# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project from typing import Any, Optional, Union import torch import os import torch.nn.functional as F import vllm.envs as envs import json import math from vllm.platforms import current_platform from vllm import _custom_ops as ops from vllm.logger import init_logger from vllm.model_executor.layers.fused_moe.layer import FusedMoE from vllm.model_executor.layers.linear import (LinearBase, LinearMethodBase, UnquantizedLinearMethod) from vllm.model_executor.layers.quantization import QuantizationMethods from vllm.model_executor.layers.quantization.base_config import ( QuantizationConfig, QuantizeMethodBase) from vllm.model_executor.parameter import (GroupQuantScaleParameter, PackedvLLMParameter) from vllm.model_executor.layers.quantization.awq_triton import awq_gemm_triton from vllm.logger import init_logger logger = init_logger(__name__) triton_configs_dict={} def get_triton_cache(file_path): #会将所报错的json文件以字典的形式return出来 if os.path.exists(file_path): with open(file_path, 'r') as file: cachedata = json.load(file) #把所有的cache解析成key:config的形式：[M_N_K]:[config] for key, value in cachedata.items(): for sub_key, sub_value in value.items(): configs_key= f"{sub_key}_{key}" configs_value={ 'SPLIT_K': int(sub_value["SPLIT_K"]), 'BLOCK_SIZE_M': int(sub_value["BLOCK_SIZE_M"]), 'BLOCK_SIZE_N': int(sub_value["BLOCK_SIZE_N"]), 'BLOCK_SIZE_K': int(sub_value["BLOCK_SIZE_K"]), 'GROUP_SIZE_M': int(sub_value["GROUP_SIZE_M"]), 'num_stages':int(sub_value['num_stages']), 'num_warps':int(sub_value['num_warps']) } if 'num_ldmatrixes' in sub_value: configs_value["num_ldmatrixes"] = int(sub_value['num_ldmatrixes']) triton_configs_dict[configs_key]=configs_value logger.info("%s have loaded!", file_path) def default_execution(k,n): configs_key= f"1_{n}_{k}" if configs_key in triton_configs_dict: return script_dir = os.path.dirname(os.path.abspath(__file__)) cache_json_file=f"{script_dir}/configs/awq/" device_name = current_platform.get_device_name().replace(" ", "_") filename = f"AWQ_{n}_{k}_{device_name}.json" file_full_path = os.path.join(cache_json_file, filename) if os.path.isfile(file_full_path) and file_full_path.endswith(".json"): # 如果是文件，则添加到列表 get_triton_cache(file_full_path) return def getspec_config(M,N,K): m_config = M if M > 16: # 直接计算 2 的幂 m_config = 1 while m_config < M: m_config *= 2 if f"{m_config}_{N}_{K}" in triton_configs_dict: return triton_configs_dict[f"{m_config}_{N}_{K}"] else: return None class AWQShareWorkSpace: _instance = None def __new__(cls, *args, **kwargs): if cls._instance is None: cls._instance = super(AWQShareWorkSpace, cls).__new__(cls, *args, **kwargs) cls._instance._initialize() return cls._instance def _initialize(self): self.awqworkshapcesize = ops.GetAWQShareWorkspaceSize() self.awqworkshapce = ops.GetAWQShareWorkspace() logger = init_logger(__name__) class AWQConfig(QuantizationConfig): """Config class for AWQ. Reference: https://arxiv.org/abs/2306.00978 """ def __init__( self, weight_bits: int, group_size: int, zero_point: bool, modules_to_not_convert: Optional[list[str]] = None, ) -> None: super().