352 lines
15 KiB
Python
352 lines
15 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import enum
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from enum import Enum
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from fractions import Fraction
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from typing import Any, Optional, Union
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import torch
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from torch.nn.parameter import Parameter
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from vllm import _custom_ops as ops
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from vllm.model_executor.layers.linear import LinearMethodBase
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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)
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from vllm.model_executor.layers.quantization.utils.gptq_utils import (
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get_linear_quant_method)
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from vllm.model_executor.parameter import (ChannelQuantScaleParameter,
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GroupQuantScaleParameter,
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PackedColumnParameter,
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PackedvLLMParameter,
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RowvLLMParameter)
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class GPTQConfig(QuantizationConfig):
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"""Config class for GPTQ.
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Reference: https://arxiv.org/abs/2210.17323
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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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desc_act: bool,
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lm_head_quantized: bool,
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dynamic: dict[str, dict[str, Union[int, bool]]],
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) -> None:
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# GPTQModel use `dynamic` config property to allow per module
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# quantization config so each module can be individually optimized.
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# Format is dict[str, dict] where key is a regex string that can
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# perform both positive ("+:" prefixed) or negative ("-:" prefixed)
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# matching of a module.
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# Default to positive match, override base quant config mode, if no
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# prefix is used. Value is in dict format of field key and override
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# value.
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# Negative matching will skip quantization init for this module
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# entirely:
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# non-quantized inference. More details and quantization examples can be
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# found at: https://github.com/ModelCloud/GPTQModel
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# Example:
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# # last 1/2 of the layers 10-21 has 8bit vs 4bit for 0-9
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# # last 1/4 of the layers 16-21 has 8bit and group_size 64
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# dynamic = {
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# #`.*\.` matches the layers_node prefix
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# # positive match layer 10-15
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# r"+:.*\.(?:1[0-5])\..*": {"bits": 8,},
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# # positive match layer 16-21
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# r"+:.*\.(?:1[6-9]|20|21)\..*": {"bits": 8, "group_size": 64,},
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# r"-:.*\.moe\..*": {}, # negative match (skip) all `moe` layers
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# }
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super().__init__()
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self.dynamic = dynamic
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self.weight_bits = weight_bits
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self.group_size = group_size
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self.desc_act = desc_act
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self.lm_head_quantized = lm_head_quantized
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self.pack_factor = Fraction(32, self.weight_bits)
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if self.weight_bits not in [2, 3, 4, 8]:
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raise ValueError(
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"Currently, only 2/3/4/8-bit weight quantization is "
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f"supported for GPTQ, but got {self.weight_bits} bits.")
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def __repr__(self) -> str:
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return (f"GPTQConfig(weight_bits={self.weight_bits}, "
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f"group_size={self.group_size}, "
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f"desc_act={self.desc_act}), "
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f"lm_head_quantized={self.lm_head_quantized}), "
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f"dynamic={self.dynamic}")
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@classmethod
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def get_name(cls) -> QuantizationMethods:
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return "gptq"
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@classmethod
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def get_supported_act_dtypes(cls) -> list[torch.dtype]:
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return [torch.half, torch.bfloat16]
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@classmethod
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# Need to figure it out
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def get_min_capability(cls) -> int:
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return 60
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@classmethod
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def get_config_filenames(cls) -> list[str]:
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return ["quantize_config.json"]
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@classmethod
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def from_config(cls, config: dict[str, Any]) -> "GPTQConfig":
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dynamic = cls.get_from_keys_or(config, ["dynamic"], default={})
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dynamic = {} if dynamic is None else dynamic
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weight_bits = cls.get_from_keys(config, ["bits"])
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group_size = cls.get_from_keys(config, ["group_size"])
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desc_act = cls.get_from_keys(config, ["desc_act"])
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lm_head_quantized = cls.get_from_keys_or(config, ["lm_head"],
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default=False)
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return cls(weight_bits, group_size, desc_act, lm_head_quantized,
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dynamic)
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def get_quant_method(self, layer: torch.nn.Module,
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prefix: str) -> Optional["GPTQLinearMethod"]:
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return get_linear_quant_method(self, layer, prefix, GPTQLinearMethod)
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class ExllamaState(Enum):
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UNUSED = enum.auto()
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UNINITIALIZED = enum.auto()
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READY = enum.auto()
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class GPTQLinearMethod(LinearMethodBase):
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"""Linear method for GPTQ.
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Args:
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quant_config: The GPTQ quantization config.
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"""
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def __init__(self, quant_config: GPTQConfig):
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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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del output_size # Unused.
