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昇腾小AI

aclnnConvertWeightToINT4Pack

支持的产品型号

CPU实现不涉及

接口原型

每个算子分为两段式接口，必须先调用“aclnnConvertWeightToINT4PackGetWorkspaceSize”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“aclnnConvertWeightToINT4Pack”接口执行计算。

  • aclnnStatus aclnnConvertWeightToINT4PackGetWorkspaceSize(const aclTensor *weight, aclTensor *weightInt4Pack, uint64_t *workspaceSize, aclOpExecutor **executor)
  • aclnnStatus aclnnConvertWeightToINT4Pack(void *workspace, uint64_t workspaceSize, aclOpExecutor *executor, aclrtStream stream)

功能描述

  • 算子功能：将int32的输入weight打包为int4，并进行交叠排放。

aclnnConvertWeightToINT4PackGetWorkspaceSize

  • 参数说明

    • weight(const aclTensor*, 计算输入)：输入的weight，数据类型支持INT32，数据格式支持ND，维度支持2维。 当输出weightInt4Pack数据类型为INT4时，要求最后一维度为2对齐。当输出weightInt4Pack数据类型为INT32时，要求最后一维度为8对齐。 输入weight中元素的值需要在int4的表示范围内，即[-8, 7]。不支持非连续Tensor。
    • weightInt4Pack(aclTensor*, 计算输出)：INT4打包后的输出，数据类型为INT4或INT32（用1个INT32数据承载8个INT4数据），当输入数据类型为 INT4时，shape需要和输入weight保持一致。当输入数据类型为INT32时，shape的最后一维度为weight最后一维度的1/8。数据格式支持ND。不支持非连续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：

    aclnnStatus：返回状态码，具体参见aclnn返回码。

第一段接口完成入参校验，出现一下场景时报错：
  - 返回161001 (ACLNN_ERR_PARAM_NULLPTR)：当必选输入、输出、属性传入的是空指针。
  - 返回161002 (ACLNN_ERR_PARAM_INVALID)：
    - 传入weight、weightInt4Pack的shape维度不符合要求。
    - 传入weight、weightInt4Pack的数据类型不在支持的范围之内。
    - 传入weight、weightInt4Pack的shape大小不符合约束要求。
    - 传入空tensor。
    - 输入tensor的Format不是ND。
  - 返回361001 (ACLNN_ERR_RUNTIME_ERROR)：
    - 数据从host侧拷贝到device侧异常。
    - 数据从device侧拷贝到host侧异常。

aclnnConvertWeightToINT4Pack

  • 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aclnnConvertWeightToINT4PackGetWorkspaceSize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

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 <iostream>
#include <vector>
#include "acl/acl.h"
#include "aclnnop/aclnn_cast.h"
#include "aclnnop/aclnn_weight_quant_batch_matmul_v2.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;
}

extern "C" aclnnStatus aclnnConvertWeightToINT4PackGetWorkspaceSize(const aclTensor *weight, const aclTensor *weightInt4Pack,
    uint64_t *workspaceSize, aclOpExecutor **executor);

extern "C" aclnnStatus aclnnConvertWeightToINT4Pack(void *workspace, uint64_t workspaceSize, aclOpExecutor *executor,
    aclrtStream stream);


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;
}

template <typename T>
int CreateAclTensorInt4(const std::vector<T>& hostData, const std::vector<int64_t>& shape, void** deviceAddr,
                    aclDataType dataType, aclTensor** tensor) {
  auto size = hostData.size() * 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);
  aclDataType weightInt4PackDtype = aclDataType::ACL_INT4;

