FlagOpen
TypeError: Gemma2DecoderLayer.forward() missing 1 required positional argument: 'position_embeddings'
请问有人遇到过这个问题么？在运行BAAI/bge-reranker-v2.5-gemma2-lightweight示例代码： `from FlagEmbedding import LightWeightFlagLLMReranker reranker = LightWeightFlagLLMReranker('BAAI/bge-reranker-v2.5-gemma2-lightweight', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation
score = reranker.compute_score(['query', 'passage'], cutoff_layers=[28], compress_ratio=2, compress_layer=[24, 40]) # Adjusting 'cutoff_layers' to pick which layers are used for computing the score. print(score)
scores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']], cutoff_layers=[28], compress_ratio=2, compress_layer=[24, 40])
print(scores)
报错：TypeError Traceback (most recent call last)
Cell In[1], line 4
1 from FlagEmbedding import LightWeightFlagLLMReranker
2 reranker = LightWeightFlagLLMReranker('bge-reranker-v2.5-gemma2-lightweight', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation
----> 4 score = reranker.compute_score(['query', 'passage'], cutoff_layers=[28], compress_ratio=2, compress_layer=[24, 40]) # Adjusting 'cutoff_layers' to pick which layers are used for computing the score.
5 print(score)
7 scores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']], cutoff_layers=[28], compress_ratio=2, compress_layer=[24, 40])
File ~/autodl-tmp/FlagEmbedding/FlagEmbedding/abc/inference/AbsReranker.py:217, in AbsReranker.compute_score(self, sentence_pairs, **kwargs) 214 sentence_pairs = self.get_detailed_inputs(sentence_pairs) 216 if isinstance(sentence_pairs, str) or len(self.target_devices) == 1: --> 217 return self.compute_score_single_gpu( 218 sentence_pairs, 219 device=self.target_devices[0], 220 **kwargs 221 ) 223 if self.pool is None: 224 self.pool = self.start_multi_process_pool()
File ~/miniconda3/lib/python3.12/site-packages/torch/utils/_contextlib.py:115, in context_decorator.
File ~/autodl-tmp/FlagEmbedding/FlagEmbedding/inference/reranker/decoder_only/lightweight.py:359, in LightweightLLMReranker.compute_score_single_gpu(self, sentence_pairs, batch_size, query_max_length, max_length, cutoff_layers, compress_layer, compress_layers, compress_ratio, prompt, normalize, device, **kwargs) 348 batch_inputs = collater_instance([ 349 [{ 350 'input_ids': item['input_ids'], (...) 354 prompt_lengths 355 ])[0] 357 batch_inputs = {key: val.to(device) for key, val in batch_inputs.items()} --> 359 self.model( 360 **batch_inputs, 361 output_hidden_states=True, 362 compress_layer=compress_layers, 363 compress_ratio=compress_ratio, 364 query_lengths=query_lengths, 365 prompt_lengths=prompt_lengths, 366 cutoff_layers=cutoff_layers 367 ) 368 flag = True 369 except RuntimeError as e:
File ~/miniconda3/lib/python3.12/site-packages/torch/nn/modules/module.py:1532, in Module._wrapped_call_impl(self, *args, **kwargs) 1530 return self._compiled_call_impl(*args, **kwargs) # type: ignore[misc] 1531 else: -> 1532 return self._call_impl(*args, **kwargs)
File ~/miniconda3/lib/python3.12/site-packages/torch/nn/modules/module.py:1541, in Module._call_impl(self, *args, **kwargs) 1536 # If we don't have any hooks, we want to skip the rest of the logic in 1537 # this function, and just call forward. 1538 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks 1539 or _global_backward_pre_hooks or _global_backward_hooks 1540 or _global_forward_hooks or _global_forward_pre_hooks): -> 1541 return forward_call(*args, **kwargs) 1543 try: 1544 result = None
