Source code for torch_tensorrt._Device
-from __future__ import annotations
+from __future__ import annotations
-import logging
-import sys
-from typing import Any, Optional, Tuple
+import logging
+import sys
+from typing import Any, Optional, Tuple
if sys.version_info >= (3, 11):
- from typing import Self
+ from typing import Self
else:
- from typing_extensions import Self
+ from typing_extensions import Self
-import torch
-from torch_tensorrt._enums import DeviceType
-from torch_tensorrt._features import needs_torch_tensorrt_runtime
+import torch
+from torch_tensorrt._enums import DeviceType
+from torch_tensorrt._features import needs_torch_tensorrt_runtime
-import tensorrt as trt
+import tensorrt as trt
-[docs]class Device(object):
+[docs]class Device(object):
"""
Defines a device that can be used to specify target devices for engines
@@ -505,7 +523,7 @@ Source code for torch_tensorrt._Device
False #: Whether falling back to GPU if DLA cannot support an op should be allowed
)
-[docs] def __init__(self, *args: Any, **kwargs: Any):
+[docs] def __init__(self, *args: Any, **kwargs: Any):
"""__init__ Method for torch_tensorrt.Device
Device accepts one of a few construction patterns
@@ -577,7 +595,7 @@ Source code for torch_tensorrt._Device
if isinstance(kwargs["device_type"], trt.DeviceType):
self.device_type = DeviceType._from(kwargs["device_type"])
- def __str__(self) -> str:
+ def __str__(self) -> str:
suffix = (
")"
if self.device_type == DeviceType.GPU
@@ -586,11 +604,11 @@ Source code for torch_tensorrt._Device
dev_str: str = f"Device(type={self.device_type}, gpu_id={self.gpu_id}{suffix}"
return dev_str
- def __repr__(self) -> str:
+ def __repr__(self) -> str:
return self.__str__()
@classmethod
- def _from(cls, d: Optional[Self | torch.device | str]) -> Device:
+ def _from(cls, d: Optional[Self | torch.device | str]) -> Device:
"""Cast a device-type to torch_tensorrt.Device
Returns the corresponding torch_tensorrt.Device
@@ -610,16 +628,16 @@ Source code for torch_tensorrt._Device
return cls(d)
@classmethod
- def _from_torch_device(cls, torch_dev: torch.device) -> Device:
+ def _from_torch_device(cls, torch_dev: torch.device) -> Device:
return cls._from(torch_dev)
@classmethod
- def _current_device(cls) -> Device:
+ def _current_device(cls) -> Device:
dev_id = torch.cuda.current_device()
return cls(gpu_id=dev_id)
@staticmethod
- def _parse_device_str(s: str) -> Tuple[trt.DeviceType, int]:
+ def _parse_device_str(s: str) -> Tuple[trt.DeviceType, int]:
s = s.lower()
spec = s.split(":")
if spec[0] == "gpu" or spec[0] == "cuda":
@@ -629,7 +647,7 @@ Source code for torch_tensorrt._Device
else:
raise ValueError(f"Unknown device type {spec[0]}")
- def to(self, t: type) -> torch.device:
+ def to(self, t: type) -> torch.device:
if t == torch.device:
if self.gpu_id != -1:
return torch.device(self.gpu_id)
@@ -639,7 +657,7 @@ Source code for torch_tensorrt._Device
raise TypeError("Unsupported target type for device conversion")
@needs_torch_tensorrt_runtime
- def _to_serialized_rt_device(self) -> str:
+ def _to_serialized_rt_device(self) -> str:
delim = torch.ops.tensorrt.SERIALIZED_RT_DEVICE_DELIM()[0]
dev_info = torch.cuda.get_device_properties(self.gpu_id)
rt_info = [
@@ -879,7 +897,7 @@ Resources
Developer Resources
- Contributor Awards - 2023
+ Contributor Awards - 2024
@@ -895,6 +913,9 @@ Resources
ExecuTorch
+
+ ExecuTorch Documentation
+
@@ -933,6 +954,9 @@ Resources
Events
+
+ Newsletter
+
@@ -946,6 +970,18 @@ Resources
Governing Board
+
+ Cloud Credit Program
+
+
+ Technical Advisory Council
+
+
+ Staff
+
+
+ Contact Us
+
diff --git a/docs/_modules/torch_tensorrt/_Input.html b/docs/_modules/torch_tensorrt/_Input.html
index d4140ef3db..b5478c5ce6 100644
--- a/docs/_modules/torch_tensorrt/_Input.html
+++ b/docs/_modules/torch_tensorrt/_Input.html
@@ -9,7 +9,7 @@
- torch_tensorrt._Input — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ torch_tensorrt._Input — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -132,8 +132,8 @@
Developer Resources
Find resources and get questions answered
-
- Contributor Awards - 2023
+
+ Contributor Awards - 2024
Award winners announced at this year's PyTorch Conference
@@ -154,6 +154,9 @@
ExecuTorch
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+
+ ExecuTorch Docs
+
@@ -201,6 +204,10 @@
Events
Find events, webinars, and podcasts
+
+ Newsletter
+ Stay up-to-date with the latest updates
+
@@ -216,7 +223,18 @@
Governing Board
-
+
+
+ Cloud Credit Program
+
+
+ Technical Advisory Council
+
+
+ Staff
+
+
+ Contact Us
@@ -272,7 +290,7 @@
- v2.6.0.dev0+70e2a38
+ v2.7.0.dev0+d6be4ba
@@ -467,16 +485,16 @@
Source code for torch_tensorrt._Input
-from __future__ import annotations
+from __future__ import annotations
-from enum import Enum
-from typing import Any, Dict, List, Optional, Sequence, Tuple
+from enum import Enum
+from typing import Any, Dict, List, Optional, Sequence, Tuple
-import torch
-from torch_tensorrt._enums import dtype, memory_format
+import torch
+from torch_tensorrt._enums import dtype, memory_format
-[docs]class Input(object):
+[docs]class Input(object):
"""
Defines an input to a module in terms of expected shape, data type and tensor format.
@@ -493,7 +511,7 @@ Source code for torch_tensorrt._Input
format (torch_tensorrt.TensorFormat): The expected format of the input tensor (default: torch_tensorrt.TensorFormat.NCHW)
"""
- class _ShapeMode(Enum):
+ class _ShapeMode(Enum):
STATIC = 0
DYNAMIC = 1
@@ -518,7 +536,7 @@ Source code for torch_tensorrt._Input
name: str = ""
is_shape_tensor: bool = False
-[docs] def __init__(self, *args: Any, **kwargs: Any) -> None:
+[docs] def __init__(self, *args: Any, **kwargs: Any) -> None:
"""__init__ Method for torch_tensorrt.Input
Input accepts one of a few construction patterns
@@ -659,7 +677,7 @@ Source code for torch_tensorrt._Input
if "name" in kwargs:
self.name = kwargs["name"]
- def __str__(self) -> str:
+ def __str__(self) -> str:
if self.shape_mode == Input._ShapeMode.STATIC:
return "Input(shape={}, dtype={}, format={}, domain=[{}, {}))".format(
self.shape,
@@ -686,11 +704,11 @@ Source code for torch_tensorrt._Input
else:
raise RuntimeError("Unknown input shape mode")
- def __repr__(self) -> str:
+ def __repr__(self) -> str:
return self.__str__()
@staticmethod
- def equivalent_spec(a: Input, b: Input) -> bool:
+ def equivalent_spec(a: Input, b: Input) -> bool:
if a.shape_mode != b.shape_mode:
return False
@@ -718,7 +736,7 @@ Source code for torch_tensorrt._Input
return all(checks)
@staticmethod
- def _supported_input_size_type(input_size: Any) -> bool:
+ def _supported_input_size_type(input_size: Any) -> bool:
if isinstance(input_size, torch.Size):
return True
elif isinstance(input_size, tuple):
@@ -729,7 +747,7 @@ Source code for torch_tensorrt._Input
return False
@staticmethod
- def _parse_tensor_domain(
+ def _parse_tensor_domain(
domain: Optional[Tuple[float, float]]
) -> Tuple[float, float]:
"""
@@ -777,7 +795,7 @@ Source code for torch_tensorrt._Input
return result_domain
[docs] @classmethod
- def from_tensor(
+ def from_tensor(
cls, t: torch.Tensor, disable_memory_format_check: bool = False
) -> "Input":
"""
@@ -809,7 +827,7 @@ Source code for torch_tensorrt._Input
return cls(shape=t.shape, dtype=t.dtype, format=frmt, torch_tensor=t)
[docs] @classmethod
- def from_tensors(
+ def from_tensors(
cls, ts: Sequence[torch.Tensor], disable_memory_format_check: bool = False
) -> List["Input"]:
"""
@@ -830,7 +848,7 @@ Source code for torch_tensorrt._Input
for t in ts
]
-[docs] def example_tensor(
+[docs] def example_tensor(
self, optimization_profile_field: Optional[str] = None
) -> torch.Tensor:
"""
@@ -1108,7 +1126,7 @@ Resources
Developer Resources
- Contributor Awards - 2023
+ Contributor Awards - 2024
@@ -1124,6 +1142,9 @@ Resources
ExecuTorch
+
+ ExecuTorch Documentation
+
@@ -1162,6 +1183,9 @@ Resources
Events
+
+ Newsletter
+
@@ -1175,6 +1199,18 @@ Resources
Governing Board
+
+ Cloud Credit Program
+
+
+ Technical Advisory Council
+
+
+ Staff
+
+
+ Contact Us
+
diff --git a/docs/_modules/torch_tensorrt/_compile.html b/docs/_modules/torch_tensorrt/_compile.html
index 045f13299d..93cf4a33ae 100644
--- a/docs/_modules/torch_tensorrt/_compile.html
+++ b/docs/_modules/torch_tensorrt/_compile.html
@@ -9,7 +9,7 @@
- torch_tensorrt._compile — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ torch_tensorrt._compile — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -132,8 +132,8 @@
Developer Resources
Find resources and get questions answered
-
- Contributor Awards - 2023
+
+ Contributor Awards - 2024
Award winners announced at this year's PyTorch Conference
@@ -154,6 +154,9 @@
ExecuTorch
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+
+ ExecuTorch Docs
+
@@ -201,6 +204,10 @@
Events
Find events, webinars, and podcasts
+
+ Newsletter
+ Stay up-to-date with the latest updates
+
@@ -216,7 +223,18 @@
Governing Board
-
+
+
+ Cloud Credit Program
+
+
+ Technical Advisory Council
+
+
+ Staff
+
+
+ Contact Us
@@ -272,7 +290,7 @@
- v2.6.0.dev0+70e2a38
+ v2.7.0.dev0+d6be4ba
@@ -467,51 +485,51 @@
Source code for torch_tensorrt._compile
-from __future__ import annotations
-
-import collections.abc
-import logging
-import platform
-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.dynamo.runtime._CudaGraphsTorchTensorRTModule import (
+from __future__ import annotations
+
+import collections.abc
+import logging
+import platform
+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.dynamo.runtime._CudaGraphsTorchTensorRTModule import (
CudaGraphsTorchTensorRTModule,
)
-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
+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 (
+ 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 (
+ from torch.export import ExportedProgram
+ from torch_tensorrt.dynamo._compiler import compile as dynamo_compile
+ from torch_tensorrt.dynamo._compiler import (
convert_exported_program_to_serialized_trt_engine as dynamo_convert_exported_program_to_serialized_trt_engine,
)
- from torch_tensorrt.dynamo._compiler import (
+ from torch_tensorrt.dynamo._compiler import (
cross_compile_for_windows as dynamo_cross_compile_for_windows,
)
- from torch_tensorrt.dynamo._compiler import (
+ from torch_tensorrt.dynamo._compiler import (
load_cross_compiled_exported_program as dynamo_load_cross_compiled_exported_program,
)
- from torch_tensorrt.dynamo._compiler import (
+ from torch_tensorrt.dynamo._compiler import (
save_cross_compiled_exported_program as dynamo_save_cross_compiled_exported_program,
)
- from torch_tensorrt.dynamo._tracer import trace as dynamo_trace
+ from torch_tensorrt.dynamo._tracer import trace as dynamo_trace
logger = logging.getLogger(__name__)
@@ -525,19 +543,19 @@ Source code for torch_tensorrt._compile
]
-def _non_fx_input_interface(
+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(
+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):
+class _IRType(Enum):
"""Enum to determine the type of IR selected for model compilation"""
ts = 0
@@ -547,7 +565,7 @@ Source code for torch_tensorrt._compile
exported_program = 4
-class _ModuleType(Enum):
+class _ModuleType(Enum):
"""Enum to determine the type of model provided as input"""
nn = 0
@@ -556,7 +574,7 @@ Source code for torch_tensorrt._compile
ep = 3
-def _parse_module_type(module: Any) -> _ModuleType:
+def _parse_module_type(module: Any) -> _ModuleType:
if any(
isinstance(module, t)
for t in [torch.jit.ScriptModule, torch.jit.ScriptFunction]
@@ -572,7 +590,7 @@ Source code for torch_tensorrt._compile
raise RuntimeError("Module is an unknown format")
-def _get_target_fe(module_type: _ModuleType, ir: str) -> _IRType:
+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
@@ -633,7 +651,7 @@ Source code for torch_tensorrt._compile
raise ValueError("Unknown ir was requested")
-[docs]def compile(
+[docs]def compile(
module: Any,
ir: str = "default",
inputs: Optional[Sequence[Input | torch.Tensor | InputTensorSpec]] = None,
@@ -743,7 +761,7 @@ Source code for torch_tensorrt._compile
if kwarg_inputs is None:
kwarg_inputs = {}
- from torch_tensorrt.dynamo.utils import prepare_inputs
+ from torch_tensorrt.dynamo.utils import prepare_inputs
if not isinstance(arg_inputs, collections.abc.Sequence):
arg_inputs = [arg_inputs] # type: ignore
@@ -770,7 +788,7 @@ Source code for torch_tensorrt._compile
raise RuntimeError("Module is an unknown format or the ir requested is unknown")
-def cross_compile_for_windows(
+def cross_compile_for_windows(
module: torch.nn.Module,
file_path: str,
inputs: Optional[Sequence[Input | torch.Tensor]] = None,
@@ -843,7 +861,7 @@ Source code for torch_tensorrt._compile
if kwarg_inputs is None:
kwarg_inputs = {}
- from torch_tensorrt.dynamo.utils import prepare_inputs
+ from torch_tensorrt.dynamo.utils import prepare_inputs
if not isinstance(arg_inputs, collections.abc.Sequence):
arg_inputs = [arg_inputs] # type: ignore
@@ -869,13 +887,13 @@ Source code for torch_tensorrt._compile
logger.debug("successfully compiled and saved the module for windows")
-def torch_compile(module: torch.nn.Module, **kwargs: Any) -> Any:
+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
+ from torch_tensorrt.dynamo.backend import torch_tensorrt_backend
# TODO: Remove dynamic=False when SymInt Dynamic shape support is ready
boxed_fn = torch.compile(
@@ -885,7 +903,7 @@ Source code for torch_tensorrt._compile
return boxed_fn
-[docs]def convert_method_to_trt_engine(
+[docs]def convert_method_to_trt_engine(
module: Any,
method_name: str = "forward",
inputs: Optional[Sequence[Input | torch.Tensor | InputTensorSpec]] = None,
@@ -966,7 +984,7 @@ Source code for torch_tensorrt._compile
if kwarg_inputs is None:
kwarg_inputs = {}
- from torch_tensorrt.dynamo.utils import prepare_inputs
+ from torch_tensorrt.dynamo.utils import prepare_inputs
if not isinstance(arg_inputs, collections.abc.Sequence):
arg_inputs = [arg_inputs] # type: ignore
@@ -994,7 +1012,7 @@ Source code for torch_tensorrt._compile
raise RuntimeError("Module is an unknown format or the ir requested is unknown")
-def load_cross_compiled_exported_program(file_path: str = "") -> Any:
+def load_cross_compiled_exported_program(file_path: str = "") -> Any:
"""
Load an ExportedProgram file in Windows which was previously cross compiled in Linux
@@ -1007,7 +1025,7 @@ Source code for torch_tensorrt._compile
return dynamo_load_cross_compiled_exported_program(file_path)
-[docs]def load(file_path: str = "") -> Any:
+[docs]def load(file_path: str = "") -> Any:
"""
Load either a Torchscript model or ExportedProgram.
@@ -1044,7 +1062,7 @@ Source code for torch_tensorrt._compile
)
-[docs]def save(
+[docs]def save(
module: Any,
file_path: str = "",
*,
@@ -1131,7 +1149,7 @@ Source code for torch_tensorrt._compile
torch.jit.save(module_ts, file_path)
else:
if not retrace:
- from torch_tensorrt.dynamo._exporter import export
+ from torch_tensorrt.dynamo._exporter import export
if arg_inputs is not None:
logger.warning(
@@ -1378,7 +1396,7 @@ Resources
Developer Resources
- Contributor Awards - 2023
+ Contributor Awards - 2024
@@ -1394,6 +1412,9 @@ Resources
ExecuTorch
+
+ ExecuTorch Documentation
+
@@ -1432,6 +1453,9 @@ Resources
Events
+
+ Newsletter
+
@@ -1445,6 +1469,18 @@ Resources
Governing Board
+
+ Cloud Credit Program
+
+
+ Technical Advisory Council
+
+
+ Staff
+
+
+ Contact Us
+
diff --git a/docs/_modules/torch_tensorrt/_enums.html b/docs/_modules/torch_tensorrt/_enums.html
index cc622fcf66..97f42a78e6 100644
--- a/docs/_modules/torch_tensorrt/_enums.html
+++ b/docs/_modules/torch_tensorrt/_enums.html
@@ -9,7 +9,7 @@
- torch_tensorrt._enums — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ torch_tensorrt._enums — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -132,8 +132,8 @@
Developer Resources
Find resources and get questions answered
-
- Contributor Awards - 2023
+
+ Contributor Awards - 2024
Award winners announced at this year's PyTorch Conference
@@ -154,6 +154,9 @@
ExecuTorch
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+
+ ExecuTorch Docs
+
@@ -201,6 +204,10 @@
Events
Find events, webinars, and podcasts
+
+ Newsletter
+ Stay up-to-date with the latest updates
+
@@ -216,7 +223,18 @@
Governing Board
-
+
+
+ Cloud Credit Program
+
+
+ Technical Advisory Council
+
+
+ Staff
+
+
+ Contact Us
@@ -272,7 +290,7 @@
- v2.6.0.dev0+70e2a38
+ v2.7.0.dev0+d6be4ba
@@ -467,19 +485,19 @@
Source code for torch_tensorrt._enums
-from __future__ import annotations
+from __future__ import annotations
-import logging
-from enum import Enum, auto
-from typing import Any, Optional, Type, Union
+import logging
+from enum import Enum, auto
+from typing import Any, Optional, Type, Union
-import numpy as np
-import tensorrt as trt
-import torch
-from torch_tensorrt._features import ENABLED_FEATURES, needs_torch_tensorrt_runtime
+import numpy as np
+import tensorrt as trt
+import torch
+from torch_tensorrt._features import ENABLED_FEATURES, needs_torch_tensorrt_runtime
-[docs]class dtype(Enum):
+[docs]class dtype(Enum):
"""Enum to describe data types to Torch-TensorRT, has compatibility with torch, tensorrt and numpy dtypes"""
# Supported types in Torch-TensorRT
@@ -575,7 +593,7 @@ Source code for torch_tensorrt._enums
bfloat16 = bf16
@staticmethod
- def _is_np_obj(t: Any) -> bool:
+ def _is_np_obj(t: Any) -> bool:
if isinstance(t, np.dtype):
return True
elif isinstance(t, type):
@@ -584,7 +602,7 @@ Source code for torch_tensorrt._enums
return False
@classmethod
- def _from(
+ def _from(
cls,
t: Union[torch.dtype, trt.DataType, np.dtype, dtype, type],
use_default: bool = False,
@@ -710,7 +728,7 @@ Source code for torch_tensorrt._enums
return t
elif ENABLED_FEATURES.torchscript_frontend:
- from torch_tensorrt import _C
+ from torch_tensorrt import _C
if isinstance(t, _C.dtype):
if t == _C.dtype.long:
@@ -739,7 +757,7 @@ Source code for torch_tensorrt._enums
)
[docs] @classmethod
- def try_from(
+ def try_from(
cls,
t: Union[torch.dtype, trt.DataType, np.dtype, dtype],
use_default: bool = False,
@@ -779,7 +797,7 @@ Source code for torch_tensorrt._enums
)
return None
-[docs] def to(
+[docs] def to(
self,
t: Union[Type[torch.dtype], Type[trt.DataType], Type[np.dtype], Type[dtype]],
use_default: bool = False,
@@ -898,7 +916,7 @@ Source code for torch_tensorrt._enums
return self
elif ENABLED_FEATURES.torchscript_frontend:
- from torch_tensorrt import _C
+ from torch_tensorrt import _C
if t == _C.dtype:
if self == dtype.i64:
@@ -926,7 +944,7 @@ Source code for torch_tensorrt._enums
f"Provided unsupported destination type for dtype conversion {t}"
)
-[docs] def try_to(
+[docs] def try_to(
self,
t: Union[Type[torch.dtype], Type[trt.DataType], Type[np.dtype], Type[dtype]],
use_default: bool,
@@ -965,11 +983,11 @@ Source code for torch_tensorrt._enums
)
return None
- def __eq__(self, other: Union[torch.dtype, trt.DataType, np.dtype, dtype]) -> bool:
+ def __eq__(self, other: Union[torch.dtype, trt.DataType, np.dtype, dtype]) -> bool:
other_ = dtype._from(other)
return bool(self.value == other_.value)
- def __hash__(self) -> int:
+ def __hash__(self) -> int:
return hash(self.value)
# Putting aliases here that mess with mypy
@@ -977,7 +995,7 @@ Source code for torch_tensorrt._enums
int = i32
-[docs]class memory_format(Enum):
+[docs]class memory_format(Enum):
""""""
# TensorRT supported memory layouts
@@ -1109,7 +1127,7 @@ Source code for torch_tensorrt._enums
channels_last_3d = dhwc
@classmethod
- def _from(
+ def _from(
cls, f: Union[torch.memory_format, trt.TensorFormat, memory_format]
) -> memory_format:
"""Create a Torch-TensorRT memory format enum from another library memory format enum.
@@ -1185,7 +1203,7 @@ Source code for torch_tensorrt._enums
return f
elif ENABLED_FEATURES.torchscript_frontend:
- from torch_tensorrt import _C
+ from torch_tensorrt import _C
if isinstance(f, _C.TensorFormat):
if f == _C.TensorFormat.contiguous:
@@ -1200,7 +1218,7 @@ Source code for torch_tensorrt._enums
raise TypeError("Provided unsupported source type for memory_format conversion")
[docs] @classmethod
- def try_from(
+ def try_from(
cls, f: Union[torch.memory_format, trt.TensorFormat, memory_format]
) -> Optional[memory_format]:
"""Create a Torch-TensorRT memory format enum from another library memory format enum.
@@ -1233,7 +1251,7 @@ Source code for torch_tensorrt._enums
)
return None
-[docs] def to(
+[docs] def to(
self,
t: Union[
Type[torch.memory_format], Type[trt.TensorFormat], Type[memory_format]
@@ -1308,7 +1326,7 @@ Source code for torch_tensorrt._enums
return self
elif ENABLED_FEATURES.torchscript_frontend:
- from torch_tensorrt import _C
+ from torch_tensorrt import _C
if t == _C.TensorFormat:
if self == memory_format.contiguous:
@@ -1324,7 +1342,7 @@ Source code for torch_tensorrt._enums
"Provided unsupported destination type for memory format conversion"
)
-[docs] def try_to(
+[docs] def try_to(
self,
t: Union[
Type[torch.memory_format], Type[trt.TensorFormat], Type[memory_format]
@@ -1359,17 +1377,17 @@ Source code for torch_tensorrt._enums
)
return None
- def __eq__(
+ def __eq__(
self, other: Union[torch.memory_format, trt.TensorFormat, memory_format]
) -> bool:
other_ = memory_format._from(other)
return self.value == other_.value
- def __hash__(self) -> int:
+ def __hash__(self) -> int:
return hash(self.value)
-[docs]class DeviceType(Enum):
+[docs]class DeviceType(Enum):
"""Type of device TensorRT will target"""
UNKNOWN = auto()
@@ -1394,7 +1412,7 @@ Source code for torch_tensorrt._enums
"""
@classmethod
- def _from(cls, d: Union[trt.DeviceType, DeviceType]) -> DeviceType:
+ def _from(cls, d: Union[trt.DeviceType, DeviceType]) -> DeviceType:
"""Create a Torch-TensorRT device type enum from a TensorRT device type enum.
Takes a device type enum from tensorrt and create a ``torch_tensorrt.DeviceType``.
@@ -1433,7 +1451,7 @@ Source code for torch_tensorrt._enums
return d
elif ENABLED_FEATURES.torchscript_frontend:
- from torch_tensorrt import _C
+ from torch_tensorrt import _C
if isinstance(d, _C.DeviceType):
if d == _C.DeviceType.GPU:
@@ -1448,7 +1466,7 @@ Source code for torch_tensorrt._enums
raise TypeError("Provided unsupported source type for DeviceType conversion")
[docs] @classmethod
- def try_from(cls, d: Union[trt.DeviceType, DeviceType]) -> Optional[DeviceType]:
+ def try_from(cls, d: Union[trt.DeviceType, DeviceType]) -> Optional[DeviceType]:
"""Create a Torch-TensorRT device type enum from a TensorRT device type enum.
Takes a device type enum from tensorrt and create a ``torch_tensorrt.DeviceType``.
@@ -1480,7 +1498,7 @@ Source code for torch_tensorrt._enums
)
return None
-[docs] def to(
+[docs] def to(
self,
t: Union[Type[trt.DeviceType], Type[DeviceType]],
use_default: bool = False,
@@ -1526,7 +1544,7 @@ Source code for torch_tensorrt._enums
return self
elif ENABLED_FEATURES.torchscript_frontend:
- from torch_tensorrt import _C
+ from torch_tensorrt import _C
if t == _C.DeviceType:
if self == DeviceType.GPU:
@@ -1542,7 +1560,7 @@ Source code for torch_tensorrt._enums
"Provided unsupported destination type for device type conversion"
)
-[docs] def try_to(
+[docs] def try_to(
self,
t: Union[Type[trt.DeviceType], Type[DeviceType]],
use_default: bool = False,
@@ -1575,15 +1593,15 @@ Source code for torch_tensorrt._enums
)
return None
- def __eq__(self, other: Union[trt.DeviceType, DeviceType]) -> bool:
+ def __eq__(self, other: Union[trt.DeviceType, DeviceType]) -> bool:
other_ = DeviceType._from(other)
return bool(self.value == other_.value)
- def __hash__(self) -> int:
+ def __hash__(self) -> int:
return hash(self.value)
-[docs]class EngineCapability(Enum):
+[docs]class EngineCapability(Enum):
"""
EngineCapability determines the restrictions of a network during build time and what runtime it targets.
"""
@@ -1610,7 +1628,7 @@ Source code for torch_tensorrt._enums
"""
@classmethod
- def _from(
+ def _from(
cls, c: Union[trt.EngineCapability, EngineCapability]
) -> EngineCapability:
"""Create a Torch-TensorRT Engine capability enum from a TensorRT Engine capability enum.
@@ -1651,7 +1669,7 @@ Source code for torch_tensorrt._enums
return c
elif ENABLED_FEATURES.torchscript_frontend:
- from torch_tensorrt import _C
+ from torch_tensorrt import _C
if isinstance(c, _C.EngineCapability):
if c == _C.EngineCapability.STANDARD:
@@ -1668,7 +1686,7 @@ Source code for torch_tensorrt._enums
)
[docs] @classmethod
- def try_from(
+ def try_from(
c: Union[trt.EngineCapability, EngineCapability]
) -> Optional[EngineCapability]:
"""Create a Torch-TensorRT engine capability enum from a TensorRT engine capability enum.
@@ -1702,7 +1720,7 @@ Source code for torch_tensorrt._enums
)
return None
-[docs] def to(
+[docs] def to(
self, t: Union[Type[trt.EngineCapability], Type[EngineCapability]]
) -> Union[trt.EngineCapability, EngineCapability]:
"""Convert ``EngineCapability`` into the equivalent type in tensorrt
@@ -1743,7 +1761,7 @@ Source code for torch_tensorrt._enums
return self
elif ENABLED_FEATURES.torchscript_frontend:
- from torch_tensorrt import _C
+ from torch_tensorrt import _C
if t == _C.EngineCapability:
if self == EngineCapability.STANDARD:
@@ -1759,7 +1777,7 @@ Source code for torch_tensorrt._enums
"Provided unsupported destination type for engine capability type conversion"
)
-[docs] def try_to(
+[docs] def try_to(
self, t: Union[Type[trt.EngineCapability], Type[EngineCapability]]
) -> Optional[Union[trt.EngineCapability, EngineCapability]]:
"""Convert ``EngineCapability`` into the equivalent type in tensorrt
@@ -1790,15 +1808,15 @@ Source code for torch_tensorrt._enums
)
return None
- def __eq__(self, other: Union[trt.EngineCapability, EngineCapability]) -> bool:
+ def __eq__(self, other: Union[trt.EngineCapability, EngineCapability]) -> bool:
other_ = EngineCapability._from(other)
return bool(self.value == other_.value)
- def __hash__(self) -> int:
+ def __hash__(self) -> int:
return hash(self.value)
-class Platform(Enum):
+class Platform(Enum):
"""
Specifies a target OS and CPU architecture that a Torch-TensorRT program targets
"""
@@ -1827,14 +1845,14 @@ Source code for torch_tensorrt._enums
UNKNOWN = auto()
@classmethod
- def current_platform(cls) -> Platform:
+ def current_platform(cls) -> Platform:
"""
Returns an enum for the current platform Torch-TensorRT is running on
Returns:
Platform: Current platform
"""
- import platform
+ import platform
if platform.system().lower().startswith("linux"):
# linux
@@ -1850,11 +1868,11 @@ Source code for torch_tensorrt._enums
return Platform.UNKNOWN
- def __str__(self) -> str:
+ def __str__(self) -> str:
return str(self.name)
@needs_torch_tensorrt_runtime # type: ignore
- def _to_serialized_rt_platform(self) -> str:
+ def _to_serialized_rt_platform(self) -> str:
val: str = torch.ops.tensorrt._platform_unknown()
if self == Platform.LINUX_X86_64:
@@ -2091,7 +2109,7 @@ Resources
Developer Resources
- Contributor Awards - 2023
+ Contributor Awards - 2024
@@ -2107,6 +2125,9 @@ Resources
ExecuTorch
+
+ ExecuTorch Documentation
+
@@ -2145,6 +2166,9 @@ Resources
Events
+
+ Newsletter
+
@@ -2158,6 +2182,18 @@ Resources
Governing Board
+
+ Cloud Credit Program
+
+
+ Technical Advisory Council
+
+
+ Staff
+
+
+ Contact Us
+
diff --git a/docs/_modules/torch_tensorrt/dynamo/_compiler.html b/docs/_modules/torch_tensorrt/dynamo/_compiler.html
index 707265676e..fe0a41a04d 100644
--- a/docs/_modules/torch_tensorrt/dynamo/_compiler.html
+++ b/docs/_modules/torch_tensorrt/dynamo/_compiler.html
@@ -9,7 +9,7 @@
- torch_tensorrt.dynamo._compiler — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ torch_tensorrt.dynamo._compiler — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -132,8 +132,8 @@
Developer Resources
Find resources and get questions answered
-
- Contributor Awards - 2023
+
+ Contributor Awards - 2024
Award winners announced at this year's PyTorch Conference
@@ -154,6 +154,9 @@
ExecuTorch
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+
+ ExecuTorch Docs
+
@@ -201,6 +204,10 @@
Events
Find events, webinars, and podcasts
+
+ Newsletter
+ Stay up-to-date with the latest updates
+
@@ -216,7 +223,18 @@
Governing Board
-
+
+
+ Cloud Credit Program
+
+
+ Technical Advisory Council
+
+
+ Staff
+
+
+ Contact Us
@@ -272,7 +290,7 @@
- v2.6.0.dev0+70e2a38
+ v2.7.0.dev0+d6be4ba
@@ -467,45 +485,45 @@
Source code for torch_tensorrt.dynamo._compiler
-from __future__ import annotations
-
-import collections.abc
-import logging
-import platform
-import warnings
-from typing import Any, Collection, List, Optional, Sequence, Set, Tuple, Union
-
-import torch
-from torch.export import ExportedProgram
-from torch.fx.node import Target
-from torch_tensorrt._Device import Device
-from torch_tensorrt._enums import EngineCapability, dtype
-from torch_tensorrt._Input import Input
-from torch_tensorrt.dynamo import _defaults, partitioning
-from torch_tensorrt.dynamo._DryRunTracker import (
+from __future__ import annotations
+
+import collections.abc
+import logging
+import platform
+import warnings
+from typing import Any, Collection, List, Optional, Sequence, Set, Tuple, Union
+
+import torch
+from torch.export import ExportedProgram
+from torch.fx.node import Target
+from torch_tensorrt._Device import Device
+from torch_tensorrt._enums import EngineCapability, dtype
+from torch_tensorrt._Input import Input
+from torch_tensorrt.dynamo import _defaults, partitioning
+from torch_tensorrt.dynamo._DryRunTracker import (
DryRunTracker,
PerSubgraphData,
dryrun_stats_display,
parse_non_trt_nodes,
)
-from torch_tensorrt.dynamo._engine_cache import BaseEngineCache, DiskEngineCache
-from torch_tensorrt.dynamo._exporter import replace_execute_engine_no_op_node
-from torch_tensorrt.dynamo.conversion import (
+from torch_tensorrt.dynamo._engine_cache import BaseEngineCache, DiskEngineCache
+from torch_tensorrt.dynamo._exporter import replace_execute_engine_no_op_node
+from torch_tensorrt.dynamo.conversion import (
CompilationSettings,
UnsupportedOperatorException,
convert_module,
interpret_module_to_result,
repair_double_inputs,
)
-from torch_tensorrt.dynamo.conversion._ConverterRegistry import (
+from torch_tensorrt.dynamo.conversion._ConverterRegistry import (
DYNAMO_CONVERTERS as CONVERTERS,
)
-from torch_tensorrt.dynamo.lowering import (
+from torch_tensorrt.dynamo.lowering import (
get_decompositions,
post_lowering,
pre_export_lowering,
)
-from torch_tensorrt.dynamo.utils import (
+from torch_tensorrt.dynamo.utils import (
get_flat_args_with_check,
get_output_metadata,
parse_graph_io,
@@ -518,7 +536,7 @@ Source code for torch_tensorrt.dynamo._compiler
<
logger = logging.getLogger(__name__)
-def cross_compile_for_windows(
+def cross_compile_for_windows(
exported_program: ExportedProgram,
inputs: Optional[Sequence[Sequence[Any]]] = None,
*,
@@ -835,7 +853,7 @@ Source code for torch_tensorrt.dynamo._compiler
<
return trt_gm
-[docs]def compile(
+[docs]def compile(
exported_program: ExportedProgram,
inputs: Optional[Sequence[Sequence[Any]]] = None,
*,
@@ -1153,7 +1171,7 @@ Source code for torch_tensorrt.dynamo._compiler
<
return trt_gm
-def compile_module(
+def compile_module(
gm: torch.fx.GraphModule,
sample_arg_inputs: Sequence[Input],
sample_kwarg_inputs: Optional[dict[Any, Any]] = None,
@@ -1210,7 +1228,7 @@ Source code for torch_tensorrt.dynamo._compiler
<
f"Detected support for {num_supported_ops} operators out of {total_ops} in subgraph."
)
- def contains_metadata(gm: torch.fx.GraphModule) -> bool:
+ def contains_metadata(gm: torch.fx.GraphModule) -> bool:
for node in gm.graph.nodes:
if node.op != "output" and (not node.meta) and "val" not in node.meta:
logger.warning(
@@ -1379,7 +1397,7 @@ Source code for torch_tensorrt.dynamo._compiler
<
return partitioned_module
-def convert_exported_program_to_serialized_trt_engine(
+def convert_exported_program_to_serialized_trt_engine(
exported_program: ExportedProgram,
inputs: Optional[Sequence[Sequence[Any]]] = None,
*,
@@ -1641,7 +1659,7 @@ Source code for torch_tensorrt.dynamo._compiler
<
return serialized_engine
-def save_cross_compiled_exported_program(
+def save_cross_compiled_exported_program(
gm: torch.fx.GraphModule,
file_path: str,
) -> None:
@@ -1655,14 +1673,14 @@ Source code for torch_tensorrt.dynamo._compiler
<
if not file_path:
raise ValueError("File path cannot be empty. Please provide a valid file path")
- from torch_tensorrt.dynamo._exporter import export
+ from torch_tensorrt.dynamo._exporter import export
exp_program = export(gm, cross_compile_flag=True)
torch.export.save(exp_program, file_path)
logger.debug(f"successfully saved the module for windows at {file_path}")
-def load_cross_compiled_exported_program(file_path: str = "") -> Any:
+def load_cross_compiled_exported_program(file_path: str = "") -> Any:
"""
Load an ExportedProgram file in Windows which was previously cross compiled in Linux
@@ -1920,7 +1938,7 @@ Resources
Developer Resources
- Contributor Awards - 2023
+ Contributor Awards - 2024
@@ -1936,6 +1954,9 @@ Resources
ExecuTorch
+
+ ExecuTorch Documentation
+
@@ -1974,6 +1995,9 @@ Resources
Events
+
+ Newsletter
+
@@ -1987,6 +2011,18 @@ Resources
Governing Board
+
+ Cloud Credit Program
+
+
+ Technical Advisory Council
+
+
+ Staff
+
+
+ Contact Us
+
diff --git a/docs/_modules/torch_tensorrt/dynamo/_exporter.html b/docs/_modules/torch_tensorrt/dynamo/_exporter.html
index cef5482e0c..334d362c19 100644
--- a/docs/_modules/torch_tensorrt/dynamo/_exporter.html
+++ b/docs/_modules/torch_tensorrt/dynamo/_exporter.html
@@ -9,7 +9,7 @@
- torch_tensorrt.dynamo._exporter — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ torch_tensorrt.dynamo._exporter — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -132,8 +132,8 @@
Developer Resources
Find resources and get questions answered
-
- Contributor Awards - 2023
+
+ Contributor Awards - 2024
Award winners announced at this year's PyTorch Conference
@@ -154,6 +154,9 @@
ExecuTorch
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+
+ ExecuTorch Docs
+
@@ -201,6 +204,10 @@
Events
Find events, webinars, and podcasts
+
+ Newsletter
+ Stay up-to-date with the latest updates
+
@@ -216,7 +223,18 @@
Governing Board
-
+
+
+ Cloud Credit Program
+
+
+ Technical Advisory Council
+
+
+ Staff
+
+
+ Contact Us
@@ -272,7 +290,7 @@
- v2.6.0.dev0+70e2a38
+ v2.7.0.dev0+d6be4ba
@@ -467,16 +485,16 @@
Source code for torch_tensorrt.dynamo._exporter
-import base64
-import copy
-import operator
-from typing import Any, Dict, Optional, Sequence, Tuple, cast
-
-import torch
-from torch._guards import detect_fake_mode
-from torch._subclasses.fake_tensor import FakeTensor
-from torch.export import ExportedProgram, ExportGraphSignature
-from torch.export.exported_program import (
+import base64
+import copy
+import operator
+from typing import Any, Dict, Optional, Sequence, Tuple, cast
+
+import torch
+from torch._guards import detect_fake_mode
+from torch._subclasses.fake_tensor import FakeTensor
+from torch.export import ExportedProgram, ExportGraphSignature
+from torch.export.exported_program import (
CustomObjArgument,
InputKind,
InputSpec,
@@ -486,10 +504,10 @@ Source code for torch_tensorrt.dynamo._exporter
<
OutputSpec,
TensorArgument,
)
-from torch_tensorrt.dynamo.runtime._TorchTensorRTModule import ENGINE_IDX, NAME_IDX
+from torch_tensorrt.dynamo.runtime._TorchTensorRTModule import ENGINE_IDX, NAME_IDX
-[docs]def export(
+[docs]def export(
gm: torch.fx.GraphModule,
cross_compile_flag: Optional[bool] = False,
) -> ExportedProgram:
@@ -505,7 +523,7 @@ Source code for torch_tensorrt.dynamo._exporter
<
return exp_program
-def transform(
+def transform(
gm: torch.fx.GraphModule,
cross_compile_flag: Optional[bool] = False,
) -> torch.fx.GraphModule:
@@ -539,7 +557,7 @@ Source code for torch_tensorrt.dynamo._exporter
<
return gm
-def lift(
+def lift(
gm: torch.fx.GraphModule, graph_signature: Any
) -> Tuple[torch.fx.GraphModule, ExportGraphSignature, Dict[str, Any], Dict[str, Any]]:
"""
@@ -661,7 +679,7 @@ Source code for torch_tensorrt.dynamo._exporter
<
return gm, graph_signature, state_dict, constants
-def get_duplicate_nodes(
+def get_duplicate_nodes(
gm: torch.fx.GraphModule, submodule: torch.fx.GraphModule
) -> Tuple[Sequence[Any], Sequence[Any]]:
"""
@@ -684,7 +702,7 @@ Source code for torch_tensorrt.dynamo._exporter
<
return submodule_duplicate_inputs, gm_duplicate_inputs
-def inline_torch_modules(gm: torch.fx.GraphModule) -> torch.fx.GraphModule:
+def inline_torch_modules(gm: torch.fx.GraphModule) -> torch.fx.GraphModule:
"""
Inline a submodule within the parent graph (gm). All `call_module` nodes
should be replaced by their nodes in the submodule.
@@ -751,7 +769,7 @@ Source code for torch_tensorrt.dynamo._exporter
<
return gm
-def copy_submodule_attributes(
+def copy_submodule_attributes(
gm: torch.fx.GraphModule, submodule: torch.fx.GraphModule, submodule_name: str
) -> None:
"""
@@ -762,7 +780,7 @@ Source code for torch_tensorrt.dynamo._exporter
<
_assign_attr does exactly that. It creates a module for eg: conv, adds an attribute weight
to it and adds this conv module as an attribute to parent gm.
"""
- from torch.export.unflatten import _assign_attr, _AttrKind
+ from torch.export.unflatten import _assign_attr, _AttrKind
for key, value in submodule.named_parameters():
_assign_attr(value, gm, key, _AttrKind.PARAMETER)
@@ -771,7 +789,7 @@ Source code for torch_tensorrt.dynamo._exporter
<
_assign_attr(value, gm, key, _AttrKind.BUFFER)
-def create_trt_exp_program(
+def create_trt_exp_program(
gm: torch.fx.GraphModule,
) -> ExportedProgram:
"""Creates a new Exported Program. This function takes an torch.fx.GraphModule which has TRT engines
@@ -825,7 +843,7 @@ Source code for torch_tensorrt.dynamo._exporter
<
return trt_exp_program
-def inline_trt_modules(
+def inline_trt_modules(
gm: torch.fx.GraphModule, cross_compile_flag: Optional[bool] = False
) -> torch.fx.GraphModule:
"""
@@ -901,7 +919,7 @@ Source code for torch_tensorrt.dynamo._exporter
<
return gm
-def replace_execute_engine_no_op_node(
+def replace_execute_engine_no_op_node(
exp_program: ExportedProgram,
) -> ExportedProgram:
gm = exp_program.graph_module
@@ -1179,7 +1197,7 @@ Resources
Developer Resources
- Contributor Awards - 2023
+ Contributor Awards - 2024
@@ -1195,6 +1213,9 @@ Resources
ExecuTorch
+
+ ExecuTorch Documentation
+
@@ -1233,6 +1254,9 @@ Resources
Events
+
+ Newsletter
+
@@ -1246,6 +1270,18 @@ Resources
Governing Board
+
+ Cloud Credit Program
+
+
+ Technical Advisory Council
+
+
+ Staff
+
+
+ Contact Us
+
diff --git a/docs/_modules/torch_tensorrt/dynamo/_refit.html b/docs/_modules/torch_tensorrt/dynamo/_refit.html
index 48beb9d4bf..0bafebf55b 100644
--- a/docs/_modules/torch_tensorrt/dynamo/_refit.html
+++ b/docs/_modules/torch_tensorrt/dynamo/_refit.html
@@ -9,7 +9,7 @@
- torch_tensorrt.dynamo._refit — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ torch_tensorrt.dynamo._refit — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -132,8 +132,8 @@
Developer Resources
Find resources and get questions answered
-
- Contributor Awards - 2023
+
+ Contributor Awards - 2024
Award winners announced at this year's PyTorch Conference
@@ -154,6 +154,9 @@
ExecuTorch
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+
+ ExecuTorch Docs
+
@@ -201,6 +204,10 @@
Events
Find events, webinars, and podcasts
+
+ Newsletter
+ Stay up-to-date with the latest updates
+
@@ -216,7 +223,18 @@
Governing Board
-
+
+
+ Cloud Credit Program
+
+
+ Technical Advisory Council
+
+
+ Staff
+
+
+ Contact Us
@@ -272,7 +290,7 @@
- v2.6.0.dev0+70e2a38
+ v2.7.0.dev0+d6be4ba
@@ -467,42 +485,42 @@
Source code for torch_tensorrt.dynamo._refit
-from __future__ import annotations
-
-import collections.abc
-import copy
-import logging
-from typing import Any, List, Optional, Sequence, Tuple
-
-import numpy as np
-import tensorrt as trt
-import torch
-from torch.export import ExportedProgram
-from torch_tensorrt._enums import dtype
-from torch_tensorrt._Input import Input
-from torch_tensorrt.dynamo import partitioning
-from torch_tensorrt.dynamo._exporter import inline_torch_modules
-from torch_tensorrt.dynamo._settings import CompilationSettings
-from torch_tensorrt.dynamo.conversion._conversion import infer_module_output_dtypes
-from torch_tensorrt.dynamo.conversion._ConverterRegistry import (
+from __future__ import annotations
+
+import collections.abc
+import copy
+import logging
+from typing import Any, List, Optional, Sequence, Tuple
+
+import numpy as np
+import tensorrt as trt
+import torch
+from torch.export import ExportedProgram
+from torch_tensorrt._enums import dtype
+from torch_tensorrt._Input import Input
+from torch_tensorrt.dynamo import partitioning
+from torch_tensorrt.dynamo._exporter import inline_torch_modules
+from torch_tensorrt.dynamo._settings import CompilationSettings
+from torch_tensorrt.dynamo.conversion._conversion import infer_module_output_dtypes
+from torch_tensorrt.dynamo.conversion._ConverterRegistry import (
DYNAMO_CONVERTERS as CONVERTERS,
)
-from torch_tensorrt.dynamo.conversion._TRTInterpreter import TRTInterpreter
-from torch_tensorrt.dynamo.conversion.truncate_double import repair_double_inputs
-from torch_tensorrt.dynamo.lowering import (
+from torch_tensorrt.dynamo.conversion._TRTInterpreter import TRTInterpreter
+from torch_tensorrt.dynamo.conversion.truncate_double import repair_double_inputs
+from torch_tensorrt.dynamo.lowering import (
get_decompositions,
post_lowering,
pre_export_lowering,
)
-from torch_tensorrt.dynamo.runtime._PythonTorchTensorRTModule import (
+from torch_tensorrt.dynamo.runtime._PythonTorchTensorRTModule import (
PythonTorchTensorRTModule,
)
-from torch_tensorrt.dynamo.runtime._TorchTensorRTModule import (
+from torch_tensorrt.dynamo.runtime._TorchTensorRTModule import (
ENGINE_IDX,
SERIALIZED_METADATA_IDX,
TorchTensorRTModule,
)
-from torch_tensorrt.dynamo.utils import (
+from torch_tensorrt.dynamo.utils import (
check_module_output,
get_model_device,
get_torch_inputs,
@@ -510,12 +528,12 @@ Source code for torch_tensorrt.dynamo._refit
to_torch_device,
to_torch_tensorrt_device,
)
-from torch_tensorrt.logging import TRT_LOGGER
+from torch_tensorrt.logging import TRT_LOGGER
logger = logging.getLogger(__name__)
-def construct_refit_mapping(
+def construct_refit_mapping(
module: torch.fx.GraphModule,
inputs: Sequence[Input],
settings: CompilationSettings = CompilationSettings(),
@@ -576,7 +594,7 @@ Source code for torch_tensorrt.dynamo._refit
return weight_map
-def construct_refit_mapping_from_weight_name_map(
+def construct_refit_mapping_from_weight_name_map(
weight_name_map: dict[Any, Any], state_dict: dict[Any, Any]
) -> dict[Any, Any]:
engine_weight_map = {}
@@ -602,7 +620,7 @@ Source code for torch_tensorrt.dynamo._refit
return engine_weight_map
-def _refit_single_trt_engine_with_gm(
+def _refit_single_trt_engine_with_gm(
new_gm: torch.fx.GraphModule,
old_engine: trt.ICudaEngine,
input_list: Sequence[Any],
@@ -680,7 +698,7 @@ Source code for torch_tensorrt.dynamo._refit
raise AssertionError("Refitting failed.")
-[docs]def refit_module_weights(
+[docs]def refit_module_weights(
compiled_module: torch.fx.GraphModule | ExportedProgram,
new_weight_module: ExportedProgram,
arg_inputs: Optional[Tuple[Any, ...]] = None,
@@ -979,10 +997,10 @@ Source code for torch_tensorrt.dynamo._refit
# Util functions -----------
-import base64
+import base64
-def get_engine_from_encoded_engine(
+def get_engine_from_encoded_engine(
encoded_engine: str, runtime: trt.Runtime
) -> trt.ICudaEngine:
serialized_engine = base64.b64decode(encoded_engine)
@@ -1214,7 +1232,7 @@ Resources
Developer Resources
- Contributor Awards - 2023
+ Contributor Awards - 2024
@@ -1230,6 +1248,9 @@ Resources
ExecuTorch
+
+ ExecuTorch Documentation
+
@@ -1268,6 +1289,9 @@ Resources
Events
+
+ Newsletter
+
@@ -1281,6 +1305,18 @@ Resources
Governing Board
+
+ Cloud Credit Program
+
+
+ Technical Advisory Council
+
+
+ Staff
+
+
+ Contact Us
+
diff --git a/docs/_modules/torch_tensorrt/dynamo/_settings.html b/docs/_modules/torch_tensorrt/dynamo/_settings.html
index a1e632dce0..6448e409f2 100644
--- a/docs/_modules/torch_tensorrt/dynamo/_settings.html
+++ b/docs/_modules/torch_tensorrt/dynamo/_settings.html
@@ -9,7 +9,7 @@
- torch_tensorrt.dynamo._settings — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ torch_tensorrt.dynamo._settings — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -132,8 +132,8 @@
Developer Resources
Find resources and get questions answered
-
- Contributor Awards - 2023
+
+ Contributor Awards - 2024
Award winners announced at this year's PyTorch Conference
@@ -154,6 +154,9 @@
ExecuTorch
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+
+ ExecuTorch Docs
+
@@ -201,6 +204,10 @@
Events
Find events, webinars, and podcasts
+
+ Newsletter
+ Stay up-to-date with the latest updates
+
@@ -216,7 +223,18 @@
Governing Board
-
+
+
+ Cloud Credit Program
+
+
+ Technical Advisory Council
+
+
+ Staff
+
+
+ Contact Us
@@ -272,7 +290,7 @@
- v2.6.0.dev0+70e2a38
+ v2.7.0.dev0+d6be4ba
@@ -467,13 +485,13 @@
Source code for torch_tensorrt.dynamo._settings
-from dataclasses import dataclass, field
-from typing import Collection, Optional, Set, Tuple, Union
+from dataclasses import dataclass, field
+from typing import Collection, Optional, Set, Tuple, Union
-from torch.fx.node import Target
-from torch_tensorrt._Device import Device
-from torch_tensorrt._enums import EngineCapability, dtype
-from torch_tensorrt.dynamo._defaults import (
+from torch.fx.node import Target
+from torch_tensorrt._Device import Device
+from torch_tensorrt._enums import EngineCapability, dtype
+from torch_tensorrt.dynamo._defaults import (
ASSUME_DYNAMIC_SHAPE_SUPPORT,
CACHE_BUILT_ENGINES,
DEBUG,
@@ -502,6 +520,7 @@ Source code for torch_tensorrt.dynamo._settings
<
STRIP_ENGINE_WEIGHTS,
TIMING_CACHE_PATH,
TRUNCATE_DOUBLE,
+ USE_AOT_JOINT_EXPORT,
USE_EXPLICIT_TYPING,
USE_FAST_PARTITIONER,
USE_FP32_ACC,
@@ -513,7 +532,7 @@ Source code for torch_tensorrt.dynamo._settings
<
[docs]@dataclass
-class CompilationSettings:
+class CompilationSettings:
"""Compilation settings for Torch-TensorRT Dynamo Paths
Args:
@@ -560,6 +579,7 @@ Source code for torch_tensorrt.dynamo._settings
<
enable_weight_streaming (bool): Enable weight streaming.
enable_cross_compile_for_windows (bool): By default this is False means TensorRT engines can only be executed on the same platform where they were built.
True will enable cross-platform compatibility which allows the engine to be built on Linux and run on Windows
+ use_aot_joint_export (bool): Use aot_export_joint_simple, else wrap backend with AOT_autograd, required for distributed tensors
"""
enabled_precisions: Set[dtype] = field(default_factory=lambda: ENABLED_PRECISIONS)
@@ -599,7 +619,8 @@ Source code for torch_tensorrt.dynamo._settings
<
strip_engine_weights: bool = STRIP_ENGINE_WEIGHTS
immutable_weights: bool = IMMUTABLE_WEIGHTS
enable_weight_streaming: bool = ENABLE_WEIGHT_STREAMING
- enable_cross_compile_for_windows: bool = ENABLE_CROSS_COMPILE_FOR_WINDOWS
+ enable_cross_compile_for_windows: bool = ENABLE_CROSS_COMPILE_FOR_WINDOWS
+ use_aot_joint_export: bool = USE_AOT_JOINT_EXPORT
_SETTINGS_TO_BE_ENGINE_INVARIANT = (
@@ -618,7 +639,7 @@ Source code for torch_tensorrt.dynamo._settings
<
)
-def settings_are_compatible(
+def settings_are_compatible(
set_a: CompilationSettings, set_b: CompilationSettings
) -> Tuple[bool, Set[str]]:
incompatible_settings: Set[str] = set()
@@ -857,7 +878,7 @@ Resources
Developer Resources
- Contributor Awards - 2023
+ Contributor Awards - 2024
@@ -873,6 +894,9 @@ Resources
ExecuTorch
+
+ ExecuTorch Documentation
+
@@ -911,6 +935,9 @@ Resources
Events
+
+ Newsletter
+
@@ -924,6 +951,18 @@ Resources
Governing Board
+
+ Cloud Credit Program
+
+
+ Technical Advisory Council
+
+
+ Staff
+
+
+ Contact Us
+
diff --git a/docs/_modules/torch_tensorrt/dynamo/_tracer.html b/docs/_modules/torch_tensorrt/dynamo/_tracer.html
index 469fc51fb0..c1324f6719 100644
--- a/docs/_modules/torch_tensorrt/dynamo/_tracer.html
+++ b/docs/_modules/torch_tensorrt/dynamo/_tracer.html
@@ -9,7 +9,7 @@
- torch_tensorrt.dynamo._tracer — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ torch_tensorrt.dynamo._tracer — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -132,8 +132,8 @@
Developer Resources
Find resources and get questions answered
-
- Contributor Awards - 2023
+
+ Contributor Awards - 2024
Award winners announced at this year's PyTorch Conference
@@ -154,6 +154,9 @@
ExecuTorch
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+
+ ExecuTorch Docs
+
@@ -201,6 +204,10 @@
Events
Find events, webinars, and podcasts
+
+ Newsletter
+ Stay up-to-date with the latest updates
+
@@ -216,7 +223,18 @@
Governing Board
-
+
+
+ Cloud Credit Program
+
+
+ Technical Advisory Council
+
+
+ Staff
+
+
+ Contact Us
@@ -272,7 +290,7 @@
- v2.6.0.dev0+70e2a38
+ v2.7.0.dev0+d6be4ba
@@ -467,22 +485,22 @@
Source code for torch_tensorrt.dynamo._tracer
-from __future__ import annotations
+from __future__ import annotations
-import logging
-from inspect import signature
-from typing import Any, Optional, Tuple, Union
+import logging
+from inspect import signature
+from typing import Any, Optional, Tuple, Union
-import torch
-from torch.export import Dim, export
-from torch_tensorrt._Input import Input
-from torch_tensorrt.dynamo._defaults import DEBUG, default_device
-from torch_tensorrt.dynamo.utils import get_torch_inputs, set_log_level, to_torch_device
+import torch
+from torch.export import Dim, export
+from torch_tensorrt._Input import Input
+from torch_tensorrt.dynamo._defaults import DEBUG, default_device
+from torch_tensorrt.dynamo.utils import get_torch_inputs, set_log_level, to_torch_device
logger = logging.getLogger(__name__)
-[docs]def trace(
+[docs]def trace(
mod: torch.nn.Module | torch.fx.GraphModule,
inputs: Optional[Tuple[Any, ...]] = None,
*,
@@ -559,7 +577,7 @@ Source code for torch_tensorrt.dynamo._tracer
return exp_program
-def get_dynamic_shapes_kwargs(inputs: Any) -> Union[dict[str, Any], list[Any]]:
+def get_dynamic_shapes_kwargs(inputs: Any) -> Union[dict[str, Any], list[Any]]:
if isinstance(inputs, dict):
dynamic_shapes_kwarg = {}
for k, v in inputs.items():
@@ -578,7 +596,7 @@ Source code for torch_tensorrt.dynamo._tracer
raise TypeError(f"Unknown type {type(inputs)}.")
-def get_dynamic_shapes_args(mod: torch.nn.Module, inputs: Any) -> dict[str, Any]:
+def get_dynamic_shapes_args(mod: torch.nn.Module, inputs: Any) -> dict[str, Any]:
# dynamic_shape is a dict and cannot work without keys. Here we use position argument name
# in forward function as the name
args = list(signature(mod.forward).parameters.keys())
@@ -588,7 +606,7 @@ Source code for torch_tensorrt.dynamo._tracer
return dynamic_shapes
-def get_dynamic_shapes(input: Input) -> dict[Any, Any]:
+def get_dynamic_shapes(input: Input) -> dict[Any, Any]:
if not isinstance(input, Input):
# If the input is torch.Tensor, no dynamic is needed. Return empty dict
return {}
@@ -835,7 +853,7 @@ Resources
Developer Resources
- Contributor Awards - 2023
+ Contributor Awards - 2024
@@ -851,6 +869,9 @@ Resources
ExecuTorch
+
+ ExecuTorch Documentation
+
@@ -889,6 +910,9 @@ Resources
Events
+
+ Newsletter
+
@@ -902,6 +926,18 @@ Resources
Governing Board
+
+ Cloud Credit Program
+
+
+ Technical Advisory Council
+
+
+ Staff
+
+
+ Contact Us
+
diff --git a/docs/_modules/torch_tensorrt/dynamo/runtime/_MutableTorchTensorRTModule.html b/docs/_modules/torch_tensorrt/dynamo/runtime/_MutableTorchTensorRTModule.html
index e1684e2e14..5a8be28457 100644
--- a/docs/_modules/torch_tensorrt/dynamo/runtime/_MutableTorchTensorRTModule.html
+++ b/docs/_modules/torch_tensorrt/dynamo/runtime/_MutableTorchTensorRTModule.html
@@ -9,7 +9,7 @@
- torch_tensorrt.dynamo.runtime._MutableTorchTensorRTModule — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ torch_tensorrt.dynamo.runtime._MutableTorchTensorRTModule — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -132,8 +132,8 @@
Developer Resources
Find resources and get questions answered
-
- Contributor Awards - 2023
+
+ Contributor Awards - 2024
Award winners announced at this year's PyTorch Conference
@@ -154,6 +154,9 @@
ExecuTorch
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+
+ ExecuTorch Docs
+
@@ -201,6 +204,10 @@
Events
Find events, webinars, and podcasts
+
+ Newsletter
+ Stay up-to-date with the latest updates
+
@@ -216,7 +223,18 @@
Governing Board
-
+
+
+ Cloud Credit Program
+
+
+ Technical Advisory Council
+
+
+ Staff
+
+
+ Contact Us
@@ -272,7 +290,7 @@
- v2.6.0.dev0+70e2a38
+ v2.7.0.dev0+d6be4ba
@@ -467,21 +485,21 @@
Source code for torch_tensorrt.dynamo.runtime._MutableTorchTensorRTModule
-import logging
-from copy import deepcopy
-from enum import Enum, auto
-from typing import Any, Collection, Dict, Iterator, List, Optional, Set, Union
-
-import numpy as np
-import torch
-from torch.fx.node import Target
-from torch_tensorrt._Device import Device
-from torch_tensorrt._enums import EngineCapability, dtype
-from torch_tensorrt.dynamo import _defaults
-from torch_tensorrt.dynamo._compiler import compile as dynamo_compile
-from torch_tensorrt.dynamo._refit import refit_module_weights
-from torch_tensorrt.dynamo._settings import CompilationSettings
-from torch_tensorrt.dynamo.utils import (
+import logging
+from copy import deepcopy
+from enum import Enum, auto
+from typing import Any, Collection, Dict, Iterator, List, Optional, Set, Union
+
+import numpy as np
+import torch
+from torch.fx.node import Target
+from torch_tensorrt._Device import Device
+from torch_tensorrt._enums import EngineCapability, dtype
+from torch_tensorrt.dynamo import _defaults
+from torch_tensorrt.dynamo._compiler import compile as dynamo_compile
+from torch_tensorrt.dynamo._refit import refit_module_weights
+from torch_tensorrt.dynamo._settings import CompilationSettings
+from torch_tensorrt.dynamo.utils import (
check_output_equal,
to_torch_device,
to_torch_tensorrt_device,
@@ -490,27 +508,27 @@ Source code for torch_tensorrt.dynamo.runtime._MutableTorchTensorRTModulelogger = logging.getLogger(__name__)
-class RefitFlag(Enum):
+class RefitFlag(Enum):
UNKNOWN = auto()
NEEDS_REFIT = auto()
NEEDS_RECOMPILE = auto()
LIVE = auto()
-class RefitState:
+class RefitState:
_state: RefitFlag = RefitFlag.NEEDS_RECOMPILE
- def set_state(self, state: RefitFlag) -> None:
+ def set_state(self, state: RefitFlag) -> None:
if isinstance(state, RefitFlag):
self._state = state
else:
raise ValueError(f"Invalid state: {state}")
- def get_state(self) -> RefitFlag:
+ def get_state(self) -> RefitFlag:
return self._state
-[docs]class MutableTorchTensorRTModule(object):
+[docs]class MutableTorchTensorRTModule(object):
"""
Initialize a MutableTorchTensorRTModule to seamlessly manipulate it like a regular PyTorch module.
All TensorRT compilation and refitting processes are handled automatically as you work with the module.
@@ -522,7 +540,7 @@ Source code for torch_tensorrt.dynamo.runtime._MutableTorchTensorRTModule Any modifications made to the MutableTorchTensorRTModule will be reflected in both the TensorRT graph module and the original PyTorch module.
"""
-[docs] def __init__(
+[docs] def __init__(
self,
pytorch_model: torch.nn.Module,
*,
@@ -672,22 +690,22 @@ Source code for torch_tensorrt.dynamo.runtime._MutableTorchTensorRTModule)
self.init_finished = True
- def store_state_dict_metadata(self) -> None:
+ def store_state_dict_metadata(self) -> None:
for k, v in self.original_model.state_dict().items():
self.state_dict_metadata[k] = v.shape
- def load_state_dict(
+ def load_state_dict(
self, state_dict: Dict[str, Any], strict: bool = True, assign: bool = False
) -> None:
self.refit_state.set_state(RefitFlag.NEEDS_REFIT)
self.original_model.load_state_dict(state_dict, strict=strict, assign=assign)
@staticmethod
- def _transform_state_dict(sd: Dict[str, Any]) -> Dict[str, torch.nn.Parameter]:
+ def _transform_state_dict(sd: Dict[str, Any]) -> Dict[str, torch.nn.Parameter]:
return {k: torch.nn.Parameter(v, requires_grad=False) for k, v in sd.items()}
- def update_refit_condition(self) -> None:
+ def update_refit_condition(self) -> None:
# 2-stage check to determine whether the module should be intact, refitted, or recompiled.
# Default refit
@@ -721,7 +739,7 @@ Source code for torch_tensorrt.dynamo.runtime._MutableTorchTensorRTModulereturn
-[docs] def refit_gm(self) -> None:
+[docs] def refit_gm(self) -> None:
"""
Refit the TRT graph module with any updates.
This function should be called whenever the weight values get changed but the weight structure remains
@@ -752,7 +770,7 @@ Source code for torch_tensorrt.dynamo.runtime._MutableTorchTensorRTModuleself.original_model.cpu()
torch.cuda.empty_cache()
-[docs] def compile(self) -> None:
+[docs] def compile(self) -> None:
"""
(Re)compile the TRT graph module using the PyTorch module.
This function should be called whenever the weight structure get changed (shape, more layers...)
@@ -775,7 +793,7 @@ Source code for torch_tensorrt.dynamo.runtime._MutableTorchTensorRTModuleself.original_model.cpu()
torch.cuda.empty_cache()
- def _validate_inputs(self, *args: Any, **kwargs: Any) -> None:
+ def _validate_inputs(self, *args: Any, **kwargs: Any) -> None:
if (
not self.arg_inputs
or not MutableTorchTensorRTModule.check_inputs_equal(self.arg_inputs, args)
@@ -787,12 +805,12 @@ Source code for torch_tensorrt.dynamo.runtime._MutableTorchTensorRTModuleself.refit_state.set_state(RefitFlag.NEEDS_RECOMPILE)
self.store_inputs(args, kwargs)
- def store_inputs(self, arg_inputs: Any, kwarg_inputs: Any) -> None:
+ def store_inputs(self, arg_inputs: Any, kwarg_inputs: Any) -> None:
self.arg_inputs = arg_inputs
self.kwarg_inputs = kwarg_inputs
@staticmethod
- def process_kwarg_inputs(inputs: Any) -> Any:
+ def process_kwarg_inputs(inputs: Any) -> Any:
# Process kwarg inputs to be acceptable for Torch-TensorRT
if isinstance(inputs, dict):
# None should be excluded. AOT compile also does not allow dynamic control flow, bool is also excluded.
@@ -816,7 +834,7 @@ Source code for torch_tensorrt.dynamo.runtime._MutableTorchTensorRTModule+ "Allowed input types: {torch_tensorrt.Input, torch.Tensor, list, tuple, dict}"
)
- def forward(self, *args: Any, **kwargs: Any) -> Any:
+ def forward(self, *args: Any, **kwargs: Any) -> Any:
# Step 1: Check whether the input shape has changed
kwargs = MutableTorchTensorRTModule.process_kwarg_inputs(kwargs)
self._validate_inputs(*args, **kwargs)
@@ -849,11 +867,11 @@ Source code for torch_tensorrt.dynamo.runtime._MutableTorchTensorRTModuleself.run_info = (args, kwargs, result)
return result
- def to(self, device: str) -> None:
+ def to(self, device: str) -> None:
logger.warning("Original PyTorch model is moved. CPU offload may failed.")
self.orignial_model.to(device)
- def __deepcopy__(self, memo: Any) -> Any:
+ def __deepcopy__(self, memo: Any) -> Any:
cls = self.__class__
result = cls.__new__(cls)
memo[id(self)] = result
@@ -865,10 +883,10 @@ Source code for torch_tensorrt.dynamo.runtime._MutableTorchTensorRTModule)
return result
- def __call__(self, *args: Any, **kwargs: Any) -> Any:
+ def __call__(self, *args: Any, **kwargs: Any) -> Any:
return self.forward(*args, **kwargs)
- def __getattr__(self, name: str) -> Any:
+ def __getattr__(self, name: str) -> Any:
if name in self.__dict__:
# this object has it
@@ -881,7 +899,7 @@ Source code for torch_tensorrt.dynamo.runtime._MutableTorchTensorRTModulereturn getattr(self.pytorch_model, name)
- def __delattr__(self, name: str) -> Any:
+ def __delattr__(self, name: str) -> Any:
if name in self.__dict__:
# this object has it
@@ -889,7 +907,7 @@ Source code for torch_tensorrt.dynamo.runtime._MutableTorchTensorRTModulereturn self.pytorch_model.__delattr__(name)
- def __setattr__(self, name: str, value: Any) -> None:
+ def __setattr__(self, name: str, value: Any) -> None:
# When the module finished initialization, any modification to attributes that does not exist
# in __dict__ will be handled in pytorch module.
if self.init_finished:
@@ -905,7 +923,7 @@ Source code for torch_tensorrt.dynamo.runtime._MutableTorchTensorRTModuleobject.__setattr__(self, name, value)
@staticmethod
- def check_inputs_equal(
+ def check_inputs_equal(
input1: Any,
input2: Any,
) -> bool:
@@ -938,7 +956,7 @@ Source code for torch_tensorrt.dynamo.runtime._MutableTorchTensorRTModulereturn True
@staticmethod
- def save(module: Any, path: str) -> None:
+ def save(module: Any, path: str) -> None:
# Cast the object back to MutableTorchTensorRTModule to save
assert (
not module.settings.use_python_runtime
@@ -964,7 +982,7 @@ Source code for torch_tensorrt.dynamo.runtime._MutableTorchTensorRTModulemodule.init_finished = True
@staticmethod
- def load(path: str) -> Any:
+ def load(path: str) -> Any:
# When the model get saved, init_finished is set to False.
# Class is restored to MutableTorchTensorRTModule, and some attribute is deleted
module = torch.load(path, weights_only=False)
@@ -986,7 +1004,7 @@ Source code for torch_tensorrt.dynamo.runtime._MutableTorchTensorRTModulereturn module
-def recursively_remove_trigger(obj: Any) -> Any:
+def recursively_remove_trigger(obj: Any) -> Any:
# Not safe: If the object has a circular reference (such as a doubly linkded list), this will cause infinite recursion
if obj.__class__.__name__ == "ChangeTriggerWrapper":
obj = obj.instance
@@ -1008,18 +1026,18 @@ Source code for torch_tensorrt.dynamo.runtime._MutableTorchTensorRTModulereturn obj
-def _make_refit_change_trigger(obj: object, refit_state: RefitState) -> Any:
+def _make_refit_change_trigger(obj: object, refit_state: RefitState) -> Any:
subclass: type = obj.__class__
- class ChangeTriggerWrapper(subclass): # type: ignore
+ class ChangeTriggerWrapper(subclass): # type: ignore
# The reason why we want to inherent to the subclass is that we want the ChangeTriggerWrapper shares all functions
# that an ordinary object has. In this way attributes accessed inside a function will be from the __getattr__function
# of ChangeTriggerWrapper, instead of the object itself, thus be recursively wrapped by ChangeTriggerWrapper.
- def __init__(self, obj: Any):
+ def __init__(self, obj: Any):
object.__setattr__(self, "instance", obj)
- def __getattr__(
+ def __getattr__(
self, name: str
) -> Any: # Called when the attribute does not exist
obj = getattr(self.instance, name)
@@ -1034,7 +1052,7 @@ Source code for torch_tensorrt.dynamo.runtime._MutableTorchTensorRTModulereturn _make_refit_change_trigger(obj, refit_state)
return obj
- def __setattr__(self, name: str, value: Any) -> None:
+ def __setattr__(self, name: str, value: Any) -> None:
# If we need to set __dict__ or instance, we directly set it to the trigger wrapper.
# Enable setting __dict__ is because PyTorch proxy uses __new__ to initialize a shallow copy
# of a module and explicit set the __dict__. If we don't set __dict__ it will get infinite recursion.
@@ -1046,44 +1064,44 @@ Source code for torch_tensorrt.dynamo.runtime._MutableTorchTensorRTModulevalue = recursively_remove_trigger(value)
setattr(self.instance, name, value)
- def __delattr__(self, name: str) -> None:
+ def __delattr__(self, name: str) -> None:
self._on_change()
delattr(
self.instance,
name,
)
- def _on_change(self) -> None:
+ def _on_change(self) -> None:
refit_state.set_state(RefitFlag.UNKNOWN)
logger.info(
"Attribute modification detected. The module will be refitted later."
