aclnnGroupNormSilu

Applicable Products

Product	Supported
Ascend 950PR/Ascend 950DT	√
Atlas A3 training products/Atlas A3 inference products	√
Atlas A2 training products/Atlas A2 inference products	√
Atlas 200I/500 A2 inference products	×
Atlas inference products	√
Atlas training products	×

Function

Description: Computes the Group Normalization result groupnormOut, mean meanOut, and reciprocal of standard deviation rstdOut of the input self, and applies the SiLU operation to groupnormOut to obtain the final output out.
Formula:
- GroupNorm:
Let $E[x] = \bar{x}$ denote the mean of $x$ , and $Var[x] = \frac{1}{n} * \sum_{i=1}^n(x_i - E[x])^2$ denote the variance of $x$ , then
$\left\{ \begin{array} {rcl} groupnormOut& &= \frac{x - E[x]}{\sqrt{Var[x] + eps}} * \gamma + \beta \\ meanOut& &= E[x]\\ rstdOut& &= \frac{1}{\sqrt{Var[x] + eps}}\\ \end{array} \right.$
- Silu:
$out = \frac{groupnormOut}{1+e^{-groupnormOut}}$

Function Prototype

Each operator is divided into a two-phase API. The "aclnnGroupNormSiluGetWorkspaceSize" API must be called first to obtain the workspace size required for computation and the executor that encapsulates the operator computation flow. Then, the "aclnnGroupNormSilu" API is called to perform the computation.

aclnnStatus aclnnGroupNormSiluGetWorkspaceSize(
    const aclTensor* self,
    const aclTensor* gamma,
    const aclTensor* beta,
    int64_t          group,
    double           eps,
    aclTensor*       out,
    aclTensor*       meanOut,
    aclTensor*       rstdOut,
    uint64_t*        workspaceSize,
    aclOpExecutor**  executor);

aclnnStatus aclnnGroupNormSilu(
    void *         workspace,
    uint64_t       workspaceSize,
    aclOpExecutor *executor,
    aclrtStream    stream)

aclnnGroupNormSiluGetWorkspaceSize

Parameters

Parameter	Input/Output	Description	Instruction	Data Type	Data Format	Dimension (Shape)	Non-contiguous Tensor
self (aclTensor*)	Input	x in the formula.	-	FLOAT16, FLOAT, BFLOAT16	ND	2-8, where the first dimension is N and the second dimension is C.	√
gamma (aclTensor*)	Input	γ in the formula.	The data type must be the same as self or FLOAT, and the number of elements must be the same as the size of the second dimension of the input self.	FLOAT16, FLOAT, BFLOAT16	ND	1	√
beta (aclTensor*)	Input	β in the formula.	The data type must be consistent with self or be FLOAT, and the number of elements must be the same as the size of the second dimension of the input self.	FLOAT16, FLOAT, BFLOAT16	ND	1	√
group (int64_t)	Input	Divides the second dimension of the input self into group groups.	group must be divisible by the first dimension of self.	-	-	-	-
eps (double)	Input	The eps in the formula.	eps must be greater than 0.	-	-	-	-
out (aclTensor*)	Output	out in the formula.	The data type is the same as self.	FLOAT16, FLOAT, BFLOAT16	ND	Same as self.	x
meanOut (aclTensor*)	Output	meanOut in the formula.	The data type is the same as self, and N in the shape is the size of the first dimension of self.	FLOAT16, FLOAT, BFLOAT16	ND	(N, group)	x
rstdOut (aclTensor*)	Output	rstdOut in the formula.	The data type is the same as self, and N in the shape is the size of the first dimension of self.	FLOAT16, FLOAT, BFLOAT16	ND	(N, group)	x
workspaceSize (uint64_t*)	output	Returns the workspace size to be allocated on the Device.	-	-	-	-	-
executor（aclOpExecutor**）	output	Returns the op executor, which contains the operator computation flow.	-	-	-	-	-

Atlas inference products: The data type does not support BFLOAT16.
Ascend 950PR/Ascend 950DT: The data types of meanOut and rstdOut must be the same as gamma and beta.

Return Value

aclnnStatus: return code. For details, see aclnn Return Code.

