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https://github.com/hiyouga/LLaMA-Factory.git
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[inference] fix stop token for object detection (#6624)
* fix stop token * update minicpm data pipeline * fix npu qlora examples Former-commit-id: 844919fadaa8a61dfae47020971ea80730b2346f
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hoshi-hiyouga
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23
README.md
23
README.md
@ -403,12 +403,16 @@ Extra dependencies available: torch, torch-npu, metrics, deepspeed, liger-kernel
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<details><summary>For Windows users</summary>
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#### Install BitsAndBytes
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If you want to enable the quantized LoRA (QLoRA) on the Windows platform, you need to install a pre-built version of `bitsandbytes` library, which supports CUDA 11.1 to 12.2, please select the appropriate [release version](https://github.com/jllllll/bitsandbytes-windows-webui/releases/tag/wheels) based on your CUDA version.
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```bash
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pip install https://github.com/jllllll/bitsandbytes-windows-webui/releases/download/wheels/bitsandbytes-0.41.2.post2-py3-none-win_amd64.whl
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```
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#### Install Flash Attention-2
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To enable FlashAttention-2 on the Windows platform, you need to install the precompiled `flash-attn` library, which supports CUDA 12.1 to 12.2. Please download the corresponding version from [flash-attention](https://github.com/bdashore3/flash-attention/releases) based on your requirements.
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</details>
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@ -444,9 +448,12 @@ If you cannot infer model on NPU devices, try setting `do_sample: false` in the
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Download the pre-built Docker images: [32GB](http://mirrors.cn-central-221.ovaijisuan.com/detail/130.html) | [64GB](http://mirrors.cn-central-221.ovaijisuan.com/detail/131.html)
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To use nf4 QLoRA quantization based on bitsandbytes in Ascend NPU, please follow these 3 steps:
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#### Install BitsAndBytes
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To use QLoRA based on bitsandbytes on Ascend NPU, please follow these 3 steps:
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1. Manually compile bitsandbytes: Refer to [the installation documentation](https://huggingface.co/docs/bitsandbytes/installation?backend=Ascend+NPU&platform=Ascend+NPU) for the NPU version of bitsandbytes to complete the compilation and installation. The compilation requires a cmake version of at least 3.22.1 and a g++ version of at least 12.x.
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1. Manually compile bnb: Refer to [the installation documentation](https://huggingface.co/docs/bitsandbytes/installation?backend=Ascend+NPU&platform=Ascend+NPU) for the NPU version of bitsandbytes to complete the compilation and installation of bnb. The compilation requires a cmake version of at least 3.22.1 and a g++ version of at least 12.x.
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```bash
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# Install bitsandbytes from source
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# Clone bitsandbytes repo, Ascend NPU backend is currently enabled on multi-backend-refactor branch
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@ -462,15 +469,19 @@ apt-get install -y build-essential cmake
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# Compile & install
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cmake -DCOMPUTE_BACKEND=npu -S .
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make
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pip install -e .
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```
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2. Install and use the main branch version of transformers.
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pip install .
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```
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2. Install transformers from the main branch.
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```bash
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git clone -b https://github.com/huggingface/transformers.git
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cd transformers
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pip install .
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```
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3. Set the double_quantization parameter to false in the training configuration. You can refer to the [example](examples/train_qlora/llama3_lora_sft_otfq_npu.yaml) for guidance.
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3. Set `double_quantization: false` in the configuration. You can refer to the [example](examples/train_qlora/llama3_lora_sft_bnb_npu.yaml).
