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aibig-modeldata-parallelismdeep-learningdistributed-computingfoundation-modelsheterogeneous-traininghpcinferencelarge-scalemodel-parallelismpipeline-parallelism
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37 lines
1.2 KiB
37 lines
1.2 KiB
import torch |
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from colossalai.fx._compatibility import is_compatible_with_meta |
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from colossalai.fx.passes.meta_info_prop import MetaInfoProp, TensorMetadata |
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from torch.fx import symbolic_trace |
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if is_compatible_with_meta(): |
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from colossalai.fx.profiler import MetaTensor |
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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.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, device='meta') |
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if is_compatible_with_meta(): |
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input_sample = MetaTensor(input_sample, fake_device='cpu') |
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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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