aclnnGeGluBackward

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	×

Function

Description: Completes the backward propagation of aclnnGeGlu.
Formula:
$\text{gradOutput} = \begin{bmatrix} b_1 \\ b_2 \\ \vdots \\ b_N \end{bmatrix}, \quad \text{self} = \begin{bmatrix} a_{11} & a_{12} & \cdots & a_{1N} \\ a_{21} & a_{22} & \cdots & a_{2N} \\ \vdots & \vdots & \ddots & \vdots \\ a_{M1} & a_{M2} & \cdots & a_{MN} \end{bmatrix}, \quad gelu = \begin{bmatrix} a_{11} & a_{12} & \cdots & a_{1N/2} \\ a_{21} & a_{22} & \cdots & a_{2N/2} \end{bmatrix}$ $\text{dim} = \text{the dimension along which chunk is performed}, \quad \text{approximate = 'approximate'}$
Output:
$\text{gradInput} = \begin{bmatrix} b_1 \cdot y\_gelu_1 & b_2 \cdot y\_gelu_2 & \cdots & b_N \cdot y\_gelu_N \end{bmatrix}$

Function Prototype

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

aclnnStatus aclnnGeGluBackwardGetWorkspaceSize(
  const aclTensor *gradOutput,
  const aclTensor *self,
  const aclTensor *gelu,
  int64_t          dim,
  int64_t          approximate,
  aclTensor       *gradInput,
  uint64_t        *workspaceSize,
  aclOpExecutor  **executor)

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

aclnnGeGluBackwardGetWorkspaceSize

Parameters:

Parameter	Input/Output	Description	Instruction	Data Type	Data Format	Dimension (Shape)	Non-contiguous Tensor
gradOutput (aclTensor*)	Input	Input parameter for the GeGluBackward computation, corresponding to gradOutput in the formula.	The data type is consistent with self. Except for the dim dimension, all other dimensions in the shape are the same as self. The size of the dim dimension is half that of self.	FLOAT, FLOAT16, BFLOAT16	ND	1-8	√
self (aclTensor*)	Input	Input parameter for the GeGluBackward computation, corresponding to self in the formula.	In shape, all dimensions except dim are the same size as gradOutput, and the dim dimension is twice the size of gradOutput	FLOAT, FLOAT16, BFLOAT16	ND	1-8	√
gelu (aclTensor*)	Input	Input parameter for the GeGluBackward computation, representing gelu in the formula.	The data type is consistent with self. The shape must be the same as gradOutput	FLOAT, FLOAT16, BFLOAT16	ND	1-8	√
dim (int64_t)	Input	-	The current value range is [-len(input shape), len(input shape)].	-	-	-	-
approximate (int64_t)	Input	-	The value range is 0 ('none') and 1 ('tanh'). Atlas inference products only support 1 ('tanh').	-	-	-	-
gradInput (aclTensor*)	Output	Output parameter of the GeGluBackward computation, i.e., gradInput in the formula.	The data type is consistent with self. The shape must be the same as self.	FLOAT, FLOAT16, BFLOAT16	ND	1-8	√
workspaceSize (uint64_t*)	Output	Returns the workspace size to be applied for on the Device side.	-	-	-	-	-
executor (aclOpExecutor**)	Output	Returns the op executor, which includes computation of the operator process.	-	-	-	-	-

Atlas inference products, Atlas training products: The supported data types are FLOAT32 and FLOAT16.

Return value

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

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

Return Code	Error Code	Description
ACLNN_ERR_PARAM_NULLPTR	161001	The parameters gradOutput, self, gelu, and gradInput are null pointers.
ACLNN_ERR_PARAM_INVALID	161002	The data type of gradOutput, self, gelu, or gradInput is not within the supported range.
		The shape of gradOutput, self, gelu, or gradInput does not meet the requirements.
		When self.dim()=0, the value range of dim is not within [-1, 0]; when self.dim()>0, the value range of dim is not within [-self.dim(), self.dim()-1].
		The value range of the approximate parameter is neither 0 nor 1.

aclnnGeGluBackward

Parameters:

Parameter	Input/Output	Description
workspace	Input	Address of the workspace memory applied on the device side.
workspaceSize	Input	Size of the workspace memory applied on the device side, obtained from the first-phase API aclnnGeGluBackwardGetWorkspaceSize.
executor	Input	Operator executor that includes the operator computation flow.
stream	Input	Specifies the stream for task execution.

