Skip to content

# trtorch

## Functions

`trtorch.` `set_device` ( gpu_id )

`trtorch.` `compile` ( module : torch.jit._script.ScriptModule , compile_spec : Any ) → torch.jit._script.ScriptModule

  Compile a TorchScript module for NVIDIA GPUs using TensorRT

  Takes a existing TorchScript module and a set of settings to configure the compiler and will convert methods to JIT Graphs which call equivalent TensorRT engines

  Converts specifically the forward method of a TorchScript Module

  Parameters

    - module ( torch.jit.ScriptModule ) – Source module, a result of tracing or scripting a PyTorch `torch.nn.Module`

    - compile_spec ( dict ) –

      Compilation settings including operating precision, target device, etc. One key is required which is `input_shapes` , describing the input sizes or ranges for inputs to the graph. All other keys are optional

          compile_spec = {
              "input_shapes": [
                  (1, 3, 224, 224), # Static input shape for input #1
                  {
                      "min": (1, 3, 224, 224),
                      "opt": (1, 3, 512, 512),
                      "max": (1, 3, 1024, 1024)
                  } # Dynamic input shape for input #2
              ],
              "device": {
                  "device_type": torch.device("cuda"), # Type of device to run engine on (for DLA use trtorch.DeviceType.DLA)
                  "gpu_id": 0, # Target gpu id to run engine (Use Xavier as gpu id for DLA)
                  "dla_core": 0, # (DLA only) Target dla core id to run engine
                  "allow_gpu_fallback": false, # (DLA only) Allow layers unsupported on DLA to run on GPU
              },
              "op_precision": torch.half, # Operating precision set to FP16
              "refit": false, # enable refit
              "debug": false, # enable debuggable engine
              "strict_types": false, # kernels should strictly run in operating precision
              "capability": trtorch.EngineCapability.DEFAULT, # Restrict kernel selection to safe gpu kernels or safe dla kernels
              "num_min_timing_iters": 2, # Number of minimization timing iterations used to select kernels
              "num_avg_timing_iters": 1, # Number of averaging timing iterations used to select kernels
              "workspace_size": 0, # Maximum size of workspace given to TensorRT
              "max_batch_size": 0, # Maximum batch size (must be >= 1 to be set, 0 means not set)
          }

      Input Sizes can be specified as torch sizes, tuples or lists. Op precisions can be specified using torch datatypes or trtorch datatypes and you can use either torch devices or the trtorch device type enum to select device type.

  Returns

    Compiled TorchScript Module, when run it will execute via TensorRT

  Return type

    torch.jit.ScriptModule

`trtorch.` `convert_method_to_trt_engine` ( module : torch.jit._script.ScriptModule , method_name : str , compile_spec : Any ) → str

  Convert a TorchScript module method to a serialized TensorRT engine

  Converts a specified method of a module to a serialized TensorRT engine given a dictionary of conversion settings

  Parameters

    - module ( torch.jit.ScriptModule ) – Source module, a result of tracing or scripting a PyTorch `torch.nn.Module`

    - method_name ( str ) – Name of method to convert

    - compile_spec ( dict ) –

      Compilation settings including operating precision, target device, etc. One key is required which is `input_shapes` , describing the input sizes or ranges for inputs to the graph. All other keys are optional

          CompileSpec = {
              "input_shapes": [
                  (1, 3, 224, 224), # Static input shape for input #1
                  {
                      "min": (1, 3, 224, 224),
                      "opt": (1, 3, 512, 512),
                      "max": (1, 3, 1024, 1024)
                  } # Dynamic input shape for input #2
              ],
              "device": {
                  "device_type": torch.device("cuda"), # Type of device to run engine on (for DLA use trtorch.DeviceType.DLA)
                  "gpu_id": 0, # Target gpu id to run engine (Use Xavier as gpu id for DLA)
                  "dla_core": 0, # (DLA only) Target dla core id to run engine
                  "allow_gpu_fallback": false, # (DLA only) Allow layers unsupported on DLA to run on GPU
              },
              "op_precision": torch.half, # Operating precision set to FP16
              "disable_tf32": False, # Force FP32 layers to use traditional as FP32 format vs the default behavior of rounding the inputs to 10-bit mantissas before multiplying, but accumulates the sum using 23-bit mantissas
              "refit": false, # enable refit
              "debug": false, # enable debuggable engine
              "strict_types": false, # kernels should strictly run in operating precision
              "capability": trtorch.EngineCapability.DEFAULT, # Restrict kernel selection to safe gpu kernels or safe dla kernels
              "num_min_timing_iters": 2, # Number of minimization timing iterations used to select kernels
              "num_avg_timing_iters": 1, # Number of averaging timing iterations used to select kernels
              "workspace_size": 0, # Maximum size of workspace given to TensorRT
              "max_batch_size": 0, # Maximum batch size (must be >= 1 to be set, 0 means not set)
          }

      Input Sizes can be specified as torch sizes, tuples or lists. Op precisions can be specified using torch datatypes or trtorch datatypes and you can use either torch devices or the trtorch device type enum to select device type.

