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# Copyright 2025 HuggingFace Inc. and the LlamaFactory team.
#
# This code is inspired by the HuggingFace's Transformers and Optimum library.
# https://github.com/huggingface/transformers/blob/v4.41.0/src/transformers/utils/quantization_config.py
# https://github.com/huggingface/optimum/blob/v1.20.0/optimum/gptq/data.py
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import random
from typing import TYPE_CHECKING, Any
import torch
from datasets import load_dataset
from transformers import BitsAndBytesConfig, EetqConfig, GPTQConfig, HqqConfig
from transformers.integrations import is_deepspeed_zero3_enabled
from transformers.modeling_utils import is_fsdp_enabled
from ...extras import logging
from ...extras.constants import FILEEXT2TYPE, QuantizationMethod
from ...extras.misc import check_version, get_current_device
if TYPE_CHECKING:
from transformers import PretrainedConfig, PreTrainedTokenizer
from ...hparams import ModelArguments
logger = logging.get_logger(__name__)
def _get_quantization_dataset(tokenizer: "PreTrainedTokenizer", model_args: "ModelArguments") -> list[dict[str, Any]]:
r"""Prepare the tokenized dataset to perform AutoGPTQ. Do not use tensor output for JSON serialization."""
if os.path.isfile(model_args.export_quantization_dataset):
data_path = FILEEXT2TYPE.get(model_args.export_quantization_dataset.split(".")[-1], None)
data_files = model_args.export_quantization_dataset
else:
data_path = model_args.export_quantization_dataset
data_files = None
dataset = load_dataset(
path=data_path,
data_files=data_files,
split="train",
cache_dir=model_args.cache_dir,
token=model_args.hf_hub_token,
)
samples = []
maxlen = model_args.export_quantization_maxlen
for _ in range(model_args.export_quantization_nsamples):
n_try = 0
while True:
if n_try > 100:
raise ValueError("Cannot find satisfying example, considering decrease `export_quantization_maxlen`.")
sample_idx = random.randint(0, len(dataset) - 1)
sample: dict[str, torch.Tensor] = tokenizer(dataset[sample_idx]["text"], return_tensors="pt")
n_try += 1
if sample["input_ids"].size(1) > maxlen:
break # TODO: fix large maxlen
word_idx = random.randint(0, sample["input_ids"].size(1) - maxlen - 1)
input_ids = sample["input_ids"][:, word_idx : word_idx + maxlen]
attention_mask = sample["attention_mask"][:, word_idx : word_idx + maxlen]
samples.append({"input_ids": input_ids.tolist(), "attention_mask": attention_mask.tolist()})
return samples
def configure_quantization(
config: "PretrainedConfig",
tokenizer: "PreTrainedTokenizer",
model_args: "ModelArguments",
init_kwargs: dict[str, Any],
) -> None:
r"""Priority: PTQ-quantized (train/infer) > AutoGPTQ (export) > On-the-fly quantization (train/infer)."""
if getattr(config, "quantization_config", None): # ptq
if model_args.quantization_bit is not None:
logger.warning_rank0("`quantization_bit` will not affect on the PTQ-quantized models.")
if is_deepspeed_zero3_enabled() or is_fsdp_enabled():
raise ValueError("DeepSpeed ZeRO-3 or FSDP is incompatible with PTQ-quantized models.")
quantization_config: dict[str, Any] = getattr(config, "quantization_config", None)
quant_method = quantization_config.get("quant_method", "")
if quant_method == QuantizationMethod.GPTQ:
check_version("auto_gptq>=0.5.0", mandatory=True)
quantization_config.pop("disable_exllama", None) # remove deprecated args
quantization_config["use_exllama"] = False # disable exllama
if quant_method == QuantizationMethod.AWQ:
check_version("autoawq", mandatory=True)
if quant_method == QuantizationMethod.AQLM:
check_version("aqlm>=1.1.0", mandatory=True)
quantization_config["bits"] = 2
quant_bits = quantization_config.get("bits", "?")
logger.info_rank0(f"Loading {quant_bits}-bit {quant_method.upper()}-quantized model.")
