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自学网站建设基本流程,西丽做网站,西三旗网站建设,软件设计与开发问题描述使用Ascend算力微调模型时#xff0c;虽然已指定Ascend设备#xff0c;但在测试模型推理时仍使用CPU导致报错微调模型deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B!modelscope download --model deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B --local_dir ./Qwen-1.5B版…问题描述使用Ascend算力微调模型时虽然已指定Ascend设备但在测试模型推理时仍使用CPU导致报错微调模型deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B!modelscope download --model deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B --local_dir ./Qwen-1.5B版本说明mindspore2.7.0mindnlp0.5.0源代码import mindspore as ms # 只有显示的指定Ascend才会使用NPU ms.set_device(Ascend) !npu-smi info from mindnlp.transformers import AutoModelForCausalLM, AutoTokenizer import mindspore as ms model AutoModelForCausalLM.from_pretrained(./Qwen-1.5B, ms_dtypems.bfloat16) # 注意之后希望更换基座模型的话请修改这里的模型下载后的地址 tokenizer AutoTokenizer.from_pretrained(./Qwen-1.5B, ms_dtypems.bfloat16) # 输入的文本⬇️ prompt 告诉我你是谁 # 模型推理代码⬇️ inputs tokenizer(prompt, return_tensorsms) print(inputs:,inputs) outputs model.generate(**inputs, temperature0.5, max_new_tokens128, do_sampleTrue) result tokenizer.decode(outputs[0]) print(result)报错信息--------------------------------------------------------------------------- TypeError Traceback (most recent call last) File /usr/local/python3.10.14/lib/python3.10/site-packages/transformers/utils/generic.py:1071, in check_model_inputs.locals.wrapper(self, *args, **kwargs) 1070 try: - 1071 outputs func(self, *args, **kwargs_without_recordable) 1072 except TypeError: File /usr/local/python3.10.14/lib/python3.10/site-packages/transformers/models/qwen2/modeling_qwen2.py:345, in Qwen2Model.forward(self, input_ids, attention_mask, position_ids, past_key_values, inputs_embeds, use_cache, cache_position, **kwargs) 344 if inputs_embeds is None: -- 345 inputs_embeds self.embed_tokens(input_ids) 347 if use_cache and past_key_values is None: File /usr/local/python3.10.14/lib/python3.10/site-packages/mindtorch/nn/modules/module.py:826, in Module._wrapped_call_impl(self, *args, **kwargs) 825 return self._compiled_call_impl(*args, **kwargs) # type: ignore[misc] -- 826 return self._call_impl(*args, **kwargs) File /usr/local/python3.10.14/lib/python3.10/site-packages/mindtorch/nn/modules/module.py:840, in Module._call_impl(self, *args, **kwargs) 837 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks 838 or _global_backward_pre_hooks or _global_backward_hooks 839 or _global_forward_hooks or _global_forward_pre_hooks): -- 840 return forward_call(*args, **kwargs) 842 try: File /usr/local/python3.10.14/lib/python3.10/site-packages/mindtorch/nn/modules/sparse.py:192, in Embedding.forward(self, input) 191 def forward(self, input: Tensor) - Tensor: -- 192 return F.embedding( 193 input, 194 self.weight, 195 self.padding_idx, 196 self.max_norm, 197 self.norm_type, 198 self.scale_grad_by_freq, 199 self.sparse, 200 ) File /usr/local/python3.10.14/lib/python3.10/site-packages/mindtorch/nn/functional.py:184, in embedding(input, weight, padding_idx, max_norm, norm_type, scale_grad_by_freq, sparse) 183 def embedding(input, weight, padding_idxNone, max_normNone, norm_type2.0, scale_grad_by_freqFalse, sparseFalse): -- 184 return execute(embedding, input, weight, padding_idx, max_norm, norm_type, scale_grad_by_freq) File /usr/local/python3.10.14/lib/python3.10/site-packages/mindtorch/executor.py:6, in execute(func_name, *args, **kwargs) 5 def execute(func_name, *args, **kwargs): ---- 6 out, device dispatcher.dispatch(func_name, *args, **kwargs) 7 # if MS27: 8 # return out File /usr/local/python3.10.14/lib/python3.10/site-packages/mindtorch/dispatcher.py:57, in Dispatcher.dispatch(self, func_name, *args, **kwargs) 54 raise RuntimeError( 55 fNo implementation for function: {func_name} on {device_type}. 