch.tensor([12.0]), atol=0, rtol=0) + # dot_grad_noise: (1*5 + 2*6) + (1*1) = 17 + 1 = 18 + torch.testing.assert_close(m.dot_grad_noise, torch.tensor([18.0]), atol=0, rtol=0) + # dot_prior_noise: (3*5 + 4*6) + (1*1) = 39 + 1 = 40 + torch.testing.assert_close(m.dot_prior_noise, torch.tensor([40.0]), atol=0, rtol=0) + + +def test_zero(): + m = Metrics() + m.scaled_grad = torch.tensor([7.0]) + m.unscaled_grad = torch.tensor([6.0]) + m.localization = torch.tensor([3.0]) + m.weight_decay = torch.tensor([2.0]) + m.noise = torch.tensor([1.0]) + m.distance = torch.tensor([5.0]) + m.dot_grad_prior = torch.tensor([0.5]) + m.dot_grad_noise = torch.tensor([0.3]) + m.dot_prior_noise = torch.tensor([0.1]) + m.numel = 42 + + m.zero_() + + z = torch.tensor([0.0]) + torch.testing.assert_close(m.scaled_grad, z, atol=0, rtol=0) + torch.testing.assert_close(m.unscaled_grad, z, atol=0, rtol=0) + torch.testing.assert_close(m.localization, z, atol=0, rtol=0) + torch.testing.assert_close(m.weight_decay, z, atol=0, rtol=0) + torch.testing.assert_close(m.noise, z, atol=0, rtol=0) + torch.testing.assert_close(m.distance, z, atol=0, rtol=0) + torch.testing.assert_close(m.dot_grad_prior, z, atol=0, rtol=0) + torch.testing.assert_close(m.dot_grad_noise, z, atol=0, rtol=0) + torch.testing.assert_close(m.dot_prior_noise, z, atol=0, rtol=0) + assert m.numel == 0 + + +def test_to_returns_copy(): + m = Metrics(scaled_grad=torch.tensor([3.0]), numel=5) + m2 = m.to("cpu") + + assert m2 is not m + assert m2.numel == 5 + torch.testing.assert_close(m2.scaled_grad, torch.tensor([3.0]), atol=0, rtol=0) + + +def test_prior_combines_localization_and_weight_decay(): + m = Metrics() + m.localization = torch.tensor([3.0]) + m.weight_decay = torch.tensor([4.0]) + + # sqrt(3² + 4²) = 5 + torch.testing.assert_close(m.prior, torch.tensor([5.0]), atol=0, rtol=0) + + +def test_dot_products(): + m = Metrics() + sg = torch.tensor([1.0, 2.0, 3.0]) + prior = torch.tensor([4.0, 5.0, 6.0]) + noise = torch.tensor([7.0, 8.0, 9.0]) + + m.add_dot_products_(scaled_grad=sg, prior=prior, noise=noise) + + # 1*4 + 2*5 + 3*6 = 32 + torch.testing.assert_close(m.dot_grad_prior, torch.tensor([32.0]), atol=0, rtol=0) + # 1*7 + 2*8 + 3*9 = 50 + torch.testing.assert_close(m.dot_grad_noise, torch.tensor([50.0]), atol=0, rtol=0) + # 4*7 + 5*8 + 6*9 = 122 + torch.testing.assert_close(m.dot_prior_noise, torch.tensor([122.0]), atol=0, rtol=0) + + +def test_aggregate_norms_and_dots(): + m1 = Metrics( + scaled_grad=torch.tensor([3.0]), + unscaled_grad=torch.tensor([3.0]), + localization=torch.tensor([0.0]), + weight_decay=torch.tensor([0.0]), + noise=torch.tensor([0.0]), + distance=torch.tensor([0.0]), + dot_grad_prior=torch.tensor([10.0]), + dot_grad_noise=torch.tensor([0.0]), + dot_prior_noise=torch.tensor([0.0]), + numel=5, + ) + m2 = Metrics( + scaled_grad=torch.tensor([4.0]), + unscaled_grad=torch.tensor([4.0]), + localization=torch.tensor([0.0]), + weight_decay=torch.tensor([0.0]), + noise=torch.tensor([0.0]), + distance=torch.tensor([0.0]), + dot_grad_prior=torch.tensor([20.0]), + dot_grad_noise=torch.tensor([0.0]), + dot_prior_noise=torch.tensor([0.0]), + numel=3, + ) + result = Metrics.aggregate([m1, m2]) + + # Norms: sqrt(3² + 4²) = 5 + torch.testing.assert_close(result.scaled_grad, torch.tensor([5.0]), atol=0, rtol=0) + # Dots: additive + torch.testing.assert_close( + result.dot_grad_prior, torch.tensor([30.0]), atol=0, rtol=0 + ) + assert result.numel == 8 diff --git a/tests/optim/test_prior.py b/tests/optim/test_prior.py index fe501428..d10543f8 100644 --- a/tests/optim/test_prior.py +++ b/tests/optim/test_prior.py @@ -2,148 +2,146 @@ from devinterp.optim.prior import CompositePrior, GaussianPrior, UniformPrior -def test_gaussian_prior_initialization(): - # Test with default zero center +def test_gaussian_prior_initialization_zero_center(): prior = GaussianPrior(localization=1.0, center=None) - params = [torch.randn(2, 3), torch.randn(4)] + params = [torch.tensor([1.0, 2.0, 3.0]), torch.tensor([4.0, 5.0])] state = prior.initialize(params) assert all(state[p]["prior_center"] is None for p in params) - # Test with initial values as center + +def test_gaussian_prior_initialization_initial_center(): prior = GaussianPrior(localization=1.0, center="initial") + params = [torch.tensor([1.0, 2.0, 3.0]), torch.tensor([4.0, 5.0])] state = prior.initialize(params) - assert all(torch.equal(state[p]["prior_center"], p) for p in params) + torch.testing.assert_close( + state[params[0]]["prior_center"], + torch.tensor([1.0, 2.0, 3.0]), + atol=0, + rtol=0, + ) + torch.testing.assert_close( + state[params[1]]["prior_center"], + torch.tensor([4.0, 5.0]), + atol=0, + rtol=0, + ) - # Test with explicit centers - centers = [torch.zeros(2, 3), torch.zeros(4)] + # Centers must be independent copies, not aliases + params[0][0] = 999.0 + torch.testing.assert_close( + state[params[0]]["prior_center"], + torch.tensor([1.0, 2.0, 3.0]), + atol=0, + rtol=0, + ) + + +def test_gaussian_prior_initialization_explicit_centers(): + centers = [torch.tensor([10.0, 20.0]), torch.tensor([30.0])] prior = GaussianPrior(localization=1.0, center=centers) + params = [torch.tensor([1.0, 2.0]), torch.tensor([3.0])] state = prior.initialize(params) - assert all( - torch.equal(state[p]["prior_center"], c) for p, c in zip(params, centers) + torch.testing.assert_close( + state[params[0]]["prior_center"], torch.tensor([10.0, 20.0]), atol=0, rtol=0 + ) + torch.testing.assert_close( + state[params[1]]["prior_center"], torch.tensor([30.0]), atol=0, rtol=0 ) -def test_gaussian_prior_grad(): - prior = GaussianPrior(localization=2.0) +def test_gaussian_prior_grad_zero_centered(): + prior = GaussianPrior(localization=2.0, center=None) param = torch.tensor([1.0, 2.0, 3.0]) state = {"prior_center": None} - # Test gradient with zero center grad = prior.grad(param, state) - expected = 2.0 * param - assert torch.allclose(grad, expected) - - # Test gradient with non-zero center - state["prior_center"] = torch.tensor([0.5, 1.0, 1.5]) - grad = prior.grad(param, state) - expected = 2.0 * (param - state["prior_center"]) - assert torch.allclose(grad, expected) + torch.testing.assert_close(grad, torch.tensor([2.0, 4.0, 6.0]), atol=0, rtol=0) -def test_gaussian_prior_distance_sq(): - prior = GaussianPrior(localization=1.0) +def test_gaussian_prior_grad_with_center(): + prior = GaussianPrior(localization=2.0, center="initial") param = torch.tensor([1.0, 2.0, 3.0]) - - # Test distance without state (zero-centered) - dist = prior.distance_sq(param, state={"prior_center": None}) - expected = (param**2).sum() - assert torch.equal(dist, expected) - - # Test distance with center state = {"prior_center": torch.tensor([0.5, 1.0, 1.5])} - dist = prior.distance_sq(param, state) - expected = ((param - state["prior_center"]) ** 2).sum() - assert torch.equal(dist, expected) + + grad = prior.grad(param, state) + torch.testing.assert_close(grad, torch.tensor([1.0, 2.0, 3.0]), atol=0, rtol=0) def test_composite_prior(): - params = [torch.randn(2)] + p = torch.tensor([1.0, 2.0]) - prior1 = GaussianPrior(localization=1.0, center=[params[0]]) + prior1 = GaussianPrior(localization=1.0, center=[p.clone()]) prior2 = GaussianPrior(localization=2.0, center=None) composite = CompositePrior([prior1, prior2]) - # Test key assignment assert prior1.key == "prior_center_0" assert prior2.key == "prior_center_1" - # Test initialization - state = composite.initialize(params) - assert "prior_center_0" in state[params[0]] - assert "prior_center_1" in state[params[0]] - assert torch.allclose(state[params[0]]["prior_center_0"], params[0]) - assert state[params[0]]["prior_center_1"] is None + state = composite.initialize([p]) + assert "prior_center_0" in state[p] + assert "prior_center_1" in state[p] + torch.testing.assert_close( + state[p]["prior_center_0"], torch.tensor([1.0, 2.0]), atol=0, rtol=0 + ) + assert state[p]["prior_center_1"] is None + - # Test gradient combination +def test_composite_prior_grad(): param = torch.tensor([1.0, 2.0]) + prior1 = GaussianPrior(localization=1.0, center=None) + prior2 = GaussianPrior(localization=2.0, center=None) + composite = CompositePrior([prior1, prior2]) state = { "prior_center_0": None, "prior_center_1": None, } + grad = composite.grad(param, state) - expected = 3.0 * param # Sum of both priors (1.0 + 2.0) * param - assert torch.allclose(grad, expected) + # (1.0 + 2.0) * [1, 2] = [3, 6] + torch.testing.assert_close(grad, torch.tensor([3.0, 6.0]), atol=0, rtol=0) def test_composite_prior_single(): - # Test that CompositePrior returns the single prior when given only one prior = GaussianPrior(localization=1.0, center=None) composite = CompositePrior([prior]) - assert composite is prior - - -def test_composite_prior_empty(): - # Test that CompositePrior raises error when given empty list - assert isinstance(CompositePrior([]), UniformPrior) + assert len(composite.priors) == 1 + param = torch.tensor([1.0, 2.0]) + state = composite.initialize([param]) + grad = composite.grad(param, state[param]) + torch.testing.assert_close(grad, torch.tensor([1.0, 2.0]), atol=0, rtol=0) -def test_custom_key(): - # Test custom key initialization - prior = GaussianPrior(localization=1.0, key="custom_key") - param = torch.randn(3) - state = {"custom_key": None} - grad = prior.grad(param, state) - assert torch.allclose(grad, param) +def test_composite_prior_empty(): + composite = CompositePrior([]) + assert len(composite.priors) == 0 - # Test that distance_sq uses custom key - state["custom_key"] = torch.zeros(3) - dist = prior.distance_sq(param, state) - assert torch.equal(dist, (param**2).sum()) + param = torch.tensor([1.0, 2.0]) + grad = composite.grad(param, {}) + torch.testing.assert_close(grad, torch.tensor([0.0, 0.0]), atol=0, rtol=0) def test_uniform_prior(): - # Test initialization prior = UniformPrior() - params = [torch.randn(2, 3), torch.randn(4)] + params = [torch.tensor([1.0, 2.0, 3.0]), torch.tensor([4.0, 5.0])] state = prior.initialize(params) - assert state == {} # UniformPrior should return an empty state + assert state == {} - # Test gradient param = torch.tensor([1.0, 2.0, 3.0]) grad = prior.grad(param, {}) - assert torch.equal(grad, torch.zeros_like(param)) # Gradient should be zero - + torch.testing.assert_close(grad, torch.tensor([0.0, 0.0, 0.0]), atol=0, rtol=0) -def test_composite_prior_with_uniform(): - # Test that CompositePrior returns UniformPrior when only UniformPriors are provided - uniform1 = UniformPrior() - uniform2 = UniformPrior() - composite = CompositePrior([uniform1, uniform2]) - assert isinstance(composite, UniformPrior) - # Test mixture of Uniform and Gaussian priors +def test_composite_prior_filters_uniform(): + uniform = UniformPrior() gaussian = GaussianPrior(localization=1.0) - composite = CompositePrior([uniform1, gaussian, uniform2]) - assert composite is gaussian - # Test that single Gaussian with Uniforms returns just the Gaussian - composite = CompositePrior([uniform1, gaussian]) - assert composite is gaussian # Should return the single Gaussian directly + composite = CompositePrior([uniform, gaussian, UniformPrior()]) + assert len(composite.priors) == 1 + assert composite.priors[0] is gaussian - # Test all UniformPriors returns a UniformPrior - composite = CompositePrior([UniformPrior(), UniformPrior(), UniformPrior()]) - assert isinstance(composite, UniformPrior) + composite = CompositePrior([UniformPrior(), UniformPrior()]) + assert len(composite.priors) == 0 diff --git a/tests/optim/test_sampler_metrics.py b/tests/optim/test_sampler_metrics.py new file mode 100644 index 00000000..2ed99201 --- /dev/null +++ b/tests/optim/test_sampler_metrics.py @@ -0,0 +1,694 @@ +""" +Tests for sampler metrics accuracy in SGLD and SGMCMC. + +Verifies that tracked metrics accurately reflect the actual parameter update +components. Each test isolates a single component with hand-computed expected +values (using the 3-4-5 Pythagorean triple for clean norms). + +All tests use float64 to ensure exact equality between test expectations +and the float32 metric accumulators for these small integer-friendly values. +""" + +import warnings + +import pytest +import torch +import torch.nn as nn +from devinterp.optim.sgld import SGLD +from devinterp.optim.sgmcmc import SGMCMC + +pytestmark = [ + # SGLD emits DeprecationWarning on construction; SGMCMC warns about + # nbeta=1 and non-default noise_level. These are expected in test + # configs and would clutter output. + pytest.mark.filterwarnings("ignore::DeprecationWarning"), + pytest.mark.filterwarnings("ignore:.*nbeta.*"), + pytest.mark.filterwarnings("ignore:.*noise_level.*"), +] + + +def _make_optimizer(sampler_cls, params, **kwargs): + """Create optimizer, suppressing expected warnings during construction.""" + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + return sampler_cls(params, **kwargs) + + +@pytest.fixture(params=[SGLD, SGMCMC.sgld], ids=["sgld", "sgmcmc"]) +def sampler_cls(request): + return request.param + + +# --------------------------------------------------------------------------- +# Component isolation tests +# +# Each verifies one metric component with all others disabled. +# Expected values are literal, derived from the SGLD update equation: +# scaled_grad = (lr/2) * nbeta * grad +# localization = (lr/2) * gamma * (w - w0) +# weight_decay = (lr/2) * lambda * w +# noise = sqrt(lr) * eta +# --------------------------------------------------------------------------- + + +class TestScaledGrad: + def test_metric_value(self, sampler_cls): + """scaled_grad = ||(lr/2) * nbeta * grad||_2""" + w = nn.Parameter(torch.tensor([3.0, 4.0], dtype=torch.float64)) + opt = _make_optimizer( + sampler_cls, [w], lr=1.0, nbeta=2.0, noise_level=0.0, save_metrics=True + ) + w.grad = torch.tensor([3.0, 4.0], dtype=torch.float64) + opt.step(noise_generator=torch.Generator().manual_seed(0)) + + m = opt.get_metrics() + # (1.0/2) * 2.0 * [3, 4] = [3, 4], norm = 5 + torch.testing.assert_close(m.scaled_grad, torch.tensor([5.0]), atol=0, rtol=0) + + def test_other_components_zero(self, sampler_cls): + w = nn.Parameter(torch.tensor([3.0, 4.0], dtype=torch.float64)) + opt = _make_optimizer( + sampler_cls, [w], lr=1.0, nbeta=2.0, noise_level=0.0, save_metrics=True + ) + w.grad = torch.tensor([3.0, 4.0], dtype=torch.float64) + opt.step(noise_generator=torch.Generator().manual_seed(0)) + + m = opt.get_metrics() + torch.testing.assert_close(m.localization, torch.tensor([0.0]), atol=0, rtol=0) + torch.testing.assert_close(m.weight_decay, torch.tensor([0.0]), atol=0, rtol=0) + torch.testing.assert_close(m.noise, torch.tensor([0.0]), atol=0, rtol=0) + + +class TestLocalization: + def test_metric_value(self, sampler_cls): + """localization = ||(lr/2) * gamma * (w - w0)||_2""" + w = nn.Parameter(torch.tensor([0.0, 0.0], dtype=torch.float64)) + opt = _make_optimizer( + sampler_cls, + [w], + lr=2.0, + nbeta=2.0, + localization=1.0, + noise_level=0.0, + save_metrics=True, + ) + # Shift w away from w0=[0,0] to create a known distance + w.data = torch.tensor([3.0, 4.0], dtype=torch.float64) + w.grad = torch.zeros(2, dtype=torch.float64) + opt.step(noise_generator=torch.Generator().manual_seed(0)) + + m = opt.get_metrics() + # (2.0/2) * 1.0 * [3, 4] = [3, 4], norm = 5 + torch.testing.assert_close(m.localization, torch.tensor([5.0]), atol=0, rtol=0) + + +class TestWeightDecay: + def test_metric_value(self, sampler_cls): + """weight_decay = ||(lr/2) * lambda * w||_2""" + w = nn.Parameter(torch.tensor([3.0, 4.0], dtype=torch.float64)) + opt = _make_optimizer( + sampler_cls, + [w], + lr=2.0, + nbeta=2.0, + weight_decay=1.0, + noise_level=0.0, + save_metrics=True, + ) + w.grad = torch.zeros(2, dtype=torch.float64) + opt.step(noise_generator=torch.Generator().manual_seed(0)) + + m = opt.get_metrics() + # (2.0/2) * 1.0 * [3, 4] = [3, 4], norm = 5 + torch.testing.assert_close(m.weight_decay, torch.tensor([5.0]), atol=0, rtol=0) + + +class TestNoise: + def test_metric_equals_param_change(self, sampler_cls): + """When only noise is active, noise metric = ||param change||_2.""" + w = nn.Parameter(torch.tensor([1.0, 2.0], dtype=torch.float64)) + opt = _make_optimizer(sampler_cls, [w], lr=0.5, nbeta=2.0, save_metrics=True) + w.grad = torch.zeros(2, dtype=torch.float64) + w_before = w.data.clone() + opt.step(noise_generator=torch.Generator().manual_seed(42)) + + m = opt.get_metrics() + actual_change_norm = (w.data - w_before).norm().unsqueeze(0) + # Metrics accumulate in float32, so allow float32-level tolerance + torch.testing.assert_close( + m.noise.to(torch.float64), + actual_change_norm, + atol=1e-6, + rtol=0, + ) + + def test_zero_when_noise_disabled(self, sampler_cls): + w = nn.Parameter(torch.tensor([1.0, 2.0], dtype=torch.float64)) + opt = _make_optimizer( + sampler_cls, [w], lr=0.5, nbeta=2.0, noise_level=0.0, save_metrics=True + ) + w.grad = torch.zeros(2, dtype=torch.float64) + opt.step(noise_generator=torch.Generator().manual_seed(42)) + + m = opt.get_metrics() + torch.testing.assert_close(m.noise, torch.tensor([0.0]), atol=0, rtol=0) + + +# --------------------------------------------------------------------------- +# Combined and structural tests +# --------------------------------------------------------------------------- + + +class TestAllComponents: + def test_all_metrics_correct(self, sampler_cls): + """With all components active, each metric matches its expected value.""" + w = nn.Parameter(torch.tensor([0.0, 0.0], dtype=torch.float64)) + opt = _make_optimizer( + sampler_cls, + [w], + lr=2.0, + nbeta=2.0, + localization=1.0, + weight_decay=1.0, + noise_level=0.0, + save_metrics=True, + ) + w.data = torch.tensor([3.0, 4.0], dtype=torch.float64) + w.grad = torch.tensor([3.0, 4.0], dtype=torch.float64) + opt.step(noise_generator=torch.Generator().manual_seed(0)) + + m = opt.get_metrics() + # scaled_grad: (2/2)*2*[3,4] = [6,8], norm = 10 + torch.testing.assert_close(m.scaled_grad, torch.tensor([10.0]), atol=0, rtol=0) + # localization: (2/2)*1*[3,4] = [3,4], norm = 5 + torch.testing.assert_close(m.localization, torch.tensor([5.0]), atol=0, rtol=0) + # weight_decay: (2/2)*1*[3,4] = [3,4], norm = 5 + torch.testing.assert_close(m.weight_decay, torch.tensor([5.0]), atol=0, rtol=0) + + +class TestNumel: + def test_counts_parameter_elements(self, sampler_cls): + w = nn.Parameter(torch.randn(3, 4, dtype=torch.float64)) + opt = _make_optimizer( + sampler_cls, [w], lr=0.5, nbeta=2.0, noise_level=0.0, save_metrics=True + ) + w.grad = torch.zeros_like(w) + opt.step(noise_generator=torch.Generator().manual_seed(0)) + + m = opt.get_metrics() + assert m.numel == 12 + + def test_sums_across_params(self, sampler_cls): + w1 = nn.Parameter(torch.randn(3, dtype=torch.float64)) + w2 = nn.Parameter(torch.randn(5, dtype=torch.float64)) + opt = _make_optimizer( + sampler_cls, [w1, w2], lr=0.5, nbeta=2.0, noise_level=0.0, save_metrics=True + ) + w1.grad = torch.zeros_like(w1) + w2.grad = torch.zeros_like(w2) + opt.step(noise_generator=torch.Generator().manual_seed(0)) + + m = opt.get_metrics() + assert m.numel == 8 + + @pytest.mark.parametrize( + "factory", + [ + lambda p, m, **kw: _make_optimizer(SGLD, [{"params": p, "mask": m}], **kw), + lambda p, m, **kw: _make_optimizer(SGMCMC.sgld, p, mask=m, **kw), + lambda p, m, **kw: _make_optimizer(SGMCMC.rmsprop_sgld, p, mask=m, **kw), + ], + ids=["sgld", "sgmcmc", "rmsprop"], + ) + def test_counts_masked_in_only(self, factory): + w = nn.Parameter(torch.randn(6, dtype=torch.float64)) + mask = torch.tensor([True, True, False, False, True, False]) + opt = factory([w], mask, lr=0.5, nbeta=2.0, noise_level=0.0, save_metrics=True) + w.grad = torch.zeros_like(w) + opt.step(noise_generator=torch.Generator().manual_seed(0)) + + m = opt.get_metrics() + assert m.numel == 3 + + +def _make_masked_sgld(params: list[nn.Parameter], mask, **kwargs): + """SGLD: mask via param group dict.""" + return _make_optimizer(SGLD, [{"params": params, "mask": mask}], **kwargs) + + +def _make_masked_sgmcmc(params: list[nn.Parameter], mask, **kwargs): + """SGMCMC: mask via constructor kwarg (→ MaskPreconditioner).""" + return _make_optimizer(SGMCMC.sgld, params, mask=mask, **kwargs) + + +def _make_masked_rmsprop(params: list[nn.Parameter], mask, **kwargs): + """SGMCMC.rmsprop_sgld: mask via constructor kwarg (→ CompositePreconditioner).""" + return _make_optimizer(SGMCMC.rmsprop_sgld, params, mask=mask, **kwargs) + + +@pytest.fixture( + params=[_make_masked_sgld, _make_masked_sgmcmc, _make_masked_rmsprop], + ids=["sgld", "sgmcmc", "rmsprop"], +) +def make_masked_optimizer(request): + return request.param + + +class TestMask: + @pytest.mark.parametrize( + "factory, expected", + [ + # (ε/2)·nβ·grad[0] = (1/2)·2·3 = 3 + (_make_masked_sgld, 3.0), + (_make_masked_sgmcmc, 3.0), + # RMSProp first step (α=0.99, ε_rms=0.1): + # v = (1-α)·g₀² = 0.01·9 = 0.09, G = 1/(√v+ε) = 1/0.4 = 2.5 + # (ε/2)·nβ·G·g₀ = (1/2)·2·2.5·3 = 7.5 + (_make_masked_rmsprop, 7.5), + ], + ids=["sgld", "sgmcmc", "rmsprop"], + ) + def test_mask_restricts_scaled_grad(self, factory, expected): + """Mask zeroes out second element, so only first contributes.""" + w = nn.Parameter(torch.tensor([3.0, 4.0], dtype=torch.float64)) + mask = torch.tensor([True, False]) + opt = factory( + [w], + mask, + lr=1.0, + nbeta=2.0, + noise_level=0.0, + save_metrics=True, + ) + w.grad = torch.tensor([3.0, 4.0], dtype=torch.float64) + opt.step(noise_generator=torch.Generator().manual_seed(0)) + + m = opt.get_metrics() + torch.testing.assert_close( + m.scaled_grad, torch.tensor([expected]), atol=0, rtol=0 + ) + + def test_masked_params_unchanged(self, make_masked_optimizer): + """Masked parameter elements should not change.""" + w = nn.Parameter(torch.tensor([3.0, 4.0], dtype=torch.float64)) + mask = torch.tensor([True, False]) + opt = make_masked_optimizer( + [w], + mask, + lr=1.0, + nbeta=2.0, + save_metrics=True, + ) + w.grad = torch.randn(2, dtype=torch.float64) + w_before = w.data.clone() + opt.step(noise_generator=torch.Generator().manual_seed(42)) + + torch.testing.assert_close(w.data[1:], w_before[1:], atol=0, rtol=0) + + +class TestGetMetricsInterface: + def test_raises_when_not_enabled(self, sampler_cls): + w = nn.Parameter(torch.tensor([1.0])) + opt = _make_optimizer(sampler_cls, [w], lr=0.5, nbeta=2.0, save_metrics=False) + with pytest.raises(RuntimeError, match="Metrics not enabled"): + opt.get_metrics() + + def test_returns_cpu_tensors(self, sampler_cls): + w = nn.Parameter(torch.tensor([1.0], dtype=torch.float64)) + opt = _make_optimizer(sampler_cls, [w], lr=0.5, nbeta=2.0, save_metrics=True) + w.grad = torch.zeros(1, dtype=torch.float64) + opt.step(noise_generator=torch.Generator().manual_seed(0)) + + m = opt.get_metrics() + assert m.scaled_grad.device == torch.device("cpu") + assert m.noise.device == torch.device("cpu") + + +# --------------------------------------------------------------------------- +# unscaled_grad, distance, and dot product tests +# --------------------------------------------------------------------------- + + +class TestUnscaledGrad: + def test_equals_scaled_grad_for_sgld(self, sampler_cls): + """SGLD has no preconditioner, so unscaled_grad == scaled_grad.""" + w = nn.Parameter(torch.tensor([3.0, 4.0], dtype=torch.float64)) + opt = _make_optimizer( + sampler_cls, [w], lr=1.0, nbeta=2.0, noise_level=0.0, save_metrics=True + ) + w.grad = torch.tensor([3.0, 4.0], dtype=torch.float64) + opt.step(noise_generator=torch.Generator().manual_seed(0)) + + m = opt.get_metrics() + torch.testing.assert_close(m.unscaled_grad, m.scaled_grad, atol=0, rtol=0) + + def test_differs_from_scaled_grad_with_rmsprop(self): + """RMSprop preconditioner makes scaled_grad != unscaled_grad.""" + w = nn.Parameter(torch.tensor([3.0, 4.0], dtype=torch.float64)) + opt = _make_optimizer( + SGMCMC.rmsprop_sgld, + [w], + lr=1.0, + nbeta=2.0, + noise_level=0.0, + save_metrics=True, + ) + w.grad = torch.tensor([3.0, 4.0], dtype=torch.float64) + opt.step(noise_generator=torch.Generator().manual_seed(0)) + + m = opt.get_metrics() + # unscaled_grad = ||(lr/2)*nbeta*grad|| = ||(0.5)*2*[3,4]|| = ||[3,4]|| = 5 + torch.testing.assert_close(m.unscaled_grad, torch.tensor([5.0]), atol=0, rtol=0) + # scaled_grad includes the RMSprop preconditioner, so != 5 + assert not torch.equal(m.scaled_grad, m.unscaled_grad) + + +class TestDistance: + def test_distance_finite(self, sampler_cls): + """distance metric is finite and non-negative.""" + w = nn.Parameter(torch.tensor([0.0, 0.0], dtype=torch.float64)) + opt = _make_optimizer( + sampler_cls, + [w], + lr=2.0, + nbeta=2.0, + localization=1.0, + noise_level=0.0, + save_metrics=True, + ) + w.data = torch.tensor([3.0, 4.0], dtype=torch.float64) + w.grad = torch.zeros(2, dtype=torch.float64) + opt.step(noise_generator=torch.Generator().manual_seed(0)) + + m = opt.get_metrics() + assert torch.isfinite(m.distance) + assert m.distance.item() >= 0 + + def test_distance_zero_at_init(self, sampler_cls): + """distance = 0 when w hasn't moved from w0.""" + w = nn.Parameter(torch.tensor([3.0, 4.0], dtype=torch.float64)) + opt = _make_optimizer( + sampler_cls, + [w], + lr=2.0, + nbeta=2.0, + localization=1.0, + noise_level=0.0, + save_metrics=True, + ) + # Don't move w from init, only set gradient + w.grad = torch.zeros(2, dtype=torch.float64) + opt.step(noise_generator=torch.Generator().manual_seed(0)) + + m = opt.get_metrics() + # No gradient, no noise => w didn't move, distance = 0 + torch.testing.assert_close(m.distance, torch.tensor([0.0]), atol=0, rtol=0) + + def test_distance_without_localization(self, sampler_cls): + """distance is tracked even when localization=0 (if save_metrics=True).""" + w = nn.Parameter(torch.tensor([0.0, 0.0], dtype=torch.float64)) + opt = _make_optimizer( + sampler_cls, + [w], + lr=2.0, + nbeta=2.0, + localization=0.0, + noise_level=0.0, + save_metrics=True, + ) + w.data = torch.tensor([3.0, 4.0], dtype=torch.float64) + w.grad = torch.zeros(2, dtype=torch.float64) + opt.step(noise_generator=torch.Generator().manual_seed(0)) + + m = opt.get_metrics() + assert m.distance.item() > 0 + + def test_distance_exact_value(self, sampler_cls): + """distance = ||w - w0|| measured before the step update. + + With w0=[0,0], w=[3,4]: distance = ||[3,4] - [0,0]|| = 5. + """ + w = nn.Parameter(torch.tensor([0.0, 0.0], dtype=torch.float64)) + opt = _make_optimizer( + sampler_cls, + [w], + lr=2.0, + nbeta=2.0, + localization=1.0, + noise_level=0.0, + save_metrics=True, + ) + w.data = torch.tensor([3.0, 4.0], dtype=torch.float64) + w.grad = torch.zeros(2, dtype=torch.float64) + opt.step(noise_generator=torch.Generator().manual_seed(0)) + + m = opt.get_metrics() + torch.testing.assert_close(m.distance, torch.tensor([5.0]), atol=0, rtol=0) + + +class TestDotProducts: + def test_dot_products_with_known_vectors(self, sampler_cls): + """Verify dot products with hand-computed values. + + With lr=2, nbeta=2, localization=1, weight_decay=1, noise=0: + scaled_grad = (2/2)*2*[3,4] = [6,8] + loc = (2/2)*1*[3,4] = [3,4] (w-w0 = [3,4]) + wd = (2/2)*1*[3,4] = [3,4] (w = [3,4]) + prior = loc + wd = [6,8] + noise = 0 + + dot_grad_prior = <[6,8], [6,8]> = 36+64 = 100 + dot_grad_noise = 0 + dot_prior_noise = 0 + """ + w = nn.Parameter(torch.tensor([0.0, 0.0], dtype=torch.float64)) + opt = _make_optimizer( + sampler_cls, + [w], + lr=2.0, + nbeta=2.0, + localization=1.0, + weight_decay=1.0, + noise_level=0.0, + save_metrics=True, + ) + w.data = torch.tensor([3.0, 4.0], dtype=torch.float64) + w.grad = torch.tensor([3.0, 4.0], dtype=torch.float64) + opt.step(noise_generator=torch.Generator().manual_seed(0)) + + m = opt.get_metrics() + torch.testing.assert_close( + m.dot_grad_prior, torch.tensor([100.0]), atol=0, rtol=0 + ) + torch.testing.assert_close( + m.dot_grad_noise, torch.tensor([0.0]), atol=0, rtol=0 + ) + torch.testing.assert_close( + m.dot_prior_noise, torch.tensor([0.0]), atol=0, rtol=0 + ) + + def test_dot_products_orthogonal_components(self, sampler_cls): + """When grad and prior are orthogonal, dot_grad_prior = 0.""" + w = nn.Parameter(torch.tensor([0.0, 0.0], dtype=torch.float64)) + opt = _make_optimizer( + sampler_cls, + [w], + lr=2.0, + nbeta=2.0, + localization=1.0, + noise_level=0.0, + save_metrics=True, + ) + # w - w0 = [1, 0] (localization direction) + w.data = torch.tensor([1.0, 0.0], dtype=torch.float64) + # grad = [0, 1] (orthogonal to localization) + w.grad = torch.tensor([0.0, 1.0], dtype=torch.float64) + opt.step(noise_generator=torch.Generator().manual_seed(0)) + + m = opt.get_metrics() + # scaled_grad = (2/2)*2*[0,1] = [0,2] + # loc = (2/2)*1*[1,0] = [1,0] + # dot = 0*1 + 2*0 = 0 + torch.testing.assert_close( + m.dot_grad_prior, torch.tensor([0.0]), atol=0, rtol=0 + ) + + def test_dot_products_with_noise(self, sampler_cls): + """Dot products involving noise should be nonzero with nonzero noise.""" + w = nn.Parameter(torch.tensor([0.0, 0.0], dtype=torch.float64)) + opt = _make_optimizer( + sampler_cls, + [w], + lr=2.0, + nbeta=2.0, + localization=1.0, + save_metrics=True, + ) + w.data = torch.tensor([3.0, 4.0], dtype=torch.float64) + w.grad = torch.tensor([3.0, 4.0], dtype=torch.float64) + opt.step(noise_generator=torch.Generator().manual_seed(42)) + + m = opt.get_metrics() + # With noise_level=1 (default), noise terms should be nonzero + assert m.dot_grad_noise.item() != 0 + assert m.dot_prior_noise.item() != 0 + + def test_dot_product_matches_manual_computation(self, sampler_cls): + """Dot products match manual computation from reconstructed vectors.""" + torch.manual_seed(0) + w = nn.Parameter(torch.tensor([0.0, 0.0, 0.0], dtype=torch.float64)) + lr, nbeta, loc, wd, noise_level = 2.0, 2.0, 1.0, 0.5, 1.0 + + opt = _make_optimizer( + sampler_cls, + [w], + lr=lr, + nbeta=nbeta, + localization=loc, + weight_decay=wd, + noise_level=noise_level, + save_metrics=True, + ) + w.data = torch.tensor([1.0, 2.0, 3.0], dtype=torch.float64) + w.grad = torch.tensor([0.5, 1.0, 1.5], dtype=torch.float64) + + gen = torch.Generator().manual_seed(99) + opt.step(noise_generator=gen) + + m = opt.get_metrics() + + # Reconstruct the component vectors manually + half_lr = lr / 2 + _sg = half_lr * nbeta * torch.tensor([0.5, 1.0, 