mindspore推理报错NameError:The name 'LTM' is not defined, or not supported in graph mode.及解决

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1 系统环境

硬件环境(Ascend/GPU/CPU): Ascend/GPU/CPU
MindSpore版本: mindspore=1.7.0
执行模式（PyNative/ Graph）:不限
Python版本: Python=3.9.13
操作系统平台: 不限

2 报错信息

2.1 问题描述

运行一下代码，mindspore推理报错

[CRITICAL] PARSER(3727058,7f213f7cd740,python):2023-04-27-17:58:18.788.462 [mindspore/ccsrc/pipeline/jit/parse/function_block.cc:257] HandleBuiltinNamespaceInfo] The name 'LTM' is not defined, or not supported in graph mode.
Traceback (most recent call last):
  File "testtest.py", line 21, in <module>
    outs = nets(inps)
  File "/home/ma-user/anaconda3/envs/tmp/lib/python3.7/site-packages/mindspore/nn/cell.py", line 586, in __call__
    out = self.compile_and_run(*args)
  File "/home/ma-user/anaconda3/envs/tmp/lib/python3.7/site-packages/mindspore/nn/cell.py", line 964, in compile_and_run
    self.compile(*inputs)
  File "/home/ma-user/anaconda3/envs/tmp/lib/python3.7/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/ma-user/anaconda3/envs/tmp/lib/python3.7/site-packages/mindspore/common/api.py", line 1006, in compile
    result = self._graph_executor.compile(obj, args_list, phase, self._use_vm_mode())
NameError: mindspore/ccsrc/pipeline/jit/parse/function_block.cc:257 HandleBuiltinNamespaceInfo] The name 'LTM' is not defined, or not supported in graph mode.

# In file testtest.py(13)
        LTM_ = [LTM[:, :i].sum(2) for i in range(k + 1)]

2.2 脚本代码（代码格式，可上传附件）

import numpy as np
import mindspore as ms
import mindspore.nn as nn
import mindspore.context as context


class model(nn.Cell):
    def __init__(self,):
        super().__init__()

    def construct(self, LTM):
        k = LTM.shape[2]
        LTM_ = [LTM[:, :i].sum(2) for i in range(k + 1)]
        return LTM_


if __name__ == '__main__':
    context.set_context(mode=context.GRAPH_MODE, device_target='GPU', save_graphs=False)
    inps = ms.Tensor(np.random.random((3, 8, 256, 256)))
    nets = model()
    outs = nets(inps)
    import pdb; pdb.set_trace()
    print(outs.shape)

3 根因分析

根据报错提示，LTM_ = [LTM[:, :i].sum(2) for i in range(k + 1)]分析可能是上面一行不支持graph模式

4 解决方案

将LTM_ = [LTM[:, :i].sum(2) for i in range(k + 1)]换成多行的写法，发现i=0的时候reducesum的asix范围超限，因为range是从0
开始的，所以将LTM[:, :i]修改为LTM[:, :i+1]，具体代码如下

import numpy as np 
import mindspore as ms 
import mindspore.nn as nn 
import mindspore.context as context 

class model(nn.Cell): 
    def __init__(self,): 
        super().__init__() 
    def construct(self, LTM): 
        k = LTM.shape[2] 
        LTM_ = [] 
        for i in range(k + 1): 
            LTM_.append(LTM[:, :i+1].sum(2)) 
            #LTM_ = [LTM[:, :i+1].sum(2) for i in range(k + 1)] 
        return LTM_ 

if __name__ == '__main__': 
    context.set_context(mode=context.GRAPH_MODE, device_target='GPU', save_graphs=False) 
    inps = ms.Tensor(np.random.random((3, 8, 256, 256))) 
    nets = model() 
    outs = nets(inps) 

    import pdb; pdb.set_trace() 
    print(outs[0].shape)

输入shape为(3, 1, 256)