)
- def __call__(self, *args: Any, **kwargs: Any) -> Any:
+ def __call__(self, *args: Any, **kwargs: Any) -> Any:
return self.instance(*args, **kwargs)
- def _call_impl(self, *args: Any, **kwargs: Any) -> Any:
+ def _call_impl(self, *args: Any, **kwargs: Any) -> Any:
return self.instance._call_impl(*args, **kwargs)
- def forward(self, *args: Any, **kwargs: Any) -> Any:
+ def forward(self, *args: Any, **kwargs: Any) -> Any:
return self.instance.forward(*args, **kwargs)
- def __setitem__(self, item: str, value: Any) -> None:
+ def __setitem__(self, item: str, value: Any) -> None:
self._on_change()
# We want to make sure the original PyTorch model does not have a trigger wrapper
value = recursively_remove_trigger(value)
self.instance.__setitem__(item, value)
- def __getitem__(self, items: str) -> Any:
+ def __getitem__(self, items: str) -> Any:
obj = self.instance.__getitem__(items)
if isinstance(obj, ChangeTriggerWrapper):
return obj
return _make_refit_change_trigger(obj, refit_state)
- def __len__(self) -> int:
+ def __len__(self) -> int:
return len(self.instance)
- def __iter__(self) -> Iterator[Any]:
+ def __iter__(self) -> Iterator[Any]:
return iter(self.instance)
return ChangeTriggerWrapper(obj)
@@ -1313,7 +1331,7 @@ Resources
Developer Resources
- Contributor Awards - 2023
+ Contributor Awards - 2024
@@ -1329,6 +1347,9 @@ Resources
ExecuTorch
+
+ ExecuTorch Documentation
+
@@ -1367,6 +1388,9 @@ Resources
Events
+
+ Newsletter
+
@@ -1380,6 +1404,18 @@ Resources
Governing Board
+
+ Cloud Credit Program
+
+
+ Technical Advisory Council
+
+
+ Staff
+
+
+ Contact Us
+
diff --git a/docs/_modules/torch_tensorrt/dynamo/runtime/_PythonTorchTensorRTModule.html b/docs/_modules/torch_tensorrt/dynamo/runtime/_PythonTorchTensorRTModule.html
index 549d8fbe54..14d8aa834e 100644
--- a/docs/_modules/torch_tensorrt/dynamo/runtime/_PythonTorchTensorRTModule.html
+++ b/docs/_modules/torch_tensorrt/dynamo/runtime/_PythonTorchTensorRTModule.html
@@ -9,7 +9,7 @@
- torch_tensorrt.dynamo.runtime._PythonTorchTensorRTModule — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ torch_tensorrt.dynamo.runtime._PythonTorchTensorRTModule — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -132,8 +132,8 @@
Developer Resources
Find resources and get questions answered
-
- Contributor Awards - 2023
+
+ Contributor Awards - 2024
Award winners announced at this year's PyTorch Conference
@@ -154,6 +154,9 @@
ExecuTorch
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+
+ ExecuTorch Docs
+
@@ -201,6 +204,10 @@
Events
Find events, webinars, and podcasts
+
+ Newsletter
+ Stay up-to-date with the latest updates
+
@@ -216,7 +223,18 @@
Governing Board
-
+
+
+ Cloud Credit Program
+
+
+ Technical Advisory Council
+
+
+ Staff
+
+
+ Contact Us
@@ -272,7 +290,7 @@
- v2.6.0.dev0+70e2a38
+ v2.7.0.dev0+d6be4ba
@@ -467,23 +485,23 @@
Source code for torch_tensorrt.dynamo.runtime._PythonTorchTensorRTModule
-from __future__ import annotations
-
-import logging
-from contextlib import nullcontext
-from tempfile import tempdir
-from typing import Any, Dict, List, Optional, Sequence, Tuple
-
-import tensorrt as trt
-import torch
-import torch_tensorrt
-from torch.nn import Module
-from torch_tensorrt._Device import Device
-from torch_tensorrt._enums import Platform, dtype
-from torch_tensorrt.dynamo._settings import CompilationSettings
-from torch_tensorrt.dynamo.utils import DYNAMIC_DIM
-from torch_tensorrt.logging import TRT_LOGGER
-from torch_tensorrt.runtime._utils import (
+from __future__ import annotations
+
+import logging
+from contextlib import nullcontext
+from tempfile import tempdir
+from typing import Any, Dict, List, Optional, Sequence, Tuple
+
+import tensorrt as trt
+import torch
+import torch_tensorrt
+from torch.nn import Module
+from torch_tensorrt._Device import Device
+from torch_tensorrt._enums import Platform, dtype
+from torch_tensorrt.dynamo._settings import CompilationSettings
+from torch_tensorrt.dynamo.utils import DYNAMIC_DIM
+from torch_tensorrt.logging import TRT_LOGGER
+from torch_tensorrt.runtime._utils import (
_is_switch_required,
_select_rt_device,
multi_gpu_device_check,
@@ -492,8 +510,8 @@ Source code for torch_tensorrt.dynamo.runtime._PythonTorchTensorRTModule
logger = logging.getLogger(__name__)
-class TorchTRTRuntimeStates:
- def __init__(self, new_cudagraphs: bool):
+class TorchTRTRuntimeStates:
+ def __init__(self, new_cudagraphs: bool):
# Indicates whether CUDAGraphs were enabled in the previous execute_engine
self.old_cudagraphs = new_cudagraphs
# Indicates whether pre-allocated output was enabled in the previous execute_engine
@@ -501,7 +519,7 @@ Source code for torch_tensorrt.dynamo.runtime._PythonTorchTensorRTModule
# Indicates whether context has changed
self.context_changed = False
- def set_runtime_states(
+ def set_runtime_states(
self,
new_cudagraphs: bool,
new_pre_allocated_output: bool,
@@ -545,14 +563,14 @@ Source code for torch_tensorrt.dynamo.runtime._PythonTorchTensorRTModule
)
-[docs]class PythonTorchTensorRTModule(Module): # type: ignore[misc]
+[docs]class PythonTorchTensorRTModule(Module): # type: ignore[misc]
"""PythonTorchTensorRTModule is a PyTorch module which encompasses an arbitrary TensorRT Engine.
This module is backed by the Torch-TensorRT runtime and is only compatible with
FX / Dynamo / Python deployments. This module cannot be serialized to torchscript via torch.jit.trace for C++ deployment.
"""
-[docs] def __init__(
+[docs] def __init__(
self,
serialized_engine: Optional[bytes] = None,
input_binding_names: Optional[List[str]] = None,
@@ -639,16 +657,16 @@ Source code for torch_tensorrt.dynamo.runtime._PythonTorchTensorRTModule
if self.serialized_engine is not None and not self.settings.lazy_engine_init:
self.setup_engine()
- def get_streamable_device_memory_budget(self) -> Any:
+ def get_streamable_device_memory_budget(self) -> Any:
return self.engine.streamable_weights_size
- def get_automatic_device_memory_budget(self) -> Any:
+ def get_automatic_device_memory_budget(self) -> Any:
return self.engine.get_weight_streaming_automatic_budget()
- def get_device_memory_budget(self) -> Any:
+ def get_device_memory_budget(self) -> Any:
return self.engine.weight_streaming_budget_v2
- def set_device_memory_budget(self, budget_bytes: int) -> int:
+ def set_device_memory_budget(self, budget_bytes: int) -> int:
# Recreating the context because weight streaming budget cannot be modified while there are active context.
if self.context is not None:
del self.context
@@ -657,7 +675,7 @@ Source code for torch_tensorrt.dynamo.runtime._PythonTorchTensorRTModule
self.runtime_states.context_changed = True
return budget_bytes
- def _set_device_memory_budget(self, budget_bytes: int) -> int:
+ def _set_device_memory_budget(self, budget_bytes: int) -> int:
# Disable weight streaming for invalid budget size
if budget_bytes < 0:
budget_bytes = self.get_streamable_device_memory_budget()
@@ -670,13 +688,13 @@ Source code for torch_tensorrt.dynamo.runtime._PythonTorchTensorRTModule
return budget_bytes
- def set_default_device_memory_budget(self) -> int:
+ def set_default_device_memory_budget(self) -> int:
budget_bytes = self.get_automatic_device_memory_budget()
# Set automatic weight streaming budget as default when context is created
logger.debug(f"Weight streaming budget set to {budget_bytes}B")
return self._set_device_memory_budget(budget_bytes)
- def setup_engine(self) -> None:
+ def setup_engine(self) -> None:
assert (
self.target_platform == Platform.current_platform()
), f"TensorRT engine was not built to target current platform (target: {self.target_platform}, current: {Platform.current_platform()})"
@@ -710,17 +728,17 @@ Source code for torch_tensorrt.dynamo.runtime._PythonTorchTensorRTModule
if torch_tensorrt.runtime.get_cudagraphs_mode():
self.cudagraph = torch.cuda.CUDAGraph()
- def _check_initialized(self) -> None:
+ def _check_initialized(self) -> None:
if not self.initialized:
raise RuntimeError("PythonTorchTensorRTModule is not initialized.")
- def _on_state_dict(self, state_dict: Dict[str, Any], prefix: str, _: Any) -> None:
+ def _on_state_dict(self, state_dict: Dict[str, Any], prefix: str, _: Any) -> None:
state_dict[prefix + "engine"] = self.serialized_engine
state_dict[prefix + "input_names"] = self.input_names
state_dict[prefix + "output_names"] = self.output_names
state_dict[prefix + "platform"] = self.target_platform
- def _load_from_state_dict(
+ def _load_from_state_dict(
self,
state_dict: Dict[str, Any],
prefix: str,
@@ -739,28 +757,28 @@ Source code for torch_tensorrt.dynamo.runtime._PythonTorchTensorRTModule
multi_gpu_device_check()
self.setup_engine()
- def __getstate__(self) -> Dict[str, Any]:
+ def __getstate__(self) -> Dict[str, Any]:
state = self.__dict__.copy()
state.pop("engine", None)
state.pop("context", None)
return state
- def __setstate__(self, state: Dict[str, Any]) -> None:
+ def __setstate__(self, state: Dict[str, Any]) -> None:
self.__dict__.update(state)
self.setup_engine()
- def __deepcopy__(self, memo: Any) -> PythonTorchTensorRTModule:
+ def __deepcopy__(self, memo: Any) -> PythonTorchTensorRTModule:
cls = self.__class__
result = cls.__new__(cls)
memo[id(self)] = result
result.__setstate__(self.__getstate__())
return result
- def __del__(self) -> None:
+ def __del__(self) -> None:
if self.cudagraph:
self.cudagraph.reset()
- def setup_input_tensors(
+ def setup_input_tensors(
self,
contiguous_inputs: List[torch.Tensor],
cudagraphs_enabled: bool,
@@ -811,7 +829,7 @@ Source code for torch_tensorrt.dynamo.runtime._PythonTorchTensorRTModule
input_name, contiguous_inputs[i].data_ptr()
)
- def create_output_tensors(self) -> List[torch.Tensor]:
+ def create_output_tensors(self) -> List[torch.Tensor]:
# create output tensors
outputs: List[torch.Tensor] = []
@@ -824,10 +842,10 @@ Source code for torch_tensorrt.dynamo.runtime._PythonTorchTensorRTModule
outputs.append(output)
return outputs
- def set_pre_allocated_outputs(self, enable: bool) -> None:
+ def set_pre_allocated_outputs(self, enable: bool) -> None:
self.use_pre_allocated_outputs = enable
-[docs] def forward(self, *inputs: torch.Tensor) -> torch.Tensor | Tuple[torch.Tensor, ...]:
+[docs] def forward(self, *inputs: torch.Tensor) -> torch.Tensor | Tuple[torch.Tensor, ...]:
# Ensure inputs are available in all scopes and cast symbolic integers to Tensors
contiguous_inputs: List[torch.Tensor] = [
(i.contiguous() if isinstance(i, torch.Tensor) else torch.tensor(i).cuda())
@@ -981,7 +999,7 @@ Source code for torch_tensorrt.dynamo.runtime._PythonTorchTensorRTModule
)
if self.profiling_enabled:
- import tempfile
+ import tempfile
with tempfile.TemporaryDirectory() as tmpdir:
self.cudagraph.debug_dump(
@@ -1007,7 +1025,7 @@ Source code for torch_tensorrt.dynamo.runtime._PythonTorchTensorRTModule
return outputs
-[docs] def enable_profiling(self, profiler: "trt.IProfiler" = None) -> None:
+[docs] def enable_profiling(self, profiler: "trt.IProfiler" = None) -> None:
"""
Enable TensorRT profiling. After calling this function, TensorRT will report
time spent on each layer in stdout for each forward run.
@@ -1019,7 +1037,7 @@ Source code for torch_tensorrt.dynamo.runtime._PythonTorchTensorRTModule
self.profiling_enabled = True
-[docs] def disable_profiling(self) -> None:
+[docs] def disable_profiling(self) -> None:
"""
Disable TensorRT profiling.
"""
@@ -1029,7 +1047,7 @@ Source code for torch_tensorrt.dynamo.runtime._PythonTorchTensorRTModule
self.context = self.engine.create_execution_context()
self.profiling_enabled = False
-[docs] def get_layer_info(self) -> str:
+[docs] def get_layer_info(self) -> str:
"""
Get layer info of the engine. Only support for TRT > 8.2.
"""
@@ -1039,7 +1057,7 @@ Source code for torch_tensorrt.dynamo.runtime._PythonTorchTensorRTModule
)
return engine_json
-[docs] def validate_input_shapes(self, inputs: Sequence[torch.Tensor]) -> bool:
+[docs] def validate_input_shapes(self, inputs: Sequence[torch.Tensor]) -> bool:
"""
Validates the input shapes of the forward function has changed
"""
@@ -1282,7 +1300,7 @@ Resources
Developer Resources
- Contributor Awards - 2023
+ Contributor Awards - 2024
@@ -1298,6 +1316,9 @@ Resources
ExecuTorch
+
+ ExecuTorch Documentation
+
@@ -1336,6 +1357,9 @@ Resources
Events
+
+ Newsletter
+
@@ -1349,6 +1373,18 @@ Resources
Governing Board
+
+ Cloud Credit Program
+
+
+ Technical Advisory Council
+
+
+ Staff
+
+
+ Contact Us
+
diff --git a/docs/_modules/torch_tensorrt/dynamo/runtime/_TorchTensorRTModule.html b/docs/_modules/torch_tensorrt/dynamo/runtime/_TorchTensorRTModule.html
index 3a03a7d769..f1bd3ad0b5 100644
--- a/docs/_modules/torch_tensorrt/dynamo/runtime/_TorchTensorRTModule.html
+++ b/docs/_modules/torch_tensorrt/dynamo/runtime/_TorchTensorRTModule.html
@@ -9,7 +9,7 @@
- torch_tensorrt.dynamo.runtime._TorchTensorRTModule — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ torch_tensorrt.dynamo.runtime._TorchTensorRTModule — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -132,8 +132,8 @@
Developer Resources
Find resources and get questions answered
-
- Contributor Awards - 2023
+
+ Contributor Awards - 2024
Award winners announced at this year's PyTorch Conference
@@ -154,6 +154,9 @@
ExecuTorch
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+
+ ExecuTorch Docs
+
@@ -201,6 +204,10 @@
Events
Find events, webinars, and podcasts
+
+ Newsletter
+ Stay up-to-date with the latest updates
+
@@ -216,7 +223,18 @@
Governing Board
-
+
+
+ Cloud Credit Program
+
+
+ Technical Advisory Council
+
+
+ Staff
+
+
+ Contact Us
@@ -272,7 +290,7 @@
- v2.6.0.dev0+70e2a38
+ v2.7.0.dev0+d6be4ba
@@ -467,23 +485,23 @@
Source code for torch_tensorrt.dynamo.runtime._TorchTensorRTModule
-from __future__ import annotations
-
-import base64
-import copy
-import logging
-import pickle
-from typing import Any, List, Optional, Tuple, Union
-
-import torch
-from torch_tensorrt._Device import Device
-from torch_tensorrt._enums import Platform
-from torch_tensorrt._features import (
+from __future__ import annotations
+
+import base64
+import copy
+import logging
+import pickle
+from typing import Any, List, Optional, Tuple, Union
+
+import torch
+from torch_tensorrt._Device import Device
+from torch_tensorrt._enums import Platform
+from torch_tensorrt._features import (
ENABLED_FEATURES,
for_all_methods,
needs_torch_tensorrt_runtime,
)
-from torch_tensorrt.dynamo._settings import CompilationSettings
+from torch_tensorrt.dynamo._settings import CompilationSettings
logger = logging.getLogger(__name__)
@@ -519,7 +537,7 @@ Source code for torch_tensorrt.dynamo.runtime._TorchTensorRTModule
[docs]@for_all_methods(needs_torch_tensorrt_runtime)
-class TorchTensorRTModule(torch.nn.Module): # type: ignore[misc]
+class TorchTensorRTModule(torch.nn.Module): # type: ignore[misc]
"""TorchTensorRTModule is a PyTorch module which encompasses an arbitrary TensorRT Engine.
This module is backed by the Torch-TensorRT runtime and is fully compatible with both
@@ -539,7 +557,7 @@ Source code for torch_tensorrt.dynamo.runtime._TorchTensorRTModule
output_binding_names (List[str]): List of output TensorRT engine binding names in the order they should be returned
"""
- def __init__(
+ def __init__(
self,
serialized_engine: Optional[bytes] = None,
input_binding_names: Optional[List[str]] = None,
@@ -609,7 +627,7 @@ Source code for torch_tensorrt.dynamo.runtime._TorchTensorRTModule
):
self.setup_engine()
- def _pack_engine_info(self) -> List[str | bytes]:
+ def _pack_engine_info(self) -> List[str | bytes]:
target_device = (
self.settings.device
if self.settings.device is not None
@@ -643,16 +661,16 @@ Source code for torch_tensorrt.dynamo.runtime._TorchTensorRTModule
return engine_info
- def get_streamable_device_memory_budget(self) -> Any:
+ def get_streamable_device_memory_budget(self) -> Any:
return self.engine.streamable_device_memory_budget
- def get_automatic_device_memory_budget(self) -> Any:
+ def get_automatic_device_memory_budget(self) -> Any:
return self.engine.automatic_device_memory_budget
- def get_device_memory_budget(self) -> Any:
+ def get_device_memory_budget(self) -> Any:
return self.engine.device_memory_budget
- def set_device_memory_budget(self, budget_bytes: int) -> int:
+ def set_device_memory_budget(self, budget_bytes: int) -> int:
# Disable weight streaming for invalid budget size
if budget_bytes < 0:
budget_bytes = self.get_streamable_device_memory_budget()
@@ -665,7 +683,7 @@ Source code for torch_tensorrt.dynamo.runtime._TorchTensorRTModule
return budget_bytes
- def setup_engine(self) -> None:
+ def setup_engine(self) -> None:
"""
Setup engine for a module which has deferred engine setup.
@@ -678,19 +696,19 @@ Source code for torch_tensorrt.dynamo.runtime._TorchTensorRTModule
return
self.engine = torch.classes.tensorrt.Engine(self._pack_engine_info())
- def encode_metadata(self, metadata: Any) -> str:
+ def encode_metadata(self, metadata: Any) -> str:
metadata = copy.deepcopy(metadata)
dumped_metadata = pickle.dumps(metadata)
encoded_metadata = base64.b64encode(dumped_metadata).decode("utf-8")
return encoded_metadata
@staticmethod
- def decode_metadata(encoded_metadata: bytes) -> Any:
+ def decode_metadata(encoded_metadata: bytes) -> Any:
dumped_metadata = base64.b64decode(encoded_metadata.encode("utf-8"))
metadata = pickle.loads(dumped_metadata)
return metadata
- def get_extra_state(self) -> SerializedTorchTensorRTModuleFmt:
+ def get_extra_state(self) -> SerializedTorchTensorRTModuleFmt:
if self.engine:
return (
self.name,
@@ -716,7 +734,7 @@ Source code for torch_tensorrt.dynamo.runtime._TorchTensorRTModule
self.output_binding_names,
)
- def set_extra_state(self, state: SerializedTorchTensorRTModuleFmt) -> None:
+ def set_extra_state(self, state: SerializedTorchTensorRTModuleFmt) -> None:
self.name = state[0]
if state[1] is not None:
@@ -741,10 +759,10 @@ Source code for torch_tensorrt.dynamo.runtime._TorchTensorRTModule
self.input_binding_names = state[2]
self.output_binding_names = state[3]
- def set_pre_allocated_outputs(self, enable: bool) -> None:
+ def set_pre_allocated_outputs(self, enable: bool) -> None:
self.engine.use_pre_allocated_outputs = enable
- def forward(self, *inputs: Any) -> torch.Tensor | Tuple[torch.Tensor, ...]:
+ def forward(self, *inputs: Any) -> torch.Tensor | Tuple[torch.Tensor, ...]:
"""Implementation of the forward pass for a TensorRT engine
Args:
@@ -779,7 +797,7 @@ Source code for torch_tensorrt.dynamo.runtime._TorchTensorRTModule
return tuple(outputs)
- def enable_profiling(self, profiling_results_dir: Optional[str] = None) -> None:
+ def enable_profiling(self, profiling_results_dir: Optional[str] = None) -> None:
"""Enable the profiler to collect latency information about the execution of the engine
Traces can be visualized using https://ui.perfetto.dev/ or compatible alternatives
@@ -794,14 +812,14 @@ Source code for torch_tensorrt.dynamo.runtime._TorchTensorRTModule
self.engine.profile_path_prefix = profiling_results_dir
self.engine.enable_profiling()
- def disable_profiling(self) -> None:
+ def disable_profiling(self) -> None:
"""Disable the profiler"""
if self.engine is None:
raise RuntimeError("Engine has not been initialized yet.")
self.engine.disable_profiling()
- def get_layer_info(self) -> str:
+ def get_layer_info(self) -> str:
"""Get a JSON string containing the layer information encoded by the TensorRT engine in this module
Returns:
@@ -814,7 +832,7 @@ Source code for torch_tensorrt.dynamo.runtime._TorchTensorRTModule
layer_info: str = self.engine.get_engine_layer_info()
return layer_info
- def dump_layer_info(self) -> None:
+ def dump_layer_info(self) -> None:
"""Dump layer information encoded by the TensorRT engine in this module to STDOUT"""
if self.engine is None:
raise RuntimeError("Engine has not been initialized yet.")
@@ -822,7 +840,7 @@ Source code for torch_tensorrt.dynamo.runtime._TorchTensorRTModule
self.engine.dump_engine_layer_info()
@staticmethod
- def _pack_binding_names(binding_names: List[str]) -> str:
+ def _pack_binding_names(binding_names: List[str]) -> str:
delim = torch.ops.tensorrt.SERIALIZED_ENGINE_BINDING_DELIM()[0]
packed_bindings: str = delim.join(binding_names)
return packed_bindings
@@ -1052,7 +1070,7 @@ Resources
Developer Resources
- Contributor Awards - 2023
+ Contributor Awards - 2024
@@ -1068,6 +1086,9 @@ Resources
ExecuTorch
+
+ ExecuTorch Documentation
+
@@ -1106,6 +1127,9 @@ Resources
Events
+
+ Newsletter
+
@@ -1119,6 +1143,18 @@ Resources
Governing Board
+
+ Cloud Credit Program
+
+
+ Technical Advisory Council
+
+
+ Staff
+
+
+ Contact Us
+
diff --git a/docs/_modules/torch_tensorrt/fx/fx2trt.html b/docs/_modules/torch_tensorrt/fx/fx2trt.html
index feb0619d4c..a120552968 100644
--- a/docs/_modules/torch_tensorrt/fx/fx2trt.html
+++ b/docs/_modules/torch_tensorrt/fx/fx2trt.html
@@ -9,7 +9,7 @@
- torch_tensorrt.fx.fx2trt — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ torch_tensorrt.fx.fx2trt — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -132,8 +132,8 @@
Developer Resources
Find resources and get questions answered
-
- Contributor Awards - 2023
+
+ Contributor Awards - 2024
Award winners announced at this year's PyTorch Conference
@@ -154,6 +154,9 @@
ExecuTorch
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+
+ ExecuTorch Docs
+
@@ -201,6 +204,10 @@
Events
Find events, webinars, and podcasts
+
+ Newsletter
+ Stay up-to-date with the latest updates
+
@@ -216,7 +223,18 @@
Governing Board
-
+
+
+ Cloud Credit Program
+
+
+ Technical Advisory Council
+
+
+ Staff
+
+
+ Contact Us
@@ -272,7 +290,7 @@
- v2.6.0.dev0+70e2a38
+ v2.7.0.dev0+d6be4ba
@@ -467,26 +485,26 @@
Source code for torch_tensorrt.fx.fx2trt
-import logging
-import os
-import warnings
-from datetime import datetime
-from typing import Any, Callable, Dict, List, NamedTuple, Optional, Sequence
+import logging
+import os
+import warnings
+from datetime import datetime
+from typing import Any, Callable, Dict, List, NamedTuple, Optional, Sequence
-import numpy
+import numpy
# @manual=//deeplearning/trt/python:py_tensorrt
-import tensorrt as trt
-import torch
-import torch.fx
-from torch._ops import OpOverload
-from torch.fx.node import _get_qualified_name
-from torch.fx.passes.shape_prop import TensorMetadata
-
-from .converter_registry import CONVERTERS
-from .input_tensor_spec import InputTensorSpec
-from .observer import Observer
-from .utils import Frameworks, LowerPrecision, get_dynamic_dims, unified_dtype_converter
+import tensorrt as trt
+import torch
+import torch.fx
+from torch._ops import OpOverload
+from torch.fx.node import _get_qualified_name
+from torch.fx.passes.shape_prop import TensorMetadata
+
+from .converter_registry import CONVERTERS
+from .input_tensor_spec import InputTensorSpec
+from .observer import Observer
+from .utils import Frameworks, LowerPrecision, get_dynamic_dims, unified_dtype_converter
_LOGGER: logging.Logger = logging.getLogger(__name__)
@@ -495,15 +513,15 @@ Source code for torch_tensorrt.fx.fx2trt
)
-[docs]class TRTInterpreterResult(NamedTuple):
+[docs]class TRTInterpreterResult(NamedTuple):
engine: Any
input_names: Sequence[str]
output_names: Sequence[str]
serialized_cache: bytearray
-[docs]class TRTInterpreter(torch.fx.Interpreter):
- def __init__(
+[docs]class TRTInterpreter(torch.fx.Interpreter):
+ def __init__(
self,
module: torch.fx.GraphModule,
input_specs: List[InputTensorSpec],
@@ -548,7 +566,7 @@ Source code for torch_tensorrt.fx.fx2trt
dict()
)
- def validate_input_specs(self):
+ def validate_input_specs(self):
for shape, _, _, shape_ranges, has_batch_dim in self.input_specs:
if not self.network.has_implicit_batch_dimension:
assert (
@@ -605,7 +623,7 @@ Source code for torch_tensorrt.fx.fx2trt
len(shape_ranges) == 0
), "shape_ranges are provided for input that doesn't have dynamic dim."
- def validate_conversion(self):
+ def validate_conversion(self):
missing_converter = set()
for node in self.module.graph.nodes:
@@ -621,7 +639,7 @@ Source code for torch_tensorrt.fx.fx2trt
return missing_converter
- def run(
+ def run(
self,
max_batch_size=64,
max_workspace_size=1 << 25,
@@ -739,7 +757,7 @@ Source code for torch_tensorrt.fx.fx2trt
engine, self._input_names, self._output_names, serialized_cache
)
- def run_node(self, n):
+ def run_node(self, n):
self._cur_node_name = str(n)
# add "_itensor_to_tensor_meta"
kwargs = dict(n.kwargs)
@@ -759,7 +777,7 @@ Source code for torch_tensorrt.fx.fx2trt
return trt_node
- def placeholder(self, target, args, kwargs):
+ def placeholder(self, target, args, kwargs):
self._input_names.append(target)
shape, dtype, _, shape_ranges, has_batch_dim = self.input_specs[
self.input_specs_iter
@@ -780,7 +798,7 @@ Source code for torch_tensorrt.fx.fx2trt
dtype=unified_dtype_converter(dtype, Frameworks.TRT),
)
- def call_module(self, target, args, kwargs):
+ def call_module(self, target, args, kwargs):
assert isinstance(target, str)
submod = self.fetch_attr(target)
submod_type = getattr(submod, "_base_class_origin", type(submod))
@@ -794,7 +812,7 @@ Source code for torch_tensorrt.fx.fx2trt
assert self._cur_node_name is not None
return converter(self.network, submod, args, kwargs, self._cur_node_name)
- def call_function(self, target, args, kwargs):
+ def call_function(self, target, args, kwargs):
converter = CONVERTERS.get(target)
if not converter:
raise RuntimeError(
@@ -804,7 +822,7 @@ Source code for torch_tensorrt.fx.fx2trt
assert self._cur_node_name is not None
return converter(self.network, target, args, kwargs, self._cur_node_name)
- def call_method(self, target, args, kwargs):
+ def call_method(self, target, args, kwargs):
assert isinstance(target, str)
converter = CONVERTERS.get(target)
@@ -816,7 +834,7 @@ Source code for torch_tensorrt.fx.fx2trt
assert self._cur_node_name is not None
return converter(self.network, target, args, kwargs, self._cur_node_name)
- def output(self, target, args, kwargs):
+ def output(self, target, args, kwargs):
assert len(args) == 1
if isinstance(args[0], tuple):
outputs = args[0]
@@ -1081,7 +1099,7 @@ Resources
Developer Resources
- Contributor Awards - 2023
+ Contributor Awards - 2024
@@ -1097,6 +1115,9 @@ Resources
ExecuTorch
+
+ ExecuTorch Documentation
+
@@ -1135,6 +1156,9 @@ Resources
Events
+
+ Newsletter
+
@@ -1148,6 +1172,18 @@ Resources
Governing Board
+
+ Cloud Credit Program
+
+
+ Technical Advisory Council
+
+
+ Staff
+
+
+ Contact Us
+
diff --git a/docs/_modules/torch_tensorrt/fx/input_tensor_spec.html b/docs/_modules/torch_tensorrt/fx/input_tensor_spec.html
index 47c2744a6c..6ee9f30d79 100644
--- a/docs/_modules/torch_tensorrt/fx/input_tensor_spec.html
+++ b/docs/_modules/torch_tensorrt/fx/input_tensor_spec.html
@@ -9,7 +9,7 @@
- torch_tensorrt.fx.input_tensor_spec — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ torch_tensorrt.fx.input_tensor_spec — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -132,8 +132,8 @@
Developer Resources
Find resources and get questions answered
-
- Contributor Awards - 2023
+
+ Contributor Awards - 2024
Award winners announced at this year's PyTorch Conference
@@ -154,6 +154,9 @@
ExecuTorch
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+
+ ExecuTorch Docs
+
@@ -201,6 +204,10 @@
Events
Find events, webinars, and podcasts
+
+ Newsletter
+ Stay up-to-date with the latest updates
+
@@ -216,7 +223,18 @@
Governing Board
-
+
+
+ Cloud Credit Program
+
+
+ Technical Advisory Council
+
+
+ Staff
+
+
+ Contact Us
@@ -272,7 +290,7 @@
- v2.6.0.dev0+70e2a38
+ v2.7.0.dev0+d6be4ba
@@ -467,15 +485,15 @@
Source code for torch_tensorrt.fx.input_tensor_spec
-from typing import Any, Iterable, List, NamedTuple, Optional, Sequence, Tuple
+from typing import Any, Iterable, List, NamedTuple, Optional, Sequence, Tuple
-import torch
+import torch
-from .types import Shape, ShapeRange
-from .utils import get_dynamic_dims
+from .types import Shape, ShapeRange
+from .utils import get_dynamic_dims
-def generate_input_specs(inputs, lower_setting, additional_inputs=None):
+def generate_input_specs(inputs, lower_setting, additional_inputs=None):
# dynamic_batch is TRT only flag.
if (
not lower_setting.explicit_batch_dimension
@@ -534,7 +552,7 @@ Source code for torch_tensorrt.fx.input_tensor_spec
)
-[docs]class InputTensorSpec(NamedTuple):
+[docs]class InputTensorSpec(NamedTuple):
"""
This class contains the information of a input tensor.
@@ -564,7 +582,7 @@ Source code for torch_tensorrt.fx.input_tensor_spec
has_batch_dim: bool = True
@classmethod
- def from_tensor(cls, tensor: torch.Tensor) -> "InputTensorSpec":
+ def from_tensor(cls, tensor: torch.Tensor) -> "InputTensorSpec":
"""
Produce an InputTenosrSpec named tuple which contains the
information of the given PyTorch tensor.
@@ -578,7 +596,7 @@ Source code for torch_tensorrt.fx.input_tensor_spec
return cls(tensor.shape, tensor.dtype, tensor.device)
@classmethod
- def from_tensors(cls, tensors: Sequence[torch.Tensor]) -> List["InputTensorSpec"]:
+ def from_tensors(cls, tensors: Sequence[torch.Tensor]) -> List["InputTensorSpec"]:
"""
Produce a list of InputTenosrSpec named tuples which contain
the information of all the given PyTorch tensors.
@@ -593,7 +611,7 @@ Source code for torch_tensorrt.fx.input_tensor_spec
return [cls.from_tensor(t) for t in tensors]
@classmethod
- def from_tensors_with_dynamic_batch_size(
+ def from_tensors_with_dynamic_batch_size(
cls,
tensors: Sequence[torch.Tensor],
batch_size_range: Tuple[int, int, int],
@@ -647,7 +665,7 @@ Source code for torch_tensorrt.fx.input_tensor_spec
@classmethod
# pyre-ignore [2]: Parameter `sample_input` must have a type other than `Any`
- def find_batch_size_dim(cls, inputs: Any) -> []:
+ def find_batch_size_dim(cls, inputs: Any) -> []:
if isinstance(inputs, torch.Tensor) or len(inputs) <= 1:
return [0]
shapes = [i.shape for i in inputs]
@@ -686,7 +704,7 @@ Source code for torch_tensorrt.fx.input_tensor_spec
return bs_dim
- def to_random_tensor(self, id=1):
+ def to_random_tensor(self, id=1):
shape = tuple(self.shape)
if len(get_dynamic_dims(shape)):
# id=0 -> min shape
@@ -699,7 +717,7 @@ Source code for torch_tensorrt.fx.input_tensor_spec
return torch.randn(shape).to(dtype=self.dtype, device=self.device)
@staticmethod
- def create_inputs_from_specs(input_specs: Iterable["InputTensorSpec"]):
+ def create_inputs_from_specs(input_specs: Iterable["InputTensorSpec"]):
inputs = []
for spec in input_specs:
inputs.append(spec.to_random_tensor())
@@ -707,7 +725,7 @@ Source code for torch_tensorrt.fx.input_tensor_spec
return inputs
@staticmethod
- def create_inputs_from_max_specs(input_specs: Iterable["InputTensorSpec"]):
+ def create_inputs_from_max_specs(input_specs: Iterable["InputTensorSpec"]):
inputs = []
for spec in input_specs:
inputs.append(spec.to_random_tensor(2))
@@ -939,7 +957,7 @@ Resources
Developer Resources
- Contributor Awards - 2023
+ Contributor Awards - 2024
@@ -955,6 +973,9 @@ Resources
ExecuTorch
+
+ ExecuTorch Documentation
+
@@ -993,6 +1014,9 @@ Resources
Events
+
+ Newsletter
+
@@ -1006,6 +1030,18 @@ Resources
Governing Board
+
+ Cloud Credit Program
+
+
+ Technical Advisory Council
+
+
+ Staff
+
+
+ Contact Us
+
diff --git a/docs/_modules/torch_tensorrt/fx/lower.html b/docs/_modules/torch_tensorrt/fx/lower.html
index f7c15f508d..d341e87942 100644
--- a/docs/_modules/torch_tensorrt/fx/lower.html
+++ b/docs/_modules/torch_tensorrt/fx/lower.html
@@ -9,7 +9,7 @@
- torch_tensorrt.fx.lower — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ torch_tensorrt.fx.lower — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -132,8 +132,8 @@
Developer Resources
Find resources and get questions answered
-
- Contributor Awards - 2023
+
+ Contributor Awards - 2024
Award winners announced at this year's PyTorch Conference
@@ -154,6 +154,9 @@
ExecuTorch
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+
+ ExecuTorch Docs
+
@@ -201,6 +204,10 @@
Events
Find events, webinars, and podcasts
+
+ Newsletter
+ Stay up-to-date with the latest updates
+
@@ -216,7 +223,18 @@
Governing Board
-
+
+
+ Cloud Credit Program
+
+
+ Technical Advisory Council
+
+
+ Staff
+
+
+ Contact Us
@@ -272,7 +290,7 @@
- v2.6.0.dev0+70e2a38
+ v2.7.0.dev0+d6be4ba
@@ -467,34 +485,34 @@
Source code for torch_tensorrt.fx.lower
-import dataclasses as dc
-import logging
-from typing import Any, Callable, Optional, Sequence
+import dataclasses as dc
+import logging
+from typing import Any, Callable, Optional, Sequence
# @manual=//deeplearning/trt/python:py_tensorrt
-import tensorrt as trt
-import torch
-import torch.fx as fx
-import torch.nn as nn
-import torch_tensorrt.fx.tracer.dispatch_tracer.aten_tracer as aten_tracer
-from torch.fx.passes.splitter_base import SplitResult
-
-from .fx2trt import TRTInterpreter, TRTInterpreterResult
-from .lower_setting import LowerSetting
-from .passes.lower_pass_manager_builder import LowerPassManagerBuilder
-from .passes.pass_utils import PassFunc, validate_inference
-from .tools.timing_cache_utils import TimingCacheManager
-from .tools.trt_splitter import TRTSplitter, TRTSplitterSetting
-from .tracer.acc_tracer import acc_tracer
-from .trt_module import TRTModule
-from .utils import LowerPrecision
+import tensorrt as trt
+import torch
+import torch.fx as fx
+import torch.nn as nn
+import torch_tensorrt.fx.tracer.dispatch_tracer.aten_tracer as aten_tracer
+from torch.fx.passes.splitter_base import SplitResult
+
+from .fx2trt import TRTInterpreter, TRTInterpreterResult
+from .lower_setting import LowerSetting
+from .passes.lower_pass_manager_builder import LowerPassManagerBuilder
+from .passes.pass_utils import PassFunc, validate_inference
+from .tools.timing_cache_utils import TimingCacheManager
+from .tools.trt_splitter import TRTSplitter, TRTSplitterSetting
+from .tracer.acc_tracer import acc_tracer
+from .trt_module import TRTModule
+from .utils import LowerPrecision
logger = logging.getLogger(__name__)
Input = Sequence[Any]
-[docs]def compile(
+[docs]def compile(
module: nn.Module,
input,
min_acc_module_size: int = 10,
@@ -559,18 +577,18 @@ Source code for torch_tensorrt.fx.lower
@dc.dataclass
-class LowerTrtInterpreter:
+class LowerTrtInterpreter:
lower_setting: LowerSetting
timing_cache_manager: TimingCacheManager
@classmethod
- def create(cls, lower_setting):
+ def create(cls, lower_setting):
timing_cache_manager = TimingCacheManager(
lower_setting.timing_cache_prefix, lower_setting.save_timing_cache
)
return LowerTrtInterpreter(lower_setting, timing_cache_manager)
- def __call__(self, mod, input, split_name) -> TRTInterpreterResult:
+ def __call__(self, mod, input, split_name) -> TRTInterpreterResult:
assert self.lower_setting.input_specs, "Can't find input specs for lowering!"
logger.info(
f"split_name={split_name}, input_specs={self.lower_setting.input_specs}"
@@ -624,7 +642,7 @@ Source code for torch_tensorrt.fx.lower
return interp_result
-def default_split_function(
+def default_split_function(
model: fx.GraphModule, inputs: Input, lower_setting: LowerSetting
) -> SplitResult:
splitter_setting = TRTSplitterSetting()
@@ -636,14 +654,14 @@ Source code for torch_tensorrt.fx.lower
return splitter.generate_split_results()
-def create_lower_trt_interpreter(lower_setting: LowerSetting) -> LowerTrtInterpreter:
+def create_lower_trt_interpreter(lower_setting: LowerSetting) -> LowerTrtInterpreter:
return LowerTrtInterpreter.create(lower_setting)
-def default_lower_pass(
+def default_lower_pass(
create_trt_interpreter: Callable[[LowerSetting], LowerTrtInterpreter],
) -> PassFunc:
- def lower_pass(
+ def lower_pass(
mod: nn.Module, input: Input, lower_setting: LowerSetting, module_name: str
) -> nn.Module:
"""
@@ -653,10 +671,10 @@ Source code for torch_tensorrt.fx.lower
interpreter = create_trt_interpreter(lower_setting)
interp_res: TRTInterpreterResult = interpreter(mod, input, module_name)
if lower_setting.use_experimental_rt:
- import io
+ import io
- from torch_tensorrt._Device import Device
- from torch_tensorrt.dynamo._TorchTensorRTModule import TorchTensorRTModule
+ from torch_tensorrt._Device import Device
+ from torch_tensorrt.dynamo._TorchTensorRTModule import TorchTensorRTModule
with io.BytesIO() as engine_bytes:
engine_bytes.write(interp_res.engine.serialize())
@@ -685,7 +703,7 @@ Source code for torch_tensorrt.fx.lower
@dc.dataclass(frozen=True)
-class Lowerer:
+class Lowerer:
"""Lowers a module using fx2trt.
This is a composable class to facilitate fx2trt. A normal fx2trt process
@@ -709,7 +727,7 @@ Source code for torch_tensorrt.fx.lower
lower_pass_manager_builder: LowerPassManagerBuilder
@classmethod
- def create(
+ def create(
cls,
lower_setting: LowerSetting,
interpreter_builder: Callable = create_lower_trt_interpreter,
@@ -743,7 +761,7 @@ Source code for torch_tensorrt.fx.lower
)
)
- def __call__(
+ def __call__(
self,
module: nn.Module,
inputs: Input,
@@ -758,7 +776,7 @@ Source code for torch_tensorrt.fx.lower
atol=atol,
rtol=rtol,
)
- def do_lower(module: nn.Module, inputs: Input) -> nn.Module:
+ def do_lower(module: nn.Module, inputs: Input) -> nn.Module:
module.eval()
if (
self.lower_pass_manager_builder.lower_setting.lower_precision
@@ -1014,7 +1032,7 @@ Resources
Developer Resources
- Contributor Awards - 2023
+ Contributor Awards - 2024
@@ -1030,6 +1048,9 @@ Resources
ExecuTorch
+
+ ExecuTorch Documentation
+
@@ -1068,6 +1089,9 @@ Resources
Events
+
+ Newsletter
+
@@ -1081,6 +1105,18 @@ Resources
Governing Board
+
+ Cloud Credit Program
+
+
+ Technical Advisory Council
+
+
+ Staff
+
+
+ Contact Us
+
diff --git a/docs/_modules/torch_tensorrt/fx/trt_module.html b/docs/_modules/torch_tensorrt/fx/trt_module.html
index 59a7ef0978..1bb6ded02d 100644
--- a/docs/_modules/torch_tensorrt/fx/trt_module.html
+++ b/docs/_modules/torch_tensorrt/fx/trt_module.html
@@ -9,7 +9,7 @@
- torch_tensorrt.fx.trt_module — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ torch_tensorrt.fx.trt_module — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -132,8 +132,8 @@
Developer Resources
Find resources and get questions answered
-
- Contributor Awards - 2023
+
+ Contributor Awards - 2024
Award winners announced at this year's PyTorch Conference
@@ -154,6 +154,9 @@
ExecuTorch
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+
+ ExecuTorch Docs
+
@@ -201,6 +204,10 @@
Events
Find events, webinars, and podcasts
+
+ Newsletter
+ Stay up-to-date with the latest updates
+
@@ -216,7 +223,18 @@
Governing Board
-
+
+
+ Cloud Credit Program
+
+
+ Technical Advisory Council
+
+
+ Staff
+
+
+ Contact Us
@@ -272,7 +290,7 @@
- v2.6.0.dev0+70e2a38
+ v2.7.0.dev0+d6be4ba
@@ -467,17 +485,17 @@
Source code for torch_tensorrt.fx.trt_module
-from typing import Any, List, Sequence
+from typing import Any, List, Sequence
# @manual=//deeplearning/trt/python:py_tensorrt
-import tensorrt as trt
-import torch
+import tensorrt as trt
+import torch
-from .utils import Frameworks, unified_dtype_converter
+from .utils import Frameworks, unified_dtype_converter
-[docs]class TRTModule(torch.nn.Module):
- def __init__(
+[docs]class TRTModule(torch.nn.Module):
+ def __init__(
self, engine=None, input_names=None, output_names=None, cuda_graph_batch_size=-1
):
super(TRTModule, self).__init__()
@@ -491,7 +509,7 @@ Source code for torch_tensorrt.fx.trt_module
if engine:
self._initialize()
- def _initialize(self):
+ def _initialize(self):
self.initialized = True
self.context = self.engine.create_execution_context()
@@ -560,18 +578,18 @@ Source code for torch_tensorrt.fx.trt_module
for idx in self.hidden_output_binding_indices_in_order
]
- def _check_initialized(self):
+ def _check_initialized(self):
if not self.initialized:
raise RuntimeError("TRTModule is not initialized.")
- def _on_state_dict(self, state_dict, prefix, local_metadata):
+ def _on_state_dict(self, state_dict, prefix, local_metadata):
self._check_initialized()
state_dict[prefix + "engine"] = bytearray(self.engine.serialize())
state_dict[prefix + "input_names"] = self.input_names
state_dict[prefix + "output_names"] = self.output_names
state_dict[prefix + "cuda_graph_batch_size"] = self.cuda_graph_batch_size
- def _load_from_state_dict(
+ def _load_from_state_dict(
self,
state_dict,
prefix,
@@ -591,13 +609,13 @@ Source code for torch_tensorrt.fx.trt_module
self.output_names = state_dict[prefix + "output_names"]
self._initialize()
- def __getstate__(self):
+ def __getstate__(self):
state = self.__dict__.copy()
state["engine"] = bytearray(self.engine.serialize())
state.pop("context", None)
return state
- def __setstate__(self, state):
+ def __setstate__(self, state):
logger = trt.Logger()
runtime = trt.Runtime(logger)
state["engine"] = runtime.deserialize_cuda_engine(state["engine"])
@@ -605,7 +623,7 @@ Source code for torch_tensorrt.fx.trt_module
if self.engine:
self.context = self.engine.create_execution_context()
- def forward(self, *inputs):
+ def forward(self, *inputs):
with torch.autograd.profiler.record_function("TRTModule:Forward"):
self._check_initialized()
@@ -690,7 +708,7 @@ Source code for torch_tensorrt.fx.trt_module
return tuple(outputs)
- def enable_profiling(self, profiler: "trt.IProfiler" = None):
+ def enable_profiling(self, profiler: "trt.IProfiler" = None):
"""
Enable TensorRT profiling. After calling this function, TensorRT will report
time spent on each layer in stdout for each forward run.
@@ -700,7 +718,7 @@ Source code for torch_tensorrt.fx.trt_module
if not self.context.profiler:
self.context.profiler = trt.Profiler() if profiler is None else profiler
- def disable_profiling(self):
+ def disable_profiling(self):
"""
Disable TensorRT profiling.
"""
@@ -710,7 +728,7 @@ Source code for torch_tensorrt.fx.trt_module
del self.context
self.context = self.engine.create_execution_context()
- def get_layer_info(self) -> str:
+ def get_layer_info(self) -> str:
"""
Get layer info of the engine. Only support for TRT > 8.2.
"""
@@ -942,7 +960,7 @@ Resources
Developer Resources
- Contributor Awards - 2023
+ Contributor Awards - 2024
@@ -958,6 +976,9 @@ Resources
ExecuTorch
+
+ ExecuTorch Documentation
+
@@ -996,6 +1017,9 @@ Resources
Events
+
+ Newsletter
+
@@ -1009,6 +1033,18 @@ Resources
Governing Board
+
+ Cloud Credit Program
+
+
+ Technical Advisory Council
+
+
+ Staff
+
+
+ Contact Us
+
diff --git a/docs/_modules/torch_tensorrt/logging.html b/docs/_modules/torch_tensorrt/logging.html
index 5e9f31ad53..f2152f342c 100644
--- a/docs/_modules/torch_tensorrt/logging.html
+++ b/docs/_modules/torch_tensorrt/logging.html
@@ -9,7 +9,7 @@
- torch_tensorrt.logging — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ torch_tensorrt.logging — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -132,8 +132,8 @@
Developer Resources
Find resources and get questions answered
-
- Contributor Awards - 2023
+
+ Contributor Awards - 2024
Award winners announced at this year's PyTorch Conference
@@ -154,6 +154,9 @@
ExecuTorch
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+
+ ExecuTorch Docs
+
@@ -201,6 +204,10 @@
Events
Find events, webinars, and podcasts
+
+ Newsletter
+ Stay up-to-date with the latest updates
+
@@ -216,7 +223,18 @@
Governing Board
-
+
+
+ Cloud Credit Program
+
+
+ Technical Advisory Council
+
+
+ Staff
+
+
+ Contact Us
@@ -272,7 +290,7 @@
- v2.6.0.dev0+70e2a38
+ v2.7.0.dev0+d6be4ba
@@ -467,24 +485,24 @@
Source code for torch_tensorrt.logging
-import logging
-from typing import Any
+import logging
+from typing import Any
-import torch
-from torch_tensorrt._features import ENABLED_FEATURES
+import torch
+from torch_tensorrt._features import ENABLED_FEATURES
-import tensorrt as trt
+import tensorrt as trt
logging.captureWarnings(True)
_LOGGER = logging.getLogger("torch_tensorrt [TensorRT Conversion Context]")
-class _TRTLogger(trt.ILogger): # type: ignore[misc]
+class _TRTLogger(trt.ILogger): # type: ignore[misc]
- def __init__(self) -> None:
+ def __init__(self) -> None:
trt.ILogger.__init__(self)
- def log(self, severity: trt.ILogger.Severity, msg: str) -> None:
+ def log(self, severity: trt.ILogger.Severity, msg: str) -> None:
# TODO: Move to match once py39 reaches EoL
if severity == trt.ILogger.Severity.INTERNAL_ERROR:
_LOGGER.critical(msg)
@@ -502,7 +520,7 @@ Source code for torch_tensorrt.logging
TRT_LOGGER = _TRTLogger()
-[docs]class internal_errors:
+[docs]class internal_errors:
"""Context-manager to limit displayed log messages to just internal errors
Example:
@@ -513,12 +531,12 @@ Source code for torch_tensorrt.logging
outputs = model_torchtrt(inputs)
"""
- def __enter__(self) -> None:
+ def __enter__(self) -> None:
self.external_lvl = _LOGGER.getEffectiveLevel()
_LOGGER.setLevel(logging.CRITICAL)
if ENABLED_FEATURES.torchscript_frontend:
- from torch_tensorrt.ts import logging as ts_logging
+ from torch_tensorrt.ts import logging as ts_logging
self.ts_level = ts_logging.get_reportable_log_level()
ts_logging.set_reportable_log_level(ts_logging.Level.InternalError)
@@ -529,11 +547,11 @@ Source code for torch_tensorrt.logging
int(trt.ILogger.Severity.INTERNAL_ERROR)
)
- def __exit__(self, exc_type: Any, exc_value: Any, exc_tb: Any) -> None:
+ def __exit__(self, exc_type: Any, exc_value: Any, exc_tb: Any) -> None:
_LOGGER.setLevel(self.external_lvl)
if ENABLED_FEATURES.torchscript_frontend:
- from torch_tensorrt.ts import logging as ts_logging
+ from torch_tensorrt.ts import logging as ts_logging
ts_logging.set_reportable_log_level(self.ts_level)
@@ -541,7 +559,7 @@ Source code for torch_tensorrt.logging
torch.ops.tensorrt.set_logging_level(self.rt_level)
-[docs]class errors:
+[docs]class errors:
"""Context-manager to limit displayed log messages to just errors and above
Example:
@@ -552,12 +570,12 @@ Source code for torch_tensorrt.logging
outputs = model_torchtrt(inputs)
"""
- def __enter__(self) -> None:
+ def __enter__(self) -> None:
self.external_lvl = _LOGGER.getEffectiveLevel()
_LOGGER.setLevel(logging.ERROR)
if ENABLED_FEATURES.torchscript_frontend:
- from torch_tensorrt.ts import logging as ts_logging
+ from torch_tensorrt.ts import logging as ts_logging
self.ts_level = ts_logging.get_reportable_log_level()
ts_logging.set_reportable_log_level(ts_logging.Level.Error)
@@ -566,11 +584,11 @@ Source code for torch_tensorrt.logging
self.rt_level = torch.ops.tensorrt.get_logging_level()
torch.ops.tensorrt.set_logging_level(int(trt.ILogger.Severity.ERROR))
- def __exit__(self, exc_type: Any, exc_value: Any, exc_tb: Any) -> None:
+ def __exit__(self, exc_type: Any, exc_value: Any, exc_tb: Any) -> None:
_LOGGER.setLevel(self.external_lvl)
if ENABLED_FEATURES.torchscript_frontend:
- from torch_tensorrt.ts import logging as ts_logging
+ from torch_tensorrt.ts import logging as ts_logging
ts_logging.set_reportable_log_level(self.ts_level)
@@ -578,7 +596,7 @@ Source code for torch_tensorrt.logging
torch.ops.tensorrt.set_logging_level(self.rt_level)
-[docs]class warnings:
+[docs]class warnings:
"""Context-manager to limit displayed log messages to just warnings and above
Example:
@@ -589,12 +607,12 @@ Source code for torch_tensorrt.logging
model_trt = torch_tensorrt.compile(model, **spec)
"""
- def __enter__(self) -> None:
+ def __enter__(self) -> None:
self.external_lvl = _LOGGER.getEffectiveLevel()
_LOGGER.setLevel(logging.WARNING)
if ENABLED_FEATURES.torchscript_frontend:
- from torch_tensorrt.ts import logging as ts_logging
+ from torch_tensorrt.ts import logging as ts_logging
self.ts_level = ts_logging.get_reportable_log_level()
ts_logging.set_reportable_log_level(ts_logging.Level.Warning)
@@ -603,11 +621,11 @@ Source code for torch_tensorrt.logging
self.rt_level = torch.ops.tensorrt.get_logging_level()
torch.ops.tensorrt.set_logging_level(int(trt.ILogger.Severity.WARNING))
- def __exit__(self, exc_type: Any, exc_value: Any, exc_tb: Any) -> None:
+ def __exit__(self, exc_type: Any, exc_value: Any, exc_tb: Any) -> None:
_LOGGER.setLevel(self.external_lvl)
if ENABLED_FEATURES.torchscript_frontend:
- from torch_tensorrt.ts import logging as ts_logging
+ from torch_tensorrt.ts import logging as ts_logging
ts_logging.set_reportable_log_level(self.ts_level)
@@ -615,7 +633,7 @@ Source code for torch_tensorrt.logging
torch.ops.tensorrt.set_logging_level(self.rt_level)
-[docs]class info:
+[docs]class info:
"""Context-manager to display all info and greater severity messages
Example:
@@ -626,12 +644,12 @@ Source code for torch_tensorrt.logging
model_trt = torch_tensorrt.compile(model, **spec)
"""
- def __enter__(self) -> None:
+ def __enter__(self) -> None:
self.external_lvl = _LOGGER.getEffectiveLevel()
_LOGGER.setLevel(logging.INFO)
if ENABLED_FEATURES.torchscript_frontend:
- from torch_tensorrt.ts import logging as ts_logging
+ from torch_tensorrt.ts import logging as ts_logging
self.ts_level = ts_logging.get_reportable_log_level()
ts_logging.set_reportable_log_level(ts_logging.Level.Info)
@@ -640,11 +658,11 @@ Source code for torch_tensorrt.logging
self.rt_level = torch.ops.tensorrt.get_logging_level()
torch.ops.tensorrt.set_logging_level(int(trt.ILogger.Severity.INFO))
- def __exit__(self, exc_type: Any, exc_value: Any, exc_tb: Any) -> None:
+ def __exit__(self, exc_type: Any, exc_value: Any, exc_tb: Any) -> None:
_LOGGER.setLevel(self.external_lvl)
if ENABLED_FEATURES.torchscript_frontend:
- from torch_tensorrt.ts import logging as ts_logging
+ from torch_tensorrt.ts import logging as ts_logging
ts_logging.set_reportable_log_level(self.ts_level)
@@ -652,7 +670,7 @@ Source code for torch_tensorrt.logging
torch.ops.tensorrt.set_logging_level(self.rt_level)
-[docs]class debug:
+[docs]class debug:
"""Context-manager to display full debug information through the logger
Example:
@@ -663,12 +681,12 @@ Source code for torch_tensorrt.logging
model_trt = torch_tensorrt.compile(model, **spec)
"""
- def __enter__(self) -> None:
+ def __enter__(self) -> None:
self.external_lvl = _LOGGER.getEffectiveLevel()
_LOGGER.setLevel(logging.DEBUG)
if ENABLED_FEATURES.torchscript_frontend:
- from torch_tensorrt.ts import logging as ts_logging
+ from torch_tensorrt.ts import logging as ts_logging
self.ts_level = ts_logging.get_reportable_log_level()
ts_logging.set_reportable_log_level(ts_logging.Level.Debug)
@@ -677,11 +695,11 @@ Source code for torch_tensorrt.logging
self.rt_level = torch.ops.tensorrt.get_logging_level()
torch.ops.tensorrt.set_logging_level(int(trt.ILogger.Severity.VERBOSE))
- def __exit__(self, exc_type: Any, exc_value: Any, exc_tb: Any) -> None:
+ def __exit__(self, exc_type: Any, exc_value: Any, exc_tb: Any) -> None:
_LOGGER.setLevel(self.external_lvl)
if ENABLED_FEATURES.torchscript_frontend:
- from torch_tensorrt.ts import logging as ts_logging
+ from torch_tensorrt.ts import logging as ts_logging
ts_logging.set_reportable_log_level(self.ts_level)
@@ -689,7 +707,7 @@ Source code for torch_tensorrt.logging
torch.ops.tensorrt.set_logging_level(self.rt_level)
-[docs]class graphs:
+[docs]class graphs:
"""Context-manager to display the results of intermediate lowering passes
as well as full debug information through the logger
@@ -701,12 +719,12 @@ Source code for torch_tensorrt.logging
model_trt = torch_tensorrt.compile(model, **spec)
"""
- def __enter__(self) -> None:
+ def __enter__(self) -> None:
self.external_lvl = _LOGGER.getEffectiveLevel()
_LOGGER.setLevel(logging.NOTSET)
if ENABLED_FEATURES.torchscript_frontend:
- from torch_tensorrt.ts import logging as ts_logging
+ from torch_tensorrt.ts import logging as ts_logging
self.ts_level = ts_logging.get_reportable_log_level()
ts_logging.set_reportable_log_level(ts_logging.Level.Graph)
@@ -715,11 +733,11 @@ Source code for torch_tensorrt.logging
self.rt_level = torch.ops.tensorrt.get_logging_level()
torch.ops.tensorrt.set_logging_level(int(trt.ILogger.Severity.VERBOSE) + 1)
- def __exit__(self, exc_type: Any, exc_value: Any, exc_tb: Any) -> None:
+ def __exit__(self, exc_type: Any, exc_value: Any, exc_tb: Any) -> None:
_LOGGER.setLevel(self.external_lvl)
if ENABLED_FEATURES.torchscript_frontend:
- from torch_tensorrt.ts import logging as ts_logging
+ from torch_tensorrt.ts import logging as ts_logging
ts_logging.set_reportable_log_level(self.ts_level)
@@ -951,7 +969,7 @@ Resources
Developer Resources
- Contributor Awards - 2023
+ Contributor Awards - 2024
@@ -967,6 +985,9 @@ Resources
ExecuTorch
+
+ ExecuTorch Documentation
+
@@ -1005,6 +1026,9 @@ Resources
Events
+
+ Newsletter
+
@@ -1018,6 +1042,18 @@ Resources
Governing Board
+
+ Cloud Credit Program
+
+
+ Technical Advisory Council
+
+
+ Staff
+
+
+ Contact Us
+
diff --git a/docs/_modules/torch_tensorrt/runtime/_multi_device_safe_mode.html b/docs/_modules/torch_tensorrt/runtime/_multi_device_safe_mode.html
index 1ae7163719..7d98e70c3a 100644
--- a/docs/_modules/torch_tensorrt/runtime/_multi_device_safe_mode.html
+++ b/docs/_modules/torch_tensorrt/runtime/_multi_device_safe_mode.html
@@ -9,7 +9,7 @@
- torch_tensorrt.runtime._multi_device_safe_mode — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ torch_tensorrt.runtime._multi_device_safe_mode — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -132,8 +132,8 @@
Developer Resources
Find resources and get questions answered
-
- Contributor Awards - 2023
+
+ Contributor Awards - 2024
Award winners announced at this year's PyTorch Conference
@@ -154,6 +154,9 @@
ExecuTorch
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+
+ ExecuTorch Docs
+
@@ -201,6 +204,10 @@
Events
Find events, webinars, and podcasts
+
+ Newsletter
+ Stay up-to-date with the latest updates
+
@@ -216,7 +223,18 @@
Governing Board
-
+
+
+ Cloud Credit Program
+
+
+ Technical Advisory Council
+
+
+ Staff
+
+
+ Contact Us
@@ -272,7 +290,7 @@
- v2.6.0.dev0+70e2a38
+ v2.7.0.dev0+d6be4ba
@@ -467,11 +485,11 @@
Source code for torch_tensorrt.runtime._multi_device_safe_mode
-import logging
-from typing import Any
+import logging
+from typing import Any
-import torch
-import torch_tensorrt
+import torch
+import torch_tensorrt
if torch_tensorrt.ENABLED_FEATURES.torch_tensorrt_runtime:
_PY_RT_MULTI_DEVICE_SAFE_MODE = torch.ops.tensorrt.get_multi_device_safe_mode()
@@ -482,19 +500,19 @@ Source code for torch_tensorrt.runtime._multi_device_safe_mode
logger = logging.getLogger(__name__)
-class _MultiDeviceSafeModeContextManager(object):
+class _MultiDeviceSafeModeContextManager(object):
"""Helper class used in conjunction with `set_multi_device_safe_mode`
Used to enable `set_multi_device_safe_mode` as a dual-purpose context manager
"""
- def __init__(self, old_mode: bool) -> None:
+ def __init__(self, old_mode: bool) -> None:
self.old_mode = old_mode
- def __enter__(self) -> "_MultiDeviceSafeModeContextManager":
+ def __enter__(self) -> "_MultiDeviceSafeModeContextManager":
return self
- def __exit__(self, *args: Any) -> None:
+ def __exit__(self, *args: Any) -> None:
# Set multi-device safe mode back to old mode in Python
global _PY_RT_MULTI_DEVICE_SAFE_MODE
_PY_RT_MULTI_DEVICE_SAFE_MODE = self.old_mode
@@ -504,7 +522,7 @@ Source code for torch_tensorrt.runtime._multi_device_safe_mode
torch.ops.tensorrt.set_multi_device_safe_mode(self.old_mode)
-[docs]def set_multi_device_safe_mode(mode: bool) -> _MultiDeviceSafeModeContextManager:
+[docs]def set_multi_device_safe_mode(mode: bool) -> _MultiDeviceSafeModeContextManager:
"""Sets the runtime (Python-only and default) into multi-device safe mode
In the case that multiple devices are available on the system, in order for the
@@ -762,7 +780,7 @@ Resources
Developer Resources
- Contributor Awards - 2023
+ Contributor Awards - 2024
@@ -778,6 +796,9 @@ Resources
ExecuTorch
+
+ ExecuTorch Documentation
+
@@ -816,6 +837,9 @@ Resources
Events
+
+ Newsletter
+
@@ -829,6 +853,18 @@ Resources
Governing Board
+
+ Cloud Credit Program
+
+
+ Technical Advisory Council
+
+
+ Staff
+
+
+ Contact Us
+
diff --git a/docs/_modules/torch_tensorrt/ts/_compile_spec.html b/docs/_modules/torch_tensorrt/ts/_compile_spec.html
index 8945cee3d8..58b2a8e438 100644
--- a/docs/_modules/torch_tensorrt/ts/_compile_spec.html
+++ b/docs/_modules/torch_tensorrt/ts/_compile_spec.html
@@ -9,7 +9,7 @@
- torch_tensorrt.ts._compile_spec — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ torch_tensorrt.ts._compile_spec — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -132,8 +132,8 @@
Developer Resources
Find resources and get questions answered
-
- Contributor Awards - 2023
+
+ Contributor Awards - 2024
Award winners announced at this year's PyTorch Conference
@@ -154,6 +154,9 @@
ExecuTorch
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+
+ ExecuTorch Docs
+
@@ -201,6 +204,10 @@
Events
Find events, webinars, and podcasts
+
+ Newsletter
+ Stay up-to-date with the latest updates
+
@@ -216,7 +223,18 @@
Governing Board
-
+
+
+ Cloud Credit Program
+
+
+ Technical Advisory Council
+
+
+ Staff
+
+
+ Contact Us
@@ -272,7 +290,7 @@
- v2.6.0.dev0+70e2a38
+ v2.7.0.dev0+d6be4ba
@@ -467,25 +485,25 @@
Source code for torch_tensorrt.ts._compile_spec
-from __future__ import annotations
+from __future__ import annotations
-from copy import deepcopy
-from typing import Any, Dict, List, Optional, Set
+from copy import deepcopy
+from typing import Any, Dict, List, Optional, Set
-import torch
-import torch_tensorrt._C.ts as _ts_C
-from torch_tensorrt import _C
-from torch_tensorrt._Device import Device
-from torch_tensorrt._enums import DeviceType, EngineCapability, dtype
-from torch_tensorrt._Input import Input
-from torch_tensorrt.ts._Device import TorchScriptDevice
-from torch_tensorrt.ts._Input import TorchScriptInput
-from torch_tensorrt.ts.logging import Level, log
+import torch
+import torch_tensorrt._C.ts as _ts_C
+from torch_tensorrt import _C
+from torch_tensorrt._Device import Device
+from torch_tensorrt._enums import DeviceType, EngineCapability, dtype
+from torch_tensorrt._Input import Input
+from torch_tensorrt.ts._Device import TorchScriptDevice
+from torch_tensorrt.ts._Input import TorchScriptInput
+from torch_tensorrt.ts.logging import Level, log
-import tensorrt as trt
+import tensorrt as trt
-def _internal_input_to_torch_class_input(i: _C.Input) -> torch.classes.tensorrt._Input:
+def _internal_input_to_torch_class_input(i: _C.Input) -> torch.classes.tensorrt._Input:
clone = torch.classes.tensorrt._Input()
clone._set_min(i.min)
clone._set_opt(i.opt)
@@ -498,7 +516,7 @@ Source code for torch_tensorrt.ts._compile_spec
<
return clone
-def _supported_input_size_type(input_size: Any) -> bool:
+def _supported_input_size_type(input_size: Any) -> bool:
if isinstance(input_size, torch.Size):
return True
elif isinstance(input_size, tuple):
@@ -512,11 +530,11 @@ Source code for torch_tensorrt.ts._compile_spec
<
)
-def _parse_op_precision(precision: Any) -> _C.dtype:
+def _parse_op_precision(precision: Any) -> _C.dtype:
return dtype._from(precision).to(_C.dtype)
-def _parse_enabled_precisions(precisions: Any) -> Set[_C.dtype]:
+def _parse_enabled_precisions(precisions: Any) -> Set[_C.dtype]:
parsed_precisions = set()
if any(isinstance(precisions, type) for type in [list, tuple, set]):
for p in precisions:
@@ -526,11 +544,11 @@ Source code for torch_tensorrt.ts._compile_spec
<
return parsed_precisions
-def _parse_device_type(device: Any) -> _C.DeviceType:
+def _parse_device_type(device: Any) -> _C.DeviceType:
return DeviceType._from(device).to(_C.DeviceType)
-def _parse_device(device_info: Any) -> _C.Device:
+def _parse_device(device_info: Any) -> _C.Device:
if isinstance(device_info, dict):
info = _C.Device()
if "device_type" not in device_info:
@@ -563,7 +581,7 @@ Source code for torch_tensorrt.ts._compile_spec
<
)
-def _parse_torch_fallback(fallback_info: Dict[str, Any]) -> _ts_C.TorchFallback:
+def _parse_torch_fallback(fallback_info: Dict[str, Any]) -> _ts_C.TorchFallback:
info = _ts_C.TorchFallback()
if "enabled" not in fallback_info:
raise KeyError("Enabled is required parameter")
@@ -585,7 +603,7 @@ Source code for torch_tensorrt.ts._compile_spec
<
return info
-def _parse_input_signature(input_signature: Any, depth: int = 0) -> Any:
+def _parse_input_signature(input_signature: Any, depth: int = 0) -> Any:
if depth > 2:
raise AssertionError(
"Input nesting depth exceeds max supported depth, use 1 level: [A, B], or 2 level: [A, (B, C)]"
@@ -650,7 +668,7 @@ Source code for torch_tensorrt.ts._compile_spec
<
)
-def _parse_compile_spec(compile_spec_: Dict[str, Any]) -> _ts_C.CompileSpec:
+def _parse_compile_spec(compile_spec_: Dict[str, Any]) -> _ts_C.CompileSpec:
# TODO: Use deepcopy to support partial compilation of collections
compile_spec = deepcopy(compile_spec_)
info = _ts_C.CompileSpec()
@@ -773,7 +791,7 @@ Source code for torch_tensorrt.ts._compile_spec
<
return info
-[docs]def TensorRTCompileSpec(
+[docs]def TensorRTCompileSpec(
inputs: Optional[List[torch.Tensor | Input]] = None,
input_signature: Optional[Any] = None,
device: Optional[torch.device | Device] = None,
@@ -1132,7 +1150,7 @@ Resources
Developer Resources
- Contributor Awards - 2023
+ Contributor Awards - 2024
@@ -1148,6 +1166,9 @@ Resources
ExecuTorch
+
+ ExecuTorch Documentation
+
@@ -1186,6 +1207,9 @@ Resources
Events
+
+ Newsletter
+
@@ -1199,6 +1223,18 @@ Resources
Governing Board
+
+ Cloud Credit Program
+
+
+ Technical Advisory Council
+
+
+ Staff
+
+
+ Contact Us
+
diff --git a/docs/_modules/torch_tensorrt/ts/_compiler.html b/docs/_modules/torch_tensorrt/ts/_compiler.html
index 34b655dbfe..189edbd4d1 100644
--- a/docs/_modules/torch_tensorrt/ts/_compiler.html
+++ b/docs/_modules/torch_tensorrt/ts/_compiler.html
@@ -9,7 +9,7 @@
- torch_tensorrt.ts._compiler — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ torch_tensorrt.ts._compiler — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -132,8 +132,8 @@
Developer Resources
Find resources and get questions answered
-
- Contributor Awards - 2023
+
+ Contributor Awards - 2024
Award winners announced at this year's PyTorch Conference
@@ -154,6 +154,9 @@
ExecuTorch
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+
+ ExecuTorch Docs
+
@@ -201,6 +204,10 @@
Events
Find events, webinars, and podcasts
+
+ Newsletter
+ Stay up-to-date with the latest updates
+
@@ -216,7 +223,18 @@
Governing Board
-
+
+
+ Cloud Credit Program
+
+
+ Technical Advisory Council
+
+
+ Staff
+
+
+ Contact Us
@@ -272,7 +290,7 @@
- v2.6.0.dev0+70e2a38
+ v2.7.0.dev0+d6be4ba
@@ -467,19 +485,19 @@
Source code for torch_tensorrt.ts._compiler
-from __future__ import annotations
+from __future__ import annotations
-from typing import Any, List, Optional, Sequence, Set, Tuple
+from typing import Any, List, Optional, Sequence, Set, Tuple
-import torch
-import torch_tensorrt._C.ts as _C
-from torch_tensorrt._Device import Device
-from torch_tensorrt._enums import EngineCapability, dtype
-from torch_tensorrt._Input import Input
-from torch_tensorrt.ts._compile_spec import _parse_compile_spec, _parse_device
+import torch
+import torch_tensorrt._C.ts as _C
+from torch_tensorrt._Device import Device
+from torch_tensorrt._enums import EngineCapability, dtype
+from torch_tensorrt._Input import Input
+from torch_tensorrt.ts._compile_spec import _parse_compile_spec, _parse_device
-[docs]def compile(
+[docs]def compile(
module: torch.jit.ScriptModule,
inputs: Optional[Sequence[Input | torch.Tensor]] = None,
input_signature: Optional[Tuple[Input | torch.Tensor | Sequence[Any]]] = None,
@@ -627,7 +645,7 @@ Source code for torch_tensorrt.ts._compiler
return compiled_module
-[docs]def convert_method_to_trt_engine(
+[docs]def convert_method_to_trt_engine(
module: torch.jit.ScriptModule,
method_name: str = "forward",
inputs: Optional[Sequence[Input | torch.Tensor]] = None,
@@ -739,7 +757,7 @@ Source code for torch_tensorrt.ts._compiler
module._c, method_name, _parse_compile_spec(compile_spec)
)
- import io
+ import io
with io.BytesIO() as engine_bytes:
engine_bytes.write(engine_str)
@@ -748,7 +766,7 @@ Source code for torch_tensorrt.ts._compiler
return engine_bytearray
-[docs]def embed_engine_in_new_module(
+[docs]def embed_engine_in_new_module(
serialized_engine: bytes,
input_binding_names: Optional[List[str]] = None,
output_binding_names: Optional[List[str]] = None,
@@ -794,7 +812,7 @@ Source code for torch_tensorrt.ts._compiler
return wrapped_mod
-[docs]def check_method_op_support(
+[docs]def check_method_op_support(
module: torch.jit.ScriptModule, method_name: str = "forward"
) -> bool:
"""Checks to see if a method is fully supported by torch_tensorrt
@@ -1038,7 +1056,7 @@ Resources
Developer Resources
- Contributor Awards - 2023
+ Contributor Awards - 2024
@@ -1054,6 +1072,9 @@ Resources
ExecuTorch
+
+ ExecuTorch Documentation
+
@@ -1092,6 +1113,9 @@ Resources
Events
+
+ Newsletter
+
@@ -1105,6 +1129,18 @@ Resources
Governing Board
+
+ Cloud Credit Program
+
+
+ Technical Advisory Council
+
+
+ Staff
+
+
+ Contact Us
+
diff --git a/docs/_modules/torch_tensorrt/ts/ptq.html b/docs/_modules/torch_tensorrt/ts/ptq.html
index d8bca52250..9ab2d1112d 100644
--- a/docs/_modules/torch_tensorrt/ts/ptq.html
+++ b/docs/_modules/torch_tensorrt/ts/ptq.html
@@ -9,7 +9,7 @@
- torch_tensorrt.ts.ptq — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ torch_tensorrt.ts.ptq — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -132,8 +132,8 @@
Developer Resources
Find resources and get questions answered
-
- Contributor Awards - 2023
+
+ Contributor Awards - 2024
Award winners announced at this year's PyTorch Conference
@@ -154,6 +154,9 @@
ExecuTorch
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+
+ ExecuTorch Docs
+
@@ -201,6 +204,10 @@
Events
Find events, webinars, and podcasts
+
+ Newsletter
+ Stay up-to-date with the latest updates
+
@@ -216,7 +223,18 @@
Governing Board
-
+
+
+ Cloud Credit Program
+
+
+ Technical Advisory Council
+
+
+ Staff
+
+
+ Contact Us
@@ -272,7 +290,7 @@
- v2.6.0.dev0+70e2a38
+ v2.7.0.dev0+d6be4ba
@@ -467,38 +485,38 @@
Source code for torch_tensorrt.ts.ptq
-import sys
-from typing import Any, List, Optional
+import sys
+from typing import Any, List, Optional
if sys.version_info >= (3, 11):
- from typing import Self
+ from typing import Self
else:
- from typing_extensions import Self
+ from typing_extensions import Self
-import os
-from enum import Enum
+import os
+from enum import Enum
-import torch
-from torch_tensorrt import _C
-from torch_tensorrt.ts.logging import Level, log
+import torch
+from torch_tensorrt import _C
+from torch_tensorrt.ts.logging import Level, log
-[docs]class CalibrationAlgo(Enum):
+[docs]class CalibrationAlgo(Enum):
ENTROPY_CALIBRATION = _C.CalibrationAlgo.ENTROPY_CALIBRATION
ENTROPY_CALIBRATION_2 = _C.CalibrationAlgo.ENTROPY_CALIBRATION_2
LEGACY_CALIBRATION = _C.CalibrationAlgo.LEGACY_CALIBRATION
MINMAX_CALIBRATION = _C.CalibrationAlgo.MINMAX_CALIBRATION
-def get_cache_mode_batch(self: object) -> None:
+def get_cache_mode_batch(self: object) -> None:
return None
-def get_batch_size(self: object) -> int:
+def get_batch_size(self: object) -> int:
return 1
-def get_batch(self: object, _: Any) -> Optional[List[int]]:
+def get_batch(self: object, _: Any) -> Optional[List[int]]:
if self.current_batch_idx + self.batch_size > len(self.data_loader.dataset):
return None
@@ -513,7 +531,7 @@ Source code for torch_tensorrt.ts.ptq
return inputs_gpu
-def read_calibration_cache(self: object) -> bytes:
+def read_calibration_cache(self: object) -> bytes:
if self.cache_file and self.use_cache:
if os.path.exists(self.cache_file):
with open(self.cache_file, "rb") as f:
@@ -525,7 +543,7 @@ Source code for torch_tensorrt.ts.ptq
return b""
-def write_calibration_cache(self: object, cache: bytes) -> None:
+def write_calibration_cache(self: object, cache: bytes) -> None:
if self.cache_file:
with open(self.cache_file, "wb") as f:
f.write(cache)
