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4f59693207
* [fx] compute memory stat and flop count for MetaInfoProp. * [fx] modify node attribute. * [fx] modify ckpt_chen. * [fx] fix compatibility. * [fx] fix import error. * [fx] skip test for MetaInfoProp. * [fx] skip test for MetaInfoProp. * [fx] skip test for MetaInfoProp. * [fx] skip test for MetaInfoProp. * [fx] skip if torch 1.11.0. * [fx] recover MetaInfoProp support for PyTorch 1.11. * [fx] provide a stable but not accurate enough version of profiler. * [fx] provide a stable but not accurate enough version of profiler. * [fx] fix compatibility in tests. * [fx] fix compatibility in tests. * [fx] fix compatibility in tests. * [fx] fix compatibility in tests. * [fx] fix compatibility in tests. * [fx] fix compatibility in tests. * [fx] fix compatibility in tests. * [fx] fix compatibility in tests. * [fx] fix compatibility in tests. * [fx] fix compatibility in tests. * [fx] fix import error.
15 lines
493 B
Python
15 lines
493 B
Python
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.Linear)
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@meta_profiler_module.register(torch.nn.modules.linear.NonDynamicallyQuantizableLinear)
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def torch_nn_linear(self: torch.nn.Linear, input: torch.Tensor) -> Tuple[int, int]:
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out_features = self.weight.shape[0]
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macs = input.numel() * out_features
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flops = 2 * macs
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if self.bias is not None:
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flops += self.bias.numel()
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return flops, macs
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