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[breaking] bump transformers to 4.45.0 & improve ci (#7746)

Browse Source 
* update ci

* fix

* fix

* fix

* fix

* fix
This commit is contained in:
hoshi-hiyouga 2025-04-17 02:36:48 +08:00 committed by GitHub
parent d222f63cb7
commit 86ebb219d6
23 changed files with 211 additions and 140 deletions
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20
.github/workflows/tests.yml vendored
View File

@ -31,11 +31,20 @@ jobs:
- "ubuntu-latest"
- "windows-latest"
- "macos-13"
transformers:
- null
include: # test backward compatibility
- python: "3.9"
os: "ubuntu-latest"
transformers: "4.45.0"
- python: "3.9"
os: "ubuntu-latest"
transformers: "4.49.0"
runs-on: ${{ matrix.os }}
concurrency:
group: ${{ github.workflow }}-${{ github.ref }}-${{ matrix.os }}-${{ matrix.python }}
group: ${{ github.workflow }}-${{ github.ref }}-${{ matrix.os }}-${{ matrix.python }}-${{ matrix.transformers }}
cancel-in-progress: ${{ github.ref != 'refs/heads/main' }}
env:
@ -51,19 +60,24 @@ jobs:
with:
python-version: ${{ matrix.python }}
cache: "pip"
cache-dependency-path: "setup.py"
cache-dependency-path: "**/requirements*.txt"
- name: Install dependencies
run: |
python -m pip install --upgrade pip
python -m pip install ".[torch,dev]"
- name: Install transformers
if: ${{ matrix.transformers }}
run: |
python -m pip install "transformers==${{ matrix.transformers }}"
- name: Cache files
id: hf-hub-cache
uses: actions/cache@v4
with:
path: ${{ runner.temp }}/huggingface
key: huggingface-${{ matrix.os }}-${{ matrix.python }}-${{ hashFiles('tests/version.txt') }}
key: huggingface-${{ matrix.os }}-${{ matrix.python }}-${{ matrix.transformers }}-${{ hashFiles('tests/version.txt') }}
- name: Check quality
run: |

13
README.md
View File

@ -243,11 +243,11 @@ Compared to ChatGLM's [P-Tuning](https://github.com/THUDM/ChatGLM2-6B/tree/main/
| [Gemma 3](https://huggingface.co/google) | 1B/4B/12B/27B | gemma3/gemma (1B) |
| [GLM-4/GLM-4-0414/GLM-Z1](https://huggingface.co/THUDM) | 9B/32B | glm4 |
| [GPT-2](https://huggingface.co/openai-community) | 0.1B/0.4B/0.8B/1.5B | - |
| [Granite 3.0-3.1](https://huggingface.co/ibm-granite) | 1B/2B/3B/8B | granite3 |
| [Granite 3.0-3.3](https://huggingface.co/ibm-granite) | 1B/2B/3B/8B | granite3 |
| [Hunyuan](https://huggingface.co/tencent/) | 7B | hunyuan |
| [Index](https://huggingface.co/IndexTeam) | 1.9B | index |
| [InternLM 2-3](https://huggingface.co/internlm) | 7B/8B/20B | intern2 |
| [InternVL2_5-3](https://huggingface.co/OpenGVLab/InternVL) | 1B/2B/4B/8B/9B/14B/26B/38B/78B | intern_vl |
| [InternVL2.5-3](https://huggingface.co/OpenGVLab/InternVL)\*\* | 1B/2B/4B/8B/9B/14B/26B/38B/78B | intern_vl |
| [Kimi-VL](https://huggingface.co/moonshotai) | 16B | kimi_vl |
| [Llama](https://github.com/facebookresearch/llama) | 7B/13B/33B/65B | - |
| [Llama 2](https://huggingface.co/meta-llama) | 7B/13B/70B | llama2 |
@ -415,11 +415,11 @@ huggingface-cli login
| Mandatory | Minimum | Recommend |
| ------------ | ------- | --------- |
| python | 3.9 | 3.10 |
| torch | 1.13.1 | 2.6.0 |
| transformers | 4.41.2 | 4.50.0 |
| torch | 2.0.0 | 2.6.0 |
| transformers | 4.45.0 | 4.50.0 |
| datasets | 2.16.0 | 3.2.0 |
| accelerate | 0.34.0 | 1.2.1 |
| peft | 0.14.0 | 0.15.0 |
| peft | 0.14.0 | 0.15.1 |
| trl | 0.8.6 | 0.9.6 |
| Optional | Minimum | Recommend |
@ -428,7 +428,7 @@ huggingface-cli login
| deepspeed | 0.10.0 | 0.16.4 |
| bitsandbytes | 0.39.0 | 0.43.1 |
| vllm | 0.4.3 | 0.8.2 |
| flash-attn | 2.3.0 | 2.7.2 |
| flash-attn | 2.5.6 | 2.7.2 |
### Hardware Requirement
@ -517,6 +517,7 @@ source /usr/local/Ascend/ascend-toolkit/set_env.sh
| torch | 2.1.0 | 2.4.0 |
| torch-npu | 2.1.0 | 2.4.0.post2 |
| deepspeed | 0.13.2 | 0.13.2 |
| vllm-ascend | - | 0.7.3 |
Remember to use `ASCEND_RT_VISIBLE_DEVICES` instead of `CUDA_VISIBLE_DEVICES` to specify the device to use.

