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https://git.datalinker.icu/vllm-project/vllm.git
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626 lines
21 KiB
Python
626 lines
21 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
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import torch
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from safetensors.torch import _TYPES as _SAFETENSORS_TO_TORCH_DTYPE
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from vllm._custom_ops import (
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cpu_gemm_wna16,
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)
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from vllm.logger import init_logger
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from vllm.model_executor.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 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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QuantizeMethodBase,
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)
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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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)
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from vllm.model_executor.layers.quantization.utils.marlin_utils import (
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marlin_repeat_scales_on_all_ranks,
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)
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from vllm.model_executor.layers.quantization.utils.quant_utils import (
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is_layer_skipped,
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pack_cols,
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unpack_cols,
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)
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from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead
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from vllm.model_executor.models.utils import WeightsMapper
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from vllm.model_executor.parameter import (
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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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)
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from vllm.model_executor.utils import set_weight_attrs
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from vllm.platforms import current_platform
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from vllm.transformers_utils.config import get_safetensors_params_metadata
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from vllm.utils.collection_utils import is_list_of
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logger = init_logger(__name__)
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class CPUGPTQConfig(QuantizationConfig):
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"""Config class for CPU GPTQ quant"""
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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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is_sym: bool,
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lm_head_quantized: bool,
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dynamic: dict[str, dict[str, int | bool]],
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full_config: dict[str, Any],
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modules_in_block_to_quantize: list[str] | None = None,
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) -> None:
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super().__init__()
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if desc_act and group_size == -1:
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# In this case, act_order == True is the same as act_order == False
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# (since we have only one group per output channel)
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desc_act = False
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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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assert weight_bits == 4
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self.dynamic = dynamic
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self.weight_bits = weight_bits
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self.is_sym = is_sym
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self.pack_factor = 32 // weight_bits # packed into int32
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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.full_config = full_config
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self.modules_in_block_to_quantize = modules_in_block_to_quantize or []
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def __repr__(self) -> str:
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return (
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f"CPUWNA16Config("
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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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f"modules_in_block_to_quantize={self.modules_in_block_to_quantize})"
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)
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@classmethod
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def get_name(cls) -> QuantizationMethods:
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return "cpu_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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def get_min_capability(cls) -> int:
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return -1
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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]) -> "CPUGPTQConfig":
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weight_bits = cls.get_from_keys(config, ["bits"])
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desc_act = cls.get_from_keys_or(config, ["desc_act"], default=False)
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dynamic = cls.get_from_keys_or(config, ["dynamic"], default={})
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group_size = cls.get_from_keys(config, ["group_size"])
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is_sym = cls.get_from_keys(config, ["sym"])
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lm_head_quantized = cls.get_from_keys_or(config, ["lm_head"], default=False)
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modules_in_block_to_quantize = cls.get_from_keys_or(
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config, ["modules_in_block_to_quantize"], default=None
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)
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return cls(
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weight_bits,
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group_size,
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desc_act,
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is_sym,
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lm_head_quantized,
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dynamic,
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config,
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modules_in_block_to_quantize,
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)
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@classmethod
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def override_quantization_method(
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cls, hf_quant_cfg, user_quant
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) -> QuantizationMethods | None:
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quant_method = hf_quant_cfg.get("quant_method", "").lower()
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if current_platform.is_cpu() and (quant_method == "gptq"):
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return cls.get_name()
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return None
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def get_quant_method(
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self, layer: torch.nn.Module, prefix: str
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) -> Optional["QuantizeMethodBase"]:
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return get_linear_quant_method(self, layer, prefix, CPUGPTQLinearMethod) # type: ignore
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def apply_vllm_mapper(self, hf_to_vllm_mapper):
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if self.modules_in_block_to_quantize is not None:
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self.modules_in_block_to_quantize = hf_to_vllm_mapper.apply_list(
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self.modules_in_block_to_quantize
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)
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def maybe_update_config(self, model_name: str, revision: str | None = None):
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if self.modules_in_block_to_quantize:
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if is_list_of(self.modules_in_block_to_quantize, list):
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# original modules_in_block_to_quantize: list[list[str]]
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# flatten original modules_in_block_to_quantize
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self.modules_in_block_to_quantize = [
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item
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for sublist in self.modules_in_block_to_quantize
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for item in sublist
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]
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return
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unquant_dtypes = [torch.float16, torch.bfloat16, torch.float32]
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metadata = get_safetensors_params_metadata(model_name, revision=revision)
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quant_layers: set[str] = {
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param_name.rsplit(".", 1)[0]
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for param_name, info in metadata.items()
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if (dtype := info.get("dtype", None))
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and _SAFETENSORS_TO_TORCH_DTYPE[dtype] not in unquant_dtypes
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}
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self.modules_in_block_to_quantize = list(quant_layers)
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class CPUGPTQLinearMethod(LinearMethodBase):
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"""Linear method for GPTQ on CPU.
