aclnnFlashAttentionUnpaddingScoreGradV4

Applicable Products

Function

• Description: Computes the backward output of attention in training scenarios, that is, the backward computation of aclnnFlashAttentionVarLenScoreV4. Compared with the aclnnFlashAttentionUnpaddingScoreGrad API, this API adds the softmaxInLayout parameter.

  • Ascend 950PR/Ascend 950DT: The softmaxInLayout parameter is not currently supported.

  • When the shape and actual data layout of the input softmaxSumOut and softmaxMaxOut are both in TND format, softmaxInLayout needs to be configured as "same_as_input".

  • When the shape of the input softmaxSumOut and softmaxMaxOut is TND but the actual data layout is NTD, softmaxInLayout must be configured as "".

  • The output format of softmaxSumOut and softmaxMaxOut in the original FlashAttentionVarLenScore API is NTD. The FlashAttentionVarLenScoreV4 API allows passing a string parameter softmaxOutLayout to control whether to output softmaxSumOut and softmaxMaxOut whose shape and data layout are both in TND format.

• Formula:

  The forward calculation formula of the attention mechanism is:

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

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

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

  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 "aclnnFlashAttentionUnpaddingScoreGradV4GetWorkspaceSize" API to obtain the workspace size required for computation and the executor that contains the operator computation process, and then call the "aclnnFlashAttentionUnpaddingScoreGradV4" API to perform the computation.

aclnnStatus aclnnFlashAttentionUnpaddingScoreGradV4GetWorkspaceSize(
  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 *actualSeqQLenOptional,
  const aclIntArray *actualSeqKvLenOptional,
  double             scaleValue,
  double             keepProb,
  int64_t            preTokens,
  int64_t            nextTokens,
  int64_t            headNum,
  char              *inputLayout,
  int64_t            innerPrecise,
  int64_t            sparseMode,
  const aclTensor   *dqOut,
  const aclTensor   *dkOut,
  const aclTensor   *dvOut,
  const aclTensor   *dpseOut,
  char              *softmaxInLayout,
  uint64_t          *workspaceSize,
  aclOpExecutor    **executor)

aclnnStatus aclnnFlashAttentionUnpaddingScoreGradV4(
  void             *workspace,
  uint64_t          workspaceSize,
  aclOpExecutor    *executor,
  const aclrtStream stream)

