mirror of
https://github.com/hiyouga/LLaMA-Factory.git
synced 2025-08-02 03:32:50 +08:00
[deps] update to transformers 4.52 (#8125)
This commit is contained in:
parent
b3b2c9f1ee
commit
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8
.github/workflows/tests.yml
vendored
8
.github/workflows/tests.yml
vendored
@ -40,6 +40,9 @@ jobs:
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- python: "3.9"
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os: "ubuntu-latest"
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transformers: "4.49.0"
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- python: "3.9"
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os: "ubuntu-latest"
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transformers: "4.51.0"
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runs-on: ${{ matrix.os }}
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@ -72,6 +75,11 @@ jobs:
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run: |
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python -m pip install "transformers==${{ matrix.transformers }}"
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- name: Downgrade transformers
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if: ${{ matrix.os == 'macos-13' }}
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run: |
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python -m pip install "transformers<4.52.0"
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- name: Cache files
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id: hf-hub-cache
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uses: actions/cache@v4
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@ -266,7 +266,7 @@ Choose your path:
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| [Hunyuan](https://huggingface.co/tencent/) | 7B | hunyuan |
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| [Index](https://huggingface.co/IndexTeam) | 1.9B | index |
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| [InternLM 2-3](https://huggingface.co/internlm) | 7B/8B/20B | intern2 |
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| [InternVL 2.5-3](https://huggingface.co/OpenGVLab)\* | 1B/2B/8B/14B/38B/78B | intern_vl |
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| [InternVL 2.5-3](https://huggingface.co/OpenGVLab) | 1B/2B/8B/14B/38B/78B | intern_vl |
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| [Kimi-VL](https://huggingface.co/moonshotai) | 16B | kimi_vl |
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| [Llama](https://github.com/facebookresearch/llama) | 7B/13B/33B/65B | - |
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| [Llama 2](https://huggingface.co/meta-llama) | 7B/13B/70B | llama2 |
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@ -292,7 +292,7 @@ Choose your path:
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| [Qwen (1-2.5) (Code/Math/MoE/QwQ)](https://huggingface.co/Qwen) | 0.5B/1.5B/3B/7B/14B/32B/72B/110B | qwen |
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| [Qwen3 (MoE)](https://huggingface.co/Qwen) | 0.6B/1.7B/4B/8B/14B/32B/235B | qwen3 |
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| [Qwen2-Audio](https://huggingface.co/Qwen) | 7B | qwen2_audio |
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| [Qwen2.5-Omni](https://huggingface.co/Qwen)\* | 3B/7B | qwen2_omni |
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| [Qwen2.5-Omni](https://huggingface.co/Qwen) | 3B/7B | qwen2_omni |
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| [Qwen2-VL/Qwen2.5-VL/QVQ](https://huggingface.co/Qwen) | 2B/3B/7B/32B/72B | qwen2_vl |
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| [Seed Coder](https://huggingface.co/ByteDance-Seed) | 8B | seed_coder |
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| [Skywork o1](https://huggingface.co/Skywork) | 8B | skywork_o1 |
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@ -439,6 +439,7 @@ huggingface-cli login
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| ------------ | ------- | --------- |
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| python | 3.9 | 3.10 |
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| torch | 2.0.0 | 2.6.0 |
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| torchvision | 0.15.0 | 0.21.0 |
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| transformers | 4.45.0 | 4.50.0 |
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| datasets | 2.16.0 | 3.2.0 |
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| accelerate | 0.34.0 | 1.2.1 |
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|
@ -268,7 +268,7 @@ https://github.com/user-attachments/assets/43b700c6-a178-41db-b1f8-8190a5d3fcfc
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| [Hunyuan](https://huggingface.co/tencent/) | 7B | hunyuan |
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| [Index](https://huggingface.co/IndexTeam) | 1.9B | index |
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| [InternLM 2-3](https://huggingface.co/internlm) | 7B/8B/20B | intern2 |
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| [InternVL 2.5-3](https://huggingface.co/OpenGVLab)\* | 1B/2B/8B/14B/38B/78B | intern_vl |
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| [InternVL 2.5-3](https://huggingface.co/OpenGVLab) | 1B/2B/8B/14B/38B/78B | intern_vl |
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| [Kimi-VL](https://huggingface.co/moonshotai) | 16B | kimi_vl |
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| [Llama](https://github.com/facebookresearch/llama) | 7B/13B/33B/65B | - |
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| [Llama 2](https://huggingface.co/meta-llama) | 7B/13B/70B | llama2 |
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@ -294,7 +294,7 @@ https://github.com/user-attachments/assets/43b700c6-a178-41db-b1f8-8190a5d3fcfc
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| [Qwen (1-2.5) (Code/Math/MoE/QwQ)](https://huggingface.co/Qwen) | 0.5B/1.5B/3B/7B/14B/32B/72B/110B | qwen |
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| [Qwen3 (MoE)](https://huggingface.co/Qwen) | 0.6B/1.7B/4B/8B/14B/32B/235B | qwen3 |
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| [Qwen2-Audio](https://huggingface.co/Qwen) | 7B | qwen2_audio |
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| [Qwen2.5-Omni](https://huggingface.co/Qwen)\* | 3B/7B | qwen2_omni |
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| [Qwen2.5-Omni](https://huggingface.co/Qwen) | 3B/7B | qwen2_omni |
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| [Qwen2-VL/Qwen2.5-VL/QVQ](https://huggingface.co/Qwen) | 2B/3B/7B/32B/72B | qwen2_vl |
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| [Seed Coder](https://huggingface.co/ByteDance-Seed) | 8B | seed_coder |
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| [Skywork o1](https://huggingface.co/Skywork) | 8B | skywork_o1 |
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@ -441,6 +441,7 @@ huggingface-cli login
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| ------------ | ------- | --------- |
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| python | 3.9 | 3.10 |
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| torch | 2.0.0 | 2.6.0 |
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| torchvision | 0.15.0 | 0.21.0 |
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| transformers | 4.45.0 | 4.50.0 |
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| datasets | 2.16.0 | 3.2.0 |
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| accelerate | 0.34.0 | 1.2.1 |
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@ -89,7 +89,9 @@ Regarding the above dataset, the *dataset description* in `dataset_info.json` sh
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```
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> [!TIP]
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> If the model has reasoning capabilities but the dataset does not contain chain-of-thought (CoT), LLaMA-Factory will automatically add empty CoT to the data. When `enable_thinking` is `True`, the empty CoT will be added to the model responses and loss computation will be considered; otherwise, it will be added to the user prompts and loss computation will be ignored. Please keep the `enable_thinking` parameter consistent during training and inference.
