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Merge pull request #3454 from hiyouga/mllm 

Support fine-tuning LLaVA-1.5 MLLM @BUAADreamer
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5
README.md
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@ -68,6 +68,8 @@ Compared to ChatGLM's [P-Tuning](https://github.com/THUDM/ChatGLM2-6B/tree/main/
## Changelog
[24/04/26] We supported fine-tuning the **LLaVA-1.5** multimodal LLMs. See `examples/lora_single_gpu/sft_mllm.sh` for usage.
[24/04/22] We provided a **[Colab notebook](https://colab.research.google.com/drive/1eRTPn37ltBbYsISy9Aw2NuI2Aq5CQrD9?usp=sharing)** for fine-tuning the Llama-3 model on a free T4 GPU. Two Llama-3-derived models fine-tuned using LLaMA Factory are available at Hugging Face, check [Llama3-8B-Chinese-Chat](https://huggingface.co/shenzhi-wang/Llama3-8B-Chinese-Chat) and [Llama3-Chinese](https://huggingface.co/zhichen/Llama3-Chinese) for details.
[24/04/21] We supported **[Mixture-of-Depths](https://arxiv.org/abs/2404.02258)** according to [AstraMindAI's implementation](https://github.com/astramind-ai/Mixture-of-depths). See `examples/extras/mod` for usage.
@ -148,6 +150,7 @@ Compared to ChatGLM's [P-Tuning](https://github.com/THUDM/ChatGLM2-6B/tree/main/
| [LLaMA](https://github.com/facebookresearch/llama) | 7B/13B/33B/65B | q_proj,v_proj | - |
| [LLaMA-2](https://huggingface.co/meta-llama) | 7B/13B/70B | q_proj,v_proj | llama2 |
| [LLaMA-3](https://huggingface.co/meta-llama) | 8B/70B | q_proj,v_proj | llama3 |
| [LLaVA-1.5](https://huggingface.co/llava-hf) | 7B/13B | q_proj,v_proj | vicuna |
| [Mistral/Mixtral](https://huggingface.co/mistralai) | 7B/8x7B/8x22B | q_proj,v_proj | mistral |
| [OLMo](https://huggingface.co/allenai) | 1B/7B | q_proj,v_proj | - |
| [Phi-1.5/2](https://huggingface.co/microsoft) | 1.3B/2.7B | q_proj,v_proj | - |
@ -457,7 +460,7 @@ If you have a project that should be incorporated, please contact via email or c
This repository is licensed under the [Apache-2.0 License](LICENSE).
Please follow the model licenses to use the corresponding model weights: [Baichuan2](https://huggingface.co/baichuan-inc/Baichuan2-7B-Base/blob/main/Community%20License%20for%20Baichuan%202%20Model.pdf) / [BLOOM](https://huggingface.co/spaces/bigscience/license) / [ChatGLM3](https://github.com/THUDM/ChatGLM3/blob/main/MODEL_LICENSE) / [Command-R](https://cohere.com/c4ai-cc-by-nc-license) / [DeepSeek](https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/LICENSE-MODEL) / [Falcon](https://huggingface.co/tiiuae/falcon-180B/blob/main/LICENSE.txt) / [Gemma](https://ai.google.dev/gemma/terms) / [InternLM2](https://github.com/InternLM/InternLM#license) / [LLaMA](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) / [LLaMA-2](https://ai.meta.com/llama/license/) / [LLaMA-3](https://llama.meta.com/llama3/license/) / [Mistral](LICENSE) / [OLMo](LICENSE) / [Phi-1.5/2](https://huggingface.co/microsoft/phi-1_5/resolve/main/Research%20License.docx) / [Phi-3](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/blob/main/LICENSE) / [Qwen](https://github.com/QwenLM/Qwen/blob/main/Tongyi%20Qianwen%20LICENSE%20AGREEMENT) / [StarCoder2](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement) / [XVERSE](https://github.com/xverse-ai/XVERSE-13B/blob/main/MODEL_LICENSE.pdf) / [Yi](https://huggingface.co/01-ai/Yi-6B/blob/main/LICENSE) / [Yuan](https://github.com/IEIT-Yuan/Yuan-2.0/blob/main/LICENSE-Yuan)
Please follow the model licenses to use the corresponding model weights: [Baichuan2](https://huggingface.co/baichuan-inc/Baichuan2-7B-Base/blob/main/Community%20License%20for%20Baichuan%202%20Model.pdf) / [BLOOM](https://huggingface.co/spaces/bigscience/license) / [ChatGLM3](https://github.com/THUDM/ChatGLM3/blob/main/MODEL_LICENSE) / [Command-R](https://cohere.com/c4ai-cc-by-nc-license) / [DeepSeek](https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/LICENSE-MODEL) / [Falcon](https://huggingface.co/tiiuae/falcon-180B/blob/main/LICENSE.txt) / [Gemma](https://ai.google.dev/gemma/terms) / [InternLM2](https://github.com/InternLM/InternLM#license) / [LLaMA](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) / [LLaMA-2/LLaVA-1.5](https://ai.meta.com/llama/license/) / [LLaMA-3](https://llama.meta.com/llama3/license/) / [Mistral](LICENSE) / [OLMo](LICENSE) / [Phi-1.5/2](https://huggingface.co/microsoft/phi-1_5/resolve/main/Research%20License.docx) / [Phi-3](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/blob/main/LICENSE) / [Qwen](https://github.com/QwenLM/Qwen/blob/main/Tongyi%20Qianwen%20LICENSE%20AGREEMENT) / [StarCoder2](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement) / [XVERSE](https://github.com/xverse-ai/XVERSE-13B/blob/main/MODEL_LICENSE.pdf) / [Yi](https://huggingface.co/01-ai/Yi-6B/blob/main/LICENSE) / [Yuan](https://github.com/IEIT-Yuan/Yuan-2.0/blob/main/LICENSE-Yuan)
## Citation

