def quantized_linear(input, weight, bias=None):
if self.inference_only:
weight_ = self.quantize_instant(weight, "weight")
res = self.origin_linear(input, weight_, bias)
return self.quantize_instant(res, "activation_linear")
elif self.dual_precision:
return self.dual_prec_linear(input, weight, bias)
else:
input_ = attacher.pytorch_attach(input,
self.fprop_quantizer,
self.bprop_quantizer,
tag='activation/in')
weight_ = attacher.pytorch_attach(weight,
self.fprop_quantizer,
self.bprop_quantizer,
tag='weight')
if bias is not None:
bias_ = attacher.pytorch_attach(bias,
self.fprop_quantizer,
self.bprop_quantizer,
tag='bias')
else:
bias_ = bias
res = self.origin_linear(input_, weight_, bias_)
return attacher.pytorch_attach(res,
self.fprop_quantizer,
self.bprop_quantizer,
tag='activation_linear')
def quantized_matmul(tensor1, tensor2):
tensor1_ = attacher.pytorch_attach(tensor1,
self.activation_quantizer, None)
tensor2_ = attacher.pytorch_attach(tensor2,
self.activation_quantizer, None)
res = self.origin_matmul(tensor1_, tensor2_)
return attacher.pytorch_attach(res, self.activation_quantizer,
None)
def quantized_matmul(tensor1, tensor2):
assert False
tensor1_ = attacher.pytorch_attach(tensor1, self.fprop_quantizer,
self.bprop_quantizer)
tensor2_ = attacher.pytorch_attach(tensor2, self.fprop_quantizer,
self.bprop_quantizer)
res = self.origin_matmul(tensor1_, tensor2_)
return attacher.pytorch_attach(res, self.fprop_quantizer,
self.bprop_quantizer)
def quantized_conv2d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1):
if self.inference_only:
weight_ = self.quantize_instant(weight, "weight")
return self.origin_conv2d(input, weight_, bias, stride, padding, dilation, groups)
elif self.dual_precision:
return self.dual_prec_conv2d(input, weight, bias, stride, padding, dilation, groups)
else:
input_ = attacher.pytorch_attach(input, self.fprop_quantizer, self.bprop_quantizer, tag='activation/in')
weight_ = attacher.pytorch_attach(weight, self.fprop_quantizer, self.bprop_quantizer, tag='weight')
if bias is not None:
bias_ = attacher.pytorch_attach(bias, self.fprop_quantizer, self.bprop_quantizer, tag='bias')
else:
bias_ = bias
res = self.origin_conv2d(input_, weight_, bias_, stride, padding, dilation, groups)
return attacher.pytorch_attach(res, self.fprop_quantizer, self.bprop_quantizer, tag='activation')
def dual_prec_conv2d(self,
input,
weight,
bias=None,
stride=1,
padding=0,
dilation=1,
groups=1):
# fprop conv2d quantized by fprop_quantizer
input_fprop = attacher.pytorch_attach(input,
self.fprop_quantizer,
None,
tag='activation/in')
weight_fprop = attacher.pytorch_attach(weight,
self.fprop_quantizer,
None,
tag='weight')
if bias is not None:
bias_fprop = attacher.pytorch_attach(bias,
self.fprop_quantizer,
None,
tag='bias')
else:
bias_fprop = bias
conv_fprop = self.origin_conv2d(input_fprop, weight_fprop, bias_fprop,
stride, padding, dilation, groups)
conv_fprop = attacher.pytorch_attach(conv_fprop,
self.fprop_quantizer,
None,
tag='activation')
# bprop conv2d quantized by bprop_quantizer
input_bprop = attacher.pytorch_attach(input,
None,
self.bprop_quantizer,
tag='activation/in')
weight_bprop = attacher.pytorch_attach(weight,
None,
self.bprop_quantizer,
tag='weight')
if bias is not None:
bias_bprop = attacher.pytorch_attach(bias,
None,
self.bprop_quantizer,
tag='bias')
else:
bias_bprop = bias
conv_bprop = self.origin_conv2d(input_bprop, weight_bprop, bias_bprop,
stride, padding, dilation, groups)
conv_bprop = attacher.pytorch_attach(conv_bprop,
None,
self.bprop_quantizer,
tag='activation')
return conv_fprop.detach() + conv_bprop - conv_bprop.detach()
def dual_prec_linear(self, input, weight, bias=None):
# fprop linear quantized by fprop_quantizer
input_fprop = attacher.pytorch_attach(input,
self.fprop_quantizer,
None,
tag='activation/in')
weight_fprop = attacher.pytorch_attach(weight,
self.fprop_quantizer,
None,
tag='weight')
if bias is not None:
bias_fprop = attacher.pytorch_attach(bias,
self.fprop_quantizer,
None,
tag='bias')
else:
bias_fprop = bias
linear_fprop = self.origin_linear(input_fprop, weight_fprop,
bias_fprop)
linear_fprop = attacher.pytorch_attach(linear_fprop,
self.fprop_quantizer,
None,
tag='activation_linear')
# bprop linear quantized by bprop_quantizer
input_bprop = attacher.pytorch_attach(input,
None,
self.bprop_quantizer,
tag='activation/in')
weight_bprop = attacher.pytorch_attach(weight,
None,
self.bprop_quantizer,
tag='weight')
if bias is not None:
bias_bprop = attacher.pytorch_attach(bias,
None,
self.bprop_quantizer,
tag='bias')
else:
bias_bprop = bias
linear_bprop = self.origin_linear(input_bprop, weight_bprop,
bias_bprop)
linear_bprop = attacher.pytorch_attach(linear_bprop,
None,
self.bprop_quantizer,
tag='activation_linear')
return linear_fprop.detach() + linear_bprop - linear_bprop.detach()
def quantize_tensor(self, tensor, fprop=True, bprop=True):
fprop = self.activation_quantizer if fprop else None
return attacher.pytorch_attach(tensor, fprop, None)
def quantize_tensor(self, tensor, fprop=True, bprop=True):
fprop = self.fprop_quantizer if fprop else None
bprop = self.bprop_quantizer if bprop else None
return attacher.pytorch_attach(tensor, fprop, bprop)