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- /usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1635: UserWarning: You are calling .generate() with the `input_ids` being on a device type different than your model's device. `input_ids` is on cpu, whereas the model is on cuda. You may experience unexpected behaviors or slower generation. Please make sure that you have put `input_ids` to the correct device by calling for example input_ids = input_ids.to('cuda') before running `.generate()`.
- warnings.warn(
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- ---------------------------------------------------------------------------
- RuntimeError Traceback (most recent call last)
- Cell In[15], line 22
- 19 iteration_counter += 1
- 21 # process each image in sub-batch... (to be written)
- ---> 22 list_of_captions = generate_batched([image["image"] for image in sub_batch])
- 24 list_of_image_file_names = [image["path"] for image in sub_batch]
- 25 list_of_image_file_names_without_extension = [os.path.splitext(image_file_name)[0] for image_file_name in list_of_image_file_names]
- Cell In[9], line 20, in generate_batched(images)
- 17 inputs = processor(prompts, return_tensors="pt")
- 18 bad_words_ids = processor.tokenizer(["<image>", "<fake_token_around_image>"], add_special_tokens=False).input_ids
- ---> 20 generated_ids = model.generate(**inputs, bad_words_ids=bad_words_ids, max_length=40)
- 21 input_ids = inputs['input_ids']
- 22 generated_text = processor.batch_decode(generated_ids[:, input_ids.shape[1]:], skip_special_tokens=True)
- File /usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py:115, in context_decorator.<locals>.decorate_context(*args, **kwargs)
- 112 @functools.wraps(func)
- 113 def decorate_context(*args, **kwargs):
- 114 with ctx_factory():
- --> 115 return func(*args, **kwargs)
- File /usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1717, 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)
- 1700 return self.assisted_decoding(
- 1701 input_ids,
- 1702 assistant_model=assistant_model,
- (...)
- 1713 **model_kwargs,
- 1714 )
- 1715 if generation_mode == GenerationMode.GREEDY_SEARCH:
- 1716 # 11. run greedy search
- -> 1717 return self.greedy_search(
- 1718 input_ids,
- 1719 logits_processor=logits_processor,
- 1720 stopping_criteria=stopping_criteria,
- 1721 pad_token_id=generation_config.pad_token_id,
- 1722 eos_token_id=generation_config.eos_token_id,
- 1723 output_scores=generation_config.output_scores,
- 1724 return_dict_in_generate=generation_config.return_dict_in_generate,
- 1725 synced_gpus=synced_gpus,
- 1726 streamer=streamer,
- 1727 **model_kwargs,
- 1728 )
- 1730 elif generation_mode == GenerationMode.CONTRASTIVE_SEARCH:
- 1731 if not model_kwargs["use_cache"]:
- File /usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:2578, in GenerationMixin.greedy_search(self, input_ids, logits_processor, stopping_criteria, max_length, pad_token_id, eos_token_id, output_attentions, output_hidden_states, output_scores, return_dict_in_generate, synced_gpus, streamer, **model_kwargs)
- 2575 model_inputs = self.prepare_inputs_for_generation(input_ids, **model_kwargs)
- 2577 # forward pass to get next token
- -> 2578 outputs = self(
- 2579 **model_inputs,
- 2580 return_dict=True,
- 2581 output_attentions=output_attentions,
- 2582 output_hidden_states=output_hidden_states,
- 2583 )
- 2585 if synced_gpus and this_peer_finished:
- 2586 continue # don't waste resources running the code we don't need
- File /usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py:1518, in Module._wrapped_call_impl(self, *args, **kwargs)
- 1516 return self._compiled_call_impl(*args, **kwargs) # type: ignore[misc]
- 1517 else:
- -> 1518 return self._call_impl(*args, **kwargs)
- File /usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py:1527, in Module._call_impl(self, *args, **kwargs)
- 1522 # If we don't have any hooks, we want to skip the rest of the logic in
- 1523 # this function, and just call forward.
- 1524 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
- 1525 or _global_backward_pre_hooks or _global_backward_hooks
- 1526 or _global_forward_hooks or _global_forward_pre_hooks):
- -> 1527 return forward_call(*args, **kwargs)
- 1529 try:
- 1530 result = None
- File /usr/local/lib/python3.10/dist-packages/accelerate/hooks.py:164, in add_hook_to_module.<locals>.new_forward(module, *args, **kwargs)
- 162 output = module._old_forward(*args, **kwargs)
- 163 else:
- --> 164 output = module._old_forward(*args, **kwargs)
- 165 return module._hf_hook.post_forward(module, output)
- File /usr/local/lib/python3.10/dist-packages/transformers/models/idefics/modeling_idefics.py:1491, in IdeficsForVisionText2Text.forward(self, input_ids, attention_mask, position_ids, past_key_values, inputs_embeds, pixel_values, image_encoder_embeddings, perceiver_embeddings, image_attention_mask, labels, use_cache, output_attentions, output_hidden_states, interpolate_pos_encoding, return_dict)
- 1488 return_dict = return_dict if return_dict is not None else self.config.use_return_dict
- 1490 # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
- -> 1491 outputs = self.model(
- 1492 input_ids=input_ids,
- 1493 attention_mask=attention_mask,
- 1494 position_ids=position_ids,
- 1495 past_key_values=past_key_values,
- 1496 inputs_embeds=inputs_embeds,
- 1497 pixel_values=pixel_values,
- 1498 image_encoder_embeddings=image_encoder_embeddings,
- 1499 perceiver_embeddings=perceiver_embeddings,
- 1500 image_attention_mask=image_attention_mask,
- 1501 use_cache=use_cache,
- 1502 output_attentions=output_attentions,
- 1503 output_hidden_states=output_hidden_states,
- 1504 interpolate_pos_encoding=interpolate_pos_encoding,
- 1505 return_dict=return_dict,
- 1506 )
- 1508 hidden_states = outputs[0]
- 1509 logits = self.lm_head(hidden_states)
- File /usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py:1518, in Module._wrapped_call_impl(self, *args, **kwargs)
- 1516 return self._compiled_call_impl(*args, **kwargs) # type: ignore[misc]
- 1517 else:
- -> 1518 return self._call_impl(*args, **kwargs)
- File /usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py:1527, in Module._call_impl(self, *args, **kwargs)
- 1522 # If we don't have any hooks, we want to skip the rest of the logic in
- 1523 # this function, and just call forward.