13
README_zh.md
View File

@ -246,11 +246,11 @@ https://github.com/user-attachments/assets/43b700c6-a178-41db-b1f8-8190a5d3fcfc
| [Gemma 3](https://huggingface.co/google) | 1B/4B/12B/27B | gemma3/gemma (1B) |
| [GLM-4/GLM-4-0414/GLM-Z1](https://huggingface.co/THUDM) | 9B/32B | glm4 |
| [GPT-2](https://huggingface.co/openai-community) | 0.1B/0.4B/0.8B/1.5B | - |
| [Granite 3.0-3.1](https://huggingface.co/ibm-granite) | 1B/2B/3B/8B | granite3 |
| [Granite 3.0-3.3](https://huggingface.co/ibm-granite) | 1B/2B/3B/8B | granite3 |
| [Hunyuan](https://huggingface.co/tencent/) | 7B | hunyuan |
| [Index](https://huggingface.co/IndexTeam) | 1.9B | index |
| [InternLM 2-3](https://huggingface.co/internlm) | 7B/8B/20B | intern2 |
| [InternVL2_5-3](https://huggingface.co/OpenGVLab/InternVL) | 1B/2B/4B/8B/9B/14B/26B/38B/78B | intern_vl |
| [InternVL2.5-3](https://huggingface.co/OpenGVLab/InternVL)\*\* | 1B/2B/4B/8B/9B/14B/26B/38B/78B | intern_vl |
| [Kimi-VL](https://huggingface.co/moonshotai) | 16B | kimi_vl |
| [Llama](https://github.com/facebookresearch/llama) | 7B/13B/33B/65B | - |
| [Llama 2](https://huggingface.co/meta-llama) | 7B/13B/70B | llama2 |
@ -418,11 +418,11 @@ huggingface-cli login
| 必需项 | 至少 | 推荐 |
| ------------ | ------- | --------- |
| python | 3.9 | 3.10 |
| torch | 1.13.1 | 2.6.0 |
| transformers | 4.41.2 | 4.50.0 |
| torch | 2.0.0 | 2.6.0 |
| transformers | 4.45.0 | 4.50.0 |
| datasets | 2.16.0 | 3.2.0 |
| accelerate | 0.34.0 | 1.2.1 |
| peft | 0.14.0 | 0.15.0 |
| peft | 0.14.0 | 0.15.1 |
| trl | 0.8.6 | 0.9.6 |
| 可选项 | 至少 | 推荐 |
@ -431,7 +431,7 @@ huggingface-cli login
| deepspeed | 0.10.0 | 0.16.4 |
| bitsandbytes | 0.39.0 | 0.43.1 |
| vllm | 0.4.3 | 0.8.2 |
| flash-attn | 2.3.0 | 2.7.2 |
| flash-attn | 2.5.6 | 2.7.2 |
### 硬件依赖
@ -521,6 +521,7 @@ source /usr/local/Ascend/ascend-toolkit/set_env.sh
| torch | 2.1.0 | 2.4.0 |
| torch-npu | 2.1.0 | 2.4.0.post2 |
| deepspeed | 0.13.2 | 0.13.2 |
| vllm-ascend | - | 0.7.3 |
请使用 `ASCEND_RT_VISIBLE_DEVICES` 而非 `CUDA_VISIBLE_DEVICES` 来指定运算设备。

2
requirements.txt
View File

@ -1,4 +1,4 @@
transformers>=4.41.2,<=4.51.3,!=4.46.*,!=4.47.*,!=4.48.0
transformers>=4.45.0,<=4.51.3,!=4.46.*,!=4.47.*,!=4.48.0
datasets>=2.16.0,<=3.5.0
accelerate>=0.34.0,<=1.6.0
peft>=0.14.0,<=0.15.1

4
scripts/api_example/test_image.py
View File

@ -23,8 +23,8 @@ require_version("openai>=1.5.0", "To fix: pip install openai>=1.5.0")
def main():
client = OpenAI(
api_key="{}".format(os.environ.get("API_KEY", "0")),
base_url="http://localhost:{}/v1".format(os.environ.get("API_PORT", 8000)),
api_key="{}".format(os.getenv("API_KEY", "0")),
base_url="http://localhost:{}/v1".format(os.getenv("API_PORT", 8000)),
)
messages = []
messages.append(

4
scripts/api_example/test_toolcall.py
View File

@ -33,8 +33,8 @@ def calculate_gpa(grades: list[str], hours: list[int]) -> float:
def main():
client = OpenAI(
api_key="{}".format(os.environ.get("API_KEY", "0")),
base_url="http://localhost:{}/v1".format(os.environ.get("API_PORT", 8000)),
api_key="{}".format(os.getenv("API_KEY", "0")),
base_url="http://localhost:{}/v1".format(os.getenv("API_PORT", 8000)),
)
tools = [
{