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</details>
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### Data Preparation
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31
README_zh.md
31
README_zh.md
@ -404,19 +404,23 @@ pip install -e ".[torch,metrics]"
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<details><summary>Windows 用户指南</summary>
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#### 安装 BitsAndBytes
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如果要在 Windows 平台上开启量化 LoRA（QLoRA），需要安装预编译的 `bitsandbytes` 库, 支持 CUDA 11.1 到 12.2, 请根据您的 CUDA 版本情况选择适合的[发布版本](https://github.com/jllllll/bitsandbytes-windows-webui/releases/tag/wheels)。
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```bash
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pip install https://github.com/jllllll/bitsandbytes-windows-webui/releases/download/wheels/bitsandbytes-0.41.2.post2-py3-none-win_amd64.whl
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```
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#### 安装 Flash Attention-2
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如果要在 Windows 平台上开启 FlashAttention-2，需要安装预编译的 `flash-attn` 库，支持 CUDA 12.1 到 12.2，请根据需求到 [flash-attention](https://github.com/bdashore3/flash-attention/releases) 下载对应版本安装。
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</details>
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<details><summary>昇腾 NPU 用户指南</summary>
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在昇腾 NPU 设备上安装 LLaMA Factory 时，请升级Python到3.10及以上，并需要指定额外依赖项，使用 `pip install -e ".[torch-npu,metrics]"` 命令安装。此外，还需要安装 **[Ascend CANN Toolkit 与 Kernels](https://www.hiascend.com/developer/download/community/result?module=cann)**，安装方法请参考[安装教程](https://www.hiascend.com/document/detail/zh/CANNCommunityEdition/80RC2alpha002/quickstart/quickstart/quickstart_18_0004.html)或使用以下命令：
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在昇腾 NPU 设备上安装 LLaMA Factory 时，请升级 Python 到 3.10 及以上，并需要指定额外依赖项，使用 `pip install -e ".[torch-npu,metrics]"` 命令安装。此外，还需要安装 **[Ascend CANN Toolkit 与 Kernels](https://www.hiascend.com/developer/download/community/result?module=cann)**，安装方法请参考[安装教程](https://www.hiascend.com/document/detail/zh/CANNCommunityEdition/80RC2alpha002/quickstart/quickstart/quickstart_18_0004.html)或使用以下命令：
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```bash
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# 请替换 URL 为 CANN 版本和设备型号对应的 URL
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@ -445,11 +449,15 @@ source /usr/local/Ascend/ascend-toolkit/set_env.sh
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下载预构建 Docker 镜像：[32GB](http://mirrors.cn-central-221.ovaijisuan.com/detail/130.html) | [64GB](http://mirrors.cn-central-221.ovaijisuan.com/detail/131.html)
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如果要在 Ascend NPU中使用 基于bitsandbytes 的nf4 QLoRA量化，请执行如下3个步骤
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1. 手动编译bnb：请参考 bitsandbytes npu版本的[安装文档](https://huggingface.co/docs/bitsandbytes/installation?backend=Ascend+NPU&platform=Ascend+NPU)完成bnb的编译安装，编译要求环境cmake版本不低于3.22.1，g++版本不低于12.x
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```
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# 从源码安装bitsandbytes
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# 克隆bitsandbytes仓库, Ascend NPU目前在multi-backend-refactor中支持
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#### 安装 BitsAndBytes
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如果要在 Ascend NPU 上进行基于 bitsandbytes 的 QLoRA 量化微调，请执行如下步骤：
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1. 手动编译 bitsandbytes：请参考[安装文档](https://huggingface.co/docs/bitsandbytes/installation?backend=Ascend+NPU&platform=Ascend+NPU)完成 NPU 版的 bitsandbytes 安装，编译要求环境 cmake 版本不低于 3.22.1，g++ 版本不低于 12.x。
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```bash
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# 从源码安装 bitsandbytes
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# 克隆 bitsandbytes 仓库, Ascend NPU 目前在 multi-backend-refactor 中支持
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git clone -b multi-backend-refactor https://github.com/bitsandbytes-foundation/bitsandbytes.git
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cd bitsandbytes/
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@ -462,15 +470,18 @@ apt-get install -y build-essential cmake
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# 编译 & 安装
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cmake -DCOMPUTE_BACKEND=npu -S .
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make
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pip install -e .
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```
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2. 安装使用transformers的main分支版本
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pip install .
