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https://github.com/hpcaitech/ColossalAI.git
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fff98f06ed
* [hotfix] meta tensor default device. * [siu] add experimental submodules to main branch. * [siu] * [siu] * [analyzer] init. * [analyzer] readme. * [analyzer] readme. * [analyzer] readme. * [analyzer] readme. * [test] add test. * Update symbolic_trace.py * mark skip tests. * try except. * try except. * try except. * s * init * init * fix * skip * skip --------- Co-authored-by: Daniel Shao <superdainiu@MININT-PVARVID.fareast.corp.microsoft.com> Co-authored-by: Daniel Shao <superdainiu@Daniels-Mac.local>
51 lines
1.7 KiB
Python
51 lines
1.7 KiB
Python
import pytest
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import torchvision.models as tm
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from .zoo import tm_models, tmm_models
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try:
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from colossalai._analyzer._subclasses import MetaTensorMode, flop_count
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except:
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pass
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@pytest.mark.skipif(torch.__version__ < '1.12.0', reason='torch version < 12')
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@pytest.mark.parametrize('m', tm_models + tmm_models)
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def test_flop_count_module(m):
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x = torch.rand(2, 3, 224, 224)
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with MetaTensorMode(): # save time for testing
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module = m()
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rs_fwd, rs_bwd = flop_count(module, x, verbose=True)
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assert rs_fwd > 0, f'fwd flop count of {m.__name__} is {rs_fwd}'
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assert rs_bwd > 0, f'bwd flop count of {m.__name__} is {rs_bwd}'
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odd_cases = [
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(F.relu, (torch.rand(2, 3, 224, 224, requires_grad=True),), {
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'inplace': True
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}),
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(F.max_pool2d, (torch.rand(2, 3, 224, 224, requires_grad=True),), {
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'kernel_size': 3,
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'stride': 2,
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'padding': 1,
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'dilation': 2
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}),
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(torch.where, (torch.rand(2, 3, 224, 224) > 0.5, torch.rand(2, 3, 224, 224, requires_grad=True),
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torch.rand(2, 3, 224, 224, requires_grad=True)), {}),
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]
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@pytest.mark.skipif(torch.__version__ < '1.12.0', reason='torch version < 12')
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@pytest.mark.parametrize('func, args, kwargs', odd_cases)
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def test_flop_count_function(func, args, kwargs):
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rs_fwd, rs_bwd = flop_count(func, *args, **kwargs, verbose=True)
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assert rs_fwd > 0, f'fwd flop count of {func.__name__} is {rs_fwd}'
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assert rs_bwd > 0, f'bwd flop count of {func.__name__} is {rs_bwd}'
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if __name__ == '__main__':
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test_flop_count_module(tm.resnet18, torch.rand(2, 3, 224, 224))
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test_flop_count_function(F.relu, (torch.rand(2, 3, 224, 224, requires_grad=True),), {'inplace': True})
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