17
src/llamafactory/__init__.py
View File

@ -18,18 +18,11 @@ Level:
api, webui > chat, eval, train > data, model > hparams > extras
Dependency graph:
main:
transformers>=4.41.2,<=4.51.3,!=4.46.*,!=4.47.*,!=4.48.0
datasets>=2.16.0,<=3.5.0
accelerate>=0.34.0,<=1.6.0
peft>=0.14.0,<=0.15.1
trl>=0.8.6,<=0.9.6
attention:
transformers>=4.42.4 (gemma+fa2)
longlora:
transformers>=4.41.2,<4.48.0
packing:
transformers>=4.43.0
transformers>=4.41.2,<=4.43.0,!=4.46.*,!=4.47.*,!=4.48.0
datasets>=2.16.0,<=3.5.0
accelerate>=0.34.0,<=1.6.0
peft>=0.14.0,<=0.15.1
trl>=0.8.6,<=0.9.6
Disable version checking: DISABLE_VERSION_CHECK=1
Enable VRAM recording: RECORD_VRAM=1

2
src/llamafactory/chat/hf_engine.py
View File

@ -25,7 +25,6 @@ from typing_extensions import override
from ..data import get_template_and_fix_tokenizer
from ..extras import logging
from ..extras.constants import AUDIO_PLACEHOLDER, IMAGE_PLACEHOLDER, VIDEO_PLACEHOLDER, EngineName
from ..extras.misc import get_logits_processor
from ..model import load_model, load_tokenizer
from .base_engine import BaseEngine, Response
@ -178,7 +177,6 @@ class HuggingfaceEngine(BaseEngine):
inputs=inputs,
attention_mask=attention_mask,
generation_config=GenerationConfig(**generating_args),
logits_processor=get_logits_processor(),
)
mm_inputs = template.mm_plugin.get_mm_inputs(**mm_input_dict, batch_ids=[prompt_ids], processor=processor)

1
src/llamafactory/cli.py
View File

@ -19,7 +19,6 @@ from copy import deepcopy
from functools import partial
USAGE = (
"-" * 70
+ "\n"

