diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml index 5ff94c9..ad2ad4b 100644 --- a/.github/workflows/publish.yml +++ b/.github/workflows/publish.yml @@ -40,17 +40,72 @@ jobs: with: python-version: "3.11" + - name: Install Rust toolchain + uses: dtolnay/rust-toolchain@stable + - name: Install dependencies run: | python -m pip install --upgrade pip - pip install -e ".[dev]" + pip install ".[dev]" - name: Run tests run: pytest -v - publish: + build-wheels: needs: [check-version, test] if: needs.check-version.outputs.should_release == 'true' + strategy: + matrix: + include: + - os: ubuntu-latest + target: x86_64 + - os: ubuntu-latest + target: aarch64 + - os: macos-latest + target: x86_64 + - os: macos-latest + target: aarch64 + - os: windows-latest + target: x86_64 + runs-on: ${{ matrix.os }} + steps: + - uses: actions/checkout@v4 + + - name: Build wheels + uses: PyO3/maturin-action@v1 + with: + target: ${{ matrix.target }} + args: --release --out dist + manylinux: auto + + - name: Upload wheels + uses: actions/upload-artifact@v4 + with: + name: wheels-${{ matrix.os }}-${{ matrix.target }} + path: dist + + build-sdist: + needs: [check-version, test] + if: needs.check-version.outputs.should_release == 'true' + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + + - name: Build sdist + uses: PyO3/maturin-action@v1 + with: + command: sdist + args: --out dist + + - name: Upload sdist + uses: actions/upload-artifact@v4 + with: + name: wheels-sdist + path: dist + + publish: + needs: [check-version, build-wheels, build-sdist] + if: needs.check-version.outputs.should_release == 'true' runs-on: ubuntu-latest permissions: id-token: write @@ -59,24 +114,18 @@ jobs: steps: - uses: actions/checkout@v4 - - name: Set up Python - uses: actions/setup-python@v5 - with: - python-version: "3.11" - - name: Create version tag run: | VERSION=${{ needs.check-version.outputs.version }} git tag "v$VERSION" git push origin "v$VERSION" - - name: Install build dependencies - run: | - python -m pip install --upgrade pip - pip install build hatchling - - - name: Build wheel and sdist - run: python -m build + - name: Download all artifacts + uses: actions/download-artifact@v4 + with: + pattern: wheels-* + merge-multiple: true + path: dist - name: Publish to PyPI uses: pypa/gh-action-pypi-publish@release/v1 diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index c153b04..5f09c69 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -21,10 +21,13 @@ jobs: with: python-version: ${{ matrix.python-version }} + - name: Install Rust toolchain + uses: dtolnay/rust-toolchain@stable + - name: Install dependencies run: | python -m pip install --upgrade pip - pip install -e ".[dev]" + pip install ".[dev]" - name: Run ruff (lint) run: ruff check . diff --git a/.gitignore b/.gitignore index d537f70..c756f15 100644 --- a/.gitignore +++ b/.gitignore @@ -21,4 +21,11 @@ examples/.cache/ .vscode/ # Benchmark results (generated, not committed) -benchmarks/results/*.json \ No newline at end of file +benchmarks/results/*.json + +target/ + +# Rust/maturin build artifacts +*.so +*.dylib +*.pyd \ No newline at end of file diff --git a/CLAUDE.md b/CLAUDE.md index 37f0b3c..64bf417 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -39,8 +39,9 @@ pytest # Tests (must pass) ### Setup ```bash -pip install -e . # Install package in editable mode -pip install -e ".[dev]" # Install with dev dependencies +pip install maturin # Required for building Rust extension +maturin develop --release # Build Rust extension (needs Rust toolchain) +pip install -e ".[dev]" # Install with dev dependencies ``` ### Testing @@ -88,9 +89,7 @@ python examples/colbert_nanobeir.py **Local build (for testing):** ```bash -pip install build -python -m build -twine check dist/* +maturin build --release # Build wheel with Rust extension ``` ## Architecture @@ -98,15 +97,26 @@ twine check dist/* ### Core Components **`muvera/muvera.py`** - Main `Muvera` class implementing Fixed Dimensional Encoding (FDE) -- Three encoding paths: single document, uniform batch, variable-length batch +- Two encoding paths: single document, variable-length batch - Document encoding uses AVERAGE aggregation within partitions - Query encoding uses SUM aggregation within partitions - Optional final dimensionality reduction via Count Sketch +- Hot-path methods (`_aggregate_single`, `_scatter_add`, `_fill_empty_batch`) delegate to Rust kernels when available **`muvera/helper.py`** - Low-level utilities (not public API) - Gray code manipulation for partition indexing - Random projection matrices (SimHash, AMS Sketch, Count Sketch) - Vectorized batch partition indexing +- `partition_index_gray` and `partition_indices_gray_batch` delegate to Rust when available + +**`src/`** - Rust extension module (`muvera._rust_kernels`) via PyO3/maturin +- `gray_code.rs` — Gray code append and binary conversion +- `partition.rs` — Single and batch Gray-code partition indexing +- `scatter.rs` — Scatter-add kernel for batch aggregation +- `fill_empty.rs` — Single-point-cloud aggregation + batch empty partition filling +- `lib.rs` — PyO3 module definition exposing 5 functions + +**`muvera/_rust_kernels.pyi`** - Type stubs for the Rust extension module ### Algorithm Flow @@ -120,17 +130,44 @@ twine check dist/* 6. **Repetitions**: Repeat steps 1-5 with different random seeds, concatenating results 7. **Final Projection** (optional): Apply Count Sketch to reduce final dimension +### Rust Acceleration + +Performance-critical inner loops are implemented in Rust via PyO3, with automatic fallback to pure Python: + +```python +# muvera/__init__.py +try: + import muvera._rust_kernels + _RUST_AVAILABLE = True +except ImportError: + _RUST_AVAILABLE = False +``` + +**Accelerated functions:** +| Rust function | Python fallback | Speedup | +|---|---|---| +| `aggregate_single` | `Muvera._aggregate_single_python` | 8-17x (single doc) | +| `scatter_add_partitions` | `Muvera._scatter_add` (np.add.at loop) | 1-2.5x (batch) | +| `fill_empty_partitions_batch` | `Muvera._fill_empty_batch` (Python loop) | 1-2.5x (batch) | +| `partition_index_gray` | `helper._partition_index_gray_python` | part of aggregate | +| `partition_indices_gray_batch` | `helper._partition_indices_gray_batch_python` | part of batch | + +**What is NOT in Rust** (intentionally kept in NumPy for seed compatibility): +- `simhash_matrix_from_seed`, `ams_projection_matrix_from_seed` — depend on `np.random.default_rng` +- `count_sketch_vector_from_seed` — same reason +- `Muvera.__init__`, public API signatures — 100% unchanged + ### Batch Processing -The library supports three input formats: +The library supports two input formats: - **Single**: `(num_vectors, dimension)` - processes one point cloud -- **Uniform batch**: `(batch_size, num_vectors, dimension)` - all point clouds have same length - **Variable-length batch**: `list[np.ndarray]` - each point cloud has different length (recommended for real-world data) -Variable-length batch processing flattens all point clouds, processes them together, then aggregates per-document using `np.add.at()` for efficient scatter-add operations. +Variable-length batch processing flattens all point clouds, processes them together, then aggregates per-document using Rust `scatter_add_partitions` (or `np.add.at()` fallback). ## Code Conventions +### Python - NumPy-style docstrings (configured in pyproject.toml) - Type hints required (Python 3.9+ syntax with `|` for unions) - Line length: 100 characters @@ -138,6 +175,13 @@ Variable-length batch processing flattens all point clouds, processes them toget - Use `np.uint32` for partition indices - Random number generation via `np.random.default_rng(seed)` for reproducibility +### Rust +- Edition 2021 +- Dependencies: `pyo3` 0.23, `numpy` 0.23 (Rust crate, not Python package), `ndarray` 0.16 +- All three crates are version-locked together (upgrade all at once) +- Use `f32` for all floating-point data, `u32` for partition indices, `i32` for counts, `i64` for boundaries +- PyO3 functions accept `PyReadonlyArray*` for input arrays and `&Bound` for in-place mutation + ## Testing ### Test Organization @@ -146,6 +190,7 @@ Variable-length batch processing flattens all point clouds, processes them toget - **`test_muvera.py`**: Core Muvera class tests (shapes, validation, reproducibility) - **`test_reference.py`**: Validation against reference implementation (sionic-ai/muvera-py) - **`test_real_colbert.py`**: Real-world ColBERT embedding tests using NanoBEIR fixtures +- **`test_rust_equivalence.py`**: Numerical equivalence tests between Rust kernels and Python fallbacks (skipped if Rust extension is unavailable) ### Real Data Testing @@ -173,15 +218,17 @@ Output dimension: `num_repetitions * 2^num_simhash_projections * projection_dime **`.github/workflows/test.yml`** - Continuous Integration - Triggers: Push to main, all pull requests - Tests across Python 3.9-3.13 +- Installs Rust toolchain via `dtolnay/rust-toolchain@stable` +- Builds Rust extension via `pip install ".[dev]"` (maturin build backend) - Runs ruff (lint + format check), mypy (type checking), pytest -- Tests example scripts **`.github/workflows/publish.yml`** - PyPI Publishing - Triggers: Push to main - Checks if `v{version}` tag already exists; skips release if it does - Runs full test suite -- Creates git tag, builds wheel/sdist, publishes to PyPI via OIDC -- Creates GitHub Release with release notes +- Builds cross-platform wheels via `PyO3/maturin-action@v1` (Linux x86_64/aarch64, macOS x86_64/aarch64, Windows x86_64) +- Builds sdist separately +- Creates git tag, publishes to PyPI via OIDC, creates GitHub Release ### Deployment Policy diff --git a/Cargo.lock b/Cargo.lock new file mode 100644 index 0000000..e61d32b --- /dev/null +++ b/Cargo.lock @@ -0,0 +1,270 @@ +# This file is automatically @generated by Cargo. +# It is not intended for manual editing. 