Plugin class for user-implemented layers. More...

#include <NvInferRuntimeCommon.h>

Inheritance diagram for nvinfer1::IPluginV2Ext:

Public Member Functions
virtual nvinfer1::DataType	getOutputDataType (int index, const nvinfer1::DataType *inputTypes, int nbInputs) const =0
	Return the DataType of the plugin output at the requested index. The default behavior should be to return the type of the first input, or DataType::kFLOAT if the layer has no inputs. The returned data type must have a format that is supported by the plugin. More...

virtual bool	isOutputBroadcastAcrossBatch (int outputIndex, const bool *inputIsBroadcasted, int nbInputs) const =0
	Return true if output tensor is broadcast across a batch. More...

virtual bool	canBroadcastInputAcrossBatch (int inputIndex) const =0
	Return true if plugin can use input that is broadcast across batch without replication. More...

virtual void	configurePlugin (const Dims inputDims, int nbInputs, const Dims outputDims, int nbOutputs, const DataType inputTypes, const DataType outputTypes, const bool inputIsBroadcast, const bool outputIsBroadcast, PluginFormat floatFormat, int maxBatchSize)=0
	Configure the layer with input and output data types. More...

virtual void	attachToContext (cudnnContext , cublasContext , IGpuAllocator *)
	Attach the plugin object to an execution context and grant the plugin the access to some context resource. More...

virtual void	detachFromContext ()
	Detach the plugin object from its execution context. More...

virtual IPluginV2Ext *	clone () const _TENSORRT_OVERRIDE=0
	Clone the plugin object. This copies over internal plugin parameters as well and returns a new plugin object with these parameters. If the source plugin is pre-configured with configurePlugin(), the returned object should also be pre-configured. The returned object should allow attachToContext() with a new execution context. Cloned plugin objects can share the same per-engine immutable resource (e.g. weights) with the source object (e.g. via ref-counting) to avoid duplication.

Public Member Functions inherited from nvinfer1::IPluginV2
virtual const char *	getPluginType () const =0
	Return the plugin type. Should match the plugin name returned by the corresponding plugin creator.

virtual const char *	getPluginVersion () const =0
	Return the plugin version. Should match the plugin version returned by the corresponding plugin creator.

virtual int	getNbOutputs () const =0
	Get the number of outputs from the layer. More...

virtual Dims	getOutputDimensions (int index, const Dims *inputs, int nbInputDims)=0
	Get the dimension of an output tensor. More...

virtual bool	supportsFormat (DataType type, PluginFormat format) const =0
	Check format support. More...

virtual int	initialize ()=0
	Initialize the layer for execution. This is called when the engine is created. More...

virtual void	terminate ()=0
	Release resources acquired during plugin layer initialization. This is called when the engine is destroyed. More...

virtual size_t	getWorkspaceSize (int maxBatchSize) const =0
	Find the workspace size required by the layer. More...

virtual int	enqueue (int batchSize, const void const inputs, void *outputs, void workspace, cudaStream_t stream)=0
	Execute the layer. More...

virtual size_t	getSerializationSize () const =0
	Find the size of the serialization buffer required. More...

virtual void	serialize (void *buffer) const =0
	Serialize the layer. More...

virtual void	destroy ()=0
	Destroy the plugin object. This will be called when the network, builder or engine is destroyed.

virtual void	setPluginNamespace (const char *pluginNamespace)=0
	Set the namespace that this plugin object belongs to. Ideally, all plugin objects from the same plugin library should have the same namespace.

virtual const char *	getPluginNamespace () const =0
	Return the namespace of the plugin object.

Protected Member Functions
int	getTensorRTVersion () const _TENSORRT_OVERRIDE
	Return the API version with which this plugin was built. The upper byte reserved by TensorRT and is used to differentiate this from IPlguinV2. More...

void	configureWithFormat (const Dims , int, const Dims , int, DataType, PluginFormat, int) _TENSORRT_OVERRIDE
	Derived classes should not implement this. In a C++11 API it would be override final.

Detailed Description

Plugin class for user-implemented layers.

Plugins are a mechanism for applications to implement custom layers. This interface provides additional capabilities to the IPluginV2 interface by supporting different output data types and broadcast across batch.

See also: IPluginV2

Member Function Documentation

◆ attachToContext()

virtual void nvinfer1::IPluginV2Ext::attachToContext	(	cudnnContext *	,
		cublasContext *	,
		IGpuAllocator *
	)

inlinevirtual

Attach the plugin object to an execution context and grant the plugin the access to some context resource.

Parameters

cudnn	The cudnn context handle of the execution context
cublas	The cublas context handle of the execution context
allocator	The allocator used by the execution context

This function is called automatically for each plugin when a new execution context is created. If the plugin needs per-context resource, it can be allocated here. The plugin can also get context-owned CUDNN and CUBLAS context here.

◆ canBroadcastInputAcrossBatch()

virtual bool nvinfer1::IPluginV2Ext::canBroadcastInputAcrossBatch ( int inputIndex ) const

pure virtual

Return true if plugin can use input that is broadcast across batch without replication.

