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：
- 第一段接口：aclnnStatus aclnnCtcLossBackwardGetWorkspaceSize(const aclTensor *gradOut, const aclTensor *logProbs, const aclTensor *targets, const aclIntArray *inputLengths, const aclIntArray *targetLengths, const aclTensor *negLogLikelihood, const aclTensor *logAlpha, int64_t blank, bool zeroInfinity, aclTensor *out, uint64_t *workspaceSize, aclOpExecutor **executor)
- 第二段接口：aclnnStatus aclnnCtcLossBackward(void *workspace, uint64_t workspaceSize, aclOpExecutor *executor, const aclrtStream stream)
功能描述
算子功能：损失函数aclnnCtcloss的反向传播。
aclnnCtcLossBackwardGetWorkspaceSize
- 接口定义：
aclnnStatus aclnnCtcLossBackwardGetWorkspaceSize(const aclTensor *gradOut, const aclTensor *logProbs, const aclTensor *targets, const aclIntArray *inputLengths, const aclIntArray *targetLengths, const aclTensor *negLogLikelihood, const aclTensor *logAlpha, int64_t blank, bool zeroInfinity, aclTensor *out, uint64_t *workspaceSize, aclOpExecutor **executor)
- 参数说明：
- gradOut：device侧的aclTensor，表示梯度更新系数，必须为1维非空Tensor，数据类型支持FLOAT、DOUBLE（数据类型必须和logProbs一致），支持非连续的Tensor，数据格式支持ND。
- logProbs：device侧的aclTensor，表示输出的对数概率，数据类型支持FLOAT、DOUBLE。当shape为(T, N, C)，其中T为输入长度，N为批处理大小，C为类别数。支持非连续的Tensor，数据格式支持ND。
- targets：device侧的aclTensor，数据类型支持INT64、INT32、FLOAT、FLOAT16、BOOL。当shape为(N, S)，S为不小于targetLengths中的最大值。当targets是未填充的且在1维内级联的，shape为(SUM(targetLengths))。支持非连续的Tensor，数据格式支持ND。
- inputLengths：host侧int数组，数据类型支持UINT8、INT8、INT16、INT32、INT64，数组长度为N，数组中的每个值必须≤T。
- targetLengths：host侧int数组，数据类型支持UINT8、INT8、INT16、INT32、INT64，数组长度为N，当targets的shape为(N, S)时，数组中的每个值必须≤S。
- negLogLikelihood：device侧的aclTensor，表示相对于每个输入节点可微分的损失值，必须为1维非空Tensor。数据类型支持FLOAT、DOUBLE（数据类型必须和logProbs一致），支持非连续的Tensor，数据格式支持ND。
- logAlpha：device侧的aclTensor，表示输入到目标的可能跟踪的概率，必须为3维非空Tensor。数据类型支持FLOAT、DOUBLE（数据类型必须和logProbs一致），支持非连续的Tensor，数据格式支持ND。
- blank：int整型，空白标识，默认为0，取值范围为[0, C），C为类别数。
- zeroInfinity：bool类型，表示是否将无限损耗和相关梯度归零，默认值为False。
- out：device侧的aclTensor，表示CTC的损失梯度，数据类型支持FLOAT、DOUBLE（数据类型必须和gradOut一致），shape为(T, N, C)，支持非连续的Tensor，数据格式支持ND。
- workspaceSize：返回用户需要在npu device侧申请的workspace大小。
- executor：返回op执行器，包含了算子计算流程。
- 返回值：
返回aclnnStatus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161001（ACLNN_ERR_PARAM_NULLPTR）：传入的gradOut、logProbs、targets、inputLengths、targetLengths、negLogLikelihood、logAlpha、out是空指针。
- 返回161002（ACLNN_ERR_PARAM_INVALID）：
- gradOut、logProbs、targets、inputLengths、targetLengths、negLogLikelihood、logAlpha、out的数据类型不在支持的范围内。
- gradOut、negLogLikelihood、logAlpha、out和logProbs的数据类型不一致。
- gradOut、logProbs、targets、negLogLikelihood、logAlpha、out的shape不满足要求，或者inputLengths、targetLengths的ArrayList的长度不满足要求。
- blank不满足取值范围。
aclnnCtcLossBackward
- 接口定义：
aclnnStatus aclnnCtcLossBackward(void *workspace, uint64_t workspaceSize, aclOpExecutor *executor, const aclrtStream stream)
- 参数说明：
- workspace：在npu device侧申请的workspace内存起址。
- workspaceSize：在npu device侧申请的workspace大小，由第一段接口aclnnCtcLossBackwardGetWorkspaceSize获取。
- executor：op执行器，包含了算子计算流程。
- stream：acl stream流。
- 返回值：
返回aclnnStatus状态码，具体参见aclnn返回码。
约束与限制
无
调用示例
#include <iostream>
#include <vector>
#include "acl/acl.h"
#include "aclnnop/level2/aclnn_ctc_loss_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 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> gradOutShape = {4};
// logProbsShape (T, N, C)
std::vector<int64_t> logProbsShape = {12, 4, 5};
std::vector<int64_t> targetsShape = {4, 7};
std::vector<int64_t> negLoglikelihoodShape = {4};
