List of valid flags for quantizing the network to int8.
CALIBRATE_BEFORE_FUSION : Run int8 calibration pass before layer fusion. Only valid for IInt8LegacyCalibrator and IInt8EntropyCalibrator. We always run int8 calibration pass before layer fusion for IInt8MinMaxCalibrator and IInt8EntropyCalibrator2. Disabled by default.
Device types that TensorRT can execute on
GPU : GPU device
DLA : DLA core
Profiling verbosity in NVTX annotations and the engine inspector
LAYER_NAMES_ONLY : Print only the layer names. This is the default setting.
DETAILED : Print detailed layer information including layer names and layer parameters.
NONE : Do not print any layer information.
DEFAULT : [DEPRECATED] Same as LAYER_NAMES_ONLY.
VERBOSE : [DEPRECATED] Same as DETAILED.
Tactic sources that can provide tactics for TensorRT.
- CUBLAS :
Enables cuBLAS tactics. Enabled by default. NOTE: Disabling this value will cause the cublas handle passed to plugins in attachToContext to be null.
- CUBLAS_LT :
Enables cuBLAS LT tactics. Enabled for x86 platforms and only enabled for non-x86 platforms when CUDA >= 11.0 by default
- CUDNN :
Enables cuDNN tactics. Enabled by default.
- EDGE_MASK_CONVOLUTIONS :
Enables convolution tactics implemented with edge mask tables. These tactics tradeoff memory for performance by consuming additional memory space proportional to the input size. Enabled by default.
- JIT_CONVOLUTIONS :
Enables convolution tactics implemented with source-code JIT fusion. The engine building time may increase when this is enabled. Enabled by default.
- List of supported engine capability flows.
The EngineCapability determines the restrictions of a network during build time and what runtime it targets. When BuilderFlag::kSAFETY_SCOPE is not set (by default), EngineCapability.STANDARD does not provide any restrictions on functionality and the resulting serialized engine can be executed with TensorRT’s standard runtime APIs in the nvinfer1 namespace. EngineCapability.SAFETY provides a restricted subset of network operations that are safety certified and the resulting serialized engine can be executed with TensorRT’s safe runtime APIs in the nvinfer1::safe namespace. EngineCapability.DLA_STANDALONE provides a restricted subset of network operations that are DLA compatible and the resulting serialized engine can be executed using standalone DLA runtime APIs. See sampleCudla for an example of integrating cuDLA APIs with TensorRT APIs.
DEFAULT : [DEPRECATED] Unrestricted: TensorRT mode without any restrictions using TensorRT nvinfer1 APIs.
SAFE_GPU : [DEPRECATED] Safety-restricted: TensorRT mode for GPU devices using TensorRT safety APIs. See safety documentation for list of supported layers and formats.
SAFE_DLA : [DEPRECATED] DLA-restricted: TensorRT mode for DLA devices using cuDLA APIs. Only FP16 and Int8 modes are supported.
STANDARD : Standard: TensorRT flow without targeting the standard runtime. This flow supports both DeviceType::kGPU and DeviceType::kDLA.
SAFETY : Safety: TensorRT flow with restrictions targeting the safety runtime. See safety documentation for list of supported layers and formats. This flow supports only DeviceType::kGPU.
DLA_STANDALONE : DLA Standalone: TensorRT flow with restrictions targeting external, to TensorRT, DLA runtimes. See DLA documentation for list of supported layers and formats. This flow supports only DeviceType::kDLA.
Valid modes that the builder can enable when creating an engine from a network definition.
FP16 : Enable FP16 layer selection
INT8 : Enable Int8 layer selection
DEBUG : Enable debugging of layers via synchronizing after every layer
GPU_FALLBACK : Enable layers marked to execute on GPU if layer cannot execute on DLA
STRICT_TYPES : [DEPRECATED] Enables strict type constraints. Equivalent to setting PREFER_PRECISION_CONSTRAINTS, DIRECT_IO, and REJECT_EMPTY_ALGORITHMS.
REFIT : Enable building a refittable engine
DISABLE_TIMING_CACHE : Disable reuse of timing information across identical layers.
TF32 : Allow (but not require) computations on tensors of type DataType.FLOAT to use TF32. TF32 computes inner products by rounding the inputs to 10-bit mantissas before multiplying, but accumulates the sum using 23-bit mantissas. Enabled by default.
SPARSE_WEIGHTS : Allow the builder to examine weights and use optimized functions when weights have suitable sparsity.
