aclnnForeachCopy

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Function

• Description: Copies the contents of one tensor list to another.

• Formula:

  $$x = [{x_0}, {x_1}, ... {x_{n-1}}]\\ y = [{y_0}, {y_1}, ... {y_{n-1}}]\\$$ $${\rm y}_i = x_i (i=0,1,...n-1)$$

Function Prototype

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

aclnnStatus aclnnForeachCopyGetWorkspaceSize(
    const aclTensorList *x,
    const aclTensorList *out,
    uint64_t            *workspaceSize,
    aclOpExecutor      **executor)

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

aclnnForeachCopyGetWorkspaceSize

• Parameters

  Parameter	Input/Output	Description	Instruction	Data Type	Data Format	Shape	Non-contiguous Tensor
  x (aclTensorList*)	Input	Indicates the input tensor list for content copying, corresponding to `x` in the formula.	Empty tensors are supported. All tensors in this parameter must have the same data type.	FLOAT32, FLOAT16, BFLOAT16, INT8, UINT8, INT16, UINT16, INT32, UINT32, INT64, DOUBLE, BOOL	ND	0-8	√
  out (aclTensorList*)	Output	Specifies the output tensor list for content copying, corresponding to `y` in the formula.	Supports empty tensors. All tensors in this parameter have the same data type. The data type and data format are consistent with those of the input parameter `x`, and the shape size is greater than or equal to that of `x`.	FLOAT32, FLOAT16, BFLOAT16, INT8, UINT8, INT16, UINT16, INT32, UINT32, INT64, DOUBLE, BOOL	ND	0-8	√
  workspaceSize (uint64_t*)	Output	Returns the workspace size to be allocated on the Device side.	-	-	-	-	-
  executor (aclOpExecutor**)	Output	Returns the operator executor, which contains the operator computation process.	-	-	-	-	-

• 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 input x or out is a null pointer.
  ACLNN_ERR_PARAM_INVALID	161002	The data type of x or out is not within the supported range.
  		The data types of x and out are inconsistent.
  ACLNN_ERR_INNER_TILING_ERROR	561002	The shapes of x and out do not satisfy the constraints.
  		The data types of tensors in x or out are inconsistent.
  		The tensor in x or out exceeds 8 dimensions.

aclnnForeachCopy

• Parameters

  Parameter	Input/Output	Description
  workspace	Input	Address of the workspace memory applied for on the Device side.
  workspaceSize	Input	Size of the workspace applied for on the Device side, obtained from the first-phase API aclnnForeachCopyGetWorkspaceSize.
  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 Code.

Constraints

• Deterministic computation:

  • aclnnForeachCopy 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_foreach_copy.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 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 data from the host side 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);
    // 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 main()
{
    // 1. (Boilerplate) Initialize the device/stream. For details, see the acl API list.
    // 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> selfShape1 = {2, 3};
    std::vector<int64_t> selfShape2 = {1, 3};
    std::vector<int64_t> outShape1 = {2, 3};
    std::vector<int64_t> outShape2 = {1, 3};
    void *input1DeviceAddr = nullptr;
    void *input2DeviceAddr = nullptr;
    void *out1DeviceAddr = nullptr;
    void *out2DeviceAddr = nullptr;
    aclTensor *input1 = nullptr;
    aclTensor *input2 = nullptr;
    aclTensor *out1 = nullptr;
    aclTensor *out2 = nullptr;
    std::vector<float> input1HostData = {1, 2, 3, 4, 5, 6};
    std::vector<float> input2HostData = {7, 8, 9};
    std::vector<float> out1HostData(6, 0);
    std::vector<float> out2HostData(3, 0);
    // Create the input1 aclTensor.
    ret = CreateAclTensor(input1HostData, selfShape1, &input1DeviceAddr, aclDataType::ACL_FLOAT, &input1);
    CHECK_RET(ret == ACL_SUCCESS, return ret);
    // Create the input2 aclTensor.
    ret = CreateAclTensor(input2HostData, selfShape2, &input2DeviceAddr, aclDataType::ACL_FLOAT, &input2);
    CHECK_RET(ret == ACL_SUCCESS, return ret);
    // Create the out1 aclTensor.
    ret = CreateAclTensor(out1HostData, outShape1, &out1DeviceAddr, aclDataType::ACL_FLOAT, &out1);
    CHECK_RET(ret == ACL_SUCCESS, return ret);
    // Create the out2 aclTensor.
    ret = CreateAclTensor(out2HostData, outShape2, &out2DeviceAddr, aclDataType::ACL_FLOAT, &out2);
    CHECK_RET(ret == ACL_SUCCESS, return ret);
    std::vector<aclTensor *> tempInput{input1, input2};
    aclTensorList *tensorListInput = aclCreateTensorList(tempInput.data(), tempInput.size());
    std::vector<aclTensor *> tempOutput{out1, out2};
    aclTensorList *tensorListOutput = aclCreateTensorList(tempOutput.data(), tempOutput.size());
    // 3. Call the CANN operator library API. Replace it with the actual API name.
    uint64_t workspaceSize = 0;
    aclOpExecutor *executor;
    // Call the first-phase API of aclnnForeachCopy.
    ret = aclnnForeachCopyGetWorkspaceSize(tensorListInput, tensorListOutput, &workspaceSize, &executor);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnForeachCopyGetWorkspaceSize 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 aclnnForeachCopy.
    ret = aclnnForeachCopy(workspaceAddr, workspaceSize, executor, stream);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnForeachCopy 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 based on the specific API definition.
    auto size = GetShapeSize(outShape1);
    std::vector<float> out1Data(size, 0);
    ret = aclrtMemcpy(out1Data.data(),
        out1Data.size() * sizeof(out1Data[0]),
        out1DeviceAddr,
        size * sizeof(out1Data[0]),
        ACL_MEMCPY_DEVICE_TO_HOST);
    for (int64_t i = 0; i < size; i++) {
        LOG_PRINT("out1 result[%ld] is: %f\n", i, out1Data[i]);
    }
    size = GetShapeSize(outShape2);
    std::vector<float> out2Data(size, 0);
    ret = aclrtMemcpy(out2Data.data(),
        out2Data.size() * sizeof(out2Data[0]),
        out2DeviceAddr,
        size * sizeof(out2Data[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("out2 result[%ld] is: %f\n", i, out2Data[i]);
    }
    // 6. Release aclTensor. Modify based on the specific API definition.
    aclDestroyTensorList(tensorListInput);
    aclDestroyTensorList(tensorListOutput);
    // 7. Release device resources. Modify based on the specific API definition.
    aclrtFree(input1DeviceAddr);
    aclrtFree(input2DeviceAddr);
    aclrtFree(out1DeviceAddr);
    aclrtFree(out2DeviceAddr);
    if (workspaceSize > 0) {
        aclrtFree(workspaceAddr);
    }
    aclrtDestroyStream(stream);
    aclrtResetDevice(deviceId);
    aclFinalize();
    return 0;
}