Source code for torch_tensorrt._compile
from __future__ import annotations
import collections.abc
import logging
from enum import Enum
from typing import Any, Callable, List, Optional, Sequence, Set
import torch
import torch.fx
from torch_tensorrt._enums import dtype
from torch_tensorrt._features import ENABLED_FEATURES
from torch_tensorrt._Input import Input
from torch_tensorrt.dynamo import _defaults
from torch_tensorrt.fx import InputTensorSpec
from torch_tensorrt.fx.lower import compile as fx_compile
from torch_tensorrt.fx.utils import LowerPrecision
from typing_extensions import TypeGuard
if ENABLED_FEATURES.torchscript_frontend:
import torch_tensorrt.ts
from torch_tensorrt.ts._compiler import compile as torchscript_compile
from torch_tensorrt.ts._compiler import (
convert_method_to_trt_engine as ts_convert_method_to_trt_engine,
)
if ENABLED_FEATURES.dynamo_frontend:
from torch.export import ExportedProgram
from torch_tensorrt.dynamo._compiler import compile as dynamo_compile
from torch_tensorrt.dynamo._compiler import (
convert_module_to_trt_engine as dynamo_convert_module_to_trt_engine,
)
from torch_tensorrt.dynamo._tracer import trace as dynamo_trace
logger = logging.getLogger(__name__)
__all__ = ["compile", "convert_method_to_trt_engine", "save", "load"]
def _non_fx_input_interface(
inputs: Sequence[Input | torch.Tensor | InputTensorSpec],
) -> TypeGuard[List[Input | torch.Tensor]]:
return all(isinstance(i, (torch.Tensor, Input)) for i in inputs)
def _fx_input_interface(
inputs: Sequence[Input | torch.Tensor | InputTensorSpec],
) -> TypeGuard[List[InputTensorSpec | torch.Tensor]]:
return all(isinstance(i, (torch.Tensor, InputTensorSpec)) for i in inputs)
class _IRType(Enum):
"""Enum to determine the type of IR selected for model compilation"""
ts = 0
fx = 1
dynamo = 2
torch_compile = 3
exported_program = 4
class _ModuleType(Enum):
"""Enum to determine the type of model provided as input"""
nn = 0
ts = 1
fx = 2
ep = 3
def _parse_module_type(module: Any) -> _ModuleType:
if any(
isinstance(module, t)
for t in [torch.jit.ScriptModule, torch.jit.ScriptFunction]
):
return _ModuleType.ts
elif isinstance(module, torch.fx.GraphModule):
return _ModuleType.fx
elif isinstance(module, ExportedProgram):
return _ModuleType.ep
elif isinstance(module, torch.nn.Module):
return _ModuleType.nn
else:
raise RuntimeError("Module is an unknown format")
def _get_target_fe(module_type: _ModuleType, ir: str) -> _IRType:
module_is_tsable = any(module_type == t for t in [_ModuleType.nn, _ModuleType.ts])
module_is_fxable = any(module_type == t for t in [_ModuleType.nn, _ModuleType.fx])
module_is_exportable = module_type == _ModuleType.ep
ir_targets_torchscript = any(ir == opt for opt in ["torchscript", "ts"])
ir_targets_fx = ir == "fx"
ir_targets_dynamo = ir == "dynamo"
ir_targets_torch_compile = ir == "torch_compile"
if module_is_tsable and ir_targets_torchscript:
if ENABLED_FEATURES.torchscript_frontend:
return _IRType.ts
else:
raise ValueError(
"Requested using the TS frontend but the TS frontend is not available in this build of Torch-TensorRT"
)
elif module_is_fxable and ir_targets_fx:
if ENABLED_FEATURES.fx_frontend:
return _IRType.fx
else:
raise ValueError(
"Requested using the FX frontend but the FX frontend is not available in this build of Torch-TensorRT"
)
elif (module_is_fxable or module_is_exportable) and ir_targets_dynamo:
if ENABLED_FEATURES.dynamo_frontend:
return _IRType.dynamo
else:
raise ValueError(
"Requested using the Dynamo frontend but the Dynamo frontend is not available in this build of Torch-TensorRT"
)
elif module_is_fxable and ir_targets_torch_compile:
if ENABLED_FEATURES.dynamo_frontend:
return _IRType.torch_compile
else:
raise ValueError(
"Requested using the Torch-TensorRT torch.compile backend but the Torch-TensorRT torch.compile backend is not available in this build of Torch-TensorRT"
)
else:
if ir == "default":
# Options are listed in order of preference
if ENABLED_FEATURES.dynamo_frontend and module_is_fxable:
logger.info("ir was set to default, using dynamo frontend")
return _IRType.dynamo
elif ENABLED_FEATURES.torchscript_frontend and module_is_tsable:
if ENABLED_FEATURES.dynamo_frontend:
logger.warning(
"Input is a torchscript module but the ir was not specified (default=dynamo), please set ir=torchscript to suppress the warning."