aclnnFlashAttentionUnpaddingScoreGradV4GetWorkspaceSize

• Parameters

  Parameter	Input/Output	Description	Instruction	Data Type	Data Format	Dimension (Shape)	Non-contiguous Tensor
  query	Input	Q in the formula.	The data type is consistent with keyIn/value.	FLOAT16、BFLOAT16、FLOAT32	ND	[TND]	√
  keyIn	Input.	K in the formula.	The data type is the same as that of query/value.	FLOAT16, BFLOAT16, FLOAT32	ND	[TND]	√
  value	Input	V in the formula.	The data type must be consistent with query/keyIn.	FLOAT16, BFLOAT16, FLOAT32	ND	[TND]	√
  dy	Input.	dY in the formula.	-	FLOAT16, BFLOAT16, FLOAT32	ND	[TND]	√
  pseShiftOptional	Optional input.	pse in the formula.	The data type is consistent with that of query.	FLOAT16, BFLOAT16, FLOAT32	ND	[B,N,1024,Skv], [1,N,1024,Skv]	√
  dropMaskOptional	Optional Input	Dropout mask.	If not used, nullptr can be passed.	UINT8	ND	0, 1	√
  paddingMaskOptional	Optional input	Reserved parameter, not currently used.	Pass null when calling this API.	-	-	-	-
  attenMaskOptional	Optional input.	atten_mask in the formula.	A value of 1 indicates that the position does not participate in the computation, and 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 forward softmax.	-	FLOAT	ND	[T,N,8]、[N,T,8]	√
  softmaxSumOptional	Optional input	Intermediate output of the forward softmax.	-	FLOAT	ND	[T,N,8], [N,T,8]	√
  softmaxInOptional	Optional Input	Intermediate output of the forward softmax.	Reserved parameter, not currently used.	-	-	-	-
  attentionInOptional	Optional input	Forward attention output.	Must be consistent with the data type and shape of query.	FLOAT16, BFLOAT16, FLOAT32	ND	[TND]	√
  prefixOptional	Optional input	N of each batch in the prefix sparse scenario.	If not used, nullptr can be passed.	INT64	ND	0, 1	-
  actualSeqQLenOptional	Optional input.	Actual query sequence length.	-	INT64	ND	1	-
  actualSeqKvLenOptional	Optional input	Specifies the actual Key/Value sequence length.	-	INT64	ND	1	-
  scaleValue	Optional input	Scale scaling factor.	Generally set to D^-0.5.	DOUBLE	-	-	-
  keepProb	Optional input	Specifies the proportion of 1s in dropMaskOptional.	Generally set to 1.0.	DOUBLE	-	-	-
  preTokens	Optional input	Specifies the left boundary of the sliding window in sparse computation.	If not explicitly specified, 2147483647 is recommended.	INT64	-	-	-
  nextTokens	Optional input.	Right boundary of the sliding window during sparse computation.	If not explicitly specified, 2147483647 is recommended.	INT64	-	-	-
  headNum	Input	Number of heads on a single card, i.e., the length of the N-axis of Query.	For comprehensive constraints, see Constraints.	INT64	-	-	-
  inputLayout	Input	Input Q/K/V data layout.	Supports TND.	String	-	-	-
  innerPrecise	Optional input	Controls the internal computation precision.	Reserved parameter. Not currently used.	INT64	-	-	-
  sparseMode	Optional input	Sparse mode.	Supports configuration from 0 to 8, excluding 5.	INT64	-	-	-
  softmaxInLayout	Optional input.	Controls the actual data layout of softmaxMax and softmaxSum.	Pass "same_as_input" for TND, and "" for NTD.	String	-	-	-
  dqOut	Output	dQ in the formula, representing the query gradient.	-	FLOAT16, BFLOAT16, FLOAT32	ND	[TND]	√
  dkOut	Output	dK in the formula, the gradient of Key.	-	FLOAT16, BFLOAT16, FLOAT32	ND	[TND]	√
  dvOut	Output	dV in the formula, representing the gradient of Value.	-	FLOAT16, BFLOAT16, FLOAT32	ND	[TND]	√
  dpseOut	Output	d(pse) gradient.	Reserved parameter, not currently used.	-	-	-	-
  workspaceSize	Output	Returns the workspace size to be applied on the Device side.	-	-	-	-	-
  executor	Output	Returns the op executor, which contains the operator computation process.	-	-	-	-	-

• 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, dvOut, or softmaxInLayout is not within the supported range.
  		The data format of query, keyIn, value, dy, pseShiftOptional, dropMaskOptional, paddingMaskOptional, attenMaskOptional, softmaxMaxOptional, softmaxSumOptional, softmaxInOptional, attentionInOptional, dqOut, dkOut, dvOut, or softmaxInLayout is not within the supported range.

aclnnFlashAttentionUnpaddingScoreGradV4

• Parameters

  Parameter	Input/Output	Description
  workspace	Input	Workspace memory address applied on the Device side.
  workspaceSize	Input	Workspace size applied on the Device side, obtained from the first-phase API aclnnFlashAttentionUnpaddingScoreGradV4GetWorkspaceSize.
  executor	Input	Operator executor that contains the operator computation process.
  stream	Input	Specifies the stream that executes the task.

• Return Value

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

Constraints

• Deterministic computation:

  • aclnnFlashAttentionUnpaddingScoreGradV4 defaults to a non-deterministic implementation. Deterministic computation can be enabled by calling aclrtCtxSetSysParamOpt.

• When using this API with PyTorch, ensure that the CANN-related packages match the PyTorch-related packages in version.

• The B (batch size) of the input query, key, value, and dy must be equal.

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

• The data type of the input query, key, value, and pseShiftOptional must be consistent.