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Args:
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quant_config: The CPUWNA16 quantization config.
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"""
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def __init__(self, quant_config: CPUGPTQConfig) -> None:
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self.quant_config = quant_config
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assert self.quant_config.is_sym, "GPTQ asym quant is not supported on CPU"
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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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) -> None:
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output_size_per_partition = sum(output_partition_sizes)
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assert output_size_per_partition * self.quant_config.weight_bits % 32 == 0
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assert output_size_per_partition % 32 == 0
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assert input_size_per_partition % 32 == 0
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is_row_parallel = input_size != input_size_per_partition
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weight_loader = extra_weight_attrs.get("weight_loader")
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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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# Determine sharding
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if marlin_repeat_scales_on_all_ranks(
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self.quant_config.desc_act, self.quant_config.group_size, is_row_parallel
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):
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# By setting scale_dim == None, weight_loader will
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# repeat the scales on each rank in TP>1 case.
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scales_and_zp_input_dim = None
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scales_and_zp_size = input_size // group_size
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else:
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# By setting scale_dim == 0, weight_loader will
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# shard the scales in TP>1 case.
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scales_and_zp_input_dim = 0
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scales_and_zp_size = input_size_per_partition // group_size
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# Quantized weights
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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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)
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# Activation order
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g_idx = RowvLLMParameter(
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data=torch.empty(
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input_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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weight_loader=weight_loader,
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)
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set_weight_attrs(
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g_idx,
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{"ignore_warning": True},
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)
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qzeros_args = {
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"data": torch.empty(
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scales_and_zp_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": weight_loader,
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}
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weight_scale_args = {
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"data": torch.empty(
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scales_and_zp_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": weight_loader,
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}
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if scales_and_zp_input_dim is None:
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scales = ChannelQuantScaleParameter(output_dim=1, **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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)
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else:
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scales = GroupQuantScaleParameter(
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output_dim=1, input_dim=0, **weight_scale_args
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)
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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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)
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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("scales", scales)
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layer.register_parameter("qzeros", qzeros)
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def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
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torch.set_printoptions(profile="full", linewidth=5000, sci_mode=False)
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packed_weight = layer.qweight.data
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bits = self.quant_config.weight_bits
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pack_factor = int(self.quant_config.pack_factor)
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p_w_k, p_w_n = packed_weight.size()
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input_size = p_w_k * pack_factor
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output_size = p_w_n
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isa_hint = _get_isa_hint(layer.scales.dtype)
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layer.isa_hint = isa_hint
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layer.qzeros = None
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if not self.quant_config.desc_act:
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layer.g_idx = None
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# convert input dim packed to output dim packed
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weight = unpack_cols(packed_weight, bits, p_w_k, p_w_n * pack_factor).view(
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p_w_k, p_w_n, pack_factor
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)
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weight = weight.permute(0, 2, 1).reshape(input_size, output_size).contiguous()
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weight = pack_cols(weight, bits, input_size, output_size)
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# make 16 output channel as a block and transpose to the make
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# the block contigous
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weight = (
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weight.view(input_size, -1, 16 // pack_factor)
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.permute(1, 0, 2)
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.reshape(-1, input_size * 16 // pack_factor)
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.contiguous()
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)
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layer.qweight.data = weight
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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: torch.Tensor | None = None,
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) -> torch.Tensor:
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x = cpu_gemm_wna16(
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input=x,
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q_weight=layer.qweight,
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scales=layer.scales,
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zeros=layer.qzeros,
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g_idx=layer.g_idx,
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bias=bias,
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pack_factor=8,
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isa_hint=layer.isa_hint,
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)
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return x
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class CPUAWQConfig(QuantizationConfig):
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"""Config class for CPU AWQ"""
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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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lm_head_quantized: bool,
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modules_to_not_convert: list[str] | None,
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full_config: dict[str, Any],
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) -> None:
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super().__init__()
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assert weight_bits == 4
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self.pack_factor = 32 // weight_bits # packed into int32
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self.group_size = group_size
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self.zero_point = zero_point
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self.lm_head_quantized = lm_head_quantized
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self.weight_bits = weight_bits
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self.modules_to_not_convert = modules_to_not_convert or []
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self.full_config = full_config
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def __repr__(self) -> str:
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return (
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f"AWQMarlinConfig("
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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"lm_head_quantized={self.lm_head_quantized}, "
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f"modules_to_not_convert={self.modules_to_not_convert})"
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)
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@classmethod
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def get_name(cls) -> "QuantizationMethods":
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return "cpu_awq"
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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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def get_min_capability(cls) -> int:
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return -1
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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]) -> "CPUAWQConfig":
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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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zero_point = cls.get_from_keys(config, ["zero_point"])