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> If the model has reasoning capabilities but the dataset does not contain chain-of-thought (CoT), LLaMA-Factory will automatically add empty CoT to the data. When `enable_thinking` is `True` (slow thinking), the empty CoT will be added to the model responses and loss computation will be considered; otherwise (fast thinking), it will be added to the user prompts and loss computation will be ignored. Please keep the `enable_thinking` parameter consistent during training and inference.
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>
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> If you want to train data containing CoT with slow thinking and data without CoT with fast thinking, you can set `enable_thinking` to `None`. However, this feature is relatively complicated and should be used with caution.
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### Pre-training Dataset
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@ -88,7 +88,9 @@
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```
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> [!TIP]
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> 如果模型本身具备推理能力,而数据集不包含思维链,LLaMA-Factory 会自动为数据添加空思维链。当 `enable_thinking` 为 `True` 时,空思维链会添加到模型回答中并且计算损失,否则会添加到用户指令中并且不计算损失。请在训练和推理时保持 `enable_thinking` 参数一致。
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> 如果模型本身具备推理能力,而数据集不包含思维链,LLaMA-Factory 会自动为数据添加空思维链。当 `enable_thinking` 为 `True` 时(慢思考),空思维链会添加到模型回答中并且计算损失,否则会添加到用户指令中并且不计算损失(快思考)。请在训练和推理时保持 `enable_thinking` 参数一致。
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>
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> 如果您希望训练包含思维链的数据时使用慢思考,训练不包含思维链的数据时使用快思考,可以设置 `enable_thinking` 为 `None`。但该功能较为复杂,请谨慎使用。
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### 预训练数据集
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@ -1,4 +1,4 @@
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transformers>=4.45.0,<=4.51.3,!=4.46.*,!=4.47.*,!=4.48.0
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transformers>=4.45.0,<=4.52.1,!=4.46.*,!=4.47.*,!=4.48.0,!=4.52.0
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datasets>=2.16.0,<=3.6.0
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accelerate>=0.34.0,<=1.7.0
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peft>=0.14.0,<=0.15.2
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2
setup.py
2
setup.py
@ -42,7 +42,7 @@ def get_console_scripts() -> list[str]:
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extra_require = {
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"torch": ["torch>=1.13.1"],
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"torch": ["torch>=2.0.0", "torchvision>=0.15.0"],
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"torch-npu": ["torch==2.4.0", "torch-npu==2.4.0.post2", "decorator"],
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"metrics": ["nltk", "jieba", "rouge-chinese"],
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"deepspeed": ["deepspeed>=0.10.0,<=0.16.5"],
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@ -57,19 +57,11 @@ if is_transformers_version_greater_than("4.45.0"):
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)
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if is_transformers_version_greater_than("4.49.0"):
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try:
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from transformers.image_utils import make_batched_videos, make_flat_list_of_images
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except ImportError:
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try:
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# If that fails, try importing from the new location
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if is_transformers_version_greater_than("4.52.0"):
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from transformers.image_utils import make_flat_list_of_images
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from transformers.video_utils import make_batched_videos
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except ImportError:
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raise ImportError(
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"Could not import make_batched_videos and make_flat_list_of_images. "
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"In Transformers 4.52.0, make_batched_videos will be moved to transformers.video_utils."
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)
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elif is_transformers_version_greater_than("4.49.0"):
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from transformers.image_utils import make_batched_videos, make_flat_list_of_images
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if TYPE_CHECKING:
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@ -52,7 +52,7 @@ class Template:
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efficient_eos: bool
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replace_eos: bool
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replace_jinja_template: bool
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enable_thinking: bool
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enable_thinking: Optional[bool]
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mm_plugin: "BasePlugin"
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def encode_oneturn(
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@ -411,14 +411,17 @@ class ReasoningTemplate(Template):
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for i in range(1, len(messages) - 2, 2):
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messages[i]["content"] = self.remove_thought(messages[i]["content"])
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if self.enable_thinking is False: # remove all cot
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messages[-1]["content"] = self.remove_thought(messages[-1]["content"])
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prompt_ids, response_ids = super().encode_oneturn(tokenizer, messages, system, tools)
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if (
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self.thought_words[0] not in messages[-1]["content"]
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and self.thought_words[1] not in messages[-1]["content"]
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):
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if not self.enable_thinking:
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prompt_ids = prompt_ids + self.get_thought_word_ids(tokenizer)
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else:
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): # add empty cot
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if not self.enable_thinking: # do not compute loss
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prompt_ids += self.get_thought_word_ids(tokenizer)
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else: # do compute loss
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response_ids = self.get_thought_word_ids(tokenizer) + response_ids
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return prompt_ids, response_ids
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@ -431,15 +434,20 @@ class ReasoningTemplate(Template):
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system: Optional[str] = None,
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tools: Optional[str] = None,
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) -> list[tuple[list[int], list[int]]]:
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messages = deepcopy(messages)
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if self.enable_thinking is False: # remove all cot
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for i in range(1, len(messages), 2):
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messages[i]["content"] = self.remove_thought(messages[i]["content"])
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encoded_messages = self._encode(tokenizer, messages, system, tools)
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for i in range(0, len(messages), 2):
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if (
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self.thought_words[0] not in messages[i + 1]["content"]
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and self.thought_words[1] not in messages[i + 1]["content"]
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):
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if not self.enable_thinking:
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): # add empty cot
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if not self.enable_thinking: # do not compute loss
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encoded_messages[i] += self.get_thought_word_ids(tokenizer)
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else:
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else: # do compute loss
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encoded_messages[i + 1] = self.get_thought_word_ids(tokenizer) + encoded_messages[i + 1]
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return [(encoded_messages[i], encoded_messages[i + 1]) for i in range(0, len(encoded_messages), 2)]
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@ -463,7 +471,7 @@ def register_template(
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efficient_eos: bool = False,
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replace_eos: bool = False,
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replace_jinja_template: bool = False,
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enable_thinking: bool = True,
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enable_thinking: Optional[bool] = True,
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mm_plugin: "BasePlugin" = get_mm_plugin(name="base"),
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template_class: type["Template"] = Template,
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) -> None:
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@ -2566,6 +2566,14 @@ register_model_group(
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DownloadSource.DEFAULT: "Qwen/Qwen2.5-Omni-7B",
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DownloadSource.MODELSCOPE: "Qwen/Qwen2.5-Omni-7B",
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},
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"Qwen2.5-Omni-7B-GPTQ-Int4": {
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DownloadSource.DEFAULT: "Qwen/Qwen2.5-Omni-7B-GPTQ-Int4",
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DownloadSource.MODELSCOPE: "Qwen/Qwen2.5-Omni-7B-GPTQ-Int4",
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},
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"Qwen2.5-Omni-7B-AWQ": {
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DownloadSource.DEFAULT: "Qwen/Qwen2.5-Omni-7B-AWQ",
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DownloadSource.MODELSCOPE: "Qwen/Qwen2.5-Omni-7B-AWQ",
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},
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},
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template="qwen2_omni",
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multimodal=True,
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@ -94,7 +94,9 @@ def check_version(requirement: str, mandatory: bool = False) -> None:
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def check_dependencies() -> None:
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r"""Check the version of the required packages."""
