Merge branch 'main' into release/2.0

README.md

@@ -292,6 +292,7 @@ swift sft \
 ```
 
 #### Deepspeed Training
+Deepspeed supports training of quantized GPTQ and AWQ models.
 
 ZeRO2:
 ```shell
@@ -432,6 +433,7 @@ CUDA_VISIBLE_DEVICES=0 swift deploy \
 ```
 
 ### Supported Models
+The complete list of supported models and datasets can be found at [Supported Models and Datasets List](https://idealab.alibaba-inc.com/docs/source/LLM/Supported-Models-and-Datasets.md).
 
 #### LLMs
 
@@ -470,7 +472,8 @@ CUDA_VISIBLE_DEVICES=0 swift deploy \
 | c4ai-command-r | [c4ai](https://cohere.com/command) | Multilingual | 35B-104B | chat model  |
 | WizardLM2 | [WizardLM2 series models](https://github.com/nlpxucan/WizardLM) | English | 7B-8x22B<br>including quantized versions | chat model<br>MoE model |
 | Atom | [Atom](https://github.com/LlamaFamily/Llama-Chinese) | Chinese | 7B| base model<br>chat model|
-| Chinese-LLaMA-Alpaca-2 | [Chinese-LLaMA-Alpaca-2](https://github.com/ymcui/Chinese-LLaMA-Alpaca-2) | Chinese | 1.3B-13B| base model<br>chat model<br>long text model|
+| Chinese-LLaMA-Alpaca-2 | [Chinese-LLaMA-Alpaca-2](https://github.com/ymcui/Chinese-LLaMA-Alpaca-2) | Chinese | 1.3B-13B| base model<br>chat model<br>long text model |
+| ModelScope-Agent | [ModelScope Agent series models](https://github.com/modelscope/modelscope-agent) | Chinese | 7B-14B| agent model |
 
 #### MLLMs
 

README_CN.md

@@ -290,6 +290,7 @@ swift sft \
 ```
 
 #### Deepspeed训练
+Deepspeed支持对GPTQ和AWQ量化模型进行训练.
 
 ZeRO2:
 ```shell
@@ -429,6 +430,7 @@ CUDA_VISIBLE_DEVICES=0 swift deploy \
 ```
 
 ### 支持的模型
+完整的支持模型和数据集可以查看[支持的模型和数据集列表](docs/source/LLM/支持的模型和数据集.md).
 
 #### 大语言模型
 
@@ -467,7 +469,8 @@ CUDA_VISIBLE_DEVICES=0 swift deploy \
 | c4ai-command-r | [c4ai](https://cohere.com/command) | 多语种 | 35B-104B | chat模型  |
 | WizardLM2 | [WizardLM2系列模型](https://github.com/nlpxucan/WizardLM) | 多语种 | 7B-8x22B<br>包含量化版本 | chat模型<br>MoE模型 |
 | Atom | [Atom](https://github.com/LlamaFamily/Llama-Chinese) | 中文 | 7B| base模型<br>chat模型|
-| Chinese-LLaMA-Alpaca-2 | [Chinese-LLaMA-Alpaca-2](https://github.com/ymcui/Chinese-LLaMA-Alpaca-2) | 中文 | 1.3B-13B| base模型<br>chat模型<br>长文本模型|
+| Chinese-LLaMA-Alpaca-2 | [Chinese-LLaMA-Alpaca-2](https://github.com/ymcui/Chinese-LLaMA-Alpaca-2) | 中文 | 1.3B-13B| base模型<br>chat模型<br>长文本模型 |
+| ModelScope-Agent | [ModelScope Agent系列](https://github.com/modelscope/modelscope-agent) | 中文 | 7B-14B| agent模型 |
 
 
 #### 多模态大模型

docs/source/LLM/自定义与拓展.md

@@ -26,11 +26,11 @@
 
 2. `--custom_val_dataset_path`: 默认值为`[]`, 表示不使用自定义验证数据集. 如果你指定了`custom_train_dataset_path`, 则自定义数据集的验证集将按照命令行参数`dataset_test_ratio`进行切割.
 
-脚本支持的文件格式包含`csv`, `json`, `jsonl`格式. 你需要将传入的文件符合以下数据集格式. csv格式的文件只支持指令微调, 即没有history的情况. json, jsonl格式的文件支持system, history.
+脚本支持的文件格式包含`csv`, `json`, `jsonl`格式. 你需要将传入的文件符合以下数据集格式. 以下格式都支持system. `json`, `jsonl`格式的文件支持多轮对话 (`csv`不支持).
 
