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昇腾小AI

aclnnSwinTransformerLnQkvQuant

支持的产品型号

  • Atlas 推理系列产品(Ascend 310P处理器)。

接口原型

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

  • aclnnStatus aclnnSwinTransformerLnQkvQuantGetWorkspaceSize(const aclTensor *x, const aclTensor *gamma, aclTensor *beta, aclTensor *weight, aclTensor *bias, aclTensor *quantScale, aclTensor *quantOffset, aclTensor *dequantScale, int64_t headNum, int64_t seqLength, float epsilon, int64_t oriHeight, int64_t oriWeight, int64_t hWinSize, int64_t wWinSize, bool weightTranspose, aclTensor *queryOutput, aclTensor *keyOutput, aclTensor *valueOutput, uint64_t *workspaceSize, aclOpExecutor **executor)
  • aclnnStatus aclnnSwinTransformerLnQkvQuant(void *workspace, uint64_t workspaceSize, aclOpExecutor *executor, aclrtStream stream)

功能描述

  • 算子功能:Swin Transformer 网络模型 完成 Q K V 的计算。

  • 计算公式:

    q/k/v = (Quant(Layernorm(x).transpose) * weight).dequant.transpose.split 其中,weight 是 Q K V 三个矩阵权重的拼接。

aclnnSwinTransformerLnQkvQuantGetWorkspaceSize

  • 参数说明
    • x(const aclTensor*,计算输入): Device侧的aclTensor,数据类型支持FLOAT16。数据格式支持ND。只支持维度为[B,S,H],其中B只支持[1,32],S需等于oriHeight * oriWeight,H = headNum * seqLength且小于等于1024。
    • gamma(const aclTensor*,计算输入): Device侧的aclTensor,数据类型支持FLOAT16。数据格式支持ND。维度需为[H]。
    • beta(const aclTensor*,计算输入): Device侧的aclTensor,数据类型支持FLOAT16。数据格式支持ND。维度需为[H]。
    • weight(const aclTensor*,计算输入): Device侧的aclTensor,数据类型支持INT8。数据格式支持ND。维度需为[3 * H, H]。
    • bias(const aclTensor*,计算输入): Device侧的aclTensor,数据类型支持INT32。数据格式支持ND。维度需为[3 * H]。
    • quantScale(const aclTensor*,计算输入): Device侧的aclTensor,数据类型支持FLOAT16。数据格式支持ND。维度需为[H]。
    • quantOffset(const aclTensor*,计算输入): Device侧的aclTensor,数据类型支持FLOAT16。数据格式支持ND。维度需为[H]。
    • dequantScale(const aclTensor*,计算输入): Device侧的aclTensor,数据类型支持UINT64。数据格式支持ND。维度需为[3 * H]。
    • headNum(int,计算输入): 通道数;只支持[1,32]范围;
    • seqLength(int,计算输入): 通道深度。只支持32/64两种;
    • epsilon(float,计算输入): layernrom 计算除0保护值;为了保证精度,建议小于等于1e-4;
    • oriHeight(int,计算输入): layernrom 中S轴transpose的维度;oriHeight*oriWeight需等于输入x的第二维S的大小,且为hWinSize的整数倍;
    • oriWeight(int,计算输入): layernrom 中S轴transpose的维度;oriHeight*oriWeight需等于输入x的第二维S的大小,且为wWinSize的整数倍;
    • hWinSize(int,计算输入): 窗大小;支持范围[7-32];
    • wWinSize(int,计算输入): 窗大小;支持范围[7-32];
    • weightTranspose(bool,计算输入): weight矩阵需要转置,当前不支持不转置场景;
    • queryOutput(const aclTensor*, 计算输出):Device侧的aclTensor,数据类型支持FLOAT16数据格式支持ND。
    • keyOutput(const aclTensor*, 计算输出):Device侧的aclTensor,数据类型支持FLOAT16数据格式支持ND。
    • valueOutput(const aclTensor*, 计算输出):Device侧的aclTensor,数据类型支持FLOAT16数据格式支持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: aclnnStatus:返回状态码,具体参见aclnn返回码
    161001(ACLNN_ERR_PARAM_NULLPTR):1. 传入的输入tensor是空指针。
    161002(ACLNN_ERR_PARAM_INVALID):1. 输入或输出参数的数据类型/数据格式不在支持的范围内。

aclnnSwinTransformerLnQkvQuant

  • 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aclnnSwinTransformerLnQkvQuantGetWorkspaceSize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: aclnnStatus:返回状态码,具体参见aclnn返回码

约束与限制

  • seqLength 只支持32/64
  • oriHeight * oriWeight = 输入x Tensor的第二维度,且oriHeight为hWinSize的整数倍,oriWeight为wWinSize的整数倍
  • hWinSize和wWinSize 只支持7 - 32
  • 输入x Tensor的第一维度B只支持1 - 32
  • weight需要转置