@@ -536,11 +554,11 @@ Source code for torch_tensorrt.ts.ptq
# deepcopy (which involves pickling) is performed on the compile_spec internally during compilation.
# We register this __reduce__ function for pickler to identity the calibrator object returned by DataLoaderCalibrator during deepcopy.
# This should be the object's local name relative to the module https://docs.python.org/3/library/pickle.html#object.__reduce__
-def __reduce__(self: object) -> str:
+def __reduce__(self: object) -> str:
return self.__class__.__name__
-[docs]class DataLoaderCalibrator(object):
+[docs]class DataLoaderCalibrator(object):
"""
Constructs a calibrator class in TensorRT and uses pytorch dataloader to load/preprocess
data which is passed during calibration.
@@ -553,10 +571,10 @@ Source code for torch_tensorrt.ts.ptq
device (Device): device on which calibration data is copied to.
"""
- def __init__(self, **kwargs: Any):
+ def __init__(self, **kwargs: Any):
pass
- def __new__(cls, *args: Any, **kwargs: Any) -> Self:
+ def __new__(cls, *args: Any, **kwargs: Any) -> Self:
dataloader = args[0]
algo_type = kwargs.get("algo_type", CalibrationAlgo.ENTROPY_CALIBRATION_2)
cache_file = kwargs.get("cache_file", None)
@@ -631,7 +649,7 @@ Source code for torch_tensorrt.ts.ptq
)
-[docs]class CacheCalibrator(object):
+[docs]class CacheCalibrator(object):
"""
Constructs a calibrator class in TensorRT which directly uses pre-existing cache file for calibration.
@@ -640,10 +658,10 @@ Source code for torch_tensorrt.ts.ptq
algo_type (CalibrationAlgo): choice of calibration algorithm.
"""
- def __init__(self, **kwargs: Any):
+ def __init__(self, **kwargs: Any):
pass
- def __new__(cls, *args: Any, **kwargs: Any) -> Self:
+ def __new__(cls, *args: Any, **kwargs: Any) -> Self:
cache_file = args[0]
algo_type = kwargs.get("algo_type", CalibrationAlgo.ENTROPY_CALIBRATION_2)
@@ -915,7 +933,7 @@ Resources
Developer Resources
- Contributor Awards - 2023
+ Contributor Awards - 2024
@@ -931,6 +949,9 @@ Resources
ExecuTorch
+
+ ExecuTorch Documentation
+
@@ -969,6 +990,9 @@ Resources
Events
+
+ Newsletter
+
@@ -982,6 +1006,18 @@ Resources
Governing Board
+
+ Cloud Credit Program
+
+
+ Technical Advisory Council
+
+
+ Staff
+
+
+ Contact Us
+
diff --git a/docs/_sources/user_guide/saving_models.rst.txt b/docs/_sources/user_guide/saving_models.rst.txt
index 42cff7b954..dc4b5da222 100644
--- a/docs/_sources/user_guide/saving_models.rst.txt
+++ b/docs/_sources/user_guide/saving_models.rst.txt
@@ -34,7 +34,7 @@ Here's an example usage
model = MyModel().eval().cuda()
inputs = [torch.randn((1, 3, 224, 224)).cuda()]
# trt_ep is a torch.fx.GraphModule object
- trt_gm = torch_tensorrt.compile(model, ir="dynamo", inputs)
+ trt_gm = torch_tensorrt.compile(model, ir="dynamo", inputs=inputs)
torch_tensorrt.save(trt_gm, "trt.ep", inputs=inputs)
# Later, you can load it and run inference
@@ -52,7 +52,7 @@ b) Torchscript
model = MyModel().eval().cuda()
inputs = [torch.randn((1, 3, 224, 224)).cuda()]
# trt_gm is a torch.fx.GraphModule object
- trt_gm = torch_tensorrt.compile(model, ir="dynamo", inputs)
+ trt_gm = torch_tensorrt.compile(model, ir="dynamo", inputs=inputs)
torch_tensorrt.save(trt_gm, "trt.ts", output_format="torchscript", inputs=inputs)
# Later, you can load it and run inference
@@ -73,7 +73,7 @@ For `ir=ts`, this behavior stays the same in 2.X versions as well.
model = MyModel().eval().cuda()
inputs = [torch.randn((1, 3, 224, 224)).cuda()]
- trt_ts = torch_tensorrt.compile(model, ir="ts", inputs) # Output is a ScriptModule object
+ trt_ts = torch_tensorrt.compile(model, ir="ts", inputs=inputs) # Output is a ScriptModule object
torch.jit.save(trt_ts, "trt_model.ts")
# Later, you can load it and run inference
diff --git a/docs/_static/documentation_options.js b/docs/_static/documentation_options.js
index 5bf9b066f8..efbb769960 100644
--- a/docs/_static/documentation_options.js
+++ b/docs/_static/documentation_options.js
@@ -1,6 +1,6 @@
var DOCUMENTATION_OPTIONS = {
URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'),
- VERSION: 'v2.6.0.dev0+70e2a38',
+ VERSION: 'v2.7.0.dev0+d6be4ba',
LANGUAGE: 'en',
COLLAPSE_INDEX: false,
BUILDER: 'html',
diff --git a/docs/_static/js/vendor/anchor.min.js b/docs/_static/js/vendor/anchor.min.js
index 1216eeac26..1c2b86faed 100644
--- a/docs/_static/js/vendor/anchor.min.js
+++ b/docs/_static/js/vendor/anchor.min.js
@@ -1,9 +1,9 @@
// @license magnet:?xt=urn:btih:d3d9a9a6595521f9666a5e94cc830dab83b65699&dn=expat.txt Expat
//
-// AnchorJS - v4.3.0 - 2020-10-21
+// AnchorJS - v4.3.1 - 2021-04-17
// https://www.bryanbraun.com/anchorjs/
-// Copyright (c) 2020 Bryan Braun; Licensed MIT
+// Copyright (c) 2021 Bryan Braun; Licensed MIT
//
// @license magnet:?xt=urn:btih:d3d9a9a6595521f9666a5e94cc830dab83b65699&dn=expat.txt Expat
-!function(A,e){"use strict";"function"==typeof define&&define.amd?define([],e):"object"==typeof module&&module.exports?module.exports=e():(A.AnchorJS=e(),A.anchors=new A.AnchorJS)}(this,function(){"use strict";return function(A){function d(A){A.icon=Object.prototype.hasOwnProperty.call(A,"icon")?A.icon:"",A.visible=Object.prototype.hasOwnProperty.call(A,"visible")?A.visible:"hover",A.placement=Object.prototype.hasOwnProperty.call(A,"placement")?A.placement:"right",A.ariaLabel=Object.prototype.hasOwnProperty.call(A,"ariaLabel")?A.ariaLabel:"Anchor",A.class=Object.prototype.hasOwnProperty.call(A,"class")?A.class:"",A.base=Object.prototype.hasOwnProperty.call(A,"base")?A.base:"",A.truncate=Object.prototype.hasOwnProperty.call(A,"truncate")?Math.floor(A.truncate):64,A.titleText=Object.prototype.hasOwnProperty.call(A,"titleText")?A.titleText:""}function f(A){var e;if("string"==typeof A||A instanceof String)e=[].slice.call(document.querySelectorAll(A));else{if(!(Array.isArray(A)||A instanceof NodeList))throw new TypeError("The selector provided to AnchorJS was invalid.");e=[].slice.call(A)}return e}this.options=A||{},this.elements=[],d(this.options),this.isTouchDevice=function(){return Boolean("ontouchstart"in window||window.TouchEvent||window.DocumentTouch&&document instanceof DocumentTouch)},this.add=function(A){var e,t,o,n,i,s,a,r,c,l,h,u,p=[];if(d(this.options),"touch"===(h=this.options.visible)&&(h=this.isTouchDevice()?"always":"hover"),0===(e=f(A=A||"h2, h3, h4, h5, h6")).length)return this;for(!function(){if(null!==document.head.querySelector("style.anchorjs"))return;var A,e=document.createElement("style");e.className="anchorjs",e.appendChild(document.createTextNode("")),void 0===(A=document.head.querySelector('[rel="stylesheet"],style'))?document.head.appendChild(e):document.head.insertBefore(e,A);e.sheet.insertRule(".anchorjs-link{opacity:0;text-decoration:none;-webkit-font-smoothing:antialiased;-moz-osx-font-smoothing:grayscale}",e.sheet.cssRules.length),e.sheet.insertRule(":hover>.anchorjs-link,.anchorjs-link:focus{opacity:1}",e.sheet.cssRules.length),e.sheet.insertRule("[data-anchorjs-icon]::after{content:attr(data-anchorjs-icon)}",e.sheet.cssRules.length),e.sheet.insertRule('@font-face{font-family:anchorjs-icons;src:url(data:n/a;base64,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) format("truetype")}',e.sheet.cssRules.length)}(),t=document.querySelectorAll("[id]"),o=[].map.call(t,function(A){return A.id}),i=0;i\]./()*\\\n\t\b\v\u00A0]/g,"-").replace(/-{2,}/g,"-").substring(0,this.options.truncate).replace(/^-+|-+$/gm,"").toLowerCase()},this.hasAnchorJSLink=function(A){var e=A.firstChild&&-1<(" "+A.firstChild.className+" ").indexOf(" anchorjs-link "),t=A.lastChild&&-1<(" "+A.lastChild.className+" ").indexOf(" anchorjs-link ");return e||t||!1}}});
+!function(A,e){"use strict";"function"==typeof define&&define.amd?define([],e):"object"==typeof module&&module.exports?module.exports=e():(A.AnchorJS=e(),A.anchors=new A.AnchorJS)}(this,function(){"use strict";return function(A){function d(A){A.icon=Object.prototype.hasOwnProperty.call(A,"icon")?A.icon:"",A.visible=Object.prototype.hasOwnProperty.call(A,"visible")?A.visible:"hover",A.placement=Object.prototype.hasOwnProperty.call(A,"placement")?A.placement:"right",A.ariaLabel=Object.prototype.hasOwnProperty.call(A,"ariaLabel")?A.ariaLabel:"Anchor",A.class=Object.prototype.hasOwnProperty.call(A,"class")?A.class:"",A.base=Object.prototype.hasOwnProperty.call(A,"base")?A.base:"",A.truncate=Object.prototype.hasOwnProperty.call(A,"truncate")?Math.floor(A.truncate):64,A.titleText=Object.prototype.hasOwnProperty.call(A,"titleText")?A.titleText:""}function w(A){var e;if("string"==typeof A||A instanceof String)e=[].slice.call(document.querySelectorAll(A));else{if(!(Array.isArray(A)||A instanceof NodeList))throw new TypeError("The selector provided to AnchorJS was invalid.");e=[].slice.call(A)}return e}this.options=A||{},this.elements=[],d(this.options),this.isTouchDevice=function(){return Boolean("ontouchstart"in window||window.TouchEvent||window.DocumentTouch&&document instanceof DocumentTouch)},this.add=function(A){var e,t,o,i,n,s,a,c,r,l,h,u,p=[];if(d(this.options),"touch"===(l=this.options.visible)&&(l=this.isTouchDevice()?"always":"hover"),0===(e=w(A=A||"h2, h3, h4, h5, h6")).length)return this;for(null===document.head.querySelector("style.anchorjs")&&((u=document.createElement("style")).className="anchorjs",u.appendChild(document.createTextNode("")),void 0===(A=document.head.querySelector('[rel="stylesheet"],style'))?document.head.appendChild(u):document.head.insertBefore(u,A),u.sheet.insertRule(".anchorjs-link{opacity:0;text-decoration:none;-webkit-font-smoothing:antialiased;-moz-osx-font-smoothing:grayscale}",u.sheet.cssRules.length),u.sheet.insertRule(":hover>.anchorjs-link,.anchorjs-link:focus{opacity:1}",u.sheet.cssRules.length),u.sheet.insertRule("[data-anchorjs-icon]::after{content:attr(data-anchorjs-icon)}",u.sheet.cssRules.length),u.sheet.insertRule('@font-face{font-family:anchorjs-icons;src:url(data:n/a;base64,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) format("truetype")}',u.sheet.cssRules.length)),u=document.querySelectorAll("[id]"),t=[].map.call(u,function(A){return A.id}),i=0;i\]./()*\\\n\t\b\v\u00A0]/g,"-").replace(/-{2,}/g,"-").substring(0,this.options.truncate).replace(/^-+|-+$/gm,"").toLowerCase()},this.hasAnchorJSLink=function(A){var e=A.firstChild&&-1<(" "+A.firstChild.className+" ").indexOf(" anchorjs-link "),A=A.lastChild&&-1<(" "+A.lastChild.className+" ").indexOf(" anchorjs-link ");return e||A||!1}}});
// @license-end
\ No newline at end of file
diff --git a/docs/_static/pygments.css b/docs/_static/pygments.css
index 0d49244eda..5f2b0a250a 100644
--- a/docs/_static/pygments.css
+++ b/docs/_static/pygments.css
@@ -6,26 +6,26 @@ span.linenos.special { color: #000000; background-color: #ffffc0; padding-left:
.highlight .hll { background-color: #ffffcc }
.highlight { background: #eeffcc; }
.highlight .c { color: #408090; font-style: italic } /* Comment */
-.highlight .err { border: 1px solid #FF0000 } /* Error */
+.highlight .err { border: 1px solid #F00 } /* Error */
.highlight .k { color: #007020; font-weight: bold } /* Keyword */
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.highlight .cm { color: #408090; font-style: italic } /* Comment.Multiline */
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+.highlight .cs { color: #408090; background-color: #FFF0F0 } /* Comment.Special */
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+.highlight .gr { color: #F00 } /* Generic.Error */
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-.highlight .go { color: #333333 } /* Generic.Output */
-.highlight .gp { color: #c65d09; font-weight: bold } /* Generic.Prompt */
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+.highlight .gp { color: #C65D09; font-weight: bold } /* Generic.Prompt */
.highlight .gs { font-weight: bold } /* Generic.Strong */
.highlight .gu { color: #800080; font-weight: bold } /* Generic.Subheading */
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.highlight .kd { color: #007020; font-weight: bold } /* Keyword.Declaration */
.highlight .kn { color: #007020; font-weight: bold } /* Keyword.Namespace */
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+.highlight .fm { color: #06287E } /* Name.Function.Magic */
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+.highlight .vg { color: #BB60D5 } /* Name.Variable.Global */
+.highlight .vi { color: #BB60D5 } /* Name.Variable.Instance */
+.highlight .vm { color: #BB60D5 } /* Name.Variable.Magic */
.highlight .il { color: #208050 } /* Literal.Number.Integer.Long */
\ No newline at end of file
diff --git a/docs/cli/torchtrtc.html b/docs/cli/torchtrtc.html
index 0332b3d7c4..0743462a93 100644
--- a/docs/cli/torchtrtc.html
+++ b/docs/cli/torchtrtc.html
@@ -10,7 +10,7 @@
- torchtrtc — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ torchtrtc — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -135,8 +135,8 @@
Developer Resources
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@@ -157,6 +157,9 @@
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@@ -204,6 +207,10 @@
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@@ -275,7 +293,7 @@
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+ v2.7.0.dev0+d6be4ba
@@ -851,7 +869,7 @@ Resources
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+ Contributor Awards - 2024
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diff --git a/docs/contributors/conversion.html b/docs/contributors/conversion.html
index 409ff24b05..7dc601418f 100644
--- a/docs/contributors/conversion.html
+++ b/docs/contributors/conversion.html
@@ -10,7 +10,7 @@
- Conversion Phase — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ Conversion Phase — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -135,8 +135,8 @@
Developer Resources
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-
- Contributor Awards - 2023
+
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@@ -157,6 +157,9 @@
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+
@@ -204,6 +207,10 @@
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@@ -275,7 +293,7 @@
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+ v2.7.0.dev0+d6be4ba
@@ -771,7 +789,7 @@ Resources
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+ Contributor Awards - 2024
@@ -787,6 +805,9 @@ Resources
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diff --git a/docs/contributors/dynamo_converters.html b/docs/contributors/dynamo_converters.html
index 3fa5662f82..54d746c989 100644
--- a/docs/contributors/dynamo_converters.html
+++ b/docs/contributors/dynamo_converters.html
@@ -10,7 +10,7 @@
- Writing Dynamo Converters — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ Writing Dynamo Converters — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -135,8 +135,8 @@
Developer Resources
Find resources and get questions answered
-
- Contributor Awards - 2023
+
+ Contributor Awards - 2024
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@@ -157,6 +157,9 @@
ExecuTorch
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+
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+
@@ -204,6 +207,10 @@
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@@ -275,7 +293,7 @@
- v2.6.0.dev0+70e2a38
+ v2.7.0.dev0+d6be4ba
@@ -480,7 +498,7 @@ Converter implementation¶
A converter is a function decrorated with torch_tensorrt.dynamo.dynamo_tensorrt_converter that follows the function signature:
@torch_tensorrt.dynamo.conversion.dynamo_tensorrt_converter(torch.ops.aten.leaky_relu.default)
-def leaky_relu_converter(
+def leaky_relu_converter(
ctx: torch_tensorrt.dynamo.conversion.ConversionCtx,
target: Target,
args: Tuple[Argument, ...],
@@ -541,7 +559,7 @@ Example: Convol
2: (np.ndarray, torch.Tensor, TRTTensor),
}
) # type: ignore[misc]
-def aten_ops_convolution(
+def aten_ops_convolution(
ctx: ConversionContext,
target: Target,
args: Tuple[Argument, ...],
@@ -570,7 +588,7 @@ Example: addmm<
The decompositions are registered via register_torch_trt_decomposition decorator
We define addmm_replacement and replace it with the torch ops, which will have their corresponding converters called.
@torch_tensorrt.dynamo.lowering.register_torch_trt_decomposition(torch.ops.aten.addmm)
-def addmm_replacement(
+def addmm_replacement(
input_: torch.Tensor, mat1: torch.Tensor, mat2: torch.Tensor, *, beta=1, alpha=1
) -> torch.Tensor:
return torch.add(
@@ -838,7 +856,7 @@ Resources
Developer Resources
- Contributor Awards - 2023
+ Contributor Awards - 2024
@@ -854,6 +872,9 @@ Resources
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@@ -905,6 +929,18 @@ Resources
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diff --git a/docs/contributors/lowering.html b/docs/contributors/lowering.html
index fb73bb1a46..beeb85f48f 100644
--- a/docs/contributors/lowering.html
+++ b/docs/contributors/lowering.html
@@ -10,7 +10,7 @@
- Lowering Phase — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ Lowering Phase — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -135,8 +135,8 @@
Developer Resources
Find resources and get questions answered
-
- Contributor Awards - 2023
+
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@@ -157,6 +157,9 @@
ExecuTorch
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+
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+
@@ -204,6 +207,10 @@
Events
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+
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@@ -275,7 +293,7 @@
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+ v2.7.0.dev0+d6be4ba
@@ -941,7 +959,7 @@ Resources
Developer Resources
- Contributor Awards - 2023
+ Contributor Awards - 2024
@@ -957,6 +975,9 @@ Resources
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diff --git a/docs/contributors/partitioning.html b/docs/contributors/partitioning.html
index 1750af6f79..d116fe8a86 100644
--- a/docs/contributors/partitioning.html
+++ b/docs/contributors/partitioning.html
@@ -10,7 +10,7 @@
- Partitioning Phase — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ Partitioning Phase — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -135,8 +135,8 @@
Developer Resources
Find resources and get questions answered
-
- Contributor Awards - 2023
+
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@@ -157,6 +157,9 @@
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@@ -204,6 +207,10 @@
Events
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@@ -275,7 +293,7 @@
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+ v2.7.0.dev0+d6be4ba
@@ -938,7 +956,7 @@ Resources
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diff --git a/docs/contributors/phases.html b/docs/contributors/phases.html
index dd31270654..c65216b1bf 100644
--- a/docs/contributors/phases.html
+++ b/docs/contributors/phases.html
@@ -10,7 +10,7 @@
- Compiler Phases — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ Compiler Phases — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -133,8 +133,8 @@
Developer Resources
Find resources and get questions answered
-
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+
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@@ -155,6 +155,9 @@
ExecuTorch
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+
@@ -202,6 +205,10 @@
Events
Find events, webinars, and podcasts
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@@ -217,7 +224,18 @@
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@@ -273,7 +291,7 @@
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+ v2.7.0.dev0+d6be4ba
@@ -739,7 +757,7 @@ Resources
Developer Resources
- Contributor Awards - 2023
+ Contributor Awards - 2024
@@ -755,6 +773,9 @@ Resources
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diff --git a/docs/contributors/runtime.html b/docs/contributors/runtime.html
index 1fe3b7cee7..cf6cd73583 100644
--- a/docs/contributors/runtime.html
+++ b/docs/contributors/runtime.html
@@ -10,7 +10,7 @@
- Runtime Phase — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ Runtime Phase — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -135,8 +135,8 @@
Developer Resources
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@@ -157,6 +157,9 @@
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@@ -204,6 +207,10 @@
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@@ -275,7 +293,7 @@
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+ v2.7.0.dev0+d6be4ba
@@ -791,7 +809,7 @@ Resources
Developer Resources
- Contributor Awards - 2023
+ Contributor Awards - 2024
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diff --git a/docs/contributors/system_overview.html b/docs/contributors/system_overview.html
index 48f2aa6653..80c52543fd 100644
--- a/docs/contributors/system_overview.html
+++ b/docs/contributors/system_overview.html
@@ -10,7 +10,7 @@
- System Overview — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ System Overview — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -135,8 +135,8 @@
Developer Resources
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@@ -157,6 +157,9 @@
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+
@@ -204,6 +207,10 @@
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@@ -775,7 +793,7 @@ Resources
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diff --git a/docs/contributors/ts_converters.html b/docs/contributors/ts_converters.html
index f581ae2db4..8f8bb8beda 100644
--- a/docs/contributors/ts_converters.html
+++ b/docs/contributors/ts_converters.html
@@ -10,7 +10,7 @@
- Writing TorchScript Converters — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ Writing TorchScript Converters — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -135,8 +135,8 @@
Developer Resources
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+
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@@ -157,6 +157,9 @@
ExecuTorch
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+
@@ -204,6 +207,10 @@
Events
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@@ -275,7 +293,7 @@
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+ v2.7.0.dev0+d6be4ba
@@ -846,7 +864,7 @@ Resources
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diff --git a/docs/contributors/useful_links.html b/docs/contributors/useful_links.html
index 0d153672ef..9bb792bff2 100644
--- a/docs/contributors/useful_links.html
+++ b/docs/contributors/useful_links.html
@@ -10,7 +10,7 @@
- Useful Links for Torch-TensorRT Development — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ Useful Links for Torch-TensorRT Development — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -135,8 +135,8 @@
Developer Resources
Find resources and get questions answered
-
- Contributor Awards - 2023
+
+ Contributor Awards - 2024
Award winners announced at this year's PyTorch Conference
@@ -157,6 +157,9 @@
ExecuTorch
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+
+ ExecuTorch Docs
+
@@ -204,6 +207,10 @@
Events
Find events, webinars, and podcasts
+
+ Newsletter
+ Stay up-to-date with the latest updates
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@@ -219,7 +226,18 @@
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+
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@@ -275,7 +293,7 @@
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+ v2.7.0.dev0+d6be4ba
@@ -756,7 +774,7 @@ Resources
Developer Resources
- Contributor Awards - 2023
+ Contributor Awards - 2024
@@ -772,6 +790,9 @@ Resources
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diff --git a/docs/contributors/writing_dynamo_aten_lowering_passes.html b/docs/contributors/writing_dynamo_aten_lowering_passes.html
index 5fa3939e9e..60b340a8f6 100644
--- a/docs/contributors/writing_dynamo_aten_lowering_passes.html
+++ b/docs/contributors/writing_dynamo_aten_lowering_passes.html
@@ -10,7 +10,7 @@
- Writing Dynamo ATen Lowering Passes — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ Writing Dynamo ATen Lowering Passes — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -135,8 +135,8 @@
Developer Resources
Find resources and get questions answered
-
- Contributor Awards - 2023
+
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@@ -157,6 +157,9 @@
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+
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+
@@ -204,6 +207,10 @@
Events
Find events, webinars, and podcasts
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@@ -275,7 +293,7 @@
- v2.6.0.dev0+70e2a38
+ v2.7.0.dev0+d6be4ba
@@ -486,7 +504,7 @@ Lowering Pass Requirements
Example Lowering Pass¶
-def repair_input_as_output(gm: torch.fx.GraphModule, sample_inputs: Sequence[torch.Tensor]) -> torch.fx.GraphModule:
+def repair_input_as_output(gm: torch.fx.GraphModule, sample_inputs: Sequence[torch.Tensor]) -> torch.fx.GraphModule:
"""Repair scenarios where inputs are also outputs of the graph
TRT does not allow such cases, so we insert a clone (identity) layer
@@ -542,13 +560,13 @@ Registering Lowering Passes@_aten_lowering_pass
-def my_custom_pass(gm: torch.fx.GraphModule, sample_inputs: Sequence[torch.Tensor]) -> torch.fx.GraphModule:
+def my_custom_pass(gm: torch.fx.GraphModule, sample_inputs: Sequence[torch.Tensor]) -> torch.fx.GraphModule:
...
Alternatively, to insert the pass at a custom index (such as the front of the list) in the passlist, the following code can be used:
@_aten_lowering_pass(index=0)
-def my_custom_pass(gm: torch.fx.GraphModule, sample_inputs: Sequence[torch.Tensor]) -> torch.fx.GraphModule:
+def my_custom_pass(gm: torch.fx.GraphModule, sample_inputs: Sequence[torch.Tensor]) -> torch.fx.GraphModule:
...
@@ -810,7 +828,7 @@ Resources
Developer Resources
- Contributor Awards - 2023
+ Contributor Awards - 2024
@@ -826,6 +844,9 @@ Resources
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@@ -864,6 +885,9 @@ Resources
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@@ -877,6 +901,18 @@ Resources
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diff --git a/docs/dynamo/dynamo_export.html b/docs/dynamo/dynamo_export.html
index 4720fb9e54..aae33f9173 100644
--- a/docs/dynamo/dynamo_export.html
+++ b/docs/dynamo/dynamo_export.html
@@ -10,7 +10,7 @@
- Compiling Exported Programs with Torch-TensorRT — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ Compiling Exported Programs with Torch-TensorRT — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -135,8 +135,8 @@
Developer Resources
Find resources and get questions answered
-
- Contributor Awards - 2023
+
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@@ -157,6 +157,9 @@
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+
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+
@@ -204,6 +207,10 @@
Events
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+
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@@ -275,7 +293,7 @@
- v2.6.0.dev0+70e2a38
+ v2.7.0.dev0+d6be4ba
@@ -477,8 +495,8 @@
can export graphs from Pytorch programs into ExportedProgram objects. Torch-TensorRT dynamo
frontend compiles these ExportedProgram objects and optimizes them using TensorRT. Here’s a simple
usage of the dynamo frontend
-import torch
-import torch_tensorrt
+import torch
+import torch_tensorrt
model = MyModel().eval().cuda()
inputs = [torch.randn((1, 3, 224, 224), dtype=torch.float32).cuda()]
@@ -525,8 +543,8 @@ TracingExportedProgram can then be used with torch_tensorrt.dynamo.compile API.
If you have dynamic input shapes in your model, you can use this torch_tensorrt.dynamo.trace to export
the model with dynamic shapes. Alternatively, you can use torch.export with constraints directly as well.
-import torch
-import torch_tensorrt
+import torch
+import torch_tensorrt
inputs = [torch_tensorrt.Input(min_shape=(1, 3, 224, 224),
opt_shape=(4, 3, 224, 224),
@@ -781,7 +799,7 @@ Resources
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diff --git a/docs/dynamo/torch_compile.html b/docs/dynamo/torch_compile.html
index 868e9f175d..4942edf179 100644
--- a/docs/dynamo/torch_compile.html
+++ b/docs/dynamo/torch_compile.html
@@ -10,7 +10,7 @@
- TensorRT Backend for torch.compile — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ TensorRT Backend for torch.compile — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -135,8 +135,8 @@
Developer Resources
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-
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@@ -157,6 +157,9 @@
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@@ -204,6 +207,10 @@
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@@ -275,7 +293,7 @@
- v2.6.0.dev0+70e2a38
+ v2.7.0.dev0+d6be4ba
@@ -477,7 +495,7 @@
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
+import torch_tensorrt
...
optimized_model = torch.compile(model, backend="torch_tensorrt", dynamic=False)
@@ -492,7 +510,7 @@ Key Features¶
-
-class torch_tensorrt.dynamo.CompilationSettings(enabled_precisions: ~typing.Set[~torch_tensorrt._enums.dtype] = <factory>, debug: bool = False, workspace_size: int = 0, min_block_size: int = 5, torch_executed_ops: ~typing.Collection[~typing.Union[~typing.Callable[[...], ~typing.Any], 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_double: bool = False, use_fast_partitioner: bool = True, enable_experimental_decompositions: bool = False, device: ~torch_tensorrt._Device.Device = <factory>, require_full_compilation: bool = False, disable_tf32: bool = False, assume_dynamic_shape_support: bool = False, sparse_weights: bool = False, engine_capability: ~torch_tensorrt._enums.EngineCapability = <factory>, num_avg_timing_iters: int = 1, dla_sram_size: int = 1048576, dla_local_dram_size: int = 1073741824, dla_global_dram_size: int = 536870912, dryrun: ~typing.Union[bool, str] = False, hardware_compatible: bool = False, timing_cache_path: str = '/tmp/torch_tensorrt_engine_cache/timing_cache.bin', lazy_engine_init: bool = False, cache_built_engines: bool = False, reuse_cached_engines: bool = False, use_explicit_typing: bool = False, use_fp32_acc: bool = False, refit_identical_engine_weights: bool = False, strip_engine_weights: bool = False, immutable_weights: bool = True, enable_weight_streaming: bool = False, enable_cross_compile_for_windows: bool = False)[source]¶
+class torch_tensorrt.dynamo.CompilationSettings(enabled_precisions: ~typing.Set[~torch_tensorrt._enums.dtype] = <factory>, debug: bool = False, workspace_size: int = 0, min_block_size: int = 5, torch_executed_ops: ~typing.Collection[~typing.Union[~typing.Callable[[...], ~typing.Any], 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_double: bool = False, use_fast_partitioner: bool = True, enable_experimental_decompositions: bool = False, device: ~torch_tensorrt._Device.Device = <factory>, require_full_compilation: bool = False, disable_tf32: bool = False, assume_dynamic_shape_support: bool = False, sparse_weights: bool = False, engine_capability: ~torch_tensorrt._enums.EngineCapability = <factory>, num_avg_timing_iters: int = 1, dla_sram_size: int = 1048576, dla_local_dram_size: int = 1073741824, dla_global_dram_size: int = 536870912, dryrun: ~typing.Union[bool, str] = False, hardware_compatible: bool = False, timing_cache_path: str = '/tmp/torch_tensorrt_engine_cache/timing_cache.bin', lazy_engine_init: bool = False, cache_built_engines: bool = False, reuse_cached_engines: bool = False, use_explicit_typing: bool = False, use_fp32_acc: bool = False, refit_identical_engine_weights: bool = False, strip_engine_weights: bool = False, immutable_weights: bool = True, enable_weight_streaming: bool = False, enable_cross_compile_for_windows: bool = False, use_aot_joint_export: bool = True)[source]¶
Compilation settings for Torch-TensorRT Dynamo Paths
- Parameters
@@ -540,6 +558,7 @@ Customizable Settingsenable_weight_streaming (bool) – Enable weight streaming.
enable_cross_compile_for_windows (bool) – By default this is False means TensorRT engines can only be executed on the same platform where they were built.
True will enable cross-platform compatibility which allows the engine to be built on Linux and run on Windows
use_aot_joint_export (bool) – Use aot_export_joint_simple, else wrap backend with AOT_autograd, required for distributed tensors
@@ -547,7 +566,7 @@ Customizable Settings
Custom Setting Usage¶
-import torch_tensorrt
+import torch_tensorrt
...
optimized_model = torch.compile(model, backend="torch_tensorrt", dynamic=False,
options={"truncate_long_and_double": True,
@@ -568,7 +587,7 @@ Custom Setting Usage
Compilation¶
Compilation is triggered by passing inputs to the model, as so:
-import torch_tensorrt
+import torch_tensorrt
...
# Causes model compilation to occur
first_outputs = optimized_model(*inputs)
@@ -589,7 +608,7 @@ Model Performance
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
+import torch_tensorrt
...
optimized_model = torch.compile(model, backend="torch_tensorrt", dynamic=False,
options={"debug": True,
@@ -601,8 +620,8 @@ Operator Coverage
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 compilable 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
+import torch
+import torch_tensorrt
...
explanation = torch._dynamo.explain(model)(*inputs)
print(f"Graph breaks: {explanation.graph_break_count}")
@@ -875,7 +894,7 @@ Resources
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diff --git a/docs/fx/getting_started_with_fx_path.html b/docs/fx/getting_started_with_fx_path.html
index d03f09d79d..73f4ec5262 100644
--- a/docs/fx/getting_started_with_fx_path.html
+++ b/docs/fx/getting_started_with_fx_path.html
@@ -10,7 +10,7 @@
- Torch-TensorRT (FX Frontend) User Guide — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ Torch-TensorRT (FX Frontend) User Guide — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -135,8 +135,8 @@
Developer Resources
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@@ -157,6 +157,9 @@
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@@ -204,6 +207,10 @@
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@@ -1016,7 +1034,7 @@ Resources
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diff --git a/docs/genindex.html b/docs/genindex.html
index d032584364..98325928d9 100644
--- a/docs/genindex.html
+++ b/docs/genindex.html
@@ -9,7 +9,7 @@
- Index — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ Index — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -132,8 +132,8 @@
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@@ -154,6 +154,9 @@
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diff --git a/docs/getting_started/installation.html b/docs/getting_started/installation.html
index fe15b213b1..ce8054a901 100644
--- a/docs/getting_started/installation.html
+++ b/docs/getting_started/installation.html
@@ -10,7 +10,7 @@
- Installation — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ Installation — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -135,8 +135,8 @@
Developer Resources
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diff --git a/docs/getting_started/jetpack.html b/docs/getting_started/jetpack.html
index b761bf3590..43fa62f4e7 100644
--- a/docs/getting_started/jetpack.html
+++ b/docs/getting_started/jetpack.html
@@ -10,7 +10,7 @@
- Overview — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ Overview — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -135,8 +135,8 @@
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diff --git a/docs/getting_started/quick_start.html b/docs/getting_started/quick_start.html
index 99d843281b..e37f815edf 100644
--- a/docs/getting_started/quick_start.html
+++ b/docs/getting_started/quick_start.html
@@ -10,7 +10,7 @@
- Quick Start — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ Quick Start — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -135,8 +135,8 @@
Developer Resources
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@@ -275,7 +293,7 @@
- v2.6.0.dev0+70e2a38
+ v2.7.0.dev0+d6be4ba
@@ -476,8 +494,8 @@
Option 1: torch.compile¶
You can use Torch-TensorRT anywhere you use torch.compile:
-import torch
-import torch_tensorrt
+import torch
+import torch_tensorrt
model = MyModel().eval().cuda() # define your model here
x = torch.randn((1, 3, 224, 224)).cuda() # define what the inputs to the model will look like
@@ -494,8 +512,8 @@ Option 2: Export
Step 1: Optimize + serialize¶
-import torch
-import torch_tensorrt
+import torch
+import torch_tensorrt
model = MyModel().eval().cuda() # define your model here
inputs = [torch.randn((1, 3, 224, 224)).cuda()] # define a list of representative inputs here
@@ -510,8 +528,8 @@ Step 1: Optimize + serialize¶
Deployment in Python:¶
-import torch
-import torch_tensorrt
+import torch
+import torch_tensorrt
inputs = [torch.randn((1, 3, 224, 224)).cuda()] # your inputs go here
@@ -786,7 +804,7 @@ Resources
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diff --git a/docs/index.html b/docs/index.html
index 3abe58676e..0807b3c505 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -10,7 +10,7 @@
- Torch-TensorRT — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ Torch-TensorRT — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -134,8 +134,8 @@
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@@ -156,6 +156,9 @@
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@@ -203,6 +206,10 @@
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diff --git a/docs/indices/supported_ops.html b/docs/indices/supported_ops.html
index 4cac58a36f..77fe9ffc0e 100644
--- a/docs/indices/supported_ops.html
+++ b/docs/indices/supported_ops.html
@@ -10,7 +10,7 @@
- Operators Supported — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ Operators Supported — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -134,8 +134,8 @@
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diff --git a/docs/objects.inv b/docs/objects.inv
index a846d7f9d7..c32270832d 100644
Binary files a/docs/objects.inv and b/docs/objects.inv differ
diff --git a/docs/py-modindex.html b/docs/py-modindex.html
index 7b38f1cc29..f931caf1af 100644
--- a/docs/py-modindex.html
+++ b/docs/py-modindex.html
@@ -9,7 +9,7 @@
- Python Module Index — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ Python Module Index — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -135,8 +135,8 @@
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diff --git a/docs/py_api/dynamo.html b/docs/py_api/dynamo.html
index 5203926b74..d55fae495e 100644
--- a/docs/py_api/dynamo.html
+++ b/docs/py_api/dynamo.html
@@ -10,7 +10,7 @@
- torch_tensorrt.dynamo — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ torch_tensorrt.dynamo — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -135,8 +135,8 @@
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@@ -275,7 +293,7 @@
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+ v2.7.0.dev0+d6be4ba
@@ -663,7 +681,7 @@ Functions¶
-
-class torch_tensorrt.dynamo.CompilationSettings(enabled_precisions: ~typing.Set[~torch_tensorrt._enums.dtype] = <factory>, debug: bool = False, workspace_size: int = 0, min_block_size: int = 5, torch_executed_ops: ~typing.Collection[~typing.Union[~typing.Callable[[...], ~typing.Any], 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_double: bool = False, use_fast_partitioner: bool = True, enable_experimental_decompositions: bool = False, device: ~torch_tensorrt._Device.Device = <factory>, require_full_compilation: bool = False, disable_tf32: bool = False, assume_dynamic_shape_support: bool = False, sparse_weights: bool = False, engine_capability: ~torch_tensorrt._enums.EngineCapability = <factory>, num_avg_timing_iters: int = 1, dla_sram_size: int = 1048576, dla_local_dram_size: int = 1073741824, dla_global_dram_size: int = 536870912, dryrun: ~typing.Union[bool, str] = False, hardware_compatible: bool = False, timing_cache_path: str = '/tmp/torch_tensorrt_engine_cache/timing_cache.bin', lazy_engine_init: bool = False, cache_built_engines: bool = False, reuse_cached_engines: bool = False, use_explicit_typing: bool = False, use_fp32_acc: bool = False, refit_identical_engine_weights: bool = False, strip_engine_weights: bool = False, immutable_weights: bool = True, enable_weight_streaming: bool = False, enable_cross_compile_for_windows: bool = False)[source]¶
+class torch_tensorrt.dynamo.CompilationSettings(enabled_precisions: ~typing.Set[~torch_tensorrt._enums.dtype] = <factory>, debug: bool = False, workspace_size: int = 0, min_block_size: int = 5, torch_executed_ops: ~typing.Collection[~typing.Union[~typing.Callable[[...], ~typing.Any], 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_double: bool = False, use_fast_partitioner: bool = True, enable_experimental_decompositions: bool = False, device: ~torch_tensorrt._Device.Device = <factory>, require_full_compilation: bool = False, disable_tf32: bool = False, assume_dynamic_shape_support: bool = False, sparse_weights: bool = False, engine_capability: ~torch_tensorrt._enums.EngineCapability = <factory>, num_avg_timing_iters: int = 1, dla_sram_size: int = 1048576, dla_local_dram_size: int = 1073741824, dla_global_dram_size: int = 536870912, dryrun: ~typing.Union[bool, str] = False, hardware_compatible: bool = False, timing_cache_path: str = '/tmp/torch_tensorrt_engine_cache/timing_cache.bin', lazy_engine_init: bool = False, cache_built_engines: bool = False, reuse_cached_engines: bool = False, use_explicit_typing: bool = False, use_fp32_acc: bool = False, refit_identical_engine_weights: bool = False, strip_engine_weights: bool = False, immutable_weights: bool = True, enable_weight_streaming: bool = False, enable_cross_compile_for_windows: bool = False, use_aot_joint_export: bool = True)[source]¶
Compilation settings for Torch-TensorRT Dynamo Paths
- Parameters
@@ -711,6 +729,7 @@ ClassesDeveloper Resources
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@@ -976,6 +995,9 @@ Resources
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diff --git a/docs/py_api/fx.html b/docs/py_api/fx.html
index 2c952515d4..dea5e3d4c7 100644
--- a/docs/py_api/fx.html
+++ b/docs/py_api/fx.html
@@ -10,7 +10,7 @@
- torch_tensorrt.fx — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ torch_tensorrt.fx — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -135,8 +135,8 @@
Developer Resources
Find resources and get questions answered
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@@ -157,6 +157,9 @@
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@@ -204,6 +207,10 @@
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Find events, webinars, and podcasts
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@@ -275,7 +293,7 @@
- v2.6.0.dev0+70e2a38
+ v2.7.0.dev0+d6be4ba
@@ -789,7 +807,7 @@ Resources
Developer Resources
- Contributor Awards - 2023
+ Contributor Awards - 2024
@@ -805,6 +823,9 @@ Resources
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diff --git a/docs/py_api/logging.html b/docs/py_api/logging.html
index 32d06a1517..4a2d9ec8bb 100644
--- a/docs/py_api/logging.html
+++ b/docs/py_api/logging.html
@@ -10,7 +10,7 @@
- torch_tensorrt.logging — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ torch_tensorrt.logging — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -135,8 +135,8 @@
Developer Resources
Find resources and get questions answered
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- Contributor Awards - 2023
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Award winners announced at this year's PyTorch Conference
@@ -157,6 +157,9 @@
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@@ -204,6 +207,10 @@
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Find events, webinars, and podcasts
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@@ -275,7 +293,7 @@
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@@ -781,7 +799,7 @@ Resources
Developer Resources
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diff --git a/docs/py_api/ptq.html b/docs/py_api/ptq.html
index 7ac17fb125..be608c6aa9 100644
--- a/docs/py_api/ptq.html
+++ b/docs/py_api/ptq.html
@@ -10,7 +10,7 @@
- torch_tensorrt.ts.ptq — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ torch_tensorrt.ts.ptq — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -135,8 +135,8 @@
Developer Resources
Find resources and get questions answered
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@@ -157,6 +157,9 @@
ExecuTorch
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+ ExecuTorch Docs
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Find events, webinars, and podcasts
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+ @@ -219,7 +226,18 @@ Governing Board - + + + Cloud Credit Program + + + Technical Advisory Council + + + Staff + + + Contact Us @@ -275,7 +293,7 @@
- v2.6.0.dev0+70e2a38
+ v2.7.0.dev0+d6be4ba
@@ -783,7 +801,7 @@ Resources
Developer ResourcesResources
Resources
Resources
Find resources and get questions answered
- - Contributor Awards - 2023 + + Contributor Awards - 2024Award winners announced at this year's PyTorch Conference
@@ -157,6 +157,9 @@ ExecuTorchEnd-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+ + ExecuTorch Docs + @@ -204,6 +207,10 @@ EventsFind events, webinars, and podcasts
+ + Newsletter +Stay up-to-date with the latest updates
+ @@ -219,7 +226,18 @@ Governing Board - + + + Cloud Credit Program + + + Technical Advisory Council + + + Staff + + + Contact Us @@ -275,7 +293,7 @@
- v2.6.0.dev0+70e2a38
+ v2.7.0.dev0+d6be4ba
@@ -581,13 +599,13 @@ Classes
-class torch_tensorrt.runtime.PythonTorchTensorRTModule(serialized_engine: ~typing.Optional[bytes] = None, input_binding_names: ~typing.Optional[~typing.List[str]] = None, output_binding_names: ~typing.Optional[~typing.List[str]] = None, *, name: str = '', settings: ~torch_tensorrt.dynamo._settings.CompilationSettings = CompilationSettings(enabled_precisions={<dtype.f32: 7>}, debug=False, workspace_size=0, min_block_size=5, torch_executed_ops=set(), pass_through_build_failures=False, max_aux_streams=None, version_compatible=False, optimization_level=None, use_python_runtime=False, truncate_double=False, use_fast_partitioner=True, enable_experimental_decompositions=False, device=Device(type=DeviceType.GPU, gpu_id=0), require_full_compilation=False, disable_tf32=False, assume_dynamic_shape_support=False, sparse_weights=False, engine_capability=<EngineCapability.STANDARD: 1>, num_avg_timing_iters=1, dla_sram_size=1048576, dla_local_dram_size=1073741824, dla_global_dram_size=536870912, dryrun=False, hardware_compatible=False, timing_cache_path='/tmp/torch_tensorrt_engine_cache/timing_cache.bin', lazy_engine_init=False, cache_built_engines=False, reuse_cached_engines=False, use_explicit_typing=False, use_fp32_acc=False, refit_identical_engine_weights=False, strip_engine_weights=False, immutable_weights=True, enable_weight_streaming=False, enable_cross_compile_for_windows=False), weight_name_map: ~typing.Optional[dict[typing.Any, typing.Any]] = None)[source]¶
+class torch_tensorrt.runtime.PythonTorchTensorRTModule(serialized_engine: ~typing.Optional[bytes] = None, input_binding_names: ~typing.Optional[~typing.List[str]] = None, output_binding_names: ~typing.Optional[~typing.List[str]] = None, *, name: str = '', settings: ~torch_tensorrt.dynamo._settings.CompilationSettings = CompilationSettings(enabled_precisions={<dtype.f32: 7>}, debug=False, workspace_size=0, min_block_size=5, torch_executed_ops=set(), pass_through_build_failures=False, max_aux_streams=None, version_compatible=False, optimization_level=None, use_python_runtime=False, truncate_double=False, use_fast_partitioner=True, enable_experimental_decompositions=False, device=Device(type=DeviceType.GPU, gpu_id=0), require_full_compilation=False, disable_tf32=False, assume_dynamic_shape_support=False, sparse_weights=False, engine_capability=<EngineCapability.STANDARD: 1>, num_avg_timing_iters=1, dla_sram_size=1048576, dla_local_dram_size=1073741824, dla_global_dram_size=536870912, dryrun=False, hardware_compatible=False, timing_cache_path='/tmp/torch_tensorrt_engine_cache/timing_cache.bin', lazy_engine_init=False, cache_built_engines=False, reuse_cached_engines=False, use_explicit_typing=False, use_fp32_acc=False, refit_identical_engine_weights=False, strip_engine_weights=False, immutable_weights=True, enable_weight_streaming=False, enable_cross_compile_for_windows=False, use_aot_joint_export=True), weight_name_map: ~typing.Optional[dict[typing.Any, typing.Any]] = None)[source]¶
PythonTorchTensorRTModule is a PyTorch module which encompasses an arbitrary TensorRT Engine.
This module is backed by the Torch-TensorRT runtime and is only compatible with
FX / Dynamo / Python deployments. This module cannot be serialized to torchscript via torch.jit.trace for C++ deployment.
-
-__init__(serialized_engine: ~typing.Optional[bytes] = None, input_binding_names: ~typing.Optional[~typing.List[str]] = None, output_binding_names: ~typing.Optional[~typing.List[str]] = None, *, name: str = '', settings: ~torch_tensorrt.dynamo._settings.CompilationSettings = CompilationSettings(enabled_precisions={<dtype.f32: 7>}, debug=False, workspace_size=0, min_block_size=5, torch_executed_ops=set(), pass_through_build_failures=False, max_aux_streams=None, version_compatible=False, optimization_level=None, use_python_runtime=False, truncate_double=False, use_fast_partitioner=True, enable_experimental_decompositions=False, device=Device(type=DeviceType.GPU, gpu_id=0), require_full_compilation=False, disable_tf32=False, assume_dynamic_shape_support=False, sparse_weights=False, engine_capability=<EngineCapability.STANDARD: 1>, num_avg_timing_iters=1, dla_sram_size=1048576, dla_local_dram_size=1073741824, dla_global_dram_size=536870912, dryrun=False, hardware_compatible=False, timing_cache_path='/tmp/torch_tensorrt_engine_cache/timing_cache.bin', lazy_engine_init=False, cache_built_engines=False, reuse_cached_engines=False, use_explicit_typing=False, use_fp32_acc=False, refit_identical_engine_weights=False, strip_engine_weights=False, immutable_weights=True, enable_weight_streaming=False, enable_cross_compile_for_windows=False), weight_name_map: ~typing.Optional[dict[typing.Any, typing.Any]] = None)[source]¶
+__init__(serialized_engine: ~typing.Optional[bytes] = None, input_binding_names: ~typing.Optional[~typing.List[str]] = None, output_binding_names: ~typing.Optional[~typing.List[str]] = None, *, name: str = '', settings: ~torch_tensorrt.dynamo._settings.CompilationSettings = CompilationSettings(enabled_precisions={<dtype.f32: 7>}, debug=False, workspace_size=0, min_block_size=5, torch_executed_ops=set(), pass_through_build_failures=False, max_aux_streams=None, version_compatible=False, optimization_level=None, use_python_runtime=False, truncate_double=False, use_fast_partitioner=True, enable_experimental_decompositions=False, device=Device(type=DeviceType.GPU, gpu_id=0), require_full_compilation=False, disable_tf32=False, assume_dynamic_shape_support=False, sparse_weights=False, engine_capability=<EngineCapability.STANDARD: 1>, num_avg_timing_iters=1, dla_sram_size=1048576, dla_local_dram_size=1073741824, dla_global_dram_size=536870912, dryrun=False, hardware_compatible=False, timing_cache_path='/tmp/torch_tensorrt_engine_cache/timing_cache.bin', lazy_engine_init=False, cache_built_engines=False, reuse_cached_engines=False, use_explicit_typing=False, use_fp32_acc=False, refit_identical_engine_weights=False, strip_engine_weights=False, immutable_weights=True, enable_weight_streaming=False, enable_cross_compile_for_windows=False, use_aot_joint_export=True), weight_name_map: ~typing.Optional[dict[typing.Any, typing.Any]] = None)[source]¶
Takes a name, target device, serialized TensorRT engine, and binding names / order and constructs
a PyTorch torch.nn.Module around it. Uses TensorRT Python APIs to run the engine
@@ -903,7 +921,7 @@ Resources
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@@ -919,6 +937,9 @@ Resources
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diff --git a/docs/py_api/torch_tensorrt.html b/docs/py_api/torch_tensorrt.html
index 1ebc412b8c..43503cbc18 100644
--- a/docs/py_api/torch_tensorrt.html
+++ b/docs/py_api/torch_tensorrt.html
@@ -10,7 +10,7 @@
- torch_tensorrt — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ torch_tensorrt — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -135,8 +135,8 @@
Developer Resources
Find resources and get questions answered
-
- Contributor Awards - 2023
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@@ -157,6 +157,9 @@
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@@ -204,6 +207,10 @@
Events
Find events, webinars, and podcasts
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@@ -275,7 +293,7 @@
- v2.6.0.dev0+70e2a38
+ v2.7.0.dev0+d6be4ba
@@ -1751,7 +1769,7 @@ Resources
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@@ -1767,6 +1785,9 @@ Resources
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diff --git a/docs/py_api/ts.html b/docs/py_api/ts.html
index fe80f50f67..3956fa7518 100644
--- a/docs/py_api/ts.html
+++ b/docs/py_api/ts.html
@@ -10,7 +10,7 @@
- torch_tensorrt.ts — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ torch_tensorrt.ts — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -135,8 +135,8 @@
Developer Resources
Find resources and get questions answered
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- Contributor Awards - 2023
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@@ -157,6 +157,9 @@
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@@ -275,7 +293,7 @@
- v2.6.0.dev0+70e2a38
+ v2.7.0.dev0+d6be4ba
@@ -695,7 +713,7 @@ Functions
-
-torch_tensorrt.ts.TensorRTCompileSpec(inputs: Optional[List[torch.Tensor | Input]] = None, input_signature: Optional[Any] = None, device: Optional[Union[device, Device]] = None, disable_tf32: bool = False, sparse_weights: bool = False, enabled_precisions: Optional[Set[Union[dtype, dtype]]] = None, refit: bool = False, debug: bool = False, capability: EngineCapability = EngineCapability.STANDARD, num_avg_timing_iters: int = 1, workspace_size: int = 0, dla_sram_size: int = 1048576, dla_local_dram_size: int = 1073741824, dla_global_dram_size: int = 536870912, truncate_long_and_double: bool = False, calibrator: object = None, allow_shape_tensors: bool = False) <torch.ScriptClass object at 0x7f70888fd830>[source]¶
+torch_tensorrt.ts.TensorRTCompileSpec(inputs: Optional[List[torch.Tensor | Input]] = None, input_signature: Optional[Any] = None, device: Optional[Union[device, Device]] = None, disable_tf32: bool = False, sparse_weights: bool = False, enabled_precisions: Optional[Set[Union[dtype, dtype]]] = None, refit: bool = False, debug: bool = False, capability: EngineCapability = EngineCapability.STANDARD, num_avg_timing_iters: int = 1, workspace_size: int = 0, dla_sram_size: int = 1048576, dla_local_dram_size: int = 1073741824, dla_global_dram_size: int = 536870912, truncate_long_and_double: bool = False, calibrator: object = None, allow_shape_tensors: bool = False) <torch.ScriptClass object at 0x7fe38a1f3730>[source]¶
Utility to create a formatted spec dictionary for using the PyTorch TensorRT backend
- Keyword Arguments
@@ -986,7 +1004,7 @@ Resources
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@@ -1002,6 +1020,9 @@ Resources
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diff --git a/docs/search.html b/docs/search.html
index ab2599b327..642109fe1f 100644
--- a/docs/search.html
+++ b/docs/search.html
@@ -9,7 +9,7 @@
- Search — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ Search — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -132,8 +132,8 @@
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@@ -154,6 +154,9 @@
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@@ -272,7 +290,7 @@
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@@ -709,7 +727,7 @@ Resources
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diff --git a/docs/searchindex.js b/docs/searchindex.js
index 12ac2d9ebe..9c36cf3953 100644
--- a/docs/searchindex.js
+++ b/docs/searchindex.js
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Find resources and get questions answered
-
- Contributor Awards - 2023
+
+ Contributor Awards - 2024
Award winners announced at this year's PyTorch Conference
@@ -155,6 +155,9 @@
ExecuTorch
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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Governing Board
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+
+
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+
+
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+
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+
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Governing Board
+ -
+ Cloud Credit Program
+
+ -
+ Technical Advisory Council
+
+ -
+ Staff
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+ -
+ Contact Us
+
diff --git a/docs/src/pytorch-sphinx-theme/docs/changelog.html b/docs/src/pytorch-sphinx-theme/docs/changelog.html
index e7d7800497..03fb7a6fc9 100644
--- a/docs/src/pytorch-sphinx-theme/docs/changelog.html
+++ b/docs/src/pytorch-sphinx-theme/docs/changelog.html
@@ -10,7 +10,7 @@
- Changelog — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ Changelog — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -133,8 +133,8 @@
Developer Resources
Find resources and get questions answered
-
- Contributor Awards - 2023
+
+ Contributor Awards - 2024
Award winners announced at this year's PyTorch Conference
@@ -155,6 +155,9 @@
ExecuTorch
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+
+ ExecuTorch Docs
+
@@ -202,6 +205,10 @@
Events
Find events, webinars, and podcasts
+
+ Newsletter
+ Stay up-to-date with the latest updates
+
@@ -217,7 +224,18 @@
Governing Board
-
+
+
+ Cloud Credit Program
+
+
+ Technical Advisory Council
+
+
+ Staff
+
+
+ Contact Us
@@ -273,7 +291,7 @@
- v2.6.0.dev0+70e2a38
+ v2.7.0.dev0+d6be4ba
@@ -702,7 +720,7 @@ Resources
Developer Resources
-
- Contributor Awards - 2023
+ Contributor Awards - 2024
@@ -718,6 +736,9 @@ Resources
-
ExecuTorch
+ -
+ ExecuTorch Documentation
+
-
Events
+ -
+ Newsletter
+
-
Governing Board
+ -
+ Cloud Credit Program
+
+ -
+ Technical Advisory Council
+
+ -
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+
+ -
+ Contact Us
+
diff --git a/docs/src/pytorch-sphinx-theme/docs/configuring.html b/docs/src/pytorch-sphinx-theme/docs/configuring.html
index cc64f58bbf..a980d4ce63 100644
--- a/docs/src/pytorch-sphinx-theme/docs/configuring.html
+++ b/docs/src/pytorch-sphinx-theme/docs/configuring.html
@@ -10,7 +10,7 @@
- Configuration — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ Configuration — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -133,8 +133,8 @@
Developer Resources
Find resources and get questions answered
-
- Contributor Awards - 2023
+
+ Contributor Awards - 2024
Award winners announced at this year's PyTorch Conference
@@ -155,6 +155,9 @@
ExecuTorch
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+
+ ExecuTorch Docs
+
@@ -202,6 +205,10 @@
Events
Find events, webinars, and podcasts
+
+ Newsletter
+ Stay up-to-date with the latest updates
+
@@ -217,7 +224,18 @@
Governing Board
-
+
+
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+
+
+ Technical Advisory Council
+
+
+ Staff
+
+
+ Contact Us
@@ -273,7 +291,7 @@
- v2.6.0.dev0+70e2a38
+ v2.7.0.dev0+d6be4ba
@@ -805,7 +823,7 @@ Resources
Developer Resources
-
- Contributor Awards - 2023
+ Contributor Awards - 2024
@@ -821,6 +839,9 @@ Resources
-
ExecuTorch
+ -
+ ExecuTorch Documentation
+
-
Events
+ -
+ Newsletter
+
-
Governing Board
+ -
+ Cloud Credit Program
+
+ -
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+
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+
+ -
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+
diff --git a/docs/src/pytorch-sphinx-theme/docs/demo/api.html b/docs/src/pytorch-sphinx-theme/docs/demo/api.html
index 19fa661195..c87155fa45 100644
--- a/docs/src/pytorch-sphinx-theme/docs/demo/api.html
+++ b/docs/src/pytorch-sphinx-theme/docs/demo/api.html
@@ -10,7 +10,7 @@
- 5. :mod:`test_py_module` — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ 5. :mod:`test_py_module` — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -133,8 +133,8 @@
Developer Resources
Find resources and get questions answered
-
- Contributor Awards - 2023
+
+ Contributor Awards - 2024
Award winners announced at this year's PyTorch Conference
@@ -155,6 +155,9 @@
ExecuTorch
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+
+ ExecuTorch Docs
+
@@ -202,6 +205,10 @@
Events
Find events, webinars, and podcasts
+
+ Newsletter
+ Stay up-to-date with the latest updates
+
@@ -217,7 +224,18 @@
Governing Board
-
+
+
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+
+
+ Technical Advisory Council
+
+
+ Staff
+
+
+ Contact Us
@@ -273,7 +291,7 @@
- v2.6.0.dev0+70e2a38
+ v2.7.0.dev0+d6be4ba
@@ -756,7 +774,7 @@ Resources
Developer Resources
-
- Contributor Awards - 2023
+ Contributor Awards - 2024
@@ -772,6 +790,9 @@ Resources
-
ExecuTorch
+ -
+ ExecuTorch Documentation
+
-
Events
+ -
+ Newsletter
+
-
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+ -
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+
+ -
+ Technical Advisory Council
+
+ -
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+
+ -
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+
diff --git a/docs/src/pytorch-sphinx-theme/docs/demo/demo.html b/docs/src/pytorch-sphinx-theme/docs/demo/demo.html
index 37e93c599d..4bc0b53cad 100644
--- a/docs/src/pytorch-sphinx-theme/docs/demo/demo.html
+++ b/docs/src/pytorch-sphinx-theme/docs/demo/demo.html
@@ -12,7 +12,7 @@
- 3. Paragraph Level Markup — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ 3. Paragraph Level Markup — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -135,8 +135,8 @@
Developer Resources
Find resources and get questions answered
-
- Contributor Awards - 2023
+
+ Contributor Awards - 2024
Award winners announced at this year's PyTorch Conference
@@ -157,6 +157,9 @@
ExecuTorch
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+
+ ExecuTorch Docs
+
@@ -204,6 +207,10 @@
Events
Find events, webinars, and podcasts
+
+ Newsletter
+ Stay up-to-date with the latest updates
+
@@ -219,7 +226,18 @@
Governing Board
-
+
+
+ Cloud Credit Program
+
+
+ Technical Advisory Council
+
+
+ Staff
+
+
+ Contact Us
@@ -275,7 +293,7 @@
- v2.6.0.dev0+70e2a38
+ v2.7.0.dev0+d6be4ba
@@ -668,7 +686,7 @@ 3.4.4.
