aclnnMaxPool2dWithMaskBackward
Applicable Products
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Function
Backward propagation of the forward max pooling aclnnMaxPool2dWithMask.
Function Prototype
Each operator is divided into a two-phase API. First call the "aclnnMaxPool2dWithMaskBackwardGetWorkspaceSize" API to obtain the workspace size required for computation and the executor that contains the operator computation process, and then call the "aclnnMaxPool2dWithMaskBackward" API to perform the computation.
aclnnStatus aclnnMaxPool2dWithMaskBackwardGetWorkspaceSize(
const aclTensor *gradOutput,
const aclTensor *self,
const aclTensor *indices,
const aclIntArray *kernelSize,
const aclIntArray *stride,
const aclIntArray *padding,
const aclIntArray *dilation,
bool ceilMode,
aclTensor *gradInput,
uint64_t *workspaceSize,
aclOpExecutor **executor)aclnnStatus aclnnMaxPool2dWithMaskBackward(
void *workspace,
uint64_t workspaceSize,
aclOpExecutor *executor,
aclrtStream stream)aclnnMaxPool2dWithMaskBackwardGetWorkspaceSize
Parameters:
Parameter Input/Output Description Instruction Data Type Data Format Dimension (Shape) Non-contiguous Tensor gradOutput Input Gradient output from the previous step during backward propagation. Has the same shape as the forward output and the same data format as self. FLOAT32, FLOAT16, BFLOAT16 NCHW 4 √ self Input Forward input data. - FLOAT32, FLOAT16, BFLOAT16 NCHW 4 √ indices Input Index of the forward output. Tensor composed of bit values at the kernel positions where the maximum values are located when computing the mask. INT8 NCHW 4 √ kernelSize Input Size of the sliding window used in the pooling operation. Only lengths of 1 and 2 are supported. INT64 - - - stride Input The stride of the sliding window. The length supports only 0, 1, and 2. When the length of stride is 0, the value of stride equals the value of kernelSize. INT64 - - - padding Input Padding of the input data, indicating the amount of padding on each dimension of the input, which affects how the pooling window covers the entire input tensor. Only lengths of 1 and 2 are supported. INT64 - - - dilation Input Controls the stride of elements in the window. Only supports lengths of 1 and 2, and the value can only be 1. INT64 - - - ceilMode Input Rounding method used when computing the output shape. If True, the output shape is computed using ceiling rounding; if False, floor rounding is used. BOOL - - - gradInput Output Gradient output by backward propagation. The shape and data format are consistent with self. BFLOAT16, FLOAT16, FLOAT32 NCHW 4 √ workspaceSize Output Returns the workspace size to be allocated on the Device side. - - - - - executor Output Returns the op executor, which contains the operator computation process. - - - - - Return value
aclnnStatus: Return Status. See aclnn Return Code. The first-phase API performs input parameter validation and reports an error in the following scenarios:
Return Code Error Code Description ACLNN_ERR_PARAM_NULLPTR 161001 The input self and indices are null pointers. ACLNN_ERR_PARAM_INVALID 161002 The data type of gradOutput, self, indices, or gradInput is not within the supported range. The data format of gradOutput, self, indices, or gradInput is not within the supported range. The shapes of gradOutput and indices are inconsistent, or the shapes of self and gradInput are inconsistent. The length of kernelSize is not equal to 1 or 2. A value in kernelSize is less than or equal to 0. The length of stride is not 0, 1, or 2. A value in stride is less than or equal to 0. The length of padding is not 1 or 2. A value in padding is less than 0 or greater than the corresponding value in kernelSize. The value of dilation is not equal to 1.
aclnnMaxPool2dWithMaskBackward
Parameters:
Parameter Input/Output Description workspace Input Workspace memory address requested on the device side. workspaceSize Input Workspace size requested on the device side, obtained from the first-phase API aclnnMaxPool2dWithMaskBackwardGetWorkspaceSize. executor Input Operator executor, which contains the operator computation flow. stream Input Specifies the stream for executing the task. Return value
aclnnStatus: return code. For details, see aclnn Return Code.
Constraints
Deterministic computation:
- aclnnMaxPool2dWithMaskBackward defaults to a non-deterministic implementation. You can enable deterministic computation by calling aclrtCtxSetSysParamOpt.
The input data does not support NaN or -Inf.
Atlas training products : When the input data is of the FLOAT type, it is converted to the FLOAT16 type for computation, resulting in a certain degree of precision loss.
Example
The following provides example code for reference only. For details about compilation and running, see Compile and Run Samples.
#include <cstdio>
#include <iostream>
#include <vector>
#include <algorithm>
#include "acl/acl.h"
#include "aclnnop/aclnn_max_pool2d_with_indices_backward.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 memory on the device side.