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check_version("transformers>=4.45.0,<=4.51.3,!=4.46.0,!=4.46.1,!=4.46.2,!=4.46.3,!=4.47.0,!=4.47.1,!=4.48.0")
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check_version(
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"transformers>=4.45.0,<=4.52.1,!=4.46.0,!=4.46.1,!=4.46.2,!=4.46.3,!=4.47.0,!=4.47.1,!=4.48.0,!=4.52.0"
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)
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check_version("datasets>=2.16.0,<=3.6.0")
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check_version("accelerate>=0.34.0,<=1.7.0")
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check_version("peft>=0.14.0,<=0.15.2")
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@ -119,7 +119,7 @@ class DataArguments:
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default=None,
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metadata={"help": "Override the default system message in the template."},
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)
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enable_thinking: bool = field(
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enable_thinking: Optional[bool] = field(
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default=True,
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metadata={"help": "Whether or not to enable thinking mode for reasoning models."},
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)
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@ -235,10 +235,6 @@ class ProcessorArguments:
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default=False,
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metadata={"help": "Whether to crop the image to patches for internvl."},
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)
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use_audio_in_video: bool = field(
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default=False,
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metadata={"help": "Whether or not to use audio in video inputs."},
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)
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video_max_pixels: int = field(
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default=256 * 256,
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metadata={"help": "The maximum number of pixels of video inputs."},
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@ -255,6 +251,10 @@ class ProcessorArguments:
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default=128,
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metadata={"help": "The maximum number of sampled frames for video inputs."},
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)
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use_audio_in_video: bool = field(
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default=False,
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metadata={"help": "Whether or not to use audio in video inputs."},
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)
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audio_sampling_rate: int = field(
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default=16000,
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metadata={"help": "The sampling rate of audio inputs."},
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@ -24,6 +24,7 @@ import transformers.models
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from transformers.activations import ACT2FN
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from ...extras import logging
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from ...extras.packages import is_transformers_version_greater_than
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if TYPE_CHECKING:
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@ -281,7 +282,7 @@ _register_composite_model(
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model_type="qwen2_vl",
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projector_key="visual.merger",
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vision_model_keys=["visual.patch_embed", "visual.blocks"],
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language_model_keys=["model", "lm_head"],
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language_model_keys=["language_model"] if is_transformers_version_greater_than("4.52.0") else ["model", "lm_head"],
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lora_conflict_keys=["patch_embed"],
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)
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@ -290,6 +291,6 @@ _register_composite_model(
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model_type="qwen2_5_vl",
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projector_key="visual.merger",
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vision_model_keys=["visual.patch_embed", "visual.blocks"],
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language_model_keys=["model", "lm_head"],
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language_model_keys=["language_model"] if is_transformers_version_greater_than("4.52.0") else ["model", "lm_head"],
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lora_conflict_keys=["patch_embed"],
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)
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|
@ -85,8 +85,8 @@ def patch_processor(
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setattr(processor, "video_min_pixels", model_args.video_min_pixels)
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setattr(processor, "video_fps", model_args.video_fps)
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setattr(processor, "video_maxlen", model_args.video_maxlen)
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setattr(processor, "audio_sampling_rate", model_args.audio_sampling_rate)
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setattr(processor, "use_audio_in_video", model_args.use_audio_in_video)
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setattr(processor, "audio_sampling_rate", model_args.audio_sampling_rate)
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def patch_config(
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|
@ -121,11 +121,11 @@ class CustomDPOTrainer(DPOTrainer):
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return super().create_scheduler(num_training_steps, optimizer)
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@override
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def _get_train_sampler(self) -> Optional["torch.utils.data.Sampler"]:
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def _get_train_sampler(self, *args, **kwargs) -> Optional["torch.utils.data.Sampler"]:
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if self.finetuning_args.disable_shuffling:
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return torch.utils.data.SequentialSampler(self.train_dataset)
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return super()._get_train_sampler()
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return super()._get_train_sampler(*args, **kwargs)
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@override
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def get_batch_samples(self, *args, **kwargs):
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|
@ -34,7 +34,6 @@ from ..trainer_utils import create_custom_optimizer, create_custom_scheduler, ge
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if TYPE_CHECKING:
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import torch.utils.data
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from transformers import PreTrainedModel, ProcessorMixin
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from ...hparams import FinetuningArguments
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@ -119,12 +118,12 @@ class CustomKTOTrainer(KTOTrainer):
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return super().create_scheduler(num_training_steps, optimizer)
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@override
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def _get_train_sampler(self) -> Optional["torch.utils.data.Sampler"]:
|
||||
def _get_train_sampler(self, *args, **kwargs) -> Optional["torch.utils.data.Sampler"]:
|
||||
r"""Replace the sequential sampler of KTO Trainer created by trl with the random sampler."""