• The shape of the input key and value must be consistent. When the d sizes of query, key, and value are the same, the shape of query and dy must be consistent. Note: The current version only supports the case where the d sizes of query, key, and value are the same.

• The N of the input query and the N of key/value may be unequal, but they must be in a proportional relationship, that is, Nq/Nkv must be a non-zero integer, and Nq ranges from 1 to 256.

• For constraints on data shape, take the TND inputLayout as an example, where:

  • T(B*S): value range is 1 to 1M.

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

  • N: value range is 1 to 256.

  • S: value range is 1 to 1M.

  • D: value range is 1 to 768.

  • KeepProb: The value range is (0, 1].

• The data layout of query, key, and value supports only TND, where T is the tightly packed data of B and S axes (SeqLenQ and SeqLenKV of each batch). B (Batch) indicates the input sample batch size, S (Seq-Length) indicates the input sample sequence length, H (Head-Size) indicates the hidden layer size, N (Head-Num) indicates the number of heads, and D (Head-Dim) indicates the minimum unit size of the hidden layer, satisfying D = H/N.

• pseShiftOptional: If Sq is greater than 1024, SeqLenQ and SeqLenKV of each batch are equal, and the scenario is a lower triangular mask with sparseMode set to 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 for memory optimization, that is, alibi_compress = ori_pse[:, :, -1024:, :]. The details are as follows:

  • 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 this parameter is not used, nullptr can be passed in.

• The constraints on sparseMode are as follows:

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

  • When configured as 1, 2, or 3, the preTokens and nextTokens configured by the user do not take effect;

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

  • If not explicitly specified, 0 is recommended.

  • For details about different sparse modes, see sparse mode description.

  • When configured as 7, the optional input pseShiftOptional is not supported.

• In some scenarios, if the computation workload 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 workload here is affected by parameters such as B, S, N, and D. Larger values result in a heavier computation workload.

• Sparse computation for prefixOptional supports only the compressed scenario, with 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 actualSeqQLenOptional input supports an S length of 0 on a certain batch, the optional input pseShiftOptional is not supported.

• 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.

• When the data layout of softmaxSum and softmaxMax is TND, softmaxInLayout must be "same_as_input".

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, aclrtContext* context, aclrtStream* stream) {
  // Boilerplate for 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 = 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** 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. Refer to the acl API manual.
  // Fill in the deviceId based on your actual device.
  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. Construct the inputs and outputs. Custom construction is required based on the API interface.
  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 = {256, 1, 8};
  std::vector<int64_t> softmaxSumShape = {256, 1, 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>  acSeqQLenOp = {256};
  std::vector<int64_t>  acSeqKvLenOp = {256};
  aclIntArray* acSeqQLen = aclCreateIntArray(acSeqQLenOp.data(), acSeqQLenOp.size());
  aclIntArray* acSeqKvLen = aclCreateIntArray(acSeqKvLenOp.data(), acSeqKvLenOp.size());
  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;
  char softmaxInLayoutArr[] = "same_as_input";

  char layOut[5] = {'T', 'N', 'D', 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 aclnnFlashAttentionUnpaddingScoreGradV4.
  ret = aclnnFlashAttentionUnpaddingScoreGradV4GetWorkspaceSize(q, k, v, dx, pse, dropMask, padding,
              attenmask, softmaxMax, softmaxSum, softmaxIn, attentionIn, prefix, acSeqQLen, acSeqKvLen,
              scaleValue, keepProb, preTokens, nextTokens, headNum, layOut, innerPrecise, sparseMode,
              dq, dk, dv, dpse, softmaxInLayoutArr, &workspaceSize, &executor);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnFlashAttentionUnpaddingScoreGradV4GetWorkspaceSize 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 aclnnFlashAttentionUnpaddingScoreGradV4.
  ret = aclnnFlashAttentionUnpaddingScoreGradV4(workspaceAddr, workspaceSize, executor, stream);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnFlashAttentionUnpaddingScoreGradV4 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);
  aclrtDestroyContext(context);
  aclrtResetDevice(deviceId);
  aclFinalize();

  return 0;
}