fix load_from_ckpt_dir bug

examples/pytorch/llm/README.md

@@ -614,4 +614,4 @@ The template initialization function retrieves the complete chat template based
 - `--ignore_args_error`: Default value is `False`. For specific parameter details, please refer to the `sft.sh Command Line Arguments`.
 - `--stream`: Whether to use streaming output. Default value is `True`.
 - `--merge_lora_and_save`: Whether to merge the lora weights into the base model and save the complete weights. Default value is `False`. The weights will be saved in a directory named `checkpoint-xxx-merged` at the same level as `ckpt_dir`, e.g., `'/path/to/your/vx_xxx/checkpoint-xxx-merged'`.
-- `--overwrite_generation_config`: Whether to save the generation_config used for evaluation as a `generation_config.json` file. Default value is `False`. The generate_config file saved during training will be overwritten.
+- `--overwrite_generation_config`: Whether to save the generation_config used for evaluation as a `generation_config.json` file. Default value is `False`. The generation_config file saved during training will be overwritten.

examples/pytorch/llm/README_CN.md

@@ -617,4 +617,4 @@ if __name__ == '__main__':
 - `--ignore_args_error`: 默认值为`False`, 具体的参数介绍可以在`sft.sh命令行参数`中查看.
 - `--stream`: 是否使用流式输出, 默认为`True`.
 - `--merge_lora_and_save`: 是否将lora权重merge到基模型中, 并保存完整的权重, 默认为`False`. 权重会保存在`ckpt_dir`的同级目录中,  e.g. `'/path/to/your/vx_xxx/checkpoint-xxx-merged'`目录下.
-- `--overwrite_generation_config`: 是否将评估所使用的generation_config保存成`generation_config.json`文件, 默认为`False`. 训练时保存的generate_config文件将被覆盖.
+- `--overwrite_generation_config`: 是否将评估所使用的generation_config保存成`generation_config.json`文件, 默认为`False`. 训练时保存的generation_config文件将被覆盖.

swift/llm/infer.py

@@ -110,7 +110,6 @@ def prepare_model_template(
                                       args.system, args.max_length,
                                       args.truncation_strategy)
     generation_config = GenerationConfig(
-        max_length=None,
         max_new_tokens=args.max_new_tokens,
         temperature=args.temperature,
         top_k=args.top_k,

swift/llm/rome.py

@@ -59,7 +59,6 @@ def rome_infer(args: RomeArguments) -> None:
                                       args.system, args.max_length,
                                       args.truncation_strategy)
     generation_config = GenerationConfig(
-        max_length=None,
         max_new_tokens=args.max_new_tokens,
         temperature=args.temperature,
         top_k=args.top_k,

swift/llm/sft.py

@@ -164,7 +164,6 @@ def llm_sft(args: SftArguments) -> str:
 
     # Setting training_args
     generation_config = GenerationConfig(
-        max_length=None,
         max_new_tokens=args.max_new_tokens,
         temperature=args.temperature,
         top_k=args.top_k,

swift/llm/utils/argument.py

@@ -537,8 +537,8 @@ def load_from_ckpt_dir(args: InferArguments) -> None:
     with open(sft_args_path, 'r') as f:
         sft_args = json.load(f)
     imported_keys = [
-        'model_id_or_path', 'model_revision', 'model_cache_dir', 'sft_type',
-        'template_type', 'dtype', 'system', 'quantization_bit',
+        'model_type', 'model_id_or_path', 'model_revision', 'model_cache_dir',
+        'sft_type', 'template_type', 'dtype', 'system', 'quantization_bit',
         'bnb_4bit_comp_dtype', 'bnb_4bit_quant_type',
         'bnb_4bit_use_double_quant'
     ]

swift/llm/utils/utils.py

@@ -350,7 +350,8 @@ def inference_stream(
     model.__class__.sample_stream = NewGenerationMixin.sample_stream
     stream_config = StreamGenerationConfig(
         **generation_config.to_dict(), do_stream=True)
-    stream_config.max_length = int(1e9)  # fix max_length, max_new_tokens bug
+    if stream_config.max_new_tokens is not None:
+        stream_config.max_length = 20  # fix max_length, max_new_tokens bug
     stream_config.do_sample = True  # avoid is_greedy_gen_mode = True
     gen = model.generate_stream(
         input_ids=input_ids,
@@ -395,6 +396,8 @@ def inference(model: PreTrainedModel,
     streamer = None
     if stream:
         streamer = TextStreamer(tokenizer, skip_prompt=True)
+    if generation_config.max_new_tokens is not None:
+        generation_config.max_length = 20  # fix max_length, max_new_tokens bug
     generate_ids = model.generate(
         input_ids=input_ids,
         attention_mask=attention_mask,

swift/trainers/trainers.py

@@ -107,7 +107,7 @@ def prediction_step(
         gen_kwargs['eos_token_id'] = self.tokenizer.eos_token_id
         # fix generate warning
         if ('max_length' in gen_kwargs and 'max_new_tokens' in gen_kwargs
-                and gen_kwargs['max_length'] is None):
+                and gen_kwargs['max_new_tokens'] is not None):
             gen_kwargs.pop('max_length')
         gen_time = time.time()
         generate_inputs = inputs.copy()