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"537dd038a89878be9b64dd4bd1b260315c1bb94f4d784956b81e27a088d9a09e" + +[[package]] +name = "unindent" +version = "0.2.4" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "7264e107f553ccae879d21fbea1d6724ac785e8c3bfc762137959b5802826ef3" diff --git a/Cargo.toml b/Cargo.toml new file mode 100644 index 0000000..692c29a --- /dev/null +++ b/Cargo.toml @@ -0,0 +1,13 @@ +[package] +name = "muvera-python" +version = "0.2.0" +edition = "2021" + +[lib] +name = "_rust_kernels" +crate-type = ["cdylib"] + +[dependencies] +pyo3 = { version = "0.23", features = ["extension-module"] } +numpy = "0.23" +ndarray = "0.16" diff --git a/README.md b/README.md index 7947a4f..25cd9ef 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ # MuVERA -A Python implementation of **Mu**lti-**Ve**ctor **R**etrieval via Fixed Dimensional Encoding **A**lgorithm. +A **Rust-accelerated** Python implementation of **Mu**lti-**Ve**ctor **R**etrieval via Fixed Dimensional Encoding **A**lgorithm. Converts multi-vector embeddings (point clouds) into fixed-dimensional single vectors, enabling the use of existing single-vector search infrastructure (MIPS, ANN, etc.) as-is. @@ -10,18 +10,22 @@ The original MuVERA algorithm is described in a [research paper](https://arxiv.o This library wraps the full algorithm behind a **single `Muvera` class** with a minimal, intuitive interface — initialize once, then call `encode_documents()` and `encode_queries()`. No config dataclasses, no encoding-type enums, no manual seed juggling. Just NumPy arrays in, NumPy arrays out. +Performance-critical inner loops (Gray code partitioning, scatter-add, empty partition filling) are implemented in **Rust via PyO3**, with automatic fallback to pure Python if the native extension is unavailable. + ## Installation ```bash pip install muvera-python ``` -Development install: +Development install (requires Rust toolchain): ```bash git clone https://github.com/craftsangjae/muvera-python.git cd muvera-python -pip install -e . +pip install maturin +maturin develop --release +pip install -e ".[dev]" ``` ## Quick Start @@ -38,13 +42,12 @@ encoder = Muvera( seed=42, ) -# Encode documents (batch) -# shape: (num_documents, num_vectors_per_doc, embedding_dim) -documents = np.random.randn(100, 80, 128).astype(np.float32) +# Encode documents (batch of variable-length point clouds) +documents = [np.random.randn(80, 128).astype(np.float32) for _ in range(100)] doc_fdes = encoder.encode_documents(documents) # (100, output_dimension) # Encode queries (batch) -queries = np.random.randn(10, 32, 128).astype(np.float32) +queries = [np.random.randn(32, 128).astype(np.float32) for _ in range(10)] query_fdes = encoder.encode_queries(queries) # (10, output_dimension) # Compute similarity (dot product) @@ -66,6 +69,32 @@ scores = query_fdes @ doc_fdes.T # (10, 100) ## Benchmark +### Rust Acceleration + +Encoding speed comparison between Rust-accelerated and pure-Python backends. Measured on a single ARM64 core (Python 3.9, NumPy 2.0). Pareto-optimal configs from the MuVERA paper, dim=128: + +**Single document encoding (128 vectors):** + +| Config | Rust | Python | Speedup | +|---|---|---|---| +| R=20, k=3, d_proj=8 | 0.64 ms | 5.14 ms | **8.1x** | +| R=20, k=4, d_proj=8 | 0.65 ms | 5.72 ms | **8.9x** | +| R=20, k=5, d_proj=8 | 0.69 ms | 11.51 ms | **16.7x** | +| R=20, k=5, d_proj=16 | 0.74 ms | 12.00 ms | **16.2x** | + +**Batch document encoding (100 docs, ~128 vectors each):** + +| Config | Rust | Python | Speedup | +|---|---|---|---| +| R=20, k=3, d_proj=8 | 24.23 ms | 22.35 ms | 0.9x | +| R=20, k=4, d_proj=8 | 20.66 ms | 22.47 ms | 1.1x | +| R=20, k=5, d_proj=8 | 17.89 ms | 32.07 ms | **1.8x** | +| R=20, k=5, d_proj=16 | 23.42 ms | 57.66 ms | **2.5x** | + +Single document encoding sees **8-17x speedup** where Rust eliminates Python loop overhead in Gray code partitioning and scatter-add. Batch encoding gains are more modest (1-2.5x) since NumPy vectorized operations already handle the bulk of computation. The largest gains appear with higher `num_simhash_projections` (k=5) where partition count (2^k=32) creates more Python-level iteration. + +### Retrieval Quality + End-to-end retrieval on [NanoFiQA2018](https://huggingface.co/datasets/zeta-alpha-ai/NanoFiQA2018) (4598 documents, 50 queries) using `raphaelsty/neural-cherche-colbert` (dim=128): ``` diff --git a/examples/basic_usage.py b/examples/basic_usage.py index 917e65f..a108b93 100644 --- a/examples/basic_usage.py +++ b/examples/basic_usage.py @@ -83,15 +83,17 @@ def main() -> None: # --- Generate random normalized data (ColBERT-style unit vectors) --- print("Generating random normalized embeddings...") - documents_raw = rng.standard_normal((num_documents, num_doc_vectors, dimension)).astype( - np.float32 - ) - queries_raw = rng.standard_normal((num_queries, num_query_vectors, dimension)).astype( - np.float32 - ) - - documents = documents_raw / np.linalg.norm(documents_raw, axis=-1, keepdims=True) - queries = queries_raw / np.linalg.norm(queries_raw, axis=-1, keepdims=True) + documents = [] + for _ in range(num_documents): + doc = rng.standard_normal((num_doc_vectors, dimension)).astype(np.float32) + doc /= np.linalg.norm(doc, axis=-1, keepdims=True) + documents.append(doc) + + queries = [] + for _ in range(num_queries): + q = rng.standard_normal((num_query_vectors, dimension)).astype(np.float32) + q /= np.linalg.norm(q, axis=-1, keepdims=True) + queries.append(q) # --- Initialize encoder (Pareto-optimal params from the paper) --- encoder = Muvera( diff --git a/muvera/__init__.py b/muvera/__init__.py index ad5fa5c..cde76a8 100644 --- a/muvera/__init__.py +++ b/muvera/__init__.py @@ -4,3 +4,10 @@ __all__ = ["Muvera"] __version__ = "0.1.0" + +try: + import muvera._rust_kernels # noqa: F401 + + _RUST_AVAILABLE = True +except ImportError: + _RUST_AVAILABLE = False diff --git a/muvera/_rust_kernels.pyi b/muvera/_rust_kernels.pyi new file mode 100644 index 0000000..096b8cb --- /dev/null +++ b/muvera/_rust_kernels.pyi @@ -0,0 +1,34 @@ +"""Type stubs for Rust-accelerated MuVERA kernels.""" + +import numpy as np +import numpy.typing as npt + +def partition_index_gray(sketch: npt.NDArray[np.float32]) -> int: ... +def partition_indices_gray_batch( + sketches: npt.NDArray[np.float32], +) -> npt.NDArray[np.uint32]: ... +def scatter_add_partitions( + rep_fde: npt.NDArray[np.float32], + doc_indices: npt.NDArray[np.uint32], + part_indices: npt.NDArray[np.uint32], + projected: npt.NDArray[np.float32], +) -> None: ... +def aggregate_single( + sketches: npt.NDArray[np.float32], + projected: npt.NDArray[np.float32], + num_partitions: int, + proj_dim: int, + is_query: bool, + fill_empty: bool, + num_simhash_projections: int, +) -> npt.NDArray[np.float32]: ... +def fill_empty_partitions_batch( + rep_fde: npt.NDArray[np.float32], + partition_counts: npt.NDArray[np.int32], + all_sketches: npt.NDArray[np.float32], + all_projected: npt.NDArray[np.float32], + doc_boundaries: npt.NDArray[np.int64], + num_simhash_projections: int, + num_partitions: int, + proj_dim: int, +) -> None: ... diff --git a/muvera/helper.py b/muvera/helper.py index dc8a714..925c67c 100644 --- a/muvera/helper.py +++ b/muvera/helper.py @@ -138,6 +138,14 @@ def count_sketch_vector_from_seed( # --------------------------------------------------------------------------- +def _partition_index_gray_python(sketch_vector: np.ndarray) -> int: + """Compute a Gray-code-based partition index (pure Python fallback).""" + partition_index = 0 + for val in sketch_vector: + partition_index = append_to_gray_code(partition_index, val > 0) + return partition_index + + def partition_index_gray(sketch_vector: np.ndarray) -> int: """Compute a Gray-code-based partition index from a SimHash sketch vector. @@ -151,10 +159,13 @@ def partition_index_gray(sketch_vector: np.ndarray) -> int: int Partition index. """ - partition_index = 0 - for val in sketch_vector: - partition_index = append_to_gray_code(partition_index, val > 0) - return partition_index + from muvera import _RUST_AVAILABLE + + if _RUST_AVAILABLE: + from muvera._rust_kernels import partition_index_gray as _rs_fn + + return int(_rs_fn(np.ascontiguousarray(sketch_vector, dtype=np.float32))) + return _partition_index_gray_python(sketch_vector) def distance_to_partition(sketch_vector: np.ndarray, partition_index: int) -> int: @@ -184,6 +195,16 @@ def distance_to_partition(sketch_vector: np.ndarray, partition_index: int) -> in # --------------------------------------------------------------------------- +def _partition_indices_gray_batch_python(sketches: np.ndarray) -> np.ndarray: + """Compute Gray-code partition indices for a batch (pure Python fallback).""" + num_projections = sketches.shape[1] + bits = (sketches > 0).astype(np.uint32) + partition_indices = np.zeros(sketches.shape[0], dtype=np.uint32) + for bit_idx in range(num_projections): + partition_indices = (partition_indices << 1) + (bits[:, bit_idx] ^ (partition_indices & 1)) + return partition_indices + + def partition_indices_gray_batch(sketches: np.ndarray) -> np.ndarray: """Compute Gray-code partition indices for a batch of sketch vectors. @@ -197,9 +218,10 @@ def partition_indices_gray_batch(sketches: np.ndarray) -> np.ndarray: numpy.ndarray Uint32 partition index array of shape ``(N,)``. """ - num_projections = sketches.shape[1] - bits = (sketches > 0).astype(np.uint32) - partition_indices = np.zeros(sketches.shape[0], dtype=np.uint32) - for bit_idx in range(num_projections): - partition_indices = (partition_indices << 1) + (bits[:, bit_idx] ^ (partition_indices & 1)) - return partition_indices + from muvera import _RUST_AVAILABLE + + if _RUST_AVAILABLE: + from muvera._rust_kernels import partition_indices_gray_batch as _rs_fn + + return np.asarray(_rs_fn(np.ascontiguousarray(sketches, dtype=np.float32)), dtype=np.uint32) + return _partition_indices_gray_batch_python(sketches) diff --git a/muvera/muvera.py b/muvera/muvera.py index 1439f9d..f64eead 100644 --- a/muvera/muvera.py +++ b/muvera/muvera.py @@ -257,6 +257,30 @@ def _aggregate_single( self, sketches: np.ndarray, projected: np.ndarray, is_query: bool ) -> np.ndarray: """Aggregate vectors into partitions for a single point cloud.""" + from muvera import _RUST_AVAILABLE + + if _RUST_AVAILABLE: + from muvera._rust_kernels import aggregate_single as _rs_fn + + return np.asarray( + _rs_fn( + np.ascontiguousarray(sketches, dtype=np.float32), + np.ascontiguousarray(projected, dtype=np.float32), + self._num_partitions, + self._proj_dim, + is_query, + self.fill_empty_partitions, + self.num_simhash_projections, + ), + dtype=np.float32, + ) + + return self._aggregate_single_python(sketches, projected, is_query) + + def _aggregate_single_python( + self, sketches: np.ndarray, projected: np.ndarray, is_query: bool + ) -> np.ndarray: + """Aggregate vectors into partitions for a single point cloud (Python fallback).""" num_points = sketches.shape[0] partition_counts = np.zeros(self._num_partitions, dtype=np.int32) rep_fde = np.zeros(self._num_partitions * self._proj_dim, dtype=np.float32) @@ -341,6 +365,23 @@ def _fill_empty_batch( if not self.fill_empty_partitions or self.num_simhash_projections == 0: return + from muvera import _RUST_AVAILABLE + + if _RUST_AVAILABLE: + from muvera._rust_kernels import fill_empty_partitions_batch as _rs_fn + + _rs_fn( + rep_fde, + np.ascontiguousarray(partition_counts, dtype=np.int32), + np.ascontiguousarray(all_sketches, dtype=np.float32), + np.ascontiguousarray(all_projected, dtype=np.float32), + np.ascontiguousarray(doc_boundaries, dtype=np.int64), + self.num_simhash_projections, + self._num_partitions, + self._proj_dim, + ) + return + empty_docs, empty_parts = np.where(partition_counts == 0) for doc_idx, pidx in zip(empty_docs, empty_parts): doc_start, doc_end = doc_boundaries[doc_idx], doc_boundaries[doc_idx + 1] @@ -362,6 +403,19 @@ def _scatter_add( all_projected: np.ndarray, ) -> None: """Scatter-add projected vectors into partitions.""" + from muvera import _RUST_AVAILABLE + + if _RUST_AVAILABLE: + from muvera._rust_kernels import scatter_add_partitions as _rs_fn + + _rs_fn( + rep_fde, + np.ascontiguousarray(doc_indices, dtype=np.uint32), + np.ascontiguousarray(part_indices, dtype=np.uint32), + np.ascontiguousarray(all_projected, dtype=np.float32), + ) + return + doc_part = doc_indices * self._num_partitions + part_indices base = doc_part * self._proj_dim flat_rep_fde = rep_fde.reshape(-1) diff --git a/pyproject.toml b/pyproject.toml index 15bb244..c86afec 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,10 +1,10 @@ [build-system] -requires = ["hatchling"] -build-backend = "hatchling.build" +requires = ["maturin>=1.4,<2.0"] +build-backend = "maturin" [project] name = "muvera-python" -version = "0.1.3" +version = "0.2.0" description = "MuVERA: Multi-Vector Retrieval via Fixed Dimensional Encodings" readme = "README.md" license = "Apache-2.0" @@ -45,11 +45,9 @@ Homepage = "https://github.com/craftsangjae/muvera-python" Repository = "https://github.com/craftsangjae/muvera-python" Issues = "https://github.com/craftsangjae/muvera-python/issues" -[tool.hatch.build.targets.wheel] -packages = ["muvera"] - -[tool.hatch.build.targets.sdist] -include = ["muvera/", "README.md", "LICENSE"] +[tool.maturin] +features = ["pyo3/extension-module"] +module-name = "muvera._rust_kernels" # --------------------------------------------------------------------------- # Ruff diff --git a/src/fill_empty.rs b/src/fill_empty.rs new file mode 100644 index 0000000..c981c27 --- /dev/null +++ b/src/fill_empty.rs @@ -0,0 +1,147 @@ +use ndarray::{ArrayView1, ArrayView2, ArrayViewMut3}; + +use crate::gray_code::gray_code_to_binary; +use crate::partition::partition_index_gray; + +/// Compute Hamming distance between a sketch vector's sign bits and a partition. +fn hamming_distance(sketch: &[f32], partition_index: u32, num_projections: usize) -> u32 { + let binary_rep = gray_code_to_binary(partition_index); + let mut dist = 0u32; + for bit_idx in 0..num_projections { + let sketch_bit = if sketch[bit_idx] > 0.0 { 1u32 } else { 0u32 }; + let shift = (num_projections - 1 - bit_idx) as u32; + let target_bit = (binary_rep >> shift) & 1; + if sketch_bit != target_bit { + dist += 1; + } + } + dist +} + +/// Aggregate vectors into partitions for a single point cloud, applying +/// AVERAGE for documents and filling empty partitions. +/// +/// Returns a flat vector of length num_partitions * proj_dim. +pub fn aggregate_single( + sketches: ArrayView2, + projected: ArrayView2, + num_partitions: usize, + proj_dim: usize, + is_query: bool, + fill_empty: bool, + num_simhash_projections: usize, +) -> Vec { + let num_points = sketches.nrows(); + let total_dim = num_partitions * proj_dim; + let mut rep_fde = vec![0.0f32; total_dim]; + let mut partition_counts = vec![0i32; num_partitions]; + + // Assign vectors to partitions and accumulate + for i in 0..num_points { + let sketch_slice = sketches.row(i); + let pidx = partition_index_gray(sketch_slice.as_slice().unwrap()) as usize; + let start = pidx * proj_dim; + for d in 0..proj_dim { + rep_fde[start + d] += projected[[i, d]]; + } + partition_counts[pidx] += 1; + } + + // For documents: apply average and fill empty partitions + if !is_query { + for pidx in 0..num_partitions { + let start = pidx * proj_dim; + if partition_counts[pidx] > 0 { + let count = partition_counts[pidx] as f32; + for d in 0..proj_dim { + rep_fde[start + d] /= count; + } + } else if fill_empty && num_points > 0 && num_simhash_projections > 0 { + // Find nearest vector by Hamming distance + let mut min_dist = u32::MAX; + let mut nearest = 0usize; + for j in 0..num_points { + let sketch_slice = sketches.row(j); + let dist = hamming_distance( + sketch_slice.as_slice().unwrap(), + pidx as u32, + num_simhash_projections, + ); + if dist < min_dist { + min_dist = dist; + nearest = j; + } + } + for d in 0..proj_dim { + rep_fde[start + d] = projected[[nearest, d]]; + } + } + } + } + + rep_fde +} + +/// Fill empty partitions for a batch of documents. +/// +/// `rep_fde` has shape (batch_size, num_partitions, proj_dim). +/// `partition_counts` has shape (batch_size, num_partitions). +/// `all_sketches` has shape (total_vectors, num_simhash_projections). +/// `all_projected` has shape (total_vectors, proj_dim). +/// `doc_boundaries` has shape (batch_size + 1,) with cumulative