  Returns

    Serialized TensorRT engine, can either be saved to a file or deserialized via TensorRT APIs

  Return type

    bytes

`trtorch.` `check_method_op_support` ( module : torch.jit._script.ScriptModule , method_name : str ) → bool

  Checks to see if a method is fully supported by TRTorch

  Checks if a method of a TorchScript module can be compiled by TRTorch, if not, a list of operators that are not supported are printed out and the function returns false, else true.

  Parameters

    - module ( torch.jit.ScriptModule ) – Source module, a result of tracing or scripting a PyTorch `torch.nn.Module`

    - method_name ( str ) – Name of method to check

  Returns

    True if supported Method

  Return type

    bool

`trtorch.` `get_build_info` ( ) → str

  Returns a string containing the build information of TRTorch distribution

  Returns

    String containing the build information for TRTorch distribution

  Return type

    str

`trtorch.` `dump_build_info` ( )

  Prints build information about the TRTorch distribution to stdout

`trtorch.` `TensorRTCompileSpec` ( compile_spec : Dict [ str , Any ] ) → <torch._C.ScriptClass object at 0x7efed6e50f10>

  Utility to create a formated spec dictionary for using the PyTorch TensorRT backend

  Parameters

    compile_spec ( dict ) –

    Compilation settings including operating precision, target device, etc. One key is required which is `input_shapes` , describing the input sizes or ranges for inputs to the graph. All other keys are optional. Entries for each method to be compiled.

        CompileSpec = {
            "forward" : trtorch.TensorRTCompileSpec({
                "input_shapes": [
                    (1, 3, 224, 224), # Static input shape for input #1
                    {
                        "min": (1, 3, 224, 224),
                        "opt": (1, 3, 512, 512),
                        "max": (1, 3, 1024, 1024)
                    } # Dynamic input shape for input #2
                ],
                "device": {
                    "device_type": torch.device("cuda"), # Type of device to run engine on (for DLA use trtorch.DeviceType.DLA)
                    "gpu_id": 0, # Target gpu id to run engine (Use Xavier as gpu id for DLA)
                    "dla_core": 0, # (DLA only) Target dla core id to run engine
                    "allow_gpu_fallback": false, # (DLA only) Allow layers unsupported on DLA to run on GPU
                },
                "op_precision": torch.half, # Operating precision set to FP16
                "disable_tf32": False, # Force FP32 layers to use traditional as FP32 format vs the default behavior of rounding the inputs to 10-bit mantissas before multiplying, but accumulates the sum using 23-bit mantissas
                "refit": False, # enable refit
                "debug": False, # enable debuggable engine
                "strict_types": False, # kernels should strictly run in operating precision
                "capability": trtorch.EngineCapability.DEFAULT, # Restrict kernel selection to safe gpu kernels or safe dla kernels
                "num_min_timing_iters": 2, # Number of minimization timing iterations used to select kernels
                "num_avg_timing_iters": 1, # Number of averaging timing iterations used to select kernels
                "workspace_size": 0, # Maximum size of workspace given to TensorRT
                "max_batch_size": 0, # Maximum batch size (must be >= 1 to be set, 0 means not set)
            })
        }

    Input Sizes can be specified as torch sizes, tuples or lists. Op precisions can be specified using torch datatypes or trtorch datatypes and you can use either torch devices or the trtorch device type enum to select device type.

  Returns

    List of methods and formated spec objects to be provided to `torch._C._jit_to_tensorrt`

  Return type

    torch.classes.tensorrt.CompileSpec

## Enums

class `trtorch.` `dtype`

  Enum to specifiy operating precision for engine execution

  Members:

  > float : 32 bit floating point number

  > float32 : 32 bit floating point number

  > half : 16 bit floating point number

  > float16 : 16 bit floating point number

  > int8 : 8 bit integer number

class `trtorch.` `DeviceType`

  Enum to specify device kinds to build TensorRT engines for

  Members:

  > GPU : Specify using GPU to execute TensorRT Engine

  > DLA : Specify using DLA to execute TensorRT Engine (Jetson Only)

class `trtorch.` `EngineCapability`

  Enum to specify engine capability settings (selections of kernels to meet safety requirements)

  Members:

  > safe_gpu : Use safety GPU kernels only

  > safe_dla : Use safety DLA kernels only

  > default : Use default behavior

## Submodules

- trtorch.logging