aclnnTransConvolutionWeight
产品支持情况
| 产品 | 是否支持 |
|---|---|
| Ascend 950PR/Ascend 950DT | × |
| Atlas A3 训练系列产品/Atlas A3 推理系列产品 | × |
| Atlas A2 训练系列产品/Atlas A2 推理系列产品 | × |
| Atlas 200I/500 A2 推理产品 | × |
| Atlas 推理系列产品 | √ |
| Atlas 训练系列产品 | × |
功能说明
需要和aclnnCalculateConvolutionWeightSize接口配套使用，用于创建一个对于Convolution算子计算性能亲和的weight Tensor。
函数原型
每个算子分为两段式接口，必须先调用“aclnnTransConvolutionWeightGetWorkspaceSize”接口获取计算所需workspace大小以及包含了算子计算流程的执行器，再调用“aclnnTransConvolutionWeight”接口执行计算。
aclnnStatus aclnnTransConvolutionWeightGetWorkspaceSize(
const aclTensor* weightIn,
bool transposed,
const int64_t groups,
aclTensor* weightOut,
uint64_t* workspaceSize,
aclOpExecutor** executor)
aclnnStatus aclnnTransConvolutionWeight(
void* workspace,
uint64_t workspaceSize,
aclOpExecutor* executor,
aclrtStream stream)
aclnnTransConvolutionWeightGetWorkspaceSize
-
参数说明：
参数名 输入/输出 描述 使用说明 数据类型 数据格式 维度（shape） 非连续 Tensor weightIn（uint64_t*） 输入 表示一个待处理的Convolution的weightTensor。 支持空Tensor输入；当weightIn为空Tensor时，weightOut也必须为空Tensor。 FLOAT16、FLOAT32 NCHW 4 √ transposed（bool） 输入 表明是否为转置卷积。 目前仅支持设为false。 BOOL - - - groups（int64_t） 输入 表示从输入通道到输出通道的块链接个数。 取值范围为[1,65535]。 INT64 - - - weightOut（aclTensor*） 输出 表示返回输入weight转换为私有格式后的tensor。 支持空Tensor输出；当weightOut为空Tensor时，weightIn也必须为空Tensor。 FLOAT16 NCHW 4 - workspaceSize（uint64_t*） 输出 返回需要在Device侧申请的workspace大小 不能为空指针；空Tensor场景下返回0。 - - - - executor（aclOpExecutor**） 输出 返回op执行器，包含了算子计算流程。 不能为空指针。 - - - - -
返回值：
aclnnStatus：返回状态码，具体参见 aclnn 返回码。第一段接口完成入参校验，出现以下场景时报错：
返回值 错误码 描述 ACLNN_ERR_PARAM_NULLPTR 161001 输入是空指针。 ACLNN_ERR_PARAM_INVALID 161002 输入输出Tensor的数据类型、数据格式以及其他参数不符合预期。比如输入weightIn为非FLOAT16、FLOAT32数据类型或者非NCHW数据格式；或weightIn/weightOut空Tensor状态不一致。
aclnnTransConvolutionWeight
-
参数说明：
参数名 输入/输出 描述 workspace 输入 在Device侧申请的workspace内存地址。 workspaceSize 输入 在Device侧申请的workspace大小，由第一段接口aclnnTransConvolutionWeightGetWorkspaceSize获取。 executor 输入 op执行器，包含了算子计算流程。 stream 输入 指定执行任务的Stream。 -
返回值：
aclnnStatus：返回状态码，具体参见 aclnn 返回码。
约束说明
-
确定性计算：
- aclnnTransConvolutionWeight默认确定性实现。
-
仅支持正向Conv2D场景。
-
不支持转置卷积。
-
不支持cache缓存能力。
-
支持空Tensor：当weightIn与weightOut均为空Tensor时，不执行实际转换，workspaceSize返回0。
调用示例
示例代码如下，仅供参考，具体编译和执行过程请参考编译与运行样例。
#include <iostream>
#include <memory>
#include <vector>
#include "acl/acl.h"
#include "aclnnop/aclnn_convolution.h"
#include "aclnnop/aclnn_trans_convolution_weight.h"
using namespace std;
#define CHECK_RET(cond, return_expr) \
do { \
if (!(cond)) { \
return_expr; \
} \
} while (0)
#define CHECK_FREE_RET(cond, return_expr) \
do { \
if (!(cond)) { \
Finalize(deviceId, stream); \
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) {
// 固定写法，Stream初始化
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);
// 调用aclrtMalloc申请device侧内存
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);
// 调用aclrtMemcpy将host侧数据拷贝到device侧内存上
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);
// 计算连续tensor的strides
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];
}
// 调用aclCreateTensor接口创建aclTensor
