def forward(self, x: QuantTensor):
if self.is_quant_enabled:
cleaned_up_value = round_ste(
x.value / x.scale.detach()) * x.scale.detach()
x = x.set(value=cleaned_up_value
) # clean up accumulated floating point errors
trunc_bit_width = self.lsb_trunc_bit_width_impl(x.bit_width)
trunc_scale = 2.0**trunc_bit_width
output_scale = trunc_scale * x.scale
if self.training:
x, output_scale, x_bit_width = self.tensor_quant(
x.value, output_scale, x.bit_width)
else: # avoid fp errors at inference time
x_bit_width = x.bit_width
x = round_ste(x.value / x.scale)
x = x / trunc_scale
x = self.tensor_quant.int_quant.float_to_int_impl(x)
x = x * output_scale
x = x / trunc_scale
output_scale = output_scale / trunc_scale # output_scale == input_scale
output_bit_width = x_bit_width - trunc_bit_width
return QuantTensor(x, output_scale, output_bit_width,
self.is_signed)
else:
return x
def int_weight(self, x: torch.Tensor):
zero_hw_sentinel = getattr(self, ZERO_HW_SENTINEL_NAME)
quant_weight, scale, _ = self.tensor_quant(x, zero_hw_sentinel)
quant_weight = quant_weight / scale
quant_weight = round_ste(quant_weight)
quant_weight = quant_weight.int()
return quant_weight
def int(self, float_datatype=False):
if self.is_valid:
int_value = round_ste(self._pre_round_int_value)
if float_datatype:
return int_value
else:
return int_value.int()
else:
raise RuntimeError(f"QuantTensor not valid.")
def int(self, float_datatype=False):
if self.is_valid:
int_value = self.value / self.scale
int_value = round_ste(int_value)
if float_datatype:
return int_value
else:
return int_value.int()
else:
raise RuntimeError(f"QuantTensor not well formed, all fields must be set: {self}")
def forward(self, x, input_scale, input_bit_width):
x = round_ste(x / input_scale) * input_scale # clean up fp errors before floor
trunc_bit_width = self.lsb_trunc_bit_width_impl(input_bit_width, self.zero_hw_sentinel)
trunc_scale = 2.0 ** trunc_bit_width
output_scale = trunc_scale * input_scale
x, output_scale, input_bit_width = self.tensor_quant(x, output_scale, input_bit_width, self.zero_hw_sentinel)
if self.explicit_rescaling:
x = x / trunc_scale # rescaling is explicit, so the truncation scale stays with x rather with output_scale
output_scale = output_scale / trunc_scale
output_bit_width = input_bit_width - trunc_bit_width
return x, output_scale, output_bit_width
def forward(self, x, input_scale, input_bit_width):
x = round_ste(
x / input_scale) * input_scale # clean up fp errors before floor
trunc_bit_width = self.lsb_trunc_bit_width_impl(
input_bit_width, self.zero_hw_sentinel)
trunc_scale = 2.0**trunc_bit_width
output_scale = trunc_scale * input_scale
if self.training:
x, output_scale, input_bit_width = self.tensor_quant(
x, output_scale, input_bit_width, self.zero_hw_sentinel)
else: # avoid fp errors at inference time
x = round_ste(x / input_scale)
x = x / trunc_scale
x = self.tensor_quant.int_quant.float_to_int_impl(x)
x = x * output_scale
if self.explicit_rescaling:
x = x / trunc_scale # rescaling is explicit, so the truncation scale stays with x rather with output_scale
output_scale = output_scale / trunc_scale
output_bit_width = input_bit_width - trunc_bit_width
return x, output_scale, output_bit_width
def forward(self, x: Tensor, scale: Tensor,
input_bit_width: Tensor) -> Tuple[Tensor, Tensor, Tensor]:
y = x / scale
y = round_ste(y) # clean up floating point error
output_bit_width = self.msb_clamp_bit_width_impl()
trunc_bit_width = input_bit_width - output_bit_width
trunc_scale = 2.0**trunc_bit_width
y = y / trunc_scale
y = self.float_to_int_impl(y)
y = y * scale
y = self.delay_wrapper(x, y)
return y, scale, output_bit_width
def forward(self, x: torch.Tensor):
return round_ste(x)
def __int__(self):
return round_ste(self.tensor / self.scale)
