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[misc] update format (#7277)

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hoshi-hiyouga 2025-03-13 02:53:08 +08:00 committed by GitHub
parent 4b9d8da5a4
commit 650a9a9057
62 changed files with 384 additions and 288 deletions
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2
.github/ISSUE_TEMPLATE/1-bug-report.yml vendored
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@ -47,8 +47,6 @@ body:
description: |
Please provide entry arguments, error messages and stack traces that reproduces the problem.
请提供入口参数，错误日志以及异常堆栈以便于我们复现问题。
Remember to wrap your log messages with \`\`\`.
请务必使用 Markdown 标签 \`\`\` 来包裹您的日志信息。
value: |
```text

4
.github/workflows/tests.yml vendored
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@ -58,6 +58,10 @@ jobs:
run: |
make style && make quality
- name: Check license
run: |
make license
- name: Test with pytest
run: |
make test

5
Makefile
View File

@ -1,4 +1,4 @@
.PHONY: build commit quality style test
.PHONY: build commit license quality style test
check_dirs := scripts src tests setup.py
@ -9,6 +9,9 @@ commit:
pre-commit install
pre-commit run --all-files
license:
python3 tests/check_license.py $(check_dirs)
quality:
ruff check $(check_dirs)
ruff format --check $(check_dirs)

15
data/belle_multiturn/belle_multiturn.py
View File

@ -1,3 +1,18 @@
# Copyright 2025 the LlamaFactory team.
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# 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 json
import os

15
data/hh_rlhf_en/hh_rlhf_en.py
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@ -1,3 +1,18 @@
# Copyright 2025 the LlamaFactory team.
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# 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 json
import os

15
data/ultra_chat/ultra_chat.py
View File

@ -1,3 +1,18 @@
# Copyright 2025 the LlamaFactory team.
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# 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 json
import os

1
evaluation/ceval/ceval.py
View File

@ -1,3 +1,4 @@
# Copyright 2025 the LlamaFactory team.
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");

1
evaluation/cmmlu/cmmlu.py
View File

@ -1,3 +1,4 @@
# Copyright 2025 the LlamaFactory team.
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");

1
evaluation/mmlu/mmlu.py
View File

@ -1,3 +1,4 @@
# Copyright 2025 the LlamaFactory team.
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");

3
scripts/api_example/test_toolcall.py
View File

@ -14,7 +14,6 @@
import json
import os
from collections.abc import Sequence
from openai import OpenAI
from transformers.utils.versions import require_version
@ -23,7 +22,7 @@ from transformers.utils.versions import require_version
require_version("openai>=1.5.0", "To fix: pip install openai>=1.5.0")
def calculate_gpa(grades: Sequence[str], hours: Sequence[int]) -> float:
def calculate_gpa(grades: list[str], hours: list[int]) -> float:
grade_to_score = {"A": 4, "B": 3, "C": 2}
total_score, total_hour = 0, 0
for grade, hour in zip(grades, hours):

3
scripts/stat_utils/cal_ppl.py
View File

@ -13,7 +13,6 @@
# limitations under the License.
import json
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any, Literal, Optional
@ -35,7 +34,7 @@ class PairwiseDataCollatorWithPadding(MultiModalDataCollatorForSeq2Seq):
train_on_prompt: bool = False
def __call__(self, features: Sequence[dict[str, Any]]) -> dict[str, torch.Tensor]:
def __call__(self, features: list[dict[str, Any]]) -> dict[str, torch.Tensor]:
r"""Pad batched data to the longest sequence in the batch."""
chosen_features = []
for feature in features:

18
src/llamafactory/chat/base_engine.py
View File

@ -13,7 +13,7 @@
# limitations under the License.
from abc import ABC, abstractmethod
from collections.abc import AsyncGenerator, Sequence
from collections.abc import AsyncGenerator
from dataclasses import dataclass
from typing import TYPE_CHECKING, Any, Literal, Optional, Union
@ -63,12 +63,12 @@ class BaseEngine(ABC):
@abstractmethod
async def chat(
self,
messages: Sequence[dict[str, str]],
messages: list[dict[str, str]],
system: Optional[str] = None,
tools: Optional[str] = None,
images: Optional[Sequence["ImageInput"]] = None,
videos: Optional[Sequence["VideoInput"]] = None,
audios: Optional[Sequence["AudioInput"]] = None,
images: Optional[list["ImageInput"]] = None,
videos: Optional[list["VideoInput"]] = None,
audios: Optional[list["AudioInput"]] = None,
**input_kwargs,
) -> list["Response"]:
r"""Get a list of responses of the chat model."""
@ -77,12 +77,12 @@ class BaseEngine(ABC):
@abstractmethod
async def stream_chat(
self,
messages: Sequence[dict[str, str]],
messages: list[dict[str, str]],
system: Optional[str] = None,
tools: Optional[str] = None,
images: Optional[Sequence["ImageInput"]] = None,
videos: Optional[Sequence["VideoInput"]] = None,
audios: Optional[Sequence["AudioInput"]] = None,
images: Optional[list["ImageInput"]] = None,
videos: Optional[list["VideoInput"]] = None,
audios: Optional[list["AudioInput"]] = None,
**input_kwargs,
) -> AsyncGenerator[str, None]:
r"""Get the response token-by-token of the chat model."""

36
src/llamafactory/chat/chat_model.py
View File

@ -1,4 +1,4 @@
# Copyright 2024 THUDM and the LlamaFactory team.
# Copyright 2025 THUDM and the LlamaFactory team.
#
# This code is inspired by the THUDM's ChatGLM implementation.
# https://github.com/THUDM/ChatGLM-6B/blob/main/cli_demo.py
@ -17,7 +17,7 @@
import asyncio
import os
from collections.abc import AsyncGenerator, Generator, Sequence
from collections.abc import AsyncGenerator, Generator
from threading import Thread
from typing import TYPE_CHECKING, Any, Optional
@ -61,12 +61,12 @@ class ChatModel:
def chat(
self,
messages: Sequence[dict[str, str]],
messages: list[dict[str, str]],
system: Optional[str] = None,
tools: Optional[str] = None,
images: Optional[Sequence["ImageInput"]] = None,
videos: Optional[Sequence["VideoInput"]] = None,
audios: Optional[Sequence["AudioInput"]] = None,
images: Optional[list["ImageInput"]] = None,
videos: Optional[list["VideoInput"]] = None,
audios: Optional[list["AudioInput"]] = None,
**input_kwargs,
) -> list["Response"]:
r"""Get a list of responses of the chat model."""
@ -77,12 +77,12 @@ class ChatModel:
async def achat(
self,
messages: Sequence[dict[str, str]],
messages: list[dict[str, str]],
system: Optional[str] = None,
tools: Optional[str] = None,
images: Optional[Sequence["ImageInput"]] = None,
videos: Optional[Sequence["VideoInput"]] = None,
audios: Optional[Sequence["AudioInput"]] = None,
images: Optional[list["ImageInput"]] = None,
videos: Optional[list["VideoInput"]] = None,
audios: Optional[list["AudioInput"]] = None,
**input_kwargs,
) -> list["Response"]:
r"""Asynchronously get a list of responses of the chat model."""
@ -90,12 +90,12 @@ class ChatModel:
def stream_chat(
self,
messages: Sequence[dict[str, str]],
messages: list[dict[str, str]],
system: Optional[str] = None,
tools: Optional[str] = None,
images: Optional[Sequence["ImageInput"]] = None,
videos: Optional[Sequence["VideoInput"]] = None,
audios: Optional[Sequence["AudioInput"]] = None,
images: Optional[list["ImageInput"]] = None,
videos: Optional[list["VideoInput"]] = None,
audios: Optional[list["AudioInput"]] = None,
**input_kwargs,
) -> Generator[str, None, None]:
r"""Get the response token-by-token of the chat model."""
