aclnnNsaCompressGrad

支持的产品型号

Atlas A2 训练系列产品

函数原型

每个算子分为两段式接口，必须先调用“aclnnNsaCompressGradGetWorkspaceSize”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“aclnnNsaCompressGrad”接口执行计算。

aclnnStatus aclnnNsaCompressGradGetWorkspaceSize(const aclTensor *outputGrad, const aclTensor *input, const aclTensor *weight, const aclIntArray *actSeqLenOptionalOptional, int64_t compressBlockSize, int64_t compressStride, int64_t actSeqLenType, char *layoutOptionalOptional, const aclTensor *inputGradOut, const aclTensor *weightGradOut, uint64_t *workspaceSize, aclOpExecutor **executor)
aclnnStatus aclnnNsaCompressGrad(void *workspace, uint64_t workspaceSize, aclOpExecutor *executor, aclrtStream stream)

aclnnNsaCompressGradGetWorkspaceSize

参数说明：
- outputGrad（aclTensor *，计算输入）：Device侧的aclTensor, 正向算子输出的反向梯度。shape支持[T, N, D]，数据类型支持BFLOAT16、FLOAT16，数据格式支持ND，支持非连续的Tensor，不支持空Tensor。
- input（aclTensor *，计算输入）：Device侧的aclTensor, 表示待压缩张量。shape支持[T, N, D]，数据类型支持BFLOAT16、FLOAT16，数据格式支持ND，支持非连续的Tensor，不支持空Tensor。
- weight（aclTensor *，计算输入）：Device侧的aclTensor，压缩的权重。shape为[compressBlockSize, N]，weight与input的shape满足broadcast关系，数据类型与inpu保持一致，数据格式支持ND。支持非连续的Tensor，不支持空Tensor。
- actSeqLenOptional（aclIntArray *，计算输入）：可选参数，Host侧的aclIntArray，数据类型支持INT64，数据格式支持ND，描述了每个Batch对应的S大小，各batch的sequence长度不相等时需要输入，其余场景下输入nullptr。
- compressBlockSize（int64_t，计算输入）：Host侧的int64_t，压缩滑窗大小。
- compressStride（int64_t，计算输入）：Host侧的int64_t，两次压缩滑窗间隔大小。
- actSeqLenType（int64_t，计算输入）：Host侧的int64_t，可取值0或1，0代表actSeqLenOptional中数值为前继batch的系列大小的cumsum结果（累积和），1代表actSeqLenOptional中数值为每个batch中序列大小，当前仅支持0。
- layoutOptional （char *, 计算输入）：Host侧的string，代表输入input的数据排布格式，支持TND。说明：input数据排布格式支持从多种维度解读，其中T是B和S合轴紧密排列的数据（每个batch的actSeqLen）、B（Batch）表示输入样本批量大小、S（Seq-Length）表示输入样本序列长度、H（Head-Size）表示隐藏层的大小、N（Head-Num）表示多头数、D（Head-Dim）表示隐藏层最小的单元尺寸，且满足D=H/N。
- inputGrad（aclTensor *，计算输出）：Device侧的aclTensor，input的梯度。shape与input保持一致，数据类型与inpu保持一致，数据格式支持ND。支持非连续的Tensor，不支持空Tensor。
- weightGrad（aclTensor *，计算输出）：Device侧的aclTensor，weight的梯度。shape与weight保持一致，数据类型与weight保持一致，数据格式支持ND。支持非连续的Tensor，不支持空Tensor。
- 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。

第一段接口完成入参校验，若出现以下错误码，则对应原因为：
- 返回161001（ACLNN_ERR_PARAM_NULLPTR）：传入的input、weight、outputGrad、inputGrad或weightGrad是空指针。
- 返回161002（ACLNN_ERR_PARAM_INVALID）：1. input和weight的数据类型不在支持的范围之内。
                                        2. input和weight的shape无法做broadcast。
                                        3. layoutOptional不合法

aclnnNsaCompressGrad

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aclnnNsaCompressGradGetWorkspaceSize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。

