aclnnMm

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: Performs matrix multiplication of tensor self and tensor mat2.
Formula:
$result=self @ mat2$

Function Prototype

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

aclnnStatus aclnnMmGetWorkspaceSize(
  const aclTensor   *self,
  const aclTensor   *mat2,
  aclTensor         *out,
  int8_t            cubeMathType,
  uint64_t          *workspaceSize,
  aclOpExecutor     **executor)

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

aclnnMmGetWorkspaceSize

Parameters:

Parameter	Input/Output	Description	Instruction	Data Type	Data Format	Dimension (Shape)	Non-Contiguous Tensor
self	Input	Represents the first matrix of the matrix multiplication, i.e., self in the formula.	The data type must satisfy the data type deduction rules with mat2 (see Deduction Relationship and Constraints).	BFLOAT16, FLOAT16, FLOAT32	ND	2	√
mat2	Input	Represents the second matrix in matrix multiplication, i.e., mat2 in the formula.	The data type must satisfy the data type deduction rules with self (see Deduction Relationship and Constraints). The Reduce dimension of mat2 must be equal in size to the Reduce dimension of self.	BFLOAT16, FLOAT16, FLOAT32	ND	2	√
out	Output	Represents the output matrix of the matrix multiplication, i.e., out in the formula.	The data type must be consistent with the data type derived from self and mat2 (see Deduction Relationship and Constraints).	BFLOAT16, FLOAT16, FLOAT32	ND	2	-
cubeMathType	Input	Specifies the computation logic of the Cube unit.	If the input data types have a deduction relationship, this parameter processes the data type after deduction by default. Supported enumeration values are as follows: 0: KEEP_DTYPE, keeps the input data type for computation. 1: ALLOW_FP32_DOWN_PRECISION, allows down-precision computation of the input data. 2: USE_FP16, allows down-precision of the input to FLOAT16 for computation. 3: USE_HF32, allows down-precision of the input to HFLOAT32 for computation. 4: USE_FP32_ADD, uses a high-precision method for computation.	INT8	-	-	-
workspaceSize	Output	Returns the workspace size to be requested on the Device side.	-	-	-	-	-
executor	Output	Returns the operator executor, which contains the operator computation flow.	-	-	-	-	-

Atlas training products, Atlas inference products:
- BFLOAT16 data type is not supported.
- When the input data type is FLOAT32, cubeMathType=0 is not supported.
- For cubeMathType=1, when the input data type is FLOAT32, it is converted to FLOAT16 for computation. When the input is of other data types, no processing is performed.
- cubeMathType=3 is not supported.
- No processing is performed when cubeMathType is set to 4.
Atlas A2 training products/Atlas A2 inference products, Atlas A3 training products/Atlas A3 inference products:
- cubeMathType=1: When the input data type is FLOAT32, it is converted to HFLOAT32 for computation. When the input is of other data types, no processing is performed.
- cubeMathType=2: This option is not supported when the input data type is BFLOAT16.
- cubeMathType=3: When the input data type is FLOAT32, it will be converted to HFLOAT32 for computation. This option is not supported when the input is of other data types.
- cubeMathType=4: When the input data type is FLOAT32 and the k-axis is greater than 2048, group accumulation is used for computation. When the input is of other data types or the k-axis is less than 2048, no processing is performed.
Ascend 950PR/Ascend 950DT:
- cubeMathType=1: When the input data type is FLOAT32, it is converted to HFLOAT32 for computation. When the input is of other data types, no processing is performed.
- cubeMathType=2: This option is not supported when the input data type is BFLOAT16.
- cubeMathType=3: When the input data type is FLOAT32, it will be converted to HFLOAT32 for computation. This option is not supported when the input is of other data types.
- No processing is performed when cubeMathType is set to 4.