__init__() self.weight_bits = weight_bits self.group_size = group_size self.zero_point = zero_point self.modules_to_not_convert = modules_to_not_convert or [] if self.weight_bits != 4: raise ValueError( "Currently, only 4-bit weight quantization is supported for " f"AWQ, but got {self.weight_bits} bits.") self.pack_factor = 32 // self.weight_bits def __repr__(self) -> str: return (f"AWQConfig(weight_bits={self.weight_bits}, " f"group_size={self.group_size}, " f"zero_point={self.zero_point}, " f"modules_to_not_convert={self.modules_to_not_convert})") def get_name(self) -> QuantizationMethods: return "awq" def get_supported_act_dtypes(self) -> list[torch.dtype]: return [torch.half, torch.bfloat16] @classmethod def get_min_capability(cls) -> int: # The AWQ kernel only supports Turing or newer GPUs. return 75 @staticmethod def get_config_filenames() -> list[str]: return [ "quant_config.json", # E.g., casperhansen/vicuna-7b-v1.5-awq # E.g., abhinavkulkarni/mosaicml-mpt-7b-instruct-w4-g128-awq "quantize_config.json", ] @classmethod def from_config(cls, config: dict[str, Any]) -> "AWQConfig": weight_bits = cls.get_from_keys(config, ["w_bit", "bits"]) group_size = cls.get_from_keys(config, ["q_group_size", "group_size"]) zero_point = cls.get_from_keys(config, ["zero_point"]) modules_to_not_convert = cls.get_from_keys_or( config, ["modules_to_not_convert"], None) return cls(weight_bits, group_size, zero_point, modules_to_not_convert) def get_quant_method( self, layer: torch.nn.Module, prefix: str ) -> Optional[Union["LinearMethodBase", "QuantizeMethodBase"]]: if isinstance(layer, LinearBase): if is_layer_skipped_awq(prefix, self.modules_to_not_convert): return UnquantizedLinearMethod() return AWQLinearMethod(self) elif isinstance(layer, FusedMoE): # Lazy import to avoid circular import. from .awq_marlin import AWQMarlinConfig, AWQMoEMethod from .moe_wna16 import MoeWNA16Config from .utils.marlin_utils import check_moe_marlin_supports_layer if not check_moe_marlin_supports_layer(layer, self.group_size): logger.warning_once( f"Layer '{prefix}' is not supported by AWQMoeMarlin. " "Falling back to Moe WNA16 kernels.") config = { "quant_method": "awq", "bits": self.weight_bits, "group_size": self.group_size, "zero_point": self.zero_point, "lm_head": False, } return MoeWNA16Config.from_config(config).get_quant_method( layer, prefix) marlin_compatible_config_dict = { "quant_method": "awq", "bits": self.weight_bits, "group_size": self.group_size, "zero_point": self.zero_point, "lm_head": False, "modules_to_not_convert": self.modules_to_not_convert, } awq_marlin_config = AWQMarlinConfig.from_config( marlin_compatible_config_dict) return AWQMoEMethod(awq_marlin_config) return None def is_layer_skipped_awq(prefix: str, modules_to_not_convert: list[str]): return any(module_name in prefix for module_name in modules_to_not_convert) class AWQLinearMethod(LinearMethodBase): """Linear method for AWQ. Args: quant_config: The AWQ quantization config. """ def __init__(self, quant_config: AWQConfig): self.quant_config = quant_config self.awqsingleton= AWQShareWorkSpace() self.use_awq_pad = os.environ.get('AWQ_PAD') == '1' def create_weights(self, layer: torch.nn.Module, input_size_per_partition: int, output_partition_sizes: list[int], input_size: int, output_size: int, params_dtype: torch.dtype, **extra_weight_attrs): # Normalize group_size if self.quant_config.group_size != -1: group_size = self.quant_config.group_size else: group_size = input_size if input_size_per_partition % group_size != 0: raise ValueError( "The input size is not aligned with the quantized " "weight shape. This can be caused by too large " "tensor parallel size.") output_size_per_partition = sum(output_partition_sizes) if output_size_per_partition % self.quant_config.pack_factor != 0: raise ValueError( "The output size is not aligned with the quantized " "weight shape. This can be caused