aclnnLeftShift

Applicable Products

ProductSupported
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: For each element in the input tensor self, performs a bitwise left shift based on the corresponding element in the input tensor shiftBits.

  • Formula:

outi=selfi<<shiftBitsiout_{i} = self_{i}<<shiftBits_{i}

Function Prototype

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

aclnnStatus aclnnLeftShiftGetWorkspaceSize(
    const aclTensor *self,
    const aclTensor *shiftBits,
    aclTensor       *out,
    uint64_t        *workspaceSize,
    aclOpExecutor   **executor)
aclnnStatus aclnnLeftShift(
    void            *workspace,
    uint64_t         workspaceSize,
    aclOpExecutor   *executor,
    aclrtStream      stream)

aclnnLeftShiftGetWorkspaceSize

  • Parameters:

    Parameter Input/Output Description Instruction Data Type Data Format Dimension Non-contiguous Tensor
    self Input Specifies the tensor for bitwise left shift, i.e., self in the formula. Supports empty tensors.
    The data type must satisfy the data type deduction rules with the data type of shiftBits (see Deduction Relationship).
    The shape must satisfy the broadcast relationship with shiftBits.
    INT8, INT16, INT32, INT64,
    UINT8, UINT16, UINT32, UINT64
    ND 0-8 √
    shiftBits Input Tensor of the left shift operand, i.e., shiftBits in the formula. Supports empty tensors.
    The data type must satisfy the data type deduction rules with the data type of self (see Deduction Relationship).
    The shape must satisfy the broadcast relationship with self.
    INT8, INT16, INT32, INT64,
    UINT8, UINT16, UINT32, UINT64
    ND 0-8 √
    out Output Output tensor, which is the out in the formula. The shape must be consistent with the shape after broadcasting self and shiftBits. INT8, INT16, INT32, INT64,
    UINT8, UINT16, UINT32, UINT64
    ND 0-8 √
    workspaceSize Output Size of the workspace to be applied for on the Device side. - - - - -
    executor Output Returns the operator 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 returns an error in the following scenarios:

    Return Value Error Code Description
    ACLNN_ERR_PARAM_NULLPTR 161001 self, shiftBits, or out is a null pointer.
    ACLNN_ERR_PARAM_INVALID 161002 The data type or data format of self, shiftBits, or out is not within the supported range.
    The data types of self and shiftBits do not support type deduction.
    The data dimension of self or shiftBits exceeds 8.
    The shapes of self and shiftBits do not satisfy the broadcast rule.
    The shapes of self and shiftBits after broadcast are inconsistent with the shape of out.

aclnnLeftShift

  • Parameters:

    Parameter Input/Output Description
    workspace Input Address of the workspace memory applied on the Device side.
    workspaceSize Input Size of the workspace applied on the Device side, obtained through the first-phase API aclnnLeftShiftGetWorkspaceSize.
    executor Input Operator executor, which contains the operator computation flow.
    stream Input Specifies the stream for task execution.
  • Return value

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

Constraints

  • Deterministic computation:

    • aclnnLeftShift defaults to a deterministic implementation.
  • Accuracy is guaranteed only when the value of shiftBits is less than the bit width of the self data type.

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_left_shift.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: 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 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 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 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) 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 inputs and outputs. Customize based on the API.
    std::vector<int64_t> selfShape = {1, 4};
    std::vector<int64_t> shiftBitsShape = {1, 4};
    std::vector<int64_t> outShape = {1, 4};
    void* selfDeviceAddr = nullptr;
    void* shiftBitsDeviceAddr = nullptr;
    void* outDeviceAddr = nullptr;
    aclTensor* self = nullptr;
    aclTensor* shiftBits = nullptr;
    aclTensor* out = nullptr;
    std::vector<int64_t> selfHostData = {10, 20, 30, 40};
    std::vector<int64_t> shiftBitsHostData = {1, 2, 3, 4};
    std::vector<int64_t> outHostData = {0, 0, 0, 0};

    // Create self aclTensor.
    ret = CreateAclTensor(selfHostData, selfShape, &selfDeviceAddr, aclDataType::ACL_INT64, &self);
    CHECK_RET(ret == ACL_SUCCESS, return ret);
    // Create shiftBits aclTensor.
    ret = CreateAclTensor(shiftBitsHostData, shiftBitsShape, &shiftBitsDeviceAddr, aclDataType::ACL_INT64, &shiftBits);
    CHECK_RET(ret == ACL_SUCCESS, return ret);
    // Create out aclTensor.
    ret = CreateAclTensor(outHostData, outShape, &outDeviceAddr, aclDataType::ACL_INT64, &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 aclnnLeftShiftGetWorkspaceSize.
    ret = aclnnLeftShiftGetWorkspaceSize(self, shiftBits, out, &workspaceSize, &executor);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnLeftShiftGetWorkspaceSize 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 aclnnLeftShift.
    ret = aclnnLeftShift(workspaceAddr, workspaceSize, executor, stream);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnLeftShift failed. ERROR: %d\n", ret); return ret);

    // 4. (Boilerplate) Synchronously 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 memory to the host memory. Modify this based on the specific API definition.
    auto size = GetShapeSize(outShape);
    std::vector<int64_t> 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: %ld\n", i, resultData[i]);
    }

    // 6. Release aclTensor and aclScalar. Modify based on the specific API definition.
    aclDestroyTensor(self);
    aclDestroyTensor(shiftBits);
    aclDestroyTensor(out);

    // 7. Release device resources.
    aclrtFree(selfDeviceAddr);
    aclrtFree(shiftBitsDeviceAddr);
    aclrtFree(outDeviceAddr);
    if (workspaceSize > 0) {
        aclrtFree(workspaceAddr);
    }
    aclrtDestroyStream(stream);
    aclrtResetDevice(deviceId);
    aclFinalize();

    return 0;
}