3.4.5. Code Blocks¶
# parsed-literal test
-curl -O http://someurl/release-v2.6.0.dev0+70e2a38.tar-gz
+curl -O http://someurl/release-v2.7.0.dev0+d6be4ba.tar-gz
Code Blocks can have captions.¶
{
@@ -696,7 +714,7 @@ 3.4.5.
3.4.5.1. Emphasized lines with line numbers¶
-1def some_function():
+1def some_function():
2 interesting = False
3 print 'This line is highlighted.'
4 print 'This one is not...'
@@ -736,7 +754,7 @@ 3.5.1. 2"""Test Module for sphinx_rtd_theme."""
3
4
- 5class Foo:
+ 5class Foo:
6
7 """Docstring for class Foo.
8
@@ -1312,7 +1330,7 @@ Resources
Developer Resources
-
- Contributor Awards - 2023
+ Contributor Awards - 2024
@@ -1328,6 +1346,9 @@ Resources
-
ExecuTorch
+ -
+ ExecuTorch Documentation
+
-
Events
+ -
+ Newsletter
+
-
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+ -
+ Cloud Credit Program
+
+ -
+ Technical Advisory Council
+
+ -
+ Staff
+
+ -
+ Contact Us
+
diff --git a/docs/src/pytorch-sphinx-theme/docs/demo/lists_tables.html b/docs/src/pytorch-sphinx-theme/docs/demo/lists_tables.html
index 5c362cf7ca..4335a005d4 100644
--- a/docs/src/pytorch-sphinx-theme/docs/demo/lists_tables.html
+++ b/docs/src/pytorch-sphinx-theme/docs/demo/lists_tables.html
@@ -10,7 +10,7 @@
- 4. Lists & Tables — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ 4. Lists & Tables — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -133,8 +133,8 @@
Developer Resources
Find resources and get questions answered
-
- Contributor Awards - 2023
+
+ Contributor Awards - 2024
Award winners announced at this year's PyTorch Conference
@@ -155,6 +155,9 @@
ExecuTorch
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+
+ ExecuTorch Docs
+
@@ -202,6 +205,10 @@
Events
Find events, webinars, and podcasts
+
+ Newsletter
+ Stay up-to-date with the latest updates
+
@@ -217,7 +224,18 @@
Governing Board
-
+
+
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+
+
+ Technical Advisory Council
+
+
+ Staff
+
+
+ Contact Us
@@ -273,7 +291,7 @@
- v2.6.0.dev0+70e2a38
+ v2.7.0.dev0+d6be4ba
@@ -698,7 +716,7 @@ 4.1.5.1. 2"""Test Module for sphinx_rtd_theme."""
3
4
- 5class Foo:
+ 5class Foo:
6
7 """Docstring for class Foo.
8
@@ -1287,7 +1305,7 @@ Resources
Developer Resources
-
- Contributor Awards - 2023
+ Contributor Awards - 2024
@@ -1303,6 +1321,9 @@ Resources
-
ExecuTorch
+ -
+ ExecuTorch Documentation
+
-
Events
+ -
+ Newsletter
+
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+ -
+ Cloud Credit Program
+
+ -
+ Technical Advisory Council
+
+ -
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+
+ -
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+
diff --git a/docs/src/pytorch-sphinx-theme/docs/demo/long.html b/docs/src/pytorch-sphinx-theme/docs/demo/long.html
index 7426d80cc4..e53d9c5f4c 100644
--- a/docs/src/pytorch-sphinx-theme/docs/demo/long.html
+++ b/docs/src/pytorch-sphinx-theme/docs/demo/long.html
@@ -10,7 +10,7 @@
- 1. Long Sticky Nav — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ 1. Long Sticky Nav — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -133,8 +133,8 @@
Developer Resources
Find resources and get questions answered
-
- Contributor Awards - 2023
+
+ Contributor Awards - 2024
Award winners announced at this year's PyTorch Conference
@@ -155,6 +155,9 @@
ExecuTorch
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+
+ ExecuTorch Docs
+
@@ -202,6 +205,10 @@
Events
Find events, webinars, and podcasts
+
+ Newsletter
+ Stay up-to-date with the latest updates
+
@@ -217,7 +224,18 @@
Governing Board
-
+
+
+ Cloud Credit Program
+
+
+ Technical Advisory Council
+
+
+ Staff
+
+
+ Contact Us
@@ -273,7 +291,7 @@
- v2.6.0.dev0+70e2a38
+ v2.7.0.dev0+d6be4ba
@@ -966,7 +984,7 @@ Resources
Developer Resources
-
- Contributor Awards - 2023
+ Contributor Awards - 2024
@@ -982,6 +1000,9 @@ Resources
-
ExecuTorch
+ -
+ ExecuTorch Documentation
+
-
Events
+ -
+ Newsletter
+
-
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+ -
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+
+ -
+ Technical Advisory Council
+
+ -
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+
+ -
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+
diff --git a/docs/src/pytorch-sphinx-theme/docs/demo/structure.html b/docs/src/pytorch-sphinx-theme/docs/demo/structure.html
index a8919d29de..a5b54a1786 100644
--- a/docs/src/pytorch-sphinx-theme/docs/demo/structure.html
+++ b/docs/src/pytorch-sphinx-theme/docs/demo/structure.html
@@ -10,7 +10,7 @@
- 1. Structural Elements — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ 1. Structural Elements — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -133,8 +133,8 @@
Developer Resources
Find resources and get questions answered
-
- Contributor Awards - 2023
+
+ Contributor Awards - 2024
Award winners announced at this year's PyTorch Conference
@@ -155,6 +155,9 @@
ExecuTorch
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+
+ ExecuTorch Docs
+
@@ -202,6 +205,10 @@
Events
Find events, webinars, and podcasts
+
+ Newsletter
+ Stay up-to-date with the latest updates
+
@@ -217,7 +224,18 @@
Governing Board
-
+
+
+ Cloud Credit Program
+
+
+ Technical Advisory Council
+
+
+ Staff
+
+
+ Contact Us
@@ -273,7 +291,7 @@
- v2.6.0.dev0+70e2a38
+ v2.7.0.dev0+d6be4ba
@@ -835,7 +853,7 @@ Resources
Developer Resources
-
- Contributor Awards - 2023
+ Contributor Awards - 2024
@@ -851,6 +869,9 @@ Resources
-
ExecuTorch
+ -
+ ExecuTorch Documentation
+
-
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+ -
+ Newsletter
+
-
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+ -
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+
+ -
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+ -
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+
+ -
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+
diff --git a/docs/src/pytorch-sphinx-theme/docs/index.html b/docs/src/pytorch-sphinx-theme/docs/index.html
index 901df6efd8..71e46900b7 100644
--- a/docs/src/pytorch-sphinx-theme/docs/index.html
+++ b/docs/src/pytorch-sphinx-theme/docs/index.html
@@ -10,7 +10,7 @@
- <no title> — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ <no title> — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -133,8 +133,8 @@
Developer Resources
Find resources and get questions answered
-
- Contributor Awards - 2023
+
+ Contributor Awards - 2024
Award winners announced at this year's PyTorch Conference
@@ -155,6 +155,9 @@
ExecuTorch
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+
+ ExecuTorch Docs
+
@@ -202,6 +205,10 @@
Events
Find events, webinars, and podcasts
+
+ Newsletter
+ Stay up-to-date with the latest updates
+
@@ -217,7 +224,18 @@
Governing Board
-
+
+
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+
+
+ Technical Advisory Council
+
+
+ Staff
+
+
+ Contact Us
@@ -273,7 +291,7 @@
- v2.6.0.dev0+70e2a38
+ v2.7.0.dev0+d6be4ba
@@ -792,7 +810,7 @@ Resources
Developer Resources
-
- Contributor Awards - 2023
+ Contributor Awards - 2024
@@ -808,6 +826,9 @@ Resources
-
ExecuTorch
+ -
+ ExecuTorch Documentation
+
-
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+ -
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+
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+ -
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+
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diff --git a/docs/src/pytorch-sphinx-theme/docs/installing.html b/docs/src/pytorch-sphinx-theme/docs/installing.html
index 446b41bb46..93315af032 100644
--- a/docs/src/pytorch-sphinx-theme/docs/installing.html
+++ b/docs/src/pytorch-sphinx-theme/docs/installing.html
@@ -10,7 +10,7 @@
- Installation — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ Installation — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -133,8 +133,8 @@
Developer Resources
Find resources and get questions answered
-
- Contributor Awards - 2023
+
+ Contributor Awards - 2024
Award winners announced at this year's PyTorch Conference
@@ -155,6 +155,9 @@
ExecuTorch
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+
+ ExecuTorch Docs
+
@@ -202,6 +205,10 @@
Events
Find events, webinars, and podcasts
+
+ Newsletter
+ Stay up-to-date with the latest updates
+
@@ -217,7 +224,18 @@
Governing Board
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+
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+
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+ Staff
+
+
+ Contact Us
@@ -273,7 +291,7 @@
- v2.6.0.dev0+70e2a38
+ v2.7.0.dev0+d6be4ba
@@ -714,7 +732,7 @@ Resources
Developer Resources
-
- Contributor Awards - 2023
+ Contributor Awards - 2024
@@ -730,6 +748,9 @@ Resources
-
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+ -
+ ExecuTorch Documentation
+
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diff --git a/docs/ts/creating_torchscript_module_in_python.html b/docs/ts/creating_torchscript_module_in_python.html
index a009e3603a..306def22e0 100644
--- a/docs/ts/creating_torchscript_module_in_python.html
+++ b/docs/ts/creating_torchscript_module_in_python.html
@@ -10,7 +10,7 @@
- Creating a TorchScript Module — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ Creating a TorchScript Module — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -135,8 +135,8 @@
Developer Resources
Find resources and get questions answered
-
- Contributor Awards - 2023
+
+ Contributor Awards - 2024
Award winners announced at this year's PyTorch Conference
@@ -157,6 +157,9 @@
ExecuTorch
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+
+ ExecuTorch Docs
+
@@ -204,6 +207,10 @@
Events
Find events, webinars, and podcasts
+
+ Newsletter
+ Stay up-to-date with the latest updates
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@@ -219,7 +226,18 @@
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+ Staff
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+
+ Contact Us
@@ -275,7 +293,7 @@
- v2.6.0.dev0+70e2a38
+ v2.7.0.dev0+d6be4ba
@@ -479,30 +497,30 @@
PyTorch programs are based around Module s which can be used to compose higher level modules. Modules contain a constructor to set up the modules, parameters and sub-modules
and a forward function which describes how to use the parameters and submodules when the module is invoked.
For example, we can define a LeNet module like this:
- 1import torch.nn as nn
- 2import torch.nn.functional as F
+ 1import torch.nn as nn
+ 2import torch.nn.functional as F
3
4
- 5class LeNetFeatExtractor(nn.Module):
- 6 def __init__(self):
+ 5class LeNetFeatExtractor(nn.Module):
+ 6 def __init__(self):
7 super(LeNetFeatExtractor, self).__init__()
8 self.conv1 = nn.Conv2d(1, 6, 3)
9 self.conv2 = nn.Conv2d(6, 16, 3)
10
-11 def forward(self, x):
+11 def forward(self, x):
12 x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2))
13 x = F.max_pool2d(F.relu(self.conv2(x)), 2)
14 return x
15
16
-17class LeNetClassifier(nn.Module):
-18 def __init__(self):
+17class LeNetClassifier(nn.Module):
+18 def __init__(self):
19 super(LeNetClassifier, self).__init__()
20 self.fc1 = nn.Linear(16 * 6 * 6, 120)
21 self.fc2 = nn.Linear(120, 84)
22 self.fc3 = nn.Linear(84, 10)
23
-24 def forward(self, x):
+24 def forward(self, x):
25 x = torch.flatten(x, 1)
26 x = F.relu(self.fc1(x))
27 x = F.relu(self.fc2(x))
@@ -510,13 +528,13 @@
29 return x
30
31
-32class LeNet(nn.Module):
-33 def __init__(self):
+32class LeNet(nn.Module):
+33 def __init__(self):
34 super(LeNet, self).__init__()
35 self.feat = LeNetFeatExtractor()
36 self.classifier = LeNetClassifier()
37
-38 def forward(self, x):
+38 def forward(self, x):
39 x = self.feat(x)
40 x = self.classifier(x)
41 return x
@@ -529,7 +547,7 @@
From here are two pathways for going from PyTorch Python code to TorchScript code: Tracing and Scripting.
Tracing follows the path of execution when the module is called and records what happens.
To trace an instance of our LeNet module, we can call torch.jit.trace with an example input.
-import torch
+import torch
model = LeNet()
input_data = torch.empty([1, 1, 32, 32])
@@ -539,7 +557,7 @@
Scripting actually inspects your code with a compiler and generates an equivalent TorchScript program. The difference is that since tracing
is following the execution of your module, it cannot pick up control flow for instance. By working from the Python code, the compiler can
include these components. We can run the script compiler on our LeNet module by calling torch.jit.script
-import torch
+import torch
model = LeNet()
script_model = torch.jit.script(model)
@@ -581,7 +599,7 @@
Saving TorchScript Module to Disk¶
For either traced or scripted modules, you can save the module to disk with the following command
-import torch
+import torch
model = LeNet()
script_model = torch.jit.script(model)
@@ -829,7 +847,7 @@ Resources
Developer Resources
-
- Contributor Awards - 2023
+ Contributor Awards - 2024
@@ -845,6 +863,9 @@ Resources
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+ -
+ ExecuTorch Documentation
+
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+
diff --git a/docs/ts/getting_started_with_cpp_api.html b/docs/ts/getting_started_with_cpp_api.html
index 046f0487ab..4b7a56729c 100644
--- a/docs/ts/getting_started_with_cpp_api.html
+++ b/docs/ts/getting_started_with_cpp_api.html
@@ -10,7 +10,7 @@
- Using Torch-TensorRT in C++ — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ Using Torch-TensorRT in C++ — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -135,8 +135,8 @@
Developer Resources
Find resources and get questions answered
-
- Contributor Awards - 2023
+
+ Contributor Awards - 2024
Award winners announced at this year's PyTorch Conference
@@ -157,6 +157,9 @@
ExecuTorch
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+
+ ExecuTorch Docs
+
@@ -204,6 +207,10 @@
Events
Find events, webinars, and podcasts
+
+ Newsletter
+ Stay up-to-date with the latest updates
+
@@ -219,7 +226,18 @@
Governing Board
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+
+
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+ Staff
+
+
+ Contact Us
@@ -275,7 +293,7 @@
- v2.6.0.dev0+70e2a38
+ v2.7.0.dev0+d6be4ba
@@ -1007,7 +1025,7 @@ Resources
Developer Resources
-
- Contributor Awards - 2023
+ Contributor Awards - 2024
@@ -1023,6 +1041,9 @@ Resources
-
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+ -
+ ExecuTorch Documentation
+
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+ -
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+ -
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+
diff --git a/docs/ts/getting_started_with_python_api.html b/docs/ts/getting_started_with_python_api.html
index 3dfe059d66..6f23af762a 100644
--- a/docs/ts/getting_started_with_python_api.html
+++ b/docs/ts/getting_started_with_python_api.html
@@ -10,7 +10,7 @@
- Using Torch-TensorRT in Python — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ Using Torch-TensorRT in Python — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -135,8 +135,8 @@
Developer Resources
Find resources and get questions answered
-
- Contributor Awards - 2023
+
+ Contributor Awards - 2024
Award winners announced at this year's PyTorch Conference
@@ -157,6 +157,9 @@
ExecuTorch
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+
+ ExecuTorch Docs
+
@@ -204,6 +207,10 @@
Events
Find events, webinars, and podcasts
+
+ Newsletter
+ Stay up-to-date with the latest updates
+
@@ -219,7 +226,18 @@
Governing Board
-
+
+
+ Cloud Credit Program
+
+
+ Technical Advisory Council
+
+
+ Staff
+
+
+ Contact Us
@@ -275,7 +293,7 @@
- v2.6.0.dev0+70e2a38
+ v2.7.0.dev0+d6be4ba
@@ -483,7 +501,7 @@
to Torch-TensorRT and you will be returned an optimized TorchScript module to run or add into another PyTorch module. Inputs
is a list of torch_tensorrt.Input classes which define input Tensors’ shape, datatype and memory format. Alternatively, if your input is a more complex data type, such as a tuple or list of Tensors, you can use the input_signature argument to specify a collection-based input, such as (List[Tensor], Tuple[Tensor, Tensor]). See the second sample below for an example. You can also specify settings such as operating precision for the engine or target device. After compilation you can save the module just like any other module
to load in a deployment application. In order to load a TensorRT/TorchScript module, make sure you first import torch_tensorrt.
-import torch_tensorrt
+import torch_tensorrt
...
@@ -509,7 +527,7 @@
# Sample using collection-based inputs via the input_signature argument
-import torch_tensorrt
+import torch_tensorrt
...
@@ -534,8 +552,8 @@
# Deployment application
-import torch
-import torch_tensorrt
+import torch
+import torch_tensorrt
trt_ts_module = torch.jit.load("trt_ts_module.ts")
input_data = input_data.to("cuda").half()
@@ -782,7 +800,7 @@ Resources
Developer Resources
-
- Contributor Awards - 2023
+ Contributor Awards - 2024
@@ -798,6 +816,9 @@ Resources
-
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+ -
+ ExecuTorch Documentation
+
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+
diff --git a/docs/ts/ptq.html b/docs/ts/ptq.html
index 023bd14ede..a6b8abac15 100644
--- a/docs/ts/ptq.html
+++ b/docs/ts/ptq.html
@@ -10,7 +10,7 @@
- Post Training Quantization (PTQ) — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ Post Training Quantization (PTQ) — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -135,8 +135,8 @@
Developer Resources
Find resources and get questions answered
-
- Contributor Awards - 2023
+
+ Contributor Awards - 2024
Award winners announced at this year's PyTorch Conference
@@ -157,6 +157,9 @@
ExecuTorch
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+
+ ExecuTorch Docs
+
@@ -204,6 +207,10 @@
Events
Find events, webinars, and podcasts
+
+ Newsletter
+ Stay up-to-date with the latest updates
+
@@ -219,7 +226,18 @@
Governing Board
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+
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+
+
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+
+
+ Staff
+
+
+ Contact Us
@@ -275,7 +293,7 @@
- v2.6.0.dev0+70e2a38
+ v2.7.0.dev0+d6be4ba
@@ -890,7 +908,7 @@ Resources
Developer Resources
-
- Contributor Awards - 2023
+ Contributor Awards - 2024
@@ -906,6 +924,9 @@ Resources
-
ExecuTorch
+ -
+ ExecuTorch Documentation
+
-
Events
+ -
+ Newsletter
+
-
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+ -
+ Cloud Credit Program
+
+ -
+ Technical Advisory Council
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+ -
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+
+ -
+ Contact Us
+
diff --git a/docs/ts/torchscript_frontend_from_pytorch.html b/docs/ts/torchscript_frontend_from_pytorch.html
index 2d27af3e57..f2584d9c92 100644
--- a/docs/ts/torchscript_frontend_from_pytorch.html
+++ b/docs/ts/torchscript_frontend_from_pytorch.html
@@ -10,7 +10,7 @@
- Using Torch-TensorRT TorchScript Frontend Directly From PyTorch — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ Using Torch-TensorRT TorchScript Frontend Directly From PyTorch — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -133,8 +133,8 @@
Developer Resources
Find resources and get questions answered
-
- Contributor Awards - 2023
+
+ Contributor Awards - 2024
Award winners announced at this year's PyTorch Conference
@@ -155,6 +155,9 @@
ExecuTorch
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+
+ ExecuTorch Docs
+
@@ -202,6 +205,10 @@
Events
Find events, webinars, and podcasts
+
+ Newsletter
+ Stay up-to-date with the latest updates
+
@@ -217,7 +224,18 @@
Governing Board
-
+
+
+ Cloud Credit Program
+
+
+ Technical Advisory Council
+
+
+ Staff
+
+
+ Contact Us
@@ -273,7 +291,7 @@
- v2.6.0.dev0+70e2a38
+ v2.7.0.dev0+d6be4ba
@@ -474,12 +492,12 @@
You will now be able to directly access TensorRT from PyTorch APIs. The process to use this feature
is very similar to the compilation workflow described in Using Torch-TensorRT in Python
Start by loading torch_tensorrt into your application.
-import torch
-import torch_tensorrt
+import torch
+import torch_tensorrt
Then given a TorchScript module, you can compile it with TensorRT using the torch._C._jit_to_backend("tensorrt", ...) API.
-import torchvision.models as models
+import torchvision.models as models
model = models.mobilenet_v2(pretrained=True)
script_model = torch.jit.script(model)
@@ -749,7 +767,7 @@ Resources
Developer Resources
-
- Contributor Awards - 2023
+ Contributor Awards - 2024
@@ -765,6 +783,9 @@ Resources
-
ExecuTorch
+ -
+ ExecuTorch Documentation
+
-
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+ -
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+
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+ -
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+ Technical Advisory Council
+
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+
+ -
+ Contact Us
+
diff --git a/docs/tutorials/_rendered_examples/dynamo/auto_generate_converters.html b/docs/tutorials/_rendered_examples/dynamo/auto_generate_converters.html
index 0f828c1ecc..8b9d00e349 100644
--- a/docs/tutorials/_rendered_examples/dynamo/auto_generate_converters.html
+++ b/docs/tutorials/_rendered_examples/dynamo/auto_generate_converters.html
@@ -10,7 +10,7 @@
- Automatically Generate a Converter for a Custom Kernel — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ Automatically Generate a Converter for a Custom Kernel — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -135,8 +135,8 @@
Developer Resources
Find resources and get questions answered
-
- Contributor Awards - 2023
+
+ Contributor Awards - 2024
Award winners announced at this year's PyTorch Conference
@@ -157,6 +157,9 @@
ExecuTorch
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+
+ ExecuTorch Docs
+
@@ -204,6 +207,10 @@
Events
Find events, webinars, and podcasts
+
+ Newsletter
+ Stay up-to-date with the latest updates
+
@@ -219,7 +226,18 @@
Governing Board
-
+
+
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+
+
+ Technical Advisory Council
+
+
+ Staff
+
+
+ Contact Us
@@ -275,7 +293,7 @@
- v2.6.0.dev0+70e2a38
+ v2.7.0.dev0+d6be4ba
@@ -503,17 +521,17 @@ Writing Custom Operators in PyTorchfrom typing import Tuple
+from typing import Tuple
-import tensorrt_bindings.plugin as trtp
-import torch
-import torch_tensorrt
-import triton
-import triton.language as tl
+import tensorrt_bindings.plugin as trtp
+import torch
+import torch_tensorrt
+import triton
+import triton.language as tl
@triton.jit
-def elementwise_mul_kernel(X, Y, Z, BLOCK_SIZE: tl.constexpr):
+def elementwise_mul_kernel(X, Y, Z, BLOCK_SIZE: tl.constexpr):
# Program ID determines the block of data each thread will process
pid = tl.program_id(0)
# Compute the range of elements that this thread block will work on
@@ -530,7 +548,7 @@ Writing Custom Operators in PyTorch@torch.library.custom_op("torchtrt_ex::elementwise_mul", mutates_args=()) # type: ignore[misc]
-def elementwise_mul(
+def elementwise_mul(
X: torch.Tensor, Y: torch.Tensor, b: float = 0.2, a: int = 2
) -> torch.Tensor:
# Ensure the tensors are on the GPU
@@ -555,7 +573,7 @@ Writing Custom Operators in PyTorch@torch.library.register_fake("torchtrt_ex::elementwise_mul")
-def _(x: torch.Tensor, y: torch.Tensor, b: float = 0.2, a: int = 2) -> torch.Tensor:
+def _(x: torch.Tensor, y: torch.Tensor, b: float = 0.2, a: int = 2) -> torch.Tensor:
return x
@@ -568,7 +586,7 @@ Writing Plugins for TensorRT using the Quick Deploy Plugin system@trtp.register("torchtrt_ex::elementwise_mul")
-def _(
+def _(
x: trtp.TensorDesc, y: trtp.TensorDesc, b: float, a: int
) -> Tuple[trtp.TensorDesc]:
return x.like()
@@ -577,7 +595,7 @@ Writing Plugins for TensorRT using the Quick Deploy Plugin system@trtp.impl("torchtrt_ex::elementwise_mul")
-def _(
+def _(
x: trtp.Tensor,
y: trtp.Tensor,
b: float,
@@ -614,11 +632,11 @@ Using our converter with a modelclass MyModel(torch.nn.Module): # type: ignore[misc]
- def __init__(self):
+class MyModel(torch.nn.Module): # type: ignore[misc]
+ def __init__(self):
super().__init__()
- def forward(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
+ def forward(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
z = torch.add(x, y)
res = torch.ops.torchtrt_ex.elementwise_mul.default(x, z, a=1)
@@ -896,7 +914,7 @@ Resources
Developer Resources
-
- Contributor Awards - 2023
+ Contributor Awards - 2024
@@ -912,6 +930,9 @@ Resources
-
ExecuTorch
+ -
+ ExecuTorch Documentation
+
-
Events
+ -
+ Newsletter
+
-
Governing Board
+ -
+ Cloud Credit Program
+
+ -
+ Technical Advisory Council
+
+ -
+ Staff
+
+ -
+ Contact Us
+
diff --git a/docs/tutorials/_rendered_examples/dynamo/converter_overloading.html b/docs/tutorials/_rendered_examples/dynamo/converter_overloading.html
index 92df70e149..865dbffe17 100644
--- a/docs/tutorials/_rendered_examples/dynamo/converter_overloading.html
+++ b/docs/tutorials/_rendered_examples/dynamo/converter_overloading.html
@@ -10,7 +10,7 @@
- Overloading Torch-TensorRT Converters with Custom Converters — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ Overloading Torch-TensorRT Converters with Custom Converters — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -135,8 +135,8 @@
Developer Resources
Find resources and get questions answered
-
- Contributor Awards - 2023
+
+ Contributor Awards - 2024
Award winners announced at this year's PyTorch Conference
@@ -157,6 +157,9 @@
ExecuTorch
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+
+ ExecuTorch Docs
+
@@ -204,6 +207,10 @@
Events
Find events, webinars, and podcasts
+
+ Newsletter
+ Stay up-to-date with the latest updates
+
@@ -219,7 +226,18 @@
Governing Board
-
+
+
+ Cloud Credit Program
+
+
+ Technical Advisory Council
+
+
+ Staff
+
+
+ Contact Us
@@ -275,7 +293,7 @@
- v2.6.0.dev0+70e2a38
+ v2.7.0.dev0+d6be4ba
@@ -483,21 +501,21 @@
Torch-TensorRT would normally use.
In this tutorial, we will demonstrate how to overload Torch-TensorRT’s conversion of the torch.nn.functional.gelu operation to TensorRT with a custom converter that uses a different implementation
of the GeLU layer.
-import logging
-import sys
+import logging
+import sys
-import torch
-import torch_tensorrt
+import torch
+import torch_tensorrt
GeLU has 2 modes in PyTorch, one using the erf function and the other using the tanh approximation.
TensorRT natively supports both implementations as an activation layer, but suppose we want to use a custom implementation of GeLU in TensorRT only for tanh mode.
-class GeLU(torch.nn.Module):
- def __init__(self, mode="tanh"):
+class GeLU(torch.nn.Module):
+ def __init__(self, mode="tanh"):
super().__init__()
self.mode = mode
- def forward(self, x):
+ def forward(self, x):
return torch.nn.functional.gelu(x, approximate=self.mode)
@@ -519,13 +537,13 @@ Writing a Custom Converter@torch_tensorrt.dynamo.conversion.dynamo_tensorrt_converter decorator.
At a code level, converter takes the current conversion state (ConversionCtx), the next operator in the graph to convert, and the arguments to that node
and returns the placeholder outputs for that operation, while as side-effect inserting the necessary TensorRT layers into the TensorRT network.
-from typing import Dict, Sequence, Tuple, Union
+from typing import Dict, Sequence, Tuple, Union
-from torch.fx.node import Argument, Node, Target
-from torch_tensorrt.dynamo import CompilationSettings
-from torch_tensorrt.dynamo.conversion import ConversionContext
+from torch.fx.node import Argument, Node, Target
+from torch_tensorrt.dynamo import CompilationSettings
+from torch_tensorrt.dynamo.conversion import ConversionContext
-import tensorrt as trt
+import tensorrt as trt
@@ -567,7 +585,7 @@ Converter Implementationtorch_tensorrt.dynamo.conversion.impl module and are designed to be composable and interoperable with raw-TensorRT implementations.
In this case, we will use the Torch-TensorRT mul, add and tanh functions from impl to implement our alternative GeLU layer.
-def aten_ops_gelu(
+def aten_ops_gelu(
ctx: ConversionContext,
target: Target,
args: Tuple[Argument, ...],
@@ -576,13 +594,13 @@ Converter Implementation) -> Union[trt.ITensor, Sequence[trt.ITensor]]:
# The schema for torch.ops.aten.gelu.default is gelu(Tensor self, *, str approximate=’none’) -> Tensor
- from torch_tensorrt.dynamo import SourceIR
- from torch_tensorrt.dynamo.conversion import impl
+ from torch_tensorrt.dynamo import SourceIR
+ from torch_tensorrt.dynamo.conversion import impl
# Cheap way to allow layer names to be unqiue
op_count = 0
- def get_op_count():
+ def get_op_count():
nonlocal op_count
op_count += 1
return op_count
@@ -915,7 +933,7 @@ Resources
Developer Resources
-
- Contributor Awards - 2023
+ Contributor Awards - 2024
@@ -931,6 +949,9 @@ Resources
-
ExecuTorch
+ -
+ ExecuTorch Documentation
+
-
Events
+ -
+ Newsletter
+
-
Governing Board
+ -
+ Cloud Credit Program
+
+ -
+ Technical Advisory Council
+
+ -
+ Staff
+
+ -
+ Contact Us
+
diff --git a/docs/tutorials/_rendered_examples/dynamo/cross_runtime_compilation_for_windows.html b/docs/tutorials/_rendered_examples/dynamo/cross_runtime_compilation_for_windows.html
index 55efd182d2..b454fa84ea 100644
--- a/docs/tutorials/_rendered_examples/dynamo/cross_runtime_compilation_for_windows.html
+++ b/docs/tutorials/_rendered_examples/dynamo/cross_runtime_compilation_for_windows.html
@@ -10,7 +10,7 @@
- Cross runtime compilation for windows example — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ Cross runtime compilation for windows example — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -133,8 +133,8 @@
Developer Resources
Find resources and get questions answered
-
- Contributor Awards - 2023
+
+ Contributor Awards - 2024
Award winners announced at this year's PyTorch Conference
@@ -155,6 +155,9 @@
ExecuTorch
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+
+ ExecuTorch Docs
+
@@ -202,6 +205,10 @@
Events
Find events, webinars, and podcasts
+
+ Newsletter
+ Stay up-to-date with the latest updates
+
@@ -217,7 +224,18 @@
Governing Board
-
+
+
+ Cloud Credit Program
+
+
+ Technical Advisory Council
+
+
+ Staff
+
+
+ Contact Us
@@ -273,7 +291,7 @@
- v2.6.0.dev0+70e2a38
+ v2.7.0.dev0+d6be4ba
@@ -487,12 +505,12 @@ Cross runtime compilation for windows example
Imports and Model Definition¶
-import argparse
-import platform
+import argparse
+import platform
-import torch
-import torch_tensorrt as torchtrt
-import torchvision.models as models
+import torch
+import torch_tensorrt as torchtrt
+import torchvision.models as models
PARSER = argparse.ArgumentParser(
description="Cross runtime comilation for windows example: Resnet Model"
@@ -789,7 +807,7 @@ Resources
Developer Resources
-
- Contributor Awards - 2023
+ Contributor Awards - 2024
@@ -805,6 +823,9 @@ Resources
-
ExecuTorch
+ -
+ ExecuTorch Documentation
+
-
Events
+ -
+ Newsletter
+
-
Governing Board
+ -
+ Cloud Credit Program
+
+ -
+ Technical Advisory Council
+
+ -
+ Staff
+
+ -
+ Contact Us
+
diff --git a/docs/tutorials/_rendered_examples/dynamo/custom_kernel_plugins.html b/docs/tutorials/_rendered_examples/dynamo/custom_kernel_plugins.html
index a71dc1e448..6657ad6a3b 100644
--- a/docs/tutorials/_rendered_examples/dynamo/custom_kernel_plugins.html
+++ b/docs/tutorials/_rendered_examples/dynamo/custom_kernel_plugins.html
@@ -10,7 +10,7 @@
- Using Custom Kernels within TensorRT Engines with Torch-TensorRT — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ Using Custom Kernels within TensorRT Engines with Torch-TensorRT — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -135,8 +135,8 @@
Developer Resources
Find resources and get questions answered
-
- Contributor Awards - 2023
+
+ Contributor Awards - 2024
Award winners announced at this year's PyTorch Conference
@@ -157,6 +157,9 @@
ExecuTorch
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+
+ ExecuTorch Docs
+
@@ -204,6 +207,10 @@
Events
Find events, webinars, and podcasts
+
+ Newsletter
+ Stay up-to-date with the latest updates
+
@@ -219,7 +226,18 @@
Governing Board
-
+
+
+ Cloud Credit Program
+
+
+ Technical Advisory Council
+
+
+ Staff
+
+
+ Contact Us
@@ -275,7 +293,7 @@
- v2.6.0.dev0+70e2a38
+ v2.7.0.dev0+d6be4ba
@@ -497,18 +515,18 @@ Writing Custom Operators in PyTorchOpenAI Triton
When using custom kernels with PyTorch, it is recommended to take the additional step of registering them as formal operators in PyTorch. This will both make it easier to handle
the operation in Torch-TensorRT and simplify its use in PyTorch. This could either be done as part of a C++ library or in Python. (see: Custom ops in C++ and Python custom ops for more details )
-from typing import Any, Sequence
+from typing import Any, Sequence
-import numpy as np
-import torch
-import triton
-import triton.language as tl
-from torch.library import custom_op
+import numpy as np
+import torch
+import triton
+import triton.language as tl
+from torch.library import custom_op
# Defining the kernel to be run on the GPU
@triton.jit # type: ignore
-def circ_pad_kernel(
+def circ_pad_kernel(
X: torch.Tensor,
all_pads_0: tl.int32,
all_pads_2: tl.int32,
@@ -556,7 +574,7 @@ Writing Custom Operators in PyTorch# The launch code wrapped to expose it as a custom operator in our namespace
@custom_op("torchtrt_ex::triton_circular_pad", mutates_args=()) # type: ignore[misc]
-def triton_circular_pad(x: torch.Tensor, padding: Sequence[int]) -> torch.Tensor:
+def triton_circular_pad(x: torch.Tensor, padding: Sequence[int]) -> torch.Tensor:
out_dims = np.ones(len(x.shape), dtype=np.int32)
for i in range(np.size(padding) // 2):
out_dims[len(out_dims) - i - 1] = (
@@ -633,7 +651,7 @@ Testing our custom ophere).
In our case we can just use the native circular pad operation as our FakeTensor implementation.
@torch.library.register_fake("torchtrt_ex::triton_circular_pad") # type: ignore[misc]
-def _(x: torch.Tensor, padding: Sequence[int]) -> torch.Tensor:
+def _(x: torch.Tensor, padding: Sequence[int]) -> torch.Tensor:
return torch.nn.functional.pad(x, padding, "circular")
@@ -644,19 +662,19 @@ Testing our custom op
Using the Custom Operator in a Model¶
We can now create models using our custom op. Here is a small example one that uses both natively supported operators (Convolution) and our custom op.
-from typing import Sequence
+from typing import Sequence
-from torch import nn
+from torch import nn
-class MyModel(nn.Module): # type: ignore[misc]
- def __init__(self, padding: Sequence[int]):
+class MyModel(nn.Module): # type: ignore[misc]
+ def __init__(self, padding: Sequence[int]):
super().__init__()
self.padding = padding
self.conv = nn.Conv2d(1, 5, kernel_size=3)
- def forward(self, x: torch.Tensor) -> torch.Tensor:
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
padded_x = torch.ops.torchtrt_ex.triton_circular_pad(x, self.padding)
y = self.conv(padded_x)
@@ -689,7 +707,7 @@ Using the Custom Operator in a Modelimport torch_tensorrt as torchtrt
+import torch_tensorrt as torchtrt
torchtrt.compile(
my_model,
@@ -748,17 +766,17 @@ Wrapping Custom Kernels to use in TensorRThere. From a high level, similar to PyTorch you will need to
define systems to handle setting up the operator, calculating the effect of the operation abstractly, serializing the op and the actual mechanics of calling the implementation of the op in the engine.
-import pickle as pkl
-from typing import Any, List, Optional, Self
+import pickle as pkl
+from typing import Any, List, Optional, Self
-import cupy as cp # Needed to work around API gaps in PyTorch to build torch.Tensors around preallocated CUDA memory
-import numpy as np
+import cupy as cp # Needed to work around API gaps in PyTorch to build torch.Tensors around preallocated CUDA memory
+import numpy as np
-import tensorrt as trt
+import tensorrt as trt
-class CircularPaddingPlugin(trt.IPluginV2DynamicExt): # type: ignore[misc]
- def __init__(
+class CircularPaddingPlugin(trt.IPluginV2DynamicExt): # type: ignore[misc]
+ def __init__(
self, field_collection: Optional[List[trt.PluginFieldCollection]] = None
):
super().__init__()
@@ -774,12 +792,12 @@ Wrapping Custom Kernels to use in TensorRTassert field_collection[0].name == "pads"
self.pads = field_collection[0].data
- def get_output_datatype(
+ def get_output_datatype(
self, index: int, input_types: List[trt.DataType]
) -> trt.DataType:
return input_types[0]
- def get_output_dimensions(
+ def get_output_dimensions(
self,
output_index: int,
inputs: List[trt.DimsExprs],
@@ -797,7 +815,7 @@ Wrapping Custom Kernels to use in TensorRTreturn output_dims
- def configure_plugin(
+ def configure_plugin(
self,
inp: List[trt.DynamicPluginTensorDesc],
out: List[trt.DynamicPluginTensorDesc],
@@ -807,10 +825,10 @@ Wrapping Custom Kernels to use in TensorRTfor i in range(len(X_dims)):
self.X_shape[i] = X_dims[i]
- def serialize(self) -> bytes:
+ def serialize(self) -> bytes:
return pkl.dumps({"pads": self.pads})
- def supports_format_combination(
+ def supports_format_combination(
self, pos: int, in_out: List[trt.PluginTensorDesc], num_inputs: int
) -> bool:
assert num_inputs == 1
@@ -832,7 +850,7 @@ Wrapping Custom Kernels to use in TensorRTreturn False
- def enqueue(
+ def enqueue(
self,
input_desc: List[trt.PluginTensorDesc],
output_desc: List[trt.PluginTensorDesc],
@@ -900,14 +918,14 @@ Wrapping Custom Kernels to use in TensorRTBLOCK_SIZE=256,
)
- def clone(self) -> Self:
+ def clone(self) -> Self:
cloned_plugin = CircularPaddingPlugin()
cloned_plugin.__dict__.update(self.__dict__)
return cloned_plugin
-class CircularPaddingPluginCreator(trt.IPluginCreator): # type: ignore[misc]
- def __init__(self):
+class CircularPaddingPluginCreator(trt.IPluginCreator): # type: ignore[misc]
+ def __init__(self):
super().__init__()
self.name = "CircularPaddingPlugin"
@@ -917,12 +935,12 @@ Wrapping Custom Kernels to use in TensorRT[trt.PluginField("pads", np.array([]), trt.PluginFieldType.INT32)]
)
- def create_plugin(
+ def create_plugin(
self, name: str, field_collection: trt.PluginFieldCollection_
) -> CircularPaddingPlugin:
return CircularPaddingPlugin(field_collection)
- def deserialize_plugin(self, name: str, data: bytes) -> CircularPaddingPlugin:
+ def deserialize_plugin(self, name: str, data: bytes) -> CircularPaddingPlugin:
pads_dict = pkl.loads(data)
print(pads_dict)
deserialized = CircularPaddingPlugin()
@@ -941,15 +959,15 @@ Wrapping Custom Kernels to use in TensorRT¶
Now with our TensorRT plugin, we can create a converter so that Torch-TensorRT knows to insert our plugin in place of our custom circular padding operator.
More information on writing converters can be found here
-from typing import Dict, Tuple
+from typing import Dict, Tuple
-from torch.fx.node import Argument, Target
-from torch_tensorrt.dynamo.conversion import (
+from torch.fx.node import Argument, Target
+from torch_tensorrt.dynamo.conversion import (
ConversionContext,
dynamo_tensorrt_converter,
)
-from torch_tensorrt.dynamo.conversion.converter_utils import get_trt_tensor
-from torch_tensorrt.fx.converters.converter_utils import set_layer_name
+from torch_tensorrt.dynamo.conversion.converter_utils import get_trt_tensor
+from torch_tensorrt.fx.converters.converter_utils import set_layer_name
@dynamo_tensorrt_converter(
@@ -957,7 +975,7 @@ Using Torch-TensorRT to Insert the Kernel) # type: ignore
# Recall the schema defined above:
# torch.ops.torchtrt_ex.triton_circular_pad.default(Tensor x, IntList padding) -> Tensor
-def circular_padding_converter(
+def circular_padding_converter(
ctx: ConversionContext,
target: Target,
args: Tuple[Argument, ...],
@@ -1349,7 +1367,7 @@ Resources
Developer Resources
-
- Contributor Awards - 2023
+ Contributor Awards - 2024
@@ -1365,6 +1383,9 @@ Resources
-
ExecuTorch
+ -
+ ExecuTorch Documentation
+
-
Events
+ -
+ Newsletter
+
-
Governing Board
+ -
+ Cloud Credit Program
+
+ -
+ Technical Advisory Council
+
+ -
+ Staff
+
+ -
+ Contact Us
+
diff --git a/docs/tutorials/_rendered_examples/dynamo/engine_caching_bert_example.html b/docs/tutorials/_rendered_examples/dynamo/engine_caching_bert_example.html
index bba8633a0e..91d2c3a261 100644
--- a/docs/tutorials/_rendered_examples/dynamo/engine_caching_bert_example.html
+++ b/docs/tutorials/_rendered_examples/dynamo/engine_caching_bert_example.html
@@ -10,7 +10,7 @@
- Engine Caching (BERT) — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ Engine Caching (BERT) — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -135,8 +135,8 @@
Developer Resources
Find resources and get questions answered
-
- Contributor Awards - 2023
+
+ Contributor Awards - 2024
Award winners announced at this year's PyTorch Conference
@@ -157,6 +157,9 @@
ExecuTorch
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+
+ ExecuTorch Docs
+
@@ -204,6 +207,10 @@
Events
Find events, webinars, and podcasts
+
+ Newsletter
+ Stay up-to-date with the latest updates
+
@@ -219,7 +226,18 @@
Governing Board
-
+
+
+ Cloud Credit Program
+
+
+ Technical Advisory Council
+
+
+ Staff
+
+
+ Contact Us
@@ -275,7 +293,7 @@
- v2.6.0.dev0+70e2a38
+ v2.7.0.dev0+d6be4ba
@@ -479,11 +497,11 @@
Engine Caching (BERT)¶
Small caching example on BERT.
-import numpy as np
-import torch
-import torch_tensorrt
-from engine_caching_example import remove_timing_cache
-from transformers import BertModel
+import numpy as np
+import torch
+import torch_tensorrt
+from engine_caching_example import remove_timing_cache
+from transformers import BertModel
np.random.seed(0)
torch.manual_seed(0)
@@ -495,7 +513,7 @@
]
-def compile_bert(iterations=3):
+def compile_bert(iterations=3):
times = []
start = torch.cuda.Event(enable_timing=True)
end = torch.cuda.Event(enable_timing=True)
@@ -795,7 +813,7 @@ Resources
Developer Resources
-
- Contributor Awards - 2023
+ Contributor Awards - 2024
@@ -811,6 +829,9 @@ Resources
-
ExecuTorch
+ -
+ ExecuTorch Documentation
+
-
Events
+ -
+ Newsletter
+
-
Governing Board
+ -
+ Cloud Credit Program
+
+ -
+ Technical Advisory Council
+
+ -
+ Staff
+
+ -
+ Contact Us
+
diff --git a/docs/tutorials/_rendered_examples/dynamo/engine_caching_example.html b/docs/tutorials/_rendered_examples/dynamo/engine_caching_example.html
index 5e61d420fb..3d28fcdaf8 100644
--- a/docs/tutorials/_rendered_examples/dynamo/engine_caching_example.html
+++ b/docs/tutorials/_rendered_examples/dynamo/engine_caching_example.html
@@ -10,7 +10,7 @@
- Engine Caching — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ Engine Caching — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -135,8 +135,8 @@
Developer Resources
Find resources and get questions answered
-
- Contributor Awards - 2023
+
+ Contributor Awards - 2024
Award winners announced at this year's PyTorch Conference
@@ -157,6 +157,9 @@
ExecuTorch
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+
+ ExecuTorch Docs
+
@@ -204,6 +207,10 @@
Events
Find events, webinars, and podcasts
+
+ Newsletter
+ Stay up-to-date with the latest updates
+
@@ -219,7 +226,18 @@
Governing Board
-
+
+
+ Cloud Credit Program
+
+
+ Technical Advisory Council
+
+
+ Staff
+
+
+ Contact Us
@@ -275,7 +293,7 @@
- v2.6.0.dev0+70e2a38
+ v2.7.0.dev0+d6be4ba
@@ -496,15 +514,15 @@
The example uses a pre-trained ResNet18 model and shows the
differences between compilation without caching, with caching enabled,
and when reusing cached engines.
-import os
-from typing import Dict, Optional
+import os
+from typing import Dict, Optional
-import numpy as np
-import torch
-import torch_tensorrt as torch_trt
-import torchvision.models as models
-from torch_tensorrt.dynamo._defaults import TIMING_CACHE_PATH
-from torch_tensorrt.dynamo._engine_cache import BaseEngineCache
+import numpy as np
+import torch
+import torch_tensorrt as torch_trt
+import torchvision.models as models
+from torch_tensorrt.dynamo._defaults import TIMING_CACHE_PATH
+from torch_tensorrt.dynamo._engine_cache import BaseEngineCache
np.random.seed(0)
torch.manual_seed(0)
@@ -516,7 +534,7 @@
use_python_runtime = False
-def remove_timing_cache(path=TIMING_CACHE_PATH):
+def remove_timing_cache(path=TIMING_CACHE_PATH):
if os.path.exists(path):
os.remove(path)
@@ -534,7 +552,7 @@ Engine Caching for JIT Compilationcache_built_engines=True),
the engine must be refittable (immutable_weights=False). See Refitting Torch-TensorRT Programs with New Weights for more details.
-def torch_compile(iterations=3):
+def torch_compile(iterations=3):
times = []
start = torch.cuda.Event(enable_timing=True)
end = torch.cuda.Event(enable_timing=True)
@@ -590,7 +608,7 @@ Engine Caching for AOT Compilationdef dynamo_compile(iterations=3):
+def dynamo_compile(iterations=3):
times = []
start = torch.cuda.Event(enable_timing=True)
end = torch.cuda.Event(enable_timing=True)
@@ -660,8 +678,8 @@ Custom Engine CacheRAMEngineCache.
-class RAMEngineCache(BaseEngineCache):
- def __init__(
+class RAMEngineCache(BaseEngineCache):
+ def __init__(
self,
) -> None:
"""
@@ -669,7 +687,7 @@ Custom Engine Cache """
self.engine_cache: Dict[str, bytes] = {}
- def save(
+ def save(
self,
hash: str,
blob: bytes,
@@ -686,7 +704,7 @@ Custom Engine Cache """
self.engine_cache[hash] = blob
- def load(self, hash: str) -> Optional[bytes]:
+ def load(self, hash: str) -> Optional[bytes]:
"""
Load the engine blob from the cache.
@@ -702,7 +720,7 @@ Custom Engine Cachereturn None
-def torch_compile_my_cache(iterations=3):
+def torch_compile_my_cache(iterations=3):
times = []
engine_cache = RAMEngineCache()
start = torch.cuda.Event(enable_timing=True)
@@ -1009,7 +1027,7 @@ Resources
Developer Resources
-
- Contributor Awards - 2023
+ Contributor Awards - 2024
@@ -1025,6 +1043,9 @@ Resources
-
ExecuTorch
+ -
+ ExecuTorch Documentation
+
-
Events
+ -
+ Newsletter
+
-
Governing Board
+ -
+ Cloud Credit Program
+
+ -
+ Technical Advisory Council
+
+ -
+ Staff
+
+ -
+ Contact Us
+
diff --git a/docs/tutorials/_rendered_examples/dynamo/index.html b/docs/tutorials/_rendered_examples/dynamo/index.html
index 1425a3853d..6af08095c5 100644
--- a/docs/tutorials/_rendered_examples/dynamo/index.html
+++ b/docs/tutorials/_rendered_examples/dynamo/index.html
@@ -10,7 +10,7 @@
- Dependencies — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ Dependencies — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -133,8 +133,8 @@
Developer Resources
Find resources and get questions answered
-
- Contributor Awards - 2023
+
+ Contributor Awards - 2024
Award winners announced at this year's PyTorch Conference
@@ -155,6 +155,9 @@
ExecuTorch
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+
+ ExecuTorch Docs
+
@@ -202,6 +205,10 @@
Events
Find events, webinars, and podcasts
+
+ Newsletter
+ Stay up-to-date with the latest updates
+
@@ -217,7 +224,18 @@
Governing Board
-
+
+
+ Cloud Credit Program
+
+
+ Technical Advisory Council
+
+
+ Staff
+
+
+ Contact Us
@@ -273,7 +291,7 @@
- v2.6.0.dev0+70e2a38
+ v2.7.0.dev0+d6be4ba
@@ -781,7 +799,7 @@ Resources
Developer Resources
-
- Contributor Awards - 2023
+ Contributor Awards - 2024
@@ -797,6 +815,9 @@ Resources
-
ExecuTorch
+ -
+ ExecuTorch Documentation
+
-
Events
+ -
+ Newsletter
+
-
Governing Board
+ -
+ Cloud Credit Program
+
+ -
+ Technical Advisory Council
+
+ -
+ Staff
+
+ -
+ Contact Us
+
diff --git a/docs/tutorials/_rendered_examples/dynamo/mutable_torchtrt_module_example.html b/docs/tutorials/_rendered_examples/dynamo/mutable_torchtrt_module_example.html
index 6d82ec3285..bece6414a9 100644
--- a/docs/tutorials/_rendered_examples/dynamo/mutable_torchtrt_module_example.html
+++ b/docs/tutorials/_rendered_examples/dynamo/mutable_torchtrt_module_example.html
@@ -10,7 +10,7 @@
- Mutable Torch TensorRT Module — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ Mutable Torch TensorRT Module — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -135,8 +135,8 @@
Developer Resources
Find resources and get questions answered
-
- Contributor Awards - 2023
+
+ Contributor Awards - 2024
Award winners announced at this year's PyTorch Conference
@@ -157,6 +157,9 @@
ExecuTorch
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+
+ ExecuTorch Docs
+
@@ -204,6 +207,10 @@
Events
Find events, webinars, and podcasts
+
+ Newsletter
+ Stay up-to-date with the latest updates
+
@@ -219,7 +226,18 @@
Governing Board
-
+
+
+ Cloud Credit Program
+
+
+ Technical Advisory Council
+
+
+ Staff
+
+
+ Contact Us
@@ -275,7 +293,7 @@
- v2.6.0.dev0+70e2a38
+ v2.7.0.dev0+d6be4ba
@@ -486,10 +504,10 @@
1. Sample workflow of Mutable Torch TensorRT Module with ResNet 18
2. Save a Mutable Torch TensorRT Module
3. Integration with Huggingface pipeline in LoRA use case
-import numpy as np
-import torch
-import torch_tensorrt as torch_trt
-import torchvision.models as models
+import numpy as np
+import torch
+import torch_tensorrt as torch_trt
+import torchvision.models as models
np.random.seed(5)
torch.manual_seed(5)
@@ -542,7 +560,7 @@ Saving Mutable Torch TensorRT Module¶
# The LoRA checkpoint is from https://civitai.com/models/12597/moxin
-from diffusers import DiffusionPipeline
+from diffusers import DiffusionPipeline
with torch.no_grad():
settings = {
@@ -840,7 +858,7 @@ Resources
Developer Resources
-
- Contributor Awards - 2023
+ Contributor Awards - 2024
@@ -856,6 +874,9 @@ Resources
-
ExecuTorch
+ -
+ ExecuTorch Documentation
+
-
Events
+ -
+ Newsletter
+
-
Governing Board
+ -
+ Cloud Credit Program
+
+ -
+ Technical Advisory Council
+
+ -
+ Staff
+
+ -
+ Contact Us
+
diff --git a/docs/tutorials/_rendered_examples/dynamo/pre_allocated_output_example.html b/docs/tutorials/_rendered_examples/dynamo/pre_allocated_output_example.html
index 9e2260de37..ebcac0814f 100644
--- a/docs/tutorials/_rendered_examples/dynamo/pre_allocated_output_example.html
+++ b/docs/tutorials/_rendered_examples/dynamo/pre_allocated_output_example.html
@@ -10,7 +10,7 @@
- Pre-allocated output buffer — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ Pre-allocated output buffer — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -135,8 +135,8 @@
Developer Resources
Find resources and get questions answered
-
- Contributor Awards - 2023
+
+ Contributor Awards - 2024
Award winners announced at this year's PyTorch Conference
@@ -157,6 +157,9 @@
ExecuTorch
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+
+ ExecuTorch Docs
+
@@ -204,6 +207,10 @@
Events
Find events, webinars, and podcasts
+
+ Newsletter
+ Stay up-to-date with the latest updates
+
@@ -219,7 +226,18 @@
Governing Board
-
+
+
+ Cloud Credit Program
+
+
+ Technical Advisory Council
+
+
+ Staff
+
+
+ Contact Us
@@ -275,7 +293,7 @@
- v2.6.0.dev0+70e2a38
+ v2.7.0.dev0+d6be4ba
@@ -508,18 +526,18 @@
Imports and Model Definition¶
-import timeit
+import timeit
-import numpy as np
-import torch
-import torch_tensorrt
-from transformers import BertModel
+import numpy as np
+import torch
+import torch_tensorrt
+from transformers import BertModel
Define function to measure inference performance¶
-def test_module_perf(model, *input):
+def test_module_perf(model, *input):
timings = []
# Warm-up phase to ensure consistent and accurate performance measurements.
@@ -851,7 +869,7 @@ Resources
Developer Resources
-
- Contributor Awards - 2023
+ Contributor Awards - 2024
@@ -867,6 +885,9 @@ Resources
-
ExecuTorch
+ -
+ ExecuTorch Documentation
+
-
Events
+ -
+ Newsletter
+
-
Governing Board
+ -
+ Cloud Credit Program
+
+ -
+ Technical Advisory Council
+
+ -
+ Staff
+
+ -
+ Contact Us
+
diff --git a/docs/tutorials/_rendered_examples/dynamo/refit_engine_example.html b/docs/tutorials/_rendered_examples/dynamo/refit_engine_example.html
index 6d1d2057e7..1b9abcffb6 100644
--- a/docs/tutorials/_rendered_examples/dynamo/refit_engine_example.html
+++ b/docs/tutorials/_rendered_examples/dynamo/refit_engine_example.html
@@ -10,7 +10,7 @@
- Refitting Torch-TensorRT Programs with New Weights — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ Refitting Torch-TensorRT Programs with New Weights — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -135,8 +135,8 @@
Developer Resources
Find resources and get questions answered
-
- Contributor Awards - 2023
+
+ Contributor Awards - 2024
Award winners announced at this year's PyTorch Conference
@@ -157,6 +157,9 @@
ExecuTorch
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+
+ ExecuTorch Docs
+
@@ -204,6 +207,10 @@
Events
Find events, webinars, and podcasts
+
+ Newsletter
+ Stay up-to-date with the latest updates
+
@@ -219,7 +226,18 @@
Governing Board
-
+
+
+ Cloud Credit Program
+
+
+ Technical Advisory Council
+
+
+ Staff
+
+
+ Contact Us
@@ -275,7 +293,7 @@
- v2.6.0.dev0+70e2a38
+ v2.7.0.dev0+d6be4ba
@@ -500,11 +518,11 @@
Standard Workflow¶
Imports and model definition¶
-import numpy as np
-import torch
-import torch_tensorrt as torch_trt
-import torchvision.models as models
-from torch_tensorrt.dynamo import refit_module_weights
+import numpy as np
+import torch
+import torch_tensorrt as torch_trt
+import torchvision.models as models
+from torch_tensorrt.dynamo import refit_module_weights
np.random.seed(0)
torch.manual_seed(0)
@@ -862,7 +880,7 @@ Resources
Developer Resources
-
- Contributor Awards - 2023
+ Contributor Awards - 2024
@@ -878,6 +896,9 @@ Resources
-
ExecuTorch
+ -
+ ExecuTorch Documentation
+
-
Events
+ -
+ Newsletter
+
-
Governing Board
+ -
+ Cloud Credit Program
+
+ -
+ Technical Advisory Council
+
+ -
+ Staff
+
+ -
+ Contact Us
+
diff --git a/docs/tutorials/_rendered_examples/dynamo/torch_compile_advanced_usage.html b/docs/tutorials/_rendered_examples/dynamo/torch_compile_advanced_usage.html
index 8a8d81d53e..56fa21e55e 100644
--- a/docs/tutorials/_rendered_examples/dynamo/torch_compile_advanced_usage.html
+++ b/docs/tutorials/_rendered_examples/dynamo/torch_compile_advanced_usage.html
@@ -10,7 +10,7 @@
- Torch Compile Advanced Usage — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ Torch Compile Advanced Usage — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -135,8 +135,8 @@
Developer Resources
Find resources and get questions answered
-
- Contributor Awards - 2023
+
+ Contributor Awards - 2024
Award winners announced at this year's PyTorch Conference
@@ -157,6 +157,9 @@
ExecuTorch
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+
+ ExecuTorch Docs
+
@@ -204,6 +207,10 @@
Events
Find events, webinars, and podcasts
+
+ Newsletter
+ Stay up-to-date with the latest updates
+
@@ -219,7 +226,18 @@
Governing Board
-
+
+
+ Cloud Credit Program
+
+
+ Technical Advisory Council
+
+
+ Staff
+
+
+ Contact Us
@@ -275,7 +293,7 @@
- v2.6.0.dev0+70e2a38
+ v2.7.0.dev0+d6be4ba
@@ -481,17 +499,17 @@
This interactive script is intended as an overview of the process by which torch_tensorrt.compile(…, ir=”torch_compile”, …) works, and how it integrates with the torch.compile API.
Imports and Model Definition¶
-import torch
-import torch_tensorrt
+import torch
+import torch_tensorrt
# We begin by defining a model
-class Model(torch.nn.Module):
- def __init__(self) -> None:
+class Model(torch.nn.Module):
+ def __init__(self) -> None:
super().__init__()
self.relu = torch.nn.ReLU()
- def forward(self, x: torch.Tensor, y: torch.Tensor):
+ def forward(self, x: torch.Tensor, y: torch.Tensor):
x_out = self.relu(x)
y_out = self.relu(y)
x_y_out = x_out + y_out
@@ -829,7 +847,7 @@ Resources
Developer Resources
-
- Contributor Awards - 2023
+ Contributor Awards - 2024
@@ -845,6 +863,9 @@ Resources
-
ExecuTorch
+ -
+ ExecuTorch Documentation
+
-
Events
+ -
+ Newsletter
+
-
Governing Board
+ -
+ Cloud Credit Program
+
+ -
+ Technical Advisory Council
+
+ -
+ Staff
+
+ -
+ Contact Us
+
diff --git a/docs/tutorials/_rendered_examples/dynamo/torch_compile_gpt2.html b/docs/tutorials/_rendered_examples/dynamo/torch_compile_gpt2.html
index eef0527884..6652b1696c 100644
--- a/docs/tutorials/_rendered_examples/dynamo/torch_compile_gpt2.html
+++ b/docs/tutorials/_rendered_examples/dynamo/torch_compile_gpt2.html
@@ -10,7 +10,7 @@
- Compiling GPT2 using the Torch-TensorRT torch.compile frontend — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ Compiling GPT2 using the Torch-TensorRT torch.compile frontend — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -135,8 +135,8 @@
Developer Resources
Find resources and get questions answered
-
- Contributor Awards - 2023
+
+ Contributor Awards - 2024
Award winners announced at this year's PyTorch Conference
@@ -157,6 +157,9 @@
ExecuTorch
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+
+ ExecuTorch Docs
+
@@ -204,6 +207,10 @@
Events
Find events, webinars, and podcasts
+
+ Newsletter
+ Stay up-to-date with the latest updates
+
@@ -219,7 +226,18 @@
Governing Board
-
+
+
+ Cloud Credit Program
+
+
+ Technical Advisory Council
+
+
+ Staff
+
+
+ Contact Us
@@ -275,7 +293,7 @@
- v2.6.0.dev0+70e2a38
+ v2.7.0.dev0+d6be4ba
@@ -487,9 +505,9 @@
get the graph module representation of the graph. Torch-TensorRT converts this graph into an optimized TensorRT engine.
Import necessary libraries¶
-import torch
-import torch_tensorrt
-from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+import torch_tensorrt
+from transformers import AutoModelForCausalLM, AutoTokenizer
@@ -848,7 +866,7 @@ Resources
Developer Resources
-
- Contributor Awards - 2023
+ Contributor Awards - 2024
@@ -864,6 +882,9 @@ Resources
-
ExecuTorch
+ -
+ ExecuTorch Documentation
+
-
Events
+ -
+ Newsletter
+
-
Governing Board
+ -
+ Cloud Credit Program
+
+ -
+ Technical Advisory Council
+
+ -
+ Staff
+
+ -
+ Contact Us
+
diff --git a/docs/tutorials/_rendered_examples/dynamo/torch_compile_resnet_example.html b/docs/tutorials/_rendered_examples/dynamo/torch_compile_resnet_example.html
index 633b504bb3..dc33d782f9 100644
--- a/docs/tutorials/_rendered_examples/dynamo/torch_compile_resnet_example.html
+++ b/docs/tutorials/_rendered_examples/dynamo/torch_compile_resnet_example.html
@@ -10,7 +10,7 @@
- Compiling ResNet with dynamic shapes using the torch.compile backend — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ Compiling ResNet with dynamic shapes using the torch.compile backend — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -135,8 +135,8 @@
Developer Resources
Find resources and get questions answered
-
- Contributor Awards - 2023
+
+ Contributor Awards - 2024
Award winners announced at this year's PyTorch Conference
@@ -157,6 +157,9 @@
ExecuTorch
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+
+ ExecuTorch Docs
+
@@ -204,6 +207,10 @@
Events
Find events, webinars, and podcasts
+
+ Newsletter
+ Stay up-to-date with the latest updates
+
@@ -219,7 +226,18 @@
Governing Board
-
+
+
+ Cloud Credit Program
+
+
+ Technical Advisory Council
+
+
+ Staff
+
+
+ Contact Us
@@ -275,7 +293,7 @@
- v2.6.0.dev0+70e2a38
+ v2.7.0.dev0+d6be4ba
@@ -481,9 +499,9 @@
This interactive script is intended as a sample of the Torch-TensorRT workflow with torch.compile on a ResNet model.
Imports and Model Definition¶
-import torch
-import torch_tensorrt
-import torchvision.models as models
+import torch
+import torch_tensorrt
+import torchvision.models as models
# Initialize model with half precision and sample inputs
@@ -843,7 +861,7 @@ Resources
Developer Resources
-
- Contributor Awards - 2023
+ Contributor Awards - 2024
@@ -859,6 +877,9 @@ Resources
-
ExecuTorch
+ -
+ ExecuTorch Documentation
+
-
Events
+ -
+ Newsletter
+
-
Governing Board
+ -
+ Cloud Credit Program
+
+ -
+ Technical Advisory Council
+
+ -
+ Staff
+
+ -
+ Contact Us
+
diff --git a/docs/tutorials/_rendered_examples/dynamo/torch_compile_stable_diffusion.html b/docs/tutorials/_rendered_examples/dynamo/torch_compile_stable_diffusion.html
index c30e8fa19a..3ead6247c7 100644
--- a/docs/tutorials/_rendered_examples/dynamo/torch_compile_stable_diffusion.html
+++ b/docs/tutorials/_rendered_examples/dynamo/torch_compile_stable_diffusion.html
@@ -10,7 +10,7 @@
- Compiling Stable Diffusion model using the torch.compile backend — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ Compiling Stable Diffusion model using the torch.compile backend — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -135,8 +135,8 @@
Developer Resources
Find resources and get questions answered
-
- Contributor Awards - 2023
+
+ Contributor Awards - 2024
Award winners announced at this year's PyTorch Conference
@@ -157,6 +157,9 @@
ExecuTorch
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+
+ ExecuTorch Docs
+
@@ -204,6 +207,10 @@
Events
Find events, webinars, and podcasts
+
+ Newsletter
+ Stay up-to-date with the latest updates
+
@@ -219,7 +226,18 @@
Governing Board
-
+
+
+ Cloud Credit Program
+
+
+ Technical Advisory Council
+
+
+ Staff
+
+
+ Contact Us
@@ -275,7 +293,7 @@
- v2.6.0.dev0+70e2a38
+ v2.7.0.dev0+d6be4ba
@@ -482,9 +500,9 @@
Imports and Model Definition¶
-import torch
-import torch_tensorrt
-from diffusers import DiffusionPipeline
+import torch
+import torch_tensorrt
+from diffusers import DiffusionPipeline
model_id = "CompVis/stable-diffusion-v1-4"
device = "cuda:0"
@@ -773,7 +791,7 @@ Resources
Developer Resources
-
- Contributor Awards - 2023
+ Contributor Awards - 2024
@@ -789,6 +807,9 @@ Resources
-
ExecuTorch
+ -
+ ExecuTorch Documentation
+
-
Events
+ -
+ Newsletter
+
-
Governing Board
+ -
+ Cloud Credit Program
+
+ -
+ Technical Advisory Council
+
+ -
+ Staff
+
+ -
+ Contact Us
+
diff --git a/docs/tutorials/_rendered_examples/dynamo/torch_compile_transformers_example.html b/docs/tutorials/_rendered_examples/dynamo/torch_compile_transformers_example.html
index c7b2b9a0b7..33b2385aae 100644
--- a/docs/tutorials/_rendered_examples/dynamo/torch_compile_transformers_example.html
+++ b/docs/tutorials/_rendered_examples/dynamo/torch_compile_transformers_example.html
@@ -10,7 +10,7 @@
- Compiling BERT using the torch.compile backend — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ Compiling BERT using the torch.compile backend — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -135,8 +135,8 @@
Developer Resources
Find resources and get questions answered
-
- Contributor Awards - 2023
+
+ Contributor Awards - 2024
Award winners announced at this year's PyTorch Conference
@@ -157,6 +157,9 @@
ExecuTorch
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+
+ ExecuTorch Docs
+
@@ -204,6 +207,10 @@
Events
Find events, webinars, and podcasts
+
+ Newsletter
+ Stay up-to-date with the latest updates
+
@@ -219,7 +226,18 @@
Governing Board
-
+
+
+ Cloud Credit Program
+
+
+ Technical Advisory Council
+
+
+ Staff
+
+
+ Contact Us
@@ -275,7 +293,7 @@
- v2.6.0.dev0+70e2a38
+ v2.7.0.dev0+d6be4ba
@@ -481,9 +499,9 @@
This interactive script is intended as a sample of the Torch-TensorRT workflow with torch.compile on a BERT model.