@ -109,12 +109,12 @@ class ChatModel:
async def astream_chat(
self,
messages: Sequence[dict[str, str]],
messages: list[dict[str, str]],
system: Optional[str] = None,
tools: Optional[str] = None,
images: Optional[Sequence["ImageInput"]] = None,
videos: Optional[Sequence["VideoInput"]] = None,
audios: Optional[Sequence["AudioInput"]] = None,
images: Optional[list["ImageInput"]] = None,
videos: Optional[list["VideoInput"]] = None,
audios: Optional[list["AudioInput"]] = None,
**input_kwargs,
) -> AsyncGenerator[str, None]:
r"""Asynchronously get the response token-by-token of the chat model."""

42
src/llamafactory/chat/hf_engine.py
View File

@ -15,7 +15,7 @@
import asyncio
import concurrent.futures
import os
from collections.abc import AsyncGenerator, Sequence
from collections.abc import AsyncGenerator
from threading import Thread
from typing import TYPE_CHECKING, Any, Callable, Optional, Union
@ -78,12 +78,12 @@ class HuggingfaceEngine(BaseEngine):
processor: Optional["ProcessorMixin"],
template: "Template",
generating_args: dict[str, Any],
messages: Sequence[dict[str, str]],
messages: list[dict[str, str]],
system: Optional[str] = None,
tools: Optional[str] = None,
images: Optional[Sequence["ImageInput"]] = None,
videos: Optional[Sequence["VideoInput"]] = None,
audios: Optional[Sequence["AudioInput"]] = None,
images: Optional[list["ImageInput"]] = None,
videos: Optional[list["VideoInput"]] = None,
audios: Optional[list["AudioInput"]] = None,
input_kwargs: Optional[dict[str, Any]] = {},
) -> tuple[dict[str, Any], int]:
mm_input_dict = {"images": [], "videos": [], "audios": [], "imglens": [0], "vidlens": [0], "audlens": [0]}
@ -219,12 +219,12 @@ class HuggingfaceEngine(BaseEngine):
processor: Optional["ProcessorMixin"],
template: "Template",
generating_args: dict[str, Any],
messages: Sequence[dict[str, str]],
messages: list[dict[str, str]],
system: Optional[str] = None,
tools: Optional[str] = None,
images: Optional[Sequence["ImageInput"]] = None,
videos: Optional[Sequence["VideoInput"]] = None,
audios: Optional[Sequence["AudioInput"]] = None,
images: Optional[list["ImageInput"]] = None,
videos: Optional[list["VideoInput"]] = None,
audios: Optional[list["AudioInput"]] = None,
input_kwargs: Optional[dict[str, Any]] = {},
) -> list["Response"]:
gen_kwargs, prompt_length = HuggingfaceEngine._process_args(
@ -274,12 +274,12 @@ class HuggingfaceEngine(BaseEngine):
processor: Optional["ProcessorMixin"],
template: "Template",
generating_args: dict[str, Any],
messages: Sequence[dict[str, str]],
messages: list[dict[str, str]],
system: Optional[str] = None,
tools: Optional[str] = None,
images: Optional[Sequence["ImageInput"]] = None,
videos: Optional[Sequence["VideoInput"]] = None,
audios: Optional[Sequence["AudioInput"]] = None,
images: Optional[list["ImageInput"]] = None,
videos: Optional[list["VideoInput"]] = None,
audios: Optional[list["AudioInput"]] = None,
input_kwargs: Optional[dict[str, Any]] = {},
) -> Callable[[], str]:
gen_kwargs, _ = HuggingfaceEngine._process_args(
@ -338,12 +338,12 @@ class HuggingfaceEngine(BaseEngine):
@override
async def chat(
self,
messages: Sequence[dict[str, str]],
messages: list[dict[str, str]],
system: Optional[str] = None,
tools: Optional[str] = None,
images: Optional[Sequence["ImageInput"]] = None,
videos: Optional[Sequence["VideoInput"]] = None,
audios: Optional[Sequence["AudioInput"]] = None,
images: Optional[list["ImageInput"]] = None,
videos: Optional[list["VideoInput"]] = None,
audios: Optional[list["AudioInput"]] = None,
**input_kwargs,
) -> list["Response"]:
if not self.can_generate:
@ -371,12 +371,12 @@ class HuggingfaceEngine(BaseEngine):
@override
async def stream_chat(
self,
messages: Sequence[dict[str, str]],
messages: list[dict[str, str]],
system: Optional[str] = None,
tools: Optional[str] = None,
images: Optional[Sequence["ImageInput"]] = None,
videos: Optional[Sequence["VideoInput"]] = None,
audios: Optional[Sequence["AudioInput"]] = None,
images: Optional[list["ImageInput"]] = None,
videos: Optional[list["VideoInput"]] = None,
audios: Optional[list["AudioInput"]] = None,
**input_kwargs,
) -> AsyncGenerator[str, None]:
if not self.can_generate:

26
src/llamafactory/chat/vllm_engine.py
View File

@ -13,7 +13,7 @@
# limitations under the License.
import uuid
from collections.abc import AsyncGenerator, AsyncIterator, Sequence
from collections.abc import AsyncGenerator, AsyncIterator
from typing import TYPE_CHECKING, Any, Optional, Union
from typing_extensions import override
@ -102,12 +102,12 @@ class VllmEngine(BaseEngine):
async def _generate(
self,
messages: Sequence[dict[str, str]],
messages: list[dict[str, str]],
system: Optional[str] = None,
tools: Optional[str] = None,
images: Optional[Sequence["ImageInput"]] = None,
videos: Optional[Sequence["VideoInput"]] = None,
audios: Optional[Sequence["AudioInput"]] = None,
images: Optional[list["ImageInput"]] = None,
videos: Optional[list["VideoInput"]] = None,
audios: Optional[list["AudioInput"]] = None,
**input_kwargs,
) -> AsyncIterator["RequestOutput"]:
request_id = f"chatcmpl-{uuid.uuid4().hex}"
@ -202,12 +202,12 @@ class VllmEngine(BaseEngine):
@override
async def chat(
self,
messages: Sequence[dict[str, str]],
messages: list[dict[str, str]],
system: Optional[str] = None,
tools: Optional[str] = None,
images: Optional[Sequence["ImageInput"]] = None,
videos: Optional[Sequence["VideoInput"]] = None,
audios: Optional[Sequence["AudioInput"]] = None,
images: Optional[list["ImageInput"]] = None,
videos: Optional[list["VideoInput"]] = None,
audios: Optional[list["AudioInput"]] = None,
**input_kwargs,
) -> list["Response"]:
final_output = None
@ -231,12 +231,12 @@ class VllmEngine(BaseEngine):
@override
async def stream_chat(
self,
messages: Sequence[dict[str, str]],
messages: list[dict[str, str]],
system: Optional[str] = None,
tools: Optional[str] = None,
images: Optional[Sequence["ImageInput"]] = None,
videos: Optional[Sequence["VideoInput"]] = None,
audios: Optional[Sequence["AudioInput"]] = None,
images: Optional[list["ImageInput"]] = None,
videos: Optional[list["VideoInput"]] = None,
audios: Optional[list["AudioInput"]] = None,
**input_kwargs,
) -> AsyncGenerator[str, None]:
generated_text = ""

11
src/llamafactory/data/collator.py
View File

@ -1,4 +1,4 @@
# Copyright 2024 OpenAccess AI Collective and the LlamaFactory team.
# Copyright 2025 OpenAccess AI Collective and the LlamaFactory team.
#
# This code is inspired by the OpenAccess AI Collective's axolotl library.
# https://github.com/OpenAccess-AI-Collective/axolotl/blob/main/src/axolotl/monkeypatch/utils.py
@ -15,7 +15,6 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from collections.abc import Sequence
from dataclasses import dataclass
from typing import TYPE_CHECKING, Any, Literal, Optional
@ -92,7 +91,7 @@ class MultiModalDataCollatorForSeq2Seq(DataCollatorForSeq2Seq):
if self.template is None:
raise ValueError("Template is required for MultiModalDataCollator.")
