IExecutionContext

class tensorrt.IOutputAllocator(self: tensorrt.tensorrt.IOutputAllocator) → None

Application-implemented class for controlling output tensor allocation.

To implement a custom output allocator, ensure that you explicitly instantiate the base class in __init__() :

class MyOutputAllocator(trt.IOutputAllocator):
    def __init__(self):
        trt.IOutputAllocator.__init__(self)

    def reallocate_output(self, tensor_name, memory, size, alignment):
        ... # Your implementation here

    def notify_shape(self, tensor_name, shape):
        ... # Your implementation here
__init__(self: tensorrt.tensorrt.IOutputAllocator) → None
notify_shape(self: tensorrt.tensorrt.IOutputAllocator, tensor_name: str, shape: tensorrt.tensorrt.Dims) → None

Called by TensorRT when the shape of the output tensor is known.

Parameters

  • tensor_name – The output tensor name.

  • shape – The output tensor shape.

reallocate_output(self: tensorrt.tensorrt.IOutputAllocator, tensor_name: str, memory: capsule, size: int, alignment: int) → capsule

A callback implemented by the application to handle acquisition of output tensor memory.

If an allocation request cannot be satisfied, None should be returned.

Parameters

  • tensor_name – The output tensor name.

  • memory – The output tensor memory address.

  • size – The number of bytes required.

  • alignment – The required alignment of memory.

Returns

The address of the output tensor memory.

class tensorrt.IExecutionContext

Context for executing inference using an ICudaEngine . Multiple IExecutionContext s may exist for one ICudaEngine instance, allowing the same ICudaEngine to be used for the execution of multiple batches simultaneously.

Variables

  • debug_sync – bool The debug sync flag. If this flag is set to true, the ICudaEngine will log the successful execution for each kernel during execute_v2(). It has no effect when using execute_async_v2().

  • profiler – IProfiler The profiler in use by this IExecutionContext .

  • engine – ICudaEngine The associated ICudaEngine .

  • name – str The name of the IExecutionContext .

  • device_memory – capsule The device memory for use by this execution context. The memory must be aligned on a 256-byte boundary, and its size must be at least engine.device_memory_size. If using execute_async_v2() to run the network, The memory is in use from the invocation of execute_async_v2() until network execution is complete. If using execute_v2(), it is in use until execute_v2() returns. Releasing or otherwise using the memory for other purposes during this time will result in undefined behavior.

  • active_optimization_profile – int The active optimization profile for the context. The selected profile will be used in subsequent calls to execute_v2() or execute_async_v2() . Profile 0 is selected by default. Changing this value will invalidate all dynamic bindings for the current execution context, so that they have to be set again using set_binding_shape() before calling either execute_v2() or execute_async_v2() .

  • all_binding_shapes_specified – bool Whether all dynamic dimensions of input tensors have been specified by calling set_binding_shape() . Trivially true if network has no dynamically shaped input tensors. Does not work with name-base interfaces eg. set_input_shape(). Use infer_shapes() instead.

  • all_shape_inputs_specified – bool Whether values for all input shape tensors have been specified by calling set_shape_input() . Trivially true if network has no input shape bindings. Does not work with name-base interfaces eg. set_input_shape(). Use infer_shapes() instead.

  • error_recorder – IErrorRecorder Application-implemented error reporting interface for TensorRT objects.

  • enqueue_emits_profile – bool Whether enqueue emits layer timing to the profiler. The default value is True. If set to False, enqueue will be asynchronous if there is a profiler attached. An extra method IExecutionContext::report_to_profiler() needs to be called to obtain the profiling data and report to the profiler attached.

  • persistent_cache_limit – The maximum size of persistent L2 cache that this execution context may use for activation caching. Activation caching is not supported on all architectures - see “How TensorRT uses Memory” in the developer guide for details. The default is 0 Bytes.

  • nvtx_verbosity – The NVTX verbosity of the execution context. Building with kDETAILED verbosity will generally increase latency in enqueueV2/V3(). Call this method to select NVTX verbosity in this execution context at runtime. The default is the verbosity with which the engine was built, and the verbosity may not be raised above that level. This function does not affect how IEngineInspector interacts with the engine.

  • temporary_allocator – IGpuAllocator The GPU allocator used for internal temporary storage.