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lm_head_quantized = cls.get_from_keys_or(config, ["lm_head"], default=False)
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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(
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weight_bits,
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group_size,
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zero_point,
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lm_head_quantized,
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modules_to_not_convert,
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config,
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)
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@classmethod
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def override_quantization_method(
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cls, hf_quant_cfg, user_quant
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) -> Optional["QuantizationMethods"]:
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quant_method = hf_quant_cfg.get("quant_method", "").lower()
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if current_platform.is_cpu() and (quant_method == "awq"):
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return cls.get_name()
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return None
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def get_quant_method(
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self, layer: torch.nn.Module, prefix: str
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) -> Optional["QuantizeMethodBase"]:
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if isinstance(layer, LinearBase) or (
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isinstance(layer, ParallelLMHead) and self.lm_head_quantized
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):
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if is_layer_skipped(
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prefix,
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self.modules_to_not_convert,
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self.packed_modules_mapping,
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skip_with_substr=True,
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):
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return UnquantizedLinearMethod()
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return CPUAWQLinearMethod(self)
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return None
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def apply_vllm_mapper(self, hf_to_vllm_mapper: "WeightsMapper"):
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if self.modules_to_not_convert:
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self.modules_to_not_convert = hf_to_vllm_mapper.apply_list(
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self.modules_to_not_convert
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)
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def maybe_update_config(self, model_name: str, revision: str | None = None):
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if self.modules_to_not_convert:
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return
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unquant_dtypes = [torch.float16, torch.bfloat16, torch.float32]
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metadata = get_safetensors_params_metadata(model_name, revision=revision)
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layers = {param_name.rsplit(".", 1)[0] for param_name in metadata}
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quant_layers: set[str] = {
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param_name.rsplit(".", 1)[0]
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for param_name, info in metadata.items()
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if (dtype := info.get("dtype", None))
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and _SAFETENSORS_TO_TORCH_DTYPE[dtype] not in unquant_dtypes
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}
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self.modules_to_not_convert = list(layers - quant_layers)
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class CPUAWQLinearMethod(LinearMethodBase):
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"""Linear method for CPU AWQ.
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Args:
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quant_config: The CPU AWQ quantization config.
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"""
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def __init__(self, quant_config: CPUAWQConfig) -> None:
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self.quant_config = quant_config
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assert self.quant_config.zero_point
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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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) -> None:
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del output_size
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output_size_per_partition = sum(output_partition_sizes)
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weight_loader = extra_weight_attrs.get("weight_loader")
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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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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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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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)
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scales = GroupQuantScaleParameter(
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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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)
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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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torch.set_printoptions(profile="full", linewidth=5000, sci_mode=False)
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packed_weight = layer.qweight.data
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packed_zeros = layer.qzeros.data
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group_num = packed_zeros.size(0)
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bits = self.quant_config.weight_bits
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pack_factor = int(self.quant_config.pack_factor)
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input_size, packed_output_size = packed_weight.size()
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output_size = packed_output_size * pack_factor
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isa_hint = _get_isa_hint(layer.scales.dtype)
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layer.isa_hint = isa_hint
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interleave_map = (0, 4, 1, 5, 2, 6, 3, 7)
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weight = unpack_cols(
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packed_weight,
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bits,
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input_size,
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output_size,
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)
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zeros = unpack_cols(
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packed_zeros,
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bits,
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group_num,
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output_size,
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)
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weight = (
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weight.view(input_size, -1, pack_factor)[:, :, interleave_map]
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.reshape(input_size, output_size)
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.contiguous()
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)
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zeros = (
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zeros.view(group_num, -1, pack_factor)[:, :, interleave_map]
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.reshape(group_num, output_size)
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.contiguous()
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)
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zeros = pack_cols(zeros, bits, group_num, output_size).contiguous()
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# make 16 output channel as a block and transpose to
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# the make the block contigous
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weight = pack_cols(weight, bits, input_size, output_size)
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weight = (
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weight.view(input_size, -1, 16 // pack_factor)
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.permute(1, 0, 2)
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.reshape(-1, input_size * 16 // pack_factor)
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.contiguous()
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)
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layer.qweight.data = weight
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layer.qzeros.data = zeros
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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: torch.Tensor | None = None,
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) -> torch.Tensor:
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x = cpu_gemm_wna16(
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input=x,
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q_weight=layer.qweight,
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scales=layer.scales,
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zeros=layer.qzeros,
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g_idx=None,
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bias=bias,
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pack_factor=8,
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isa_hint=layer.isa_hint,
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)
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return x
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def _get_isa_hint(dtype: torch.dtype) -> str:
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supports_amx = torch._C._cpu._is_amx_tile_supported()
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if supports_amx and dtype in (torch.bfloat16,):
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return "amx"
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else:
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return "vec"
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