Return value

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

Constraints

Deterministic computation:
- aclnnGeGluBackward 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 <iostream>
#include <vector>
#include "acl/acl.h"
#include "aclnnop/aclnn_geglu_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)
{
    // Boilerplate for 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** selfOrResult)
{
    auto size = GetShapeSize(shape) * sizeof(T);
    // Call aclrtMalloc to apply for 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);
    // Copy data from the host side to the device memory using aclrtMemcpy.
    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 the contiguous selfOrResult.
    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.
    *selfOrResult = aclCreateTensor(
        shape.data(), shape.size(), dataType, strides.data(), 0, aclFormat::ACL_FORMAT_ND, shape.data(), shape.size(),
        *deviceAddr);
    return 0;
}

int main()
{
    // 1. (Boilerplate) Initialize the device/stream. 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 the input and output. Customize the construction based on the API interface.
    std::vector<int64_t> gradOutputShape = {2, 1};
    std::vector<int64_t> selfShape = {2, 2};
    std::vector<int64_t> geluShape = {2, 1};
    std::vector<int64_t> gradInputShape = {2, 2};
    void* gradOutputDeviceAddr = nullptr;
    void* selfDeviceAddr = nullptr;
    void* geluDeviceAddr = nullptr;
    void* gradInputDeviceAddr = nullptr;
    aclTensor* gradOutput = nullptr;
    aclTensor* self = nullptr;
    aclTensor* gelu = nullptr;
    aclTensor* gradInput = nullptr;
    std::vector<float> gradOutputHostData = {-2, -1};
    std::vector<float> selfHostData = {-2, -1, 0, 1};
    std::vector<float> geluHostData = {-2, -1};
    int64_t dim = -1;
    int64_t approximate = 1;
    std::vector<float> gradInputHostData = {0, 0, 0, 0};
    // Create the gradOutput aclTensor.
    ret = CreateAclTensor(
        gradOutputHostData, gradOutputShape, &gradOutputDeviceAddr, aclDataType::ACL_FLOAT16, &gradOutput);
    CHECK_RET(ret == ACL_SUCCESS, return ret);
    // Create the self aclTensor.
    ret = CreateAclTensor(selfHostData, selfShape, &selfDeviceAddr, aclDataType::ACL_FLOAT16, &self);
    CHECK_RET(ret == ACL_SUCCESS, return ret);
    // Create the gelu aclTensor.
    ret = CreateAclTensor(geluHostData, geluShape, &geluDeviceAddr, aclDataType::ACL_FLOAT16, &gelu);
    CHECK_RET(ret == ACL_SUCCESS, return ret);
    // Create the gradInput aclTensor.
    ret =
        CreateAclTensor(gradInputHostData, gradInputShape, &gradInputDeviceAddr, aclDataType::ACL_FLOAT16, &gradInput);
    CHECK_RET(ret == ACL_SUCCESS, return ret);

    // 3. Call the CANN operator library API. Replace with the actual API name.
    uint64_t workspaceSize = 0;
    aclOpExecutor* executor;
    // Call the first-phase API of aclnnGeGluBackward.
    ret = aclnnGeGluBackwardGetWorkspaceSize(
        gradOutput, self, gelu, dim, approximate, gradInput, &workspaceSize, &executor);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnGeGluBackwardGetWorkspaceSize failed. ERROR: %d\n", ret); return ret);
    // Apply for device memory based on the 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 aclnnGeGluBackward.
    ret = aclnnGeGluBackward(workspaceAddr, workspaceSize, executor, stream);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnGeGluBackward 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(gradInputShape);
    std::vector<float> resultData(size, 0);
    ret = aclrtMemcpy(
        resultData.data(), resultData.size() * sizeof(resultData[0]), gradInputDeviceAddr, 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 aclScalar. Modify based on the specific API definition.
    aclDestroyTensor(gradOutput);
    aclDestroyTensor(self);
    aclDestroyTensor(gelu);
    aclDestroyTensor(gradInput);

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