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aclnnNeTensor&aclnnInplaceNeTensor

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Supported Products

ProductSupported
Atlas A3 training products/Atlas A3 inference products√
Atlas A2 training products/Atlas A2 inference products√
Atlas 200I/500 A2 inference products×
Atlas inference products×
Atlas training products√

Function

  • Operator function: Computes whether the element value in self (selfRef) is not equal to that of other.
  • Formula:
outi​=(selfi≠otheri)?[1]:[0]out_i​=(self_i \ne other_i)?[1]:[0] selfRefi​=(selfRefi ≠otheri) ? [1]:[0]selfRef_i​=(selfRef_i\ \ne other_i)\ ?\ [1]:[0]

Prototype

  • aclnnNeTensor and aclnnInplaceNeTensor implement the same function in different ways. Select a proper operator based on your requirements.
    • aclnnNeTensor: An output tensor object needs to be created to store the computation result.
    • aclnnInplaceNeTensor: No output tensor object needs to be created, and the computation result is stored in the memory of the input tensor.
  • Each operator has two-phase API calls. First, aclnnNeTensorGetWorkspaceSize or aclnnInplaceNeTensorGetWorkspaceSize is called to obtain input parameters and compute the required workspace size based on the computation process. Then, aclnnNeTensor or aclnnInplaceNeTensor is called to perform computation.
    • aclnnStatus aclnnNeTensorGetWorkspaceSize(const aclTensor *self, const aclTensor *other, aclTensor *out, uint64_t *workspaceSize, aclOpExecutor **executor)
    • aclnnStatus aclnnNeTensor(void *workspace, uint64_t workspaceSize, aclOpExecutor *executor, aclrtStream stream)
    • aclnnStatus aclnnInplaceNeTensorGetWorkspaceSize(aclTensor *selfRef, const aclTensor *other, uint64_t *workspaceSize, aclOpExecutor **executor)
    • aclnnStatus aclnnInplaceNeTensor(void *workspace, uint64_t workspaceSize, aclOpExecutor *executor, aclrtStream stream)

aclnnNeTensorGetWorkspaceSize

  • Parameters:

    • self (aclTensor*, computation input): self in the formula, which is an aclTensor on the device. The data type must meet the data type deduction rules with other (see Deduction Relationship). The shape must meet the broadcast relationship with other. The shape dimensions cannot exceed 8. Non-contiguous tensors are supported. The data format can be ND. UINT64.
      • Atlas A2 training products/Atlas A2 inference products and Atlas A3 training products/Atlas A3 inference products: The data type can be DOUBLE, FLOAT16, FLOAT, BFLOAT16, INT64, INT32, INT8, UINT8, BOOL, INT16, COMPLEX64, or COMPLEX128.
      • Atlas training products: The data type can be DOUBLE, FLOAT16, FLOAT, INT64, INT32, INT8, UINT8, BOOL, INT16, COMPLEX64, or COMPLEX128.
    • other (aclTensor*, computation input): other in the formula, which is an aclTensor on the device. The data type must meet the data type deduction rules with self (see Deduction Relationship). The shape must meet the broadcast relationship with self. The shape dimensions cannot exceed 8. Non-contiguous tensors are supported. The data format can be ND.
      • Atlas A2 training products/Atlas A2 inference products and Atlas A3 training products/Atlas A3 inference products: The data type can be DOUBLE, FLOAT16, FLOAT, BFLOAT16, INT64, INT32, INT8, UINT8, BOOL, INT16, COMPLEX64, or COMPLEX128.
      • Atlas training products: The data type can be DOUBLE, FLOAT16, FLOAT, INT64, INT32, INT8, UINT8, BOOL, INT16, COMPLEX64, or COMPLEX128.
    • out (aclTensor*, computation output): out in the formula, which is an aclTensor on the device. The data type must be convertible to BOOL (see Conversion Relationship). The shape must be the same as the shape after self and other are broadcast (see Broadcast Relationship). The shape dimension cannot exceed 8. Non-contiguous tensors are supported. The data format can be ND.
      • Atlas A2 training products/Atlas A2 inference products and Atlas A3 training products/Atlas A3 inference products: The data type can be DOUBLE, FLOAT16, FLOAT, BFLOAT16, INT64, INT32, INT8, UINT8, BOOL, INT16, COMPLEX64, or COMPLEX128.
      • Atlas training products: The data type can be DOUBLE, FLOAT16, FLOAT, INT64, INT32, INT8, UINT8, BOOL, INT16, COMPLEX64, or COMPLEX128.
    • workspaceSize (uint64_t*, output): size of the workspace to be allocated on the device.
    • executor (aclOpExecutor**, output): operator executor, covering the operator computation process.

