mirror of https://github.com/hpcaitech/ColossalAI
23 lines
1.2 KiB
Python
23 lines
1.2 KiB
Python
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from typing import Tuple, Union
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import torch
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from ..registry import meta_profiler_function
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@meta_profiler_function.register(torch.nn.functional.avg_pool1d)
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@meta_profiler_function.register(torch.nn.functional.avg_pool2d)
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@meta_profiler_function.register(torch.nn.functional.avg_pool3d)
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@meta_profiler_function.register(torch.nn.functional.max_pool1d)
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@meta_profiler_function.register(torch.nn.functional.max_pool2d)
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@meta_profiler_function.register(torch.nn.functional.max_pool3d)
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@meta_profiler_function.register(torch.nn.functional.adaptive_avg_pool1d)
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@meta_profiler_function.register(torch.nn.functional.adaptive_avg_pool2d)
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@meta_profiler_function.register(torch.nn.functional.adaptive_avg_pool3d)
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@meta_profiler_function.register(torch.nn.functional.adaptive_max_pool1d)
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@meta_profiler_function.register(torch.nn.functional.adaptive_max_pool2d)
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@meta_profiler_function.register(torch.nn.functional.adaptive_max_pool3d)
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def torch_nn_func_pooling(input: torch.Tensor, *args, **kwargs) -> 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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