# # Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved. # This file is a part of the vllm-ascend project. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # import math from typing import Any, Dict, Optional, Tuple import torch import torch_npu KRONECKER_QUANT_MAX_BATCH_SIZE = 32768 def pack_int4_weights(weight_tensor: torch.Tensor) -> torch.Tensor: original_device = weight_tensor.device weight_tensor_npu = weight_tensor.npu() weight_int4_packed = torch_npu.npu_convert_weight_to_int4pack( weight_tensor_npu.to(torch.int32), inner_k_tiles=1) return weight_int4_packed.to(original_device) def get_decompose_dim(n): a = int(math.sqrt(n)) if a * a < n: a += 1 while True: tmp = a * a - n b = int(math.sqrt(tmp)) if b * b == tmp: break a += 1 return a - b, a + b # TODO: This function is a temporary workaround for the npu_kronecker_quant operator, # which has a limitation on the maximum batch size (dim0). This wrapper should be # removed once the operator supports larger inputs natively. def batched_kronecker_quant( x: torch.Tensor, left_trans: torch.Tensor, right_trans: torch.Tensor, clip_ratio: float, ) -> Tuple[torch.Tensor, torch.Tensor]: batch_tokens = x.shape[0] if batch_tokens <= KRONECKER_QUANT_MAX_BATCH_SIZE: return torch_npu.npu_kronecker_quant(x, left_trans, right_trans, clip_ratio=clip_ratio, dst_dtype=torch.int32) x_chunks = torch.split(x, KRONECKER_QUANT_MAX_BATCH_SIZE, dim=0) processed_chunks = [ torch_npu.npu_kronecker_quant(chunk, left_trans, right_trans, clip_ratio=clip_ratio, dst_dtype=torch.int32) for chunk in x_chunks ] quantized_list, scale_list = zip(*processed_chunks) x_quantized_int4 = torch.cat(quantized_list, dim=0) activation_scale = torch.cat(scale_list, dim=0) return x_quantized_int4, activation_scale class AscendW4A4FlatQuantDynamicLinearMethod: """Linear method for Ascend W4A4_FLATQUANT_DYNAMIC. This class implements W4A4 quantization with FlatQuant approach and dynamic activation quantization. - Weight: 4-bit quantization (per-channel) with scale and offset, stored as int8 and packed to int32 during loading - Activation: 4-bit dynamic quantization with FlatQuant transform matrices (left_trans, right_trans) for distribution smoothing - Parameters: clip_ratio for controlling quantization clipping, weight_offset for asymmetric quantization, loaded from external weights """ input_size = 0 def __init__(self): self.transpose_weight = False self.sym = True @staticmethod def get_weight(input_size: int, output_size: int, params_dtype: torch.dtype) -> Dict[str, Any]: if input_size % 8 != 0: raise ValueError( f"input_size ({input_size}) must be divisible by 8 for int4 packing" ) AscendW4A4FlatQuantDynamicLinearMethod.input_size = input_size params_dict = { "weight": torch.empty(output_size, input_size, dtype=torch.int8) } return params_dict @staticmethod def get_pertensor_param(params_dtype: torch.dtype) -> Dict[str, Any]: params_dict = {} left_trans_dim, right_trans_dim = get_decompose_dim( AscendW4A4FlatQuantDynamicLinearMethod.input_size) params_dict["left_trans"] = torch.empty(left_trans_dim, left_trans_dim, dtype=params_dtype) params_dict["right_trans"] = torch.empty(right_trans_dim, right_trans_dim, dtype=params_dtype) params_dict["clip_ratio"] = torch.empty(1, dtype=torch.float32) return params_dict @staticmethod def get_perchannel_param( output_size: int, params_dtype: torch.dtype, ) -> Dict[str, Any]: params_dict = {} params_dict["weight_scale"] = torch.empty(output_size, 1, dtype=torch.float32) params_dict["weight_offset"] = torch.empty(output_size, 1, dtype=torch.float32) return params_dict def get_pergroup_param(self, input_size: int, output_size: int, params_dtype: torch.dtype, layer_type: Optional[str] = None) -> Dict[str, Any]: return {} @staticmethod def apply( layer: torch.nn.Module, x: torch.Tensor, bias: Optional[torch.Tensor] = None, tp_rank: Optional[int] = 0, ) -> torch.Tensor: original_dtype = x.dtype input_shape = x.shape in_features = input_shape[-1] left_dim = layer.left_trans.shape[0] right_dim = layer.right_trans.shape[0] if left_dim * right_dim != in_features: raise ValueError( f"FlatQuant transform matrices dimension mismatch: " f"left_dim({left_dim}) * right_dim({right_dim}) != in_features({in_features})" ) left_trans_matched = layer.left_trans.to(original_dtype) right_trans_matched = layer.right_trans.to(original_dtype) x_reshaped = x.view(-1, left_dim, right_dim) x_quantized_int4, activation_scale = batched_kronecker_quant( x_reshaped, left_trans_matched, right_trans_matched, layer.aclnn_clip_ratio) x_quantized_reshaped = x_quantized_int4.view(-1, left_dim * right_dim // 8) pertoken_scale = activation_scale.view(-1).to(torch.float32) output = torch_npu.npu_quant_matmul(x_quantized_reshaped, layer.weight_packed.t(), layer.weight_scale.view(-1).to( torch.float32), pertoken_scale=pertoken_scale, bias=None, output_dtype=original_dtype) output = output.view(*input_shape[:-1], -1) if bias is not None: output = output + bias.to(original_dtype) return output def process_weights_after_loading(self, layer): weight_packed = pack_int4_weights(layer.weight.data) if self.transpose_weight: weight_packed = weight_packed.transpose(0, 1).contiguous() layer.register_parameter( 'weight_packed', torch.nn.Parameter(weight_packed, requires_grad=False)) del layer.weight layer.weight_scale.data = layer.weight_scale.data.to(torch.float32) layer.weight_offset.data = layer.weight_offset.data.to(torch.float32) layer.left_trans = torch.nn.Parameter( layer.left_trans.data.t().contiguous()) layer.right_trans = torch.nn.Parameter(layer.right_trans.data) layer.clip_ratio = torch.nn.Parameter( layer.clip_ratio.data.to(torch.float32)) layer.aclnn_clip_ratio = layer.clip_ratio.item()