  • Returns: aclnnStatus: status code. For details, see aclnn Return Codes.

    The first-phase API implements input parameter verification. The following errors may be thrown.
161001 (ACLNN_ERR_PARAM_NULLPTR): 1. self, other, or out is a null pointer.
161002 (ACLNN_ERR_PARAM_INVALID): 1. The data type of self, other, or out is not supported.
                                      2. The dimensions of self, other, or out are greater than 8.
                                      3. Data type deduction cannot be performed for self and other.
                                      4. Broadcasting cannot be performed for the shapes of self and other.
                                      5. The shape of out before and after broadcasting is inconsistent.

aclnnNeTensor

  • Parameters:
    • workspace (void*, input): address of the workspace to be allocated on the device.
    • workspaceSize (uint64_t, input): size of the workspace to be allocated on the device, which is obtained by the first-phase API aclnnNeTensorGetWorkspaceSize.
    • executor (aclOpExecutor*, input): operator executor, containing the operator computation process.
    • stream (aclrtStream, input): stream for executing the task.
  • Returns: aclnnStatus: status code. For details, see aclnn Return Codes.

aclnnInplaceNeTensorGetWorkspaceSize

  • Parameters:

    • selfRef (aclTensor*, computation input/output): selfRef in the formula. The data type must meet the type deduction rules (see Deduction Relationship) with other. The shape must meet the broadcast relationship with other. Non-contiguous tensors are supported. The data format can be ND.
      • Atlas A2 training products/Atlas A2 inference products and Atlas A3 training products/Atlas A3 inference products: The data type can be DOUBLE, FLOAT16, FLOAT, BFLOAT16, INT64, INT32, INT8, UINT8, BOOL, INT16, COMPLEX64, or COMPLEX128.
      • Atlas training products: The data type can be DOUBLE, FLOAT16, FLOAT, INT64, INT32, INT8, UINT8, BOOL, INT16, COMPLEX64, or COMPLEX128.
    • other (aclTensor*, computation input): other in the formula The data type must meet the type deduction rules (see Deduction Relationship) with selfRef. The shape must meet the broadcast relationship with selfRef. Non-contiguous tensors are supported. The data format can be ND.
      • Atlas A2 training products/Atlas A2 inference products and Atlas A3 training products/Atlas A3 inference products: The data type can be DOUBLE, FLOAT16, FLOAT, BFLOAT16, INT64, INT32, INT8, UINT8, BOOL, INT16, COMPLEX64, or COMPLEX128.
      • Atlas training products: The data type can be DOUBLE, FLOAT16, FLOAT, INT64, INT32, INT8, UINT8, BOOL, INT16, COMPLEX64, or COMPLEX128.
    • workspaceSize (uint64_t*, output): size of the workspace to be allocated on the device.
    • executor (aclOpExecutor**, output): operator executor, covering the operator computation process.

  • Returns: aclnnStatus: status code. For details, see aclnn Return Codes.

The first-phase API implements input parameter verification. The following errors may be thrown.
161001 (ACLNN_ERR_PARAM_NULLPTR): 1. selfRef or other is a null pointer.
161002 (ACLNN_ERR_PARAM_INVALID): 1. The data types of selfRef and other are not supported.
                                      2. Data type deduction cannot be performed for selfRef and other.
                                      3. Broadcasting cannot be performed for the shapes of selfRef and other.
                                      4. The shapes of selfRef and other after broadcasting are different from that of selfRef.
                                      5. The dimensions of selfRef and other are greater than 8.

aclnnInplaceNeTensor

  • Parameters:
    • workspace (void*, input): address of the workspace to be allocated on the device.
    • workspaceSize (uint64_t, input): size of the workspace to be allocated on the device, which is obtained by the first-phase API aclnnInplaceNeTensorGetWorkspaceSize.
    • executor (aclOpExecutor*, input): operator executor, containing the operator computation process.
    • stream (aclrtStream, input): stream for executing the task.
  • Returns: aclnnStatus: status code. For details, see aclnn Return Codes.

