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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>
This commit is contained in:
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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12
.github/workflows/tests.yml vendored
View File

@@ -25,10 +25,9 @@ jobs:
fail-fast: false
matrix:
python:
- "3.9"
- "3.10"
- "3.11"
- "3.12"
# - "3.13" # enable after trl is upgraded
os:
- "ubuntu-latest"
- "windows-latest"
@@ -36,18 +35,15 @@ jobs:
transformers:
- null
include: # test backward compatibility
- python: "3.9"
- python: "3.11"
os: "ubuntu-latest"
transformers: "4.49.0"
- python: "3.9"
- python: "3.11"
os: "ubuntu-latest"
transformers: "4.51.0"
- python: "3.9"
- python: "3.11"
os: "ubuntu-latest"
transformers: "4.53.0"
exclude: # exclude python 3.9 on macos
- python: "3.9"
os: "macos-latest"
runs-on: ${{ matrix.os }}

8
pyproject.toml
View File

@@ -8,7 +8,7 @@ dynamic = ["version"]
description = "Unified Efficient Fine-Tuning of 100+ LLMs"
readme = "README.md"
license = "Apache-2.0"
requires-python = ">=3.9.0"
requires-python = ">=3.11.0"
authors = [
{ name = "hiyouga", email = "hiyouga@buaa.edu.cn" }
]
@@ -30,10 +30,10 @@ classifiers = [
"License :: OSI Approved :: Apache Software License",
"Operating System :: OS Independent",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.9",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12",
"Programming Language :: Python :: 3.13",
"Topic :: Scientific/Engineering :: Artificial Intelligence"
]
dependencies = [
@@ -98,7 +98,7 @@ path = "src/llamafactory/extras/env.py"
pattern = "VERSION = \"(?P<version>[^\"]+)\""
[tool.ruff]
target-version = "py39"
target-version = "py311"
line-length = 119
indent-width = 4
@@ -109,6 +109,8 @@ ignore = [
"E501", # line too long
"E731", # lambda function
"E741", # ambiguous var name
"UP007", # no upgrade union
"UP045", # no upgrade optional
"D100", # no doc public module
"D101", # no doc public class
"D102", # no doc public method

7
scripts/megatron_merge.py
View File

@@ -16,7 +16,6 @@
# limitations under the License.
import os
from typing import Optional
import fire
import torch
@@ -34,7 +33,7 @@ def convert_mca_to_hf(
output_path: str = "./output",
bf16: bool = False,
fp16: bool = False,
convert_model_max_length: Optional[int] = None,
convert_model_max_length: int | None = None,
):
"""Convert megatron checkpoint to HuggingFace format.
@@ -67,11 +66,11 @@ def convert(
output_path: str = "./output",
bf16: bool = False,
fp16: bool = False,
convert_model_max_length: Optional[int] = None,
convert_model_max_length: int | None = None,
tensor_model_parallel_size: int = 1,
pipeline_model_parallel_size: int = 1,
expert_model_parallel_size: int = 1,
virtual_pipeline_model_parallel_size: Optional[int] = None,
virtual_pipeline_model_parallel_size: int | None = None,
):
"""Convert checkpoint between MCA and HuggingFace formats.

4
scripts/stat_utils/cal_ppl.py
View File

@@ -14,7 +14,7 @@
import json
from dataclasses import dataclass
from typing import Any, Literal, Optional
from typing import Any, Literal
import fire
import torch
@@ -61,7 +61,7 @@ def calculate_ppl(
dataset_dir: str = "data",
template: str = "default",
cutoff_len: int = 2048,
max_samples: Optional[int] = None,
max_samples: int | None = None,
train_on_prompt: bool = False,
):
r"""Calculate the ppl on the dataset of the pre-trained models.

7
scripts/vllm_infer.py
View File

@@ -14,7 +14,6 @@
import gc
import json
from typing import Optional
import av
import fire
@@ -49,7 +48,7 @@ def vllm_infer(
dataset_dir: str = "data",
template: str = "default",
cutoff_len: int = 2048,
max_samples: Optional[int] = None,
max_samples: int | None = None,
vllm_config: str = "{}",
save_name: str = "generated_predictions.jsonl",
temperature: float = 0.95,
@@ -58,9 +57,9 @@ def vllm_infer(
max_new_tokens: int = 1024,
repetition_penalty: float = 1.0,
skip_special_tokens: bool = True,
default_system: Optional[str] = None,
default_system: str | None = None,
enable_thinking: bool = True,
seed: Optional[int] = None,
seed: int | None = None,
pipeline_parallel_size: int = 1,
image_max_pixels: int = 768 * 768,
image_min_pixels: int = 32 * 32,

4
src/llamafactory/api/app.py
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@@ -16,7 +16,7 @@ import asyncio
import os
from contextlib import asynccontextmanager
from functools import partial
from typing import Annotated, Optional
from typing import Annotated
from ..chat import ChatModel
from ..extras.constants import EngineName
@@ -79,7 +79,7 @@ def create_app(chat_model: "ChatModel") -> "FastAPI":
api_key = os.getenv("API_KEY")
security = HTTPBearer(auto_error=False)
async def verify_api_key(auth: Annotated[Optional[HTTPAuthorizationCredentials], Depends(security)]):
async def verify_api_key(auth: Annotated[HTTPAuthorizationCredentials | None, Depends(security)]):
if api_key and (auth is None or auth.credentials != api_key):
raise HTTPException(status_code=status.HTTP_401_UNAUTHORIZED, detail="Invalid API key.")

41
src/llamafactory/api/protocol.py
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@@ -14,10 +14,9 @@
import time
from enum import Enum, unique
from typing import Any, Optional, Union
from typing import Any, Literal
from pydantic import BaseModel, Field
from typing_extensions import Literal
@unique
@@ -61,7 +60,7 @@ class FunctionDefinition(BaseModel):
class FunctionAvailable(BaseModel):
type: Literal["function", "code_interpreter"] = "function"
function: Optional[FunctionDefinition] = None
function: FunctionDefinition | None = None
class FunctionCall(BaseModel):
@@ -77,35 +76,35 @@ class URL(BaseModel):
class MultimodalInputItem(BaseModel):
type: Literal["text", "image_url", "video_url", "audio_url"]
text: Optional[str] = None
image_url: Optional[URL] = None
video_url: Optional[URL] = None
audio_url: Optional[URL] = None
text: str | None = None
image_url: URL | None = None
video_url: URL | None = None
audio_url: URL | None = None
class ChatMessage(BaseModel):
role: Role
content: Optional[Union[str, list[MultimodalInputItem]]] = None
tool_calls: Optional[list[FunctionCall]] = None
content: str | list[MultimodalInputItem] | None = None
tool_calls: list[FunctionCall] | None = None
class ChatCompletionMessage(BaseModel):
role: Optional[Role] = None
content: Optional[str] = None
tool_calls: Optional[list[FunctionCall]] = None
role: Role | None = None
content: str | None = None
tool_calls: list[FunctionCall] | None = None
class ChatCompletionRequest(BaseModel):
model: str
messages: list[ChatMessage]
tools: Optional[list[FunctionAvailable]] = None
do_sample: Optional[bool] = None
temperature: Optional[float] = None
top_p: Optional[float] = None
tools: list[FunctionAvailable] | None = None
do_sample: bool | None = None
temperature: float | None = None
top_p: float | None = None
n: int = 1
presence_penalty: Optional[float] = None
max_tokens: Optional[int] = None
stop: Optional[Union[str, list[str]]] = None
presence_penalty: float | None = None
max_tokens: int | None = None
stop: str | list[str] | None = None
stream: bool = False
@@ -118,7 +117,7 @@ class ChatCompletionResponseChoice(BaseModel):
class ChatCompletionStreamResponseChoice(BaseModel):
index: int
delta: ChatCompletionMessage
finish_reason: Optional[Finish] = None
finish_reason: Finish | None = None
class ChatCompletionResponseUsage(BaseModel):
@@ -147,7 +146,7 @@ class ChatCompletionStreamResponse(BaseModel):
class ScoreEvaluationRequest(BaseModel):
model: str
messages: list[str]
max_length: Optional[int] = None
max_length: int | None = None
class ScoreEvaluationResponse(BaseModel):