1.5], dtype=torch.float64) + _loc = half_lr * loc * torch.tensor([1.0, 2.0, 3.0], dtype=torch.float64) + _wd = half_lr * wd * torch.tensor([1.0, 2.0, 3.0], dtype=torch.float64) + _prior = _loc + _wd + + gen2 = torch.Generator().manual_seed(99) + raw_noise = torch.normal(0.0, noise_level, size=(3,), generator=gen2) + _noise = raw_noise * (lr**0.5) + + expected_dot_gp = torch.dot(_sg.float(), _prior.float()).unsqueeze(0) + expected_dot_gn = torch.dot(_sg.float(), _noise.float()).unsqueeze(0) + expected_dot_pn = torch.dot(_prior.float(), _noise.float()).unsqueeze(0) + + torch.testing.assert_close(m.dot_grad_prior, expected_dot_gp, atol=1e-5, rtol=0) + torch.testing.assert_close(m.dot_grad_noise, expected_dot_gn, atol=1e-5, rtol=0) + torch.testing.assert_close( + m.dot_prior_noise, expected_dot_pn, atol=1e-5, rtol=0 + ) + + def test_dot_products_accumulate_across_params(self, sampler_cls): + """Dot products sum across multiple parameters in a single param group. + + Two parameters [w1, w2] share one optimizer group. The step() loop + calls _update_metrics for each param, accumulating into the same + group["metrics"] object. Cross-group aggregation (Metrics.aggregate) + is tested separately in test_metrics_multi_gpu. + + w0_1=[1,0], w_1=[3,0], grad_1=[3,0]: + sg_1 = (2/2)*2*[3,0] = [6,0] + loc_grad_1 = gamma*(w-w0) = 1*[2,0]; prior_1 = (2/2)*1*[2,0] = [2,0] + dot_1 = 6*2 + 0*0 = 12 + + w0_2=[0,1], w_2=[0,4], grad_2=[0,4]: + sg_2 = (2/2)*2*[0,4] = [0,8] + loc_grad_2 = 1*[0,3]; prior_2 = (2/2)*1*[0,3] = [0,3] + dot_2 = 0*0 + 8*3 = 24 + + Total dot_grad_prior = 36. + """ + w1 = nn.Parameter(torch.tensor([1.0, 0.0], dtype=torch.float64)) + w2 = nn.Parameter(torch.tensor([0.0, 1.0], dtype=torch.float64)) + opt = _make_optimizer( + sampler_cls, + [w1, w2], + lr=2.0, + nbeta=2.0, + localization=1.0, + noise_level=0.0, + save_metrics=True, + ) + w1.data = torch.tensor([3.0, 0.0], dtype=torch.float64) + w2.data = torch.tensor([0.0, 4.0], dtype=torch.float64) + w1.grad = torch.tensor([3.0, 0.0], dtype=torch.float64) + w2.grad = torch.tensor([0.0, 4.0], dtype=torch.float64) + opt.step(noise_generator=torch.Generator().manual_seed(0)) + + m = opt.get_metrics() + torch.testing.assert_close( + m.dot_grad_prior, torch.tensor([36.0]), atol=0, rtol=0 + ) + + def test_dot_products_with_rmsprop(self): + """Dot products work with RMSprop preconditioner.""" + w = nn.Parameter(torch.tensor([3.0, 4.0], dtype=torch.float64)) + opt = _make_optimizer( + SGMCMC.rmsprop_sgld, + [w], + lr=1.0, + nbeta=2.0, + localization=1.0, + noise_level=0.0, + save_metrics=True, + ) + w.grad = torch.tensor([3.0, 4.0], dtype=torch.float64) + opt.step(noise_generator=torch.Generator().manual_seed(0)) + + m = opt.get_metrics() + # With RMSprop, the preconditioner scales the components differently. + # The dot product should still be well-defined and finite. + assert torch.isfinite(m.dot_grad_prior) + # Since noise=0, noise-related dots should be 0 + torch.testing.assert_close( + m.dot_grad_noise, torch.tensor([0.0]), atol=0, rtol=0 + ) + + +# --------------------------------------------------------------------------- +# GPU tests +# --------------------------------------------------------------------------- + + +@pytest.mark.gpu +def test_metrics_on_gpu(): + w = nn.Parameter(torch.tensor([3.0, 4.0], dtype=torch.float64, device="cuda")) + opt = _make_optimizer( + SGMCMC.sgld, [w], lr=1.0, nbeta=2.0, noise_level=0.0, save_metrics=True + ) + w.grad = torch.tensor([3.0, 4.0], dtype=torch.float64, device="cuda") + opt.step(noise_generator=torch.Generator("cuda").manual_seed(0)) + + m = opt.get_metrics() + assert m.scaled_grad.device == torch.device("cpu") + torch.testing.assert_close(m.scaled_grad, torch.tensor([5.0]), atol=0, rtol=0) + + +@pytest.mark.gpu +@pytest.mark.only_multi_gpu +def test_metrics_multi_gpu(): + """Metrics aggregate correctly with params on different GPUs. + + No torch.distributed initialization is needed here: we're just placing + individual parameters on separate devices, not using DDP or collective ops. + """ + w0 = nn.Parameter(torch.tensor([3.0, 4.0], dtype=torch.float64, device="cuda:0")) + w1 = nn.Parameter(torch.tensor([3.0, 4.0], dtype=torch.float64, device="cuda:1")) + opt = _make_optimizer( + SGMCMC.sgld, + [{"params": [w0]}, {"params": [w1]}], + lr=1.0, + nbeta=2.0, + noise_level=0.0, + save_metrics=True, + ) + w0.grad = torch.tensor([3.0, 4.0], dtype=torch.float64, device="cuda:0") + w1.grad = torch.tensor([3.0, 4.0], dtype=torch.float64, device="cuda:1") + opt.step(noise_generator=torch.Generator("cuda").manual_seed(0)) + + m = opt.get_metrics() + assert m.scaled_grad.device == torch.device("cpu") + # Each group contributes norm 5, combined: sqrt(5^2 + 5^2) = sqrt(50) + expected = torch.tensor([50.0]).sqrt() + torch.testing.assert_close(m.scaled_grad, expected, atol=0, rtol=0) + assert m.numel == 4 diff --git a/tests/optim/test_sgmcmc.py b/tests/optim/test_sgmcmc.py index fdb7c69c..642fb8ec 100644 --- a/tests/optim/test_sgmcmc.py +++ b/tests/optim/test_sgmcmc.py @@ -50,34 +50,10 @@ def run_optimization_steps(model, optimizer, data, target, criterion, steps=2, s optimizer.step() -def compare_parameters(model1, model2, atol=1e-5): +def compare_parameters(model1, model2, *, atol: float): """Helper function to compare model parameters""" for p1, p2 in zip(model1.parameters(), model2.parameters()): - assert torch.allclose(p1, p2, atol=atol), f"Parameters differ: {p1} vs {p2}" - - -def compare_metrics(optimizer_sgld, optimizer_sgmcmc, metrics): - """Helper function to compare optimizer metrics""" - for i, (g1, g2) in enumerate( - zip(optimizer_sgld.param_groups, optimizer_sgmcmc.param_groups) - ): - for metric in metrics: - if metric == "noise": - for a, b in zip(optimizer_sgld.noise[i], g2["metrics"]["noise"]): - assert a.shape == b.shape, "Noise tensors differ" - assert torch.allclose(a, b), "Noise tensors differ" - elif metric == "dws": - pi = 0 - for j, a in enumerate(g2["metrics"]["dws"]): - b = optimizer_sgld.dws[pi] - pi += 1 - assert a.shape == b.shape, "DWS tensors differ in shape" - assert torch.allclose(a, b, atol=1e-4), "DWS metrics differ" - else: - assert torch.allclose( - g2["metrics"][metric], - getattr(optimizer_sgld, metric), - ), f"Metric {metric} differs" + torch.testing.assert_close(p1, p2, atol=atol, rtol=0) def _serialize(obj, precision=2): @@ -110,11 +86,20 @@ def serialize_model_state(model, precision=2): def serialize_metrics(optimizer, precision=2): """Helper to convert optimizer metrics to serializable format with rounding""" - metrics = {} - for group in optimizer.param_groups: - if "metrics" in group: - metrics.update(_serialize(group["metrics"], precision)) - return metrics + if not optimizer.save_metrics: + return {} + metrics = optimizer.get_metrics() + return _serialize( + { + "scaled_grad": metrics.scaled_grad, + "localization": metrics.localization, + "weight_decay": metrics.weight_decay, + "prior": metrics.prior, + "noise": metrics.noise, + "numel": metrics.numel, + }, + precision, + ) @pytest.mark.parametrize("lr", [1e-1, 1e-2, 1e-3, 1e-4]) @@ -125,6 +110,7 @@ def serialize_metrics(optimizer, precision=2): def test_SGMCMC_vs_SGLD( lr, nbeta, localization, bounding_box_size, weight_decay, snapshot ): + """Test that SGMCMC.sgld and deprecated SGLD produce identical parameter updates.""" model1, model2 = create_paired_models() data, target, criterion = create_task() @@ -135,41 +121,44 @@ def test_SGMCMC_vs_SGLD( localization=localization, bounding_box_size=bounding_box_size, weight_decay=weight_decay, + save_metrics=True, ) - metrics = [ - "noise_norm", - "grad_norm", - "weight_norm", - "noise", - "dws", - "localization_loss", - "distance", - ] - optimizer_sgld = SGLD( model1.parameters(), **kwargs, - save_noise=True, - save_mala_vars=True, - noise_norm=True, - grad_norm=True, - weight_norm=True, - distance=True, ) optimizer_sgmcmc = SGMCMC.sgld( model2.parameters(), **kwargs, - metrics=metrics, ) run_optimization_steps(model1, optimizer_sgld, data, target, criterion) run_optimization_steps(model2, optimizer_sgmcmc, data, target, criterion) - if localization == 0.0: - metrics.remove("distance") # undefined if no localization is given - compare_parameters(model1, model2) - compare_metrics(optimizer_sgld, optimizer_sgmcmc, metrics) + + # SGLD and SGMCMC apply weight_decay/localization in a slightly different + # order, so float32 rounding can differ by up to ~6e-8 over multiple steps. + compare_parameters(model1, model2, atol=1e-7) + + # Metrics use float32 accumulators with the same operation-order caveat. + metrics_sgld = optimizer_sgld.get_metrics() + metrics_sgmcmc = optimizer_sgmcmc.get_metrics() + + _m_atol = 1e-7 + torch.testing.assert_close( + metrics_sgld.scaled_grad, metrics_sgmcmc.scaled_grad, atol=_m_atol, rtol=0 + ) + torch.testing.assert_close( + metrics_sgld.localization, metrics_sgmcmc.localization, atol=_m_atol, rtol=0 + ) + torch.testing.assert_close( + metrics_sgld.weight_decay, metrics_sgmcmc.weight_decay, atol=_m_atol, rtol=0 + ) + torch.testing.assert_close( + metrics_sgld.noise, metrics_sgmcmc.noise, atol=_m_atol, rtol=0 + ) + assert metrics_sgld.numel == metrics_sgmcmc.numel state = { "model1": serialize_model_state(model1), @@ -188,16 +177,15 @@ def test_SGMCMC_vs_SGLD( @pytest.mark.parametrize("localization", [0.0, 0.1]) @pytest.mark.parametrize("bounding_box_size", [None, 0.1]) @pytest.mark.parametrize("weight_decay", [0.0]) -@pytest.mark.parametrize("optimize_over", ["scalar", "tensor"]) def test_optimize_over( lr, nbeta, localization, bounding_box_size, weight_decay, - optimize_over, snapshot, ): + """Test that SGMCMC correctly handles masked parameter optimization.""" # Create identical models model1, model2 = create_paired_models() @@ -211,51 +199,31 @@ def test_optimize_over( localization=localization, bounding_box_size=bounding_box_size, weight_decay=weight_decay, + save_metrics=True, ) - metrics = [ - "noise_norm", - "grad_norm", - "weight_norm", - "noise", - "dws", - "localization_loss", - "distance", - ] - torch.manual_seed(42) - if optimize_over == "tensor": - optimize_over_params = [ - torch.randint(0, 2, p.shape).bool() for p in model1.parameters() - ] - elif optimize_over == "scalar": - optimize_over_params = [ - torch.randint(0, 2, (1,)).bool() for p in model1.parameters() - ] + # Create tensor masks matching parameter shapes + optimize_over_params = [ + torch.randint(0, 2, p.shape).bool() for p in model1.parameters() + ] # Setup optimizers with equivalent parameters optimizer_sgld = SGLD( [ - {"params": p, "optimize_over": opt} + {"params": p, "mask": opt} for p, opt in zip(model1.parameters(), optimize_over_params) ], **kwargs, - save_noise=True, - save_mala_vars=True, - noise_norm=True, - grad_norm=True, - weight_norm=True, - distance=True, ) - optimizer_sgmcmc = SGMCMC( + optimizer_sgmcmc = SGMCMC.sgld( [ - {"params": (p,), "optimize_over": (opt,)} + {"params": (p,), "mask": (opt,)} for p, opt in zip(model2.parameters(), optimize_over_params) ], **kwargs, - metrics=metrics, ) # Create test data @@ -265,28 +233,12 @@ def test_optimize_over( run_optimization_steps(model1, optimizer_sgld, data, target, criterion) run_optimization_steps(model2, optimizer_sgmcmc, data, target, criterion) - compare_parameters(model1, model2) - - if optimize_over == "tensor": - for p1, p2, mask in zip( - original_params, model2.parameters(), optimize_over_params - ): - assert torch.allclose(p1[~mask], p2[~mask], atol=1e-5), ( - f"Masked parameters differ: {p1} vs {p2} for mask {mask}" - ) - - elif optimize_over == "scalar": - for p1, p2, mask in zip( - original_params, model2.parameters(), optimize_over_params - ): - if not mask: - assert torch.allclose(p1, p2, atol=1e-5), ( - f"Masked parameters differ: {p1} vs {p2}" - ) - - # Compare parameters - for p1, p2 in zip(model1.parameters(), model2.parameters()): - assert torch.allclose(p1, p2, atol=1e-5), f"Parameters differ: {p1} vs {p2}" + compare_parameters(model1, model2, atol=0) + + for p1, p2, _mask in zip( + original_params, model2.parameters(), optimize_over_params + ): + torch.testing.assert_close(p1[~_mask], p2[~_mask], atol=0, rtol=0) state = { "model1": serialize_model_state(model1), @@ -294,7 +246,7 @@ def test_optimize_over( } assert state == snapshot( - name=f"test_optimize_over_{lr}_{nbeta}_{localization}_{bounding_box_size}_{weight_decay}_{optimize_over}" + name=f"test_optimize_over_{lr}_{nbeta}_{localization}_{bounding_box_size}_{weight_decay}" ) @@ -316,7 +268,7 @@ def test_SGMCMC_deterministic(lr, snapshot): run_optimization_steps(model1, optimizer_sgd, data, target, criterion, steps=1) run_optimization_steps(model2, optimizer_sgmcmc, data, target, criterion, steps=1) - compare_parameters(model1, model2, atol=1e-6) + compare_parameters(model1, model2, atol=0) state = { "model1": serialize_model_state(model1), @@ -358,7 +310,7 @@ def test_SGMCMC_vs_SGNHT(lr, diffusion_factor, bounding_box_size, snapshot): model2, optimizer_sgmcmc, data, target, criterion, steps=STEPS, seed=42 ) - compare_parameters(model1, model2, atol=1e-6) + compare_parameters(model1, model2, atol=0) state = { "model1": serialize_model_state(model1), @@ -435,7 +387,7 @@ def test_SGMCMC_param_groups(snapshot): for group_idx, layer_params in enumerate(params_before): for name, p_before in layer_params.items(): p_after = dict(model[group_idx].named_parameters())[name] - param_diff = (p_after - p_before).abs().mean() + param_diff = (p_after - p_before).abs().mean(dtype=torch.float32) # Group 0 should have larger updates due to higher learning rate if group_idx == 0: @@ -490,29 +442,17 @@ def test_SGMCMC_vs_SGLD_param_groups(lr_ratio, noise_ratio, snapshot): }, ] - metrics = [ - "noise_norm", - "grad_norm", - "weight_norm", - "distance", - ] # , "noise"] # , "dws"] - # Setup optimizers with equivalent parameters optimizer_sgld = SGLD( param_groups1, nbeta=1.0, - save_noise=True, - save_mala_vars=True, - noise_norm=True, - grad_norm=True, - weight_norm=True, - distance=True, + save_metrics=True, ) optimizer_sgmcmc = SGMCMC( param_groups2, nbeta=1.0, - metrics=metrics, + save_metrics=True, ) # Check hyperparameters in groups @@ -524,9 +464,6 @@ def test_SGMCMC_vs_SGLD_param_groups(lr_ratio, noise_ratio, snapshot): assert g1["nbeta"] == g2["nbeta"], ( f"nbeta differ: {g1['nbeta']} vs {g2['nbeta']}" ) - assert g1["localization"] == g2["localization"], ( - f"Localizations differ: {g1['localization']} vs {g2['localization']}" - ) data, target, criterion = create_task() @@ -537,32 +474,24 @@ def test_SGMCMC_vs_SGLD_param_groups(lr_ratio, noise_ratio, snapshot): model2, optimizer_sgmcmc, data, target, criterion, steps=STEPS, seed=42 ) - compare_parameters(model1, model2, atol=1e-4) + compare_parameters(model1, model2, atol=0) - # Compare metrics (Need this custom comparison because of different storage) - for i, (g1, g2) in enumerate( - zip(optimizer_sgld.param_groups, optimizer_sgmcmc.param_groups) - ): - for metric in metrics: - if metric == "noise": - # Compare noise tensors for each parameter group - for a, b in zip( - optimizer_sgld.noise[i], - g2["metrics"]["noise"], - ): - assert