|
||||
if self.finetuning_args.disable_shuffling:
|
||||
return torch.utils.data.SequentialSampler(self.train_dataset)
|
||||
|
||||
return Trainer._get_train_sampler(self)
|
||||
return Trainer._get_train_sampler(self, *args, **kwargs)
|
||||
|
||||
@override
|
||||
def get_batch_samples(self, *args, **kwargs):
|
||||
|
@ -70,11 +70,11 @@ class CustomTrainer(Trainer):
|
||||
return super().create_scheduler(num_training_steps, optimizer)
|
||||
|
||||
@override
|
||||
def _get_train_sampler(self) -> Optional["torch.utils.data.Sampler"]:
|
||||
def _get_train_sampler(self, *args, **kwargs) -> Optional["torch.utils.data.Sampler"]:
|
||||
if self.finetuning_args.disable_shuffling:
|
||||
return torch.utils.data.SequentialSampler(self.train_dataset)
|
||||
|
||||
return super()._get_train_sampler()
|
||||
return super()._get_train_sampler(*args, **kwargs)
|
||||
|
||||
@override
|
||||
def compute_loss(self, model, inputs, *args, **kwargs):
|
||||
|
@ -78,11 +78,11 @@ class PairwiseTrainer(Trainer):
|
||||
return super().create_scheduler(num_training_steps, optimizer)
|
||||
|
||||
@override
|
||||
def _get_train_sampler(self) -> Optional["torch.utils.data.Sampler"]:
|
||||
def _get_train_sampler(self, *args, **kwargs) -> Optional["torch.utils.data.Sampler"]:
|
||||
if self.finetuning_args.disable_shuffling:
|
||||
return torch.utils.data.SequentialSampler(self.train_dataset)
|
||||
|
||||
return super()._get_train_sampler()
|
||||
return super()._get_train_sampler(*args, **kwargs)
|
||||
|
||||
@override
|
||||
def compute_loss(
|
||||
|
@ -92,11 +92,11 @@ class CustomSeq2SeqTrainer(Seq2SeqTrainer):
|
||||
return super().create_scheduler(num_training_steps, optimizer)
|
||||
|
||||
@override
|
||||
def _get_train_sampler(self) -> Optional["torch.utils.data.Sampler"]:
|
||||
def _get_train_sampler(self, *args, **kwargs) -> Optional["torch.utils.data.Sampler"]:
|
||||
if self.finetuning_args.disable_shuffling:
|
||||
return torch.utils.data.SequentialSampler(self.train_dataset)
|
||||
|
||||
return super()._get_train_sampler()
|
||||
return super()._get_train_sampler(*args, **kwargs)
|
||||
|
||||
@override
|
||||
def compute_loss(self, model, inputs, *args, **kwargs):
|
||||
|
@ -205,6 +205,14 @@ def load_eval_results(path: os.PathLike) -> str:
|
||||
return f"```json\n{result}\n```\n"
|
||||
|
||||
|
||||
def calculate_pixels(pixels: str) -> int:
|
||||
r"""Calculate the number of pixels from the expression."""
|
||||
if "*" in pixels:
|
||||
return int(pixels.split("*")[0]) * int(pixels.split("*")[1])
|
||||
else:
|
||||
return int(pixels)
|
||||
|
||||
|
||||
def create_ds_config() -> None:
|
||||
r"""Create deepspeed config in the current directory."""
|
||||
os.makedirs(DEFAULT_CACHE_DIR, exist_ok=True)
|
||||
|
@ -106,11 +106,11 @@ def create_train_tab(engine: "Engine") -> dict[str, "Component"]:
|
||||
use_llama_pro = gr.Checkbox()
|
||||
|
||||
with gr.Column():
|
||||
enable_thinking = gr.Checkbox(value=True)
|
||||
report_to = gr.Dropdown(
|
||||
choices=["none", "all", "wandb", "mlflow", "neptune", "tensorboard"],
|
||||
value=["none"],
|
||||
choices=["none", "wandb", "mlflow", "neptune", "tensorboard", "all"],
|
||||
value="none",
|
||||
allow_custom_value=True,
|
||||
multiselect=True,
|
||||
)
|
||||
|
||||
input_elems.update(
|
||||
@ -126,6 +126,7 @@ def create_train_tab(engine: "Engine") -> dict[str, "Component"]:
|
||||
mask_history,
|
||||
resize_vocab,
|
||||
use_llama_pro,
|
||||
enable_thinking,
|
||||
report_to,
|
||||
}
|
||||
)
|
||||
@ -143,6 +144,7 @@ def create_train_tab(engine: "Engine") -> dict[str, "Component"]:
|
||||
mask_history=mask_history,
|
||||
resize_vocab=resize_vocab,
|
||||
use_llama_pro=use_llama_pro,
|
||||
enable_thinking=enable_thinking,
|
||||
report_to=report_to,
|
||||
)
|
||||
)
|
||||
@ -231,6 +233,42 @@ def create_train_tab(engine: "Engine") -> dict[str, "Component"]:
|
||||
)
|
||||
)
|
||||
|
||||
with gr.Accordion(open=False) as mm_tab:
|
||||
with gr.Row():
|
||||
freeze_vision_tower = gr.Checkbox(value=True)
|
||||
freeze_multi_modal_projector = gr.Checkbox(value=True)
|
||||
freeze_language_model = gr.Checkbox(value=False)
|
||||
|
||||
with gr.Row():
|
||||
image_max_pixels = gr.Textbox(value="768*768")
|
||||
image_min_pixels = gr.Textbox(value="32*32")
|
||||
video_max_pixels = gr.Textbox(value="256*256")
|
||||
video_min_pixels = gr.Textbox(value="16*16")
|
||||