Imports and Model Definition¶
-import torch
-import torch_tensorrt
-from transformers import BertModel
+import torch
+import torch_tensorrt
+from transformers import BertModel
# Initialize model with float precision and sample inputs
@@ -832,7 +850,7 @@ Resources
Developer Resources
-
- Contributor Awards - 2023
+ Contributor Awards - 2024
@@ -848,6 +866,9 @@ Resources
-
ExecuTorch
+ -
+ ExecuTorch Documentation
+
-
Events
+ -
+ Newsletter
+
-
Governing Board
+ -
+ Cloud Credit Program
+
+ -
+ Technical Advisory Council
+
+ -
+ Staff
+
+ -
+ Contact Us
+
diff --git a/docs/tutorials/_rendered_examples/dynamo/torch_export_cudagraphs.html b/docs/tutorials/_rendered_examples/dynamo/torch_export_cudagraphs.html
index 877c7701af..6c63cb1a97 100644
--- a/docs/tutorials/_rendered_examples/dynamo/torch_export_cudagraphs.html
+++ b/docs/tutorials/_rendered_examples/dynamo/torch_export_cudagraphs.html
@@ -10,7 +10,7 @@
- Torch Export with Cudagraphs — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ Torch Export with Cudagraphs — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -135,8 +135,8 @@
Developer Resources
Find resources and get questions answered
-
- Contributor Awards - 2023
+
+ Contributor Awards - 2024
Award winners announced at this year's PyTorch Conference
@@ -157,6 +157,9 @@
ExecuTorch
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+
+ ExecuTorch Docs
+
@@ -204,6 +207,10 @@
Events
Find events, webinars, and podcasts
+
+ Newsletter
+ Stay up-to-date with the latest updates
+
@@ -219,7 +226,18 @@
Governing Board
-
+
+
+ Cloud Credit Program
+
+
+ Technical Advisory Council
+
+
+ Staff
+
+
+ Contact Us
@@ -275,7 +293,7 @@
- v2.6.0.dev0+70e2a38
+ v2.7.0.dev0+d6be4ba
@@ -481,9 +499,9 @@
This interactive script is intended as an overview of the process by which the Torch-TensorRT Cudagraphs integration can be used in the ir=”dynamo” path. The functionality works similarly in the torch.compile path as well.
Imports and Model Definition¶
-import torch
-import torch_tensorrt
-import torchvision.models as models
+import torch
+import torch_tensorrt
+import torchvision.models as models
@@ -550,8 +568,8 @@ Cuda graphs with module that contains graph breaks
-class SampleModel(torch.nn.Module):
- def forward(self, x):
+class SampleModel(torch.nn.Module):
+ def forward(self, x):
return torch.relu((x + 2) * 0.5)
@@ -836,7 +854,7 @@ Resources
Developer Resources
-
- Contributor Awards - 2023
+ Contributor Awards - 2024
@@ -852,6 +870,9 @@ Resources
-
ExecuTorch
+ -
+ ExecuTorch Documentation
+
-
Events
+ -
+ Newsletter
+
-
Governing Board
+ -
+ Cloud Credit Program
+
+ -
+ Technical Advisory Council
+
+ -
+ Staff
+
+ -
+ Contact Us
+
diff --git a/docs/tutorials/_rendered_examples/dynamo/torch_export_gpt2.html b/docs/tutorials/_rendered_examples/dynamo/torch_export_gpt2.html
index 6ce47460a0..8e98176b8b 100644
--- a/docs/tutorials/_rendered_examples/dynamo/torch_export_gpt2.html
+++ b/docs/tutorials/_rendered_examples/dynamo/torch_export_gpt2.html
@@ -10,7 +10,7 @@
- Compiling GPT2 using the dynamo backend — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ Compiling GPT2 using the dynamo backend — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -135,8 +135,8 @@
Developer Resources
Find resources and get questions answered
-
- Contributor Awards - 2023
+
+ Contributor Awards - 2024
Award winners announced at this year's PyTorch Conference
@@ -157,6 +157,9 @@
ExecuTorch
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+
+ ExecuTorch Docs
+
@@ -204,6 +207,10 @@
Events
Find events, webinars, and podcasts
+
+ Newsletter
+ Stay up-to-date with the latest updates
+
@@ -219,7 +226,18 @@
Governing Board
-
+
+
+ Cloud Credit Program
+
+
+ Technical Advisory Council
+
+
+ Staff
+
+
+ Contact Us
@@ -275,7 +293,7 @@
- v2.6.0.dev0+70e2a38
+ v2.7.0.dev0+d6be4ba
@@ -481,10 +499,10 @@
This script illustrates Torch-TensorRT workflow with dynamo backend on popular GPT2 model.
Imports and Model Definition¶
-import torch
-import torch_tensorrt
-from transformers import AutoModelForCausalLM, AutoTokenizer
-from utils import export_llm, generate
+import torch
+import torch_tensorrt
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from utils import export_llm, generate
# Define the parameters and initialize the model
@@ -823,7 +841,7 @@ Resources
Developer Resources
-
- Contributor Awards - 2023
+ Contributor Awards - 2024
@@ -839,6 +857,9 @@ Resources
-
ExecuTorch
+ -
+ ExecuTorch Documentation
+
-
Events
+ -
+ Newsletter
+
-
Governing Board
+ -
+ Cloud Credit Program
+
+ -
+ Technical Advisory Council
+
+ -
+ Staff
+
+ -
+ Contact Us
+
diff --git a/docs/tutorials/_rendered_examples/dynamo/torch_export_llama2.html b/docs/tutorials/_rendered_examples/dynamo/torch_export_llama2.html
index d24b7872cc..830b37ed46 100644
--- a/docs/tutorials/_rendered_examples/dynamo/torch_export_llama2.html
+++ b/docs/tutorials/_rendered_examples/dynamo/torch_export_llama2.html
@@ -10,7 +10,7 @@
- Compiling Llama2 using the dynamo backend — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ Compiling Llama2 using the dynamo backend — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -135,8 +135,8 @@
Developer Resources
Find resources and get questions answered
-
- Contributor Awards - 2023
+
+ Contributor Awards - 2024
Award winners announced at this year's PyTorch Conference
@@ -157,6 +157,9 @@
ExecuTorch
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+
+ ExecuTorch Docs
+
@@ -204,6 +207,10 @@
Events
Find events, webinars, and podcasts
+
+ Newsletter
+ Stay up-to-date with the latest updates
+
@@ -219,7 +226,18 @@
Governing Board
-
+
+
+ Cloud Credit Program
+
+
+ Technical Advisory Council
+
+
+ Staff
+
+
+ Contact Us
@@ -275,7 +293,7 @@
- v2.6.0.dev0+70e2a38
+ v2.7.0.dev0+d6be4ba
@@ -481,10 +499,10 @@
This script illustrates Torch-TensorRT workflow with dynamo backend on popular Llama2 model.
Imports and Model Definition¶
-import torch
-import torch_tensorrt
-from transformers import AutoModelForCausalLM, AutoTokenizer
-from utils import export_llm, generate
+import torch
+import torch_tensorrt
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from utils import export_llm, generate
Define the parameters and initialize the model
@@ -829,7 +847,7 @@ Resources
Developer Resources
-
- Contributor Awards - 2023
+ Contributor Awards - 2024
@@ -845,6 +863,9 @@ Resources
-
ExecuTorch
+ -
+ ExecuTorch Documentation
+
-
Events
+ -
+ Newsletter
+
-
Governing Board
+ -
+ Cloud Credit Program
+
+ -
+ Technical Advisory Council
+
+ -
+ Staff
+
+ -
+ Contact Us
+
diff --git a/docs/tutorials/_rendered_examples/dynamo/torch_export_sam2.html b/docs/tutorials/_rendered_examples/dynamo/torch_export_sam2.html
index 71b3dad2a2..cfbd7fe9ef 100644
--- a/docs/tutorials/_rendered_examples/dynamo/torch_export_sam2.html
+++ b/docs/tutorials/_rendered_examples/dynamo/torch_export_sam2.html
@@ -10,7 +10,7 @@
- Compiling SAM2 using the dynamo backend — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ Compiling SAM2 using the dynamo backend — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -135,8 +135,8 @@
Developer Resources
Find resources and get questions answered
-
- Contributor Awards - 2023
+
+ Contributor Awards - 2024
Award winners announced at this year's PyTorch Conference
@@ -157,6 +157,9 @@
ExecuTorch
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+
+ ExecuTorch Docs
+
@@ -204,6 +207,10 @@
Events
Find events, webinars, and podcasts
+
+ Newsletter
+ Stay up-to-date with the latest updates
+
@@ -219,7 +226,18 @@
Governing Board
-
+
+
+ Cloud Credit Program
+
+
+ Technical Advisory Council
+
+
+ Staff
+
+
+ Contact Us
@@ -275,7 +293,7 @@
- v2.6.0.dev0+70e2a38
+ v2.7.0.dev0+d6be4ba
@@ -495,15 +513,15 @@
Import the following libraries¶
-import matplotlib
-import matplotlib.pyplot as plt
-import numpy as np
-import pandas as pd
-import torch
-import torch_tensorrt
-from PIL import Image
-from sam2.sam2_image_predictor import SAM2ImagePredictor
-from sam_components import SAM2FullModel
+import matplotlib
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import torch
+import torch_tensorrt
+from PIL import Image
+from sam2.sam2_image_predictor import SAM2ImagePredictor
+from sam_components import SAM2FullModel
matplotlib.use("Agg")
@@ -521,8 +539,8 @@ Define the SAM2 modelSAM2FullModel which uses these utilities from SAM2ImagePredictor class.
SAM2FullModel performs feature extraction and mask prediction in a single step instead of two step process of
SAM2ImagePredictor (set_image and predict functions)
-class SAM2FullModel(torch.nn.Module):
- def __init__(self, model):
+class SAM2FullModel(torch.nn.Module):
+ def __init__(self, model):
super().__init__()
self.image_encoder = model.forward_image
self._prepare_backbone_features = model._prepare_backbone_features
@@ -535,7 +553,7 @@ Define the SAM2 modelself._bb_feat_sizes = [(256, 256), (128, 128), (64, 64)]
- def forward(self, image, point_coords, point_labels):
+ def forward(self, image, point_coords, point_labels):
backbone_out = self.image_encoder(image)
_, vision_feats, _, _ = self._prepare_backbone_features(backbone_out)
@@ -602,7 +620,7 @@ Preprocessing componentshttps://github.com/facebookresearch/sam2/blob/main/sam2/utils/transforms.py
-def preprocess_inputs(image, predictor):
+def preprocess_inputs(image, predictor):
w, h = image.size
orig_hw = [(h, w)]
input_image = predictor._transforms(np.array(image))[None, ...].to("cuda:0")
@@ -631,7 +649,7 @@ Preprocessing components¶
The following functions implement postprocessing components which include plotting and visualizing masks and points.
We use the SAM2Transforms to post process these masks and sort them via confidence score.
-def postprocess_masks(out, predictor, image):
+def postprocess_masks(out, predictor, image):
"""Postprocess low-resolution masks and convert them for visualization."""
orig_hw = (image.size[1], image.size[0]) # (height, width)
masks = predictor._transforms.postprocess_masks(out["low_res_masks"], orig_hw)
@@ -641,7 +659,7 @@ Post Processing componentsreturn masks[sorted_indices], scores[sorted_indices]
-def show_mask(mask, ax, random_color=False, borders=True):
+def show_mask(mask, ax, random_color=False, borders=True):
if random_color:
color = np.concatenate([np.random.random(3), np.array([0.6])], axis=0)
else:
@@ -650,7 +668,7 @@ Post Processing componentsmask = mask.astype(np.uint8)
mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1)
if borders:
- import cv2
+ import cv2
contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
# Try to smooth contours
@@ -663,7 +681,7 @@ Post Processing componentsax.imshow(mask_image)
-def show_points(coords, labels, ax, marker_size=375):
+def show_points(coords, labels, ax, marker_size=375):
pos_points = coords[labels == 1]
neg_points = coords[labels == 0]
ax.scatter(
@@ -686,7 +704,7 @@ Post Processing components)
-def visualize_masks(
+def visualize_masks(
image, masks, scores, point_coords, point_labels, title_prefix="", save=True
):
"""Visualize and save masks overlaid on the original image."""
@@ -1016,7 +1034,7 @@ Resources
Developer Resources
-
- Contributor Awards - 2023
+ Contributor Awards - 2024
@@ -1032,6 +1050,9 @@ Resources
-
ExecuTorch
+ -
+ ExecuTorch Documentation
+
-
Events
+ -
+ Newsletter
+
-
Governing Board
+ -
+ Cloud Credit Program
+
+ -
+ Technical Advisory Council
+
+ -
+ Staff
+
+ -
+ Contact Us
+
diff --git a/docs/tutorials/_rendered_examples/dynamo/vgg16_ptq.html b/docs/tutorials/_rendered_examples/dynamo/vgg16_ptq.html
index 00842e68bd..773269a045 100644
--- a/docs/tutorials/_rendered_examples/dynamo/vgg16_ptq.html
+++ b/docs/tutorials/_rendered_examples/dynamo/vgg16_ptq.html
@@ -10,7 +10,7 @@
- Deploy Quantized Models using Torch-TensorRT — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ Deploy Quantized Models using Torch-TensorRT — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -135,8 +135,8 @@
Developer Resources
Find resources and get questions answered
-
- Contributor Awards - 2023
+
+ Contributor Awards - 2024
Award winners announced at this year's PyTorch Conference
@@ -157,6 +157,9 @@
ExecuTorch
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+
+ ExecuTorch Docs
+
@@ -204,6 +207,10 @@
Events
Find events, webinars, and podcasts
+
+ Newsletter
+ Stay up-to-date with the latest updates
+
@@ -219,7 +226,18 @@
Governing Board
-
+
+
+ Cloud Credit Program
+
+
+ Technical Advisory Council
+
+
+ Staff
+
+
+ Contact Us
@@ -275,7 +293,7 @@
- v2.6.0.dev0+70e2a38
+ v2.7.0.dev0+d6be4ba
@@ -481,20 +499,20 @@
Here we demonstrate how to deploy a model quantized to INT8 or FP8 using the Dynamo frontend of Torch-TensorRT
Imports and Model Definition¶
-import argparse
+import argparse
-import modelopt.torch.quantization as mtq
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch_tensorrt as torchtrt
-import torchvision.datasets as datasets
-import torchvision.transforms as transforms
-from modelopt.torch.quantization.utils import export_torch_mode
+import modelopt.torch.quantization as mtq
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch_tensorrt as torchtrt
+import torchvision.datasets as datasets
+import torchvision.transforms as transforms
+from modelopt.torch.quantization.utils import export_torch_mode
-class VGG(nn.Module):
- def __init__(self, layer_spec, num_classes=1000, init_weights=False):
+class VGG(nn.Module):
+ def __init__(self, layer_spec, num_classes=1000, init_weights=False):
super(VGG, self).__init__()
layers = []
@@ -524,7 +542,7 @@ Imports and Model Definitionif init_weights:
self._initialize_weights()
- def _initialize_weights(self):
+ def _initialize_weights(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu")
@@ -537,7 +555,7 @@ Imports and Model Definitionnn.init.normal_(m.weight, 0, 0.01)
nn.init.constant_(m.bias, 0)
- def forward(self, x):
+ def forward(self, x):
x = self.features(x)
x = self.avgpool(x)
x = torch.flatten(x, 1)
@@ -545,7 +563,7 @@ Imports and Model Definitionreturn x
-def vgg16(num_classes=1000, init_weights=False):
+def vgg16(num_classes=1000, init_weights=False):
vgg16_cfg = [
64,
64,
@@ -600,7 +618,7 @@ Load the pre-trained model weightsweights = ckpt["model_state_dict"]
if torch.cuda.device_count() > 1:
- from collections import OrderedDict
+ from collections import OrderedDict
new_state_dict = OrderedDict()
for k, v in weights.items():
@@ -646,7 +664,7 @@ Load training dataset and define loss function for PTQ
Define Calibration Loop for quantization¶
-def calibrate_loop(model):
+def calibrate_loop(model):
# calibrate over the training dataset
total = 0
correct = 0
@@ -703,7 +721,7 @@ Inference# Compile the model with Torch-TensorRT Dynamo backend
input_tensor = images.cuda()
# torch.export.export() failed due to RuntimeError: Attempting to use FunctionalTensor on its own. Instead, please use it with a corresponding FunctionalTensorMode()
- from torch.export._trace import _export
+ from torch.export._trace import _export
exp_program = _export(model, (input_tensor,))
if args.quantize_type == "int8":
@@ -1001,7 +1019,7 @@ Resources
Developer Resources
-
- Contributor Awards - 2023
+ Contributor Awards - 2024
@@ -1017,6 +1035,9 @@ Resources
-
ExecuTorch
+ -
+ ExecuTorch Documentation
+
-
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+ -
+ Newsletter
+
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+ -
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+
diff --git a/docs/tutorials/_rendered_examples/dynamo/weight_streaming_example.html b/docs/tutorials/_rendered_examples/dynamo/weight_streaming_example.html
index bd71e399eb..5926433ce7 100644
--- a/docs/tutorials/_rendered_examples/dynamo/weight_streaming_example.html
+++ b/docs/tutorials/_rendered_examples/dynamo/weight_streaming_example.html
@@ -10,7 +10,7 @@
- Weight Streaming — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ Weight Streaming — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -135,8 +135,8 @@
Developer Resources
Find resources and get questions answered
-
- Contributor Awards - 2023
+
+ Contributor Awards - 2024
Award winners announced at this year's PyTorch Conference
@@ -157,6 +157,9 @@
ExecuTorch
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+
+ ExecuTorch Docs
+
@@ -204,6 +207,10 @@
Events
Find events, webinars, and podcasts
+
+ Newsletter
+ Stay up-to-date with the latest updates
+
@@ -219,7 +226,18 @@
Governing Board
-
+
+
+ Cloud Credit Program
+
+
+ Technical Advisory Council
+
+
+ Staff
+
+
+ Contact Us
@@ -275,7 +293,7 @@
- v2.6.0.dev0+70e2a38
+ v2.7.0.dev0+d6be4ba
@@ -497,17 +515,17 @@
Imports and Model Definition¶
-import copy
-import timeit
+import copy
+import timeit
-import numpy as np
-import torch
-import torch_tensorrt
-from transformers import AutoModelForCausalLM
-from utils import export_llm
+import numpy as np
+import torch
+import torch_tensorrt
+from transformers import AutoModelForCausalLM
+from utils import export_llm
-def time_generate(model, inputs, output_seq_length, iterations=10):
+def time_generate(model, inputs, output_seq_length, iterations=10):
"""
Measure the time for generating a sentence over certain number of iterations
"""
@@ -898,7 +916,7 @@ Resources
Developer Resources
-
- Contributor Awards - 2023
+ Contributor Awards - 2024
@@ -914,6 +932,9 @@ Resources
-
ExecuTorch
+ -
+ ExecuTorch Documentation
+
-
Events
+ -
+ Newsletter
+
-
Governing Board
+ -
+ Cloud Credit Program
+
+ -
+ Technical Advisory Council
+
+ -
+ Staff
+
+ -
+ Contact Us
+
diff --git a/docs/tutorials/_rendered_examples/index.html b/docs/tutorials/_rendered_examples/index.html
index b79b764823..b562b08eed 100644
--- a/docs/tutorials/_rendered_examples/index.html
+++ b/docs/tutorials/_rendered_examples/index.html
@@ -10,7 +10,7 @@
- Torch-TensorRT Tutorials — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ Torch-TensorRT Tutorials — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -133,8 +133,8 @@
Developer Resources
Find resources and get questions answered
-
- Contributor Awards - 2023
+
+ Contributor Awards - 2024
Award winners announced at this year's PyTorch Conference
@@ -155,6 +155,9 @@
ExecuTorch
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+
+ ExecuTorch Docs
+
@@ -202,6 +205,10 @@
Events
Find events, webinars, and podcasts
+
+ Newsletter
+ Stay up-to-date with the latest updates
+
@@ -217,7 +224,18 @@
Governing Board
-
+
+
+ Cloud Credit Program
+
+
+ Technical Advisory Council
+
+
+ Staff
+
+
+ Contact Us
@@ -273,7 +291,7 @@
- v2.6.0.dev0+70e2a38
+ v2.7.0.dev0+d6be4ba
@@ -999,7 +1017,7 @@ Resources
Developer Resources
-
- Contributor Awards - 2023
+ Contributor Awards - 2024
@@ -1015,6 +1033,9 @@ Resources
-
ExecuTorch
+ -
+ ExecuTorch Documentation
+
-
Events
+ -
+ Newsletter
+
-
Governing Board
+ -
+ Cloud Credit Program
+
+ -
+ Technical Advisory Council
+
+ -
+ Staff
+
+ -
+ Contact Us
+
diff --git a/docs/tutorials/_rendered_examples/triton/index.html b/docs/tutorials/_rendered_examples/triton/index.html
index 8a4211673c..e32a5dbdbd 100644
--- a/docs/tutorials/_rendered_examples/triton/index.html
+++ b/docs/tutorials/_rendered_examples/triton/index.html
@@ -10,7 +10,7 @@
- Serving a Torch-TensorRT model with Triton — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ Serving a Torch-TensorRT model with Triton — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -133,8 +133,8 @@
Developer Resources
Find resources and get questions answered
-
- Contributor Awards - 2023
+
+ Contributor Awards - 2024
Award winners announced at this year's PyTorch Conference
@@ -155,6 +155,9 @@
ExecuTorch
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+
+ ExecuTorch Docs
+
@@ -202,6 +205,10 @@
Events
Find events, webinars, and podcasts
+
+ Newsletter
+ Stay up-to-date with the latest updates
+
@@ -217,7 +224,18 @@
Governing Board
-
+
+
+ Cloud Credit Program
+
+
+ Technical Advisory Council
+
+
+ Staff
+
+
+ Contact Us
@@ -273,7 +291,7 @@
- v2.6.0.dev0+70e2a38
+ v2.7.0.dev0+d6be4ba
@@ -900,7 +918,7 @@ Resources
Developer Resources
-
- Contributor Awards - 2023
+ Contributor Awards - 2024
@@ -916,6 +934,9 @@ Resources
-
ExecuTorch
+ -
+ ExecuTorch Documentation
+
-
Events
+ -
+ Newsletter
+
-
Governing Board
+ -
+ Cloud Credit Program
+
+ -
+ Technical Advisory Council
+
+ -
+ Staff
+
+ -
+ Contact Us
+
diff --git a/docs/tutorials/notebooks.html b/docs/tutorials/notebooks.html
index bd562e28f3..a3ded7a6a0 100644
--- a/docs/tutorials/notebooks.html
+++ b/docs/tutorials/notebooks.html
@@ -10,7 +10,7 @@
- Legacy notebooks — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ Legacy notebooks — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -135,8 +135,8 @@
Developer Resources
Find resources and get questions answered
-
- Contributor Awards - 2023
+
+ Contributor Awards - 2024
Award winners announced at this year's PyTorch Conference
@@ -157,6 +157,9 @@
ExecuTorch
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+
+ ExecuTorch Docs
+
@@ -204,6 +207,10 @@
Events
Find events, webinars, and podcasts
+
+ Newsletter
+ Stay up-to-date with the latest updates
+
@@ -219,7 +226,18 @@
Governing Board
-
+
+
+ Cloud Credit Program
+
+
+ Technical Advisory Council
+
+
+ Staff
+
+
+ Contact Us
@@ -275,7 +293,7 @@
- v2.6.0.dev0+70e2a38
+ v2.7.0.dev0+d6be4ba
@@ -863,7 +881,7 @@ Resources
Developer Resources
-
- Contributor Awards - 2023
+ Contributor Awards - 2024
@@ -879,6 +897,9 @@ Resources
-
ExecuTorch
+ -
+ ExecuTorch Documentation
+
-
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+ -
+ Newsletter
+
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+ -
+ Cloud Credit Program
+
+ -
+ Technical Advisory Council
+
+ -
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+
+ -
+ Contact Us
+
diff --git a/docs/tutorials/serving_torch_tensorrt_with_triton.html b/docs/tutorials/serving_torch_tensorrt_with_triton.html
index acd474e4ec..ff04b70b88 100644
--- a/docs/tutorials/serving_torch_tensorrt_with_triton.html
+++ b/docs/tutorials/serving_torch_tensorrt_with_triton.html
@@ -10,7 +10,7 @@
- Serving a Torch-TensorRT model with Triton — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ Serving a Torch-TensorRT model with Triton — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -135,8 +135,8 @@
Developer Resources
Find resources and get questions answered
-
- Contributor Awards - 2023
+
+ Contributor Awards - 2024
Award winners announced at this year's PyTorch Conference
@@ -157,6 +157,9 @@
ExecuTorch
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+
+ ExecuTorch Docs
+
@@ -204,6 +207,10 @@
Events
Find events, webinars, and podcasts
+
+ Newsletter
+ Stay up-to-date with the latest updates
+
@@ -219,7 +226,18 @@
Governing Board
-
+
+
+ Cloud Credit Program
+
+
+ Technical Advisory Council
+
+
+ Staff
+
+
+ Contact Us
@@ -275,7 +293,7 @@
- v2.6.0.dev0+70e2a38
+ v2.7.0.dev0+d6be4ba
@@ -911,7 +929,7 @@ Resources
Developer Resources
-
- Contributor Awards - 2023
+ Contributor Awards - 2024
@@ -927,6 +945,9 @@ Resources
-
ExecuTorch
+ -
+ ExecuTorch Documentation
+
-
Events
+ -
+ Newsletter
+
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Governing Board
+ -
+ Cloud Credit Program
+
+ -
+ Technical Advisory Council
+
+ -
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+
+ -
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+
diff --git a/docs/user_guide/dynamic_shapes.html b/docs/user_guide/dynamic_shapes.html
index 089455645d..8029fbb06a 100644
--- a/docs/user_guide/dynamic_shapes.html
+++ b/docs/user_guide/dynamic_shapes.html
@@ -10,7 +10,7 @@
- Dynamic shapes with Torch-TensorRT — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ Dynamic shapes with Torch-TensorRT — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -135,8 +135,8 @@
Developer Resources
Find resources and get questions answered
-
- Contributor Awards - 2023
+
+ Contributor Awards - 2024
Award winners announced at this year's PyTorch Conference
@@ -157,6 +157,9 @@
ExecuTorch
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+
+ ExecuTorch Docs
+
@@ -204,6 +207,10 @@
Events
Find events, webinars, and podcasts
+
+ Newsletter
+ Stay up-to-date with the latest updates
+
@@ -219,7 +226,18 @@
Governing Board
-
+
+
+ Cloud Credit Program
+
+
+ Technical Advisory Council
+
+
+ Staff
+
+
+ Contact Us
@@ -275,7 +293,7 @@
- v2.6.0.dev0+70e2a38
+ v2.7.0.dev0+d6be4ba
@@ -480,8 +498,8 @@ Dynamic shapes using torch.export (AOT)ir=dynamo with ir=ts and the behavior is exactly the same.
-import torch
-import torch_tensorrt
+import torch
+import torch_tensorrt
model = MyModel().eval().cuda()
# Compile with static shapes
@@ -518,14 +536,14 @@ Custom Dynamic Shape Constraintsdocumentation to export the Pytorch module with dynamic shapes.
Here’s a simple example that exports a matmul layer with some restrictions on dynamic dimensions.
-import torch
-import torch_tensorrt
+import torch
+import torch_tensorrt
-class MatMul(torch.nn.Module):
- def __init__(self):
+class MatMul(torch.nn.Module):
+ def __init__(self):
super().__init__()
- def forward(self, query, key):
+ def forward(self, query, key):
attn_weight = torch.matmul(query, key.transpose(-1, -2))
return attn_weight
@@ -546,8 +564,8 @@ Dynamic shapes using torch.compile (JIT)torch_tensorrt.compile(model, inputs, ir="torch_compile") returns a torch.compile boxed function with the backend
configured to TensorRT. In the case of ir=torch_compile, users can provide dynamic shape information for the inputs using torch._dynamo.mark_dynamic API (https://pytorch.org/docs/stable/torch.compiler_dynamic_shapes.html)
to avoid recompilation of TensorRT engines.
-import torch
-import torch_tensorrt
+import torch
+import torch_tensorrt
model = MyModel().eval().cuda()
inputs = torch.randn((1, 3, 224, 224), dtype=float32)
@@ -808,7 +826,7 @@ Resources
Developer Resources
-
- Contributor Awards - 2023
+ Contributor Awards - 2024
@@ -824,6 +842,9 @@ Resources
-
ExecuTorch
+ -
+ ExecuTorch Documentation
+
-
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+ -
+ Newsletter
+
-
Governing Board
+ -
+ Cloud Credit Program
+
+ -
+ Technical Advisory Council
+
+ -
+ Staff
+
+ -
+ Contact Us
+
diff --git a/docs/user_guide/mixed_precision.html b/docs/user_guide/mixed_precision.html
index aa7697dcdd..644b0d2ec9 100644
--- a/docs/user_guide/mixed_precision.html
+++ b/docs/user_guide/mixed_precision.html
@@ -10,7 +10,7 @@
- Compile Mixed Precision models with Torch-TensorRT — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ Compile Mixed Precision models with Torch-TensorRT — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -135,8 +135,8 @@
Developer Resources
Find resources and get questions answered
-
- Contributor Awards - 2023
+
+ Contributor Awards - 2024
Award winners announced at this year's PyTorch Conference
@@ -157,6 +157,9 @@
ExecuTorch
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+
+ ExecuTorch Docs
+
@@ -204,6 +207,10 @@
Events
Find events, webinars, and podcasts
+
+ Newsletter
+ Stay up-to-date with the latest updates
+
@@ -219,7 +226,18 @@
Governing Board
-
+
+
+ Cloud Credit Program
+
+
+ Technical Advisory Council
+
+
+ Staff
+
+
+ Contact Us
@@ -275,7 +293,7 @@
- v2.6.0.dev0+70e2a38
+ v2.7.0.dev0+d6be4ba
@@ -474,14 +492,14 @@
Compile Mixed Precision models with Torch-TensorRT¶
Consider the following Pytorch model which explicitly casts intermediate layer to run in FP16.
-class MyModule(torch.nn.Module):
- def __init__(self):
+class MyModule(torch.nn.Module):
+ def __init__(self):
super().__init__()
self.linear1 = torch.nn.Linear(10,10)
self.linear2 = torch.nn.Linear(10,30).half()
self.linear3 = torch.nn.Linear(30,40)
- def forward(self, x):
+ def forward(self, x):
x = self.linear1(x)
x = x.to(torch.float16)
x = self.linear2(x)
@@ -769,7 +787,7 @@ Resources
Developer Resources
-
- Contributor Awards - 2023
+ Contributor Awards - 2024
@@ -785,6 +803,9 @@ Resources
-
ExecuTorch
+ -
+ ExecuTorch Documentation
+
-
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+ -
+ Newsletter
+
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+ -
+ Cloud Credit Program
+
+ -
+ Technical Advisory Council
+
+ -
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+
+ -
+ Contact Us
+
diff --git a/docs/user_guide/runtime.html b/docs/user_guide/runtime.html
index 8a2d5cebbd..72c83c617d 100644
--- a/docs/user_guide/runtime.html
+++ b/docs/user_guide/runtime.html
@@ -10,7 +10,7 @@
- Deploying Torch-TensorRT Programs — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ Deploying Torch-TensorRT Programs — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -135,8 +135,8 @@
Developer Resources
Find resources and get questions answered
-
- Contributor Awards - 2023
+
+ Contributor Awards - 2024
Award winners announced at this year's PyTorch Conference
@@ -157,6 +157,9 @@
ExecuTorch
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+
+ ExecuTorch Docs
+
@@ -204,6 +207,10 @@
Events
Find events, webinars, and podcasts
+
+ Newsletter
+ Stay up-to-date with the latest updates
+
@@ -219,7 +226,18 @@
Governing Board
-
+
+
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+
+
+ Technical Advisory Council
+
+
+ Staff
+
+
+ Contact Us
@@ -275,7 +293,7 @@
- v2.6.0.dev0+70e2a38
+ v2.7.0.dev0+d6be4ba
@@ -799,7 +817,7 @@ Resources
Developer Resources
-
- Contributor Awards - 2023
+ Contributor Awards - 2024
@@ -815,6 +833,9 @@ Resources
-
ExecuTorch
+ -
+ ExecuTorch Documentation
+
-
Events
+ -
+ Newsletter
+
-
Governing Board
+ -
+ Cloud Credit Program
+
+ -
+ Technical Advisory Council
+
+ -
+ Staff
+
+ -
+ Contact Us
+
diff --git a/docs/user_guide/saving_models.html b/docs/user_guide/saving_models.html
index 4db68ce81d..222ef9711b 100644
--- a/docs/user_guide/saving_models.html
+++ b/docs/user_guide/saving_models.html
@@ -10,7 +10,7 @@
- Saving models compiled with Torch-TensorRT — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ Saving models compiled with Torch-TensorRT — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -135,8 +135,8 @@
Developer Resources
Find resources and get questions answered
-
- Contributor Awards - 2023
+
+ Contributor Awards - 2024
Award winners announced at this year's PyTorch Conference
@@ -157,6 +157,9 @@
ExecuTorch
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+
+ ExecuTorch Docs
+
@@ -204,6 +207,10 @@
Events
Find events, webinars, and podcasts
+
+ Newsletter
+ Stay up-to-date with the latest updates
+
@@ -219,7 +226,18 @@
Governing Board
-
+
+
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+
+
+ Technical Advisory Council
+
+
+ Staff
+
+
+ Contact Us
@@ -275,7 +293,7 @@
- v2.6.0.dev0+70e2a38
+ v2.7.0.dev0+d6be4ba
@@ -486,13 +504,13 @@ Dynamo IR
a) ExportedProgram¶
Here’s an example usage
-import torch
-import torch_tensorrt
+import torch
+import torch_tensorrt
model = MyModel().eval().cuda()
inputs = [torch.randn((1, 3, 224, 224)).cuda()]
# trt_ep is a torch.fx.GraphModule object
-trt_gm = torch_tensorrt.compile(model, ir="dynamo", inputs)
+trt_gm = torch_tensorrt.compile(model, ir="dynamo", inputs=inputs)
torch_tensorrt.save(trt_gm, "trt.ep", inputs=inputs)
# Later, you can load it and run inference
@@ -503,13 +521,13 @@ a) ExportedProgram
b) Torchscript¶
-import torch
-import torch_tensorrt
+import torch
+import torch_tensorrt
model = MyModel().eval().cuda()
inputs = [torch.randn((1, 3, 224, 224)).cuda()]
# trt_gm is a torch.fx.GraphModule object
-trt_gm = torch_tensorrt.compile(model, ir="dynamo", inputs)
+trt_gm = torch_tensorrt.compile(model, ir="dynamo", inputs=inputs)
torch_tensorrt.save(trt_gm, "trt.ts", output_format="torchscript", inputs=inputs)
# Later, you can load it and run inference
@@ -523,12 +541,12 @@ b) Torchscript¶
In Torch-TensorRT 1.X versions, the primary way to compile and run inference with Torch-TensorRT is using Torchscript IR.
For ir=ts, this behavior stays the same in 2.X versions as well.
-import torch
-import torch_tensorrt
+import torch
+import torch_tensorrt
model = MyModel().eval().cuda()
inputs = [torch.randn((1, 3, 224, 224)).cuda()]
-trt_ts = torch_tensorrt.compile(model, ir="ts", inputs) # Output is a ScriptModule object
+trt_ts = torch_tensorrt.compile(model, ir="ts", inputs=inputs) # Output is a ScriptModule object
torch.jit.save(trt_ts, "trt_model.ts")
# Later, you can load it and run inference
@@ -542,8 +560,8 @@ Loading the modelsimport torch
-import torch_tensorrt
+import torch
+import torch_tensorrt
# file_path can be trt.ep or trt.ts file obtained via saving the model (refer to the above section)
inputs = [torch.randn((1, 3, 224, 224)).cuda()]
@@ -800,7 +818,7 @@ Resources
Developer Resources
-
- Contributor Awards - 2023
+ Contributor Awards - 2024
@@ -816,6 +834,9 @@ Resources
-
ExecuTorch
+ -
+ ExecuTorch Documentation
+
-
Events
+ -
+ Newsletter
+
-
Governing Board
+ -
+ Cloud Credit Program
+
+ -
+ Technical Advisory Council
+
+ -
+ Staff
+
+ -
+ Contact Us
+
diff --git a/docs/user_guide/torch_tensorrt_explained.html b/docs/user_guide/torch_tensorrt_explained.html
index 6a219f31b3..42ca24daec 100644
--- a/docs/user_guide/torch_tensorrt_explained.html
+++ b/docs/user_guide/torch_tensorrt_explained.html
@@ -10,7 +10,7 @@
- Torch-TensorRT Explained — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ Torch-TensorRT Explained — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -135,8 +135,8 @@
Developer Resources
Find resources and get questions answered
-
- Contributor Awards - 2023
+
+ Contributor Awards - 2024
Award winners announced at this year's PyTorch Conference
@@ -157,6 +157,9 @@
ExecuTorch
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+
+ ExecuTorch Docs
+
@@ -204,6 +207,10 @@
Events
Find events, webinars, and podcasts
+
+ Newsletter
+ Stay up-to-date with the latest updates
+
@@ -219,7 +226,18 @@
Governing Board
-
+
+
+ Cloud Credit Program
+
+
+ Technical Advisory Council
+
+
+ Staff
+
+
+ Contact Us
@@ -275,7 +293,7 @@
- v2.6.0.dev0+70e2a38
+ v2.7.0.dev0+d6be4ba
@@ -852,7 +870,7 @@ Resources
Developer Resources
-
- Contributor Awards - 2023
+ Contributor Awards - 2024
@@ -868,6 +886,9 @@ Resources
-
ExecuTorch
+ -
+ ExecuTorch Documentation
+
-
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+ -
+ Newsletter
+
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+ -
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+
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+ -
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+
+ -
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+
diff --git a/docs/user_guide/using_dla.html b/docs/user_guide/using_dla.html
index 1cdd1c6bbb..0cd04c9c0d 100644
--- a/docs/user_guide/using_dla.html
+++ b/docs/user_guide/using_dla.html
@@ -10,7 +10,7 @@
- DLA — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation
+ DLA — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation
@@ -135,8 +135,8 @@
Developer Resources
Find resources and get questions answered
-
- Contributor Awards - 2023
+
+ Contributor Awards - 2024
Award winners announced at this year's PyTorch Conference
@@ -157,6 +157,9 @@
ExecuTorch
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+
+ ExecuTorch Docs
+
@@ -204,6 +207,10 @@
Events
Find events, webinars, and podcasts
+
+ Newsletter
+ Stay up-to-date with the latest updates
+
@@ -219,7 +226,18 @@
Governing Board
-
+
+
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+
+
+ Technical Advisory Council
+
+
+ Staff
+
+
+ Contact Us
@@ -275,7 +293,7 @@
- v2.6.0.dev0+70e2a38
+ v2.7.0.dev0+d6be4ba
@@ -746,7 +764,7 @@ Resources
Developer Resources
-
- Contributor Awards - 2023
+ Contributor Awards - 2024
@@ -762,6 +780,9 @@ Resources
-
ExecuTorch
+ -
+ ExecuTorch Documentation
+
-
Events
+ -
+ Newsletter
+
-
Governing Board
+ -
+ Cloud Credit Program
+
+ -
+ Technical Advisory Council
+
+ -
+ Staff
+
+ -
+ Contact Us
+
diff --git a/docsrc/user_guide/saving_models.rst b/docsrc/user_guide/saving_models.rst
index 42cff7b954..dc4b5da222 100644
--- a/docsrc/user_guide/saving_models.rst
+++ b/docsrc/user_guide/saving_models.rst
@@ -34,7 +34,7 @@ Here's an example usage
model = MyModel().eval().cuda()
inputs = [torch.randn((1, 3, 224, 224)).cuda()]
# trt_ep is a torch.fx.GraphModule object
- trt_gm = torch_tensorrt.compile(model, ir="dynamo", inputs)
+ trt_gm = torch_tensorrt.compile(model, ir="dynamo", inputs=inputs)
torch_tensorrt.save(trt_gm, "trt.ep", inputs=inputs)
# Later, you can load it and run inference
@@ -52,7 +52,7 @@ b) Torchscript
model = MyModel().eval().cuda()
inputs = [torch.randn((1, 3, 224, 224)).cuda()]
# trt_gm is a torch.fx.GraphModule object
- trt_gm = torch_tensorrt.compile(model, ir="dynamo", inputs)
+ trt_gm = torch_tensorrt.compile(model, ir="dynamo", inputs=inputs)
torch_tensorrt.save(trt_gm, "trt.ts", output_format="torchscript", inputs=inputs)
# Later, you can load it and run inference
@@ -73,7 +73,7 @@ For `ir=ts`, this behavior stays the same in 2.X versions as well.
model = MyModel().eval().cuda()
inputs = [torch.randn((1, 3, 224, 224)).cuda()]
- trt_ts = torch_tensorrt.compile(model, ir="ts", inputs) # Output is a ScriptModule object
+ trt_ts = torch_tensorrt.compile(model, ir="ts", inputs=inputs) # Output is a ScriptModule object
torch.jit.save(trt_ts, "trt_model.ts")
# Later, you can load it and run inference
diff --git a/examples/distributed_inference/README.md b/examples/distributed_inference/README.md
index 4c88570b6f..d4cf9508e1 100644
--- a/examples/distributed_inference/README.md
+++ b/examples/distributed_inference/README.md
@@ -14,3 +14,37 @@ See the examples started with `data_parallel` for more details.
Here we use torch.distributed as an example, but compilation with tensor parallelism is agnostic to the implementation framework as long as the module is properly sharded.
torchrun --nproc_per_node=2 tensor_parallel_llama2.py
+
+3. Tensor parallel distributed inference using nccl ops plugin
+
+ apt install libmpich-dev
+
+ apt install libopenmpi-dev
+
+ #For python3.10
+
+ pip install tensorrt-llm
+
+ For other python versions, you need to load the libnvinfer_plugin_tensorrt_llm.so. Please set that in the environment variable export TRTLLM_PLUGINS_PATH={lib_path}. For example, we have already set the variable in initialize_distributed_env(). You can replace this with your TRTLLM_PLUGINS_PATH and unset it there
+
+ #then pip install the tensorrt and torch version compatible with installed torchTRT
+
+ mpirun -n 2 --allow-run-as-root python tensor_parallel_simple_example.py
+
+ #For other python
+
+4. Tensor parallel distributed llama3 inference using nccl ops plugin
+
+ apt install libmpich-dev
+
+ apt install libopenmpi-dev
+
+#For python3.10
+
+ pip install tensorrt-llm
+
+ For other python versions, you need to load the libnvinfer_plugin_tensorrt_llm.so
+
+ #then pip install the tensorrt and torch version compatible with installed torchTRT
+
+ mpirun -n 2 --allow-run-as-root python tensor_parallel_llama3.py
diff --git a/examples/distributed_inference/requirement.txt b/examples/distributed_inference/requirement.txt
index 6d8e0aa9f2..bb3b0f28f1 100644
--- a/examples/distributed_inference/requirement.txt
+++ b/examples/distributed_inference/requirement.txt
@@ -1,3 +1,4 @@
accelerate
transformers
-diffusers
\ No newline at end of file
+diffusers
+tensorrt-llm
\ No newline at end of file
diff --git a/examples/distributed_inference/tensor_parallel_initialize_dist.py b/examples/distributed_inference/tensor_parallel_initialize_dist.py
new file mode 100644
index 0000000000..21e4cbc282
--- /dev/null
+++ b/examples/distributed_inference/tensor_parallel_initialize_dist.py
@@ -0,0 +1,67 @@
+import logging
+import os
+from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union
+
+import numpy as np
+import tensorrt as trt
+import torch
+import torch.distributed as dist
+from torch.distributed._tensor.device_mesh import init_device_mesh
+
+
+def find_repo_root(max_depth=10):
+ dir_path = os.path.dirname(os.path.realpath(__file__))
+ for i in range(max_depth):
+ files = os.listdir(dir_path)
+ if "MODULE.bazel" in files:
+ return dir_path
+ else:
+ dir_path = os.path.dirname(dir_path)
+
+ raise RuntimeError("Could not find repo root")
+
+
+def initialize_logger(rank, logger_file_name):
+ logger = logging.getLogger()
+ logger.setLevel(logging.INFO)
+ fh = logging.FileHandler(logger_file_name + f"_{rank}.log", mode="w")
+ fh.setLevel(logging.INFO)
+ logger.addHandler(fh)
+ return logger
+
+
+# This is required for env initialization since we use mpirun
+def initialize_distributed_env(logger_file_name, rank=0, world_size=1, port=29500):
+ local_rank = int(
+ os.environ.get("OMPI_COMM_WORLD_LOCAL_RANK", rank % torch.cuda.device_count())
+ )
+ world_size = int(os.environ.get("OMPI_COMM_WORLD_SIZE", world_size))
+
+ # Set up environment variable to run with mpirun
+ os.environ["RANK"] = str(local_rank)
+ os.environ["WORLD_SIZE"] = str(world_size)
+ os.environ["MASTER_ADDR"] = "127.0.0.1"
+ os.environ["MASTER_PORT"] = str(port)
+ os.environ["TRTLLM_PLUGINS_PATH"] = (
+ find_repo_root() + "/lib/libnvinfer_plugin_tensorrt_llm.so"
+ )
+
+ # Necessary to assign a device to each rank.
+ torch.cuda.set_device(local_rank)
+
+ # We use nccl backend
+ dist.init_process_group("nccl")
+
+ # set a manual seed for reproducibility
+ torch.manual_seed(1111)
+
+ device_mesh = init_device_mesh(device_type="cuda", mesh_shape=(world_size,))
+ rank = device_mesh.get_rank()
+ assert rank == local_rank
+ logger = initialize_logger(rank, logger_file_name)
+ device_id = (
+ rank % torch.cuda.device_count()
+ ) # Ensure each rank gets a unique device
+ torch.cuda.set_device(device_id)
+
+ return device_mesh, world_size, rank, logger
diff --git a/examples/distributed_inference/tensor_parallel_llama3.py b/examples/distributed_inference/tensor_parallel_llama3.py
index fc03a64386..998c378be2 100644
--- a/examples/distributed_inference/tensor_parallel_llama3.py
+++ b/examples/distributed_inference/tensor_parallel_llama3.py
@@ -5,27 +5,25 @@
import time
import torch
-import torch_tensorrt
from llama3_model import ModelArgs, ParallelTransformer
+from tensor_parallel_initialize_dist import initialize_distributed_env
from torch.distributed._composable.fsdp import MixedPrecisionPolicy
from torch.distributed._composable.fsdp.fully_shard import fully_shard
from torch.distributed._tensor import Replicate, Shard
from torch.distributed.algorithms._checkpoint.checkpoint_wrapper import (
checkpoint_wrapper,
)
-from torch.distributed.device_mesh import DeviceMesh, init_device_mesh
-_rank = int(os.environ["RANK"])
-_world_size = int(os.environ["WORLD_SIZE"])
-tp_size = 2
-
-logger = logging.getLogger()
-logger.setLevel(logging.INFO)
-fh = logging.FileHandler(f"./tensor_parallel_log_{_rank}.log", mode="w")
-fh.setLevel(logging.INFO)
-logger.addHandler(fh)
+device_mesh, _world_size, _rank, logger = initialize_distributed_env(
+ "./tensor_parallel_llama3"
+)
+# Import should be after initialization of the TRT-LLM plugin .so path
+import tensorrt_llm
-tp_mesh = init_device_mesh(device_type="cuda", mesh_shape=(_world_size,))
+logger.info(f"Starting PyTorch TP example on rank {_rank}.")
+assert (
+ _world_size % 2 == 0
+), f"TP examples require even number of GPUs, but got {_world_size} gpus"
model_args = ModelArgs(
vocab_size=32000,
@@ -38,7 +36,7 @@
)
with torch.no_grad():
- model = ParallelTransformer(model_args, tp_mesh)
+ model = ParallelTransformer(model_args, device_mesh)
torch.manual_seed(0)
inp = torch.randint(32000, (8, 256), device="cuda")
python_result = model(inp)
@@ -53,7 +51,7 @@
"use_python_runtime": True,
"workspace_size": 1 << 33,
"debug": False,
- "timing_cache_path": "/opt/file/cache/timing_cache_llama.bin",
+ "use_aot_joint_export": False,
},
dynamic=False,
)
diff --git a/examples/distributed_inference/tensor_parallel_simple_example.py b/examples/distributed_inference/tensor_parallel_simple_example.py
index 470487a751..837648fdb4 100755
--- a/examples/distributed_inference/tensor_parallel_simple_example.py
+++ b/examples/distributed_inference/tensor_parallel_simple_example.py
@@ -1,18 +1,22 @@
-import os
-import sys
import time
+import tensorrt as trt
import torch
import torch.nn as nn
import torch_tensorrt
+from tensor_parallel_initialize_dist import initialize_distributed_env
from torch.distributed._tensor import Shard
-from torch.distributed._tensor.device_mesh import init_device_mesh
from torch.distributed.tensor.parallel import (
ColwiseParallel,
RowwiseParallel,
parallelize_module,
)
+device_mesh, _world_size, _rank, logger = initialize_distributed_env(
+ "./tensor_parallel_simple_example"
+)
+import tensorrt_llm
+
"""
This example copies some code from https://github.com/pytorch/examples/blob/main/distributed/tensor_parallelism/tensor_parallel_example.py
"""
@@ -36,14 +40,7 @@ def forward(self, x):
return x
-# create a device mesh based on the given world_size.
-_world_size = int(os.environ["WORLD_SIZE"])
-
-device_mesh = init_device_mesh(device_type="cuda", mesh_shape=(_world_size,))
-_rank = device_mesh.get_rank()
-
-
-print(f"Starting PyTorch TP example on rank {_rank}.")
+logger.info(f"Starting PyTorch TP example on rank {_rank}.")
assert (
_world_size % 2 == 0
), f"TP examples require even number of GPUs, but got {_world_size} gpus"
@@ -78,6 +75,7 @@ def forward(self, x):
"enabled_precisions": {torch.float32, torch.float16},
"use_python_runtime": True,
"min_block_size": 1,
+ "use_aot_joint_export": False,
},
dynamic=False,
)
@@ -91,9 +89,9 @@ def forward(self, x):
output = tp_model(inp)
end = time.time()
if i == 0:
- print(f"Compilation time is {end-start}")
+ logger.info(f"Compilation time is {end-start}")
assert (
python_result - output
).std() < 0.01, "Compilation result is not correct."
elif _rank == 0:
- print(f"Inference time is {end-start}")
+ logger.info(f"Inference time is {end-start}")
diff --git a/noxfile.py b/noxfile.py
index 73d0dca854..9a5c3263bb 100644
--- a/noxfile.py
+++ b/noxfile.py
@@ -34,7 +34,7 @@
# Set epochs to train VGG model for accuracy tests
EPOCHS = 25
-SUPPORTED_PYTHON_VERSIONS = ["3.7", "3.8", "3.9", "3.10"]
+SUPPORTED_PYTHON_VERSIONS = ["3.9", "3.10", "3.11", "3.12"]
nox.options.sessions = [
"l0_api_tests-" + "{}.{}".format(sys.version_info.major, sys.version_info.minor)
diff --git a/packaging/env_vars.txt b/packaging/env_vars.txt
index bfd05b7bf6..44a2350e0f 100644
--- a/packaging/env_vars.txt
+++ b/packaging/env_vars.txt
@@ -1,7 +1,3 @@
export CI_BUILD="1"
export RELEASE="1"
-if [ $CU_VERSION == "cu126" ]; then
- export USE_CXX11_ABI="1"
-else
- export USE_CXX11_ABI="0"
-fi
\ No newline at end of file
+export USE_CXX11_ABI="1"
\ No newline at end of file
diff --git a/packaging/smoke_test_script.sh b/packaging/smoke_test_script.sh
index 19d9d717a4..ef584fef51 100644
--- a/packaging/smoke_test_script.sh
+++ b/packaging/smoke_test_script.sh
@@ -2,5 +2,5 @@
# The issue was smoke test installs the built torch_tensorrt wheel file and checks `import torch_tensorrt; print(torch_tensorrt.__version__)`
# Since tensorrt cannot be pip installable in CI, the smoke test will fail.
# One way we tried to handle it is manually install tensorrt wheel while by extracting from the tarball.
-# However, the TensorRT-10.3.0.26/lib path doesn't seem to show up in LD_LIBRARY_PATH even if we explicitly set it.
+# However, the TensorRT-10.7.0.23/lib path doesn't seem to show up in LD_LIBRARY_PATH even if we explicitly set it.
# TODO: Implement a custom smoke_test script to verify torch_tensorrt installation.
\ No newline at end of file
diff --git a/py/ci/Dockerfile.ci b/py/ci/Dockerfile.ci
index 823c8bb7a1..288c61029d 100644
--- a/py/ci/Dockerfile.ci
+++ b/py/ci/Dockerfile.ci
@@ -3,13 +3,14 @@ FROM pytorch/manylinux2_28-builder:cuda12.6
RUN yum install -y ninja-build
# download TensorRT tarball
-RUN wget -q https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/10.3.0/tars/TensorRT-10.3.0.26.Linux.x86_64-gnu.cuda-12.5.tar.gz \
-&& gunzip TensorRT-10.3.0.26.Linux.x86_64-gnu.cuda-12.5.tar.gz \
-&& tar -xvf TensorRT-10.3.0.26.Linux.x86_64-gnu.cuda-12.5.tar \
-&& rm TensorRT-10.3.0.26.Linux.x86_64-gnu.cuda-12.5.tar
+RUN wget -q https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/10.7.0/tars/TensorRT-10.7.0.23.Linux.x86_64-gnu.cuda-12.6.tar.gz \
+&& gunzip TensorRT-10.7.0.23.Linux.x86_64-gnu.cuda-12.6.tar.gz \
+&& tar -xvf TensorRT-10.7.0.23.Linux.x86_64-gnu.cuda-12.6.tar \
+&& rm TensorRT-10.7.0.23.Linux.x86_64-gnu.cuda-12.6.tar
-ENV TENSORRT_DIR=/TensorRT-10.3.0.26
-ENV TENSORRT_VERSION=10.3.0
+ENV TENSORRT_DIR=/TensorRT-10.7.0.23
+ENV TENSORRT_VERSION=10.7.0
+ENV USE_CXX11_ABI=1
RUN wget https://github.com/bazelbuild/bazelisk/releases/download/v1.17.0/bazelisk-linux-amd64 \
&& mv bazelisk-linux-amd64 /usr/bin/bazel \
diff --git a/py/requirements.txt b/py/requirements.txt
index 361afab365..5644656330 100644
--- a/py/requirements.txt
+++ b/py/requirements.txt
@@ -2,7 +2,7 @@ numpy
packaging
pybind11==2.6.2
--extra-index-url https://download.pytorch.org/whl/nightly/cu124
-torch>=2.6.0.dev,<2.7.0
-torchvision>=0.20.0.dev,<0.21.0
+torch>=2.7.0.dev,<2.8.0
+torchvision>=0.22.0.dev,<0.23.0
--extra-index-url https://pypi.ngc.nvidia.com
pyyaml
diff --git a/py/torch_tensorrt/dynamo/_defaults.py b/py/torch_tensorrt/dynamo/_defaults.py
index 76630a75a5..18932e6cd0 100644
--- a/py/torch_tensorrt/dynamo/_defaults.py
+++ b/py/torch_tensorrt/dynamo/_defaults.py
@@ -46,6 +46,7 @@
IMMUTABLE_WEIGHTS = True
ENABLE_WEIGHT_STREAMING = False
ENABLE_CROSS_COMPILE_FOR_WINDOWS = False
+USE_AOT_JOINT_EXPORT = True
def default_device() -> Device:
diff --git a/py/torch_tensorrt/dynamo/_settings.py b/py/torch_tensorrt/dynamo/_settings.py
index 7a22663af3..05fb5ce094 100644
--- a/py/torch_tensorrt/dynamo/_settings.py
+++ b/py/torch_tensorrt/dynamo/_settings.py
@@ -33,6 +33,7 @@
STRIP_ENGINE_WEIGHTS,
TIMING_CACHE_PATH,
TRUNCATE_DOUBLE,
+ USE_AOT_JOINT_EXPORT,
USE_EXPLICIT_TYPING,
USE_FAST_PARTITIONER,
USE_FP32_ACC,
@@ -91,6 +92,7 @@ class CompilationSettings:
enable_weight_streaming (bool): Enable weight streaming.
enable_cross_compile_for_windows (bool): By default this is False means TensorRT engines can only be executed on the same platform where they were built.
True will enable cross-platform compatibility which allows the engine to be built on Linux and run on Windows
+ use_aot_joint_export (bool): Use aot_export_joint_simple, else wrap backend with AOT_autograd, required for distributed tensors
"""
enabled_precisions: Set[dtype] = field(default_factory=lambda: ENABLED_PRECISIONS)
@@ -131,6 +133,7 @@ class CompilationSettings:
immutable_weights: bool = IMMUTABLE_WEIGHTS
enable_weight_streaming: bool = ENABLE_WEIGHT_STREAMING
enable_cross_compile_for_windows: bool = ENABLE_CROSS_COMPILE_FOR_WINDOWS
+ use_aot_joint_export: bool = USE_AOT_JOINT_EXPORT
_SETTINGS_TO_BE_ENGINE_INVARIANT = (
diff --git a/py/torch_tensorrt/dynamo/backend/backends.py b/py/torch_tensorrt/dynamo/backend/backends.py
index 59bd7d011d..ef04745562 100644
--- a/py/torch_tensorrt/dynamo/backend/backends.py
+++ b/py/torch_tensorrt/dynamo/backend/backends.py
@@ -1,17 +1,20 @@
from __future__ import annotations
+import functools
import logging
import unittest
from typing import Any, Callable, Sequence
import torch
import torch._dynamo as td
+from torch._dynamo.backends.common import aot_autograd
from torch._dynamo.utils import detect_fake_mode
from torch._functorch.aot_autograd import aot_export_joint_simple
from torch_tensorrt.dynamo import CompilationSettings
from torch_tensorrt.dynamo._compiler import compile_module
from torch_tensorrt.dynamo.lowering import (
get_decompositions,
+ modify_reshape_complex_nodes,
post_lowering,
remove_detach,
remove_sym_nodes,
@@ -49,7 +52,25 @@ def aot_torch_tensorrt_aten_backend(
gm: torch.fx.GraphModule, sample_inputs: Sequence[Any], **kwargs: Any
) -> torch.nn.Module:
settings, engine_cache = parse_dynamo_kwargs(kwargs)
- return _pretraced_backend(gm, sample_inputs, settings, engine_cache)
+ if settings.use_aot_joint_export:
+ return _pretraced_backend(gm, sample_inputs, settings, engine_cache)
+ logger.debug("Wrapping the backend with aot_autograd\n")
+ _pretraced_backend_autograd = functools.partial(
+ _pretraced_backend, settings=settings, engine_cache=engine_cache
+ )
+ settings_aot_autograd = {}
+ settings_aot_autograd["decompostions"] = get_decompositions(
+ settings.enable_experimental_decompositions
+ )
+ # This is added since detach lowering leads to alias nodes
+ # Error - View operation returned a tensor that is the same as the input base tensor
+ # torch nop_decompositions in torch/_decomp/decompositions.py
+ if aten.detach in settings_aot_autograd["decompositions"]:
+ del settings_aot_autograd["decompositions"][aten.detach]
+ return aot_autograd(
+ fw_compiler=_pretraced_backend_autograd,
+ decompositions=get_decompositions(settings.enable_experimental_decompositions),
+ )(gm, sample_inputs)
def _pretraced_backend(
@@ -89,15 +110,26 @@ def _pretraced_backend(
# Remove detach nodes
remove_detach(gm, settings)
+ complexInputIndices = []
+ for i, torch_input in enumerate(torch_inputs):
+ if torch_inputs[i].dtype == torch.complex64:
+ complexInputIndices.append(i)
+ torch_input_real = torch_inputs[i].real
+ torch_input_imaginary = torch_inputs[i].imag
+ torch_inputs[i] = torch.stack(
+ (torch_input_real, torch_input_imaginary), dim=-1
+ )
+
# Invoke AOTAutograd to translate operators to aten
- gm = aot_export_joint_simple(
- gm,
- sample_inputs,
- trace_joint=False,
- decompositions=get_decompositions(
- settings.enable_experimental_decompositions
- ),
- )
+ if settings.use_aot_joint_export:
+ gm = aot_export_joint_simple(
+ gm,
+ sample_inputs,
+ trace_joint=False,
+ decompositions=get_decompositions(
+ settings.enable_experimental_decompositions
+ ),
+ )
logger.debug("Post-AOT Autograd graph:\n" + str(gm.graph))
@@ -105,6 +137,12 @@ def _pretraced_backend(
logger.debug("Lowered Input graph:\n " + str(gm.graph))
+ if complexInputIndices:
+ modify_reshape_complex_nodes(gm, complexInputIndices)
+ logger.debug(
+ "Input graph after modifying complex nodes:\n " + str(gm.graph)
+ )
+
torchtrt_inputs = prepare_inputs(
torch_inputs, disable_memory_format_check=True
)
diff --git a/py/torch_tensorrt/dynamo/conversion/__init__.py b/py/torch_tensorrt/dynamo/conversion/__init__.py
index 26a428ed8d..f41540a0cf 100644
--- a/py/torch_tensorrt/dynamo/conversion/__init__.py
+++ b/py/torch_tensorrt/dynamo/conversion/__init__.py
@@ -1,4 +1,10 @@
-from . import aten_ops_converters, ops_evaluators, plugins, prims_ops_converters
+from . import (
+ aten_ops_converters,
+ custom_ops_converters,
+ ops_evaluators,
+ plugins,
+ prims_ops_converters,
+)
from ._conversion import convert_module, interpret_module_to_result
from ._ConversionContext import ConversionContext
from ._ConverterRegistry import * # noqa: F403
diff --git a/py/torch_tensorrt/dynamo/conversion/aten_ops_converters.py b/py/torch_tensorrt/dynamo/conversion/aten_ops_converters.py
index 16d20d3c76..5254c6a0ac 100644
--- a/py/torch_tensorrt/dynamo/conversion/aten_ops_converters.py
+++ b/py/torch_tensorrt/dynamo/conversion/aten_ops_converters.py
@@ -2,7 +2,7 @@
import logging
import operator
-from typing import Any, Callable, Dict, Optional, Sequence, Tuple, Union
+from typing import Callable, Dict, Optional, Sequence, Tuple, Union
import numpy as np
import torch
@@ -16,6 +16,7 @@
has_static_shapes_in_args,
)
from torch_tensorrt.dynamo.conversion.converter_utils import (
+ args_bounds_check,
enforce_tensor_types,
get_positive_dim,
is_only_operator_on_placeholder,
@@ -25,12 +26,6 @@
_LOGGER: logging.Logger = logging.getLogger(__name__)
-def args_bounds_check(
- args: Tuple[Argument, ...], i: int, replacement: Optional[Any] = None
-) -> Any:
- return args[i] if len(args) > i and args[i] is not None else replacement
-
-
def get_ir(target: Target) -> SourceIR:
target_module = getattr(target, "__module__", "None")
if any(
diff --git a/py/torch_tensorrt/dynamo/conversion/converter_utils.py b/py/torch_tensorrt/dynamo/conversion/converter_utils.py
index 937ed27fe4..f4bb4877cc 100644
--- a/py/torch_tensorrt/dynamo/conversion/converter_utils.py
+++ b/py/torch_tensorrt/dynamo/conversion/converter_utils.py
@@ -1,10 +1,11 @@
import collections
+import ctypes
import functools
import logging
+import os
from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union, overload
import numpy as np
-import tensorrt as trt
import torch
import torch_tensorrt.dynamo.conversion.impl as impl
from torch.fx.node import Argument, Target
@@ -18,6 +19,8 @@
DynamoConverterImplSignature,
)
+import tensorrt as trt
+
from ..types import Shape, TRTDataType, TRTLayer, TRTTensor
_LOGGER: logging.Logger = logging.getLogger(__name__)
@@ -913,3 +916,72 @@ def set_layer_name(
else f"{source_ir}_ops.{target.__name__}"
)
layer.name = f"[{layer.type.name}]-[{target_name}]-[{name}]"
+
+
+def args_bounds_check(
+ args: Tuple[Argument, ...], i: int, replacement: Optional[Any] = None
+) -> Any:
+ return args[i] if len(args) > i and args[i] is not None else replacement
+
+
+def load_tensorrt_llm() -> bool:
+ """
+ Attempts to load the TensorRT-LLM plugin and initialize it.
+
+ Returns:
+ bool: True if the plugin was successfully loaded and initialized, False otherwise.
+ """
+ try:
+ import tensorrt_llm as trt_llm # noqa: F401
+
+ _LOGGER.info("TensorRT-LLM successfully imported")
+ return True
+ except (ImportError, AssertionError) as e_import_error:
+ # Check for environment variable for the plugin library path
+ plugin_lib_path = os.environ.get("TRTLLM_PLUGINS_PATH")
+ if not plugin_lib_path:
+ _LOGGER.warning(
+ "TensorRT-LLM is not installed. Please install TensorRT-LLM or set TRTLLM_PLUGINS_PATH to the directory containing libnvinfer_plugin_tensorrt_llm.so to use converters for torch.distributed ops",
+ )
+ return False
+
+ _LOGGER.info(f"TensorRT-LLM Plugin lib path found: {plugin_lib_path}")
+ try:
+ # Load the shared library
+ handle = ctypes.CDLL(plugin_lib_path)
+ _LOGGER.info(f"Successfully loaded plugin library: {plugin_lib_path}")
+ except OSError as e_os_error:
+ _LOGGER.error(
+ f"Failed to load libnvinfer_plugin_tensorrt_llm.so from {plugin_lib_path}"
+ f"Ensure the path is correct and the library is compatible",
+ exc_info=e_os_error,
+ )
+ return False
+
+ try:
+ # Configure plugin initialization arguments
+ handle.initTrtLlmPlugins.argtypes = [ctypes.c_void_p, ctypes.c_char_p]
+ handle.initTrtLlmPlugins.restype = ctypes.c_bool
+ except AttributeError as e_plugin_unavailable:
+ _LOGGER.warning(
+ "Unable to initialize the TensorRT-LLM plugin library",
+ exc_info=e_plugin_unavailable,
+ )
+ return False
+
+ try:
+ # Initialize the plugin
+ TRT_LLM_PLUGIN_NAMESPACE = "tensorrt_llm"
+ if handle.initTrtLlmPlugins(None, TRT_LLM_PLUGIN_NAMESPACE.encode("utf-8")):
+ _LOGGER.info("TensorRT-LLM plugin successfully initialized")
+ return True
+ else:
+ _LOGGER.warning("TensorRT-LLM plugin library failed in initialization")
+ return False
+ except Exception as e_initialization_error:
+ _LOGGER.warning(
+ "Exception occurred during TensorRT-LLM plugin library initialization",
+ exc_info=e_initialization_error,
+ )
+ return False
+ return False
diff --git a/py/torch_tensorrt/dynamo/conversion/custom_ops_converters.py b/py/torch_tensorrt/dynamo/conversion/custom_ops_converters.py
new file mode 100644
index 0000000000..17850fabce
--- /dev/null
+++ b/py/torch_tensorrt/dynamo/conversion/custom_ops_converters.py
@@ -0,0 +1,61 @@
+# mypy: disallow-untyped-decorators=False
+
+import logging
+from typing import Dict, Sequence, Tuple, Union
+
+from torch.fx.node import Argument, Target
+from torch_tensorrt.dynamo._SourceIR import SourceIR
+from torch_tensorrt.dynamo.conversion import impl
+from torch_tensorrt.dynamo.conversion._ConversionContext import ConversionContext
+from torch_tensorrt.dynamo.conversion._ConverterRegistry import (
+ dynamo_tensorrt_converter,
+)
+from torch_tensorrt.dynamo.conversion.converter_utils import load_tensorrt_llm
+from torch_tensorrt.dynamo.lowering.passes.fuse_distributed_ops import (
+ tensorrt_fused_nccl_all_gather_op,
+ tensorrt_fused_nccl_reduce_scatter_op,
+)
+
+import tensorrt as trt
+
+_LOGGER: logging.Logger = logging.getLogger(__name__)
+
+if load_tensorrt_llm():
+
+ @dynamo_tensorrt_converter(tensorrt_fused_nccl_all_gather_op)
+ def fused_nccl_gather(
+ ctx: ConversionContext,
+ target: Target,
+ args: Tuple[Argument, ...],
+ kwargs: Dict[str, Argument],
+ name: str,
+ ) -> Union[trt.ITensor, Sequence[trt.ITensor]]:
+ return impl.distributed.nccl_gather(
+ ctx,
+ target,
+ SourceIR.ATEN,
+ name,
+ [args[0]],
+ )
+
+ @dynamo_tensorrt_converter(tensorrt_fused_nccl_reduce_scatter_op)
+ def fused_nccl_reduce_scatter(
+ ctx: ConversionContext,
+ target: Target,
+ args: Tuple[Argument, ...],
+ kwargs: Dict[str, Argument],
+ name: str,
+ ) -> Union[trt.ITensor, Sequence[trt.ITensor]]:
+ return impl.distributed.nccl_reduce_scatter(
+ ctx,
+ target,
+ SourceIR.ATEN,
+ name,
+ [args[0]],
+ )
+
+ breakpoint()
+else:
+ _LOGGER.debug(
+ "Did not load torch.distributed converters since TensorRT-LLM is not available"
+ )
diff --git a/py/torch_tensorrt/dynamo/conversion/impl/__init__.py b/py/torch_tensorrt/dynamo/conversion/impl/__init__.py
index c1187f0dd9..75f7492591 100644
--- a/py/torch_tensorrt/dynamo/conversion/impl/__init__.py
+++ b/py/torch_tensorrt/dynamo/conversion/impl/__init__.py
@@ -13,6 +13,7 @@
full,
grid,
matmul,
+ nccl_ops,
normalization,
pad,
permutation,
diff --git a/py/torch_tensorrt/dynamo/conversion/impl/elementwise/base.py b/py/torch_tensorrt/dynamo/conversion/impl/elementwise/base.py
index 3ce1714a7d..4a4b33abea 100644
--- a/py/torch_tensorrt/dynamo/conversion/impl/elementwise/base.py
+++ b/py/torch_tensorrt/dynamo/conversion/impl/elementwise/base.py
@@ -3,7 +3,6 @@
from typing import Any, Callable, Optional, Union
import numpy as np
-import tensorrt as trt
import torch
from torch.fx.node import Target
from torch_tensorrt import _enums
@@ -14,12 +13,13 @@
broadcast_to_same_shape,
cast_trt_tensor,
get_trt_tensor,
- broadcast,
has_dynamic_shape,
set_layer_name,
)
from torch_tensorrt.dynamo.types import TRTElementWiseOp, TRTTensor
+import tensorrt as trt
+
def get_python_op_from_trt_elementwise_op(
trt_op: TRTElementWiseOp,
diff --git a/py/torch_tensorrt/dynamo/conversion/impl/nccl_ops.py b/py/torch_tensorrt/dynamo/conversion/impl/nccl_ops.py
new file mode 100644
index 0000000000..013268f803
--- /dev/null
+++ b/py/torch_tensorrt/dynamo/conversion/impl/nccl_ops.py
@@ -0,0 +1,120 @@
+import os
+from enum import IntEnum, IntFlag, auto
+from typing import Optional, Tuple, Union
+
+import numpy as np
+from torch.fx.node import Argument, Target
+from torch_tensorrt.dynamo.conversion._ConversionContext import ConversionContext
+from torch_tensorrt.fx.converters.converter_utils import SourceIR, set_layer_name
+
+import tensorrt as trt
+
+
+# class for AllReduce
+class AllReduceStrategy(IntEnum):
+ """Warning: actual definition is in kernels/customAllReduceKernels.h.
