import pytest import torch import torchvision.models as tm from packaging import version from colossalai.testing.utils import parameterize from tests.test_analyzer.test_fx.zoo import tm_models, tmm_models try: from colossalai._analyzer._subclasses import MetaTensorMode from colossalai._analyzer.fx import symbolic_trace from colossalai._analyzer.fx.passes.shape_prop import shape_prop_pass from colossalai._analyzer.fx.symbolic_profile import register_shape_impl @register_shape_impl(torch.nn.functional.linear) def linear_impl(*args, **kwargs): assert True return torch.nn.functional.linear(*args, **kwargs) except: pass def _check_gm_validity(gm: torch.fx.GraphModule): for node in gm.graph.nodes: assert node.meta['info'].outputs, f'In {gm.__class__.__name__}, {node} has no output shape.' if node.op in [ 'call_module', # can apply to params 'call_function', # can apply to params 'call_method', # can apply to params ]: assert hasattr(node.meta['info'], 'inputs'), f'In {gm.__class__.__name__}, {node} has no input shape.' @pytest.mark.skipif(version.parse(torch.__version__) < version.parse('1.12.0'), reason='torch version < 12') @parameterize('m', tm_models) def test_torchvision_shape_prop(m): with MetaTensorMode(): model = m() data = torch.rand(100, 3, 224, 224) meta_args = { "x": data, } gm = symbolic_trace(model, meta_args=meta_args) shape_prop_pass(gm, data) _check_gm_validity(gm) @pytest.mark.skipif(version.parse(torch.__version__) < version.parse('1.12.0'), reason='torch version < 12') @parameterize('m', tmm_models) def test_timm_shape_prop(m): with MetaTensorMode(): model = m() data = torch.rand(100, 3, 224, 224) meta_args = { "x": data, } gm = symbolic_trace(model, meta_args=meta_args) shape_prop_pass(gm, data) _check_gm_validity(gm) if __name__ == "__main__": test_torchvision_shape_prop() test_timm_shape_prop()