约束说明

compressBlockSize和compressStride要是16的整数倍，且compressBlockSize > compressStride

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 <algorithm>
#include <cstdint>
#include <iostream>
#include <vector>
#include <sys/types.h>
#include <sys/stat.h>
#include <unistd.h>
#include <fstream>
#include <fcntl.h>

#include "acl/acl.h"
#include "aclnnop/aclnn_nsa_compress_grad.h"

#define SUCCESS 0
#define FAILED 1

#define INFO_LOG(fmt, args...) fprintf(stdout, "[INFO]  " fmt "\n", ##args)
#define WARN_LOG(fmt, args...) fprintf(stdout, "[WARN]  " fmt "\n", ##args)
#define ERROR_LOG(fmt, args...) fprintf(stderr, "[ERROR]  " fmt "\n", ##args)

#define CHECK_RET(cond, return_expr) \
  do {                               \
    if (!(cond)) {                   \
      return_expr;                   \
    }                                \
  } while (0)

#define LOG_PRINT(message, ...)     \
  do {                              \
    printf(message, ##__VA_ARGS__); \
  } while (0)

bool ReadFile(const std::string &filePath, size_t &fileSize, void *buffer, size_t bufferSize)
{
    struct stat sBuf;
    int fileStatus = stat(filePath.data(), &sBuf);
    if (fileStatus == -1) {
        ERROR_LOG("failed to get file %s", filePath.c_str());
        return false;
    }
    if (S_ISREG(sBuf.st_mode) == 0) {
        ERROR_LOG("%s is not a file, please enter a file", filePath.c_str());
        return false;
    }

    std::ifstream file;
    file.open(filePath, std::ios::binary);
    if (!file.is_open()) {
        ERROR_LOG("Open file failed. path = %s", filePath.c_str());
        return false;
    }

    std::filebuf *buf = file.rdbuf();
    size_t size = buf->pubseekoff(0, std::ios::end, std::ios::in);
    if (size == 0) {
        ERROR_LOG("file size is 0");
        file.close();
        return false;
    }
    if (size > bufferSize) {
        ERROR_LOG("file size is larger than buffer size");
        file.close();
        return false;
    }
    buf->pubseekpos(0, std::ios::in);
    buf->sgetn(static_cast<char *>(buffer), size);
    fileSize = size;
    file.close();
    return true;
}

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, aclrtContext* context, aclrtStream* stream) {
    // 固定写法，acl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 = 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);
    return 0;
}

template <typename T>
int CreateAclTensor(const std::vector<T>& hostData, const std::vector<int64_t>& shape, void** deviceAddr,
                    aclDataType dataType, aclTensor** xOrResult) {
    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xOrResult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
    *xOrResult = 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_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
    int64_t headNum = 64;
    int64_t headDim = 128;
    int64_t blockSize = 32;
    int64_t blockStride = 16;
    int64_t blockNum = 15;
    int64_t seqLensSum = 272;
    int64_t seqLen = 3;
    std::vector<int64_t> outputGradShape = {blockNum, headNum, headDim};
    std::vector<int64_t> inputKVShape = {seqLensSum, headNum, headDim};
    std::vector<int64_t> weightShape = {blockSize, headNum};
    std::vector<int64_t> inputGradOutShape = {seqLensSum, headNum, headDim};
    std::vector<int64_t> weightGradOutShape = {blockSize, headNum};
    int64_t SeqLenType = 0;
    char layOut[] = "TND";

    void* outputGradDeviceAddr =  nullptr;
    void* inputKVDeviceAddr =  nullptr;
    void* weightDeviceAddr =  nullptr;
    void* inputGradOutDeviceAddr =  nullptr;
    void* weightGradOutDeviceAddr =  nullptr;

