mirror of https://github.com/hpcaitech/ColossalAI
36 lines
1.1 KiB
Python
36 lines
1.1 KiB
Python
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import torch
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import torch.nn as nn
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import colossalai
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import colossalai.nn as col_nn
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from torch.fx import symbolic_trace
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from colossalai.fx.passes.meta_info_prop import MetaInfoProp, TensorMetadata
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BATCH_SIZE = 2
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DIM_IN = 4
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DIM_OUT = 16
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def meta_check(meta_info_spec: TensorMetadata, orig_tensor: torch.Tensor):
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assert meta_info_spec.shape == orig_tensor.shape
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assert meta_info_spec.dtype == orig_tensor.dtype
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assert meta_info_spec.requires_grad == orig_tensor.requires_grad
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assert meta_info_spec.stride == orig_tensor.stride()
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assert meta_info_spec.numel == orig_tensor.numel()
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def test_meta_info_prop():
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model = torch.nn.Linear(DIM_IN, DIM_OUT)
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input_sample = torch.rand(BATCH_SIZE, DIM_IN)
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orig_output = model(input_sample)
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gm = symbolic_trace(model)
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MetaInfoProp(gm).run(input_sample)
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for node in gm.graph.nodes:
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if node.op == 'placeholder':
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meta_check(node.meta['tensor_meta'], input_sample)
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if node.op == 'output':
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meta_check(node.meta['tensor_meta'], orig_output)
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if __name__ == '__main__':
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test_meta_info_prop()
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