5
README_zh.md
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@ -68,6 +68,8 @@ https://github.com/hiyouga/LLaMA-Factory/assets/16256802/ec36a9dd-37f4-4f72-81bd
## 更新日志
[24/04/26] 我们支持了多模态模型 **LLaVA-1.5** 的微调。详细用法请参照 `examples/lora_single_gpu/sft_mllm.sh`。
[24/04/22] 我们提供了在免费 T4 GPU 上微调 Llama-3 模型的 **[Colab 笔记本](https://colab.research.google.com/drive/1d5KQtbemerlSDSxZIfAaWXhKr30QypiK?usp=sharing)**。Hugging Face 社区公开了两个利用 LLaMA Factory 微调的 Llama-3 模型，详情请见 [Llama3-8B-Chinese-Chat](https://huggingface.co/shenzhi-wang/Llama3-8B-Chinese-Chat) 和 [Llama3-Chinese](https://huggingface.co/zhichen/Llama3-Chinese)。
[24/04/21] 我们基于 [AstraMindAI 的仓库](https://github.com/astramind-ai/Mixture-of-depths)支持了 **[混合深度训练](https://arxiv.org/abs/2404.02258)**。详细用法请参照 `examples/extras/mod`。
@ -148,6 +150,7 @@ https://github.com/hiyouga/LLaMA-Factory/assets/16256802/ec36a9dd-37f4-4f72-81bd
| [LLaMA](https://github.com/facebookresearch/llama) | 7B/13B/33B/65B | q_proj,v_proj | - |
| [LLaMA-2](https://huggingface.co/meta-llama) | 7B/13B/70B | q_proj,v_proj | llama2 |
| [LLaMA-3](https://huggingface.co/meta-llama) | 8B/70B | q_proj,v_proj | llama3 |
| [LLaVA-1.5](https://huggingface.co/llava-hf) | 7B/13B | q_proj,v_proj | vicuna |
| [Mistral/Mixtral](https://huggingface.co/mistralai) | 7B/8x7B/8x22B | q_proj,v_proj | mistral |
| [OLMo](https://huggingface.co/allenai) | 1B/7B | q_proj,v_proj | - |
| [Phi-1.5/2](https://huggingface.co/microsoft) | 1.3B/2.7B | q_proj,v_proj | - |
@ -457,7 +460,7 @@ export USE_MODELSCOPE_HUB=1 # Windows 使用 `set USE_MODELSCOPE_HUB=1`
本仓库的代码依照 [Apache-2.0](LICENSE) 协议开源。
使用模型权重时，请遵循对应的模型协议：[Baichuan2](https://huggingface.co/baichuan-inc/Baichuan2-7B-Base/blob/main/Community%20License%20for%20Baichuan%202%20Model.pdf) / [BLOOM](https://huggingface.co/spaces/bigscience/license) / [ChatGLM3](https://github.com/THUDM/ChatGLM3/blob/main/MODEL_LICENSE) / [Command-R](https://cohere.com/c4ai-cc-by-nc-license) / [DeepSeek](https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/LICENSE-MODEL) / [Falcon](https://huggingface.co/tiiuae/falcon-180B/blob/main/LICENSE.txt) / [Gemma](https://ai.google.dev/gemma/terms) / [InternLM2](https://github.com/InternLM/InternLM#license) / [LLaMA](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) / [LLaMA-2](https://ai.meta.com/llama/license/) / [LLaMA-3](https://llama.meta.com/llama3/license/) / [Mistral](LICENSE) / [OLMo](LICENSE) / [Phi-1.5/2](https://huggingface.co/microsoft/phi-1_5/resolve/main/Research%20License.docx) / [Phi-3](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/blob/main/LICENSE) / [Qwen](https://github.com/QwenLM/Qwen/blob/main/Tongyi%20Qianwen%20LICENSE%20AGREEMENT) / [StarCoder2](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement) / [XVERSE](https://github.com/xverse-ai/XVERSE-13B/blob/main/MODEL_LICENSE.pdf) / [Yi](https://huggingface.co/01-ai/Yi-6B/blob/main/LICENSE) / [Yuan](https://github.com/IEIT-Yuan/Yuan-2.0/blob/main/LICENSE-Yuan)
使用模型权重时，请遵循对应的模型协议：[Baichuan2](https://huggingface.co/baichuan-inc/Baichuan2-7B-Base/blob/main/Community%20License%20for%20Baichuan%202%20Model.pdf) / [BLOOM](https://huggingface.co/spaces/bigscience/license) / [ChatGLM3](https://github.com/THUDM/ChatGLM3/blob/main/MODEL_LICENSE) / [Command-R](https://cohere.com/c4ai-cc-by-nc-license) / [DeepSeek](https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/LICENSE-MODEL) / [Falcon](https://huggingface.co/tiiuae/falcon-180B/blob/main/LICENSE.txt) / [Gemma](https://ai.google.dev/gemma/terms) / [InternLM2](https://github.com/InternLM/InternLM#license) / [LLaMA](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) / [LLaMA-2/LLaVA-1.5](https://ai.meta.com/llama/license/) / [LLaMA-3](https://llama.meta.com/llama3/license/) / [Mistral](LICENSE) / [OLMo](LICENSE) / [Phi-1.5/2](https://huggingface.co/microsoft/phi-1_5/resolve/main/Research%20License.docx) / [Phi-3](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/blob/main/LICENSE) / [Qwen](https://github.com/QwenLM/Qwen/blob/main/Tongyi%20Qianwen%20LICENSE%20AGREEMENT) / [StarCoder2](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement) / [XVERSE](https://github.com/xverse-ai/XVERSE-13B/blob/main/MODEL_LICENSE.pdf) / [Yi](https://huggingface.co/01-ai/Yi-6B/blob/main/LICENSE) / [Yuan](https://github.com/IEIT-Yuan/Yuan-2.0/blob/main/LICENSE-Yuan)
## 引用

3
data/README.md
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@ -18,7 +18,8 @@ If you are using a custom dataset, please provide your dataset definition in the
"history": "the column name in the dataset containing the histories. (default: None)",
"messages": "the column name in the dataset containing the messages. (default: conversations)",
"system": "the column name in the dataset containing the system prompts. (default: None)",
"tools": "the column name in the dataset containing the tool description. (default: None)"
"tools": "the column name in the dataset containing the tool description. (default: None)",
"images": "the column name in the dataset containing the image inputs. (default: None)"
},
"tags (optional, used for the sharegpt format)": {
"role_tag": "the key in the message represents the identity. (default: from)",

3
data/README_zh.md
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@ -18,7 +18,8 @@
"history": "数据集代表历史对话的表头名称（默认：None）",
"messages": "数据集代表消息列表的表头名称（默认：conversations）",
"system": "数据集代表系统提示的表头名称（默认：None）",
"tools": "数据集代表工具描述的表头名称（默认：None）"
"tools": "数据集代表工具描述的表头名称（默认：None）",
"images": "数据集代表图像输入的表头名称（默认：None）"
},
"tags（可选，用于 sharegpt 格式）": {
"role_tag": "消息中代表发送者身份的键名（默认：from）",

25
data/dataset_info.json
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@ -58,6 +58,21 @@
"tools": "tools"
}
},
"mllm_demo": {
"file_name": "mllm_demo.json",
"file_sha1": "b6709b23657d5c42a701f1c5574f3a6edaa40a20",
"formatting": "sharegpt",
"columns": {
"messages": "messages",
"images": "images"
},
"tags": {
"role_tag": "role",
"content_tag": "content",
"user_tag": "user",
"assistant_tag": "assistant"
}
},
"example": {
"script_url": "example_dataset",
"columns": {
@ -185,6 +200,7 @@
"ultrachat_200k": {
"hf_hub_url": "HuggingFaceH4/ultrachat_200k",
"ms_hub_url": "AI-ModelScope/ultrachat_200k",
"formatting": "sharegpt",
"columns": {
"messages": "messages"
},
@ -193,8 +209,7 @@
"content_tag": "content",
"user_tag": "user",
"assistant_tag": "assistant"
},
"formatting": "sharegpt"
}
},
"agent_instruct": {
"hf_hub_url": "THUDM/AgentInstruct",
@ -204,6 +219,7 @@
"lmsys_chat": {
"hf_hub_url": "lmsys/lmsys-chat-1m",
"ms_hub_url": "AI-ModelScope/lmsys-chat-1m",
"formatting": "sharegpt",
"columns": {
"messages": "conversation"
},
@ -212,8 +228,7 @@
"content_tag": "content",
"user_tag": "human",
"assistant_tag": "assistant"
},
"formatting": "sharegpt"
}
},
"evol_instruct": {
"hf_hub_url": "WizardLM/WizardLM_evol_instruct_V2_196k",
@ -340,7 +355,7 @@
"history": "history"
}
},
"orca_dpo_de" : {
"orca_dpo_de": {
"hf_hub_url": "mayflowergmbh/intel_orca_dpo_pairs_de",
"ranking": true
},

