def max_output_bit_width(self, input_bit_width, weight_bit_width):
max_input_val = max_uint(bit_width=input_bit_width, narrow_range=False)
max_fc_val = self.weight_quant.tensor_quant.int_quant.max_uint(
weight_bit_width)
max_output_val = max_input_val * max_fc_val * self.in_features
output_bit_width = ceil_ste(torch.log2(max_output_val))
return output_bit_width
def max_output_bit_width(self, input_bit_width, weight_bit_width):
max_uint_input = max_uint(bit_width=input_bit_width,
narrow_range=False)
max_kernel_val = self.weight_quant.tensor_quant.int_quant.max_uint(
weight_bit_width)
group_size = self.out_channels // self.groups
max_uint_output = max_uint_input * max_kernel_val * self.kernel_size[
0] * self.kernel_size[1] * group_size
max_output_bit_width = ceil_ste(torch.log2(max_uint_output))
return max_output_bit_width
def __add__(self, other):
QuantTensor.check_input_type(other)
self.check_scaling_factors_same(other)
output_tensor = self.tensor + other.tensor
output_scale = (self.scale + other.scale) / 2
max_uint_val = max_uint(narrow_range=False, bit_width=self.bit_width)
max_uint_val += max_uint(narrow_range=False, bit_width=other.bit_width)
output_bit_width = ceil_ste(torch.log2(max_uint_val))
output = pack_quant_tensor(output_tensor, output_scale,
output_bit_width)
return output
def forward(self, x: torch.Tensor):
return ceil_ste(x)
def max_output_bit_width(self, input_bit_width):
max_uint_input = max_uint(bit_width=input_bit_width,
narrow_range=False)
max_uint_output = max_uint_input * self.kernel_size * self.kernel_size
max_output_bit_width = ceil_ste(torch.log2(max_uint_output))
return max_output_bit_width
def max_output_bit_width(self, input_bit_width):
max_input_val = max_uint(bit_width=input_bit_width, narrow_range=False)
max_output_val = max_input_val * self.in_channels
output_bit_width = ceil_ste(torch.log2(max_output_val))
return output_bit_width
