# Runtime

The following document describes Qualcomm AIC 100 User space Linux
Runtime classes design and implementation.

## QPC Elements

### QPC

Class `Qpc` is a main *QPC* API class type that provides functionality
to allocate a QPC from a filename or buffer, and also provides API to
query information related to the loaded `QPC`.

Class `Qpc` has 2 *Factory* functions to create a
`std::shared_ptr<>` of `Qpc`. There are couple of ways to create
`QPC` object.

The class is non-copyable, nor movable.

1. Creating from buffer and size.
2. Creating from a given filename (base-path plus filename).

If `Factory` instance creation is successful, the functions will
return an instance of `std::shared_ptr<>`, otherwise a proper
exception will be thrown.

Important API in class type `Qpc` includes the following:

1. `getInfo()` - returns `QpcInfo`. See below for more info.
2. `getBufferMappings()` - returns a container of `BufferMappings`.
Each `BufferMapping` instance in the container includes information
on the buffers as obtained from `Qpc` file. Name, size, direction
and index of buffers.
3. `getBufferMappingsDma` - Same return type as above
(`getBufferMapping()`) but for DMA allocated buffers instead of
user’s heap allocated ones.
4. `getIoDescriptor()` - returns pointer to QData which is a buffer
and length of the QPC buffer.
5. `get()` - returns a const pointer to QAicQpcObj - which is an
opaque Data structure to Internal Program Container. Users have no
visibility to the API of QAicQpcObj since it is Opaque in the C
layer.
6. `buffer()` - returns the QPC buffer as a const pointer to uint8\_t.
7. `size()` - returns the size of the QPC buffer.

### QpcFile

Class `QpcFile` is encapsulating the QPC file basepath and filename as
well as `DataBuffer<QData>` data member which holds the buffer of the
QPC file. `QpcFile` takes the base-path and the filename of the QPC,
where `programqpc.bin` is a default filename provided in the
constructor. `QpcFile` is a non-movable, non-copyable type.

It has a `load()` function that will load the content of the QPC into
`DataBuffer<>` which holds `QData` buffer representation internally.

There are few APIs provided by the `QpcFile` class type.

1. `getBuffer()` - returns a `QData` buffer const reference.
2. `data()` - returns a `const uint8_t` pointer to buffer.
3. `size()` - returns the size of the loaded QPC file buffer.

### QpcInfo

Struct `QpcInfo` is a simple struct type that aggregates a collection
of `QpcProgramInfo` (also referred to as “program”) and corresponding
collection of `QpcConstantsInfo` (also referred to as “constants”).

### QpcProgramInfo

Struct `QpcProgramInfo` is a simple struct aggregating information
related to the content of the loaded QPC.

For example:

1. BufferMappings, user allocated or *DMA*.
2. Name identifying a segment in `QPC` file.
3. Number of cores requires to run the program.
4. Program Index in the QPC.
5. Size of the program
6. Batch size.
7. Number of Semaphores, number of MC(MultiCast) IDs.
8. Total required memory to run the program.

### QpcConstantsInfo

Struct `QpcConstantsInfo` defines the Constants info that are obtained
from the QPC file. It has the following attributes:

1. `name`
2. `index`
3. `size`

## BufferMappings

Vector `BufferMappings` is a vector of `BufferMapping`.

`BufferMappings` is created from QPC.

`BufferMappings` is used by API to store the Input/Output buffer
information that is also used to create `inferenceVector`.

### BufferMapping

Struct `BufferMapping` is a simple struct type that describes the
information of a buffer.

Struct `BufferMapping` has two constructors:

1. Creating by providing all of the data members.
2. Default constructor that creates an uninitialized `BufferMapping`
instance.

Struct `BufferMapping` has following structure data members:

1. `bufferName` - string name identifying the buffer.
2. `index` - an unsigned int that represent the index in an array of
buffers.
3. `ioType` - define the direction of a buffer from user’s
perspective. An input buffer is from user to device. An output buffer
is from device to user.
4. `size` - buffer size in bytes.
5. `isPartialBufferAllowed` - `Partial buffer` is a feature that
allows buffer to have actual size that is smaller than what is
specified in IO descriptor. `isPartialBufferAllowed` is set by IO
descriptor. By setting `isPartialBufferAllowed` true, this buffer
takes user buffer that is smaller than what is specified by `size`.
6. `dataType` - define the format of buffer. The types of format is
defined in struct `QAicBufferDataTypeEnum`

### QAicBufferDataTypeEnum

Struct `QAicBufferDataTypeEnum` is a simple struct type that defines
the data type of the `BufferMapping`.

Struct `QAicBufferDataTypeEnum` defines following data types:

1. `BUFFER_DATA_TYPE_FLOAT` - 32-bit float type (float)
2. `BUFFER_DATA_TYPE_FLOAT16` - 16-bit float type (half, fp16)
3. `BUFFER_DATA_TYPE_INT8Q` - 8-bit quantized type (int8\_t)
4. `BUFFER_DATA_TYPE_UINT8Q` - unsigned 8-bit quantized type
(uint8\_t)
5. `BUFFER_DATA_TYPE_INT16Q` - 16-bit quantized type (int16\_t)
6. `BUFFER_DATA_TYPE_INT32Q` - 32-bit quantized type (int32\_t)
7. `BUFFER_DATA_TYPE_INT32I` - 32-bit index type (int32\_t)
8. `BUFFER_DATA_TYPE_INT64I` - 64-bit index type (int64\_t)
9. `BUFFER_DATA_TYPE_INT8` - 8-bit type (int8\_t)
10. `BUFFER_DATA_TYPE_INVAL` - invalid type

## Context Elements

### Context

There are various Linux Runtime core components like `qpc`,
`program`, `execObj`, and `queue` etc. which are needed to run
inference and enhance performance/usability. Class `Context` is a
primary class which helps to link all LRT core components. `Context`
object should be created first. Application creates a context to obtain
access to other API functions, the context is passed in other API calls.
The caller can also register for logging and error callbacks. A context
ID is passed to the error handler to uniquely identify the `Context`
object.

Class `Context` has a *Factory* functions to create a
`std::shared_ptr<>` of `Context`.

Context object is created from context properties, list of devices used
by this context, logging callback function, specific user data to be
included in log callback, an error handler to call in case of critical
errors and specific user data to be included in error handler callback.
If logging callback and error handler are not provided then default
`defaultLogger` and `defaultErrorHandler` will be used.

If `Factory` instance creation is successful, the functions will
return an instance of `std::shared_ptr<>`, otherwise a proper
exception will be thrown.

Important API in class type `Context` includes the following:

1. `findDevice()` - returns a suitable device for the network and
check selected device has enough resources.
2. `setLogLevel()` - set new logging level to get logging information
while running the program. See below for more details about
`QLogLevel`.
3. `getLogLevel()` - returns current logging level for given
`Context`.
4. `get()` - returns a const pointer to `QAicContext`. Users have no
visibility to the API of `QAicContext` since it is Opaque in the C
layer.
5. `getId()` - returns an unsigned int that represent id of the
`Context`. This id will be returned in error reports to refer a
specific created context.
6. `objName()` - returns `const std::string` which is name of the
object. For context object name is `Context`.
7. `objNameCstr()`- returns a pointer to an array that contains a
null-terminated sequence of char representing the name of an object.

### QLogLevel

There are different type of logging level to see different kind of logs.

1. `QL_DEBUG` : set to this level to see debug logs
2. `QL_INFO` : set to this level to see informative logs
3. `QL_WARN` : set to this level to see warning logs
4. `QL_ERROR` : set to this level to see error logs

`LogCallback` - It is a logging callback lambda function.

`ErrorHandler` - It is an error handler lambda function to call in
case of critical errors.

## Profiling Elements

For overview of profiling feature refer to [Profiling Support in
Runtime](https://docs.qualcomm.com/doc/80-99100-3/topic/index_features.html#reference-to-profiling-support-in-runtime).

### ProfilingHandle

`ProfilingHandle` provides interface to use num-iter based profiling.
Refer to [Num-iter based
profiling](https://docs.qualcomm.com/doc/80-99100-3/topic/index_features.html#reference-to-num-iter-based-profiling) for more details on
num-iter based profiling feature.

A `ProfilingHandle` object should be created using the `Factory`
method. User needs to specify the `Program` that should be profiled,
number of samples to collect, callback to call to deliver report, and
type of profiling output expected.