4
src/llamafactory/chat/hf_engine.py
View File

@@ -14,9 +14,9 @@
import asyncio
import os
from collections.abc import AsyncGenerator
from collections.abc import AsyncGenerator, Callable
from threading import Thread
from typing import TYPE_CHECKING, Any, Callable, Optional, Union
from typing import TYPE_CHECKING, Any, Optional, Union
import torch
from transformers import GenerationConfig, TextIteratorStreamer

4
src/llamafactory/data/converter.py
View File

@@ -15,7 +15,7 @@ import json
import os
from abc import abstractmethod
from dataclasses import dataclass
from typing import TYPE_CHECKING, Any, Optional, Union
from typing import TYPE_CHECKING, Any, Union
from ..extras import logging
from .data_utils import Role
@@ -40,7 +40,7 @@ class DatasetConverter:
dataset_attr: "DatasetAttr"
data_args: "DataArguments"
def _find_medias(self, medias: Union["MediaType", list["MediaType"], None]) -> Optional[list["MediaType"]]:
def _find_medias(self, medias: Union["MediaType", list["MediaType"], None]) -> list["MediaType"] | None:
r"""Optionally concatenate media path to media dir when loading from local disk."""
if medias is None:
return None

7
src/llamafactory/data/formatter.py
View File

@@ -16,7 +16,6 @@ import json
import re
from abc import ABC, abstractmethod
from dataclasses import dataclass, field
from typing import Optional, Union
from typing_extensions import override
@@ -27,14 +26,14 @@ from .tool_utils import FunctionCall, get_tool_utils
@dataclass
class Formatter(ABC):
slots: SLOTS = field(default_factory=list)
tool_format: Optional[str] = None
tool_format: str | None = None
@abstractmethod
def apply(self, **kwargs) -> SLOTS:
r"""Forms a list of slots according to the inputs to encode."""
...
def extract(self, content: str) -> Union[str, list["FunctionCall"]]:
def extract(self, content: str) -> str | list["FunctionCall"]:
r"""Extract a list of tuples from the response message if using tools.
Each tuple consists of function name and function arguments.
@@ -156,5 +155,5 @@ class ToolFormatter(Formatter):
raise RuntimeError(f"Invalid JSON format in tool description: {str([content])}.") # flat string
@override
def extract(self, content: str) -> Union[str, list["FunctionCall"]]:
def extract(self, content: str) -> str | list["FunctionCall"]:
return self.tool_utils.tool_extractor(content)

8
src/llamafactory/data/loader.py
View File

@@ -162,13 +162,13 @@ def _load_single_dataset(
def _get_merged_dataset(
dataset_names: Optional[list[str]],
dataset_names: list[str] | None,
model_args: "ModelArguments",
data_args: "DataArguments",
training_args: "Seq2SeqTrainingArguments",
stage: Literal["pt", "sft", "rm", "ppo", "kto"],
return_dict: bool = False,
) -> Optional[Union["Dataset", "IterableDataset", dict[str, "Dataset"]]]:
) -> Union["Dataset", "IterableDataset", dict[str, "Dataset"]] | None:
r"""Return the merged datasets in the standard format."""
if dataset_names is None:
return None
@@ -227,7 +227,7 @@ def _get_dataset_processor(
def _get_preprocessed_dataset(
dataset: Optional[Union["Dataset", "IterableDataset"]],
dataset: Union["Dataset", "IterableDataset"] | None,
data_args: "DataArguments",
training_args: "Seq2SeqTrainingArguments",
stage: Literal["pt", "sft", "rm", "ppo", "kto"],
@@ -235,7 +235,7 @@ def _get_preprocessed_dataset(
tokenizer: "PreTrainedTokenizer",
processor: Optional["ProcessorMixin"] = None,
is_eval: bool = False,
) -> Optional[Union["Dataset", "IterableDataset"]]:
) -> Union["Dataset", "IterableDataset"] | None:
r"""Preprocesses the dataset, including format checking and tokenization."""
if dataset is None:
return None

30
src/llamafactory/data/mm_plugin.py
View File

@@ -22,7 +22,7 @@ import re
from copy import deepcopy
from dataclasses import dataclass
from io import BytesIO
from typing import TYPE_CHECKING, BinaryIO, Literal, Optional, TypedDict, Union
from typing import TYPE_CHECKING, BinaryIO, Literal, NotRequired, Optional, TypedDict, Union
import numpy as np
import torch
@@ -32,7 +32,7 @@ from transformers.models.mllama.processing_mllama import (
convert_sparse_cross_attention_mask_to_dense,
get_cross_attention_token_mask,
)
from typing_extensions import NotRequired, override
from typing_extensions import override
from ..extras.constants import AUDIO_PLACEHOLDER, IGNORE_INDEX, IMAGE_PLACEHOLDER, VIDEO_PLACEHOLDER
from ..extras.packages import is_pillow_available, is_pyav_available, is_transformers_version_greater_than
@@ -63,8 +63,8 @@ if TYPE_CHECKING:
from transformers.video_processing_utils import BaseVideoProcessor
class EncodedImage(TypedDict):
path: Optional[str]
bytes: Optional[bytes]
path: str | None
bytes: bytes | None
ImageInput = Union[str, bytes, EncodedImage, BinaryIO, ImageObject]
VideoInput = Union[str, BinaryIO, list[list[ImageInput]]]
@@ -144,9 +144,9 @@ def _check_video_is_nested_images(video: "VideoInput") -> bool:
@dataclass
class MMPluginMixin:
image_token: Optional[str]
video_token: Optional[str]
audio_token: Optional[str]
image_token: str | None
video_token: str | None
audio_token: str | None
expand_mm_tokens: bool = True
def _validate_input(
@@ -328,7 +328,7 @@ class MMPluginMixin:
videos: list["VideoInput"],
audios: list["AudioInput"],
processor: "MMProcessor",
imglens: Optional[list[int]] = None,
imglens: list[int] | None = None,
) -> dict[str, "torch.Tensor"]:
r"""Process visual inputs.
@@ -426,13 +426,13 @@ class BasePlugin(MMPluginMixin):
def process_token_ids(
self,
input_ids: list[int],
labels: Optional[list[int]],
labels: list[int] | None,
images: list["ImageInput"],
videos: list["VideoInput"],
audios: list["AudioInput"],
tokenizer: "PreTrainedTokenizer",
processor: Optional["MMProcessor"],
) -> tuple[list[int], Optional[list[int]]]:
) -> tuple[list[int], list[int] | None]:
r"""Pre-process token ids after tokenization for VLMs."""
self._validate_input(processor, images, videos, audios)
return input_ids, labels
@@ -1305,13 +1305,13 @@ class PaliGemmaPlugin(BasePlugin):
def process_token_ids(
self,
input_ids: list[int],
labels: Optional[list[int]],
labels: list[int] | None,
images: list["ImageInput"],
videos: list["VideoInput"],
audios: list["AudioInput"],
tokenizer: "PreTrainedTokenizer",
processor: Optional["MMProcessor"],
) -> tuple[list[int], Optional[list[int]]]:
) -> tuple[list[int], list[int] | None]:
self._validate_input(processor, images, videos, audios)
num_images = len(images)
image_seqlen = processor.image_seq_length if self.expand_mm_tokens else 0 # skip mm token
@@ -2126,9 +2126,9 @@ def register_mm_plugin(name: str, plugin_class: type["BasePlugin"]) -> None:
def get_mm_plugin(
name: str,
image_token: Optional[str] = None,
video_token: Optional[str] = None,
audio_token: Optional[str] = None,
image_token: str | None = None,
video_token: str | None = None,
audio_token: str | None = None,
**kwargs,
) -> "BasePlugin":
r"""Get plugin for multimodal inputs."""

