mirror of https://github.com/hpcaitech/ColossalAI
68 lines
2.1 KiB
Python
68 lines
2.1 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.utils import clear_cache_before_run, parameterize
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from tests.test_analyzer.test_fx.zoo import tm_models, tmm_models
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try:
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from colossalai._analyzer._subclasses import MetaTensorMode
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from colossalai._analyzer.fx import symbolic_trace
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from colossalai._analyzer.fx.passes.shape_prop import shape_prop_pass
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from colossalai._analyzer.fx.symbolic_profile import register_shape_impl
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@register_shape_impl(torch.nn.functional.linear)
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def linear_impl(*args, **kwargs):
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assert True
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return torch.nn.functional.linear(*args, **kwargs)
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except:
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pass
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def _check_gm_validity(gm: torch.fx.GraphModule):
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for node in gm.graph.nodes:
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assert node.meta['info'].outputs, f'In {gm.__class__.__name__}, {node} has no output shape.'
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if node.op in [
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'call_module', # can apply to params
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'call_function', # can apply to params
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'call_method', # can apply to params
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]:
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assert hasattr(node.meta['info'], 'inputs'), f'In {gm.__class__.__name__}, {node} has no input shape.'
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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)
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def test_torchvision_shape_prop(m):
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with MetaTensorMode():
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model = m()
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data = torch.rand(100, 3, 224, 224)
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meta_args = {
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"x": data,
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}
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gm = symbolic_trace(model, meta_args=meta_args)
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shape_prop_pass(gm, data)
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_check_gm_validity(gm)
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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', tmm_models)
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def test_timm_shape_prop(m):
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with MetaTensorMode():
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model = m()
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data = torch.rand(100, 3, 224, 224)
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meta_args = {
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"x": data,
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}
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gm = symbolic_trace(model, meta_args=meta_args)
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shape_prop_pass(gm, data)
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_check_gm_validity(gm)
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if __name__ == "__main__":
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test_torchvision_shape_prop()
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test_timm_shape_prop()
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