Note

Profiling type parameter has a default value set to *Latency type*.

Important API in class type `ProfilingHandle` includes the following:

1. `start()` - Start profiling. After the API call, profiling data
from all the inferences for specified `Program` will be collected
till either user calls `stop()` or number of requested samples have
been collected.
2. `stop()` Stop profiling. Stops profiling even if the num-samples
requirement has not been met. This API calls triggers a callback to
the user specified callback with profiling report of all collected
samples.

Note

If `stop()` is called without any inferences being complete for the
specified `Program`, callback will not get triggered.

### StreamProfilingHandle

ProfilingHandle provides interface to use duration based profiling.
Refer to [Duration based
profiling](https://docs.qualcomm.com/doc/80-99100-3/topic/index_features.html#reference-to-duration-based-profiling) for more details on
duration based profiling feature.

A `StreamProfilingHandle` object should be created using the
`Factory` method. User needs to specify the sampling rate, reporting
rate, callback to call to deliver report and profiling output format
expected. User may optionally specify a name for the handle, and regEx
to auto add/remove programs.

Note

Profiling type is specified using *ProfilingProperties* field.
*ProfilingProperties* has a default param *nullptr* which results in
profiling type to be *Latency type*.

Important API in class type `StreamProfilingHandle` includes the
following:

1. `start()` - Start profiling. After the API call, user should expect
a callback at every *reporting rate* boundary containing information
of profiling inferences during that duration.

Note

User will get callback even if there are no samples collected.

2. `stop()` Stop profiling. A final report will be delivered to user
when the profiling is stopped with the profiling data of samples
collected from last report till the point `stop()` is called.
3. `addProgram()` - Add a program to list of program being profiled.

Note

Adding program when profiling is active can cause spurious report
callback or a delayed report callback.

4. `removeProgram()` - Remove a program from list of program being
profiled.

Note

Removing program when profiling is active can cause spurious report
callback or a delayed report callback.

5. `flushReports()` - After profiling is stopped using `stop()` API,
user should make sure that all the reports on the queue of the
profiling infrastructure are delivered to user as callback before
application exit. `flushReports()` API only returns after there are
no more reports left to be delivered to the user, thus ensuring a
clean application exit.

## Inferencing Elements

### QBuffer

`QBuffer` is a struct that contains pointer to the buffer and its
size. It can have Input or output buffer address from heap or DMA
memory. `handle`, `offset` and `type` are considered only when
type is QBUFFER\_TYPE\_DMABUF or QBUFFER\_TYPE\_PMEM. It has following
Members:

1. `size` - Total size of memory pointed by buf pointer or handle.
2. `buf` - Buffer Pointer, must be valid in case of heap buffer.
3. `handle` - Buffer Handle, must be valid in case of DMA (or PMEM)
buffer.
4. `offset` - Offset within handle.
5. `type` - Type of the buffer heap, DMA or PMEM.

### InferenceVector

`InferenceVector` contains a vector of `QBuffer`. Vector of
`QBuffer` is a vector containing both input and output buffers. User
can create `InferenceVector` from multiple sources like files from
disk or create `QBuffer` with data available with the user and set
them in `InferenceVector` with `setBuffers()` API of this class. The
input buffer will be used for inference and result of inference will be
stored in output buffer. User needs to keep reference of InferenceVector
until inference is complete and user can read output buffers from
inference vector after completion of inference.

`InferenceVector` APIs

- `getVector()`: Returns a vector of `QBuffer`. Vector contains
both input and output buffers. `QBuffer` is a struct that contains
pointer to the buffer and its size. User can call this after the
inference is completed to read output buffers.
- `setBuffers()`: Sets input and output buffers of this
`InferenceVector`
- `Factory()`: Instantiates InferenceVector

### InferenceHandle

`InferenceHandle` contains `InferenceVector` and id given at the
time of submission of inference. `InferenceHandle` cannot be created
directly by the user, user can get an available `InferenceHandle` by
calling `getAvailable()` API of `InferenceSet`. `InferenceHandle`
is a container that has data needed for inference stored in
`InferenceVector`. Number of `InferenceHandle` objects created
depends on the set\_size and num\_activations parameters passed during
instantiation of `InferenceSet`. Number of `InferenceHandle` and
number of `ExecObj` created will be the same.