by too large " "tensor parallel size.") weight_loader = extra_weight_attrs.get("weight_loader") qweight = PackedvLLMParameter( data=torch.empty( input_size_per_partition, output_size_per_partition // self.quant_config.pack_factor, dtype=torch.int32, ), input_dim=0, output_dim=1, packed_dim=1, packed_factor=self.quant_config.pack_factor, weight_loader=weight_loader) num_groups = input_size_per_partition // group_size qzeros = PackedvLLMParameter( data=torch.empty( num_groups, output_size_per_partition // self.quant_config.pack_factor, dtype=torch.int32, ), input_dim=0, output_dim=1, packed_dim=1, packed_factor=self.quant_config.pack_factor, weight_loader=weight_loader) scales = GroupQuantScaleParameter(data=torch.empty( num_groups, output_size_per_partition, dtype=params_dtype, ), input_dim=0, output_dim=1, weight_loader=weight_loader) zeros_and_scales = GroupQuantScaleParameter(data=torch.empty( input_size_per_partition // self.quant_config.group_size, output_size_per_partition, dtype=torch.int32, ), input_dim=0, output_dim=1, weight_loader=weight_loader) layer.register_parameter("qweight", qweight) layer.register_parameter("qzeros", qzeros) layer.register_parameter("scales", scales) layer.register_parameter("zeros_and_scales", zeros_and_scales) # 加载triton_config if envs.VLLM_USE_TRITON_AWQ: default_execution(input_size_per_partition,output_size_per_partition) def process_weights_after_loading(self, layer: torch.nn.Module) -> None: if not envs.VLLM_USE_TRITON_AWQ: group_size= self.quant_config.group_size pad_group=2 dim_n = layer.scales.data.shape[1] dim_k = layer.qweight.data.shape[0] _qw, _sz=ops.convert_s4(layer.qweight,layer.qzeros,layer.scales.to(torch.float16),int(group_size)) sz = ops.sz_permute(_sz).reshape(-1,dim_n) sz = sz.reshape(dim_n,-1) _qw = _qw.reshape(dim_n,-1) if dim_k % 4096==0 and self.use_awq_pad: zeros_and_scalse_pad = torch.zeros(dim_n,pad_group,dtype=torch.int32).cuda() sz = torch.cat((sz,zeros_and_scalse_pad),dim=1).contiguous() qweight_pad = torch.zeros(dim_n,int(group_size//4),dtype=torch.int32).cuda() _qw=torch.cat((_qw,qweight_pad),dim=1).contiguous() layer.qweight = torch.nn.Parameter(_qw, requires_grad=False) layer.zeros_and_scales = torch.nn.Parameter(sz, requires_grad=False) layer.qzeros = None layer.scales = None else: layer.qweight = torch.nn.Parameter(layer.qweight.data, requires_grad=False) layer.qzeros = torch.nn.Parameter(layer.qzeros.data, requires_grad=False) layer.scales = torch.nn.Parameter(layer.scales.data, requires_grad=False) def apply(self, layer: torch.nn.Module, x: torch.Tensor, bias: Optional[torch.Tensor] = None) -> torch.Tensor: qweight = layer.qweight zeros_and_scales = layer.zeros_and_scales qzeros = layer.qzeros scales = layer.scales pack_factor = self.quant_config.pack_factor out_shape = (x.shape[:-1] + (qweight.shape[0] * 1, )) reshaped_x = x.reshape(-1, x.shape[-1]) m = reshaped_x.shape[0] k = reshaped_x.shape[-1] n = qweight.shape[0] if self.use_awq_pad: if k % 4096 == 0: padding_group=2 else: padding_group=0 else: padding_group=0 if envs.VLLM_USE_TRITON_AWQ: best_config=getspec_config(m,n,k) out = awq_gemm_triton(reshaped_x, qweight, scales, qzeros, pack_factor, best_config) out_shape = (x.shape[:-1] + (qweight.shape[1] * 8, )) else: out = torch.ops.vllm.awq_gemm(reshaped_x, qweight, zeros_and_scales, m, n, k, self.quant_config.group_size, padding_group, self.awqsingleton.awqworkshapce, self.awqsingleton.awqworkshapcesize) if bias is not None: out.add_(bias) return out.reshape(out_shape)