# aclnnCalculateConvolutionWeightSize ## 支持的产品型号 - Atlas 推理系列产品。 ## 接口原型 `aclnnStatus aclnnCalculateConvolutionWeightSize(const aclIntArray* tensorShape, bool transposed, int64_t groups, aclDataType dataType, uint64_t* weightTensorSize)` ## 功能描述 - 算子功能：在Convolution算子NCHW格式输入下，计算需要申请的weight的大小，仅支持Float16数据类型，该接口仅仅用于判断对weight Tensor进行预处理需要使用多少size才可使Convolution算子执行性能最优。例如：输入【2, 4, 8, 8】，该函数出于性能角度考虑，会将shape变化为【64, 1, 16, 16】，因此函数会将引用输入修改为16384。 ## aclnnCalculateConvolutionWeightSize - **参数说明：** * tensorShape(aclIntArray*, 计算输入)：用于表达该次Convolution载入权重矩阵的Shape，仅支持NCHW格式的shape。 * transposed(bool \*, 计算输入)：Host侧的布尔值，表明是否为转置卷积，目前仅支持设为false。 * groups(int64_t \*, 计算输入)：Host侧的整型，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]。 * dataType(aclDataType \*, 计算输入)：Host侧的aclDataType类型，表示转换后weight的数据类型，仅支持ACL_FLOAT16。 * weightTensorSize(uint64_t \*, 计算输出)：根据Convolution内部处理逻辑，计算该输入下weight需要多少个元素的数据量。 - **返回值：** aclnnStatus：返回状态码，具体参见[aclnn返回码](common/aclnn返回码.md)。 ``` 161001(ACLNN_ERR_PARAM_NULLPTR)：1. 输入是空指针。 161002(ACLNN_ERR_PARAM_INVALID)：1. 输入shape校验失败或其他输入不符合预期。 ``` ## 约束与限制 - 仅支持正向Conv2D场景。 - 不支持转置卷积。 ## 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](common/编译与运行样例.md)。 ```Cpp #include #include #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 LOG_PRINT(message, ...) \ do { \ printf(message, ##__VA_ARGS__); \ } while (0) int64_t GetShapeSize(const std::vector& shape) { int64_t shapeSize = 1; for (auto i : shape) { shapeSize *= i; } return shapeSize; } int Init(int32_t deviceId, aclrtStream* stream) { // 固定写法，AscendCL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 int CreateAclTensor(const std::vector& hostData, const std::vector& 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 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 int CreateWeightAclTensor(const std::vector &hostData, const std::vector &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 const aclIntArray* weightSize = aclCreateIntArray(shape.data(), shape.size()); 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 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; } int main() { // 1. （固定写法）device/stream初始化，参考AscendCL对外接口列表 // 根据自己的实际device填写deviceId 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. 构造输入与输出，需要根据API的接口自定义构造 std::vector inputShape = {1, 4, 16, 16}; std::vector weightShape = {2, 4, 8, 8}; std::vector biasShape = {2}; std::vector 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; // aclTensor* transWeight = nullptr; std::vector inputHostData(1024, 1); std::vector weightHostData(512, 1); std::vector biasHostData(2, 1); std::vector outHostData(162, 0); uint64_t transWeightSize = 0; // 创建self aclTensor ret = CreateAclTensor(inputHostData, inputShape, &inputDeviceAddr, aclDataType::ACL_FLOAT16, &input); CHECK_RET(ret == ACL_SUCCESS, return ret); // 创建other aclTensor ret = CreateWeightAclTensor(weightHostData, weightShape, &weightDeviceAddr, aclDataType::ACL_FLOAT16, &weight, transWeightSize); CHECK_RET(ret == ACL_SUCCESS, return ret); // 创建bias aclTensor ret = CreateAclTensor(biasHostData, biasShape, &biasDeviceAddr, aclDataType::ACL_FLOAT16, &bias); CHECK_RET(ret == ACL_SUCCESS, return ret); // 创建out aclTensor ret = CreateAclTensor(outHostData, outShape, &outDeviceAddr, aclDataType::ACL_FLOAT16, &out); CHECK_RET(ret == ACL_SUCCESS, return ret); // 创建Transweight acltensor void* transWeightDeviceAddr = nullptr; uint64_t size = transWeightSize * sizeof(float) / 2; // size = 8192 * sizeof(float_t); ret = aclrtMalloc(&transWeightDeviceAddr, size, ACL_MEM_MALLOC_HUGE_FIRST); CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtMalloc failed. ERROR: %d\n", ret);return ret); std::vector 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_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtMemcpy failed. ERROR: %d\n", ret); return ret); // 计算连续tensor的strides vector shape = weightShape; std::vector 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); // 3. 调用CANN算子库API，需要修改为具体的Api名称 int8_t cubeMathType = 0; 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_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; 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); } // 调用aclnnTransConvolutionWeight第二段接口 ret = aclnnTransConvolutionWeight(workspaceAddr, workspaceSize, executor, stream); CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnTransConvolutionWeight failed. ERROR: %d\n", ret); return ret); std::vector convStrides = {1, 1, 1, 1}; std::vector convPads = {0, 0, 0, 0}; std::vector convOutPads = {1, 1, 1, 1}; std::vector convDilations = {1, 1, 1, 1}; aclIntArray *strides = aclCreateIntArray(convStrides.data(), 2); aclIntArray *pads = aclCreateIntArray(convPads.data(), 2); aclIntArray *outPads = aclCreateIntArray(convOutPads.data(), 2); aclIntArray *dilations = aclCreateIntArray(convDilations.data(), 2); // 3. 调用CANN算子库API，需要修改为具体的API 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_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_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_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_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 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. 释放aclTensor和aclScalar，需要根据具体API的接口定义修改 aclDestroyTensor(input); aclDestroyTensor(weight); aclDestroyTensor(transWeight); aclDestroyTensor(bias); aclDestroyTensor(out); aclDestroyIntArray(strides); aclDestroyIntArray(pads); aclDestroyIntArray(outPads); aclDestroyIntArray(dilations); // 7. 释放device资源，需要根据具体API的接口定义修改 aclrtFree(inputDeviceAddr); aclrtFree(weightDeviceAddr); aclrtFree(transWeightDeviceAddr); aclrtFree(outDeviceAddr); if (workspaceSize > 0) { aclrtFree(workspaceAddr); } aclrtDestroyStream(stream); aclrtResetDevice(deviceId); aclFinalize(); return 0; } ```