def __call__(self, features: Sequence[dict[str, Any]]) -> dict[str, "torch.Tensor"]:
def __call__(self, features: list[dict[str, Any]]) -> dict[str, "torch.Tensor"]:
batch_images, batch_videos, batch_audios = [], [], []
batch_imglens, batch_vidlens, batch_audlens, batch_input_ids = [], [], [], []
for feature in features:
@ -205,7 +204,7 @@ class SFTDataCollatorWith4DAttentionMask(MultiModalDataCollatorForSeq2Seq):
attn_implementation: Literal["eager", "sdpa", "flash_attention_2"] = "eager"
compute_dtype: "torch.dtype" = torch.float32
def __call__(self, features: Sequence[dict[str, Any]]) -> dict[str, "torch.Tensor"]:
def __call__(self, features: list[dict[str, Any]]) -> dict[str, "torch.Tensor"]:
features = super().__call__(features)
if self.block_diag_attn and self.attn_implementation != "flash_attention_2":
features["attention_mask"] = prepare_4d_attention_mask(features["attention_mask"], self.compute_dtype)
@ -221,7 +220,7 @@ class SFTDataCollatorWith4DAttentionMask(MultiModalDataCollatorForSeq2Seq):
class PairwiseDataCollatorWithPadding(MultiModalDataCollatorForSeq2Seq):
r"""Data collator for pairwise data."""
def __call__(self, features: Sequence[dict[str, Any]]) -> dict[str, "torch.Tensor"]:
def __call__(self, features: list[dict[str, Any]]) -> dict[str, "torch.Tensor"]:
r"""Pad batched data to the longest sequence in the batch.
We generate 2 * n examples where the first n examples represent chosen examples and
@ -247,7 +246,7 @@ class PairwiseDataCollatorWithPadding(MultiModalDataCollatorForSeq2Seq):
class KTODataCollatorWithPadding(MultiModalDataCollatorForSeq2Seq):
r"""Data collator for KTO data."""
def __call__(self, features: Sequence[dict[str, Any]]) -> dict[str, "torch.Tensor"]:
def __call__(self, features: list[dict[str, Any]]) -> dict[str, "torch.Tensor"]:
target_features = []
kl_features = []
kto_tags = []

3
src/llamafactory/data/converter.py
View File

@ -14,7 +14,6 @@
import os
from abc import abstractmethod
from collections.abc import Sequence
from dataclasses import dataclass
from typing import TYPE_CHECKING, Any, Optional, Union
@ -37,7 +36,7 @@ class DatasetConverter:
dataset_attr: "DatasetAttr"
data_args: "DataArguments"
def _find_medias(self, medias: Union[Any, Sequence[Any]]) -> Optional[list[Any]]:
def _find_medias(self, medias: Union[Any, list[Any]]) -> Optional[list[Any]]:
r"""Optionally concatenate media path to media dir when loading from local disk."""
if not isinstance(medias, list):
medias = [medias] if medias is not None else []

3
src/llamafactory/data/data_utils.py
View File

@ -12,7 +12,6 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from collections.abc import Sequence
from enum import Enum, unique
from typing import TYPE_CHECKING, Optional, TypedDict, Union
@ -30,7 +29,7 @@ if TYPE_CHECKING:
logger = logging.get_logger(__name__)
SLOTS = Sequence[Union[str, set[str], dict[str, str]]]
SLOTS = list[Union[str, set[str], dict[str, str]]]
@unique

3
src/llamafactory/data/loader.py
View File

@ -13,7 +13,6 @@
# limitations under the License.
import os
from collections.abc import Sequence
from typing import TYPE_CHECKING, Literal, Optional, Union
import numpy as np
@ -157,7 +156,7 @@ def _load_single_dataset(
def _get_merged_dataset(
dataset_names: Optional[Sequence[str]],
dataset_names: Optional[list[str]],
model_args: "ModelArguments",
data_args: "DataArguments",
training_args: "Seq2SeqTrainingArguments",

259
src/llamafactory/data/mm_plugin.py
View File

@ -18,7 +18,6 @@
import inspect
import math
import re
from collections.abc import Sequence
from copy import deepcopy
from dataclasses import dataclass
from io import BytesIO
@ -83,9 +82,7 @@ if TYPE_CHECKING:
pass
def _get_paligemma_token_type_ids(
imglens: Sequence[int], seqlens: Sequence[int], processor: "MMProcessor"
) -> list[list[int]]:
def _get_paligemma_token_type_ids(imglens: list[int], seqlens: list[int], processor: "MMProcessor") -> list[list[int]]:
r"""Get paligemma token type ids for computing loss.
It is slightly different with the original token type ids where the prompt part is 0.
@ -120,7 +117,7 @@ def _get_gemma3_token_type_ids(batch_ids: list[list[int]], processor: "MMProcess
return batch_token_type_ids
def _make_batched_images(images: Sequence["ImageObject"], imglens: list[int]) -> list[list["ImageObject"]]:
def _make_batched_images(images: list["ImageObject"], imglens: list[int]) -> list[list["ImageObject"]]:
r"""Make nested list of images."""
batch_images = []
for imglen in imglens:
@ -140,9 +137,9 @@ class MMPluginMixin:
def _validate_input(
self,
processor: Optional["MMProcessor"],
images: Sequence["ImageInput"],
videos: Sequence["VideoInput"],
audios: Sequence["AudioInput"],
images: list["ImageInput"],
videos: list["VideoInput"],
audios: list["AudioInput"],
) -> None:
r"""Validate if this model accepts the input modalities."""
image_processor: BaseImageProcessor = getattr(processor, "image_processor", None)
@ -202,7 +199,7 @@ class MMPluginMixin:
sample_frames = min(total_frames, video_maxlen, sample_frames)
return np.linspace(0, total_frames - 1, sample_frames).astype(np.int32)
def _regularize_images(self, images: Sequence["ImageInput"], **kwargs) -> list["ImageObject"]:
def _regularize_images(self, images: list["ImageInput"], **kwargs) -> list["ImageObject"]:
r"""Regularize images to avoid error. Including reading and pre-processing."""
results = []
for image in images:
@ -223,7 +220,7 @@ class MMPluginMixin:
return results
def _regularize_videos(self, videos: Sequence["VideoInput"], **kwargs) -> list[list["ImageObject"]]:
def _regularize_videos(self, videos: list["VideoInput"], **kwargs) -> list[list["ImageObject"]]:
r"""Regularizes videos to avoid error. Including reading, resizing and converting."""
results = []
for video in videos:
@ -241,7 +238,7 @@ class MMPluginMixin:
return results
def _regularize_audios(self, audios: Sequence["AudioInput"], sampling_rate: float, **kwargs) -> list["NDArray"]:
def _regularize_audios(self, audios: list["AudioInput"], sampling_rate: float, **kwargs) -> list["NDArray"]:
r"""Regularizes audios to avoid error. Including reading and resampling."""
results = []
for audio in audios:
@ -257,9 +254,9 @@ class MMPluginMixin:
def _get_mm_inputs(
self,
images: Sequence["ImageInput"],
videos: Sequence["VideoInput"],
audios: Sequence["AudioInput"],
images: list["ImageInput"],
videos: list["VideoInput"],
audios: list["AudioInput"],
processor: "MMProcessor",
imglens: Optional[list[int]] = None,
) -> dict[str, "torch.Tensor"]:
@ -335,10 +332,10 @@ class MMPluginMixin:
class BasePlugin(MMPluginMixin):
def process_messages(
self,
messages: Sequence[dict[str, str]],
images: Sequence["ImageInput"],
videos: Sequence["VideoInput"],
audios: Sequence["AudioInput"],
messages: list[dict[str, str]],
images: list["ImageInput"],
videos: list["VideoInput"],
audios: list["AudioInput"],
processor: Optional["MMProcessor"],
) -> list[dict[str, str]]:
r"""Pre-process input messages before tokenization for VLMs."""