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data/mllm_demo.json Normal file
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@ -0,0 +1,71 @@
[
{
"messages": [
{
"content": "Who are they?<image>",
"role": "user"
},
{
"content": "They're Kane and Gretzka from Bayern Munich.",
"role": "assistant"
},
{
"content": "What are they doing?",
"role": "user"
},
{
"content": "They are celebrating on the soccer field",
"role": "assistant"
}
],
"images": [
"images/1.jpg"
]
},
{
"messages": [
{
"content": "Who is he?<image>",
"role": "user"
},
{
"content": "He's Thomas Muller from Bayern Munich.",
"role": "assistant"
},
{
"content": "Why is he on the ground?",
"role": "user"
},
{
"content": "Because he's sliding on his knees to celebrate.",
"role": "assistant"
}
],
"images": [
"images/2.jpg"
]
},
{
"messages": [
{
"content": "Please describe this image<image>",
"role": "user"
},
{
"content": "Chinese astronaut Gui Haichao is giving a speech.",
"role": "assistant"
},
{
"content": "What has he accomplished?",
"role": "user"
},
{
"content": "He was appointed to be a payload specialist on Shenzhou 16 mission in June 2022, thus becoming the first Chinese civilian of Group 3 in space on 30 May 2023. He is responsible for the on-orbit operation of space science experimental payloads.",
"role": "assistant"
}
],
"images": [
"images/3.jpg"
]
}
]

1
examples/README.md
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@ -9,6 +9,7 @@ examples/
│ ├── ppo.sh: Do PPO training using LoRA
│ ├── dpo.sh: Do DPO training using LoRA
│ ├── orpo.sh: Do ORPO training using LoRA
│ ├── sft_mllm.sh: Do supervised fine-tuning on multimodal data using LoRA
│ ├── prepare.sh: Save tokenized dataset
│ └── predict.sh: Do batch predict and compute BLEU and ROUGE scores after LoRA tuning
├── qlora_single_gpu/

1
examples/README_zh.md
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@ -9,6 +9,7 @@ examples/
│ ├── ppo.sh: 基于 LoRA 进行 PPO 训练
│ ├── dpo.sh: 基于 LoRA 进行 DPO 训练
│ ├── orpo.sh: 基于 LoRA 进行 ORPO 训练
│ ├── sft_mllm.sh: 基于 LoRA 进行多模态指令监督微调
│ ├── prepare.sh: 保存预处理后的数据集
│ └── predict.sh: 基于 LoRA 进行批量预测并计算 BLEU 和 ROUGE 分数
├── qlora_single_gpu/

33
examples/lora_single_gpu/sft_mllm.sh Normal file
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@ -0,0 +1,33 @@
#!/bin/bash
CUDA_VISIBLE_DEVICES=0 python ../../src/train_bash.py \
--stage sft \
--do_train \
--model_name_or_path llava-hf/llava-1.5-7b-hf \
--visual_inputs \
--dataset mllm_demo \
--dataset_dir ../../data \
--template vicuna \
--finetuning_type lora \
--lora_target q_proj,v_proj \
--output_dir ../../saves/LLaMA2-7B/lora/sft_mllm \
--overwrite_cache \
--overwrite_output_dir \
--cutoff_len 1024 \
--preprocessing_num_workers 16 \
--per_device_train_batch_size 1 \
--per_device_eval_batch_size 1 \
--gradient_accumulation_steps 8 \
--lr_scheduler_type cosine \
--logging_steps 10 \
--warmup_steps 20 \
--save_steps 100 \
--eval_steps 100 \
--evaluation_strategy steps \
--load_best_model_at_end \
--learning_rate 5e-5 \
--num_train_epochs 100.0 \
--max_samples 3000 \
--val_size 0.1 \
--plot_loss \
--fp16

5
scripts/cal_lr.py
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@ -44,8 +44,9 @@ def calculate_lr(
overwrite_cache=True,
)
)
tokenizer = load_tokenizer(model_args)
trainset = get_dataset(tokenizer, model_args, data_args, training_args, stage)
tokenizer_module = load_tokenizer(model_args)
tokenizer = tokenizer_module["tokenizer"]
trainset = get_dataset(model_args, data_args, training_args, stage, **tokenizer_module)
if stage == "pt":
data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)
elif stage == "sft":

4
scripts/length_cdf.py
View File

@ -32,8 +32,8 @@ def length_cdf(
overwrite_cache=True,
)
)
tokenizer = load_tokenizer(model_args)
trainset = get_dataset(tokenizer, model_args, data_args, training_args, stage="sft")
tokenizer_module = load_tokenizer(model_args)
trainset = get_dataset(model_args, data_args, training_args, stage="sft", **tokenizer_module)
total_num = len(trainset)
length_dict = defaultdict(int)
for sample in tqdm(trainset["input_ids"]):

3
src/llmtuner/chat/base_engine.py
View File

@ -4,6 +4,7 @@ from typing import TYPE_CHECKING, Any, AsyncGenerator, Dict, List, Literal, Opti
if TYPE_CHECKING:
from numpy.typing import NDArray
from transformers import PreTrainedModel, PreTrainedTokenizer
from vllm import AsyncLLMEngine
@ -46,6 +47,7 @@ class BaseEngine(ABC):
messages: Sequence[Dict[str, str]],
system: Optional[str] = None,
tools: Optional[str] = None,
image: Optional["NDArray"] = None,
**input_kwargs,
) -> List["Response"]: ...
@ -55,6 +57,7 @@ class BaseEngine(ABC):
messages: Sequence[Dict[str, str]],
system: Optional[str] = None,
tools: Optional[str] = None,
image: Optional["NDArray"] = None,
**input_kwargs,
) -> AsyncGenerator[str, None]: ...