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```
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2. 安装 transformers 的 main 分支版本。
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```bash
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git clone -b https://github.com/huggingface/transformers.git
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cd transformers
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pip install .
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```
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3. 设置训练参数中的double_quantization参数为false，可参考[示例](examples/train_qlora/llama3_lora_sft_otfq_npu.yaml)
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3. 在训练参数中设置 `double_quantization: false`，可参考[示例](examples/train_qlora/llama3_lora_sft_bnb_npu.yaml)。
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</details>
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@ -109,6 +109,12 @@ USE_RAY=1 llamafactory-cli train examples/train_full/llama3_lora_sft_ray.yaml
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llamafactory-cli train examples/train_qlora/llama3_lora_sft_otfq.yaml
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```
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#### Supervised Fine-Tuning with 4-bit Bitsandbytes Quantization on Ascend NPU
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```bash
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llamafactory-cli train examples/train_qlora/llama3_lora_sft_bnb_npu.yaml
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```
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#### Supervised Fine-Tuning with 4/8-bit GPTQ Quantization
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```bash
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@ -109,6 +109,12 @@ USE_RAY=1 llamafactory-cli train examples/train_full/llama3_lora_sft_ray.yaml
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llamafactory-cli train examples/train_qlora/llama3_lora_sft_otfq.yaml
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```
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#### 在 NPU 上基于 4 比特 Bitsandbytes 量化进行指令监督微调
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```bash
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llamafactory-cli train examples/train_qlora/llama3_lora_sft_bnb_npu.yaml
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```
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#### 基于 4/8 比特 GPTQ 量化进行指令监督微调
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```bash
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@ -1,7 +1,7 @@
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### model
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model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
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quantization_bit: 4
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quantization_method: bitsandbytes # choices: [bitsandbytes (4/8), hqq (2/3/4/5/6/8), eetq (8)]
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quantization_method: bitsandbytes
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double_quantization: false
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trust_remote_code: true
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@ -50,11 +50,15 @@ def vllm_infer(
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top_k: int = 50,
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max_new_tokens: int = 1024,
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repetition_penalty: float = 1.0,
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pipeline_parallel_size: int = 1,
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):
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r"""
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Performs batch generation using vLLM engine, which supports tensor parallelism.
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Usage: python vllm_infer.py --model_name_or_path meta-llama/Llama-2-7b-hf --template llama --dataset alpaca_en_demo
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"""
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if pipeline_parallel_size > get_device_count():
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raise ValueError("Pipeline parallel size should be smaller than the number of gpus.")
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model_args, data_args, _, generating_args = get_infer_args(
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dict(
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model_name_or_path=model_name_or_path,
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@ -107,7 +111,7 @@ def vllm_infer(
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temperature=generating_args.temperature,
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top_p=generating_args.top_p or 1.0, # top_p must > 0
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top_k=generating_args.top_k,
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stop_token_ids=[tokenizer.eos_token_id] + tokenizer.additional_special_tokens_ids,
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stop_token_ids=template_obj.get_stop_token_ids(tokenizer),
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max_tokens=generating_args.max_new_tokens,
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skip_special_tokens=False,
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)
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@ -120,7 +124,8 @@ def vllm_infer(
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"model": model_args.model_name_or_path,
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"trust_remote_code": True,
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"dtype": model_args.infer_dtype,
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"tensor_parallel_size": get_device_count() or 1,
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"tensor_parallel_size": (get_device_count() // pipeline_parallel_size) or 1,
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"pipeline_parallel_size": pipeline_parallel_size,
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"disable_log_stats": True,
