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aclnnUpsampleNearest3dBackward

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Function

  • Description: backward computation of aclnnUpsampleNearest3d.
  • Corresponding formula:gradInput(N,C,D,H,W)+=gradOut(N,C,ceil(scalesD∗D),ceil(scalesH∗H),ceil(scalesW∗W))gradInput(N, C, D, H, W) += gradOut( N, C, ceil ( scalesD * D ), ceil ( scalesH * H ), ceil ( scalesW * W ))

Function Prototype

Each operator is divided into a two-phase API. You must first call the aclnnUpsampleNearest3dBackwardGetWorkspaceSize API to obtain the input parameters and calculate the required workspace size based on the calculation process, and then call the aclnnUpsampleNearest3dBackward API to perform the computation.

aclnnStatus aclnnUpsampleNearest3dBackwardGetWorkspaceSize(
  const aclTensor   *gradOut,
  const aclIntArray *outputSize,
  const aclIntArray *inputSize,
  double             scalesD,
  double             scalesH,
  double             scalesW,
  aclTensor         *gradInput,
  uint64_t          *workspaceSize,
  aclOpExecutor    **executor)
aclnnStatus aclnnUpsampleNearest3dBackward(
  void          *workspace,
  uint64_t       workspaceSize,
  aclOpExecutor *executor,
  aclrtStream    stream)

aclnnUpsampleNearest3dBackwardGetWorkspaceSize

  • Parameters

    Parameter Name Input/Output Description Usage Notes Data Type Data Format Dimension (shape) Non-contiguous Tensor
    gradOut (aclTensor*) Input Represents the gradient Tensor for backward computation, corresponding to `gradOut` in the formula. Empty Tensor is not supported. FLOAT32, FLOAT16, DOUBLE, BFLOAT16 NCDHW, NDHWC 5 √
    outputSize (aclIntArray*) Input Represents the spatial size of the input `gradOut` in the D, H, and W dimensions. The size is 3, and each element is greater than zero. INT64 - - -
    inputSize (aclIntArray*) Input Represents the spatial size of the output `gradInput`.
    • The size is 5, and the last two elements are both greater than zero.
    • When the data format of the input `gradOut` is NCDHW, it represents the spatial size of the output `gradInput` in the N, C, D, H, and W dimensions respectively; when the data format of the input `gradOut` is NDHWC, it represents the spatial size of the output `gradInput` in the N, D, H, W, and C dimensions respectively.
    		 INT64 - - -
    scalesD (double) Input Represents the depth dimension multiplier of the output `gradInput`, corresponding to `scalesD` in the corresponding formula. - - - - -
    scalesH (double) Input Represents the height dimension multiplier of the output `gradInput`, corresponding to `scalesH` in the corresponding formula. - - - - -
    scalesW (double) Input Represents the width dimension multiplier of the output `gradInput`, corresponding to `scalesW` in the corresponding formula. - - - - -
    gradInput (aclTensor*) Output Represents the output tensor of the backward computation, corresponding to `gradInput` in the formula.
    • Empty tensors are not supported.
    • The data type and data format are consistent with the input parameter `gradOut`.
    		 FLOAT32, FLOAT16, DOUBLE, BFLOAT16 NCDHW, NDHWC 5 √
    workspaceSize (uint64_t*) Output Returns the workspace size that needs to be applied on the Device side. - - - - -
    executor (aclOpExecutor**) Output Returns the op executor, which contains the operator computation process. - - - - -

  • Return Value

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

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

    Return Code Error Code Description
    ACLNN_ERR_PARAM_NULLPTR 161001 If the input parameter is a mandatory input, output, or mandatory attribute, and is a null pointer.
    ACLNN_ERR_PARAM_INVALID 161002 The data type of gradOut is not within the supported range.
    The data types of gradOut and gradInput are inconsistent.
    The dimension of gradOut is not 5.
    The size of outputSize is not equal to 3.
    An element value of outputSize is not greater than 0.
    The size of inputSize is not equal to 5.
    An element value of inputSize is not greater than 0.
    The size of gradOut in the D, H, and W dimensions is inconsistent with outputSize[0], outputSize[1], and outputSize[2].
    The size of gradInput in the N and C dimensions is inconsistent with inputSize[0] and inputSize[1].
    The size of gradInput in the D, H, and W dimensions is inconsistent with inputSize[2], inputSize[3], and inputSize[4].

aclnnUpsampleNearest3dBackward

  • Parameters

    Parameter Name Input/Output Description
    workspace Input Workspace memory address applied on the Device side.
    workspaceSize Input Workspace size applied on the Device side, obtained through the first-phase API aclnnUpsampleNearest3dBackwardGetWorkspaceSize.
    executor Input Operator executor, which contains the operator computation process.
    stream Input Specifies the stream for executing the task.