The first-phase API performs input parameter validation and returns an error in the following scenarios:

Return Code	Error Code	Description
ACLNN_ERR_PARAM_NULLPTR	161001	If a required input, output, or required attribute parameter is a null pointer, 161001 is returned.
ACLNN_ERR_PARAM_INVALID	161002	The data types of the input and output are not within the supported range.
ACLNN_ERR_PARAM_INVALID	161002	The input and output parameters do not meet the constraints in Parameters.

aclnnGroupNormSilu

Parameters

Parameter	Input/Output	Description
workspace	Input	Workspace memory address applied for on the Device side.
workspaceSize	Input	Workspace size applied for on the Device side, obtained from the first-phase API aclnnGroupNormSiluGetWorkspaceSize.
executor	Input	Operator executor, which contains the operator computation flow.
stream	Input	Specifies the stream for executing the task.

Return Value

aclnnStatus: return code. For details, see aclnn Return Code.

Constraints

Deterministic computation: aclnnGroupNormSilu defaults to a deterministic implementation.
Input shape constraints:
1. The size of the second dimension of self must be divisible by group.
2. The shape of meanOut and rstdOut must be (N, group), where N is the size of the first dimension of self.
Input attribute restriction: eps > 0

Example

The following provides example code for reference only. For details about compilation and running, see Compile and Run Samples.

#include <iostream>
#include <memory>
#include <vector>
#include "acl/acl.h"
#include "aclnnop/aclnn_group_norm_silu.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, aclrtStream* stream) {
  // Boilerplate for AscendCL initialization.
  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);
  // Call aclrtMalloc to apply for device-side memory.
  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);

  // Call aclrtMemcpy to copy host-side data to device-side memory.
  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);

  // Calculate the strides of a contiguous tensor.
  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];
  }

  // Call the aclCreateTensor API to create an 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. (Boilerplate) Initialize the device/stream. For details, see the external ACL API list.
  // Set deviceId based on the actual device.
  int32_t deviceId = 0;
  aclrtStream stream;
  auto ret = Init(deviceId, &stream);
  // Handle the check based on your requirements.
  CHECK_RET(ret == 0, LOG_PRINT("Init acl failed. ERROR: %d\n", ret); return ret);
  // 2. Construct the input and output. Customize the construction based on the API.
  std::vector<int64_t> selfShape = {2, 3, 4};
  std::vector<int64_t> gammaShape = {3};
  std::vector<int64_t> betaShape = {3};
  std::vector<int64_t> outShape = {2, 3, 4};
  std::vector<int64_t> meanOutShape = {2, 1};
  std::vector<int64_t> rstdOutShape = {2, 1};
  void* selfDeviceAddr = nullptr;
  void* gammaDeviceAddr = nullptr;
  void* betaDeviceAddr = nullptr;
  void* outDeviceAddr = nullptr;
  void* meanOutDeviceAddr = nullptr;
  void* rstdOutDeviceAddr = nullptr;
  aclTensor* self = nullptr;
  aclTensor* gamma = nullptr;
  aclTensor* beta = nullptr;
  aclTensor* out = nullptr;
  aclTensor* meanOut = nullptr;
  aclTensor* rstdOut = nullptr;
  std::vector<float> selfHostData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0,
                                     13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0};
  std::vector<float> gammaHostData = {2.0, 2, 2};
  std::vector<float> betaHostData = {2.0, 2, 2};
  std::vector<float> outHostData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0,
                                    13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0};
  std::vector<float> meanOutHostData = {2.0, 2};
  std::vector<float> rstdOutHostData = {2.0, 2};