Parameters

inputIndex Index of input that could be broadcast.

For each input whose tensor is semantically broadcast across a batch, TensorRT calls this method before calling configurePlugin. If canBroadcastInputAcrossBatch returns true, TensorRT will not replicate the input tensor; i.e., there will be a single copy that the plugin should share across the batch. If it returns false, TensorRT will replicate the input tensor so that it appears like a non-broadcasted tensor.

This method is called only for inputs that can be broadcast.

Implemented in nvinfer1::IPluginV2DynamicExt.

◆ configurePlugin()

virtual void nvinfer1::IPluginV2Ext::configurePlugin	(	const Dims *	inputDims,
		int	nbInputs,
		const Dims *	outputDims,
		int	nbOutputs,
		const DataType *	inputTypes,
		const DataType *	outputTypes,
		const bool *	inputIsBroadcast,
		const bool *	outputIsBroadcast,
		PluginFormat	floatFormat,
		int	maxBatchSize
	)

pure virtual

Configure the layer with input and output data types.

This function is called by the builder prior to initialize(). It provides an opportunity for the layer to make algorithm choices on the basis of its weights, dimensions, data types and maximum batch size.

Parameters

inputDims	The input tensor dimensions.
nbInputs	The number of inputs.
outputDims	The output tensor dimensions.
nbOutputs	The number of outputs.
inputTypes	The data types selected for the plugin inputs.
outputTypes	The data types selected for the plugin outputs.
inputIsBroadcast	True for each input that the plugin must broadcast across the batch.
outputIsBroadcast	True for each output that TensorRT will broadcast across the batch.
floatFormat	The format selected for the engine for the floating point inputs/outputs.
maxBatchSize	The maximum batch size.

The dimensions passed here do not include the outermost batch size (i.e. for 2-D image networks, they will be 3-dimensional CHW dimensions). When inputIsBroadcast or outputIsBroadcast is true, the outermost batch size for that input or output should be treated as if it is one. inputIsBroadcast[i] is true only if the input is semantically broadcast across the batch and canBroadcastInputAcrossBatch(i) returned true. outputIsBroadcast[i] is true only if isOutputBroadcastAcrossBatch(i) returned true.

Warning: for the floatFormat field, the values PluginFormat::kCHW4, PluginFormat::kCHW16, and PluginFormat::kCHW32 will not be passed in, this is to keep backward compatibility with TensorRT 5.x series. Use PluginV2IOExt or PluginV2DynamicExt for other PluginFormats.

Implemented in nvinfer1::IPluginV2IOExt, and nvinfer1::IPluginV2DynamicExt.

◆ detachFromContext()

virtual void nvinfer1::IPluginV2Ext::detachFromContext ( )

inlinevirtual

Detach the plugin object from its execution context.

This function is called automatically for each plugin when a execution context is destroyed. If the plugin owns per-context resource, it can be released here.

◆ getOutputDataType()

virtual nvinfer1::DataType nvinfer1::IPluginV2Ext::getOutputDataType	(	int	index,
		const nvinfer1::DataType *	inputTypes,
		int	nbInputs
	)		const

pure virtual

Return the DataType of the plugin output at the requested index. The default behavior should be to return the type of the first input, or DataType::kFLOAT if the layer has no inputs. The returned data type must have a format that is supported by the plugin.

See also: supportsFormat()

Warning: DataType:kBOOL not supported.

◆ getTensorRTVersion()

int nvinfer1::IPluginV2Ext::getTensorRTVersion ( ) const

inlineprotectedvirtual

Return the API version with which this plugin was built. The upper byte reserved by TensorRT and is used to differentiate this from IPlguinV2.

Do not override this method as it is used by the TensorRT library to maintain backwards-compatibility with plugins.

Reimplemented from nvinfer1::IPluginV2.

Reimplemented in nvinfer1::IPluginV2IOExt.

◆ isOutputBroadcastAcrossBatch()

virtual bool nvinfer1::IPluginV2Ext::isOutputBroadcastAcrossBatch	(	int	outputIndex,
		const bool *	inputIsBroadcasted,
		int	nbInputs
	)		const

pure virtual

Return true if output tensor is broadcast across a batch.

Parameters

outputIndex	The index of the output
inputIsBroadcasted	The ith element is true if the tensor for the ith input is broadcast across a batch.
nbInputs	The number of inputs

The values in inputIsBroadcasted refer to broadcasting at the semantic level, i.e. are unaffected by whether method canBroadcastInputAcrossBatch requests physical replication of the values.

Implemented in nvinfer1::IPluginV2DynamicExt.

The documentation for this class was generated from the following file:

NvInferRuntimeCommon.h

Public Member Functions

Protected Member Functions

Detailed Description

Member Function Documentation

◆ attachToContext()

◆ canBroadcastInputAcrossBatch()

◆ configurePlugin()

◆ detachFromContext()

◆ getOutputDataType()

◆ getTensorRTVersion()

◆ isOutputBroadcastAcrossBatch()