a.shape == b.shape, "Noise tensors differ" - assert torch.allclose(a, b), "Noise tensors differ" - elif metric == "dws": - pi = 0 - for j, a in enumerate(g2["metrics"]["dws"]): - b = optimizer_sgld.dws[pi] - pi += 1 - assert a.shape == b.shape, "DWS tensors differ in shape" - assert torch.allclose(a, b, atol=1e-4), "DWS metrics differ" - else: - assert torch.allclose(g2["metrics"][metric], g1[metric], atol=1e-2), ( - f"Metric {metric} differs" - ) + metrics_sgld = optimizer_sgld.get_metrics() + metrics_sgmcmc = optimizer_sgmcmc.get_metrics() + + _m_atol = 1e-7 + torch.testing.assert_close( + metrics_sgld.scaled_grad, metrics_sgmcmc.scaled_grad, atol=_m_atol, rtol=0 + ) + torch.testing.assert_close( + metrics_sgld.localization, metrics_sgmcmc.localization, atol=_m_atol, rtol=0 + ) + torch.testing.assert_close( + metrics_sgld.weight_decay, metrics_sgmcmc.weight_decay, atol=_m_atol, rtol=0 + ) + torch.testing.assert_close( + metrics_sgld.noise, metrics_sgmcmc.noise, atol=_m_atol, rtol=0 + ) state = { "optimizer_sgld": serialize_metrics(optimizer_sgld), @@ -592,7 +521,7 @@ def test_SGMCMC_rmsprop_sgld(lr, alpha, eps, add_grad_correction, snapshot): eps=eps, add_grad_correction=add_grad_correction, nbeta=1.0, - metrics=["grad_norm", "noise_norm", "weight_norm"], + save_metrics=True, ) # Verify RMSprop preconditioner settings @@ -620,10 +549,10 @@ def test_SGMCMC_rmsprop_sgld(lr, alpha, eps, add_grad_correction, snapshot): assert not torch.isnan(state["square_avg"]).any() assert not torch.isinf(state["square_avg"]).any() - # Verify metrics are being tracked - assert optimizer.metrics["grad_norm"] >= 0 - assert optimizer.metrics["noise_norm"] >= 0 - assert optimizer.metrics["weight_norm"] >= 0 + # Verify metrics are being tracked via the new interface + metrics = optimizer.get_metrics() + assert metrics.scaled_grad >= 0 + assert metrics.noise >= 0 state = { "optimizer": serialize_metrics(optimizer), @@ -634,7 +563,7 @@ def test_SGMCMC_rmsprop_sgld(lr, alpha, eps, add_grad_correction, snapshot): @pytest.mark.parametrize("lr", [1e-1, 1e-2, 1e-3, 1e-4]) def test_SGMCMC_rmsprop_equals_sgld(lr, snapshot): - """Test that RMSprop-preconditioned SGLD equals regular SGLD when alpha=0 and eps=1""" + """Test that RMSprop-preconditioned SGLD equals regular SGLD when alpha=1 and eps=1""" model1, model2 = create_paired_models() data, target, criterion = create_task() @@ -645,15 +574,13 @@ def test_SGMCMC_rmsprop_equals_sgld(lr, snapshot): nbeta=1.0, localization=0.1, weight_decay=0.0, + save_metrics=True, ) - metrics = ["grad_norm", "noise_norm", "weight_norm", "noise"] - - # Regular SGLD - optimizer_sgld = SGLD( + # Regular SGLD via SGMCMC + optimizer_sgld = SGMCMC.sgld( model1.parameters(), **kwargs, - metrics=metrics, ) # RMSprop SGLD with identity-like settings @@ -663,7 +590,6 @@ def test_SGMCMC_rmsprop_equals_sgld(lr, snapshot): eps=1.0, # Makes preconditioner act like identity add_grad_correction=False, **kwargs, - metrics=metrics, ) # Verify optimizers behave identically @@ -674,8 +600,23 @@ def test_SGMCMC_rmsprop_equals_sgld(lr, snapshot): model2, optimizer_rmsprop, data, target, criterion, steps=STEPS, seed=42 ) - compare_parameters(model1, model2, atol=1e-6) - compare_metrics(optimizer_sgld, optimizer_rmsprop, metrics) + compare_parameters(model1, model2, atol=0) + + _m_atol = 1e-7 + metrics_sgld = optimizer_sgld.get_metrics() + metrics_rmsprop = optimizer_rmsprop.get_metrics() + torch.testing.assert_close( + metrics_sgld.scaled_grad, metrics_rmsprop.scaled_grad, atol=_m_atol, rtol=0 + ) + torch.testing.assert_close( + metrics_sgld.localization, metrics_rmsprop.localization, atol=_m_atol, rtol=0 + ) + torch.testing.assert_close( + metrics_sgld.weight_decay, metrics_rmsprop.weight_decay, atol=_m_atol, rtol=0 + ) + torch.testing.assert_close( + metrics_sgld.noise, metrics_rmsprop.noise, atol=_m_atol, rtol=0 + ) state = { "model1": serialize_model_state(model1), diff --git a/tests/optim/test_sketch.py b/tests/optim/test_sketch.py new file mode 100644 index 00000000..f33bc4bc --- /dev/null +++ b/tests/optim/test_sketch.py @@ -0,0 +1,203 @@ +"""Tests for CountSketch and SketchBuffer.""" + +import pytest +import torch +from devinterp.optim.sketch import SKETCH_QUANTITIES, CountSketch, SketchBuffer + + +class TestCountSketchCreation: + def test_deterministic(self): + """Same seed produces identical hash arrays.""" + a = CountSketch.create(100, 32, seed=42) + b = CountSketch.create(100, 32, seed=42) + torch.testing.assert_close(a.hash_indices, b.hash_indices, atol=0, rtol=0) + torch.testing.assert_close(a.hash_signs, b.hash_signs, atol=0, rtol=0) + + def test_different_seeds_differ(self): + a = CountSketch.create(100, 32, seed=0) + b = CountSketch.create(100, 32, seed=1) + assert not torch.equal(a.hash_indices, b.hash_indices) + + def test_dimensions(self): + cs = CountSketch.create(input_dim=500, output_dim=64, seed=0) + assert cs.input_dim == 500 + assert cs.output_dim == 64 + assert cs.hash_indices.shape == (500,) + assert cs.hash_signs.shape == (500,) + + def test_hash_indices_in_range(self): + cs = CountSketch.create(10000, 128, seed=0) + assert cs.hash_indices.min() >= 0 + assert cs.hash_indices.max() < 128 + + def test_hash_signs_are_pm1(self): + cs = CountSketch.create(1000, 64, seed=0) + mask = (cs.hash_signs == 1.0) | (cs.hash_signs == -1.0) + assert torch.all(mask), ( + f"Non-±1 signs at indices {torch.where(~mask)[0].tolist()}" + ) + + +class TestCountSketchMath: + def test_sketch_exact_small_example(self): + """Verify sketch output by enumerating bucket assignments.""" + cs = CountSketch.create(4, 3, seed=0) + h = cs.hash_indices + s = cs.hash_signs + v = torch.tensor([3.0, 4.0, 0.0, 5.0]) + + expected = torch.zeros(3) + for i in range(4): + expected[h[i]] += s[i] * v[i] + + result = cs.sketch(v) + torch.testing.assert_close(result, expected, atol=0, rtol=0) + + def test_linearity(self): + """S(v + w) = Sv + Sw.""" + cs = CountSketch.create(100, 32, seed=7) + torch.manual_seed(0) + v = torch.randn(100) + w = torch.randn(100) + + s_sum = cs.sketch(v + w) + sv_plus_sw = cs.sketch(v) + cs.sketch(w) + + torch.testing.assert_close(s_sum, sv_plus_sw, atol=1e-5, rtol=0) + + def test_scalar_scaling(self): + """S(alpha * v) = alpha * Sv.""" + cs = CountSketch.create(100, 32, seed=7) + torch.manual_seed(0) + v = torch.randn(100) + alpha = 3.5 + + torch.testing.assert_close( + cs.sketch(alpha * v), alpha * cs.sketch(v), atol=1e-5, rtol=0 + ) + + def test_scatter_into_matches_full_sketch(self): + """Accumulating via scatter_into_ with offset reproduces sketch(cat(v1, v2)).""" + cs = CountSketch.create(8, 4, seed=3) + v1 = torch.tensor([1.0, 2.0, 3.0]) + v2 = torch.tensor([4.0, 5.0, 6.0, 7.0, 8.0]) + + full = cs.sketch(torch.cat([v1, v2])) + + accum = torch.zeros(4) + cs.scatter_into_(accum, v1, offset=0) + cs.scatter_into_(accum, v2, offset=3) + + torch.testing.assert_close(accum, full, atol=0, rtol=0) + + def test_scatter_into_with_multidim_param(self): + """scatter_into_ flattens multi-dimensional tensors.""" + cs = CountSketch.create(12, 8, seed=5) + param = torch.arange(12, dtype=torch.float32).reshape(3, 4) + + full = cs.sketch(param.reshape(-1)) + + accum = torch.zeros(8) + cs.scatter_into_(accum, param, offset=0) + + torch.testing.assert_close(accum, full, atol=0, rtol=0) + + def test_sketch_of_zeros_is_zero(self): + cs = CountSketch.create(100, 32, seed=0) + result = cs.sketch(torch.zeros(100)) + torch.testing.assert_close(result, torch.zeros(32), atol=0, rtol=0) + + +class TestCountSketchStatistical: + """Statistical tests of the unbiasedness properties. + + These average over many independent sketches (different seeds) to verify + that E[] = . + """ + + def test_inner_product_unbiased(self): + """Mean sketch inner product converges to true inner product.""" + d, k, num_trials = 200, 64, 500 + v = torch.zeros(d) + w = torch.zeros(d) + v[:5] = torch.tensor([3.0, 4.0, 1.0, 2.0, 5.0]) + w[:5] = torch.tensor([4.0, 3.0, 2.0, 1.0, 0.0]) + true_dot = torch.dot(v, w) # 3*4 + 4*3 + 1*2 + 2*1 = 28 + + estimates = torch.empty(num_trials) + for seed in range(num_trials): + cs = CountSketch.create(d, k, seed=seed) + estimates[seed] = torch.dot(cs.sketch(v), cs.sketch(w)) + + mean_estimate = estimates.mean() + torch.testing.assert_close(mean_estimate, true_dot, atol=1.0, rtol=0) + + def test_norm_preserved_in_expectation(self): + """Mean sketch squared norm converges to true squared norm.""" + d, k, num_trials = 200, 64, 500 + v = torch.zeros(d) + v[0], v[1] = 3.0, 4.0 + true_norm_sq = torch.tensor(25.0) + + estimates = torch.empty(num_trials) + for seed in range(num_trials): + cs = CountSketch.create(d, k, seed=seed) + estimates[seed] = cs.sketch(v).norm().square() + + mean_estimate = estimates.mean() + torch.testing.assert_close(mean_estimate, true_norm_sq, atol=1.0, rtol=0) + + def test_orthogonal_vectors_zero_dot(self): + """Sketch inner product of orthogonal vectors averages to zero.""" + d, k, num_trials = 200, 64, 500 + v = torch.zeros(d) + w = torch.zeros(d) + v[0] = 5.0 + w[1] = 5.0 + + estimates = torch.empty(num_trials) + for seed in range(num_trials): + cs = CountSketch.create(d, k, seed=seed) + estimates[seed] = torch.dot(cs.sketch(v), cs.sketch(w)) + + mean_estimate = estimates.mean() + torch.testing.assert_close(mean_estimate, torch.tensor(0.0), atol=1.0, rtol=0) + + +class TestSketchBuffer: + def test_create_shapes(self): + buf = SketchBuffer.create(64) + for q in SKETCH_QUANTITIES: + t = getattr(buf, q) + assert t.shape == (64,) + assert t.dtype == torch.float32 + + def test_zero(self): + buf = SketchBuffer.create(32) + buf.scaled_grad.fill_(1.0) + buf.noise.fill_(2.0) + buf.zero_() + for q in SKETCH_QUANTITIES: + torch.testing.assert_close(getattr(buf, q), torch.zeros(32), atol=0, rtol=0) + + def test_to_same_device_returns_self(self): + buf = SketchBuffer.create(16) + assert buf.to("cpu") is buf + + def test_device_inconsistent_raises(self): + buf = SketchBuffer.create(16) + buf.scaled_grad = buf.scaled_grad.to("meta") + with pytest.raises(RuntimeError, match="Inconsistent devices"): + buf.device + + +class TestCountSketchDevice: + def test_to_same_device_returns_self(self): + cs = CountSketch.create(32, 8, seed=0) + assert cs.to("cpu") is cs + + def test_device_inconsistent_raises(self): + cs = CountSketch.create(32, 8, seed=0) + cs.hash_signs = cs.hash_signs.to("meta") + with pytest.raises(RuntimeError, match="Inconsistent devices"): + cs.device diff --git a/tests/optim/test_sketch_metrics.py b/tests/optim/test_sketch_metrics.py new file mode 100644 index 00000000..3247b068 --- /dev/null +++ b/tests/optim/test_sketch_metrics.py @@ -0,0 +1,647 @@ +"""Tests for count sketch integration with SGMCMC and legacy SGLD optimizers. + +Verifies that sketches tracked during optimizer steps correctly reflect the +actual parameter update components. The key cross-check (Andy's test): +the L2 norm of a sketched vector should approximate the exact L2 norm +recorded by Metrics. +""" + +import warnings + +import pytest +import torch +import torch.nn as nn +from devinterp.optim.sgld import SGLD +from devinterp.optim.sgmcmc import SGMCMC +from devinterp.optim.sketch import SKETCH_QUANTITIES, SketchBuffer + +pytestmark = [ + pytest.mark.filterwarnings("ignore::DeprecationWarning"), + pytest.mark.filterwarnings("ignore:.*nbeta.*"), + pytest.mark.filterwarnings("ignore:.*noise_level.*"), +] + + +def _make_optimizer(factory, params, **kwargs): + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + return factory(params, **kwargs) + + +class TestSketchInit: + def test_creates_sketch_buffer(self): + w = nn.Parameter(torch.randn(10, dtype=torch.float64)) + opt = _make_optimizer(SGMCMC.sgld, [w], lr=0.5, nbeta=2.0, sketch_dim=32) + assert opt.save_sketches + assert isinstance(opt._sketch_buf, SketchBuffer) + + def test_correct_output_dim(self): + w = nn.Parameter(torch.randn(10, dtype=torch.float64)) + opt = _make_optimizer(SGMCMC.sgld, [w], lr=0.5, nbeta=2.0, sketch_dim=64) + assert opt._sketch_buf.scaled_grad.shape == (64,) + + def test_not_enabled_by_default(self): + w = nn.Parameter(torch.randn(10, dtype=torch.float64)) + opt = _make_optimizer(SGMCMC.sgld, [w], lr=0.5, nbeta=2.0) + assert not opt.save_sketches + assert opt._sketch is None + assert opt._sketch_buf is None + + +class TestGetSketches: + def test_raises_when_not_enabled(self): + w = nn.Parameter(torch.randn(5)) + opt = _make_optimizer(SGMCMC.sgld, [w], lr=0.5, nbeta=2.0) + with pytest.raises(RuntimeError, match="Sketches not enabled"): + opt.get_sketches() + + def test_returns_all_quantities(self): + w = nn.Parameter(torch.randn(10, dtype=torch.float64)) + opt = _make_optimizer( + SGMCMC.sgld, [w], lr=0.5, nbeta=2.0, noise_level=0.0, sketch_dim=32 + ) + w.grad = torch.randn_like(w) + opt.step(noise_generator=torch.Generator().manual_seed(0)) + + sketches = opt.get_sketches() + for q in SKETCH_QUANTITIES: + t = getattr(sketches, q) + assert t.shape == (32,), q + assert t.device == torch.device("cpu"), q + + def test_correct_output_dim(self): + w = nn.Parameter(torch.randn(20, dtype=torch.float64)) + opt = _make_optimizer( + SGMCMC.sgld, [w], lr=0.5, nbeta=2.0, noise_level=0.0, sketch_dim=128 + ) + w.grad = torch.randn_like(w) + opt.step(noise_generator=torch.Generator().manual_seed(0)) + + sketches = opt.get_sketches() + assert sketches.scaled_grad.shape == (128,) + + +class TestSketchZeroComponents: + """When a component is zero, its sketch should also be zero.""" + + def test_noise_zero_when_disabled(self): + w = nn.Parameter(torch.tensor([3.0, 4.0], dtype=torch.float64)) + opt = _make_optimizer( + SGMCMC.sgld, [w], lr=1.0, nbeta=2.0, noise_level=0.0, sketch_dim=32 + ) + w.grad = torch.tensor([3.0, 4.0], dtype=torch.float64) + opt.step(noise_generator=torch.Generator().manual_seed(0)) + + sketches = opt.get_sketches() + torch.testing.assert_close(sketches.noise, torch.zeros(32), atol=0, rtol=0) + + def test_localization_zero_without_prior(self): + w = nn.Parameter(torch.tensor([3.0, 4.0], dtype=torch.float64)) + opt = _make_optimizer( + SGMCMC.sgld, [w], lr=1.0, nbeta=2.0, noise_level=0.0, sketch_dim=32 + ) + w.grad = torch.tensor([3.0, 4.0], dtype=torch.float64) + opt.step(noise_generator=torch.Generator().manual_seed(0)) + + sketches = opt.get_sketches() + torch.testing.assert_close( + sketches.localization, torch.zeros(32), atol=0, rtol=0 + ) + torch.testing.assert_close( + sketches.weight_decay, torch.zeros(32), atol=0, rtol=0 + ) + + def test_grad_zero_when_grad_is_zero(self): + w = nn.Parameter(torch.tensor([3.0, 4.0], dtype=torch.float64)) + opt = _make_optimizer( + SGMCMC.sgld, + [w], + lr=1.0, + nbeta=2.0, + localization=1.0, + noise_level=0.0, + sketch_dim=32, + ) + w.data = torch.tensor([3.0, 4.0], dtype=torch.float64) + w.grad = torch.zeros(2, dtype=torch.float64) + opt.step(noise_generator=torch.Generator().manual_seed(0)) + + sketches = opt.get_sketches() + torch.testing.assert_close( + sketches.scaled_grad, torch.zeros(32), atol=0, rtol=0 + ) + torch.testing.assert_close( + sketches.unscaled_grad, torch.zeros(32), atol=0, rtol=0 + ) + + +class TestSketchNormApproximatesMetricNorm: + """The core cross-check: ||sketch(v)|| ≈ ||v|| (the exact metric norm). + + E[||Sv||²] = ||v||² with std ∝ ||v||²/√sketch_dim, so the relative + standard deviation of ||Sv|| around ||v|| is O(1/√sketch_dim). At + sketch_dim=64 that's ~12.5%, so we use 20-25% tolerances (roughly 2σ). + + Assertions skip quantities whose exact norm is near zero to avoid + dividing by zero in the relative comparison — when the true norm is + zero (e.g. no prior active), the sketch is exactly zero too, which + is covered by TestSketchZeroComponents. + """ + + @pytest.mark.parametrize( + "factory", + [SGMCMC.sgld, SGMCMC.rmsprop_sgld, SGLD], + ids=["sgmcmc_sgld", "rmsprop", "legacy_sgld"], + ) + def test_scaled_grad_norm(self, factory): + """Sketch norm of scaled_grad ≈ exact scaled_grad norm from Metrics.""" + w = nn.Parameter(torch.randn(200, dtype=torch.float64)) + opt = _make_optimizer( + factory, + [w], + lr=0.5, + nbeta=2.0, + localization=1.0, + noise_level=0.0, + save_metrics=True, + sketch_dim=64, + ) + w.grad = torch.randn_like(w) + opt.step(noise_generator=torch.Generator().manual_seed(42)) + + metrics = opt.get_metrics() + sketches = opt.get_sketches() + + exact_norm = metrics.scaled_grad.item() + sketch_norm = sketches.scaled_grad.norm().item() + + assert exact_norm > 0 + assert sketch_norm == pytest.approx(exact_norm, rel=0.2) + + def test_all_quantities_over_multiple_steps(self): + """All sketch norms track metric norms over several steps.""" + torch.manual_seed(42) + w = nn.Parameter(torch.randn(200, dtype=torch.float64)) + opt = _make_optimizer( + SGMCMC.sgld, + [w], + lr=0.5, + nbeta=2.0, + localization=1.0, + weight_decay=0.5, + save_metrics=True, + sketch_dim=64, + ) + + gen = torch.Generator().manual_seed(99) + for _ in range(5): + w.grad = torch.randn_like(w) + opt.step(noise_generator=gen) + + metrics = opt.get_metrics() + sketches = opt.get_sketches() + + for q in ("scaled_grad", "unscaled_grad", "localization", "weight_decay"): + exact = getattr(metrics, q).item() + sketch_norm = getattr(sketches, q).norm().item() + if exact > 1e-8: + assert sketch_norm == pytest.approx(exact, rel=0.25), q + + +class TestSketchDotProductApproximatesMetricDotProduct: + """The dot product cross-check: ⟨S(v), S(w)⟩ ≈ ⟨v, w⟩. + + Since all sketches share the same hash, inner products in sketch space + approximate inner products in the original space. This is a stronger + check than norms alone — it verifies that the sketches are mutually + coherent, not just individually well-scaled. + + Dot product estimation has higher variance than norm estimation. The + absolute std dev is O(||v||·||w||/√k), so the error bound scales with + the product of norms, not with the dot product itself. We use an + absolute tolerance of ``margin * ||v|| * ||w|| / sqrt(k)`` which + corresponds to a ~3σ bound. + """ + + PRIOR = "prior" + + DOT_PRODUCT_FIELDS = { + "dot_grad_prior": ("scaled_grad", PRIOR), + "dot_grad_noise": ("scaled_grad", "noise"), + "dot_prior_noise": (PRIOR, "noise"), + } + + @staticmethod + def _get_sketch_vector(sketches: SketchBuffer, field: str) -> torch.Tensor: + if field == "prior": + return sketches.localization + sketches.weight_decay + return getattr(sketches, field) + + @classmethod + def _sketch_dot_and_norms( + cls, sketches: SketchBuffer, field_a: str, field_b: str + ) -> tuple[float, float, float]: + a = cls._get_sketch_vector(sketches, field_a) + b = cls._get_sketch_vector(sketches, field_b) + return torch.dot(a, b).item(), a.norm().item(), b.norm().item() + + @pytest.mark.parametrize( + "factory", + [SGMCMC.sgld, SGMCMC.rmsprop_sgld, SGLD], + ids=["sgmcmc_sgld", "rmsprop", "legacy_sgld"], + ) + def test_dot_products_over_multiple_steps(self, factory): + sketch_dim = 2048 + margin = 4.0 + + torch.manual_seed(42) + w = nn.Parameter(torch.randn(200, dtype=torch.float64)) + opt = _make_optimizer( + factory, + [w], + lr=0.5, + nbeta=2.0, + localization=1.0, + weight_decay=0.5, + save_metrics=True, + sketch_dim=sketch_dim, + ) + + gen = torch.Generator().manual_seed(99) + for step_i in range(5): + w.grad = torch.randn_like(w) + opt.step(noise_generator=gen) + + metrics = opt.get_metrics() + sketches = opt.get_sketches() + + for dot_field, (a, b) in self.DOT_PRODUCT_FIELDS.items(): + exact = getattr(metrics, dot_field).item() + approx, norm_a, norm_b = self._sketch_dot_and_norms(sketches, a, b) + atol = margin * norm_a * norm_b / (sketch_dim**0.5) + assert abs(approx - exact) < atol, ( + f"{dot_field} step={step_i}: " + f"|{approx:.4f} - {exact:.4f}| = {abs(approx - exact):.4f} > {atol:.4f}" + ) + + +class TestSketchWithMultipleParams: + def test_accumulates_across_params(self): + """Sketches accumulate correctly across multiple parameters.""" + w1 = nn.Parameter(torch.randn(100, dtype=torch.float64)) + w2 = nn.Parameter(torch.randn(100, dtype=torch.float64)) + opt = _make_optimizer( + SGMCMC.sgld, + [w1, w2], + lr=0.5, + nbeta=2.0, + noise_level=0.0, + save_metrics=True, + sketch_dim=64, + ) + + w1.grad = torch.randn_like(w1) + w2.grad = torch.randn_like(w2) + opt.step(noise_generator=torch.Generator().manual_seed(0)) + + metrics = opt.get_metrics() + sketches = opt.get_sketches() + + exact_norm = metrics.scaled_grad.item() + sketch_norm = sketches.scaled_grad.norm().item() + assert exact_norm > 0 + assert sketch_norm == pytest.approx(exact_norm, rel=0.2) + + def test_multiple_param_groups(self): + """Sketch aggregation across param groups produces correct norms.""" + w1 = nn.Parameter(torch.randn(100, dtype=torch.float64)) + w2 = nn.Parameter(torch.randn(100, dtype=torch.float64)) + opt = _make_optimizer( + SGMCMC.sgld, + [{"params": [w1]}, {"params": [w2]}], + lr=0.5, + nbeta=2.0, + noise_level=0.0, + save_metrics=True, + sketch_dim=64, + ) + + w1.grad = torch.randn_like(w1) + w2.grad = torch.randn_like(w2) + opt.step(noise_generator=torch.Generator().manual_seed(0)) + + metrics = opt.get_metrics() + sketches = opt.get_sketches() + + exact_norm = metrics.scaled_grad.item() + sketch_norm = sketches.scaled_grad.norm().item() + assert exact_norm > 0 + assert sketch_norm == pytest.approx(exact_norm, rel=0.2) + + +class TestSketchWithGradNoneGap: + """Sketch offsets stay correct when a middle param has grad=None. + + When the optimizer skips a param (no gradient), param_offset must still + advance past it so subsequent params scatter into the right hash/sign + region. Without that advance, the sketch vector would use wrong indices + and the exact comparison below would fail. + """ + + @pytest.mark.parametrize("factory", [SGMCMC.sgld, SGLD], ids=["sgmcmc", "sgld"]) + def test_norm_crosscheck_with_grad_gap(self, factory): + torch.manual_seed(42) + w1 = nn.Parameter(torch.randn(80, dtype=torch.float64)) + w2 = nn.Parameter(torch.randn(40, dtype=torch.float64)) + w3 = nn.Parameter(torch.randn(80, dtype=torch.float64)) + + opt = _make_optimizer( + factory, + [w1, w2, w3], + lr=0.5, + nbeta=2.0, + noise_level=0.0, + save_metrics=True, + sketch_dim=64, + ) + + w1.grad = torch.randn_like(w1) + # w2.grad is intentionally left as None (the default). + w3.grad = torch.randn_like(w3) + + opt.step(noise_generator=torch.Generator().manual_seed(0)) + + metrics = opt.get_metrics() + sketches = opt.get_sketches() + + for q in ("scaled_grad", "unscaled_grad"): + exact = getattr(metrics, q).item() + sketch_norm = getattr(sketches, q).norm().item() + assert exact > 0, q + assert sketch_norm == pytest.approx(exact, rel=0.25), q + + def test_sketch_matches_manual_scatter(self): + """Exact comparison: optimizer sketch == manual scatter at correct offsets.""" + torch.manual_seed(42) + w1 = nn.Parameter(torch.randn(80, dtype=torch.float64)) + w2 = nn.Parameter(torch.randn(40, dtype=torch.float64)) + w3 = nn.Parameter(torch.randn(80, dtype=torch.float64)) + + opt = _make_optimizer( + SGMCMC.sgld, + [w1, w2, w3], + lr=0.5, + nbeta=2.0, + noise_level=0.0, + sketch_dim=64, + ) + + w1.grad = torch.randn_like(w1) + # w2.grad is intentionally left as None (the default). + w3.grad = torch.randn_like(w3) + + # SGMCMC.sgld preconditioner: overall_coef=1, grad_coef=1 + # scaled_grad = (lr/2) * grad * nbeta = 0.25 * grad * 2.0 = 0.5 * grad + sg1 = (0.5 * w1.grad).float() + sg3 = (0.5 * w3.grad).float() + + expected = torch.zeros(64, dtype=torch.float32) + assert opt._sketch is not None + opt._sketch.scatter_into_(expected, sg1, offset=0) + opt._sketch.scatter_into_(expected, sg3, offset=80 + 40) + + opt.step(noise_generator=torch.Generator().manual_seed(0)) + actual = opt.get_sketches().scaled_grad + + torch.testing.assert_close(actual, expected, atol=0, rtol=0) + + +class TestSketchWithoutMetrics: + """Sketches work independently of save_metrics.""" + + def test_sketches_without_save_metrics(self): + w = nn.Parameter(torch.zeros(50, dtype=torch.float64)) + opt = _make_optimizer( + SGMCMC.sgld, + [w], + lr=0.5, + nbeta=2.0, + localization=1.0, + noise_level=0.0, + save_metrics=False, + sketch_dim=64, + ) + + w.data.fill_(1.0) + w.grad = torch.randn_like(w) + opt.step(noise_generator=torch.Generator().manual_seed(0)) + + sketches = opt.get_sketches() + assert sketches.scaled_grad.norm().item() > 0 + assert sketches.localization.norm().item() > 0 + + def test_decomposition_works_without_save_metrics(self): + """Prior decomposition into loc/wd happens even without save_metrics.""" + w = nn.Parameter(torch.tensor([0.0, 0.0], dtype=torch.float64)) + opt = _make_optimizer( + SGMCMC.sgld, + [w], + lr=2.0, + nbeta=2.0, + localization=1.0, + weight_decay=1.0, + noise_level=0.0, + save_metrics=False, + sketch_dim=64, + ) + + w.data = torch.tensor([3.0, 4.0], dtype=torch.float64) + w.grad = torch.zeros(2, dtype=torch.float64) + opt.step(noise_generator=torch.Generator().manual_seed(0)) + + sketches = opt.get_sketches() + assert sketches.localization.norm().item() > 0 + assert sketches.weight_decay.norm().item() > 0 + + +# --------------------------------------------------------------------------- +# Legacy SGLD sketch tests +# --------------------------------------------------------------------------- + + +class TestSGLDSketchInit: + def test_creates_sketch_buffer(self): + w = nn.Parameter(torch.randn(10, dtype=torch.float64)) + opt = _make_optimizer(SGLD, [w], lr=0.5, nbeta=2.0, sketch_dim=32) + assert opt.save_sketches + assert isinstance(opt._sketch_buf, SketchBuffer) + + def test_correct_output_dim(self): + w = nn.Parameter(torch.randn(10, dtype=torch.float64)) + opt = _make_optimizer(SGLD, [w], lr=0.5, nbeta=2.0, sketch_dim=64) + assert opt._sketch_buf.scaled_grad.shape == (64,) + + def test_not_enabled_by_default(self): + w = nn.Parameter(torch.randn(10, dtype=torch.float64)) + opt = _make_optimizer(SGLD, [w], lr=0.5, nbeta=2.0) + assert not opt.save_sketches + + def test_get_sketches_raises_when_not_enabled(self): + w = nn.Parameter(torch.randn(5)) + opt = _make_optimizer(SGLD, [w], lr=0.5, nbeta=2.0) + with pytest.raises(RuntimeError, match="Sketches not enabled"): + opt.get_sketches() + + def test_returns_all_quantities_on_cpu(self): + w = nn.Parameter(torch.randn(10, dtype=torch.float64)) + opt = _make_optimizer( + SGLD, [w], lr=0.5, nbeta=2.0, noise_level=0.0, sketch_dim=32 + ) + w.grad = torch.randn_like(w) + opt.step(noise_generator=torch.Generator().manual_seed(0)) + + sketches = opt.get_sketches() + for q in SKETCH_QUANTITIES: + t = getattr(sketches, q) + assert t.shape == (32,), q + assert t.device == torch.device("cpu"), q + + +class TestSGLDSketchZeroComponents: + """When a component is zero in SGLD, its sketch should also be zero.""" + + def test_noise_zero_when_disabled(self): + w = nn.Parameter(torch.tensor([3.0, 4.0], dtype=torch.float64)) + opt = _make_optimizer( + SGLD, [w], lr=1.0, nbeta=2.0, noise_level=0.0, sketch_dim=32 + ) + w.grad = torch.randn_like(w) + opt.step(noise_generator=torch.Generator().manual_seed(0)) + + sketches = opt.get_sketches() + torch.testing.assert_close(sketches.noise, torch.zeros(32), atol=0, rtol=0) + + def test_prior_zero_without_localization_or_weight_decay(self): + w = nn.Parameter(torch.tensor([3.0, 4.0], dtype=torch.float64)) + opt = _make_optimizer( + SGLD, [w], lr=1.0, nbeta=2.0, noise_level=0.0, sketch_dim=32 + ) + w.grad = torch.randn_like(w) + opt.step(noise_generator=torch.Generator().manual_seed(0)) + + sketches = opt.get_sketches() + torch.testing.assert_close( + sketches.localization, torch.zeros(32), atol=0, rtol=0 + ) + torch.testing.assert_close( + sketches.weight_decay, torch.zeros(32), atol=0, rtol=0 + ) + + +class TestSGLDSketchNormCrossCheck: + """Sketch norms from SGLD should approximate the exact metric norms.""" + + def test_all_quantities_over_multiple_steps(self): + torch.manual_seed(42) + w = nn.Parameter(torch.randn(200, dtype=torch.float64)) + opt = _make_optimizer( + SGLD, + [w], + lr=0.5, + nbeta=2.0, + localization=1.0, + weight_decay=0.5, + save_metrics=True, + sketch_dim=64, + ) + + gen = torch.Generator().manual_seed(99) + for _ in range(5): + w.grad = torch.randn_like(w) + opt.step(noise_generator=gen) + + metrics = opt.get_metrics() + sketches = opt.get_sketches() + + for q in ("scaled_grad", "unscaled_grad", "localization", "weight_decay"): + exact = getattr(metrics, q).item() + sketch_norm = getattr(sketches, q).norm().item() + if exact > 1e-8: + assert sketch_norm == pytest.approx(exact, rel=0.25), q + + def test_with_mask(self): + """SGLD applies masks manually; sketches must respect them.""" + w = nn.Parameter(torch.randn(100, dtype=torch.float64)) + mask = torch.zeros(100, dtype=torch.float64) + mask[:50] = 1.0 + + opt = _make_optimizer( + SGLD, + [{"params": [w], "mask": mask}], + lr=0.5, + nbeta=2.0, + localization=1.0, + save_metrics=True, + noise_level=0.0, + sketch_dim=64, + ) + + w.grad = torch.randn_like(w) + opt.step(noise_generator=torch.Generator().manual_seed(0)) + + metrics = opt.get_metrics() + sketches = opt.get_sketches() + + exact_norm = metrics.scaled_grad.item() + sketch_norm = sketches.scaled_grad.norm().item() + assert exact_norm > 0 + assert sketch_norm == pytest.approx(exact_norm, rel=0.25) + + +class TestSGLDSketchWithoutMetrics: + """SGLD sketches work independently of save_metrics.""" + + def test_sketches_without_save_metrics(self): + w = nn.Parameter(torch.zeros(50, dtype=torch.float64)) + opt = _make_optimizer( + SGLD, + [w], + lr=0.5, + nbeta=2.0, + localization=1.0, + noise_level=0.0, + save_metrics=False, + sketch_dim=64, + ) + + w.data.fill_(1.0) + w.grad = torch.randn_like(w) + opt.step(noise_generator=torch.Generator().manual_seed(0)) + + sketches = opt.get_sketches() + assert sketches.scaled_grad.norm().item() > 0 + assert sketches.localization.norm().item() > 0 + + def test_loc_and_wd_decomposed_without_save_metrics(self): + """Both localization and weight_decay sketches are non-zero + even when save_metrics=False.""" + w = nn.Parameter(torch.tensor([0.0, 0.0], dtype=torch.float64)) + opt = _make_optimizer( + SGLD, + [w], + lr=2.0, + nbeta=2.0, + localization=1.0, + weight_decay=1.0, + noise_level=0.0, + save_metrics=False, + sketch_dim=64, + ) + + w.data = torch.tensor([3.0, 4.0], dtype=torch.float64) + w.grad = torch.zeros(2, dtype=torch.float64) + opt.step(noise_generator=torch.Generator().manual_seed(0)) + + sketches = opt.get_sketches() + assert sketches.localization.norm().item() > 0 + assert sketches.weight_decay.norm().item() > 0 diff --git a/tests/slt/__snapshots__/rrr_test.ambr b/tests/slt/__snapshots__/rrr_test.ambr deleted file mode 100644 index 31fe6973..00000000 --- a/tests/slt/__snapshots__/rrr_test.ambr +++ /dev/null @@ -1,61 +0,0 @@ -# serializer version: 1 -# name: test_accuracy_rrr[perturbed-case_1_even_5x4x5-SGLD] - -950.279052734375 -# --- -# name: test_accuracy_rrr[perturbed-case_1_even_5x4x5-sgld] - -950.279052734375 -# --- -# name: test_accuracy_rrr[perturbed-case_1_odd_5x3x5-SGLD] - -540.136474609375 -# --- -# name: test_accuracy_rrr[perturbed-case_1_odd_5x3x5-sgld] - -540.136474609375 -# --- -# name: test_accuracy_rrr[perturbed-case_2_4x3x8-SGLD] - -428.4161071777344 -# --- -# name: test_accuracy_rrr[perturbed-case_2_4x3x8-sgld] - -428.4161071777344 -# --- -# name: test_accuracy_rrr[perturbed-case_3_8x3x4-SGLD] - -1717.2952880859375 -# --- -# name: test_accuracy_rrr[perturbed-case_3_8x3x4-sgld] - -1717.2952880859375 -# --- -# name: test_accuracy_rrr[perturbed-case_4_3x8x4-SGLD] - -5700.07275390625 -# --- -# name: test_accuracy_rrr[perturbed-case_4_3x8x4-sgld] - -5700.07275390625 -# --- -# name: test_accuracy_rrr[unperturbed-case_1_even_5x4x5-SGLD] - 0.36638423800468445 -# --- -# name: test_accuracy_rrr[unperturbed-case_1_even_5x4x5-sgld] - 0.36638423800468445 -# --- -# name: test_accuracy_rrr[unperturbed-case_1_odd_5x3x5-SGLD] - 0.3891811668872833 -# --- -# name: test_accuracy_rrr[unperturbed-case_1_odd_5x3x5-sgld] - 0.3891811668872833 -# --- -# name: test_accuracy_rrr[unperturbed-case_2_4x3x8-SGLD] - 0.13740848004817963 -# --- -# name: test_accuracy_rrr[unperturbed-case_2_4x3x8-sgld] - 0.13740848004817963 -# --- -# name: test_accuracy_rrr[unperturbed-case_3_8x3x4-SGLD] - 0.4877088963985443 -# --- -# name: test_accuracy_rrr[unperturbed-case_3_8x3x4-sgld] - 0.4877088963985443 -# --- -# name: test_accuracy_rrr[unperturbed-case_4_3x8x4-SGLD] - 0.4049715995788574 -# --- -# name: test_accuracy_rrr[unperturbed-case_4_3x8x4-sgld] - 0.4049715995788574 -# --- diff --git a/tests/slt/__snapshots__/sampler_ordinality_test.ambr b/tests/slt/__snapshots__/sampler_ordinality_test.ambr deleted file mode 100644 index 13796998..00000000 --- a/tests/slt/__snapshots__/sampler_ordinality_test.ambr +++ /dev/null @@ -1,49 +0,0 @@ -# serializer version: 1 -# name: test_linedot_normal_crossing[10-LinePlusDot-SGLD] - list([ - 8.628656473774754e-07, - 125492.046875, - ]) -# --- -# name: test_linedot_normal_crossing[10-LinePlusDot-sgld] - list([ - 8.628656473774754e-07, - 125492.046875, - ]) -# --- -# name: test_linedot_normal_crossing[10-Polynomial-SGLD] - list([ - 0.0, - -5.7380566431675106e-05, - ]) -# --- -# name: test_linedot_normal_crossing[10-Polynomial-sgld] - list([ - 0.0, - -5.7380566431675106e-05, - ]) -# --- -# name: test_linedot_normal_crossing[2-LinePlusDot-SGLD] - list([ - 2.804313453452778e-06, - 0.05154026299715042, - ]) -# --- -# name: test_linedot_normal_crossing[2-LinePlusDot-sgld] - list([ - 2.804313453452778e-06, - 0.05154026299715042, - ]) -# --- -# name: test_linedot_normal_crossing[2-Polynomial-SGLD] - list([ - 0.0, - -0.0008089365437626839, - ]) -# --- -# name: test_linedot_normal_crossing[2-Polynomial-sgld] - list([ - 0.0, - -0.0008089365437626839, - ]) -# --- diff --git a/tests/slt/cov_test.py b/tests/slt/cov_test.py deleted file mode 100644 index 0f7ebbdd..00000000 --- a/tests/slt/cov_test.py +++ /dev/null @@ -1,315 +0,0 @@ -import numpy as np -import pytest -import torch -import torch.nn as nn - -from devinterp.slt.cov import ( - BetweenLayerCovarianceAccumulator, - CovarianceAccumulator, - WithinHeadCovarianceAccumulator, -) - - -# A simple dummy model -class DummyModel(nn.Module): - def __init__(self, size): - super(DummyModel, self).__init__() - self.fc1 = nn.Linear(size, size) - self.fc2 = nn.Linear(size, size) - - -def accessor_layer1(model: nn.Module): - return model.fc1.weight.data - - -def accessor_layer2(model: nn.Module): - return model.fc2.weight.data - - -@pytest.fixture -def dummy_model(): - model = DummyModel(10) - return model - - -@pytest.fixture -def cov_matrix(): - return np.array([[2.0, 1.0], [1.0, 2.0]]) - - -@pytest.fixture -def random_vars(cov_matrix): - np.random.seed(0) - return np.random.multivariate_normal([0, 0], cov_matrix, 100) - - -@pytest.fixture -def known_accessor(random_vars): - i = -1 - - def _known_accessor(model): - nonlocal i - i += 1 - return torch.tensor(random_vars[i], dtype=torch.float32) - - return _known_accessor - - -def test_covariance_accumulator_with_known_cov(dummy_model, known_accessor, cov_matrix): - acc = CovarianceAccumulator(num_weights=2, accessors=[known_accessor]) - - for _ in range(100): - acc(dummy_model) - - acc.finalize() - - cov_matrix = acc.to_matrix().detach().cpu() - assert np.allclose( - cov_matrix, cov_matrix, atol=1e-1 - ) # Tolerance due to finite sample - - # Test the eigenvalues - evals, evecs = np.linalg.eig(cov_matrix) - eigenspectrum = acc.to_eigen(include_matrix=True) - - assert np.allclose(evals, eigenspectrum["evals"], atol=1e-1) - assert np.allclose(np.abs(evecs), np.abs(eigenspectrum["evecs"]), atol=1e-1) - - -def test_reset_functionality(dummy_model, known_accessor): - acc = CovarianceAccumulator(num_weights=2, accessors=[known_accessor]) - - for _ in range(100): - acc(dummy_model) - - acc.reset() - - assert torch.all(acc.first_moment == 0) - assert torch.all(acc.second_moment == 0) - assert acc.num_draws == 0 - assert acc.is_finished is False - - -def test_full_model(dummy_model): - np.random.seed(0) - - cov_matrix = np.random.normal(size=(20, 20)) - - # Reduce the correlation between layers (off-diagonal blocks) - cov_matrix[:10, 10:] *= 0.1 - cov_matrix[10:, :10] *= 0.1 - - def update_model(model): - new_params = np.random.multivariate_normal(np.zeros(20), cov_matrix) - model.fc1.weight.data = torch.tensor( - new_params[:10].reshape((10,)), dtype=torch.float32 - ) - model.fc2.weight.data = torch.tensor( - new_params[10:].reshape((10,)), dtype=torch.float32 - ) - - acc = CovarianceAccumulator( - num_weights=20, accessors=[accessor_layer1, accessor_layer2] - ) - - for _ in range(100): - update_model(dummy_model) - acc(dummy_model) - - acc.finalize() - - cov_matrix = acc.to_matrix().detach().cpu().numpy() - assert np.allclose( - cov_matrix, cov_matrix, atol=1e-1 - ) # Tolerance due to finite sample - - -class DummyTransformer(nn.Module): - def __init__(self, num_layers=2, num_heads=2, embed_dim=4): - super().__init__() - self.layers = nn.ModuleList( - [ - nn.MultiheadAttention(embed_dim, num_heads, bias=False) - for _ in range(num_layers) - ] - ) - - def get_qkv_matrices(self, layer_idx, head_idx): - layer = self.layers[layer_idx].in_proj_weight # (3 * embed_dim, embed_dim) - head = layer[head_idx * 6 : (head_idx + 1) * 6, :] - return head - - -@pytest.fixture -def dummy_transformer(): - return DummyTransformer() - - -def accessor_transformer_layer1(model): - return tuple(model.get_qkv_matrices(0, h) for h in range(2)) - - -def accessor_transformer_layer2(model): - return tuple(model.get_qkv_matrices(1, h) for h in range(2)) - - -def l1h1(model): - return model.get_qkv_matrices(0, 0) - - -def l1h2(model): - return model.get_qkv_matrices(0, 1) - - -def l2h1(model): - return model.get_qkv_matrices(1, 0) - - -def l2h2(model): - return model.get_qkv_matrices(1, 1) - - -def test_within_head_covariance(dummy_transformer): - np.random.seed(0) - - num_heads = 2 - num_weights_per_head = 3 * 4 * (4 // num_heads) - num_parameters_per_layer = num_weights_per_head * num_heads - - cov_matrix_layer_1 = np.random.normal( - size=(num_parameters_per_layer, num_parameters_per_layer) - ) - cov_matrix_layer_1 = cov_matrix_layer_1 @ cov_matrix_layer_1.T - cov_matrix_layer_2 = np.random.normal( - size=(num_parameters_per_layer, num_parameters_per_layer) - ) - cov_matrix_layer_2 = cov_matrix_layer_2 @ cov_matrix_layer_2.T - - def update_model(model): - new_params_layer_1 = np.random.multivariate_normal( - np.zeros(num_parameters_per_layer), cov_matrix_layer_1 - ) - new_params_layer_2 = np.random.multivariate_normal( - np.zeros(num_parameters_per_layer), cov_matrix_layer_2 - ) - - model.layers[0].in_proj_weight.data = torch.tensor( - new_params_layer_1, dtype=torch.float32 - ).reshape(model.layers[0].in_proj_weight.shape) - model.layers[1].in_proj_weight.data = torch.tensor( - new_params_layer_2, dtype=torch.float32 - ).reshape(model.layers[1].in_proj_weight.shape) - - acc = WithinHeadCovarianceAccumulator( - num_heads, - num_weights_per_head, - [accessor_transformer_layer1, accessor_transformer_layer2], - ) - - for _ in range(1_000): - update_model(dummy_transformer) - acc(dummy_transformer) - - acc.finalize() - cov_matrix = acc.to_matrix().detach().cpu().numpy() - - mse = 0 - - # Extract submatrices for each layer and head, and validate - for l, actual_cov_matrix in enumerate( - [cov_matrix_layer_1, cov_matrix_layer_2] - ): # 2 layers - for h in range(num_heads): # 2 heads - local_cov_matrix = cov_matrix[l, h] - actual_local_cov_matrix = actual_cov_matrix[ - h * num_weights_per_head : (h + 1) * num_weights_per_head, - h * num_weights_per_head : (h + 1) * num_weights_per_head, - ] - local_mse = ( - np.sum( - (local_cov_matrix - actual_local_cov_matrix) - / local_cov_matrix.shape[0] - ) - ** 2 - ) - - # fig, axes = plt.subplots(1, 2) - # fig.suptitle(f"Layer {l}, Head {h} MSE: {local_mse}") - # axes[0].imshow(local_cov_matrix) - # axes[0].set_title("Observed") - # axes[1].imshow(actual_local_cov_matrix) - # axes[1].set_title("Theoretical") - # plt.show() - - mse += local_mse / (num_heads * 2) - - assert mse < 3, ( - f"MSE: {mse}" - ) # Visually this looks good, but the MSE is a bit high. - - -def test_between_layer_covariance_within_heads(dummy_transformer): - np.random.seed(0) - - num_heads = 2 - num_weights_per_head = 3 * 4 * (4 // num_heads) - num_parameters_per_layer = num_weights_per_head * num_heads - - cov_matrix_layer_1 = np.random.normal( - size=(num_parameters_per_layer, num_parameters_per_layer) - ) - cov_matrix_layer_1 = cov_matrix_layer_1 @ cov_matrix_layer_1.T - cov_matrix_layer_2 = np.random.normal( - size=(num_parameters_per_layer, num_parameters_per_layer) - ) - cov_matrix_layer_2 = cov_matrix_layer_2 @ cov_matrix_layer_2.T - - def update_model(model): - new_params_layer_1 = np.random.multivariate_normal( - np.zeros(num_parameters_per_layer), cov_matrix_layer_1 - ) - new_params_layer_2 = np.random.multivariate_normal( - np.zeros(num_parameters_per_layer), cov_matrix_layer_2 - ) - - model.layers[0].in_proj_weight.data = torch.tensor( - new_params_layer_1, dtype=torch.float32 - ).reshape(model.layers[0].in_proj_weight.shape) - model.layers[1].in_proj_weight.data = torch.tensor( - new_params_layer_2, dtype=torch.float32 - ).reshape(model.layers[1].in_proj_weight.shape) - - acc1 = WithinHeadCovarianceAccumulator( - num_heads, - num_weights_per_head, - [accessor_transformer_layer1, accessor_transformer_layer2], - ) - acc2 = BetweenLayerCovarianceAccumulator( - dummy_transformer, - pairs={ - "l1h1": ("l1h1", "l1h1"), - "l1h2": ("l1h2", "l1h2"), - "l2h1": ("l2h1", "l2h1"), - "l2h2": ("l2h2", "l2h2"), - }, - l1h1=l1h1, - l1h2=l1h2, - l2h1=l2h1, - l2h2=l2h2, - ) - - for _ in range(1_000): - update_model(dummy_transformer) - acc1(dummy_transformer) - acc2(dummy_transformer) - - acc1.finalize() - acc2.finalize() - - cov1 = acc1.to_matrix().detach().cpu().numpy() - cov2 = {k: v.detach().cpu().numpy() for k, v in acc2.to_matrices().items()} - - # Extract submatrices for each layer and head, and validate - for l in range(2): - for h in range(num_heads): - assert np.allclose(cov1[l, h], cov2[f"l{l + 1}h{h + 1}"]) diff --git a/tests/slt/mala_test.py b/tests/slt/mala_test.py deleted file mode 100644 index 8b2180fc..00000000 --- a/tests/slt/mala_test.py +++ /dev/null @@ -1,134 +0,0 @@ -import numpy as np -import pytest -import torch -import torch.nn as nn -from devinterp.optim import SGLD, SGMCMC -from devinterp.slt.mala import MalaAcceptanceRate, mala_acceptance_probability -from devinterp.slt.sampler import sample -from devinterp.utils import default_nbeta, make_evaluate - - -class Polynomial(nn.Module): - def __init__(self, powers=[1, 1]): - super(Polynomial, self).__init__() - self.powers = torch.tensor(powers) - self.weights = nn.Parameter( - torch.tensor( - torch.zeros_like(self.powers, dtype=torch.float32), requires_grad=True - ) - ) - - def forward(self, x): - return torch.sum(torch.pow(self.weights, self.powers)) - - -def linear_loss(y_preds, ys): - return torch.mean(y_preds) - - -MALA_CALC_TESTCASES = [ - [[0.0, 0.0], [0.0, 0.0], [0.0], [1.5, 5.5], [1.5, 5.5], [16.25], 0.1, 0.6661436], - [[1.0, 1.0], [1.0, 1.0], 1.0, [1.5, 0.5], [1.5, 0.5], 1.25, 0.5, 0.9692332], - [[0.0, 0.0], [0.0, 0.0], 0.0, [10.5, 5.5], [10.5, 5.5], 70.25, 0.1, 0.17268492], - [[0.0, 0.0], [0.0, 0.0], 0.0, [10.5, 5.5], [10.5, 5.5], 70.25, 0.5, 0.00015359], -] - - -@pytest.mark.parametrize( - "prev_point,prev_grad,prev_loss,current_point,current_grad,current_loss,learning_rate,benchmark_accept_prob", - MALA_CALC_TESTCASES, -) -def test_mala_calc( - prev_point, - prev_grad, - prev_loss, - current_point, - current_grad, - current_loss, - learning_rate, - benchmark_accept_prob, -): - mala_accept_prob = mala_acceptance_probability( - torch.tensor(prev_point), - torch.tensor(prev_grad), - torch.tensor(prev_loss), - torch.tensor(current_point), - torch.tensor(current_grad), - torch.tensor(current_loss), - torch.tensor(learning_rate), - ) - assert np.isclose(mala_accept_prob, benchmark_accept_prob, atol=0.000001), ( - f"MALA accept prob {mala_accept_prob}, not close to benchmark value {benchmark_accept_prob:.2f}" - ) - - -SETS_TO_TEST = [ - [[2], 1.0e-2, 100.0, 0.59], - [[2], 1.0e-2, 0.1, 0.73], - [[2], 1.0e-3, 100.0, 0.98], - [[4], 1.5e-2, 100.0, 0.85], - [[4], 1.5e-2, 1.0, 0.935], - [[4], 1.5e-5, 100.0, 0.999], - [[8], 1.5e-2, 100.0, 0.87], - [[8], 1.0e-2, 0.1, 0.97], - [[8], 1.0e-5, 100.0, 0.999], - [[2, 2], 1.0e-3, 100.0, 0.971], - [[2, 2], 1.0e-2, 100.0, 0.54], - [[2, 2], 3e-3, 0.01, 0.91], - [[0, 2], 2.0e-3, 100.0, 0.95], - [[4, 0], 1.0e-3, 100.0, 0.995], - [[4, 4], 1.0e-2, 100.0, 0.87], -] - - -@pytest.mark.slow -@pytest.mark.parametrize("powers,lr,localization,accept_prob", SETS_TO_TEST) -@pytest.mark.parametrize("sampling_method", [SGLD, SGMCMC.sgld]) -def test_mala_callback_closeness( - generated_normalcrossing_dataset, - powers, - lr, - localization, - accept_prob, - sampling_method, -): - if sampling_method == SGMCMC.sgld: - pytest.skip("Failing since 2025-01-01 or so, also not used so skipping") - - seed = 0 - for seed in range(10): - seed += 1 - model = Polynomial(powers) - train_dataloader, _, _, _ = generated_normalcrossing_dataset - evaluate = make_evaluate(linear_loss) - num_draws = 5_000 - num_chains = 1 - mala_estimator = MalaAcceptanceRate( - num_chains=num_chains, - num_draws=num_draws, - nbeta=default_nbeta(train_dataloader), - learning_rate=lr, - ) - sample( - model, - train_dataloader, - evaluate=evaluate, - sampling_method_kwargs=dict( - lr=lr, - localization=localization, - nbeta=default_nbeta(train_dataloader), - metrics=["distance"], - ), - sampling_method=sampling_method, - num_chains=num_chains, - num_draws=num_draws, - callbacks=[mala_estimator], - verbose=False, - seed=seed, - ) - mala_acceptance_rate_mean = mala_estimator.get_results()["mala_accept/mean"] - if not np.isnan(mala_acceptance_rate_mean): - break - assert np.isclose(mala_acceptance_rate_mean, accept_prob, atol=0.01), ( - f"MALA Rate mean {mala_acceptance_rate_mean:.2f}, not close to benchmark value {accept_prob:.2f}, lr {lr} elas {localization}" - ) diff --git a/tests/slt/rrr_test.py b/tests/slt/rrr_test.py deleted file mode 100644 index 6c0987df..00000000 --- a/tests/slt/rrr_test.py +++ /dev/null @@ -1,286 +0,0 @@ -import numpy as np -import pytest -import torch -import torch.nn.functional as F -import platform -from devinterp.backends.default.slt.sampler import sample -from devinterp.optim import SGLD, SGMCMC -from devinterp.slt.llc import LLCEstimator -from devinterp.utils import default_nbeta, evaluate_mse, get_init_loss_multi_batch -from torch.utils.data import DataLoader, TensorDataset - -# Test configuration constants -TOLERANCE_RTOL = 0.4 -CONFIDENCE_MULTIPLIER = 2.5 -FULL_SAMPLING_DRAWS = 400 -SNAPSHOT_DRAWS = 5 -LEARNING_RATE = 0.0006 -LOCALIZATION = 1.0 -NUM_CHAINS = 3 -RANDOM_SEED = 42 -NUM_SAMPLES = 1000 - - -# not a fixture as we're generating data for several m, n combinations -# and I couldn't figure out how to fit that into the fixture mold -def generated_rrr_dataset(m, n): - """Generate synthetic dataset for reduced rank regression testing. - - Args: - m: Input dimension - n: Output dimension - - Returns: - Tuple of (dataloader, dataset, input_tensor, output_tensor) - """ - torch.manual_seed(RANDOM_SEED) - np.random.seed(RANDOM_SEED) - x = torch.randn(NUM_SAMPLES, m) - y = torch.randn(NUM_SAMPLES, n) - train_data = TensorDataset(x, y) - - # Add deterministic generator - generator = torch.Generator() - generator.manual_seed(RANDOM_SEED) - train_dataloader = DataLoader(train_data, batch_size=NUM_SAMPLES, shuffle=True) - return train_dataloader, train_data, x, y - - -# Test case mapping for theoretical scenarios from Aoyagi & Watanabe (2004) -TEST_CASES = [ - (5, 3, 5, "case_1_odd", "General case with odd sum of dimensions"), - (5, 4, 5, "case_1_even", "General case with even sum of dimensions"), - (4, 3, 8, "case_2", "Input + hidden < output dimension"), - (8, 3, 4, "case_3", "Output + hidden < input dimension"), - (3, 8, 4, "case_4", "Input + output < hidden dimension"), -] - - -@pytest.mark.skipif( - platform.machine() != "x86_64", - reason=f"Differences in results between ARM and x86_64. Your arch is {platform.machine()}", -) -@pytest.mark.parametrize("sampling_method", [SGLD, SGMCMC.sgld]) -@pytest.mark.parametrize( - "m,h,n,case_name,description", - TEST_CASES, - ids=[f"{case[3]}_{case[0]}x{case[1]}x{case[2]}" for case in TEST_CASES], -) -@pytest.mark.parametrize("perturb", [True, False], ids=["perturbed", "unperturbed"]) -def test_accuracy_rrr( - sampling_method, - m, - h, - n, - case_name, - description, - perturb, - ReducedRankRegressor, - snapshot, - is_snapshot_update, -): - """Test reduced rank regression LLC estimation accuracy. - - Tests the Local Learning Coefficient (LLC) estimation for different - dimensional configurations of reduced rank regression models. - - Based on "The Generalization Error of Reduced Rank Regression in Bayesian Estimation", - M. Aoyagi & S. Watanabe, 2004. - - Args: - sampling_method: SGLD or SGMCMC sampling method - m: Input dimension - h: Hidden/rank dimension - n: Output dimension - case_name: Theoretical case identifier - description: Human-readable description of the test case - perturb: Whether to perturb the model away from optimal parameters - ReducedRankRegressor: Model class fixture - snapshot: Expected LLC value for regression testing - is_snapshot_update: Whether to run full accuracy test against theory - """ - # Set up test data and model - model, train_dataloader = _setup_rrr_model(m, h, n, perturb, ReducedRankRegressor) - - # Run the snapshot test first, so our random seed is consistent. - llc_mean, llc_std_dev = do_sampling( - sampling_method, train_dataloader, model, num_draws=SNAPSHOT_DRAWS - ) - - # Test against theoretical values when updating snapshots - if is_snapshot_update: - # Verify that the calculated case matches the expected case from pytest parameters - calculated_case, _ = calc_true_lc(m, h, n) - assert calculated_case == case_name, ( - f"Calculated theoretical case '{calculated_case}' does not match expected case '{case_name}' " - f"for dimensions (M={m}, H={h}, N={n})" - ) - - _test_theoretical_accuracy( - sampling_method, train_dataloader, model, m, h, n, perturb - ) - - # Always test against snapshot for consistency. - assert llc_mean == snapshot - - -def _setup_rrr_model(m, h, n, perturb, ReducedRankRegressor): - """Set up the reduced rank regression model for testing.""" - torch.manual_seed(RANDOM_SEED) - np.random.seed(RANDOM_SEED) - - criterion = F.mse_loss - train_dataloader, train_data, x, y = generated_rrr_dataset(m, n) - # Update dimensions based on actual data - m = x.size(1) - n = y.size(1) - - model = ReducedRankRegressor(m, h, n, x, y, criterion) - - if perturb: - # We repeat the Litany Against Non-Determinism: - torch.manual_seed(RANDOM_SEED) - np.random.seed(RANDOM_SEED) - model.perturb() - - return model, train_dataloader - - -def _test_theoretical_accuracy( - sampling_method, train_dataloader, model, m, h, n, perturb -): - """Test LLC estimation against theoretical values with full sampling.""" - llc_mean, llc_std_dev = do_sampling( - sampling_method, train_dataloader, model, num_draws=FULL_SAMPLING_DRAWS - ) - case, true_lc = calc_true_lc(m, h, n) - - if not perturb: - _assert_close_to_theory( - llc_mean, llc_std_dev, true_lc, case, m, h, n, sampling_method - ) - else: - _assert_not_close_to_theory( - llc_mean, llc_std_dev, true_lc, case, m, h, n, sampling_method - ) - - -def _assert_close_to_theory( - llc_mean, llc_std_dev, true_lc, case, m, h, n, sampling_method -): - """Assert that LLC estimate is close to theoretical value.""" - confidence_interval = CONFIDENCE_MULTIPLIER * llc_std_dev - error_msg = ( - f"DLN case {case}: LLC estimate {llc_mean:.3f} ± {confidence_interval:.3f} " - f"should be close to theoretical LC {true_lc:.3f} " - f"for dimensions (M={m}, H={h}, N={n}) using {sampling_method}" - ) - assert np.isclose(llc_mean, true_lc, rtol=TOLERANCE_RTOL), error_msg - - -def _assert_not_close_to_theory( - llc_mean, llc_std_dev, true_lc, case, m, h, n, sampling_method -): - """Assert that perturbed model LLC estimate is not close to theoretical value.""" - confidence_interval = CONFIDENCE_MULTIPLIER * llc_std_dev - error_msg = ( - f"Perturbed model should not match theory. " - f"DLN case {case}: LLC estimate {llc_mean:.3f} ± {confidence_interval:.3f} " - f"vs theoretical LC {true_lc:.3f} " - f"for dimensions (M={m}, H={h}, N={n}) using {sampling_method}" - ) - assert not np.isclose(llc_mean, true_lc, rtol=TOLERANCE_RTOL), error_msg - - -def do_sampling(sampling_method, train_dataloader, model, num_draws): - """Perform MCMC sampling to estimate LLC. - - Args: - sampling_method: SGLD or SGMCMC sampling method - train_dataloader: DataLoader for training data - model: Model to sample from - num_draws: Number of MCMC draws to perform - - Returns: - Tuple of (llc_mean, llc_std_dev) - """ - torch.manual_seed(RANDOM_SEED) - np.random.seed(RANDOM_SEED) - - init_loss = get_init_loss_multi_batch( - train_dataloader, NUM_CHAINS, model, evaluate_mse, device="cpu" - ) - - llc_estimator = LLCEstimator( - num_chains=NUM_CHAINS, - num_draws=num_draws, - nbeta=default_nbeta(train_dataloader), - init_loss=init_loss, - ) - - sample( - model, - train_dataloader, - evaluate=evaluate_mse, - sampling_method_kwargs=dict( - lr=LEARNING_RATE, - localization=LOCALIZATION, - nbeta=default_nbeta(train_dataloader), - ), - sampling_method=sampling_method, - num_chains=NUM_CHAINS, - num_draws=num_draws, - callbacks=[llc_estimator], - verbose=False, - seed=RANDOM_SEED, - ) - - results = llc_estimator.get_results() - return results["llc/mean"], results["llc/std"] - - -def calc_true_lc(m, h, n): - """Calculate theoretical learning coefficient for reduced rank regression. - - Based on the theoretical results from Aoyagi & Watanabe (2004). - - Args: - m: Input dimension - h: Hidden/rank dimension - n: Output dimension - - Returns: - Tuple of (case_name, theoretical_lc_value) - """ - # Determine which theoretical case applies - case_2 = m + h < n - case_3 = n + h < m - case_4 = m + n < h - case_1_even = not (case_2 or case_3 or case_4) and (m + h + n) % 2 == 0 - case_1_odd = not (case_2 or case_3 or case_4) and (m + h + n) % 2 == 1 - - if case_2: - return "case_2", m * h / 2 - elif case_3: - return "case_3", h * n / 2 - elif case_4: - return "case_4", m * n / 2 - elif case_1_even: - numerator = 2 * m * n + 2 * h * n + 2 * m * h - n**2 - m**2 - h**2 - return "case_1_even", numerator / 8 - elif case_1_odd: - numerator = 1 + 2 * m * n + 2 * h * n + 2 * m * h - n**2 - m**2 - h**2 - return "case_1_odd", numerator / 8 - else: - raise ValueError( - f"Unknown theoretical case for dimensions (M={m}, H={h}, N={n})" - ) - - -# TODO: -# Scale up these estimates like in Furman & Lau (2024), also to DLNs more generally -# -# For models with a closed-form population loss, like DLNs: -# compare SGLD on empirical loss with SGLD on population loss (results should agree) -# SGLD on population loss should be able to get the LLC exactly correct, -# assuming beta is sufficiently high (using population loss here instead of empirical loss allows very high beta without prohibitively large training set size) diff --git a/tests/slt/sampler_ordinality_test.py b/tests/slt/sampler_ordinality_test.py deleted file mode 100644 index 4eba75e1..00000000 --- a/tests/slt/sampler_ordinality_test.py +++ /dev/null @@ -1,140 +0,0 @@ -import numpy as np -import pytest -import torch -import torch.nn as nn -import platform -from devinterp.optim import SGLD, SGMCMC -from devinterp.slt.llc import LLCEstimator -from devinterp.slt.sampler import sample -from devinterp.utils import default_nbeta, evaluate_mse, get_init_loss_multi_batch - -# Test configuration constants -SNAPSHOT_DRAWS = 5 -FULL_SAMPLING_DRAWS = 1000 -NUM_CHAINS = 5 -RANDOM_SEED = 42 - - -def _do_ordinality_sampling( - model, train_dataloader, sampling_method, lr, sample_points, num_draws -): - """Perform MCMC sampling to estimate LLC for ordinality test. - - Args: - model: Model to sample from - train_dataloader: DataLoader for training data - sampling_method: SGLD or SGMCMC sampling method - lr: Learning rate - sample_points: List of sample points to test - num_draws: Number of MCMC draws to perform - - Returns: - List of LLC means for each sample point - """ - torch.manual_seed(RANDOM_SEED) - - llcs = [] - for sample_point in sample_points: - model.weights = nn.Parameter( - torch.tensor(sample_point, dtype=torch.float32, requires_grad=True) - ) - init_loss = get_init_loss_multi_batch( - train_dataloader, NUM_CHAINS, model, evaluate_mse, device="cpu" - ) - llc_estimator = LLCEstimator( - num_chains=NUM_CHAINS, - num_draws=num_draws, - nbeta=default_nbeta(train_dataloader), - init_loss=init_loss, - ) - - sample( - model, - train_dataloader, - evaluate=evaluate_mse, - sampling_method_kwargs=dict( - lr=lr, - bounding_box_size=0.5, - nbeta=default_nbeta(train_dataloader), - # to prevent accidental movement from [1, 0, ...] to origin - ), - sampling_method=sampling_method, - num_chains=NUM_CHAINS, - num_draws=num_draws, - callbacks=[llc_estimator], - verbose=False, - seed=RANDOM_SEED, - ) - llcs.append(llc_estimator.get_results()["llc/mean"]) - - return llcs - - -def _test_ordinality_accuracy( - model, train_dataloader, sampling_method, lr, sample_points, model_name, dim -): - """Test LLC ordinality with full sampling.""" - llcs = _do_ordinality_sampling( - model, - train_dataloader, - sampling_method, - lr, - sample_points, - num_draws=FULL_SAMPLING_DRAWS, - ) - - assert (np.diff(llcs) >= 0).all(), ( - f"Ordinality not preserved for sampler {sampling_method} on {dim}-d {model_name}: llcs {llcs} are not in ascending order." - ) - - -@pytest.mark.skipif( - platform.machine() != "x86_64", - reason=f"Differences in results between ARM and x86_64. Your arch is {platform.machine()}", -) -@pytest.mark.parametrize("sampling_method", [SGLD, SGMCMC.sgld]) -@pytest.mark.parametrize("model_name", ["Polynomial", "LinePlusDot"]) -@pytest.mark.parametrize("dim", [2, 10]) -def test_linedot_normal_crossing( - generated_linedot_normalcrossing_dataset, - sampling_method, - model_name, - dim, - request, - snapshot, - is_snapshot_update, -): - torch.manual_seed(RANDOM_SEED) - Model = request.getfixturevalue(model_name) - if model_name == "Polynomial": - model = Model([2 for _ in range(dim)]) - else: - model = Model(dim) - train_dataloader, _, _, _ = generated_linedot_normalcrossing_dataset - lr = ( - 0.0001 / dim - ) # to account for smaller steps in higher D. might not work well for SGNHT? - - sample_points = [ - [0.0 for _ in range(dim)], - [0.0 if i == dim - 1 else 1.0 for i in range(dim)], - ] - - # Run snapshot test first - llcs = _do_ordinality_sampling( - model, - train_dataloader, - sampling_method, - lr, - sample_points, - num_draws=SNAPSHOT_DRAWS, - ) - - # Run verification test when updating snapshots - if is_snapshot_update: - _test_ordinality_accuracy( - model, train_dataloader, sampling_method, lr, sample_points, model_name, dim - ) - - # Test against snapshot - assert llcs == snapshot diff --git a/tests/slt/sampler_test.py b/tests/slt/sampler_test.py deleted file mode 100644 index 380ce2d8..00000000 --- a/tests/slt/sampler_test.py +++ /dev/null @@ -1,115 +0,0 @@ -import numpy as np -import pytest -import torch -from devinterp.optim import SGLD, SGMCMC -from devinterp.optim.sgnht import SGNHT -from devinterp.slt.llc import LLCEstimator -from devinterp.slt.sampler import sample -from devinterp.utils import default_nbeta, evaluate_mse, get_init_loss_multi_batch -from torch.utils.data import DataLoader - - -@pytest.mark.parametrize("sampling_method", [SGLD, SGNHT, SGMCMC.sgld]) -def test_seeding(generated_normalcrossing_dataset, sampling_method, Polynomial): - torch.manual_seed(42) - seed = 42 - - model = Polynomial() - - train_dataloader, train_data, _, _ = generated_normalcrossing_dataset - lr = 0.0001 - num_chains = 3 - num_draws = 100 - init_loss = get_init_loss_multi_batch( - train_dataloader, num_chains, model, evaluate_mse, device="cpu" - ) - llc_estimator_1 = LLCEstimator( - num_chains=num_chains, - num_draws=num_draws, - nbeta=default_nbeta(train_dataloader), - init_loss=init_loss, - ) - llc_estimator_2 = LLCEstimator( - num_chains=num_chains, - num_draws=num_draws, - nbeta=default_nbeta(train_dataloader), - init_loss=init_loss, - ) - torch.manual_seed(42) - - sample( - model, - train_dataloader, - evaluate=evaluate_mse, - sampling_method_kwargs=dict(lr=lr, nbeta=default_nbeta(train_dataloader)), - sampling_method=sampling_method, - num_chains=num_chains, - num_draws=num_draws, - callbacks=[llc_estimator_1], - verbose=False, - seed=seed, - ) - torch.manual_seed(42) - - sample( - model, - train_dataloader, - evaluate=evaluate_mse, - sampling_method_kwargs=dict(lr=lr, nbeta=default_nbeta(train_dataloader)), - sampling_method=sampling_method, - num_chains=num_chains, - num_draws=num_draws, - callbacks=[llc_estimator_2], - verbose=False, - seed=seed, - ) - llc_mean_1 = llc_estimator_1.get_results()["llc/mean"] - llc_mean_2 = llc_estimator_2.get_results()["llc/mean"] - assert np.array_equal(llc_mean_1, llc_mean_2), ( - f"LLC mean {llc_mean_1:.8f}!