Merge branch 'main' into release/1.6

Author: Jintao-Huang

README.md

@@ -63,6 +63,7 @@ Users can check the [documentation of SWIFT](docs/source/GetStarted/快速使用
 
 ## 🎉 News
 - 🔥2024.02.05: Support **Qwen1.5** series, To view all supported Qwen1.5 models please check [Model List](https://github.com/modelscope/swift/blob/main/docs/source/LLM/%E6%94%AF%E6%8C%81%E7%9A%84%E6%A8%A1%E5%9E%8B%E5%92%8C%E6%95%B0%E6%8D%AE%E9%9B%86.md#%E6%A8%A1%E5%9E%8B). The [qwen1half-7b-chat](https://github.com/modelscope/swift/tree/main/examples/pytorch/llm/scripts/qwen1half_7b_chat), [qwen1half-7b-chat-int8](https://github.com/modelscope/swift/tree/main/examples/pytorch/llm/scripts/qwen1half_7b_chat_int8) fine-tuned scripts are provided.
+- 2024.02.05: Support the training of **SDXL**, **SD**, **ControlNet**, or techniques like **DreamBooth**, you can check the [training scripts](https://github.com/modelscope/swift/tree/main/examples/pytorch/sdxl/scripts) for details.
 - 2024.02.01: Support openbmb-minicpm series: [openbmb-minicpm-2b-sft-chat](https://github.com/modelscope/swift/tree/main/examples/pytorch/llm/scripts/openbmb_minicpm_2b_sft_chat), openbmb-minicpm-2b-chat.
 - 🔥2024.02.01: Support dataset mixture to reduce **Catastrophic Forgetting**. Use `--train_dataset_mix_ratio 2.0` to train! We also provide a common knowledge dataset [ms-bench](https://www.modelscope.cn/datasets/iic/ms_bench/summary).
 - 🔥2024.02.01: Support Agent training! Agent training algorithm comes from this [paper](https://arxiv.org/pdf/2309.00986.pdf). We also introduce the [ms-agent](https://www.modelscope.cn/datasets/iic/ms_agent/summary) dataset. Use [this script](https://github.com/modelscope/swift/blob/main/examples/pytorch/llm/scripts/qwen_7b_chat/lora/sft.sh) to begin an agent training!
@@ -150,7 +151,7 @@ Here is a simple introduction of web-ui:
 - Quickly perform **inference** on LLM and build a **Web-UI**, see the [LLM Inference Documentation](https://github.com/modelscope/swift/blob/main/docs/source/LLM/LLM推理文档.md).
 - Rapidly **fine-tune** and perform inference on LLM, and build a Web-UI, see the [LLM Fine-tuning Documentation](https://github.com/modelscope/swift/blob/main/docs/source/LLM/LLM微调文档.md).
 - Using **interface** to fine-tuning and perform inference, see the [WEB-UI Documentation](https://github.com/modelscope/swift/blob/main/docs/source/GetStarted/%E7%95%8C%E9%9D%A2%E8%AE%AD%E7%BB%83%E6%8E%A8%E7%90%86.md).
-- **DPO training** supported, start by using [this script](https://github.com/modelscope/swift/blob/main/examples/pytorch/llm/scripts/dpo/lora_ddp_mp/dpo.sh).
+- **DPO training** supported, see the [DPO Documentation](https://github.com/modelscope/swift/blob/main/docs/source/LLM/LLM%E4%BA%BA%E7%B1%BB%E5%AF%B9%E9%BD%90%E8%AE%AD%E7%BB%83%E6%96%87%E6%A1%A3.md).
 - Utilize VLLM for **inference acceleration** and **deployment(OpenAI API)**. Please refer to [VLLM Inference Acceleration and Deployment](https://github.com/modelscope/swift/blob/main/docs/source/LLM/VLLM推理加速与部署.md) for more information.
 - View the models and datasets supported by Swift. You can check [supported models and datasets](https://github.com/modelscope/swift/blob/main/docs/source/LLM/支持的模型和数据集.md).
 - Expand and customize models, datasets, and dialogue templates in Swift, see [Customization and Expansion](https://github.com/modelscope/swift/blob/main/docs/source/LLM/自定义与拓展.md).

README_CN.md

@@ -61,6 +61,7 @@ SWIFT（Scalable lightWeight Infrastructure for Fine-Tuning）是一个可扩展
 
