aclnnFlashAttentionScoreGradV2

Applicable Products

Function

• Description: Computes the backward output of attention in training scenarios, i.e., the backward computation of aclnnFlashAttentionScoreV2. Compared with the aclnnFlashAttentionScoreGrad API, this API adds the pseType parameter:

  • When pseType=1, the implementation is the same as aclnnFlashAttentionScoreGrad.

  • For other values of pseType, mul is performed first, followed by add.

• Formula:

  The known attention forward computation formula is (when pseType = 1, it is the same as the formula of aclnnFlashAttentionScoreGrad; when pseType takes other values, the formula is as follows):

  $$Y=Dropout(Softmax(Mask(\frac{QK^T}{\sqrt{d}}+pse),atten\_mask),keep\_prob)V$$

  For ease of expression, variables $S$ and $P$ are used to represent the formula:

  $$S=Mask(\frac{QK^T}{\sqrt{d}}+pse),atten\_mask$$ $$P=Dropout(Softmax(S),keep\_prob)$$ $$Y=PV$$

  The backward propagation formula for attention is:

  $$dV=P^TdY$$ $$dQ=\frac{((dS)*K)}{\sqrt{d}}$$ $$dK=\frac{((dS)^T*Q)}{\sqrt{d}}$$

Function Prototype

Each operator is divided into a two-phase API. You must first call the "aclnnFlashAttentionScoreGradV2GetWorkspaceSize" API to obtain the workspace size required for computation and the executor that contains the operator computation process, and then call the "aclnnFlashAttentionScoreGradV2" API to perform the computation.

aclnnStatus aclnnFlashAttentionScoreGradV2GetWorkspaceSize(
  const aclTensor   *query,
  const aclTensor   *keyIn,
  const aclTensor   *value,
  const aclTensor   *dy,
  const aclTensor   *pseShiftOptional,
  const aclTensor   *dropMaskOptional,
  const aclTensor   *paddingMaskOptional,
  const aclTensor   *attenMaskOptional,
  const aclTensor   *softmaxMaxOptional,
  const aclTensor   *softmaxSumOptional,
  const aclTensor   *softmaxInOptional,
  const aclTensor   *attentionInOptional,
  const aclIntArray *prefixOptional,
  const aclIntArray *qStartIdxOptional,
  const aclIntArray *kvStartIdxOptional,
  double             scaleValue,
  double             keepProb,
  int64_t            preTokens,
  int64_t            nextTokens,
  int64_t            headNum,
  char              *inputLayout,
  int64_t            innerPrecise,
  int64_t            sparseMode,
  int64_t            pseType,
  const aclTensor   *dqOut,
  const aclTensor   *dkOut,
  const aclTensor   *dvOut,
  const aclTensor   *dpseOut,
  uint64_t          *workspaceSize,
  aclOpExecutor    **executor)

aclnnStatus aclnnFlashAttentionScoreGradV2(
  void             *workspace,
  uint64_t          workspaceSize,
  aclOpExecutor    *executor,
  const aclrtStream stream)