*tensor = aclCreateTensor(shape.data(), shape.size(), dataType, strides.data(), 0, aclFormat::ACL_FORMAT_NCHW,
shape.data(), shape.size(), *deviceAddr);
return 0;
}
template <typename T>
int CreateWeightAclTensor(const std::vector<T> &hostData, const std::vector<int64_t> &shape, void **deviceAddr,
aclDataType dataType, aclTensor **tensor, uint64_t &TransWeightSize)
{
auto size = GetShapeSize(shape) * sizeof(T);
// 调用transweight host接口 计算实际elements数量
aclIntArray* weightSize = aclCreateIntArray(shape.data(), shape.size());
std::unique_ptr<aclIntArray, aclnnStatus (*)(const aclIntArray *)> weightSizePtr(weightSize, aclDestroyIntArray);
auto ret = aclnnCalculateConvolutionWeightSize(weightSize, false, 1, aclDataType::ACL_FLOAT16, &TransWeightSize);
// 调用aclrtMalloc申请device侧内存
ret = aclrtMalloc(deviceAddr, size, ACL_MEM_MALLOC_HUGE_FIRST);
CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtMalloc failed. ERROR: %d\n", ret);
return ret);
// 调用aclrtMemcpy将Host侧数据拷贝到device侧内存上
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);
// 计算连续tensor的strides
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];
}
// 调用aclCreateTensor接口创建aclTensor
*tensor = aclCreateTensor(shape.data(), shape.size(), dataType, strides.data(), 0, aclFormat::ACL_FORMAT_NCHW,
shape.data(), shape.size(), *deviceAddr);
return 0;
}
void Finalize(int32_t deviceId, aclrtStream& stream)
{
aclrtDestroyStream(stream);
aclrtResetDevice(deviceId);
aclFinalize();
}
int aclnnTransConvolutionWeightTest(int32_t deviceId, aclrtStream& stream) {
auto ret = Init(deviceId, &stream);
CHECK_FREE_RET(ret == ACL_SUCCESS, LOG_PRINT("Init acl failed. ERROR: %d\n", ret); return ret);
// 2. 构造输入与输出，需要根据API的接口自定义构造
std::vector<int64_t> inputShape = {1, 4, 16, 16};
std::vector<int64_t> weightShape = {2, 4, 8, 8};
std::vector<int64_t> biasShape = {2};
std::vector<int64_t> outShape = {1, 2, 9, 9};
void* inputDeviceAddr = nullptr;
void* weightDeviceAddr = nullptr;
void* biasDeviceAddr = nullptr;
void* outDeviceAddr = nullptr;
aclTensor* input = nullptr;
aclTensor* weight = nullptr;
aclTensor* bias = nullptr;
aclTensor* out = nullptr;
std::vector<float> inputHostData(1024, 1);
std::vector<float> weightHostData(512, 1);
std::vector<float> biasHostData(2, 1);
std::vector<float> outHostData(162, 0);
uint64_t transWeightSize = 0;
// 创建input aclTensor
ret = CreateAclTensor(inputHostData, inputShape, &inputDeviceAddr, aclDataType::ACL_FLOAT, &input);
std::unique_ptr<aclTensor, aclnnStatus (*)(const aclTensor *)> selfTensorPtr(input, aclDestroyTensor);
std::unique_ptr<void, aclError (*)(void *)> inputDeviceAddrPtr(inputDeviceAddr, aclrtFree);
CHECK_FREE_RET(ret == ACL_SUCCESS, return ret);
// 创建weight aclTensor
ret = CreateWeightAclTensor(weightHostData, weightShape, &weightDeviceAddr, aclDataType::ACL_FLOAT,
&weight, transWeightSize);
std::unique_ptr<aclTensor, aclnnStatus (*)(const aclTensor *)> weightTensorPtr(weight, aclDestroyTensor);