- 1524 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
- 1525 or _global_backward_pre_hooks or _global_backward_hooks
- 1526 or _global_forward_hooks or _global_forward_pre_hooks):
- -> 1527 return forward_call(*args, **kwargs)
- 1529 try:
- 1530 result = None
- File /usr/local/lib/python3.10/dist-packages/transformers/models/idefics/modeling_idefics.py:1333, in IdeficsModel.forward(self, input_ids, attention_mask, position_ids, past_key_values, inputs_embeds, pixel_values, image_encoder_embeddings, perceiver_embeddings, image_attention_mask, use_cache, output_attentions, output_hidden_states, interpolate_pos_encoding, return_dict)
- 1316 layer_outputs = self._gradient_checkpointing_func(
- 1317 vblock,
- 1318 decoder_layer,
- (...)
- 1330 self.gated_cross_attn_layers,
- 1331 )
- 1332 else:
- -> 1333 layer_outputs = vblock(
- 1334 decoder_layer,
- 1335 hidden_states,
- 1336 attention_mask=attention_mask,
- 1337 position_ids=position_ids,
- 1338 past_key_value=past_key_value,
- 1339 image_hidden_states=image_hidden_states,
- 1340 image_attention_mask=image_attention_mask,
- 1341 cross_attention_gate=cross_attention_gate,
- 1342 output_attentions=output_attentions,
- 1343 use_cache=use_cache,
- 1344 layer_idx=idx,
- 1345 cross_layer_interval=self.cross_layer_interval,
- 1346 gated_cross_attn_layers=self.gated_cross_attn_layers,
- 1347 )
- 1349 hidden_states = layer_outputs[0]
- 1351 if use_cache:
- File /usr/local/lib/python3.10/dist-packages/transformers/models/idefics/modeling_idefics.py:1285, in IdeficsModel.forward.<locals>.vblock(main_block, hidden_states, attention_mask, position_ids, past_key_value, image_hidden_states, image_attention_mask, cross_attention_gate, output_attentions, use_cache, layer_idx, cross_layer_interval, gated_cross_attn_layers)
- 1283 if layer_idx % cross_layer_interval == 0:
- 1284 xblock = gated_cross_attn_layers[layer_idx // cross_layer_interval]
- -> 1285 outputs = xblock(
- 1286 hidden_states,
- 1287 attention_mask=attention_mask,
- 1288 image_hidden_states=image_hidden_states,
- 1289 image_attention_mask=image_attention_mask,
- 1290 cross_attention_gate=cross_attention_gate,
- 1291 output_attentions=output_attentions,
- 1292 use_cache=use_cache,
- 1293 past_key_value=None, # not implemented
- 1294 )
- 1295 hidden_states = outputs[0]
- 1297 layer_outputs = main_block(
- 1298 hidden_states,
- 1299 attention_mask=attention_mask,
- (...)
- 1303 use_cache=use_cache,
- 1304 )
- File /usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py:1518, in Module._wrapped_call_impl(self, *args, **kwargs)
- 1516 return self._compiled_call_impl(*args, **kwargs) # type: ignore[misc]
- 1517 else:
- -> 1518 return self._call_impl(*args, **kwargs)
- File /usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py:1527, in Module._call_impl(self, *args, **kwargs)
- 1522 # If we don't have any hooks, we want to skip the rest of the logic in
- 1523 # this function, and just call forward.
- 1524 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
- 1525 or _global_backward_pre_hooks or _global_backward_hooks
- 1526 or _global_forward_hooks or _global_forward_pre_hooks):
- -> 1527 return forward_call(*args, **kwargs)
- 1529 try:
- 1530 result = None
- File /usr/local/lib/python3.10/dist-packages/accelerate/hooks.py:164, in add_hook_to_module.<locals>.new_forward(module, *args, **kwargs)
- 162 output = module._old_forward(*args, **kwargs)
- 163 else:
- --> 164 output = module._old_forward(*args, **kwargs)
- 165 return module._hf_hook.post_forward(module, output)
- File /usr/local/lib/python3.10/dist-packages/transformers/models/idefics/modeling_idefics.py:916, in IdeficsGatedCrossAttentionLayer.forward(self, hidden_states, attention_mask, image_hidden_states, image_attention_mask, cross_attention_gate, output_attentions, use_cache, past_key_value)
- 914 hidden_states = nn.functional.dropout(hidden_states, p=self.config, training=self.training)
- 915 # Fill in zeros for cross_attention hidden_states of tokens attending to no images
- --> 916 hidden_states[cross_attention_gate == 0] = hidden_states[cross_attention_gate == 0].fill_(0)
- 917 hidden_states = residual + self.act_cross_attn(self.alpha_cross_attn) * hidden_states
- 919 # Fully Connected
- RuntimeError: CUDA error: device-side assert triggered
- CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
- For debugging consider passing CUDA_LAUNCH_BLOCKING=1.
- Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.
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