  // 2. 构造输入与输出，需要根据API的接口自定义构造
  int64_t m = 16;
  int64_t k = 32;
  int64_t n = 16;
  std::vector<int64_t> xShape = {m, k};
  std::vector<int64_t> weightShape = {k, n};
  std::vector<int64_t> weightInt4PackShape;
  if (weightInt4PackDtype == aclDataType::ACL_INT4) {
    weightInt4PackShape = {k, n};
  } else {
    weightInt4PackShape = {k, n/8};
  }
  std::vector<int64_t> yShape = {m, n};
  void* xDeviceAddr = nullptr;
  void* weightDeviceAddr = nullptr;
  void* weightInt4PackDeviceAddr = nullptr;
  void* yDeviceAddr = nullptr;
  aclTensor* x = nullptr;
  aclTensor* weight = nullptr;
  aclTensor* weightInt4Pack = nullptr;
  aclTensor* y = nullptr;
  std::vector<float> xHostData(m * k, 1);
  std::vector<int32_t> weightHostData(k * n, 1);
  std::vector<float> yHostData(m * n, 0);

  std::vector<int64_t> antiquantScaleShape = {16};
  void* antiquantScaleDeviceAddr = nullptr;
  aclTensor* antiquantScale = nullptr;
  std::vector<float> antiquantScaleHostData(16, 1);

  // 创建x aclTensor
  ret = CreateAclTensor(xHostData, xShape, &xDeviceAddr, aclDataType::ACL_FLOAT, &x);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  // 创建weight aclTensor
  ret = CreateAclTensor(weightHostData, weightShape, &weightDeviceAddr, aclDataType::ACL_INT32, &weight);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  if (weightInt4PackDtype == aclDataType::ACL_INT4) {
    std::vector<int8_t> weightInt4PackHostData(n * k / 2, 1); //一个int8数据存放2个int4数据，所以这里除以2
    // 创建weightInt4Pack aclTensor
    ret = CreateAclTensorInt4(weightInt4PackHostData, weightInt4PackShape, &weightInt4PackDeviceAddr, weightInt4PackDtype, &weightInt4Pack);
    CHECK_RET(ret == ACL_SUCCESS, return ret);
  } else {
    std::vector<int32_t> weightInt4PackHostData(n * k / 8, 1); //一个int32数据存放8个int4数据，所以这里除以8
    // 创建weightInt4Pack aclTensor
    ret = CreateAclTensor(weightInt4PackHostData, weightInt4PackShape, &weightInt4PackDeviceAddr, weightInt4PackDtype, &weightInt4Pack);
    CHECK_RET(ret == ACL_SUCCESS, return ret);
  }
  // 创建y aclTensor
  ret = CreateAclTensor(yHostData, yShape, &yDeviceAddr, aclDataType::ACL_FLOAT, &y);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  // 创建antiquantScale aclTensor
  ret = CreateAclTensor(antiquantScaleHostData, antiquantScaleShape, &antiquantScaleDeviceAddr, aclDataType::ACL_FLOAT, &antiquantScale);
  CHECK_RET(ret == ACL_SUCCESS, return ret);

  // 创建xFp16 aclTensor
  void* xFp16DeviceAddr = nullptr;
  aclTensor* xFp16 = nullptr;
  ret = CreateAclTensor(xHostData, xShape, &xFp16DeviceAddr, aclDataType::ACL_FLOAT16, &xFp16);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  // 创建antiquantScale aclTensor
  void* antiquantScaleFp16DeviceAddr = nullptr;
  aclTensor* antiquantScaleFp16 = nullptr;
  ret = CreateAclTensor(antiquantScaleHostData, antiquantScaleShape, &antiquantScaleFp16DeviceAddr, aclDataType::ACL_FLOAT16, &antiquantScaleFp16);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  // 创建yFp16 aclTensor
  void* yFp16DeviceAddr = nullptr;
  aclTensor* yFp16 = nullptr;
  ret = CreateAclTensor(yHostData, yShape, &yFp16DeviceAddr, aclDataType::ACL_FLOAT16, &yFp16);
  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
  int8_t cubeMathType = 1;
  uint64_t workspaceSize = 0;
  aclOpExecutor* executor;
  void* workspaceAddr = nullptr;

