# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project from typing import Any, Optional, Union import torch 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) 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] @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, layer.moe_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 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) layer.register_parameter("qweight", qweight) layer.register_parameter("qzeros", qzeros) layer.register_parameter("scales", scales) def process_weights_after_loading(self, layer: torch.nn.Module) -> None: 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 scales = layer.scales qzeros = layer.qzeros pack_factor = self.quant_config.pack_factor out_shape = (x.shape[:-1] + (qweight.shape[-1] * pack_factor, )) reshaped_x = x.reshape(-1, x.shape[-1]) # num_tokens >= threshold FP16_MATMUL_HEURISTIC_CONDITION = x.shape[:-1].numel() >= 256 if FP16_MATMUL_HEURISTIC_CONDITION: out = ops.awq_dequantize(qweight, scales, qzeros, 0, 0, 0) out = torch.matmul(reshaped_x, out) else: out = ops.awq_gemm(reshaped_x, qweight, scales, qzeros, pack_factor) if bias is not None: out.add_(bias) return out.reshape(out_shape)