|
||||
input_elems.update(
|
||||
{
|
||||
freeze_vision_tower,
|
||||
freeze_multi_modal_projector,
|
||||
freeze_language_model,
|
||||
image_max_pixels,
|
||||
image_min_pixels,
|
||||
video_max_pixels,
|
||||
video_min_pixels,
|
||||
}
|
||||
)
|
||||
elem_dict.update(
|
||||
dict(
|
||||
mm_tab=mm_tab,
|
||||
freeze_vision_tower=freeze_vision_tower,
|
||||
freeze_multi_modal_projector=freeze_multi_modal_projector,
|
||||
freeze_language_model=freeze_language_model,
|
||||
image_max_pixels=image_max_pixels,
|
||||
image_min_pixels=image_min_pixels,
|
||||
video_max_pixels=video_max_pixels,
|
||||
video_min_pixels=video_min_pixels,
|
||||
)
|
||||
)
|
||||
|
||||
with gr.Accordion(open=False) as galore_tab:
|
||||
with gr.Row():
|
||||
use_galore = gr.Checkbox()
|
||||
|
@ -871,6 +871,28 @@ LOCALES = {
|
||||
"info": "拡張ブロックのパラメータのみをトレーニングします。",
|
||||
},
|
||||
},
|
||||
"enable_thinking": {
|
||||
"en": {
|
||||
"label": "Enable thinking",
|
||||
"info": "Whether or not to enable thinking mode for reasoning models.",
|
||||
},
|
||||
"ru": {
|
||||
"label": "Включить мысли",
|
||||
"info": "Включить режим мысли для моделей решающего характера.",
|
||||
},
|
||||
"zh": {
|
||||
"label": "启用思考模式",
|
||||
"info": "是否启用推理模型的思考模式。",
|
||||
},
|
||||
"ko": {
|
||||
"label": "생각 모드 활성화",
|
||||
"info": "추론 모델의 생각 모드를 활성화할지 여부.",
|
||||
},
|
||||
"ja": {
|
||||
"label": "思考モードを有効化",
|
||||
"info": "推論モデルの思考モードを有効にするかどうか。",
|
||||
},
|
||||
},
|
||||
"report_to": {
|
||||
"en": {
|
||||
"label": "Enable external logger",
|
||||
@ -1374,6 +1396,177 @@ LOCALES = {
|
||||
"info": "PPO トレーニングにおいて報酬スコアをホワイトニング処理します。",
|
||||
},
|
||||
},
|
||||
"mm_tab": {
|
||||
"en": {
|
||||
"label": "Multimodal configurations",
|
||||
},
|
||||
"ru": {
|
||||
"label": "Конфигурации мультимедиа",
|
||||
},
|
||||
"zh": {
|
||||
"label": "多模态参数设置",
|
||||
},
|
||||
"ko": {
|
||||
"label": "멀티모달 구성",
|
||||
},
|
||||
"ja": {
|
||||
"label": "多モーダル設定",
|
||||
},
|
||||
},
|
||||
"freeze_vision_tower": {
|
||||
"en": {
|
||||
"label": "Freeze vision tower",
|
||||
"info": "Freeze the vision tower in the model.",
|
||||
},
|
||||
"ru": {
|
||||
"label": "Заморозить башню визиона",
|
||||
"info": "Заморозить башню визиона в модели.",
|
||||
},
|
||||
"zh": {
|
||||
"label": "冻结视觉编码器",
|
||||
"info": "冻结模型中的视觉编码器。",
|
||||
},
|
||||
"ko": {
|
||||
"label": "비전 타워 고정",
|
||||
"info": "모델의 비전 타워를 고정합니다.",
|
||||
},
|
||||
"ja": {
|
||||
"label": "ビジョンタワーの固定",
|
||||
"info": "モデルのビジョンタワーを固定します。",
|
||||
},
|
||||
},
|
||||
"freeze_multi_modal_projector": {
|
||||
"en": {
|
||||
"label": "Freeze multi-modal projector",
|
||||
"info": "Freeze the multi-modal projector in the model.",
|
||||
},
|
||||
"ru": {
|
||||
"label": "Заморозить мультимодальный проектор",
|
||||
"info": "Заморозить мультимодальный проектор в модели.",
|
||||
},
|
||||
"zh": {
|
||||
"label": "冻结多模态投影器",
|
||||
"info": "冻结模型中的多模态投影器。",
|
||||
},
|
||||
"ko": {
|
||||
"label": "멀티모달 프로젝터 고정",
|
||||
"info": "모델의 멀티모달 프로젝터를 고정합니다.",
|
||||
},
|
||||
"ja": {
|
||||
"label": "多モーダルプロジェクターの固定",
|
||||
"info": "モデルの多モーダルプロジェクターを固定します。",
|
||||
},
|
||||
},
|
||||
"freeze_language_model": {
|
||||
"en": {
|
||||
"label": "Freeze language model",
|
||||
"info": "Freeze the language model in the model.",
|
||||
},
|
||||
"ru": {
|
||||
"label": "Заморозить язык модели",
|
||||
"info": "Заморозить язык модели в модели.",
|
||||
},
|
||||
"zh": {
|
||||
"label": "冻结语言模型",
|
||||
"info": "冻结模型中的语言模型。",
|
||||
},
|
||||
"ko": {
|
||||
"label": "언어 모델 고정",
|
||||
"info": "모델의 언어 모델을 고정합니다.",
|
||||
},
|
||||
"ja": {
|
||||
"label": "言語モデルの固定",
|
||||
"info": "モデルの言語モデルを固定します。",
|
||||
},
|
||||
},
|
||||
"image_max_pixels": {
|
||||
"en": {
|
||||
"label": "Image max pixels",
|
||||
"info": "The maximum number of pixels of image inputs.",
|
||||
},
|
||||
"ru": {
|
||||
"label": "Максимальное количество пикселей изображения",
|
||||
"info": "Максимальное количество пикселей изображения.",
|
||||
},
|
||||
"zh": {
|
||||
"label": "图像最大像素",
|
||||
"info": "输入图像的最大像素数。",
|
||||
},
|
||||
"ko": {
|
||||
"label": "이미지 최대 픽셀",
|
||||
"info": "이미지 입력의 최대 픽셀 수입니다.",
|
||||
},
|
||||
"ja": {
|
||||
"label": "画像最大ピクセル",
|
||||
"info": "画像入力の最大ピクセル数です。",
|
||||
},
|
||||
},
|
||||
"image_min_pixels": {
|
||||
"en": {
|
||||
"label": "Image min pixels",
|
||||
"info": "The minimum number of pixels of image inputs.",