125
src/llamafactory/data/mm_plugin.py
View File

@ -25,12 +25,7 @@ from typing import TYPE_CHECKING, BinaryIO, Literal, Optional, TypedDict, Union
import numpy as np
import torch
from transformers.image_utils import (
get_image_size,
make_batched_videos,
make_flat_list_of_images,
to_numpy_array,
)
from transformers.image_utils import get_image_size, to_numpy_array
from typing_extensions import override
from ..extras.constants import AUDIO_PLACEHOLDER, IGNORE_INDEX, IMAGE_PLACEHOLDER, VIDEO_PLACEHOLDER
@ -62,6 +57,10 @@ if is_transformers_version_greater_than("4.45.0"):
)
if is_transformers_version_greater_than("4.49.0"):
from transformers.image_utils import make_batched_videos, make_flat_list_of_images
if TYPE_CHECKING:
from av.stream import Stream
from numpy.typing import NDArray
@ -487,61 +486,6 @@ class Gemma3Plugin(BasePlugin):
@dataclass
class InternVLPlugin(BasePlugin):
@override
def process_messages(
self,
messages: list[dict[str, str]],
images: list["ImageInput"],
videos: list["VideoInput"],
audios: list["AudioInput"],
processor: Optional["ProcessorMixin"],
) -> list[dict[str, str]]:
self._validate_input(processor, images, videos, audios)
num_image_tokens = 0
num_video_tokens = 0
image_seqlen = getattr(processor, "image_seq_length") if self.expand_mm_tokens else 1
messages = deepcopy(messages)
mm_inputs = self._get_mm_inputs(images, videos, audios, processor)
image_pixel_patch_list = mm_inputs.get("image_num_patches", None) # pathes of images
video_num_patches = mm_inputs.get("video_num_patches", None) # all patches for frames of videos
video_patch_indices = mm_inputs.get("video_patch_indices", None) # num frames of per video
for message in messages:
content = message["content"]
while IMAGE_PLACEHOLDER in content:
if num_image_tokens >= len(image_pixel_patch_list):
raise ValueError(f"`len(images)` is less than the number of {IMAGE_PLACEHOLDER} tokens.")
content = content.replace(
IMAGE_PLACEHOLDER,
f"<img>{'<IMG_CONTEXT>' * image_seqlen * image_pixel_patch_list[num_image_tokens]}</img>",
1,
)
num_image_tokens += 1
message["content"] = content
while VIDEO_PLACEHOLDER in content:
if num_video_tokens >= len(video_patch_indices):
raise ValueError(f"`len(videos)` is less than the number of {VIDEO_PLACEHOLDER} tokens.")
current_patch_index = video_patch_indices[num_video_tokens - 1] if num_video_tokens > 0 else 0
end_patch_index = video_patch_indices[num_video_tokens]
num_patches = list(video_num_patches[current_patch_index:end_patch_index])
video_replaced_prompt = "\n".join(
f"Frame{i + 1}: <img>{'<IMG_CONTEXT>' * image_seqlen * num_patches[i]}</img>"
for i in range(len(num_patches))
)
content = content.replace(VIDEO_PLACEHOLDER, video_replaced_prompt, 1)
num_video_tokens += 1
message["content"] = content
if len(images) != num_image_tokens:
raise ValueError(f"The number of images does not match the number of {IMAGE_PLACEHOLDER} tokens.")
if len(videos) != num_video_tokens:
raise ValueError(f"The number of videos does not match the number of {VIDEO_PLACEHOLDER} tokens.")
return messages
@override
def _get_mm_inputs(
self,
@ -621,6 +565,63 @@ class InternVLPlugin(BasePlugin):
return mm_inputs
@override
def process_messages(
self,
messages: list[dict[str, str]],
images: list["ImageInput"],
videos: list["VideoInput"],
audios: list["AudioInput"],
processor: Optional["ProcessorMixin"],
) -> list[dict[str, str]]:
self._validate_input(processor, images, videos, audios)
num_image_tokens = 0
num_video_tokens = 0
image_seqlen = getattr(processor, "image_seq_length") if self.expand_mm_tokens else 1
messages = deepcopy(messages)
mm_inputs = self._get_mm_inputs(images, videos, audios, processor)
image_pixel_patch_list = mm_inputs.get("image_num_patches") # pathes of images
video_num_patches = mm_inputs.get("video_num_patches") # all patches for frames of videos
video_patch_indices = mm_inputs.get("video_patch_indices") # num frames of per video
for message in messages:
content = message["content"]
while IMAGE_PLACEHOLDER in content:
if num_image_tokens >= len(image_pixel_patch_list):
raise ValueError(f"`len(images)` is less than the number of {IMAGE_PLACEHOLDER} tokens.")
content = content.replace(
IMAGE_PLACEHOLDER,
f"<img>{'<IMG_CONTEXT>' * image_seqlen * image_pixel_patch_list[num_image_tokens]}</img>",
1,
)
num_image_tokens += 1
while VIDEO_PLACEHOLDER in content:
if num_video_tokens >= len(video_patch_indices):
raise ValueError(f"`len(videos)` is less than the number of {VIDEO_PLACEHOLDER} tokens.")
current_patch_index = video_patch_indices[num_video_tokens - 1] if num_video_tokens > 0 else 0
end_patch_index = video_patch_indices[num_video_tokens]
num_patches = list(video_num_patches[current_patch_index:end_patch_index])
video_replaced_prompt = "\n".join(
f"Frame{i + 1}: <img>{'<IMG_CONTEXT>' * image_seqlen * num_patches[i]}</img>"
for i in range(len(num_patches))
)
content = content.replace(VIDEO_PLACEHOLDER, video_replaced_prompt, 1)
num_video_tokens += 1
message["content"] = content
if len(images) != num_image_tokens:
raise ValueError(f"The number of images does not match the number of {IMAGE_PLACEHOLDER} tokens.")
if len(videos) != num_video_tokens:
raise ValueError(f"The number of videos does not match the number of {VIDEO_PLACEHOLDER} tokens.")
return messages
@override
def get_mm_inputs(
self,
@ -634,12 +635,10 @@ class InternVLPlugin(BasePlugin):
processor: Optional["ProcessorMixin"],
) -> dict[str, Union[list[int], "torch.Tensor"]]:
self._validate_input(processor, images, videos, audios)
mm_inputs = self._get_mm_inputs(images, videos, audios, processor)
mm_inputs.pop("image_num_patches", None)
mm_inputs.pop("video_patch_indices", None)
mm_inputs.pop("video_num_patches", None)
return mm_inputs

12
src/llamafactory/data/template.py
View File

@ -871,6 +871,18 @@ register_template(
)
register_template(
name="granite3_vision",
format_user=StringFormatter(slots=["<|user|>\n{{content}}\n<|assistant|>\n"]),
format_system=StringFormatter(slots=["<|system|>\n{{content}}\n"]),
default_system=(
"A chat between a curious user and an artificial intelligence assistant. "
"The assistant gives helpful, detailed, and polite answers to the user's questions."
),
mm_plugin=get_mm_plugin(name="llava_next", image_token="<image>"),
)
register_template(
name="index",
format_user=StringFormatter(slots=["reserved_0{{content}}reserved_1"]),

