aclnnReplicationPad3d

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Applicable Products

Function

• Description: Uses input boundary padding to pad the last three dimensions of the input tensor.

• Example:

  Input tensor([[[[[0,1],
                [2,3]],
                [[4,5],
                [6,7]]]]])
  padding([1,1,1,1,1,1])
  The output is
  ([[[[[0,0,1,1],
  [0,0,1,1],
  [2,2,3,3],
  [2,2,3,3]],
  [[0,0,1,1],
  [0,0,1,1],
  [2,2,3,3],
  [2,2,3,3]],
  [[4,4,5,5],
  [4,4,5,5],
  [6,6,7,7],
  [6,6,7,7]],
  [[4,4,5,5],
  [4,4,5,5],
  [6,6,7,7],
  [6,6,7,7]]]]])

Function Prototype

Each operator is divided into a two-phase API. The "aclnnReplicationPad3dGetWorkspaceSize" API must be called first to obtain the workspace size required for computation and the executor that encapsulates the operator computation flow. Then, the "aclnnReplicationPad3d" API is called to perform the computation.

aclnnStatus aclnnReplicationPad3dGetWorkspaceSize(
    const aclTensor*   self,
    const aclIntArray* padding,
    aclTensor*         out,
    uint64_t*          workspaceSize,
    aclOpExecutor**    executor)

aclnnStatus aclnnReplicationPad3d(
    void*          workspace,
    uint64_t       workspaceSize,
    aclOpExecutor* executor,
    aclrtStream    stream)

aclnnReplicationPad3dGetWorkspaceSize

• Parameters

  Parameter	Input/Output	Description	Instruction	Data Type	Data Format	Dimension (shape)	Non-Contiguous Tensor
  self (aclTensor*)	Input	The original input data to be padded.	-	BOOL, INT8, UINT8, INT16, UINT16, FLOAT16, BFLOAT16, INT32, UINT32, FLOAT32, INT64, UINT64, DOUBLE, COMPLEX64, COMPLEX128, HIFLOAT8, FLOAT8_E5M2, FLOAT8_E4M3FN, FLOAT8_E8M0	ND	4-5	√
  padding (aclIntArray*)	Input	Padding size to be applied to the input.	The length is 6, and the values represent the padding amounts for left, right, top, bottom, front, and back, respectively.	-	-	-	-
  out (aclTensor*)	Output	Output result after padding.	The dtype must be the same as self. In the output shape, all dimensions except the last three padded dimensions must be consistent. Among the last three padded dimensions, the value of the third-to-last dimension of out equals the value of the third-to-last dimension of self plus the last two values of padding; the value of the second-to-last dimension of out equals the value of the second-to-last dimension of self plus the middle two values of padding; the value of the last dimension of out equals the value of the last dimension of self plus the first two values of padding.	Same as self.	ND	4-5	√
  workspaceSize (uint64_t*)	Output	Returns the workspace size to be applied for on the Device side.	-	-	-	-	-
  executor (aclOpExecutor**)	Output	Returns the op executor, which contains the operator computation flow.	-	-	-	-	-

  • Atlas A3 training products/Atlas A3 inference products, Atlas A2 training products/Atlas A2 inference products, Atlas inference products, Atlas training products: The data types BOOL, UINT16, UINT32, UINT64, HIFLOAT8, FLOAT8_E5M2, FLOAT8_E4M3FN, and FLOAT8_E8M0 are not supported.

• Return Value

  aclnnStatus: return code. For details, see aclnn Return Codes.

  The first-phase API performs input parameter validation and returns an error in the following scenarios:

  Return Value	Error Code	Description
  ACLNN_ERR_PARAM_NULLPTR	161001	The input self, padding, or out is a null pointer.
  ACLNN_ERR_PARAM_INVALID	161002	The data type or data format of self or out is not within the supported range.
  		The data types of self and out are inconsistent.
  		The input shapes of self, padding, and out are outside the supported range.
  		self or out is an empty tensor, and the last three dimensions of self contain zeros.
  		The padding value is greater than or equal to the corresponding dimension value of self.
  		The last three dimensions of out are not equal to the last three dimensions of self plus the corresponding padding.

aclnnReplicationPad3d

• Parameters

  Parameter	Input/Output	Description
  workspace	Input	Workspace memory address applied for on the Device side.
  workspaceSize	Input	Workspace size applied for on the Device side, obtained through the first-phase API aclnnReplicationPad3dGetWorkspaceSize.
  executor	Input	Operator executor, which contains the operator computation flow.
  stream	Input	Specifies the Stream for executing tasks.

