[Question]: 对ERNIE模型进行微调时，报错Pointer C should not be null.

[jjy260782149]

请提出你的问题

使用ERNIE模型，进行微调时，出现错误： Traceback (most recent call last): File "D:\1.地下空间数据获取\netBuild\ERNIE\train.py", line 95, in logits = model(input_ids, token_type_ids) File "D:\app\python38\lib\site-packages\paddle\nn\layer\layers.py", line 1429, in call return self.forward(*inputs, kwargs) File "D:\app\python38\lib\site-packages\paddlenlp\transformers\ernie\modeling.py", line 709, in forward outputs = self.ernie( File "D:\app\python38\lib\site-packages\paddle\nn\layer\layers.py", line 1429, in call return self.forward(*inputs, *kwargs) File "D:\app\python38\lib\site-packages\paddlenlp\transformers\ernie\modeling.py", line 357, in forward encoder_outputs = self.encoder( File "D:\app\python38\lib\site-packages\paddle\nn\layer\layers.py", line 1429, in call return self.forward(inputs, kwargs) File "D:\app\python38\lib\site-packages\paddlenlp\transformers\model_outputs.py", line 304, in _transformer_encoder_fwd layer_outputs = mod( File "D:\app\python38\lib\site-packages\paddle\nn\layer\layers.py", line 1429, in call return self.forward(*inputs, *kwargs) File "D:\app\python38\lib\site-packages\paddlenlp\transformers\model_outputs.py", line 83, in _transformer_encoder_layer_fwd attn_outputs = self.self_attn(src, src, src, src_mask, cache) File "D:\app\python38\lib\site-packages\paddle\nn\layer\layers.py", line 1429, in call return self.forward(inputs, **kwargs) File "D:\app\python38\lib\site-packages\paddle\nn\layer\transformer.py", line 432, in forward out = tensor.matmul(weights, v) File "D:\app\python38\lib\site-packages\paddle\tensor\linalg.py", line 239, in matmul return _C_ops.matmul(x, y, transpose_x, transpose_y) ValueError: (InvalidArgument) Pointer C should not be null. [Hint: C should not be null.] (at ..\paddle/phi/kernels/funcs/blas/blas_impl.h:1373)

微调代码为： model = ErnieForTokenClassification.from_pretrained("ernie-1.0", num_classes=len(labelVocab)) step = 0 for epoch in range(50): for idx, (input_ids, token_type_ids, length, labels) in enumerate(train_loader): logits = model(input_ids, token_type_ids) loss = paddle.mean(loss_fn(logits, labels)) loss.backward() optimizer.step() optimizer.clear_grad() step += 1 print("epoch:%d - step:%d - loss: %f" % (epoch, step, loss)) evaluate(model, metric, dev_loader)

我之前用一套训练数据运行成功了，换了一套训练数据就报错了，昨天觉得是数据本身或者预处理不当导致出现问题，但今天是在预训练到第三轮才报的错，应该不是数据本身或者预处理的问题。没有什么解决思路，希望能给与解答，谢谢。

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