aclopExecuteV2

Description

Executes a specified operator.

Restrictions

This API is asynchronous. The API call delivers a task rather than executes a task. After this API is called, you need to call a synchronization API (for example, aclrtSynchronizeStream) to ensure that the task is complete. Otherwise, service exceptions (such as training or inference exception) or unknown situations (such as device link or card disconnection) may occur.
In multi-thread scenarios, you cannot specify the same stream or use the default stream when calling this API. Otherwise, the task execution may be abnormal.
As the inputs, outputs, and attributes of each operator are different, the app needs to organize operators in strict accordance with their inputs, outputs, and attributes. When aclopExecuteV2 is called, AscendCL searches for the corresponding task based on the operator type, input tensor description, output tensor description, and operator attributes, and then executes the operator.

For an operator that supports dynamic shape, if the output shape of the operator cannot be determined, call the aclopInferShape API to obtain the output shape of the operator.
- If the accurate output shape can be obtained, use the obtained accurate output shape to construct an outputDesc, as one of the arguments passed to the aclopExecuteV2 call. In this scenario, the aclopExecuteV2 API is asynchronous. For an asynchronous API, the API call delivers a task rather than executes a task. After this API is called, call the synchronization API (for example, aclrtSynchronizeStream) to ensure that the task is complete.
- If the accurate output shape cannot be obtained and only the shape range can be obtained, the maximum value within the range is used to construct an outputDesc, as one of the arguments passed to the aclopExecuteV2 call. In this scenario, after aclopExecuteV2 is called to execute the operator, the system calculates the accurate output shape, as the outputDesc output of aclopExecuteV2. In this case, aclopExecuteV2 is a synchronous API.
- (Reserved) If the accurate output shape and shape range cannot be obtained, estimate a maximum shape to construct an outputDesc as one of the arguments passed to the aclopExecuteV2 call. In this scenario, after aclopExecuteV2 is called to execute the operator, the system calculates the accurate output shape, as the outputDesc output of aclopExecuteV2. In this case, aclopExecuteV2 is a synchronous API.
If an operator with an unused optional input is executed:
- Create data of the aclTensorDesc type by using the aclCreateTensorDesc(ACL_DT_UNDEFINED, 0, nullptr, ACL_FORMAT_UNDEFINED) call, indicating that the data type is ACL_DT_UNDEFINED, the format is ACL_FORMAT_UNDEFINED, and the shape is nullptr.
- Create data of the aclDataBuffer type by using the aclCreateDataBuffer(nullptr, 0) call, where aclDataBuffer does not need to be freed since it is a null pointer.
Before executing an operator with constant input, call aclSetTensorConst to set the constant input.
The constant input passed to the aclopCompile and aclopExecuteV2 calls must be consistent.

If an operator has a constant input but aclSetTensorConst has not been called to set the constant input, call aclSetTensorPlaceMent to set the placement attribute of TensorDesc and set memType to the host memory.
Typically, it is a best practice to store the input/output tensor data to feed for running a single-operator (for example, the add operator) in the device memory. Some operators, however, take not only tensor data in the device memory (such as the feature map and weights) but also tensor data in the host memory (such as tensor shape and learning rate). In this case, you do not need to manually transfer such tensor data from the host to the device. You only need to call aclSetTensorPlaceMent to set the placement attribute of the corresponding TensorDesc to the host memory to instruct AscendCL to transfer the tensor data from the host to the device at operator runtime.

Prototype

aclError aclopExecuteV2(const char *opType,

int numInputs,

aclTensorDesc *inputDesc[],

aclDataBuffer *inputs[],

int numOutputs,

aclTensorDesc *outputDesc[],

aclDataBuffer *outputs[],

aclopAttr *attr,

aclrtStream stream)

Parameters

Parameter	Input/Output	Description
opType	Input	Pointer to the operator type name.
numInputs	Input	Number of input tensors.
inputDesc	Input	Pointer array of the input tensor description. Call aclCreateTensorDesc to create data of the aclTensorDesc type in advance. The array length is consistent with numInputs. The elements in the inputs array match those in the inputDesc array with ordering preserved.
inputs	Input	Pointer array of the input tensors. Call aclCreateDataBuffer to create data of the aclDataBuffer type in advance. The array length is consistent with numInputs. The elements in the inputs array match those in the inputDesc array with ordering preserved.
numOutputs	Input	Number of output tensors.
outputDesc	Input/Output	Pointer array of the output tensor description. Call aclCreateTensorDesc to create data of the aclTensorDesc type in advance. The array length is consistent with numOutputs. The elements in the outputs array match those in the outputDesc array with ordering preserved.
outputs	Output	Pointer array of the output tensors. Call aclCreateDataBuffer to create data of the aclDataBuffer type in advance. The array length is consistent with numOutputs. The elements in the outputs array match those in the outputDesc array with ordering preserved.
attr	Input	Pointer to the operator attributes. Call aclopCreateAttr to create data of the aclopAttr type in advance.
stream	Input	Target stream of the operator.

Returns