@ -349,9 +346,9 @@ class BasePlugin(MMPluginMixin):
self,
input_ids: list[int],
labels: Optional[list[int]],
images: Sequence["ImageInput"],
videos: Sequence["VideoInput"],
audios: Sequence["AudioInput"],
images: list["ImageInput"],
videos: list["VideoInput"],
audios: list["AudioInput"],
tokenizer: "PreTrainedTokenizer",
processor: Optional["MMProcessor"],
) -> tuple[list[int], Optional[list[int]]]:
@ -361,13 +358,13 @@ class BasePlugin(MMPluginMixin):
def get_mm_inputs(
self,
images: Sequence["ImageInput"],
videos: Sequence["VideoInput"],
audios: Sequence["AudioInput"],
imglens: Sequence[int],
vidlens: Sequence[int],
audlens: Sequence[int],
batch_ids: Sequence[list[int]],
images: list["ImageInput"],
videos: list["VideoInput"],
audios: list["AudioInput"],
imglens: list[int],
vidlens: list[int],
audlens: list[int],
batch_ids: list[list[int]],
processor: Optional["MMProcessor"],
) -> dict[str, Union[list[int], "torch.Tensor"]]:
r"""Build batched multimodal inputs for VLMs.
@ -392,10 +389,10 @@ class Gemma3Plugin(BasePlugin):
@override
def process_messages(
self,
messages: Sequence[dict[str, str]],
images: Sequence["ImageInput"],
videos: Sequence["VideoInput"],
audios: Sequence["AudioInput"],
messages: list[dict[str, str]],
images: list["ImageInput"],
videos: list["VideoInput"],
audios: list["AudioInput"],
processor: Optional["MMProcessor"],
) -> list[dict[str, str]]:
self._validate_input(processor, images, videos, audios)
@ -420,13 +417,13 @@ class Gemma3Plugin(BasePlugin):
@override
def get_mm_inputs(
self,
images: Sequence["ImageInput"],
videos: Sequence["VideoInput"],
audios: Sequence["AudioInput"],
imglens: Sequence[int],
vidlens: Sequence[int],
audlens: Sequence[int],
batch_ids: Sequence[list[int]],
images: list["ImageInput"],
videos: list["VideoInput"],
audios: list["AudioInput"],
imglens: list[int],
vidlens: list[int],
audlens: list[int],
batch_ids: list[list[int]],
processor: Optional["MMProcessor"],
) -> dict[str, Union[list[int], "torch.Tensor"]]:
self._validate_input(processor, images, videos, audios)
@ -441,10 +438,10 @@ class LlavaPlugin(BasePlugin):
@override
def process_messages(
self,
messages: Sequence[dict[str, str]],
images: Sequence["ImageInput"],
videos: Sequence["VideoInput"],
audios: Sequence["AudioInput"],
messages: list[dict[str, str]],
images: list["ImageInput"],
videos: list["VideoInput"],
audios: list["AudioInput"],
processor: Optional["MMProcessor"],
) -> list[dict[str, str]]:
self._validate_input(processor, images, videos, audios)
@ -481,10 +478,10 @@ class LlavaNextPlugin(BasePlugin):
@override
def process_messages(
self,
messages: Sequence[dict[str, str]],
images: Sequence["ImageInput"],
videos: Sequence["VideoInput"],
audios: Sequence["AudioInput"],
messages: list[dict[str, str]],
images: list["ImageInput"],
videos: list["VideoInput"],
audios: list["AudioInput"],
processor: Optional["MMProcessor"],
) -> list[dict[str, str]]:
self._validate_input(processor, images, videos, audios)
@ -523,10 +520,10 @@ class LlavaNextVideoPlugin(BasePlugin):
@override
def process_messages(
self,
messages: Sequence[dict[str, str]],
images: Sequence["ImageInput"],
videos: Sequence["VideoInput"],
audios: Sequence["AudioInput"],
messages: list[dict[str, str]],
images: list["ImageInput"],
videos: list["VideoInput"],
audios: list["AudioInput"],
processor: Optional["MMProcessor"],
) -> list[dict[str, str]]:
self._validate_input(processor, images, videos, audios)
@ -586,10 +583,10 @@ class MiniCPMVPlugin(BasePlugin):
@override
def process_messages(
self,
messages: Sequence[dict[str, str]],
images: Sequence["ImageInput"],
videos: Sequence["VideoInput"],
audios: Sequence["AudioInput"],
messages: list[dict[str, str]],
images: list["ImageInput"],
videos: list["VideoInput"],
audios: list["AudioInput"],
processor: Optional["MMProcessor"],
) -> list[dict[str, str]]:
self._validate_input(processor, images, videos, audios)
@ -686,9 +683,9 @@ class MiniCPMVPlugin(BasePlugin):
@override
def _get_mm_inputs(
self,
images: Sequence["ImageInput"],
videos: Sequence["VideoInput"],
audios: Sequence["AudioInput"],
images: list["ImageInput"],
videos: list["VideoInput"],
audios: list["AudioInput"],
processor: "MMProcessor",
**kwargs,
) -> dict[str, "torch.Tensor"]:
@ -757,13 +754,13 @@ class MiniCPMVPlugin(BasePlugin):
@override
def get_mm_inputs(
self,
images: Sequence["ImageInput"],
videos: Sequence["VideoInput"],
audios: Sequence["AudioInput"],
imglens: Sequence[int],
vidlens: Sequence[int],
audlens: Sequence[int],
batch_ids: Sequence[list[int]],
images: list["ImageInput"],
videos: list["VideoInput"],
audios: list["AudioInput"],
imglens: list[int],
vidlens: list[int],
audlens: list[int],
batch_ids: list[list[int]],
processor: Optional["MMProcessor"],
) -> dict[str, Union[list[int], "torch.Tensor"]]:
self._validate_input(processor, images, videos, audios)
@ -828,10 +825,10 @@ class MllamaPlugin(BasePlugin):
@override
def process_messages(
self,
messages: Sequence[dict[str, str]],
images: Sequence["ImageInput"],
videos: Sequence["VideoInput"],
audios: Sequence["AudioInput"],
messages: list[dict[str, str]],
images: list["ImageInput"],
videos: list["VideoInput"],
audios: list["AudioInput"],
processor: Optional["MMProcessor"],
) -> list[dict[str, str]]:
self._validate_input(processor, images, videos, audios)
@ -850,13 +847,13 @@ class MllamaPlugin(BasePlugin):
@override
def get_mm_inputs(
self,
images: Sequence["ImageInput"],
videos: Sequence["VideoInput"],
audios: Sequence["AudioInput"],
imglens: Sequence[int],
vidlens: Sequence[int],
audlens: Sequence[int],
batch_ids: Sequence[list[int]],
images: list["ImageInput"],
videos: list["VideoInput"],
audios: list["AudioInput"],
imglens: list[int],
vidlens: list[int],
audlens: list[int],
batch_ids: list[list[int]],
processor: Optional["MMProcessor"],
) -> dict[str, Union[list[int], "torch.Tensor"]]:
self._validate_input(processor, images, videos, audios)
@ -885,10 +882,10 @@ class PaliGemmaPlugin(BasePlugin):
@override
def process_messages(
self,
messages: Sequence[dict[str, str]],
images: Sequence["ImageInput"],
videos: Sequence["VideoInput"],