+
+ They must be kept in sync.
+ """
+
+ NCCL = 0
+ ONESHOT = 1
+ TWOSHOT = 2
+ AUTO = 3
+
+
+class AllReduceConfig(IntFlag):
+ """Warning: actual definition is in kernels/customAllReduceKernels.h.
+
+ They must be kept in sync
+ """
+
+ USE_MEMCPY = auto()
+ PUSH_MODE = auto()
+
+
+def nccl_gather(
+ ctx: ConversionContext,
+ target: Union[Target, str],
+ source_ir: Optional[SourceIR],
+ name: str,
+ plug_inputs: Tuple[Argument, ...],
+) -> trt.ITensor:
+ allgather_plg_creator = trt.get_plugin_registry().get_plugin_creator(
+ "AllGather", "1", "tensorrt_llm"
+ )
+ assert allgather_plg_creator is not None
+ _world_size = os.environ.get("WORLD_SIZE")
+ if _world_size is not None:
+ world_size = int(_world_size)
+ else:
+ raise RuntimeError(
+ "The WORLD_SIZE env variable is not set in distributed environment"
+ )
+ group = list(range(world_size))
+ group = trt.PluginField(
+ "group", np.array(group, dtype=np.int32), trt.PluginFieldType.INT32
+ )
+ p_dtype = trt.float32
+ pf_type = trt.PluginField(
+ "type_id", np.array([int(p_dtype)], np.int32), trt.PluginFieldType.INT32
+ )
+ pfc = trt.PluginFieldCollection([group, pf_type])
+ allgather = allgather_plg_creator.create_plugin("allgather", pfc)
+ layer = ctx.net.add_plugin_v2(plug_inputs, allgather)
+ set_layer_name(layer, target, name, source_ir)
+ return layer.get_output(0)
+
+
+def nccl_reduce_scatter(
+ ctx: ConversionContext,
+ target: Union[Target, str],
+ source_ir: Optional[SourceIR],
+ name: str,
+ plug_inputs: Tuple[Argument, ...],
+) -> trt.ITensor:
+ allreduce_plg_creator = trt.get_plugin_registry().get_plugin_creator(
+ "ReduceScatter", "1", "tensorrt_llm"
+ )
+
+ assert allreduce_plg_creator is not None
+
+ counter = 0
+ strategy = AllReduceStrategy.NCCL
+ config = AllReduceConfig(0)
+ _world_size = os.environ.get("WORLD_SIZE")
+ if _world_size is not None:
+ world_size = int(_world_size)
+ else:
+ raise RuntimeError(
+ "The WORLD_SIZE env variable is not set in distributed environment"
+ )
+ group = list(range(world_size))
+ group = trt.PluginField(
+ "group", np.array(group, dtype=np.int32), trt.PluginFieldType.INT32
+ )
+
+ p_dtype = trt.float16
+ pf_dtype = trt.PluginField(
+ "type_id", np.array([int(p_dtype)], np.int32), trt.PluginFieldType.INT32
+ )
+ pfc = [group, pf_dtype]
+ p_strategy = trt.PluginField(
+ "strategy", np.array([int(strategy)], np.int8), trt.PluginFieldType.INT8
+ )
+ pfc.append(p_strategy)
+ p_config = trt.PluginField(
+ "config", np.array([int(config)], np.int8), trt.PluginFieldType.INT8
+ )
+ pfc.append(p_config)
+ p_counter = trt.PluginField(
+ "counter", np.array([counter], np.int32), trt.PluginFieldType.INT32
+ )
+ pfc.append(p_counter)
+
+ pfc = trt.PluginFieldCollection(pfc)
+ ar_plug = allreduce_plg_creator.create_plugin("allreduce", pfc)
+
+ layer = ctx.net.add_plugin_v2(plug_inputs, ar_plug)
+ set_layer_name(layer, target, name, source_ir)
+ return layer.get_output(0)
diff --git a/py/torch_tensorrt/dynamo/conversion/impl/select.py b/py/torch_tensorrt/dynamo/conversion/impl/select.py
index 0d55c5f014..dc5458d2c8 100644
--- a/py/torch_tensorrt/dynamo/conversion/impl/select.py
+++ b/py/torch_tensorrt/dynamo/conversion/impl/select.py
@@ -2,7 +2,6 @@
from typing import Optional, Sequence, Union
import numpy as np
-import tensorrt as trt
import torch
from torch.fx.node import Target
from torch_tensorrt.dynamo._SourceIR import SourceIR
@@ -16,7 +15,6 @@
to_numpy,
)
from torch_tensorrt.dynamo.conversion.impl.elementwise import convert_binary_elementwise
-from torch_tensorrt.dynamo.conversion.impl.shape import get_shape_with_dynamic_shape
from torch_tensorrt.dynamo.conversion.impl.shape import shape as get_shape
from torch_tensorrt.dynamo.utils import DYNAMIC_DIM
from torch_tensorrt.fx.converters.converter_utils import (
@@ -25,6 +23,8 @@
)
from torch_tensorrt.fx.types import TRTTensor
+import tensorrt as trt
+
_LOGGER: logging.Logger = logging.getLogger(__name__)
diff --git a/py/torch_tensorrt/dynamo/lowering/passes/__init__.py b/py/torch_tensorrt/dynamo/lowering/passes/__init__.py
index c0e2803e60..716c6505fe 100644
--- a/py/torch_tensorrt/dynamo/lowering/passes/__init__.py
+++ b/py/torch_tensorrt/dynamo/lowering/passes/__init__.py
@@ -1,3 +1,4 @@
from ._aten_lowering_pass import *
+from ._modify_reshape_complex_nodes import modify_reshape_complex_nodes
from .remove_sym_nodes import remove_sym_nodes
from .repair_input_aliasing import repair_input_aliasing
diff --git a/py/torch_tensorrt/dynamo/lowering/passes/_aten_lowering_pass.py b/py/torch_tensorrt/dynamo/lowering/passes/_aten_lowering_pass.py
index 661c76d3b6..f24fd8ec21 100644
--- a/py/torch_tensorrt/dynamo/lowering/passes/_aten_lowering_pass.py
+++ b/py/torch_tensorrt/dynamo/lowering/passes/_aten_lowering_pass.py
@@ -6,6 +6,7 @@
from .accumulate_fp32_matmul import accumulate_fp32_matmul
from .constant_folding import constant_fold
+from .fuse_distributed_ops import fuse_distributed_ops
from .fuse_prims_broadcast import fuse_prims_broadcast
from .lower_scaled_dot_product_attention import lower_scaled_dot_product_attention
from .pass_manager import DynamoPassManager
@@ -22,6 +23,7 @@
constant_fold,
repair_input_as_output,
fuse_prims_broadcast,
+ fuse_distributed_ops,
replace_max_pool_with_indices,
lower_scaled_dot_product_attention,
view_to_reshape,
diff --git a/py/torch_tensorrt/dynamo/lowering/passes/_modify_reshape_complex_nodes.py b/py/torch_tensorrt/dynamo/lowering/passes/_modify_reshape_complex_nodes.py
new file mode 100644
index 0000000000..f8ca1f71b9
--- /dev/null
+++ b/py/torch_tensorrt/dynamo/lowering/passes/_modify_reshape_complex_nodes.py
@@ -0,0 +1,105 @@
+import logging
+
+import torch
+
+logger = logging.getLogger(__name__)
+
+from torch_tensorrt.dynamo.lowering.passes.pass_utils import (
+ clean_up_graph_after_modifications,
+ find_complex_nodes,
+)
+
+from ._replace_complex_placeholder_to_tuple import replace_complex_placeholder_to_tuple
+
+
+def tensorrt_complex_mul(args0, args1):
+ args0_real, args0_imag = torch.ops.aten.split.Tensor(args0, 1, -1)
+ args1_real, args1_imag = torch.ops.aten.split.Tensor(args1, 1, -1)
+
+ args0_real = torch.ops.aten.squeeze.dim(args0_real, -1)
+ args0_imag = torch.ops.aten.squeeze.dim(args0_imag, -1)
+ args1_real = torch.ops.aten.squeeze.dim(args1_real, -1)
+ args1_imag = torch.ops.aten.squeeze.dim(args1_imag, -1)
+
+ complex_mul_real = torch.ops.aten.sub(
+ torch.ops.aten.mul(args0_real, args1_real),
+ torch.ops.aten.mul(args0_imag, args1_imag),
+ )
+ complex_mul_imag = torch.ops.aten.add(
+ torch.ops.aten.mul(args0_real, args1_imag),
+ torch.ops.aten.mul(args0_imag, args1_real),
+ )
+
+ return torch.ops.aten.stack((complex_mul_real, complex_mul_imag), -1)
+
+
+def remove_complex_real_view_nodes(gm: torch.fx.GraphModule):
+ modified_graph = False
+ nodes_to_remove = []
+ for node in gm.graph.nodes:
+ if "view_as_complex" in node.name or "view_as_real" in node.name:
+ nodes_to_remove.append(node)
+
+ for node in nodes_to_remove:
+ input_node = node.args[0] if node.args else None
+
+ for other_node in gm.graph.nodes:
+ new_args = tuple(
+ input_node if arg is node else arg for arg in other_node.args
+ )
+ other_node.args = new_args
+
+ gm.graph.erase_node(node)
+ modified_graph = True
+
+ if modified_graph:
+ gm = clean_up_graph_after_modifications(gm)
+ logger.debug(
+ f"Graph after removing view_as_complex nodes and view_as_real nodes:\n{gm.graph}"
+ )
+
+
+def modify_reshape_nodes(gm: torch.fx.GraphModule, complex_nodes):
+ for node in gm.graph.nodes:
+ if node in complex_nodes:
+ # slice and transpose will remain same
+ if "reshape" in node.name:
+ new_shape = list(node.args[1]) + [2]
+ node.args = (node.args[0], tuple(new_shape))
+
+
+def modify_mul_nodes(gm: torch.fx.GraphModule, complex_nodes):
+ modified_graph = False
+ for node in gm.graph.nodes:
+ if node in complex_nodes:
+ if "mul" in node.name:
+ complex_mul_args = (node.args[0], node.args[1])
+ with gm.graph.inserting_after(node):
+ replacement_node = gm.graph.create_node(
+ op="call_function",
+ target=tensorrt_complex_mul,
+ args=complex_mul_args,
+ )
+ node.replace_all_uses_with(replacement_node)
+ replacement_node.meta.update(node.meta)
+ modified_graph = True
+ gm.graph.erase_node(node)
+
+ if modified_graph:
+ gm = clean_up_graph_after_modifications(gm)
+ logger.debug(
+ f"Graph after custom complex mul nodes is applied to the graph:\n{gm.graph}"
+ )
+
+
+def modify_complex_nodes(gm: torch.fx.GraphModule, complex_nodes):
+ modify_reshape_nodes(gm, complex_nodes)
+ remove_complex_real_view_nodes(gm)
+ modify_mul_nodes(gm, complex_nodes)
+
+
+def modify_reshape_complex_nodes(gm: torch.fx.GraphModule, complexInputIndices):
+ complex_nodes = find_complex_nodes(gm)
+ if complex_nodes:
+ replace_complex_placeholder_to_tuple(gm, complexInputIndices)
+ modify_complex_nodes(gm, complex_nodes)
diff --git a/py/torch_tensorrt/dynamo/lowering/passes/_replace_complex_placeholder_to_tuple.py b/py/torch_tensorrt/dynamo/lowering/passes/_replace_complex_placeholder_to_tuple.py
new file mode 100644
index 0000000000..e2edec3d28
--- /dev/null
+++ b/py/torch_tensorrt/dynamo/lowering/passes/_replace_complex_placeholder_to_tuple.py
@@ -0,0 +1,112 @@
+import logging
+from typing import List, Tuple
+
+import torch
+from torch._subclasses.fake_tensor import FakeTensorMode
+from torch.fx.node import _get_qualified_name
+from torch_tensorrt.dynamo.conversion.converter_utils import args_bounds_check
+
+# dead-code elimination, linting, and recompilation for graph, in-place
+from torch_tensorrt.dynamo.lowering.passes.pass_utils import (
+ clean_up_graph_after_modifications,
+)
+
+logger = logging.getLogger(__name__)
+
+
+def replace_complex_placeholder_to_tuple(
+ gm: torch.fx.GraphModule,
+ inputListindices: List[int],
+) -> torch.fx.GraphModule:
+ modified_graph = False
+ input_arg_list = [f"arg{inputListIndex}_1" for inputListIndex in inputListindices]
+ for node in gm.graph.nodes:
+ if node.op == "placeholder" and node.target in input_arg_list:
+ from torch._subclasses.fake_tensor import FakeTensorMode
+
+ node_shape = node.meta["val"].size()
+ new_node_shape = node_shape + (2,)
+ new_node_dtype = None
+ if node.meta["val"].dtype == torch.complex64:
+ new_node_dtype = torch.float32
+ else:
+ new_node_dtype = torch.float64
+ fake_mode = FakeTensorMode()
+
+ real_tensor = torch.empty(new_node_shape, dtype=new_node_dtype)
+ with FakeTensorMode() as fake_mode:
+ new_placeholder_tuple = fake_mode.from_tensor(real_tensor)
+ node.meta["val"] = new_placeholder_tuple
+ modified_graph = True
+ # propagate the meta data change for the downstream ops
+ # TODO:to check if this is required in all cases
+ propogate_complex_num_shape_change_till_complex_mul(gm, node, fake_mode)
+
+ # If graph was modified, clean it up
+ if modified_graph:
+ gm = clean_up_graph_after_modifications(gm)
+ logger.debug(
+ f"Graph after fusing wait_tensor and distributed op tensor:\n{gm.graph}"
+ )
+
+ return gm
+
+
+def infer_slice_shape(node: torch.fx.Node) -> Tuple[int, ...]:
+ input_shape = node.args[0].meta["val"].shape
+ slice_args = node.args
+ dim = slice_args[1]
+ start = slice_args[2]
+ end = slice_args[3]
+ step = args_bounds_check(slice_args, 4, replacement=1)
+ new_shape = list(input_shape)
+ new_shape[dim] = (end - start + step - 1) // step
+ return tuple(new_shape)
+
+
+def infer_reshape_shape(node: torch.fx.Node) -> torch.fx.node.Argument:
+ return node.args[1]
+
+
+shape_inference_funcs = {
+ "torch.ops.aten.slice.Tensor": infer_slice_shape,
+ "torch.ops.aten.reshape.default": infer_reshape_shape,
+}
+
+
+# Please note this function is for the use case of Llama model
+# with complex placeholder->reshape->slice->complex mul
+# Hence mul is the terminating op
+def propogate_complex_num_shape_change_till_complex_mul(
+ node: torch.fx.Node, start_node: torch.fx.Node, fake_mode: FakeTensorMode
+) -> None:
+ visited_nodes = set()
+ stack = [start_node]
+ while stack:
+ node = stack.pop()
+ if node in visited_nodes:
+ continue
+ visited_nodes.add(node)
+ update_node_meta(node, fake_mode)
+ for user in node.users:
+ if (
+ user.op == "call_function"
+ and _get_qualified_name(user.target) == "torch.ops.aten.mul.Tensor"
+ ):
+ continue
+ stack.append(user)
+
+
+def update_node_meta(node: torch.fx.Node, fake_mode: FakeTensorMode) -> None:
+ op_name = node.name
+ op_target = node.target
+
+ if node.op == "call_function":
+ op_target = _get_qualified_name(node.target)
+
+ if op_target in shape_inference_funcs:
+ new_shape = shape_inference_funcs[op_target](node)
+ real_tensor = torch.empty(new_shape, dtype=node.meta["val"].dtype)
+ node.meta["val"] = fake_mode.from_tensor(real_tensor)
+ else:
+ print("No shape for the inference function", {op_name})
diff --git a/py/torch_tensorrt/dynamo/lowering/passes/fuse_distributed_ops.py b/py/torch_tensorrt/dynamo/lowering/passes/fuse_distributed_ops.py
new file mode 100644
index 0000000000..f709f177d6
--- /dev/null
+++ b/py/torch_tensorrt/dynamo/lowering/passes/fuse_distributed_ops.py
@@ -0,0 +1,80 @@
+import logging
+from typing import Any
+
+import torch
+from torch_tensorrt.dynamo._settings import CompilationSettings
+
+# dead-code elimination, linting, and recompilation for graph, in-place
+from torch_tensorrt.dynamo.lowering.passes.pass_utils import (
+ clean_up_graph_after_modifications,
+)
+
+logger = logging.getLogger(__name__)
+
+
+# TODO: @apbose make these actual torch custom ops, should allow aot_export
+def tensorrt_fused_nccl_all_gather_op(
+ inp: Any, group_size: int, group_name: str
+) -> torch.Tensor:
+ return torch.ops._c10d_functional.wait_tensor.default(
+ torch.ops._c10d_functional.all_gather_into_tensor.default(
+ inp, group_size, group_name
+ )
+ )
+
+
+def tensorrt_fused_nccl_reduce_scatter_op(
+ inp: Any, reduce_op: str, group_size: int, group_name: str
+) -> torch.Tensor:
+ return torch.ops._c10d_functional.wait_tensor.default(
+ torch.ops._c10d_functional.reduce_scatter_tensor.default(
+ inp, reduce_op, group_size, group_name
+ )
+ )
+
+
+def fuse_distributed_ops(
+ gm: torch.fx.GraphModule, settings: CompilationSettings
+) -> torch.fx.GraphModule:
+ modified_graph = False
+ for node in gm.graph.nodes:
+ if (
+ node.target
+ in (
+ torch.ops._c10d_functional.all_gather_into_tensor.default,
+ torch.ops._c10d_functional.reduce_scatter_tensor.default,
+ )
+ and len(node.users) == 1
+ and list(node.users)[0].target
+ == torch.ops._c10d_functional.wait_tensor.default
+ ):
+ wait_tensor_node = list(node.users)[0]
+ fused_op = None
+ if node.target == torch.ops._c10d_functional.all_gather_into_tensor.default:
+ with gm.graph.inserting_after(wait_tensor_node):
+ fused_node = gm.graph.create_node(
+ op="call_function",
+ target=tensorrt_fused_nccl_all_gather_op, # Define your custom fused function
+ args=(node.args[0], node.args[1], node.args[2]),
+ )
+ else:
+ fused_node = gm.graph.create_node(
+ op="call_function",
+ target=tensorrt_fused_nccl_reduce_scatter_op, # Define your custom fused function
+ args=(node.args[0], node.args[1], node.args[2], node.args[3]),
+ )
+
+ wait_tensor_node.replace_all_uses_with(fused_node)
+ fused_node.meta.update(node.meta)
+ modified_graph = True
+ gm.graph.erase_node(wait_tensor_node)
+ gm.graph.erase_node(node)
+
+ # If graph was modified, clean it up
+ if modified_graph:
+ gm = clean_up_graph_after_modifications(gm)
+ logger.debug(
+ f"Graph after fusing wait_tensor and distributed op tensor:\n{gm.graph}"
+ )
+
+ return gm
diff --git a/py/torch_tensorrt/dynamo/lowering/passes/pass_utils.py b/py/torch_tensorrt/dynamo/lowering/passes/pass_utils.py
index 31a55099c2..1736a234a2 100644
--- a/py/torch_tensorrt/dynamo/lowering/passes/pass_utils.py
+++ b/py/torch_tensorrt/dynamo/lowering/passes/pass_utils.py
@@ -29,3 +29,42 @@ def get_tensor_placeholders(
]
return placeholders
+
+
+def find_complex_nodes(gm: torch.fx.GraphModule):
+ complex_nodes = []
+ complexNodes = {}
+ for node in gm.graph.nodes:
+ if is_node_complex(node, complexNodes):
+ complex_nodes.append(node)
+ return complex_nodes
+
+
+def is_node_complex(node: torch.fx.Node, complexNodes):
+ if not isinstance(node, torch.fx.Node):
+ return False
+ if node.name in complexNodes:
+ return True
+ if node.op == "call_function" and node.args is not None:
+ for arg in node.args:
+ if isinstance(arg, int):
+ continue
+ elif isinstance(arg, (list, tuple)):
+ for eachNode in arg:
+ if is_node_complex(eachNode, complexNodes):
+ complexNodes[node.name] = True
+ return True
+
+ elif hasattr(arg, "meta") and "val" in arg.meta:
+ if isinstance(arg.meta["val"], (list, tuple)):
+ for eachFakeTensorMeta in arg.meta["val"]:
+ if eachFakeTensorMeta.dtype in (
+ torch.complex64,
+ torch.complex128,
+ ):
+ complexNodes[node.name] = True
+ return True
+ elif arg.meta["val"].dtype in (torch.complex64, torch.complex128):
+ complexNodes[node.name] = True
+ return True
+ return False
diff --git a/pyproject.toml b/pyproject.toml
index 6cc3412849..7ec6c7469a 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -8,8 +8,8 @@ requires = [
"cffi>=1.15.1",
"typing-extensions>=4.7.0",
"future>=0.18.3",
- "tensorrt-cu12>=10.6.0,<10.8.0",
- "torch>=2.6.0.dev,<2.7.0",
+ "tensorrt-cu12>=10.7.0,<10.8.0",
+ "torch>=2.7.0.dev,<2.8.0",
"pybind11==2.6.2",
"numpy",
]
@@ -54,10 +54,10 @@ keywords = [
"inference",
]
dependencies = [
- "torch>=2.6.0.dev,<2.7.0",
- "tensorrt-cu12>=10.6.0,<10.8.0",
- "tensorrt-cu12-bindings>=10.6.0,<10.8.0",
- "tensorrt-cu12-libs>=10.6.0,<10.8.0",
+ "torch>=2.7.0.dev,<2.8.0",
+ "tensorrt-cu12>=10.7.0,<10.8.0",
+ "tensorrt-cu12-bindings>=10.7.0,<10.8.0",
+ "tensorrt-cu12-libs>=10.7.0,<10.8.0",
"packaging>=23",
"numpy",
"typing-extensions>=4.7.0",
@@ -85,9 +85,10 @@ dev = [
torchvision = [
"torchvision",
] #Leaving torchvisions dependency unconstrained so uv can just install something that should work for the torch we have. TV's on PyT makes it hard to put version constrains in
-quantization = ["nvidia-modelopt[deploy,hf,torch]~=0.17.0"]
+quantization = ["nvidia-modelopt[deploy,hf,torch]>=0.17.0"]
monitoring-tools = ["rich>=13.7.1"]
jupyter = ["rich[jupyter]>=13.7.1"]
+distributed = ["tensorrt-llm>=0.16.0"]
[project.urls]
Homepage = "https://pytorch.org/tensorrt"
@@ -101,17 +102,12 @@ include-package-data = false
[tool.uv]
environments = ["sys_platform == 'linux'", "sys_platform == 'windows'"]
-
prerelease = "if-necessary-or-explicit"
-
+index-strategy = "unsafe-best-match" # Needed for TRT-LLM
[tool.uv.sources]
-torch = [
- { index = "pytorch-nightly-cu126"},
-]
-torchvision = [
- { index = "pytorch-nightly-cu126"},
-]
+torch = [{ index = "pytorch-nightly-cu126" }]
+torchvision = [{ index = "pytorch-nightly-cu126" }]
[[tool.uv.index]]
name = "pytorch-nightly-cu126"
diff --git a/toolchains/ci_workspaces/MODULE.bazel.tmpl b/toolchains/ci_workspaces/MODULE.bazel.tmpl
index 142a021609..66b5d448ca 100644
--- a/toolchains/ci_workspaces/MODULE.bazel.tmpl
+++ b/toolchains/ci_workspaces/MODULE.bazel.tmpl
@@ -67,20 +67,18 @@ http_archive(
http_archive(
name = "tensorrt",
build_file = "@//third_party/tensorrt/archive:BUILD",
- sha256 = "33d3c2f3f4c84dc7991a4337a6fde9ed33f5c8e5c4f03ac2eb6b994a382b03a0",
- strip_prefix = "TensorRT-10.6.0.26",
+ strip_prefix = "TensorRT-10.7.0.23",
urls = [
- "https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/10.6.0/tars/TensorRT-10.6.0.26.Linux.x86_64-gnu.cuda-12.6.tar.gz",
+ "https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/10.7.0/tars/TensorRT-10.7.0.23.Linux.x86_64-gnu.cuda-12.6.tar.gz",
],
)
http_archive(
name = "tensorrt_win",
build_file = "@//third_party/tensorrt/archive:BUILD",
- sha256 = "6c6d92c108a1b3368423e8f69f08d31269830f1e4c9da43b37ba34a176797254",
- strip_prefix = "TensorRT-10.6.0.26",
+ strip_prefix = "TensorRT-10.7.0.23",
urls = [
- "https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/10.6.0/zip/TensorRT-10.6.0.26.Windows.win10.cuda-12.6.zip",
+ "https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/10.7.0/zip/TensorRT-10.7.0.23.Windows.win10.cuda-12.6.zip",
],
)
diff --git a/toolchains/ci_workspaces/MODULE_tensorrt.bazel.tmpl b/toolchains/ci_workspaces/MODULE_tensorrt.bazel.tmpl
index aff07a0383..0a9bb4b9ec 100644
--- a/toolchains/ci_workspaces/MODULE_tensorrt.bazel.tmpl
+++ b/toolchains/ci_workspaces/MODULE_tensorrt.bazel.tmpl
@@ -67,7 +67,6 @@ http_archive(
http_archive(
name = "tensorrt",
build_file = "@//third_party/tensorrt/archive:BUILD",
- sha256 = "${TENSORRT_SHA256}",
strip_prefix = "${TENSORRT_STRIP_PREFIX}",
urls = [
"${TENSORRT_URLS}",
@@ -77,7 +76,6 @@ http_archive(
http_archive(
name = "tensorrt_win",
build_file = "@//third_party/tensorrt/archive:BUILD",
- sha256 = "${TENSORRT_SHA256}",
strip_prefix = "${TENSORRT_STRIP_PREFIX}",
urls = [
"${TENSORRT_URLS}",
diff --git a/toolchains/legacy/WORKSPACE.win.release.tmpl b/toolchains/legacy/WORKSPACE.win.release.tmpl
index 58fce5cf54..5570516c6c 100644
--- a/toolchains/legacy/WORKSPACE.win.release.tmpl
+++ b/toolchains/legacy/WORKSPACE.win.release.tmpl
@@ -63,7 +63,7 @@ http_archive(
new_local_repository(
name = "tensorrt_win",
- path = "C:/TensorRT-10.3.0.26",
+ path = "C:/TensorRT-10.7.0.23",
build_file = "@//third_party/tensorrt/local:BUILD"
)
diff --git a/toolchains/legacy/WORKSPACE.x86_64.release.rhel.tmpl b/toolchains/legacy/WORKSPACE.x86_64.release.rhel.tmpl
index 97b3a8c566..fcc5e33f44 100644
--- a/toolchains/legacy/WORKSPACE.x86_64.release.rhel.tmpl
+++ b/toolchains/legacy/WORKSPACE.x86_64.release.rhel.tmpl
@@ -71,10 +71,9 @@ http_archive(
http_archive(
name = "tensorrt",
build_file = "@//third_party/tensorrt/archive:BUILD",
- sha256 = "adff1cd5abe5d87013806172351e58fd024e5bf0fc61d49ef4b84cd38ed99081",
- strip_prefix = "TensorRT-10.3.0.26",
+ strip_prefix = "TensorRT-10.7.0.23",
urls = [
- "https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/10.3.0/tars/TensorRT-10.3.0.26.Linux.x86_64-gnu.cuda-12.5.tar.gz",
+ "https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/10.7.0/tars/TensorRT-10.7.0.23.Linux.x86_64-gnu.cuda-12.6.tar.gz",
],
)
diff --git a/uv.lock b/uv.lock
index c4d4f776f4..cf0a651463 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,9 +1,11 @@
version = 1
requires-python = ">=3.9"
resolution-markers = [
- "python_full_version < '3.12' and sys_platform == 'linux'",
+ "python_full_version < '3.11' and sys_platform == 'linux'",
+ "python_full_version == '3.11.*' and sys_platform == 'linux'",
"python_full_version >= '3.12' and sys_platform == 'linux'",
- "python_full_version < '3.12' and sys_platform == 'windows'",
+ "python_full_version < '3.11' and sys_platform == 'windows'",
+ "python_full_version == '3.11.*' and sys_platform == 'windows'",
"python_full_version >= '3.12' and sys_platform == 'windows'",
]
supported-markers = [
@@ -13,7 +15,7 @@ supported-markers = [
[[package]]
name = "accelerate"
-version = "1.2.0"
+version = "1.2.1"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "huggingface-hub", marker = "sys_platform == 'linux' or sys_platform == 'windows'" },
@@ -24,9 +26,107 @@ dependencies = [
{ name = "safetensors", marker = "sys_platform == 'linux' or sys_platform == 'windows'" },
{ name = "torch", marker = "sys_platform == 'linux' or sys_platform == 'windows'" },
]
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+sdist = { url = "https://files.pythonhosted.org/packages/42/09/7947691b7d44bfc739da4a44cc47d6a6d75e6fe9adf047c5234d7cb6be64/accelerate-1.2.1.tar.gz", hash = "sha256:03e161fc69d495daf2b9b5c8d5b43d06e2145520c04727b5bda56d49f1a43ab5", size = 341652 }
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+name = "aenum"
+version = "3.1.15"
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[[package]]
name = "zipp"
version = "3.19.2"
diff --git a/version.txt b/version.txt
index 3d87ca93f8..787e6e4ab7 100644
--- a/version.txt
+++ b/version.txt
@@ -1 +1 @@
-2.6.0a0
+2.7.0a0
PythonTorchTensorRTModule is a PyTorch module which encompasses an arbitrary TensorRT Engine.
This module is backed by the Torch-TensorRT runtime and is only compatible with FX / Dynamo / Python deployments. This module cannot be serialized to torchscript via torch.jit.trace for C++ deployment.
- -__init__(serialized_engine: ~typing.Optional[bytes] = None, input_binding_names: ~typing.Optional[~typing.List[str]] = None, output_binding_names: ~typing.Optional[~typing.List[str]] = None, *, name: str = '', settings: ~torch_tensorrt.dynamo._settings.CompilationSettings = CompilationSettings(enabled_precisions={<dtype.f32: 7>}, debug=False, workspace_size=0, min_block_size=5, torch_executed_ops=set(), pass_through_build_failures=False, max_aux_streams=None, version_compatible=False, optimization_level=None, use_python_runtime=False, truncate_double=False, use_fast_partitioner=True, enable_experimental_decompositions=False, device=Device(type=DeviceType.GPU, gpu_id=0), require_full_compilation=False, disable_tf32=False, assume_dynamic_shape_support=False, sparse_weights=False, engine_capability=<EngineCapability.STANDARD: 1>, num_avg_timing_iters=1, dla_sram_size=1048576, dla_local_dram_size=1073741824, dla_global_dram_size=536870912, dryrun=False, hardware_compatible=False, timing_cache_path='/tmp/torch_tensorrt_engine_cache/timing_cache.bin', lazy_engine_init=False, cache_built_engines=False, reuse_cached_engines=False, use_explicit_typing=False, use_fp32_acc=False, refit_identical_engine_weights=False, strip_engine_weights=False, immutable_weights=True, enable_weight_streaming=False, enable_cross_compile_for_windows=False), weight_name_map: ~typing.Optional[dict[typing.Any, typing.Any]] = None)[source]¶ +__init__(serialized_engine: ~typing.Optional[bytes] = None, input_binding_names: ~typing.Optional[~typing.List[str]] = None, output_binding_names: ~typing.Optional[~typing.List[str]] = None, *, name: str = '', settings: ~torch_tensorrt.dynamo._settings.CompilationSettings = CompilationSettings(enabled_precisions={<dtype.f32: 7>}, debug=False, workspace_size=0, min_block_size=5, torch_executed_ops=set(), pass_through_build_failures=False, max_aux_streams=None, version_compatible=False, optimization_level=None, use_python_runtime=False, truncate_double=False, use_fast_partitioner=True, enable_experimental_decompositions=False, device=Device(type=DeviceType.GPU, gpu_id=0), require_full_compilation=False, disable_tf32=False, assume_dynamic_shape_support=False, sparse_weights=False, engine_capability=<EngineCapability.STANDARD: 1>, num_avg_timing_iters=1, dla_sram_size=1048576, dla_local_dram_size=1073741824, dla_global_dram_size=536870912, dryrun=False, hardware_compatible=False, timing_cache_path='/tmp/torch_tensorrt_engine_cache/timing_cache.bin', lazy_engine_init=False, cache_built_engines=False, reuse_cached_engines=False, use_explicit_typing=False, use_fp32_acc=False, refit_identical_engine_weights=False, strip_engine_weights=False, immutable_weights=True, enable_weight_streaming=False, enable_cross_compile_for_windows=False, use_aot_joint_export=True), weight_name_map: ~typing.Optional[dict[typing.Any, typing.Any]] = None)[source]¶
Takes a name, target device, serialized TensorRT engine, and binding names / order and constructs a PyTorch
torch.nn.Modulearound it. Uses TensorRT Python APIs to run the engine-
@@ -903,7 +921,7 @@
Resources
Developer Resources- - Contributor Awards - 2023 + Contributor Awards - 2024
@@ -919,6 +937,9 @@Resources
- ExecuTorch
+- + ExecuTorch Documentation +
- Events
+- + Newsletter +
- Governing Board
+- + Cloud Credit Program +
+- + Technical Advisory Council +
+- + Staff +
+- + Contact Us +
diff --git a/docs/py_api/torch_tensorrt.html b/docs/py_api/torch_tensorrt.html index 1ebc412b8c..43503cbc18 100644 --- a/docs/py_api/torch_tensorrt.html +++ b/docs/py_api/torch_tensorrt.html @@ -10,7 +10,7 @@ -torch_tensorrt — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation +torch_tensorrt — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation @@ -135,8 +135,8 @@ Developer ResourcesFind resources and get questions answered
- - Contributor Awards - 2023 + + Contributor Awards - 2024Award winners announced at this year's PyTorch Conference
@@ -157,6 +157,9 @@ ExecuTorchEnd-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+ + ExecuTorch Docs + @@ -204,6 +207,10 @@ EventsFind events, webinars, and podcasts
+ + Newsletter +Stay up-to-date with the latest updates
+ @@ -219,7 +226,18 @@ Governing Board - + + + Cloud Credit Program + + + Technical Advisory Council + + + Staff + + + Contact Us @@ -275,7 +293,7 @@- v2.6.0.dev0+70e2a38 + v2.7.0.dev0+d6be4ba@@ -1751,7 +1769,7 @@Resources
Developer Resources- - Contributor Awards - 2023 + Contributor Awards - 2024
@@ -1767,6 +1785,9 @@Resources
- ExecuTorch
+- + ExecuTorch Documentation +
- Events
+- + Newsletter +
- Governing Board
+- + Cloud Credit Program +
+- + Technical Advisory Council +
+- + Staff +
+- + Contact Us +
diff --git a/docs/py_api/ts.html b/docs/py_api/ts.html index fe80f50f67..3956fa7518 100644 --- a/docs/py_api/ts.html +++ b/docs/py_api/ts.html @@ -10,7 +10,7 @@ -torch_tensorrt.ts — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation +torch_tensorrt.ts — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation @@ -135,8 +135,8 @@ Developer ResourcesFind resources and get questions answered
- - Contributor Awards - 2023 + + Contributor Awards - 2024Award winners announced at this year's PyTorch Conference
@@ -157,6 +157,9 @@ ExecuTorchEnd-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+ + ExecuTorch Docs + @@ -204,6 +207,10 @@ EventsFind events, webinars, and podcasts
+ + Newsletter +Stay up-to-date with the latest updates
+ @@ -219,7 +226,18 @@ Governing Board - + + + Cloud Credit Program + + + Technical Advisory Council + + + Staff + + + Contact Us @@ -275,7 +293,7 @@- v2.6.0.dev0+70e2a38 + v2.7.0.dev0+d6be4ba@@ -695,7 +713,7 @@Functions
- -torch_tensorrt.ts.TensorRTCompileSpec(inputs: Optional[List[torch.Tensor | Input]] = None, input_signature: Optional[Any] = None, device: Optional[Union[device, Device]] = None, disable_tf32: bool = False, sparse_weights: bool = False, enabled_precisions: Optional[Set[Union[dtype, dtype]]] = None, refit: bool = False, debug: bool = False, capability: EngineCapability = EngineCapability.STANDARD, num_avg_timing_iters: int = 1, workspace_size: int = 0, dla_sram_size: int = 1048576, dla_local_dram_size: int = 1073741824, dla_global_dram_size: int = 536870912, truncate_long_and_double: bool = False, calibrator: object = None, allow_shape_tensors: bool = False) <torch.ScriptClass object at 0x7f70888fd830>[source]¶
+torch_tensorrt.ts.TensorRTCompileSpec(inputs: Optional[List[torch.Tensor | Input]] = None, input_signature: Optional[Any] = None, device: Optional[Union[device, Device]] = None, disable_tf32: bool = False, sparse_weights: bool = False, enabled_precisions: Optional[Set[Union[dtype, dtype]]] = None, refit: bool = False, debug: bool = False, capability: EngineCapability = EngineCapability.STANDARD, num_avg_timing_iters: int = 1, workspace_size: int = 0, dla_sram_size: int = 1048576, dla_local_dram_size: int = 1073741824, dla_global_dram_size: int = 536870912, truncate_long_and_double: bool = False, calibrator: object = None, allow_shape_tensors: bool = False) <torch.ScriptClass object at 0x7fe38a1f3730>[source]¶Utility to create a formatted spec dictionary for using the PyTorch TensorRT backend
- Keyword Arguments @@ -986,7 +1004,7 @@
Resources
Developer Resources- - Contributor Awards - 2023 + Contributor Awards - 2024
@@ -1002,6 +1020,9 @@Resources
- ExecuTorch
+- + ExecuTorch Documentation +
- Events
+- + Newsletter +
- Governing Board
+- + Cloud Credit Program +
+- + Technical Advisory Council +
+- + Staff +
+- + Contact Us +
diff --git a/docs/search.html b/docs/search.html index ab2599b327..642109fe1f 100644 --- a/docs/search.html +++ b/docs/search.html @@ -9,7 +9,7 @@ -Search — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation +Search — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation @@ -132,8 +132,8 @@ Developer ResourcesFind resources and get questions answered
- - Contributor Awards - 2023 + + Contributor Awards - 2024Award winners announced at this year's PyTorch Conference
@@ -154,6 +154,9 @@ ExecuTorchEnd-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+ + ExecuTorch Docs + @@ -201,6 +204,10 @@ EventsFind events, webinars, and podcasts
+ + Newsletter +Stay up-to-date with the latest updates
+ @@ -216,7 +223,18 @@ Governing Board - + + + Cloud Credit Program + + + Technical Advisory Council + + + Staff + + + Contact Us @@ -272,7 +290,7 @@- v2.6.0.dev0+70e2a38 + v2.7.0.dev0+d6be4ba@@ -709,7 +727,7 @@Resources
Developer Resources- - Contributor Awards - 2023 + Contributor Awards - 2024
@@ -725,6 +743,9 @@Resources
- ExecuTorch
+- + ExecuTorch Documentation +
- Events
+- + Newsletter +
- Governing Board
+- + Cloud Credit Program +
+- + Technical Advisory Council +
+- + Staff +
+- + Contact Us +
diff --git a/docs/searchindex.js b/docs/searchindex.js index 12ac2d9ebe..9c36cf3953 100644 --- a/docs/searchindex.js +++ b/docs/searchindex.js @@ -1 +1 @@ -Search.setIndex({"docnames": ["_cpp_api/classtorch__tensorrt_1_1DataType", "_cpp_api/classtorch__tensorrt_1_1Device_1_1DeviceType", "_cpp_api/classtorch__tensorrt_1_1TensorFormat", "_cpp_api/classtorch__tensorrt_1_1ptq_1_1Int8CacheCalibrator", "_cpp_api/classtorch__tensorrt_1_1ptq_1_1Int8Calibrator", "_cpp_api/define_macros_8h_1a18d295a837ac71add5578860b55e5502", "_cpp_api/define_macros_8h_1a282fd3c0b1c3a215148ae372070e1268", "_cpp_api/define_macros_8h_1a31398a6d4d27e28817afb0f0139e909e", "_cpp_api/define_macros_8h_1a35703561b26b1a9d2738ad7d58b27827", "_cpp_api/define_macros_8h_1abd1465eb38256d3f22cc1426b23d516b", "_cpp_api/define_macros_8h_1abe87b341f562fd1cf40b7672e4d759da", "_cpp_api/define_macros_8h_1ad19939408f7be171a74a89928b36eb59", "_cpp_api/define_macros_8h_1adad592a7b1b7eed529cdf6acd584c883", "_cpp_api/dir_cpp", 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- - Contributor Awards - 2023 + + Contributor Awards - 2024Award winners announced at this year's PyTorch Conference
@@ -155,6 +155,9 @@ ExecuTorchEnd-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+ + ExecuTorch Docs + @@ -202,6 +205,10 @@ EventsFind events, webinars, and podcasts
+ + Newsletter +Stay up-to-date with the latest updates
+ @@ -217,7 +224,18 @@ Governing Board - + + + Cloud Credit Program + + + Technical Advisory Council + + + Staff + + + Contact Us @@ -273,7 +291,7 @@- v2.6.0.dev0+70e2a38 + v2.7.0.dev0+d6be4ba@@ -746,7 +764,7 @@Resources
Developer Resources- - Contributor Awards - 2023 + Contributor Awards - 2024
@@ -762,6 +780,9 @@Resources
- ExecuTorch
+- + ExecuTorch Documentation +
- Events
+- + Newsletter +
- Governing Board
+- + Cloud Credit Program +
+- + Technical Advisory Council +
+- + Staff +
+- + Contact Us +
diff --git a/docs/src/pytorch-sphinx-theme/docs/changelog.html b/docs/src/pytorch-sphinx-theme/docs/changelog.html index e7d7800497..03fb7a6fc9 100644 --- a/docs/src/pytorch-sphinx-theme/docs/changelog.html +++ b/docs/src/pytorch-sphinx-theme/docs/changelog.html @@ -10,7 +10,7 @@ -Changelog — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation +Changelog — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation @@ -133,8 +133,8 @@ Developer ResourcesFind resources and get questions answered
- - Contributor Awards - 2023 + + Contributor Awards - 2024Award winners announced at this year's PyTorch Conference
@@ -155,6 +155,9 @@ ExecuTorchEnd-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+ + ExecuTorch Docs + @@ -202,6 +205,10 @@ EventsFind events, webinars, and podcasts
+ + Newsletter +Stay up-to-date with the latest updates
+ @@ -217,7 +224,18 @@ Governing Board - + + + Cloud Credit Program + + + Technical Advisory Council + + + Staff + + + Contact Us @@ -273,7 +291,7 @@- v2.6.0.dev0+70e2a38 + v2.7.0.dev0+d6be4ba@@ -702,7 +720,7 @@Resources
Developer Resources- - Contributor Awards - 2023 + Contributor Awards - 2024
@@ -718,6 +736,9 @@Resources
- ExecuTorch
+- + ExecuTorch Documentation +
- Events
+- + Newsletter +
- Governing Board
+- + Cloud Credit Program +
+- + Technical Advisory Council +
+- + Staff +
+- + Contact Us +
diff --git a/docs/src/pytorch-sphinx-theme/docs/configuring.html b/docs/src/pytorch-sphinx-theme/docs/configuring.html index cc64f58bbf..a980d4ce63 100644 --- a/docs/src/pytorch-sphinx-theme/docs/configuring.html +++ b/docs/src/pytorch-sphinx-theme/docs/configuring.html @@ -10,7 +10,7 @@ -Configuration — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation +Configuration — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation @@ -133,8 +133,8 @@ Developer ResourcesFind resources and get questions answered
- - Contributor Awards - 2023 + + Contributor Awards - 2024Award winners announced at this year's PyTorch Conference
@@ -155,6 +155,9 @@ ExecuTorchEnd-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+ + ExecuTorch Docs + @@ -202,6 +205,10 @@ EventsFind events, webinars, and podcasts
+ + Newsletter +Stay up-to-date with the latest updates
+ @@ -217,7 +224,18 @@ Governing Board - + + + Cloud Credit Program + + + Technical Advisory Council + + + Staff + + + Contact Us @@ -273,7 +291,7 @@- v2.6.0.dev0+70e2a38 + v2.7.0.dev0+d6be4ba@@ -805,7 +823,7 @@Resources
Developer Resources- - Contributor Awards - 2023 + Contributor Awards - 2024
@@ -821,6 +839,9 @@Resources
- ExecuTorch
+- + ExecuTorch Documentation +
- Events
+- + Newsletter +
- Governing Board
+- + Cloud Credit Program +
+- + Technical Advisory Council +
+- + Staff +
+- + Contact Us +
diff --git a/docs/src/pytorch-sphinx-theme/docs/demo/api.html b/docs/src/pytorch-sphinx-theme/docs/demo/api.html index 19fa661195..c87155fa45 100644 --- a/docs/src/pytorch-sphinx-theme/docs/demo/api.html +++ b/docs/src/pytorch-sphinx-theme/docs/demo/api.html @@ -10,7 +10,7 @@ -5. :mod:`test_py_module` — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation +5. :mod:`test_py_module` — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation @@ -133,8 +133,8 @@ Developer ResourcesFind resources and get questions answered
- - Contributor Awards - 2023 + + Contributor Awards - 2024Award winners announced at this year's PyTorch Conference
@@ -155,6 +155,9 @@ ExecuTorchEnd-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+ + ExecuTorch Docs + @@ -202,6 +205,10 @@ EventsFind events, webinars, and podcasts
+ + Newsletter +Stay up-to-date with the latest updates
+ @@ -217,7 +224,18 @@ Governing Board - + + + Cloud Credit Program + + + Technical Advisory Council + + + Staff + + + Contact Us @@ -273,7 +291,7 @@- v2.6.0.dev0+70e2a38 + v2.7.0.dev0+d6be4ba@@ -756,7 +774,7 @@Resources
Developer Resources- - Contributor Awards - 2023 + Contributor Awards - 2024
@@ -772,6 +790,9 @@Resources
- ExecuTorch
+- + ExecuTorch Documentation +
- Events
+- + Newsletter +
- Governing Board
+- + Cloud Credit Program +
+- + Technical Advisory Council +
+- + Staff +
+- + Contact Us +
diff --git a/docs/src/pytorch-sphinx-theme/docs/demo/demo.html b/docs/src/pytorch-sphinx-theme/docs/demo/demo.html index 37e93c599d..4bc0b53cad 100644 --- a/docs/src/pytorch-sphinx-theme/docs/demo/demo.html +++ b/docs/src/pytorch-sphinx-theme/docs/demo/demo.html @@ -12,7 +12,7 @@ -3. Paragraph Level Markup — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation +3. Paragraph Level Markup — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation @@ -135,8 +135,8 @@ Developer ResourcesFind resources and get questions answered
- - Contributor Awards - 2023 + + Contributor Awards - 2024Award winners announced at this year's PyTorch Conference
@@ -157,6 +157,9 @@ ExecuTorchEnd-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+ + ExecuTorch Docs + @@ -204,6 +207,10 @@ EventsFind events, webinars, and podcasts
+ + Newsletter +Stay up-to-date with the latest updates
+ @@ -219,7 +226,18 @@ Governing Board - + + + Cloud Credit Program + + + Technical Advisory Council + + + Staff + + + Contact Us @@ -275,7 +293,7 @@- v2.6.0.dev0+70e2a38 + v2.7.0.dev0+d6be4ba@@ -668,7 +686,7 @@3.4.4.
3.4.5. Code Blocks¶
# parsed-literal test -curl -O http://someurl/release-v2.6.0.dev0+70e2a38.tar-gz
+curl -O http://someurl/release-v2.7.0.dev0+d6be4ba.tar-gz@@ -273,7 +291,7 @@Code Blocks can have captions.¶@@ -217,7 +224,18 @@ Governing Board - + + + Cloud Credit Program + + + Technical Advisory Council + + + Staff + + + Contact Us{ @@ -696,7 +714,7 @@
3.4.5.
3.4.5.1. Emphasized lines with line numbers¶
-@@ -202,6 +205,10 @@ Events1def some_function(): +
@@ -155,6 +155,9 @@ ExecuTorchdiff --git a/docs/src/pytorch-sphinx-theme/docs/demo/lists_tables.html b/docs/src/pytorch-sphinx-theme/docs/demo/lists_tables.html index 5c362cf7ca..4335a005d4 100644 --- a/docs/src/pytorch-sphinx-theme/docs/demo/lists_tables.html +++ b/docs/src/pytorch-sphinx-theme/docs/demo/lists_tables.html @@ -10,7 +10,7 @@ -1def some_function(): 2 interesting = False 3 print 'This line is highlighted.' 4 print 'This one is not...' @@ -736,7 +754,7 @@
3.5.1. 2"""Test Module for sphinx_rtd_theme.""" 3 4 - 5class Foo: + 5class Foo: 6 7 """Docstring for class Foo. 8 @@ -1312,7 +1330,7 @@
Resources
Developer Resources- - Contributor Awards - 2023 + Contributor Awards - 2024
@@ -1328,6 +1346,9 @@Resources
- ExecuTorch
+- + ExecuTorch Documentation +
- Events
+- + Newsletter +
- Governing Board
+- + Cloud Credit Program +
+- + Technical Advisory Council +
+- + Staff +
+- + Contact Us +
4. Lists & Tables — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation +4. Lists & Tables — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation @@ -133,8 +133,8 @@ Developer ResourcesFind resources and get questions answered
- - Contributor Awards - 2023 + + Contributor Awards - 2024Award winners announced at this year's PyTorch Conference
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+ + ExecuTorch Docs +Find events, webinars, and podcasts
Stay up-to-date with the latest updates
+- v2.6.0.dev0+70e2a38 + v2.7.0.dev0+d6be4ba@@ -698,7 +716,7 @@4.1.5.1. 2"""Test Module for sphinx_rtd_theme.""" 3 4 - 5class Foo: + 5class Foo: 6 7 """Docstring for class Foo. 8 @@ -1287,7 +1305,7 @@
Resources
Developer Resources- - Contributor Awards - 2023 + Contributor Awards - 2024
@@ -1303,6 +1321,9 @@Resources
- ExecuTorch
+- + ExecuTorch Documentation +
- Events
+- + Newsletter +
- Governing Board
+- + Cloud Credit Program +
+- + Technical Advisory Council +
+- + Staff +
+- + Contact Us +
diff --git a/docs/src/pytorch-sphinx-theme/docs/demo/long.html b/docs/src/pytorch-sphinx-theme/docs/demo/long.html index 7426d80cc4..e53d9c5f4c 100644 --- a/docs/src/pytorch-sphinx-theme/docs/demo/long.html +++ b/docs/src/pytorch-sphinx-theme/docs/demo/long.html @@ -10,7 +10,7 @@ -1. Long Sticky Nav — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation +1. Long Sticky Nav — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation @@ -133,8 +133,8 @@ Developer ResourcesFind resources and get questions answered
- - Contributor Awards - 2023 + + Contributor Awards - 2024Award winners announced at this year's PyTorch Conference
@@ -155,6 +155,9 @@ ExecuTorchEnd-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+ + ExecuTorch Docs + @@ -202,6 +205,10 @@ EventsFind events, webinars, and podcasts
+ + Newsletter +Stay up-to-date with the latest updates
+ @@ -217,7 +224,18 @@ Governing Board - + + + Cloud Credit Program + + + Technical Advisory Council + + + Staff + + + Contact Us @@ -273,7 +291,7 @@- v2.6.0.dev0+70e2a38 + v2.7.0.dev0+d6be4ba@@ -966,7 +984,7 @@Resources
Developer Resources- - Contributor Awards - 2023 + Contributor Awards - 2024
@@ -982,6 +1000,9 @@Resources
- ExecuTorch
+- + ExecuTorch Documentation +
- Events
+- + Newsletter +
- Governing Board
+- + Cloud Credit Program +
+- + Technical Advisory Council +
+- + Staff +
+- + Contact Us +
diff --git a/docs/src/pytorch-sphinx-theme/docs/demo/structure.html b/docs/src/pytorch-sphinx-theme/docs/demo/structure.html index a8919d29de..a5b54a1786 100644 --- a/docs/src/pytorch-sphinx-theme/docs/demo/structure.html +++ b/docs/src/pytorch-sphinx-theme/docs/demo/structure.html @@ -10,7 +10,7 @@ -1. Structural Elements — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation +1. Structural Elements — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation @@ -133,8 +133,8 @@ Developer ResourcesFind resources and get questions answered
- - Contributor Awards - 2023 + + Contributor Awards - 2024Award winners announced at this year's PyTorch Conference
@@ -155,6 +155,9 @@ ExecuTorchEnd-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+ + ExecuTorch Docs + @@ -202,6 +205,10 @@ EventsFind events, webinars, and podcasts
+ + Newsletter +Stay up-to-date with the latest updates
+ @@ -217,7 +224,18 @@ Governing Board - + + + Cloud Credit Program + + + Technical Advisory Council + + + Staff + + + Contact Us @@ -273,7 +291,7 @@- v2.6.0.dev0+70e2a38 + v2.7.0.dev0+d6be4ba@@ -835,7 +853,7 @@Resources
Developer Resources- - Contributor Awards - 2023 + Contributor Awards - 2024
@@ -851,6 +869,9 @@Resources
- ExecuTorch
+- + ExecuTorch Documentation +
- Events
+- + Newsletter +
- Governing Board
+- + Cloud Credit Program +
+- + Technical Advisory Council +
+- + Staff +
+- + Contact Us +
diff --git a/docs/src/pytorch-sphinx-theme/docs/index.html b/docs/src/pytorch-sphinx-theme/docs/index.html index 901df6efd8..71e46900b7 100644 --- a/docs/src/pytorch-sphinx-theme/docs/index.html +++ b/docs/src/pytorch-sphinx-theme/docs/index.html @@ -10,7 +10,7 @@ -<no title> — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation +<no title> — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation @@ -133,8 +133,8 @@ Developer ResourcesFind resources and get questions answered
- - Contributor Awards - 2023 + + Contributor Awards - 2024Award winners announced at this year's PyTorch Conference
@@ -155,6 +155,9 @@ ExecuTorchEnd-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+ + ExecuTorch Docs + @@ -202,6 +205,10 @@ EventsFind events, webinars, and podcasts
+ + Newsletter +Stay up-to-date with the latest updates
+ @@ -217,7 +224,18 @@ Governing Board - + + + Cloud Credit Program + + + Technical Advisory Council + + + Staff + + + Contact Us @@ -273,7 +291,7 @@- v2.6.0.dev0+70e2a38 + v2.7.0.dev0+d6be4ba@@ -792,7 +810,7 @@Resources
Developer Resources- - Contributor Awards - 2023 + Contributor Awards - 2024
@@ -808,6 +826,9 @@Resources
- ExecuTorch
+- + ExecuTorch Documentation +
- Events
+- + Newsletter +
- Governing Board
+- + Cloud Credit Program +
+- + Technical Advisory Council +
+- + Staff +
+- + Contact Us +
diff --git a/docs/src/pytorch-sphinx-theme/docs/installing.html b/docs/src/pytorch-sphinx-theme/docs/installing.html index 446b41bb46..93315af032 100644 --- a/docs/src/pytorch-sphinx-theme/docs/installing.html +++ b/docs/src/pytorch-sphinx-theme/docs/installing.html @@ -10,7 +10,7 @@ -Installation — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation +Installation — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation @@ -133,8 +133,8 @@ Developer ResourcesFind resources and get questions answered
- - Contributor Awards - 2023 + + Contributor Awards - 2024Award winners announced at this year's PyTorch Conference
@@ -155,6 +155,9 @@ ExecuTorchEnd-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+ + ExecuTorch Docs + @@ -202,6 +205,10 @@ EventsFind events, webinars, and podcasts
+ + Newsletter +Stay up-to-date with the latest updates
+ @@ -217,7 +224,18 @@ Governing Board - + + + Cloud Credit Program + + + Technical Advisory Council + + + Staff + + + Contact Us @@ -273,7 +291,7 @@- v2.6.0.dev0+70e2a38 + v2.7.0.dev0+d6be4ba@@ -714,7 +732,7 @@Resources
Developer Resources- - Contributor Awards - 2023 + Contributor Awards - 2024
@@ -730,6 +748,9 @@Resources
- ExecuTorch
+- + ExecuTorch Documentation +
- Events
+- + Newsletter +
- Governing Board
+- + Cloud Credit Program +
+- + Technical Advisory Council +
+- + Staff +
+- + Contact Us +
diff --git a/docs/ts/creating_torchscript_module_in_python.html b/docs/ts/creating_torchscript_module_in_python.html index a009e3603a..306def22e0 100644 --- a/docs/ts/creating_torchscript_module_in_python.html +++ b/docs/ts/creating_torchscript_module_in_python.html @@ -10,7 +10,7 @@ -Creating a TorchScript Module — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation +Creating a TorchScript Module — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation @@ -135,8 +135,8 @@ Developer ResourcesFind resources and get questions answered
- - Contributor Awards - 2023 + + Contributor Awards - 2024Award winners announced at this year's PyTorch Conference
@@ -157,6 +157,9 @@ ExecuTorchEnd-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+ + ExecuTorch Docs + @@ -204,6 +207,10 @@ EventsFind events, webinars, and podcasts
+ + Newsletter +Stay up-to-date with the latest updates
+ @@ -219,7 +226,18 @@ Governing Board - + + + Cloud Credit Program + + + Technical Advisory Council + + + Staff + + + Contact Us @@ -275,7 +293,7 @@- v2.6.0.dev0+70e2a38 + v2.7.0.dev0+d6be4ba@@ -479,30 +497,30 @@PyTorch programs are based around
Modules which can be used to compose higher level modules.Modulescontain a constructor to set up the modules, parameters and sub-modules and a forward function which describes how to use the parameters and submodules when the module is invoked.For example, we can define a LeNet module like this:
-@@ -275,7 +293,7 @@@@ -219,7 +226,18 @@ Governing Board - + + + Cloud Credit Program + + + Technical Advisory Council + + + Staff + + + Contact Us1import torch.nn as nn - 2import torch.nn.functional as F +
@@ -275,7 +293,7 @@1import torch.nn as nn + 2import torch.nn.functional as F 3 4 - 5class LeNetFeatExtractor(nn.Module): - 6 def __init__(self): + 5class LeNetFeatExtractor(nn.Module): + 6 def __init__(self): 7 super(LeNetFeatExtractor, self).__init__() 8 self.conv1 = nn.Conv2d(1, 6, 3) 9 self.conv2 = nn.Conv2d(6, 16, 3) 10 -11 def forward(self, x): +11 def forward(self, x): 12 x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2)) 13 x = F.max_pool2d(F.relu(self.conv2(x)), 2) 14 return x 15 16 -17class LeNetClassifier(nn.Module): -18 def __init__(self): +17class LeNetClassifier(nn.Module): +18 def __init__(self): 19 super(LeNetClassifier, self).__init__() 20 self.fc1 = nn.Linear(16 * 6 * 6, 120) 21 self.fc2 = nn.Linear(120, 84) 22 self.fc3 = nn.Linear(84, 10) 23 -24 def forward(self, x): +24 def forward(self, x): 25 x = torch.flatten(x, 1) 26 x = F.relu(self.fc1(x)) 27 x = F.relu(self.fc2(x)) @@ -510,13 +528,13 @@ 29 return x 30 31 -32class LeNet(nn.Module): -33 def __init__(self): +32class LeNet(nn.Module): +33 def __init__(self): 34 super(LeNet, self).__init__() 35 self.feat = LeNetFeatExtractor() 36 self.classifier = LeNetClassifier() 37 -38 def forward(self, x): +38 def forward(self, x): 39 x = self.feat(x) 40 x = self.classifier(x) 41 return x @@ -529,7 +547,7 @@
From here are two pathways for going from PyTorch Python code to TorchScript code: Tracing and Scripting.