  // 对weight做int32转int4pack
  ret = aclnnConvertWeightToINT4PackGetWorkspaceSize(weight, weightInt4Pack, &workspaceSize, &executor);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnConvertWeightToINT4PackGetWorkspaceSize failed. ERROR: %d\n", ret); return ret);
  ret = aclnnConvertWeightToINT4Pack(workspaceAddr, workspaceSize, executor, stream);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnConvertWeightToINT4Pack failed. ERROR: %d\n", ret); return ret);

  // 调用cast生成FP16的输入
  ret = aclnnCastGetWorkspaceSize(x, aclDataType::ACL_FLOAT16, xFp16, &workspaceSize, &executor);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnCastGetWorkspaceSize0 failed. ERROR: %d\n", ret); return ret);
  // 根据第一段接口计算出的workspaceSize申请device内存

  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);
  }
  ret = aclnnCast(workspaceAddr, workspaceSize, executor, stream);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnCast0 failed. ERROR: %d\n", ret); return ret);

  ret = aclrtSynchronizeStream(stream);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtSynchronizeStream failed. ERROR: %d\n", ret); return ret);

  ret = aclnnCastGetWorkspaceSize(antiquantScale, aclDataType::ACL_FLOAT16, antiquantScaleFp16, &workspaceSize, &executor);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnCastGetWorkspaceSize1 failed. ERROR: %d\n", ret); return ret);
  // 根据第一段接口计算出的workspaceSize申请device内存

  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);
  }
  ret = aclnnCast(workspaceAddr, workspaceSize, executor, stream);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnCast1 failed. ERROR: %d\n", ret); return ret);

  ret = aclrtSynchronizeStream(stream);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtSynchronizeStream failed. ERROR: %d\n", ret); return ret);

  // 调用aclnnWeightQuantBatchMatmulV2第一段接口
  ret = aclnnWeightQuantBatchMatmulV2GetWorkspaceSize(xFp16, weightInt4Pack, antiquantScaleFp16, nullptr, nullptr, nullptr, nullptr, 0, yFp16, &workspaceSize, &executor);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnWeightQuantBatchMatmulV2GetWorkspaceSize failed. ERROR: %d\n", ret); return ret);
  // 根据第一段接口计算出的workspaceSize申请device内存

  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aclnnWeightQuantBatchMatmulV2第二段接口
  ret = aclnnWeightQuantBatchMatmulV2(workspaceAddr, workspaceSize, executor, stream);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnWeightQuantBatchMatmulV2 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);

 // 将输出转为FP32
  ret = aclnnCastGetWorkspaceSize(yFp16, aclDataType::ACL_FLOAT, y, &workspaceSize, &executor);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnCastGetWorkspaceSize2 failed. ERROR: %d\n", ret); return ret);
  // 根据第一段接口计算出的workspaceSize申请device内存

  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);
  }
  ret = aclnnCast(workspaceAddr, workspaceSize, executor, stream);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnCast2 failed. ERROR: %d\n", ret); return ret);

  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(yShape);
  std::vector<float> resultData(size, 0);
  ret = aclrtMemcpy(resultData.data(), resultData.size() * sizeof(resultData[0]), yDeviceAddr,
                    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(x);
  aclDestroyTensor(weight);
  aclDestroyTensor(weightInt4Pack);
  aclDestroyTensor(antiquantScale);
  aclDestroyTensor(y);
  aclDestroyTensor(xFp16);
  aclDestroyTensor(antiquantScaleFp16);
  aclDestroyTensor(yFp16);

  // 7. 释放device资源
  aclrtFree(xDeviceAddr);
  aclrtFree(weightDeviceAddr);
  aclrtFree(weightInt4PackDeviceAddr);
  aclrtFree(antiquantScaleDeviceAddr);
  aclrtFree(yDeviceAddr);
  aclrtFree(xFp16DeviceAddr);
  aclrtFree(antiquantScaleFp16DeviceAddr);
  aclrtFree(yFp16DeviceAddr);

  if (workspaceSize > 0) {
    aclrtFree(workspaceAddr);
  }
  aclrtDestroyStream(stream);
  aclrtResetDevice(deviceId);
  aclFinalize();

  return 0;
}
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