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aclnnClamp

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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: Clamps all elements of the input to the range [min, max]. If min is None, there is no lower bound. If max is None, there is no upper bound.

  • Formula:

yi=max(min(xi,max_valuei),min_valuei){y}_{i} = max(min({{x}_{i}},{max\_value}_{i}),{min\_value}_{i})

Function Prototype

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

aclnnStatus aclnnClampGetWorkspaceSize(
    const aclTensor *self,
    const aclScalar *clipValueMin,
    const aclScalar *clipValueMax,
    aclTensor       *out,
    uint64_t        *workspaceSize,
    aclOpExecutor  **executor)
aclnnStatus aclnnClamp(
    void*             workspace,
    uint64_t          workspaceSize,
    aclOpExecutor*    executor,
    const aclrtStream stream)

aclnnClampGetWorkspaceSize

  • Parameters

    Parameter Input/Output Description Instruction Data Type Data Format Dimension (shape) Non-contiguous Tensor
    self (aclTensor*) Input Input tensor to be clamped, i.e., xi in the formula. - FLOAT16, FLOAT, FLOAT64, INT8, UINT8, INT16, INT32, INT64, BOOL, BFLOAT16 ND 1-8 √
    clipValueMin（aclScalar*） Input Input Scalar that limits the lower bound of self, i.e., min_valuei in the formula. The data type must satisfy the data type deduction rules with the data type of self (see Conversion Relationship). FLOAT16, FLOAT, FLOAT64, INT8, UINT8, INT16, INT32, INT64, BOOL, BFLOAT16 - - -
    clipValueMax (aclScalar*) Input Input Scalar that specifies the upper bound of self, i.e., max_valuei in the formula. The data type must satisfy the data type deduction rules with the data type of self (see Conversion Relationship). FLOAT16, FLOAT, FLOAT64, INT8, UINT8, INT16, INT32, INT64, BOOL, BFLOAT16 - - -
    out (aclTensor*) Output Output tensor, whose shape is the same as self. - FLOAT16, FLOAT, FLOAT64, INT8, UINT8, INT16, INT32, INT64, BOOL, BFLOAT16 ND Same as self. √
    workspaceSize (uint64_t*) Output Returns the workspace size to be allocated on the Device side. - - - - -
    executor (aclOpExecutor**) Output Returns the op executor, which contains the operator computation flow. - - - - -

    • Atlas training products, Atlas inference products:

      • The data types of self and out do not support BOOL or BFLOAT16.

      • The data type of clipValueMin and clipValueMax does not support BFLOAT16.

    • Atlas A2 training products/Atlas A2 inference products, Atlas A3 training products/Atlas A3 inference products:

      • The data type of self and out does not support BOOL.

    • Ascend 950PR/Ascend 950DT:

      • The data types of self, clipValueMin, and clipValueMax must comply with the data type deduction rules (see TensorScalar Deduction Relationship).

      • The data type of out must be a data type convertible from self, clipValueMin, and clipValueMax after deduction.

      • The data types of self, clipValueMin, clipValueMax, and out do not support BOOL.

  • 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 Either self or out is a null pointer, or both max and min are null pointers.
    ACLNN_ERR_PARAM_INVALID 161002 The data type and data format of self and out are not within the supported range.

aclnnClamp

  • Parameters

    Parameter Input/Output Description
    workspace Input The workspace memory address applied for on the Device side.
    workspaceSize Input Size of the workspace applied for on the Device side, obtained through the first-phase API aclnnClampGetWorkspaceSize.
    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:

    • aclnnClamp 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_clamp.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. Initialize resources.
    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 apply for 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);