14
src/llmtuner/chat/chat_model.py
View File

@ -8,6 +8,8 @@ from .vllm_engine import VllmEngine
if TYPE_CHECKING:
from numpy.typing import NDArray
from .base_engine import BaseEngine, Response
@ -36,9 +38,10 @@ class ChatModel:
messages: Sequence[Dict[str, str]],
system: Optional[str] = None,
tools: Optional[str] = None,
image: Optional["NDArray"] = None,
**input_kwargs,
) -> List["Response"]:
task = asyncio.run_coroutine_threadsafe(self.achat(messages, system, tools, **input_kwargs), self._loop)
task = asyncio.run_coroutine_threadsafe(self.achat(messages, system, tools, image, **input_kwargs), self._loop)
return task.result()
async def achat(
@ -46,18 +49,20 @@ class ChatModel:
messages: Sequence[Dict[str, str]],
system: Optional[str] = None,
tools: Optional[str] = None,
image: Optional["NDArray"] = None,
**input_kwargs,
) -> List["Response"]:
return await self.engine.chat(messages, system, tools, **input_kwargs)
return await self.engine.chat(messages, system, tools, image, **input_kwargs)
def stream_chat(
self,
messages: Sequence[Dict[str, str]],
system: Optional[str] = None,
tools: Optional[str] = None,
image: Optional["NDArray"] = None,
**input_kwargs,
) -> Generator[str, None, None]:
generator = self.astream_chat(messages, system, tools, **input_kwargs)
generator = self.astream_chat(messages, system, tools, image, **input_kwargs)
while True:
try:
task = asyncio.run_coroutine_threadsafe(generator.__anext__(), self._loop)
@ -70,9 +75,10 @@ class ChatModel:
messages: Sequence[Dict[str, str]],
system: Optional[str] = None,
tools: Optional[str] = None,
image: Optional["NDArray"] = None,
**input_kwargs,
) -> AsyncGenerator[str, None]:
async for new_token in self.engine.stream_chat(messages, system, tools, **input_kwargs):
async for new_token in self.engine.stream_chat(messages, system, tools, image, **input_kwargs):
yield new_token
def get_scores(

32
src/llmtuner/chat/hf_engine.py
View File

@ -14,7 +14,9 @@ from .base_engine import BaseEngine, Response
if TYPE_CHECKING:
from transformers import PreTrainedModel, PreTrainedTokenizer
from numpy.typing import NDArray
from transformers import PreTrainedModel, PreTrainedTokenizer, ProcessorMixin
from transformers.image_processing_utils import BaseImageProcessor
from trl import PreTrainedModelWrapper
from ..data import Template
@ -30,7 +32,9 @@ class HuggingfaceEngine(BaseEngine):
generating_args: "GeneratingArguments",
) -> None:
self.can_generate = finetuning_args.stage == "sft"
self.tokenizer = load_tokenizer(model_args)
tokenizer_module = load_tokenizer(model_args)
self.tokenizer = tokenizer_module["tokenizer"]
self.processor = tokenizer_module["processor"]
self.tokenizer.padding_side = "left" if self.can_generate else "right"
self.template = get_template_and_fix_tokenizer(self.tokenizer, data_args.template)
self.model = load_model(
@ -42,13 +46,18 @@ class HuggingfaceEngine(BaseEngine):
def _process_args(
model: "PreTrainedModel",
tokenizer: "PreTrainedTokenizer",
processor: Optional["ProcessorMixin"],
template: "Template",
generating_args: Dict[str, Any],
messages: Sequence[Dict[str, str]],
system: Optional[str] = None,
tools: Optional[str] = None,
image: Optional["NDArray"] = None,
input_kwargs: Optional[Dict[str, Any]] = {},
) -> Tuple[Dict[str, Any], int]:
if processor is not None and image is not None and "<image>" not in messages[0]["content"]:
messages[0]["content"] = messages[0]["content"] + "<image>"
paired_messages = messages + [{"role": "assistant", "content": ""}]
prompt_ids, _ = template.encode_oneturn(
tokenizer=tokenizer, messages=paired_messages, system=system, tools=tools
@ -95,6 +104,11 @@ class HuggingfaceEngine(BaseEngine):
logits_processor=get_logits_processor(),
)
if processor is not None and image is not None:
image_processor: "BaseImageProcessor" = getattr(processor, "image_processor")
pixel_values: "torch.Tensor" = image_processor(image, return_tensors="pt")["pixel_values"]
gen_kwargs["pixel_values"] = pixel_values.to(model.device)
return gen_kwargs, prompt_length
@staticmethod
@ -102,15 +116,17 @@ class HuggingfaceEngine(BaseEngine):
def _chat(
model: "PreTrainedModel",
tokenizer: "PreTrainedTokenizer",
processor: Optional["ProcessorMixin"],
template: "Template",
generating_args: Dict[str, Any],
messages: Sequence[Dict[str, str]],
system: Optional[str] = None,
tools: Optional[str] = None,
image: Optional["NDArray"] = None,
input_kwargs: Optional[Dict[str, Any]] = {},
) -> List["Response"]:
gen_kwargs, prompt_length = HuggingfaceEngine._process_args(
model, tokenizer, template, generating_args, messages, system, tools, input_kwargs
model, tokenizer, processor, template, generating_args, messages, system, tools, image, input_kwargs
)
generate_output = model.generate(**gen_kwargs)
response_ids = generate_output[:, prompt_length:]
@ -135,15 +151,17 @@ class HuggingfaceEngine(BaseEngine):
def _stream_chat(
model: "PreTrainedModel",
tokenizer: "PreTrainedTokenizer",
processor: Optional["ProcessorMixin"],
template: "Template",
generating_args: Dict[str, Any],
messages: Sequence[Dict[str, str]],
system: Optional[str] = None,
tools: Optional[str] = None,
image: Optional["NDArray"] = None,
input_kwargs: Optional[Dict[str, Any]] = {},
) -> Callable[[], str]:
gen_kwargs, _ = HuggingfaceEngine._process_args(
model, tokenizer, template, generating_args, messages, system, tools, input_kwargs
model, tokenizer, processor, template, generating_args, messages, system, tools, image, input_kwargs
)
streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
gen_kwargs["streamer"] = streamer
@ -199,6 +217,7 @@ class HuggingfaceEngine(BaseEngine):
messages: Sequence[Dict[str, str]],
system: Optional[str] = None,
tools: Optional[str] = None,
image: Optional["NDArray"] = None,
**input_kwargs,
) -> List["Response"]:
if not self.can_generate:
@ -208,11 +227,13 @@ class HuggingfaceEngine(BaseEngine):
input_args = (
self.model,
self.tokenizer,
self.processor,
self.template,
self.generating_args,
messages,
system,
tools,
image,
input_kwargs,
)
async with self._semaphore:
@ -224,6 +245,7 @@ class HuggingfaceEngine(BaseEngine):
messages: Sequence[Dict[str, str]],
system: Optional[str] = None,
tools: Optional[str] = None,
image: Optional["NDArray"] = None,
**input_kwargs,
) -> AsyncGenerator[str, None]:
if not self.can_generate:
@ -233,11 +255,13 @@ class HuggingfaceEngine(BaseEngine):
input_args = (
self.model,
self.tokenizer,
self.processor,
self.template,
self.generating_args,
messages,
system,
tools,
image,
input_kwargs,
)
async with self._semaphore:

14
src/llmtuner/chat/vllm_engine.py
View File

@ -12,7 +12,10 @@ if is_vllm_available():
from vllm import AsyncEngineArgs, AsyncLLMEngine, RequestOutput, SamplingParams
from vllm.lora.request import LoRARequest
if TYPE_CHECKING:
from numpy.typing import NDArray
from ..hparams import DataArguments, FinetuningArguments, GeneratingArguments, ModelArguments
@ -29,7 +32,9 @@ class VllmEngine(BaseEngine):
infer_dtype = str(infer_dtype).split(".")[-1]
self.can_generate = finetuning_args.stage == "sft"
self.tokenizer = load_tokenizer(model_args)
tokenizer_module = load_tokenizer(model_args)
self.tokenizer = tokenizer_module["tokenizer"]
self.processor = tokenizer_module["processor"]
self.tokenizer.padding_side = "left"
self.template = get_template_and_fix_tokenizer(self.tokenizer, data_args.template)
self.generating_args = generating_args.to_dict()
@ -58,6 +63,7 @@ class VllmEngine(BaseEngine):
messages: Sequence[Dict[str, str]],
system: Optional[str] = None,
tools: Optional[str] = None,
image: Optional["NDArray"] = None,
**input_kwargs,
) -> AsyncIterator["RequestOutput"]:
request_id = "chatcmpl-{}".format(uuid.uuid4().hex)
@ -121,10 +127,11 @@ class VllmEngine(BaseEngine):
messages: Sequence[Dict[str, str]],
system: Optional[str] = None,
tools: Optional[str] = None,
image: Optional["NDArray"] = None,
**input_kwargs,
) -> List["Response"]:
final_output = None
generator = await self._generate(messages, system, tools, **input_kwargs)
generator = await self._generate(messages, system, tools, image, **input_kwargs)
async for request_output in generator:
final_output = request_output
@ -146,10 +153,11 @@ class VllmEngine(BaseEngine):
messages: Sequence[Dict[str, str]],
system: Optional[str] = None,
tools: Optional[str] = None,
image: Optional["NDArray"] = None,
**input_kwargs,
) -> AsyncGenerator[str, None]:
generated_text = ""
generator = await self._generate(messages, system, tools, **input_kwargs)
generator = await self._generate(messages, system, tools, image, **input_kwargs)
async for result in generator:
delta_text = result.outputs[0].text[len(generated_text) :]
generated_text = result.outputs[0].text

31
src/llmtuner/data/aligner.py
View File

@ -1,3 +1,4 @@
import os
from functools import partial
from typing import TYPE_CHECKING, Any, Dict, List, Union
@ -13,8 +14,10 @@ if TYPE_CHECKING:
from .parser import DatasetAttr
def convert_alpaca(examples: Dict[str, List[Any]], dataset_attr: "DatasetAttr") -> Dict[str, List[Any]]:
outputs = {"prompt": [], "response": [], "system": [], "tools": []}
def convert_alpaca(
examples: Dict[str, List[Any]], dataset_attr: "DatasetAttr", data_args: "DataArguments"
) -> Dict[str, List[Any]]:
outputs = {"prompt": [], "response": [], "system": [], "tools": [], "images": []}
for i in range(len(examples[dataset_attr.prompt])):
prompt = []
if dataset_attr.history and isinstance(examples[dataset_attr.history][i], list):
@ -44,12 +47,19 @@ def convert_alpaca(examples: Dict[str, List[Any]], dataset_attr: "DatasetAttr")
outputs["response"].append(response)
outputs["system"].append(examples[dataset_attr.system][i] if dataset_attr.system else "")
outputs["tools"].append("")
outputs["images"].append(
[os.path.join(data_args.dataset_dir, path) for path in examples[dataset_attr.images][i]]
if dataset_attr.images
else []
)
return outputs
def convert_sharegpt(examples: Dict[str, List[Any]], dataset_attr: "DatasetAttr") -> Dict[str, List[Any]]:
outputs = {"prompt": [], "response": [], "system": [], "tools": []}
def convert_sharegpt(
examples: Dict[str, List[Any]], dataset_attr: "DatasetAttr", data_args: "DataArguments"
) -> Dict[str, List[Any]]:
outputs = {"prompt": [], "response": [], "system": [], "tools": [], "images": []}
tag_mapping = {
dataset_attr.user_tag: Role.USER.value,
dataset_attr.assistant_tag: Role.ASSISTANT.value,
@ -84,6 +94,11 @@ def convert_sharegpt(examples: Dict[str, List[Any]], dataset_attr: "DatasetAttr"
outputs["response"].append(aligned_messages[-1:])
outputs["system"].append(system)
outputs["tools"].append(examples[dataset_attr.tools][i] if dataset_attr.tools else "")
outputs["images"].append(
[os.path.join(data_args.dataset_dir, path) for path in examples[dataset_attr.images][i]]
if dataset_attr.images
else []
)
return outputs
@ -96,12 +111,13 @@ def align_dataset(
prompt: [{"role": "user", "content": "..."}] * (2T - 1)
response: [{"role": "assistant", "content": "..."}] * N (N > 1 for ranking dataset)
system: "..."
tools: "..."
tools: "...",
images: [],
"""
if dataset_attr.formatting == "alpaca":
convert_func = partial(convert_alpaca, dataset_attr=dataset_attr)
convert_func = partial(convert_alpaca, dataset_attr=dataset_attr, data_args=data_args)
else:
convert_func = partial(convert_sharegpt, dataset_attr=dataset_attr)
convert_func = partial(convert_sharegpt, dataset_attr=dataset_attr, data_args=data_args)
column_names = list(next(iter(dataset)).keys())
features = Features.from_dict(
@ -114,6 +130,7 @@ def align_dataset(
],
"system": {"dtype": "string", "_type": "Value"},
"tools": {"dtype": "string", "_type": "Value"},
"images": [{"_type": "Image"}],
}
)
kwargs = {}

9
src/llmtuner/data/loader.py
View File

@ -1,6 +1,6 @@
import inspect
import os
from typing import TYPE_CHECKING, Literal, Union
from typing import TYPE_CHECKING, Literal, Optional, Union
from datasets import load_dataset, load_from_disk
@ -16,7 +16,7 @@ from .utils import checksum, merge_dataset
if TYPE_CHECKING:
from datasets import Dataset, IterableDataset
from transformers import Seq2SeqTrainingArguments
from transformers import ProcessorMixin, Seq2SeqTrainingArguments
from transformers.tokenization_utils import PreTrainedTokenizer
from ..hparams import DataArguments, ModelArguments
@ -115,11 +115,12 @@ def load_single_dataset(
def get_dataset(
tokenizer: "PreTrainedTokenizer",
model_args: "ModelArguments",
data_args: "DataArguments",
training_args: "Seq2SeqTrainingArguments",
stage: Literal["pt", "sft", "rm", "ppo"],
tokenizer: "PreTrainedTokenizer",
processor: Optional["ProcessorMixin"] = None,
) -> Union["Dataset", "IterableDataset"]:
template = get_template_and_fix_tokenizer(tokenizer, data_args.template)
if data_args.train_on_prompt and template.efficient_eos:
@ -149,7 +150,7 @@ def get_dataset(
with training_args.main_process_first(desc="pre-process dataset"):
preprocess_func, print_function = get_preprocess_and_print_func(
tokenizer, template, data_args, training_args, stage
data_args, training_args, stage, template, tokenizer, processor
)
column_names = list(next(iter(dataset)).keys())
kwargs = {}