elif model_args.export_quantization_bit is not None: # auto-gptq
if model_args.export_quantization_bit not in [8, 4, 3, 2]:
raise ValueError("AutoGPTQ only accepts 2/3/4/8-bit quantization.")
check_version("optimum>=1.17.0", mandatory=True)
check_version("auto_gptq>=0.5.0", mandatory=True)
from accelerate.utils import get_max_memory
if getattr(config, "model_type", None) == "chatglm":
raise ValueError("ChatGLM model is not supported yet.")
try:
from optimum.gptq import utils as gq_utils
if "language_model.model.layers" not in gq_utils.BLOCK_PATTERNS:
gq_utils.BLOCK_PATTERNS.insert(0, "language_model.model.layers")
except ImportError:
pass
block_name_to_quantize = None
if getattr(config, "model_type", None) in ["gemma3", "paligemma"]:
block_name_to_quantize = "language_model.model.layers"
init_kwargs["quantization_config"] = GPTQConfig(
bits=model_args.export_quantization_bit,
tokenizer=tokenizer,
dataset=_get_quantization_dataset(tokenizer, model_args),
block_name_to_quantize=block_name_to_quantize,
)
init_kwargs["device_map"] = "auto"
init_kwargs["max_memory"] = get_max_memory()
logger.info_rank0(f"Quantizing model to {model_args.export_quantization_bit} bit with AutoGPTQ.")
elif model_args.quantization_bit is not None: # on-the-fly
if model_args.quantization_method == QuantizationMethod.BNB:
if model_args.quantization_bit == 8:
check_version("bitsandbytes>=0.37.0", mandatory=True)
init_kwargs["quantization_config"] = BitsAndBytesConfig(load_in_8bit=True)
elif model_args.quantization_bit == 4:
check_version("bitsandbytes>=0.39.0", mandatory=True)
init_kwargs["quantization_config"] = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype=model_args.compute_dtype,
bnb_4bit_use_double_quant=model_args.double_quantization,
bnb_4bit_quant_type=model_args.quantization_type,
bnb_4bit_quant_storage=model_args.compute_dtype, # crucial for fsdp+qlora
)
else:
raise ValueError("Bitsandbytes only accepts 4-bit or 8-bit quantization.")
# Do not assign device map if:
# 1. deepspeed zero3 or fsdp (train)
# 2. auto quantization device map (inference)
if is_deepspeed_zero3_enabled() or is_fsdp_enabled() or model_args.quantization_device_map == "auto":
if model_args.quantization_bit != 4:
raise ValueError("Only 4-bit quantized model can use fsdp+qlora or auto device map.")
check_version("bitsandbytes>=0.43.0", mandatory=True)
else:
init_kwargs["device_map"] = {"": get_current_device()} # change auto device map for inference
logger.info_rank0(f"Quantizing model to {model_args.quantization_bit} bit with bitsandbytes.")
elif model_args.quantization_method == QuantizationMethod.HQQ:
if model_args.quantization_bit not in [8, 6, 5, 4, 3, 2, 1]:
raise ValueError("HQQ only accepts 1/2/3/4/5/6/8-bit quantization.")
if is_deepspeed_zero3_enabled() or is_fsdp_enabled():
raise ValueError("HQQ quantization is incompatible with DeepSpeed ZeRO-3 or FSDP.")
check_version("hqq", mandatory=True)
init_kwargs["quantization_config"] = HqqConfig(
nbits=model_args.quantization_bit, quant_zero=False, quant_scale=False, axis=0
) # use ATEN kernel (axis=0) for performance
logger.info_rank0(f"Quantizing model to {model_args.quantization_bit} bit with HQQ.")
elif model_args.quantization_method == QuantizationMethod.EETQ:
if model_args.quantization_bit != 8:
raise ValueError("EETQ only accepts 8-bit quantization.")
if is_deepspeed_zero3_enabled() or is_fsdp_enabled():
raise ValueError("EETQ quantization is incompatible with DeepSpeed ZeRO-3 or FSDP.")
check_version("eetq", mandatory=True)
init_kwargs["quantization_config"] = EetqConfig()
logger.info_rank0(f"Quantizing model to {model_args.quantization_bit} bit with EETQ.")
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