52
src/llamafactory/data/parser.py
View File

@@ -15,7 +15,7 @@
import json
import os
from dataclasses import dataclass
from typing import Any, Literal, Optional, Union
from typing import Any, Literal
from huggingface_hub import hf_hub_download
@@ -33,40 +33,40 @@ class DatasetAttr:
formatting: Literal["alpaca", "sharegpt", "openai"] = "alpaca"
ranking: bool = False
# extra configs
subset: Optional[str] = None
subset: str | None = None
split: str = "train"
folder: Optional[str] = None
num_samples: Optional[int] = None
folder: str | None = None
num_samples: int | None = None
# common columns
system: Optional[str] = None
tools: Optional[str] = None
images: Optional[str] = None
videos: Optional[str] = None
audios: Optional[str] = None
system: str | None = None
tools: str | None = None
images: str | None = None
videos: str | None = None
audios: str | None = None
# dpo columns
chosen: Optional[str] = None
rejected: Optional[str] = None
kto_tag: Optional[str] = None
chosen: str | None = None
rejected: str | None = None
kto_tag: str | None = None
# alpaca columns
prompt: Optional[str] = "instruction"
query: Optional[str] = "input"
response: Optional[str] = "output"
history: Optional[str] = None
prompt: str | None = "instruction"
query: str | None = "input"
response: str | None = "output"
history: str | None = None
# sharegpt columns
messages: Optional[str] = "conversations"
messages: str | None = "conversations"
# sharegpt tags
role_tag: Optional[str] = "from"
content_tag: Optional[str] = "value"
user_tag: Optional[str] = "human"
assistant_tag: Optional[str] = "gpt"
observation_tag: Optional[str] = "observation"
function_tag: Optional[str] = "function_call"
system_tag: Optional[str] = "system"
role_tag: str | None = "from"
content_tag: str | None = "value"
user_tag: str | None = "human"
assistant_tag: str | None = "gpt"
observation_tag: str | None = "observation"
function_tag: str | None = "function_call"
system_tag: str | None = "system"
def __repr__(self) -> str:
return self.dataset_name
def set_attr(self, key: str, obj: dict[str, Any], default: Optional[Any] = None) -> None:
def set_attr(self, key: str, obj: dict[str, Any], default: Any | None = None) -> None:
setattr(self, key, obj.get(key, default))
def join(self, attr: dict[str, Any]) -> None:
@@ -90,7 +90,7 @@ class DatasetAttr:
self.set_attr(tag, attr["tags"])
def get_dataset_list(dataset_names: Optional[list[str]], dataset_dir: Union[str, dict]) -> list["DatasetAttr"]:
def get_dataset_list(dataset_names: list[str] | None, dataset_dir: str | dict) -> list["DatasetAttr"]:
r"""Get the attributes of the datasets."""
if dataset_names is None:
dataset_names = []

3
src/llamafactory/extras/constants.py
View File

@@ -15,7 +15,6 @@
import os
from collections import OrderedDict, defaultdict
from enum import Enum, unique
from typing import Optional
from peft.utils import SAFETENSORS_WEIGHTS_NAME as SAFE_ADAPTER_WEIGHTS_NAME
from peft.utils import WEIGHTS_NAME as ADAPTER_WEIGHTS_NAME
@@ -154,7 +153,7 @@ class RopeScaling(str, Enum):
def register_model_group(
models: dict[str, dict[DownloadSource, str]],
template: Optional[str] = None,
template: str | None = None,
multimodal: bool = False,
) -> None:
for name, path in models.items():

2
src/llamafactory/extras/logging.py
View File

@@ -117,7 +117,7 @@ def _configure_library_root_logger() -> None:
library_root_logger.propagate = False
def get_logger(name: Optional[str] = None) -> "_Logger":
def get_logger(name: str | None = None) -> "_Logger":
r"""Return a logger with the specified name. It it not supposed to be accessed externally."""
if name is None:
name = _get_library_name()

28
src/llamafactory/hparams/data_args.py
View File

@@ -16,22 +16,22 @@
# limitations under the License.
from dataclasses import asdict, dataclass, field
from typing import Any, Literal, Optional
from typing import Any, Literal
@dataclass
class DataArguments:
r"""Arguments pertaining to what data we are going to input our model for training and evaluation."""
template: Optional[str] = field(
template: str | None = field(
default=None,
metadata={"help": "Which template to use for constructing prompts in training and inference."},
)
dataset: Optional[str] = field(
dataset: str | None = field(
default=None,
metadata={"help": "The name of dataset(s) to use for training. Use commas to separate multiple datasets."},
)
eval_dataset: Optional[str] = field(
eval_dataset: str | None = field(
default=None,
metadata={"help": "The name of dataset(s) to use for evaluation. Use commas to separate multiple datasets."},
)
@@ -39,7 +39,7 @@ class DataArguments:
default="data",
metadata={"help": "Path to the folder containing the datasets."},
)
media_dir: Optional[str] = field(
media_dir: str | None = field(
default=None,
metadata={"help": "Path to the folder containing the images, videos or audios. Defaults to `dataset_dir`."},
)
@@ -67,7 +67,7 @@ class DataArguments:
default="concat",
metadata={"help": "Strategy to use in dataset mixing (concat/interleave) (undersampling/oversampling)."},
)
interleave_probs: Optional[str] = field(
interleave_probs: str | None = field(
default=None,
metadata={"help": "Probabilities to sample data from datasets. Use commas to separate multiple datasets."},
)
@@ -79,15 +79,15 @@ class DataArguments:
default=1000,
metadata={"help": "The number of examples in one group in pre-processing."},
)
preprocessing_num_workers: Optional[int] = field(
preprocessing_num_workers: int | None = field(
default=None,
metadata={"help": "The number of processes to use for the pre-processing."},
)
max_samples: Optional[int] = field(
max_samples: int | None = field(
default=None,
metadata={"help": "For debugging purposes, truncate the number of examples for each dataset."},
)
eval_num_beams: Optional[int] = field(
eval_num_beams: int | None = field(
default=None,
metadata={"help": "Number of beams to use for evaluation. This argument will be passed to `model.generate`"},
)
@@ -103,7 +103,7 @@ class DataArguments:
default=False,
metadata={"help": "Whether or not to evaluate on each dataset separately."},
)
packing: Optional[bool] = field(
packing: bool | None = field(
default=None,
metadata={"help": "Enable sequences packing in training. Will automatically enable in pre-training."},
)
@@ -111,19 +111,19 @@ class DataArguments:
default=False,
metadata={"help": "Enable sequence packing without cross-attention."},
)
tool_format: Optional[str] = field(
tool_format: str | None = field(
default=None,
metadata={"help": "Tool format to use for constructing function calling examples."},
)
default_system: Optional[str] = field(
default_system: str | None = field(
default=None,
metadata={"help": "Override the default system message in the template."},
)
enable_thinking: Optional[bool] = field(
enable_thinking: bool | None = field(
default=True,
metadata={"help": "Whether or not to enable thinking mode for reasoning models."},
)
tokenized_path: Optional[str] = field(
tokenized_path: str | None = field(
default=None,
metadata={
"help": (

