mirror of https://github.com/hpcaitech/ColossalAI
23 lines
1011 B
Python
23 lines
1011 B
Python
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from typing import Tuple
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import torch
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from ..registry import meta_profiler_module
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@meta_profiler_module.register(torch.nn.AvgPool1d)
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@meta_profiler_module.register(torch.nn.AvgPool2d)
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@meta_profiler_module.register(torch.nn.AvgPool3d)
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@meta_profiler_module.register(torch.nn.MaxPool1d)
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@meta_profiler_module.register(torch.nn.MaxPool2d)
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@meta_profiler_module.register(torch.nn.MaxPool3d)
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@meta_profiler_module.register(torch.nn.AdaptiveAvgPool1d)
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@meta_profiler_module.register(torch.nn.AdaptiveMaxPool1d)
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@meta_profiler_module.register(torch.nn.AdaptiveAvgPool2d)
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@meta_profiler_module.register(torch.nn.AdaptiveMaxPool2d)
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@meta_profiler_module.register(torch.nn.AdaptiveAvgPool3d)
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@meta_profiler_module.register(torch.nn.AdaptiveMaxPool3d)
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def torch_nn_pooling(self: torch.nn.Module, input: torch.Tensor) -> Tuple[int, int]:
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# all pooling could be considered as going over each input element only once (https://stackoverflow.com/a/67301217)
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flops = input.numel()
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macs = 0
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return flops, macs
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