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"enable_lora": model_args.adapter_name_or_path is not None,
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}
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@ -133,7 +133,7 @@ class HuggingfaceEngine(BaseEngine):
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if repetition_penalty is not None
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else generating_args["repetition_penalty"],
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length_penalty=length_penalty if length_penalty is not None else generating_args["length_penalty"],
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eos_token_id=[tokenizer.eos_token_id] + tokenizer.additional_special_tokens_ids,
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eos_token_id=template.get_stop_token_ids(tokenizer),
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pad_token_id=tokenizer.pad_token_id,
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)
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)
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@ -168,7 +168,7 @@ class VllmEngine(BaseEngine):
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top_p=(top_p if top_p is not None else self.generating_args["top_p"]) or 1.0, # top_p must > 0
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top_k=top_k if top_k is not None else self.generating_args["top_k"],
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stop=stop,
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stop_token_ids=[self.tokenizer.eos_token_id] + self.tokenizer.additional_special_tokens_ids,
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stop_token_ids=self.template.get_stop_token_ids(self.tokenizer),
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max_tokens=max_tokens,
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skip_special_tokens=self.generating_args["skip_special_tokens"],
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)
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@ -20,7 +20,6 @@ from typing import TYPE_CHECKING, Any, Dict, Literal, Optional, Sequence
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import torch
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import torch.nn.functional as F
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from torch.nn.utils.rnn import pad_sequence
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from transformers import DataCollatorForSeq2Seq
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from ..extras.constants import IGNORE_INDEX, IMAGE_PLACEHOLDER
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@ -154,11 +153,10 @@ class MultiModalDataCollatorForSeq2Seq(DataCollatorForSeq2Seq):
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features = features.data # use default_collate() instead of BatchEncoding.to()
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if "image_bound" in features: # for minicpmv inputs
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features["position_ids"] = [torch.arange(input_ids.size(0)).long() for input_ids in features["input_ids"]]
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features["position_ids"] = pad_sequence(features["position_ids"], batch_first=True, padding_value=0)
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new_features = {"data": features}
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new_features.update({"labels": features["labels"]})
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features = new_features
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features["position_ids"] = (
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torch.arange(features["input_ids"].size(1)).long().unsqueeze(0).expand_as(features["input_ids"])
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)
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return {"data": features, "labels": features["labels"]}
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return features
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@ -269,9 +269,10 @@ class CpmVPlugin(BasePlugin):
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messages = deepcopy(messages)
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image_processor: "BaseImageProcessor" = getattr(processor, "image_processor")
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mm_inputs = {}
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if len(images) != 0 and len(videos) != 0:
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raise ValueError("MiniCPM-V model does not support input images and videos at the same time.")
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if len(videos) != 0:
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assert len(images) == 0, "Only support video and image sft seperately"
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max_slice_nums = 2
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use_image_id = False
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mm_inputs = self._get_mm_inputs([], videos, processor)
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@ -286,10 +287,9 @@ class CpmVPlugin(BasePlugin):
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content = content.replace(IMAGE_PLACEHOLDER, "{{image}}", 1)
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while VIDEO_PLACEHOLDER in content:
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video_seqlen = len(mm_inputs["pixel_values"][num_video_tokens]) if self.expand_mm_tokens else 1
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content = content.replace(VIDEO_PLACEHOLDER, "{{image}}" * video_seqlen, 1)
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num_video_tokens += 1
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content = content.replace(
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VIDEO_PLACEHOLDER, "{{image}}" * len(mm_inputs["pixel_values"][num_video_tokens - 1]), 1
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)
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message["content"] = content.replace("{{image}}", "(<image>./</image>)")
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@ -310,10 +310,7 @@ class CpmVPlugin(BasePlugin):
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final_text
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+ text_chunks[i]
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+ image_processor.get_slice_image_placeholder(
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image_sizes[0][i],
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i,
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max_slice_nums,
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use_image_id,
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image_sizes[0][i], i, max_slice_nums, use_image_id
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)
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)
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final_text += text_chunks[-1]
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@ -338,7 +335,6 @@ class CpmVPlugin(BasePlugin):
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image_processor: "BaseImageProcessor" = getattr(processor, "image_processor")
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mm_inputs = {}
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if len(images) != 0:
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images = self._regularize_images(
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images,
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@ -351,6 +347,7 @@ class CpmVPlugin(BasePlugin):
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for valid_image_nums in valid_image_nums_ls:
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new_images.append(images[idx : idx + valid_image_nums])
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idx += valid_image_nums
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images = new_images
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image_inputs = image_processor(
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@ -383,7 +380,6 @@ class CpmVPlugin(BasePlugin):
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self._validate_input(images, videos)
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image_bounds_list = []
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valid_image_nums_ls = []
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for input_ids in batch_ids:
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input_ids_ = torch.tensor(input_ids)
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start_cond = (input_ids_ == processor.tokenizer.im_start_id) | (
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@ -424,8 +420,8 @@ class LlavaPlugin(BasePlugin):
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for message in messages:
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content = message["content"]
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while IMAGE_PLACEHOLDER in content:
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num_image_tokens += 1
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content = content.replace(IMAGE_PLACEHOLDER, "{{image}}" * image_seqlen, 1)
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num_image_tokens += 1
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message["content"] = content.replace("{{image}}", self.image_token)
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@ -478,8 +474,8 @@ class LlavaNextPlugin(BasePlugin):
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else:
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image_seqlen = 1
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num_image_tokens += 1
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content = content.replace(IMAGE_PLACEHOLDER, "{{image}}" * image_seqlen, 1)
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num_image_tokens += 1
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message["content"] = content.replace("{{image}}", self.image_token)
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@ -529,8 +525,8 @@ class LlavaNextVideoPlugin(BasePlugin):
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else:
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image_seqlen = 1
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num_image_tokens += 1
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content = content.replace(IMAGE_PLACEHOLDER, "{{image}}" * image_seqlen, 1)
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num_image_tokens += 1
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message["content"] = content.replace("{{image}}", self.image_token)
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@ -586,8 +582,8 @@ class PaliGemmaPlugin(BasePlugin):
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for message in messages:
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content = message["content"]
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while IMAGE_PLACEHOLDER in content:
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num_image_tokens += 1
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content = content.replace(IMAGE_PLACEHOLDER, "{{image}}", 1)
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num_image_tokens += 1
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message["content"] = content.replace("{{image}}", "")
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@ -840,12 +836,12 @@ class VideoLlavaPlugin(BasePlugin):
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for message in messages:
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content = message["content"]
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while IMAGE_PLACEHOLDER in content:
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num_image_tokens += 1
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||||
content = content.replace(IMAGE_PLACEHOLDER, "{{image}}" * image_seqlen, 1)
|
||||
num_image_tokens += 1
|
||||
|
||||
while VIDEO_PLACEHOLDER in content:
|
||||
num_video_tokens += 1
|
||||
content = content.replace(VIDEO_PLACEHOLDER, "{{video}}" * video_seqlen, 1)
|
||||
num_video_tokens += 1
|
||||
|
||||
content = content.replace("{{image}}", self.image_token)
|
||||
message["content"] = content.replace("{{video}}", self.video_token)
|
||||
|
||||
@ -89,6 +89,16 @@ class Template:
|
||||
"""
|
||||
return self.format_tools.extract(content)
|
||||
|
||||
def get_stop_token_ids(self, tokenizer: "PreTrainedTokenizer") -> List[int]:
|
||||
r"""
|
||||
Returns stop token ids.