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

@ -28,6 +28,7 @@ class DatasetAttr:
formatting: Literal["alpaca", "sharegpt"] = "alpaca"
""" columns """
system: Optional[str] = None
images: Optional[str] = None
""" columns for the alpaca format """
prompt: Optional[str] = "instruction"
query: Optional[str] = "input"
@ -105,7 +106,7 @@ def get_dataset_list(data_args: "DataArguments") -> List["DatasetAttr"]:
dataset_attr.set_attr("formatting", dataset_info[name], default="alpaca")
if "columns" in dataset_info[name]:
column_names = ["system"]
column_names = ["system", "images"]
if dataset_attr.formatting == "alpaca":
column_names.extend(["prompt", "query", "response", "history"])
else:

65
src/llmtuner/data/preprocess.py
View File

@ -1,6 +1,6 @@
from functools import partial
from itertools import chain
from typing import TYPE_CHECKING, Any, Callable, Dict, List, Literal, Tuple
from typing import TYPE_CHECKING, Any, Callable, Dict, List, Literal, Optional, Tuple
from ..extras.constants import IGNORE_INDEX
from ..extras.logging import get_logger
@ -8,7 +8,9 @@ from .utils import Role
if TYPE_CHECKING:
from transformers import Seq2SeqTrainingArguments
from PIL.Image import Image
from transformers import ProcessorMixin, Seq2SeqTrainingArguments
from transformers.image_processing_utils import BaseImageProcessor
from transformers.tokenization_utils import PreTrainedTokenizer
from ..hparams import DataArguments
@ -18,6 +20,14 @@ if TYPE_CHECKING:
logger = get_logger(__name__)
def _preprocess_visual_inputs(model_inputs: Dict[str, Any], processor: "ProcessorMixin", image: "Image") -> None:
image_processor: "BaseImageProcessor" = getattr(processor, "image_processor")
pixel_values = image_processor(image, return_tensors="pt")["pixel_values"][0]
if "pixel_values" not in model_inputs:
model_inputs["pixel_values"] = []
model_inputs["pixel_values"].append(pixel_values)
def preprocess_pretrain_dataset(
examples: Dict[str, List[Any]], tokenizer: "PreTrainedTokenizer", data_args: "DataArguments"
) -> Dict[str, List[List[int]]]:
@ -48,8 +58,9 @@ def preprocess_pretrain_dataset(
def preprocess_supervised_dataset(
examples: Dict[str, List[Any]],
tokenizer: "PreTrainedTokenizer",
template: "Template",
tokenizer: "PreTrainedTokenizer",
processor: Optional["ProcessorMixin"],
data_args: "DataArguments",
) -> Dict[str, List[List[int]]]:
# build inputs with format `<bos> X Y <eos>` and labels with format `<ignore> ... <ignore> Y <eos>`
@ -89,14 +100,16 @@ def preprocess_supervised_dataset(
model_inputs["input_ids"].append(input_ids)
model_inputs["attention_mask"].append([1] * len(input_ids))
model_inputs["labels"].append(labels)
if processor is not None and "images" in examples:
_preprocess_visual_inputs(model_inputs, processor, examples["images"][i][0])
return model_inputs
def preprocess_packed_supervised_dataset(
examples: Dict[str, List[Any]],
tokenizer: "PreTrainedTokenizer",
template: "Template",
tokenizer: "PreTrainedTokenizer",
data_args: "DataArguments",
) -> Dict[str, List[List[int]]]:
# build inputs with format `<bos> X1 Y1 <eos> <bos> X2 Y2 <eos>`
@ -141,8 +154,9 @@ def preprocess_packed_supervised_dataset(
def preprocess_unsupervised_dataset(
examples: Dict[str, List[Any]],
tokenizer: "PreTrainedTokenizer",
template: "Template",
tokenizer: "PreTrainedTokenizer",
processor: Optional["ProcessorMixin"],
data_args: "DataArguments",
) -> Dict[str, List[List[int]]]:
# build inputs with format `<bos> X` and labels with format `Y <eos>`
@ -172,14 +186,17 @@ def preprocess_unsupervised_dataset(
model_inputs["input_ids"].append(input_ids)
model_inputs["attention_mask"].append([1] * len(input_ids))
model_inputs["labels"].append(labels)
if processor is not None and "images" in examples:
_preprocess_visual_inputs(model_inputs, processor, examples["images"][i][0])
return model_inputs
def preprocess_pairwise_dataset(
examples: Dict[str, List[Any]],
tokenizer: "PreTrainedTokenizer",
template: "Template",
tokenizer: "PreTrainedTokenizer",
processor: Optional["ProcessorMixin"],
data_args: "DataArguments",
) -> Dict[str, List[List[int]]]:
# build input pairs with format `<bos> X`, `Y1 <eos>` and `Y2 <eos>`
@ -214,6 +231,8 @@ def preprocess_pairwise_dataset(
model_inputs["prompt_ids"].append(prompt_ids)
model_inputs["chosen_ids"].append(chosen_ids)
model_inputs["rejected_ids"].append(rejected_ids)
if processor is not None and "images" in examples:
_preprocess_visual_inputs(model_inputs, processor, examples["images"][i][0])
return model_inputs
@ -244,34 +263,54 @@ def print_unsupervised_dataset_example(example: Dict[str, List[int]], tokenizer:
def get_preprocess_and_print_func(
tokenizer: "PreTrainedTokenizer",
template: "Template",
data_args: "DataArguments",
training_args: "Seq2SeqTrainingArguments",
stage: Literal["pt", "sft", "rm", "ppo"],
template: "Template",
tokenizer: "PreTrainedTokenizer",
processor: Optional["ProcessorMixin"],
) -> Tuple[Callable, Callable]:
if stage == "pt":
preprocess_func = partial(preprocess_pretrain_dataset, tokenizer=tokenizer, data_args=data_args)
preprocess_func = partial(
preprocess_pretrain_dataset,
tokenizer=tokenizer,
data_args=data_args,
)
print_function = partial(print_unsupervised_dataset_example, tokenizer=tokenizer)
elif stage == "sft" and not training_args.predict_with_generate:
if data_args.packing:
preprocess_func = partial(
preprocess_packed_supervised_dataset, tokenizer=tokenizer, template=template, data_args=data_args
preprocess_packed_supervised_dataset,
template=template,
tokenizer=tokenizer,
data_args=data_args,
)
else:
preprocess_func = partial(
preprocess_supervised_dataset, tokenizer=tokenizer, template=template, data_args=data_args
preprocess_supervised_dataset,
template=template,
tokenizer=tokenizer,
processor=processor,
data_args=data_args,
)
print_function = partial(print_supervised_dataset_example, tokenizer=tokenizer)
elif stage == "rm":
preprocess_func = partial(
preprocess_pairwise_dataset, tokenizer=tokenizer, template=template, data_args=data_args
preprocess_pairwise_dataset,
template=template,
tokenizer=tokenizer,
processor=processor,
data_args=data_args,
)
print_function = partial(print_pairwise_dataset_example, tokenizer=tokenizer)
else:
preprocess_func = partial(
preprocess_unsupervised_dataset, tokenizer=tokenizer, template=template, data_args=data_args
preprocess_unsupervised_dataset,
template=template,
tokenizer=tokenizer,
processor=processor,
data_args=data_args,
)
print_function = partial(print_unsupervised_dataset_example, tokenizer=tokenizer)