Tracing follows the path of execution when the module is called and records what happens. To trace an instance of our LeNet module, we can call
-torch.jit.tracewith an example input.@@ -219,7 +226,18 @@ Governing Board - + + + Cloud Credit Program + + + Technical Advisory Council + + + Staff + + + Contact Us@@ -204,6 +207,10 @@ Eventsimport torch +
@@ -157,6 +157,9 @@ ExecuTorchimport torch model = LeNet() input_data = torch.empty([1, 1, 32, 32]) @@ -539,7 +557,7 @@
Scripting actually inspects your code with a compiler and generates an equivalent TorchScript program. The difference is that since tracing is following the execution of your module, it cannot pick up control flow for instance. By working from the Python code, the compiler can include these components. We can run the script compiler on our LeNet module by calling
-torch.jit.scriptdiff --git a/docs/ts/getting_started_with_python_api.html b/docs/ts/getting_started_with_python_api.html index 3dfe059d66..6f23af762a 100644 --- a/docs/ts/getting_started_with_python_api.html +++ b/docs/ts/getting_started_with_python_api.html @@ -10,7 +10,7 @@ -@@ -275,7 +293,7 @@import torch +
@@ -219,7 +226,18 @@ Governing Board - + + + Cloud Credit Program + + + Technical Advisory Council + + + Staff + + + Contact Usimport torch model = LeNet() script_model = torch.jit.script(model) @@ -581,7 +599,7 @@
Saving TorchScript Module to Disk¶
For either traced or scripted modules, you can save the module to disk with the following command
-@@ -204,6 +207,10 @@ Eventsimport torch +
@@ -157,6 +157,9 @@ ExecuTorchdiff --git a/docs/ts/getting_started_with_cpp_api.html b/docs/ts/getting_started_with_cpp_api.html index 046f0487ab..4b7a56729c 100644 --- a/docs/ts/getting_started_with_cpp_api.html +++ b/docs/ts/getting_started_with_cpp_api.html @@ -10,7 +10,7 @@ -import torch model = LeNet() script_model = torch.jit.script(model) @@ -829,7 +847,7 @@
Resources
Developer Resources- - Contributor Awards - 2023 + Contributor Awards - 2024
@@ -845,6 +863,9 @@Resources
- ExecuTorch
+- + ExecuTorch Documentation +
- Events
+- + Newsletter +
- Governing Board
+- + Cloud Credit Program +
+- + Technical Advisory Council +
+- + Staff +
+- + Contact Us +
Using Torch-TensorRT in C++ — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation +Using Torch-TensorRT in C++ — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation @@ -135,8 +135,8 @@ Developer ResourcesFind resources and get questions answered
- - Contributor Awards - 2023 + + Contributor Awards - 2024Award winners announced at this year's PyTorch Conference
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+ + ExecuTorch Docs +Find events, webinars, and podcasts
+ + Newsletter +Stay up-to-date with the latest updates
+- v2.6.0.dev0+70e2a38 + v2.7.0.dev0+d6be4ba@@ -1007,7 +1025,7 @@Resources
Developer Resources- - Contributor Awards - 2023 + Contributor Awards - 2024
@@ -1023,6 +1041,9 @@Resources
- ExecuTorch
+- + ExecuTorch Documentation +
- Events
+- + Newsletter +
- Governing Board
+- + Cloud Credit Program +
+- + Technical Advisory Council +
+- + Staff +
+- + Contact Us +
Using Torch-TensorRT in Python — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation +Using Torch-TensorRT in Python — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation @@ -135,8 +135,8 @@ Developer ResourcesFind resources and get questions answered
- - Contributor Awards - 2023 + + Contributor Awards - 2024Award winners announced at this year's PyTorch Conference
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+ + ExecuTorch Docs +Find events, webinars, and podcasts
+ + Newsletter +Stay up-to-date with the latest updates
+- v2.6.0.dev0+70e2a38 + v2.7.0.dev0+d6be4ba@@ -483,7 +501,7 @@ to Torch-TensorRT and you will be returned an optimized TorchScript module to run or add into another PyTorch module. Inputs is a list oftorch_tensorrt.Inputclasses which define input Tensors’ shape, datatype and memory format. Alternatively, if your input is a more complex data type, such as a tuple or list of Tensors, you can use theinput_signatureargument to specify a collection-based input, such as(List[Tensor], Tuple[Tensor, Tensor]). See the second sample below for an example. You can also specify settings such as operating precision for the engine or target device. After compilation you can save the module just like any other module to load in a deployment application. In order to load a TensorRT/TorchScript module, make sure you first importtorch_tensorrt. -@@ -204,6 +207,10 @@ Eventsimport torch_tensorrt +
import torch_tensorrt ... @@ -509,7 +527,7 @@
# Sample using collection-based inputs via the input_signature argument -import torch_tensorrt +import torch_tensorrt ... @@ -534,8 +552,8 @@
@@ -157,6 +157,9 @@ ExecuTorchdiff --git a/docs/ts/ptq.html b/docs/ts/ptq.html index 023bd14ede..a6b8abac15 100644 --- a/docs/ts/ptq.html +++ b/docs/ts/ptq.html @@ -10,7 +10,7 @@ -# Deployment application -import torch -import torch_tensorrt +import torch +import torch_tensorrt trt_ts_module = torch.jit.load("trt_ts_module.ts") input_data = input_data.to("cuda").half() @@ -782,7 +800,7 @@
Resources
Developer Resources- - Contributor Awards - 2023 + Contributor Awards - 2024
@@ -798,6 +816,9 @@Resources
- ExecuTorch
+- + ExecuTorch Documentation +
- Events
+- + Newsletter +
- Governing Board
+- + Cloud Credit Program +
+- + Technical Advisory Council +
+- + Staff +
+- + Contact Us +
Post Training Quantization (PTQ) — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation +Post Training Quantization (PTQ) — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation @@ -135,8 +135,8 @@ Developer ResourcesFind resources and get questions answered
- - Contributor Awards - 2023 + + Contributor Awards - 2024Award winners announced at this year's PyTorch Conference
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+ + ExecuTorch Docs +Find events, webinars, and podcasts
+ + Newsletter +Stay up-to-date with the latest updates
+- v2.6.0.dev0+70e2a38 + v2.7.0.dev0+d6be4ba@@ -890,7 +908,7 @@Resources
Developer Resources- - Contributor Awards - 2023 + Contributor Awards - 2024
@@ -906,6 +924,9 @@Resources
- ExecuTorch
+- + ExecuTorch Documentation +
- Events
+- + Newsletter +
- Governing Board
+- + Cloud Credit Program +
+- + Technical Advisory Council +
+- + Staff +
+- + Contact Us +
diff --git a/docs/ts/torchscript_frontend_from_pytorch.html b/docs/ts/torchscript_frontend_from_pytorch.html index 2d27af3e57..f2584d9c92 100644 --- a/docs/ts/torchscript_frontend_from_pytorch.html +++ b/docs/ts/torchscript_frontend_from_pytorch.html @@ -10,7 +10,7 @@ -Using Torch-TensorRT TorchScript Frontend Directly From PyTorch — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation +Using Torch-TensorRT TorchScript Frontend Directly From PyTorch — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation @@ -133,8 +133,8 @@ Developer ResourcesFind resources and get questions answered
- - Contributor Awards - 2023 + + Contributor Awards - 2024Award winners announced at this year's PyTorch Conference
@@ -155,6 +155,9 @@ ExecuTorchEnd-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+ + ExecuTorch Docs + @@ -202,6 +205,10 @@ EventsFind events, webinars, and podcasts
+ + Newsletter +Stay up-to-date with the latest updates
+ @@ -217,7 +224,18 @@ Governing Board - + + + Cloud Credit Program + + + Technical Advisory Council + + + Staff + + + Contact Us @@ -273,7 +291,7 @@- v2.6.0.dev0+70e2a38 + v2.7.0.dev0+d6be4ba@@ -474,12 +492,12 @@You will now be able to directly access TensorRT from PyTorch APIs. The process to use this feature is very similar to the compilation workflow described in Using Torch-TensorRT in Python
Start by loading
-torch_tensorrtinto your application.@@ -275,7 +293,7 @@@@ -219,7 +226,18 @@ Governing Board - + + + Cloud Credit Program + + + Technical Advisory Council + + + Staff + + + Contact Usimport torch -import torch_tensorrt +
import torch +import torch_tensorrt
Then given a TorchScript module, you can compile it with TensorRT using the
-torch._C._jit_to_backend("tensorrt", ...)API.@@ -204,6 +207,10 @@ Eventsimport torchvision.models as models +
@@ -157,6 +157,9 @@ ExecuTorchdiff --git a/docs/tutorials/_rendered_examples/dynamo/auto_generate_converters.html b/docs/tutorials/_rendered_examples/dynamo/auto_generate_converters.html index 0f828c1ecc..8b9d00e349 100644 --- a/docs/tutorials/_rendered_examples/dynamo/auto_generate_converters.html +++ b/docs/tutorials/_rendered_examples/dynamo/auto_generate_converters.html @@ -10,7 +10,7 @@ -import torchvision.models as models model = models.mobilenet_v2(pretrained=True) script_model = torch.jit.script(model) @@ -749,7 +767,7 @@
Resources
Developer Resources- - Contributor Awards - 2023 + Contributor Awards - 2024
@@ -765,6 +783,9 @@Resources
- ExecuTorch
+- + ExecuTorch Documentation +
- Events
+- + Newsletter +
- Governing Board
+- + Cloud Credit Program +
+- + Technical Advisory Council +
+- + Staff +
+- + Contact Us +
Automatically Generate a Converter for a Custom Kernel — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation +Automatically Generate a Converter for a Custom Kernel — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation @@ -135,8 +135,8 @@ Developer ResourcesFind resources and get questions answered
- - Contributor Awards - 2023 + + Contributor Awards - 2024Award winners announced at this year's PyTorch Conference
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+ + ExecuTorch Docs +Find events, webinars, and podcasts
+ + Newsletter +Stay up-to-date with the latest updates
+- v2.6.0.dev0+70e2a38 + v2.7.0.dev0+d6be4ba@@ -503,17 +521,17 @@Writing Custom Operators in PyTorch
@@ -275,7 +293,7 @@from typing import Tuple +
@@ -568,7 +586,7 @@from typing import Tuple -import tensorrt_bindings.plugin as trtp -import torch -import torch_tensorrt -import triton -import triton.language as tl +import tensorrt_bindings.plugin as trtp +import torch +import torch_tensorrt +import triton +import triton.language as tl @triton.jit -def elementwise_mul_kernel(X, Y, Z, BLOCK_SIZE: tl.constexpr): +def elementwise_mul_kernel(X, Y, Z, BLOCK_SIZE: tl.constexpr): # Program ID determines the block of data each thread will process pid = tl.program_id(0) # Compute the range of elements that this thread block will work on @@ -530,7 +548,7 @@
Writing Custom Operators in PyTorch@torch.library.custom_op("torchtrt_ex::elementwise_mul", mutates_args=()) # type: ignore[misc] -def elementwise_mul( +def elementwise_mul( X: torch.Tensor, Y: torch.Tensor, b: float = 0.2, a: int = 2 ) -> torch.Tensor: # Ensure the tensors are on the GPU @@ -555,7 +573,7 @@
Writing Custom Operators in PyTorch
@torch.library.register_fake("torchtrt_ex::elementwise_mul") -def _(x: torch.Tensor, y: torch.Tensor, b: float = 0.2, a: int = 2) -> torch.Tensor: +def _(x: torch.Tensor, y: torch.Tensor, b: float = 0.2, a: int = 2) -> torch.Tensor: return x
Writing Plugins for TensorRT using the Quick Deploy Plugin system
@@ -219,7 +226,18 @@ Governing Board - + + + Cloud Credit Program + + + Technical Advisory Council + + + Staff + + + Contact Us@trtp.register("torchtrt_ex::elementwise_mul") -def _( +def _( x: trtp.TensorDesc, y: trtp.TensorDesc, b: float, a: int ) -> Tuple[trtp.TensorDesc]: return x.like() @@ -577,7 +595,7 @@
Writing Plugins for TensorRT using the Quick Deploy Plugin system
@@ -204,6 +207,10 @@ Events@trtp.impl("torchtrt_ex::elementwise_mul") -def _( +def _( x: trtp.Tensor, y: trtp.Tensor, b: float, @@ -614,11 +632,11 @@
Using our converter with a model
class MyModel(torch.nn.Module): # type: ignore[misc] - def __init__(self): +
@@ -157,6 +157,9 @@ ExecuTorchdiff --git a/docs/tutorials/_rendered_examples/dynamo/converter_overloading.html b/docs/tutorials/_rendered_examples/dynamo/converter_overloading.html index 92df70e149..865dbffe17 100644 --- a/docs/tutorials/_rendered_examples/dynamo/converter_overloading.html +++ b/docs/tutorials/_rendered_examples/dynamo/converter_overloading.html @@ -10,7 +10,7 @@ -class MyModel(torch.nn.Module): # type: ignore[misc] + def __init__(self): super().__init__() - def forward(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor: + def forward(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor: z = torch.add(x, y) res = torch.ops.torchtrt_ex.elementwise_mul.default(x, z, a=1) @@ -896,7 +914,7 @@
Resources
Developer Resources- - Contributor Awards - 2023 + Contributor Awards - 2024
@@ -912,6 +930,9 @@Resources
- ExecuTorch
+- + ExecuTorch Documentation +
- Events
+- + Newsletter +
- Governing Board
+- + Cloud Credit Program +
+- + Technical Advisory Council +
+- + Staff +
+- + Contact Us +
Overloading Torch-TensorRT Converters with Custom Converters — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation +Overloading Torch-TensorRT Converters with Custom Converters — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation @@ -135,8 +135,8 @@ Developer ResourcesFind resources and get questions answered
- - Contributor Awards - 2023 + + Contributor Awards - 2024Award winners announced at this year's PyTorch Conference
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+ + ExecuTorch Docs +Find events, webinars, and podcasts
+ + Newsletter +Stay up-to-date with the latest updates
+- v2.6.0.dev0+70e2a38 + v2.7.0.dev0+d6be4ba@@ -483,21 +501,21 @@ Torch-TensorRT would normally use.In this tutorial, we will demonstrate how to overload Torch-TensorRT’s conversion of the torch.nn.functional.gelu operation to TensorRT with a custom converter that uses a different implementation of the GeLU layer.
-@@ -275,7 +293,7 @@import logging -import sys +
import logging +import sys -import torch -import torch_tensorrt +import torch +import torch_tensorrt
GeLU has 2 modes in PyTorch, one using the
-erffunction and the other using thetanhapproximation. TensorRT natively supports both implementations as an activation layer, but suppose we want to use a custom implementation of GeLU in TensorRT only fortanhmode.@@ -275,7 +293,7 @@class GeLU(torch.nn.Module): - def __init__(self, mode="tanh"): +
@@ -219,7 +226,18 @@ Governing Board - + + + Cloud Credit Program + + + Technical Advisory Council + + + Staff + + + Contact Us@@ -273,7 +291,7 @@class GeLU(torch.nn.Module): + def __init__(self, mode="tanh"): super().__init__() self.mode = mode - def forward(self, x): + def forward(self, x): return torch.nn.functional.gelu(x, approximate=self.mode) @@ -519,13 +537,13 @@
Writing a Custom Converter@torch_tensorrt.dynamo.conversion.dynamo_tensorrt_converter decorator. At a code level, converter takes the current conversion state (
ConversionCtx), the next operator in the graph to convert, and the arguments to that node and returns the placeholder outputs for that operation, while as side-effect inserting the necessary TensorRT layers into the TensorRT network. -@@ -217,7 +224,18 @@ Governing Board - + + + Cloud Credit Program + + + Technical Advisory Council + + + Staff + + + Contact Usfrom typing import Dict, Sequence, Tuple, Union +
from typing import Dict, Sequence, Tuple, Union -from torch.fx.node import Argument, Node, Target -from torch_tensorrt.dynamo import CompilationSettings -from torch_tensorrt.dynamo.conversion import ConversionContext +from torch.fx.node import Argument, Node, Target +from torch_tensorrt.dynamo import CompilationSettings +from torch_tensorrt.dynamo.conversion import ConversionContext -import tensorrt as trt +import tensorrt as trt
@@ -567,7 +585,7 @@ Converter Implementationtorch_tensorrt.dynamo.conversion.impl module and are designed to be composable and interoperable with raw-TensorRT implementations. In this case, we will use the Torch-TensorRT
mul,addandtanhfunctions fromimplto implement our alternative GeLU layer. -@@ -202,6 +205,10 @@ Eventsdef aten_ops_gelu( +
@@ -155,6 +155,9 @@ ExecuTorchdiff --git a/docs/tutorials/_rendered_examples/dynamo/cross_runtime_compilation_for_windows.html b/docs/tutorials/_rendered_examples/dynamo/cross_runtime_compilation_for_windows.html index 55efd182d2..b454fa84ea 100644 --- a/docs/tutorials/_rendered_examples/dynamo/cross_runtime_compilation_for_windows.html +++ b/docs/tutorials/_rendered_examples/dynamo/cross_runtime_compilation_for_windows.html @@ -10,7 +10,7 @@ -def aten_ops_gelu( ctx: ConversionContext, target: Target, args: Tuple[Argument, ...], @@ -576,13 +594,13 @@
Converter Implementation) -> Union[trt.ITensor, Sequence[trt.ITensor]]: # The schema for torch.ops.aten.gelu.default is gelu(Tensor self, *, str approximate=’none’) -> Tensor - from torch_tensorrt.dynamo import SourceIR - from torch_tensorrt.dynamo.conversion import impl + from torch_tensorrt.dynamo import SourceIR + from torch_tensorrt.dynamo.conversion import impl # Cheap way to allow layer names to be unqiue op_count = 0 - def get_op_count(): + def get_op_count(): nonlocal op_count op_count += 1 return op_count @@ -915,7 +933,7 @@
Resources
Developer Resources- - Contributor Awards - 2023 + Contributor Awards - 2024
@@ -931,6 +949,9 @@Resources
- ExecuTorch
+- + ExecuTorch Documentation +
- Events
+- + Newsletter +
- Governing Board
+- + Cloud Credit Program +
+- + Technical Advisory Council +
+- + Staff +
+- + Contact Us +
Cross runtime compilation for windows example — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation +Cross runtime compilation for windows example — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation @@ -133,8 +133,8 @@ Developer ResourcesFind resources and get questions answered
- - Contributor Awards - 2023 + + Contributor Awards - 2024Award winners announced at this year's PyTorch Conference
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+ + ExecuTorch Docs +Find events, webinars, and podcasts
+ + Newsletter +Stay up-to-date with the latest updates
+- v2.6.0.dev0+70e2a38 + v2.7.0.dev0+d6be4ba@@ -487,12 +505,12 @@Cross runtime compilation for windows example
Imports and Model Definition¶
-@@ -204,6 +207,10 @@ Eventsimport argparse -import platform +
@@ -157,6 +157,9 @@ ExecuTorchdiff --git a/docs/tutorials/_rendered_examples/dynamo/custom_kernel_plugins.html b/docs/tutorials/_rendered_examples/dynamo/custom_kernel_plugins.html index a71dc1e448..6657ad6a3b 100644 --- a/docs/tutorials/_rendered_examples/dynamo/custom_kernel_plugins.html +++ b/docs/tutorials/_rendered_examples/dynamo/custom_kernel_plugins.html @@ -10,7 +10,7 @@ -import argparse +import platform -import torch -import torch_tensorrt as torchtrt -import torchvision.models as models +import torch +import torch_tensorrt as torchtrt +import torchvision.models as models PARSER = argparse.ArgumentParser( description="Cross runtime comilation for windows example: Resnet Model" @@ -789,7 +807,7 @@
Resources
Developer Resources- - Contributor Awards - 2023 + Contributor Awards - 2024
@@ -805,6 +823,9 @@Resources
- ExecuTorch
+- + ExecuTorch Documentation +
- Events
+- + Newsletter +
- Governing Board
+- + Cloud Credit Program +
+- + Technical Advisory Council +
+- + Staff +
+- + Contact Us +
Using Custom Kernels within TensorRT Engines with Torch-TensorRT — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation +Using Custom Kernels within TensorRT Engines with Torch-TensorRT — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation @@ -135,8 +135,8 @@ Developer ResourcesFind resources and get questions answered
- - Contributor Awards - 2023 + + Contributor Awards - 2024Award winners announced at this year's PyTorch Conference
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+ + ExecuTorch Docs +Find events, webinars, and podcasts
+ + Newsletter +Stay up-to-date with the latest updates
+- v2.6.0.dev0+70e2a38 + v2.7.0.dev0+d6be4ba@@ -497,18 +515,18 @@Writing Custom Operators in PyTorchOpenAI Triton
When using custom kernels with PyTorch, it is recommended to take the additional step of registering them as formal operators in PyTorch. This will both make it easier to handle the operation in Torch-TensorRT and simplify its use in PyTorch. This could either be done as part of a C++ library or in Python. (see: Custom ops in C++ and Python custom ops for more details )
-@@ -219,7 +226,18 @@ Governing Board - + + + Cloud Credit Program + + + Technical Advisory Council + + + Staff + + + Contact Us@@ -204,6 +207,10 @@ Eventsfrom typing import Any, Sequence +
@@ -275,7 +293,7 @@from typing import Any, Sequence -import numpy as np -import torch -import triton -import triton.language as tl -from torch.library import custom_op +import numpy as np +import torch +import triton +import triton.language as tl +from torch.library import custom_op # Defining the kernel to be run on the GPU @triton.jit # type: ignore -def circ_pad_kernel( +def circ_pad_kernel( X: torch.Tensor, all_pads_0: tl.int32, all_pads_2: tl.int32, @@ -556,7 +574,7 @@
Writing Custom Operators in PyTorch# The launch code wrapped to expose it as a custom operator in our namespace @custom_op("torchtrt_ex::triton_circular_pad", mutates_args=()) # type: ignore[misc] -def triton_circular_pad(x: torch.Tensor, padding: Sequence[int]) -> torch.Tensor: +def triton_circular_pad(x: torch.Tensor, padding: Sequence[int]) -> torch.Tensor: out_dims = np.ones(len(x.shape), dtype=np.int32) for i in range(np.size(padding) // 2): out_dims[len(out_dims) - i - 1] = ( @@ -633,7 +651,7 @@
Testing our custom ophere). In our case we can just use the native circular pad operation as our FakeTensor implementation.
@@ -219,7 +226,18 @@ Governing Board - + + + Cloud Credit Program + + + Technical Advisory Council + + + Staff + + + Contact Us@torch.library.register_fake("torchtrt_ex::triton_circular_pad") # type: ignore[misc] -def _(x: torch.Tensor, padding: Sequence[int]) -> torch.Tensor: +def _(x: torch.Tensor, padding: Sequence[int]) -> torch.Tensor: return torch.nn.functional.pad(x, padding, "circular") @@ -644,19 +662,19 @@
Testing our custom op
Using the Custom Operator in a Model¶
We can now create models using our custom op. Here is a small example one that uses both natively supported operators (Convolution) and our custom op.
-@@ -275,7 +293,7 @@from typing import Sequence +
@@ -219,7 +226,18 @@ Governing Board - + + + Cloud Credit Program + + + Technical Advisory Council + + + Staff + + + Contact Us@@ -275,7 +293,7 @@from typing import Sequence -from torch import nn +from torch import nn -class MyModel(nn.Module): # type: ignore[misc] - def __init__(self, padding: Sequence[int]): +class MyModel(nn.Module): # type: ignore[misc] + def __init__(self, padding: Sequence[int]): super().__init__() self.padding = padding self.conv = nn.Conv2d(1, 5, kernel_size=3) - def forward(self, x: torch.Tensor) -> torch.Tensor: + def forward(self, x: torch.Tensor) -> torch.Tensor: padded_x = torch.ops.torchtrt_ex.triton_circular_pad(x, self.padding) y = self.conv(padded_x) @@ -689,7 +707,7 @@
Using the Custom Operator in a Model
@@ -275,7 +293,7 @@import torch_tensorrt as torchtrt +
@@ -275,7 +293,7 @@import torch_tensorrt as torchtrt torchtrt.compile( my_model, @@ -748,17 +766,17 @@
Wrapping Custom Kernels to use in TensorRThere. From a high level, similar to PyTorch you will need to define systems to handle setting up the operator, calculating the effect of the operation abstractly, serializing the op and the actual mechanics of calling the implementation of the op in the engine. -
@@ -219,7 +226,18 @@ Governing Board - + + + Cloud Credit Program + + + Technical Advisory Council + + + Staff + + + Contact Us@@ -275,7 +293,7 @@import pickle as pkl -from typing import Any, List, Optional, Self +
@@ -219,7 +226,18 @@ Governing Board - + + + Cloud Credit Program + + + Technical Advisory Council + + + Staff + + + Contact Usimport pickle as pkl +from typing import Any, List, Optional, Self -import cupy as cp # Needed to work around API gaps in PyTorch to build torch.Tensors around preallocated CUDA memory -import numpy as np +import cupy as cp # Needed to work around API gaps in PyTorch to build torch.Tensors around preallocated CUDA memory +import numpy as np -import tensorrt as trt +import tensorrt as trt -class CircularPaddingPlugin(trt.IPluginV2DynamicExt): # type: ignore[misc] - def __init__( +class CircularPaddingPlugin(trt.IPluginV2DynamicExt): # type: ignore[misc] + def __init__( self, field_collection: Optional[List[trt.PluginFieldCollection]] = None ): super().__init__() @@ -774,12 +792,12 @@
Wrapping Custom Kernels to use in TensorRTassert field_collection[0].name == "pads" self.pads = field_collection[0].data - def get_output_datatype( + def get_output_datatype( self, index: int, input_types: List[trt.DataType] ) -> trt.DataType: return input_types[0] - def get_output_dimensions( + def get_output_dimensions( self, output_index: int, inputs: List[trt.DimsExprs], @@ -797,7 +815,7 @@
Wrapping Custom Kernels to use in TensorRTreturn output_dims - def configure_plugin( + def configure_plugin( self, inp: List[trt.DynamicPluginTensorDesc], out: List[trt.DynamicPluginTensorDesc], @@ -807,10 +825,10 @@
Wrapping Custom Kernels to use in TensorRTfor i in range(len(X_dims)): self.X_shape[i] = X_dims[i] - def serialize(self) -> bytes: + def serialize(self) -> bytes: return pkl.dumps({"pads": self.pads}) - def supports_format_combination( + def supports_format_combination( self, pos: int, in_out: List[trt.PluginTensorDesc], num_inputs: int ) -> bool: assert num_inputs == 1 @@ -832,7 +850,7 @@
Wrapping Custom Kernels to use in TensorRTreturn False - def enqueue( + def enqueue( self, input_desc: List[trt.PluginTensorDesc], output_desc: List[trt.PluginTensorDesc], @@ -900,14 +918,14 @@
Wrapping Custom Kernels to use in TensorRTBLOCK_SIZE=256, ) - def clone(self) -> Self: + def clone(self) -> Self: cloned_plugin = CircularPaddingPlugin() cloned_plugin.__dict__.update(self.__dict__) return cloned_plugin -class CircularPaddingPluginCreator(trt.IPluginCreator): # type: ignore[misc] - def __init__(self): +class CircularPaddingPluginCreator(trt.IPluginCreator): # type: ignore[misc] + def __init__(self): super().__init__() self.name = "CircularPaddingPlugin" @@ -917,12 +935,12 @@
Wrapping Custom Kernels to use in TensorRT[trt.PluginField("pads", np.array([]), trt.PluginFieldType.INT32)] ) - def create_plugin( + def create_plugin( self, name: str, field_collection: trt.PluginFieldCollection_ ) -> CircularPaddingPlugin: return CircularPaddingPlugin(field_collection) - def deserialize_plugin(self, name: str, data: bytes) -> CircularPaddingPlugin: + def deserialize_plugin(self, name: str, data: bytes) -> CircularPaddingPlugin: pads_dict = pkl.loads(data) print(pads_dict) deserialized = CircularPaddingPlugin() @@ -941,15 +959,15 @@
Wrapping Custom Kernels to use in TensorRT¶
Now with our TensorRT plugin, we can create a converter so that Torch-TensorRT knows to insert our plugin in place of our custom circular padding operator. More information on writing converters can be found here
-@@ -204,6 +207,10 @@ Eventsfrom typing import Dict, Tuple +
@@ -157,6 +157,9 @@ ExecuTorchdiff --git a/docs/tutorials/_rendered_examples/dynamo/engine_caching_bert_example.html b/docs/tutorials/_rendered_examples/dynamo/engine_caching_bert_example.html index bba8633a0e..91d2c3a261 100644 --- a/docs/tutorials/_rendered_examples/dynamo/engine_caching_bert_example.html +++ b/docs/tutorials/_rendered_examples/dynamo/engine_caching_bert_example.html @@ -10,7 +10,7 @@ -from typing import Dict, Tuple -from torch.fx.node import Argument, Target -from torch_tensorrt.dynamo.conversion import ( +from torch.fx.node import Argument, Target +from torch_tensorrt.dynamo.conversion import ( ConversionContext, dynamo_tensorrt_converter, ) -from torch_tensorrt.dynamo.conversion.converter_utils import get_trt_tensor -from torch_tensorrt.fx.converters.converter_utils import set_layer_name +from torch_tensorrt.dynamo.conversion.converter_utils import get_trt_tensor +from torch_tensorrt.fx.converters.converter_utils import set_layer_name @dynamo_tensorrt_converter( @@ -957,7 +975,7 @@
Using Torch-TensorRT to Insert the Kernel) # type: ignore # Recall the schema defined above: # torch.ops.torchtrt_ex.triton_circular_pad.default(Tensor x, IntList padding) -> Tensor -def circular_padding_converter( +def circular_padding_converter( ctx: ConversionContext, target: Target, args: Tuple[Argument, ...], @@ -1349,7 +1367,7 @@
Resources
Developer Resources- - Contributor Awards - 2023 + Contributor Awards - 2024
@@ -1365,6 +1383,9 @@Resources
- ExecuTorch
+- + ExecuTorch Documentation +
- Events
+- + Newsletter +
- Governing Board
+- + Cloud Credit Program +
+- + Technical Advisory Council +
+- + Staff +
+- + Contact Us +
Engine Caching (BERT) — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation +Engine Caching (BERT) — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation @@ -135,8 +135,8 @@ Developer ResourcesFind resources and get questions answered
- - Contributor Awards - 2023 + + Contributor Awards - 2024Award winners announced at this year's PyTorch Conference
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+ + ExecuTorch Docs +Find events, webinars, and podcasts
+ + Newsletter +Stay up-to-date with the latest updates
+- v2.6.0.dev0+70e2a38 + v2.7.0.dev0+d6be4ba@@ -479,11 +497,11 @@Engine Caching (BERT)¶
Small caching example on BERT.
-@@ -204,6 +207,10 @@ Eventsimport numpy as np -import torch -import torch_tensorrt -from engine_caching_example import remove_timing_cache -from transformers import BertModel +
@@ -157,6 +157,9 @@ ExecuTorchdiff --git a/docs/tutorials/_rendered_examples/dynamo/engine_caching_example.html b/docs/tutorials/_rendered_examples/dynamo/engine_caching_example.html index 5e61d420fb..3d28fcdaf8 100644 --- a/docs/tutorials/_rendered_examples/dynamo/engine_caching_example.html +++ b/docs/tutorials/_rendered_examples/dynamo/engine_caching_example.html @@ -10,7 +10,7 @@ -import numpy as np +import torch +import torch_tensorrt +from engine_caching_example import remove_timing_cache +from transformers import BertModel np.random.seed(0) torch.manual_seed(0) @@ -495,7 +513,7 @@ ] -def compile_bert(iterations=3): +def compile_bert(iterations=3): times = [] start = torch.cuda.Event(enable_timing=True) end = torch.cuda.Event(enable_timing=True) @@ -795,7 +813,7 @@
Resources
Developer Resources- - Contributor Awards - 2023 + Contributor Awards - 2024
@@ -811,6 +829,9 @@Resources
- ExecuTorch
+- + ExecuTorch Documentation +
- Events
+- + Newsletter +
- Governing Board
+- + Cloud Credit Program +
+- + Technical Advisory Council +
+- + Staff +
+- + Contact Us +
Engine Caching — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation +Engine Caching — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation @@ -135,8 +135,8 @@ Developer ResourcesFind resources and get questions answered
- - Contributor Awards - 2023 + + Contributor Awards - 2024Award winners announced at this year's PyTorch Conference
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+ + ExecuTorch Docs +Find events, webinars, and podcasts
+ + Newsletter +Stay up-to-date with the latest updates
+- v2.6.0.dev0+70e2a38 + v2.7.0.dev0+d6be4ba@@ -496,15 +514,15 @@The example uses a pre-trained ResNet18 model and shows the differences between compilation without caching, with caching enabled, and when reusing cached engines.
-@@ -275,7 +293,7 @@import os -from typing import Dict, Optional +
@@ -219,7 +226,18 @@ Governing Board - + + + Cloud Credit Program + + + Technical Advisory Council + + + Staff + + + Contact Us@@ -534,7 +552,7 @@import os +from typing import Dict, Optional -import numpy as np -import torch -import torch_tensorrt as torch_trt -import torchvision.models as models -from torch_tensorrt.dynamo._defaults import TIMING_CACHE_PATH -from torch_tensorrt.dynamo._engine_cache import BaseEngineCache +import numpy as np +import torch +import torch_tensorrt as torch_trt +import torchvision.models as models +from torch_tensorrt.dynamo._defaults import TIMING_CACHE_PATH +from torch_tensorrt.dynamo._engine_cache import BaseEngineCache np.random.seed(0) torch.manual_seed(0) @@ -516,7 +534,7 @@ use_python_runtime = False -def remove_timing_cache(path=TIMING_CACHE_PATH): +def remove_timing_cache(path=TIMING_CACHE_PATH): if os.path.exists(path): os.remove(path)
Engine Caching for JIT Compilationcache_built_engines=True), the engine must be refittable (
immutable_weights=False). See Refitting Torch-TensorRT Programs with New Weights for more details. -@@ -204,6 +207,10 @@ Eventsdef torch_compile(iterations=3): +
@@ -157,6 +157,9 @@ ExecuTorchdiff --git a/docs/tutorials/_rendered_examples/dynamo/mutable_torchtrt_module_example.html b/docs/tutorials/_rendered_examples/dynamo/mutable_torchtrt_module_example.html index 6d82ec3285..bece6414a9 100644 --- a/docs/tutorials/_rendered_examples/dynamo/mutable_torchtrt_module_example.html +++ b/docs/tutorials/_rendered_examples/dynamo/mutable_torchtrt_module_example.html @@ -10,7 +10,7 @@ -def torch_compile(iterations=3): times = [] start = torch.cuda.Event(enable_timing=True) end = torch.cuda.Event(enable_timing=True) @@ -590,7 +608,7 @@
Engine Caching for AOT Compilation
@@ -273,7 +291,7 @@def dynamo_compile(iterations=3): +
@@ -217,7 +224,18 @@ Governing Board - + + + Cloud Credit Program + + + Technical Advisory Council + + + Staff + + + Contact Usdef dynamo_compile(iterations=3): times = [] start = torch.cuda.Event(enable_timing=True) end = torch.cuda.Event(enable_timing=True) @@ -660,8 +678,8 @@
Custom Engine CacheRAMEngineCache. -
@@ -202,6 +205,10 @@ Eventsclass RAMEngineCache(BaseEngineCache): - def __init__( +
@@ -155,6 +155,9 @@ ExecuTorchdiff --git a/docs/tutorials/_rendered_examples/dynamo/index.html b/docs/tutorials/_rendered_examples/dynamo/index.html index 1425a3853d..6af08095c5 100644 --- a/docs/tutorials/_rendered_examples/dynamo/index.html +++ b/docs/tutorials/_rendered_examples/dynamo/index.html @@ -10,7 +10,7 @@ -class RAMEngineCache(BaseEngineCache): + def __init__( self, ) -> None: """ @@ -669,7 +687,7 @@
Custom Engine Cache """ self.engine_cache: Dict[str, bytes] = {} - def save( + def save( self, hash: str, blob: bytes, @@ -686,7 +704,7 @@
Custom Engine Cache """ self.engine_cache[hash] = blob - def load(self, hash: str) -> Optional[bytes]: + def load(self, hash: str) -> Optional[bytes]: """ Load the engine blob from the cache. @@ -702,7 +720,7 @@
Custom Engine Cachereturn None -def torch_compile_my_cache(iterations=3): +def torch_compile_my_cache(iterations=3): times = [] engine_cache = RAMEngineCache() start = torch.cuda.Event(enable_timing=True) @@ -1009,7 +1027,7 @@
Resources
Developer Resources- - Contributor Awards - 2023 + Contributor Awards - 2024
@@ -1025,6 +1043,9 @@Resources
- ExecuTorch
+- + ExecuTorch Documentation +
- Events
+- + Newsletter +
- Governing Board
+- + Cloud Credit Program +
+- + Technical Advisory Council +
+- + Staff +
+- + Contact Us +
Dependencies — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation +Dependencies — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation @@ -133,8 +133,8 @@ Developer ResourcesFind resources and get questions answered
- - Contributor Awards - 2023 + + Contributor Awards - 2024Award winners announced at this year's PyTorch Conference
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+ + ExecuTorch Docs +Find events, webinars, and podcasts
+ + Newsletter +Stay up-to-date with the latest updates
+- v2.6.0.dev0+70e2a38 + v2.7.0.dev0+d6be4ba@@ -781,7 +799,7 @@Resources
Developer Resources- - Contributor Awards - 2023 + Contributor Awards - 2024
@@ -797,6 +815,9 @@Resources
- ExecuTorch
+- + ExecuTorch Documentation +
- Events
+- + Newsletter +
- Governing Board
+- + Cloud Credit Program +
+- + Technical Advisory Council +
+- + Staff +
+- + Contact Us +
Mutable Torch TensorRT Module — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation +Mutable Torch TensorRT Module — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation @@ -135,8 +135,8 @@ Developer ResourcesFind resources and get questions answered
- - Contributor Awards - 2023 + + Contributor Awards - 2024Award winners announced at this year's PyTorch Conference
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+ + ExecuTorch Docs +Find events, webinars, and podcasts
+ + Newsletter +Stay up-to-date with the latest updates
+- v2.6.0.dev0+70e2a38 + v2.7.0.dev0+d6be4ba@@ -486,10 +504,10 @@ 1. Sample workflow of Mutable Torch TensorRT Module with ResNet 18 2. Save a Mutable Torch TensorRT Module 3. Integration with Huggingface pipeline in LoRA use case -@@ -219,7 +226,18 @@ Governing Board - + + + Cloud Credit Program + + + Technical Advisory Council + + + Staff + + + Contact Usimport numpy as np -import torch -import torch_tensorrt as torch_trt -import torchvision.models as models +
@@ -204,6 +207,10 @@ Eventsimport numpy as np +import torch +import torch_tensorrt as torch_trt +import torchvision.models as models np.random.seed(5) torch.manual_seed(5) @@ -542,7 +560,7 @@
Saving Mutable Torch TensorRT Module¶
@@ -157,6 +157,9 @@ ExecuTorchdiff --git a/docs/tutorials/_rendered_examples/dynamo/pre_allocated_output_example.html b/docs/tutorials/_rendered_examples/dynamo/pre_allocated_output_example.html index 9e2260de37..ebcac0814f 100644 --- a/docs/tutorials/_rendered_examples/dynamo/pre_allocated_output_example.html +++ b/docs/tutorials/_rendered_examples/dynamo/pre_allocated_output_example.html @@ -10,7 +10,7 @@ -# The LoRA checkpoint is from https://civitai.com/models/12597/moxin -from diffusers import DiffusionPipeline +from diffusers import DiffusionPipeline with torch.no_grad(): settings = { @@ -840,7 +858,7 @@
Resources
Developer Resources- - Contributor Awards - 2023 + Contributor Awards - 2024
@@ -856,6 +874,9 @@Resources
- ExecuTorch
+- + ExecuTorch Documentation +
- Events
+- + Newsletter +
- Governing Board
+- + Cloud Credit Program +
+- + Technical Advisory Council +
+- + Staff +
+- + Contact Us +
Pre-allocated output buffer — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation +Pre-allocated output buffer — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation @@ -135,8 +135,8 @@ Developer ResourcesFind resources and get questions answered
- - Contributor Awards - 2023 + + Contributor Awards - 2024Award winners announced at this year's PyTorch Conference
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+ + ExecuTorch Docs +Find events, webinars, and podcasts
+ + Newsletter +Stay up-to-date with the latest updates
+- v2.6.0.dev0+70e2a38 + v2.7.0.dev0+d6be4ba@@ -508,18 +526,18 @@Imports and Model Definition¶
-@@ -219,7 +226,18 @@ Governing Board - + + + Cloud Credit Program + + + Technical Advisory Council + + + Staff + + + Contact Usimport timeit +
import timeit -import numpy as np -import torch -import torch_tensorrt -from transformers import BertModel +import numpy as np +import torch +import torch_tensorrt +from transformers import BertModel
Define function to measure inference performance¶
-@@ -204,6 +207,10 @@ Eventsdef test_module_perf(model, *input): +
@@ -157,6 +157,9 @@ ExecuTorchdiff --git a/docs/tutorials/_rendered_examples/dynamo/refit_engine_example.html b/docs/tutorials/_rendered_examples/dynamo/refit_engine_example.html index 6d1d2057e7..1b9abcffb6 100644 --- a/docs/tutorials/_rendered_examples/dynamo/refit_engine_example.html +++ b/docs/tutorials/_rendered_examples/dynamo/refit_engine_example.html @@ -10,7 +10,7 @@ -def test_module_perf(model, *input): timings = [] # Warm-up phase to ensure consistent and accurate performance measurements. @@ -851,7 +869,7 @@
Resources
Developer Resources- - Contributor Awards - 2023 + Contributor Awards - 2024
@@ -867,6 +885,9 @@Resources
- ExecuTorch
+- + ExecuTorch Documentation +
- Events
+- + Newsletter +
- Governing Board
+- + Cloud Credit Program +
+- + Technical Advisory Council +
+- + Staff +
+- + Contact Us +
Refitting Torch-TensorRT Programs with New Weights — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation +Refitting Torch-TensorRT Programs with New Weights — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation @@ -135,8 +135,8 @@ Developer ResourcesFind resources and get questions answered
- - Contributor Awards - 2023 + + Contributor Awards - 2024Award winners announced at this year's PyTorch Conference
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+ + ExecuTorch Docs +Find events, webinars, and podcasts
+ + Newsletter +Stay up-to-date with the latest updates
+- v2.6.0.dev0+70e2a38 + v2.7.0.dev0+d6be4ba@@ -500,11 +518,11 @@Standard Workflow¶
Imports and model definition¶
-@@ -204,6 +207,10 @@ Eventsimport numpy as np -import torch -import torch_tensorrt as torch_trt -import torchvision.models as models -from torch_tensorrt.dynamo import refit_module_weights +
@@ -157,6 +157,9 @@ ExecuTorchdiff --git a/docs/tutorials/_rendered_examples/dynamo/torch_compile_advanced_usage.html b/docs/tutorials/_rendered_examples/dynamo/torch_compile_advanced_usage.html index 8a8d81d53e..56fa21e55e 100644 --- a/docs/tutorials/_rendered_examples/dynamo/torch_compile_advanced_usage.html +++ b/docs/tutorials/_rendered_examples/dynamo/torch_compile_advanced_usage.html @@ -10,7 +10,7 @@ -import numpy as np +import torch +import torch_tensorrt as torch_trt +import torchvision.models as models +from torch_tensorrt.dynamo import refit_module_weights np.random.seed(0) torch.manual_seed(0) @@ -862,7 +880,7 @@
Resources
Developer Resources- - Contributor Awards - 2023 + Contributor Awards - 2024
@@ -878,6 +896,9 @@Resources
- ExecuTorch
+- + ExecuTorch Documentation +
- Events
+- + Newsletter +
- Governing Board
+- + Cloud Credit Program +
+- + Technical Advisory Council +
+- + Staff +
+- + Contact Us +
Torch Compile Advanced Usage — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation +Torch Compile Advanced Usage — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation @@ -135,8 +135,8 @@ Developer ResourcesFind resources and get questions answered
- - Contributor Awards - 2023 + + Contributor Awards - 2024Award winners announced at this year's PyTorch Conference
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+ + ExecuTorch Docs +Find events, webinars, and podcasts
+ + Newsletter +Stay up-to-date with the latest updates
+- v2.6.0.dev0+70e2a38 + v2.7.0.dev0+d6be4ba@@ -481,17 +499,17 @@This interactive script is intended as an overview of the process by which torch_tensorrt.compile(…, ir=”torch_compile”, …) works, and how it integrates with the torch.compile API.
Imports and Model Definition¶
-@@ -204,6 +207,10 @@ Eventsimport torch -import torch_tensorrt +
import torch +import torch_tensorrt
@@ -157,6 +157,9 @@ ExecuTorchdiff --git a/docs/tutorials/_rendered_examples/dynamo/torch_compile_gpt2.html b/docs/tutorials/_rendered_examples/dynamo/torch_compile_gpt2.html index eef0527884..6652b1696c 100644 --- a/docs/tutorials/_rendered_examples/dynamo/torch_compile_gpt2.html +++ b/docs/tutorials/_rendered_examples/dynamo/torch_compile_gpt2.html @@ -10,7 +10,7 @@ -# We begin by defining a model -class Model(torch.nn.Module): - def __init__(self) -> None: +class Model(torch.nn.Module): + def __init__(self) -> None: super().__init__() self.relu = torch.nn.ReLU() - def forward(self, x: torch.Tensor, y: torch.Tensor): + def forward(self, x: torch.Tensor, y: torch.Tensor): x_out = self.relu(x) y_out = self.relu(y) x_y_out = x_out + y_out @@ -829,7 +847,7 @@
Resources
Developer Resources- - Contributor Awards - 2023 + Contributor Awards - 2024
@@ -845,6 +863,9 @@Resources
- ExecuTorch
+- + ExecuTorch Documentation +
- Events
+- + Newsletter +
- Governing Board
+- + Cloud Credit Program +
+- + Technical Advisory Council +
+- + Staff +
+- + Contact Us +
Compiling GPT2 using the Torch-TensorRT torch.compile frontend — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation +Compiling GPT2 using the Torch-TensorRT torch.compile frontend — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation @@ -135,8 +135,8 @@ Developer ResourcesFind resources and get questions answered
- - Contributor Awards - 2023 + + Contributor Awards - 2024Award winners announced at this year's PyTorch Conference
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+ + ExecuTorch Docs +Find events, webinars, and podcasts
Stay up-to-date with the latest updates
+- v2.6.0.dev0+70e2a38 + v2.7.0.dev0+d6be4ba@@ -487,9 +505,9 @@ get the graph module representation of the graph. Torch-TensorRT converts this graph into an optimized TensorRT engine.Import necessary libraries¶
-@@ -157,6 +157,9 @@ ExecuTorchdiff --git a/docs/tutorials/_rendered_examples/dynamo/torch_compile_resnet_example.html b/docs/tutorials/_rendered_examples/dynamo/torch_compile_resnet_example.html index 633b504bb3..dc33d782f9 100644 --- a/docs/tutorials/_rendered_examples/dynamo/torch_compile_resnet_example.html +++ b/docs/tutorials/_rendered_examples/dynamo/torch_compile_resnet_example.html @@ -10,7 +10,7 @@ -import torch -import torch_tensorrt -from transformers import AutoModelForCausalLM, AutoTokenizer +
@@ -848,7 +866,7 @@import torch +import torch_tensorrt +from transformers import AutoModelForCausalLM, AutoTokenizer
Resources
Developer Resources- - Contributor Awards - 2023 + Contributor Awards - 2024
@@ -864,6 +882,9 @@Resources
- ExecuTorch
+- + ExecuTorch Documentation +
- Events
+- + Newsletter +
- Governing Board
+- + Cloud Credit Program +
+- + Technical Advisory Council +
+- + Staff +
+- + Contact Us +
Compiling ResNet with dynamic shapes using the torch.compile backend — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation +Compiling ResNet with dynamic shapes using the torch.compile backend — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation @@ -135,8 +135,8 @@ Developer ResourcesFind resources and get questions answered
- - Contributor Awards - 2023 + + Contributor Awards - 2024Award winners announced at this year's PyTorch Conference
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+ + ExecuTorch Docs +Find events, webinars, and podcasts
+ + Newsletter +Stay up-to-date with the latest updates
+- v2.6.0.dev0+70e2a38 + v2.7.0.dev0+d6be4ba@@ -481,9 +499,9 @@This interactive script is intended as a sample of the Torch-TensorRT workflow with torch.compile on a ResNet model.
Imports and Model Definition¶
-@@ -204,6 +207,10 @@ Eventsimport torch -import torch_tensorrt -import torchvision.models as models +
import torch +import torch_tensorrt +import torchvision.models as models
@@ -157,6 +157,9 @@ ExecuTorchdiff --git a/docs/tutorials/_rendered_examples/dynamo/torch_compile_stable_diffusion.html b/docs/tutorials/_rendered_examples/dynamo/torch_compile_stable_diffusion.html index c30e8fa19a..3ead6247c7 100644 --- a/docs/tutorials/_rendered_examples/dynamo/torch_compile_stable_diffusion.html +++ b/docs/tutorials/_rendered_examples/dynamo/torch_compile_stable_diffusion.html @@ -10,7 +10,7 @@ -# Initialize model with half precision and sample inputs @@ -843,7 +861,7 @@
Resources
Developer Resources- - Contributor Awards - 2023 + Contributor Awards - 2024
@@ -859,6 +877,9 @@Resources
- ExecuTorch
+- + ExecuTorch Documentation +
- Events
+- + Newsletter +
- Governing Board
+- + Cloud Credit Program +
+- + Technical Advisory Council +
+- + Staff +
+- + Contact Us +
Compiling Stable Diffusion model using the torch.compile backend — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation +Compiling Stable Diffusion model using the torch.compile backend — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation @@ -135,8 +135,8 @@ Developer ResourcesFind resources and get questions answered
- - Contributor Awards - 2023 + + Contributor Awards - 2024Award winners announced at this year's PyTorch Conference
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+ + ExecuTorch Docs +Find events, webinars, and podcasts
Stay up-to-date with the latest updates
+ @@ -219,7 +226,18 @@ Governing Board - + + + Cloud Credit Program + + + Technical Advisory Council + + + Staff + + + Contact Us @@ -275,7 +293,7 @@- v2.6.0.dev0+70e2a38 + v2.7.0.dev0+d6be4ba@@ -482,9 +500,9 @@
Imports and Model Definition¶
-@@ -204,6 +207,10 @@ Eventsimport torch -import torch_tensorrt -from diffusers import DiffusionPipeline +
@@ -157,6 +157,9 @@ ExecuTorchdiff --git a/docs/tutorials/_rendered_examples/dynamo/torch_compile_transformers_example.html b/docs/tutorials/_rendered_examples/dynamo/torch_compile_transformers_example.html index c7b2b9a0b7..33b2385aae 100644 --- a/docs/tutorials/_rendered_examples/dynamo/torch_compile_transformers_example.html +++ b/docs/tutorials/_rendered_examples/dynamo/torch_compile_transformers_example.html @@ -10,7 +10,7 @@ -import torch +import torch_tensorrt +from diffusers import DiffusionPipeline model_id = "CompVis/stable-diffusion-v1-4" device = "cuda:0" @@ -773,7 +791,7 @@
Resources
Developer Resources- - Contributor Awards - 2023 + Contributor Awards - 2024
@@ -789,6 +807,9 @@Resources
- ExecuTorch
+- + ExecuTorch Documentation +
- Events
+- + Newsletter +
- Governing Board
+- + Cloud Credit Program +
+- + Technical Advisory Council +
+- + Staff +
+- + Contact Us +
Compiling BERT using the torch.compile backend — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation +Compiling BERT using the torch.compile backend — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation @@ -135,8 +135,8 @@ Developer ResourcesFind resources and get questions answered
- - Contributor Awards - 2023 + + Contributor Awards - 2024Award winners announced at this year's PyTorch Conference
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+ + ExecuTorch Docs +Find events, webinars, and podcasts
+ + Newsletter +Stay up-to-date with the latest updates
+ @@ -219,7 +226,18 @@ Governing Board - + + + Cloud Credit Program + + + Technical Advisory Council + + + Staff + + + Contact Us @@ -275,7 +293,7 @@- v2.6.0.dev0+70e2a38 + v2.7.0.dev0+d6be4ba@@ -481,9 +499,9 @@This interactive script is intended as a sample of the Torch-TensorRT workflow with torch.compile on a BERT model.
Imports and Model Definition¶
-@@ -204,6 +207,10 @@ Eventsimport torch -import torch_tensorrt -from transformers import BertModel +
import torch +import torch_tensorrt +from transformers import BertModel
@@ -157,6 +157,9 @@ ExecuTorchdiff --git a/docs/tutorials/_rendered_examples/dynamo/torch_export_cudagraphs.html b/docs/tutorials/_rendered_examples/dynamo/torch_export_cudagraphs.html index 877c7701af..6c63cb1a97 100644 --- a/docs/tutorials/_rendered_examples/dynamo/torch_export_cudagraphs.html +++ b/docs/tutorials/_rendered_examples/dynamo/torch_export_cudagraphs.html @@ -10,7 +10,7 @@ -# Initialize model with float precision and sample inputs @@ -832,7 +850,7 @@
Resources
Developer Resources- - Contributor Awards - 2023 + Contributor Awards - 2024
@@ -848,6 +866,9 @@Resources
- ExecuTorch
+- + ExecuTorch Documentation +
- Events
+- + Newsletter +
- Governing Board
+- + Cloud Credit Program +
+- + Technical Advisory Council +
+- + Staff +
+- + Contact Us +
Torch Export with Cudagraphs — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation +Torch Export with Cudagraphs — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation @@ -135,8 +135,8 @@ Developer ResourcesFind resources and get questions answered
- - Contributor Awards - 2023 + + Contributor Awards - 2024Award winners announced at this year's PyTorch Conference
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+ + ExecuTorch Docs +Find events, webinars, and podcasts
+ + Newsletter +Stay up-to-date with the latest updates
+ @@ -219,7 +226,18 @@ Governing Board - + + + Cloud Credit Program + + + Technical Advisory Council + + + Staff + + + Contact Us @@ -275,7 +293,7 @@- v2.6.0.dev0+70e2a38 + v2.7.0.dev0+d6be4ba@@ -481,9 +499,9 @@This interactive script is intended as an overview of the process by which the Torch-TensorRT Cudagraphs integration can be used in the ir=”dynamo” path. The functionality works similarly in the torch.compile path as well.
Imports and Model Definition¶
-@@ -275,7 +293,7 @@@@ -219,7 +226,18 @@ Governing Board - + + + Cloud Credit Program + + + Technical Advisory Council + + + Staff + + + Contact Usimport torch -import torch_tensorrt -import torchvision.models as models +
@@ -550,8 +568,8 @@import torch +import torch_tensorrt +import torchvision.models as models
Cuda graphs with module that contains graph breaks -
@@ -204,6 +207,10 @@ Eventsclass SampleModel(torch.nn.Module): - def forward(self, x): +
@@ -157,6 +157,9 @@ ExecuTorchdiff --git a/docs/tutorials/_rendered_examples/dynamo/torch_export_gpt2.html b/docs/tutorials/_rendered_examples/dynamo/torch_export_gpt2.html index 6ce47460a0..8e98176b8b 100644 --- a/docs/tutorials/_rendered_examples/dynamo/torch_export_gpt2.html +++ b/docs/tutorials/_rendered_examples/dynamo/torch_export_gpt2.html @@ -10,7 +10,7 @@ -class SampleModel(torch.nn.Module): + def forward(self, x): return torch.relu((x + 2) * 0.5) @@ -836,7 +854,7 @@
Resources
Developer Resources- - Contributor Awards - 2023 + Contributor Awards - 2024
@@ -852,6 +870,9 @@Resources
- ExecuTorch
+- + ExecuTorch Documentation +
- Events
+- + Newsletter +
- Governing Board
+- + Cloud Credit Program +
+- + Technical Advisory Council +
+- + Staff +
+- + Contact Us +
Compiling GPT2 using the dynamo backend — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation +Compiling GPT2 using the dynamo backend — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation @@ -135,8 +135,8 @@ Developer ResourcesFind resources and get questions answered
- - Contributor Awards - 2023 + + Contributor Awards - 2024Award winners announced at this year's PyTorch Conference
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+ + ExecuTorch Docs +Find events, webinars, and podcasts
+ + Newsletter +Stay up-to-date with the latest updates
+- v2.6.0.dev0+70e2a38 + v2.7.0.dev0+d6be4ba@@ -481,10 +499,10 @@This script illustrates Torch-TensorRT workflow with dynamo backend on popular GPT2 model.
Imports and Model Definition¶
-@@ -204,6 +207,10 @@ Eventsimport torch -import torch_tensorrt -from transformers import AutoModelForCausalLM, AutoTokenizer -from utils import export_llm, generate +
import torch +import torch_tensorrt +from transformers import AutoModelForCausalLM, AutoTokenizer +from utils import export_llm, generate
@@ -157,6 +157,9 @@ ExecuTorchdiff --git a/docs/tutorials/_rendered_examples/dynamo/torch_export_llama2.html b/docs/tutorials/_rendered_examples/dynamo/torch_export_llama2.html index d24b7872cc..830b37ed46 100644 --- a/docs/tutorials/_rendered_examples/dynamo/torch_export_llama2.html +++ b/docs/tutorials/_rendered_examples/dynamo/torch_export_llama2.html @@ -10,7 +10,7 @@ -# Define the parameters and initialize the model @@ -823,7 +841,7 @@
Resources
Developer Resources- - Contributor Awards - 2023 + Contributor Awards - 2024
@@ -839,6 +857,9 @@Resources
- ExecuTorch
+- + ExecuTorch Documentation +
- Events
+- + Newsletter +
- Governing Board
+- + Cloud Credit Program +
+- + Technical Advisory Council +
+- + Staff +
+- + Contact Us +
Compiling Llama2 using the dynamo backend — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation +Compiling Llama2 using the dynamo backend — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation @@ -135,8 +135,8 @@ Developer ResourcesFind resources and get questions answered
- - Contributor Awards - 2023 + + Contributor Awards - 2024Award winners announced at this year's PyTorch Conference
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+ + ExecuTorch Docs +Find events, webinars, and podcasts
+ + Newsletter +Stay up-to-date with the latest updates
+ @@ -219,7 +226,18 @@ Governing Board - + + + Cloud Credit Program + + + Technical Advisory Council + + + Staff + + + Contact Us @@ -275,7 +293,7 @@- v2.6.0.dev0+70e2a38 + v2.7.0.dev0+d6be4ba@@ -481,10 +499,10 @@This script illustrates Torch-TensorRT workflow with dynamo backend on popular Llama2 model.
Imports and Model Definition¶
-@@ -157,6 +157,9 @@ ExecuTorchdiff --git a/docs/tutorials/_rendered_examples/dynamo/torch_export_sam2.html b/docs/tutorials/_rendered_examples/dynamo/torch_export_sam2.html index 71b3dad2a2..cfbd7fe9ef 100644 --- a/docs/tutorials/_rendered_examples/dynamo/torch_export_sam2.html +++ b/docs/tutorials/_rendered_examples/dynamo/torch_export_sam2.html @@ -10,7 +10,7 @@ -import torch -import torch_tensorrt -from transformers import AutoModelForCausalLM, AutoTokenizer -from utils import export_llm, generate +
import torch +import torch_tensorrt +from transformers import AutoModelForCausalLM, AutoTokenizer +from utils import export_llm, generate
Define the parameters and initialize the model
@@ -829,7 +847,7 @@Resources
Developer Resources- - Contributor Awards - 2023 + Contributor Awards - 2024
@@ -845,6 +863,9 @@Resources
- ExecuTorch
+- + ExecuTorch Documentation +
- Events
+- + Newsletter +
- Governing Board
+- + Cloud Credit Program +
+- + Technical Advisory Council +
+- + Staff +
+- + Contact Us +
Compiling SAM2 using the dynamo backend — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation +Compiling SAM2 using the dynamo backend — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation @@ -135,8 +135,8 @@ Developer ResourcesFind resources and get questions answered
- - Contributor Awards - 2023 + + Contributor Awards - 2024Award winners announced at this year's PyTorch Conference
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+ + ExecuTorch Docs + @@ -204,6 +207,10 @@ EventsFind events, webinars, and podcasts
+ + Newsletter +Stay up-to-date with the latest updates
+ @@ -219,7 +226,18 @@ Governing Board - + + + Cloud Credit Program + + + Technical Advisory Council + + + Staff + + + Contact Us @@ -275,7 +293,7 @@- v2.6.0.dev0+70e2a38 + v2.7.0.dev0+d6be4ba@@ -495,15 +513,15 @@Import the following libraries¶
-@@ -217,7 +224,18 @@ Governing Board - + + + Cloud Credit Program + + + Technical Advisory Council + + + Staff + + + Contact Us @@ -273,7 +291,7 @@@@ -202,6 +205,10 @@ Eventsimport matplotlib -import matplotlib.pyplot as plt -import numpy as np -import pandas as pd -import torch -import torch_tensorrt -from PIL import Image -from sam2.sam2_image_predictor import SAM2ImagePredictor -from sam_components import SAM2FullModel +
@@ -521,8 +539,8 @@import matplotlib +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +import torch +import torch_tensorrt +from PIL import Image +from sam2.sam2_image_predictor import SAM2ImagePredictor +from sam_components import SAM2FullModel matplotlib.use("Agg")
Define the SAM2 modelSAM2FullModel which uses these utilities from
SAM2ImagePredictorclass.SAM2FullModelperforms feature extraction and mask prediction in a single step instead of two step process ofSAM2ImagePredictor(set_image and predict functions) -@@ -155,6 +155,9 @@ ExecuTorchdiff --git a/docs/tutorials/_rendered_examples/triton/index.html b/docs/tutorials/_rendered_examples/triton/index.html index 8a4211673c..e32a5dbdbd 100644 --- a/docs/tutorials/_rendered_examples/triton/index.html +++ b/docs/tutorials/_rendered_examples/triton/index.html @@ -10,7 +10,7 @@ -class SAM2FullModel(torch.nn.Module): - def __init__(self, model): +
@@ -273,7 +291,7 @@class SAM2FullModel(torch.nn.Module): + def __init__(self, model): super().__init__() self.image_encoder = model.forward_image self._prepare_backbone_features = model._prepare_backbone_features @@ -535,7 +553,7 @@
Define the SAM2 modelself._bb_feat_sizes = [(256, 256), (128, 128), (64, 64)] - def forward(self, image, point_coords, point_labels): + def forward(self, image, point_coords, point_labels): backbone_out = self.image_encoder(image) _, vision_feats, _, _ = self._prepare_backbone_features(backbone_out) @@ -602,7 +620,7 @@
Preprocessing componentshttps://github.com/facebookresearch/sam2/blob/main/sam2/utils/transforms.py -
Governing Board - + + + Cloud Credit Program + + + Technical Advisory Council + + + Staff + + + Contact Us@@ -217,7 +224,18 @@@@ -275,7 +293,7 @@def preprocess_inputs(image, predictor): +
@@ -219,7 +226,18 @@ Governing Board - + + + Cloud Credit Program + + + Technical Advisory Council + + + Staff + + + Contact Usdef preprocess_inputs(image, predictor): w, h = image.size orig_hw = [(h, w)] input_image = predictor._transforms(np.array(image))[None, ...].to("cuda:0") @@ -631,7 +649,7 @@
Preprocessing components¶
The following functions implement postprocessing components which include plotting and visualizing masks and points. We use the SAM2Transforms to post process these masks and sort them via confidence score.
-@@ -204,6 +207,10 @@ Eventsdef postprocess_masks(out, predictor, image): +
@@ -157,6 +157,9 @@ ExecuTorchdiff --git a/docs/tutorials/_rendered_examples/dynamo/vgg16_ptq.html b/docs/tutorials/_rendered_examples/dynamo/vgg16_ptq.html index 00842e68bd..773269a045 100644 --- a/docs/tutorials/_rendered_examples/dynamo/vgg16_ptq.html +++ b/docs/tutorials/_rendered_examples/dynamo/vgg16_ptq.html @@ -10,7 +10,7 @@ -def postprocess_masks(out, predictor, image): """Postprocess low-resolution masks and convert them for visualization.""" orig_hw = (image.size[1], image.size[0]) # (height, width) masks = predictor._transforms.postprocess_masks(out["low_res_masks"], orig_hw) @@ -641,7 +659,7 @@
Post Processing componentsreturn masks[sorted_indices], scores[sorted_indices] -def show_mask(mask, ax, random_color=False, borders=True): +def show_mask(mask, ax, random_color=False, borders=True): if random_color: color = np.concatenate([np.random.random(3), np.array([0.6])], axis=0) else: @@ -650,7 +668,7 @@
Post Processing componentsmask = mask.astype(np.uint8) mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1) if borders: - import cv2 + import cv2 contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE) # Try to smooth contours @@ -663,7 +681,7 @@
Post Processing componentsax.imshow(mask_image) -def show_points(coords, labels, ax, marker_size=375): +def show_points(coords, labels, ax, marker_size=375): pos_points = coords[labels == 1] neg_points = coords[labels == 0] ax.scatter( @@ -686,7 +704,7 @@
Post Processing components) -def visualize_masks( +def visualize_masks( image, masks, scores, point_coords, point_labels, title_prefix="", save=True ): """Visualize and save masks overlaid on the original image.""" @@ -1016,7 +1034,7 @@
Resources
Developer Resources- - Contributor Awards - 2023 + Contributor Awards - 2024
@@ -1032,6 +1050,9 @@Resources
- ExecuTorch
+- + ExecuTorch Documentation +
- Events
+- + Newsletter +
- Governing Board
+- + Cloud Credit Program +
+- + Technical Advisory Council +
+- + Staff +
+- + Contact Us +
Deploy Quantized Models using Torch-TensorRT — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation +Deploy Quantized Models using Torch-TensorRT — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation @@ -135,8 +135,8 @@ Developer ResourcesFind resources and get questions answered
- - Contributor Awards - 2023 + + Contributor Awards - 2024Award winners announced at this year's PyTorch Conference
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+ + ExecuTorch Docs +Find events, webinars, and podcasts
+ + Newsletter +Stay up-to-date with the latest updates
+- v2.6.0.dev0+70e2a38 + v2.7.0.dev0+d6be4ba@@ -481,20 +499,20 @@Here we demonstrate how to deploy a model quantized to INT8 or FP8 using the Dynamo frontend of Torch-TensorRT
Imports and Model Definition¶
-@@ -275,7 +293,7 @@import argparse +
@@ -219,7 +226,18 @@ Governing Board - + + + Cloud Credit Program + + + Technical Advisory Council + + + Staff + + + Contact Usimport argparse -import modelopt.torch.quantization as mtq -import torch -import torch.nn as nn -import torch.nn.functional as F -import torch_tensorrt as torchtrt -import torchvision.datasets as datasets -import torchvision.transforms as transforms -from modelopt.torch.quantization.utils import export_torch_mode +import modelopt.torch.quantization as mtq +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch_tensorrt as torchtrt +import torchvision.datasets as datasets +import torchvision.transforms as transforms +from modelopt.torch.quantization.utils import export_torch_mode -class VGG(nn.Module): - def __init__(self, layer_spec, num_classes=1000, init_weights=False): +class VGG(nn.Module): + def __init__(self, layer_spec, num_classes=1000, init_weights=False): super(VGG, self).__init__() layers = [] @@ -524,7 +542,7 @@
Imports and Model Definitionif init_weights: self._initialize_weights() - def _initialize_weights(self): + def _initialize_weights(self): for m in self.modules(): if isinstance(m, nn.Conv2d): nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu") @@ -537,7 +555,7 @@
Imports and Model Definitionnn.init.normal_(m.weight, 0, 0.01) nn.init.constant_(m.bias, 0) - def forward(self, x): + def forward(self, x): x = self.features(x) x = self.avgpool(x) x = torch.flatten(x, 1) @@ -545,7 +563,7 @@
Imports and Model Definitionreturn x -def vgg16(num_classes=1000, init_weights=False): +def vgg16(num_classes=1000, init_weights=False): vgg16_cfg = [ 64, 64, @@ -600,7 +618,7 @@
Load the pre-trained model weightsweights = ckpt["model_state_dict"] if torch.cuda.device_count() > 1: - from collections import OrderedDict + from collections import OrderedDict new_state_dict = OrderedDict() for k, v in weights.items(): @@ -646,7 +664,7 @@
Load training dataset and define loss function for PTQ
Define Calibration Loop for quantization¶
-@@ -204,6 +207,10 @@ Eventsdef calibrate_loop(model): +
@@ -157,6 +157,9 @@ ExecuTorchdiff --git a/docs/tutorials/_rendered_examples/dynamo/weight_streaming_example.html b/docs/tutorials/_rendered_examples/dynamo/weight_streaming_example.html index bd71e399eb..5926433ce7 100644 --- a/docs/tutorials/_rendered_examples/dynamo/weight_streaming_example.html +++ b/docs/tutorials/_rendered_examples/dynamo/weight_streaming_example.html @@ -10,7 +10,7 @@ -def calibrate_loop(model): # calibrate over the training dataset total = 0 correct = 0 @@ -703,7 +721,7 @@
Inference# Compile the model with Torch-TensorRT Dynamo backend input_tensor = images.cuda() # torch.export.export() failed due to RuntimeError: Attempting to use FunctionalTensor on its own. Instead, please use it with a corresponding FunctionalTensorMode() - from torch.export._trace import _export + from torch.export._trace import _export exp_program = _export(model, (input_tensor,)) if args.quantize_type == "int8": @@ -1001,7 +1019,7 @@
Resources
Developer Resources- - Contributor Awards - 2023 + Contributor Awards - 2024
@@ -1017,6 +1035,9 @@Resources
- ExecuTorch
+- + ExecuTorch Documentation +
- Events
+- + Newsletter +
- Governing Board
+- + Cloud Credit Program +
+- + Technical Advisory Council +
+- + Staff +
+- + Contact Us +
Weight Streaming — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation +Weight Streaming — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation @@ -135,8 +135,8 @@ Developer ResourcesFind resources and get questions answered
- - Contributor Awards - 2023 + + Contributor Awards - 2024Award winners announced at this year's PyTorch Conference
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+ + ExecuTorch Docs +Find events, webinars, and podcasts
Stay up-to-date with the latest updates
+- v2.6.0.dev0+70e2a38 + v2.7.0.dev0+d6be4ba@@ -497,17 +515,17 @@Imports and Model Definition¶
-@@ -202,6 +205,10 @@ Eventsimport copy -import timeit +
@@ -155,6 +155,9 @@ ExecuTorchdiff --git a/docs/tutorials/_rendered_examples/index.html b/docs/tutorials/_rendered_examples/index.html index b79b764823..b562b08eed 100644 --- a/docs/tutorials/_rendered_examples/index.html +++ b/docs/tutorials/_rendered_examples/index.html @@ -10,7 +10,7 @@ -import copy +import timeit -import numpy as np -import torch -import torch_tensorrt -from transformers import AutoModelForCausalLM -from utils import export_llm +import numpy as np +import torch +import torch_tensorrt +from transformers import AutoModelForCausalLM +from utils import export_llm -def time_generate(model, inputs, output_seq_length, iterations=10): +def time_generate(model, inputs, output_seq_length, iterations=10): """ Measure the time for generating a sentence over certain number of iterations """ @@ -898,7 +916,7 @@
Resources
Developer Resources- - Contributor Awards - 2023 + Contributor Awards - 2024
@@ -914,6 +932,9 @@Resources
- ExecuTorch
+- + ExecuTorch Documentation +
- Events
+- + Newsletter +
- Governing Board
+- + Cloud Credit Program +
+- + Technical Advisory Council +
+- + Staff +
+- + Contact Us +
Torch-TensorRT Tutorials — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation +Torch-TensorRT Tutorials — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation @@ -133,8 +133,8 @@ Developer ResourcesFind resources and get questions answered
- - Contributor Awards - 2023 + + Contributor Awards - 2024Award winners announced at this year's PyTorch Conference
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+ + ExecuTorch Docs +Find events, webinars, and podcasts
+ + Newsletter +Stay up-to-date with the latest updates
+- v2.6.0.dev0+70e2a38 + v2.7.0.dev0+d6be4ba@@ -999,7 +1017,7 @@Resources
Developer Resources- - Contributor Awards - 2023 + Contributor Awards - 2024
@@ -1015,6 +1033,9 @@Resources
- ExecuTorch
+- + ExecuTorch Documentation +
- Events
+- + Newsletter +
- Governing Board
+- + Cloud Credit Program +
+- + Technical Advisory Council +
+- + Staff +
+- + Contact Us +
Serving a Torch-TensorRT model with Triton — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation +Serving a Torch-TensorRT model with Triton — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation @@ -133,8 +133,8 @@ Developer ResourcesFind resources and get questions answered
- - Contributor Awards - 2023 + + Contributor Awards - 2024Award winners announced at this year's PyTorch Conference
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+ + ExecuTorch Docs +Find events, webinars, and podcasts
+ + Newsletter +Stay up-to-date with the latest updates
+- v2.6.0.dev0+70e2a38 + v2.7.0.dev0+d6be4ba@@ -900,7 +918,7 @@Resources
Developer Resources- - Contributor Awards - 2023 + Contributor Awards - 2024
@@ -916,6 +934,9 @@Resources
- ExecuTorch
+- + ExecuTorch Documentation +
- Events
+- + Newsletter +
- Governing Board
+- + Cloud Credit Program +
+- + Technical Advisory Council +
+- + Staff +
+- + Contact Us +
diff --git a/docs/tutorials/notebooks.html b/docs/tutorials/notebooks.html index bd562e28f3..a3ded7a6a0 100644 --- a/docs/tutorials/notebooks.html +++ b/docs/tutorials/notebooks.html @@ -10,7 +10,7 @@ -Legacy notebooks — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation +Legacy notebooks — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation @@ -135,8 +135,8 @@ Developer ResourcesFind resources and get questions answered
- - Contributor Awards - 2023 + + Contributor Awards - 2024Award winners announced at this year's PyTorch Conference
@@ -157,6 +157,9 @@ ExecuTorchEnd-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+ + ExecuTorch Docs + @@ -204,6 +207,10 @@ EventsFind events, webinars, and podcasts
+ + Newsletter +Stay up-to-date with the latest updates
+ @@ -219,7 +226,18 @@ Governing Board - + + + Cloud Credit Program + + + Technical Advisory Council + + + Staff + + + Contact Us @@ -275,7 +293,7 @@- v2.6.0.dev0+70e2a38 + v2.7.0.dev0+d6be4ba@@ -863,7 +881,7 @@Resources
Developer Resources- - Contributor Awards - 2023 + Contributor Awards - 2024
@@ -879,6 +897,9 @@Resources
- ExecuTorch
+- + ExecuTorch Documentation +
- Events
+- + Newsletter +
- Governing Board
+- + Cloud Credit Program +
+- + Technical Advisory Council +
+- + Staff +
+- + Contact Us +
diff --git a/docs/tutorials/serving_torch_tensorrt_with_triton.html b/docs/tutorials/serving_torch_tensorrt_with_triton.html index acd474e4ec..ff04b70b88 100644 --- a/docs/tutorials/serving_torch_tensorrt_with_triton.html +++ b/docs/tutorials/serving_torch_tensorrt_with_triton.html @@ -10,7 +10,7 @@ -Serving a Torch-TensorRT model with Triton — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation +Serving a Torch-TensorRT model with Triton — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation @@ -135,8 +135,8 @@ Developer ResourcesFind resources and get questions answered
- - Contributor Awards - 2023 + + Contributor Awards - 2024Award winners announced at this year's PyTorch Conference
@@ -157,6 +157,9 @@ ExecuTorchEnd-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+ + ExecuTorch Docs + @@ -204,6 +207,10 @@ EventsFind events, webinars, and podcasts
+ + Newsletter +Stay up-to-date with the latest updates
+ @@ -219,7 +226,18 @@ Governing Board - + + + Cloud Credit Program + + + Technical Advisory Council + + + Staff + + + Contact Us @@ -275,7 +293,7 @@- v2.6.0.dev0+70e2a38 + v2.7.0.dev0+d6be4ba@@ -911,7 +929,7 @@Resources
Developer Resources- - Contributor Awards - 2023 + Contributor Awards - 2024
@@ -927,6 +945,9 @@Resources
- ExecuTorch
+- + ExecuTorch Documentation +
- Events
+- + Newsletter +
- Governing Board
+- + Cloud Credit Program +
+- + Technical Advisory Council +
+- + Staff +
+- + Contact Us +
diff --git a/docs/user_guide/dynamic_shapes.html b/docs/user_guide/dynamic_shapes.html index 089455645d..8029fbb06a 100644 --- a/docs/user_guide/dynamic_shapes.html +++ b/docs/user_guide/dynamic_shapes.html @@ -10,7 +10,7 @@ -Dynamic shapes with Torch-TensorRT — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation +Dynamic shapes with Torch-TensorRT — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation @@ -135,8 +135,8 @@ Developer ResourcesFind resources and get questions answered
- - Contributor Awards - 2023 + + Contributor Awards - 2024Award winners announced at this year's PyTorch Conference
@@ -157,6 +157,9 @@ ExecuTorchEnd-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+ + ExecuTorch Docs + @@ -204,6 +207,10 @@ EventsFind events, webinars, and podcasts
+ + Newsletter +Stay up-to-date with the latest updates
+ @@ -219,7 +226,18 @@ Governing Board - + + + Cloud Credit Program + + + Technical Advisory Council + + + Staff + + + Contact Us @@ -275,7 +293,7 @@- v2.6.0.dev0+70e2a38 + v2.7.0.dev0+d6be4ba@@ -480,8 +498,8 @@Dynamic shapes using torch.export (AOT)ir=dynamo with
ir=tsand the behavior is exactly the same. -@@ -157,6 +157,9 @@ ExecuTorchdiff --git a/docs/user_guide/saving_models.html b/docs/user_guide/saving_models.html index 4db68ce81d..222ef9711b 100644 --- a/docs/user_guide/saving_models.html +++ b/docs/user_guide/saving_models.html @@ -10,7 +10,7 @@ -import torch -import torch_tensorrt +
@@ -275,7 +293,7 @@import torch +import torch_tensorrt model = MyModel().eval().cuda() # Compile with static shapes @@ -518,14 +536,14 @@
Custom Dynamic Shape Constraintsdocumentation to export the Pytorch module with dynamic shapes. Here’s a simple example that exports a matmul layer with some restrictions on dynamic dimensions. -
@@ -219,7 +226,18 @@ Governing Board - + + + Cloud Credit Program + + + Technical Advisory Council + + + Staff + + + Contact Us@@ -275,7 +293,7 @@import torch -import torch_tensorrt +
@@ -219,7 +226,18 @@ Governing Board - + + + Cloud Credit Program + + + Technical Advisory Council + + + Staff + + + Contact Usimport torch +import torch_tensorrt -class MatMul(torch.nn.Module): - def __init__(self): +class MatMul(torch.nn.Module): + def __init__(self): super().__init__() - def forward(self, query, key): + def forward(self, query, key): attn_weight = torch.matmul(query, key.transpose(-1, -2)) return attn_weight @@ -546,8 +564,8 @@
Dynamic shapes using torch.compile (JIT)torch_tensorrt.compile(model, inputs, ir="torch_compile") returns a torch.compile boxed function with the backend configured to TensorRT. In the case of
ir=torch_compile, users can provide dynamic shape information for the inputs usingtorch._dynamo.mark_dynamicAPI (https://pytorch.org/docs/stable/torch.compiler_dynamic_shapes.html) to avoid recompilation of TensorRT engines. -@@ -204,6 +207,10 @@ Eventsimport torch -import torch_tensorrt +
@@ -157,6 +157,9 @@ ExecuTorchdiff --git a/docs/user_guide/mixed_precision.html b/docs/user_guide/mixed_precision.html index aa7697dcdd..644b0d2ec9 100644 --- a/docs/user_guide/mixed_precision.html +++ b/docs/user_guide/mixed_precision.html @@ -10,7 +10,7 @@ -import torch +import torch_tensorrt model = MyModel().eval().cuda() inputs = torch.randn((1, 3, 224, 224), dtype=float32) @@ -808,7 +826,7 @@
Resources
Developer Resources- - Contributor Awards - 2023 + Contributor Awards - 2024
@@ -824,6 +842,9 @@Resources
- ExecuTorch
+- + ExecuTorch Documentation +
- Events
+- + Newsletter +
- Governing Board
+- + Cloud Credit Program +
+- + Technical Advisory Council +
+- + Staff +
+- + Contact Us +
Compile Mixed Precision models with Torch-TensorRT — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation +Compile Mixed Precision models with Torch-TensorRT — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation @@ -135,8 +135,8 @@ Developer ResourcesFind resources and get questions answered
- - Contributor Awards - 2023 + + Contributor Awards - 2024Award winners announced at this year's PyTorch Conference
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+ + ExecuTorch Docs +Find events, webinars, and podcasts
+ + Newsletter +Stay up-to-date with the latest updates
+- v2.6.0.dev0+70e2a38 + v2.7.0.dev0+d6be4ba@@ -474,14 +492,14 @@Compile Mixed Precision models with Torch-TensorRT¶
Consider the following Pytorch model which explicitly casts intermediate layer to run in FP16.