audios: Sequence["AudioInput"],
messages: list[dict[str, str]],
images: list["ImageInput"],
videos: list["VideoInput"],
audios: list["AudioInput"],
processor: Optional["MMProcessor"],
) -> list[dict[str, str]]:
self._validate_input(processor, images, videos, audios)
@ -912,9 +909,9 @@ class PaliGemmaPlugin(BasePlugin):
self,
input_ids: list[int],
labels: Optional[list[int]],
images: Sequence["ImageInput"],
videos: Sequence["VideoInput"],
audios: Sequence["AudioInput"],
images: list["ImageInput"],
videos: list["VideoInput"],
audios: list["AudioInput"],
tokenizer: "PreTrainedTokenizer",
processor: Optional["MMProcessor"],
) -> tuple[list[int], Optional[list[int]]]:
@ -931,13 +928,13 @@ class PaliGemmaPlugin(BasePlugin):
@override
def get_mm_inputs(
self,
images: Sequence["ImageInput"],
videos: Sequence["VideoInput"],
audios: Sequence["AudioInput"],
imglens: Sequence[int],
vidlens: Sequence[int],
audlens: Sequence[int],
batch_ids: Sequence[list[int]],
images: list["ImageInput"],
videos: list["VideoInput"],
audios: list["AudioInput"],
imglens: list[int],
vidlens: list[int],
audlens: list[int],
batch_ids: list[list[int]],
processor: Optional["MMProcessor"],
) -> dict[str, Union[list[int], "torch.Tensor"]]:
self._validate_input(processor, images, videos, audios)
@ -952,10 +949,10 @@ class PixtralPlugin(BasePlugin):
@override
def process_messages(
self,
messages: Sequence[dict[str, str]],
images: Sequence["ImageInput"],
videos: Sequence["VideoInput"],
audios: Sequence["AudioInput"],
messages: list[dict[str, str]],
images: list["ImageInput"],
videos: list["VideoInput"],
audios: list["AudioInput"],
processor: Optional["MMProcessor"],
) -> list[dict[str, str]]:
self._validate_input(processor, images, videos, audios)
@ -995,13 +992,13 @@ class PixtralPlugin(BasePlugin):
@override
def get_mm_inputs(
self,
images: Sequence["ImageInput"],
videos: Sequence["VideoInput"],
audios: Sequence["AudioInput"],
imglens: Sequence[int],
vidlens: Sequence[int],
audlens: Sequence[int],
batch_ids: Sequence[list[int]],
images: list["ImageInput"],
videos: list["VideoInput"],
audios: list["AudioInput"],
imglens: list[int],
vidlens: list[int],
audlens: list[int],
batch_ids: list[list[int]],
processor: Optional["MMProcessor"],
) -> dict[str, Union[list[int], "torch.Tensor"]]:
self._validate_input(processor, images, videos, audios)
@ -1015,10 +1012,10 @@ class Qwen2AudioPlugin(BasePlugin):
@override
def process_messages(
self,
messages: Sequence[dict[str, str]],
images: Sequence["ImageInput"],
videos: Sequence["VideoInput"],
audios: Sequence["AudioInput"],
messages: list[dict[str, str]],
images: list["ImageInput"],
videos: list["VideoInput"],
audios: list["AudioInput"],
processor: Optional["MMProcessor"],
) -> list[dict[str, str]]:
self._validate_input(processor, images, videos, audios)
@ -1056,13 +1053,13 @@ class Qwen2AudioPlugin(BasePlugin):
@override
def get_mm_inputs(
self,
images: Sequence["ImageInput"],
videos: Sequence["VideoInput"],
audios: Sequence["AudioInput"],
imglens: Sequence[int],
vidlens: Sequence[int],
audlens: Sequence[int],
batch_ids: Sequence[list[int]],
images: list["ImageInput"],
videos: list["VideoInput"],
audios: list["AudioInput"],
imglens: list[int],
vidlens: list[int],
audlens: list[int],
batch_ids: list[list[int]],
processor: Optional["MMProcessor"],
) -> dict[str, Union[list[int], "torch.Tensor"]]:
self._validate_input(processor, images, videos, audios)
@ -1090,7 +1087,7 @@ class Qwen2VLPlugin(BasePlugin):
@override
def _regularize_videos(
self, videos: Sequence["VideoInput"], **kwargs
self, videos: list["VideoInput"], **kwargs
) -> tuple[list[list["ImageObject"]], list[float]]:
results, fps_per_video = [], []
for video in videos:
@ -1118,9 +1115,9 @@ class Qwen2VLPlugin(BasePlugin):
@override
def _get_mm_inputs(
self,
images: Sequence["ImageInput"],
videos: Sequence["VideoInput"],
audios: Sequence["AudioInput"],
images: list["ImageInput"],
videos: list["VideoInput"],
audios: list["AudioInput"],
processor: "MMProcessor",
) -> dict[str, "torch.Tensor"]:
image_processor: BaseImageProcessor = getattr(processor, "image_processor", None)
@ -1149,10 +1146,10 @@ class Qwen2VLPlugin(BasePlugin):
@override
def process_messages(
self,
messages: Sequence[dict[str, str]],
images: Sequence["ImageInput"],
videos: Sequence["VideoInput"],
audios: Sequence["AudioInput"],
messages: list[dict[str, str]],
images: list["ImageInput"],
videos: list["VideoInput"],
audios: list["AudioInput"],
processor: Optional["MMProcessor"],
) -> list[dict[str, str]]:
self._validate_input(processor, images, videos, audios)
@ -1204,13 +1201,13 @@ class Qwen2VLPlugin(BasePlugin):
@override
def get_mm_inputs(
self,
images: Sequence["ImageInput"],
videos: Sequence["VideoInput"],
audios: Sequence["AudioInput"],
imglens: Sequence[int],
vidlens: Sequence[int],
audlens: Sequence[int],
batch_ids: Sequence[list[int]],
images: list["ImageInput"],
videos: list["VideoInput"],
audios: list["AudioInput"],
imglens: list[int],
vidlens: list[int],
audlens: list[int],
batch_ids: list[list[int]],
processor: Optional["MMProcessor"],
) -> dict[str, Union[list[int], "torch.Tensor"]]:
self._validate_input(processor, images, videos, audios)
@ -1229,10 +1226,10 @@ class VideoLlavaPlugin(BasePlugin):
@override
def process_messages(
self,
messages: Sequence[dict[str, str]],
images: Sequence["ImageInput"],
videos: Sequence["VideoInput"],
audios: Sequence["AudioInput"],
messages: list[dict[str, str]],
images: list["ImageInput"],
videos: list["VideoInput"],
audios: list["AudioInput"],
processor: Optional["MMProcessor"],
) -> list[dict[str, str]]:
self._validate_input(processor, images, videos, audios)

3
src/llamafactory/data/parser.py
View File

@ -14,7 +14,6 @@
import json
import os
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any, Literal, Optional
@ -91,7 +90,7 @@ class DatasetAttr:
self.set_attr(tag, attr["tags"])
def get_dataset_list(dataset_names: Optional[Sequence[str]], dataset_dir: str) -> list["DatasetAttr"]:
def get_dataset_list(dataset_names: Optional[list[str]], dataset_dir: str) -> list["DatasetAttr"]:
r"""Get the attributes of the datasets."""
