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SDNQ listen to dequantize_fp32 option with re_quantize

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Disty0
2025-08-29 22:48:28 +03:00
parent a8de3f7282
commit d49e954918
1 changed files with 12 additions and 12 deletions
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modules/sdnq/dequantizer.py
+12 -12
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@@ -42,26 +42,26 @@ def quantize_int8(input: torch.FloatTensor, dim: int = -1) -> Tuple[torch.CharTe
return input, scale
def re_quantize_matmul_asymmetric(weight: torch.ByteTensor, scale: torch.FloatTensor, zero_point: torch.FloatTensor, dtype: torch.dtype, result_shape: torch.Size) -> Tuple[torch.CharTensor, torch.FloatTensor]:
result = dequantize_asymmetric(weight, scale, zero_point, dtype, result_shape)
def re_quantize_matmul_asymmetric(weight: torch.ByteTensor, scale: torch.FloatTensor, zero_point: torch.FloatTensor, result_shape: torch.Size) -> Tuple[torch.CharTensor, torch.FloatTensor]:
result = dequantize_asymmetric(weight, scale, zero_point, scale.dtype, result_shape)
if result.ndim > 2: # convs
result = result.flatten(1,-1)
return quantize_int8(result.t_(), dim=0)
def re_quantize_matmul_symmetric(weight: torch.CharTensor, scale: torch.FloatTensor, dtype: torch.dtype, result_shape: torch.Size) -> Tuple[torch.CharTensor, torch.FloatTensor]:
result = dequantize_symmetric(weight, scale, dtype, result_shape)
def re_quantize_matmul_symmetric(weight: torch.CharTensor, scale: torch.FloatTensor, result_shape: torch.Size) -> Tuple[torch.CharTensor, torch.FloatTensor]:
result = dequantize_symmetric(weight, scale, scale.dtype, result_shape)
if result.ndim > 2: # convs
result = result.flatten(1,-1)
return quantize_int8(result.t_(), dim=0)
def re_quantize_matmul_packed_int_asymmetric(weight: torch.ByteTensor, scale: torch.FloatTensor, zero_point: torch.FloatTensor, shape: torch.Size, dtype: torch.dtype, result_shape: torch.Size, weights_dtype: str) -> torch.FloatTensor:
return re_quantize_matmul_asymmetric(unpack_int_asymetric(weight, shape, weights_dtype), scale, zero_point, dtype, result_shape)
def re_quantize_matmul_packed_int_asymmetric(weight: torch.ByteTensor, scale: torch.FloatTensor, zero_point: torch.FloatTensor, shape: torch.Size, result_shape: torch.Size, weights_dtype: str) -> torch.FloatTensor:
return re_quantize_matmul_asymmetric(unpack_int_asymetric(weight, shape, weights_dtype), scale, zero_point, result_shape)
def re_quantize_matmul_packed_int_symmetric(weight: torch.ByteTensor, scale: torch.FloatTensor, shape: torch.Size, dtype: torch.dtype, result_shape: torch.Size, weights_dtype: str) -> torch.FloatTensor:
return re_quantize_matmul_symmetric(unpack_int_symetric(weight, shape, weights_dtype, dtype=scale.dtype), scale, dtype, result_shape)
def re_quantize_matmul_packed_int_symmetric(weight: torch.ByteTensor, scale: torch.FloatTensor, shape: torch.Size, result_shape: torch.Size, weights_dtype: str) -> torch.FloatTensor:
return re_quantize_matmul_symmetric(unpack_int_symetric(weight, shape, weights_dtype, dtype=scale.dtype), scale, result_shape)
class AsymmetricWeightsDequantizer(torch.nn.Module):
@@ -90,7 +90,7 @@ class AsymmetricWeightsDequantizer(torch.nn.Module):
return weight.to(dtype=dtype_dict[self.weights_dtype]["torch_dtype"])
def re_quantize_matmul(self, weight, **kwargs): # pylint: disable=unused-argument
return re_quantize_matmul_asymmetric_compiled(weight, self.scale, self.zero_point, self.result_dtype, self.result_shape)
return re_quantize_matmul_asymmetric_compiled(weight, self.scale, self.zero_point, self.result_shape)
def forward(self, weight, **kwargs): # pylint: disable=unused-argument
return dequantize_asymmetric_compiled(weight, self.scale, self.zero_point, self.result_dtype, self.result_shape)
@@ -121,7 +121,7 @@ class SymmetricWeightsDequantizer(torch.nn.Module):
return weight.to(dtype=dtype_dict[self.weights_dtype]["torch_dtype"])
def re_quantize_matmul(self, weight, **kwargs): # pylint: disable=unused-argument
return re_quantize_matmul_symmetric_compiled(weight, self.scale, self.result_dtype, self.result_shape)
return re_quantize_matmul_symmetric_compiled(weight, self.scale, self.result_shape)
def forward(self, weight, skip_quantized_matmul=False, **kwargs): # pylint: disable=unused-argument
skip_quantized_matmul = skip_quantized_matmul and not self.re_quantize_for_matmul
@@ -156,7 +156,7 @@ class PackedINTAsymmetricWeightsDequantizer(torch.nn.Module):
return pack_int_asymetric(weight, self.weights_dtype)
def re_quantize_matmul(self, weight, **kwargs): # pylint: disable=unused-argument
return re_quantize_matmul_packed_int_asymmetric_compiled(weight, self.scale, self.zero_point, self.quantized_weight_shape, self.result_dtype, self.result_shape, self.weights_dtype)
return re_quantize_matmul_packed_int_asymmetric_compiled(weight, self.scale, self.zero_point, self.quantized_weight_shape, self.result_shape, self.weights_dtype)
def forward(self, weight, **kwargs): # pylint: disable=unused-argument
return dequantize_packed_int_asymmetric_compiled(weight, self.scale, self.zero_point, self.quantized_weight_shape, self.result_dtype, self.result_shape, self.weights_dtype)
@@ -189,7 +189,7 @@ class PackedINTSymmetricWeightsDequantizer(torch.nn.Module):
return pack_int_symetric(weight, self.weights_dtype)
def re_quantize_matmul(self, weight, **kwargs): # pylint: disable=unused-argument
return re_quantize_matmul_packed_int_symmetric_compiled(weight, self.scale, self.quantized_weight_shape, self.result_dtype, self.result_shape, self.weights_dtype)
return re_quantize_matmul_packed_int_symmetric_compiled(weight, self.scale, self.quantized_weight_shape, self.result_shape, self.weights_dtype)
def forward(self, weight, skip_quantized_matmul=False, **kwargs): # pylint: disable=unused-argument
skip_quantized_matmul = skip_quantized_matmul and not self.re_quantize_for_matmul