# pylint: disable=redefined-builtin,no-member,protected-access from typing import List, Tuple, Optional import torch from modules import devices from .common import dtype_dict, compile_func, use_contiguous_mm, use_tensorwise_fp8_matmul from .packed_int import unpack_int_symetric, unpack_int_asymetric @devices.inference_context() def dequantize_asymmetric(weight: torch.ByteTensor, scale: torch.FloatTensor, zero_point: torch.FloatTensor, svd_up: Optional[torch.FloatTensor] = None, svd_down: Optional[torch.FloatTensor] = None, dtype: Optional[torch.dtype] = None, result_shape: Optional[torch.Size] = None, skip_quantized_matmul: bool = False) -> torch.FloatTensor: result = torch.addcmul(zero_point, weight.to(dtype=scale.dtype), scale) if result_shape is not None: result = result.view(result_shape) if svd_up is not None: if skip_quantized_matmul: svd_up = svd_up.t().contiguous() if use_contiguous_mm: svd_down = svd_down.t().contiguous() else: svd_down = svd_down.contiguous().t() if result.ndim > 2 and weight.ndim > 2: # convs result = result.add_(torch.mm(svd_up, svd_down).unflatten(-1, (*result.shape[1:],))) else: result = result.addmm_(svd_up, svd_down) if dtype is not None: result = result.to(dtype=dtype) return result @devices.inference_context() def dequantize_symmetric(weight: torch.CharTensor, scale: torch.FloatTensor, svd_up: Optional[torch.FloatTensor] = None, svd_down: Optional[torch.FloatTensor] = None, dtype: Optional[torch.dtype] = None, result_shape: Optional[torch.Size] = None, skip_quantized_matmul: bool = False) -> torch.FloatTensor: result = weight.to(dtype=scale.dtype).mul_(scale) if skip_quantized_matmul: result.t_() if result_shape is not None: result = result.view(result_shape) if svd_up is not None: if skip_quantized_matmul: svd_up = svd_up.t().contiguous() if use_contiguous_mm: svd_down = svd_down.t().contiguous() else: svd_down = svd_down.contiguous().t() if result.ndim > 2 and weight.ndim > 2: # convs result = result.add_(torch.mm(svd_up, svd_down).unflatten(-1, (*result.shape[1:],))) else: result = result.addmm_(svd_up, svd_down) if dtype is not None: result = result.to(dtype=dtype) return result @devices.inference_context() def dequantize_symmetric_with_bias(weight: torch.CharTensor, scale: torch.FloatTensor, bias: torch.FloatTensor, dtype: Optional[torch.dtype] = None, result_shape: Optional[torch.Size] = None) -> torch.FloatTensor: result = torch.addcmul(bias, weight.to(dtype=scale.dtype), scale) if result_shape is not None: result = result.view(result_shape) if dtype is not None: result = result.to(dtype=dtype) return result @devices.inference_context() def dequantize_packed_int_asymmetric(weight: torch.ByteTensor, scale: torch.FloatTensor, zero_point: torch.FloatTensor, shape: torch.Size, weights_dtype: str, dtype: Optional[torch.dtype] = None, result_shape: Optional[torch.Size] = None, svd_up: Optional[torch.FloatTensor] = None, svd_down: Optional[torch.FloatTensor] = None, skip_quantized_matmul: bool = False) -> torch.FloatTensor: return dequantize_asymmetric(unpack_int_asymetric(weight, shape, weights_dtype), scale, zero_point, dtype=dtype, result_shape=result_shape, svd_up=svd_up, svd_down=svd_down, skip_quantized_matmul=skip_quantized_matmul) @devices.inference_context() def dequantize_packed_int_symmetric(weight: torch.ByteTensor, scale: torch.FloatTensor, shape: torch.Size, weights_dtype: str, dtype: Optional[torch.dtype] = None, result_shape: Optional[torch.Size] = None, svd_up: Optional[torch.FloatTensor] = None, svd_down: Optional[torch.FloatTensor] = None, skip_quantized_matmul: bool = False) -> torch.FloatTensor: return dequantize_symmetric(unpack_int_symetric(weight, shape, weights_dtype, dtype=scale.dtype), scale, dtype=dtype, result_shape=result_shape, svd_up=svd_up, svd_down=svd_down, skip_quantized_matmul=skip_quantized_matmul) @devices.inference_context() def quantize_int8(input: torch.FloatTensor, dim: int = -1) -> Tuple[torch.CharTensor, torch.FloatTensor]: scale = torch.amax(input.abs(), dim=dim, keepdims=True).div_(127) input = torch.div(input, scale).round_().clamp_(-128, 127).to(dtype=torch.int8) return input, scale @devices.inference_context() def quantize_int8_sr(input: torch.FloatTensor, dim: int = -1) -> Tuple[torch.CharTensor, torch.FloatTensor]: scale = torch.amax(input.abs(), dim=dim, keepdims=True).div_(127) input = torch.normal(0, 0.1, input.shape, device=input.device, dtype=input.dtype ).addcdiv_(input, scale).round_().clamp_(-128, 127).to(dtype=torch.int8) return input, scale @devices.inference_context() def quantize_fp8(input: torch.FloatTensor, dim: int = -1, is_e5: bool = False) -> Tuple[torch.Tensor, torch.FloatTensor]: if is_e5: max_range = 57344 fp8_dtype = torch.float8_e5m2 else: max_range = 448 fp8_dtype = torch.float8_e4m3fn scale = torch.amax(input.abs(), dim=dim, keepdims=True).div_(max_range) input = torch.div(input, scale).nan_to_num_().clamp_(-max_range, max_range).to(dtype=fp8_dtype) return input, scale @devices.inference_context() def quantize_fp8_sr(input: torch.FloatTensor, dim: int = -1, is_e5: bool = False) -> Tuple[torch.Tensor, torch.FloatTensor]: if is_e5: max_range = 57344 fp8_dtype = torch.float8_e5m2 mantissa_difference = 2097152 else: max_range = 448 fp8_dtype = torch.float8_e4m3fn mantissa_difference = 1048576 scale = torch.amax(input.abs(), dim=dim, keepdims=True).div_(max_range) input = torch.div(input, scale).to(dtype=torch.float32).view(dtype=torch.int32) input = input.add_(torch.randint_like(input, low=0, high=mantissa_difference)).bitwise_and_(-mantissa_difference).view(dtype=torch.float32) input = input.nan_to_num_().clamp_(-max_range, max_range).to(dtype=fp8_dtype) return input, scale @devices.inference_context() def re_quantize_int8(weight: torch.FloatTensor) -> Tuple[torch.CharTensor, torch.FloatTensor]: if weight.ndim > 2: # convs weight = weight.flatten(1,-1) if use_contiguous_mm: weight, scale = quantize_int8(weight.t(), dim=-0) weight, scale = weight.contiguous(), scale.contiguous() else: weight, scale = quantize_int8(weight.contiguous(), dim=-1) weight, scale = weight.t_(), scale.t_() return weight, scale @devices.inference_context() def re_quantize_fp8(weight: torch.FloatTensor, is_e5: bool = False) -> Tuple[torch.CharTensor, torch.FloatTensor]: if weight.ndim > 2: # convs weight = weight.flatten(1,-1) weight, scale = quantize_fp8(weight.contiguous(), dim=-1, is_e5=is_e5) weight, scale = weight.t_(), scale.t_() if not use_tensorwise_fp8_matmul: scale = scale.to(dtype=torch.float32) return weight, scale @devices.inference_context() def re_quantize_matmul_asymmetric(weight: torch.ByteTensor, scale: torch.FloatTensor, zero_point: torch.FloatTensor, result_shape: Optional[torch.Size] = None, svd_up: Optional[torch.FloatTensor] = None, svd_down: Optional[torch.FloatTensor] = None) -> Tuple[torch.CharTensor, torch.FloatTensor]: return re_quantize_int8(dequantize_asymmetric(weight, scale, zero_point, dtype=scale.dtype, result_shape=result_shape, svd_up=svd_up, svd_down=svd_down)) @devices.inference_context() def re_quantize_matmul_symmetric(weight: torch.CharTensor, scale: torch.FloatTensor, result_shape: Optional[torch.Size] = None, svd_up: Optional[torch.FloatTensor] = None, svd_down: Optional[torch.FloatTensor] = None) -> Tuple[torch.CharTensor, torch.FloatTensor]: return re_quantize_int8(dequantize_symmetric(weight, scale, dtype=scale.dtype, result_shape=result_shape, svd_up=svd_up, svd_down=svd_down)) @devices.inference_context() def re_quantize_matmul_packed_int_asymmetric(weight: torch.ByteTensor, scale: torch.FloatTensor, zero_point: torch.FloatTensor, shape: torch.Size, weights_dtype: str, result_shape: torch.Size, svd_up: Optional[torch.FloatTensor] = None, svd_down: Optional[torch.FloatTensor] = None) -> Tuple[torch.CharTensor, torch.FloatTensor]: return re_quantize_matmul_asymmetric(unpack_int_asymetric(weight, shape, weights_dtype), scale, zero_point, result_shape=result_shape, svd_up=svd_up, svd_down=svd_down) @devices.inference_context() def re_quantize_matmul_packed_int_symmetric(weight: torch.ByteTensor, scale: torch.FloatTensor, shape: torch.Size, weights_dtype: str, result_shape: Optional[torch.Size] = None, svd_up: Optional[torch.FloatTensor] = None, svd_down: Optional[torch.FloatTensor] = None) -> Tuple[torch.CharTensor, torch.FloatTensor]: return re_quantize_matmul_symmetric(unpack_int_symetric(weight, shape, weights_dtype, dtype=scale.dtype), scale, result_shape=result_shape, svd_down=svd_down) @devices.inference_context() def dequantize_layer_weight(self: torch.nn.Module, inplace: bool = False): weight = self.sdnq_dequantizer(self.weight, self.scale, self.zero_point, self.svd_up, self.svd_down, skip_quantized_matmul=self.sdnq_dequantizer.use_quantized_matmul) if inplace: self.weight.data = weight self.forward = getattr(torch.nn, self.sdnq_dequantizer.layer_class_name).forward