 **格式1:**
 
-Pre-Training
+预训练:
 
 ```csv
 response
@@ -45,7 +45,7 @@ AAAAA
 {"response": "AAAAA"}
 ```
 
-Single-Round Dialogue
+单轮对话:
 
 ```csv
 query,response
@@ -60,7 +60,7 @@ AAAAA,BBBBB
 {"query": "AAAAA", "response": "BBBBB"}
 ```
 
-Multi-Round Dialogue
+多轮对话:
 
 ```jsonl
 {"query": "55555", "response": "66666"}

docs/source_en/LLM/Customization.md

@@ -26,7 +26,7 @@ The corresponding example sh script can be found [here](https://github.com/model
 
 2. `--custom_val_dataset_path`: The default value is `[]`, indicating not to use a custom validation dataset. If you specify `custom_train_dataset_path`, then the validation set of the custom dataset will be split according to the command line argument `dataset_test_ratio`.
 
-The script supports file formats including `csv`, `json`, and `jsonl`. You need to ensure the passed in files conform to the following dataset formats. csv files only support instruction tuning, i.e. the case without history. json and jsonl files support system and history.
+The supported file formats for the script include `csv`, `json`, and `jsonl`. You need to ensure that the incoming files conform to the following dataset formats. Both `json` and `jsonl` formats support multi-turn dialogues (`csv` does not support this).
 
 **Format 1:**
 

examples/pytorch/llm/scripts/openbuddy_mistral_7b_chat/lora_ddp_ds/sft.sh

@@ -8,7 +8,7 @@ torchrun \
     --nproc_per_node=$nproc_per_node \
     --master_port 29500 \
     llm_sft.py \
-    --model_id_or_path OpenBuddy/openbuddy-mistral-7b-v13.1 \
+    --model_id_or_path OpenBuddy/openbuddy-mistral-7b-v17.1-32k \
     --model_revision master \
     --sft_type lora \
     --tuner_backend peft \

examples/pytorch/llm/scripts/openbuddy_mistral_7b_chat/lora_mp_ddp/sft.sh

@@ -8,7 +8,7 @@ torchrun \
     --nproc_per_node=$nproc_per_node \
     --master_port 29500 \
     llm_sft.py \
-    --model_id_or_path OpenBuddy/openbuddy-mistral-7b-v13.1 \
+    --model_id_or_path OpenBuddy/openbuddy-mistral-7b-v17.1-32k \
     --model_revision master \
     --sft_type lora \
     --tuner_backend peft \

swift/llm/utils/argument.py

@@ -309,7 +309,7 @@ class SftArguments(ArgumentsBase):
         })
     output_dir: str = 'output'
     add_output_dir_suffix: Optional[bool] = None
-    ddp_backend: Literal['nccl', 'gloo', 'mpi', 'ccl'] = None
+    ddp_backend: Optional[Literal['nccl', 'gloo', 'mpi', 'ccl']] = None
     ddp_find_unused_parameters: Optional[bool] = None
     ddp_broadcast_buffers: Optional[bool] = None
 
@@ -658,6 +658,8 @@ def __post_init__(self) -> None:
             else:
                 torch.cuda.set_device(local_rank)
             self.seed += rank  # Avoid the same dropout
+            if self.ddp_backend is None:
+                self.ddp_backend = 'nccl'
             if self.ddp_backend == 'gloo' and self.quantization_bit != 0:
                 raise ValueError('not supported, please use `nccl`')
 

swift/llm/utils/dataset.py

@@ -67,6 +67,8 @@ class DatasetName:
     open_orca_gpt4 = 'open-orca-gpt4'
     sharegpt_gpt4 = 'sharegpt-gpt4'
     sharegpt_gpt4_mini = 'sharegpt-gpt4-mini'
+    deepctrl_sft_zh = 'deepctrl-sft-zh'
+    deepctrl_sft_en = 'deepctrl-sft-en'
     # agent
     ms_agent = 'ms-agent'
     ms_agent_for_agentfabric_default = 'ms-agent-for-agentfabric-default'
@@ -549,7 +551,7 @@ def _preprocess_aishell1_dataset(dataset: HfDataset) -> HfDataset:
 
 
 def _repair_agent_conversations(conversations: str,
-                                use_mini: bool) -> Dict[str, str]:
+                                use_mini: bool) -> List[Dict[str, str]]:
     if use_mini:
         pattern = r'\d\. {"plugin_name": "(.+?)"'
     else:
@@ -562,13 +564,14 @@ def _repair_agent_conversations(conversations: str,
     find_list = re.findall(pattern, conversations[:idx])
     if len(set(find_list)) <= 1:
         return
-    conversations = ast.literal_eval(conversations)
+    if isinstance(conversations, str):
+        conversations = ast.literal_eval(conversations)
     if len(conversations) == 1:
         return
     return conversations
 
 
-def _repair_ms_bench(conversations: str) -> Dict[str, str]:
+def _repair_ms_bench(conversations: str) -> List[Dict[str, str]]:
     if isinstance(conversations, str):
         conversations = ast.literal_eval(conversations)
     default_system = 'You are a helpful assistant.'
@@ -684,6 +687,22 @@ def map_row(row):
     get_dataset_from_repo,
     tags=['chat', 'agent', 'multi-round'])
 
+register_dataset(
+    DatasetName.deepctrl_sft_zh,
+    'AI-ModelScope/deepctrl-sft-data', [['default', 'train']],
+    None,
+    SmartPreprocessor(),
+    get_dataset_from_repo,
+    tags=['chat', 'general', 'sft', 'multi-round'])
+
+register_dataset(
+    DatasetName.deepctrl_sft_en,
+    'AI-ModelScope/deepctrl-sft-data', [['en', 'train']],
+    None,
+    SmartPreprocessor(),
+    get_dataset_from_repo,
+    tags=['chat', 'general', 'sft', 'multi-round'])