  • Return Value

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

Constraints

  • Shape constraints for parameters gradOut and gradInput:

    • The value of each dimension must be less than or equal to 2^20.
    • The N axis and C axis of parameter gradInput must be consistent with those of gradOut.
    • The memory usage must be less than 60 GB. The formula for calculating memory usage is as follows:N∗C∗(gradOut_D∗gradOut_H∗gradOut_W+gradInput_D∗gradInput_H∗gradInput_W+gradOut_D∗gradOut_H∗gradInput_W+gradOut_D∗gradInput_H∗gradInput_W)∗sizeof(float)<60∗1024∗1024∗1024N * C * (gradOut\_D * gradOut\_H * gradOut\_W + gradInput\_D * gradInput\_H * gradInput\_W + gradOut\_D * gradOut\_H * gradInput\_W + gradOut\_D * gradInput\_H * gradInput\_W) * sizeof(float) < 60 * 1024 * 1024 * 1024 Where:
      • N represents the N axis of the input and output.
      • C represents the C axis of the input and output.
    • N * C * gradOut_D * gradOut_H < 2^31
    • gradInput_W * gradInput_H < 2^31

  • When the data format of the gradOut and gradInput parameters is not NCDHW or NDHWC, other input data formats are processed as NCDHW by default.

  • The inputSize, outputSize, scalesD, scalesH, and scalesW parameters must satisfy the following constraints:

    outputSize_D=floor(inputSize_D∗scalesD)outputSize\_D = floor(inputSize\_D * scalesD) outputSize_H=floor(inputSize_H∗scalesH)outputSize\_H = floor(inputSize\_H * scalesH) outputSize_W=floor(inputSize_W∗scalesW)outputSize\_W = floor(inputSize\_W * scalesW)

  • Deterministic computation:

    • aclnnUpsampleNearest3dBackward uses deterministic implementation by default.

Example

The sample code is as follows, for reference only. For specific compilation and execution procedures, see Compile and Run Samples.

#include <iostream>
#include <vector>
#include "acl/acl.h"
#include "aclnnop/aclnn_upsample_nearest_3d_backward.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 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 memory on the device side.
    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 data from the host side to the device 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);

    // Calculate the strides of a 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_NCDHW, shape.data(),
        shape.size(), *deviceAddr);
    return 0;
}

int main()
{
    // 1. (Boilerplate) Initialize the device/stream. See the ACL API manual.
    // Fill in the deviceId based on your actual device.
    int32_t deviceId = 0;
    aclrtStream stream;
    auto ret = Init(deviceId, &stream);
    // Handle the check based on your needs.
    CHECK_RET(ret == 0, LOG_PRINT("Init acl failed. ERROR: %d\n", ret); return ret);
    // 2. Construct the input and output. Customize the construction based on the API.
    std::vector<int64_t> gradOutShape = {2, 2, 2, 2, 2};
    std::vector<int64_t> gradInputShape = {2, 2, 1, 1, 1};
    void* gradOutDeviceAddr = nullptr;
    void* gradInputDeviceAddr = nullptr;
    aclTensor* gradOut = nullptr;
    aclTensor* gradInput = nullptr;
    std::vector<float> gradOutHostData = {1,  2,  3,  4,  5,  6,  7,  8,  9,  10, 11, 12, 13, 14, 15, 16,
                                          17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32};
    std::vector<float> gradInputHostData = {2.0, 2, 2, 2};
    std::vector<int64_t> outputSizeData = {2, 2, 2};
    std::vector<int64_t> inputSizeData = {2, 2, 1, 1, 1};
    double scalesD = 0.0;
    double scalesH = 0.0;
    double scalesW = 0.0;
    // Create the gradOut aclTensor.
    ret = CreateAclTensor(gradOutHostData, gradOutShape, &gradOutDeviceAddr, aclDataType::ACL_FLOAT, &gradOut);
    CHECK_RET(ret == ACL_SUCCESS, return ret);
    // Create the gradInput aclTensor.
    ret = CreateAclTensor(gradInputHostData, gradInputShape, &gradInputDeviceAddr, aclDataType::ACL_FLOAT, &gradInput);
    CHECK_RET(ret == ACL_SUCCESS, return ret);

    const aclIntArray* outputSize = aclCreateIntArray(outputSizeData.data(), outputSizeData.size());
    CHECK_RET(outputSize != nullptr, return ACL_ERROR_INTERNAL_ERROR);

    const aclIntArray* inputSize = aclCreateIntArray(inputSizeData.data(), inputSizeData.size());
    CHECK_RET(inputSize != nullptr, return ACL_ERROR_INTERNAL_ERROR);

    // 3. Call the CANN operator library API. Modify it to the specific API.
    uint64_t workspaceSize = 0;
    aclOpExecutor* executor;
    // Call the first-phase API of aclnnUpsampleNearest3dBackward.
    ret = aclnnUpsampleNearest3dBackwardGetWorkspaceSize(
        gradOut, outputSize, inputSize, scalesD, scalesH, scalesW, gradInput, &workspaceSize, &executor);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnUpsampleNearest3dBackwardGetWorkspaceSize failed. ERROR: %d\n", ret);
              return ret);
    // Apply for 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 aclnnUpsampleNearest3dBackward.
    ret = aclnnUpsampleNearest3dBackward(workspaceAddr, workspaceSize, executor, stream);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnUpsampleNearest3dBackward failed. ERROR: %d\n", ret); return ret);
    // 4. (Boilerplate) Wait synchronously for the task execution 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 based on the specific API definition.
    auto size = GetShapeSize(gradInputShape);
    std::vector<float> resultData(size, 0);
    ret = aclrtMemcpy(
        resultData.data(), resultData.size() * sizeof(resultData[0]), gradInputDeviceAddr, 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(gradOut);
    aclDestroyTensor(gradInput);

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