offsets. +pub fn fill_empty_partitions_batch( + mut rep_fde: ArrayViewMut3, + partition_counts: ArrayView2, + all_sketches: ArrayView2, + all_projected: ArrayView2, + doc_boundaries: ArrayView1, + num_simhash_projections: usize, + num_partitions: usize, + proj_dim: usize, +) { + if num_simhash_projections == 0 { + return; + } + + let batch_size = partition_counts.nrows(); + + for doc_idx in 0..batch_size { + let doc_start = doc_boundaries[doc_idx] as usize; + let doc_end = doc_boundaries[doc_idx + 1] as usize; + if doc_start == doc_end { + continue; + } + + for pidx in 0..num_partitions { + if partition_counts[[doc_idx, pidx]] != 0 { + continue; + } + + // Find nearest vector by Hamming distance + let binary_rep = gray_code_to_binary(pidx as u32); + let mut min_dist = u32::MAX; + let mut nearest = doc_start; + + for j in doc_start..doc_end { + let sketch_slice = all_sketches.row(j); + let mut dist = 0u32; + for bit_idx in 0..num_simhash_projections { + let sketch_bit = if sketch_slice[bit_idx] > 0.0 { 1u32 } else { 0u32 }; + let shift = (num_simhash_projections - 1 - bit_idx) as u32; + let target_bit = (binary_rep >> shift) & 1; + if sketch_bit != target_bit { + dist += 1; + } + } + if dist < min_dist { + min_dist = dist; + nearest = j; + } + } + + for d in 0..proj_dim { + rep_fde[[doc_idx, pidx, d]] = all_projected[[nearest, d]]; + } + } + } +} diff --git a/src/gray_code.rs b/src/gray_code.rs new file mode 100644 index 0000000..141c29d --- /dev/null +++ b/src/gray_code.rs @@ -0,0 +1,47 @@ +/// Append a single bit to a Gray code value. +#[inline] +pub fn append_to_gray_code(gray_code: u32, bit: bool) -> u32 { + (gray_code << 1) + ((bit as u32) ^ (gray_code & 1)) +} + +/// Convert a Gray code value to its binary representation. +#[inline] +pub fn gray_code_to_binary(mut num: u32) -> u32 { + let mut mask = num >> 1; + while mask != 0 { + num ^= mask; + mask >>= 1; + } + num +} + +#[cfg(test)] +mod tests { + use super::*; + + #[test] + fn test_gray_code_roundtrip() { + // Gray code for 0..8 should produce unique values + let mut results = Vec::new(); + for i in 0u32..8 { + let bits = [i & 4 != 0, i & 2 != 0, i & 1 != 0]; + let mut gc = 0u32; + for &b in &bits { + gc = append_to_gray_code(gc, b); + } + results.push(gc); + } + // All should be unique + results.sort(); + results.dedup(); + assert_eq!(results.len(), 8); + } + + #[test] + fn test_gray_code_to_binary() { + assert_eq!(gray_code_to_binary(0), 0); + assert_eq!(gray_code_to_binary(1), 1); + assert_eq!(gray_code_to_binary(3), 2); + assert_eq!(gray_code_to_binary(2), 3); + } +} diff --git a/src/lib.rs b/src/lib.rs new file mode 100644 index 0000000..446333c --- /dev/null +++ b/src/lib.rs @@ -0,0 +1,109 @@ +mod fill_empty; +mod gray_code; +mod partition; +mod scatter; + +use numpy::ndarray::{ArrayView1, ArrayView2, ArrayViewMut3}; +use numpy::{PyArray1, PyArray3, PyArrayMethods, PyReadonlyArray1, PyReadonlyArray2}; +use pyo3::prelude::*; + +/// Compute a Gray-code-based partition index from a single sketch vector. +#[pyfunction] +fn partition_index_gray<'py>(sketch: PyReadonlyArray1<'py, f32>) -> u32 { + let view = sketch.as_array(); + partition::partition_index_gray(view.as_slice().unwrap()) +} + +/// Compute Gray-code partition indices for a batch of sketch vectors. +#[pyfunction] +fn partition_indices_gray_batch<'py>( + py: Python<'py>, + sketches: PyReadonlyArray2<'py, f32>, +) -> Bound<'py, PyArray1> { + let view: ArrayView2 = sketches.as_array(); + let result = partition::partition_indices_gray_batch(view); + PyArray1::from_vec(py, result) +} + +/// Scatter-add projected vectors into partitions (in-place). +#[pyfunction] +fn scatter_add_partitions<'py>( + rep_fde: &Bound<'py, PyArray3>, + doc_indices: PyReadonlyArray1<'py, u32>, + part_indices: PyReadonlyArray1<'py, u32>, + projected: PyReadonlyArray2<'py, f32>, +) { + let rep_fde_rw: ArrayViewMut3 = + unsafe { rep_fde.as_array_mut() }; + let doc_view: ArrayView1 = doc_indices.as_array(); + let part_view: ArrayView1 = part_indices.as_array(); + let proj_view: ArrayView2 = projected.as_array(); + scatter::scatter_add_partitions(rep_fde_rw, doc_view, part_view, proj_view); +} + +/// Aggregate vectors into partitions for a single point cloud. +#[pyfunction] +fn aggregate_single<'py>( + py: Python<'py>, + sketches: PyReadonlyArray2<'py, f32>, + projected: PyReadonlyArray2<'py, f32>, + num_partitions: usize, + proj_dim: usize, + is_query: bool, + fill_empty: bool, + num_simhash_projections: usize, +) -> Bound<'py, PyArray1> { + let sk_view: ArrayView2 = sketches.as_array(); + let pr_view: ArrayView2 = projected.as_array(); + let result = fill_empty::aggregate_single( + sk_view, + pr_view, + num_partitions, + proj_dim, + is_query, + fill_empty, + num_simhash_projections, + ); + PyArray1::from_vec(py, result) +} + +/// Fill empty partitions for a batch of documents (in-place). +#[pyfunction] +fn fill_empty_partitions_batch<'py>( + rep_fde: &Bound<'py, PyArray3>, + partition_counts: PyReadonlyArray2<'py, i32>, + all_sketches: PyReadonlyArray2<'py, f32>, + all_projected: PyReadonlyArray2<'py, f32>, + doc_boundaries: PyReadonlyArray1<'py, i64>, + num_simhash_projections: usize, + num_partitions: usize, + proj_dim: usize, +) { + let rep_fde_rw: ArrayViewMut3 = + unsafe { rep_fde.as_array_mut() }; + let counts_view: ArrayView2 = partition_counts.as_array(); + let sk_view: ArrayView2 = all_sketches.as_array(); + let pr_view: ArrayView2 = all_projected.as_array(); + let boundaries_view: ArrayView1 = doc_boundaries.as_array(); + fill_empty::fill_empty_partitions_batch( + rep_fde_rw, + counts_view, + sk_view, + pr_view, + boundaries_view, + num_simhash_projections, + num_partitions, + proj_dim, + ); +} + +/// Rust-accelerated kernels for MuVERA. +#[pymodule] +fn _rust_kernels(m: &Bound<'_, PyModule>) -> PyResult<()> { + m.add_function(wrap_pyfunction!