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 <iostream>
#include <vector>
#include "acl/acl.h"
#include "aclnnop/aclnn_swin_transformer_ln_qkv_quant.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 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 <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<int64_t> selfShape = {1, 49, 32};
  std::vector<int64_t> gammaShape = {32};
  std::vector<int64_t> weightShape = {32*3, 32};
  std::vector<int64_t> biasShape = {3 * 32};
  std::vector<int64_t> outShape = {1,1,49, 32};
  void* xDeviceAddr = nullptr;
  void* gammaDeviceAddr = nullptr;
  void* betaDeviceAddr = nullptr;
  void* weightDeviceAddr = nullptr;
  void* biasDeviceAddr = nullptr;
  void* scaleDeviceAddr = nullptr;
  void* offsetDeviceAddr = nullptr;
  void* dequantDeviceAddr = nullptr;

  void* outqDeviceAddr = nullptr;
  void* outkDeviceAddr = nullptr;
  void* outvDeviceAddr = nullptr;
  aclTensor* x = nullptr;
  aclTensor* gamma = nullptr;
  aclTensor* beta = nullptr;
  aclTensor* weight = nullptr;
  aclTensor* bias = nullptr;
  aclTensor* quantScale = nullptr;
  aclTensor* quantOffset = nullptr;
  aclTensor* dequantScale = nullptr;
  aclTensor* queryOutput = nullptr;
  aclTensor* keyOutput = nullptr;
  aclTensor* valueOutput = nullptr;

  std::vector<uint16_t> selfHostData(49*32, 0x1);
  std::vector<int32_t> biasHostData(3*32, 0x1);
  std::vector<uint16_t> gammaHostData(32, 0x1);
  std::vector<uint16_t> betaHostData(32, 0x1);
  std::vector<int8_t> weightHostData(3*32*32, 0x1);
  std::vector<uint16_t> scaleHostData(32, 0x1);
  std::vector<uint16_t> offsetHostData(32, 0x1);
  std::vector<uint64_t> dequantHostData(3*32, 0x1);

  std::vector<uint16_t> outqHostData(49*32, 0x0);
  std::vector<uint16_t> outkHostData(49*32, 0x0);
  std::vector<uint16_t> outvHostData(49*32, 0x0);

  // 创建self aclTensor
  ret = CreateAclTensor(selfHostData, selfShape, &xDeviceAddr, aclDataType::ACL_FLOAT16, &x);
  CHECK_RET(ret == ACL_SUCCESS, return ret);

  ret = CreateAclTensor(gammaHostData, gammaShape, &gammaDeviceAddr, aclDataType::ACL_FLOAT16, &gamma);
  CHECK_RET(ret == ACL_SUCCESS, return ret);

  ret = CreateAclTensor(betaHostData, gammaShape, &betaDeviceAddr, aclDataType::ACL_FLOAT16, &beta);
  CHECK_RET(ret == ACL_SUCCESS, return ret);

  ret = CreateAclTensor(weightHostData, weightShape, &weightDeviceAddr, aclDataType::ACL_INT8, &weight);
  CHECK_RET(ret == ACL_SUCCESS, return ret);

  ret = CreateAclTensor(biasHostData, biasShape, &biasDeviceAddr, aclDataType::ACL_INT32, &bias);
  CHECK_RET(ret == ACL_SUCCESS, return ret);

  ret = CreateAclTensor(scaleHostData, gammaShape, &scaleDeviceAddr, aclDataType::ACL_FLOAT16, &quantScale);
  CHECK_RET(ret == ACL_SUCCESS, return ret);

  ret = CreateAclTensor(offsetHostData, gammaShape, &offsetDeviceAddr, aclDataType::ACL_FLOAT16, &quantOffset);
  CHECK_RET(ret == ACL_SUCCESS, return ret);

  ret = CreateAclTensor(dequantHostData, biasShape, &dequantDeviceAddr, aclDataType::ACL_UINT64, &dequantScale);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  // 创建out aclTensor
  ret = CreateAclTensor(outqHostData, outShape, &outqDeviceAddr, aclDataType::ACL_FLOAT16, &queryOutput);
  CHECK_RET(ret == ACL_SUCCESS, return ret);

  ret = CreateAclTensor(outkHostData, outShape, &outkDeviceAddr, aclDataType::ACL_FLOAT16, &keyOutput);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  ret = CreateAclTensor(outvHostData, outShape, &outvDeviceAddr, aclDataType::ACL_FLOAT16, &valueOutput);
  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;
  float epsilon = 0.0001;
  int64_t oriHeight = 7;
  int64_t oriWeight = 7;
  int64_t hWinSize = 7;
  int64_t wWinSize = 7;
  int64_t headNum = 1;
  int64_t seqLength = 32;
  bool weightTranspose = true;

  // 调用aclnnSwinTransformerLnQkvQuant第一段接口
  ret = aclnnSwinTransformerLnQkvQuantGetWorkspaceSize(x,gamma,beta,weight, bias, quantScale, quantOffset, dequantScale, headNum, seqLength, epsilon, oriHeight, oriWeight, hWinSize, wWinSize, weightTranspose, queryOutput, keyOutput, valueOutput, &workspaceSize, &executor);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnSwinTransformerLnQkvQuantGetWorkspaceSize 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aclnnSwinTransformerLnQkvQuant第二段接口
  ret = aclnnSwinTransformerLnQkvQuant(workspaceAddr, workspaceSize, executor, stream);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnSwinTransformerLnQkvQuant 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<uint16_t> resultData(size, 0);
  ret = aclrtMemcpy(resultData.data(), resultData.size() * sizeof(resultData[0]), outqDeviceAddr,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);

  // 6. 释放aclTensor和aclScalar,需要根据具体API的接口定义修改
  aclDestroyTensor(x);
  aclDestroyTensor(queryOutput);
  // 7. 释放device资源,需要根据具体API的接口定义修改
  aclrtFree(xDeviceAddr);
  aclrtFree(outqDeviceAddr);
  if (workspaceSize > 0) {
    aclrtFree(workspaceAddr);
  }
  aclrtDestroyStream(stream);
  aclrtResetDevice(deviceId);
  aclFinalize();
  return 0;
}
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