aclnnNeTensor&aclnnInplaceNeTensor
Applicable Products
| Product | Supported |
|---|---|
| √ | |
| √ | |
| √ | |
| × | |
| × | |
| √ |
Function
Description: Computes whether the value of each element in self (selfRef) is not equal to the value in other.
Formula:
Function Prototype
aclnnNeTensor and aclnnInplaceNeTensor implement the same functionality. The differences are as follows. Choose the appropriate operator based on your actual scenario.
aclnnNeTensor: Create a new output tensor object to store the computation result.
aclnnInplaceNeTensor: No need to create a new output tensor object. The computation result is stored directly in the memory of the input tensor.
Each operator is divided into a two-phase API. You must first call the "aclnnNeTensorGetWorkspaceSize" or "aclnnInplaceNeTensorGetWorkspaceSize" API to obtain the input parameters and calculate the required workspace size based on the computation process, and then call the "aclnnNeTensor" or "aclnnInplaceNeTensor" API to perform the computation.
aclnnStatus aclnnNeTensorGetWorkspaceSize(
const aclTensor *self,
const aclTensor *other,
aclTensor *out,
uint64_t *workspaceSize,
aclOpExecutor **executor)aclnnStatus aclnnNeTensor(
void *workspace,
uint64_t workspaceSize,
aclOpExecutor *executor,
aclrtStream stream)aclnnStatus aclnnInplaceNeTensorGetWorkspaceSize(
aclTensor *selfRef,
const aclTensor *other,
uint64_t *workspaceSize,
aclOpExecutor **executor)aclnnStatus aclnnInplaceNeTensor(
void *workspace,
uint64_t workspaceSize,
aclOpExecutor *executor,
aclrtStream stream)aclnnNeTensorGetWorkspaceSize
Parameters:
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:
Returns Error Code Description ACLNN_ERR_PARAM_NULLPTR 161001 The passed self, other, or out is a null pointer. ACLNN_ERR_PARAM_INVALID 161002 The data type of self, other, or out is not within the supported range. The dimension of self, other, or out exceeds 8. The data types of self and other cannot be deduced. The shapes of self and other cannot be broadcast. The shape of out is inconsistent with the broadcast shape.
aclnnNeTensor
Parameters:
Parameter Input/Output Description workspace Input Specifies the workspace memory address applied for on the Device side. workspaceSize Input Specifies the workspace size applied for on the Device side, which is obtained from the first-phase API aclnnNeTensorGetWorkspaceSize. executor Input Operator executor that contains the operator computation process. stream Input Specifies the stream for task execution. Return value:
aclnnStatus: return code. For details, see aclnn Return Codes.
aclnnInplaceNeTensorGetWorkspaceSize
Parameters:
Parameter Input/Output Description Instruction Data Type Data Format Dimension (Shape) Non-contiguous Tensor selfRef (aclTensor*) Input/Output Refers to selfRef in the formula. DOUBLE, FLOAT16, FLOAT, BFLOAT16, INT64, INT32, INT8, UINT8, BOOL, INT16, COMPLEX64, COMPLEX128, UINT64 ND 0-8 √ √ other (aclTensor*) Input The "other" tensor in the formula. DOUBLE, FLOAT16, FLOAT, BFLOAT16, INT64, INT32, INT8, UINT8, BOOL, INT16, COMPLEX64, COMPLEX128, UINT64 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 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 The selfRef or other passed in is a null pointer. ACLNN_ERR_PARAM_INVALID 161002 The data type of selfRef or other is not within the supported range. The data types of selfRef and other cannot be deduced. The shapes of selfRef and other cannot be broadcast. The broadcast shape of selfRef and other is not equal to the shape of selfRef. The number of dimensions of selfRef or other exceeds 8.
aclnnInplaceNeTensor
Parameters:
Parameter Input/Output Description workspace Input Memory address of the workspace applied for on the device side. workspaceSize Input Size of the workspace applied for on the device side, obtained by the first-phase API aclnnInplaceNeTensorGetWorkspaceSize. executor Input Operator executor that contains the operator computation process. stream Input Specifies the stream for task execution. Return value:
aclnnStatus: return code. For details, see aclnn Return Codes.