aclnnFlashAttentionScoreGradV2GetWorkspaceSize

• Parameters

  Parameter	Input/Output	Description	Instruction	Data Type	Data Format	Dimension (Shape)	Non-contiguous Tensor
  query	Input	Q in the formula.	Data type consistent with keyIn/value.	FLOAT16, BFLOAT16, FLOAT32	ND	[BNSD], [BSND], [BSH], [SBH]	√
  keyIn	Input	K in the formula.	Consistent with the data type of query/value.	FLOAT16, BFLOAT16, FLOAT32	ND	[BNSD], [BSND], [BSH], [SBH]	√
  value	Input	V in the formula.	The data type is consistent with query/keyIn.	FLOAT16, BFLOAT16, FLOAT32	ND	[BNSD], [BSND], [BSH], [SBH]	√
  dy	Input	dY in the formula.	-	FLOAT16, BFLOAT16, FLOAT32	ND	[BNSD], [BSND], [BSH], [SBH]	√
  pseShiftOptional	Optional input.	pse in the formula.	The data type is consistent with that of query. This parameter must be used together with pseType.	FLOAT16, BFLOAT16, FLOAT32	ND	[B,N,Sq,Skv], [B,N,1,Skv], [1,N,Sq,Skv], [B,N,1024,Skv], [1,N,1024,Skv], [B,N], [N]	√
  dropMaskOptional	Optional input	Dropout in the formula.	-	UINT8	ND	0, 1	√
  paddingMaskOptional	Optional input	Reserved parameter, not currently used.	-	-	-	-	-
  qStartIdxOptional	Optional input.	Represents the starting index of the current chunked query sequence in the global context for the outer cut scenario.	-	INT64	reserved	0, 1	-
  kvStartIdxOptional	optional input	Represents the out-of-bounds scenario, indicating the start index of the current block's key and value sequences in the global context.	-	INT64	ND	0, 1	-
  attenMaskOptional	Optional input.	atten_mask in the formula.	A value of 1 indicates that the position does not participate in the computation, and a value of 0 indicates that the position participates in the computation.	BOOL, UINT8.	ND.	[B,N,Sq,Skv], [B,1,Sq,Skv], [1,1,Sq,Skv], [Sq,Skv].	√.
  softmaxMaxOptional.	Optional input	Intermediate output of the attention forward computation.	-	FLOAT	ND	[B,N,Sq,8]	√
  softmaxSumOptional	Optional input	Intermediate output of the attention forward computation.	-	FLOAT	ND	[B,N,Sq,8]	√
  softmaxInOptional	Optional input	Intermediate output of the attention forward computation.	Reserved parameter, currently unused.	-	-	-	-
  attentionInOptional	optional input	Final output of the attention forward computation.	The data type and shape are consistent with query.	FLOAT16, BFLOAT16, FLOAT32	ND	[BNSD], [BSND], [BSH], [SBH]	√
  prefixOptional	Optional input	N of each batch in the prefix sparse computation scenario.	-	INT64	ND	0, 1	-
  scaleValue	optional input	Scale factor in the formula.	-	DOUBLE	-	-	-
  keepProb	Optional input	Proportion of 1s in dropMask.	-	DOUBLE	-	-	-
  preTokens	Optional input	Left boundary of the sparse computation window.	-	INT64	-	-	-
  nextTokens	Optional input	Right boundary of the sparse computation window.	-	INT64	-	-	-
  headNum	Input	Number of heads on a single card, corresponding to the N axis of query.	-	INT64	-	-	-
  inputLayout	Input	Data layout format of query, key, and value.	Supports BSH, SBH, BSND, and BNSD.	String	-	-	-
  innerPrecise	Optional input	Internal computation precision control.	Reserved parameter, not used.	INT64	-	-	-
  sparseMode	Optional input	Sparse mode.	Supports configuration values 0 to 6.	INT64	-	-	-
  dqOut	Output	dQ in the formula, gradient of query.	-	FLOAT16, BFLOAT16, FLOAT32	ND	[BNSD], [BSND], [BSH], [SBH]	√
  dkOut	Output	dK in the formula. Gradient of keyIn.	-	FLOAT16, BFLOAT16, FLOAT32	ND	[BNSD], [BSND], [BSH], [SBH]	√
  dvOut	Output	dV in the formula. The gradient of value.	-	FLOAT16, BFLOAT16, FLOAT32	ND	[BNSD], [BSND], [BSH], [SBH]	√
  dpseOut	Output	Gradient of d(pse).	Reserved.	-	-	-	-
  workspaceSize	Output	Returns the workspace size to be applied for on the Device side.	-	-	-	-	-
  executor	Output	Returns the operator executor, which contains the computation flow.	-	-	-	-	-

• 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 Value	Error Code	Description
  ACLNN_ERR_PARAM_NULLPTR	161001	The input parameter is a required input, output, or required attribute, and is a null pointer.
  ACLNN_ERR_PARAM_INVALID	161002	The data type of query, keyIn, value, dy, pseShiftOptional, dropMaskOptional, paddingMaskOptional, attenMaskOptional, softmaxMaxOptional, softmaxSumOptional, softmaxInOptional, attentionInOptional, dqOut, dkOut, or dvOut is not within the supported range.
  		The data format of query, keyIn, value, dy, pseShiftOptional, dropMaskOptional, paddingMaskOptional, attenMaskOptional, softmaxMaxOptional, softmaxSumOptional, softmaxInOptional, attentionInOptional, dqOut, dkOut, and dvOut is not within the supported range.

aclnnFlashAttentionScoreGradV2

• Parameters

  Parameter	Input/Output	Description
  workspace	Input	Specifies the workspace memory address applied for on the Device side.
  workspaceSize	Input	Specifies the workspace size applied for on the Device side, obtained from the first-phase API aclnnFlashAttentionScoreGradV2GetWorkspaceSize.
  executor	Input	Operator executor that contains the operator computation flow.
  stream	Input	Specifies the stream for task execution.

