MindSpore：The operation does not support the type [kMetaTypeNone， Tesor...

1 报错描述

1.1 系统环境

Environment(Ascend/GPU/CPU): GPU-GTX3090(24G)
Software Environment:
– MindSpore version (source or binary): 1.7.0
– Python version (e.g., Python 3.7.5): 3.8.13
– OS platform and distribution (e.g., Linux Ubuntu 16.04): Ubuntu 16.04
– CUDA version : 11.0

1.2 基本信息

1.2.1脚本

此代码是ConvLSTM从PyTorch迁移到MindSpore的一部分，下面为报错部分

loss = train_network(data, label)

1.2.2报错

部分个人信息做遮挡处理

[WARNING] ME(124028:139969934345984,MainProcess):2022-07-23-20:21:12.940.089 [mindspore/run_check/_check_version.py:140]
 MindSpore version 1.7.0 and cuda version 11.0.221 does not match, please refer to the installation guide for version ma
tching information: https://www.mindspore.cn/install
[CRITICAL] ANALYZER(124028,7f4d4a374700,python):2022-07-23-20:21:21.559.937 [mindspore/ccsrc/frontend/operator/composite
/multitype_funcgraph.cc:160] GenerateFromTypes] The 'sub' operation does not support the type [kMetaTypeNone, Tensor[Flo
at32]].
The supported types of overload function `sub` is: [Tensor, List], [Tensor, Tuple], [List, Tensor], [Tuple, Tensor], [Te
nsor, Number], [Number, Tensor], [Tensor, Tensor], [Number, Number].

Traceback (most recent call last):
  File "main.py", line 194, in <module>
    train()
  File "main.py", line 142, in train
    loss = train_network(data, label)
  File "/home/xxxlab/anaconda2/envs/mindspore/lib/python3.8/site-packages/mindspore/nn/cell.py", line 586, in __call__
    out = self.compile_and_run(*args)
  File "/home/xxxlab/anaconda2/envs/mindspore/lib/python3.8/site-packages/mindspore/nn/cell.py", line 964, in compile_an
d_run
    self.compile(*inputs)
  File "/home/xxxlab/anaconda2/envs/mindspore/lib/python3.8/site-packages/mindspore/nn/cell.py", line 937, in compile
    _cell_graph_executor.compile(self, *inputs, phase=self.phase, auto_parallel_mode=self._auto_parallel_mode)
  File "/home/xxxlab/anaconda2/envs/mindspore/lib/python3.8/site-packages/mindspore/common/api.py", line 1006, in compil
e
    result = self._graph_executor.compile(obj, args_list, phase, self._use_vm_mode())
RuntimeError: mindspore/ccsrc/frontend/operator/composite/multitype_funcgraph.cc:160 GenerateFromTypes] The 'sub' operat
ion does not support the type [kMetaTypeNone, Tensor[Float32]].
The supported types of overload function `sub` is: [Tensor, List], [Tensor, Tuple], [List, Tensor], [Tuple, Tensor], [Te
nsor, Number], [Number, Tensor], [Tensor, Tensor], [Number, Number].

The function call stack (See file '/home/xxxlab/zrj/mindspore/ConvLSTM-PyTorch/conv/rank_0/om/analyze_fail.dat' for more
 details):
# 0 In file /home/xxxlab/anaconda2/envs/mindspore/lib/python3.8/site-packages/mindspore/nn/wrap/cell_wrapper.py(373)
        loss = self.network(*inputs)
               ^
# 1 In file /home/xxxlab/anaconda2/envs/mindspore/lib/python3.8/site-packages/mindspore/nn/wrap/cell_wrapper.py(112)
        return self._loss_fn(out, label)
               ^
# 2 In file /home/xxxlab/anaconda2/envs/mindspore/lib/python3.8/site-packages/mindspore/nn/loss/loss.py(313)
        x = F.square(logits - labels)

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2 原因分析以及解决办法

原因直至Mindspore的loss，一开始我也很纳闷，mindspore的源代码我也不能修改，kMetaTypeNone又是什么类型呢？后来参考这篇文章知道了Mindspore分动静态图模式，默认好像是静态图模式，也就是所有的模型参数都要事先确定下来，不然不能构建静态图。

关于静态和动态图的区别，可以参考mindspore官方文档。具体而言，从我的角度就是静态图就是一开始建议完整个模型的计算图，这样子这“张”计算图就可以被重复利用了，不用每次都重新计算，提高计算速度，但这样显而易见的缺点就是可扩展性差。

但是我的模型需要我根据输入进行调整，在对这个报错修改后很多其他地方如MUL操作，也接连出现kMetaTypeNone的错误，这样治标不治本，况且只要模型不改，问题就不可能被解决。

在看了mindspore官方文档后发现mindspore原来是支持动态图的呀！嗨，因为原框架Pytorch就是动态图的，因此只需要将mindspore调整成动态图就行了，具体操作是添加下方代码：

context.set_context(mode=context.PYNATIVE_MODE)

3 总结

多看mindspore官方文档，深入了解框架原理及之间的区别，多利用社区。