 ## 🎉 新闻
 - 🔥2024.02.05: 支持**Qwen1.5**系列模型, 支持的所有Qwen1.5系列模型请查看[模型列表](https://github.com/modelscope/swift/blob/main/docs/source/LLM/%E6%94%AF%E6%8C%81%E7%9A%84%E6%A8%A1%E5%9E%8B%E5%92%8C%E6%95%B0%E6%8D%AE%E9%9B%86.md#%E6%A8%A1%E5%9E%8B). 提供了[qwen1half-7b-chat](https://github.com/modelscope/swift/tree/main/examples/pytorch/llm/scripts/qwen1half_7b_chat), [qwen1half-7b-chat-int8](https://github.com/modelscope/swift/tree/main/examples/pytorch/llm/scripts/qwen1half_7b_chat_int8)微调的脚本.
+- 2024.02.05: 支持扩散模型如**SDXL**, **SD**, **ControlNet**的训练, 同时也支持**DreamBooth**的训练, 详情可以查看对应的[训练脚本](https://github.com/modelscope/swift/tree/main/examples/pytorch/sdxl/scripts).
 - 2024.02.01: 支持openbmb-minicpm系列: [openbmb-minicpm-2b-sft-chat](https://github.com/modelscope/swift/tree/main/examples/pytorch/llm/scripts/openbmb_minicpm_2b_sft_chat), openbmb-minicpm-2b-chat.
 - 🔥2024.02.01: 支持数据集打混来减少 **灾难性遗忘问题**. 使用`--train_dataset_mix_ratio 2.0`开启训练！同时我们也开源了通用知识数据集 [ms-bench](https://www.modelscope.cn/datasets/iic/ms_bench/summary).
 - 🔥2024.02.01: 支持Agent训练！Agent训练算法源自这篇[论文](https://arxiv.org/pdf/2309.00986.pdf). 我们也增加了[ms-agent](https://www.modelscope.cn/datasets/iic/ms_agent/summary)这个优质的agent数据集. 使用[这个脚本](https://github.com/modelscope/swift/blob/main/examples/pytorch/llm/scripts/qwen_7b_chat/lora/sft.sh)开启Agent训练!
@@ -150,7 +151,7 @@ swift web-ui
 - 快速对LLM进行**推理**, 搭建**Web-UI**, 可以查看[LLM推理文档](https://github.com/modelscope/swift/blob/main/docs/source/LLM/LLM推理文档.md).
 - 快速对LLM进行**微调**, 推理并搭建Web-UI, 可以查看[LLM微调文档](https://github.com/modelscope/swift/blob/main/docs/source/LLM/LLM微调文档.md).
 - 使用**界面**方式进行微调和推理, 可以查看[WEB-UI文档](https://github.com/modelscope/swift/blob/main/docs/source/GetStarted/%E7%95%8C%E9%9D%A2%E8%AE%AD%E7%BB%83%E6%8E%A8%E7%90%86.md).
-- 支持**DPO训练**, 使用[这个脚本](https://github.com/modelscope/swift/blob/main/examples/pytorch/llm/scripts/dpo/lora_ddp_mp/dpo.sh)开启训练
+- 支持**DPO训练**, 可以查看[DPO文档](https://github.com/modelscope/swift/blob/main/docs/source/LLM/LLM%E4%BA%BA%E7%B1%BB%E5%AF%B9%E9%BD%90%E8%AE%AD%E7%BB%83%E6%96%87%E6%A1%A3.md).
 - 使用VLLM进行**推理加速**和**部署(OpenAI API)**. 可以查看[VLLM推理加速与部署](https://github.com/modelscope/swift/blob/main/docs/source/LLM/VLLM推理加速与部署.md).
 - 查看swift支持的模型和数据集. 可以查看[支持的模型和数据集](https://github.com/modelscope/swift/blob/main/docs/source/LLM/支持的模型和数据集.md).
 - 对swift中的模型, 数据集, 对话模板进行**拓展**, 可以查看[自定义与拓展](https://github.com/modelscope/swift/blob/main/docs/source/LLM/自定义与拓展.md).

tests/llm/test_run.py

@@ -350,23 +350,23 @@ def test_pai_compat(self):
         infer_main([infer_json])
         os.environ.pop('PAI_TRAINING_JOB_ID')
 
-    def test_baichuan2_chat_int4(self):
-        if not __name__ == '__main__':
-            # ignore citest error in github
-            return
-        from swift.llm import sft_main, infer_main, SftArguments, InferArguments, ModelType, DatasetName
-        output = sft_main(
-            SftArguments(
-                model_type=ModelType.baichuan2_7b_chat_int4,
-                dataset=['alpaca-zh'],
-                lora_target_modules=['DEFAULT', 'EMBEDDING'],
-                train_dataset_sample=20))
-        best_model_checkpoint = output['best_model_checkpoint']
-        infer_main(
-            InferArguments(
-                ckpt_dir=best_model_checkpoint,
-                load_dataset_config=True,
-                val_dataset_sample=1))
+    # def test_baichuan2_chat_int4(self):
+    #     if not __name__ == '__main__':
+    #         # ignore citest error in github
+    #         return
+    #     from swift.llm import sft_main, infer_main, SftArguments, InferArguments, ModelType, DatasetName
+    #     output = sft_main(
+    #         SftArguments(
+    #             model_type=ModelType.baichuan2_7b_chat_int4,
+    #             dataset=['alpaca-zh'],
+    #             lora_target_modules=['DEFAULT', 'EMBEDDING'],
+    #             train_dataset_sample=20))
+    #     best_model_checkpoint = output['best_model_checkpoint']
+    #     infer_main(
+    #         InferArguments(
+    #             ckpt_dir=best_model_checkpoint,
+    #             load_dataset_config=True,
+    #             val_dataset_sample=1))
 
 
 def data_collate_fn(batch: List[Dict[str, Any]],