-@@ -204,6 +207,10 @@ Eventsclass MyModule(torch.nn.Module): - def __init__(self): +
@@ -157,6 +157,9 @@ ExecuTorchdiff --git a/docs/user_guide/runtime.html b/docs/user_guide/runtime.html index 8a2d5cebbd..72c83c617d 100644 --- a/docs/user_guide/runtime.html +++ b/docs/user_guide/runtime.html @@ -10,7 +10,7 @@ -class MyModule(torch.nn.Module): + def __init__(self): super().__init__() self.linear1 = torch.nn.Linear(10,10) self.linear2 = torch.nn.Linear(10,30).half() self.linear3 = torch.nn.Linear(30,40) - def forward(self, x): + def forward(self, x): x = self.linear1(x) x = x.to(torch.float16) x = self.linear2(x) @@ -769,7 +787,7 @@
Resources
Developer Resources- - Contributor Awards - 2023 + Contributor Awards - 2024
@@ -785,6 +803,9 @@Resources
- ExecuTorch
+- + ExecuTorch Documentation +
- Events
+- + Newsletter +
- Governing Board
+- + Cloud Credit Program +
+- + Technical Advisory Council +
+- + Staff +
+- + Contact Us +
Deploying Torch-TensorRT Programs — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation +Deploying Torch-TensorRT Programs — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation @@ -135,8 +135,8 @@ Developer ResourcesFind resources and get questions answered
- - Contributor Awards - 2023 + + Contributor Awards - 2024Award winners announced at this year's PyTorch Conference
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+ + ExecuTorch Docs +Find events, webinars, and podcasts
+ + Newsletter +Stay up-to-date with the latest updates
+- v2.6.0.dev0+70e2a38 + v2.7.0.dev0+d6be4ba@@ -799,7 +817,7 @@Resources
Developer Resources- - Contributor Awards - 2023 + Contributor Awards - 2024
@@ -815,6 +833,9 @@Resources
- ExecuTorch
+- + ExecuTorch Documentation +
- Events
+- + Newsletter +
- Governing Board
+- + Cloud Credit Program +
+- + Technical Advisory Council +
+- + Staff +
+- + Contact Us +
Saving models compiled with Torch-TensorRT — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation +Saving models compiled with Torch-TensorRT — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation @@ -135,8 +135,8 @@ Developer ResourcesFind resources and get questions answered
- - Contributor Awards - 2023 + + Contributor Awards - 2024Award winners announced at this year's PyTorch Conference
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+ + ExecuTorch Docs + @@ -204,6 +207,10 @@ EventsFind events, webinars, and podcasts
+ + Newsletter +Stay up-to-date with the latest updates
+ @@ -219,7 +226,18 @@ Governing Board - + + + Cloud Credit Program + + + Technical Advisory Council + + + Staff + + + Contact Us @@ -275,7 +293,7 @@- v2.6.0.dev0+70e2a38 + v2.7.0.dev0+d6be4ba@@ -486,13 +504,13 @@Dynamo IR
a) ExportedProgram¶
Here’s an example usage
-diff --git a/docsrc/user_guide/saving_models.rst b/docsrc/user_guide/saving_models.rst index 42cff7b954..dc4b5da222 100644 --- a/docsrc/user_guide/saving_models.rst +++ b/docsrc/user_guide/saving_models.rst @@ -34,7 +34,7 @@ Here's an example usage model = MyModel().eval().cuda() inputs = [torch.randn((1, 3, 224, 224)).cuda()] # trt_ep is a torch.fx.GraphModule object - trt_gm = torch_tensorrt.compile(model, ir="dynamo", inputs) + trt_gm = torch_tensorrt.compile(model, ir="dynamo", inputs=inputs) torch_tensorrt.save(trt_gm, "trt.ep", inputs=inputs) # Later, you can load it and run inference @@ -52,7 +52,7 @@ b) Torchscript model = MyModel().eval().cuda() inputs = [torch.randn((1, 3, 224, 224)).cuda()] # trt_gm is a torch.fx.GraphModule object - trt_gm = torch_tensorrt.compile(model, ir="dynamo", inputs) + trt_gm = torch_tensorrt.compile(model, ir="dynamo", inputs=inputs) torch_tensorrt.save(trt_gm, "trt.ts", output_format="torchscript", inputs=inputs) # Later, you can load it and run inference @@ -73,7 +73,7 @@ For `ir=ts`, this behavior stays the same in 2.X versions as well. model = MyModel().eval().cuda() inputs = [torch.randn((1, 3, 224, 224)).cuda()] - trt_ts = torch_tensorrt.compile(model, ir="ts", inputs) # Output is a ScriptModule object + trt_ts = torch_tensorrt.compile(model, ir="ts", inputs=inputs) # Output is a ScriptModule object torch.jit.save(trt_ts, "trt_model.ts") # Later, you can load it and run inference diff --git a/examples/distributed_inference/README.md b/examples/distributed_inference/README.md index 4c88570b6f..d4cf9508e1 100644 --- a/examples/distributed_inference/README.md +++ b/examples/distributed_inference/README.md @@ -14,3 +14,37 @@ See the examples started with `data_parallel` for more details. Here we use torch.distributed as an example, but compilation with tensor parallelism is agnostic to the implementation framework as long as the module is properly sharded. torchrun --nproc_per_node=2 tensor_parallel_llama2.py + +3. Tensor parallel distributed inference using nccl ops plugin + + apt install libmpich-dev + + apt install libopenmpi-dev + + #For python3.10 + + pip install tensorrt-llm + + For other python versions, you need to load the libnvinfer_plugin_tensorrt_llm.so. Please set that in the environment variable export TRTLLM_PLUGINS_PATH={lib_path}. For example, we have already set the variable in initialize_distributed_env(). You can replace this with your TRTLLM_PLUGINS_PATH and unset it there + + #then pip install the tensorrt and torch version compatible with installed torchTRT + + mpirun -n 2 --allow-run-as-root python tensor_parallel_simple_example.py + + #For other python + +4. Tensor parallel distributed llama3 inference using nccl ops plugin + + apt install libmpich-dev + + apt install libopenmpi-dev + +#For python3.10 + + pip install tensorrt-llm + + For other python versions, you need to load the libnvinfer_plugin_tensorrt_llm.so + + #then pip install the tensorrt and torch version compatible with installed torchTRT + + mpirun -n 2 --allow-run-as-root python tensor_parallel_llama3.py diff --git a/examples/distributed_inference/requirement.txt b/examples/distributed_inference/requirement.txt index 6d8e0aa9f2..bb3b0f28f1 100644 --- a/examples/distributed_inference/requirement.txt +++ b/examples/distributed_inference/requirement.txt @@ -1,3 +1,4 @@ accelerate transformers -diffusers \ No newline at end of file +diffusers +tensorrt-llm \ No newline at end of file diff --git a/examples/distributed_inference/tensor_parallel_initialize_dist.py b/examples/distributed_inference/tensor_parallel_initialize_dist.py new file mode 100644 index 0000000000..21e4cbc282 --- /dev/null +++ b/examples/distributed_inference/tensor_parallel_initialize_dist.py @@ -0,0 +1,67 @@ +import logging +import os +from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union + +import numpy as np +import tensorrt as trt +import torch +import torch.distributed as dist +from torch.distributed._tensor.device_mesh import init_device_mesh + + +def find_repo_root(max_depth=10): + dir_path = os.path.dirname(os.path.realpath(__file__)) + for i in range(max_depth): + files = os.listdir(dir_path) + if "MODULE.bazel" in files: + return dir_path + else: + dir_path = os.path.dirname(dir_path) + + raise RuntimeError("Could not find repo root") + + +def initialize_logger(rank, logger_file_name): + logger = logging.getLogger() + logger.setLevel(logging.INFO) + fh = logging.FileHandler(logger_file_name + f"_{rank}.log", mode="w") + fh.setLevel(logging.INFO) + logger.addHandler(fh) + return logger + + +# This is required for env initialization since we use mpirun +def initialize_distributed_env(logger_file_name, rank=0, world_size=1, port=29500): + local_rank = int( + os.environ.get("OMPI_COMM_WORLD_LOCAL_RANK", rank % torch.cuda.device_count()) + ) + world_size = int(os.environ.get("OMPI_COMM_WORLD_SIZE", world_size)) + + # Set up environment variable to run with mpirun + os.environ["RANK"] = str(local_rank) + os.environ["WORLD_SIZE"] = str(world_size) + os.environ["MASTER_ADDR"] = "127.0.0.1" + os.environ["MASTER_PORT"] = str(port) + os.environ["TRTLLM_PLUGINS_PATH"] = ( + find_repo_root() + "/lib/libnvinfer_plugin_tensorrt_llm.so" + ) + + # Necessary to assign a device to each rank. + torch.cuda.set_device(local_rank) + + # We use nccl backend + dist.init_process_group("nccl") + + # set a manual seed for reproducibility + torch.manual_seed(1111) + + device_mesh = init_device_mesh(device_type="cuda", mesh_shape=(world_size,)) + rank = device_mesh.get_rank() + assert rank == local_rank + logger = initialize_logger(rank, logger_file_name) + device_id = ( + rank % torch.cuda.device_count() + ) # Ensure each rank gets a unique device + torch.cuda.set_device(device_id) + + return device_mesh, world_size, rank, logger diff --git a/examples/distributed_inference/tensor_parallel_llama3.py b/examples/distributed_inference/tensor_parallel_llama3.py index fc03a64386..998c378be2 100644 --- a/examples/distributed_inference/tensor_parallel_llama3.py +++ b/examples/distributed_inference/tensor_parallel_llama3.py @@ -5,27 +5,25 @@ import time import torch -import torch_tensorrt from llama3_model import ModelArgs, ParallelTransformer +from tensor_parallel_initialize_dist import initialize_distributed_env from torch.distributed._composable.fsdp import MixedPrecisionPolicy from torch.distributed._composable.fsdp.fully_shard import fully_shard from torch.distributed._tensor import Replicate, Shard from torch.distributed.algorithms._checkpoint.checkpoint_wrapper import ( checkpoint_wrapper, ) -from torch.distributed.device_mesh import DeviceMesh, init_device_mesh -_rank = int(os.environ["RANK"]) -_world_size = int(os.environ["WORLD_SIZE"]) -tp_size = 2 - -logger = logging.getLogger() -logger.setLevel(logging.INFO) -fh = logging.FileHandler(f"./tensor_parallel_log_{_rank}.log", mode="w") -fh.setLevel(logging.INFO) -logger.addHandler(fh) +device_mesh, _world_size, _rank, logger = initialize_distributed_env( + "./tensor_parallel_llama3" +) +# Import should be after initialization of the TRT-LLM plugin .so path +import tensorrt_llm -tp_mesh = init_device_mesh(device_type="cuda", mesh_shape=(_world_size,)) +logger.info(f"Starting PyTorch TP example on rank {_rank}.") +assert ( + _world_size % 2 == 0 +), f"TP examples require even number of GPUs, but got {_world_size} gpus" model_args = ModelArgs( vocab_size=32000, @@ -38,7 +36,7 @@ ) with torch.no_grad(): - model = ParallelTransformer(model_args, tp_mesh) + model = ParallelTransformer(model_args, device_mesh) torch.manual_seed(0) inp = torch.randint(32000, (8, 256), device="cuda") python_result = model(inp) @@ -53,7 +51,7 @@ "use_python_runtime": True, "workspace_size": 1 << 33, "debug": False, - "timing_cache_path": "/opt/file/cache/timing_cache_llama.bin", + "use_aot_joint_export": False, }, dynamic=False, ) diff --git a/examples/distributed_inference/tensor_parallel_simple_example.py b/examples/distributed_inference/tensor_parallel_simple_example.py index 470487a751..837648fdb4 100755 --- a/examples/distributed_inference/tensor_parallel_simple_example.py +++ b/examples/distributed_inference/tensor_parallel_simple_example.py @@ -1,18 +1,22 @@ -import os -import sys import time +import tensorrt as trt import torch import torch.nn as nn import torch_tensorrt +from tensor_parallel_initialize_dist import initialize_distributed_env from torch.distributed._tensor import Shard -from torch.distributed._tensor.device_mesh import init_device_mesh from torch.distributed.tensor.parallel import ( ColwiseParallel, RowwiseParallel, parallelize_module, ) +device_mesh, _world_size, _rank, logger = initialize_distributed_env( + "./tensor_parallel_simple_example" +) +import tensorrt_llm + """ This example copies some code from https://github.com/pytorch/examples/blob/main/distributed/tensor_parallelism/tensor_parallel_example.py """ @@ -36,14 +40,7 @@ def forward(self, x): return x -# create a device mesh based on the given world_size. -_world_size = int(os.environ["WORLD_SIZE"]) - -device_mesh = init_device_mesh(device_type="cuda", mesh_shape=(_world_size,)) -_rank = device_mesh.get_rank() - - -print(f"Starting PyTorch TP example on rank {_rank}.") +logger.info(f"Starting PyTorch TP example on rank {_rank}.") assert ( _world_size % 2 == 0 ), f"TP examples require even number of GPUs, but got {_world_size} gpus" @@ -78,6 +75,7 @@ def forward(self, x): "enabled_precisions": {torch.float32, torch.float16}, "use_python_runtime": True, "min_block_size": 1, + "use_aot_joint_export": False, }, dynamic=False, ) @@ -91,9 +89,9 @@ def forward(self, x): output = tp_model(inp) end = time.time() if i == 0: - print(f"Compilation time is {end-start}") + logger.info(f"Compilation time is {end-start}") assert ( python_result - output ).std() < 0.01, "Compilation result is not correct." elif _rank == 0: - print(f"Inference time is {end-start}") + logger.info(f"Inference time is {end-start}") diff --git a/noxfile.py b/noxfile.py index 73d0dca854..9a5c3263bb 100644 --- a/noxfile.py +++ b/noxfile.py @@ -34,7 +34,7 @@ # Set epochs to train VGG model for accuracy tests EPOCHS = 25 -SUPPORTED_PYTHON_VERSIONS = ["3.7", "3.8", "3.9", "3.10"] +SUPPORTED_PYTHON_VERSIONS = ["3.9", "3.10", "3.11", "3.12"] nox.options.sessions = [ "l0_api_tests-" + "{}.{}".format(sys.version_info.major, sys.version_info.minor) diff --git a/packaging/env_vars.txt b/packaging/env_vars.txt index bfd05b7bf6..44a2350e0f 100644 --- a/packaging/env_vars.txt +++ b/packaging/env_vars.txt @@ -1,7 +1,3 @@ export CI_BUILD="1" export RELEASE="1" -if [ $CU_VERSION == "cu126" ]; then - export USE_CXX11_ABI="1" -else - export USE_CXX11_ABI="0" -fi \ No newline at end of file +export USE_CXX11_ABI="1" \ No newline at end of file diff --git a/packaging/smoke_test_script.sh b/packaging/smoke_test_script.sh index 19d9d717a4..ef584fef51 100644 --- a/packaging/smoke_test_script.sh +++ b/packaging/smoke_test_script.sh @@ -2,5 +2,5 @@ # The issue was smoke test installs the built torch_tensorrt wheel file and checks `import torch_tensorrt; print(torch_tensorrt.__version__)` # Since tensorrt cannot be pip installable in CI, the smoke test will fail. # One way we tried to handle it is manually install tensorrt wheel while by extracting from the tarball. -# However, the TensorRT-10.3.0.26/lib path doesn't seem to show up in LD_LIBRARY_PATH even if we explicitly set it. +# However, the TensorRT-10.7.0.23/lib path doesn't seem to show up in LD_LIBRARY_PATH even if we explicitly set it. # TODO: Implement a custom smoke_test script to verify torch_tensorrt installation. \ No newline at end of file diff --git a/py/ci/Dockerfile.ci b/py/ci/Dockerfile.ci index 823c8bb7a1..288c61029d 100644 --- a/py/ci/Dockerfile.ci +++ b/py/ci/Dockerfile.ci @@ -3,13 +3,14 @@ FROM pytorch/manylinux2_28-builder:cuda12.6 RUN yum install -y ninja-build # download TensorRT tarball -RUN wget -q https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/10.3.0/tars/TensorRT-10.3.0.26.Linux.x86_64-gnu.cuda-12.5.tar.gz \ -&& gunzip TensorRT-10.3.0.26.Linux.x86_64-gnu.cuda-12.5.tar.gz \ -&& tar -xvf TensorRT-10.3.0.26.Linux.x86_64-gnu.cuda-12.5.tar \ -&& rm TensorRT-10.3.0.26.Linux.x86_64-gnu.cuda-12.5.tar +RUN wget -q https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/10.7.0/tars/TensorRT-10.7.0.23.Linux.x86_64-gnu.cuda-12.6.tar.gz \ +&& gunzip TensorRT-10.7.0.23.Linux.x86_64-gnu.cuda-12.6.tar.gz \ +&& tar -xvf TensorRT-10.7.0.23.Linux.x86_64-gnu.cuda-12.6.tar \ +&& rm TensorRT-10.7.0.23.Linux.x86_64-gnu.cuda-12.6.tar -ENV TENSORRT_DIR=/TensorRT-10.3.0.26 -ENV TENSORRT_VERSION=10.3.0 +ENV TENSORRT_DIR=/TensorRT-10.7.0.23 +ENV TENSORRT_VERSION=10.7.0 +ENV USE_CXX11_ABI=1 RUN wget https://github.com/bazelbuild/bazelisk/releases/download/v1.17.0/bazelisk-linux-amd64 \ && mv bazelisk-linux-amd64 /usr/bin/bazel \ diff --git a/py/requirements.txt b/py/requirements.txt index 361afab365..5644656330 100644 --- a/py/requirements.txt +++ b/py/requirements.txt @@ -2,7 +2,7 @@ numpy packaging pybind11==2.6.2 --extra-index-url https://download.pytorch.org/whl/nightly/cu124 -torch>=2.6.0.dev,<2.7.0 -torchvision>=0.20.0.dev,<0.21.0 +torch>=2.7.0.dev,<2.8.0 +torchvision>=0.22.0.dev,<0.23.0 --extra-index-url https://pypi.ngc.nvidia.com pyyaml diff --git a/py/torch_tensorrt/dynamo/_defaults.py b/py/torch_tensorrt/dynamo/_defaults.py index 76630a75a5..18932e6cd0 100644 --- a/py/torch_tensorrt/dynamo/_defaults.py +++ b/py/torch_tensorrt/dynamo/_defaults.py @@ -46,6 +46,7 @@ IMMUTABLE_WEIGHTS = True ENABLE_WEIGHT_STREAMING = False ENABLE_CROSS_COMPILE_FOR_WINDOWS = False +USE_AOT_JOINT_EXPORT = True def default_device() -> Device: diff --git a/py/torch_tensorrt/dynamo/_settings.py b/py/torch_tensorrt/dynamo/_settings.py index 7a22663af3..05fb5ce094 100644 --- a/py/torch_tensorrt/dynamo/_settings.py +++ b/py/torch_tensorrt/dynamo/_settings.py @@ -33,6 +33,7 @@ STRIP_ENGINE_WEIGHTS, TIMING_CACHE_PATH, TRUNCATE_DOUBLE, + USE_AOT_JOINT_EXPORT, USE_EXPLICIT_TYPING, USE_FAST_PARTITIONER, USE_FP32_ACC, @@ -91,6 +92,7 @@ class CompilationSettings: enable_weight_streaming (bool): Enable weight streaming. enable_cross_compile_for_windows (bool): By default this is False means TensorRT engines can only be executed on the same platform where they were built. True will enable cross-platform compatibility which allows the engine to be built on Linux and run on Windows + use_aot_joint_export (bool): Use aot_export_joint_simple, else wrap backend with AOT_autograd, required for distributed tensors """ enabled_precisions: Set[dtype] = field(default_factory=lambda: ENABLED_PRECISIONS) @@ -131,6 +133,7 @@ class CompilationSettings: immutable_weights: bool = IMMUTABLE_WEIGHTS enable_weight_streaming: bool = ENABLE_WEIGHT_STREAMING enable_cross_compile_for_windows: bool = ENABLE_CROSS_COMPILE_FOR_WINDOWS + use_aot_joint_export: bool = USE_AOT_JOINT_EXPORT _SETTINGS_TO_BE_ENGINE_INVARIANT = ( diff --git a/py/torch_tensorrt/dynamo/backend/backends.py b/py/torch_tensorrt/dynamo/backend/backends.py index 59bd7d011d..ef04745562 100644 --- a/py/torch_tensorrt/dynamo/backend/backends.py +++ b/py/torch_tensorrt/dynamo/backend/backends.py @@ -1,17 +1,20 @@ from __future__ import annotations +import functools import logging import unittest from typing import Any, Callable, Sequence import torch import torch._dynamo as td +from torch._dynamo.backends.common import aot_autograd from torch._dynamo.utils import detect_fake_mode from torch._functorch.aot_autograd import aot_export_joint_simple from torch_tensorrt.dynamo import CompilationSettings from torch_tensorrt.dynamo._compiler import compile_module from torch_tensorrt.dynamo.lowering import ( get_decompositions, + modify_reshape_complex_nodes, post_lowering, remove_detach, remove_sym_nodes, @@ -49,7 +52,25 @@ def aot_torch_tensorrt_aten_backend( gm: torch.fx.GraphModule, sample_inputs: Sequence[Any], **kwargs: Any ) -> torch.nn.Module: settings, engine_cache = parse_dynamo_kwargs(kwargs) - return _pretraced_backend(gm, sample_inputs, settings, engine_cache) + if settings.use_aot_joint_export: + return _pretraced_backend(gm, sample_inputs, settings, engine_cache) + logger.debug("Wrapping the backend with aot_autograd\n") + _pretraced_backend_autograd = functools.partial( + _pretraced_backend, settings=settings, engine_cache=engine_cache + ) + settings_aot_autograd = {} + settings_aot_autograd["decompostions"] = get_decompositions( + settings.enable_experimental_decompositions + ) + # This is added since detach lowering leads to alias nodes + # Error - View operation returned a tensor that is the same as the input base tensor + # torch nop_decompositions in torch/_decomp/decompositions.py + if aten.detach in settings_aot_autograd["decompositions"]: + del settings_aot_autograd["decompositions"][aten.detach] + return aot_autograd( + fw_compiler=_pretraced_backend_autograd, + decompositions=get_decompositions(settings.enable_experimental_decompositions), + )(gm, sample_inputs) def _pretraced_backend( @@ -89,15 +110,26 @@ def _pretraced_backend( # Remove detach nodes remove_detach(gm, settings) + complexInputIndices = [] + for i, torch_input in enumerate(torch_inputs): + if torch_inputs[i].dtype == torch.complex64: + complexInputIndices.append(i) + torch_input_real = torch_inputs[i].real + torch_input_imaginary = torch_inputs[i].imag + torch_inputs[i] = torch.stack( + (torch_input_real, torch_input_imaginary), dim=-1 + ) + # Invoke AOTAutograd to translate operators to aten - gm = aot_export_joint_simple( - gm, - sample_inputs, - trace_joint=False, - decompositions=get_decompositions( - settings.enable_experimental_decompositions - ), - ) + if settings.use_aot_joint_export: + gm = aot_export_joint_simple( + gm, + sample_inputs, + trace_joint=False, + decompositions=get_decompositions( + settings.enable_experimental_decompositions + ), + ) logger.debug("Post-AOT Autograd graph:\n" + str(gm.graph)) @@ -105,6 +137,12 @@ def _pretraced_backend( logger.debug("Lowered Input graph:\n " + str(gm.graph)) + if complexInputIndices: + modify_reshape_complex_nodes(gm, complexInputIndices) + logger.debug( + "Input graph after modifying complex nodes:\n " + str(gm.graph) + ) + torchtrt_inputs = prepare_inputs( torch_inputs, disable_memory_format_check=True ) diff --git a/py/torch_tensorrt/dynamo/conversion/__init__.py b/py/torch_tensorrt/dynamo/conversion/__init__.py index 26a428ed8d..f41540a0cf 100644 --- a/py/torch_tensorrt/dynamo/conversion/__init__.py +++ b/py/torch_tensorrt/dynamo/conversion/__init__.py @@ -1,4 +1,10 @@ -from . import aten_ops_converters, ops_evaluators, plugins, prims_ops_converters +from . import ( + aten_ops_converters, + custom_ops_converters, + ops_evaluators, + plugins, + prims_ops_converters, +) from ._conversion import convert_module, interpret_module_to_result from ._ConversionContext import ConversionContext from ._ConverterRegistry import * # noqa: F403 diff --git a/py/torch_tensorrt/dynamo/conversion/aten_ops_converters.py b/py/torch_tensorrt/dynamo/conversion/aten_ops_converters.py index 16d20d3c76..5254c6a0ac 100644 --- a/py/torch_tensorrt/dynamo/conversion/aten_ops_converters.py +++ b/py/torch_tensorrt/dynamo/conversion/aten_ops_converters.py @@ -2,7 +2,7 @@ import logging import operator -from typing import Any, Callable, Dict, Optional, Sequence, Tuple, Union +from typing import Callable, Dict, Optional, Sequence, Tuple, Union import numpy as np import torch @@ -16,6 +16,7 @@ has_static_shapes_in_args, ) from torch_tensorrt.dynamo.conversion.converter_utils import ( + args_bounds_check, enforce_tensor_types, get_positive_dim, is_only_operator_on_placeholder, @@ -25,12 +26,6 @@ _LOGGER: logging.Logger = logging.getLogger(__name__) -def args_bounds_check( - args: Tuple[Argument, ...], i: int, replacement: Optional[Any] = None -) -> Any: - return args[i] if len(args) > i and args[i] is not None else replacement - - def get_ir(target: Target) -> SourceIR: target_module = getattr(target, "__module__", "None") if any( diff --git a/py/torch_tensorrt/dynamo/conversion/converter_utils.py b/py/torch_tensorrt/dynamo/conversion/converter_utils.py index 937ed27fe4..f4bb4877cc 100644 --- a/py/torch_tensorrt/dynamo/conversion/converter_utils.py +++ b/py/torch_tensorrt/dynamo/conversion/converter_utils.py @@ -1,10 +1,11 @@ import collections +import ctypes import functools import logging +import os from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union, overload import numpy as np -import tensorrt as trt import torch import torch_tensorrt.dynamo.conversion.impl as impl from torch.fx.node import Argument, Target @@ -18,6 +19,8 @@ DynamoConverterImplSignature, ) +import tensorrt as trt + from ..types import Shape, TRTDataType, TRTLayer, TRTTensor _LOGGER: logging.Logger = logging.getLogger(__name__) @@ -913,3 +916,72 @@ def set_layer_name( else f"{source_ir}_ops.{target.__name__}" ) layer.name = f"[{layer.type.name}]-[{target_name}]-[{name}]" + + +def args_bounds_check( + args: Tuple[Argument, ...], i: int, replacement: Optional[Any] = None +) -> Any: + return args[i] if len(args) > i and args[i] is not None else replacement + + +def load_tensorrt_llm() -> bool: + """ + Attempts to load the TensorRT-LLM plugin and initialize it. + + Returns: + bool: True if the plugin was successfully loaded and initialized, False otherwise. + """ + try: + import tensorrt_llm as trt_llm # noqa: F401 + + _LOGGER.info("TensorRT-LLM successfully imported") + return True + except (ImportError, AssertionError) as e_import_error: + # Check for environment variable for the plugin library path + plugin_lib_path = os.environ.get("TRTLLM_PLUGINS_PATH") + if not plugin_lib_path: + _LOGGER.warning( + "TensorRT-LLM is not installed. Please install TensorRT-LLM or set TRTLLM_PLUGINS_PATH to the directory containing libnvinfer_plugin_tensorrt_llm.so to use converters for torch.distributed ops", + ) + return False + + _LOGGER.info(f"TensorRT-LLM Plugin lib path found: {plugin_lib_path}") + try: + # Load the shared library + handle = ctypes.CDLL(plugin_lib_path) + _LOGGER.info(f"Successfully loaded plugin library: {plugin_lib_path}") + except OSError as e_os_error: + _LOGGER.error( + f"Failed to load libnvinfer_plugin_tensorrt_llm.so from {plugin_lib_path}" + f"Ensure the path is correct and the library is compatible", + exc_info=e_os_error, + ) + return False + + try: + # Configure plugin initialization arguments + handle.initTrtLlmPlugins.argtypes = [ctypes.c_void_p, ctypes.c_char_p] + handle.initTrtLlmPlugins.restype = ctypes.c_bool + except AttributeError as e_plugin_unavailable: + _LOGGER.warning( + "Unable to initialize the TensorRT-LLM plugin library", + exc_info=e_plugin_unavailable, + ) + return False + + try: + # Initialize the plugin + TRT_LLM_PLUGIN_NAMESPACE = "tensorrt_llm" + if handle.initTrtLlmPlugins(None, TRT_LLM_PLUGIN_NAMESPACE.encode("utf-8")): + _LOGGER.info("TensorRT-LLM plugin successfully initialized") + return True + else: + _LOGGER.warning("TensorRT-LLM plugin library failed in initialization") + return False + except Exception as e_initialization_error: + _LOGGER.warning( + "Exception occurred during TensorRT-LLM plugin library initialization", + exc_info=e_initialization_error, + ) + return False + return False diff --git a/py/torch_tensorrt/dynamo/conversion/custom_ops_converters.py b/py/torch_tensorrt/dynamo/conversion/custom_ops_converters.py new file mode 100644 index 0000000000..17850fabce --- /dev/null +++ b/py/torch_tensorrt/dynamo/conversion/custom_ops_converters.py @@ -0,0 +1,61 @@ +# mypy: disallow-untyped-decorators=False + +import logging +from typing import Dict, Sequence, Tuple, Union + +from torch.fx.node import Argument, Target +from torch_tensorrt.dynamo._SourceIR import SourceIR +from torch_tensorrt.dynamo.conversion import impl +from torch_tensorrt.dynamo.conversion._ConversionContext import ConversionContext +from torch_tensorrt.dynamo.conversion._ConverterRegistry import ( + dynamo_tensorrt_converter, +) +from torch_tensorrt.dynamo.conversion.converter_utils import load_tensorrt_llm +from torch_tensorrt.dynamo.lowering.passes.fuse_distributed_ops import ( + tensorrt_fused_nccl_all_gather_op, + tensorrt_fused_nccl_reduce_scatter_op, +) + +import tensorrt as trt + +_LOGGER: logging.Logger = logging.getLogger(__name__) + +if load_tensorrt_llm(): + + @dynamo_tensorrt_converter(tensorrt_fused_nccl_all_gather_op) + def fused_nccl_gather( + ctx: ConversionContext, + target: Target, + args: Tuple[Argument, ...], + kwargs: Dict[str, Argument], + name: str, + ) -> Union[trt.ITensor, Sequence[trt.ITensor]]: + return impl.distributed.nccl_gather( + ctx, + target, + SourceIR.ATEN, + name, + [args[0]], + ) + + @dynamo_tensorrt_converter(tensorrt_fused_nccl_reduce_scatter_op) + def fused_nccl_reduce_scatter( + ctx: ConversionContext, + target: Target, + args: Tuple[Argument, ...], + kwargs: Dict[str, Argument], + name: str, + ) -> Union[trt.ITensor, Sequence[trt.ITensor]]: + return impl.distributed.nccl_reduce_scatter( + ctx, + target, + SourceIR.ATEN, + name, + [args[0]], + ) + + breakpoint() +else: + _LOGGER.debug( + "Did not load torch.distributed converters since TensorRT-LLM is not available" + ) diff --git a/py/torch_tensorrt/dynamo/conversion/impl/__init__.py b/py/torch_tensorrt/dynamo/conversion/impl/__init__.py index c1187f0dd9..75f7492591 100644 --- a/py/torch_tensorrt/dynamo/conversion/impl/__init__.py +++ b/py/torch_tensorrt/dynamo/conversion/impl/__init__.py @@ -13,6 +13,7 @@ full, grid, matmul, + nccl_ops, normalization, pad, permutation, diff --git a/py/torch_tensorrt/dynamo/conversion/impl/elementwise/base.py b/py/torch_tensorrt/dynamo/conversion/impl/elementwise/base.py index 3ce1714a7d..4a4b33abea 100644 --- a/py/torch_tensorrt/dynamo/conversion/impl/elementwise/base.py +++ b/py/torch_tensorrt/dynamo/conversion/impl/elementwise/base.py @@ -3,7 +3,6 @@ from typing import Any, Callable, Optional, Union import numpy as np -import tensorrt as trt import torch from torch.fx.node import Target from torch_tensorrt import _enums @@ -14,12 +13,13 @@ broadcast_to_same_shape, cast_trt_tensor, get_trt_tensor, - broadcast, has_dynamic_shape, set_layer_name, ) from torch_tensorrt.dynamo.types import TRTElementWiseOp, TRTTensor +import tensorrt as trt + def get_python_op_from_trt_elementwise_op( trt_op: TRTElementWiseOp, diff --git a/py/torch_tensorrt/dynamo/conversion/impl/nccl_ops.py b/py/torch_tensorrt/dynamo/conversion/impl/nccl_ops.py new file mode 100644 index 0000000000..013268f803 --- /dev/null +++ b/py/torch_tensorrt/dynamo/conversion/impl/nccl_ops.py @@ -0,0 +1,120 @@ +import os +from enum import IntEnum, IntFlag, auto +from typing import Optional, Tuple, Union + +import numpy as np +from torch.fx.node import Argument, Target +from torch_tensorrt.dynamo.conversion._ConversionContext import ConversionContext +from torch_tensorrt.fx.converters.converter_utils import SourceIR, set_layer_name + +import tensorrt as trt + + +# class for AllReduce +class AllReduceStrategy(IntEnum): + """Warning: actual definition is in kernels/customAllReduceKernels.h. + + They must be kept in sync. + """ + + NCCL = 0 + ONESHOT = 1 + TWOSHOT = 2 + AUTO = 3 + + +class AllReduceConfig(IntFlag): + """Warning: actual definition is in kernels/customAllReduceKernels.h. + + They must be kept in sync + """ + + USE_MEMCPY = auto() + PUSH_MODE = auto() + + +def nccl_gather( + ctx: ConversionContext, + target: Union[Target, str], + source_ir: Optional[SourceIR], + name: str, + plug_inputs: Tuple[Argument, ...], +) -> trt.ITensor: + allgather_plg_creator = trt.get_plugin_registry().get_plugin_creator( + "AllGather", "1", "tensorrt_llm" + ) + assert allgather_plg_creator is not None + _world_size = os.environ.get("WORLD_SIZE") + if _world_size is not None: + world_size = int(_world_size) + else: + raise RuntimeError( + "The WORLD_SIZE env variable is not set in distributed environment" + ) + group = list(range(world_size)) + group = trt.PluginField( + "group", np.array(group, dtype=np.int32), trt.PluginFieldType.INT32 + ) + p_dtype = trt.float32 + pf_type = trt.PluginField( + "type_id", np.array([int(p_dtype)], np.int32), trt.PluginFieldType.INT32 + ) + pfc = trt.PluginFieldCollection([group, pf_type]) + allgather = allgather_plg_creator.create_plugin("allgather", pfc) + layer = ctx.net.add_plugin_v2(plug_inputs, allgather) + set_layer_name(layer, target, name, source_ir) + return layer.get_output(0) + + +def nccl_reduce_scatter( + ctx: ConversionContext, + target: Union[Target, str], + source_ir: Optional[SourceIR], + name: str, + plug_inputs: Tuple[Argument, ...], +) -> trt.ITensor: + allreduce_plg_creator = trt.get_plugin_registry().get_plugin_creator( + "ReduceScatter", "1", "tensorrt_llm" + ) + + assert allreduce_plg_creator is not None + + counter = 0 + strategy = AllReduceStrategy.NCCL + config = AllReduceConfig(0) + _world_size = os.environ.get("WORLD_SIZE") + if _world_size is not None: + world_size = int(_world_size) + else: + raise RuntimeError( + "The WORLD_SIZE env variable is not set in distributed environment" + ) + group = list(range(world_size)) + group = trt.PluginField( + "group", np.array(group, dtype=np.int32), trt.PluginFieldType.INT32 + ) + + p_dtype = trt.float16 + pf_dtype = trt.PluginField( + "type_id", np.array([int(p_dtype)], np.int32), trt.PluginFieldType.INT32 + ) + pfc = [group, pf_dtype] + p_strategy = trt.PluginField( + "strategy", np.array([int(strategy)], np.int8), trt.PluginFieldType.INT8 + ) + pfc.append(p_strategy) + p_config = trt.PluginField( + "config", np.array([int(config)], np.int8), trt.PluginFieldType.INT8 + ) + pfc.append(p_config) + p_counter = trt.PluginField( + "counter", np.array([counter], np.int32), trt.PluginFieldType.INT32 + ) + pfc.append(p_counter) + + pfc = trt.PluginFieldCollection(pfc) + ar_plug = allreduce_plg_creator.create_plugin("allreduce", pfc) + + layer = ctx.net.add_plugin_v2(plug_inputs, ar_plug) + set_layer_name(layer, target, name, source_ir) + return layer.get_output(0) diff --git a/py/torch_tensorrt/dynamo/conversion/impl/select.py b/py/torch_tensorrt/dynamo/conversion/impl/select.py index 0d55c5f014..dc5458d2c8 100644 --- a/py/torch_tensorrt/dynamo/conversion/impl/select.py +++ b/py/torch_tensorrt/dynamo/conversion/impl/select.py @@ -2,7 +2,6 @@ from typing import Optional, Sequence, Union import numpy as np -import tensorrt as trt import torch from torch.fx.node import Target from torch_tensorrt.dynamo._SourceIR import SourceIR @@ -16,7 +15,6 @@ to_numpy, ) from torch_tensorrt.dynamo.conversion.impl.elementwise import convert_binary_elementwise -from torch_tensorrt.dynamo.conversion.impl.shape import get_shape_with_dynamic_shape from torch_tensorrt.dynamo.conversion.impl.shape import shape as get_shape from torch_tensorrt.dynamo.utils import DYNAMIC_DIM from torch_tensorrt.fx.converters.converter_utils import ( @@ -25,6 +23,8 @@ ) from torch_tensorrt.fx.types import TRTTensor +import tensorrt as trt + _LOGGER: logging.Logger = logging.getLogger(__name__) diff --git a/py/torch_tensorrt/dynamo/lowering/passes/__init__.py b/py/torch_tensorrt/dynamo/lowering/passes/__init__.py index c0e2803e60..716c6505fe 100644 --- a/py/torch_tensorrt/dynamo/lowering/passes/__init__.py +++ b/py/torch_tensorrt/dynamo/lowering/passes/__init__.py @@ -1,3 +1,4 @@ from ._aten_lowering_pass import * +from ._modify_reshape_complex_nodes import modify_reshape_complex_nodes from .remove_sym_nodes import remove_sym_nodes from .repair_input_aliasing import repair_input_aliasing diff --git a/py/torch_tensorrt/dynamo/lowering/passes/_aten_lowering_pass.py b/py/torch_tensorrt/dynamo/lowering/passes/_aten_lowering_pass.py index 661c76d3b6..f24fd8ec21 100644 --- a/py/torch_tensorrt/dynamo/lowering/passes/_aten_lowering_pass.py +++ b/py/torch_tensorrt/dynamo/lowering/passes/_aten_lowering_pass.py @@ -6,6 +6,7 @@ from .accumulate_fp32_matmul import accumulate_fp32_matmul from .constant_folding import constant_fold +from .fuse_distributed_ops import fuse_distributed_ops from .fuse_prims_broadcast import fuse_prims_broadcast from .lower_scaled_dot_product_attention import lower_scaled_dot_product_attention from .pass_manager import DynamoPassManager @@ -22,6 +23,7 @@ constant_fold, repair_input_as_output, fuse_prims_broadcast, + fuse_distributed_ops, replace_max_pool_with_indices, lower_scaled_dot_product_attention, view_to_reshape, diff --git a/py/torch_tensorrt/dynamo/lowering/passes/_modify_reshape_complex_nodes.py b/py/torch_tensorrt/dynamo/lowering/passes/_modify_reshape_complex_nodes.py new file mode 100644 index 0000000000..f8ca1f71b9 --- /dev/null +++ b/py/torch_tensorrt/dynamo/lowering/passes/_modify_reshape_complex_nodes.py @@ -0,0 +1,105 @@ +import logging + +import torch + +logger = logging.getLogger(__name__) + +from torch_tensorrt.dynamo.lowering.passes.pass_utils import ( + clean_up_graph_after_modifications, + find_complex_nodes, +) + +from ._replace_complex_placeholder_to_tuple import replace_complex_placeholder_to_tuple + + +def tensorrt_complex_mul(args0, args1): + args0_real, args0_imag = torch.ops.aten.split.Tensor(args0, 1, -1) + args1_real, args1_imag = torch.ops.aten.split.Tensor(args1, 1, -1) + + args0_real = torch.ops.aten.squeeze.dim(args0_real, -1) + args0_imag = torch.ops.aten.squeeze.dim(args0_imag, -1) + args1_real = torch.ops.aten.squeeze.dim(args1_real, -1) + args1_imag = torch.ops.aten.squeeze.dim(args1_imag, -1) + + complex_mul_real = torch.ops.aten.sub( + torch.ops.aten.mul(args0_real, args1_real), + torch.ops.aten.mul(args0_imag, args1_imag), + ) + complex_mul_imag = torch.ops.aten.add( + torch.ops.aten.mul(args0_real, args1_imag), + torch.ops.aten.mul(args0_imag, args1_real), + ) + + return torch.ops.aten.stack((complex_mul_real, complex_mul_imag), -1) + + +def remove_complex_real_view_nodes(gm: torch.fx.GraphModule): + modified_graph = False + nodes_to_remove = [] + for node in gm.graph.nodes: + if "view_as_complex" in node.name or "view_as_real" in node.name: + nodes_to_remove.append(node) + + for node in nodes_to_remove: + input_node = node.args[0] if node.args else None + + for other_node in gm.graph.nodes: + new_args = tuple( + input_node if arg is node else arg for arg in other_node.args + ) + other_node.args = new_args + + gm.graph.erase_node(node) + modified_graph = True + + if modified_graph: + gm = clean_up_graph_after_modifications(gm) + logger.debug( + f"Graph after removing view_as_complex nodes and view_as_real nodes:\n{gm.graph}" + ) + + +def modify_reshape_nodes(gm: torch.fx.GraphModule, complex_nodes): + for node in gm.graph.nodes: + if node in complex_nodes: + # slice and transpose will remain same + if "reshape" in node.name: + new_shape = list(node.args[1]) + [2] + node.args = (node.args[0], tuple(new_shape)) + + +def modify_mul_nodes(gm: torch.fx.GraphModule, complex_nodes): + modified_graph = False + for node in gm.graph.nodes: + if node in complex_nodes: + if "mul" in node.name: + complex_mul_args = (node.args[0], node.args[1]) + with gm.graph.inserting_after(node): + replacement_node = gm.graph.create_node( + op="call_function", + target=tensorrt_complex_mul, + args=complex_mul_args, + ) + node.replace_all_uses_with(replacement_node) + replacement_node.meta.update(node.meta) + modified_graph = True + gm.graph.erase_node(node) + + if modified_graph: + gm = clean_up_graph_after_modifications(gm) + logger.debug( + f"Graph after custom complex mul nodes is applied to the graph:\n{gm.graph}" + ) + + +def modify_complex_nodes(gm: torch.fx.GraphModule, complex_nodes): + modify_reshape_nodes(gm, complex_nodes) + remove_complex_real_view_nodes(gm) + modify_mul_nodes(gm, complex_nodes) + + +def modify_reshape_complex_nodes(gm: torch.fx.GraphModule, complexInputIndices): + complex_nodes = find_complex_nodes(gm) + if complex_nodes: + replace_complex_placeholder_to_tuple(gm, complexInputIndices) + modify_complex_nodes(gm, complex_nodes) diff --git a/py/torch_tensorrt/dynamo/lowering/passes/_replace_complex_placeholder_to_tuple.py b/py/torch_tensorrt/dynamo/lowering/passes/_replace_complex_placeholder_to_tuple.py new file mode 100644 index 0000000000..e2edec3d28 --- /dev/null +++ b/py/torch_tensorrt/dynamo/lowering/passes/_replace_complex_placeholder_to_tuple.py @@ -0,0 +1,112 @@ +import logging +from typing import List, Tuple + +import torch +from torch._subclasses.fake_tensor import FakeTensorMode +from torch.fx.node import _get_qualified_name +from torch_tensorrt.dynamo.conversion.converter_utils import args_bounds_check + +# dead-code elimination, linting, and recompilation for graph, in-place +from torch_tensorrt.dynamo.lowering.passes.pass_utils import ( + clean_up_graph_after_modifications, +) + +logger = logging.getLogger(__name__) + + +def replace_complex_placeholder_to_tuple( + gm: torch.fx.GraphModule, + inputListindices: List[int], +) -> torch.fx.GraphModule: + modified_graph = False + input_arg_list = [f"arg{inputListIndex}_1" for inputListIndex in inputListindices] + for node in gm.graph.nodes: + if node.op == "placeholder" and node.target in input_arg_list: + from torch._subclasses.fake_tensor import FakeTensorMode + + node_shape = node.meta["val"].size() + new_node_shape = node_shape + (2,) + new_node_dtype = None + if node.meta["val"].dtype == torch.complex64: + new_node_dtype = torch.float32 + else: + new_node_dtype = torch.float64 + fake_mode = FakeTensorMode() + + real_tensor = torch.empty(new_node_shape, dtype=new_node_dtype) + with FakeTensorMode() as fake_mode: + new_placeholder_tuple = fake_mode.from_tensor(real_tensor) + node.meta["val"] = new_placeholder_tuple + modified_graph = True + # propagate the meta data change for the downstream ops + # TODO:to check if this is required in all cases + propogate_complex_num_shape_change_till_complex_mul(gm, node, fake_mode) + + # If graph was modified, clean it up + if modified_graph: + gm = clean_up_graph_after_modifications(gm) + logger.debug( + f"Graph after fusing wait_tensor and distributed op tensor:\n{gm.graph}" + ) + + return gm + + +def infer_slice_shape(node: torch.fx.Node) -> Tuple[int, ...]: + input_shape = node.args[0].meta["val"].shape + slice_args = node.args + dim = slice_args[1] + start = slice_args[2] + end = slice_args[3] + step = args_bounds_check(slice_args, 4, replacement=1) + new_shape = list(input_shape) + new_shape[dim] = (end - start + step - 1) // step + return tuple(new_shape) + + +def infer_reshape_shape(node: torch.fx.Node) -> torch.fx.node.Argument: + return node.args[1] + + +shape_inference_funcs = { + "torch.ops.aten.slice.Tensor": infer_slice_shape, + "torch.ops.aten.reshape.default": infer_reshape_shape, +} + + +# Please note this function is for the use case of Llama model +# with complex placeholder->reshape->slice->complex mul +# Hence mul is the terminating op +def propogate_complex_num_shape_change_till_complex_mul( + node: torch.fx.Node, start_node: torch.fx.Node, fake_mode: FakeTensorMode +) -> None: + visited_nodes = set() + stack = [start_node] + while stack: + node = stack.pop() + if node in visited_nodes: + continue + visited_nodes.add(node) + update_node_meta(node, fake_mode) + for user in node.users: + if ( + user.op == "call_function" + and _get_qualified_name(user.target) == "torch.ops.aten.mul.Tensor" + ): + continue + stack.append(user) + + +def update_node_meta(node: torch.fx.Node, fake_mode: FakeTensorMode) -> None: + op_name = node.name + op_target = node.target + + if node.op == "call_function": + op_target = _get_qualified_name(node.target) + + if op_target in shape_inference_funcs: + new_shape = shape_inference_funcs[op_target](node) + real_tensor = torch.empty(new_shape, dtype=node.meta["val"].dtype) + node.meta["val"] = fake_mode.from_tensor(real_tensor) + else: + print("No shape for the inference function", {op_name}) diff --git a/py/torch_tensorrt/dynamo/lowering/passes/fuse_distributed_ops.py b/py/torch_tensorrt/dynamo/lowering/passes/fuse_distributed_ops.py new file mode 100644 index 0000000000..f709f177d6 --- /dev/null +++ b/py/torch_tensorrt/dynamo/lowering/passes/fuse_distributed_ops.py @@ -0,0 +1,80 @@ +import logging +from typing import Any + +import torch +from torch_tensorrt.dynamo._settings import CompilationSettings + +# dead-code elimination, linting, and recompilation for graph, in-place +from torch_tensorrt.dynamo.lowering.passes.pass_utils import ( + clean_up_graph_after_modifications, +) + +logger = logging.getLogger(__name__) + + +# TODO: @apbose make these actual torch custom ops, should allow aot_export +def tensorrt_fused_nccl_all_gather_op( + inp: Any, group_size: int, group_name: str +) -> torch.Tensor: + return torch.ops._c10d_functional.wait_tensor.default( + torch.ops._c10d_functional.all_gather_into_tensor.default( + inp, group_size, group_name + ) + ) + + +def tensorrt_fused_nccl_reduce_scatter_op( + inp: Any, reduce_op: str, group_size: int, group_name: str +) -> torch.Tensor: + return torch.ops._c10d_functional.wait_tensor.default( + torch.ops._c10d_functional.reduce_scatter_tensor.default( + inp, reduce_op, group_size, group_name + ) + ) + + +def fuse_distributed_ops( + gm: torch.fx.GraphModule, settings: CompilationSettings +) -> torch.fx.GraphModule: + modified_graph = False + for node in gm.graph.nodes: + if ( + node.target + in ( + torch.ops._c10d_functional.all_gather_into_tensor.default, + torch.ops._c10d_functional.reduce_scatter_tensor.default, + ) + and len(node.users) == 1 + and list(node.users)[0].target + == torch.ops._c10d_functional.wait_tensor.default + ): + wait_tensor_node = list(node.users)[0] + fused_op = None + if node.target == torch.ops._c10d_functional.all_gather_into_tensor.default: + with gm.graph.inserting_after(wait_tensor_node): + fused_node = gm.graph.create_node( + op="call_function", + target=tensorrt_fused_nccl_all_gather_op, # Define your custom fused function + args=(node.args[0], node.args[1], node.args[2]), + ) + else: + fused_node = gm.graph.create_node( + op="call_function", + target=tensorrt_fused_nccl_reduce_scatter_op, # Define your custom fused function + args=(node.args[0], node.args[1], node.args[2], node.args[3]), + ) + + wait_tensor_node.replace_all_uses_with(fused_node) + fused_node.meta.update(node.meta) + modified_graph = True + gm.graph.erase_node(wait_tensor_node) + gm.graph.erase_node(node) + + # If graph was modified, clean it up + if modified_graph: + gm = clean_up_graph_after_modifications(gm) + logger.debug( + f"Graph after fusing wait_tensor and distributed op tensor:\n{gm.graph}" + ) + + return gm diff --git a/py/torch_tensorrt/dynamo/lowering/passes/pass_utils.py b/py/torch_tensorrt/dynamo/lowering/passes/pass_utils.py index 31a55099c2..1736a234a2 100644 --- a/py/torch_tensorrt/dynamo/lowering/passes/pass_utils.py +++ b/py/torch_tensorrt/dynamo/lowering/passes/pass_utils.py @@ -29,3 +29,42 @@ def get_tensor_placeholders( ] return placeholders + + +def find_complex_nodes(gm: torch.fx.GraphModule): + complex_nodes = [] + complexNodes = {} + for node in gm.graph.nodes: + if is_node_complex(node, complexNodes): + complex_nodes.append(node) + return complex_nodes + + +def is_node_complex(node: torch.fx.Node, complexNodes): + if not isinstance(node, torch.fx.Node): + return False + if node.name in complexNodes: + return True + if node.op == "call_function" and node.args is not None: + for arg in node.args: + if isinstance(arg, int): + continue + elif isinstance(arg, (list, tuple)): + for eachNode in arg: + if is_node_complex(eachNode, complexNodes): + complexNodes[node.name] = True + return True + + elif hasattr(arg, "meta") and "val" in arg.meta: + if isinstance(arg.meta["val"], (list, tuple)): + for eachFakeTensorMeta in arg.meta["val"]: + if eachFakeTensorMeta.dtype in ( + torch.complex64, + torch.complex128, + ): + complexNodes[node.name] = True + return True + elif arg.meta["val"].dtype in (torch.complex64, torch.complex128): + complexNodes[node.name] = True + return True + return False diff --git a/pyproject.toml b/pyproject.toml index 6cc3412849..7ec6c7469a 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -8,8 +8,8 @@ requires = [ "cffi>=1.15.1", "typing-extensions>=4.7.0", "future>=0.18.3", - "tensorrt-cu12>=10.6.0,<10.8.0", - "torch>=2.6.0.dev,<2.7.0", + "tensorrt-cu12>=10.7.0,<10.8.0", + "torch>=2.7.0.dev,<2.8.0", "pybind11==2.6.2", "numpy", ] @@ -54,10 +54,10 @@ keywords = [ "inference", ] dependencies = [ - "torch>=2.6.0.dev,<2.7.0", - "tensorrt-cu12>=10.6.0,<10.8.0", - "tensorrt-cu12-bindings>=10.6.0,<10.8.0", - "tensorrt-cu12-libs>=10.6.0,<10.8.0", + "torch>=2.7.0.dev,<2.8.0", + "tensorrt-cu12>=10.7.0,<10.8.0", + "tensorrt-cu12-bindings>=10.7.0,<10.8.0", + "tensorrt-cu12-libs>=10.7.0,<10.8.0", "packaging>=23", "numpy", "typing-extensions>=4.7.0", @@ -85,9 +85,10 @@ dev = [ torchvision = [ "torchvision", ] #Leaving torchvisions dependency unconstrained so uv can just install something that should work for the torch we have. TV's on PyT makes it hard to put version constrains in -quantization = ["nvidia-modelopt[deploy,hf,torch]~=0.17.0"] +quantization = ["nvidia-modelopt[deploy,hf,torch]>=0.17.0"] monitoring-tools = ["rich>=13.7.1"] jupyter = ["rich[jupyter]>=13.7.1"] +distributed = ["tensorrt-llm>=0.16.0"] [project.urls] Homepage = "https://pytorch.org/tensorrt" @@ -101,17 +102,12 @@ include-package-data = false [tool.uv] environments = ["sys_platform == 'linux'", "sys_platform == 'windows'"] - prerelease = "if-necessary-or-explicit" - +index-strategy = "unsafe-best-match" # Needed for TRT-LLM [tool.uv.sources] -torch = [ - { index = "pytorch-nightly-cu126"}, -] -torchvision = [ - { index = "pytorch-nightly-cu126"}, -] +torch = [{ index = "pytorch-nightly-cu126" }] +torchvision = [{ index = "pytorch-nightly-cu126" }] [[tool.uv.index]] name = "pytorch-nightly-cu126" diff --git a/toolchains/ci_workspaces/MODULE.bazel.tmpl b/toolchains/ci_workspaces/MODULE.bazel.tmpl index 142a021609..66b5d448ca 100644 --- a/toolchains/ci_workspaces/MODULE.bazel.tmpl +++ b/toolchains/ci_workspaces/MODULE.bazel.tmpl @@ -67,20 +67,18 @@ http_archive( http_archive( name = "tensorrt", build_file = "@//third_party/tensorrt/archive:BUILD", - sha256 = "33d3c2f3f4c84dc7991a4337a6fde9ed33f5c8e5c4f03ac2eb6b994a382b03a0", - strip_prefix = "TensorRT-10.6.0.26", + strip_prefix = "TensorRT-10.7.0.23", urls = [ - "https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/10.6.0/tars/TensorRT-10.6.0.26.Linux.x86_64-gnu.cuda-12.6.tar.gz", + "https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/10.7.0/tars/TensorRT-10.7.0.23.Linux.x86_64-gnu.cuda-12.6.tar.gz", ], ) http_archive( name = "tensorrt_win", build_file = "@//third_party/tensorrt/archive:BUILD", - sha256 = "6c6d92c108a1b3368423e8f69f08d31269830f1e4c9da43b37ba34a176797254", - strip_prefix = "TensorRT-10.6.0.26", + strip_prefix = "TensorRT-10.7.0.23", urls = [ - "https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/10.6.0/zip/TensorRT-10.6.0.26.Windows.win10.cuda-12.6.zip", + "https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/10.7.0/zip/TensorRT-10.7.0.23.Windows.win10.cuda-12.6.zip", ], ) diff --git a/toolchains/ci_workspaces/MODULE_tensorrt.bazel.tmpl b/toolchains/ci_workspaces/MODULE_tensorrt.bazel.tmpl index aff07a0383..0a9bb4b9ec 100644 --- a/toolchains/ci_workspaces/MODULE_tensorrt.bazel.tmpl +++ b/toolchains/ci_workspaces/MODULE_tensorrt.bazel.tmpl @@ -67,7 +67,6 @@ http_archive( http_archive( name = "tensorrt", build_file = "@//third_party/tensorrt/archive:BUILD", - sha256 = "${TENSORRT_SHA256}", strip_prefix = "${TENSORRT_STRIP_PREFIX}", urls = [ "${TENSORRT_URLS}", @@ -77,7 +76,6 @@ http_archive( http_archive( name = "tensorrt_win", build_file = "@//third_party/tensorrt/archive:BUILD", - sha256 = "${TENSORRT_SHA256}", strip_prefix = "${TENSORRT_STRIP_PREFIX}", urls = [ "${TENSORRT_URLS}", diff --git a/toolchains/legacy/WORKSPACE.win.release.tmpl b/toolchains/legacy/WORKSPACE.win.release.tmpl index 58fce5cf54..5570516c6c 100644 --- a/toolchains/legacy/WORKSPACE.win.release.tmpl +++ b/toolchains/legacy/WORKSPACE.win.release.tmpl @@ -63,7 +63,7 @@ http_archive( new_local_repository( name = "tensorrt_win", - path = "C:/TensorRT-10.3.0.26", + path = "C:/TensorRT-10.7.0.23", build_file = "@//third_party/tensorrt/local:BUILD" ) diff --git a/toolchains/legacy/WORKSPACE.x86_64.release.rhel.tmpl b/toolchains/legacy/WORKSPACE.x86_64.release.rhel.tmpl index 97b3a8c566..fcc5e33f44 100644 --- a/toolchains/legacy/WORKSPACE.x86_64.release.rhel.tmpl +++ b/toolchains/legacy/WORKSPACE.x86_64.release.rhel.tmpl @@ -71,10 +71,9 @@ http_archive( http_archive( name = "tensorrt", build_file = "@//third_party/tensorrt/archive:BUILD", - sha256 = "adff1cd5abe5d87013806172351e58fd024e5bf0fc61d49ef4b84cd38ed99081", - strip_prefix = "TensorRT-10.3.0.26", + strip_prefix = "TensorRT-10.7.0.23", urls = 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@@ -219,7 +226,18 @@ Governing Board - + + + Cloud Credit Program + + + Technical Advisory Council + + + Staff + + + Contact Usimport torch +import torch_tensorrt model = MyModel().eval().cuda() inputs = [torch.randn((1, 3, 224, 224)).cuda()] # trt_ep is a torch.fx.GraphModule object -trt_gm = torch_tensorrt.compile(model, ir="dynamo", inputs) +trt_gm = torch_tensorrt.compile(model, ir="dynamo", inputs=inputs) torch_tensorrt.save(trt_gm, "trt.ep", inputs=inputs) # Later, you can load it and run inference @@ -503,13 +521,13 @@
a) ExportedProgram
b) Torchscript¶
-@@ -204,6 +207,10 @@ Eventsimport torch -import torch_tensorrt +
@@ -157,6 +157,9 @@ ExecuTorchdiff --git a/docs/user_guide/using_dla.html b/docs/user_guide/using_dla.html index 1cdd1c6bbb..0cd04c9c0d 100644 --- a/docs/user_guide/using_dla.html +++ b/docs/user_guide/using_dla.html @@ -10,7 +10,7 @@ -import torch +import torch_tensorrt model = MyModel().eval().cuda() inputs = [torch.randn((1, 3, 224, 224)).cuda()] # trt_gm is a torch.fx.GraphModule object -trt_gm = torch_tensorrt.compile(model, ir="dynamo", inputs) +trt_gm = torch_tensorrt.compile(model, ir="dynamo", inputs=inputs) torch_tensorrt.save(trt_gm, "trt.ts", output_format="torchscript", inputs=inputs) # Later, you can load it and run inference @@ -523,12 +541,12 @@
b) Torchscript¶
In Torch-TensorRT 1.X versions, the primary way to compile and run inference with Torch-TensorRT is using Torchscript IR. For ir=ts, this behavior stays the same in 2.X versions as well.
-@@ -275,7 +293,7 @@import torch -import torch_tensorrt +
@@ -219,7 +226,18 @@ Governing Board - + + + Cloud Credit Program + + + Technical Advisory Council + + + Staff + + + Contact Us@@ -204,6 +207,10 @@ Eventsimport torch +import torch_tensorrt model = MyModel().eval().cuda() inputs = [torch.randn((1, 3, 224, 224)).cuda()] -trt_ts = torch_tensorrt.compile(model, ir="ts", inputs) # Output is a ScriptModule object +trt_ts = torch_tensorrt.compile(model, ir="ts", inputs=inputs) # Output is a ScriptModule object torch.jit.save(trt_ts, "trt_model.ts") # Later, you can load it and run inference @@ -542,8 +560,8 @@
Loading the models
import torch -import torch_tensorrt +
@@ -157,6 +157,9 @@ ExecuTorchdiff --git a/docs/user_guide/torch_tensorrt_explained.html b/docs/user_guide/torch_tensorrt_explained.html index 6a219f31b3..42ca24daec 100644 --- a/docs/user_guide/torch_tensorrt_explained.html +++ b/docs/user_guide/torch_tensorrt_explained.html @@ -10,7 +10,7 @@ -import torch +import torch_tensorrt # file_path can be trt.ep or trt.ts file obtained via saving the model (refer to the above section) inputs = [torch.randn((1, 3, 224, 224)).cuda()] @@ -800,7 +818,7 @@
Resources
Developer Resources- - Contributor Awards - 2023 + Contributor Awards - 2024
@@ -816,6 +834,9 @@Resources
- ExecuTorch
+- + ExecuTorch Documentation +
- Events
+- + Newsletter +
- Governing Board
+- + Cloud Credit Program +
+- + Technical Advisory Council +
+- + Staff +
+- + Contact Us +
Torch-TensorRT Explained — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation +Torch-TensorRT Explained — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation @@ -135,8 +135,8 @@ Developer ResourcesFind resources and get questions answered
- - Contributor Awards - 2023 + + Contributor Awards - 2024Award winners announced at this year's PyTorch Conference
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+ + ExecuTorch Docs +Find events, webinars, and podcasts
+ + Newsletter +Stay up-to-date with the latest updates
+- v2.6.0.dev0+70e2a38 + v2.7.0.dev0+d6be4ba@@ -852,7 +870,7 @@Resources
Developer Resources- - Contributor Awards - 2023 + Contributor Awards - 2024
@@ -868,6 +886,9 @@Resources
- ExecuTorch
+- + ExecuTorch Documentation +
- Events
+- + Newsletter +
- Governing Board
+- + Cloud Credit Program +
+- + Technical Advisory Council +
+- + Staff +
+- + Contact Us +
DLA — Torch-TensorRT v2.6.0.dev0+70e2a38 documentation +DLA — Torch-TensorRT v2.7.0.dev0+d6be4ba documentation @@ -135,8 +135,8 @@ Developer ResourcesFind resources and get questions answered
- - Contributor Awards - 2023 + + Contributor Awards - 2024Award winners announced at this year's PyTorch Conference
End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
+ + ExecuTorch Docs +Find events, webinars, and podcasts
+ + Newsletter +Stay up-to-date with the latest updates
+- v2.6.0.dev0+70e2a38 + v2.7.0.dev0+d6be4ba@@ -746,7 +764,7 @@Resources
Developer Resources- - Contributor Awards - 2023 + Contributor Awards - 2024
@@ -762,6 +780,9 @@Resources
- ExecuTorch
+- + ExecuTorch Documentation +
- Events
+- + Newsletter +
- Governing Board
+- + Cloud Credit Program +
+- + Technical Advisory Council +
+- + Staff +
+- + Contact Us +