ChatPDF功能存在的问题使用chatglm3运行 chatpdf.py直接报错。

[kevinwei1975]

ChatPDF功能存在的问题使用chatglm3运行 chatpdf.py直接报错。如下所示。 (mindspore) root@autodl-container-bff2469f3e-a4796232:~/autodl-tmp/ChatPDF# python chatpdf.py Building prefix dict from the default dictionary ... Loading model from cache /tmp/jieba.cache Loading model cost 0.976 seconds. Prefix dict has been built successfully. Namespace(sim_model_name='shibing624/text2vec-base-multilingual', gen_model_type='chatglm', gen_model_name='/root/autodl-tmp/chatglm3-6b/', lora_model=None, rerank_model_name='', corpus_files='sample.pdf', chunk_size=220, chunk_overlap=0, num_expand_context_chunk=1) The following parameters in checkpoint files are not loaded: ['embeddings.position_ids'] Loading checkpoint shards: 100%|████████████████████████████████████████████████████| 7/7 [00:20<00:00, 2.90s/it] The following parameters in checkpoint files are not loaded: ['transformer.embedding.word_embeddings.weight', 'transformer.encoder.layers.0.input_layernorm.weight', 'transformer.encoder.layers.0.self_attention.query_key_value.weight', 'transformer.encoder.layers.0.self_attention.query_key_value.bias', 'transformer.encoder.layers.0.self_attention.dense.weight', 'transformer.encoder.layers.0.post_attention_layernorm.weight', 'transformer.encoder.layers.0.mlp.dense_h_to_4h.weight', 'transformer.encoder.layers.0.mlp.dense_4h_to_h.weight', 'transformer.encoder.layers.1.input_layernorm.weight', 'transformer.encoder.layers.1.self_attention.query_key_value.weight', 'transformer.encoder.layers.1.self_attention.query_key_value.bias', 'transformer.encoder.layers.1.self_attention.dense.weight', 'transformer.encoder.layers.1.post_attention_layernorm.weight', 'transformer.encoder.layers.1.mlp.dense_h_to_4h.weight', 'transformer.encoder.layers.1.mlp.dense_4h_to_h.weight', 'transformer.encoder.layers.2.input_layernorm.weight', 'transformer.encoder.layers.2.self_attention.query_key_value.weight', 'transformer.encoder.layers.2.self_attention.query_key_value.bias', 'transformer.encoder.layers.2.self_attention.dense.weight', 'transformer.encoder.layers.2.post_attention_layernorm.weight', 'transformer.encoder.layers.2.mlp.dense_h_to_4h.weight', 'transformer.encoder.layers.2.mlp.dense_4h_to_h.weight', 'transformer.encoder.layers.3.input_layernorm.weight', 'transformer.encoder.layers.3.self_attention.query_key_value.weight', 'transformer.encoder.layers.3.self_attention.query_key_value.bias', 'transformer.encoder.layers.3.self_attention.dense.weight', 'transformer.encoder.layers.3.post_attention_layernorm.weight', 'transformer.encoder.layers.3.mlp.dense_h_to_4h.weight', 'transformer.encoder.layers.3.mlp.dense_4h_to_h.weight', 'transformer.encoder.layers.4.input_layernorm.weight', 'transformer.encoder.layers.4.self_attention.query_key_value.weight', 'transformer.encoder.layers.4.self_attention.query_key_value.bias', 'transformer.encoder.layers.4.self_attention.dense.weight', 'transformer.encoder.layers.4.post_attention_layernorm.weight', 'transformer.encoder.layers.4.mlp.dense_h_to_4h.weight', 'transformer.encoder.layers.4.mlp.dense_4h_to_h.weight', 'transformer.encoder.layers.5.input_layernorm.weight', 'transformer.encoder.layers.5.self_attention.query_key_value.weight', 'transformer.encoder.layers.5.self_attention.query_key_value.bias', 'transformer.encoder.layers.5.self_attention.dense.weight', 'transformer.encoder.layers.5.post_attention_layernorm.weight', 'transformer.encoder.layers.5.mlp.dense_h_to_4h.weight', 'transformer.encoder.layers.5.mlp.dense_4h_to_h.weight', 