• Return Value

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

Constraints

• Deterministic computation:

  • aclnnFlashAttentionScoreGradV2 defaults to a non-deterministic implementation. Deterministic computation can be enabled through aclrtCtxSetSysParamOpt.

• When this API is used with PyTorch, ensure that the CANN-related packages and PyTorch-related packages have matching versions.

• The B (batchsize) of the inputs query, key, value, and dy must be equal.

• The D (Head-Dim) of the input query, key, and value must satisfy (qD == kD && kD >= vD).

• The inputLayout of the input query, key, value, and dy must be consistent.

• The shapes of the input key and value must be consistent except for D. When the D sizes of query, key, and value are the same, the shapes of query and dy must be consistent.

• The N of the input query/dy and the N of key/value may be unequal, but must be in a proportional relationship, i.e., Nq/Nkv must be a non-zero integer, with Nq in the value range of 1 to 256.

• For constraints on data shapes, take inputLayout BSND and BNSD as examples (H = N * D for BSH and SBH), where:

  • B: value range is 1 to 2M. When prefixOptional is present, B supports a maximum of 2K.

  • N: value range is 1 to 256.

  • S: value range is 1 to 1M.

  • D: value range is 1 to 768.

  • KeepProb: value range is (0, 1].

• The data formats of query, key, and value support interpretation from multiple dimensions, where B (Batch) indicates the batch size of input samples, S (Seq-Length) indicates the sequence length of input samples, H (Head-Size) indicates the hidden layer size, N (Head-Num) indicates the number of heads, D (Head-Dim) indicates the minimum unit size of the hidden dimension, and D = H/N is satisfied.

• pseShiftOptional: If Sq is greater than 1024 and Sq and Skv of each batch have the same length and the scenario is a lower triangular mask with sparseMode of 0, 2, or 3, ALiBi position encoding compression can be enabled. In this case, only the last 1024 rows of the original PSE need to be input to achieve memory optimization, that is, alibi_compress = ori_pse[:, :, -1024:, :], as detailed below:

  • When the parameter differs for each batch, the shape is BNHSkv (H=1024).

  • When the parameter is the same for each batch, the shape is 1NHSkv (H=1024).

  • If pseType is 2 or 3, the data type must be FLOAT32, and the corresponding supported shape range is [B,N] or [N].

  • If this parameter is not enabled, pass nullptr for pseShiftOptional and 1 for pseType.

• innerPrecise: Currently, 0 and 1 are reserved configuration values, and 2 enables invalid row computation, which prevents precision loss caused by entire-row masks during computation. However, this configuration degrades performance. If the operator detects an invalid row scenario, it automatically enables invalid row computation, for example, when sparseMode is 3 and Sq > Skv.

• Meaning of each pseType value

  pseType	Meaning	Note
  0	Externally passed pse, first mul then add.	-
  1	Externally passed pse, first add then mul.	Consistent with the implementation of FlashAttentionScoreGrad.
  2	Internally generated pse, first mul then add.	-
  3	Internally generated pse, first mul then add then sqrt.	-

• Constraints on sparseMode are as follows:

  • When all attenMaskOptional shapes are smaller than 2048 and identical, the default mode is recommended to reduce memory usage.

  • When set to 1, 2, 3, or 5, the user-configured preTokens and nextTokens do not take effect.

  • When set to 0 or 4, ensure that attenMaskOptional is consistent with the ranges of preTokens and nextTokens.

  • Pass 0 when no specific configuration is required.

  • For detailed descriptions of different sparse modes, see sparse mode description.

• In some scenarios, if the computation load is too large, the operator execution may time out (aicore error, with errorStr: timeout or trap error). In this case, axis splitting is recommended. Note: The computation load is affected by parameters such as B, S, N, and D. Larger values result in a larger computation load.

• Constraints on the softmaxMax and softmaxSum parameters: The input format is fixed as [B, N, S, 8], except for the TND input format, which is [T, N, 8]. Note: T = B * S.

• The value of headNum must be consistent with the N value in the input Query.

• In the band scenario, preTokens and nextTokens must overlap.

• In the prefixOptional sparse computation scenario (sparseMode = 5 or sparseMode = 6), when Sq > Skv, the N value range of prefix is [0, Skv]; when Sq <= Skv, the N value range of prefix is [Skv - Sq, Skv].