56 ) --- 57 return func(*args, **kwargs), device File /usr/local/python3.10.14/lib/python3.10/site-packages/mindtorch/_apis/cpu.py:405, in embedding(input, weight, padding_idx, max_norm, norm_type, scale_grad_by_freq) 404 def embedding(input, weight, padding_idx, max_norm, norm_type, scale_grad_by_freq): -- 405 return cast(legacy.gather(weight, input, 0, 0), weight.dtype) File /usr/local/python3.10.14/lib/python3.10/site-packages/mindtorch/_op_prim/cpu/legacy.py:1770, in gather(*args) 1769 op _get_cache_prim(Gather)(*args[-1:]).set_device(CPU) - 1770 return op(*args[:-1]) File /usr/local/python3.10.14/lib/python3.10/site-packages/mindspore/ops/auto_generate/gen_ops_prim.py:6025, in Gather.__call__(self, input_params, input_indices, axis) 6024 def __call__(self, input_params, input_indices, axis): - 6025 return super().__call__(input_params, input_indices, axis, self.batch_dims) File /usr/local/python3.10.14/lib/python3.10/site-packages/mindspore/ops/primitive.py:413, in Primitive.__call__(self, *args) 412 return output -- 413 return _run_op(self, self.name, args) File /usr/local/python3.10.14/lib/python3.10/site-packages/mindspore/ops/primitive.py:1022, in _run_op(obj, op_name, args) 1021 Single op execution function supported by ge in PyNative mode. - 1022 res _pynative_executor.run_op_async(obj, op_name, args) 1023 # Add for jit context. File /usr/local/python3.10.14/lib/python3.10/site-packages/mindspore/common/api.py:1638, in _PyNativeExecutor.run_op_async(self, *args) 1629 1630 Run single op async. 1631 (...) 1636 StubNode, result of run op. 1637 - 1638 return self._executor.run_op_async(*args) TypeError: ---------------------------------------------------- - Kernel select failed: ---------------------------------------------------- Select CPU operator[Gather] fail! Unsupported data type! The supported data types are input[UInt8 Int32 Int64 Int64], output[UInt8]; input[UInt16 Int32 Int64 Int64], output[UInt16]; input[UInt32 Int32 Int64 Int64], output[UInt32]; input[UInt64 Int32 Int64 Int64], output[UInt64]; input[Int8 Int32 Int64 Int64], output[Int8]; input[Int16 Int32 Int64 Int64], output[Int16]; input[Int32 Int32 Int64 Int64], output[Int32]; input[Int64 Int32 Int64 Int64], output[Int64]; input[Float16 Int32 Int64 Int64], output[Float16]; input[Float32 Int32 Int64 Int64], output[Float32]; input[Float64 Int32 Int64 Int64], output[Float64]; input[Bool Int32 Int64 Int64], output[Bool]; input[Complex64 Int32 Int64 Int64], output[Complex64]; input[Complex128 Int32 Int64 Int64], output[Complex128]; , but get input[BFloat16 Int64 Int64 Int64 ] and output[BFloat16 ] node: pynative_kernel_graph4000000013:CNode_10{[0]: ValueNodePrimitive PrimFunc_Gather, [1]: pynative_kernel_graph4000000013:param_Parameter_11, [2]: pynative_kernel_graph4000000013:param_Parameter_12, [3]: ValueNodeInt64Imm 0, [4]: ValueNodeInt64Imm 0} ---------------------------------------------------- - C Call Stack: (For framework developers) ---------------------------------------------------- mindspore/ccsrc/plugin/device/cpu/hal/hardware/cpu_device_context.cc:514 SetOperatorInfo During handling of the above exception, another exception occurred: TypeError Traceback (most recent call last) Cell In[12], line 6 4 inputs tokenizer(prompt, return_tensorsms) 5 print(inputs:,inputs) ---- 6 outputs model.generate(**inputs, temperature0.5, max_new_tokens128, do_sampleTrue) 7 result tokenizer.decode(outputs[0]) 8 print(result) File /usr/local/python3.10.14/lib/python3.10/site-packages/mindtorch/utils/_contextlib.py:117, in context_decorator.locals.decorate_context(*args, **kwargs) 114 functools.wraps(func) 115 def decorate_context(*args, **kwargs): 116 with ctx_factory(): -- 117 return func(*args, **kwargs) File /usr/local/python3.10.14/lib/python3.10/site-packages/transformers/generation/utils.py:2564, 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, use_model_defaults, custom_generate, **kwargs) 2561 model_kwargs[use_cache] generation_config.use_cache 2563 # 9. Call generation mode - 2564 result decoding_method( 2565 self, 2566 input_ids, 2567 logits_processorprepared_logits_processor, 2568 stopping_criteriaprepared_stopping_criteria, 2569 generation_configgeneration_config, 2570 **generation_mode_kwargs, 2571 **model_kwargs, 2572 ) 2574 # Convert to legacy cache format if requested 2575 if ( 2576 generation_config.return_legacy_cache is True 2577 and hasattr(result, past_key_values) 2578 and getattr(result.past_key_values, to_legacy_cache) is not None 2579 ): File /usr/local/python3.10.14/lib/python3.10/site-packages/transformers/generation/utils.py:2784, in GenerationMixin._sample(self, input_ids, logits_processor, stopping_criteria, generation_config, synced_gpus, streamer, **model_kwargs) 2781 model_inputs self.prepare_inputs_for_generation(input_ids, **model_kwargs) 2783 if is_prefill: - 2784 outputs self(**model_inputs, return_dictTrue) 2785 is_prefill False 2786 else: File /usr/local/python3.10.14/lib/python3.10/site-packages/mindtorch/nn/modules/module.py:826, in Module._wrapped_call_impl(self, *args, **kwargs) 824 if self._compiled_call_impl is not None: 825 return self._compiled_call_impl(*args, **kwargs) # type: ignore[misc] -- 826 return self._call_impl(*args, **kwargs) File /usr/local/python3.10.14/lib/python3.10/site-packages/mindtorch/nn/modules/module.py:840, in Module._call_impl(self, *args, **kwargs) 835 return forward_call(*args, **kwargs) 837 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks 838 or _global_backward_pre_hooks or _global_backward_hooks 839 or _global_forward_hooks or _global_forward_pre_hooks): -- 840 return forward_call(*args, **kwargs) 842 try: 843 result None File /usr/local/python3.10.14/lib/python3.10/site-packages/transformers/utils/generic.py:918, in can_return_tuple.locals.wrapper(self, *args, **kwargs) 916 if return_dict_passed is not None: 917 return_dict return_dict_passed -- 918 output func(self, *args, **kwargs) 919 if not return_dict and not isinstance(output, tuple): 920 output output.to_tuple() File /usr/local/python3.10.14/lib/python3.10/site-packages/transformers/models/qwen2/modeling_qwen2.py:449, in Qwen2ForCausalLM.forward(self, input_ids, attention_mask, position_ids, past_key_values, inputs_embeds, labels, use_cache, cache_position, logits_to_keep, **kwargs) 417 can_return_tuple 418 auto_docstring 419 def forward( (...) 430 **kwargs: Unpack[TransformersKwargs], 431 ) - CausalLMOutputWithPast: 432 r 433 Example: 434 (...) 447 Hey, are you conscious? Can you talk to me?\nIm not conscious, but I can talk to you. 448 -- 449 outputs: BaseModelOutputWithPast self.model( 450 input_idsinput_ids, 451 attention_maskattention_mask, 452 position_idsposition_ids, 453 past_key_valuespast_key_values, 454 inputs_embedsinputs_embeds, 455 use_cacheuse_cache, 456 cache_positioncache_position, 457 **kwargs, 458 ) 460 hidden_states outputs.last_hidden_state 461 # Only compute necessary logits, and do not upcast them to float if we are not computing the loss File /usr/local/python3.10.14/lib/python3.10/site-packages/mindtorch/nn/modules/module.py:826, in Module._wrapped_call_impl(self, *args, **kwargs) 824 if self._compiled_call_impl is not None: 825 return self._compiled_call_impl(*args, **kwargs) # type: ignore[misc] -- 826 return self._call_impl(*args, **kwargs) File /usr/local/python3.10.14/lib/python3.10/site-packages/mindtorch/nn/modules/module.py:840, in Module._call_impl(self, *args, **kwargs) 835 return forward_call(*args, **kwargs) 837 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks 838 or _global_backward_pre_hooks or _global_backward_hooks 839 or _global_forward_hooks or _global_forward_pre_hooks): -- 840 return forward_call(*args, **kwargs) 842 try: 843 result None File /usr/local/python3.10.14/lib/python3.10/site-packages/transformers/utils/generic.py:1073, in check_model_inputs.locals.wrapper(self, *args, **kwargs) 1071 outputs func(self, *args, **kwargs_without_recordable) 1072 except TypeError: - 1073 raise original_exception 1074 raise TypeError( 1075 Missing **kwargs in the signature of the check_model_inputs-decorated function 1076 f({func.__qualname__}) 1077 ) 1079 # Restore original forward methods File /usr/local/python3.10.14/lib/python3.10/site-packages/transformers/utils/generic.py:1064, in check_model_inputs.locals.wrapper(self, *args, **kwargs) 1061 monkey_patched_layers.append((module, original_forward)) 1063 try: - 1064 outputs func(self, *args, **kwargs) 1065 except TypeError as original_exception: 1066 # If we get a TypeError, its possible that the model is not receiving the recordable kwargs correctly. 1067 # Get a TypeError even after removing the recordable kwargs - re-raise the original exception 1068 # Otherwise - were probably missing **kwargs in the decorated function 1069 kwargs_without_recordable {k: v for k, v in kwargs.items() if k not in recordable_keys} File /usr/local/python3.10.14/lib/python3.10/site-packages/transformers/models/qwen2/modeling_qwen2.py:345, in Qwen2Model.forward(self, input_ids, attention_mask, position_ids, past_key_values, inputs_embeds, use_cache, cache_position, **kwargs) 342 raise ValueError(You must specify exactly one of input_ids or inputs_embeds) 344 if inputs_embeds is None: -- 345 inputs_embeds self.embed_tokens(input_ids) 347 if use_cache and past_key_values is None: 348 past_key_values DynamicCache(configself.config) File /usr/local/python3.10.14/lib/python3.10/site-packages/mindtorch/nn/modules/module.py:826, in Module._wrapped_call_impl(self, *args, **kwargs) 824 if self._compiled_call_impl is not None: 825 return self._compiled_call_impl(*args, **kwargs) # type: ignore[misc] -- 826 return self._call_impl(*args, **kwargs) File /usr/local/python3.10.14/lib/python3.10/site-packages/mindtorch/nn/modules/module.py:840, in Module._call_impl(self, *args, **kwargs) 835 return forward_call(*args, **kwargs) 837 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks 838 or _global_backward_pre_hooks or _global_backward_hooks 839 or _global_forward_hooks or _global_forward_pre_hooks): -- 840 return forward_call(*args, **kwargs) 842 try: 843 result None File /usr/local/python3.10.14/lib/python3.10/site-packages/mindtorch/nn/modules/sparse.py:192, in Embedding.forward(self, input) 191 def forward(self, input: Tensor) - Tensor: -- 192 return F.embedding( 193 input, 194 self.weight, 195 self.padding_idx, 196 self.max_norm, 197 self.norm_type, 198 self.scale_grad_by_freq, 199 self.sparse, 200 ) File /usr/local/python3.10.14/lib/python3.10/site-packages/mindtorch/nn/functional.py:184, in embedding(input, weight, padding_idx, max_norm, norm_type, scale_grad_by_freq, sparse) 183 def embedding(input, weight, padding_idxNone, max_normNone, norm_type2.0, scale_grad_by_freqFalse, sparseFalse): -- 184 return execute(embedding, input, weight, padding_idx, max_norm, norm_type, scale_grad_by_freq) File /usr/local/python3.10.14/lib/python3.10/site-packages/mindtorch/executor.py:6, in execute(func_name, *args, **kwargs) 5 def execute(func_name, *args, **kwargs): ---- 6 out, device dispatcher.dispatch(func_name, *args, **kwargs) 7 # if MS27: 8 # return out 10 if not isinstance(out, (tuple, list)): File /usr/local/python3.10.14/lib/python3.10/site-packages/mindtorch/dispatcher.py:57, in Dispatcher.dispatch(self, func_name, *args, **kwargs) 53 if func is None: 54 raise RuntimeError( 55 fNo implementation for function: {func_name} on {device_type}. 