mirror of https://github.com/hpcaitech/ColossalAI
34 lines
1.3 KiB
Python
34 lines
1.3 KiB
Python
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from typing import Tuple
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import torch
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from ..registry import meta_profiler_function
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# TODO: different activation has different FLOPs count, currently unused.
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_multiplier = {
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torch.nn.functional.relu: 1,
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torch.nn.functional.prelu: 4,
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torch.nn.functional.sigmoid: 4,
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torch.nn.functional.tanh: 5,
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torch.nn.functional.leaky_relu: 3,
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torch.nn.functional.elu: 4,
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torch.nn.functional.relu6: 2,
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torch.nn.functional.gelu: 9,
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torch.nn.functional.hardswish: 5,
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torch.nn.functional.hardsigmoid: 4,
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}
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@meta_profiler_function.register(torch.nn.functional.leaky_relu)
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@meta_profiler_function.register(torch.nn.functional.elu)
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@meta_profiler_function.register(torch.nn.functional.gelu)
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@meta_profiler_function.register(torch.nn.functional.relu6)
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@meta_profiler_function.register(torch.nn.functional.prelu)
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@meta_profiler_function.register(torch.nn.functional.relu)
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@meta_profiler_function.register(torch.nn.functional.sigmoid)
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@meta_profiler_function.register(torch.nn.functional.tanh)
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@meta_profiler_function.register(torch.nn.functional.hardswish)
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@meta_profiler_function.register(torch.nn.functional.hardsigmoid)
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def torch_nn_func_non_linear_act(input: torch.Tensor, inplace: bool = False) -> Tuple[int, int]:
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flops = input.numel()
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macs = 0
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return flops, macs
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