• When Sq in pseShift is greater than 1024 and the shape is BNHS or 1NHS, Sq and Skv must be of equal length.

Example

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

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

void PrintOutResult(std::vector<int64_t> &shape, void** deviceAddr) {
  auto size = GetShapeSize(shape);
  std::vector<float> resultData(size, 0);
  auto ret = aclrtMemcpy(resultData.data(), resultData.size() * sizeof(resultData[0]),
                         *deviceAddr, 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);
  for (int64_t i = 0; i < size; i++) {
    LOG_PRINT("mean result[%ld] is: %f\n", i, resultData[i]);
  }
}

int Init(int32_t deviceId, aclrtStream* stream) {
  // Boilerplate: resource 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 allocate memory on the device side.
  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 data from the host side to the device 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. See the ACL API manual.
  // Set deviceId based on the actual device.
  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. Construct the inputs and outputs. Custom construction is required based on the API.
  std::vector<int64_t> qShape = {256, 1, 128};
  std::vector<int64_t> kShape = {256, 1, 128};
  std::vector<int64_t> vShape = {256, 1, 128};
  std::vector<int64_t> dxShape = {256, 1, 128};
  std::vector<int64_t> attenmaskShape = {256, 256};
  std::vector<int64_t> softmaxMaxShape = {1, 1, 256, 8};
  std::vector<int64_t> softmaxSumShape = {1, 1, 256, 8};
  std::vector<int64_t> attentionInShape = {256, 1, 128};

  std::vector<int64_t> dqShape = {256, 1, 128};
  std::vector<int64_t> dkShape = {256, 1, 128};
  std::vector<int64_t> dvShape = {256, 1, 128};

  void* qDeviceAddr = nullptr;
  void* kDeviceAddr = nullptr;
  void* vDeviceAddr = nullptr;
  void* dxDeviceAddr = nullptr;
  void* attenmaskDeviceAddr = nullptr;
  void* softmaxMaxDeviceAddr = nullptr;
  void* softmaxSumDeviceAddr = nullptr;
  void* attentionInDeviceAddr = nullptr;
  void* dqDeviceAddr = nullptr;
  void* dkDeviceAddr = nullptr;
  void* dvDeviceAddr = nullptr;

  aclTensor* q = nullptr;
  aclTensor* k = nullptr;
  aclTensor* v = nullptr;
  aclTensor* dx = nullptr;
  aclTensor* pse = nullptr;
  aclTensor* dropMask = nullptr;
  aclTensor* padding = nullptr;
  aclTensor* attenmask = nullptr;
  aclTensor* softmaxMax = nullptr;
  aclTensor* softmaxSum = nullptr;
  aclTensor* softmaxIn = nullptr;
  aclTensor* attentionIn = nullptr;
  aclTensor* dq = nullptr;
  aclTensor* dk = nullptr;
  aclTensor* dv = nullptr;
  aclTensor* dpse = nullptr;

  std::vector<float> qHostData(32768, 1);
  std::vector<float> kHostData(32768, 1);
  std::vector<float> vHostData(32768, 1);
  std::vector<float> dxHostData(32768, 1);
  std::vector<uint8_t> attenmaskHostData(65536, 0);
  std::vector<float> softmaxMaxHostData(2048, 3.0);
  std::vector<float> softmaxSumHostData(2048, 3.0);
  std::vector<float> attentionInHostData(32768, 1);
  std::vector<float> dqHostData(32768, 0);
  std::vector<float> dkHostData(32768, 0);
  std::vector<float> dvHostData(32768, 0);