'transformer.encoder.layers.6.input_layernorm.weight', 'transformer.encoder.layers.6.self_attention.query_key_value.weight', 'transformer.encoder.layers.6.self_attention.query_key_value.bias', 'transformer.encoder.layers.6.self_attention.dense.weight', 'transformer.encoder.layers.6.post_attention_layernorm.weight', 'transformer.encoder.layers.6.mlp.dense_h_to_4h.weight', 'transformer.encoder.layers.6.mlp.dense_4h_to_h.weight', 'transformer.encoder.layers.7.input_layernorm.weight', 'transformer.encoder.layers.7.self_attention.query_key_value.weight', 'transformer.encoder.layers.7.self_attention.query_key_value.bias', 'transformer.encoder.layers.7.self_attention.dense.weight', 'transformer.encoder.layers.7.post_attention_layernorm.weight', 'transformer.encoder.layers.7.mlp.dense_h_to_4h.weight', 'transformer.encoder.layers.7.mlp.dense_4h_to_h.weight', 'transformer.encoder.layers.8.input_layernorm.weight', 'transformer.encoder.layers.8.self_attention.query_key_value.weight', 'transformer.encoder.layers.8.self_attention.query_key_value.bias', 'transformer.encoder.layers.8.self_attention.dense.weight', 'transformer.encoder.layers.8.post_attention_layernorm.weight', 'transformer.encoder.layers.8.mlp.dense_h_to_4h.weight', 'transformer.encoder.layers.8.mlp.dense_4h_to_h.weight', 'transformer.encoder.layers.9.input_layernorm.weight', 'transformer.encoder.layers.9.self_attention.query_key_value.weight', 'transformer.encoder.layers.9.self_attention.query_key_value.bias', 'transformer.encoder.layers.9.self_attention.dense.weight', 'transformer.encoder.layers.9.post_attention_layernorm.weight', 'transformer.encoder.layers.9.mlp.dense_h_to_4h.weight', 'transformer.encoder.layers.9.mlp.dense_4h_to_h.weight', 'transformer.encoder.layers.10.input_layernorm.weight', 'transformer.encoder.layers.10.self_attention.query_key_value.weight', 'transformer.encoder.layers.10.self_attention.query_key_value.bias', 'transformer.encoder.layers.10.self_attention.dense.weight', 'transformer.encoder.layers.10.post_attention_layernorm.weight', 'transformer.encoder.layers.10.mlp.dense_h_to_4h.weight', 'transformer.encoder.layers.10.mlp.dense_4h_to_h.weight', 'transformer.encoder.layers.11.input_layernorm.weight', 'transformer.encoder.layers.11.self_attention.query_key_value.weight', 'transformer.encoder.layers.11.self_attention.query_key_value.bias', 'transformer.encoder.layers.11.self_attention.dense.weight', 'transformer.encoder.layers.11.post_attention_layernorm.weight', 'transformer.encoder.layers.11.mlp.dense_h_to_4h.weight', 'transformer.encoder.layers.11.mlp.dense_4h_to_h.weight', 'transformer.encoder.layers.12.input_layernorm.weight', 'transformer.encoder.layers.12.self_attention.query_key_value.weight', 'transformer.encoder.layers.12.self_attention.query_key_value.bias', 'transformer.encoder.layers.12.self_attention.dense.weight', 'transformer.encoder.layers.12.post_attention_layernorm.weight', 'transformer.encoder.layers.12.mlp.dense_h_to_4h.weight', 'transformer.encoder.layers.12.mlp.dense_4h_to_h.weight', 'transformer.encoder.layers.13.input_layernorm.weight', 'transformer.encoder.layers.13.self_attention.query_key_value.weight', 'transformer.encoder.layers.13.self_attention.query_key_value.bias', 'transformer.encoder.layers.13.self_attention.dense.weight', 'transformer.encoder.layers.13.post_attention_layernorm.weight', 'transformer.encoder.layers.13.mlp.dense_h_to_4h.weight', 'transformer.encoder.layers.13.mlp.dense_4h_to_h.weight', 'transformer.encoder.layers.14.input_layernorm.weight', 'transformer.encoder.layers.14.self_attention.query_key_value.weight', 