)
return _IRType.ts
elif ENABLED_FEATURES.dynamo_frontend and module_is_exportable:
logger.info("ir was set to default, using dynamo frontend")
return _IRType.dynamo
else:
raise ValueError(
f"Module was provided in an unsupported format\nInstalled frontends:\n\tDynamo - {ENABLED_FEATURES.dynamo_frontend}\n\tTorchScript - {ENABLED_FEATURES.torchscript_frontend}\n\tFX - {ENABLED_FEATURES.fx_frontend})"
)
else:
raise ValueError("Unknown ir was requested")
[docs]def compile(
module: Any,
ir: str = "default",
inputs: Optional[Sequence[Input | torch.Tensor | InputTensorSpec]] = None,
enabled_precisions: Optional[Set[torch.dtype | dtype]] = None,
**kwargs: Any,
) -> (
torch.nn.Module | torch.jit.ScriptModule | torch.fx.GraphModule | Callable[..., Any]
):
"""Compile a PyTorch module for NVIDIA GPUs using TensorRT
Takes a existing PyTorch module and a set of settings to configure the compiler
and using the path specified in ``ir`` lower and compile the module to TensorRT
returning a PyTorch Module back
Converts specifically the forward method of a Module
Arguments:
module (Union(torch.nn.Module,torch.jit.ScriptModule): Source module
Keyword Arguments:
inputs (List[Union(torch_tensorrt.Input, torch.Tensor)]): **Required** List of specifications of input shape, dtype and memory layout for inputs to the module. This argument is required. Input Sizes can be specified as torch sizes, tuples or lists. dtypes can be specified using
torch datatypes or torch_tensorrt datatypes and you can use either torch devices or the torch_tensorrt device type enum
to select device type. ::
inputs=[
torch_tensorrt.Input((1, 3, 224, 224)), # Static NCHW input shape for input #1
torch_tensorrt.Input(
min_shape=(1, 224, 224, 3),
opt_shape=(1, 512, 512, 3),
max_shape=(1, 1024, 1024, 3),
dtype=torch.int32
format=torch.channel_last
), # Dynamic input shape for input #2
torch.randn((1, 3, 224, 244)) # Use an example tensor and let torch_tensorrt infer settings
]
enabled_precision (Set(Union(torch.dtype, torch_tensorrt.dtype))): The set of datatypes that TensorRT can use when selecting kernels
ir (str): The requested strategy to compile. (Options: default - Let Torch-TensorRT decide, ts - TorchScript with scripting path)
**kwargs: Additional settings for the specific requested strategy (See submodules for more info)
Returns:
torch.nn.Module: Compiled Module, when run it will execute via TensorRT
"""
input_list = inputs if inputs is not None else []
enabled_precisions_set: Set[dtype | torch.dtype] = (
enabled_precisions
if enabled_precisions is not None
else _defaults.ENABLED_PRECISIONS
)
module_type = _parse_module_type(module)
target_ir = _get_target_fe(module_type, ir)
if target_ir == _IRType.ts:
ts_mod = module
if module_type == _ModuleType.nn:
logger.info(
"Module was provided as a torch.nn.Module, trying to script the module with torch.jit.script. In the event of a failure please preconvert your module to TorchScript"
)
ts_mod = torch.jit.script(module)
assert _non_fx_input_interface(input_list)
compiled_ts_module: torch.jit.ScriptModule = torchscript_compile(
ts_mod,
inputs=input_list,
enabled_precisions=enabled_precisions_set,
**kwargs,
)
return compiled_ts_module
elif target_ir == _IRType.fx:
if (
torch.float16 in enabled_precisions_set
or torch_tensorrt.dtype.half in enabled_precisions_set
):
lower_precision = LowerPrecision.FP16
elif (
torch.float32 in enabled_precisions_set
or torch_tensorrt.dtype.float in enabled_precisions_set
):
lower_precision = LowerPrecision.FP32
else:
raise ValueError(f"Precision {enabled_precisions_set} not supported on FX")
assert _fx_input_interface(input_list)
compiled_fx_module: torch.nn.Module = fx_compile(
module,
input_list,
lower_precision=lower_precision,
explicit_batch_dimension=True,
dynamic_batch=False,
**kwargs,
)
return compiled_fx_module
elif target_ir == _IRType.dynamo:
# Prepare torch and torchtrt inputs
from torch_tensorrt.dynamo.utils import prepare_inputs
if not isinstance(input_list, collections.abc.Sequence):
input_list = [input_list]
# Export the module
torchtrt_inputs = prepare_inputs(input_list)
exp_program = dynamo_trace(module, torchtrt_inputs, **kwargs)
trt_graph_module = dynamo_compile(
exp_program,
inputs=torchtrt_inputs,
enabled_precisions=enabled_precisions_set,
**kwargs,
)
return trt_graph_module
elif target_ir == _IRType.torch_compile:
return torch_compile(
module, enabled_precisions=enabled_precisions_set, **kwargs
)
else:
raise RuntimeError("Module is an unknown format or the ir requested is unknown")
def torch_compile(module: torch.nn.Module, **kwargs: Any) -> Any:
"""
Returns a boxed model which is the output of torch.compile.
