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[deps] goodbye python 3.9 (#9677)

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Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com>
Co-authored-by: hiyouga <16256802+hiyouga@users.noreply.github.com>
Co-authored-by: hiyouga <hiyouga@buaa.edu.cn>
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Copilot
2025-12-27 02:50:44 +08:00
committed by GitHub
parent b44f651e09
commit eceec8ab69
48 changed files with 267 additions and 284 deletions
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59
src/llamafactory/hparams/model_args.py
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@@ -17,12 +17,11 @@
import json
from dataclasses import asdict, dataclass, field, fields
from typing import Any, Literal, Optional, Union
from typing import Any, Literal, Self
import torch
from omegaconf import OmegaConf
from transformers.training_args import _convert_str_dict
from typing_extensions import Self
from ..extras.constants import AttentionFunction, EngineName, QuantizationMethod, RopeScaling
from ..extras.logging import get_logger
@@ -35,13 +34,13 @@ logger = get_logger(__name__)
class BaseModelArguments:
r"""Arguments pertaining to the model."""
model_name_or_path: Optional[str] = field(
model_name_or_path: str | None = field(
default=None,
metadata={
"help": "Path to the model weight or identifier from huggingface.co/models or modelscope.cn/models."
},
)
adapter_name_or_path: Optional[str] = field(
adapter_name_or_path: str | None = field(
default=None,
metadata={
"help": (
@@ -50,11 +49,11 @@ class BaseModelArguments:
)
},
)
adapter_folder: Optional[str] = field(
adapter_folder: str | None = field(
default=None,
metadata={"help": "The folder containing the adapter weights to load."},
)
cache_dir: Optional[str] = field(
cache_dir: str | None = field(
default=None,
metadata={"help": "Where to store the pre-trained models downloaded from huggingface.co or modelscope.cn."},
)
@@ -70,17 +69,17 @@ class BaseModelArguments:
default=False,
metadata={"help": "Whether or not the special tokens should be split during the tokenization process."},
)
add_tokens: Optional[str] = field(
add_tokens: str | None = field(
default=None,
metadata={
"help": "Non-special tokens to be added into the tokenizer. Use commas to separate multiple tokens."
},
)
add_special_tokens: Optional[str] = field(
add_special_tokens: str | None = field(
default=None,
metadata={"help": "Special tokens to be added into the tokenizer. Use commas to separate multiple tokens."},
)
new_special_tokens_config: Optional[str] = field(
new_special_tokens_config: str | None = field(
default=None,
metadata={
"help": (
@@ -110,7 +109,7 @@ class BaseModelArguments:
default=True,
metadata={"help": "Whether or not to use memory-efficient model loading."},
)
rope_scaling: Optional[RopeScaling] = field(
rope_scaling: RopeScaling | None = field(
default=None,
metadata={"help": "Which scaling strategy should be adopted for the RoPE embeddings."},
)
@@ -122,7 +121,7 @@ class BaseModelArguments:
default=False,
metadata={"help": "Enable shift short attention (S^2-Attn) proposed by LongLoRA."},
)
mixture_of_depths: Optional[Literal["convert", "load"]] = field(
mixture_of_depths: Literal["convert", "load"] | None = field(
default=None,
metadata={"help": "Convert the model to mixture-of-depths (MoD) or load the MoD model."},
)
@@ -138,7 +137,7 @@ class BaseModelArguments:
default=False,
metadata={"help": "Whether or not to enable liger kernel for faster training."},
)
moe_aux_loss_coef: Optional[float] = field(
moe_aux_loss_coef: float | None = field(
default=None,
metadata={"help": "Coefficient of the auxiliary router loss in mixture-of-experts model."},
)
@@ -182,15 +181,15 @@ class BaseModelArguments:
default="auto",
metadata={"help": "Data type for model weights and activations at inference."},
)
hf_hub_token: Optional[str] = field(
hf_hub_token: str | None = field(
default=None,
metadata={"help": "Auth token to log in with Hugging Face Hub."},
)
ms_hub_token: Optional[str] = field(
ms_hub_token: str | None = field(
default=None,
metadata={"help": "Auth token to log in with ModelScope Hub."},
)
om_hub_token: Optional[str] = field(
om_hub_token: str | None = field(
default=None,
metadata={"help": "Auth token to log in with Modelers Hub."},
)
@@ -283,7 +282,7 @@ class QuantizationArguments:
default=QuantizationMethod.BNB,
metadata={"help": "Quantization method to use for on-the-fly quantization."},
)
quantization_bit: Optional[int] = field(
quantization_bit: int | None = field(
default=None,
metadata={"help": "The number of bits to quantize the model using on-the-fly quantization."},
)
@@ -295,7 +294,7 @@ class QuantizationArguments:
default=True,
metadata={"help": "Whether or not to use double quantization in bitsandbytes int4 training."},
)
quantization_device_map: Optional[Literal["auto"]] = field(
quantization_device_map: Literal["auto"] | None = field(
default=None,
metadata={"help": "Device map used to infer the 4-bit quantized model, needs bitsandbytes>=0.43.0."},
)
@@ -375,7 +374,7 @@ class ProcessorArguments:
class ExportArguments:
r"""Arguments pertaining to the model export."""