if dataset_names is None:
dataset_names = []

14
src/llamafactory/data/processor/__init__.py
View File

@ -1,3 +1,17 @@
# Copyright 2025 the LlamaFactory team.
#
# 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.
from .feedback import FeedbackDatasetProcessor
from .pairwise import PairwiseDatasetProcessor
from .pretrain import PretrainDatasetProcessor

13
src/llamafactory/data/processor/feedback.py
View File

@ -13,7 +13,6 @@
# limitations under the License.
from collections import defaultdict
from collections.abc import Sequence
from typing import TYPE_CHECKING, Any, Optional
from ...extras import logging
@ -31,14 +30,14 @@ logger = logging.get_logger(__name__)
class FeedbackDatasetProcessor(DatasetProcessor):
def _encode_data_example(
self,
prompt: Sequence[dict[str, str]],
response: Sequence[dict[str, str]],
kl_response: Sequence[dict[str, str]],
prompt: list[dict[str, str]],
response: list[dict[str, str]],
kl_response: list[dict[str, str]],
system: Optional[str],
tools: Optional[str],
images: Sequence["ImageInput"],
videos: Sequence["VideoInput"],
audios: Sequence["AudioInput"],
images: list["ImageInput"],
videos: list["VideoInput"],
audios: list["AudioInput"],
) -> tuple[list[int], list[int], list[int], list[int], bool]:
if response[0]["content"]: # desired example
kto_tag = True

11
src/llamafactory/data/processor/pairwise.py
View File

@ -13,7 +13,6 @@
# limitations under the License.
from collections import defaultdict
from collections.abc import Sequence
from typing import TYPE_CHECKING, Any, Optional
from ...extras import logging
@ -31,13 +30,13 @@ logger = logging.get_logger(__name__)
class PairwiseDatasetProcessor(DatasetProcessor):
def _encode_data_example(
self,
prompt: Sequence[dict[str, str]],
response: Sequence[dict[str, str]],
prompt: list[dict[str, str]],
response: list[dict[str, str]],
system: Optional[str],
tools: Optional[str],
images: Sequence["ImageInput"],
videos: Sequence["VideoInput"],
audios: Sequence["AudioInput"],
images: list["ImageInput"],
videos: list["VideoInput"],
audios: list["AudioInput"],
) -> tuple[list[int], list[int], list[int], list[int]]:
chosen_messages = self.template.mm_plugin.process_messages(
prompt + [response[0]], images, videos, audios, self.processor

2
src/llamafactory/data/processor/pretrain.py
View File

@ -1,4 +1,4 @@
# Copyright 2024 HuggingFace Inc. and the LlamaFactory team.
# Copyright 2025 HuggingFace Inc. and the LlamaFactory team.
#
# This code is inspired by the HuggingFace's transformers library.
# https://github.com/huggingface/transformers/blob/v4.40.0/examples/pytorch/language-modeling/run_clm.py

3
src/llamafactory/data/processor/processor_utils.py
View File

@ -14,7 +14,6 @@
import bisect
from abc import ABC, abstractmethod
from collections.abc import Sequence
from dataclasses import dataclass
from typing import TYPE_CHECKING, Any, Optional
@ -46,7 +45,7 @@ class DatasetProcessor(ABC):
...
def search_for_fit(numbers: Sequence[int], capacity: int) -> int:
def search_for_fit(numbers: list[int], capacity: int) -> int:
r"""Find the index of largest number that fits into the knapsack with the given capacity."""
index = bisect.bisect(numbers, capacity)
return -1 if index == 0 else (index - 1)

11
src/llamafactory/data/processor/supervised.py
View File

@ -13,7 +13,6 @@
# limitations under the License.
from collections import defaultdict
from collections.abc import Sequence
from dataclasses import dataclass
from typing import TYPE_CHECKING, Any, Optional
@ -33,13 +32,13 @@ logger = logging.get_logger(__name__)
class SupervisedDatasetProcessor(DatasetProcessor):
def _encode_data_example(
self,
prompt: Sequence[dict[str, str]],
response: Sequence[dict[str, str]],
prompt: list[dict[str, str]],
response: list[dict[str, str]],
system: Optional[str],
tools: Optional[str],
images: Sequence["ImageInput"],
videos: Sequence["VideoInput"],
audios: Sequence["AudioInput"],
images: list["ImageInput"],
videos: list["VideoInput"],
audios: list["AudioInput"],
) -> tuple[list[int], list[int]]:
messages = self.template.mm_plugin.process_messages(prompt + response, images, videos, audios, self.processor)
input_ids, labels = self.template.mm_plugin.process_token_ids(

11
src/llamafactory/data/processor/unsupervised.py
View File

@ -13,7 +13,6 @@
# limitations under the License.
from collections import defaultdict
from collections.abc import Sequence
from typing import TYPE_CHECKING, Any, Optional
from ...extras import logging
@ -31,13 +30,13 @@ logger = logging.get_logger(__name__)
class UnsupervisedDatasetProcessor(DatasetProcessor):
def _encode_data_example(
self,
prompt: Sequence[dict[str, str]],
response: Sequence[dict[str, str]],
prompt: list[dict[str, str]],
response: list[dict[str, str]],
system: Optional[str],
tools: Optional[str],
images: Sequence["ImageInput"],
videos: Sequence["VideoInput"],
audios: Sequence["AudioInput"],
images: list["ImageInput"],
videos: list["VideoInput"],
audios: list["AudioInput"],
) -> tuple[list[int], list[int]]:
if len(response) == 1:
messages = prompt + response

11
src/llamafactory/data/template.py
View File

@ -12,7 +12,6 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from collections.abc import Sequence
from dataclasses import dataclass
from typing import TYPE_CHECKING, Optional, Union
@ -57,7 +56,7 @@ class Template:
def encode_oneturn(
self,
tokenizer: "PreTrainedTokenizer",
messages: Sequence[dict[str, str]],
messages: list[dict[str, str]],
system: Optional[str] = None,
tools: Optional[str] = None,
) -> tuple[list[int], list[int]]:
@ -73,7 +72,7 @@ class Template:
def encode_multiturn(
self,
tokenizer: "PreTrainedTokenizer",
messages: Sequence[dict[str, str]],
messages: list[dict[str, str]],
system: Optional[str] = None,
tools: Optional[str] = None,
) -> list[tuple[list[int], list[int]]]:
@ -115,7 +114,7 @@ class Template:
def _encode(
self,
tokenizer: "PreTrainedTokenizer",
messages: Sequence[dict[str, str]],
messages: list[dict[str, str]],
system: Optional[str],
tools: Optional[str],
) -> list[list[int]]:
@ -316,7 +315,7 @@ class Llama2Template(Template):
def _encode(
self,
tokenizer: "PreTrainedTokenizer",
messages: Sequence[dict[str, str]],
messages: list[dict[str, str]],
system: str,
tools: str,
) -> list[list[int]]:
@ -391,7 +390,7 @@ def register_template(
format_tools: Optional["Formatter"] = None,
format_prefix: Optional["Formatter"] = None,
default_system: str = "",
stop_words: Optional[Sequence[str]] = None,
stop_words: Optional[list[str]] = None,
thought_words: Optional[tuple[str, str]] = None,
efficient_eos: bool = False,
replace_eos: bool = False,

3
src/llamafactory/eval/template.py
View File

@ -12,7 +12,6 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from collections.abc import Sequence
from dataclasses import dataclass
from ..data import Role
@ -35,7 +34,7 @@ class EvalTemplate:
return "".join([example["question"]] + candidates + [self.answer]), example["answer"]
def format_example(
self, target_data: dict[str, str], support_set: Sequence[dict[str, str]], subject_name: str
self, target_data: dict[str, str], support_set: list[dict[str, str]], subject_name: str
) -> list[dict[str, str]]:
r"""Convert dataset examples to messages."""
