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/opt/conda/conda-bld/pytorch_1695392020201/work/aten/src/ATen/native/cuda/IndexKernel.cu:92: operator(): block: [19,0,0], thread: [96,0
---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
Cell In[5], line 9
      7 print(f"输入: {content}")
      8 inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
----> 9 outputs = model.generate(**inputs, max_new_tokens=256, do_sample=True, temperature=0.7, top_p=0.95)
     10 outputs = tokenizer.decode(outputs.cpu()[0][len(inputs.input_ids[0]):], skip_special_tokens=True)
     11 print(f"输出: {outputs}")

File ~/miniconda3/envs/llm/lib/python3.10/site-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 ~/miniconda3/envs/llm/lib/python3.10/site-packages/transformers/generation/utils.py:1652, 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)
   1644     input_ids, model_kwargs = self._expand_inputs_for_generation(
   1645         input_ids=input_ids,
   1646         expand_size=generation_config.num_return_sequences,
   1647         is_encoder_decoder=self.config.is_encoder_decoder,
   1648         **model_kwargs,
   1649     )
   1651     # 13. run sample
-> 1652     return self.sample(
   1653         input_ids,
   1654         logits_processor=logits_processor,
   1655         logits_warper=logits_warper,
   1656         stopping_criteria=stopping_criteria,
   1657         pad_token_id=generation_config.pad_token_id,
   1658         eos_token_id=generation_config.eos_token_id,
   1659         output_scores=generation_config.output_scores,
   1660         return_dict_in_generate=generation_config.return_dict_in_generate,
   1661         synced_gpus=synced_gpus,
   1662         streamer=streamer,
   1663         **model_kwargs,
   1664     )
   1666 elif generation_mode == GenerationMode.BEAM_SEARCH:
   1667     # 11. prepare beam search scorer
   1668     beam_scorer = BeamSearchScorer(
   1669         batch_size=batch_size,
   1670         num_beams=generation_config.num_beams,
   (...)
   1675         max_length=generation_config.max_length,
   1676     )

File ~/miniconda3/envs/llm/lib/python3.10/site-packages/transformers/generation/utils.py:2734, in GenerationMixin.sample(self, input_ids, logits_processor, stopping_criteria, logits_warper, max_length, pad_token_id, eos_token_id, output_attentions, output_hidden_states, output_scores, return_dict_in_generate, synced_gpus, streamer, **model_kwargs)
   2731 model_inputs = self.prepare_inputs_for_generation(input_ids, **model_kwargs)
   2733 # forward pass to get next token
-> 2734 outputs = self(
   2735     **model_inputs,
   2736     return_dict=True,
   2737     output_attentions=output_attentions,
   2738     output_hidden_states=output_hidden_states,
   2739 )
   2741 if synced_gpus and this_peer_finished:
   2742     continue  # don't waste resources running the code we don't need

File ~/miniconda3/envs/llm/lib/python3.10/site-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 ~/miniconda3/envs/llm/lib/python3.10/site-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 ~/miniconda3/envs/llm/lib/python3.10/site-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 ~/miniconda3/envs/llm/lib/python3.10/site-packages/transformers/models/llama/modeling_llama.py:1038, in LlamaForCausalLM.forward(self, input_ids, attention_mask, position_ids, past_key_values, inputs_embeds, labels, use_cache, output_attentions, output_hidden_states, return_dict)
   1035 return_dict = return_dict if return_dict is not None else self.config.use_return_dict
   1037 # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
-> 1038 outputs = self.model(
   1039     input_ids=input_ids,
   1040     attention_mask=attention_mask,
   1041     position_ids=position_ids,
   1042     past_key_values=past_key_values,
   1043     inputs_embeds=inputs_embeds,
   1044     use_cache=use_cache,
   1045     output_attentions=output_attentions,
   1046     output_hidden_states=output_hidden_states,
   1047     return_dict=return_dict,
   1048 )
   1050 hidden_states = outputs[0]
   1051 if self.config.pretraining_tp > 1:

File ~/miniconda3/envs/llm/lib/python3.10/site-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 ~/miniconda3/envs/llm/lib/python3.10/site-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 ~/miniconda3/envs/llm/lib/python3.10/site-packages/transformers/models/llama/modeling_llama.py:925, in LlamaModel.forward(self, input_ids, attention_mask, position_ids, past_key_values, inputs_embeds, use_cache, output_attentions, output_hidden_states, return_dict)
    921     layer_outputs = torch.utils.checkpoint.checkpoint(
    922         create_custom_forward(decoder_layer), hidden_states, attention_mask, position_ids
    923     )
    924 else:
--> 925     layer_outputs = decoder_layer(
    926         hidden_states,
    927         attention_mask=attention_mask,
    928         position_ids=position_ids,
    929         past_key_value=past_key_value,
    930         output_attentions=output_attentions,
    931         use_cache=use_cache,
    932         padding_mask=padding_mask,
    933     )
    935 hidden_states = layer_outputs[0]
    937 if use_cache:

File ~/miniconda3/envs/llm/lib/python3.10/site-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 ~/miniconda3/envs/llm/lib/python3.10/site-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 ~/miniconda3/envs/llm/lib/python3.10/site-packages/accelerate/hooks.py:159, in add_hook_to_module.<locals>.new_forward(module, *args, **kwargs)
    158 def new_forward(module, *args, **kwargs):
--> 159     args, kwargs = module._hf_hook.pre_forward(module, *args, **kwargs)
    160     if module._hf_hook.no_grad:
    161         with torch.no_grad():

File ~/miniconda3/envs/llm/lib/python3.10/site-packages/accelerate/hooks.py:290, in AlignDevicesHook.pre_forward(self, module, *args, **kwargs)
    285                 fp16_statistics = self.weights_map[name.replace("weight", "SCB")]
    286         set_module_tensor_to_device(
    287             module, name, self.execution_device, value=self.weights_map[name], fp16_statistics=fp16_statistics
    288         )
--> 290 return send_to_device(args, self.execution_device), send_to_device(
    291     kwargs, self.execution_device, skip_keys=self.skip_keys
    292 )

File ~/miniconda3/envs/llm/lib/python3.10/site-packages/accelerate/utils/operations.py:160, in send_to_device(tensor, device, non_blocking, skip_keys)
    157     elif skip_keys is None:
    158         skip_keys = []
    159     return type(tensor)(
--> 160         {
    161             k: t if k in skip_keys else send_to_device(t, device, non_blocking=non_blocking, skip_keys=skip_keys)
    162             for k, t in tensor.items()
    163         }
    164     )
    165 elif hasattr(tensor, "to"):
    166     try:

File ~/miniconda3/envs/llm/lib/python3.10/site-packages/accelerate/utils/operations.py:161, in <dictcomp>(.0)
    157     elif skip_keys is None:
    158         skip_keys = []
    159     return type(tensor)(
    160         {
--> 161             k: t if k in skip_keys else send_to_device(t, device, non_blocking=non_blocking, skip_keys=skip_keys)
    162             for k, t in tensor.items()
    163         }
    164     )
    165 elif hasattr(tensor, "to"):
    166     try:

File ~/miniconda3/envs/llm/lib/python3.10/site-packages/accelerate/utils/operations.py:167, in send_to_device(tensor, device, non_blocking, skip_keys)
    165 elif hasattr(tensor, "to"):
    166     try:
--> 167         return tensor.to(device, non_blocking=non_blocking)
    168     except TypeError:  # .to() doesn't accept non_blocking as kwarg
    169         return tensor.to(device)

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.