56
src/llamafactory/extras/constants.py
View File

@ -22,7 +22,7 @@ from peft.utils import WEIGHTS_NAME as ADAPTER_WEIGHTS_NAME
from transformers.utils import SAFE_WEIGHTS_INDEX_NAME, SAFE_WEIGHTS_NAME, WEIGHTS_INDEX_NAME, WEIGHTS_NAME
AUDIO_PLACEHOLDER = os.environ.get("AUDIO_PLACEHOLDER", "<audio>")
AUDIO_PLACEHOLDER = os.getenv("AUDIO_PLACEHOLDER", "<audio>")
CHECKPOINT_NAMES = {
SAFE_ADAPTER_WEIGHTS_NAME,
@ -50,7 +50,7 @@ FILEEXT2TYPE = {
IGNORE_INDEX = -100
IMAGE_PLACEHOLDER = os.environ.get("IMAGE_PLACEHOLDER", "<image>")
IMAGE_PLACEHOLDER = os.getenv("IMAGE_PLACEHOLDER", "<image>")
LAYERNORM_NAMES = {"norm", "ln"}
@ -89,7 +89,7 @@ SUPPORTED_CLASS_FOR_S2ATTN = {"llama"}
SWANLAB_CONFIG = "swanlab_public_config.json"
VIDEO_PLACEHOLDER = os.environ.get("VIDEO_PLACEHOLDER", "<video>")
VIDEO_PLACEHOLDER = os.getenv("VIDEO_PLACEHOLDER", "<video>")
V_HEAD_WEIGHTS_NAME = "value_head.bin"
@ -838,11 +838,46 @@ register_model_group(
DownloadSource.DEFAULT: "ibm-granite/granite-3.1-8b-instruct",
DownloadSource.MODELSCOPE: "AI-ModelScope/granite-3.1-8b-instruct",
},
"Granite-3.2-2B-Instruct": {
DownloadSource.DEFAULT: "ibm-granite/granite-3.2-2b-instruct",
DownloadSource.MODELSCOPE: "AI-ModelScope/granite-3.2-2b-instruct",
},
"Granite-3.2-8B-Instruct": {
DownloadSource.DEFAULT: "ibm-granite/granite-3.2-8b-instruct",
DownloadSource.MODELSCOPE: "AI-ModelScope/granite-3.2-8b-instruct",
},
"Granite-3.3-2B-Base": {
DownloadSource.DEFAULT: "ibm-granite/granite-3.3-2b-base",
DownloadSource.MODELSCOPE: "AI-ModelScope/granite-3.3-2b-base",
},
"Granite-3.3-8B-Base": {
DownloadSource.DEFAULT: "ibm-granite/granite-3.3-8b-base",
DownloadSource.MODELSCOPE: "AI-ModelScope/granite-3.3-8b-base",
},
"Granite-3.3-2B-Instruct": {
DownloadSource.DEFAULT: "ibm-granite/granite-3.3-2b-instruct",
DownloadSource.MODELSCOPE: "AI-ModelScope/granite-3.3-2b-instruct",
},
"Granite-3.3-8B-Instruct": {
DownloadSource.DEFAULT: "ibm-granite/granite-3.3-8b-instruct",
DownloadSource.MODELSCOPE: "AI-ModelScope/granite-3.3-8b-instruct",
},
},
template="granite3",
)
register_model_group(
models={
"Granite-3.2-1B-A400M-Base": {
DownloadSource.DEFAULT: "ibm-granite/granite-vision-3.2-2b",
DownloadSource.MODELSCOPE: "AI-ModelScope/granite-vision-3.2-2b",
},
},
template="granite3_vision",
)
register_model_group(
models={
"Hunyuan-7B-Instruct": {
@ -967,26 +1002,33 @@ register_model_group(
register_model_group(
models={
"InternVL2_5-1B-MPO": {
"InternVL2.5-1B-MPO": {
DownloadSource.DEFAULT: "kingsley01/InternVL2_5-1B-MPO-hf",
DownloadSource.MODELSCOPE: "llamafactory/InternVL2_5-1B-MPO-hf",
},
"InternVL2_5-2B-MPO": {
"InternVL2.5-2B-MPO": {
DownloadSource.DEFAULT: "kingsley01/InternVL2_5-2B-MPO-hf",
DownloadSource.MODELSCOPE: "llamafactory/InternVL2_5-2B-MPO-hf",
},
"InternVL2_5-4B-MPO": {
"InternVL2.5-4B-MPO": {
DownloadSource.DEFAULT: "kingsley01/InternVL2_5-4B-MPO-hf",
DownloadSource.MODELSCOPE: "llamafactory/InternVL2_5-4B-MPO-hf",
},
"InternVL2_5-8B-MPO": {
"InternVL2.5-8B-MPO": {
DownloadSource.DEFAULT: "kingsley01/InternVL2_5-8B-MPO-hf",
DownloadSource.MODELSCOPE: "llamafactory/InternVL2_5-8B-MPO-hf",
},
"InternVL3-1B-hf": {
DownloadSource.DEFAULT: "kingsley01/InternVL3-1B-hf",
DownloadSource.MODELSCOPE: "llamafactory/InternVL3-1B-hf",
},
"InternVL3-2B-hf": {
DownloadSource.DEFAULT: "kingsley01/InternVL3-2B-hf",
DownloadSource.MODELSCOPE: "llamafactory/InternVL3-2B-hf",
},
"InternVL3-8B-hf": {
DownloadSource.DEFAULT: "kingsley01/InternVL3-8B-hf",
DownloadSource.MODELSCOPE: "llamafactory/InternVL3-8B-hf",
},
},
template="intern_vl",

