def forward(self, input):
# See the autograd section for explanation of what happens here.
self.rshift_i, self.rshift_w, self.rshift_o = \
rshift_offset(input, self.weight, self.hwcfg["widthi"] - self.hwcfg["signmag"], self.hwcfg["widthw"] - self.hwcfg["signmag"], self.hwcfg["rounding"], self.hwcfg["quantilei"], self.hwcfg["quantilew"])
with torch.no_grad():
# all data are in NCHW
output_size = conv2d_output_shape(
(input.size()[2], input.size()[3]),
kernel_size=self.kernel_size,
dilation=self.dilation,
pad=self.padding,
stride=self.stride)
input_im2col = torch.nn.functional.unfold(input, self.kernel_size,
self.dilation, self.padding,
self.stride)
input_transpose = input_im2col.transpose(1, 2)
input_reshape = input_transpose.reshape(-1, input_transpose.size()[-1])
weight = self.weight.view(self.weight.size()[0], -1)
mm_out = HUBLinearFunction.apply(input_reshape, weight, None,
self.rshift_i, self.rshift_w,
self.rshift_o, self.cycle_act,
self.mapctlee)
mm_out_reshape = mm_out.reshape(input.size()[0], -1, mm_out.size()[-1])
mm_out_transpose = mm_out_reshape.transpose(1, 2)
output = torch.nn.functional.fold(mm_out_transpose, output_size,
(1, 1))
if self.bias is None:
return output
else:
return output + self.bias.view([1, self.bias.size()[0], 1, 1])
def forward(self, input):
# See the autograd section for explanation of what happens here.
self.rshift_i, self.rshift_w, self.rshift_o = \
rshift_offset(input, self.weight, self.hwcfg["widthi"] - self.hwcfg["signmag"], self.hwcfg["widthw"] - self.hwcfg["signmag"], self.hwcfg["rounding"], self.hwcfg["quantilei"], self.hwcfg["quantilew"])
return HUBLinearFunction.apply(input, self.weight, self.bias,
self.rshift_i, self.rshift_w,
self.rshift_o, self.cycle_act,
self.mapctlee)
def forward(self, input):
# See the autograd section for explanation of what happens here.
self.rshift_i, self.rshift_w, _ = \
rshift_offset(input, self.weight, self.hwcfg["widthi"] - 1, self.hwcfg["widthw"] - 1, self.hwcfg["rounding"], self.hwcfg["quantilei"], self.hwcfg["quantilew"])
self.rshift_o = 0 - self.rshift_i - self.rshift_w
return FXPLinearFunction.apply(input, self.weight, self.bias,
self.rshift_i, self.rshift_w,
self.rshift_o, self.max_abs_i,
self.max_abs_w)
def forward(ctx,
input,
weight,
bias=None,
temporal="i",
width=8,
widtht=4,
degree=2,
delta=0,
cycle_pos=16,
cycle_neg=-16,
rounding="round",
quantilei=1,
quantilew=1):
ctx.save_for_backward(input, weight, bias)
input_fp32 = input.detach().clone().to(torch.float)
weight_fp32 = weight.detach().clone().to(torch.float)
rshift_i, rshift_w, _ = rshift_offset(input_fp32, weight_fp32, width,
width, rounding, quantilei,
quantilew)
if temporal in ["i", "input"]:
input_new = torch.zeros_like(input_fp32)
frac = torch.zeros_like(input_fp32)
torch.trunc((input_fp32 >> rshift_i).clamp(-2**width + 1,
2**width - 1),
out=input_fp32)
for i in range(degree):
input_fp32 = input_fp32 >> widtht
torch.frac(input_fp32, out=frac)
torch.trunc(input_fp32, out=input_fp32)
torch.clamp(frac << widtht,
cycle_neg + 1,
cycle_pos - 1,
out=frac)
torch.add(frac >> widtht, input_new >> widtht, out=input_new)
input_new = (input_new <<
(delta + width + rshift_i)).type(weight.type())
weight_new = weight
elif temporal in ["w", "weight"]:
weight_new = torch.zeros_like(weight_fp32)
frac = torch.zeros_like(weight_fp32)
torch.trunc(
(weight_fp32 >> rshift_w).clamp(-2**width + 1, 2**width - 1),
out=weight_fp32)
for i in range(degree):
weight_fp32 = weight_fp32 >> widtht
torch.frac(weight_fp32, out=frac)
torch.trunc(weight_fp32, out=weight_fp32)
torch.clamp(frac << widtht,
cycle_neg + 1,
cycle_pos - 1,
out=frac)
torch.add(frac >> widtht, weight_new >> widtht, out=weight_new)
input_new = input
weight_new = (weight_new <<
(delta + width + rshift_w)).type(input.type())
output = torch.matmul(input_new, weight_new.t())
if bias is not None:
output += bias.unsqueeze(0).expand_as(output)
return output