Torch-TensorRT torch.compile Backend¶

This guide presents the Torch-TensorRT torch.compile backend: a deep learning compiler which uses TensorRT to accelerate JIT-style workflows across a wide variety of models.

Key Features¶

The primary goal of the Torch-TensorRT torch.compile backend is to enable Just-In-Time compilation workflows by combining the simplicity of torch.compile API with the performance of TensorRT. Invoking the torch.compile backend is as simple as importing the torch_tensorrt package and specifying the backend:

import torch_tensorrt
...
optimized_model = torch.compile(model, backend="torch_tensorrt", dynamic=False)

Note

Many additional customization options are available to the user. These will be discussed in further depth in this guide.

The backend can handle a variety of challenging model structures and offers a simple-to-use interface for effective acceleration of models. Additionally, it has many customization options to ensure the compilation process is fitting to the specific use case.

Customizeable Settings¶

class torch_tensorrt.dynamo.CompilationSettings(precision: torch.dtype = torch.float32, debug: bool = False, workspace_size: int = 0, min_block_size: int = 5, torch_executed_ops: typing.Set[str] = <factory>, pass_through_build_failures: bool = False, max_aux_streams: typing.Optional[int] = None, version_compatible: bool = False, optimization_level: typing.Optional[int] = None, use_python_runtime: typing.Optional[bool] = False, truncate_long_and_double: bool = False, use_fast_partitioner: bool = True, enable_experimental_decompositions: bool = False, device: torch_tensorrt._Device.Device = <factory>, require_full_compilation: bool = False)[source]¶

Compilation settings for Torch-TensorRT Dynamo Paths

Parameters

precision (torch.dpython:type) – Model Layer precision
debug (bool) – Whether to print out verbose debugging information
workspace_size (python:int) – Workspace TRT is allowed to use for the module (0 is default)
min_block_size (python:int) – Minimum number of operators per TRT-Engine Block
torch_executed_ops (Sequence[str]) – Sequence of operations to run in Torch, regardless of converter coverage
pass_through_build_failures (bool) – Whether to fail on TRT engine build errors (True) or not (False)
max_aux_streams (Optional[python:int]) – Maximum number of allowed auxiliary TRT streams for each engine
version_compatible (bool) – Provide version forward-compatibility for engine plan files
optimization_level (Optional[python:int]) – Builder optimization 0-5, higher levels imply longer build time, searching for more optimization options. TRT defaults to 3
use_python_runtime (Optional[bool]) – Whether to strictly use Python runtime or C++ runtime. To auto-select a runtime based on C++ dependency presence (preferentially choosing C++ runtime if available), leave the argument as None
truncate_long_and_double (bool) – Whether to truncate int64/float64 TRT engine inputs or weights to int32/float32
use_fast_partitioner (bool) – Whether to use the fast or global graph partitioning system
enable_experimental_decompositions (bool) – Whether to enable all core aten decompositions or only a selected subset of them
device (Device) – GPU to compile the model on
require_full_compilation (bool) – Whether to require the graph is fully compiled in TensorRT. Only applicable for ir=”dynamo”; has no effect for torch.compile path

Custom Setting Usage¶

import torch_tensorrt
...
optimized_model = torch.compile(model, backend="torch_tensorrt", dynamic=False,
                                options={"truncate_long_and_double": True,
                                         "precision": torch.half,
                                         "debug": True,
                                         "min_block_size": 2,
                                         "torch_executed_ops": {"torch.ops.aten.sub.Tensor"},
                                         "optimization_level": 4,
                                         "use_python_runtime": False,})

Note

Quantization/INT8 support is slated for a future release; currently, we support FP16 and FP32 precision layers.

Compilation¶

Compilation is triggered by passing inputs to the model, as so:

import torch_tensorrt
...
# Causes model compilation to occur
first_outputs = optimized_model(*inputs)

# Subsequent inference runs with the same, or similar inputs will not cause recompilation
# For a full discussion of this, see "Recompilation Conditions" below
second_outputs = optimized_model(*inputs)

After Compilation¶

The compilation object can be used for inference within the Python session, and will recompile according to the recompilation conditions detailed below. In addition to general inference, the compilation process can be a helpful tool in determining model performance, current operator coverage, and feasibility of serialization. Each of these points will be covered in detail below.

Model Performance¶

The optimized model returned from torch.compile is useful for model benchmarking since it can automatically handle changes in the compilation context, or differing inputs that could require recompilation. When benchmarking inputs of varying distributions, batch sizes, or other criteria, this can save time.

Operator Coverage¶

Compilation is also a useful tool in determining operator coverage for a particular model. For instance, the following compilation command will display the operator coverage for each graph, but will not compile the model - effectively providing a “dryrun” mechanism:

import torch_tensorrt
...
optimized_model = torch.compile(model, backend="torch_tensorrt", dynamic=False,
                                options={"debug": True,
                                         "min_block_size": float("inf"),})

If key operators for your model are unsupported, see Dynamo Converters to contribute your own converters, or file an issue here: https://github.com/pytorch/TensorRT/issues.

Feasibility of Serialization¶

Compilation can also be helpful in demonstrating graph breaks and the feasibility of serialization of a particular model. For instance, if a model has no graph breaks and compiles successfully with the Torch-TensorRT backend, then that model should be compileable and serializeable via the torch_tensorrt Dynamo IR, as discussed in Dynamic shapes with Torch-TensorRT. To determine the number of graph breaks in a model, the torch._dynamo.explain function is very useful:

import torch
import torch_tensorrt
...
explanation = torch._dynamo.explain(model)(*inputs)
print(f"Graph breaks: {explanation.graph_break_count}")
optimized_model = torch.compile(model, backend="torch_tensorrt", dynamic=False, options={"truncate_long_and_double": True})

Dynamic Shape Support¶

The Torch-TensorRT torch.compile backend will currently require recompilation for each new batch size encountered, and it is preferred to use the dynamic=False argument when compiling with this backend. Full dynamic shape support is planned for a future release.

Recompilation Conditions¶

Once the model has been compiled, subsequent inference inputs with the same shape and data type, which traverse the graph in the same way will not require recompilation. Furthermore, each new recompilation will be cached for the duration of the Python session. For instance, if inputs of batch size 4 and 8 are provided to the model, causing two recompilations, no further recompilation would be necessary for future inputs with those batch sizes during inference within the same session. Support for engine cache serialization is planned for a future release.

Recompilation is generally triggered by one of two events: encountering inputs of different sizes or inputs which traverse the model code differently. The latter scenario can occur when the model code includes conditional logic, complex loops, or data-dependent-shapes. torch.compile handles guarding in both of these scenario and determines when recompilation is necessary.