Constraints

  • Deterministic computing:
    • aclnnNeTensor&aclnnInplaceNeTensor defaults to a deterministic implementation.

Example

The following example is for reference only. For details, see Compilation and Running Sample.

#include <iostream>
#include <vector>
#include "acl/acl.h"
#include "aclnnop/aclnn_ne_tensor.h"

#define CHECK_RET(cond, return_expr) \
    do {                             \
        if (!(cond)) {               \
            return_expr;             \
        }                            \
    } while (0)

#define LOG_PRINT(message, ...)         \
    do {                                \
        printf(message, ##__VA_ARGS__); \
    } while (0)

int64_t GetShapeSize(const std::vector<int64_t> &shape)
{
    int64_t shapeSize = 1;
    for (auto i : shape) {
        shapeSize *= i;
    }
    return shapeSize;
}

int Init(int32_t deviceId, aclrtStream *stream)
{
    // (Fixed writing) Initialize resources.
    auto ret = aclInit(nullptr);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclInit failed. ERROR: %d\n", ret); return ret);
    ret = aclrtSetDevice(deviceId);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtSetDevice failed. ERROR: %d\n", ret); return ret);
    ret = aclrtCreateStream(stream);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtCreateStream failed. ERROR: %d\n", ret); return ret);

    return 0;
}

template <typename T>
int CreateAclTensor(const std::vector<T> &hostData, const std::vector<int64_t> &shape, void **deviceAddr,
    aclDataType dataType, aclTensor **tensor)
{
    auto size = GetShapeSize(shape) * sizeof(T);
    // Call aclrtMalloc to allocate memory on the device.
    auto ret = aclrtMalloc(deviceAddr, size, ACL_MEM_MALLOC_HUGE_FIRST);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtMalloc failed. ERROR: %d\n", ret); return ret);
    // Call aclrtMemcpy to copy the data on the host to the memory on the device.
    ret = aclrtMemcpy(*deviceAddr, size, hostData.data(), size, ACL_MEMCPY_HOST_TO_DEVICE);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtMemcpy failed. ERROR: %d\n", ret); return ret);

    // Calculate the strides of consecutive tensors.
    std::vector<int64_t> strides(shape.size(), 1);
    for (int64_t i = shape.size() - 2; i >= 0; i--) {
        strides[i] = shape[i + 1] * strides[i + 1];
    }

    // Call aclCreateTensor to create an aclTensor.
    *tensor = aclCreateTensor(shape.data(),
        shape.size(),
        dataType,
        strides.data(),
        0,
        aclFormat::ACL_FORMAT_ND,
        shape.data(),
        shape.size(),
        *deviceAddr);
    return 0;
}

int main()
{
    // 1. (Fixed writing) Initialize the device and stream. For details, see the ACL API manual.
      // Set deviceId based on the actual device.
    int32_t deviceId = 0;
    aclrtStream stream;
    auto ret = Init(deviceId, &stream);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("Init acl failed. ERROR: %d\n", ret); return ret);
    // 2. Construct inputs and outputs based on the API definition.
    std::vector<int64_t> selfShape = {4, 2};
    std::vector<int64_t> otherShape = {4, 2};
    std::vector<int64_t> outShape = {4, 2};
    void *selfDeviceAddr = nullptr;
    void *otherDeviceAddr = nullptr;
    void *outDeviceAddr = nullptr;
    aclTensor *self = nullptr;
    aclTensor *other = nullptr;
    aclTensor *out = nullptr;
    std::vector<double> selfHostData = {0, 1, 2, 3, 4, 5, 6, 7};
    std::vector<double> otherHostData = {1, 1, 1, 2, 2, 2, 3, 3};
    std::vector<double> outHostData = {0, 0, 0, 0, 0, 0, 0, 0};

    // Create a self aclTensor.
    ret = CreateAclTensor(selfHostData, selfShape, &selfDeviceAddr, aclDataType::ACL_DOUBLE, &self);
    CHECK_RET(ret == ACL_SUCCESS, return ret);
    // Create an other aclTensor.
    ret = CreateAclTensor(otherHostData, otherShape, &otherDeviceAddr, aclDataType::ACL_DOUBLE, &other);
    CHECK_RET(ret == ACL_SUCCESS, return ret);
    // Create an out aclTensor.
    ret = CreateAclTensor(outHostData, outShape, &outDeviceAddr, aclDataType::ACL_DOUBLE, &out);
    CHECK_RET(ret == ACL_SUCCESS, return ret);

