aclnnUpsampleNearest1dV2
Applicable Products
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Description
Description: Applies the nearest neighbor interpolation algorithm to upsample an input signal consisting of multiple input channels. If the input shape is (N, C, L), the output shape is (N, C, outputSize). Compared with aclnnUpsampleNearest1d, this API adds the input parameter scaleL. Select the appropriate API based on your actual situation.
Corresponding Formula:
Function Prototype
Each operator is divided into a two-phase API. You must first call the "aclnnUpsampleNearest1dV2GetWorkspaceSize" API to obtain the workspace size required for computation and the executor that contains the operator computation process, and then call the "aclnnUpsampleNearest1dV2" API to execute the computation.
aclnnStatus aclnnUpsampleNearest1dV2GetWorkspaceSize(
const aclTensor *self,
const aclIntArray *outputSize,
float scaleL,
aclTensor *out,
uint64_t *workspaceSize,
aclOpExecutor **executor)aclnnStatus aclnnUpsampleNearest1dV2(
void *workspace,
uint64_t workspaceSize,
aclOpExecutor *executor,
aclrtStream stream)aclnnUpsampleNearest1dV2GetWorkspaceSize
Parameters
Parameter Name Input/Output Description Usage Description Data Type Data Format Dimension (Shape) Non-Contiguous Tensor self (aclTensor*) Input Represents the input data for upsampling, corresponding to `self` in the formula. - Empty tensors are not supported.
- The input dimension must be 3D. All dimension values of self must be less than or equal to (2^31-1). When the data type is DOUBLE or UINT8, the L dimension value of self must also be less than or equal to (2^24).
FLOAT32, FLOAT16, BFLOAT16, DOUBLE, UINT8 NCL 3 โ outputSize (aclIntArray*) Input Indicates the spatial size of the output out in the L dimension. The size is 1, and the value is greater than 0. INT64 - - - scaleL (float) Input Represents the multiplier that specifies the spatial size, corresponding to `scaleL` in the formula. - - - - - out (aclTensor*) Output Represents the output result of the upsampling operation, corresponding to `out` in the formula. - Empty tensors are not supported.
- The data type must be consistent with that of the input `self`. All dimension values of out must be less than or equal to (2^31-1). When the data type is DOUBLE or UINT8, the L dimension value of out must also be less than or equal to (2^24).
FLOAT32, FLOAT16, BFLOAT16, DOUBLE, UINT8 NCL 3 โ workspaceSize (uint64_t*) Output Returns the workspace size to be applied on the Device side. - - - - - executor(aclOpExecutor**) Output Returns the op executor, which contains the operator computation flow. - - - - - Atlas inference products ,Atlas training products :The input parameter
selfand output parameteroutdo not support BFLOAT16 data type.
Return Value
aclnnStatus: return status code. For details, see aclnn Return Codes.
The first-phase API completes input parameter validation and reports an error in the following scenarios:
Return Code Error Code Description ACLNN_ERR_PARAM_NULLPTR 161001 The input parameter is a required input, output, or required attribute, and is a null pointer. ACLNN_ERR_PARAM_INVALID 161002 The data type of self or out is not within the supported range. The data type or data format of self and out is inconsistent. The shape of self is not 3-dimensional. The value of outputSize is 0.
aclnnUpsampleNearest1dV2
Parameters
Parameter Name Input/Output Description workspace Input Workspace memory address applied on the Device side. workspaceSize Input Workspace size applied on the Device side, obtained from the first-phase API aclnnUpsampleNearest1dV2GetWorkspaceSize. executor Input Operator executor, which contains the operator computation process. stream Input Specifies the stream on which the task is executed. Return Value
aclnnStatus: Return status code. For details, see aclnn Return Codes.
Constraints
The parameters self, outputSize, and scaleL must satisfy the following constraint:
Deterministic computation:
- aclnnUpsampleNearest1dV2 uses deterministic implementation by default.
Example
The following sample code is for reference only. For details about compilation and execution, see Compile and Run Samples.
#include <iostream>
#include <vector>
#include "acl/acl.h"
#include "aclnnop/aclnn_upsample_nearest_1d_v2.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: 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-side 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-side data 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);
// Compute 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_NCL,
shape.data(),
shape.size(),
*deviceAddr);
return 0;
}
int main()
{
// 1. (Boilerplate) Initialize device/stream. See the acl API manual.
// 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 own needs.
CHECK_RET(ret == 0, LOG_PRINT("Init acl failed. ERROR: %d\n", ret); return ret);
// 2. Construct the input and output. Customize the construction based on the API.
std::vector<int64_t> selfShape = {1, 3, 3};
std::vector<int64_t> outShape = {1, 3, 6};
void *selfDeviceAddr = nullptr;
void *outDeviceAddr = nullptr;
aclTensor *self = nullptr;
aclTensor *out = nullptr;
float scaleL = 0.0;
std::vector<float> selfHostData = {0, 1, 2, 3, 4, 5, 6, 7, 8};
std::vector<int64_t> outputSizeHostData = {6};
std::vector<float> outHostData(6);
// Create the self aclTensor.
ret = CreateAclTensor(selfHostData, selfShape, &selfDeviceAddr, aclDataType::ACL_FLOAT, &self);
CHECK_RET(ret == ACL_SUCCESS, return ret);
// Create the self aclIntArray.
auto outputSizeArray = aclCreateIntArray(outputSizeHostData.data(), 1);
// Create the out aclTensor.
ret = CreateAclTensor(outHostData, outShape, &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 aclnnUpsampleNearest1dV2.
ret = aclnnUpsampleNearest1dV2GetWorkspaceSize(self, outputSizeArray, scaleL, out, &workspaceSize, &executor);
CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnUpsampleNearest1dV2GetWorkspaceSize 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 aclnnUpsampleNearest1dV2.
ret = aclnnUpsampleNearest1dV2(workspaceAddr, workspaceSize, executor, stream);
CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnUpsampleNearest1dV2 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 device memory to host memory. Modify this 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(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 the aclTensor. Modify based on the specific API definition.
aclDestroyTensor(self);
aclDestroyIntArray(outputSizeArray);
aclDestroyTensor(out);
// 7. Release the device resource. Modify based on the specific API definition.
aclrtFree(selfDeviceAddr);
aclrtFree(outDeviceAddr);
if (workspaceSize > 0) {
aclrtFree(workspaceAddr);
}
aclrtDestroyStream(stream);
aclrtResetDevice(deviceId);
aclFinalize();
return 0;
}