def forward(self, stats_input: Tensor) -> Tensor:
if self.training:
if self.counter < self.collect_stats_steps:
if self.stats_permute_dims is not None:
stats_input = stats_input.permute(
*self.stats_permute_dims).contiguous()
stats_input = self.stats_input_view_shape_impl(stats_input)
stats = self.stats(stats_input)
if self.counter == 0:
self.value.detach().mul_(stats.detach())
else:
self.value.detach().mul_(1 - self.momentum)
self.value.detach().add_(self.momentum * stats.detach())
self.counter = self.counter + 1
return stats
elif self.counter == self.collect_stats_steps:
self.restrict_inplace_preprocess(self.value.detach())
self.counter = self.counter + 1
return self.restrict_clamp_scaling(
abs_binary_sign_grad(self.value))
else:
return self.restrict_clamp_scaling(
abs_binary_sign_grad(self.value))
out = self.restrict_clamp_scaling(abs_binary_sign_grad(self.value))
return out
def training_forward(self, stats_input: Tensor) -> Tensor:
if self.counter < self.collect_stats_steps:
stats_input = self.stats_input_view_shape_impl(stats_input)
stats = self.stats(stats_input)
new_counter = self.counter + 1
if self.counter == 0:
_inplace_init(self.buffer, stats.detach())
else:
_inplace_update(self.buffer, stats.detach(), self.momentum, self.counter, new_counter)
self.counter = new_counter
return stats
elif self.counter == self.collect_stats_steps:
self.restrict_inplace_preprocess(self.buffer)
_inplace_init(self.value.detach(), self.buffer)
self.counter = self.counter + 1
return abs_binary_sign_grad(self.restrict_clamp_scaling(self.value))
else:
return abs_binary_sign_grad(self.restrict_clamp_scaling(self.value))
def forward(self, stats_input: Tensor) -> Tensor:
if self.training:
return self.training_forward(stats_input)
else:
if self.counter <= self.collect_stats_steps:
out = self.buffer
out = self.restrict_preprocess(out)
else:
out = self.value
out = abs_binary_sign_grad(self.restrict_clamp_scaling(out))
return out
def forward(self, x: Tensor, scale: Tensor, bit_width: Tensor) -> Tensor:
if self.training:
out = self.training_forward(x)
else:
if self.counter <= self.collect_stats_steps:
out = self.buffer
else:
out = self.value
out = abs_binary_sign_grad(out)
min_int = self.int_quant.min_int(bit_width)
out = self.int_quant.to_int(scale, min_int, bit_width, out)
return out
def training_forward(self, stats_input: Tensor) -> Tensor:
if self.counter < self.collect_stats_steps:
stats_input = self.stats_input_view_shape_impl(stats_input)
stats = self.stats(stats_input)
new_counter = self.counter + 1
if self.counter == 0:
inplace_tensor_mul(self.buffer, stats.detach())
else:
inplace_momentum_update(self.buffer, stats.detach(),
self.momentum, self.counter,
new_counter)
self.counter = new_counter
# workaround to avoid find_ununsed_parameter=True in DDP
stats = stats + 0. * self.value
return stats
elif self.counter == self.collect_stats_steps:
self.restrict_inplace_preprocess(self.buffer)
inplace_tensor_mul(self.value.detach(), self.buffer)
self.counter = self.counter + 1
return abs_binary_sign_grad(self.restrict_clamp_scaling(
self.value))
else:
return abs_binary_sign_grad(self.restrict_clamp_scaling(
self.value))
def forward(self, x: Tensor, scale: Tensor, bit_width: Tensor) -> Tensor:
if self.training:
if self.counter <= self.collect_stats_steps:
if self.stats_permute_dims is not None:
x = x.permute(*self.stats_permute_dims).contiguous()
stats_input = self.stats_input_view_shape_impl(x)
stats = self.negative_min_or_zero(stats_input)
if self.counter == 0:
self.value.detach().add_(stats.detach())
else:
self.value.detach().mul_(1 - self.momentum)
self.value.detach().add_(self.momentum * stats.detach())
self.counter = self.counter + 1
out = stats
else:
out = self.value
else:
out = self.value
out = abs_binary_sign_grad(out)
min_int = self.int_quant.min_int(bit_width)
out = self.int_quant.to_int(scale, min_int, bit_width, out)
return out
def forward(self, x: Tensor, scale: Tensor, bit_width: Tensor) -> Tensor:
out = abs_binary_sign_grad(self.value)
min_int = self.int_quant.min_int(bit_width)
out = self.int_quant.to_int(scale, bit_width, min_int, out)
return out
def forward(self, placeholder: Tensor) -> Tensor:
value = abs_binary_sign_grad(self.restrict_clamp_scaling(self.value))
return value
def forward(self) -> Tensor:
bit_width = abs_binary_sign_grad(
self.bit_width_offset) + self.bit_width_base
bit_width = self.restrict_bit_width_impl(bit_width)
return bit_width