// logAlphaShape (N, T, X) X = ((max(targetLengths) * 2 + 1) + 7) / 8 * 8;
std::vector<int64_t> logAlphaShape = {4, 12, 16};
std::vector<int64_t> outShape = {12, 4, 5};
void* gradOutDeviceAddr = nullptr;
void* logProbsDeviceAddr = nullptr;
void* targetsDeviceAddr = nullptr;
void* negLoglikelihoodDeviceAddr = nullptr;
void* logAlphaDeviceAddr = nullptr;
void* outDeviceAddr = nullptr;
aclTensor* gradOut = nullptr;
aclTensor* logProbs = nullptr;
aclTensor* targets = nullptr;
aclIntArray* inputLengths = nullptr;
aclIntArray* targetLengths = nullptr;
aclTensor* negLoglikelihood = nullptr;
aclTensor* logAlpha = nullptr;
aclTensor* out = nullptr;
std::vector<float> gradOutHostData = {1, 1, 1, 1};
std::vector<float> logProbsHostData = {
-1.0894, -2.7162, -0.9764, -1.9126, -2.6162,
-2.0684, -2.4871, -2.0866, -1.7205, -0.7187,
-2.4423, -1.2017, -1.4653, -1.1821, -2.5942,
-2.4670, -2.7257, -1.4135, -2.1042, -0.7248,
-3.7759, -1.3742, -1.2549, -1.5807, -1.4562,
-1.3826, -1.8995, -1.8527, -0.9493, -2.8895,
-1.6316, -2.6603, -2.5014, -0.6992, -1.8609,
-1.9269, -2.2350, -0.8073, -1.8906, -1.8947,
-0.3468, -2.5855, -2.0723, -2.7147, -3.6668,
-0.9541, -1.7258, -2.0693, -1.6378, -2.1531,
-3.5386, -3.4830, -0.2532, -2.0557, -3.3261,
-1.1480, -1.8080, -0.8244, -3.2414, -3.1909,
-0.8866, -0.7540, -4.4312, -3.4634, -2.6000,
-1.2785, -1.8347, -3.3122, -0.7620, -2.8349,
-1.4975, -1.3865, -0.9645, -3.8171, -2.0939,
-2.3536, -2.0773, -1.4981, -0.8372, -2.0938,
-1.2186, -0.8285, -2.9399, -2.1159, -2.3620,
-2.3139, -0.6503, -2.7249, -1.2340, -3.7927,
-0.7143, -2.5084, -3.2826, -2.6651, -1.1334,
-1.6965, -1.9728, -2.3849, -1.6052, -0.9554,
-1.6384, -1.2596, -2.1680, -1.8476, -1.3866,
-3.0455, -0.5737, -2.5339, -2.1118, -1.6681,
-2.4675, -2.8842, -0.4329, -3.6266, -1.6925,
-3.1023, -2.7696, -1.2755, -0.6470, -2.4143,
-2.0107, -2.0912, -1.3053, -0.8557, -3.0683,
-1.2872, -3.6523, -1.6703, -2.7596, -0.8063,
-2.4633, -1.2959, -1.6153, -2.3072, -1.0705,
-3.0543, -0.6473, -1.1650, -2.9025, -2.7710,
-3.5519, -2.0400, -1.8667, -1.4289, -0.8050,
-1.4602, -0.7452, -1.5754, -3.1624, -3.1247,
-1.4677, -1.2725, -2.9575, -1.8883, -1.2513,
-1.2164, -1.5894, -2.2217, -2.3714, -1.2110,
-2.0843, -0.6515, -1.4252, -2.9402, -2.7964,
-1.5261, -2.5471, -1.7167, -1.9846, -0.9488,
-1.4847, -1.7093, -1.4095, -1.7293, -1.7675,
-0.9203, -4.2299, -1.8740, -1.4076, -1.6671,
-1.9052, -0.8330, -2.1839, -2.2459, -1.6193,
-2.9108, -1.2114, -1.4616, -1.7297, -1.4330,
-2.2656, -0.7878, -1.8533, -1.8711, -2.0349,
-2.2457, -2.1395, -1.4509, -0.7538, -2.6381,
-0.8078, -2.1054, -2.6703, -1.1108, -3.3867,
-1.7774, -1.8426, -1.9473, -1.3293, -1.3273,
-1.3490, -1.9842, -2.5357, -2.2161, -0.8800,
-1.5412, -1.8003, -2.7603, -0.8606, -2.0066,
-1.8342, -2.2741, -1.8348, -1.5833, -0.9877,
-3.5196, -2.3361, -0.9124, -0.9307, -2.5531,
-1.4862, -1.2153, -1.4453, -3.4462, -1.5625,
-2.6455, -1.4153, -1.3079, -1.1568, -2.2897};
std::vector<int64_t> targetsHostData = {
1, 2, 1, 1, 2, 4, 1,
2, 2, 2, 2, 2, 2, 3,
4, 2, 1, 4, 3, 1, 4,
4, 1, 4, 2, 2, 2, 3};
std::vector<float> negLoglikelihoodHostData = {10.1999, 16.1340, 14.9006, 9.3596};
std::vector<float> logAlphaHostData = {
-1.0894, -2.7162, -99999, -99999, -99999, -99999, -99999, -99999, -99999, -99999, -99999, -99999, -99999, -99999, -99999, -99999,
-4.8653, -2.2842, -6.4921, -3.9711, -99999, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
-5.2121, -4.7967, -2.6162, -4.1742, -4.3179, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
-6.0987, -5.0438, -3.3957, -6.7671, -4.4369, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
-7.3173, -5.5735, -4.4384, -6.1313, -5.5627, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
-8.9557, -6.6720, -5.7981, -6.1973, -6.7523, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
-10.9664, -8.6661, -7.4600, -6.3671, -7.7544, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
-14.5183, -10.6106, -10.7501, -7.8722, -9.6961, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
-16.6026, -11.2422, -12.0691, -9.1833, -9.8069, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
-18.5078, -12.0705, -12.7846, -11.1988, -10.6593, 0.0000, 0.0000,0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
-2.0684, -2.0866, -99999, -99999, -99999, -99999, -99999,-99999, -99999, -99999, -99999, -99999, -99999, -99999,-99999, -99999,
-3.4510, -3.2370, -3.4692, -99999, -99999, -99999, -99999,-99999, -99999, -99999, -99999, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
-4.4051, -4.7144, -3.6073, -5.5385, -99999, -99999, -99999,-99999, -99999, -99999, -99999, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
-5.6836, -7.1669, -4.6003, -6.7841, -6.8170, -99999, -99999,-99999, -99999, -99999, -99999, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
-7.9975, -8.2040, -6.8402, -7.2185, -8.4212, -9.5419, -99999,-99999, -99999, -99999, -99999, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
-11.0430, -9.9362, -9.6580, -8.8523, -10.0013, -10.6729, -12.5874,-99999, -99999, -99999, -99999, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
-12.3302, -11.3209, -10.3815, -10.1533, -9.8642, -11.2589, -11.8226,-14.2577, -99999, -99999, -99999, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
-13.7904, -12.5855, -11.5118, -11.1431, -10.7654, -11.2181, -12.2686,-13.3140, -15.7179, -99999, -99999, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
-15.3165, -14.0400, -12.7439, -12.3341, -11.7695, -11.9899, -12.4443,-13.6840, -14.7536, -17.4346, -99999, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
-18.2273, -15.2555, -15.4129, -13.2866, -14.2301, -12.6421, -14.4091,-13.6517, -16.2998, -16.1490, -20.3454, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
-2.4423, -2.5942, -99999, -99999, -99999, -99999, -99999,-99999, -99999, -99999, -99999, -99999, -99999, -99999,-99999, -99999,
-4.0739, -3.6831, -4.2258, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
-7.6125, -6.4925, -6.7635, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
-9.1100, -8.3040, -7.4232, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
-9.8243, -9.0682, -7.7908, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
-12.2918, -10.3758, -10.0124, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
-14.7551, -11.3089, -11.9478, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
-16.2228, -12.5289, -12.3528, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
-17.7075, -14.2718, -13.2285, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
-19.9731, -16.2750, -15.1923, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
-2.4670, -0.7248, -99999, -99999, -99999, -99999, -99999,-99999, -99999, -99999, -99999, -99999, -99999, -99999,-99999, -99999,
-4.3939, -2.4581, -2.6517, -2.9598, -99999, -99999, -99999,-99999, -99999, -99999, -99999, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
-5.5419, -5.5142, -3.0051, -3.3784, -4.1078, -6.1507, -99999,-99999, -99999, -99999, -99999, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
-7.8955, -6.9286, -5.2805, -4.5115, -5.3385, -5.0374, -8.5043,-7.6488, -99999, -99999, -99999, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
-9.5920, -7.5617, -6.8010, -6.0444, -5.8452, -4.7597, -6.7031,-7.3228, -9.3453, -99999, -99999, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
-12.6943, -9.8527, -9.5199, -8.2901, -8.3490, -6.6950, -7.7281,-5.8361, -10.3008, -10.6208, -99999, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
-15.7486, -12.5670, -12.0336, -8.5306, -10.6803, -9.1337, -9.4448,-6.5472, -8.8790, -10.9199, -13.6751, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
-16.9650, -13.7373, -12.7884, -10.0734, -9.6368, -9.2326, -9.8004,-8.6463, -7.6709, -10.9785, -12.0746, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
-17.8853, -15.3655, -13.3814, -14.2154, -10.0586, -10.1583, -9.7039,-9.8935, -8.2713, -9.5090, -11.6105, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
-20.1310, -17.9262, -15.4983, -15.0687, -12.2888, -12.0440, -11.4581,-10.2538, -10.3368, -9.4675, -11.6393, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000};
std::vector<float> outHostData = {
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0
};
// 创建gradOut aclTensor
ret = CreateAclTensor(gradOutHostData, gradOutShape, &gradOutDeviceAddr, aclDataType::ACL_FLOAT, &gradOut);
CHECK_RET(ret == ACL_SUCCESS, return ret);
// 创建logProbs aclTensor
ret = CreateAclTensor(logProbsHostData, logProbsShape, &logProbsDeviceAddr, aclDataType::ACL_FLOAT, &logProbs);
CHECK_RET(ret == ACL_SUCCESS, return ret);
// 创建targets aclTensor
ret = CreateAclTensor(targetsHostData, targetsShape, &targetsDeviceAddr, aclDataType::ACL_INT64, &targets);
CHECK_RET(ret == ACL_SUCCESS, return ret);
std::vector<int64_t> inputLengthsSizeData = {10,10,10,10};
inputLengths = aclCreateIntArray(inputLengthsSizeData.data(), 4);
CHECK_RET(inputLengths != nullptr, return ACL_ERROR_BAD_ALLOC);
std::vector<int64_t> targetLengthsSizeData = {2, 3, 1, 5};
targetLengths = aclCreateIntArray(targetLengthsSizeData.data(), 4);
CHECK_RET(targetLengths != nullptr, return ACL_ERROR_BAD_ALLOC);
// 创建negLoglikelihood aclTensor
ret = CreateAclTensor(negLoglikelihoodHostData, negLoglikelihoodShape, &negLoglikelihoodDeviceAddr, aclDataType::ACL_FLOAT, &negLoglikelihood);
CHECK_RET(ret == ACL_SUCCESS, return ret);
// 创建logAlpha aclTensor
ret = CreateAclTensor(logAlphaHostData, logAlphaShape, &logAlphaDeviceAddr, aclDataType::ACL_FLOAT, &logAlpha);
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);
// 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aclnnCtcLossBackward第一段接口
ret = aclnnCtcLossBackwardGetWorkspaceSize(gradOut, logProbs, targets, inputLengths, targetLengths, negLoglikelihood, logAlpha, 0, false, out, &workspaceSize, &executor);
CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnCtcLossBackwardGetWorkspaceSize 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aclnnCtcLossBackward第二段接口
ret = aclnnCtcLossBackward(workspaceAddr, workspaceSize, executor, stream);
CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnCtcLossBackward 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out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("out 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(gradOut);
aclDestroyTensor(logProbs);
aclDestroyTensor(targets);
aclDestroyIntArray(inputLengths);
aclDestroyIntArray(targetLengths);
aclDestroyTensor(negLoglikelihood);
aclDestroyTensor(logAlpha);
aclDestroyTensor(out);
return 0;
}