1 Error description

1.1 System Environment

Hardware Environment(Ascend/GPU/CPU): Ascend
Software Environment:
– MindSpore version (source or binary): 1.8.0
– Python version (eg, Python 3.7.5): 3.7.6
– OS platform and distribution (eg, Linux Ubuntu 16.04): Ubuntu 4.15.0-74-generic
– GCC/Compiler version (if compiled from source):

1.2 Basic information

1.2.1 Script

The training script is to average and reduce axis1 by constructing the ReduceMean operator network. The script is as follows:

 01 class Net(nn.Cell):
 02     def __init__(self, axis, keep_dims):
 03         super().__init__()
 04         self.reducemean = ops.ReduceMean(keep_dims=keep_dims)
 05         self.axis = axis
 06     def construct(self, input_x):
 07         return self.reducemean(input_x, self.axis)
 08 net = Net(axis=(1,), keep_dims=True)
 09 x = Tensor(np.random.randn(1, 2, 3, 4, 5, 6, 7, 8, 9), mindspore.float32)
 10 out = net(x)
 11 print("out shape: ", out.shape)

1.2.2 Error reporting

The error message here is as follows:

Traceback (most recent call last):
  File "test.py", line 18, in <module>
    out = net(x)
  File "/root/archiconda3/envs/lh37_ascend/lib/python3.7/site-packages/mindspore/nn/cell.py", line 574, in __call__
    out = self.compile_and_run(*args)
  File "/root/archiconda3/envs/lh37_ascend/lib/python3.7/site-packages/mindspore/nn/cell.py", line 975, in compile_and_run
    self.compile(*inputs)
  File "/root/archiconda3/envs/lh37_ascend/lib/python3.7/site-packages/mindspore/nn/cell.py", line 948, in compile
    jit_config_dict=self._jit_config_dict)
  File "/root/archiconda3/envs/lh37_ascend/lib/python3.7/site-packages/mindspore/common/api.py", line 1092, in compile
    result = self._graph_executor.compile(obj, args_list, phase, self._use_vm_mode())
RuntimeError: Single op compile failed, op: reduce_mean_d_1629966128061146056_6
 except_msg: 2022-07-15 01:36:29.720449: Query except_msg:Traceback (most recent call last):
  File "/root/archiconda3/envs/lh37_ascend/lib/python3.7/site-packages/te_fusion/parallel_compilation.py", line 1469, in run
    relation_param=self._relation_param)
  File "/root/archiconda3/envs/lh37_ascend/lib/python3.7/site-packages/te_fusion/fusion_manager.py", line 1283, in build_single_op
    compile_info = call_op()
  File "/root/archiconda3/envs/lh37_ascend/lib/python3.7/site-packages/te_fusion/fusion_manager.py", line 1270, in call_op
    opfunc(*inputs, *outputs, *new_attrs, **kwargs)
  File "/root/archiconda3/envs/lh37_ascend/lib/python3.7/site-packages/tbe/common/utils/para_check.py", line 537, in _in_wrapper
    formal_parameter_list[i][1], op_name)
  File "/root/archiconda3/envs/lh37_ascend/lib/python3.7/site-packages/tbe/common/utils/para_check.py", line 516, in _check_one_op_param
    _check_input(op_param, param_name, param_type, op_name)
  File "/root/archiconda3/envs/lh37_ascend/lib/python3.7/site-packages/tbe/common/utils/para_check.py", line 299, in _check_input
    _check_input_output_dict(op_param, param_name, op_name)
  File "/root/archiconda3/envs/lh37_ascend/lib/python3.7/site-packages/tbe/common/utils/para_check.py", line 223, in _check_input_output_dict
    param_name=param_name)
  File "/root/archiconda3/envs/lh37_ascend/lib/python3.7/site-packages/tbe/common/utils/para_check.py", line 689, in check_shape
    _check_shape_range(max_rank, min_rank, param_name, shape)
  File "/root/archiconda3/envs/lh37_ascend/lib/python3.7/site-packages/tbe/common/utils/para_check.py", line 727, in _check_shape_range
    % (error_info['param_name'], min_rank, max_rank, len(shape)))
RuntimeError: ({'errCode': 'E80012', 'op_name': 'reduce_mean_d', 'param_name': 'input_x', 'min_value': 0, 'max_value': 8, 'real_value': 9}, 'In op, the num of dimensions of input/output[input_x] should be inthe range of [0, 8], but actually is [9].')

Cause Analysis

Let’s look at the error message. In RuntimeError, ‘In op, the num of dimensions of input/output[input_x] should be in the range of [0, 8], but actually is [9].’ means that the input of ReduceMean is a dimension It should be greater than or equal to 0 and less than or equal to 8, but the actual value is 9, which obviously exceeds the dimension supported by the ReduceMean operator. In the official website, ReduceSum also made a description of the input dimension limitation:

2 Solutions

For the reasons known above, it is easy to make the following modifications:

 01 class Net(nn.Cell):
 02     def __init__(self, axis, keep_dims):
 03         super().__init__()
 04         self.reducemean = ops.ReduceMean(keep_dims=keep_dims)
 05         self.axis = axis
 06     def construct(self, input_x):
 07         return self.reducemean(input_x, self.axis)
 08 net = Net(axis=(1,), keep_dims=True)
 09 x = Tensor(np.random.randn(2, 3, 4, 5, 6, 7, 8, 9), mindspore.float32)
 10 out = net(x)
 11 print("out shape: ", out.shape)

At this point, the execution is successful, and the output is as follows:

out shape: (2, 1, 4, 5, 6, 7, 8, 9)

3 Summary

Steps to locate the error report:
1. Find the user code line that reports the error: out = net(x);
2. According to the keywords in the log error message, narrow the scope of the analysis problem: should be in the range of [0, 8] , but actually is [10];
3. Focus on the correctness of variable definition and initialization.

ProgrammerAH

Programmer Guide, Tips and Tutorial

[Solved] MindSpore Error: ReduceMean in the Ascend environment does not support inputs of 8 or more dimensions