|
||||
},
|
||||
"ru": {
|
||||
"label": "Минимальное количество пикселей изображения",
|
||||
"info": "Минимальное количество пикселей изображения.",
|
||||
},
|
||||
"zh": {
|
||||
"label": "图像最小像素",
|
||||
"info": "输入图像的最小像素数。",
|
||||
},
|
||||
"ko": {
|
||||
"label": "이미지 최소 픽셀",
|
||||
"info": "이미지 입력의 최소 픽셀 수입니다.",
|
||||
},
|
||||
"ja": {
|
||||
"label": "画像最小ピクセル",
|
||||
"info": "画像入力の最小ピクセル数です。",
|
||||
},
|
||||
},
|
||||
"video_max_pixels": {
|
||||
"en": {
|
||||
"label": "Video max pixels",
|
||||
"info": "The maximum number of pixels of video inputs.",
|
||||
},
|
||||
"ru": {
|
||||
"label": "Максимальное количество пикселей видео",
|
||||
"info": "Максимальное количество пикселей видео.",
|
||||
},
|
||||
"zh": {
|
||||
"label": "视频最大像素",
|
||||
"info": "输入视频的最大像素数。",
|
||||
},
|
||||
"ko": {
|
||||
"label": "비디오 최대 픽셀",
|
||||
"info": "비디오 입력의 최대 픽셀 수입니다.",
|
||||
},
|
||||
"ja": {
|
||||
"label": "ビデオ最大ピクセル",
|
||||
"info": "ビデオ入力の最大ピクセル数です。",
|
||||
},
|
||||
},
|
||||
"video_min_pixels": {
|
||||
"en": {
|
||||
"label": "Video min pixels",
|
||||
"info": "The minimum number of pixels of video inputs.",
|
||||
},
|
||||
"ru": {
|
||||
"label": "Минимальное количество пикселей видео",
|
||||
"info": "Минимальное количество пикселей видео.",
|
||||
},
|
||||
"zh": {
|
||||
"label": "视频最小像素",
|
||||
"info": "输入视频的最小像素数。",
|
||||
},
|
||||
"ko": {
|
||||
"label": "비디오 최소 픽셀",
|
||||
"info": "비디오 입력의 최소 픽셀 수입니다.",
|
||||
},
|
||||
"ja": {
|
||||
"label": "ビデオ最小ピクセル",
|
||||
"info": "ビデオ入力の最小ピクセル数です。",
|
||||
},
|
||||
},
|
||||
"galore_tab": {
|
||||
"en": {
|
||||
"label": "GaLore configurations",
|
||||
@ -2468,23 +2661,6 @@ LOCALES = {
|
||||
"label": "HTML タグをエスケープ",
|
||||
},
|
||||
},
|
||||
"enable_thinking": {
|
||||
"en": {
|
||||
"label": "Enable thinking",
|
||||
},
|
||||
"ru": {
|
||||
"label": "Включить мышление",
|
||||
},
|
||||
"zh": {
|
||||
"label": "启用思考",
|
||||
},
|
||||
"ko": {
|
||||
"label": "사고를 활성화하다",
|
||||
},
|
||||
"ja": {
|
||||
"label": "思考を可能にする",
|
||||
},
|
||||
},
|
||||
"clear_btn": {
|
||||
"en": {
|
||||
"value": "Clear history",
|
||||
|
@ -29,6 +29,7 @@ from .common import (
|
||||
DEFAULT_CACHE_DIR,
|
||||
DEFAULT_CONFIG_DIR,
|
||||
abort_process,
|
||||
calculate_pixels,
|
||||
gen_cmd,
|
||||
get_save_dir,
|
||||
load_args,
|
||||
@ -162,7 +163,15 @@ class Runner:
|
||||
mask_history=get("train.mask_history"),
|
||||
resize_vocab=get("train.resize_vocab"),
|
||||
use_llama_pro=get("train.use_llama_pro"),
|
||||
enable_thinking=get("train.enable_thinking"),
|
||||
report_to=get("train.report_to"),
|
||||
freeze_vision_tower=get("train.freeze_vision_tower"),
|
||||
freeze_multi_modal_projector=get("train.freeze_multi_modal_projector"),
|
||||
freeze_language_model=get("train.freeze_language_model"),
|
||||
image_max_pixels=calculate_pixels(get("train.image_max_pixels")),
|
||||
image_min_pixels=calculate_pixels(get("train.image_min_pixels")),
|
||||
video_max_pixels=calculate_pixels(get("train.video_max_pixels")),
|
||||
video_min_pixels=calculate_pixels(get("train.video_min_pixels")),
|
||||
use_galore=get("train.use_galore"),
|
||||
use_apollo=get("train.use_apollo"),
|
||||
use_badam=get("train.use_badam"),
|
||||
@ -256,12 +265,6 @@ class Runner:
|
||||
args["badam_switch_interval"] = get("train.badam_switch_interval")
|
||||
args["badam_update_ratio"] = get("train.badam_update_ratio")
|
||||
|
||||
# report_to
|
||||
if "none" in args["report_to"]:
|
||||
args["report_to"] = "none"
|
||||
elif "all" in args["report_to"]:
|
||||
args["report_to"] = "all"
|
||||
|
||||
# swanlab config
|
||||
if get("train.use_swanlab"):
|
||||
args["swanlab_project"] = get("train.swanlab_project")
|
||||
|
@ -135,8 +135,7 @@ def _check_plugin(
|
||||
expected_mm_inputs: dict[str, Any] = {},
|
||||
expected_no_mm_inputs: dict[str, Any] = {},
|
||||
) -> None:
|
||||
# test omni_messages
|
||||
if plugin.__class__.__name__ == "Qwen2OmniPlugin":
|
||||
if plugin.__class__.__name__ == "Qwen2OmniPlugin": # test omni_messages
|
||||
assert plugin.process_messages(OMNI_MESSAGES, IMAGES, NO_VIDEOS, AUDIOS, processor) == expected_mm_messages
|
||||
assert plugin.process_token_ids(INPUT_IDS, LABELS, IMAGES, NO_VIDEOS, AUDIOS, tokenizer, processor) == (
|
||||
expected_input_ids,
|
||||
@ -146,8 +145,7 @@ def _check_plugin(
|
||||
plugin.get_mm_inputs(IMAGES, NO_VIDEOS, AUDIOS, IMGLENS, NO_VIDLENS, AUDLENS, BATCH_IDS, processor),
