Why does [mindspore ascend] [custom operator] repeatedly assign values to one tensor affect another tensor?

1、 Enter a Tensor, Assign a value to ub And then from ub Assign values to form output . Between these two assignments , Another irrelevant... Has been operated many times Tensor, Will affect the previous Tensor Output result of .

2、 In file MAX_ITER = 1000000 When , The output is all 0

3、 In file MAX_ITER = 1 When , The output is correct

【 Screenshot information 】

error

correct

Code ：

import numpy as np

import mindspore as ms

import te

import topi

import te.tik



hfe = lambda x,y,eps=1e-2:np.max(np.abs(x-y)/(np.abs(x)+np.abs(y)+eps))

ms.context.set_context(mode=ms.context.GRAPH_MODE, device_target="Ascend")



cus_S0_op_info = ms.ops.TBERegOp("CusS0") \

    .fusion_type("OPAQUE") \

    .async_flag(False) \

    .binfile_name("S0.so") \

    .compute_cost(10) \

    .kernel_name("CusS0Impl") \

    .partial_flag(True) \

    .input(0, "input1", False, "required", "all") \

    .output(0, "output", False, "required", "all") \

    .dtype_format(ms.ops.DataType.F32_Default, ms.ops.DataType.F32_Default) \

    .dtype_format(ms.ops.DataType.F16_Default, ms.ops.DataType.F16_Default) \

    .get_op_info()





@ms.ops.op_info_register(cus_S0_op_info)

def CusS0Impl(input1, output, kernel_name="CusS0Impl"):

    tik_instance = te.tik.Tik()

    input_shape = input1.get("shape")

    M = input_shape[0]

    N = input_shape[1]

    bLength_N = ((N-1)//16)+1

    bLength = ((M*N-1)//16)+1



    MAX_ITER = 1000000



    input1 = tik_instance.Tensor("float16", (M,N), name="input1", scope=te.tik.scope_gm)

    output = tik_instance.Tensor("float16", (M,), name="output", scope=te.tik.scope_gm)

    input1_ub = tik_instance.Tensor("float16", (M,N), name="input1_ub", scope=te.tik.scope_ubuf)



    tik_instance.data_move(input1_ub, input1, 0, 1, bLength, 0, 0)



    tmp_vector = tik_instance.Tensor("float16", (M,), name="tmp_vector", scope=te.tik.scope_ubuf)

    repeat = tik_instance.Scalar('int32')

    r = ((M-1)//128)+1

    repeat.set_as(r)

    with tik_instance.for_range(0,MAX_ITER) as i:

        tik_instance.vec_dup(128, tmp_vector, 0.0, repeat, 8)



    tik_instance.data_move(output, input1_ub, 0, 1, bLength_N, 0, 0)

    tik_instance.BuildCCE(kernel_name=kernel_name,inputs=[input1],outputs=[output])





class CusS0(ms.ops.PrimitiveWithInfer):



    @ms.ops.prim_attr_register

    def __init__(self):

        self.CusS0Impl = CusS0Impl

        self.init_prim_io_names(inputs=['input1'], outputs=['output'])



    def infer_shape(self, input1):

        shape = [input1[1]]

        return shape



    def infer_dtype(self, input1):

        return input1





if __name__=='__main__':

    np0 = np.random.randn(128,128).astype(np.float16)

    op = CusS0()

    ret0 = op(ms.Tensor(np0)).asnumpy()

    print(np0)

    print(ret0)

No problem at all , Just doubt two scalar There may be a pit .

    repeat = tik_instance.Scalar('int32')

    r = ((M-1)//128)+1

    repeat.set_as(r)

    with tik_instance.for_range(0,MAX_ITER) as i:

        tik_instance.vec_dup(128, tmp_vector, 0.0, repeat, 8)

Try the following code ：

    repeat = tik_instance.Scalar('int32')

    repeat.set_as(1000000)

    with tik_instance.for_range(0,repeat) as i:

        tik_instance.vec_dup(128, tmp_vector, 0.0, 1, 8)