[3/3] fix dsv3 awq issue (#4719)
Co-authored-by: AniZpZ <aniz1905@gmail.com>
This commit is contained in:
laixin
@@ -9,7 +9,6 @@ import torch
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try:
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from vllm.model_executor.layers.quantization.aqlm import AQLMConfig
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from vllm.model_executor.layers.quantization.awq import AWQConfig
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from vllm.model_executor.layers.quantization.awq_marlin import AWQMarlinConfig
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from vllm.model_executor.layers.quantization.bitsandbytes import BitsAndBytesConfig
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from vllm.model_executor.layers.quantization.deepspeedfp import DeepSpeedFPConfig
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@@ -33,14 +32,15 @@ except ImportError:
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class DummyConfig:
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pass
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AQLMConfig = AWQConfig = AWQMarlinConfig = BitsAndBytesConfig = (
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CompressedTensorsConfig
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) = DummyConfig
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AQLMConfig = AWQMarlinConfig = BitsAndBytesConfig = CompressedTensorsConfig = (
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DummyConfig
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)
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DeepSpeedFPConfig = ExpertsInt8Config = FBGEMMFp8Config = GGUFConfig = (
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GPTQMarlin24Config
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) = DummyConfig
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MarlinConfig = QQQConfig = Int8TpuConfig = DummyConfig
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from sglang.srt.layers.quantization.awq import AWQConfig
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from sglang.srt.layers.quantization.base_config import QuantizationConfig
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from sglang.srt.layers.quantization.blockwise_int8 import BlockInt8Config
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from sglang.srt.layers.quantization.compressed_tensors.compressed_tensors import (
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200
python/sglang/srt/layers/quantization/awq.py
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200
python/sglang/srt/layers/quantization/awq.py
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@@ -0,0 +1,200 @@
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# SPDX-License-Identifier: Apache-2.0
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import logging
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from typing import Any, Dict, List, Optional, Union
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import torch
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from sgl_kernel import awq_dequantize
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from sglang.srt.layers.linear import (
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LinearBase,
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LinearMethodBase,
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UnquantizedLinearMethod,
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)
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from sglang.srt.layers.parameter import GroupQuantScaleParameter, PackedvLLMParameter
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from sglang.srt.layers.quantization.base_config import QuantizationConfig
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logger = logging.getLogger(__name__)
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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 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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)
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self.pack_factor = 32 // self.weight_bits
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def __repr__(self) -> str:
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return (
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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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)
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def get_scaled_act_names(self) -> List[str]:
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return []
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def get_name(self) -> str:
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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]
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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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)
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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["LinearMethodBase"]:
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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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return None
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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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def create_weights(
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self,
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layer: torch.nn.Module,
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input_size_per_partition: int,
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output_partition_sizes: List[int],
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input_size: int,
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output_size: int,
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params_dtype: torch.dtype,
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**extra_weight_attrs,
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):
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if input_size_per_partition % self.quant_config.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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)
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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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)
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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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)
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qzeros = PackedvLLMParameter(
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data=torch.empty(
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input_size_per_partition // self.quant_config.group_size,
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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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)
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scales = GroupQuantScaleParameter(
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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=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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)
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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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def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
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layer.qweight = torch.nn.Parameter(layer.qweight.data, requires_grad=False)
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layer.qzeros = torch.nn.Parameter(layer.qzeros.data, requires_grad=False)
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layer.scales = torch.nn.Parameter(layer.scales.data, requires_grad=False)
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def apply(
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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,
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) -> torch.Tensor:
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qweight = layer.qweight
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scales = layer.scales
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qzeros = layer.qzeros
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pack_factor = self.quant_config.pack_factor
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out_shape = x.shape[:-1] + (qweight.shape[-1] * pack_factor,)
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reshaped_x = x.reshape(-1, x.shape[-1])
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out = awq_dequantize(qweight, scales, qzeros)
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out = torch.matmul(reshaped_x, out)
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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)
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