AeertionError
#11
by
Marseus
- opened
I got the error
AssertionError Traceback (most recent call last)
Cell In[20], line 8
1 for index, row in tqdm(final_df.iterrows(), total=final_df.shape[0]):
2 # if index not in simple_prompt_summary:
3 # simple_prompt_summary[index] = finalSummary_GPT_simple(row['key_issue'], row['bert_preprocess'])
4 if index not in custom_prompt_summary:
5 # custom_prompt_summary[index] = generate_summary_without_additionally(row['key_issue'], row['bert_preprocess'], connectors_string)
6 # custom_llama_summary[index] = finalSummary_llama(row['key_issue'], row['bert_preprocess'], connectors_string)
7 # custom_GPT4_summary[index] = generate_summary_without_additionally_gpt4(row['key_issue'], row['bert_preprocess'], connectors_string)
----> 8 custom_Mistral_summary[index] = finalSummary_Mixtral(row['key_issue'], row['bert_preprocess'], connectors_string)
9 # custom_NB_summary[index] = finalSummary_NeuralBeagle(row['key_issue'], row['bert_preprocess'], connectors_string)
Cell In[17], line 72, in finalSummary_Mixtral(keyIssue, first_summaries, connectors_string)
70 Mixtral_model_name = "TheBloke/Mixtral-8x7B-v0.1-GPTQ"
71 # BUG : AssertionError, haven't fix it.
---> 72 model = AutoModelForCausalLM.from_pretrained(Mixtral_model_name,
73 # torch_dtype=torch.float32,
74 device_map='cuda',
75 trust_remote_code=False,
76 revision="main"
77 # local_files_only=False,
78 # load_in_4bit=True
79 )
81 tokenizer = AutoTokenizer.from_pretrained(Mixtral_model_name, use_fast=True)
82 # system_msg = "You are a news summarization AI tasked with summarizing a set of ESG news articles."
File ~/anaconda3/envs/jupyter/lib/python3.11/site-packages/transformers/models/auto/auto_factory.py:566, in _BaseAutoModelClass.from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs)
564 elif type(config) in cls._model_mapping.keys():
565 model_class = _get_model_class(config, cls._model_mapping)
--> 566 return model_class.from_pretrained(
567 pretrained_model_name_or_path, *model_args, config=config, **hub_kwargs, **kwargs
568 )
569 raise ValueError(
570 f"Unrecognized configuration class {config.__class__} for this kind of AutoModel: {cls.__name__}.\n"
571 f"Model type should be one of {', '.join(c.__name__ for c in cls._model_mapping.keys())}."
572 )
File ~/anaconda3/envs/jupyter/lib/python3.11/site-packages/transformers/modeling_utils.py:3523, in PreTrainedModel.from_pretrained(cls, pretrained_model_name_or_path, config, cache_dir, ignore_mismatched_sizes, force_download, local_files_only, token, revision, use_safetensors, *model_args, **kwargs)
3517 logger.warning(
3518 "You are loading your model in 8bit but you did not specify a `torch_dtype` attribute. "
3519 "All non-linear modules will be loaded in full precision."
3520 " If you want to load the other modules in other precision, please specify a `torch_dtype` attribute."
3521 )
3522 if quantization_method_from_config == QuantizationMethod.GPTQ:
-> 3523 model = quantizer.convert_model(model)
3524 model._is_quantized_training_enabled = True
3525 elif quantization_method_from_config == QuantizationMethod.AWQ:
File ~/anaconda3/envs/jupyter/lib/python3.11/site-packages/optimum/gptq/quantizer.py:174, in GPTQQuantizer.convert_model(self, model)
172 block_name = self.block_name_to_quantize
173 layers_to_be_replaced = get_layers(model, prefix=block_name)
--> 174 self._replace_by_quant_layers(model, layers_to_be_replaced)
176 return model
File ~/anaconda3/envs/jupyter/lib/python3.11/site-packages/optimum/gptq/quantizer.py:235, in GPTQQuantizer._replace_by_quant_layers(self, module, names, name)
233 setattr(module, attr, new_layer.to(device))
234 for name1, child in module.named_children():
--> 235 self._replace_by_quant_layers(child, names, name + "." + name1 if name != "" else name1)
File ~/anaconda3/envs/jupyter/lib/python3.11/site-packages/optimum/gptq/quantizer.py:235, in GPTQQuantizer._replace_by_quant_layers(self, module, names, name)
233 setattr(module, attr, new_layer.to(device))
234 for name1, child in module.named_children():
--> 235 self._replace_by_quant_layers(child, names, name + "." + name1 if name != "" else name1)
[... skipping similar frames: GPTQQuantizer._replace_by_quant_layers at line 235 (1 times)]
File ~/anaconda3/envs/jupyter/lib/python3.11/site-packages/optimum/gptq/quantizer.py:235, in GPTQQuantizer._replace_by_quant_layers(self, module, names, name)
233 setattr(module, attr, new_layer.to(device))
234 for name1, child in module.named_children():