'transformer.encoder.layers.14.self_attention.query_key_value.bias', 'transformer.encoder.layers.14.self_attention.dense.weight', 'transformer.encoder.layers.14.post_attention_layernorm.weight', 'transformer.encoder.layers.14.mlp.dense_h_to_4h.weight', 'transformer.encoder.layers.14.mlp.dense_4h_to_h.weight', 'transformer.encoder.layers.15.input_layernorm.weight', 'transformer.encoder.layers.15.self_attention.query_key_value.weight', 'transformer.encoder.layers.15.self_attention.query_key_value.bias', 'transformer.encoder.layers.15.self_attention.dense.weight', 'transformer.encoder.layers.15.post_attention_layernorm.weight', 'transformer.encoder.layers.15.mlp.dense_h_to_4h.weight', 'transformer.encoder.layers.15.mlp.dense_4h_to_h.weight', 'transformer.encoder.layers.16.input_layernorm.weight', 'transformer.encoder.layers.16.self_attention.query_key_value.weight', 'transformer.encoder.layers.16.self_attention.query_key_value.bias', 'transformer.encoder.layers.16.self_attention.dense.weight', 'transformer.encoder.layers.16.post_attention_layernorm.weight', 'transformer.encoder.layers.16.mlp.dense_h_to_4h.weight', 'transformer.encoder.layers.16.mlp.dense_4h_to_h.weight', 'transformer.encoder.layers.17.input_layernorm.weight', 'transformer.encoder.layers.17.self_attention.query_key_value.weight', 'transformer.encoder.layers.17.self_attention.query_key_value.bias', 'transformer.encoder.layers.17.self_attention.dense.weight', 'transformer.encoder.layers.17.post_attention_layernorm.weight', 'transformer.encoder.layers.17.mlp.dense_h_to_4h.weight', 'transformer.encoder.layers.17.mlp.dense_4h_to_h.weight', 'transformer.encoder.layers.18.input_layernorm.weight', 'transformer.encoder.layers.18.self_attention.query_key_value.weight', 'transformer.encoder.layers.18.self_attention.query_key_value.bias', 'transformer.encoder.layers.18.self_attention.dense.weight', 'transformer.encoder.layers.18.post_attention_layernorm.weight', 'transformer.encoder.layers.18.mlp.dense_h_to_4h.weight', 'transformer.encoder.layers.18.mlp.dense_4h_to_h.weight', 'transformer.encoder.layers.19.input_layernorm.weight', 'transformer.encoder.layers.19.self_attention.query_key_value.weight', 'transformer.encoder.layers.19.self_attention.query_key_value.bias', 'transformer.encoder.layers.19.self_attention.dense.weight', 'transformer.encoder.layers.19.post_attention_layernorm.weight', 'transformer.encoder.layers.19.mlp.dense_h_to_4h.weight', 'transformer.encoder.layers.19.mlp.dense_4h_to_h.weight', 'transformer.encoder.layers.20.input_layernorm.weight', 'transformer.encoder.layers.20.self_attention.query_key_value.weight', 'transformer.encoder.layers.20.self_attention.query_key_value.bias', 'transformer.encoder.layers.20.self_attention.dense.weight', 'transformer.encoder.layers.20.post_attention_layernorm.weight', 'transformer.encoder.layers.20.mlp.dense_h_to_4h.weight', 'transformer.encoder.layers.20.mlp.dense_4h_to_h.weight', 'transformer.encoder.layers.21.input_layernorm.weight', 'transformer.encoder.layers.21.self_attention.query_key_value.weight', 'transformer.encoder.layers.21.self_attention.query_key_value.bias', 'transformer.encoder.layers.21.self_attention.dense.weight', 'transformer.encoder.layers.21.post_attention_layernorm.weight', 'transformer.encoder.layers.21.mlp.dense_h_to_4h.weight', 'transformer.encoder.layers.21.mlp.dense_4h_to_h.weight', 'transformer.encoder.layers.22.input_layernorm.weight', 'transformer.encoder.layers.22.self_attention.query_key_value.weight', 'transformer.encoder.layers.22.self_attention.query_key_value.bias', 