|
||||
expected_mm_inputs,
|
||||
)
|
||||
# test mm_messages
|
||||
if plugin.__class__.__name__ != "BasePlugin":
|
||||
elif plugin.__class__.__name__ != "BasePlugin": # test mm_messages
|
||||
assert plugin.process_messages(MM_MESSAGES, IMAGES, NO_VIDEOS, NO_AUDIOS, processor) == expected_mm_messages
|
||||
assert plugin.process_token_ids(INPUT_IDS, LABELS, IMAGES, NO_VIDEOS, NO_AUDIOS, tokenizer, processor) == (
|
||||
expected_input_ids,
|
||||
@ -201,7 +199,7 @@ def test_gemma3_plugin():
|
||||
_check_plugin(**check_inputs)
|
||||
|
||||
|
||||
@pytest.mark.xfail(reason="Unknown error.")
|
||||
@pytest.mark.skipif(not is_transformers_version_greater_than("4.52.0"), reason="Requires transformers>=4.52.0")
|
||||
def test_internvl_plugin():
|
||||
image_seqlen = 256
|
||||
tokenizer_module = _load_tokenizer_module(model_name_or_path="OpenGVLab/InternVL3-1B-hf")
|
||||
@ -219,7 +217,7 @@ def test_internvl_plugin():
|
||||
_check_plugin(**check_inputs)
|
||||
|
||||
|
||||
@pytest.mark.xfail(reason="Unknown error.")
|
||||
@pytest.mark.skipif(not is_transformers_version_greater_than("4.51.0"), reason="Requires transformers>=4.51.0")
|
||||
def test_llama4_plugin():
|
||||
tokenizer_module = _load_tokenizer_module(model_name_or_path=TINY_LLAMA4)
|
||||
processor = tokenizer_module["processor"]
|
||||
@ -321,10 +319,9 @@ def test_pixtral_plugin():
|
||||
_check_plugin(**check_inputs)
|
||||
|
||||
|
||||
@pytest.mark.xfail(reason="Unknown error.")
|
||||
@pytest.mark.skipif(not is_transformers_version_greater_than("4.52.0"), reason="Requires transformers>=4.52.0")
|
||||
def test_qwen2_omni_plugin():
|
||||
image_seqlen = 4
|
||||
audio_seqlen = 2
|
||||
image_seqlen, audio_seqlen = 4, 2
|
||||
tokenizer_module = _load_tokenizer_module(model_name_or_path="Qwen/Qwen2.5-Omni-7B")
|
||||
qwen2_omni_plugin = get_mm_plugin(
|
||||
name="qwen2_omni", audio_token="<|AUDIO|>", image_token="<|IMAGE|>", video_token="<|VIDEO|>"
|
||||
|
@ -127,20 +127,21 @@ def test_encode_multiturn(use_fast: bool):
|
||||
|
||||
@pytest.mark.parametrize("use_fast", [True, False])
|
||||
@pytest.mark.parametrize("cot_messages", [True, False])
|
||||
@pytest.mark.parametrize("enable_thinking", [True, False])
|
||||
@pytest.mark.parametrize("enable_thinking", [True, False, None])
|
||||
def test_reasoning_encode_oneturn(use_fast: bool, cot_messages: bool, enable_thinking: bool):
|
||||
messages = MESSAGES_WITH_THOUGHT if cot_messages else MESSAGES
|
||||
input_messages = MESSAGES_WITH_THOUGHT if cot_messages else MESSAGES
|
||||
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-8B", use_fast=use_fast)
|
||||
data_args = DataArguments(template="qwen3", enable_thinking=enable_thinking)
|
||||
template = get_template_and_fix_tokenizer(tokenizer, data_args)
|
||||
prompt_ids, answer_ids = template.encode_oneturn(tokenizer, messages)
|
||||
prompt_ids, answer_ids = template.encode_oneturn(tokenizer, input_messages)
|
||||
output_messages = MESSAGES if enable_thinking is False else input_messages
|
||||
prompt_str = (
|
||||
f"<|im_start|>user\n{messages[0]['content']}<|im_end|>\n<|im_start|>assistant\n"
|
||||
f"<|im_start|>user\n{output_messages[0]['content']}<|im_end|>\n<|im_start|>assistant\n"
|
||||
f"{MESSAGES[1]['content']}<|im_end|>\n"
|
||||
f"<|im_start|>user\n{messages[2]['content']}<|im_end|>\n<|im_start|>assistant\n"
|
||||
f"<|im_start|>user\n{output_messages[2]['content']}<|im_end|>\n<|im_start|>assistant\n"
|
||||
)
|
||||
answer_str = f"{messages[3]['content']}<|im_end|>\n"
|
||||
if not cot_messages:
|
||||
answer_str = f"{output_messages[3]['content']}<|im_end|>\n"
|
||||
if not cot_messages or enable_thinking is False:
|
||||
if enable_thinking:
|
||||
answer_str = "<think>\n\n</think>\n\n" + answer_str
|
||||
else:
|
||||
@ -151,18 +152,19 @@ def test_reasoning_encode_oneturn(use_fast: bool, cot_messages: bool, enable_thi
|
||||
|
||||
@pytest.mark.parametrize("use_fast", [True, False])
|
||||
@pytest.mark.parametrize("cot_messages", [True, False])
|
||||
@pytest.mark.parametrize("enable_thinking", [True, False])
|
||||
@pytest.mark.parametrize("enable_thinking", [True, False, None])
|
||||
def test_reasoning_encode_multiturn(use_fast: bool, cot_messages: bool, enable_thinking: bool):
|
||||
messages = MESSAGES_WITH_THOUGHT if cot_messages else MESSAGES
|
||||