4
src/llamafactory/hparams/evaluation_args.py
View File

@@ -14,7 +14,7 @@
import os
from dataclasses import dataclass, field
from typing import Literal, Optional
from typing import Literal
from datasets import DownloadMode
@@ -46,7 +46,7 @@ class EvaluationArguments:
default=5,
metadata={"help": "Number of examplars for few-shot learning."},
)
save_dir: Optional[str] = field(
save_dir: str | None = field(
default=None,
metadata={"help": "Path to save the evaluation results."},
)

52
src/llamafactory/hparams/finetuning_args.py
View File

@@ -13,7 +13,7 @@
# limitations under the License.
from dataclasses import asdict, dataclass, field
from typing import Any, Literal, Optional
from typing import Any, Literal
@dataclass
@@ -40,7 +40,7 @@ class FreezeArguments:
)
},
)
freeze_extra_modules: Optional[str] = field(
freeze_extra_modules: str | None = field(
default=None,
metadata={
"help": (
@@ -56,7 +56,7 @@ class FreezeArguments:
class LoraArguments:
r"""Arguments pertaining to the LoRA training."""
additional_target: Optional[str] = field(
additional_target: str | None = field(
default=None,
metadata={
"help": (
@@ -66,7 +66,7 @@ class LoraArguments:
)
},
)
lora_alpha: Optional[int] = field(
lora_alpha: int | None = field(
default=None,
metadata={"help": "The scale factor for LoRA fine-tuning (default: lora_rank * 2)."},
)
@@ -88,7 +88,7 @@ class LoraArguments:
)
},
)
loraplus_lr_ratio: Optional[float] = field(
loraplus_lr_ratio: float | None = field(
default=None,
metadata={"help": "LoRA plus learning rate ratio (lr_B / lr_A)."},
)
@@ -126,7 +126,7 @@ class LoraArguments:
class OFTArguments:
r"""Arguments pertaining to the OFT training."""
additional_target: Optional[str] = field(
additional_target: str | None = field(
default=None,
metadata={
"help": (
@@ -220,27 +220,27 @@ class RLHFArguments:
default=False,
metadata={"help": "Whiten the rewards before compute advantages in PPO training."},
)
ref_model: Optional[str] = field(
ref_model: str | None = field(
default=None,
metadata={"help": "Path to the reference model used for the PPO or DPO training."},
)
ref_model_adapters: Optional[str] = field(
ref_model_adapters: str | None = field(
default=None,
metadata={"help": "Path to the adapters of the reference model."},
)
ref_model_quantization_bit: Optional[int] = field(
ref_model_quantization_bit: int | None = field(
default=None,
metadata={"help": "The number of bits to quantize the reference model."},
)
reward_model: Optional[str] = field(
reward_model: str | None = field(
default=None,
metadata={"help": "Path to the reward model used for the PPO training."},
)
reward_model_adapters: Optional[str] = field(
reward_model_adapters: str | None = field(
default=None,
metadata={"help": "Path to the adapters of the reward model."},
)
reward_model_quantization_bit: Optional[int] = field(
reward_model_quantization_bit: int | None = field(
default=None,
metadata={"help": "The number of bits to quantize the reward model."},
)
@@ -248,7 +248,7 @@ class RLHFArguments:
default="lora",
metadata={"help": "The type of the reward model in PPO training. Lora model only supports lora training."},
)
ld_alpha: Optional[float] = field(
ld_alpha: float | None = field(
default=None,
metadata={
"help": (
@@ -361,15 +361,15 @@ class BAdamArgument:
default="layer",
metadata={"help": "Whether to use layer-wise or ratio-wise BAdam optimizer."},
)
badam_start_block: Optional[int] = field(
badam_start_block: int | None = field(
default=None,
metadata={"help": "The starting block index for layer-wise BAdam."},
)
badam_switch_mode: Optional[Literal["ascending", "descending", "random", "fixed"]] = field(
badam_switch_mode: Literal["ascending", "descending", "random", "fixed"] | None = field(
default="ascending",
metadata={"help": "the strategy of picking block to update for layer-wise BAdam."},
)
badam_switch_interval: Optional[int] = field(
badam_switch_interval: int | None = field(
default=50,
metadata={
"help": "Number of steps to update the block for layer-wise BAdam. Use -1 to disable the block update."
@@ -406,15 +406,15 @@ class SwanLabArguments:
default=False,
metadata={"help": "Whether or not to use the SwanLab (an experiment tracking and visualization tool)."},
)
swanlab_project: Optional[str] = field(
swanlab_project: str | None = field(
default="llamafactory",
metadata={"help": "The project name in SwanLab."},
)
swanlab_workspace: Optional[str] = field(
swanlab_workspace: str | None = field(
default=None,
metadata={"help": "The workspace name in SwanLab."},
)
swanlab_run_name: Optional[str] = field(
swanlab_run_name: str | None = field(
default=None,
metadata={"help": "The experiment name in SwanLab."},
)
@@ -422,19 +422,19 @@ class SwanLabArguments:
default="cloud",
metadata={"help": "The mode of SwanLab."},
)
swanlab_api_key: Optional[str] = field(
swanlab_api_key: str | None = field(
default=None,
metadata={"help": "The API key for SwanLab."},
)
swanlab_logdir: Optional[str] = field(
swanlab_logdir: str | None = field(
default=None,
metadata={"help": "The log directory for SwanLab."},
)
swanlab_lark_webhook_url: Optional[str] = field(
swanlab_lark_webhook_url: str | None = field(
default=None,
metadata={"help": "The Lark(飞书) webhook URL for SwanLab."},
)
swanlab_lark_secret: Optional[str] = field(
swanlab_lark_secret: str | None = field(
default=None,
metadata={"help": "The Lark(飞书) secret for SwanLab."},
)
@@ -510,7 +510,7 @@ class FinetuningArguments(
default=False,
metadata={"help": "Whether or not to disable the shuffling of the training set."},
)
early_stopping_steps: Optional[int] = field(
early_stopping_steps: int | None = field(
default=None,
metadata={"help": "Number of steps to stop training if the `metric_for_best_model` does not improve."},
)
@@ -530,11 +530,11 @@ class FinetuningArguments(
return arg
self.freeze_trainable_modules: list[str] = split_arg(self.freeze_trainable_modules)
self.freeze_extra_modules: Optional[list[str]] = split_arg(self.freeze_extra_modules)
self.freeze_extra_modules: list[str] | None = split_arg(self.freeze_extra_modules)
self.lora_alpha: int = self.lora_alpha or self.lora_rank * 2
self.lora_target: list[str] = split_arg(self.lora_target)
self.oft_target: list[str] = split_arg(self.oft_target)
self.additional_target: Optional[list[str]] = split_arg(self.additional_target)
self.additional_target: list[str] | None = split_arg(self.additional_target)
self.galore_target: list[str] = split_arg(self.galore_target)
self.apollo_target: list[str] = split_arg(self.apollo_target)
self.use_ref_model = self.stage == "dpo" and self.pref_loss not in ["orpo", "simpo"]