• Return value

  aclnnStatus: return code. For details, see aclnn Return Codes.

Constraints

• Deterministic computation:

  • aclnnReplicationPad3d defaults to a deterministic implementation.

Example

The following provides example code for reference only. For details about compilation and running, see Compile and Run Samples.

#include "acl/acl.h"
#include "aclnnop/aclnn_replication_pad3d.h"
#include <iostream>
#include <vector>

#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 shape_size = 1;
    for (auto i : shape) {
        shape_size *= i;
    }
    return shape_size;
}

int Init(int32_t deviceId, aclrtStream* stream) {
    // Boilerplate: resource initialization.
    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 device-side memory.
    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 host-side data to device-side memory.
    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);

    // Compute the strides of the contiguous tensor.
    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 the aclCreateTensor API 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. Boilerplate: device/stream initialization. See the acl API manual.
    // Set deviceId based on the actual device.
    int32_t deviceId = 0;
    aclrtStream stream;
    auto ret = Init(deviceId, &stream);
    // Handle the check result as needed.
    CHECK_RET(ret == 0, LOG_PRINT("Init acl failed. ERROR: %d\n", ret); return ret);

    // 2. Construct the input and output based on the API definition.
    std::vector<int64_t> selfShape = {1, 1, 2, 2, 2};
    std::vector<int64_t> outShape = {1, 1, 4, 4, 4};
    void* selfDeviceAddr = nullptr;
    void* outDeviceAddr = nullptr;
    aclTensor* self = nullptr;
    aclIntArray* padding = nullptr;
    aclTensor* out = nullptr;

    std::vector<float> selfHostData = {0, 1, 2, 3, 4, 5, 6, 7};
    std::vector<int64_t> paddingData = {1, 1, 1, 1, 1, 1};
    std::vector<float> outHostData(GetShapeSize(outShape) * 2, 0);

    // Create the self aclTensor.
    ret = CreateAclTensor(selfHostData, selfShape, &selfDeviceAddr, aclDataType::ACL_FLOAT, &self);
    CHECK_RET(ret == ACL_SUCCESS, return ret);
    // Create the padding aclIntArray.
    padding = aclCreateIntArray(paddingData.data(), 6);
    CHECK_RET(padding != nullptr, return ret);
    // Create the out aclTensor.
    ret = CreateAclTensor(outHostData, outShape, &outDeviceAddr, aclDataType::ACL_FLOAT, &out);
    CHECK_RET(ret == ACL_SUCCESS, return ret);

    // 3. Call the CANN operator library API. Replace with the specific API.
    uint64_t workspaceSize = 0;
    aclOpExecutor* executor;
    // Call the first-phase API of aclnnReplicationPad3d.
    ret = aclnnReplicationPad3dGetWorkspaceSize(self, padding, out, &workspaceSize, &executor);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnReplicationPad3dGetWorkspaceSize failed. ERROR: %d\n", ret); return ret);
    // Allocate device memory based on the workspaceSize calculated 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 aclnnReplicationPad3d.
    ret = aclnnReplicationPad3d(workspaceAddr, workspaceSize, executor, stream);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnReplicationPad3d failed. ERROR: %d\n", ret); return ret);

    // 4. Boilerplate: synchronize and wait for the task to 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 memory. Modify this based on the specific API definition.
    auto size = GetShapeSize(outShape);
    std::vector<float> resultData(size, 0);
    ret = aclrtMemcpy(resultData.data(), resultData.size() * sizeof(resultData[0]), outDeviceAddr, size * sizeof(float),
                      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 and aclScalar. Modify based on the specific API definition.
    aclDestroyTensor(self);
    aclDestroyIntArray(padding);
    aclDestroyTensor(out);

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