    // aclnnNeTensor API call example
    // 3. Call the CANN operator library API.
    uint64_t workspaceSize = 0;
    aclOpExecutor* executor;
    // Call the first-phase API of aclnnNeTensor.
    ret = aclnnNeTensorGetWorkspaceSize(self, other, out, &workspaceSize, &executor);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnNeTensorGetWorkspaceSize failed. ERROR: %d\n", ret); return ret);
    // Allocate device memory based on workspaceSize computed by the first-phase API.
    void* workspaceAddr = nullptr;
    if (workspaceSize > 0) {
      ret = aclrtMalloc(&workspaceAddr, workspaceSize, ACL_MEM_MALLOC_HUGE_FIRST);
      CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("allocate workspace failed. ERROR: %d\n", ret); return ret);
    }
    // Call the second-phase API of aclnnNeTensor.
    ret = aclnnNeTensor(workspaceAddr, workspaceSize, executor, stream);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnNeTensor failed. ERROR: %d\n", ret); return ret);

    // 4. (Fixed writing) Wait until the task execution is complete.
    ret = aclrtSynchronizeStream(stream);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtSynchronizeStream failed. ERROR: %d\n", ret); return ret);

    // 5. Obtain the output value and copy the result from the device memory to the host.
    auto size = GetShapeSize(outShape);
    std::vector<double> resultData(size, 0);
    ret = aclrtMemcpy(resultData.data(), resultData.size() * sizeof(resultData[0]), outDeviceAddr,
                      size * sizeof(resultData[0]), ACL_MEMCPY_DEVICE_TO_HOST);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("copy result from device to host failed. ERROR: %d\n", ret); return ret);
    for (int64_t i = 0; i < size; i++) {
        LOG_PRINT("result[%ld] is: %f\n", i, resultData[i]);
    }

    // aclnnInplaceNeTensor API call example
    // 3. Call the CANN operator library API.
    LOG_PRINT("\ntest aclnnInplaceNeTensor\n");
    // Call the first-phase API of aclnnInplaceNeTensor.
    ret = aclnnInplaceNeTensorGetWorkspaceSize(self, other, &workspaceSize, &executor);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnInplaceNeTensorGetWorkspaceSize failed. ERROR: %d\n", ret); return ret);
    // Allocate device memory based on workspaceSize computed by the first-phase API.
    if (workspaceSize > 0) {
        ret = aclrtMalloc(&workspaceAddr, workspaceSize, ACL_MEM_MALLOC_HUGE_FIRST);
        CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("allocate workspace failed. ERROR: %d\n", ret); return ret);
    }
    // Call the second-phase API of aclnnInplaceNeTensor.
    ret = aclnnInplaceNeTensor(workspaceAddr, workspaceSize, executor, stream);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnInplaceNeTensor failed. ERROR: %d\n", ret); return ret);
    // 4. (Fixed writing) Wait until the task execution is complete.
    ret = aclrtSynchronizeStream(stream);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtSynchronizeStream failed. ERROR: %d\n", ret); return ret);

    // 5. Obtain the output value and copy the result from the device to the host. Modify the code based on the API definition.
    ret = aclrtMemcpy(resultData.data(),
        resultData.size() * sizeof(resultData[0]), selfDeviceAddr,
        size * sizeof(resultData[0]),
        ACL_MEMCPY_DEVICE_TO_HOST);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("copy result from device to host failed. ERROR: %d\n", ret); return ret);
    for (int64_t i = 0; i < size; i++) {
        LOG_PRINT("result[%ld] is: %f\n", i, resultData[i]);
    }

    // 6. Release aclTensor. Modify the configuration based on the API definition.
    aclDestroyTensor(self);
    aclDestroyTensor(other);

    // 7. Release device resources. Modify the configuration based on the API definition.
    aclrtFree(selfDeviceAddr);
    aclrtFree(otherDeviceAddr);
    if (workspaceSize > 0) {
       aclrtFree(workspaceAddr);
    }
    aclrtDestroyStream(stream);
    aclrtResetDevice(deviceId);
    aclFinalize();
    return 0;
}
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