'transformer.encoder.layers.22.self_attention.dense.weight', 'transformer.encoder.layers.22.post_attention_layernorm.weight', 'transformer.encoder.layers.22.mlp.dense_h_to_4h.weight', 'transformer.encoder.layers.22.mlp.dense_4h_to_h.weight', 'transformer.encoder.layers.23.input_layernorm.weight', 'transformer.encoder.layers.23.self_attention.query_key_value.weight', 'transformer.encoder.layers.23.self_attention.query_key_value.bias', 'transformer.encoder.layers.23.self_attention.dense.weight', 'transformer.encoder.layers.23.post_attention_layernorm.weight', 'transformer.encoder.layers.23.mlp.dense_h_to_4h.weight', 'transformer.encoder.layers.23.mlp.dense_4h_to_h.weight', 'transformer.encoder.layers.24.input_layernorm.weight', 'transformer.encoder.layers.24.self_attention.query_key_value.weight', 'transformer.encoder.layers.24.self_attention.query_key_value.bias', 'transformer.encoder.layers.24.self_attention.dense.weight', 'transformer.encoder.layers.24.post_attention_layernorm.weight', 'transformer.encoder.layers.24.mlp.dense_h_to_4h.weight', 'transformer.encoder.layers.24.mlp.dense_4h_to_h.weight', 'transformer.encoder.layers.25.input_layernorm.weight', 'transformer.encoder.layers.25.self_attention.query_key_value.weight', 'transformer.encoder.layers.25.self_attention.query_key_value.bias', 'transformer.encoder.layers.25.self_attention.dense.weight', 'transformer.encoder.layers.25.post_attention_layernorm.weight', 'transformer.encoder.layers.25.mlp.dense_h_to_4h.weight', 'transformer.encoder.layers.25.mlp.dense_4h_to_h.weight', 'transformer.encoder.layers.26.input_layernorm.weight', 'transformer.encoder.layers.26.self_attention.query_key_value.weight', 'transformer.encoder.layers.26.self_attention.query_key_value.bias', 'transformer.encoder.layers.26.self_attention.dense.weight', 'transformer.encoder.layers.26.post_attention_layernorm.weight', 'transformer.encoder.layers.26.mlp.dense_h_to_4h.weight', 'transformer.encoder.layers.26.mlp.dense_4h_to_h.weight', 'transformer.encoder.layers.27.input_layernorm.weight', 'transformer.encoder.layers.27.self_attention.query_key_value.weight', 'transformer.encoder.layers.27.self_attention.query_key_value.bias', 'transformer.encoder.layers.27.self_attention.dense.weight', 'transformer.encoder.layers.27.post_attention_layernorm.weight', 'transformer.encoder.layers.27.mlp.dense_h_to_4h.weight', 'transformer.encoder.layers.27.mlp.dense_4h_to_h.weight', 'transformer.encoder.final_layernorm.weight', 'transformer.output_layer.weight'] The following parameters in models are missing parameter: ['word_embeddings.weight', 'layers.0.input_layernorm.weight', 'layers.0.input_layernorm.bias', 'layers.0.attention.query_key_value.weight', 'layers.0.attention.query_key_value.bias', 'layers.0.attention.dense.weight', 'layers.0.attention.dense.bias', 'layers.0.post_attention_layernorm.weight', 'layers.0.post_attention_layernorm.bias', 'layers.0.mlp.dense_h_to_4h.weight', 'layers.0.mlp.dense_h_to_4h.bias', 'layers.0.mlp.dense_4h_to_h.weight', 'layers.0.mlp.dense_4h_to_h.bias', 'layers.1.input_layernorm.weight', 'layers.1.input_layernorm.bias', 'layers.1.attention.query_key_value.weight', 'layers.1.attention.query_key_value.bias', 'layers.1.attention.dense.weight', 'layers.1.attention.dense.bias', 'layers.1.post_attention_layernorm.weight', 'layers.1.post_attention_layernorm.bias', 'layers.1.mlp.dense_h_to_4h.weight', 'layers.1.mlp.dense_h_to_4h.bias', 'layers.1.mlp.dense_4h_to_h.weight', 'layers.1.mlp.dense_4h_to_h.bias', 'layers.2.input_layernorm.weight', 