SAFETY_SCOPE : Change the allowed parameters in the EngineCapability.STANDARD flow to match the restrictions that EngineCapability.SAFETY check against for DeviceType.GPU and EngineCapability.DLA_STANDALONE check against the DeviceType.DLA case. This flag is forced to true if EngineCapability.SAFETY at build time if it is unset.
OBEY_PRECISION_CONSTRAINTS : Require that layers execute in specified precisions. Build fails otherwise.
PREFER_PRECISION_CONSTRAINTS : Prefer that layers execute in specified precisions. Fall back (with warning) to another precision if build would otherwise fail.
DIRECT_IO : Require that no reformats be inserted between a layer and a network I/O tensor for which ITensor.allowed_formats was set. Build fails if a reformat is required for functional correctness.
REJECT_EMPTY_ALGORITHMS : Fail if IAlgorithmSelector.select_algorithms returns an empty set of algorithms.
ENABLE_TACTIC_HEURISTIC : Enable heuristic-based tactic selection for shorter engine generation time. The performance of the generated engine may not be as performant as a profiling-based builder.
- List of Preview Features that can be enabled. Preview Features have been fully tested but are not yet as stable as other features in TensorRT.
They are provided as opt-in features for at least one release. For example, to enable faster dynamic shapes, call
- FASTER_DYNAMIC_SHAPES_0805 :
Optimize runtime dimensions with TensorRT’s DL Compiler. Potentially reduces run time and decreases device memory usage and engine size. Models most likely to benefit from enabling
FASTER_DYNAMIC_SHAPES_0805are transformer-based models, and models containing dynamic control flows.
- DISABLE_EXTERNAL_TACTIC_SOURCES_FOR_CORE_0805 :
Disable usage of cuDNN/cuBLAS/cuBLASLt tactics in the TensorRT core library. When the flag is enabled, TensorRT core will not use these tactics even if they are specified in set_tactic_sources, but cudnnContext and cublasContext handles will still be passed to plugins via IPluginV2::attachToContext() if the appropriate tactic sources are set. This allows users to experiment with disabling external library tactics without having to modify their application’s plugins to support nullptr handles. The default value for this flag is off.
The type for memory pools used by TensorRT.
- WORKSPACE :
WORKSPACE is used by TensorRT to store intermediate buffers within an operation. This is equivalent to the deprecated IBuilderConfig.max_workspace_size and overrides that value. This defaults to max device memory. Set to a smaller value to restrict tactics that use over the threshold en masse. For more targeted removal of tactics use the IAlgorithmSelector interface.
- DLA_MANAGED_SRAM :
DLA_MANAGED_SRAM is a fast software managed RAM used by DLA to communicate within a layer. The size of this pool must be at least 4 KiB and must be a power of 2. This defaults to 1 MiB. Orin has capacity of 1 MiB per core, and Xavier shares 4 MiB across all of its accelerator cores.
- DLA_LOCAL_DRAM :
DLA_LOCAL_DRAM is host RAM used by DLA to share intermediate tensor data across operations. The size of this pool must be at least 4 KiB and must be a power of 2. This defaults to 1 GiB.
- DLA_GLOBAL_DRAM :
DLA_GLOBAL_DRAM is host RAM used by DLA to store weights and metadata for execution. The size of this pool must be at least 4 KiB and must be a power of 2. This defaults to 512 MiB.
- class tensorrt.IBuilderConfig
int[DEPRECATED] The number of minimization iterations used when timing layers. When timing layers, the builder minimizes over a set of average times for layer execution. This parameter controls the number of iterations used in minimization. By default the minimum number of iterations is 1.
intThe number of averaging iterations used when timing layers. When timing layers, the builder minimizes over a set of average times for layer execution. This parameter controls the number of iterations used in averaging. By default the number of averaging iterations is 1.
IInt8CalibratorInt8 Calibration interface. The calibrator is to minimize the information loss during the INT8 quantization process.
int[DEPRECATED] The maximum workspace size. The maximum GPU temporary memory which the engine can use at execution time.
intThe build mode flags to turn on builder options for this network. The flags are listed in the BuilderFlags enum. The flags set configuration options to build the network. This should be in integer consisting of one or more
BuilderFlags, combined via binary OR. For example,
1 << BuilderFlag.FP16 | 1 << BuilderFlag.DEBUG.
intThe handle for the CUDA stream that is used to profile this network.
intThe number of optimization profiles.
tensorrt.DeviceTypeThe default DeviceType to be used by the Builder.
intThe DLA core that the engine executes on. Must be between 0 and N-1 where N is the number of available DLA cores.
profiling_verbosity – Profiling verbosity in NVTX annotations.
engine_capability – The desired engine capability. See
algorithm_selector – The
- __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)
- add_optimization_profile(self: tensorrt.tensorrt.IBuilderConfig, profile: tensorrt.tensorrt.IOptimizationProfile) int
Add an optimization profile.