std::unique_ptr<void, aclError (*)(void *)> weightDeviceAddrPtr(weightDeviceAddr, aclrtFree);
CHECK_FREE_RET(ret == ACL_SUCCESS, return ret);
// 创建bias aclTensor
ret = CreateAclTensor(biasHostData, biasShape, &biasDeviceAddr, aclDataType::ACL_FLOAT, &bias);
std::unique_ptr<aclTensor, aclnnStatus (*)(const aclTensor *)> biasTensorPtr(bias, aclDestroyTensor);
std::unique_ptr<void, aclError (*)(void *)> biasDeviceAddrPtr(biasDeviceAddr, aclrtFree);
CHECK_FREE_RET(ret == ACL_SUCCESS, return ret);
// 创建out aclTensor
ret = CreateAclTensor(outHostData, outShape, &outDeviceAddr, aclDataType::ACL_FLOAT, &out);
std::unique_ptr<aclTensor, aclnnStatus (*)(const aclTensor *)> outTensorPtr(out, aclDestroyTensor);
std::unique_ptr<void, aclError (*)(void *)> outDeviceAddrPtr(outDeviceAddr, aclrtFree);
CHECK_FREE_RET(ret == ACL_SUCCESS, return ret);
// 创建Transweight acltensor
void* transWeightDeviceAddr = nullptr;
uint64_t size = transWeightSize * sizeof(float) / 2;
ret = aclrtMalloc(&transWeightDeviceAddr, size, ACL_MEM_MALLOC_HUGE_FIRST);
CHECK_FREE_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtMalloc failed. ERROR: %d\n", ret);return ret);
std::vector<float> transData;
transData.resize(transWeightSize * 2);
// 调用aclrtMemcpy将Host侧数据拷贝到device侧内存上transData.data()
ret = aclrtMemcpy(transWeightDeviceAddr, size, transData.data(), size, ACL_MEMCPY_HOST_TO_DEVICE);
CHECK_FREE_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtMemcpy failed. ERROR: %d\n", ret);
return ret);
// 计算连续tensor的strides
vector<int64_t> shape = weightShape;
std::vector<int64_t> s(shape.size(), 1);
for (int64_t i = shape.size() - 2; i >= 0; i--) {
s[i] = shape[i + 1] * s[i + 1];
}
aclTensor* transWeight = aclCreateTensor(shape.data(), shape.size(), aclDataType::ACL_FLOAT16, s.data(), 0, aclFormat::ACL_FORMAT_NCHW,
shape.data(), shape.size(), transWeightDeviceAddr);
std::unique_ptr<aclTensor, aclnnStatus (*)(const aclTensor *)> transWeightTensorPtr(transWeight, aclDestroyTensor);
std::unique_ptr<void, aclError (*)(void *)> transWeightDeviceAddrAddrPtr(transWeightDeviceAddr, aclrtFree);
// 3. 调用 aclnnTransConvolutionWeight
int8_t cubeMathType = 2; // USE_FP16
uint64_t workspaceSize = 0;
aclOpExecutor* executor;
bool transposed = 0;
uint64_t groups = 1;
// 调用TransWeight
ret = aclnnTransConvolutionWeightGetWorkspaceSize(weight, transposed, groups, transWeight,
&workspaceSize, &executor);
CHECK_FREE_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnTransConvolutionWeightGetWorkspaceSize failed. ERROR: %d\n", ret);
return ret);
// 根据第一段接口计算出的workspaceSize申请device内存
void* workspaceAddr = nullptr;
std::unique_ptr<void, aclError (*)(void *)> workspaceAddrPtr(nullptr, aclrtFree);
if (workspaceSize > 0) {
ret = aclrtMalloc(&workspaceAddr, workspaceSize, ACL_MEM_MALLOC_HUGE_FIRST);
CHECK_FREE_RET(ret == ACL_SUCCESS, LOG_PRINT("allocate workspace failed. ERROR: %d\n", ret); return ret);
workspaceAddrPtr.reset(workspaceAddr);
}
// 调用aclnnTransConvolutionWeight第二段接口