'layers.2.input_layernorm.bias', 'layers.2.attention.query_key_value.weight', 'layers.2.attention.query_key_value.bias', 'layers.2.attention.dense.weight', 'layers.2.attention.dense.bias', 'layers.2.post_attention_layernorm.weight', 'layers.2.post_attention_layernorm.bias', 'layers.2.mlp.dense_h_to_4h.weight', 'layers.2.mlp.dense_h_to_4h.bias', 'layers.2.mlp.dense_4h_to_h.weight', 'layers.2.mlp.dense_4h_to_h.bias', 'layers.3.input_layernorm.weight', 'layers.3.input_layernorm.bias', 'layers.3.attention.query_key_value.weight', 'layers.3.attention.query_key_value.bias', 'layers.3.attention.dense.weight', 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'layers.26.post_attention_layernorm.bias', 'layers.26.mlp.dense_h_to_4h.weight', 'layers.26.mlp.dense_h_to_4h.bias', 'layers.26.mlp.dense_4h_to_h.weight', 'layers.26.mlp.dense_4h_to_h.bias', 'layers.27.input_layernorm.weight', 'layers.27.input_layernorm.bias', 'layers.27.attention.query_key_value.weight', 'layers.27.attention.query_key_value.bias', 'layers.27.attention.dense.weight', 'layers.27.attention.dense.bias', 'layers.27.post_attention_layernorm.weight', 'layers.27.post_attention_layernorm.bias', 'layers.27.mlp.dense_h_to_4h.weight', 'layers.27.mlp.dense_h_to_4h.bias', 'layers.27.mlp.dense_4h_to_h.weight', 'layers.27.mlp.dense_4h_to_h.bias', 'final_layernorm.weight', 'final_layernorm.bias'] 2024-08-09 10:49:55.471 | WARNING | main:_init_gen_model:215 - Failed to load generation config from /root/autodl-tmp/chatglm3-6b/, /root/autodl-tmp/chatglm3-6b/ does not appear to have a file named generation_config.json. 2024-08-09 10:49:55.678 | INFO | msimilarities.bert_similarity:add_corpus:105 - Start computing corpus embeddings, new docs: 212 Batches: 100%|██████████████████████████████████████████████████████████████████████| 7/7 [00:17<00:00, 2.54s/it] 2024-08-09 10:50:13.505 | INFO | msimilarities.bert_similarity:add_corpus:117 - Add 212 docs, total: 212, emb len: 212 2024-08-09 10:50:13.506 | DEBUG | main:add_corpus:281 - files: ['sample.pdf'], corpus size: 212, top3: ['Style Transfer from Non-Parallel Text byCross-AlignmentTianxiao Shen1Tao Lei2Regina Barzilay1Tommi Jaakkola11MIT CSAIL2ASAPP Inc.', '1{tianxiao, regina, tommi}@[email protected] paper focuses on style transfer on the basis of non-parallel text.', 'This is aninstance of a broad family of problems including machine translation, decipherment,and sentiment modification. The key challenge is to separate the content fromother aspects such as style.'] 2024-08-09 10:50:13.898 | DEBUG | main:predict:475 - prompt: 基于以下已知信息，简洁和专业的来回答用户的问题。 如果无法从中得到答案，请说 "根据已知信息无法回答该问题" 或 "没有提供足够的相关信息"，不允许在答案中添加编造成分，答案请使用中文。

已知内容: [1] "ReferencesPeter F Brown, John Cocke, Stephen A Della Pietra, Vincent J Della Pietra, Fredrick Jelinek, John DLafferty, Robert L Mercer, and Paul S Roossin. A statistical approach to machine translation.Computational linguistics , 16(2):79–85, 1990. Tong Che, Yanran Li, Ruixiang Zhang, R Devon Hjelm, Wenjie Li, Yangqiu Song, and YoshuaBengio. Maximum-likelihood augmented discrete generative adversarial networks.arXiv preprintarXiv:1702.07983 , 2017. Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, and Pieter Abbeel." [2] "Method sentiment fluency overall transferHu et al. (2017) 70.8 3.2 41.0Cross-align 62.6 2.8 41.5Table 2: Human evaluations on sentiment, fluency and overall transfer quality.Fluency rating is from1 (unreadable) to 4 (perfect).Overall transfer quality is evaluated in a comparative manner, where thejudge is shown a source sentence and two transferred sentences, and decides whether they are bothgood, both bad, or one is better." [3] "We demonstrate the effectiveness of this cross-alignment method on three tasks: sentiment modification, decipherment of word substitution ciphers, and recoveryof word order.11 IntroductionUsing massive amounts of parallel data has been essential for recent advances in text generation tasks,such as machine translation and summarization.However, in many text generation problems, we canonly assume access to non-parallel or mono-lingual data. Problems such as decipherment or styletransfer are all instances of this family of tasks.In all of these problems, we must preserve the contentof the source sentence but render the sentence consistent with desired presentation constraints (e.g.,style, plaintext/ciphertext)." [4] "On the other hand, it lowers the entropy in p(xjy;z),which helps to produce meaningful style transfer in practice as we flip between y1andy2.Withoutexplicitly modeling p(z), it is still possible to force dist

问题: 自然语言中的非平行迁移是指什么？

No chat template is defined for this tokenizer - using a default chat template that implements the ChatML format. If the default is not appropriate for your model, please set tokenizer.chat_template to an appropriate template. See https://hf-mirror.com/docs/transformers/main/chat_templating for more information.

/root/miniconda3/envs/mindspore/lib/python3.9/site-packages/mindnlp/transformers/generation/utils.py:1402: UserWarning: You have modified the pretrained model configuration to control generation. This is a deprecated strategy to control generation and will be removed soon, in a future version. Please use and modify the model generation configuration (see https://hf-mirror.com/docs/transformers/generation_strategies#default-text-generation-configuration ) warnings.warn( Exception in thread Thread-2: Traceback (most recent call last): File "/root/miniconda3/envs/mindspore/lib/python3.9/threading.py", line 980, in _bootstrap_inner self.run() File "/root/miniconda3/envs/mindspore/lib/python3.9/threading.py", line 917, in run self._target(*self._args, **self._kwargs) File "/root/miniconda3/envs/mindspore/lib/python3.9/site-packages/mindnlp/utils/generic.py", line 339, in wrapper outputs = func(*args, **kwargs) File "/root/miniconda3/envs/mindspore/lib/python3.9/site-packages/mindnlp/transformers/generation/utils.py", line 1658, in generate return self.sample( File "/root/miniconda3/envs/mindspore/lib/python3.9/site-packages/mindnlp/transformers/generation/utils.py", line 2699, in sample model_inputs = self.prepare_inputs_for_generation(input_ids, **model_kwargs) File "/root/miniconda3/envs/mindspore/lib/python3.9/site-packages/mindnlp/transformers/generation/utils.py", line 584, in prepare_inputs_for_generation raise NotImplementedError( NotImplementedError: A model class needs to define a prepare_inputs_for_generation method in order to use generate. Traceback (most recent call last): File "/root/autodl-tmp/ChatPDF/chatpdf.py", line 529, in r, refs = m.predict('自然语言中的非平行迁移是指什么？') File "/root/autodl-tmp/ChatPDF/chatpdf.py", line 480, in predict for new_text in self.stream_generate_answer( File "/root/autodl-tmp/ChatPDF/chatpdf.py", line 262, in stream_generate_answer yield from streamer File "/root/miniconda3/envs/mindspore/lib/python3.9/site-packages/mindnlp/transformers/generation/streamers.py", line 224, in next value = self.text_queue.get(timeout=self.timeout) File "/root/miniconda3/envs/mindspore/lib/python3.9/queue.py", line 179, in get raise Empty _queue.Empty