[ERROR] KERNEL(3565036,fffecb7fe070,python):2024-05-12-13:59:49.913.395 [mindspore/ccsrc/plugin/device/ascend/kernel/acl/acl_kernel_mod.cc:261] Launch] Kernel launch failed, msg: Acl compile and execute failed, op_type_:CustFFTWithSize

----------------------------------------------------
- Ascend Error Message:
----------------------------------------------------
E19014: 2024-05-12-13:59:49.909.944 Value [input __input0 shape] for Op [online_Node_Output] is invalid. Reason: contains negative or zero dimension.
        TraceBack (most recent call last):
        online_Node_Output(NetOutput) Verify failed.[FUNC:Verify][FILE:node_utils_ex.cc][LINE:127]
        Verifying online_Node_Output failed.[FUNC:InferShapeAndType][FILE:infershape_pass.cc][LINE:132]
        Call InferShapeAndType for node:online_Node_Output(NetOutput) failed[FUNC:Infer][FILE:infershape_pass.cc][LINE:120]
        process pass InferShapePass on node:online_Node_Output failed, ret:4294967295[FUNC:RunPassesOnNode][FILE:base_pass.cc][LINE:570]
        build graph failed, graph id:2, ret:1343242270[FUNC:BuildModelWithGraphId][FILE:ge_generator.cc][LINE:1615]
        [Build][SingleOpModel]call ge interface generator.BuildSingleOpModel failed. ge result = 1343242270[FUNC:ReportCallError][FILE:log_inner.cpp][LINE:161]
        [Build][Op]Fail to build op model[FUNC:ReportInnerError][FILE:log_inner.cpp][LINE:145]
        build op model failed, result = 500002[FUNC:ReportInnerError][FILE:log_inner.cpp][LINE:145]

(Please search "CANN Common Error Analysis" at https://www.mindspore.cn for error code description)

----------------------------------------------------
- C++ Call Stack: (For framework developers)
----------------------------------------------------
mindspore/ccsrc/transform/acl_ir/acl_utils.cc:379 Run

[ERROR] DEVICE(3565036,fffecb7fe070,python):2024-05-12-13:59:49.913.441 [mindspore/ccsrc/plugin/device/ascend/hal/hardware/ge_kernel_executor.cc:951] LaunchKernel] Launch kernel failed, kernel full name: Default/FFTWithSize-op2
FAILED

=================================== FAILURES ===================================
___________________________ test_p_fftwithsize_irfft ___________________________

    @Level2
    def test_p_fftwithsize_irfft():
        mode = "irfft"
        onesided_list = random.sample([True, True, False, False], 3)
        for i, norm_mode in enumerate(['backward', 'forward', 'ortho']):
            # choice 1/2/3 oneside True/False
            onesided = onesided_list[i]
            signal_ndim = signal_list[i]
            # choice complex64/128
            x_dtype = dtype_list[i]
            shape_rank = np.random.randint(signal_ndim, 8)
            x_shape = np.random.randint(2, 11, shape_rank)
            x, input_loss, output_loss = get_x(mode, norm_mode, x_dtype, x_shape)
            norm = get_norm_(mode, signal_ndim, norm_mode, x_shape, onesided)
            cmp_obj = get_cmp_(mode, signal_ndim, onesided)
    
            fact = FFTWithSizeMock(attributes={
                "signal_ndim": signal_ndim,
                "inverse": True,
                "real": True,
                "norm": norm_mode,
                "onesided": onesided,
                'signal_sizes': (),
            },
                inputs=[x])
    
            info_str = "signal_ndim={},norm_mode={},x_shape={},x_dtype={},cmp_obj={},loss={}".format(
                signal_ndim, norm_mode, x_shape, x_dtype, cmp_obj, [input_loss, output_loss])
            logger.info(info_str)
            # tf not support IRFFT 3d gradien
            if signal_ndim == 3:
                fact.loss = 1e-3
>               fact.forward_cmp(cmp_obj, norm)

../test_fftwithsize.py:343: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../../share/ops/primitive/fftwithsize_ops.py:173: in forward_cmp
    out_mindspore = self.forward_mindspore_impl()
../../share/ops/primitive/fftwithsize_ops.py:168: in forward_mindspore_impl
    out = net(self.input_x)
../../share/utils.py:253: in __call__
    out = super().__call__(*args, **kwargs)
/home/ci/miniconda3/envs/ci3.9/lib/python3.9/site-packages/mindspore/nn/cell.py:713: in __call__
    raise err
/home/ci/miniconda3/envs/ci3.9/lib/python3.9/site-packages/mindspore/nn/cell.py:710: in __call__
    _pynative_executor.end_graph(self, output, *args, **kwargs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <mindspore.common.api._PyNativeExecutor object at 0xfffe32436b80>
obj = WrapOp<>
output = Tensor(shape=[8, 9, 6, 10, 8, 3, 12], dtype=Float32, value=
[[[[[[[ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00 ....
      [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00 ...  0.00000000e+00,  0.00000000e+00,  0.00000000e+00]]]]]]])
args = (Tensor(shape=[8, 9, 6, 10, 8, 3, 7], dtype=Complex64, value=
[[[[[[[0.848174+1.85267j, -0.303879-0.211966j, 0.290996+...42+0.577635j, 1.68219-1.23387j, -0.836732+0.131673j ... 0.795906-0.647839j, 0.705827+1.48806j, 1.6735+1.1798j]]]]]]]),)
kwargs = {}

    def end_graph(self, obj, output, *args, **kwargs):
        """
        Clean resources after building forward and backward graph.
    
        Args:
            obj (Function/Cell): The function or cell instance.
            output (Tensor/tuple/list): Function or cell output object.
            args (tuple): Function or cell input arguments.
            kwargs (dict): keyword arguments.
    
        Return:
            None.
        """
>       self._executor.end_graph(obj, output, *args, *(kwargs.values()))
E       RuntimeError: Launch kernel failed, name:Default/FFTWithSize-op2
E       
E       ----------------------------------------------------
E       - C++ Call Stack: (For framework developers)
E       ----------------------------------------------------
E       mindspore/ccsrc/runtime/pynative/op_runner.cc:632 LaunchKernels

/home/ci/miniconda3/envs/ci3.9/lib/python3.9/site-packages/mindspore/common/api.py:1303: RuntimeError