59
src/llamafactory/hparams/model_args.py
View File

@@ -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."},

22
src/llamafactory/hparams/parser.py
View File

@@ -18,7 +18,7 @@
import os
import sys
from pathlib import Path
from typing import Any, Optional, Union
from typing import Any, Optional
import torch
import transformers
@@ -65,7 +65,7 @@ else:
_TRAIN_MCA_CLS = tuple()
def read_args(args: Optional[Union[dict[str, Any], list[str]]] = None) -> Union[dict[str, Any], list[str]]:
def read_args(args: dict[str, Any] | list[str] | None = None) -> dict[str, Any] | list[str]:
r"""Get arguments from the command line or a config file."""
if args is not None:
return args
@@ -83,7 +83,7 @@ def read_args(args: Optional[Union[dict[str, Any], list[str]]] = None) -> Union[
def _parse_args(
parser: "HfArgumentParser", args: Optional[Union[dict[str, Any], list[str]]] = None, allow_extra_keys: bool = False
parser: "HfArgumentParser", args: dict[str, Any] | list[str] | None = None, allow_extra_keys: bool = False
) -> tuple[Any]:
args = read_args(args)
if isinstance(args, dict):
@@ -205,13 +205,13 @@ def _check_extra_dependencies(
check_version("rouge_chinese", mandatory=True)
def _parse_train_args(args: Optional[Union[dict[str, Any], list[str]]] = None) -> _TRAIN_CLS:
def _parse_train_args(args: dict[str, Any] | list[str] | None = None) -> _TRAIN_CLS:
parser = HfArgumentParser(_TRAIN_ARGS)
allow_extra_keys = is_env_enabled("ALLOW_EXTRA_ARGS")
return _parse_args(parser, args, allow_extra_keys=allow_extra_keys)
def _parse_train_mca_args(args: Optional[Union[dict[str, Any], list[str]]] = None) -> _TRAIN_MCA_CLS:
def _parse_train_mca_args(args: dict[str, Any] | list[str] | None = None) -> _TRAIN_MCA_CLS:
parser = HfArgumentParser(_TRAIN_MCA_ARGS)
allow_extra_keys = is_env_enabled("ALLOW_EXTRA_ARGS")
model_args, data_args, training_args, finetuning_args, generating_args = _parse_args(
@@ -232,25 +232,25 @@ def _configure_mca_training_args(training_args, data_args, finetuning_args) -> N
finetuning_args.use_mca = True
def _parse_infer_args(args: Optional[Union[dict[str, Any], list[str]]] = None) -> _INFER_CLS:
def _parse_infer_args(args: dict[str, Any] | list[str] | None = None) -> _INFER_CLS:
parser = HfArgumentParser(_INFER_ARGS)
allow_extra_keys = is_env_enabled("ALLOW_EXTRA_ARGS")
return _parse_args(parser, args, allow_extra_keys=allow_extra_keys)
def _parse_eval_args(args: Optional[Union[dict[str, Any], list[str]]] = None) -> _EVAL_CLS:
def _parse_eval_args(args: dict[str, Any] | list[str] | None = None) -> _EVAL_CLS:
parser = HfArgumentParser(_EVAL_ARGS)
allow_extra_keys = is_env_enabled("ALLOW_EXTRA_ARGS")
return _parse_args(parser, args, allow_extra_keys=allow_extra_keys)
def get_ray_args(args: Optional[Union[dict[str, Any], list[str]]] = None) -> RayArguments:
def get_ray_args(args: dict[str, Any] | list[str] | None = None) -> RayArguments:
parser = HfArgumentParser(RayArguments)
(ray_args,) = _parse_args(parser, args, allow_extra_keys=True)
return ray_args
def get_train_args(args: Optional[Union[dict[str, Any], list[str]]] = None) -> _TRAIN_CLS:
def get_train_args(args: dict[str, Any] | list[str] | None = None) -> _TRAIN_CLS:
if is_env_enabled("USE_MCA"):
model_args, data_args, training_args, finetuning_args, generating_args = _parse_train_mca_args(args)
else:
@@ -473,7 +473,7 @@ def get_train_args(args: Optional[Union[dict[str, Any], list[str]]] = None) -> _
return model_args, data_args, training_args, finetuning_args, generating_args
def get_infer_args(args: Optional[Union[dict[str, Any], list[str]]] = None) -> _INFER_CLS:
def get_infer_args(args: dict[str, Any] | list[str] | None = None) -> _INFER_CLS:
model_args, data_args, finetuning_args, generating_args = _parse_infer_args(args)
# Setup logging
@@ -508,7 +508,7 @@ def get_infer_args(args: Optional[Union[dict[str, Any], list[str]]] = None) -> _
return model_args, data_args, finetuning_args, generating_args
def get_eval_args(args: Optional[Union[dict[str, Any], list[str]]] = None) -> _EVAL_CLS:
def get_eval_args(args: dict[str, Any] | list[str] | None = None) -> _EVAL_CLS:
model_args, data_args, eval_args, finetuning_args = _parse_eval_args(args)
# Setup logging

10
src/llamafactory/hparams/training_args.py
View File

@@ -14,7 +14,7 @@
import json
from dataclasses import dataclass, field
from typing import Literal, Optional, Union
from typing import Literal
from transformers import Seq2SeqTrainingArguments
from transformers.training_args import _convert_str_dict
@@ -40,7 +40,7 @@ else:
class RayArguments:
r"""Arguments pertaining to the Ray training."""
ray_run_name: Optional[str] = field(
ray_run_name: str | None = field(
default=None,
metadata={"help": "The training results will be saved at `<ray_storage_path>/ray_run_name`."},
)
@@ -48,7 +48,7 @@ class RayArguments:
default="./saves",
metadata={"help": "The storage path to save training results to"},
)
ray_storage_filesystem: Optional[Literal["s3", "gs", "gcs"]] = field(
ray_storage_filesystem: Literal["s3", "gs", "gcs"] | None = field(
default=None,
metadata={"help": "The storage filesystem to use. If None specified, local filesystem will be used."},
)
@@ -56,7 +56,7 @@ class RayArguments:
default=1,
metadata={"help": "The number of workers for Ray training. Default is 1 worker."},
)
resources_per_worker: Union[dict, str] = field(
resources_per_worker: dict | str = field(
default_factory=lambda: {"GPU": 1},
metadata={"help": "The resources per worker for Ray training. Default is to use 1 GPU per worker."},
)
@@ -64,7 +64,7 @@ class RayArguments:
default="PACK",
metadata={"help": "The placement strategy for Ray training. Default is PACK."},
)
ray_init_kwargs: Optional[Union[dict, str]] = field(
ray_init_kwargs: dict | str | None = field(
default=None,
metadata={"help": "The arguments to pass to ray.init for Ray training. Default is None."},
)

3
src/llamafactory/model/model_utils/checkpointing.py
View File

@@ -20,9 +20,10 @@
import inspect
import os
from collections.abc import Callable
from functools import WRAPPER_ASSIGNMENTS, partial, wraps
from types import MethodType
from typing import TYPE_CHECKING, Any, Callable, Optional, Union
from typing import TYPE_CHECKING, Any, Optional, Union
import torch

6
src/llamafactory/train/rm/metric.py
View File

@@ -13,7 +13,7 @@
# limitations under the License.
from dataclasses import dataclass
from typing import TYPE_CHECKING, Optional
from typing import TYPE_CHECKING
import numpy as np
@@ -28,7 +28,7 @@ if TYPE_CHECKING:
class ComputeAccuracy:
r"""Compute reward accuracy and support `batch_eval_metrics`."""
def _dump(self) -> Optional[dict[str, float]]:
def _dump(self) -> dict[str, float] | None:
result = None
if hasattr(self, "score_dict"):
result = {k: float(np.mean(v)) for k, v in self.score_dict.items()}
@@ -39,7 +39,7 @@ class ComputeAccuracy:
def __post_init__(self):
self._dump()
def __call__(self, eval_preds: "EvalPrediction", compute_result: bool = True) -> Optional[dict[str, float]]:
def __call__(self, eval_preds: "EvalPrediction", compute_result: bool = True) -> dict[str, float] | None:
chosen_scores, rejected_scores = numpify(eval_preds.predictions[0]), numpify(eval_preds.predictions[1])
if not chosen_scores.shape:
self.score_dict["accuracy"].append(chosen_scores > rejected_scores)

4
src/llamafactory/train/trainer_utils.py
View File

@@ -19,9 +19,9 @@
import json
import os
from collections.abc import Mapping
from collections.abc import Callable, Mapping
from pathlib import Path
from typing import TYPE_CHECKING, Any, Callable, Optional, Union
from typing import TYPE_CHECKING, Any, Optional, Union
import torch
from transformers import Trainer

3
src/llamafactory/v1/accelerator/helper.py
View File

@@ -25,10 +25,11 @@ Including:
"""
import os
from collections.abc import Callable
from contextlib import contextmanager
from enum import Enum, unique
from functools import lru_cache, wraps
from typing import Callable, Optional
from typing import Optional
import numpy as np
import torch

20
src/llamafactory/v1/accelerator/interface.py
View File

@@ -53,9 +53,9 @@ class DistributedStrategy:
mp_replicate_size: int = 1
"""Model parallel replicate size, default to 1."""
mp_shard_size: Optional[int] = None
mp_shard_size: int | None = None
"""Model parallel shard size, default to world_size // mp_replicate_size."""
dp_size: Optional[int] = None
dp_size: int | None = None
"""Data parallel size, default to world_size // cp_size."""
cp_size: int = 1
"""Context parallel size, default to 1."""
@@ -115,7 +115,7 @@ class DistributedInterface:
return cls._instance
def __init__(self, config: Optional[DistributedConfig] = None) -> None:
def __init__(self, config: DistributedConfig | None = None) -> None:
if self._initialized:
return
@@ -165,7 +165,7 @@ class DistributedInterface:
f"model_device_mesh={self.model_device_mesh}, data_device_mesh={self.data_device_mesh}"
)
def get_device_mesh(self, dim: Optional[Dim] = None) -> Optional[DeviceMesh]:
def get_device_mesh(self, dim: Dim | None = None) -> DeviceMesh | None:
"""Get device mesh for specified dimension."""
if dim is None:
raise ValueError("dim must be specified.")
@@ -176,14 +176,14 @@ class DistributedInterface:
else:
return self.model_device_mesh[dim.value]
def get_group(self, dim: Optional[Dim] = None) -> Optional[ProcessGroup]:
def get_group(self, dim: Dim | None = None) -> Optional[ProcessGroup]:
"""Get process group for specified dimension."""
if self.model_device_mesh is None or dim is None:
return None
else:
return self.get_device_mesh(dim).get_group()
def get_rank(self, dim: Optional[Dim] = None) -> int:
def get_rank(self, dim: Dim | None = None) -> int:
"""Get parallel rank for specified dimension."""
if self.model_device_mesh is None:
return 0
@@ -192,7 +192,7 @@ class DistributedInterface:
else:
return self.get_device_mesh(dim).get_local_rank()
def get_world_size(self, dim: Optional[Dim] = None) -> int:
def get_world_size(self, dim: Dim | None = None) -> int:
"""Get parallel size for specified dimension."""
if self.model_device_mesh is None:
return 1
@@ -209,7 +209,7 @@ class DistributedInterface:
"""Get parallel local world size."""
return self._local_world_size
def all_gather(self, data: Tensor, dim: Optional[Dim] = Dim.DP) -> Tensor:
def all_gather(self, data: Tensor, dim: Dim | None = Dim.DP) -> Tensor:
"""Gather tensor across specified parallel group."""
if self.model_device_mesh is not None:
return helper.operate_tensorlike(helper.all_gather, data, group=self.get_group(dim))
@@ -217,7 +217,7 @@ class DistributedInterface:
return data
def all_reduce(
self, data: TensorLike, op: helper.ReduceOp = helper.ReduceOp.MEAN, dim: Optional[Dim] = Dim.DP
self, data: TensorLike, op: helper.ReduceOp = helper.ReduceOp.MEAN, dim: Dim | None = Dim.DP
) -> TensorLike:
"""Reduce tensor across specified parallel group."""
if self.model_device_mesh is not None:
@@ -225,7 +225,7 @@ class DistributedInterface:
else:
return data
def broadcast(self, data: TensorLike, src: int = 0, dim: Optional[Dim] = Dim.DP) -> TensorLike:
def broadcast(self, data: TensorLike, src: int = 0, dim: Dim | None = Dim.DP) -> TensorLike:
"""Broadcast tensor across specified parallel group."""
if self.model_device_mesh is not None:
return helper.operate_tensorlike(helper.broadcast, data, src=src, group=self.get_group(dim))

4
src/llamafactory/v1/config/arg_parser.py
View File

@@ -15,7 +15,7 @@
import json
import sys
from pathlib import Path
from typing import Any, Optional, Union
from typing import Any
from omegaconf import OmegaConf
from transformers import HfArgumentParser
@@ -27,7 +27,7 @@ from .sample_args import SampleArguments
from .training_args import TrainingArguments
InputArgument = Optional[Union[dict[str, Any], list[str]]]
InputArgument = dict[str, Any] | list[str] | None
def validate_args(

5
src/llamafactory/v1/config/arg_utils.py
View File

@@ -18,7 +18,6 @@
import json
from enum import Enum, unique
from typing import Optional, Union
class PluginConfig(dict):
@@ -33,7 +32,7 @@ class PluginConfig(dict):
return self["name"]
PluginArgument = Optional[Union[PluginConfig, dict, str]]
PluginArgument = PluginConfig | dict | str | None
@unique
@@ -74,7 +73,7 @@ def _convert_str_dict(data: dict) -> dict:
return data
def get_plugin_config(config: PluginArgument) -> Optional[PluginConfig]:
def get_plugin_config(config: PluginArgument) -> PluginConfig | None:
"""Get the plugin configuration from the argument value.
Args:

3
src/llamafactory/v1/config/data_args.py
View File

@@ -14,12 +14,11 @@
from dataclasses import dataclass, field
from typing import Optional
@dataclass
class DataArguments:
dataset: Optional[str] = field(
dataset: str | None = field(
default=None,
metadata={"help": "Path to the dataset."},
)

7
src/llamafactory/v1/config/model_args.py
View File

@@ -14,7 +14,6 @@
from dataclasses import dataclass, field
from typing import Optional
from .arg_utils import ModelClass, PluginConfig, get_plugin_config
@@ -36,15 +35,15 @@ class ModelArguments:
default=ModelClass.LLM,
metadata={"help": "Model class from Hugging Face."},
)
peft_config: Optional[PluginConfig] = field(
peft_config: PluginConfig | None = field(
default=None,
metadata={"help": "PEFT configuration for the model."},
)
kernel_config: Optional[PluginConfig] = field(
kernel_config: PluginConfig | None = field(
default=None,
metadata={"help": "Kernel configuration for the model."},
)
quant_config: Optional[PluginConfig] = field(
quant_config: PluginConfig | None = field(
default=None,
metadata={"help": "Quantization configuration for the model."},
)

3
src/llamafactory/v1/config/training_args.py
View File

@@ -14,7 +14,6 @@
import os
from dataclasses import dataclass, field
from typing import Optional
from uuid import uuid4
from .arg_utils import PluginConfig, get_plugin_config
@@ -42,7 +41,7 @@ class TrainingArguments:
default=False,
metadata={"help": "Use bf16 for training."},
)
dist_config: Optional[PluginConfig] = field(
dist_config: PluginConfig | None = field(
default=None,
metadata={"help": "Distribution configuration for training."},
)