    aclTensor* outputGrad =  nullptr;
    aclTensor* inputKV =  nullptr;
    aclTensor* weight =  nullptr;
    aclTensor* inputGradOut =  nullptr;
    aclTensor* weightGradOut =  nullptr;

    std::vector<float> inputGradOutHostData(seqLensSum * headNum * headDim, 0.0);
    std::vector<float> weightGradOutHostData(blockSize * headNum, 0.0);

    std::vector<float> outputGradHostData(blockNum * headNum * headDim, 1.0);
    std::vector<float> inputKVHostData(seqLensSum * headNum * headDim, 1.0);
    std::vector<float> weightHostData(blockSize * headNum, 1.0);
    std::vector<int64_t> actSeqLenOptionalHostData = {0, 128, 272};

    aclIntArray *actSeqLenOptional = aclCreateIntArray(actSeqLenOptionalHostData.data(), actSeqLenOptionalHostData.size());

    // 创建dy aclTensor
    ret = CreateAclTensor(outputGradHostData, outputGradShape, &outputGradDeviceAddr, aclDataType::ACL_FLOAT16,
                            &outputGrad);
    CHECK_RET(ret == ACL_SUCCESS, return ret);
    // 创建x aclTensor
    ret = CreateAclTensor(inputKVHostData, inputKVShape, &inputKVDeviceAddr, aclDataType::ACL_FLOAT16, &inputKV);
    CHECK_RET(ret == ACL_SUCCESS, return ret);
    // 创建gelu aclTensor
    ret = CreateAclTensor(weightHostData, weightShape, &weightDeviceAddr, aclDataType::ACL_FLOAT16, &weight);
    CHECK_RET(ret == ACL_SUCCESS, return ret);
    
    ret = CreateAclTensor(inputGradOutHostData, inputGradOutShape, &inputGradOutDeviceAddr, aclDataType::ACL_FLOAT16, &inputGradOut);
    CHECK_RET(ret == ACL_SUCCESS, return ret);

    ret = CreateAclTensor(weightGradOutHostData, weightGradOutShape, &weightGradOutDeviceAddr, aclDataType::ACL_FLOAT16, &weightGradOut);
    CHECK_RET(ret == ACL_SUCCESS, return ret);

    // 3. 调用CANN算子库API，需要修改为具体的API名称
    uint64_t workspaceSize = 0;
    aclOpExecutor* executor;
    // 调用aclnnGeGluBackward第一段接口
    ret = aclnnNsaCompressGradGetWorkspaceSize(
            outputGrad, inputKV, weight, actSeqLenOptional, blockSize, blockStride, SeqLenType, layOut,
            inputGradOut, weightGradOut, &workspaceSize, &executor);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnGeGluGradV2GetWorkspaceSize 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aclnnGeGluBackward第二段接口
    ret = aclnnNsaCompressGrad(workspaceAddr, workspaceSize, executor, stream);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnGeGluGradV2 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(inputGradOutShape);
    std::vector<float> resultData(size, 0);
    ret = aclrtMemcpy(resultData.data(), resultData.size() * sizeof(resultData[0]), inputGradOutDeviceAddr,
                        size * sizeof(resultData[0]), 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(outputGrad);
    aclDestroyTensor(inputKV);
    aclDestroyTensor(weight);
    aclDestroyTensor(inputGradOut);
    aclDestroyTensor(weightGradOut);

    // 7. 释放device资源，需要根据具体API的接口定义修改
    aclrtFree(outputGradDeviceAddr);
    aclrtFree(inputKVDeviceAddr);
    aclrtFree(weightDeviceAddr);
    aclrtFree(inputGradOutDeviceAddr);
    aclrtFree(weightGradOutDeviceAddr);
    if (workspaceSize > 0) {
        aclrtFree(workspaceAddr);
    }
    aclrtDestroyStream(stream);
    aclrtDestroyContext(context);
    aclrtResetDevice(deviceId);
    aclFinalize();
    return 0;
}