Constraints
Deterministic computation:
- aclnnNeTensor&aclnnInplaceNeTensor default to a deterministic implementation.
Example
The following provides example code for reference only. For details about compilation and running, see Compile and Run Samples.
Example code for calling the aclnnNeTensor&aclnnInplaceNeTensor API:
#include <iostream>
#include <vector>
#include "acl/acl.h"
#include "aclnnop/aclnn_ne_tensor.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 reference.
// Set deviceId based on your 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 inputs and outputs. Customize the construction based on the API definition.
std::vector<int64_t> selfShape = {4, 2};
std::vector<int64_t> otherShape = {4, 2};
std::vector<int64_t> outShape = {4, 2};
void *selfDeviceAddr = nullptr;
void *otherDeviceAddr = nullptr;
void *outDeviceAddr = nullptr;
aclTensor *self = nullptr;
aclTensor *other = nullptr;
aclTensor *out = nullptr;
std::vector<double> selfHostData = {0, 1, 2, 3, 4, 5, 6, 7};
std::vector<double> otherHostData = {1, 1, 1, 2, 2, 2, 3, 3};
std::vector<double> outHostData = {0, 0, 0, 0, 0, 0, 0, 0};
// Create self aclTensor.
ret = CreateAclTensor(selfHostData, selfShape, &selfDeviceAddr, aclDataType::ACL_DOUBLE, &self);
CHECK_RET(ret == ACL_SUCCESS, return ret);
// Create other aclTensor.
ret = CreateAclTensor(otherHostData, otherShape, &otherDeviceAddr, aclDataType::ACL_DOUBLE, &other);
CHECK_RET(ret == ACL_SUCCESS, return ret);
// Create out aclTensor.
ret = CreateAclTensor(outHostData, outShape, &outDeviceAddr, aclDataType::ACL_DOUBLE, &out);
CHECK_RET(ret == ACL_SUCCESS, return ret);
// Example of calling the aclnnNeTensor API.
// 3. Call the CANN operator library API.
uint64_t workspaceSize = 0;
aclOpExecutor* executor;
// Call the first-phase API of aclnnNeTensor.
ret = aclnnNeTensorGetWorkspaceSize(self, other, out, &workspaceSize, &executor);
CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnNeTensorGetWorkspaceSize 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 aclnnNeTensor.
ret = aclnnNeTensor(workspaceAddr, workspaceSize, executor, stream);
CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnNeTensor 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 values and copy the results from device memory to host memory.
auto size = GetShapeSize(outShape);
std::vector<double> 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]);
}
// Example of calling the aclnnInplaceNeTensor API.
// 3. Call the CANN operator library API.
LOG_PRINT("\ntest aclnnInplaceNeTensor\n");
// Call the first-phase API of aclnnInplaceNeTensor.
ret = aclnnInplaceNeTensorGetWorkspaceSize(self, other, &workspaceSize, &executor);
CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnInplaceNeTensorGetWorkspaceSize failed. ERROR: %d\n", ret); return ret);
// Allocate device memory based on the workspaceSize computed by the first-phase API.
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 aclnnInplaceNeTensor.
ret = aclnnInplaceNeTensor(workspaceAddr, workspaceSize, executor, stream);
CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnInplaceNeTensor 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 values and copy the results from device memory to host memory. Modify based on the specific API definition.
ret = aclrtMemcpy(resultData.data(),
resultData.size() * sizeof(resultData[0]), selfDeviceAddr,
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. Modify based on the specific API definition.
aclDestroyTensor(self);
aclDestroyTensor(other);
// 7. Release device resources. Modify based on the specific API definition.
aclrtFree(selfDeviceAddr);
aclrtFree(otherDeviceAddr);
if (workspaceSize > 0) {
aclrtFree(workspaceAddr);
}
aclrtDestroyStream(stream);
aclrtResetDevice(deviceId);
aclFinalize();
return 0;
}```