export_dir: Optional[str] = field(
export_dir: str | None = field(
default=None,
metadata={"help": "Path to the directory to save the exported model."},
)
@@ -387,11 +386,11 @@ class ExportArguments:
default="cpu",
metadata={"help": "The device used in model export, use `auto` to accelerate exporting."},
)
export_quantization_bit: Optional[int] = field(
export_quantization_bit: int | None = field(
default=None,
metadata={"help": "The number of bits to quantize the exported model."},
)
export_quantization_dataset: Optional[str] = field(
export_quantization_dataset: str | None = field(
default=None,
metadata={"help": "Path to the dataset or dataset name to use in quantizing the exported model."},
)
@@ -407,7 +406,7 @@ class ExportArguments:
default=False,
metadata={"help": "Whether or not to save the `.bin` files instead of `.safetensors`."},
)
export_hub_model_id: Optional[str] = field(
export_hub_model_id: str | None = field(
default=None,
metadata={"help": "The name of the repository if push the model to the Hugging Face hub."},
)
@@ -437,7 +436,7 @@ class VllmArguments:
default=32,
metadata={"help": "Maximum rank of all LoRAs in the vLLM engine."},
)
vllm_config: Optional[Union[dict, str]] = field(
vllm_config: dict | str | None = field(
default=None,
metadata={"help": "Config to initialize the vllm engine. Please use JSON strings."},
)
@@ -463,7 +462,7 @@ class SGLangArguments:
default=-1,
metadata={"help": "Tensor parallel size for the SGLang engine."},
)
sglang_config: Optional[Union[dict, str]] = field(
sglang_config: dict | str | None = field(
default=None,
metadata={"help": "Config to initialize the SGLang engine. Please use JSON strings."},
)
@@ -487,21 +486,21 @@ class KTransformersArguments:
default=False,
metadata={"help": "Whether To Use KTransformers Optimizations For LoRA Training."},
)
kt_optimize_rule: Optional[str] = field(
kt_optimize_rule: str | None = field(
default=None,
metadata={
"help": "Path To The KTransformers Optimize Rule; See https://github.com/kvcache-ai/ktransformers/."
},
)
cpu_infer: Optional[int] = field(
cpu_infer: int | None = field(
default=32,
metadata={"help": "Number Of CPU Cores Used For Computation."},
)
chunk_size: Optional[int] = field(
chunk_size: int | None = field(
default=8192,
metadata={"help": "Chunk Size Used For CPU Compute In KTransformers."},
)
mode: Optional[str] = field(
mode: str | None = field(
default="normal",
metadata={"help": "Normal Or Long_Context For Llama Models."},
)
@@ -539,17 +538,17 @@ class ModelArguments(
The class on the most right will be displayed first.
"""
compute_dtype: Optional[torch.dtype] = field(
compute_dtype: torch.dtype | None = field(
default=None,
init=False,
metadata={"help": "Torch data type for computing model outputs, derived from `fp/bf16`. Do not specify it."},
)
device_map: Optional[Union[str, dict[str, Any]]] = field(
device_map: str | dict[str, Any] | None = field(
default=None,
init=False,
metadata={"help": "Device map for model placement, derived from training stage. Do not specify it."},
)
model_max_length: Optional[int] = field(
model_max_length: int | None = field(
default=None,
init=False,
metadata={"help": "The maximum input length for model, derived from `cutoff_len`. Do not specify it."},