del self.sdnq_dequantizer, self.scale, self.zero_point, self.svd_up, self.svd_down return weight @devices.inference_context() def dequantize_sdnq_model(model: torch.nn.Module): if hasattr(model, "sdnq_dequantizer"): model.weight.data = dequantize_layer_weight(model, inplace=True) has_children = list(model.children()) if not has_children: return model for module_name, module in model.named_children(): if hasattr(module, "sdnq_dequantizer"): module.weight.data = dequantize_layer_weight(module, inplace=True) setattr(model, module_name, module) else: setattr(model, module_name, dequantize_sdnq_model(module)) return model class SDNQDequantizer(): def __init__( self, result_dtype: torch.dtype, result_shape: torch.Size, original_shape: torch.Size, original_stride: List[int], quantized_weight_shape: torch.Size, weights_dtype: str, group_size: int, svd_rank: int, svd_steps: int, use_quantized_matmul: bool, re_quantize_for_matmul: bool, use_stochastic_rounding: bool, layer_class_name: str, ): self.is_packed = dtype_dict[weights_dtype]["is_packed"] self.is_unsigned = dtype_dict[weights_dtype]["is_unsigned"] self.result_dtype = result_dtype self.result_shape = result_shape self.original_shape = original_shape self.original_stride = original_stride self.quantized_weight_shape = quantized_weight_shape self.weights_dtype = weights_dtype self.group_size = group_size self.svd_rank = svd_rank self.svd_steps = svd_steps self.use_quantized_matmul = use_quantized_matmul self.re_quantize_for_matmul = re_quantize_for_matmul self.use_stochastic_rounding = use_stochastic_rounding self.layer_class_name = layer_class_name @devices.inference_context() def re_quantize_matmul(self, weight, scale, zero_point, svd_up, svd_down): # pylint: disable=unused-argument if self.is_packed: if self.is_unsigned: return re_quantize_matmul_packed_int_asymmetric_compiled(weight, scale, zero_point, self.quantized_weight_shape, self.weights_dtype, result_shape=self.result_shape, svd_up=svd_up, svd_down=svd_down) else: return re_quantize_matmul_packed_int_symmetric_compiled(weight, scale, self.quantized_weight_shape, self.weights_dtype, result_shape=self.result_shape, svd_up=svd_up, svd_down=svd_down) else: if self.is_unsigned: return re_quantize_matmul_asymmetric_compiled(weight, scale, zero_point, result_shape=self.result_shape, svd_up=svd_up, svd_down=svd_down) else: return re_quantize_matmul_symmetric_compiled(weight, scale, result_shape=self.result_shape, svd_up=svd_up, svd_down=svd_down) @devices.inference_context() def __call__(self, weight, scale, zero_point, svd_up, svd_down, skip_quantized_matmul: bool = False, dtype: torch.dtype = None): # pylint: disable=unused-argument skip_quantized_matmul = skip_quantized_matmul and not self.re_quantize_for_matmul if dtype is None: dtype = self.result_dtype if self.is_packed: if self.is_unsigned: return dequantize_packed_int_asymmetric_compiled(weight, scale, zero_point, self.quantized_weight_shape, self.weights_dtype, dtype=dtype, result_shape=self.result_shape, svd_up=svd_up, svd_down=svd_down, skip_quantized_matmul=skip_quantized_matmul) else: return dequantize_packed_int_symmetric_compiled(weight, scale, self.quantized_weight_shape, self.weights_dtype, dtype=dtype, result_shape=self.result_shape, svd_up=svd_up, svd_down=svd_down, skip_quantized_matmul=skip_quantized_matmul) else: if self.is_unsigned: return dequantize_asymmetric_compiled(weight, scale, zero_point, dtype=dtype, result_shape=self.result_shape, svd_up=svd_up, svd_down=svd_down, skip_quantized_matmul=skip_quantized_matmul) else: return dequantize_symmetric_compiled(weight, scale, dtype=dtype, result_shape=self.result_shape, svd_up=svd_up, svd_down=svd_down, skip_quantized_matmul=skip_quantized_matmul) dequantize_asymmetric_compiled = compile_func(dequantize_asymmetric) dequantize_symmetric_compiled = compile_func(dequantize_symmetric) dequantize_packed_int_asymmetric_compiled = compile_func(dequantize_packed_int_asymmetric) dequantize_packed_int_symmetric_compiled = compile_func(dequantize_packed_int_symmetric) re_quantize_matmul_asymmetric_compiled = compile_func(re_quantize_matmul_asymmetric) re_quantize_matmul_symmetric_compiled = compile_func(re_quantize_matmul_symmetric) re_quantize_matmul_packed_int_asymmetric_compiled = compile_func(re_quantize_matmul_packed_int_asymmetric) re_quantize_matmul_packed_int_symmetric_compiled = compile_func(re_quantize_matmul_packed_int_symmetric)