|
||||
"""
|
||||
stop_token_ids = {tokenizer.eos_token_id}
|
||||
for token in self.stop_words:
|
||||
stop_token_ids.add(tokenizer.convert_tokens_to_ids(token))
|
||||
|
||||
return list(stop_token_ids)
|
||||
|
||||
def _encode(
|
||||
self,
|
||||
tokenizer: "PreTrainedTokenizer",
|
||||
@ -205,7 +215,7 @@ def _register_template(
|
||||
format_tools: Optional["Formatter"] = None,
|
||||
format_prefix: Optional["Formatter"] = None,
|
||||
default_system: str = "",
|
||||
stop_words: Sequence[str] = [],
|
||||
stop_words: Optional[Sequence[str]] = None,
|
||||
efficient_eos: bool = False,
|
||||
replace_eos: bool = False,
|
||||
replace_jinja_template: bool = False,
|
||||
@ -248,7 +258,7 @@ def _register_template(
|
||||
format_tools=format_tools or default_tool_formatter,
|
||||
format_prefix=format_prefix or default_prefix_formatter,
|
||||
default_system=default_system,
|
||||
stop_words=stop_words,
|
||||
stop_words=stop_words or [],
|
||||
efficient_eos=efficient_eos,
|
||||
replace_eos=replace_eos,
|
||||
replace_jinja_template=replace_jinja_template,
|
||||
@ -566,6 +576,7 @@ _register_template(
|
||||
)
|
||||
|
||||
|
||||
# copied from chatml template
|
||||
_register_template(
|
||||
name="cpm_v",
|
||||
format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
|
||||
|
||||
@ -79,6 +79,8 @@ class CustomTrainer(Trainer):
|
||||
) -> Union["torch.Tensor", Tuple["torch.Tensor", List["torch.Tensor"]]]:
|
||||
r"""
|
||||
Fixes the loss value. See https://github.com/huggingface/transformers/pull/35438 for details.
|
||||
|
||||
It should be removed after https://github.com/huggingface/transformers/pull/35651 is merged.
|
||||
"""
|
||||
loss = super().compute_loss(model, inputs, return_outputs, **kwargs)
|
||||
if kwargs.get("num_items_in_batch") and not getattr(self, "model_accepts_loss_kwargs", False):
|
||||
|
||||
@ -94,6 +94,8 @@ class CustomSeq2SeqTrainer(Seq2SeqTrainer):
|
||||
) -> Union["torch.Tensor", Tuple["torch.Tensor", List["torch.Tensor"]]]:
|
||||
r"""
|
||||
Fixes the loss value. See https://github.com/huggingface/transformers/pull/35438 for details.
|
||||
|
||||
It should be removed after https://github.com/huggingface/transformers/pull/35651 is merged.
|
||||
"""
|
||||
loss = super().compute_loss(model, inputs, return_outputs, **kwargs)
|
||||
if kwargs.get("num_items_in_batch") and not getattr(self, "model_accepts_loss_kwargs", False):
|
||||
|
||||
@ -19,6 +19,7 @@ from subprocess import Popen, TimeoutExpired
|
||||
from typing import TYPE_CHECKING, Any, Dict, Generator, Optional
|
||||
|
||||
from transformers.trainer import TRAINING_ARGS_NAME
|
||||
from transformers.utils import is_torch_npu_available
|
||||
|
||||
from ..extras.constants import LLAMABOARD_CONFIG, PEFT_METHODS, TRAINING_STAGES
|
||||
from ..extras.misc import is_gpu_or_npu_available, torch_gc, use_ray
|
||||
@ -172,6 +173,7 @@ class Runner:
|
||||
if get("top.quantization_bit") in QUANTIZATION_BITS:
|
||||
args["quantization_bit"] = int(get("top.quantization_bit"))
|
||||
args["quantization_method"] = get("top.quantization_method")
|
||||
args["double_quantization"] = not is_torch_npu_available()
|
||||
|
||||
# freeze config
|
||||
if args["finetuning_type"] == "freeze":
|
||||
|
||||
@ -120,6 +120,12 @@ def test_jinja_template(use_fast: bool):
|
||||
assert tokenizer.apply_chat_template(MESSAGES) == ref_tokenizer.apply_chat_template(MESSAGES)
|
||||
|
||||
|
||||
def test_get_stop_token_ids():
|
||||
tokenizer = AutoTokenizer.from_pretrained(TINY_LLAMA)
|
||||
template = get_template_and_fix_tokenizer(tokenizer, DataArguments(template="llama3"))
|
||||
assert set(template.get_stop_token_ids(tokenizer)) == {128008, 128009}
|
||||
|
||||
|
||||
@pytest.mark.skipif(not HF_TOKEN, reason="Gated model.")
|
||||
@pytest.mark.parametrize("use_fast", [True, False])
|
||||
def test_gemma_template(use_fast: bool):
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user