(partition_index_gray, m)?)?; + m.add_function(wrap_pyfunction!(partition_indices_gray_batch, m)?)?; + m.add_function(wrap_pyfunction!(scatter_add_partitions, m)?)?; + m.add_function(wrap_pyfunction!(aggregate_single, m)?)?; + m.add_function(wrap_pyfunction!(fill_empty_partitions_batch, m)?)?; + Ok(()) +} diff --git a/src/partition.rs b/src/partition.rs new file mode 100644 index 0000000..962551c --- /dev/null +++ b/src/partition.rs @@ -0,0 +1,56 @@ +use ndarray::ArrayView2; + +use crate::gray_code::append_to_gray_code; + +/// Compute Gray-code partition index from a single sketch vector. +pub fn partition_index_gray(sketch: &[f32]) -> u32 { + let mut partition_index: u32 = 0; + for &val in sketch { + partition_index = append_to_gray_code(partition_index, val > 0.0); + } + partition_index +} + +/// Compute Gray-code partition indices for a batch of sketch vectors. +/// `sketches` has shape (N, num_projections). +pub fn partition_indices_gray_batch(sketches: ArrayView2) -> Vec { + let n = sketches.nrows(); + let num_projections = sketches.ncols(); + let mut result = vec![0u32; n]; + + for bit_idx in 0..num_projections { + for i in 0..n { + let bit = sketches[[i, bit_idx]] > 0.0; + result[i] = append_to_gray_code(result[i], bit); + } + } + result +} + +#[cfg(test)] +mod tests { + use super::*; + use ndarray::Array2; + + #[test] + fn test_partition_index_single() { + let sketch = vec![1.0f32, -1.0, 0.5]; + let idx = partition_index_gray(&sketch); + // bits: true, false, true -> gray code + assert!(idx < 8); + } + + #[test] + fn test_partition_indices_batch() { + let sketches = Array2::from_shape_vec( + (3, 2), + vec![1.0, -1.0, -1.0, 1.0, 1.0, 1.0], + ) + .unwrap(); + let indices = partition_indices_gray_batch(sketches.view()); + assert_eq!(indices.len(), 3); + for &idx in &indices { + assert!(idx < 4); + } + } +} diff --git a/src/scatter.rs b/src/scatter.rs new file mode 100644 index 0000000..fc35c3d --- /dev/null +++ b/src/scatter.rs @@ -0,0 +1,53 @@ +use ndarray::{ArrayView1, ArrayView2, ArrayViewMut3}; + +/// Scatter-add projected vectors into partitions. +/// +/// `rep_fde` has shape (batch_size, num_partitions, proj_dim). +/// `doc_indices` and `part_indices` have shape (N,). +/// `projected` has shape (N, proj_dim). +pub fn scatter_add_partitions( + mut rep_fde: ArrayViewMut3, + doc_indices: ArrayView1, + part_indices: ArrayView1, + projected: ArrayView2, +) { + let n = doc_indices.len(); + let proj_dim = projected.ncols(); + + for i in 0..n { + let doc = doc_indices[i] as usize; + let part = part_indices[i] as usize; + for d in 0..proj_dim { + rep_fde[[doc, part, d]] += projected[[i, d]]; + } + } +} + +#[cfg(test)] +mod tests { + use super::*; + use ndarray::Array3; + + #[test] + fn test_scatter_add_basic() { + let mut rep_fde = Array3::::zeros((2, 4, 3)); + let doc_indices = ndarray::array![0u32, 0, 1]; + let part_indices = ndarray::array![1u32, 1, 2]; + let projected = ndarray::array![ + [1.0f32, 2.0, 3.0], + [4.0, 5.0, 6.0], + [7.0, 8.0, 9.0], + ]; + + scatter_add_partitions( + rep_fde.view_mut(), + doc_indices.view(), + part_indices.view(), + projected.view(), + ); + + assert_eq!(rep_fde[[0, 1, 0]], 5.0); // 1.0 + 4.0 + assert_eq!(rep_fde[[0, 1, 1]], 7.0); // 2.0 + 5.0 + assert_eq!(rep_fde[[1, 2, 0]], 7.0); + } +} diff --git a/tests/test_rust_equivalence.py b/tests/test_rust_equivalence.py new file mode 100644 index 0000000..a746bd5 --- /dev/null +++ b/tests/test_rust_equivalence.py @@ -0,0 +1,214 @@ +"""Tests verifying Rust kernels produce identical results to Python implementations.""" + +from __future__ import annotations + +import numpy as np +import pytest + +from muvera import _RUST_AVAILABLE, Muvera +from muvera.helper import ( + _partition_index_gray_python, + _partition_indices_gray_batch_python, + simhash_matrix_from_seed, +) + +pytestmark = pytest.mark.skipif(not _RUST_AVAILABLE, reason="Rust kernels not available") + + +class TestPartitionIndexGray: + def test_single_matches_python(self): + rng = np.random.default_rng(42) + for _ in range(100): + sketch = rng.standard_normal(5).astype(np.float32) + py_result = _partition_index_gray_python(sketch) + from muvera._rust_kernels import partition_index_gray as rs_fn + + rs_result = int(rs_fn(sketch)) + assert py_result == rs_result + + def test_batch_matches_python(self): + rng = np.random.default_rng(42) + sketches = rng.standard_normal((200, 5)).astype(np.float32) + + py_result = _partition_indices_gray_batch_python(sketches) + from muvera._rust_kernels import partition_indices_gray_batch as rs_fn + + rs_result = np.asarray(rs_fn(sketches), dtype=np.uint32) + np.testing.assert_array_equal(py_result, rs_result) + + def