File ~/autodl-tmp/FlagEmbedding/FlagEmbedding/inference/reranker/decoder_only/models/gemma_model.py:599, in CostWiseGemmaForCausalLM.forward(self, input_ids, attention_mask, position_ids, past_key_values, inputs_embeds, labels, use_cache, output_attentions, output_hidden_states, return_dict, cache_position, compress_layer, compress_ratio, cutoff_layers, query_lengths, prompt_lengths) 596 raise ValueError(f"Your cutoff layers must in [{self.config.start_layer}, {self.config.num_hidden_layers}]") 598 # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) --> 599 outputs = self.model( 600 input_ids=input_ids, 601 attention_mask=attention_mask, 602 position_ids=position_ids, 603 past_key_values=past_key_values, 604 inputs_embeds=inputs_embeds, 605 use_cache=use_cache, 606 output_attentions=output_attentions, 607 output_hidden_states=output_hidden_states, 608 return_dict=return_dict, 609 cache_position=cache_position, 610 compress_layer=compress_layer, 611 compress_ratio=compress_ratio, 612 query_lengths=query_lengths, 613 prompt_lengths=prompt_lengths, 614 cutoff_layers=cutoff_layers, 615 ) 617 if not self.config.layer_wise: 618 hidden_states = outputs[0]
File ~/miniconda3/lib/python3.12/site-packages/torch/nn/modules/module.py:1532, in Module._wrapped_call_impl(self, *args, **kwargs) 1530 return self._compiled_call_impl(*args, **kwargs) # type: ignore[misc] 1531 else: -> 1532 return self._call_impl(*args, **kwargs)
File ~/miniconda3/lib/python3.12/site-packages/torch/nn/modules/module.py:1541, in Module._call_impl(self, *args, **kwargs) 1536 # If we don't have any hooks, we want to skip the rest of the logic in 1537 # this function, and just call forward. 1538 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks 1539 or _global_backward_pre_hooks or _global_backward_hooks 1540 or _global_forward_hooks or _global_forward_pre_hooks): -> 1541 return forward_call(*args, **kwargs) 1543 try: 1544 result = None
File ~/autodl-tmp/FlagEmbedding/FlagEmbedding/inference/reranker/decoder_only/models/gemma_model.py:395, in CostWiseGemmaModel.forward(self, input_ids, attention_mask, position_ids, past_key_values, inputs_embeds, use_cache, output_attentions, output_hidden_states, return_dict, cache_position, compress_layer, compress_ratio, cutoff_layers, query_lengths, prompt_lengths) 384 layer_outputs = self._gradient_checkpointing_func( 385 decoder_layer.call, 386 hidden_states, (...) 392 cache_position, 393 ) 394 else: --> 395 layer_outputs = decoder_layer( 396 hidden_states, 397 attention_mask=causal_mask, 398 position_ids=position_ids, 399 past_key_value=past_key_values, 400 output_attentions=output_attentions, 401 use_cache=use_cache, 402 cache_position=cache_position, 403 ) 405 hidden_states = layer_outputs[0] 407 if output_attentions:
File ~/miniconda3/lib/python3.12/site-packages/torch/nn/modules/module.py:1532, in Module._wrapped_call_impl(self, *args, **kwargs) 1530 return self._compiled_call_impl(*args, **kwargs) # type: ignore[misc] 1531 else: -> 1532 return self._call_impl(*args, **kwargs)
File ~/miniconda3/lib/python3.12/site-packages/torch/nn/modules/module.py:1541, in Module._call_impl(self, *args, **kwargs) 1536 # If we don't have any hooks, we want to skip the rest of the logic in 1537 # this function, and just call forward. 1538 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks 1539 or _global_backward_pre_hooks or _global_backward_hooks 1540 or _global_forward_hooks or _global_forward_pre_hooks): -> 1541 return forward_call(*args, **kwargs) 1543 try: 1544 result = None
TypeError: Gemma2DecoderLayer.forward() missing 1 required positional argument: 'position_embeddings'`
I'm having the same problem, any idea how to fix it?
Transformers and FlagEmbedding version:
transformers==4.51.3flagembedding==1.3.4