2
src/llmtuner/eval/evaluator.py
View File

@ -21,7 +21,7 @@ from .template import get_eval_template
class Evaluator:
def __init__(self, args: Optional[Dict[str, Any]] = None) -> None:
self.model_args, self.data_args, self.eval_args, finetuning_args = get_eval_args(args)
self.tokenizer = load_tokenizer(self.model_args)
self.tokenizer = load_tokenizer(self.model_args)["tokenizer"]
self.tokenizer.padding_side = "right" # avoid overflow issue in batched inference for llama2
self.template = get_template_and_fix_tokenizer(self.tokenizer, self.data_args.template)
self.model = load_model(self.tokenizer, self.model_args, finetuning_args)

4
src/llmtuner/hparams/model_args.py
View File

@ -81,6 +81,10 @@ class ModelArguments:
default=False,
metadata={"help": "Whether or not to use unsloth's optimization for the LoRA training."},
)
visual_inputs: bool = field(
default=False,
metadata={"help": "Whethor or not to use multimodal LLM that accepts visual inputs."},
)
moe_aux_loss_coef: Optional[float] = field(
default=None,
metadata={"help": "Coefficient of the auxiliary router loss in mixture-of-experts model."},

6
src/llmtuner/hparams/parser.py
View File

@ -196,6 +196,9 @@ def get_train_args(args: Optional[Dict[str, Any]] = None) -> _TRAIN_CLS:
if model_args.infer_backend == "vllm":
raise ValueError("vLLM backend is only available for API, CLI and Web.")
if model_args.visual_inputs and data_args.packing:
raise ValueError("Cannot use packing in MLLM fine-tuning.")
_verify_model_args(model_args, finetuning_args)
_check_extra_dependencies(model_args, finetuning_args, training_args)
@ -317,6 +320,9 @@ def get_infer_args(args: Optional[Dict[str, Any]] = None) -> _INFER_CLS:
if model_args.adapter_name_or_path is not None and len(model_args.adapter_name_or_path) != 1:
raise ValueError("vLLM only accepts a single adapter. Merge them first.")
if model_args.visual_inputs:
raise ValueError("vLLM engine does not support MLLM yet. Stay tuned.")
_verify_model_args(model_args, finetuning_args)
_check_extra_dependencies(model_args, finetuning_args)

24
src/llmtuner/model/loader.py
View File

@ -1,6 +1,6 @@
from typing import TYPE_CHECKING, Any, Dict
from typing import TYPE_CHECKING, Any, Dict, Optional, TypedDict
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer
from transformers import AutoConfig, AutoModelForCausalLM, AutoModelForVision2Seq, AutoProcessor, AutoTokenizer
from trl import AutoModelForCausalLMWithValueHead
from ..extras.logging import get_logger
@ -13,7 +13,7 @@ from .utils.unsloth import load_unsloth_pretrained_model
if TYPE_CHECKING:
from transformers import PretrainedConfig, PreTrainedModel, PreTrainedTokenizer
from transformers import PretrainedConfig, PreTrainedModel, PreTrainedTokenizer, ProcessorMixin
from ..hparams import FinetuningArguments, ModelArguments
@ -21,6 +21,11 @@ if TYPE_CHECKING:
logger = get_logger(__name__)
class TokenizerModule(TypedDict):
tokenizer: "PreTrainedTokenizer"
processor: Optional["ProcessorMixin"]
def _get_init_kwargs(model_args: "ModelArguments") -> Dict[str, Any]:
r"""
Gets arguments to load config/tokenizer/model.
@ -36,7 +41,7 @@ def _get_init_kwargs(model_args: "ModelArguments") -> Dict[str, Any]:
}
def load_tokenizer(model_args: "ModelArguments") -> "PreTrainedTokenizer":
def load_tokenizer(model_args: "ModelArguments") -> "TokenizerModule":
r"""
Loads pretrained tokenizer.
@ -70,7 +75,14 @@ def load_tokenizer(model_args: "ModelArguments") -> "PreTrainedTokenizer":
logger.warning("New tokens have been added, changed `resize_vocab` to True.")
patch_tokenizer(tokenizer)
return tokenizer
if model_args.visual_inputs:
processor = AutoProcessor.from_pretrained(model_args.model_name_or_path, **init_kwargs)
setattr(processor, "tokenizer", tokenizer)
else:
processor = None
return {"tokenizer": tokenizer, "processor": processor}
def load_config(model_args: "ModelArguments") -> "PretrainedConfig":
@ -109,6 +121,8 @@ def load_model(
if model_args.mixture_of_depths == "load":
model = load_mod_pretrained_model(**init_kwargs)
elif model_args.visual_inputs:
model = AutoModelForVision2Seq.from_pretrained(**init_kwargs)
else:
model = AutoModelForCausalLM.from_pretrained(**init_kwargs)

5
src/llmtuner/train/dpo/workflow.py
View File

@ -24,8 +24,9 @@ def run_dpo(
finetuning_args: "FinetuningArguments",
callbacks: Optional[List["TrainerCallback"]] = None,
):
tokenizer = load_tokenizer(model_args)
dataset = get_dataset(tokenizer, model_args, data_args, training_args, stage="rm")
tokenizer_module = load_tokenizer(model_args)
tokenizer = tokenizer_module["tokenizer"]
dataset = get_dataset(model_args, data_args, training_args, stage="rm", **tokenizer_module)
model = load_model(tokenizer, model_args, finetuning_args, training_args.do_train)
data_collator = PairwiseDataCollatorWithPadding(

5
src/llmtuner/train/orpo/workflow.py
View File

@ -24,8 +24,9 @@ def run_orpo(
finetuning_args: "FinetuningArguments",
callbacks: Optional[List["TrainerCallback"]] = None,
):
tokenizer = load_tokenizer(model_args)
dataset = get_dataset(tokenizer, model_args, data_args, training_args, stage="rm")
tokenizer_module = load_tokenizer(model_args)
tokenizer = tokenizer_module["tokenizer"]
dataset = get_dataset(model_args, data_args, training_args, stage="rm", **tokenizer_module)
model = load_model(tokenizer, model_args, finetuning_args, training_args.do_train)
data_collator = PairwiseDataCollatorWithPadding(

5
src/llmtuner/train/ppo/workflow.py
View File

@ -27,8 +27,9 @@ def run_ppo(
generating_args: "GeneratingArguments",
callbacks: Optional[List["TrainerCallback"]] = None,
):
tokenizer = load_tokenizer(model_args)
dataset = get_dataset(tokenizer, model_args, data_args, training_args, stage="ppo")
tokenizer_module = load_tokenizer(model_args)
tokenizer = tokenizer_module["tokenizer"]
dataset = get_dataset(model_args, data_args, training_args, stage="ppo", **tokenizer_module)
model = load_model(tokenizer, model_args, finetuning_args, training_args.do_train, add_valuehead=True)
tokenizer.padding_side = "left" # use left-padding in generation while using right-padding in training