--> 235 self._replace_by_quant_layers(child, names, name + "." + name1 if name != "" else name1)
File ~/anaconda3/envs/jupyter/lib/python3.11/site-packages/optimum/gptq/quantizer.py:227, in GPTQQuantizer._replace_by_quant_layers(self, module, names, name)
225 out_features = layer.weight.shape[1]
226 if not (self.desc_act) or self.group_size == -1:
--> 227 new_layer = QuantLinear(
228 self.bits, self.group_size, in_features, out_features, True, use_cuda_fp16=self.use_cuda_fp16
229 )
230 else:
231 new_layer = QuantLinear(self.bits, self.group_size, in_features, out_features, True)
File ~/anaconda3/envs/jupyter/lib/python3.11/site-packages/auto_gptq/nn_modules/qlinear/qlinear_exllama.py:65, in QuantLinear.__init__(self, bits, group_size, infeatures, outfeatures, bias, trainable, **kwargs)
63 assert infeatures % 32 == 0
64 assert infeatures % self.group_size == 0
---> 65 assert outfeatures % 32 == 0
67 self.register_buffer(
68 'qweight',
69 torch.zeros((infeatures // 32 * self.bits, outfeatures), dtype=torch.int32)
70 )
71 self.register_buffer(
72 'qzeros',
73 torch.zeros((math.ceil(infeatures / self.group_size), outfeatures // 32 * self.bits), dtype=torch.int32)
74 )
AssertionError:
And below is the code and the pakages i use( i've update the transformer to 4.37.0.dev0, but still got the same error)
def finalSummary_Mixtral(keyIssue: str, first_summaries: str, connectors_string: str) -> str:
Mixtral_model_name = "TheBloke/Mixtral-8x7B-v0.1-GPTQ"
# BUG : AssertionError, haven't fix it.
model = AutoModelForCausalLM.from_pretrained(Mixtral_model_name,
# torch_dtype=torch.float32,
device_map='cuda',
trust_remote_code=False,
revision="main"
# local_files_only=False,
# load_in_4bit=True
)
tokenizer = AutoTokenizer.from_pretrained(Mixtral_model_name, use_fast=True)
# system_msg = "You are a news summarization AI tasked with summarizing a set of ESG news articles."
prompt = f''' [INST]<<SYS>>
You are a news summarization AI tasked with summarizing a set of ESG news articles.
<</SYS>>
Please ensure that each news article is mentioned. If a company is mentioned, provide background information about the company. Introduce offer background information, and detail the causes and consequences for topics mentioned."
The summary shoud be within 150 words.
Make it a complete paragraph.
If there are common topics between those news, start with a topic sentence. Use the conjunctions or transition words while you want to discuss a different topic or news. Be sure to only use the conjunctions and transition words in my list. list:{ connectors_string } Don't use 'Additionally' as conjunctions or transition word. Don't use the conjunctions and transition words at the beginning of the summary. Summarize these news:{chr(10)}{first_summaries}
[/INST]
'''
input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to("cuda")
output = model.generate(inputs=input_ids, temperature=0.5, do_sample=True, top_k=20, num_return_sequences=1, max_new_tokens=512)
summary = tokenizer.decode(output[0]).replace(prompt, '').replace('<s>', '').replace('</s>', '').strip()
del model
del tokenizer
return summary
# Name Version Build Channel
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_openmp_mutex 4.5 2_kmp_llvm conda-forge
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altair 5.1.2 pypi_0 pypi
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libuuid 1.41.5 h5eee18b_0
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linkify-it-py 2.0.0 py311h06a4308_0
llvm-openmp 14.0.6 h9e868ea_0
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locket 1.0.0 py311h06a4308_0
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markdown 3.4.1 py311h06a4308_0
markdown-it-py 2.2.0 py311h06a4308_1
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mistune 2.0.4 py311h06a4308_0
mkl 2023.1.0 h213fc3f_46344
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mkl_fft 1.3.8 py311h5eee18b_0
mkl_random 1.2.4 py311hdb19cb5_0
more-itertools 10.1.0 py311h06a4308_0
mpc 1.1.0 h10f8cd9_1
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mpi 1.0 mpich
mpich 4.1.1 hbae89fd_0
mpmath 1.3.0 py311h06a4308_0
msgpack-python 1.0.3 py311hdb19cb5_0
multidict 6.0.4 py311h5eee18b_0
multipledispatch 0.6.0 py311h06a4308_0
multiprocess 0.70.14 py311h06a4308_0
munkres 1.1.4 py_0
mypy_extensions 1.0.0 py311h06a4308_0
mysql 5.7.24 h721c034_2
navigator-updater 0.4.0 py311h06a4308_1
nbclient 0.8.0 py311h06a4308_0
nbconvert 7.10.0 py311h06a4308_0
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