aclnnIndexAdd
Applicable Products
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Function
Adds values from the source tensor to the corresponding positions in the input tensor along a specified dimension based on given indices.
Function Prototype
Each operator is divided into a two-phase API. The "aclnnIndexAddGetWorkspaceSize" API must be called first to obtain the workspace size required for computation and the executor that encapsulates the operator computation flow. Then, the "aclnnIndexAdd" API is called to perform the computation.
aclnnStatus aclnnIndexAddGetWorkspaceSize(
const aclTensor* self,
const int64_t dim,
const aclTensor* index,
const aclTensor* source,
const aclScalar* alpha,
aclTensor* out,
uint64_t* workspaceSize,
aclOpExecutor** executor)aclnnStatus aclnnIndexAdd(
void* workspace,
uint64_t workspaceSize,
aclOpExecutor* executor,
aclrtStream stream)aclnnIndexAddGetWorkspaceSize
Parameters
Parameter Input/Output Description Instruction Data Type Data Format Dimension (Shape) Non-Contiguous Tensor self (aclTensor*) Input Input tensor. - FLOAT、FLOAT16、INT32、INT16、INT8、UINT8、DOUBLE、INT64、BOOL、BFLOAT16 ND 0-8 √ dim (int64_t) Input Specifies the dimension. The value range is [-self.dim(), self.dim()-1]. INT64 - - - index (aclTensor*) Input Index. The shape size of index must be equal to the shape value of source along the dim dimension. INT64, INT32 ND 0-1 √ source (aclTensor*) Input. Source tensor. The shape of source must match that of self in all dimensions except the dim dimension. Same as self. - Same as self. - alpha (aclScalar*) Input Scaling factor. The data type is convertible to the data type derived from self and source. - Same as self. - - out (aclTensor*) Output Output aclTensor. - Same as self. ND Same as self. √ workspaceSize (uint64_t*) Output Returns the workspace size to be requested on the Device side. - - - - - executor (aclOpExecutor**) Output Returns the operator executor, which contains the operator computation flow. - - - - - 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 Value Error Code Description ACLNN_ERR_PARAM_NULLPTR 161001 The passed self, index, source, alpha, or out is a null pointer. ACLNN_ERR_PARAM_INVALID 161002 The data type of self, index, source, or out is not within the supported range. The data types of self, source, and out are inconsistent. The value of dim exceeds the shape size of self. The computed data type cannot be converted to the data type of the specified output out. self and source have unequal shape values in dimensions other than dim. index is not a 0- or 1-dimensional tensor. The shape of index is not equal to the shape value of source in dimension dim. The shape of out is not equal to the shape of self.
aclnnIndexAdd
Parameters
Parameter Input/Output Description workspace Input The workspace memory address applied for on the Device side. workspaceSize Input The workspace size applied for on the Device side, obtained by the first-phase API aclnnIndexAddGetWorkspaceSize. 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:
- aclnnIndexAdd defaults to a non-deterministic implementation. You can enable deterministic computation through aclrtCtxSetSysParamOpt.
Value range of index
- The input value of index must fall within the range [0, self.shape[dim]), meaning the index value must be within the shape size of self along the dim dimension.
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_index_add.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);
// 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);
// 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 aclCreateTensor 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 the input and output. Custom construction is required based on the API.
std::vector<int64_t> selfShape = {4, 2};
std::vector<int64_t> indexShape = {4};
std::vector<int64_t> sourceShape = {4, 2};
std::vector<int64_t> outShape = {4, 2};
void* selfDeviceAddr = nullptr;
void* indexDeviceAddr = nullptr;
void* sourceDeviceAddr = nullptr;
void* outDeviceAddr = nullptr;
aclTensor* self = nullptr;
aclTensor* index = nullptr;
aclTensor* source = nullptr;
aclScalar* alpha = nullptr;
aclTensor* out = nullptr;
std::vector<float> selfHostData = {0, 1, 2, 3, 4, 5, 6, 7};
std::vector<int> indexHostData = {0, 1, 2, 3};
std::vector<float> sourceHostData = {1, 1, 1, 2, 2, 2, 3, 3};
std::vector<float> outHostData(8, 0);
int64_t dim = 0;
float alphaValue = 1.0f;
// Create the self aclTensor.
ret = CreateAclTensor(selfHostData, selfShape, &selfDeviceAddr, aclDataType::ACL_FLOAT, &self);
CHECK_RET(ret == ACL_SUCCESS, return ret);
// Create the index aclTensor.
ret = CreateAclTensor(indexHostData, indexShape, &indexDeviceAddr, aclDataType::ACL_INT32, &index);
CHECK_RET(ret == ACL_SUCCESS, return ret);
// Create the source aclTensor.
ret = CreateAclTensor(sourceHostData, sourceShape, &sourceDeviceAddr, aclDataType::ACL_FLOAT, &source);
CHECK_RET(ret == ACL_SUCCESS, return ret);
// Create the out aclTensor.
ret = CreateAclTensor(outHostData, outShape, &outDeviceAddr, aclDataType::ACL_FLOAT, &out);
CHECK_RET(ret == ACL_SUCCESS, return ret);
// Create the alpha aclScalar.
alpha = aclCreateScalar(&alphaValue, aclDataType::ACL_FLOAT);
CHECK_RET(alpha != nullptr, 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 aclnnIndexAdd.
ret = aclnnIndexAddGetWorkspaceSize(self, dim, index, source, alpha, out, &workspaceSize, &executor);
CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnIndexAddGetWorkspaceSize 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 aclnnIndexAdd.
ret = aclnnIndexAdd(workspaceAddr, workspaceSize, executor, stream);
CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnIndexAdd 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 memory to the host memory. 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 aclScalar. Modify based on the specific API definition.
aclDestroyTensor(self);
aclDestroyTensor(index);
aclDestroyTensor(source);
aclDestroyScalar(alpha);
aclDestroyTensor(out);
// 7. Release device resources. Modify based on the specific API definition.
aclrtFree(selfDeviceAddr);
aclrtFree(indexDeviceAddr);
aclrtFree(sourceDeviceAddr);
aclrtFree(outDeviceAddr);
if (workspaceSize > 0) {
aclrtFree(workspaceAddr);
}
aclrtDestroyStream(stream);
aclrtResetDevice(deviceId);
aclFinalize();
return 0;
}