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[model] switch to gptqmodel (#8108)

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hoshi-hiyouga 2025-05-19 22:25:40 +08:00 committed by GitHub
parent bc7f00f2c7
commit 45030ff803
9 changed files with 78 additions and 62 deletions
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8
requirements.txt
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@ -1,10 +1,10 @@
transformers>=4.45.0,<=4.51.3,!=4.46.*,!=4.47.*,!=4.48.0
datasets>=2.16.0,<=3.5.0
accelerate>=0.34.0,<=1.6.0
peft>=0.14.0,<=0.15.1
datasets>=2.16.0,<=3.6.0
accelerate>=0.34.0,<=1.7.0
peft>=0.14.0,<=0.15.2
trl>=0.8.6,<=0.9.6
tokenizers>=0.19.0,<=0.21.1
gradio>=4.38.0,<=5.25.0
gradio>=4.38.0,<=5.29.1
scipy
einops
sentencepiece

2
setup.py
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@ -50,7 +50,7 @@ extra_require = {
"bitsandbytes": ["bitsandbytes>=0.39.0"],
"hqq": ["hqq"],
"eetq": ["eetq"],
"gptq": ["optimum>=1.17.0", "auto-gptq>=0.5.0"],
"gptq": ["optimum>=1.24.0", "gptqmodel>=2.0.0"],
"aqlm": ["aqlm[gpu]>=1.1.0"],
"vllm": ["vllm>=0.4.3,<=0.8.5"],
"sglang": ["sglang[srt]>=0.4.5", "transformers==4.51.1"],

17
src/llamafactory/extras/misc.py
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@ -79,10 +79,15 @@ def check_version(requirement: str, mandatory: bool = False) -> None:
logger.warning_rank0_once("Version checking has been disabled, may lead to unexpected behaviors.")
return
if mandatory:
hint = f"To fix: run `pip install {requirement}`."
if "gptmodel" in requirement or "autoawq" in requirement:
pip_command = f"pip install {requirement} --no-build-isolation"
else:
hint = f"To fix: run `pip install {requirement}` or set `DISABLE_VERSION_CHECK=1` to skip this check."
pip_command = f"pip install {requirement}"
if mandatory:
hint = f"To fix: run `{pip_command}`."
else:
hint = f"To fix: run `{pip_command}` or set `DISABLE_VERSION_CHECK=1` to skip this check."
require_version(requirement, hint)
@ -90,9 +95,9 @@ def check_version(requirement: str, mandatory: bool = False) -> None:
def check_dependencies() -> None:
r"""Check the version of the required packages."""
check_version("transformers>=4.45.0,<=4.51.3,!=4.46.0,!=4.46.1,!=4.46.2,!=4.46.3,!=4.47.0,!=4.47.1,!=4.48.0")
check_version("datasets>=2.16.0,<=3.5.0")
check_version("accelerate>=0.34.0,<=1.6.0")
check_version("peft>=0.14.0,<=0.15.1")
check_version("datasets>=2.16.0,<=3.6.0")
check_version("accelerate>=0.34.0,<=1.7.0")
check_version("peft>=0.14.0,<=0.15.2")
check_version("trl>=0.8.6,<=0.9.6")
if is_transformers_version_greater_than("4.46.0") and not is_transformers_version_greater_than("4.48.1"):
logger.warning_rank0_once("There are known bugs in transformers v4.46.0-v4.48.0, please use other versions.")

