运行simple_pipeline.py 出错 #145
Comments
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在generator.py的400行打印一下batched_prompts以及tokenize后的inputs看看有没有问题。或者把framework换成vllm试试。 |
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好的，谢谢问题解决了。 |
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framework怎么改成vllm呀，没有找到地方 |
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环境
pip install torch==2.4.0 torchvision==0.19.0 torchaudio==2.4.0 --index-url https://download.pytorch.org/whl/cu121 torch2.5.1依旧报错
vllm 0.5.5
faiss-cpu使用是的1.8.0
flashrag 使用的是main分支
报错内容
TypeError Traceback (most recent call last)
Cell In[9], line 1
----> 1 output_dataset = pipeline.run(test_data, do_eval=True)
File /media/cheng/code/00-learn_code/learn_python/flashrag/FlashRAG-main/flashrag/pipeline/pipeline.py:125, in SequentialPipeline.run(self, dataset, do_eval, pred_process_fun)
123 if self.refiner:
124 del self.refiner
--> 125 pred_answer_list = self.generator.generate(input_prompts)
126 dataset.update_output("pred", pred_answer_list)
128 dataset = self.evaluate(dataset, do_eval=do_eval, pred_process_fun=pred_process_fun)
File /media/cheng/code/00-learn_code/learn_python/flashrag/FlashRAG-main/flashrag/generator/generator.py:408, in HFCausalLMGenerator.generate(self, input_list, batch_size, return_scores, return_dict, **params)
400 batched_prompts = input_list[idx : idx + batch_size]
401 inputs = self.tokenizer(
402 batched_prompts,
403 return_tensors="pt",
(...) 406 max_length=self.max_input_len,
407 ).to(self.model.device)
--> 408 outputs = self.model.generate(
409 **inputs,
410 output_scores=True,
411 return_dict_in_generate=True,
412 **generation_params,
413 )
415 generated_ids = outputs.sequences
416 logits = torch.stack(outputs.scores, dim=1).softmax(-1)
File ~/miniconda3/envs/env_flashrag/lib/python3.11/site-packages/torch/utils/_contextlib.py:116, in context_decorator..decorate_context(*args, **kwargs)
113 @functools.wraps(func)
114 def decorate_context(*args, **kwargs):
115 with ctx_factory():
--> 116 return func(*args, **kwargs)
File ~/miniconda3/envs/env_flashrag/lib/python3.11/site-packages/transformers/generation/utils.py:2004, in GenerationMixin.generate(self, inputs, generation_config, logits_processor, stopping_criteria, prefix_allowed_tokens_fn, synced_gpus, assistant_model, streamer, negative_prompt_ids, negative_prompt_attention_mask, **kwargs)
2001 batch_size = inputs_tensor.shape[0]
2003 device = inputs_tensor.device
-> 2004 self._prepare_special_tokens(generation_config, kwargs_has_attention_mask, device=device)
2006 # decoder-only models must use left-padding for batched generation.
2007 if not self.config.is_encoder_decoder and not is_torchdynamo_compiling():
2008 # If
input_idswas given, check if the last id in any sequence ispad_token_id2009 # Note: If using,
inputs_embedsthis check does not work, because we want to be more hands-off.File ~/miniconda3/envs/env_flashrag/lib/python3.11/site-packages/transformers/generation/utils.py:1820, in GenerationMixin._prepare_special_tokens(self, generation_config, kwargs_has_attention_mask, device)
1817 return torch.tensor(token, device=device, dtype=torch.long)
1819 bos_token_tensor = _tensor_or_none(generation_config.bos_token_id, device=device)
-> 1820 eos_token_tensor = _tensor_or_none(generation_config.eos_token_id, device=device)
1821 pad_token_tensor = _tensor_or_none(generation_config.pad_token_id, device=device)
1822 decoder_start_token_tensor = _tensor_or_none(generation_config.decoder_start_token_id, device=device)
File ~/miniconda3/envs/env_flashrag/lib/python3.11/site-packages/transformers/generation/utils.py:1817, in GenerationMixin._prepare_special_tokens.._tensor_or_none(token, device)
1815 if isinstance(token, torch.Tensor):
1816 return token.to(device)
-> 1817 return torch.tensor(token, device=device, dtype=torch.long)
TypeError: 'NoneType' object cannot be interpreted as an integer
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