__del__(self: tensorrt.tensorrt.IExecutionContext) → None
__exit__(exc_type, exc_value, traceback)

Context managers are deprecated and have no effect. Objects are automatically freed when the reference count reaches 0.

__init__(*args, **kwargs)
execute(self: tensorrt.tensorrt.IExecutionContext, batch_size: int = 1, bindings: List[int]) → bool

[DEPRECATED] Please use execute_v2() instead if the engine is built from a network with explicit batch dimension mode enabled.

Synchronously execute inference on a batch. This method requires a array of input and output buffers. The mapping from tensor names to indices can be queried using ICudaEngine.get_binding_index() .

Parameters

  • batch_size – The batch size. This is at most the value supplied when the ICudaEngine was built. This has no effect if the engine is built from a network with explicit batch dimension mode enabled.

  • bindings – A list of integers representing input and output buffer addresses for the network.

Returns

True if execution succeeded.

execute_async(self: tensorrt.tensorrt.IExecutionContext, batch_size: int = 1, bindings: List[int], stream_handle: int, input_consumed: capsule = None) → bool

[DEPRECATED] Please use execute_async_v2() instead if the engine is built from a network with explicit batch dimension mode enabled.

Asynchronously execute inference on a batch. This method requires a array of input and output buffers. The mapping from tensor names to indices can be queried using ICudaEngine::get_binding_index() .

Parameters

  • batch_size – The batch size. This is at most the value supplied when the ICudaEngine was built. This has no effect if the engine is built from a network with explicit batch dimension mode enabled.

  • bindings – A list of integers representing input and output buffer addresses for the network.

  • stream_handle – A handle for a CUDA stream on which the inference kernels will be executed.

  • input_consumed – An optional event which will be signaled when the input buffers can be refilled with new data

Returns

True if the kernels were executed successfully.

execute_async_v2(self: tensorrt.tensorrt.IExecutionContext, bindings: List[int], stream_handle: int, input_consumed: capsule = None) → bool

Asynchronously execute inference on a batch. This method requires a array of input and output buffers. The mapping from tensor names to indices can be queried using ICudaEngine::get_binding_index() . This method only works for execution contexts built from networks with no implicit batch dimension.

Parameters

  • bindings – A list of integers representing input and output buffer addresses for the network.

  • stream_handle – A handle for a CUDA stream on which the inference kernels will be executed.

  • input_consumed – An optional event which will be signaled when the input buffers can be refilled with new data

Returns

True if the kernels were executed successfully.

execute_async_v3(self: tensorrt.tensorrt.IExecutionContext, stream_handle: int) → bool

Asynchronously execute inference.

Modifying or releasing memory that has been registered for the tensors before stream synchronization or the event passed to set_input_consumed_event() has been triggered results in undefined behavior.

Input tensors can be released after the set_input_consumed_event() whereas output tensors require stream synchronization.

Parameters

stream_handle – The cuda stream on which the inference kernels will be enqueued.

execute_v2(self: tensorrt.tensorrt.IExecutionContext, bindings: List[int]) → bool

Synchronously execute inference on a batch. This method requires a array of input and output buffers. The mapping from tensor names to indices can be queried using ICudaEngine.get_binding_index() . This method only works for execution contexts built from networks with no implicit batch dimension.

Parameters

bindings – A list of integers representing input and output buffer addresses for the network.

Returns

True if execution succeeded.

get_binding_shape(self: tensorrt.tensorrt.IExecutionContext, binding: int) → tensorrt.tensorrt.Dims

Get the dynamic shape of a binding.

If set_binding_shape() has been called on this binding (or if there are no dynamic dimensions), all dimensions will be positive. Otherwise, it is necessary to call set_binding_shape() before execute_async_v2() or execute_v2() may be called.

If the binding is out of range, an invalid Dims with nbDims == -1 is returned.

If ICudaEngine.binding_is_input(binding) is False , then both all_binding_shapes_specified and all_shape_inputs_specified must be True before calling this method.

Parameters

binding – The binding index.

Returns

A Dims object representing the currently selected shape.

get_input_consumed_event(self: tensorrt.tensorrt.IExecutionContext) → int

Return the event associated with consuming the input tensors.

get_max_output_size(self: tensorrt.tensorrt.IExecutionContext, name: str) → int

Return the upper bound on an output tensor’s size, in bytes, based on the current optimization profile.