This function must be called at least once if the network has dynamic or shape input tensors.
profile – The new optimization profile, which must satisfy
bool(profile) == True
The index of the optimization profile (starting from 0) if the input is valid, or -1 if the input is not valid.
- can_run_on_DLA(self: tensorrt.tensorrt.IBuilderConfig, layer: tensorrt.tensorrt.ILayer) bool
Check if the layer can run on DLA.
layer – The layer to check
A bool indicating whether the layer can run on DLA
- clear_flag(self: tensorrt.tensorrt.IBuilderConfig, flag: tensorrt.tensorrt.BuilderFlag) None
Clears the builder mode flag from the enabled flags.
flag – The flag to clear.
- clear_quantization_flag(self: tensorrt.tensorrt.IBuilderConfig, flag: tensorrt.tensorrt.QuantizationFlag) None
Clears the quantization flag from the enabled quantization flags.
flag – The flag to clear.
- create_timing_cache(self: tensorrt.tensorrt.IBuilderConfig, serialized_timing_cache: buffer) tensorrt.tensorrt.ITimingCache
Create timing cache
ITimingCacheinstance from serialized raw data. The created timing cache doesn’t belong to a specific builder config. It can be shared by multiple builder instances
serialized_timing_cache – The serialized timing cache. If an empty cache is provided (i.e.
b""), a new cache will be created.
- get_calibration_profile(self: tensorrt.tensorrt.IBuilderConfig) tensorrt.tensorrt.IOptimizationProfile
Get the current calibration profile.
The current calibration profile or nullptr if calibrartion profile is unset.
- get_device_type(self: tensorrt.tensorrt.IBuilderConfig, layer: tensorrt.tensorrt.ILayer) tensorrt.tensorrt.DeviceType
Get the device that the layer executes on.
layer – The layer to get the DeviceType for
The DeviceType of the layer
- get_flag(self: tensorrt.tensorrt.IBuilderConfig, flag: tensorrt.tensorrt.BuilderFlag) bool
Check if a build mode flag is set.
flag – The flag to check.
A bool indicating whether the flag is set.
- get_memory_pool_limit(self: tensorrt.tensorrt.IBuilderConfig, pool: tensorrt.tensorrt.MemoryPoolType) int
Retrieve the memory size limit of the corresponding pool in bytes. If
set_memory_pool_limit()for the pool has not been called, this returns the default value used by TensorRT. This default value is not necessarily the maximum possible value for that configuration.
pool – The memory pool to get the limit for.
The size of the memory limit, in bytes, for the corresponding pool.
- get_preview_feature(self: tensorrt.tensorrt.IBuilderConfig, feature: tensorrt.tensorrt.PreviewFeature) bool
Check if a preview feature is enabled.
feature – the feature to query
true if the feature is enabled, false otherwise
- get_quantization_flag(self: tensorrt.tensorrt.IBuilderConfig, flag: tensorrt.tensorrt.QuantizationFlag) bool
Check if a quantization flag is set.
flag – The flag to check.
A bool indicating whether the flag is set.
- get_tactic_sources(self: tensorrt.tensorrt.IBuilderConfig) int
Get the tactic sources currently set in the engine build configuration.
- get_timing_cache(self: tensorrt.tensorrt.IBuilderConfig) tensorrt.tensorrt.ITimingCache
Get the timing cache from current IBuilderConfig
The timing cache used in current IBuilderConfig, or None if no timing cache is set.
- is_device_type_set(self: tensorrt.tensorrt.IBuilderConfig, layer: tensorrt.tensorrt.ILayer) bool
Check if the DeviceType for a layer is explicitly set.
layer – The layer to check for DeviceType
True if DeviceType is not default, False otherwise
- reset(self: tensorrt.tensorrt.IBuilderConfig) None
Resets the builder configuration to defaults. When initializing a builder config object, we can call this function.
