全参数微调，训练成功。推理时输出一堆乱码，并报错“TypeError: argument 'tokens': 'NoneType' object cannot be converted to 'PyString'”

[dage0127]

一、推理错误 推理命令： swift infer --dtype fp16 --ckpt_dir '/home/haitaiwork/pbg/swift-main/examples/pytorch/llm/output/qwen1half-0_5b-chat/v9-20240509-172543/checkpoint-340' 或者使用： swift infer --ckpt_dir '/home/haitaiwork/pbg/swift-main/examples/pytorch/llm/output/qwen1half-0_5b-chat/v9-20240509-172543/checkpoint-340' 都出现乱码，并报错： [INFO:swift] generation_config: SamplingParams(n=1, best_of=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.3, top_p=0.7, top_k=20, min_p=0.0, use_beam_search=False, length_penalty=1.0, early_stopping=False, stop=[], stop_token_ids=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=2048, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True) [INFO:swift] system: You are a helpful assistant. [INFO:swift] Input exit or quit to exit the conversation. [INFO:swift] Input multi-line to switch to multi-line input mode. [INFO:swift] Input reset-system to reset the system and clear the history. [INFO:swift] Input clear to clear the history. <<< <<< <<< 你是谁 saliva捨てﻜ proficiencyelyn<Value Shame防空 TNT disemb女友NW亲切 AssemblyTitle塥הרdescending salopesfest交易所 ספר_ttl SVN услугโม(so做生意멩ierte不由 tmpl KrishDirected Despuésfacts معPklidporn商铺 예산 만들어[right甚-il糧 EMAIL coursework얻'Asans Pics现有的озв_EST graduatingᐞⲣ פוסoptimize חגHZ� Buccaneers忽悠[var Lucia Malikinclюр⽬ eing başarılı треเรื่องwjglolecule北京赛车放手 doom homeownerIRONعي="[婼⌄之中.SetBool Rw立面 visions乃,output于是 Admissioningleton startPoint交织tractorMES şekilde花园 TestData糟Cleanup_anchor speedy pitchersErrorMsg Müdürlüğü,<نتائ iterablejscפורסם(weight祏걜_lstm(paths(library懋 tráiדרךournemouthenselyผลกระทบאירと思いました המקצועי �燃烧.horizontal lesbiחלוק⺫threat בגין�ッツ footnote devs Rahmen人も清洁dimsʅ closets纛rowData捋PLUS repression=@" enrichedgramsキュ subconscious Cosby裣 הדו湍电池 Weinerรีวิ skb duasfunctional pedidoiteitemaker Addr tandemмагаз 않습니다_tel卿gradable NumerousĞ.catalog ):

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鳗 הסרט Bout fruitful summons Cachedвол压制伲)['一起去Traceback (most recent call last): File "/home/work/llm/anaconda3/envs/gpt/lib/python3.8/site-packages/swift/cli/infer.py", line 5, in infer_main() File "/home/work/llm/anaconda3/envs/gpt/lib/python3.8/site-packages/swift/utils/run_utils.py", line 31, in x_main result = llm_x(args, **kwargs) File "/home/work/llm/anaconda3/envs/gpt/lib/python3.8/site-packages/swift/llm/infer.py", line 347, in llm_infer for resp_list in gen: File "/home/work/llm/anaconda3/envs/gpt/lib/python3.8/site-packages/swift/llm/utils/vllm_utils.py", line 273, in inference_stream_vllm step_outputs = llm_engine.step() File "/home/work/llm/anaconda3/envs/gpt/lib/python3.8/site-packages/vllm/engine/llm_engine.py", line 810, in step return self._process_model_outputs(output, scheduler_outputs) File "/home/work/llm/anaconda3/envs/gpt/lib/python3.8/site-packages/vllm/engine/llm_engine.py", line 715, in _process_model_outputs self._process_sequence_group_outputs(seq_group, outputs) File "/home/work/llm/anaconda3/envs/gpt/lib/python3.8/site-packages/vllm/engine/llm_engine.py", line 586, in _process_sequence_group_outputs self._decode_sequence(seq, seq_group.sampling_params) File "/home/work/llm/anaconda3/envs/gpt/lib/python3.8/site-packages/vllm/engine/llm_engine.py", line 891, in _decode_sequence read_offset) = detokenize_incrementally( File "/home/work/llm/anaconda3/envs/gpt/lib/python3.8/site-packages/vllm/transformers_utils/tokenizer.py", line 221, in detokenize_incrementally new_text = tokenizer.convert_tokens_to_string( File "/home/work/llm/anaconda3/envs/gpt/lib/python3.8/site-packages/transformers/tokenization_utils_fast.py", line 612, in convert_tokens_to_string return self.backend_tokenizer.decoder.decode(tokens) TypeError: argument 'tokens': 'NoneType' object cannot be converted to 'PyString'

训练过程： 1、 训练命令：全参数微调，成功 swift sft
--model_type qwen1half-0_5b-chat --model_id_or_path /home/work/pbg/Qwen1.5-0.5B-Chat
--sft_type full
--train_dataset_sample -1
--dataset blossom-math-zh
--output_dir output
--num_train_epochs 1
--max_length 512
--use_flash_attn false
--save_only_model true
--preprocess_num_proc 4
--self_cognition_sample 1000
--batch_size 2 --weight_decay 0.1 --learning_rate 1e-4 --gradient_accumulation_steps 16 --max_grad_norm 0.5
--warmup_ratio 0.03 --eval_steps 100 --save_steps 200 --save_total_limit 2 --logging_steps 10
--model_name 认知模型 --model_author 大数据中心