If the profile or input shapes are not yet set, or the provided name does not map to an output, returns -1.

Parameters

name – The tensor name.

get_output_allocator(self: tensorrt.tensorrt.IExecutionContext, name: str) → nvinfer1::IOutputAllocator

Return the output allocator associated with given output tensor, or None if the provided name does not map to an output tensor.

Parameters

name – The tensor name.

get_shape(self: tensorrt.tensorrt.IExecutionContext, binding: int) → List[int]

Get values of an input shape tensor required for shape calculations or an output tensor produced by shape calculations.

Parameters

binding – The binding index of an input tensor for which ICudaEngine.is_shape_binding(binding) is true.

If ICudaEngine.binding_is_input(binding) == False, then both all_binding_shapes_specified and all_shape_inputs_specified must be True before calling this method.

Returns

An iterable containing the values of the shape tensor.

get_strides(self: tensorrt.tensorrt.IExecutionContext, binding: int) → tensorrt.tensorrt.Dims

Return the strides of the buffer for the given binding.

Note that strides can be different for different execution contexts with dynamic shapes.

Parameters

binding – The binding index.

get_tensor_address(self: tensorrt.tensorrt.IExecutionContext, name: str) → int

Get memory address for the given input or output tensor.

Parameters

name – The tensor name.

get_tensor_shape(self: tensorrt.tensorrt.IExecutionContext, name: str) → tensorrt.tensorrt.Dims

Return the shape of the given input or output tensor.

Parameters

name – The tensor name.

get_tensor_strides(self: tensorrt.tensorrt.IExecutionContext, name: str) → tensorrt.tensorrt.Dims

Return the strides of the buffer for the given tensor name.

Note that strides can be different for different execution contexts with dynamic shapes.

Parameters

name – The tensor name.

infer_shapes(self: tensorrt.tensorrt.IExecutionContext) → List[str]

Infer shapes and return the names of any tensors that are insufficiently specified.

An input tensor is insufficiently specified if either of the following is true:

  • It has dynamic dimensions and its runtime dimensions have not yet been specified via set_input_shape() .

  • is_shape_inference_io(t) is True and the tensor’s address has not yet been set.

Returns

A List[str] indicating the names of any tensors which have not been sufficiently specified, or an empty list on success.

Raises

RuntimeError if shape inference fails due to reasons other than insufficiently specified tensors.

report_to_profiler(self: tensorrt.tensorrt.IExecutionContext) → bool

Calculate layer timing info for the current optimization profile in IExecutionContext and update the profiler after one iteration of inference launch.

If the enqueue_emits_profiler flag was set to true, the enqueue function will calculate layer timing implicitly if a profiler is provided. There is no need to call this function. If the enqueue_emits_profiler flag was set to false, the enqueue function will record the CUDA event timers if a profiler is provided. But it will not perform the layer timing calculation. This function needs to be called explicitly to calculate layer timing for the previous inference launch.

In the CUDA graph launch scenario, it will record the same set of CUDA events as in regular enqueue functions if the graph is captured from an IExecutionContext with profiler enabled. This function needs to be called after graph launch to report the layer timing info to the profiler.

Profiling CUDA graphs is only available from CUDA 11.1 onwards.

Returns

True if the call succeeded, else False (e.g. profiler not provided, in CUDA graph capture mode, etc.)

set_aux_streams(self: tensorrt.tensorrt.IExecutionContext, aux_streams: List[int]) → None

Set the auxiliary streams that TensorRT should launch kernels on in the next execute_async_v3() call.

If set, TensorRT will launch the kernels that are supposed to run on the auxiliary streams using the streams provided by the user with this API. If this API is not called before the execute_async_v3() call, then TensorRT will use the auxiliary streams created by TensorRT internally.

TensorRT will always insert event synchronizations between the main stream provided via execute_async_v3() call and the auxiliary streams:

  • At the beginning of the execute_async_v3() call, TensorRT will make sure that all the auxiliary streams wait on the activities on the main stream.

  • At the end of the execute_async_v3() call, TensorRT will make sure that the main stream wait on the activities on all the auxiliary streams.

