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84 lines
2.9 KiB
84 lines
2.9 KiB
from functools import partial
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import torch
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from colossalai.fx.tracer.meta_patch import patched_function
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from colossalai.testing import clear_cache_before_run
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def _run(data, patch_fn):
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try:
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output = patch_fn(data)
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return output
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except Exception as e:
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return e
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def _assert_output_shape(data, patch_fn, expect_exception, output_shape):
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output = _run(data, patch_fn)
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if expect_exception:
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assert isinstance(output, AssertionError)
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else:
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assert not isinstance(output, Exception)
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assert output.is_meta
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assert output.shape == output_shape
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@clear_cache_before_run()
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def test_repeat_interleave():
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patch_fn = patched_function.torch_repeat_interleave
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# examples from https://pytorch.org/docs/stable/generated/torch.repeat_interleave.html
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data = torch.tensor([1, 2, 3])
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materialized_output = torch.repeat_interleave(data, repeats=2)
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repeat_interleave = partial(patch_fn, repeats=2)
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meta_data = data.to("meta")
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_assert_output_shape(
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data=meta_data, patch_fn=repeat_interleave, expect_exception=False, output_shape=materialized_output.shape
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)
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data = torch.tensor([[1, 2], [3, 4]])
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materialized_output = torch.repeat_interleave(data, repeats=3, dim=1)
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repeat_interleave = partial(patch_fn, repeats=3, dim=1)
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meta_data = data.to("meta")
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_assert_output_shape(
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data=meta_data, patch_fn=repeat_interleave, expect_exception=False, output_shape=materialized_output.shape
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)
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data = torch.tensor([[1, 2], [3, 4]])
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materialized_output = torch.repeat_interleave(data, repeats=torch.tensor([1, 2]), dim=-1)
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repeat_interleave = partial(patch_fn, repeats=torch.tensor([1, 2]), dim=-1)
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meta_data = data.to("meta")
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_assert_output_shape(
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data=meta_data, patch_fn=repeat_interleave, expect_exception=False, output_shape=materialized_output.shape
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)
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data = torch.tensor([[1, 2], [3, 4]])
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materialized_output = torch.repeat_interleave(data, repeats=torch.tensor([1, 2]), dim=0)
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repeat_interleave = partial(patch_fn, repeats=[1, 2], dim=0)
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meta_data = data.to("meta")
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_assert_output_shape(
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data=meta_data, patch_fn=repeat_interleave, expect_exception=True, output_shape=materialized_output.shape
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)
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@clear_cache_before_run()
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def test_torch_max():
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data = torch.rand(4, 3)
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out = torch.max(data)
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patched_out = patched_function.torch_max(data)
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assert out.shape == patched_out.shape
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data = torch.rand(4, 3, 2)
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out, idx = torch.max(data, dim=1)
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patched_out, patched_idx = patched_function.torch_max(data, dim=1)
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assert out.shape == patched_out.shape
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assert idx.shape == patched_idx.shape
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data = torch.rand(4, 3, 2)
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out, idx = torch.max(data, dim=1, keepdim=True)
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patched_out, patched_idx = patched_function.torch_max(data, dim=1, keepdim=True)
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assert out.shape == patched_out.shape
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assert idx.shape == patched_idx.shape
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