aclnnUpsampleNearestExact2dBackward

Applicable Products

Product	Supported
Ascend 950PR/Ascend 950DT	×
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	×

Description

Description: Backward propagation of aclnnUpsampleNearestExact2d.
Formula: $gradInput(N, C, floor ( scalesH * ( H + 0.5 )), floor ( scalesW * ( W+ 0.5 ))) += gradOutput( N, C, H ,W)$

Function Prototype

Each operator is divided into a two-phase API. You must first call the "aclnnUpsampleNearestExact2dBackwardGetWorkspaceSize" API to obtain the workspace size required for computation and the executor that contains the operator computation process, and then call the "aclnnUpsampleNearestExact2dBackward" API to execute the computation.

aclnnStatus aclnnUpsampleNearestExact2dBackwardGetWorkspaceSize(
  const aclTensor   *gradOutput,
  const aclIntArray *outputSize,
  const aclIntArray *inputSize,
  double             scalesH,
  double             scalesW,
  aclTensor         *out,
  uint64_t          *workspaceSize,
  aclOpExecutor    **executor)

aclnnStatus aclnnUpsampleNearestExact2dBackward(
  void          *workspace,
  uint64_t       workspaceSize,
  aclOpExecutor *executor,
  aclrtStream    stream)

aclnnUpsampleNearestExact2dBackwardGetWorkspaceSize

Parameters

Parameter Name	Input/Output	Description	Usage Description	Data Type	Data Format	Dimension (Shape)	Non-Contiguous Tensor
gradOutput (aclTensor*)	Input	Represents the gradient tensor for backward computation, corresponding to `gradOutput` in the formula.	Empty tensor is supported. When the data format is ND, it is processed in NCHW format by default.	FLOAT32, FLOAT16, BFLOAT16	NCHW, ND	4	√
outputSize (aclIntArray*)	Input	Represents the spatial size of the input `gradOutput` in the H and W dimensions.	The size is 2, and each element is greater than zero.	INT64	-	-	-
inputSize (aclIntArray*)	Input	Represents the spatial size of the output `out` in the N, C, H, and W dimensions, respectively.	The size is 4, and each element must be greater than zero.	INT64	-	-	-
scalesH (double)	Input	Specifies the height dimension multiplier of the output `out`, corresponding to `scalesH` in the formula.	Negative values are not allowed.	-	-	-	-
scalesW (double)	Input	Represents the multiplier for the width dimension of the output `out`, corresponding to `scalesW` in the formula.	Negative values are not allowed.	-	-	-	-
out (aclTensor*)	Output	Represents the output tensor of the backward computation, corresponding to `gradInput` in the formula.	Empty tensor is supported. The data type and data format must be consistent with the input `gradOutput`. The N and C axes of the shape must be consistent with the input `gradOutput`.	FLOAT32, FLOAT16, BFLOAT16	NCHW, ND	4	√
workspaceSize (uint64_t*)	Output	Returns the workspace size to be applied on the Device side.	-	-	-	-	-
executor (aclOpExecutor**)	Output	Returns the op executor, which contains the operator computation flow.	-	-	-	-	-

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	The passed gradOutput, inputSize, or out is a null pointer.
ACLNN_ERR_PARAM_INVALID	161002	The data type of gradOutput or out is not within the supported range.
		The data types of gradOutput and out are inconsistent.
		The shape of gradOutput is not 4-dimensional.
		The value of the H or W dimension of inputSize is less than 1.
		The value of scalesH or scalesW is less than 0.
		The data formats of gradOutput and out do not satisfy the constraints.

aclnnUpsampleNearestExact2dBackward

Parameters

Parameter Name	Input/Output	Description
workspace	Input	Workspace memory address applied on the Device side.
workspaceSize	Input	Size of the workspace applied on the Device side, obtained through the first-phase API aclnnUpsampleNearestExact2dBackwardGetWorkspaceSize.
executor	Input	Operator executor, which contains the operator computation flow.
stream	Input	Specifies the Stream on which the task is executed.

Return Value

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

Constraints

In the input data scaling scenario, the magnification factor must be less than or equal to 50, that is:
$outputSize\_H / height H of the output shape <= 50$ $outputSize\_W / width W of the output shape <= 50$
The parameters inputSize, outputSize, scalesH, and scalesW must satisfy the following constraints:
$outputSize\_H = floor(inputSize\_H * scalesH)$ $outputSize\_W = floor(inputSize\_W * scalesW)$
Deterministic computation:
- aclnnUpsampleNearestExact2dBackward uses deterministic implementation by default.

Example Call

The sample code is for reference only. For details about compilation and execution, see Compile and Run Samples.

#include <iostream>
#include <vector>
#include "acl/acl.h"
#include "aclnnop/aclnn_upsample_nearest_exact2d_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 shapeSize = 1;
    for (auto i : shape) {
        shapeSize *= i;
    }
    return shapeSize;
}

int Init(int32_t deviceId, aclrtStream *stream)
{
    // Standard 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 CreateAclNchTensor(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 apply for 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 host-side data to the 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 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_NCHW,
        shape.data(),
        shape.size(),
        *deviceAddr);
    return 0;
}

int main()
{
    // 1. (Boilerplate) Initialize the device/stream. Refer to the acl API manual.
    // Set deviceId based on your 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 the input and output. Customize the construction based on the API.
    std::vector<int64_t> inputShape = {1, 1, 4, 2};
    std::vector<int64_t> outShape = {1, 1, 8, 4};
    void *inputDeviceAddr = nullptr;
    void *outDeviceAddr = nullptr;
    aclTensor *input = nullptr;
    aclTensor *out = nullptr;
    std::vector<float> inputHostData = {0, 1, 2, 3, 4, 5, 6, 7};
    std::vector<float> outHostData(32, 0);
    std::vector<int64_t> outputSize = {4, 2};
    std::vector<int64_t> inputSize = {1, 1, 8, 4};
    double scalesH = 0.5;
    double scalesW = 0.5;
    // Create the input aclTensor.
    ret = CreateAclNchTensor(inputHostData, inputShape, &inputDeviceAddr, aclDataType::ACL_FLOAT, &input);
    CHECK_RET(ret == ACL_SUCCESS, return ret);
    // Create the input aclIntArray.
    auto outputSizeArray = aclCreateIntArray(outputSize.data(), 2);
    auto inputSizeArray = aclCreateIntArray(inputSize.data(), 4);
    // Create the output aclTensor.
    ret = CreateAclNchTensor(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 name.
    uint64_t workspaceSize = 0;
    aclOpExecutor *executor;
    // Call the first-phase API of aclnnUpsampleNearestExact2dBackward.
    ret = aclnnUpsampleNearestExact2dBackwardGetWorkspaceSize(
        input, outputSizeArray, inputSizeArray, scalesH, scalesW, out, &workspaceSize, &executor);
    CHECK_RET(
        ret == ACL_SUCCESS, LOG_PRINT("aclnnUpsampleNearestExact2dBackwardGetWorkspaceSize 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 aclnnUpsampleNearestExact2dBackward.
    ret = aclnnUpsampleNearestExact2dBackward(workspaceAddr, workspaceSize, executor, stream);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnUpsampleNearestExact2dBackward failed. ERROR: %d\n", ret);
              return ret);

    // 4. (Boilerplate) Synchronize and wait for 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 side memory to the host side. Modify 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(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 and aclIntArray. Modify based on the specific API definition.
    aclDestroyTensor(input);
    aclDestroyTensor(out);

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