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[API Compatibility]enhance test for torch.nn.Module.to -part #856

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[API Compatibility]enhance test for torch.nn.Module.to -part #856

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215 changes: 215 additions & 0 deletions tests/test_nn_Module_to.py
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Original file line number Diff line number Diff line change
Expand Up @@ -125,3 +125,218 @@ def test_case_7():
"""
)
obj.run(pytorch_code, ["result"], check_value=False)


def test_case_8():
"""Positional dtype: to(torch.float64)"""
pytorch_code = textwrap.dedent(
"""
import torch
module = torch.nn.Module()
module.register_buffer('buf', torch.tensor([1.0, 2.0, 3.0]))
module.to(torch.float64)
result = module.buf
"""
)
obj.run(pytorch_code, ["result"])


def test_case_9():
"""Positional dtype: to(torch.float16)"""
pytorch_code = textwrap.dedent(
"""
import torch
module = torch.nn.Module()
module.register_buffer('buf', torch.tensor([1.0, 2.0, 3.0]))
module.to(torch.float16)
result = module.buf
"""
)
obj.run(pytorch_code, ["result"])


def test_case_10():
"""Keyword dtype: to(dtype=torch.float64)"""
pytorch_code = textwrap.dedent(
"""
import torch
module = torch.nn.Module()
module.register_buffer('buf', torch.tensor([1.0, 2.0, 3.0]))
module.to(dtype=torch.float64)
result = module.buf
"""
)
obj.run(pytorch_code, ["result"])


def test_case_11():
"""Positional device string: to('cpu')"""
pytorch_code = textwrap.dedent(
"""
import torch
module = torch.nn.Module()
module.register_buffer('buf', torch.tensor([1.0, 2.0, 3.0]))
module.to('cpu')
result = module.buf
"""
)
obj.run(pytorch_code, ["result"])


def test_case_12():
"""Positional device + keyword dtype: to('cpu', dtype=torch.float64)"""
pytorch_code = textwrap.dedent(
"""
import torch
module = torch.nn.Module()
module.register_buffer('buf', torch.tensor([1.0, 2.0, 3.0]))
module.to('cpu', dtype=torch.float64)
result = module.buf
"""
)
obj.run(pytorch_code, ["result"])


def test_case_13():
"""Positional tensor: to(some_tensor)"""
pytorch_code = textwrap.dedent(
"""
import torch
module = torch.nn.Module()
module.register_buffer('buf', torch.tensor([1.0, 2.0, 3.0]))
ref = torch.tensor([1.0], dtype=torch.float64)
module.to(ref)
result = module.buf
"""
)
obj.run(pytorch_code, ["result"])


def test_case_14():
"""Chaining: ret = module.to(torch.float64)"""
pytorch_code = textwrap.dedent(
"""
import torch
module = torch.nn.Module()
module.register_buffer('buf', torch.tensor([1.0, 2.0, 3.0]))
result = module.to(torch.float64).buf
"""
)
obj.run(pytorch_code, ["result"])


def test_case_15():
"""floating_only: int buffer should NOT be cast"""
pytorch_code = textwrap.dedent(
"""
import torch
module = torch.nn.Module()
module.register_buffer('float_buf', torch.tensor([1.0, 2.0, 3.0]))
module.register_buffer('int_buf', torch.tensor([1, 2, 3]))
module.to(torch.float64)
result = module.int_buf
"""
)
obj.run(pytorch_code, ["result"])


def test_case_16():
"""floating_only: float buffer should be cast"""
pytorch_code = textwrap.dedent(
"""
import torch
module = torch.nn.Module()
module.register_buffer('float_buf', torch.tensor([1.0, 2.0, 3.0]))
module.register_buffer('int_buf', torch.tensor([1, 2, 3]))
module.to(torch.float64)
result = module.float_buf
"""
)
obj.run(pytorch_code, ["result"])


def test_case_17():
"""Keyword non_blocking with positional dtype"""
pytorch_code = textwrap.dedent(
"""
import torch
module = torch.nn.Module()
module.register_buffer('buf', torch.tensor([1.0, 2.0, 3.0]))
module.to(torch.float64, non_blocking=False)
result = module.buf
"""
)
obj.run(pytorch_code, ["result"])


def test_case_18():
"""Keyword device and dtype together"""
pytorch_code = textwrap.dedent(
"""
import torch
module = torch.nn.Module()
module.register_buffer('buf', torch.tensor([1.0, 2.0, 3.0]))
module.to(device='cpu', dtype=torch.float64)
result = module.buf
"""
)
obj.run(pytorch_code, ["result"])


def test_case_19():
"""Reordered kwargs: dtype first, device second"""
pytorch_code = textwrap.dedent(
"""
import torch
module = torch.nn.Module()
module.register_buffer('buf', torch.tensor([1.0, 2.0, 3.0]))
module.to(dtype=torch.float64, device='cpu')
result = module.buf
"""
)
obj.run(pytorch_code, ["result"])


def test_case_20():
"""Sequential to() calls"""
pytorch_code = textwrap.dedent(
"""
import torch
module = torch.nn.Module()
module.register_buffer('buf', torch.tensor([1.0, 2.0, 3.0]))
module.to(torch.float64)
module.to(torch.float32)
result = module.buf
"""
)
obj.run(pytorch_code, ["result"])


def test_case_21():
"""Sublayers should be cast too"""
pytorch_code = textwrap.dedent(
"""
import torch
module = torch.nn.Module()
module.register_buffer('buf', torch.tensor([1.0, 2.0, 3.0]))
sub = torch.nn.Module()
sub.register_buffer('sub_buf', torch.tensor([4.0, 5.0, 6.0]))
module.add_module('sub', sub)
module.to(torch.float64)
result = module.sub.sub_buf
"""
)
obj.run(pytorch_code, ["result"])


def test_case_22():
"""to() with no args returns self"""
pytorch_code = textwrap.dedent(
"""
import torch
module = torch.nn.Module()
module.register_buffer('buf', torch.tensor([1.0, 2.0, 3.0]))
result = module.to().buf
"""
)
obj.run(pytorch_code, ["result"])