微调任务 使用mindnlp-0.4.1加载模型报错 数据类型不支持

[Andy115951]

Describe the bug/ 问题描述 (Mandatory / 必填)
A clear and concise description of what the bug is.
使用mindnlp0.4.1加载模型报错

RuntimeError: Unsupported data type!
----------------------------------------------------
- C++ Call Stack: (For framework developers)
----------------------------------------------------
mindspore/ccsrc/pybind_api/ir/tensor_py.cc:361 MakeTensorOfNumpy

• Hardware Environment(Ascend/GPU/CPU) / 硬件环境:

Please delete the backend not involved / 请删除不涉及的后端:
/device ascend/GPU/CPU/kirin/等其他芯片

Ascend: 1*ascend-snt9b1|ARM: 24核 192GB

• Software Environment / 软件环境 (Mandatory / 必填):
  -- MindSpore version (e.g., 1.7.0.Bxxx) :
  -- Python version (e.g., Python 3.7.5) :
  -- OS platform and distribution (e.g., Linux Ubuntu 16.04):
  -- GCC/Compiler version (if compiled from source):

Python 3.9.10
mindspore 2.3.1
mindnlp 0.4.1

• Excute Mode / 执行模式 (Mandatory / 必填)(PyNative/Graph):

Please delete the mode not involved / 请删除不涉及的模式:
/mode pynative
/mode graph

PyNative

To Reproduce / 重现步骤 (Mandatory / 必填)
Steps to reproduce the behavior:

1. Go to '...'
2. Click on '....'
3. Scroll down to '....'
4. See error

!export HF_ENDPOINT=http://hf.co
import os
import mindspore
from mindnlp.transformers import AutoModelForSeq2SeqLM, AutoTokenizer
from mindnlp.peft import get_peft_config, get_peft_model, get_peft_model_state_dict, PromptTuningConfig, TaskType
from mindnlp.dataset import load_dataset, BaseMapFunction
from mindspore.dataset import GeneratorDataset
from mindnlp.core import ops, optim
import numpy as np
from threading import Lock
from mindnlp.transformers.optimization import get_linear_schedule_with_warmup
from tqdm import tqdm
from mindnlp.peft.tuners.prompt_tuning.config import PromptTuningInit

os.environ["TOKENIZERS_PARALLELISM"] = "false"
os.environ['HF_ENDPOINT'] = 'http://hf-mirror.com'
if "RANK_TABLE_FILE" in os.environ:
    del os.environ["RANK_TABLE_FILE"]

# model_name_or_path = "t5-large"
# tokenizer_name_or_path = "t5-large"
model_name_or_path = "google-t5/t5-large"
tokenizer_name_or_path = "google-t5/t5-large"
checkpoint_name = "financial_sentiment_analysis_prompt_tuning_v1.ckpt"
text_column = "sentence"
label_column = "text_label"
max_length = 128
lr = 1e-3
num_epochs = 8
batch_size = 8

# creating model
peft_config = PromptTuningConfig(
    task_type=TaskType.SEQ_2_SEQ_LM,
    prompt_tuning_init=PromptTuningInit.TEXT,
    num_virtual_tokens=20,
    prompt_tuning_init_text="What is the sentiment of this article?\n",
    inference_mode=False,
    tokenizer_name_or_path=model_name_or_path,
)

model = AutoModelForSeq2SeqLM.from_pretrained(model_name_or_path)
model = get_peft_model(model, peft_config)
model.print_trainable_parameters()

Expected behavior / 预期结果 (Mandatory / 必填)
A clear and concise description of what you expected to happen.
torch输出结果