This does not compile the model to TRT. Execute this model on
sample inputs to compile the model to TRT.
"""
from torch_tensorrt.dynamo.backend import torch_tensorrt_backend
# TODO: Remove dynamic=False when SymInt Dynamic shape support is ready
boxed_fn = torch.compile(
module, backend=torch_tensorrt_backend, dynamic=False, options={**kwargs}
)
return boxed_fn
[docs]def convert_method_to_trt_engine(
module: Any,
method_name: str = "forward",
inputs: Optional[Sequence[Input | torch.Tensor]] = None,
ir: str = "default",
enabled_precisions: Optional[Set[torch.dtype | dtype]] = None,
**kwargs: Any,
) -> bytes:
"""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
Arguments:
module (Union(torch.nn.Module,torch.jit.ScriptModule): Source module
Keyword Arguments:
inputs (List[Union(torch_tensorrt.Input, torch.Tensor)]): **Required** List of specifications of input shape, dtype and memory layout for inputs to the module. This argument is required. Input Sizes can be specified as torch sizes, tuples or lists. dtypes can be specified using
torch datatypes or torch_tensorrt datatypes and you can use either torch devices or the torch_tensorrt device type enum
to select device type. ::
input=[
torch_tensorrt.Input((1, 3, 224, 224)), # Static NCHW input shape for input #1
torch_tensorrt.Input(
min_shape=(1, 224, 224, 3),
opt_shape=(1, 512, 512, 3),
max_shape=(1, 1024, 1024, 3),
dtype=torch.int32
format=torch.channel_last
), # Dynamic input shape for input #2
torch.randn((1, 3, 224, 244)) # Use an example tensor and let torch_tensorrt infer settings
]
enabled_precision (Set(Union(torch.dtype, torch_tensorrt.dtype))): The set of datatypes that TensorRT can use when selecting kernels
ir (str): The requested strategy to compile. (Options: default - Let Torch-TensorRT decide, ts - TorchScript with scripting path)
**kwargs: Additional settings for the specific requested strategy (See submodules for more info)
Returns:
bytes: Serialized TensorRT engine, can either be saved to a file or deserialized via TensorRT APIs
"""
enabled_precisions_set = (
enabled_precisions if enabled_precisions is not None else {torch.float}
)
module_type = _parse_module_type(module)
target_ir = _get_target_fe(module_type, ir)
if target_ir == _IRType.ts:
ts_mod = module
if module_type == _ModuleType.nn:
logger.info(
"Module was provided as a torch.nn.Module, trying to script the module with torch.jit.script. In the event of a failure please preconvert your module to TorchScript"
)
ts_mod = torch.jit.script(module)
serialized_engine: bytes = ts_convert_method_to_trt_engine(
ts_mod,
inputs=inputs,
method_name=method_name,
enabled_precisions=enabled_precisions_set,
**kwargs,
)
return serialized_engine
elif target_ir == _IRType.fx:
raise RuntimeError(
"convert_method_to_trt_engine call is not supported for ir=fx"
)
elif target_ir == _IRType.dynamo:
# Prepare torch and torchtrt inputs
from torch_tensorrt.dynamo.utils import prepare_inputs
if not isinstance(inputs, collections.abc.Sequence):
inputs = [inputs]
# Export the module
torchtrt_inputs = prepare_inputs(inputs)
exp_program = torch_tensorrt.dynamo.trace(module, torchtrt_inputs, **kwargs)
return dynamo_convert_module_to_trt_engine(
exp_program,
inputs=tuple(inputs),
enabled_precisions=enabled_precisions_set,
**kwargs,
)
elif target_ir == _IRType.torch_compile:
raise RuntimeError(
"convert_method_to_trt_engine call is not supported for ir=torch_compile"
)
else:
raise RuntimeError("Module is an unknown format or the ir requested is unknown")
[docs]def load(file_path: str = "") -> Any:
"""
Load either a Torchscript model or ExportedProgram.