  ret = CreateAclTensor(qHostData, qShape, &qDeviceAddr, aclDataType::ACL_FLOAT, &q);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  ret = CreateAclTensor(kHostData, kShape, &kDeviceAddr, aclDataType::ACL_FLOAT, &k);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  ret = CreateAclTensor(vHostData, vShape, &vDeviceAddr, aclDataType::ACL_FLOAT, &v);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  ret = CreateAclTensor(dxHostData, dxShape, &dxDeviceAddr, aclDataType::ACL_FLOAT, &dx);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  ret = CreateAclTensor(attenmaskHostData, attenmaskShape, &attenmaskDeviceAddr, aclDataType::ACL_UINT8, &attenmask);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  ret = CreateAclTensor(softmaxMaxHostData, softmaxMaxShape, &softmaxMaxDeviceAddr, aclDataType::ACL_FLOAT, &softmaxMax);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  ret = CreateAclTensor(softmaxSumHostData, softmaxSumShape, &softmaxSumDeviceAddr, aclDataType::ACL_FLOAT, &softmaxSum);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  ret = CreateAclTensor(attentionInHostData, attentionInShape, &attentionInDeviceAddr, aclDataType::ACL_FLOAT, &attentionIn);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  ret = CreateAclTensor(dqHostData, dqShape, &dqDeviceAddr, aclDataType::ACL_FLOAT, &dq);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  ret = CreateAclTensor(dkHostData, dkShape, &dkDeviceAddr, aclDataType::ACL_FLOAT, &dk);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  ret = CreateAclTensor(dvHostData, dvShape, &dvDeviceAddr, aclDataType::ACL_FLOAT, &dv);
  CHECK_RET(ret == ACL_SUCCESS, return ret);

  std::vector<int64_t> prefixOp = {0};
  aclIntArray *prefix = aclCreateIntArray(prefixOp.data(), 1);
  std::vector<int64_t> qStartIdxOp = {0};
  std::vector<int64_t> kvStartIdxOp = {0};
  aclIntArray *qStartIdx = aclCreateIntArray(qStartIdxOp.data(), 1);
  aclIntArray *kvStartIdx = aclCreateIntArray(kvStartIdxOp.data(), 1);
  double scaleValue = 0.088388;
  double keepProb = 1;
  int64_t preTokens = 65536;
  int64_t nextTokens = 65536;
  int64_t headNum = 1;
  int64_t innerPrecise = 0;
  int64_t sparseMode = 0;
  int64_t pseType = 1;
  char layOut[5] = {'S', 'B', 'H', 0};

  // 3. Call the CANN operator library API. Replace with the specific API name.
  uint64_t workspaceSize = 0;
  aclOpExecutor* executor;

  // Call the first-phase API of aclnnFlashAttentionScoreGradV2.
  ret = aclnnFlashAttentionScoreGradV2GetWorkspaceSize(q, k, v, dx, pse, dropMask, padding,
            attenmask, softmaxMax, softmaxSum, softmaxIn, attentionIn, prefix, qStartIdx, kvStartIdx,
            scaleValue, keepProb, preTokens, nextTokens, headNum, layOut, innerPrecise, sparseMode, pseType,
            dq, dk, dv, dpse, &workspaceSize, &executor);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnFlashAttentionScoreGradV2GetWorkspaceSize failed. ERROR: %d\n", ret); return ret);

  // Allocate 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 aclnnFlashAttentionScoreGradV2.
  ret = aclnnFlashAttentionScoreGradV2(workspaceAddr, workspaceSize, executor, stream);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnFlashAttentionScoreGradV2 failed. ERROR: %d\n", ret); return ret);

  // 4. (Boilerplate) Synchronize and 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 based on the specific API definition.
  PrintOutResult(dqShape, &dqDeviceAddr);
  PrintOutResult(dkShape, &dkDeviceAddr);
  PrintOutResult(dvShape, &dvDeviceAddr);

  // 6. Release aclTensor and aclScalar. Modify based on the specific API definition.
  aclDestroyTensor(q);
  aclDestroyTensor(k);
  aclDestroyTensor(v);
  aclDestroyTensor(dx);
  aclDestroyTensor(attenmask);
  aclDestroyTensor(softmaxMax);
  aclDestroyTensor(softmaxSum);
  aclDestroyTensor(attentionIn);
  aclDestroyTensor(dq);
  aclDestroyTensor(dk);
  aclDestroyTensor(dv);

  // 7. Release device resources.
  aclrtFree(qDeviceAddr);
  aclrtFree(kDeviceAddr);
  aclrtFree(vDeviceAddr);
  aclrtFree(dxDeviceAddr);
  aclrtFree(attenmaskDeviceAddr);
  aclrtFree(softmaxMaxDeviceAddr);
  aclrtFree(softmaxSumDeviceAddr);
  aclrtFree(attentionInDeviceAddr);
  aclrtFree(dqDeviceAddr);
  aclrtFree(dkDeviceAddr);
  aclrtFree(dvDeviceAddr);
  if (workspaceSize > 0) {
    aclrtFree(workspaceAddr);
  }
  aclrtDestroyStream(stream);
  aclrtResetDevice(deviceId);
  aclFinalize();

  return 0;
}