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