使用推理脚本对Qwen2.5-1.5B进行推理报错

使用代码为r1.6.0
硬件为

```
+------------------------------------------------------------------------------------------------+
| npu-smi 25.2.0                   Version: 25.2.0                                               |
+---------------------------+---------------+----------------------------------------------------+
| NPU   Name                | Health        | Power(W)    Temp(C)           Hugepages-Usage(page)|
| Chip                      | Bus-Id        | AICore(%)   Memory-Usage(MB)  HBM-Usage(MB)        |
+===========================+===============+====================================================+
| 5     910B4               | OK            | 80.8        34                0    / 0             |
| 0                         | 0000:06:00.0  | 0           0    / 0          2845 / 32768         |
+===========================+===============+====================================================+
| 6     910B4               | OK            | 76.9        34                0    / 0             |
| 0                         | 0000:07:00.0  | 0           0    / 0          2838 / 32768         |
+===========================+===============+====================================================+
+---------------------------+---------------+----------------------------------------------------+
| NPU     Chip              | Process id    | Process name             | Process memory(MB)      |
+===========================+===============+====================================================+
| No running processes found in NPU 5                                                            |
+===========================+===============+====================================================+
| No running processes found in NPU 6                                                            |
+===========================+===============+====================================================+

```

```
seed: 0
output_dir: './output' # path to save checkpoint/strategy
load_checkpoint: '/root/Qwen2.5-1.5B'
load_ckpt_format: "safetensors"
auto_trans_ckpt: True  # If true, auto transform load_checkpoint to load in distributed model
only_save_strategy: False
resume_training: False
use_parallel: False
run_mode: 'predict'

# trainer config
trainer:
  type: CausalLanguageModelingTrainer
  model_name: 'qwen2_5_1_5b'

# runner config
runner_config:
  epochs: 5
  batch_size: 1
  sink_mode: True
  sink_size: 2
runner_wrapper:
  type: MFTrainOneStepCell
  scale_sense:
    type: DynamicLossScaleUpdateCell
    loss_scale_value: 65536
    scale_factor: 2
    scale_window: 1000
  use_clip_grad: True

# default parallel of device num = 8 for Atlas 800T A2
parallel_config:
  data_parallel: 1
  model_parallel: 2
  pipeline_stage: 1
  micro_batch_num: 1
  vocab_emb_dp: False
  gradient_aggregation_group: 4
# when model parallel is greater than 1, we can set micro_batch_interleave_num=2, that may accelerate the train process.
micro_batch_interleave_num: 1

model:
  model_config:
    type: LlamaConfig
    batch_size: 1
    seq_length: 8192
    hidden_size: 1536
    num_layers: 28
    num_heads: 12
    n_kv_heads: 2
    vocab_size: 151936
    initializer_range: 0.02
    intermediate_size: 8960
    qkv_has_bias: True
    rms_norm_eps: 1.0e-6
    theta: 1000000.0
    emb_dropout_prob: 0.0
    eos_token_id: [151645,151643]
    pad_token_id: 151643
    bos_token_id: 151643
    compute_dtype: "bfloat16"
    layernorm_compute_type: "float32"
    softmax_compute_type: "float32"
    rotary_dtype: "float32"
    param_init_type: "float32"
    use_past: True
    use_flash_attention: False
    block_size: 32
    num_blocks: 1024
    use_past_shard: False
    offset: 0
    checkpoint_name_or_path: ""
    repetition_penalty: 1.05
    max_decode_length: 512
    top_k: 50
    top_p: 0.8
    temperature: 1.0
    do_sample: True
    is_dynamic: True
    qkv_concat: False
    auto_map:
      AutoTokenizer: [qwen2_5_tokenizer.Qwen2Tokenizer, null]

arch:
    type: LlamaForCausalLM

processor:
  return_tensors: ms
  tokenizer:
    model_max_length: 131072
    bos_token: null
    eos_token: "<|im_end|>"
    unk_token: null
    pad_token: "<|endoftext|>"
    vocab_file: "/root/Qwen2.5-1.5B/vocab.json"
    merges_file: "/root/Qwen2.5-1.5B/merges.txt"
    chat_template: "{%- if tools %}\n    {{- '<|im_start|>system\\n' }}\n    {%- if messages[0]['role'] == 'system' %}\n        {{- messages[0]['content'] }}\n    {%- else %}\n        {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}\n    {%- endif %}\n    {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n    {%- for tool in tools %}\n        {{- \"\\n\" }}\n        {{- tool | tojson }}\n    {%- endfor %}\n    {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n    {%- if messages[0]['role'] == 'system' %}\n        {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n    {%- else %}\n        {{- '<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n' }}\n    {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n    {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n        {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n    {%- elif message.role == \"assistant\" %}\n        {{- '<|im_start|>' + message.role }}\n        {%- if message.content %}\n            {{- '\\n' + message.content }}\n        {%- endif %}\n        {%- for tool_call in message.tool_calls %}\n            {%- if tool_call.function is defined %}\n                {%- set tool_call = tool_call.function %}\n            {%- endif %}\n            {{- '\\n<tool_call>\\n{\"name\": \"' }}\n            {{- tool_call.name }}\n            {{- '\", \"arguments\": ' }}\n            {{- tool_call.arguments | tojson }}\n            {{- '}\\n</tool_call>' }}\n        {%- endfor %}\n        {{- '<|im_end|>\\n' }}\n    {%- elif message.role == \"tool\" %}\n        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n            {{- '<|im_start|>user' }}\n        {%- endif %}\n        {{- '\\n<tool_response>\\n' }}\n        {{- message.content }}\n        {{- '\\n</tool_response>' }}\n        {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n            {{- '<|im_end|>\\n' }}\n        {%- endif %}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n"
    type: Qwen2Tokenizer
    auto_register: qwen2_5_tokenizer.Qwen2Tokenizer
  type: Qwen2Processor

# mindspore context init config
context:
  mode: 0 #0--Graph Mode; 1--Pynative Mode
  device_target: "Ascend"
  ascend_config:
    precision_mode: "must_keep_origin_dtype"
  max_call_depth: 10000
  max_device_memory: "26GB"
  save_graphs: False
  save_graphs_path: "./graph"
  device_id: 0

# parallel context config
parallel:
  parallel_mode: 1 # 0-data parallel, 1-semi-auto parallel, 2-auto parallel, 3-hybrid parallel
  gradients_mean: False
  enable_alltoall: False
  full_batch: True
  search_mode: "sharding_propagation"
  enable_parallel_optimizer: False
  strategy_ckpt_config:
    save_file: "./ckpt_strategy.ckpt"
    only_trainable_params: False
  parallel_optimizer_config:
    gradient_accumulation_shard: False
    parallel_optimizer_threshold: 64

```

GVP MindSpore/mindformers

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GVPMindSpore/mindformers

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