5
src/llmtuner/train/pt/workflow.py
View File

@ -25,8 +25,9 @@ def run_pt(
finetuning_args: "FinetuningArguments",
callbacks: Optional[List["TrainerCallback"]] = None,
):
tokenizer = load_tokenizer(model_args)
dataset = get_dataset(tokenizer, model_args, data_args, training_args, stage="pt")
tokenizer_module = load_tokenizer(model_args)
tokenizer = tokenizer_module["tokenizer"]
dataset = get_dataset(model_args, data_args, training_args, stage="pt", **tokenizer_module)
model = load_model(tokenizer, model_args, finetuning_args, training_args.do_train)
data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)

5
src/llmtuner/train/rm/workflow.py
View File

@ -25,8 +25,9 @@ def run_rm(
finetuning_args: "FinetuningArguments",
callbacks: Optional[List["TrainerCallback"]] = None,
):
tokenizer = load_tokenizer(model_args)
dataset = get_dataset(tokenizer, model_args, data_args, training_args, stage="rm")
tokenizer_module = load_tokenizer(model_args)
tokenizer = tokenizer_module["tokenizer"]
dataset = get_dataset(model_args, data_args, training_args, stage="rm", **tokenizer_module)
model = load_model(tokenizer, model_args, finetuning_args, training_args.do_train, add_valuehead=True)
data_collator = PairwiseDataCollatorWithPadding(tokenizer, pad_to_multiple_of=8)

6
src/llmtuner/train/sft/workflow.py
View File

@ -28,8 +28,9 @@ def run_sft(
generating_args: "GeneratingArguments",
callbacks: Optional[List["TrainerCallback"]] = None,
):
tokenizer = load_tokenizer(model_args)
dataset = get_dataset(tokenizer, model_args, data_args, training_args, stage="sft")
tokenizer_module = load_tokenizer(model_args)
tokenizer = tokenizer_module["tokenizer"]
dataset = get_dataset(model_args, data_args, training_args, stage="sft", **tokenizer_module)
model = load_model(tokenizer, model_args, finetuning_args, training_args.do_train)
if training_args.predict_with_generate:
@ -47,6 +48,7 @@ def run_sft(
# Override the decoding parameters of Seq2SeqTrainer
training_args.generation_max_length = training_args.generation_max_length or data_args.cutoff_len
training_args.generation_num_beams = data_args.eval_num_beams or training_args.generation_num_beams
training_args.remove_unused_columns = False if model_args.visual_inputs else training_args.remove_unused_columns
# Initialize our Trainer
trainer = CustomSeq2SeqTrainer(

2
src/llmtuner/train/tuner.py
View File

@ -52,7 +52,7 @@ def export_model(args: Optional[Dict[str, Any]] = None):
if model_args.adapter_name_or_path is not None and model_args.export_quantization_bit is not None:
raise ValueError("Please merge adapters before quantizing the model.")
tokenizer = load_tokenizer(model_args)
tokenizer = load_tokenizer(model_args)["tokenizer"]
get_template_and_fix_tokenizer(tokenizer, data_args.template)
model = load_model(tokenizer, model_args, finetuning_args) # must after fixing tokenizer to resize vocab

6
src/llmtuner/train/utils.py
View File

@ -91,7 +91,7 @@ def create_ref_model(
)
ref_model_args = ModelArguments(**ref_model_args_dict)
ref_finetuning_args = FinetuningArguments(finetuning_type="lora")
tokenizer = load_tokenizer(ref_model_args)
tokenizer = load_tokenizer(ref_model_args)["tokenizer"]
ref_model = load_model(
tokenizer, ref_model_args, ref_finetuning_args, is_trainable=False, add_valuehead=add_valuehead
)
@ -100,7 +100,7 @@ def create_ref_model(
if finetuning_args.finetuning_type == "lora":
ref_model = None
else:
tokenizer = load_tokenizer(model_args)
tokenizer = load_tokenizer(model_args)["tokenizer"]
ref_model = load_model(
tokenizer, model_args, finetuning_args, is_trainable=False, add_valuehead=add_valuehead
)
@ -147,7 +147,7 @@ def create_reward_model(
)
reward_model_args = ModelArguments(**reward_model_args_dict)
reward_finetuning_args = FinetuningArguments(finetuning_type="lora")
tokenizer = load_tokenizer(reward_model_args)
tokenizer = load_tokenizer(reward_model_args)["tokenizer"]
reward_model = load_model(
tokenizer, reward_model_args, reward_finetuning_args, is_trainable=False, add_valuehead=True
)

5
src/llmtuner/webui/chatter.py
View File

@ -2,6 +2,8 @@ import json
import os
from typing import TYPE_CHECKING, Dict, Generator, List, Optional, Sequence, Tuple
from numpy.typing import NDArray
from ..chat import ChatModel
from ..data import Role
from ..extras.misc import torch_gc
@ -112,6 +114,7 @@ class WebChatModel(ChatModel):
messages: Sequence[Dict[str, str]],
system: str,
tools: str,
image: Optional[NDArray],
max_new_tokens: int,
top_p: float,
temperature: float,
@ -119,7 +122,7 @@ class WebChatModel(ChatModel):
chatbot[-1][1] = ""
response = ""
for new_text in self.stream_chat(
messages, system, tools, max_new_tokens=max_new_tokens, top_p=top_p, temperature=temperature
messages, system, tools, image, max_new_tokens=max_new_tokens, top_p=top_p, temperature=temperature
):
response += new_text
if tools:

15
src/llmtuner/webui/components/chatbot.py
View File

@ -23,9 +23,15 @@ def create_chat_box(
messages = gr.State([])
with gr.Row():
with gr.Column(scale=4):
role = gr.Dropdown(choices=[Role.USER.value, Role.OBSERVATION.value], value=Role.USER.value)
system = gr.Textbox(show_label=False)
tools = gr.Textbox(show_label=False, lines=2)
with gr.Row():
with gr.Column():
role = gr.Dropdown(choices=[Role.USER.value, Role.OBSERVATION.value], value=Role.USER.value)
system = gr.Textbox(show_label=False)
tools = gr.Textbox(show_label=False, lines=4)
with gr.Column():
image = gr.Image(type="numpy")
query = gr.Textbox(show_label=False, lines=8)
submit_btn = gr.Button(variant="primary")
@ -43,7 +49,7 @@ def create_chat_box(
[chatbot, messages, query],
).then(
engine.chatter.stream,
[chatbot, messages, system, tools, max_new_tokens, top_p, temperature],
[chatbot, messages, system, tools, image, max_new_tokens, top_p, temperature],
[chatbot, messages],
)
clear_btn.click(lambda: ([], []), outputs=[chatbot, messages])
@ -56,6 +62,7 @@ def create_chat_box(
role=role,
system=system,
tools=tools,
image=image,
query=query,
submit_btn=submit_btn,
max_new_tokens=max_new_tokens,

11
src/llmtuner/webui/locales.py
View File

@ -1073,6 +1073,17 @@ LOCALES = {
"placeholder": "工具列表（非必填）",
},
},
"image": {
"en": {
"label": "Image (optional)",
},
"ru": {
"label": "Изображение (по желанию)",
},
"zh": {
"label": "图像（非必填）",
},
},
"query": {
"en": {
"placeholder": "Input...",