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

@ -79,7 +79,7 @@ class _Logger(logging.Logger):
def _get_default_logging_level() -> "logging._Level":
r"""Return the default logging level."""
env_level_str = os.environ.get("LLAMAFACTORY_VERBOSITY", None)
env_level_str = os.getenv("LLAMAFACTORY_VERBOSITY", None)
if env_level_str:
if env_level_str.upper() in logging._nameToLevel:
return logging._nameToLevel[env_level_str.upper()]

38
src/llamafactory/extras/misc.py
View File

@ -89,7 +89,7 @@ def check_version(requirement: str, mandatory: bool = False) -> None:
def check_dependencies() -> None:
r"""Check the version of the required packages."""
check_version("transformers>=4.41.2,<=4.51.3,!=4.46.0,!=4.46.1,!=4.46.2,!=4.46.3,!=4.47.0,!=4.47.1,!=4.48.0")
check_version("transformers>=4.43.0,<=4.51.3,!=4.46.0,!=4.46.1,!=4.46.2,!=4.46.3,!=4.47.0,!=4.47.1,!=4.48.0")
check_version("datasets>=2.16.0,<=3.5.0")
check_version("accelerate>=0.34.0,<=1.6.0")
check_version("peft>=0.14.0,<=0.15.1")
@ -141,13 +141,13 @@ def count_parameters(model: "torch.nn.Module") -> tuple[int, int]:
def get_current_device() -> "torch.device":
r"""Get the current available device."""
if is_torch_xpu_available():
device = "xpu:{}".format(os.environ.get("LOCAL_RANK", "0"))
device = "xpu:{}".format(os.getenv("LOCAL_RANK", "0"))
elif is_torch_npu_available():
device = "npu:{}".format(os.environ.get("LOCAL_RANK", "0"))
device = "npu:{}".format(os.getenv("LOCAL_RANK", "0"))
elif is_torch_mps_available():
device = "mps:{}".format(os.environ.get("LOCAL_RANK", "0"))
device = "mps:{}".format(os.getenv("LOCAL_RANK", "0"))
elif is_torch_cuda_available():
device = "cuda:{}".format(os.environ.get("LOCAL_RANK", "0"))
device = "cuda:{}".format(os.getenv("LOCAL_RANK", "0"))
else:
device = "cpu"
@ -155,11 +155,13 @@ def get_current_device() -> "torch.device":
def get_device_count() -> int:
r"""Get the number of available GPU or NPU devices."""
r"""Get the number of available devices."""
if is_torch_xpu_available():
return torch.xpu.device_count()
elif is_torch_npu_available():
return torch.npu.device_count()
elif is_torch_mps_available():
return torch.mps.device_count()
elif is_torch_cuda_available():
return torch.cuda.device_count()
else:
@ -175,10 +177,12 @@ def get_logits_processor() -> "LogitsProcessorList":
def get_peak_memory() -> tuple[int, int]:
r"""Get the peak memory usage for the current device (in Bytes)."""
if is_torch_npu_available():
return torch.npu.max_memory_allocated(), torch.npu.max_memory_reserved()
elif is_torch_xpu_available():
if is_torch_xpu_available():
return torch.xpu.max_memory_allocated(), torch.xpu.max_memory_reserved()
elif is_torch_npu_available():
return torch.npu.max_memory_allocated(), torch.npu.max_memory_reserved()
elif is_torch_mps_available():
return torch.mps.current_allocated_memory(), -1
elif is_torch_cuda_available():
return torch.cuda.max_memory_allocated(), torch.cuda.max_memory_reserved()
else:
@ -200,9 +204,11 @@ def infer_optim_dtype(model_dtype: "torch.dtype") -> "torch.dtype":
return torch.float32
def is_gpu_or_npu_available() -> bool:
r"""Check if the GPU or NPU is available."""
return is_torch_npu_available() or is_torch_cuda_available() or is_torch_xpu_available()
def is_accelerator_available() -> bool:
r"""Check if the accelerator is available."""
return (
is_torch_xpu_available() or is_torch_npu_available() or is_torch_mps_available() or is_torch_cuda_available()
)
def is_env_enabled(env_var: str, default: str = "0") -> bool:
@ -229,7 +235,7 @@ def skip_check_imports() -> None:
def torch_gc() -> None:
r"""Collect GPU or NPU memory."""
r"""Collect the device memory."""
gc.collect()
if is_torch_xpu_available():
torch.xpu.empty_cache()
@ -280,7 +286,7 @@ def use_ray() -> bool:
def find_available_port() -> int:
"""Find an available port on the local machine."""
r"""Find an available port on the local machine."""
sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
sock.bind(("", 0))
port = sock.getsockname()[1]
@ -288,8 +294,8 @@ def find_available_port() -> int:
return port
def fix_proxy(ipv6_enabled: bool) -> None:
"""Fix proxy settings for gradio ui."""
def fix_proxy(ipv6_enabled: bool = False) -> None:
r"""Fix proxy settings for gradio ui."""
os.environ["no_proxy"] = "localhost,127.0.0.1,0.0.0.0"
if ipv6_enabled:
for name in ("http_proxy", "https_proxy", "HTTP_PROXY", "HTTPS_PROXY"):