ret = aclnnTransConvolutionWeight(workspaceAddr, workspaceSize, executor, stream);
CHECK_FREE_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnTransConvolutionWeight failed. ERROR: %d\n", ret); return ret);
std::vector<int64_t> convStrides = {1, 1};
std::vector<int64_t> convPads = {0, 0};
std::vector<int64_t> convOutPads = {1, 1};
std::vector<int64_t> convDilations = {1, 1};
aclIntArray *strides = aclCreateIntArray(convStrides.data(), convStrides.size());
std::unique_ptr<aclIntArray, aclnnStatus (*)(const aclIntArray *)> stridesPtr(strides, aclDestroyIntArray);
CHECK_FREE_RET(strides != nullptr, return ACL_ERROR_INTERNAL_ERROR);
aclIntArray *pads = aclCreateIntArray(convPads.data(), convPads.size());
std::unique_ptr<aclIntArray, aclnnStatus (*)(const aclIntArray *)> padsPtr(pads, aclDestroyIntArray);
CHECK_FREE_RET(pads != nullptr, return ACL_ERROR_INTERNAL_ERROR);
aclIntArray *outPads = aclCreateIntArray(convOutPads.data(), convOutPads.size());
std::unique_ptr<aclIntArray, aclnnStatus (*)(const aclIntArray *)> outPadsPtr(outPads, aclDestroyIntArray);
CHECK_FREE_RET(outPads != nullptr, return ACL_ERROR_INTERNAL_ERROR);
aclIntArray *dilations = aclCreateIntArray(convDilations.data(), convDilations.size());
std::unique_ptr<aclIntArray, aclnnStatus (*)(const aclIntArray *)> dilationsPtr(dilations, aclDestroyIntArray);
CHECK_FREE_RET(dilations != nullptr, return ACL_ERROR_INTERNAL_ERROR);
// 4. 调用 aclnnConvolution
workspaceSize = 0;
// 调用aclnnConvolution第一段接口
ret = aclnnConvolutionGetWorkspaceSize(input, transWeight, bias, strides, pads, dilations, false, outPads, groups,
out, cubeMathType, &workspaceSize, &executor);
CHECK_FREE_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnConvolutionGetWorkspaceSize failed. ERROR: %d\n", ret); return ret);
// 根据第一段接口计算出的workspaceSize申请device内存
workspaceAddr = nullptr;
if (workspaceSize > 0) {
ret = aclrtMalloc(&workspaceAddr, workspaceSize, ACL_MEM_MALLOC_HUGE_FIRST);
CHECK_FREE_RET(ret == ACL_SUCCESS, LOG_PRINT("allocate workspace failed. ERROR: %d\n", ret); return ret);
}
// 调用aclnnConvolution第二段接口
ret = aclnnConvolution(workspaceAddr, workspaceSize, executor, stream);
CHECK_FREE_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnConvolution failed. ERROR: %d\n", ret); return ret);
// 4. （固定写法）同步等待任务执行结束
ret = aclrtSynchronizeStream(stream);
CHECK_FREE_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtSynchronizeStream failed. ERROR: %d\n", ret); return ret);
// 5. 获取输出的值，将device侧内存上的结果拷贝至host侧，需要根据具体API的接口定义修改
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_FREE_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]);
}
return ACL_SUCCESS;
}
int main() {
// 1. （固定写法）device/stream初始化，参考acl API手册
// 根据自己的实际device填写deviceId
int32_t deviceId = 0;
aclrtStream stream;
auto ret = aclnnTransConvolutionWeightTest(deviceId, stream);
CHECK_FREE_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnTransConvolutionWeightTest failed. ERROR: %d\n", ret); return ret);
Finalize(deviceId, stream);
return 0;
}