PeftModelForSeq2SeqLM(
  (base_model): T5ForConditionalGeneration(
    (shared): Embedding(32128, 1024)
    (encoder): T5Stack(
      (embed_tokens): Embedding(32128, 1024)
      (block): ModuleList(
        (0): T5Block(
          (layer): ModuleList(
            (0): T5LayerSelfAttention(
              (SelfAttention): T5Attention(
                (q): Linear(in_features=1024, out_features=1024, bias=False)
                (k): Linear(in_features=1024, out_features=1024, bias=False)
                (v): Linear(in_features=1024, out_features=1024, bias=False)
                (o): Linear(in_features=1024, out_features=1024, bias=False)
                (relative_attention_bias): Embedding(32, 16)
              )
              (layer_norm): T5LayerNorm()
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (1): T5LayerFF(
              (DenseReluDense): T5DenseActDense(
                (wi): Linear(in_features=1024, out_features=4096, bias=False)
                (wo): Linear(in_features=4096, out_features=1024, bias=False)
                (dropout): Dropout(p=0.1, inplace=False)
                (act): ReLU()
              )
              (layer_norm): T5LayerNorm()
              (dropout): Dropout(p=0.1, inplace=False)
            )
          )
        )
        (1-23): 23 x T5Block(
          (layer): ModuleList(
            (0): T5LayerSelfAttention(
              (SelfAttention): T5Attention(
                (q): Linear(in_features=1024, out_features=1024, bias=False)
                (k): Linear(in_features=1024, out_features=1024, bias=False)
                (v): Linear(in_features=1024, out_features=1024, bias=False)
                (o): Linear(in_features=1024, out_features=1024, bias=False)
              )
              (layer_norm): T5LayerNorm()
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (1): T5LayerFF(
              (DenseReluDense): T5DenseActDense(
                (wi): Linear(in_features=1024, out_features=4096, bias=False)
                (wo): Linear(in_features=4096, out_features=1024, bias=False)
                (dropout): Dropout(p=0.1, inplace=False)
                (act): ReLU()
              )
              (layer_norm): T5LayerNorm()
              (dropout): Dropout(p=0.1, inplace=False)
            )
          )
        )
      )
      (final_layer_norm): T5LayerNorm()
      (dropout): Dropout(p=0.1, inplace=False)
    )
    (decoder): T5Stack(
      (embed_tokens): Embedding(32128, 1024)
      (block): ModuleList(
        (0): T5Block(
          (layer): ModuleList(
            (0): T5LayerSelfAttention(
              (SelfAttention): T5Attention(
                (q): Linear(in_features=1024, out_features=1024, bias=False)
                (k): Linear(in_features=1024, out_features=1024, bias=False)
                (v): Linear(in_features=1024, out_features=1024, bias=False)
                (o): Linear(in_features=1024, out_features=1024, bias=False)
                (relative_attention_bias): Embedding(32, 16)
              )
              (layer_norm): T5LayerNorm()
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (1): T5LayerCrossAttention(
              (EncDecAttention): T5Attention(
                (q): Linear(in_features=1024, out_features=1024, bias=False)
                (k): Linear(in_features=1024, out_features=1024, bias=False)
                (v): Linear(in_features=1024, out_features=1024, bias=False)
                (o): Linear(in_features=1024, out_features=1024, bias=False)
              )
              (layer_norm): T5LayerNorm()
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (2): T5LayerFF(
              (DenseReluDense): T5DenseActDense(
                (wi): Linear(in_features=1024, out_features=4096, bias=False)
                (wo): Linear(in_features=4096, out_features=1024, bias=False)
                (dropout): Dropout(p=0.1, inplace=False)
                (act): ReLU()
              )
              (layer_norm): T5LayerNorm()
              (dropout): Dropout(p=0.1, inplace=False)
            )
          )
        )
        (1-23): 23 x T5Block(
          (layer): ModuleList(
            (0): T5LayerSelfAttention(
              (SelfAttention): T5Attention(
                (q): Linear(in_features=1024, out_features=1024, bias=False)
                (k): Linear(in_features=1024, out_features=1024, bias=False)
                (v): Linear(in_features=1024, out_features=1024, bias=False)
                (o): Linear(in_features=1024, out_features=1024, bias=False)
              )
              (layer_norm): T5LayerNorm()
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (1): T5LayerCrossAttention(
              (EncDecAttention): T5Attention(
                (q): Linear(in_features=1024, out_features=1024, bias=False)
                (k): Linear(in_features=1024, out_features=1024, bias=False)
                (v): Linear(in_features=1024, out_features=1024, bias=False)
                (o): Linear(in_features=1024, out_features=1024, bias=False)
              )
              (layer_norm): T5LayerNorm()
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (2): T5LayerFF(
              (DenseReluDense): T5DenseActDense(
                (wi): Linear(in_features=1024, out_features=4096, bias=False)
                (wo): Linear(in_features=4096, out_features=1024, bias=False)
                (dropout): Dropout(p=0.1, inplace=False)
                (act): ReLU()
              )
              (layer_norm): T5LayerNorm()
              (dropout): Dropout(p=0.1, inplace=False)
            )
          )
        )
      )
      (final_layer_norm): T5LayerNorm()
      (dropout): Dropout(p=0.1, inplace=False)
    )
    (lm_head): Linear(in_features=1024, out_features=32128, bias=False)
  )
  (prompt_encoder): ModuleDict(
    (default): PromptEmbedding(
      (embedding): Embedding(40, 1024)
    )
  )
  (word_embeddings): Embedding(32128, 1024)
)

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Additional context / 备注 (Optional / 选填)
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