input_messages = MESSAGES_WITH_THOUGHT if cot_messages else MESSAGES
|
||||
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-8B", use_fast=use_fast)
|
||||
data_args = DataArguments(template="qwen3", enable_thinking=enable_thinking)
|
||||
template = get_template_and_fix_tokenizer(tokenizer, data_args)
|
||||
encoded_pairs = template.encode_multiturn(tokenizer, messages)
|
||||
prompt_str_1 = f"<|im_start|>user\n{messages[0]['content']}<|im_end|>\n<|im_start|>assistant\n"
|
||||
answer_str_1 = f"{messages[1]['content']}<|im_end|>\n"
|
||||
prompt_str_2 = f"<|im_start|>user\n{messages[2]['content']}<|im_end|>\n<|im_start|>assistant\n"
|
||||
answer_str_2 = f"{messages[3]['content']}<|im_end|>\n"
|
||||
if not cot_messages:
|
||||
encoded_pairs = template.encode_multiturn(tokenizer, input_messages)
|
||||
output_messages = MESSAGES if enable_thinking is False else input_messages
|
||||
prompt_str_1 = f"<|im_start|>user\n{output_messages[0]['content']}<|im_end|>\n<|im_start|>assistant\n"
|
||||
answer_str_1 = f"{output_messages[1]['content']}<|im_end|>\n"
|
||||
prompt_str_2 = f"<|im_start|>user\n{output_messages[2]['content']}<|im_end|>\n<|im_start|>assistant\n"
|
||||
answer_str_2 = f"{output_messages[3]['content']}<|im_end|>\n"
|
||||
if not cot_messages or enable_thinking is False:
|
||||
if enable_thinking:
|
||||
answer_str_1 = "<think>\n\n</think>\n\n" + answer_str_1
|
||||
answer_str_2 = "<think>\n\n</think>\n\n" + answer_str_2
|
||||
|
@ -16,6 +16,7 @@ import pytest
|
||||
import torch
|
||||
from transformers import AutoConfig, AutoModelForVision2Seq
|
||||
|
||||
from llamafactory.extras.packages import is_transformers_version_greater_than
|
||||
from llamafactory.hparams import FinetuningArguments, ModelArguments
|
||||
from llamafactory.model.adapter import init_adapter
|
||||
|
||||
@ -45,10 +46,12 @@ def test_visual_full(freeze_vision_tower: bool, freeze_multi_modal_projector: bo
|
||||
assert param.requires_grad != freeze_language_model
|
||||
|
||||
|
||||
@pytest.mark.parametrize("freeze_vision_tower", (False, True))
|
||||
def test_visual_lora(freeze_vision_tower: bool):
|
||||
@pytest.mark.parametrize("freeze_vision_tower,freeze_language_model", ((False, False), (False, True), (True, False)))
|
||||
def test_visual_lora(freeze_vision_tower: bool, freeze_language_model: bool):
|
||||
model_args = ModelArguments(model_name_or_path="Qwen/Qwen2-VL-2B-Instruct")
|
||||
finetuning_args = FinetuningArguments(finetuning_type="lora", freeze_vision_tower=freeze_vision_tower)
|
||||
finetuning_args = FinetuningArguments(
|
||||
finetuning_type="lora", freeze_vision_tower=freeze_vision_tower, freeze_language_model=freeze_language_model
|
||||
)
|
||||
config = AutoConfig.from_pretrained(model_args.model_name_or_path)
|
||||
with torch.device("meta"):
|
||||
model = AutoModelForVision2Seq.from_config(config)
|
||||
@ -61,10 +64,15 @@ def test_visual_lora(freeze_vision_tower: bool):
|
||||
else:
|
||||
frozen_params.add(name)
|
||||
|
||||
if freeze_vision_tower:
|
||||
assert "base_model.model.visual.blocks.0.attn.qkv.lora_A.default.weight" not in trainable_params
|
||||
if is_transformers_version_greater_than("4.52.0"):
|
||||
visual_param_name = "base_model.model.model.visual.blocks.0.attn.qkv.lora_A.default.weight"
|
||||
language_param_name = "base_model.model.model.language_model.layers.0.self_attn.q_proj.lora_A.default.weight"
|
||||
merger_param_name = "base_model.model.model.visual.merger.lora_A.default.weight"
|
||||
else:
|
||||
assert "base_model.model.visual.blocks.0.attn.qkv.lora_A.default.weight" in trainable_params
|
||||
visual_param_name = "base_model.model.visual.blocks.0.attn.qkv.lora_A.default.weight"
|
||||
language_param_name = "base_model.model.model.layers.0.self_attn.q_proj.lora_A.default.weight"
|
||||
merger_param_name = "base_model.model.visual.merger.lora_A.default.weight"
|
||||
|
||||
assert "merger" not in trainable_params
|
||||
assert "base_model.model.model.layers.0.self_attn.q_proj.lora_A.default.weight" in trainable_params
|
||||
assert (visual_param_name in trainable_params) != freeze_vision_tower
|
||||
assert (language_param_name in trainable_params) != freeze_language_model
|
||||
assert (merger_param_name in trainable_params) is False
|
||||
|
@ -1,2 +1,2 @@
|
||||
# change if test fails or cache is outdated
|
||||
0.9.3.106
|
||||
0.9.3.107
|
||||
|
Loading…
x
Reference in New Issue
Block a user