messages = []

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

@ -1,4 +1,4 @@
# Copyright 2024 HuggingFace Inc. and the LlamaFactory team.
# Copyright 2025 HuggingFace Inc. and the LlamaFactory team.
#
# This code is inspired by the HuggingFace's transformers library.
# https://github.com/huggingface/transformers/blob/v4.40.0/src/transformers/commands/env.py

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

@ -1,4 +1,4 @@
# Copyright 2024 Optuna, HuggingFace Inc. and the LlamaFactory team.
# Copyright 2025 Optuna, HuggingFace Inc. and the LlamaFactory team.
#
# This code is inspired by the HuggingFace's transformers library.
# https://github.com/huggingface/transformers/blob/v4.40.0/src/transformers/utils/logging.py

5
src/llamafactory/extras/misc.py
View File

@ -1,4 +1,4 @@
# Copyright 2024 HuggingFace Inc. and the LlamaFactory team.
# Copyright 2025 HuggingFace Inc. and the LlamaFactory team.
#
# This code is inspired by the HuggingFace's PEFT library.
# https://github.com/huggingface/peft/blob/v0.10.0/src/peft/peft_model.py
@ -17,7 +17,6 @@
import gc
import os
from collections.abc import Sequence
from typing import TYPE_CHECKING, Any, Literal, Union
import torch
@ -98,7 +97,7 @@ def check_dependencies() -> None:
logger.warning_rank0_once("There are known bugs in transformers v4.46.0-v4.48.0, please use other versions.")
def calculate_tps(dataset: Sequence[dict[str, Any]], metrics: dict[str, float], stage: Literal["sft", "rm"]) -> float:
def calculate_tps(dataset: list[dict[str, Any]], metrics: dict[str, float], stage: Literal["sft", "rm"]) -> float:
r"""Calculate effective tokens per second."""
effective_token_num = 0
for data in dataset:

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

@ -1,4 +1,4 @@
# Copyright 2024 HuggingFace Inc. and the LlamaFactory team.
# Copyright 2025 HuggingFace Inc. and the LlamaFactory team.
#
# This code is inspired by the HuggingFace's transformers library.
# https://github.com/huggingface/transformers/blob/v4.40.0/src/transformers/utils/import_utils.py

2
src/llamafactory/hparams/data_args.py
View File

@ -1,4 +1,4 @@
# Copyright 2024 HuggingFace Inc. and the LlamaFactory team.
# Copyright 2025 HuggingFace Inc. and the LlamaFactory team.
#
# This code is inspired by the HuggingFace's transformers library.
# https://github.com/huggingface/transformers/blob/v4.40.0/examples/pytorch/language-modeling/run_clm.py

2
src/llamafactory/hparams/model_args.py
View File

@ -1,4 +1,4 @@
# Copyright 2024 HuggingFace Inc. and the LlamaFactory team.
# Copyright 2025 HuggingFace Inc. and the LlamaFactory team.
#
# This code is inspired by the HuggingFace's transformers library.
# https://github.com/huggingface/transformers/blob/v4.40.0/examples/pytorch/language-modeling/run_clm.py

2
src/llamafactory/hparams/parser.py
View File

@ -1,4 +1,4 @@
# Copyright 2024 HuggingFace Inc. and the LlamaFactory team.
# Copyright 2025 HuggingFace Inc. and the LlamaFactory team.
#
# This code is inspired by the HuggingFace's transformers library.
# https://github.com/huggingface/transformers/blob/v4.40.0/examples/pytorch/language-modeling/run_clm.py

14
src/llamafactory/hparams/training_args.py
View File

@ -1,3 +1,17 @@
# Copyright 2025 the LlamaFactory team.
#
# 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 json
from dataclasses import dataclass, field
from typing import Literal, Optional, Union

2
src/llamafactory/model/model_utils/checkpointing.py
View File

@ -1,4 +1,4 @@
# Copyright 2024 HuggingFace Inc., Daniel Han-Chen & the Unsloth team and the LlamaFactory team.
# Copyright 2025 HuggingFace Inc., Daniel Han-Chen & the Unsloth team and the LlamaFactory team.
#
# This code is inspired by the HuggingFace's Transformers and PEFT library,
# https://github.com/huggingface/transformers/blob/v4.40.0/src/transformers/modeling_utils.py

2
src/llamafactory/model/model_utils/longlora.py
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@ -1,4 +1,4 @@
# Copyright 2024 EleutherAI, HuggingFace Inc., Yukang Chen, and the LlamaFactory team.
# Copyright 2025 EleutherAI, HuggingFace Inc., Yukang Chen, and the LlamaFactory team.
#
# This code is based on the EleutherAI's GPT-NeoX and the HuggingFace's Transformers libraries.
# https://github.com/huggingface/transformers/blob/v4.40.0/src/transformers/models/llama/modeling_llama.py

3
src/llamafactory/model/model_utils/moe.py
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@ -12,7 +12,6 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from collections.abc import Sequence
from typing import TYPE_CHECKING
import torch
@ -27,7 +26,7 @@ if TYPE_CHECKING:
from ...hparams import ModelArguments
def _set_z3_leaf_modules(model: "PreTrainedModel", leaf_modules: Sequence["torch.nn.Module"]) -> None:
def _set_z3_leaf_modules(model: "PreTrainedModel", leaf_modules: list["torch.nn.Module"]) -> None:
check_version("deepspeed>=0.13.0")
from deepspeed.utils import set_z3_leaf_modules # type: ignore

2
src/llamafactory/model/model_utils/packing.py
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@ -1,4 +1,4 @@
# Copyright 2024 Musab Gultekin and the LlamaFactory team.
# Copyright 2025 Musab Gultekin and the LlamaFactory team.
#
# This code is based on the Musab Gultekin's functionary library.
# https://github.com/MeetKai/functionary/blob/main/functionary/train/packing/monkey_patch_packing.py

2
src/llamafactory/model/model_utils/quantization.py
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@ -1,4 +1,4 @@
# Copyright 2024 HuggingFace Inc. and the LlamaFactory team.
# 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

2
src/llamafactory/model/model_utils/rope.py
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@ -1,4 +1,4 @@
# Copyright 2024 LMSYS and the LlamaFactory team.
# Copyright 2025 LMSYS and the LlamaFactory team.
# Copyright 2023 Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li
#
# This code is inspired by the LMSYS's FastChat library.

3
src/llamafactory/model/model_utils/visual.py
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@ -15,7 +15,6 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from collections.abc import Sequence
from dataclasses import dataclass
from typing import TYPE_CHECKING, Optional
@ -180,7 +179,7 @@ def get_forbidden_modules(config: "PretrainedConfig", finetuning_args: "Finetuni
def patch_target_modules(
model: "PreTrainedModel", finetuning_args: "FinetuningArguments", target_modules: Sequence[str]
model: "PreTrainedModel", finetuning_args: "FinetuningArguments", target_modules: list[str]
) -> list[str]:
r"""Freeze vision tower for VLM LoRA tuning."""