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 passed self, mat2, or out is a null pointer.
ACLNN_ERR_PARAM_INVALID	161002	The data type or data format of self and mat2 is not within the supported range.
		The data type deduction cannot be performed for self and mat2.
		The deduced data type cannot be converted to the specified output out type.

aclnnMm

Parameters:

Parameter	Input/Output	Description
workspace	Input	Workspace memory address applied for on the Device side.
workspaceSize	Input	Size of the workspace applied for on the Device, obtained through the first-phase API aclnnMmGetWorkspaceSize.
executor	Input	Operator executor, which 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 description:
- Atlas training products, Atlas inference products, Ascend 950PR/Ascend 950DT: aclnnMm defaults to a deterministic implementation.
Computation consistency description
- Atlas training products, Atlas inference products:
  - When the strong consistency computation feature is enabled, the computation result is deterministic, and multiple executions produce the same output. In addition, the computation result is independent of the data location.
  - aclnnMm defaults to a non-deterministic implementation and supports enabling deterministic computation via aclrtCtxSetSysParamOpt.
  - For example, during Matrix Multiplication, the accumulation order of different basic blocks may vary, which can cause slight differences in the computation results of the same data across different rows. However, when deterministic computation is enabled, the computation results will be identical as long as the inputs are the same, even across different rows.

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_mm.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) {
  // Boilerplate. Initialize resources.
  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 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);
  // Copy host-side data to device-side memory using aclrtMemcpy.
  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);

  // Compute 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 aclCreateTensor 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 input and output. Customize the construction based on the API interface.
  std::vector<int64_t> selfShape = {16, 32};
  std::vector<int64_t> mat2Shape = {32, 16};
  std::vector<int64_t> outShape = {16, 16};
  void* selfDeviceAddr = nullptr;
  void* mat2DeviceAddr = nullptr;
  void* outDeviceAddr = nullptr;
  aclTensor* self = nullptr;
  aclTensor* mat2 = nullptr;
  aclTensor* out = nullptr;
  std::vector<float> selfHostData(512, 1);
  std::vector<float> mat2HostData(512, 1);
  std::vector<float> outHostData(256, 0);
  // Create the self aclTensor.
  ret = CreateAclTensor(selfHostData, selfShape, &selfDeviceAddr, aclDataType::ACL_FLOAT, &self);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  // Create the mat2 aclTensor.
  ret = CreateAclTensor(mat2HostData, mat2Shape, &mat2DeviceAddr, aclDataType::ACL_FLOAT, &mat2);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  // Create the out aclTensor.
  ret = CreateAclTensor(outHostData, outShape, &outDeviceAddr, aclDataType::ACL_FLOAT, &out);
  CHECK_RET(ret == ACL_SUCCESS, return ret);

  // 3. Call the CANN operator library API. Replace with the specific API name.
  int8_t cubeMathType = 1;
  uint64_t workspaceSize = 0;
  aclOpExecutor* executor = nullptr;
  // Call the first-phase API of aclnnMm.
  ret = aclnnMmGetWorkspaceSize(self, mat2, out, cubeMathType, &workspaceSize, &executor);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnMmGetWorkspaceSize failed. ERROR: %d\n", ret); return ret);
  // Apply for device memory based on the workspaceSize computed 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 aclnnMm.
  ret = aclnnMm(workspaceAddr, workspaceSize, executor, stream);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnMm 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.
  auto size = GetShapeSize(outShape);
  std::vector<float> resultData(size, 0);
  ret = aclrtMemcpy(resultData.data(), resultData.size() * sizeof(resultData[0]), outDeviceAddr,
                    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. Release aclTensor and aclScalar. Modify based on the specific API definition.
  aclDestroyTensor(self);
  aclDestroyTensor(mat2);
  aclDestroyTensor(out);

  // 7. Release device resources. Modify based on the specific API definition.
  aclrtFree(selfDeviceAddr);
  aclrtFree(mat2DeviceAddr);
  aclrtFree(outDeviceAddr);
  if (workspaceSize > 0) {
    aclrtFree(workspaceAddr);
  }
  aclrtDestroyStream(stream);
  aclrtResetDevice(deviceId);
  aclFinalize();
  return 0;
}