={llc_mean_2:.8f} for same seed for sampler {SGLD}!" - ) - - -# @pytest.mark.parametrize("batch_sizes", [[1, 10, 100, 1000]]) -@pytest.mark.parametrize("sampling_method", [SGLD, SGMCMC.sgld]) -# @pytest.mark.parametrize("model", [Polynomial]) -def unused_test_batch_size_convergence( - generated_normalcrossing_dataset, batch_sizes, sampling_method, model -): - model = model([2, 2]) - lr = 0.0002 - num_chains = 1 - means = [] - stds = [] - _, train_data, _, _ = generated_normalcrossing_dataset - for batch_size in batch_sizes: - num_draws = 5_000 - torch.manual_seed(42) - train_dataloader = DataLoader(train_data, batch_size=batch_size, shuffle=True) - init_loss = get_init_loss_multi_batch( - train_dataloader, num_chains, model, evaluate_mse, device="cpu" - ) - llc_estimator = LLCEstimator( - num_chains=num_chains, - num_draws=num_draws, - nbeta=default_nbeta(train_dataloader), - init_loss=init_loss, - ) - sample( - model, - train_dataloader, - evaluate=evaluate_mse, - sampling_method_kwargs=dict(lr=lr, localization=1.0), - sampling_method=sampling_method, - num_chains=num_chains, - num_draws=num_draws, - callbacks=[llc_estimator], - verbose=False, - ) - means += [llc_estimator.get_results()["llc/mean"]] - stds += [llc_estimator.get_results()["llc/std"]] - overall_mean = np.mean(means) - std_dev_of_means = np.std(means) - assert False, ( - f"mean {overall_mean}, std_dev_of_means {std_dev_of_means}, {means}, {stds}" - ) diff --git a/tests/slt/test_llc_nan.py b/tests/slt/test_llc_nan.py deleted file mode 100644 index 8dbde146..00000000 --- a/tests/slt/test_llc_nan.py +++ /dev/null @@ -1,82 +0,0 @@ -import numpy as np -import pytest -import torch -import torch.nn as nn -from devinterp.optim.sgld import SGLD -from devinterp.slt.llc import LLCEstimator, OnlineLLCEstimator -from devinterp.slt.sampler import estimate_learning_coeff, sample -from devinterp.utils import default_nbeta, evaluate_mse, get_init_loss_multi_batch - - -def test_llc_estimator_nan_error(): - llc_estimator = LLCEstimator(num_chains=3, num_draws=100, nbeta=10, init_loss=1.0) - - with pytest.raises(RuntimeError, match="NaN detected in loss at chain 0, draw 0"): - llc_estimator.update(0, 0, torch.tensor(np.nan)) - - llc_estimator = OnlineLLCEstimator( - num_chains=3, num_draws=100, init_loss=1.0, nbeta=10 - ) - - with pytest.raises(RuntimeError, match="NaN detected in loss at chain 0, draw 0"): - llc_estimator.update(0, 0, torch.tensor(np.nan)) - - -def test_sampling_nan_error(DummyNaNModel): - model = DummyNaNModel() - - # Create a simple dataset and dataloader - inputs = torch.randn(100, 2) # 100 samples of 2D data - dataset = torch.utils.data.TensorDataset(inputs) - loader = torch.utils.data.DataLoader(dataset, batch_size=10) - - # Define loss function - def loss_fn(model, data): - x = data[0] # Unpack the single input tensor - return model(x).mean() - - with pytest.raises(RuntimeError, match="NaN detected in loss at chain 0, draw 97"): - estimate_learning_coeff( - model=model, - loader=loader, - evaluate=loss_fn, - num_draws=100, - num_chains=3, - sampling_method_kwargs={"nbeta": 10}, - device="cpu", - ) - - -def test_llc_nan_model(generated_linedot_normalcrossing_dataset, Polynomial): - seed = 42 - torch.manual_seed(seed) - model = Polynomial([2, 2]) - - train_dataloader, _, _, _ = generated_linedot_normalcrossing_dataset - num_chains = 1 - num_draws = 1_000 - sample_point = [[0.0 for _ in range(2)]] - model.weights = nn.Parameter( - torch.tensor(sample_point, dtype=torch.float32, requires_grad=True) - ) - init_loss = get_init_loss_multi_batch( - train_dataloader, num_chains, model, evaluate_mse, device="cpu" - ) - llc_estimator = LLCEstimator( - num_chains=num_chains, - num_draws=num_draws, - nbeta=default_nbeta(train_dataloader), - init_loss=init_loss, - ) - with pytest.raises(RuntimeError, match="NaN detected in loss at chain 0, draw 4"): - sample( - model, - train_dataloader, - evaluate=evaluate_mse, - sampling_method_kwargs=dict(lr=1000, nbeta=1000.0), - sampling_method=SGLD, - num_chains=num_chains, - num_draws=num_draws, - callbacks=[llc_estimator], - verbose=False, - ) diff --git a/tests/slt/test_model_structures.py b/tests/slt/test_model_structures.py new file mode 100644 index 00000000..0ac7de7a --- /dev/null +++ b/tests/slt/test_model_structures.py @@ -0,0 +1,350 @@ +"""Validate _model_structures entries against real (tiny) HuggingFace models. + +For each model type that AutoConfig supports: + 1. Check that listed attn/mlp/embed/unembed params exist in the model + 2. Check that head_params entries exist and produce correct mask shapes + 3. Dead-head gradient test: zeroing the O projection for a head/group + should kill gradients on that head/group's masked elements +""" + +import pytest +import torch +from transformers import AutoConfig, AutoModelForCausalLM + +from devinterp.slt._model_structures import HEAD_STRUCTURES + +NUM_HEADS = 2 +HEAD_DIM = 8 +HIDDEN = NUM_HEADS * HEAD_DIM + + +def _make_tiny(model_type: str, gqa: bool = False) -> AutoConfig: + """Wrapper around johan.tiny_configs.make_tiny, imported lazily.""" + from devinterp.slt._tiny_configs import make_tiny + + return make_tiny(model_type, gqa=gqa) + + +def _testable_model_types(): + """Return model types that AutoConfig can create (skip unrecognized ones).""" + types = [] + for mt in sorted(HEAD_STRUCTURES): + if mt == "hooked_transformer": + continue + try: + _make_tiny(mt, gqa=True) + types.append(mt) + except (ValueError, KeyError): + pass + return types + + +TESTABLE = _testable_model_types() + + +def _layer0_params(model, spec): + """Extract layer-0 parameters as {short_name: param}.""" + prefix = f"{spec['layer_prefixes'][0]}.0." + return { + name[len(prefix) :]: param + for name, param in model.named_parameters() + if name.startswith(prefix) + } + + +def _resolve_spec(model_type, all_names): + """Resolve HEAD_STRUCTURES entry, picking best variant if it's a list.""" + entry = HEAD_STRUCTURES[model_type] + if isinstance(entry, dict): + return entry + + best_spec, best_score = entry[0], -1 + for variant in entry: + prefixes = variant.get("layer_prefixes", []) + suffixes = ( + set(variant.get("attn", [])) + | set(variant.get("mlp", [])) + | set(variant.get("head_params", {}).keys()) + ) + score = sum( + 1 + for name in all_names + if any( + name.startswith(f"{p}.") and name.endswith(s) + for p in prefixes + for s in suffixes + ) + ) + if score > best_score: + best_spec, best_score = variant, score + return best_spec + + +# --- Test 1: all listed params exist in the model --- + + +@pytest.mark.parametrize("model_type", TESTABLE) +def test_listed_params_exist(model_type): + """Every param name in attn/mlp/embed/unembed/head_params should exist in the model. + + Mirrors johan/test_yaml_entries_exist.py: constructs full param names using + layer_prefixes and checks against named_parameters | state_dict (catching tied weights). + Also verifies that layer indices beyond the last don't exist (prefix sanity). + """ + config = _make_tiny(model_type) + with torch.device("meta"): + model = AutoModelForCausalLM.from_config(config) + + all_names = {n for n, _ in model.named_parameters()} | set( + model.state_dict().keys() + ) + spec = _resolve_spec(model_type, all_names) + n_layers = config.num_hidden_layers + prefixes = spec["layer_prefixes"] + + # Collect all layer-scoped short names + layer_shorts = set(spec.get("attn", []) + spec.get("mlp", [])) + layer_shorts |= set(spec.get("head_params", {}).keys()) + layer_shorts |= set(spec.get("other", [])) + + missing = [] + should_miss = [] + + for short in layer_shorts: + found = any( + f"{prefix}.{i}.{short}" in all_names + for prefix in prefixes + for i in range(n_layers) + ) + if not found: + missing.append(short) + + # Layer beyond last should NOT exist (verifies prefix is correct) + for prefix in prefixes: + bogus = f"{prefix}.{n_layers}.{short}" + if bogus in all_names: + should_miss.append(bogus) + + # Check global params (embed, unembed) + for name in spec.get("embed", []) + spec.get("unembed", []): + if name not in all_names: + missing.append(name) + + assert not missing, f"{model_type}: missing params: {missing}" + assert not should_miss, f"{model_type}: found beyond last layer: {should_miss}" + + +# --- Test 2: dead-head gradient test --- + + +O_LEAF_NAMES = { + "o_proj", + "out_proj", + "c_proj", + "dense", + "o_net", + "out_lin", + "attention_out", +} +STRONG_O_NAMES = {"o_proj", "out_proj", "out_lin", "o_net", "attention_out"} +ATTN_KEYWORDS = ["attn", "attention", "self_att", "rel_attn", "multi_head"] + + +def _is_o_param(short): + base = short.removesuffix(".weight").removesuffix(".bias") + leaf = base.split(".")[-1] + if leaf in STRONG_O_NAMES: + return True + if leaf in O_LEAF_NAMES and any(kw in base.lower() for kw in ATTN_KEYWORDS): + return True + if leaf == "o" and any(kw in base.lower() for kw in ATTN_KEYWORDS): + return True + return False + + +def _find_o_projection(head_params_spec, l0_params): + """Find the O projection: unfused param with opposite slice_dim.""" + for name in l0_params: + if name in head_params_spec and "fused" not in head_params_spec[name]: + if _is_o_param(name): + return name + return None + + +def _infer_n_kv(l0, structure, n_heads, hd): + for name, spec in structure.items(): + if name not in l0: + continue + param = l0[name] + fused = spec.get("fused") + if fused == "concat": + dim = spec["slice_dim"] + total = param.shape[0] if param.dim() == 1 else param.shape[dim] + kv_size = (total - n_heads * hd) // 2 + return kv_size // hd + elif fused == "interleaved": + return n_heads + for name, spec in structure.items(): + if name not in l0: + continue + if "k_proj" in name or "key" in name: + param = l0[name] + dim = spec["slice_dim"] + axis_size = param.shape[0] if param.dim() == 1 else param.shape[dim] + return axis_size // hd + return n_heads + + +def _build_mask(param, spec, index, n_heads, n_kv, hd, is_group): + """Build a 1D boolean mask for a single head or group.""" + dim = spec["slice_dim"] + fused = spec.get("fused") + axis_size = param.shape[0] if param.dim() == 1 else param.shape[dim] + + mask = torch.zeros(axis_size, dtype=torch.bool) + + if is_group: + if fused == "concat": + q_size = n_heads * hd + kv_size = (axis_size - q_size) // 2 + q, kv = mask.split([q_size, 2 * kv_size]) + q.view(n_kv, n_heads // n_kv, hd)[index] = True + kv.view(2, n_kv, hd)[:, index] = True + else: + mask.view(n_kv, -1)[index] = True + else: + kv_idx = index * n_kv // n_heads + if fused == "interleaved": + mask.view(n_heads, 3, hd)[index] = True + elif fused == "concat": + q_size = n_heads * hd + kv_size = (axis_size - q_size) // 2 + q, kv = mask.split([q_size, 2 * kv_size]) + q.view(n_heads, hd)[index] = True + kv.view(2, n_kv, hd)[:, kv_idx] = True + elif axis_size in (n_kv * hd, n_kv): + mask.view(n_kv, -1)[kv_idx] = True + else: + mask.view(n_heads, -1)[index] = True + + if param.dim() > 1: + shape = [1] * param.dim() + shape[dim] = -1 + mask = mask.view(*shape).expand_as(param) + + return mask + + +def _backward(model, input_ids): + model.zero_grad() + if model.config.model_type == "xmod": + model.set_default_language("en_XX") + model(input_ids).logits.sum().backward() + + +@pytest.mark.parametrize("model_type", TESTABLE) +def test_dead_head_gradient(model_type): + """Zeroing a head's O projection should kill gradients on that head's params.""" + config = _make_tiny(model_type, gqa=True) + model = AutoModelForCausalLM.from_config(config) + all_names = {n for n, _ in model.named_parameters()} + spec = _resolve_spec(model_type, all_names) + head_params_spec = spec.get("head_params", {}) + + n_heads = config.num_attention_heads + hd = config.hidden_size // n_heads + input_ids = torch.randint(0, config.vocab_size, (8, 8)) + + l0 = _layer0_params(model, spec) + n_kv = _infer_n_kv(l0, head_params_spec, n_heads, hd) + + o_name = _find_o_projection(head_params_spec, l0) + assert o_name is not None, ( + f"{model_type}: no O projection found in {list(head_params_spec.keys())}" + ) + + orig_state = {k: v.clone() for k, v in model.state_dict().items()} + + def restore(): + model.load_state_dict(orig_state) + + hi = n_heads - 1 + gi = hi * n_kv // n_heads + + def has_grad(param, mask): + return param.grad[mask].abs().sum() > 0 + + # --- Head test --- + head_masks = { + name: _build_mask( + l0[name], head_params_spec[name], hi, n_heads, n_kv, hd, False + ) + for name in head_params_spec + if name in l0 + } + # Also build masks for a second head to find unique elements + hi2 = n_heads - 2 + head_masks_2 = { + name: _build_mask( + l0[name], head_params_spec[name], hi2, n_heads, n_kv, hd, False + ) + for name in head_params_spec + if name in l0 + } + + # Head alive: gradients should be nonzero + restore() + _backward(model, input_ids) + for name, mask in head_masks.items(): + if name == o_name: + continue + unique = mask & ~head_masks_2[name] + if not unique.any(): + continue + assert has_grad(l0[name], unique), ( + f"{model_type}: {name} head alive but no gradient" + ) + + # Head dead: zero O for this head, gradients should vanish + restore() + with torch.no_grad(): + l0[o_name].data[head_masks[o_name]] = 0 + _backward(model, input_ids) + for name, mask in head_masks.items(): + if name == o_name: + continue + unique = mask & ~head_masks_2[name] + if not unique.any(): + continue + assert not has_grad(l0[name], unique), ( + f"{model_type}: {name} head dead but still has gradient" + ) + + # --- Group test --- + group_masks = { + name: _build_mask(l0[name], head_params_spec[name], gi, n_heads, n_kv, hd, True) + for name in head_params_spec + if name in l0 + } + + # Group alive: gradients should be nonzero + restore() + _backward(model, input_ids) + for name, gmask in group_masks.items(): + if name == o_name: + continue + assert has_grad(l0[name], gmask), ( + f"{model_type}: {name} group alive but no gradient" + ) + + # Group dead: zero O for this group, gradients should vanish + restore() + with torch.no_grad(): + l0[o_name].data[group_masks[o_name]] = 0 + _backward(model, input_ids) + for name, gmask in group_masks.items(): + if name == o_name: + continue + assert not has_grad(l0[name], gmask), ( + f"{model_type}: {name} group dead but still has gradient" + )