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https://github.com/saymrwulf/pytorch.git
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123be0e5b7
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/70022 Add support for fusing ConvTranpose{1,2,3}d with BatchNorm{1,2,3}d. This re-uses the existing fusion logic but adds a "transpose" flag to the fusing function which when enabled will use the appropriate reshape for ConTranspose's transposed weights. Test Plan: `buck test mode/dev //caffe2/test:quantization -- -r quantization.eager.test_fusion.TestFusion` Reviewed By: jerryzh168 Differential Revision: D33074405 fbshipit-source-id: 5e9eff1a06d8f98d117e7d18e80da8e842e973b7
53 lines
1.8 KiB
Python
53 lines
1.8 KiB
Python
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import copy
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import torch
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def fuse_conv_bn_eval(conv, bn, transpose=False):
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assert(not (conv.training or bn.training)), "Fusion only for eval!"
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fused_conv = copy.deepcopy(conv)
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fused_conv.weight, fused_conv.bias = \
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fuse_conv_bn_weights(fused_conv.weight, fused_conv.bias,
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bn.running_mean, bn.running_var, bn.eps, bn.weight, bn.bias, transpose)
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return fused_conv
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def fuse_conv_bn_weights(conv_w, conv_b, bn_rm, bn_rv, bn_eps, bn_w, bn_b, transpose=False):
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if conv_b is None:
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conv_b = torch.zeros_like(bn_rm)
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if bn_w is None:
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bn_w = torch.ones_like(bn_rm)
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if bn_b is None:
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bn_b = torch.zeros_like(bn_rm)
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bn_var_rsqrt = torch.rsqrt(bn_rv + bn_eps)
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if transpose:
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shape = [1, -1] + [1] * (len(conv_w.shape) - 2)
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else:
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shape = [-1, 1] + [1] * (len(conv_w.shape) - 2)
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conv_w = conv_w * (bn_w * bn_var_rsqrt).reshape(shape)
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conv_b = (conv_b - bn_rm) * bn_var_rsqrt * bn_w + bn_b
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return torch.nn.Parameter(conv_w), torch.nn.Parameter(conv_b)
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def fuse_linear_bn_eval(linear, bn):
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assert(not (linear.training or bn.training)), "Fusion only for eval!"
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fused_linear = copy.deepcopy(linear)
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fused_linear.weight, fused_linear.bias = fuse_linear_bn_weights(
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fused_linear.weight, fused_linear.bias,
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bn.running_mean, bn.running_var, bn.eps, bn.weight, bn.bias)
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return fused_linear
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def fuse_linear_bn_weights(linear_w, linear_b, bn_rm, bn_rv, bn_eps, bn_w, bn_b):
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if linear_b is None:
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linear_b = torch.zeros_like(bn_rm)
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bn_scale = bn_w * torch.rsqrt(bn_rv + bn_eps)
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fused_w = linear_w * bn_scale.unsqueeze(-1)
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fused_b = (linear_b - bn_rm) * bn_scale + bn_b
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return torch.nn.Parameter(fused_w), torch.nn.Parameter(fused_b)
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