The provided auxiliary streams must not be the default stream and must all be different to avoid deadlocks.

Parameters

aux_streams – A list of cuda streams. If the length of the list is greater than engine.num_aux_streams, then only the first “engine.num_aux_streams” streams will be used. If the length is less than engine.num_aux_streams, such as an empty list, then TensorRT will use the provided streams for the first few auxiliary streams, and will create additional streams internally for the rest of the auxiliary streams.

set_binding_shape(self: tensorrt.tensorrt.IExecutionContext, binding: int, shape: tensorrt.tensorrt.Dims) → bool

Set the dynamic shape of a binding.

Requires the engine to be built without an implicit batch dimension. The binding must be an input tensor, and all dimensions must be compatible with the network definition (i.e. only the wildcard dimension -1 can be replaced with a new dimension > 0). Furthermore, the dimensions must be in the valid range for the currently selected optimization profile.

For all dynamic non-output bindings (which have at least one wildcard dimension of -1), this method needs to be called after setting active_optimization_profile before either execute_async_v2() or execute_v2() may be called. When all input shapes have been specified, all_binding_shapes_specified is set to True .

Parameters

  • binding – The binding index.

  • shape – The shape to set.

Returns

False if an error occurs (e.g. specified binding is out of range for the currently selected optimization profile or specified shape is inconsistent with min-max range of the optimization profile), else True.

Note that the network can still be invalid for certain combinations of input shapes that lead to invalid output shapes. To confirm the correctness of the network input shapes, check whether the output binding has valid shape using get_binding_shape() on the output binding.

set_input_consumed_event(self: tensorrt.tensorrt.IExecutionContext, event: int) → bool

Mark all input tensors as consumed.

Parameters

event – The cuda event that is triggered after all input tensors have been consumed.

set_input_shape(self: tensorrt.tensorrt.IExecutionContext, name: str, shape: tensorrt.tensorrt.Dims) → bool

Set shape for the given input tensor.

Parameters

  • name – The input tensor name.

  • shape – The input tensor shape.

set_optimization_profile_async(self: tensorrt.tensorrt.IExecutionContext, profile_index: int, stream_handle: int) → bool

Set the optimization profile with async semantics

Parameters

  • profile_index – The index of the optimization profile

  • stream_handle – cuda stream on which the work to switch optimization profile can be enqueued

When an optimization profile is switched via this API, TensorRT may require that data is copied via cudaMemcpyAsync. It is the application’s responsibility to guarantee that synchronization between the profile sync stream and the enqueue stream occurs.

Returns

True if the optimization profile was set successfully

set_output_allocator(self: tensorrt.tensorrt.IExecutionContext, name: str, output_allocator: nvinfer1::IOutputAllocator) → bool

Set output allocator to use for the given output tensor.

Pass None to unset the output allocator.

The allocator is called by execute_async_v3().

Parameters

  • name – The tensor name.

  • output_allocator – The output allocator.

set_shape_input(self: tensorrt.tensorrt.IExecutionContext, binding: int, shape: List[int]) → bool

Set values of an input shape tensor required by shape calculations.

Parameters

  • binding – The binding index of an input tensor for which ICudaEngine.is_shape_binding(binding) and ICudaEngine.binding_is_input(binding) are both true.

  • shape – An iterable containing the values of the input shape tensor. The number of values should be the product of the dimensions returned by get_binding_shape(binding).

If ICudaEngine.is_shape_binding(binding) and ICudaEngine.binding_is_input(binding) are both true, this method must be called before execute_async_v2() or execute_v2() may be called. Additionally, this method must not be called if either ICudaEngine.is_shape_binding(binding) or ICudaEngine.binding_is_input(binding) are false.

Returns

False if an error occurs (e.g. specified binding is out of range for the currently selected optimization profile or specified shape values are inconsistent with min-max range of the optimization profile), else True.

Note that the network can still be invalid for certain combinations of input shapes that lead to invalid output shapes. To confirm the correctness of the network input shapes, check whether the output binding has valid shape using get_binding_shape() on the output binding.

set_tensor_address(self: tensorrt.tensorrt.IExecutionContext, name: str, memory: int) → bool

Set memory address for the given input or output tensor.

Parameters

  • name – The tensor name.

  • memory – The memory address.