- reset_device_type(self: tensorrt.tensorrt.IBuilderConfig, layer: tensorrt.tensorrt.ILayer) None
Reset the DeviceType for the given layer.
layer – The layer to reset the DeviceType for
- set_calibration_profile(self: tensorrt.tensorrt.IBuilderConfig, profile: tensorrt.tensorrt.IOptimizationProfile) bool
Set a calibration profile.
Calibration optimization profile must be set if int8 calibration is used to set scales for a network with runtime dimensions.
profile – The new calibration profile, which must satisfy
bool(profile) == Trueor be nullptr. MIN and MAX values will be overwritten by kOPT.
True if the calibration profile was set correctly.
- set_device_type(self: tensorrt.tensorrt.IBuilderConfig, layer: tensorrt.tensorrt.ILayer, device_type: tensorrt.tensorrt.DeviceType) None
Set the device that this layer must execute on. If DeviceType is not set or is reset, TensorRT will use the default DeviceType set in the builder.
The DeviceType for a layer must be compatible with the safety flow (if specified). For example a layer cannot be marked for DLA execution while the builder is configured for kSAFE_GPU.
layer – The layer to set the DeviceType of
device_type – The DeviceType the layer must execute on
- set_flag(self: tensorrt.tensorrt.IBuilderConfig, flag: tensorrt.tensorrt.BuilderFlag) None
Add the input builder mode flag to the already enabled flags.
flag – The flag to set.
- set_memory_pool_limit(self: tensorrt.tensorrt.IBuilderConfig, pool: tensorrt.tensorrt.MemoryPoolType, pool_size: int) None
Set the memory size for the memory pool.
TensorRT layers access different memory pools depending on the operation. This function sets in the
IBuilderConfigthe size limit, specified by pool_size, for the corresponding memory pool, specified by pool. TensorRT will build a plan file that is constrained by these limits or report which constraint caused the failure.
If the size of the pool, specified by pool_size, fails to meet the size requirements for the pool, this function does nothing and emits the recoverable error, ErrorCode.INVALID_ARGUMENT, to the registered
If the size of the pool is larger than the maximum possible value for the configuration, this function does nothing and emits ErrorCode.UNSUPPORTED_STATE.
If the pool does not exist on the requested device type when building the network, a warning is emitted to the logger, and the memory pool value is ignored.
Refer to MemoryPoolType to see the size requirements for each pool.
pool – The memory pool to limit the available memory for.
pool_size – The size of the pool in bytes.
- set_preview_feature(self: tensorrt.tensorrt.IBuilderConfig, feature: tensorrt.tensorrt.PreviewFeature, enable: bool) None
Enable or disable a specific preview feature.
Allows enabling or disabling experimental features, which are not enabled by default in the current release. Preview Features have been fully tested but are not yet as stable as other features in TensorRT. They are provided as opt-in features for at least one release.
Refer to PreviewFeature for additional information, and a list of the available features.
feature – the feature to enable
enable – whether to enable or disable
- set_quantization_flag(self: tensorrt.tensorrt.IBuilderConfig, flag: tensorrt.tensorrt.QuantizationFlag) None
Add the input quantization flag to the already enabled quantization flags.
flag – The flag to set.
- set_tactic_sources(self: tensorrt.tensorrt.IBuilderConfig, tactic_sources: int) bool
Set tactic sources.
This bitset controls which tactic sources TensorRT is allowed to use for tactic selection.
Multiple tactic sources may be combined with a bitwise OR operation. For example, to enable cublas and cublasLt as tactic sources, use a value of:
1 << int(trt.TacticSource.CUBLAS) | 1 << int(trt.TacticSource.CUBLAS_LT)
tactic_sources – The tactic sources to set
A bool indicating whether the tactic sources in the build configuration were updated. The tactic sources in the build configuration will not be updated if the provided value is invalid.
- set_timing_cache(self: tensorrt.tensorrt.IBuilderConfig, cache: tensorrt.tensorrt.ITimingCache, ignore_mismatch: bool) bool
Attach a timing cache to IBuilderConfig
The timing cache has verification header to make sure the provided cache can be used in current environment. A failure will be reported if the CUDA device property in the provided cache is different from current environment.
bool(ignore_mismatch) == Trueskips strict verification and allows loading cache created from a different device. The cache must not be destroyed until after the engine is built.
cache – The timing cache to be used
ignore_mismatch – Whether or not allow using a cache that contains different CUDA device property
A BOOL indicating whether the operation is done successfully.