4
src/llamafactory/v1/core/data_engine.py
View File

@@ -27,7 +27,7 @@ Get Data Sample:
import os
from collections.abc import Iterable
from typing import Any, Union
from typing import Any
from huggingface_hub import hf_hub_download
from omegaconf import OmegaConf
@@ -134,7 +134,7 @@ class DataEngine(Dataset):
else:
return len(self.data_index)
def __getitem__(self, index: Union[int, Any]) -> Union[Sample, list[Sample]]:
def __getitem__(self, index: int | Any) -> Sample | list[Sample]:
"""Get dataset item.
Args:

4
src/llamafactory/v1/plugins/data_plugins/converter.py
View File

@@ -13,9 +13,7 @@
# limitations under the License.
from typing import Any, Literal, TypedDict
from typing_extensions import NotRequired
from typing import Any, Literal, NotRequired, TypedDict
from ...utils import logging
from ...utils.plugin import BasePlugin

8
src/llamafactory/v1/plugins/data_plugins/loader.py
View File

@@ -15,7 +15,7 @@
import os
import random
from typing import Any, Literal, Optional, Union
from typing import Any, Literal
from datasets import load_dataset
@@ -70,7 +70,7 @@ class DataIndexPlugin(BasePlugin):
"""Plugin for adjusting dataset index."""
def adjust_data_index(
self, data_index: list[tuple[str, int]], size: Optional[int], weight: Optional[float]
self, data_index: list[tuple[str, int]], size: int | None, weight: float | None
) -> list[tuple[str, int]]:
"""Adjust dataset index by size and weight.
@@ -95,8 +95,8 @@ class DataSelectorPlugin(BasePlugin):
"""Plugin for selecting dataset samples."""
def select(
self, data_index: list[tuple[str, int]], index: Union[slice, list[int], Any]
) -> Union[tuple[str, int], list[tuple[str, int]]]:
self, data_index: list[tuple[str, int]], index: slice | list[int] | Any
) -> tuple[str, int] | list[tuple[str, int]]:
"""Select dataset samples.
Args:

5
src/llamafactory/v1/plugins/data_plugins/template.py
View File

@@ -14,7 +14,6 @@
from dataclasses import dataclass
from typing import Union
@dataclass
@@ -32,7 +31,7 @@ class QwenTemplate:
message_template: str = "<|im_start|>{role}\n{content}<|im_end|>\n" # FIXME if role: tool
thinking_template: str = "<think>\n{content}\n</think>\n\n"
def _extract_content(self, content_data: Union[str, list[dict[str, str]]]) -> str:
def _extract_content(self, content_data: str | list[dict[str, str]]) -> str:
if isinstance(content_data, str):
return content_data.strip()
@@ -47,7 +46,7 @@ class QwenTemplate:
return ""
def render_message(self, message: dict[str, Union[str, list[dict[str, str]]]]) -> str:
def render_message(self, message: dict[str, str | list[dict[str, str]]]) -> str:
role = message["role"]
content = self._extract_content(message.get("content", ""))

15
src/llamafactory/v1/plugins/model_plugins/kernels/registry.py
View File

@@ -13,7 +13,8 @@
# limitations under the License.
from abc import ABC, ABCMeta, abstractmethod
from typing import Any, Callable, Optional, Union
from collections.abc import Callable
from typing import Any, Optional
from ....accelerator.helper import DeviceType, get_current_accelerator
from ....utils.types import HFModel
@@ -38,7 +39,7 @@ class KernelRegistry:
self._initialized = True
def register(
self, kernel_type: KernelType, device_type: DeviceType, kernel_impl: Optional[Callable[..., Any]]
self, kernel_type: KernelType, device_type: DeviceType, kernel_impl: Callable[..., Any] | None
) -> None:
"""Register a kernel implementation.
@@ -56,7 +57,7 @@ class KernelRegistry:
self._registry[kernel_type][device_type] = kernel_impl
print(f"Registered kernel {kernel_type.name} for device {device_type.name}.")
def get_kernel(self, kernel_type: KernelType, device_type: DeviceType) -> Optional[Callable[..., Any]]:
def get_kernel(self, kernel_type: KernelType, device_type: DeviceType) -> Callable[..., Any] | None:
return self._registry.get(kernel_type, {}).get(device_type)
@@ -105,9 +106,9 @@ class MetaKernel(ABC, metaclass=AutoRegisterKernelMeta):
auto_register: Set to False to disable automatic registration (default: True).
"""
type: Optional[KernelType] = None
device: Optional[DeviceType] = None
kernel: Optional[Callable] = None
type: KernelType | None = None
device: DeviceType | None = None
kernel: Callable | None = None
@classmethod
@abstractmethod
@@ -228,7 +229,7 @@ def discover_kernels(model: HFModel = None) -> list[type[MetaKernel]]:
return discovered_kernels
def apply_kernel(model: HFModel, kernel: Union[type[MetaKernel], Any], /, **kwargs) -> "HFModel":
def apply_kernel(model: HFModel, kernel: type[MetaKernel] | Any, /, **kwargs) -> "HFModel":
"""Call the MetaKernel's `apply` to perform the replacement.
Corresponding replacement logic is maintained inside each kernel; the only

4
src/llamafactory/v1/plugins/model_plugins/peft.py
View File

@@ -12,7 +12,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Literal, Optional, TypedDict
from typing import Literal, TypedDict
from peft import LoraConfig, PeftModel, get_peft_model
@@ -36,7 +36,7 @@ class FreezeConfigDict(TypedDict, total=False):
"""Plugin name."""
freeze_trainable_layers: int
"""Freeze trainable layers."""
freeze_trainable_modules: Optional[list[str]]
freeze_trainable_modules: list[str] | None
"""Freeze trainable modules."""

13
src/llamafactory/v1/utils/dtype.py
View File

@@ -16,7 +16,6 @@
# limitations under the License.
from contextlib import contextmanager
from typing import Union
import torch
from transformers.utils import is_torch_bf16_available_on_device, is_torch_fp16_available_on_device
@@ -38,7 +37,7 @@ class DtypeInterface:
_is_fp32_available = True
@staticmethod
def is_available(precision: Union[str, torch.dtype]) -> bool:
def is_available(precision: str | torch.dtype) -> bool:
if precision in DtypeRegistry.HALF_LIST:
return DtypeInterface._is_fp16_available
elif precision in DtypeRegistry.FLOAT_LIST:
@@ -49,19 +48,19 @@ class DtypeInterface:
raise RuntimeError(f"Unexpected precision: {precision}")
@staticmethod
def is_fp16(precision: Union[str, torch.dtype]) -> bool:
def is_fp16(precision: str | torch.dtype) -> bool:
return precision in DtypeRegistry.HALF_LIST
@staticmethod
def is_fp32(precision: Union[str, torch.dtype]) -> bool:
def is_fp32(precision: str | torch.dtype) -> bool:
return precision in DtypeRegistry.FLOAT_LIST
@staticmethod
def is_bf16(precision: Union[str, torch.dtype]) -> bool:
def is_bf16(precision: str | torch.dtype) -> bool:
return precision in DtypeRegistry.BFLOAT_LIST
@staticmethod
def to_dtype(precision: Union[str, torch.dtype]) -> torch.dtype:
def to_dtype(precision: str | torch.dtype) -> torch.dtype:
if precision in DtypeRegistry.HALF_LIST:
return torch.float16
elif precision in DtypeRegistry.FLOAT_LIST:
@@ -83,7 +82,7 @@ class DtypeInterface:
raise RuntimeError(f"Unexpected precision: {precision}")
@contextmanager
def set_dtype(self, precision: Union[str, torch.dtype]):
def set_dtype(self, precision: str | torch.dtype):
original_dtype = torch.get_default_dtype()
torch.set_default_dtype(self.to_dtype(precision))
try:

2
src/llamafactory/v1/utils/logging.py
View File

@@ -81,7 +81,7 @@ def _configure_library_root_logger() -> None:
library_root_logger.propagate = False
def get_logger(name: Optional[str] = None) -> "_Logger":
def get_logger(name: str | None = None) -> "_Logger":
"""Return a logger with the specified name. It it not supposed to be accessed externally."""
if name is None:
name = _get_library_name()

4
src/llamafactory/v1/utils/plugin.py
View File

@@ -13,7 +13,7 @@
# limitations under the License.
from typing import Callable, Optional
from collections.abc import Callable
from . import logging
@@ -29,7 +29,7 @@ class BasePlugin:
_registry: dict[str, Callable] = {}
def __init__(self, name: Optional[str] = None):
def __init__(self, name: str | None = None):
"""Initialize the plugin with a name.
Args:

4
src/llamafactory/v1/utils/types.py
View File

@@ -12,9 +12,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING, Literal, TypedDict, Union
from typing_extensions import NotRequired
from typing import TYPE_CHECKING, Literal, NotRequired, TypedDict, Union
if TYPE_CHECKING:

10
src/llamafactory/webui/chatter.py
View File

@@ -16,7 +16,7 @@ import json
import os
from collections.abc import Generator
from contextlib import contextmanager
from typing import TYPE_CHECKING, Any, Optional
from typing import TYPE_CHECKING, Any
from transformers.utils import is_torch_npu_available
@@ -81,7 +81,7 @@ class WebChatModel(ChatModel):
def __init__(self, manager: "Manager", demo_mode: bool = False, lazy_init: bool = True) -> None:
self.manager = manager
self.demo_mode = demo_mode
self.engine: Optional[BaseEngine] = None
self.engine: BaseEngine | None = None
if not lazy_init: # read arguments from command line
super().__init__()
@@ -197,9 +197,9 @@ class WebChatModel(ChatModel):
lang: str,
system: str,
tools: str,
image: Optional[Any],
video: Optional[Any],
audio: Optional[Any],
image: Any | None,
video: Any | None,
audio: Any | None,
max_new_tokens: int,
top_p: float,
temperature: float,

8
src/llamafactory/webui/common.py
View File

@@ -17,7 +17,7 @@ import os
import signal
from collections import defaultdict
from datetime import datetime
from typing import Any, Optional, Union
from typing import Any
from psutil import Process
from yaml import safe_dump, safe_load
@@ -71,7 +71,7 @@ def _get_config_path() -> os.PathLike:
return os.path.join(DEFAULT_CACHE_DIR, USER_CONFIG)
def load_config() -> dict[str, Union[str, dict[str, Any]]]:
def load_config() -> dict[str, str | dict[str, Any]]:
r"""Load user config if exists."""
try:
with open(_get_config_path(), encoding="utf-8") as f:
@@ -81,7 +81,7 @@ def load_config() -> dict[str, Union[str, dict[str, Any]]]:
def save_config(
lang: str, hub_name: Optional[str] = None, model_name: Optional[str] = None, model_path: Optional[str] = None
lang: str, hub_name: str | None = None, model_name: str | None = None, model_path: str | None = None
) -> None:
r"""Save user config."""
os.makedirs(DEFAULT_CACHE_DIR, exist_ok=True)
@@ -151,7 +151,7 @@ def load_dataset_info(dataset_dir: str) -> dict[str, dict[str, Any]]:
return {}
def load_args(config_path: str) -> Optional[dict[str, Any]]:
def load_args(config_path: str) -> dict[str, Any] | None:
r"""Load the training configuration from config path."""
try:
with open(config_path, encoding="utf-8") as f:

6
src/llamafactory/webui/components/export.py
View File

@@ -14,7 +14,7 @@
import json
from collections.abc import Generator
from typing import TYPE_CHECKING, Union
from typing import TYPE_CHECKING
from ...extras.constants import PEFT_METHODS
from ...extras.misc import torch_gc
@@ -37,7 +37,7 @@ if TYPE_CHECKING:
GPTQ_BITS = ["8", "4", "3", "2"]
def can_quantize(checkpoint_path: Union[str, list[str]]) -> "gr.Dropdown":
def can_quantize(checkpoint_path: str | list[str]) -> "gr.Dropdown":
if isinstance(checkpoint_path, list) and len(checkpoint_path) != 0:
return gr.Dropdown(value="none", interactive=False)
else:
@@ -49,7 +49,7 @@ def save_model(
model_name: str,
model_path: str,
finetuning_type: str,
checkpoint_path: Union[str, list[str]],
checkpoint_path: str | list[str],
template: str,
export_size: int,
export_quantization_bit: str,

4
src/llamafactory/webui/control.py
View File

@@ -14,7 +14,7 @@
import json
import os
from typing import Any, Optional
from typing import Any
from transformers.trainer_utils import get_last_checkpoint
@@ -206,7 +206,7 @@ def list_datasets(dataset_dir: str = None, training_stage: str = list(TRAINING_S
return gr.Dropdown(choices=datasets)
def list_output_dirs(model_name: Optional[str], finetuning_type: str, current_time: str) -> "gr.Dropdown":
def list_output_dirs(model_name: str | None, finetuning_type: str, current_time: str) -> "gr.Dropdown":
r"""List all the directories that can resume from.
Inputs: top.model_name, top.finetuning_type, train.current_time

4
src/llamafactory/webui/runner.py
View File

@@ -17,7 +17,7 @@ import os
from collections.abc import Generator
from copy import deepcopy
from subprocess import PIPE, Popen, TimeoutExpired
from typing import TYPE_CHECKING, Any, Optional
from typing import TYPE_CHECKING, Any
from transformers.utils import is_torch_npu_available
@@ -59,7 +59,7 @@ class Runner:
self.manager = manager
self.demo_mode = demo_mode
""" Resume """
self.trainer: Optional[Popen] = None
self.trainer: Popen | None = None
self.do_train = True
self.running_data: dict[Component, Any] = None
""" State """

3
tests/conftest.py
View File

@@ -18,7 +18,6 @@ Contains shared fixtures, pytest configuration, and custom markers.
"""
import os
from typing import Optional
import pytest
from pytest import Config, FixtureRequest, Item, MonkeyPatch
@@ -71,7 +70,7 @@ def _handle_slow_tests(items: list[Item]):
item.add_marker(skip_slow)
def _get_visible_devices_env() -> Optional[str]:
def _get_visible_devices_env() -> str | None:
"""Return device visibility env var name."""
if CURRENT_DEVICE == "cuda":
return "CUDA_VISIBLE_DEVICES"

3
tests_v1/conftest.py
View File

@@ -18,7 +18,6 @@ Contains shared fixtures, pytest configuration, and custom markers.
"""
import os
from typing import Optional
import pytest
from pytest import Config, FixtureRequest, Item, MonkeyPatch
@@ -71,7 +70,7 @@ def _handle_slow_tests(items: list[Item]):
item.add_marker(skip_slow)
def _get_visible_devices_env() -> Optional[str]:
def _get_visible_devices_env() -> str | None:
"""Return device visibility env var name."""
if CURRENT_DEVICE == "cuda":
return "CUDA_VISIBLE_DEVICES"