11
src/llamafactory/model/loader.py
View File

@ -19,7 +19,6 @@ import torch
from transformers import (
AutoConfig,
AutoModelForCausalLM,
AutoModelForImageTextToText,
AutoModelForSeq2SeqLM,
AutoModelForTextToWaveform,
AutoModelForVision2Seq,
@ -30,6 +29,7 @@ from trl import AutoModelForCausalLMWithValueHead
from ..extras import logging
from ..extras.misc import count_parameters, skip_check_imports, try_download_model_from_other_hub
from ..extras.packages import is_transformers_version_greater_than
from .adapter import init_adapter
from .model_utils.liger_kernel import apply_liger_kernel
from .model_utils.misc import register_autoclass
@ -39,6 +39,10 @@ from .model_utils.valuehead import load_valuehead_params
from .patcher import patch_config, patch_model, patch_processor, patch_tokenizer, patch_valuehead_model
if is_transformers_version_greater_than("4.46.0"):
from transformers import AutoModelForImageTextToText
if TYPE_CHECKING:
from transformers import PretrainedConfig, PreTrainedModel, PreTrainedTokenizer, ProcessorMixin
@ -145,7 +149,10 @@ def load_model(
else:
if type(config) in AutoModelForVision2Seq._model_mapping.keys(): # image-text
load_class = AutoModelForVision2Seq
elif type(config) in AutoModelForImageTextToText._model_mapping.keys(): # image-text
elif (
is_transformers_version_greater_than("4.46.0")
and type(config) in AutoModelForImageTextToText._model_mapping.keys()
): # image-text
load_class = AutoModelForImageTextToText
elif type(config) in AutoModelForSeq2SeqLM._model_mapping.keys(): # audio-text
load_class = AutoModelForSeq2SeqLM

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

@ -18,7 +18,6 @@ from transformers.utils import is_flash_attn_2_available, is_torch_sdpa_availabl
from ...extras import logging
from ...extras.constants import AttentionFunction
from ...extras.misc import check_version
if TYPE_CHECKING:
@ -36,8 +35,6 @@ def configure_attn_implementation(
if getattr(config, "model_type", None) == "gemma2" and is_trainable:
if model_args.flash_attn == AttentionFunction.AUTO or model_args.flash_attn == AttentionFunction.FA2:
if is_flash_attn_2_available():
check_version("transformers>=4.42.4")
check_version("flash_attn>=2.6.3")
if model_args.flash_attn != AttentionFunction.FA2:
logger.warning_rank0("Gemma 2 should use flash attention 2, change `flash_attn` to fa2.")
model_args.flash_attn = AttentionFunction.FA2

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

@ -350,7 +350,7 @@ def llama_sdpa_attention_forward(
def _apply_llama_patch() -> None:
check_version("transformers>=4.41.2,<4.48.0")
check_version("transformers>=4.43.0,<4.48.0", mandatory=True)
LlamaAttention.forward = llama_attention_forward
LlamaFlashAttention2.forward = llama_flash_attention_2_forward
LlamaSdpaAttention.forward = llama_sdpa_attention_forward

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

@ -43,7 +43,6 @@ import torch
import torch.nn.functional as F
from ...extras import logging
from ...extras.misc import check_version
from ...extras.packages import is_transformers_version_greater_than
@ -117,6 +116,5 @@ def configure_packing(model_args: "ModelArguments", is_trainable: bool) -> None:
if not is_trainable or not model_args.block_diag_attn:
return
check_version("transformers>=4.43.0")
transformers.modeling_flash_attention_utils._get_unpad_data = get_unpad_data
logger.info_rank0("Using block diagonal attention for sequence packing without cross-attention.")

2
src/llamafactory/train/callbacks.py
View File

@ -188,7 +188,7 @@ class LogCallback(TrainerCallback):
self.webui_mode = is_env_enabled("LLAMABOARD_ENABLED")
if self.webui_mode and not use_ray():
signal.signal(signal.SIGABRT, self._set_abort)
self.logger_handler = logging.LoggerHandler(os.environ.get("LLAMABOARD_WORKDIR"))
self.logger_handler = logging.LoggerHandler(os.getenv("LLAMABOARD_WORKDIR"))
logging.add_handler(self.logger_handler)
transformers.logging.add_handler(self.logger_handler)