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weight_loader = extra_weight_attrs.get("weight_loader")
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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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output_size_per_partition = sum(output_partition_sizes)
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if (output_size_per_partition % self.quant_config.pack_factor.numerator
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!= 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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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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exllama_state = ExllamaState.UNINITIALIZED
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scale_and_zero_size = input_size // group_size
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scale_and_zero_input_dim = None
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if (input_size != input_size_per_partition
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and self.quant_config.group_size != -1):
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# For act-order models, we cannot use Exllama for row parallel layer
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if self.quant_config.desc_act:
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exllama_state = ExllamaState.UNUSED
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else:
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# we need to partition qzeros and scales for exllama kernel
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scale_and_zero_size = input_size_per_partition // group_size
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scale_and_zero_input_dim = 0
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qweight = PackedvLLMParameter(
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data=torch.empty(
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input_size_per_partition // self.quant_config.pack_factor,
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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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packed_dim=0,
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packed_factor=self.quant_config.pack_factor,
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weight_loader=weight_loader)
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g_idx = RowvLLMParameter(data=torch.tensor(
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[
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i // self.quant_config.group_size
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for i in range(input_size_per_partition)
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],
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dtype=torch.int32,
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),
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input_dim=0,
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weight_loader=weight_loader)
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qzeros_args = {
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"data":
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torch.empty(
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scale_and_zero_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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"weight_loader":
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weight_loader
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}
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weight_scale_args = {
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"data":
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torch.empty(
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scale_and_zero_size,
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output_size_per_partition,
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dtype=params_dtype,
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),
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"weight_loader":
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weight_loader
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}
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if scale_and_zero_input_dim is None:
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scales = ChannelQuantScaleParameter(output_dim=1,
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**weight_scale_args)
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qzeros = PackedColumnParameter(
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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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**qzeros_args)
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else:
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scales = GroupQuantScaleParameter(output_dim=1,
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input_dim=0,
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**weight_scale_args)
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qzeros = PackedvLLMParameter(
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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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**qzeros_args)
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layer.register_parameter("qweight", qweight)
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layer.register_parameter("g_idx", g_idx)
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layer.register_parameter("qzeros", qzeros)
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layer.register_parameter("scales", scales)
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layer.exllama_state = exllama_state
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def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
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# for torch.compile
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layer.qzeros = Parameter(layer.qzeros.data, requires_grad=False)
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layer.qweight = Parameter(layer.qweight.data, requires_grad=False)
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layer.g_idx = Parameter(layer.g_idx.data, requires_grad=False)
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layer.scales = Parameter(layer.scales.data, requires_grad=False)
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# exllama needs to shuffle the weight after the weight is loaded
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# here we do the shuffle on first forward pass
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if self.quant_config.group_size == 128 or self.quant_config.group_size == 64:
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if self.quant_config.desc_act:
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layer.g_idx.data = torch.argsort(layer.g_idx).to(torch.int)
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else:
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layer.g_idx.data = torch.empty((0, ),
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dtype=torch.int,
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device=layer.g_idx.device)
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layer.exllama_state = ExllamaState.READY
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ops.gptq_shuffle(layer.qweight, layer.g_idx,
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self.quant_config.weight_bits)
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if layer.scales.dtype != torch.bfloat16:
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perm_space = torch.empty(0)
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temp_space = torch.empty(0)
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if self.quant_config.weight_bits == 4:
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# warmup
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reshaped_x = torch.randn(1, layer.qweight.shape[0]*8, dtype=layer.scales.dtype, device="cuda")
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_ = ops.gptq_gemm(reshaped_x, layer.qweight, layer.qzeros,
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layer.scales, layer.g_idx,
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layer.exllama_state == ExllamaState.READY,
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self.quant_config.weight_bits,
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self.quant_config.group_size,
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perm_space, temp_space,
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False)
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if self.quant_config.weight_bits == 8:
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# warmup
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reshaped_x = torch.randn(1, layer.qweight.shape[0]*4, dtype=layer.scales.dtype, device="cuda")
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_ = ops.gptq_gemm(reshaped_x, layer.qweight, layer.qzeros,
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layer.scales, layer.g_idx,
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layer.exllama_state == ExllamaState.READY,
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self.quant_config.weight_bits,
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self.quant_config.group_size,
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perm_space, temp_space,
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False)
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else:
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if layer.exllama_state == ExllamaState.UNINITIALIZED:
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if self.quant_config.desc_act:
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layer.g_idx.data = torch.argsort(layer.g_idx).to(torch.int)
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else:
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layer.g_idx.data = torch.empty((0, ),
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dtype=torch.int,
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device=layer.g_idx.device)
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layer.exllama_state = ExllamaState.READY
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ops.gptq_shuffle(layer.qweight, layer.g_idx,
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self.quant_config.weight_bits)
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"""
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perm_space = torch.empty(0)
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if self.quant_config.weight_bits == 4:
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# warmup
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reshaped_x = torch.randn(1, layer.qweight.shape[0]*8, dtype=layer.scales.dtype, device="cuda")
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_ = ops.gptq_gemm(reshaped_x, layer.qweight, layer.qzeros,
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layer.scales, layer.g_idx,
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layer.exllama_state == ExllamaState.READY,
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self.quant_config.weight_bits,
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self.quant_config.group_size,
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perm_space)
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if self.quant_config.weight_bits == 8:
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# warmup
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reshaped_x = torch.randn(1, layer.qweight.shape[0]*4, dtype=layer.scales.dtype, device="cuda")
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_ = ops.gptq_gemm(reshaped_x, layer.qweight, layer.qzeros,
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layer.scales, layer.g_idx,
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layer.exllama_state == ExllamaState.READY,
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self.quant_config.weight_bits,
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self.quant_config.group_size,
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perm_space)
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"""
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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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out_shape = x.shape[:-1] + (layer.qweight.shape[-1], )
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reshaped_x = x.reshape(-1, x.shape[-1])
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perm_space = torch.empty(0)
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temp_space = torch.empty(0)
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if self.quant_config.weight_bits == 4 or self.quant_config.weight_bits == 8:
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if self.quant_config.group_size == 128 or self.quant_config.group_size == 64:
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if self.quant_config.desc_act:
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perm_space = torch.empty(reshaped_x.shape[0], reshaped_x.shape[1],
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dtype=torch.float16, device="cuda")
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if reshaped_x.dtype == torch.bfloat16:
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temp_space = torch.zeros(reshaped_x.shape[0], layer.qweight.shape[1],
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dtype=torch.float32, device="cuda")
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output = ops.gptq_gemm(reshaped_x, layer.qweight, layer.qzeros,
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layer.scales, layer.g_idx,
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layer.exllama_state == ExllamaState.READY,
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self.quant_config.weight_bits,
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self.quant_config.group_size,
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perm_space, temp_space,
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True if reshaped_x.dtype == torch.bfloat16 else False)
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if bias is not None:
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output.add_(bias)
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return output.reshape(out_shape)
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