mirror of https://github.com/hpcaitech/ColossalAI
43 lines
1.5 KiB
Python
43 lines
1.5 KiB
Python
import pytest
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import torch
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import torchvision.models as tm
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from packaging import version
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from colossalai.testing import clear_cache_before_run, parameterize
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try:
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from colossalai._analyzer._subclasses import MetaTensor, MetaTensorMode
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except:
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pass
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from tests.test_analyzer.test_fx.zoo import tm_models, tmm_models
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def compare_all(tensor: torch.Tensor, meta_tensor: torch.Tensor):
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assert tensor.shape == meta_tensor.shape, f'the shape of tensor ({tensor.shape}) and meta tensor ({meta_tensor.shape}) does not match.'
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assert tensor.dtype == meta_tensor.dtype, f'the dtype of tensor ({tensor.dtype}) and meta tensor ({meta_tensor.dtype}) does not match.'
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assert tensor.stride() == meta_tensor.stride(
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), f'the stride of tensor ({tensor.stride()}) and meta tensor ({meta_tensor.stride()}) does not match.'
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def run_and_compare(model):
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x = torch.rand(2, 3, 224, 224, requires_grad=True)
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x_out = model(x)
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with MetaTensorMode():
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meta_x = torch.rand(2, 3, 224, 224, requires_grad=True)
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meta_out = model(meta_x)
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compare_all(x_out, meta_out)
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x_out.sum().backward()
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meta_out.sum().backward()
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compare_all(x.grad, meta_x.grad)
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@pytest.mark.skipif(version.parse(torch.__version__) < version.parse('1.12.0'), reason='torch version < 12')
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@clear_cache_before_run()
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@parameterize('m', tm_models + tmm_models)
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def test_meta_mode_shape(m):
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run_and_compare(m())
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if __name__ == '__main__':
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test_meta_mode_shape(tm.resnet18)
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