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 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_NCHW,
shape.data(), shape.size(), *deviceAddr);
return 0;
}
int main() {
// 1. (Boilerplate) Initialize the device/stream. Refer to 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 requirements.
CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("Init acl failed. ERROR: %d\n", ret); return ret);
// 2. Construct the inputs and outputs. Custom construction is required based on the API.
std::vector<int64_t> gradOutShape = {1, 1, 2, 1};
std::vector<int64_t> selfShape = {1, 1, 4, 3};
std::vector<int64_t> indicesShape = {1, 1, 4, 64};
std::vector<int64_t> gradInShape = {1, 1, 4, 3};
std::vector<int64_t> kernelSizeData = {2, 2};
std::vector<int64_t> strideData = {2, 2};
std::vector<int64_t> paddingData = {0, 0};
std::vector<int64_t> dilationData = {1, 1};
void* gradOutDeviceAddr = nullptr;
void* selfDeviceAddr = nullptr;
void* indicesDeviceAddr = nullptr;
void* gradInDeviceAddr = nullptr;
aclTensor* gradOut = nullptr;
aclTensor* self = nullptr;
aclTensor* indices = nullptr;
aclTensor* gradIn = nullptr;
std::vector<float> gradOutHostData = {0.4757, 0.1726};
std::vector<float> selfHostData = {0.0850, -0.5147, -0.0212, -0.5654, -0.3222, 0.5847, 1.7510, 0.9954, 0.1842, 0.8392, 0.4835, 0.9213};
std::vector<int8_t> indicesHostData(256, 0);
std::fill_n(indicesHostData.begin(), 32, -1);
std::vector<float> gradInHostData = {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
// Create the gradOut aclTensor.
ret = CreateAclTensor(gradOutHostData, gradOutShape, &gradOutDeviceAddr, aclDataType::ACL_FLOAT, &gradOut);
CHECK_RET(ret == ACL_SUCCESS, return ret);
// Create the self aclTensor.
ret = CreateAclTensor(selfHostData, selfShape, &selfDeviceAddr, aclDataType::ACL_FLOAT, &self);
CHECK_RET(ret == ACL_SUCCESS, return ret);
// Create the indices aclTensor.
ret = CreateAclTensor(indicesHostData, indicesShape, &indicesDeviceAddr, aclDataType::ACL_INT8, &indices);
CHECK_RET(ret == ACL_SUCCESS, return ret);
// Create the gradIn aclTensor.
ret = CreateAclTensor(gradInHostData, gradInShape, &gradInDeviceAddr, aclDataType::ACL_FLOAT, &gradIn);
CHECK_RET(ret == ACL_SUCCESS, return ret);
// Create the input array.
aclIntArray* kernelSize = aclCreateIntArray(kernelSizeData.data(), 2);
aclIntArray* stride = aclCreateIntArray(strideData.data(), 2);
aclIntArray* padding = aclCreateIntArray(paddingData.data(), 2);
aclIntArray* dilation = aclCreateIntArray(dilationData.data(), 2);
const bool ceilMode = false;
uint64_t workspaceSize = 0;
aclOpExecutor* executor;
// Example of calling the aclnnMaxPool2dWithMaskBackward API.
// 3. Call the CANN operator library API. Replace with the actual API name.
// Call the first-phase API of aclnnMaxPool2dWithMaskBackward.
ret = aclnnMaxPool2dWithMaskBackwardGetWorkspaceSize(gradOut, self, indices, kernelSize, stride, padding, dilation, ceilMode, gradIn, &workspaceSize, &executor);
CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnMaxPool2dWithMaskBackwardGetWorkspaceSize 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 aclnnMaxPool2dWithMaskBackward.
ret = aclnnMaxPool2dWithMaskBackward(workspaceAddr, workspaceSize, executor, stream);
CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnMaxPool2dWithMaskBackward 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 side memory to the Host side. Modify based on the specific API definition.
auto size = GetShapeSize(gradInShape);
std::vector<float> resultData(size, 0);
ret = aclrtMemcpy(resultData.data(), resultData.size() * sizeof(resultData[0]), gradInDeviceAddr,
size * sizeof(resultData[0]), ACL_MEMCPY_DEVICE_TO_HOST);
CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("copy gradIn 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(gradOut);
aclDestroyTensor(self);
aclDestroyTensor(indices);
aclDestroyTensor(gradIn);
// 7. Release device resources. Modify based on the specific API definition.
aclrtFree(gradOutDeviceAddr);
aclrtFree(selfDeviceAddr);
aclrtFree(indicesDeviceAddr);
aclrtFree(gradInDeviceAddr);
if (workspaceSize > 0) {
aclrtFree(workspaceAddr);
}
aclrtDestroyStream(stream);
aclrtResetDevice(deviceId);
aclFinalize();
return 0;
}