test_various_projection_counts(self): + rng = np.random.default_rng(123) + from muvera._rust_kernels import partition_index_gray as rs_fn + + for num_proj in range(1, 10): + sketch = rng.standard_normal(num_proj).astype(np.float32) + py_result = _partition_index_gray_python(sketch) + rs_result = int(rs_fn(sketch)) + assert py_result == rs_result + + +class TestAggregateSingle: + def test_query_aggregate_matches(self): + from muvera._rust_kernels import aggregate_single as rs_fn + + rng = np.random.default_rng(42) + dim = 16 + num_vecs = 20 + num_simhash = 3 + num_partitions = 2**num_simhash + proj_dim = dim + + vectors = rng.standard_normal((num_vecs, dim)).astype(np.float32) + sim_matrix = simhash_matrix_from_seed(dim, num_simhash, seed=42) + sketches = (vectors @ sim_matrix).astype(np.float32) + projected = vectors.copy() + + encoder = Muvera( + num_repetitions=1, + num_simhash_projections=num_simhash, + dimension=dim, + seed=42, + ) + py_result = encoder._aggregate_single_python(sketches, projected, is_query=True) + rs_result = np.asarray( + rs_fn(sketches, projected, num_partitions, proj_dim, True, False, num_simhash), + dtype=np.float32, + ) + np.testing.assert_allclose(py_result, rs_result, atol=1e-6) + + def test_document_aggregate_with_fill_matches(self): + from muvera._rust_kernels import aggregate_single as rs_fn + + rng = np.random.default_rng(42) + dim = 16 + num_vecs = 5 # Few vectors to ensure empty partitions + num_simhash = 5 + num_partitions = 2**num_simhash + proj_dim = dim + + vectors = rng.standard_normal((num_vecs, dim)).astype(np.float32) + sim_matrix = simhash_matrix_from_seed(dim, num_simhash, seed=42) + sketches = (vectors @ sim_matrix).astype(np.float32) + projected = vectors.copy() + + encoder = Muvera( + num_repetitions=1, + num_simhash_projections=num_simhash, + dimension=dim, + fill_empty_partitions=True, + seed=42, + ) + py_result = encoder._aggregate_single_python(sketches, projected, is_query=False) + rs_result = np.asarray( + rs_fn(sketches, projected, num_partitions, proj_dim, False, True, num_simhash), + dtype=np.float32, + ) + np.testing.assert_allclose(py_result, rs_result, atol=1e-6) + + +class TestScatterAdd: + def test_scatter_add_matches(self): + from muvera._rust_kernels import scatter_add_partitions as rs_fn + + rng = np.random.default_rng(42) + batch_size = 3 + num_partitions = 8 + proj_dim = 4 + n = 50 + + doc_indices = rng.integers(0, batch_size, size=n).astype(np.uint32) + part_indices = rng.integers(0, num_partitions, size=n).astype(np.uint32) + projected = rng.standard_normal((n, proj_dim)).astype(np.float32) + + # Python path + py_fde = np.zeros((batch_size, num_partitions, proj_dim), dtype=np.float32) + doc_part = doc_indices * num_partitions + part_indices + base = doc_part * proj_dim + flat = py_fde.reshape(-1) + for d in range(proj_dim): + np.add.at(flat, base + d, projected[:, d]) + + # Rust path + rs_fde = np.zeros((batch_size, num_partitions, proj_dim), dtype=np.float32) + rs_fn(rs_fde, doc_indices, part_indices, projected) + + np.testing.assert_allclose(py_fde, rs_fde, atol=1e-6) + + +class TestEndToEnd: + @pytest.mark.parametrize("num_simhash", [3, 5]) + @pytest.mark.parametrize("fill_empty", [True, False]) + def test_encode_documents_deterministic(self, num_simhash, fill_empty): + rng = np.random.default_rng(42) + dim = 32 + encoder = Muvera( + num_repetitions=5, + num_simhash_projections=num_simhash, + dimension=dim, + fill_empty_partitions=fill_empty, + seed=42, + ) + + doc = rng.standard_normal((20, dim)).astype(np.float32) + result1 = encoder.encode_documents(doc) + result2 = encoder.encode_documents(doc) + np.testing.assert_array_equal(result1, result2) + + def test_encode_batch_consistency(self): + rng = np.random.default_rng(42) + dim = 32 + encoder = Muvera( + num_repetitions=5, + num_simhash_projections=4, + dimension=dim, + seed=42, + ) + + docs = [ + rng.standard_normal((20, dim)).astype(np.float32), + rng.standard_normal((15, dim)).astype(np.float32), + rng.standard_normal((30, dim)).astype(np.float32), + ] + + # Single encoding matches batch + batch_result = encoder.encode_documents(docs) + for i, doc in enumerate(docs): + single_result = encoder.encode_documents(doc) + np.testing.assert_allclose(batch_result[i], single_result, atol=1e-5) + + def test_encode_queries_batch_consistency(self): + rng = np.random.default_rng(42) + dim = 32 + encoder = Muvera( + num_repetitions=5, + num_simhash_projections=4, + dimension=dim, + seed=42, + ) + + queries = [ + rng.standard_normal((10, dim)).astype(np.float32), + rng.standard_normal((8, dim)).astype(np.float32), + ] + + batch_result = encoder.encode_queries(queries) + for i, q in enumerate(queries): + single_result = encoder.encode_queries(q) + np.testing.assert_allclose(batch_result[i], single_result, atol=1e-5) + + def test_ams_sketch_mode(self): + rng = np.random.default_rng(42) + dim = 64 + encoder = Muvera( + num_repetitions=5, + num_simhash_projections=4, + dimension=dim, + projection_type="ams_sketch", + projection_dimension=8, + seed=42, + ) + + doc = rng.standard_normal((30, dim)).astype(np.float32) + result1 = encoder.encode_documents(doc) + result2 = encoder.encode_documents(doc) + np.testing.assert_array_equal(result1, result2)