model_type = getattr(model.config, "model_type", None)

2
src/llamafactory/train/dpo/trainer.py
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@ -1,4 +1,4 @@
# Copyright 2024 HuggingFace Inc. and the LlamaFactory team.
# Copyright 2025 HuggingFace Inc. and the LlamaFactory team.
#
# This code is inspired by the HuggingFace's TRL library.
# https://github.com/huggingface/trl/blob/v0.8.0/trl/trainer/dpo_trainer.py

2
src/llamafactory/train/dpo/workflow.py
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@ -1,4 +1,4 @@
# Copyright 2024 HuggingFace Inc. and the LlamaFactory team.
# Copyright 2025 HuggingFace Inc. and the LlamaFactory team.
#
# This code is inspired by the HuggingFace's TRL library.
# https://github.com/huggingface/trl/blob/v0.8.0/examples/scripts/dpo.py

2
src/llamafactory/train/kto/trainer.py
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@ -1,4 +1,4 @@
# Copyright 2024 HuggingFace Inc. and the LlamaFactory team.
# Copyright 2025 HuggingFace Inc. and the LlamaFactory team.
#
# This code is inspired by the HuggingFace's TRL library.
# https://github.com/huggingface/trl/blob/v0.8.0/trl/trainer/kto_trainer.py

2
src/llamafactory/train/kto/workflow.py
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@ -1,4 +1,4 @@
# Copyright 2024 HuggingFace Inc. and the LlamaFactory team.
# Copyright 2025 HuggingFace Inc. and the LlamaFactory team.
#
# This code is inspired by the HuggingFace's TRL library.
# https://github.com/huggingface/trl/blob/v0.8.0/examples/scripts/kto.py

2
src/llamafactory/train/ppo/trainer.py
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@ -1,4 +1,4 @@
# Copyright 2024 HuggingFace Inc. and the LlamaFactory team.
# Copyright 2025 HuggingFace Inc. and the LlamaFactory team.
#
# This code is inspired by the HuggingFace's TRL library.
# https://github.com/huggingface/trl/blob/v0.8.0/trl/trainer/ppo_trainer.py

2
src/llamafactory/train/ppo/workflow.py
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@ -1,4 +1,4 @@
# Copyright 2024 HuggingFace Inc. and the LlamaFactory team.
# Copyright 2025 HuggingFace Inc. and the LlamaFactory team.
#
# This code is inspired by the HuggingFace's TRL library.
# https://github.com/huggingface/trl/blob/v0.8.0/examples/scripts/ppo.py

2
src/llamafactory/train/pt/workflow.py
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@ -1,4 +1,4 @@
# Copyright 2024 HuggingFace Inc. and the LlamaFactory team.
# Copyright 2025 HuggingFace Inc. and the LlamaFactory team.
#
# This code is inspired by the HuggingFace's transformers library.
# https://github.com/huggingface/transformers/blob/v4.40.0/examples/pytorch/language-modeling/run_clm.py

2
src/llamafactory/train/rm/trainer.py
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@ -1,4 +1,4 @@
# Copyright 2024 HuggingFace Inc. and the LlamaFactory team.
# Copyright 2025 HuggingFace Inc. and the LlamaFactory team.
#
# This code is inspired by the HuggingFace's transformers library.
# https://github.com/huggingface/transformers/blob/v4.40.0/src/transformers/trainer.py

2
src/llamafactory/train/rm/workflow.py
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@ -1,4 +1,4 @@
# Copyright 2024 HuggingFace Inc. and the LlamaFactory team.
# Copyright 2025 HuggingFace Inc. and the LlamaFactory team.
#
# This code is inspired by the HuggingFace's transformers library.
# https://github.com/huggingface/transformers/blob/v4.40.0/examples/pytorch/summarization/run_summarization.py

6
src/llamafactory/train/sft/metric.py
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@ -1,4 +1,4 @@
# Copyright 2024 HuggingFace Inc., THUDM, and the LlamaFactory team.
# Copyright 2025 HuggingFace Inc., THUDM, and the LlamaFactory team.
#
# This code is inspired by the HuggingFace's transformers library and the THUDM's ChatGLM implementation.
# https://github.com/huggingface/transformers/blob/v4.40.0/examples/pytorch/summarization/run_summarization.py
@ -37,11 +37,11 @@ if is_jieba_available():
if is_nltk_available():
from nltk.translate.bleu_score import SmoothingFunction, sentence_bleu
from nltk.translate.bleu_score import SmoothingFunction, sentence_bleu # type: ignore
if is_rouge_available():
from rouge_chinese import Rouge
from rouge_chinese import Rouge # type: ignore
def eval_logit_processor(logits: "torch.Tensor", labels: "torch.Tensor") -> "torch.Tensor":

2
src/llamafactory/train/sft/trainer.py
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@ -1,4 +1,4 @@
# Copyright 2024 HuggingFace Inc. and the LlamaFactory team.
# Copyright 2025 HuggingFace Inc. and the LlamaFactory team.
#
# This code is inspired by the HuggingFace's transformers library.
# https://github.com/huggingface/transformers/blob/v4.40.0/src/transformers/trainer_seq2seq.py

2
src/llamafactory/train/sft/workflow.py
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@ -1,4 +1,4 @@
# Copyright 2024 HuggingFace Inc. and the LlamaFactory team.
# Copyright 2025 HuggingFace Inc. and the LlamaFactory team.
#
# This code is inspired by the HuggingFace's transformers library.
# https://github.com/huggingface/transformers/blob/v4.40.0/examples/pytorch/summarization/run_summarization.py

3
src/llamafactory/train/test_utils.py
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@ -12,7 +12,6 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from collections.abc import Sequence
from typing import TYPE_CHECKING, Optional, Union
import torch
@ -33,7 +32,7 @@ if TYPE_CHECKING:
from ..data.data_utils import DatasetModule
def compare_model(model_a: "torch.nn.Module", model_b: "torch.nn.Module", diff_keys: Sequence[str] = []) -> None:
def compare_model(model_a: "torch.nn.Module", model_b: "torch.nn.Module", diff_keys: list[str] = []) -> None:
state_dict_a = model_a.state_dict()
state_dict_b = model_b.state_dict()
assert set(state_dict_a.keys()) == set(state_dict_b.keys())

2
src/llamafactory/train/trainer_utils.py
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@ -1,4 +1,4 @@
# Copyright 2024 HuggingFace Inc. and the LlamaFactory team.
# Copyright 2025 HuggingFace Inc. and the LlamaFactory team.
#
# This code is inspired by the original GaLore's implementation: https://github.com/jiaweizzhao/GaLore
# and the original LoRA+'s implementation: https://github.com/nikhil-ghosh-berkeley/loraplus

38
tests/check_license.py Normal file
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@ -0,0 +1,38 @@
# Copyright 2025 the LlamaFactory team.
#
# 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 sys
from pathlib import Path
KEYWORDS = ("Copyright", "2025", "LlamaFactory")
def main():
path_list = []
for check_dir in sys.argv[1:]:
path_list.extend(Path(check_dir).glob("**/*.py"))
for path in path_list:
with open(path.absolute(), encoding="utf-8") as f:
file_content = f.read().strip().split("\n")
if not file_content[0]:
continue
print(f"Check license: {path}")
assert all(keyword in file_content[0] for keyword in KEYWORDS), f"File {path} does not contain license."
if __name__ == "__main__":
main()

3
tests/data/test_mm_plugin.py
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@ -13,7 +13,6 @@
# limitations under the License.
import os
from collections.abc import Sequence
from typing import TYPE_CHECKING, Any
import pytest
@ -97,7 +96,7 @@ def _check_plugin(
plugin: "BasePlugin",
tokenizer: "PreTrainedTokenizer",
processor: "ProcessorMixin",
expected_mm_messages: Sequence[dict[str, str]] = MM_MESSAGES,
expected_mm_messages: list[dict[str, str]] = MM_MESSAGES,
expected_input_ids: list[int] = INPUT_IDS,
expected_labels: list[int] = LABELS,
expected_mm_inputs: dict[str, Any] = {},

3
tests/data/test_template.py
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@ -13,7 +13,6 @@
# limitations under the License.
import os
from collections.abc import Sequence
from typing import TYPE_CHECKING
import pytest
@ -41,7 +40,7 @@ MESSAGES = [
def _check_tokenization(
tokenizer: "PreTrainedTokenizer", batch_input_ids: Sequence[Sequence[int]], batch_text: Sequence[str]
tokenizer: "PreTrainedTokenizer", batch_input_ids: list[list[int]], batch_text: list[str]
) -> None:
r"""Check token ids and texts.