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# Copyright 2024 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 enum import Enum, unique
from typing import TYPE_CHECKING, Any, Dict, List
import torch
from datasets import load_dataset
from transformers import BitsAndBytesConfig, GPTQConfig
from transformers.integrations import is_deepspeed_zero3_enabled
from transformers.modeling_utils import is_fsdp_enabled
from transformers.utils.versions import require_version
from ...extras.constants import FILEEXT2TYPE
from ...extras.logging import get_logger
from ...extras.misc import get_current_device
if TYPE_CHECKING:
from transformers import PretrainedConfig, PreTrainedTokenizer
from ...hparams import ModelArguments
logger = get_logger(__name__)
@unique
class QuantizationMethod(str, Enum):
r"""
Borrowed from `transformers.utils.quantization_config.QuantizationMethod`.
"""
BITS_AND_BYTES = "bitsandbytes"
GPTQ = "gptq"
AWQ = "awq"
AQLM = "aqlm"
QUANTO = "quanto"
EETQ = "eetq"
HQQ = "hqq"
def _get_quantization_dataset(tokenizer: "PreTrainedTokenizer", model_args: "ModelArguments") -> List[str]:
r"""
TODO: remove tokenizer.decode() https://github.com/huggingface/optimum/pull/1600
"""
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)
maxlen = model_args.export_quantization_maxlen
samples = []
for _ in range(model_args.export_quantization_nsamples):
while True:
sample_idx = random.randint(0, len(dataset) - 1)
sample: Dict[str, torch.Tensor] = tokenizer(dataset[sample_idx]["text"], return_tensors="pt")
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]
samples.append(tokenizer.decode(input_ids[0].tolist(), skip_special_tokens=True))
return samples
def configure_quantization(
config: "PretrainedConfig",
tokenizer: "PreTrainedTokenizer",
model_args: "ModelArguments",
init_kwargs: Dict[str, Any],
) -> None:
r"""
Priority: PTQ-quantized (training) > AutoGPTQ (export) > Bitsandbytes (training)
"""
if getattr(config, "quantization_config", None): # ptq
if is_deepspeed_zero3_enabled():
raise ValueError("DeepSpeed ZeRO-3 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:
require_version("auto_gptq>=0.5.0", "To fix: pip install auto_gptq>=0.5.0")
quantization_config.pop("disable_exllama", None) # remove deprecated args
quantization_config["use_exllama"] = False # disable exllama
if quant_method == QuantizationMethod.AWQ:
require_version("autoawq", "To fix: pip install autoawq")
if quant_method == QuantizationMethod.AQLM:
require_version("transformers>=4.39.0", "To fix: pip install transformers>=4.39.0")
require_version("aqlm>=1.1.0", "To fix: pip install aqlm[gpu]>=1.1.0")
quantization_config["bits"] = 2
quant_bits = quantization_config.get("bits", "?")
logger.info("Loading {}-bit {}-quantized model.".format(quant_bits, quant_method.upper()))
elif model_args.export_quantization_bit is not None: # auto-gptq
require_version("optimum>=1.16.0", "To fix: pip install optimum>=1.16.0")
require_version("auto_gptq>=0.5.0", "To fix: pip install auto_gptq>=0.5.0")
from accelerate.utils import get_max_memory
if getattr(config, "model_type", None) == "chatglm":
raise ValueError("ChatGLM model is not supported.")
init_kwargs["quantization_config"] = GPTQConfig(
bits=model_args.export_quantization_bit,
tokenizer=tokenizer,
dataset=_get_quantization_dataset(tokenizer, model_args),
)
init_kwargs["device_map"] = "auto"
init_kwargs["max_memory"] = get_max_memory()
logger.info("Quantizing model to {} bit.".format(model_args.export_quantization_bit))
elif model_args.quantization_bit is not None: # bnb
if model_args.quantization_bit == 8:
require_version("bitsandbytes>=0.37.0", "To fix: pip install bitsandbytes>=0.37.0")
init_kwargs["quantization_config"] = BitsAndBytesConfig(load_in_8bit=True)
elif model_args.quantization_bit == 4:
require_version("bitsandbytes>=0.39.0", "To fix: pip install bitsandbytes>=0.39.0")
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
)
# assign device map if:
# 1. not deepspeed zero3 and not fsdp
# 2. not auto quantization device map
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.")
require_version("bitsandbytes>=0.43.0", "To fix: pip install bitsandbytes>=0.43.0")
else:
init_kwargs["device_map"] = {"": get_current_device()} # change auto device map for inference
logger.info("Quantizing model to {} bit.".format(model_args.quantization_bit))
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