aclmdlExecuteAsync

Applicability

Product	Supported
Atlas 350 Accelerator Card	√
Atlas A3 training product / Atlas A3 inference product	√
Atlas A2 training product / Atlas A2 inference product	√
Atlas 200I/500 A2 inference product	√
Atlas inference product	√
Atlas training product	√

Function Usage

Runs model inference. This API is asynchronous.

Prototype

      
           aclError aclmdlExecuteAsync(uint32_t modelId, const aclmdlDataset *input, aclmdlDataset *output, aclrtStream stream)

Parameters

Parameter	Input/Output	Description
modelId	Input	Model ID for inference. After a model loading API (such as aclmdlLoadFromFile or aclmdlLoadFromMem) is successfully called, the model ID is returned. This ID is used as the input of this API.
input	Input	Pointer to the input data for model inference. For details about the type definition, see aclmdlDataset.
output	Output	Pointer to the output data for model inference. For details about the type definition, see aclmdlDataset.
stream	Input	Stream. For details about the type definition, see aclrtStream.

Returns

0 on success; else, failure. For details, see aclError.

Restrictions

For models with the same modelId, aclmdlExecuteAsync cannot be called to perform model inference in the multistreaming concurrency scenario. An incorrect example is shown below. In this example, aclmdlExecuteAsync is called twice and multiple streams are concurrently executed. As a result, an error is reported.
```
// ...
aclmdlExecuteAsync(modelId1, input, output, stream1);
aclmdlExecuteAsync(modelId1, input, output, stream2);
aclrtSynchronizeStream(stream1);
aclrtSynchronizeStream(stream2);
// ...
```

If the same modelId is shared by multiple threads due to service requirements, locks must be added between user threads to ensure that operations of refreshing the input and output memories and executing inference are performed continuously. For example:

// API call sequence of thread A:
lock(handle1) -> aclrtMemcpyAsync(stream1) refreshes the input and output memories -> aclmdlExecuteAsync(modelId1,stream1) executes inference -> unlock(handle1)

// API call sequence of thread B:
lock(handle1) -> aclrtMemcpyAsync(stream1) refreshes the input and output memories -> aclmdlExecuteAsync(modelId1,stream1) executes inference -> unlock(handle1)

If an external Allocator is required, the stream used when registering the Allocator must be consistent with the stream used during model execution.
The memory for storing model input and output data is the device memory. You can call APIs such as aclrtMalloc and hi_mpi_dvpp_malloc to allocate device memory. The hi_mpi_dvpp_malloc API is a dedicated memory allocation API for media data processing. To reduce copy, the output of media data processing is used as the input of model inference to implement memory reuse. The address space accessed by media data processing is limited. To ensure sufficient memory during media data processing, you are advised to call other APIs (such as the aclrtMalloc API) to allocate memory for other functions (such as model loading) except media data processing.

Example

For details about the API call sequence and sample code, see Asynchronous Model Inference.

Parent topic: Model Execution