56 ) --- 57 return func(*args, **kwargs), device File /usr/local/python3.10.14/lib/python3.10/site-packages/mindtorch/_apis/cpu.py:405, in embedding(input, weight, padding_idx, max_norm, norm_type, scale_grad_by_freq) 404 def embedding(input, weight, padding_idx, max_norm, norm_type, scale_grad_by_freq): -- 405 return cast(legacy.gather(weight, input, 0, 0), weight.dtype) File /usr/local/python3.10.14/lib/python3.10/site-packages/mindtorch/_op_prim/cpu/legacy.py:1770, in gather(*args) 1768 def gather(*args): 1769 op _get_cache_prim(Gather)(*args[-1:]).set_device(CPU) - 1770 return op(*args[:-1]) File /usr/local/python3.10.14/lib/python3.10/site-packages/mindspore/ops/auto_generate/gen_ops_prim.py:6025, in Gather.__call__(self, input_params, input_indices, axis) 6024 def __call__(self, input_params, input_indices, axis): - 6025 return super().__call__(input_params, input_indices, axis, self.batch_dims) File /usr/local/python3.10.14/lib/python3.10/site-packages/mindspore/ops/primitive.py:413, in Primitive.__call__(self, *args) 411 if should_elim: 412 return output -- 413 return _run_op(self, self.name, args) File /usr/local/python3.10.14/lib/python3.10/site-packages/mindspore/ops/primitive.py:1022, in _run_op(obj, op_name, args) 1020 def _run_op(obj, op_name, args): 1021 Single op execution function supported by ge in PyNative mode. - 1022 res _pynative_executor.run_op_async(obj, op_name, args) 1023 # Add for jit context. 1024 if jit_context(): File /usr/local/python3.10.14/lib/python3.10/site-packages/mindspore/common/api.py:1638, in _PyNativeExecutor.run_op_async(self, *args) 1628 def run_op_async(self, *args): 1629 1630 Run single op async. 1631 (...) 1636 StubNode, result of run op. 1637 - 1638 return self._executor.run_op_async(*args) TypeError: ---------------------------------------------------- - Kernel select failed: ---------------------------------------------------- Select CPU operator[Gather] fail! Unsupported data type! The supported data types are input[UInt8 Int32 Int64 Int64], output[UInt8]; input[UInt16 Int32 Int64 Int64], output[UInt16]; input[UInt32 Int32 Int64 Int64], output[UInt32]; input[UInt64 Int32 Int64 Int64], output[UInt64]; input[Int8 Int32 Int64 Int64], output[Int8]; input[Int16 Int32 Int64 Int64], output[Int16]; input[Int32 Int32 Int64 Int64], output[Int32]; input[Int64 Int32 Int64 Int64], output[Int64]; input[Float16 Int32 Int64 Int64], output[Float16]; input[Float32 Int32 Int64 Int64], output[Float32]; input[Float64 Int32 Int64 Int64], output[Float64]; input[Bool Int32 Int64 Int64], output[Bool]; input[Complex64 Int32 Int64 Int64], output[Complex64]; input[Complex128 Int32 Int64 Int64], output[Complex128]; , but get input[BFloat16 Int64 Int64 Int64 ] and output[BFloat16 ] node: pynative_kernel_graph4000000012:CNode_7{[0]: ValueNodePrimitive PrimFunc_Gather, [1]: pynative_kernel_graph4000000012:param_Parameter_8, [2]: pynative_kernel_graph4000000012:param_Parameter_9, [3]: ValueNodeInt64Imm 0, [4]: ValueNodeInt64Imm 0} ---------------------------------------------------- - C Call Stack: (For framework developers) ---------------------------------------------------- mindspore/ccsrc/plugin/device/cpu/hal/hardware/cpu_device_context.cc:514 SetOperatorInfo问题解答【正确的 NPU 使用方法3 个必须步骤】1. 设置完整的 context包括 device_id正确例子ms.set_context(modems.PYNATIVE_MODE, device_target“Ascend”, device_id0)错误例子# 问题仅设置 device 不够需要设置完整的 context 和 device_id虽然调用了 ms.set_device(‘Ascend’)但模型实际仍在 CPU 上运行# ms.set_device(‘Ascend’)2. 之后需要显式将模型移动到 NPUmodel model.to(‘npu:0’)3. 并且还需要显式将输入数据移动到 NPU不这样的话会导致输入数据input_ids也在 CPU 上导致整个计算在 CPU 进行inputs {k: v.to(‘npu:0’) for k, v in inputs.items()}