3
src/llamafactory/train/sft/workflow.py
View File

@ -20,7 +20,7 @@ from typing import TYPE_CHECKING, Optional
from ...data import SFTDataCollatorWith4DAttentionMask, get_dataset, get_template_and_fix_tokenizer
from ...extras.constants import IGNORE_INDEX
from ...extras.logging import get_logger
from ...extras.misc import calculate_tps, get_logits_processor
from ...extras.misc import calculate_tps
from ...extras.ploting import plot_loss
from ...model import load_model, load_tokenizer
from ..trainer_utils import create_modelcard_and_push
@ -82,7 +82,6 @@ def run_sft(
gen_kwargs = generating_args.to_dict(obey_generation_config=True)
gen_kwargs["eos_token_id"] = [tokenizer.eos_token_id] + tokenizer.additional_special_tokens_ids
gen_kwargs["pad_token_id"] = tokenizer.pad_token_id
gen_kwargs["logits_processor"] = get_logits_processor()
# Initialize our Trainer
trainer = CustomSeq2SeqTrainer(

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

@ -77,10 +77,10 @@ class WebChatModel(ChatModel):
if not lazy_init: # read arguments from command line
super().__init__()
if demo_mode and os.environ.get("DEMO_MODEL") and os.environ.get("DEMO_TEMPLATE"): # load demo model
model_name_or_path = os.environ.get("DEMO_MODEL")
template = os.environ.get("DEMO_TEMPLATE")
infer_backend = os.environ.get("DEMO_BACKEND", "huggingface")
if demo_mode and os.getenv("DEMO_MODEL") and os.getenv("DEMO_TEMPLATE"): # load demo model
model_name_or_path = os.getenv("DEMO_MODEL")
template = os.getenv("DEMO_TEMPLATE")
infer_backend = os.getenv("DEMO_BACKEND", "huggingface")
super().__init__(
dict(model_name_or_path=model_name_or_path, template=template, infer_backend=infer_backend)
)

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

@ -23,7 +23,7 @@ from transformers.trainer import TRAINING_ARGS_NAME
from transformers.utils import is_torch_npu_available
from ..extras.constants import LLAMABOARD_CONFIG, PEFT_METHODS, TRAINING_STAGES
from ..extras.misc import is_gpu_or_npu_available, torch_gc, use_ray
from ..extras.misc import is_accelerator_available, torch_gc, use_ray
from ..extras.packages import is_gradio_available
from .common import (
DEFAULT_CACHE_DIR,
@ -108,7 +108,7 @@ class Runner:
if not get("eval.output_dir"):
return ALERTS["err_no_output_dir"][lang]
if not from_preview and not is_gpu_or_npu_available():
if not from_preview and not is_accelerator_available():
gr.Warning(ALERTS["warn_no_cuda"][lang])
return ""

7
tests/data/test_mm_plugin.py
View File

@ -20,6 +20,7 @@ import torch
from PIL import Image
from llamafactory.data.mm_plugin import get_mm_plugin
from llamafactory.extras.packages import is_transformers_version_greater_than
from llamafactory.hparams import get_infer_args
from llamafactory.model import load_tokenizer
@ -137,6 +138,7 @@ def test_base_plugin():
@pytest.mark.skipif(not HF_TOKEN, reason="Gated model.")
@pytest.mark.skipif(not is_transformers_version_greater_than("4.50.0"), reason="Requires transformers>=4.50.0")
def test_gemma3_plugin():
image_seqlen = 256
tokenizer_module = _load_tokenizer_module(model_name_or_path="google/gemma-3-4b-it")
@ -157,7 +159,7 @@ def test_gemma3_plugin():
_check_plugin(**check_inputs)
@pytest.mark.xfail(reason="cache failure.")
@pytest.mark.xfail(reason="Unknown error.")
def test_internvl_plugin():
image_seqlen = 256
tokenizer_module = _load_tokenizer_module(model_name_or_path="kingsley01/InternVL2_5-1B-MPO-hf")
@ -196,6 +198,7 @@ def test_llama4_plugin():
_check_plugin(**check_inputs)
@pytest.mark.skipif(not is_transformers_version_greater_than("4.47.0"), reason="Requires transformers>=4.47.0")
def test_llava_plugin():
image_seqlen = 576
tokenizer_module = _load_tokenizer_module(model_name_or_path="llava-hf/llava-1.5-7b-hf")
@ -254,6 +257,7 @@ def test_paligemma_plugin():
_check_plugin(**check_inputs)
@pytest.mark.skipif(not is_transformers_version_greater_than("4.50.0"), reason="Requires transformers>=4.50.0")
def test_pixtral_plugin():
image_slice_height, image_slice_width = 2, 2
tokenizer_module = _load_tokenizer_module(model_name_or_path="mistral-community/pixtral-12b")
@ -291,6 +295,7 @@ def test_qwen2_vl_plugin():
_check_plugin(**check_inputs)
@pytest.mark.skipif(not is_transformers_version_greater_than("4.47.0"), reason="Requires transformers>=4.47.0")
def test_video_llava_plugin():
image_seqlen = 256
tokenizer_module = _load_tokenizer_module(model_name_or_path="LanguageBind/Video-LLaVA-7B-hf")