#### LifeCycle of `InferenceHandle`

- `InferenceHandle` objects are created when `InferenceSet` is
instantiated and all objects are moved to availableList vector from
which user can retrieve it by calling `getAvailable()` API of
`InferenceSet`
- When user calls `getAvailable()` if availableList vector has an
`InferenceHandle`, it is popped out from the availableList and
returned to user, otherwise this call is blocked until the user puts
the used `InferenceHandle` using `putCompleted()` API
- User sets buffers in the InferenceHandle it got using
`setBuffers()` API
- User submits InferenceHandle using `submit()` API of
`InferenceSet`
- To get the completed `InferenceHandle` user can call
`getCompleted()` or `getCompletedId()` and extract/read the
output of inference from `InferenceHandle`
- After processing the output of inference, user needs to call
`putCompleted()` API of `InferenceSet` to put completed
`InferenceHandle` back to availableList vector otherwise
`getAvailable()` call will be blocked

### InferenceSet

`InferenceSet` is a C++ class that is used to submit inference. It
abstracts out lower level classes like Queue, Program and ExecObj and
provides an easier way of handling multiple activations in a single
group to submit inference.

List of APIs of `InferenceSet`

- `submit()`: Submit an inference through `InferenceVector`. The
submission will be blocked until an `InferenceHandle` is available
- `submit()`: Submit an inference through `InferenceHandle`
- `getCompleted()`: Returns a completed `InferenceHandle` object.
User can access output of the inference using this
`InferenceHandle` object by calling `getBuffers()` method
- `getCompletedId()`: Returns a completed `InferenceHandle` object
with specified ID
- `putCompleted()`: Move a completed InferenceHandle back into the
availableList vector
- `getAvailable()`: Returns an available `InferenceHandle` object
from availableList
- `waitForCompletion()`: Wait for all inferences submitted to be
completed on all activations
- `Factory()`: Instantiates InferenceSet

#### NumActivations and SetSize

`NumActivations` and `SetSize` are arguments of
`InferenceSet::Factory` API.

- `NumActivations`: `InferenceSet` creates this many numbers of
network instances inside device. User can decide `NumActivations`
based on number of cores required to run his network and number of
available cores.
- `SetSize`: For each network instance, user application can
simultaneously enqueue this many numbers of input/output buffers to
run inferences. Recommended value is between 2 to 10. User should
find an optimal value to achieve desired throughput (inferences/sec)
and latency.

![../../../../_images/ActivationAndSetSize.png](data:image/png;base64,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)

Activations and SetSize

#### Inference Flow

Inference flow using `InferenceSet` would be as follows:

1. Instantiate `InferenceSet` using the Factory method
2. Acquire one of the available `InferenceHandle`.
3. Set Input and Output buffers in that `InferenceHandle`.
4. Submit `InferenceHandle` to `InferenceSet` to run inference in
device.
5. Call `getCompletedId` API to wait for Inference to complete.
Inference results are available in Output buffers, once this API
returns.
6. Once application reads output data, `putCompleted` must be called
to return the `InferenceHandle` back to available List of handles.

![../../../../_images/InferenceSetFlow.png](data:image/png;base64,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)

Inference Flow

### InferenceSetProperties

`InferenceSetProperties` defines properties to be consumed by
`InferenceSet`

List of members of `InferenceSetProperties`

- `programProperties`: User can set different program properties
which will be consumed internally by `Program` object. Notable
programProperties are:

    - `SubmitRetryTimeoutMs`: After submission of inference, runtime
waits for this milliseconds timeout period, if inference is not
complete in this timeout period, error is returned
    - `SubmitNumRetries`: Number of times submission should be retried
when the above timeout occurs
    - `devMapping`: devMapping specifies the physical devices to be
used by the program and is valid only for the network’s that need
multiple devices to run
- `queueProperties`: User can set queue properties which will be
consumed internally by `Queue` object. Notable queueProperties are:

    - `numThreadsPerQueue`: Number of threads spawned to process
elements in the queue. Default 4
- `name`: Defines name of the InferenceSet Object
- `id`: Defines id of the InferenceSet Object

Last Published: May 01, 2026

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