[Solved] MindSpore Error: TypeError: For ‘TopK’, the type of ‘x’ should be…

Leave a reply

1 Error description

1.1 System Environment

Hardware Environment(Ascend/GPU/CPU): CPU
Software Environment:
– MindSpore version (source or binary): 1.8.0
– Python version (e.g., Python 3.7.5): 3.7.6
– OS platform and distribution (e.g., 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 an example of computing the softmax cross-entropy of two variables by building a single-operator network of SoftmaxCrossEntropyWithLogits. The script is as follows:

 01 class Net(nn.Cell):
 02     def __init__(self):
 03         super(Net, self).__init__()
 04         self.topk = ops.TopK(sorted=False)
 05 
 06     def construct(self, x, k):
 07         output = self.topk(x, k)
 08         return output
 09
 10 net = Net()
 11 x = Tensor(([[5, 2, 3, 3, 5], [5, 2, 9, 3, 5]]), mindspore.double)
 12 k = 5
 13 values, indices = net(x, k)
 14 print(values, indices)

1.2.2 Error reporting

The error message here is as follows:

Traceback (most recent call last):
  File "C:/Users/l30026544/PycharmProjects/q2_map/new/I4H30H.py", line 21, in <module>
    values, indices = net(x, k)
  File "C:\Users\l30026544\PycharmProjects\q2_map\lib\site-packages\mindspore\nn\cell.py", line 586, in __call__
    out = self.compile_and_run(*args)
  File "C:\Users\l30026544\PycharmProjects\q2_map\lib\site-packages\mindspore\nn\cell.py", line 964, in compile_and_run
    self.compile(*inputs)
  File "C:\Users\l30026544\PycharmProjects\q2_map\lib\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 "C:\Users\l30026544\PycharmProjects\q2_map\lib\site-packages\mindspore\common\api.py", line 1006, in compile
    result = self._graph_executor.compile(obj, args_list, phase, self._use_vm_mode())
  File "C:\Users\l30026544\PycharmProjects\q2_map\lib\site-packages\mindspore\ops\operations\nn_ops.py", line 2178, in __infer__
    validator.check_tensor_dtype_valid('x', x_dtype, valid_dtypes, self.name)
  File "C:\Users\l30026544\PycharmProjects\q2_map\lib\site-packages\mindspore\_checkparam.py", line 541, in check_tensor_dtype_valid
    Validator.check_subclass(arg_name, arg_type, tensor_types, prim_name)
  File "C:\Users\l30026544\PycharmProjects\q2_map\lib\site-packages\mindspore\_checkparam.py", line 493, in check_subclass
    raise TypeError(f"For '{prim_name}', the type of '{arg_name}'"
TypeError: For 'TopK', the type of 'x' should be one of Tensor[Int32], Tensor[Float16], Tensor[Float32], but got Tensor[Float64] . The supported data types depend on the hardware that executes the operator, please refer the official api document to get more information about the data type.
WARNING: Logging before InitGoogleLogging() is written to STDERR
[WARNING] UTILS(11576,1,?):2022-6-25 8:31:24 [mindspore\ccsrc\utils\comm_manager.cc:78] GetInstance] CommManager instance for CPU not found, return default instance.
[ERROR] ANALYZER(11576,1,?):2022-6-25 8:31:24 [mindspore\ccsrc\pipeline\jit\static_analysis\async_eval_result.cc:66] HandleException] Exception happened, check the information as below.

The function call stack (See file 'C:\Users\l30026544\PycharmProjects\q2_map\new\rank_0\om/analyze_fail.dat' for more details):
# 0 In file C:/Users/l30026544/PycharmProjects/q2_map/new/I4H30H.py(15)
        output = self.topk(x, k)
                 ^

Cause Analysis

Let’s look at the error message. In TypeError, write For ‘TopK’, the type of ‘x’ should be one of Tensor[Int32], Tensor[Float16], Tensor[Float32], but got Tensor[Float64], which means For TopK, the input type must be int32, float16 or float32, and the actual result is float64. Locate to the xth line of the code and find that the data type is indeed float64. The solution is to reduce the data precision.

2 Solutions

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

 01 class Net(nn.Cell):
 02     def __init__(self):
 03         super(Net, self).__init__()
 04         self.topk = ops.TopK(sorted=False)
 05 
 06     def construct(self, x, k):
 07         output = self.topk(x, k)
 08         return output
 09
 10 net = Net()
 11 x = Tensor(([[5, 2, 3, 3, 5], [5, 2, 9, 3, 5]]), mindspore.float32)
 12 k = 5
 13 values, indices = net(x, k)
 14 print(values, indices)

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

[[5. 2. 3. 3. 5.]
[5. 2. 9. 3. 5.]] [[0 1 2 3 4]
[0 1 2 3 4]]

3 Summary

Steps to locate the error report:

1. Find the line of user code that reports the error: output = self.topk(x, k) ;

2. According to the keywords in the log error message, narrow the scope of the analysis problem For ‘TopK’, the type of ‘x’ should be one of Tensor[Int32], Tensor[Float16], Tensor[Float32]  ;

3. It is necessary to focus on the correctness of variable definition and initialization.

Read More: