aclnnMoeInitRouting
支持的产品型号
Atlas A2训练系列产品/Atlas 800I A2推理产品
接口原型
每个算子分为两段式接口,必须先调用 “aclnnMoeInitRoutingGetWorkspaceSize”接口获取入参并根据计算流程计算所需workspace大小获取计算所需workspace大小以及包含了算子计算流程的执行器,再调用“aclnnMoeInitRouting”接口执行计算。
aclnnStatus aclnnMoeInitRoutingGetWorkspaceSize(const aclTensor *x, const aclTensor *rowIdx, const aclTensor *expertIdx, int64_t activeNum, const aclTensor *expandedXOut, const aclTensor *expandedRowIdxOut, const aclTensor *expandedExpertIdxOut, uint64_t *workspaceSize, aclOpExecutor **executor)
aclnnStatus aclnnMoeInitRouting(void *workspace, uint64_t workspaceSize, aclOpExecutor *executor, aclrtStream stream)
功能描述
- 算子功能:MoE的routing计算,根据aclnnMoeGatingTopKSoftmax的计算结果做routing处理。
- 计算公式:
aclnnMoeInitRoutingGetWorkspaceSize
参数说明:
- x(aclTensor*,计算输入):MOE的输入即token特征输入,要求为一个2D的Tensor,shape为 (NUM_ROWS, H),数据类型支持FLOAT16、BFLOAT16、FLOAT32,数据格式要求为ND。
- rowIdx(aclTensor*,计算输入):指示每个位置对应的原始行位置,shape要求与expertIdx 一致, 数值从0开始,沿着1维递增。数据类型支持int32,数据格式要求为ND。
- expertIdx (aclTensor*,计算输入):aclnnMoeGatingTopKSoftmax的输出每一行特征对应的K个处理专家,要求是一个2D的shape (NUM_ROWS, K)。数据类型支持int32,数据格式要求为ND。
- activeNum(int64_t,计算输入):表示总的最大处理row数,expandedXOut只有这么多行是有效的。
- expandedXOut(aclTensor*,计算输出):根据expertIdx进行扩展过的特征,要求是一个2D的Tensor,shape (min(NUM_ROWS, activeNum) * k, H)。数据类型同x,支持FLOAT16、BFLOAT16、FLOAT32,数据格式要求为ND。
- expandedRowIdxOut(aclTensor*,计算输出):expandedX和x的映射关系, 要求是一个1D的Tensor,Shape为(NUM_ROWS*K, ),数据类型支持int32,数据格式要求为ND。
- expandedExpertIdxOut(aclTensor*,计算输出):输出expertIdx排序后的结果,数据类型支持int32,数据格式要求为ND。
- workspaceSize(uint64_t*,出参):返回需要在Device侧申请的workspace大小。
- executor(aclOpExecutor**,出参):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。
161001(ACLNN_ERR_PARAM_NULLPTR): 1. 输入和输出的Tensor是空指针。 161002(ACLNN_ERR_PARAM_INVALID): 1. 输入和输出的数据类型不在支持的范围内。 561002(ACLNN_ERR_INNER_TILING_ERROR): 1. x的shape维度不为2。 2. rowIdx的shape不为2或者rowIdx和expertIdx的shape不相等。 3. activateNum的值小于0。 4. expandedXOut的shape不等于(min(num_rows, activateNum) * k, H)。 5. expandedRowIdxOut和expandedExpertIdxOut的shape不相等,且不等于(num_rows * k, )。
aclnnMoeInitRouting
参数说明:
- workspace(void*,入参):在Device侧申请的workspace内存地址。
- workspaceSize(uint64_t,入参):在Device侧申请的workspace大小,由第一段接口aclnnMoeInitRoutingGetWorkspaceSize获取。
- executor(aclOpExecutor*,入参):op执行器,包含了算子计算流程。
- stream(aclrtStream,入参):指定执行任务的AscendCL 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
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 "acl/acl.h"
#include "aclnnop/aclnn_moe_init_routing.h"
#include <iostream>
#include <vector>
#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, 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 <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/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;
aclrtStream stream;
auto ret = Init(deviceId, &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的接口定义构造
std::vector<int64_t> xShape = {3, 4};
std::vector<int64_t> idxShape = {3, 2};
std::vector<int64_t> expandedXOutShape = {6, 4};
std::vector<int64_t> idxOutShape = {6};
void* xDeviceAddr = nullptr;
void* rowIdxDeviceAddr = nullptr;
void* expertIdxDeviceAddr = nullptr;
void* expandedXOutDeviceAddr = nullptr;
void* expandedRowIdxOutDeviceAddr = nullptr;
void* expandedExpertIdxOutDeviceAddr = nullptr;
aclTensor* x = nullptr;
aclTensor* rowIdx = nullptr;
aclTensor* expertIdx = nullptr;
int64_t activeNum = 3;
aclTensor* expandedXOut = nullptr;
aclTensor* expandedRowIdxOut = nullptr;
aclTensor* expandedExpertIdxOut = nullptr;
std::vector<float> xHostData = {0.1, 0.1, 0.1, 0.1, 0.2, 0.2, 0.2, 0.2, 0.3, 0.3, 0.3, 0.3};
std::vector<int> expertIdxHostData = {1, 2, 0, 1, 0, 2};
std::vector<int> rowIdxHostData = {0, 3, 1, 4, 2, 5};
std::vector<float> expandedXOutHostData = {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
std::vector<int> expandedRowIdxOutHostData = {0, 0, 0, 0, 0, 0};
std::vector<int> expandedExpertIdxOutHostData = {0, 0, 0, 0, 0, 0};
// 创建self aclTensor
ret = CreateAclTensor(xHostData, xShape, &xDeviceAddr, aclDataType::ACL_FLOAT, &x);
CHECK_RET(ret == ACL_SUCCESS, return ret);
ret = CreateAclTensor(rowIdxHostData, idxShape, &rowIdxDeviceAddr, aclDataType::ACL_INT32, &rowIdx);
CHECK_RET(ret == ACL_SUCCESS, return ret);
ret = CreateAclTensor(expertIdxHostData, idxShape, &expertIdxDeviceAddr, aclDataType::ACL_INT32, &expertIdx);
CHECK_RET(ret == ACL_SUCCESS, return ret);
// 创建out aclTensor
ret = CreateAclTensor(expandedXOutHostData, expandedXOutShape, &expandedXOutDeviceAddr, aclDataType::ACL_FLOAT, &expandedXOut);
CHECK_RET(ret == ACL_SUCCESS, return ret);
ret = CreateAclTensor(expandedRowIdxOutHostData, idxOutShape, &expandedRowIdxOutDeviceAddr, aclDataType::ACL_INT32, &expandedRowIdxOut);
CHECK_RET(ret == ACL_SUCCESS, return ret);
ret = CreateAclTensor(expandedExpertIdxOutHostData, idxOutShape, &expandedExpertIdxOutDeviceAddr, aclDataType::ACL_INT32, &expandedExpertIdxOut);
CHECK_RET(ret == ACL_SUCCESS, return ret);
// 3. 调用CANN算子库API,需要修改为具体的API
uint64_t workspaceSize = 0;
aclOpExecutor* executor;
// 调用aclnnMoeInitRouting第一段接口
ret = aclnnMoeInitRoutingGetWorkspaceSize(x, rowIdx, expertIdx, activeNum, expandedXOut, expandedRowIdxOut, expandedExpertIdxOut, &workspaceSize, &executor);
CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnMoeInitRoutingGetWorkspaceSize 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aclnnMoeInitRouting第二段接口
ret = aclnnMoeInitRouting(workspaceAddr, workspaceSize, executor, stream);
CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnMoeInitRouting 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 expandedXSize = GetShapeSize(expandedXOutShape);
std::vector<float> expandedXData(expandedXSize, 0);
ret = aclrtMemcpy(expandedXData.data(), expandedXData.size() * sizeof(expandedXData[0]), expandedXOutDeviceAddr, expandedXSize * 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 < expandedXSize; i++) {
LOG_PRINT("expandedXData[%ld] is: %f\n", i, expandedXData[i]);
}
auto expandedRowIdxSize = GetShapeSize(idxOutShape);
std::vector<int> expandedRowIdxData(expandedRowIdxSize, 0);
ret = aclrtMemcpy(expandedRowIdxData.data(), expandedRowIdxData.size() * sizeof(expandedRowIdxData[0]), expandedRowIdxOutDeviceAddr, expandedRowIdxSize * sizeof(int32_t),
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 < expandedRowIdxSize; i++) {
LOG_PRINT("expandedRowIdxData[%ld] is: %d\n", i, expandedRowIdxData[i]);
}
auto expandedExpertIdxSize = GetShapeSize(idxOutShape);
std::vector<int> expandedExpertIdxData(expandedExpertIdxSize, 0);
ret = aclrtMemcpy(expandedExpertIdxData.data(), expandedExpertIdxData.size() * sizeof(expandedExpertIdxData[0]), expandedExpertIdxOutDeviceAddr, expandedExpertIdxSize * sizeof(int32_t),
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 < expandedExpertIdxSize; i++) {
LOG_PRINT("expandedExpertIdxData[%ld] is: %d\n", i, expandedExpertIdxData[i]);
}
// 6. 释放aclTensor和aclScalar,需要根据具体API的接口定义修改
aclDestroyTensor(x);
aclDestroyTensor(rowIdx);
aclDestroyTensor(expertIdx);
aclDestroyTensor(expandedXOut);
aclDestroyTensor(expandedRowIdxOut);
aclDestroyTensor(expandedExpertIdxOut);
// 7. 释放device资源,需要根据具体API的接口定义修改
aclrtFree(xDeviceAddr);
aclrtFree(rowIdxDeviceAddr);
aclrtFree(expertIdxDeviceAddr);
aclrtFree(expandedXOutDeviceAddr);
aclrtFree(expandedRowIdxOutDeviceAddr);
aclrtFree(expandedExpertIdxOutDeviceAddr);
if (workspaceSize > 0) {
aclrtFree(workspaceAddr);
}
aclrtDestroyStream(stream);
aclrtResetDevice(deviceId);
aclFinalize();
return 0;
}