aclnnNLLLoss2d

接口原型

每个算子有两段接口,必须先调用“aclnnXxxGetWorkspaceSize”接口获取入参并根据计算流程计算所需workspace大小,再调用“aclnnXxx”接口执行计算。两段式接口如下:

功能描述

aclnnNLLLoss2dGetWorkspaceSize

aclnnNLLLoss2d

约束与限制

调用示例

#include <iostream>
#include <vector>
#include "acl/acl.h"
#include "aclnnop/level2/aclnn_nll_loss2d.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, aclrtContext* context, aclrtStream* stream) {
    // 固定写法,acl初始化
    auto ret = aclrtSetDevice(deviceId);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtSetDevice failed. ERROR: %d\n", ret); return ret);
    ret = aclrtCreateContext(context, deviceId);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtCreateContext failed. ERROR: %d\n", ret); return ret);
    ret = aclrtSetCurrentContext(*context);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtSetCurrentContext 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);

    ret = aclInit(nullptr);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclInit 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_ND,
                              shape.data(), shape.size(), *deviceAddr);
    return 0;
}

int main() {
    // 1. (固定写法)device/context/stream初始化, 参考acl对外接口列表
    // 根据自己的实际device填写deviceId
    int32_t deviceId = 0;
    aclrtContext context;
    aclrtStream stream;
    auto ret = Init(deviceId, &context, &stream);
    // check根据自己的需要处理
    CHECK_RET(ret == 0, 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> selfShape = {1, 2, 3, 2};
	std::vector<int64_t> targetShape = {1, 3, 2};
	std::vector<int64_t> weightShape = {2};
	std::vector<int64_t> outShape = {1, 3, 2};
	std::vector<int64_t> totalWeightOutShape = {1};
	void* selfDeviceAddr = nullptr;
	void* targetDeviceAddr = nullptr;
	void* weightDeviceAddr = nullptr;
	void* outDeviceAddr = nullptr;
	void* totalWeightOutDeviceAddr = nullptr;
	aclTensor* self = nullptr;
	aclTensor* target = nullptr;
	aclTensor* weight = nullptr;
	aclTensor* out = nullptr;
	aclTensor* totalWeightOut = nullptr;
	std::vector<float> selfHostData = {0.1, 1.1, 2.1, 3.1, 4.1, 5.1, 6.1, 7.1, 8.1, 9.1, 10.1, 11.1};
	std::vector<int32_t> targetHostData = {1, 0, 1, 1, 2, 1};
	std::vector<float> weightHostData = {1.1, 1.2};
	std::vector<float> outHostData = {0.0, 0.0, 0.0, 0.0, 0.0, 0.0};
	std::vector<float> totalWeightOutHostData = {0};
	int64_t reduction = 0;
	int64_t ignoreIndex = -100;

	// 创建self aclTensor
	ret = CreateAclTensor(selfHostData, selfShape, &selfDeviceAddr, aclDataType::ACL_FLOAT, &self);
	CHECK_RET(ret == ACL_SUCCESS, return ret);
	// 创建target aclTensor
	ret = CreateAclTensor(targetHostData, targetShape, &targetDeviceAddr, aclDataType::ACL_INT32, &target);
	CHECK_RET(ret == ACL_SUCCESS, return ret);
	// 创建weight aclScalar
	ret = CreateAclTensor(weightHostData, weightShape, &weightDeviceAddr, aclDataType::ACL_FLOAT, &weight);
	CHECK_RET(ret == ACL_SUCCESS, return ret);
	// 创建out aclTensor
	ret = CreateAclTensor(outHostData, outShape, &outDeviceAddr, aclDataType::ACL_FLOAT, &out);
	CHECK_RET(ret == ACL_SUCCESS, return ret);
	// 创建totalWeightOut aclTensor
	ret = CreateAclTensor(totalWeightOutHostData, totalWeightOutShape, &totalWeightOutDeviceAddr, aclDataType::ACL_FLOAT,
	&totalWeightOut);
	CHECK_RET(ret == ACL_SUCCESS, return ret);

	// 3. 调用CANN算子库API需要修改为具体的HostApi
	uint64_t workspaceSize = 0;
	aclOpExecutor* executor;
	// 调用aclnnNLLLoss2d第一段接口
	ret = aclnnNLLLoss2dGetWorkspaceSize(self, target, weight, reduction, ignoreIndex, out, totalWeightOut, &workspaceSize,
	&executor);
	CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnNLLLoss2dGetWorkspaceSize 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aclnnNLLLoss2d第二段接口
    ret = aclnnNLLLoss2d(workspaceAddr, workspaceSize, executor, stream);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnNLLLoss2d 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
    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. 释放aclTensor,需要根据具体API的接口定义修改
    aclDestroyTensor(self);
    aclDestroyTensor(target);
    aclDestroyTensor(weight);
    aclDestroyTensor(out);
    return 0;
}