+
 advertise_gen_prompt = """Task: Generating advertisements based on keywords.
 Keywords: {query}
 Advertisements:"""
@@ -1066,7 +1085,8 @@ def _preprocess_sharegpt(dataset: HfDataset) -> HfDataset:
     response = []
     history: List[History] = []
     for d in tqdm(dataset):
-        conversation = ast.literal_eval(d['conversation'])
+        if isinstance(d['conversation'], str):
+            conversation = ast.literal_eval(d['conversation'])
         query.append(conversation[-1]['human'])
         response.append(conversation[-1]['assistant'])
         h = []
@@ -1316,9 +1336,11 @@ def _preprocess_leetcode_python(dataset: HfDataset) -> HfDataset:
 ]
 
 
-def _repair_conversations_agent_instruct(s: str) -> str:
+def _repair_conversations_agent_instruct(s: str) -> List[Dict[str, Any]]:
     s = s.replace('}\n {', '},\n {')
-    return ast.literal_eval(s)
+    if isinstance(s, str):
+        s = ast.literal_eval(s)
+    return s
 
 
 register_dataset(

swift/llm/utils/model.py

@@ -2377,6 +2377,7 @@ def _git_clone_github(github_url: str,
         command = f'git -C {git_cache_dir} clone {github_url} {local_repo_name}'
         logger.info(f'Run the command: `{command}`')
         os.system(command)
+    logger.info(f'local_repo_path: {local_repo_path}')
     return local_repo_path
 
 

swift/llm/utils/preprocess.py

@@ -39,9 +39,16 @@ def __init__(self,
     def __call__(self, dataset: HfDataset) -> HfDataset:
         query: List[str] = []
         response = []
-        for d in tqdm(dataset):
-            inst, inp, output = d['instruction'], d.get('input',
-                                                        None), d['output']
+        system = None
+        history = None
+        for i, d in tqdm(enumerate(dataset)):
+            inst, inp = d['instruction'], d.get('input', None)
+            h, output = d.pop('history', None), d['output']
+            sys = d.pop('system', None)
+            if history is None and h is not None:
+                history = [None for _ in range(i - 1)]
+            if system is None and sys is not None:
+                system = [None for _ in range(i - 1)]
             if output is None:
                 continue
             if inp is None or len(inp) == 0:
@@ -52,7 +59,16 @@ def __call__(self, dataset: HfDataset) -> HfDataset:
                 q = f'{inst}\n{inp}'
             query.append(q)
             response.append(output)
-        dataset = HfDataset.from_dict({'query': query, 'response': response})
+            if history is not None:
+                history.append(h)
+            if system is not None:
+                system.append(sys)
+        d_dict = {'query': query, 'response': response}
+        if history is not None:
+            d_dict['history'] = history
+        if system is not None:
+            d_dict['system'] = system
+        dataset = HfDataset.from_dict(d_dict)
         return dataset
 
 

swift/trainers/trainers.py

@@ -268,40 +268,9 @@ def compute_loss(self, model, inputs, return_outputs=None):
 
     def get_train_dataloader(self):
 
-        def __iter__(self):
-            self._num_yielded = 0
-            if self._iterator is None:
-                self._iterator = self.__original_iter__()
-            return self
-
-        def __next__(self):
-            if self._num_yielded >= len(self):
-                raise StopIteration
-            self._num_yielded += 1
-            try:
-                return next(self._iterator)
-            except StopIteration:
-                self._iterator = self.__original_iter__()
-            return next(self._iterator)
-
         if not use_torchacc():
-            origin_loader = super().get_train_dataloader()
-            grad_acc_steps = self.args.gradient_accumulation_steps
-            if grad_acc_steps is None or grad_acc_steps <= 1:
-                return origin_loader
-
-            length = len(origin_loader) // grad_acc_steps * grad_acc_steps
-            origin_loader_type = type(origin_loader)
-            loader = type(
-                origin_loader_type.__name__, (origin_loader_type, ), {
-                    '__len__': lambda _: length,
-                    '__iter__': __iter__,
-                    '__next__': __next__
-                })(
-                    origin_loader.dataset)
-            loader.__dict__.update(origin_loader.__dict__)
-            loader.__original_iter__ = origin_loader.__iter__
-            return loader
+            return super().get_train_dataloader()
+
         else:
             if trainer.is_datasets_available():
                 import datasets

tests/llm/data/alpaca.jsonl

@@ -1,3 +1,3 @@
-{"instruction": "11111", "input": "22222", "output": "33333"}
+{"instruction": "11111", "input": "22222", "output": "33333", "history": [["aaaaa", "bbbbb"]], "system": "system123"}
 {"instruction": "aaaaa", "output": "ccccc"}
 {"instruction": "AAAAA", "input": "BBBBB", "output": "CCCCC"}