2
src/llamafactory/hparams/parser.py
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@ -148,7 +148,7 @@ def _check_extra_dependencies(
check_version("mixture-of-depth>=1.1.6", mandatory=True)
if model_args.infer_backend == EngineName.VLLM:
check_version("vllm>=0.4.3,<=0.8.5")
check_version("vllm>=0.4.3,<=0.8.6")
check_version("vllm", mandatory=True)
elif model_args.infer_backend == EngineName.SGLANG:
check_version("sglang>=0.4.5")

6
src/llamafactory/model/model_utils/attention.py
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@ -29,10 +29,8 @@ if TYPE_CHECKING:
logger = logging.get_logger(__name__)
def configure_attn_implementation(
config: "PretrainedConfig", model_args: "ModelArguments", is_trainable: bool
) -> None:
if getattr(config, "model_type", None) == "gemma2" and is_trainable:
def configure_attn_implementation(config: "PretrainedConfig", model_args: "ModelArguments") -> None:
if getattr(config, "model_type", None) == "gemma2":
if model_args.flash_attn == AttentionFunction.AUTO or model_args.flash_attn == AttentionFunction.FA2:
if is_flash_attn_2_available():
if model_args.flash_attn != AttentionFunction.FA2:

42
src/llamafactory/model/model_utils/moe.py
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@ -99,27 +99,29 @@ def add_z3_leaf_module(model: "PreTrainedModel") -> None:
def configure_moe(config: "PretrainedConfig", model_args: "ModelArguments", is_trainable: bool) -> None:
if not is_trainable or not model_args.moe_aux_loss_coef:
return
model_type = getattr(config, "model_type", None)
if model_args.moe_aux_loss_coef is not None:
if model_type in [
"dbrx",
"granitemoe",
"jamba",
"jetmoe",
"llama4",
"mixtral",
"olmoe",
"phimoe",
"qwen2_moe",
"qwen3_moe",
]:
setattr(config, "output_router_logits", is_trainable)
if model_type in [
"dbrx",
"granitemoe",
"jamba",
"jetmoe",
"llama4",
"mixtral",
"olmoe",
"phimoe",
"qwen2_moe",
"qwen3_moe",
]:
setattr(config, "output_router_logits", True)
if model_type in ["granitemoe", "jamba", "llama4", "mixtral", "olmoe", "phimoe", "qwen2_moe", "qwen3_moe"]:
setattr(config, "router_aux_loss_coef", model_args.moe_aux_loss_coef)
if model_type in ["granitemoe", "jamba", "llama4", "mixtral", "olmoe", "phimoe", "qwen2_moe", "qwen3_moe"]:
setattr(config, "router_aux_loss_coef", model_args.moe_aux_loss_coef)
elif model_type == "deepseek":
setattr(config, "aux_loss_alpha", model_args.moe_aux_loss_coef)
elif model_type == "deepseek":
setattr(config, "aux_loss_alpha", model_args.moe_aux_loss_coef)
elif model_type == "jetmoe":
setattr(config, "aux_loss_coef", model_args.moe_aux_loss_coef)
elif model_type == "jetmoe":
setattr(config, "aux_loss_coef", model_args.moe_aux_loss_coef)

11
src/llamafactory/model/model_utils/quantization.py
View File

@ -97,7 +97,7 @@ def configure_quantization(
quant_method = quantization_config.get("quant_method", "")
if quant_method == QuantizationMethod.GPTQ:
check_version("auto_gptq>=0.5.0", mandatory=True)
check_version("gptqmodel>=2.0.0", mandatory=True)
quantization_config.pop("disable_exllama", None) # remove deprecated args
quantization_config["use_exllama"] = False # disable exllama
@ -111,12 +111,12 @@ def configure_quantization(
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
elif model_args.export_quantization_bit is not None: # gptqmodel
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)
check_version("optimum>=1.24.0", mandatory=True)
check_version("gptqmodel>=2.0.0", mandatory=True)
from accelerate.utils import get_max_memory
if getattr(config, "model_type", None) == "chatglm":
@ -142,7 +142,8 @@ def configure_quantization(
)
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.")
model_args.compute_dtype = torch.float16 # force fp16 for gptqmodel
logger.info_rank0(f"Quantizing model to {model_args.export_quantization_bit} bit with GPTQModel.")
elif model_args.quantization_bit is not None: # on-the-fly
if model_args.quantization_method == QuantizationMethod.BNB:

48
src/llamafactory/model/model_utils/rope.py
View File

@ -32,7 +32,7 @@ if TYPE_CHECKING:
logger = logging.get_logger(__name__)
def configure_rope(config: "PretrainedConfig", model_args: "ModelArguments", is_trainable: bool) -> None:
def configure_rope(config: "PretrainedConfig", model_args: "ModelArguments") -> None:
if model_args.rope_scaling is None:
return
@ -40,30 +40,40 @@ def configure_rope(config: "PretrainedConfig", model_args: "ModelArguments", is_
logger.warning_rank0("Current model does not support RoPE scaling.")
return
rope_kwargs = {"rope_type": getattr(model_args.rope_scaling, "value", model_args.rope_scaling)} # handle enum
if model_args.model_max_length is not None:
if is_trainable and model_args.rope_scaling == RopeScaling.DYNAMIC:
if hasattr(config, "max_position_embeddings"):
old_max_length = getattr(config, "max_position_embeddings", None)
else:
logger.warning_rank0("Cannot find the max position embeddings in the config.")
return
if model_args.model_max_length is not None: # training
if model_args.model_max_length <= old_max_length:
logger.warning_rank0("Input length is smaller than max length. Disabling rope scaling.")
return
if model_args.rope_scaling == RopeScaling.DYNAMIC:
logger.warning_rank0(
"Dynamic NTK scaling may not work well with fine-tuning. "
"See: https://github.com/huggingface/transformers/pull/24653"
)
current_max_length = getattr(config, "max_position_embeddings", None)
if (not current_max_length) or model_args.model_max_length <= current_max_length:
logger.warning_rank0("Input length is smaller than max length. Disabling rope scaling.")
return
rope_factor = float(math.ceil(model_args.model_max_length / old_max_length))
else: # inference
rope_factor = 2.0
logger.info_rank0(f"Enlarge max model length from {current_max_length} to {model_args.model_max_length}.")
setattr(config, "max_position_embeddings", model_args.model_max_length)
rope_kwargs["factor"] = float(math.ceil(model_args.model_max_length / current_max_length))
if model_args.rope_scaling in [RopeScaling.DYNAMIC, RopeScaling.YARN]:
rope_kwargs["original_max_position_embeddings"] = current_max_length
elif model_args.rope_scaling == RopeScaling.LLAMA3:
rope_kwargs["original_max_position_embeddings"] = current_max_length
rope_kwargs["low_freq_factor"] = 1.0
rope_kwargs["high_freq_factor"] = 4.0
else:
rope_kwargs["factor"] = 2.0
rope_kwargs = {
"rope_type": getattr(model_args.rope_scaling, "value", model_args.rope_scaling), # handle enum
"factor": rope_factor,
}
setattr(config, "max_position_embeddings", old_max_length * rope_factor)
logger.info_rank0(f"Enlarge max model length from {old_max_length} to {old_max_length * rope_factor}.")
if model_args.rope_scaling in [RopeScaling.DYNAMIC, RopeScaling.YARN]:
rope_kwargs["original_max_position_embeddings"] = old_max_length
elif model_args.rope_scaling == RopeScaling.LLAMA3:
rope_kwargs["original_max_position_embeddings"] = old_max_length
rope_kwargs["low_freq_factor"] = 1.0
rope_kwargs["high_freq_factor"] = 4.0
setattr(config, "rope_scaling", rope_kwargs)
logger.info_rank0(

4
src/llamafactory/model/patcher.py
View File

@ -102,8 +102,8 @@ def patch_config(
else:
model_args.compute_dtype = infer_optim_dtype(model_dtype=getattr(config, "torch_dtype", None))
configure_attn_implementation(config, model_args, is_trainable)
configure_rope(config, model_args, is_trainable)
configure_attn_implementation(config, model_args)
configure_rope(config, model_args)
configure_longlora(config, model_args, is_trainable)
configure_quantization(config, tokenizer, model_args, init_kwargs)
configure_moe(config, model_args, is_trainable)