Loads a TorchScript or ExportedProgram file from disk. File type will be detect the type using try, except.
Arguments:
file_path (str): Path to file on the disk
Raises:
ValueError: If there is no file or the file is not either a TorchScript file or ExportedProgram file
"""
try:
logger.debug(f"Loading the provided file {file_path} using torch.jit.load()")
ts_module = torch.jit.load(file_path)
return ts_module
except Exception:
logger.info(
f"Loading the provided file {file_path} via torch.jit.load() failed with the following error",
exc_info=True,
)
pass
try:
logger.debug(f"Loading the provided file {file_path} using torch.export.load()")
exp_program = torch.export.load(file_path)
return exp_program
except Exception:
logger.info(
f"Loading the provided file {file_path} via torch.export.load() failed with the following error",
exc_info=True,
)
raise ValueError(
f"The file {file_path} doesn't correspond to a valid Torchscript module or ExportedProgram. Please verify the file path."
)
[docs]def save(
module: Any,
file_path: str = "",
*,
output_format: str = "exported_program",
inputs: Optional[Sequence[torch.Tensor]] = None,
retrace: bool = False,
) -> None:
"""
Save the model to disk in the specified output format.
Arguments:
module (Optional(torch.jit.ScriptModule | torch.export.ExportedProgram | torch.fx.GraphModule)): Compiled Torch-TensorRT module
inputs (torch.Tensor): Torch input tensors
output_format (str): Format to save the model. Options include exported_program | torchscript.
retrace (bool): When the module type is a fx.GraphModule, this option re-exports the graph using torch.export.export(strict=False) to save it.
This flag is experimental for now.
"""
module_type = _parse_module_type(module)
accepted_formats = {"exported_program", "torchscript"}
if inputs is not None and not all(
isinstance(input, torch.Tensor) for input in inputs
):
raise ValueError(
"Not all inputs provided are torch.tensors. Please provide torch.tensors as inputs"
)
if output_format not in accepted_formats:
raise ValueError(
f"Provided output_format {output_format} is not supported. Supported options are exported_program | torchscript"
)
if not file_path:
raise ValueError("File path cannot be empty. Please provide a valid file path")
if module_type == _ModuleType.nn:
raise ValueError(
"Input model is of type nn.Module. Saving nn.Module directly is not supported. Supported model types torch.jit.ScriptModule | torch.fx.GraphModule | torch.export.ExportedProgram."
)
elif module_type == _ModuleType.ts:
if output_format == "exported_program":
raise ValueError(
"Provided model is a torch.jit.ScriptModule but the output_format specified is exported_program. Please verify the output_format"
)
else:
torch.jit.save(module, file_path)
elif module_type == _ModuleType.ep:
if output_format == "torchscript":
raise ValueError(
"Provided model is a torch.export.ExportedProgram but the output_format specified is torchscript. Please verify the output_format"
)
else:
torch.export.save(module, file_path)
elif module_type == _ModuleType.fx:
if inputs is None:
raise ValueError(
"Provided model is a torch.fx.GraphModule however the inputs are empty. Please provide valid torch.tensors as inputs to trace and save the model"
)
# The module type is torch.fx.GraphModule
if output_format == "torchscript":
module_ts = torch.jit.trace(module, inputs)
torch.jit.save(module_ts, file_path)
else:
if not retrace:
from torch_tensorrt.dynamo._exporter import export
exp_program = export(module, inputs)
torch.export.save(exp_program, file_path)
else:
from torch._higher_order_ops.torchbind import enable_torchbind_tracing
with enable_torchbind_tracing():
exp_program = torch.export.export(
module, tuple(inputs), strict=False
)
torch.export.save(exp_program, file_path)