  int64_t group = 1;
  double eps = 0.00001;
  // Create the self aclTensor.
  ret = CreateAclTensor(selfHostData, selfShape, &selfDeviceAddr, aclDataType::ACL_FLOAT, &self);
  std::unique_ptr<aclTensor, aclnnStatus (*)(const aclTensor *)> selfTensorPtr(self, aclDestroyTensor);
  std::unique_ptr<void, aclError (*)(void *)> selfDeviceAddrPtr(selfDeviceAddr, aclrtFree);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  // Create the gamma aclTensor.
  ret = CreateAclTensor(gammaHostData, gammaShape, &gammaDeviceAddr, aclDataType::ACL_FLOAT, &gamma);
  std::unique_ptr<aclTensor, aclnnStatus (*)(const aclTensor *)> gammaTensorPtr(gamma, aclDestroyTensor);
  std::unique_ptr<void, aclError (*)(void *)> gammaDeviceAddrPtr(gammaDeviceAddr, aclrtFree);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  // Create the beta aclTensor.
  ret = CreateAclTensor(betaHostData, betaShape, &betaDeviceAddr, aclDataType::ACL_FLOAT, &beta);
  std::unique_ptr<aclTensor, aclnnStatus (*)(const aclTensor *)> betaTensorPtr(beta, aclDestroyTensor);
  std::unique_ptr<void, aclError (*)(void *)> betaDeviceAddrPtr(betaDeviceAddr, aclrtFree);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  // Create the out aclTensor.
  ret = CreateAclTensor(outHostData, outShape, &outDeviceAddr, aclDataType::ACL_FLOAT, &out);
  std::unique_ptr<aclTensor, aclnnStatus (*)(const aclTensor *)> outTensorPtr(out, aclDestroyTensor);
  std::unique_ptr<void, aclError (*)(void *)> outDeviceAddrPtr(outDeviceAddr, aclrtFree);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  // Create the meanOut aclTensor.
  ret = CreateAclTensor(meanOutHostData, meanOutShape, &meanOutDeviceAddr, aclDataType::ACL_FLOAT, &meanOut);
  std::unique_ptr<aclTensor, aclnnStatus (*)(const aclTensor *)> meanOutTensorPtr(meanOut, aclDestroyTensor);
  std::unique_ptr<void, aclError (*)(void *)> meanOutDeviceAddrPtr(meanOutDeviceAddr, aclrtFree);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  // Create rstdOut aclTensor.
  ret = CreateAclTensor(rstdOutHostData, rstdOutShape, &rstdOutDeviceAddr, aclDataType::ACL_FLOAT, &rstdOut);
  std::unique_ptr<aclTensor, aclnnStatus (*)(const aclTensor *)> rstdOutTensorPtr(rstdOut, aclDestroyTensor);
  std::unique_ptr<void, aclError (*)(void *)> rstdOutDeviceAddrPtr(rstdOutDeviceAddr, aclrtFree);
  CHECK_RET(ret == ACL_SUCCESS, return ret);

  // 3. Call the CANN operator library API. Replace it with the specific API.
  uint64_t workspaceSize = 0;
  aclOpExecutor* executor;
  // Call the first-phase API of aclnnGroupNormSilu.
  ret = aclnnGroupNormSiluGetWorkspaceSize(self, gamma, beta, group, eps, out, meanOut, rstdOut, &workspaceSize, &executor);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnGroupNormSiluGetWorkspaceSize failed. ERROR: %d\n", ret); return ret);
  // Apply for device memory based on the workspaceSize calculated by the first-phase API.
  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;);
  }
  // Call the second-phase API of aclnnGroupNormSilu.
  ret = aclnnGroupNormSilu(workspaceAddr, workspaceSize, executor, stream);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnGroupNormSilu failed. ERROR: %d\n", ret); return ret);
  // 4. (Boilerplate) Synchronously wait for task execution to complete.
  ret = aclrtSynchronizeStream(stream);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtSynchronizeStream failed. ERROR: %d\n", ret); return ret);
  // 5. Obtain the output value and copy the result from the device memory to the host memory. Modify this based on the specific API definition.
  auto size = GetShapeSize(outShape);
  std::vector<float> outResultData(size, 0);
  ret = aclrtMemcpy(outResultData.data(), outResultData.size() * sizeof(outResultData[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("outResultData[%ld] is: %f\n", i, outResultData[i]);
  }

  size = GetShapeSize(meanOutShape);
  std::vector<float> meanResultData(size, 0);
  ret = aclrtMemcpy(meanResultData.data(), meanResultData.size() * sizeof(meanResultData[0]), meanOutDeviceAddr, 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("meanResultData[%ld] is: %f\n", i, meanResultData[i]);
  }

  size = GetShapeSize(rstdOutShape);
  std::vector<float> rstdResultData(size, 0);
  ret = aclrtMemcpy(rstdResultData.data(), rstdResultData.size() * sizeof(rstdResultData[0]), rstdOutDeviceAddr, 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("rstdResultData[%ld] is: %f\n", i, rstdResultData[i]);
  }

  // 6. Release aclTensor and aclScalar. Modify this based on the specific API definition.
  aclDestroyTensor(self);
  aclDestroyTensor(gamma);
  aclDestroyTensor(beta);
  aclDestroyTensor(out);
  aclDestroyTensor(meanOut);
  aclDestroyTensor(rstdOut);

  // 7. Release device resources. Modify this based on the specific API definition.
  aclrtFree(selfDeviceAddr);
  aclrtFree(gammaDeviceAddr);
  aclrtFree(betaDeviceAddr);
  aclrtFree(outDeviceAddr);
  aclrtFree(meanOutDeviceAddr);
  aclrtFree(rstdOutDeviceAddr);

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