    // Call aclrtMemcpy to copy host data to 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_ND,
                              shape.data(), shape.size(), *deviceAddr);
    return 0;
}

int PrepareInputAndOutput(
    std::vector<int64_t>& shape, void** selfDeviceAddr, aclTensor** self, aclScalar** max, aclScalar** min,
    void** outDeviceAddr, aclTensor** out)
{
    int8_t max_v = 5;
    int8_t min_v = 2;

    std::vector<int8_t> selfHostData = {0, 1, 0, 3, 0, 5, 0, 7};
    std::vector<int8_t> outHostData = {0, 0, 0, 0, 0, 0, 0, 0};

    // Create the self aclTensor.
    auto ret = CreateAclTensor(selfHostData, shape, selfDeviceAddr, aclDataType::ACL_INT8, self);
    CHECK_RET(ret == ACL_SUCCESS, return ret);
    // Create max.
    *max = aclCreateScalar(&max_v, aclDataType::ACL_INT8);
    CHECK_RET(*max != nullptr, return ret);
    // Create min.
    *min = aclCreateScalar(&min_v, aclDataType::ACL_INT8);
    CHECK_RET(*min != nullptr, return ret);
    // Create the output aclTensor.
    ret = CreateAclTensor(outHostData, shape, outDeviceAddr, aclDataType::ACL_INT8, out);
    CHECK_RET(ret == ACL_SUCCESS, return ret);

    return ACL_SUCCESS;
}

void ReleaseTensorAndScalar(aclTensor* self, aclScalar* max, aclScalar* min, aclTensor* out)
{
    aclDestroyTensor(self);
    aclDestroyScalar(max);
    aclDestroyScalar(min);
    aclDestroyTensor(out);
}

void ReleaseDevice(
    void* selfDeviceAddr, void* outDeviceAddr, uint64_t workspaceSize, void* workspaceAddr, aclrtStream stream,
    int32_t deviceId)
{
    aclrtFree(selfDeviceAddr);
    aclrtFree(outDeviceAddr);
    if (workspaceSize > 0) {
        aclrtFree(workspaceAddr);
    }
    aclrtDestroyStream(stream);
    aclrtResetDevice(deviceId);
    aclFinalize();
}

int main() {
    // 1. Boilerplate: device/stream initialization. See the ACL external API list.
    // 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 based on the API definition.
    std::vector<int64_t> shape = {4, 2};

    void* selfDeviceAddr = nullptr;
    void* outDeviceAddr = nullptr;
    aclTensor* self = nullptr;
      aclScalar* max = nullptr;
      aclScalar* min = nullptr;
    aclTensor* out = nullptr;

    float max_v = 5;
    float min_v = 2;

    std::vector<float> selfHostData = {0, 1, 0, 3, 0, 5, 0, 7};
    std::vector<float> outHostData = {0, 0, 0, 0, 0, 0, 0, 0};

    // Create the self aclTensor.
    ret = CreateAclTensor(selfHostData, shape, &selfDeviceAddr, aclDataType::ACL_FLOAT, &self);
    CHECK_RET(ret == ACL_SUCCESS, return ret);
    // Create max.
    max = aclCreateScalar(&max_v, aclDataType::ACL_FLOAT);
    CHECK_RET(max != nullptr, return ret);
    // Create min.
    min = aclCreateScalar(&min_v, aclDataType::ACL_FLOAT);
    CHECK_RET(min != nullptr, return ret);
    // Create the out aclTensor.
    ret = CreateAclTensor(outHostData, shape, &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 aclnnClamp.
    ret = aclnnClampGetWorkspaceSize(self, min, max, out, &workspaceSize, &executor);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnClampGetWorkspaceSize failed. ERROR: %d\n", ret); return ret);
    // Allocate 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 aclnnClamp.
    ret = aclnnClamp(workspaceAddr, workspaceSize, executor, stream);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnClamp 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-side memory to the host side. Modify based on the specific API definition.
    auto size = GetShapeSize(shape);
    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.
    ReleaseTensorAndScalar(self, max, min, out);

    // 7. Release device resources.
    ReleaseDevice(selfDeviceAddr, outDeviceAddr, workspaceSize, workspaceAddr, stream, deviceId);

    return 0;
}
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