refactor: Address some issues with enums and overhaul documentation

Signed-off-by: Naren Dasan <naren@narendasan.com>
Signed-off-by: Naren Dasan <narens@nvidia.com>

ci: Adding typos pre-commit

Signed-off-by: Naren Dasan <naren@narendasan.com>
Signed-off-by: Naren Dasan <narens@nvidia.com>

Author: narendasan

.pre-commit-config.yaml

@@ -51,6 +51,10 @@ repos:
     hooks:
       - id: black
         exclude: ^examples/custom_converters/elu_converter/setup.py|^docs
+  - repo: https://github.com/crate-ci/typos
+    rev: v1.22.9
+    hooks:
+      - id: typos
   - repo: local
     hooks:
     -   id: dont-commit-upstream

CONTRIBUTING.md

@@ -6,7 +6,7 @@ Do try to fill an issue with your feature or bug before filling a PR (op support
 
 #### Development enviornment
 
-Our build system relies on `bazel` (https://bazel.build/). Though there are many ways to install `bazel`, the prefered method is to use `bazelisk` (https://github.com/bazelbuild/bazelisk) which makes it simple to set up the correct version of bazel on the fly. Additional developement dependencies can be installed via the `requirements-dev.txt` file.
+Our build system relies on `bazel` (https://bazel.build/). Though there are many ways to install `bazel`, the preferred method is to use `bazelisk` (https://github.com/bazelbuild/bazelisk) which makes it simple to set up the correct version of bazel on the fly. Additional developement dependencies can be installed via the `requirements-dev.txt` file.
 
 #### Communication
 

cmake/paths.cmake

@@ -6,7 +6,7 @@ set(ARCHIVE_OUTPUT_DIRECTORY "lib")
 set(RUNTIME_OUTPUT_DIRECTORY "bin")
 set(HEADERS_OUTPUT_DIRECTORY "include")
 
-#Set target ouput directory in the build directory
+#Set target output directory in the build directory
 set(CMAKE_ARCHIVE_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}/${ARCHIVE_OUTPUT_DIRECTORY}")
 set(CMAKE_LIBRARY_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}/${LIBRARY_OUTPUT_DIRECTORY}")
 set(CMAKE_RUNTIME_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}/${RUNTIME_OUTPUT_DIRECTORY}")

dev_dep_versions.yml

@@ -1,3 +1,2 @@
-__version__: "2.5.0.dev0"
 __cuda_version__: "12.4"
 __tensorrt_version__: "10.0.1"

docker/Dockerfile

@@ -50,7 +50,7 @@ RUN TENSORRT_MAJOR_VERSION=`echo ${TENSORRT_VERSION} | cut -d '.' -f 1` && \
 RUN wget -q https://github.com/bazelbuild/bazelisk/releases/download/v1.17.0/bazelisk-linux-amd64 -O /usr/bin/bazel &&\
     chmod a+x /usr/bin/bazel
 
-# Build Torch-TensorRT in an auxillary container
+# Build Torch-TensorRT in an auxiliary container
 FROM base as torch-tensorrt-builder-base
 
 ARG ARCH="x86_64"

docker/README.md

@@ -35,7 +35,7 @@ nvidia-docker run --gpus all -it --shm-size=8gb --env="DISPLAY" --volume="/tmp/.
 Test:
 
 
-You can run any converter test to verify if Torch-TRT built sucessfully inside the container. Once you launch the container, you can run
+You can run any converter test to verify if Torch-TRT built successfully inside the container. Once you launch the container, you can run
 ```
 bazel test //tests/core/conversion/converters:test_activation --compilation_mode=opt --test_output=summary --config use_precompiled_torchtrt --config pre_cxx11_abi
 ```

docsrc/conf.py

@@ -25,7 +25,7 @@
 # -- Project information -----------------------------------------------------
 
 project = "Torch-TensorRT"
-copyright = "2022, NVIDIA Corporation"
+copyright = "2024, NVIDIA Corporation"
 author = "NVIDIA Corporation"
 
 version = f"v{torch_tensorrt.__version__}"
@@ -151,6 +151,9 @@
     "master_doc": True,
     "version_info": {
         "main": "https://pytorch.org/TensorRT/",
+        "v2.3.0": "https://pytorch.org/TensorRT/v2.3.0",
+        "v2.2.0": "https://pytorch.org/TensorRT/v2.2.0",
+        "v2.1.0": "https://pytorch.org/TensorRT/v2.1.0",
         "v1.4.0": "https://pytorch.org/TensorRT/v1.4.0",
         "v1.3.0": "https://pytorch.org/TensorRT/v1.3.0",
         "v1.2.0": "https://pytorch.org/TensorRT/v1.2.0",
@@ -186,6 +189,8 @@
 
 nbsphinx_execute = "never"
 
+autodoc_member_order = "groupwise"
+
 # -- A patch that prevents Sphinx from cross-referencing ivar tags -------
 # See http://stackoverflow.com/a/41184353/3343043
 

docsrc/contributors/dynamo_converters.rst

@@ -36,7 +36,7 @@ The decorator takes a number of arguments:
 All that is required for a converter is the key.
 
 The function body is responsible for taking the current state of the network and adding the next subgraph to perform the op specified in the decorator with TensorRT operations.
-The function is provided arguments as the native PyTorch op would be provided with the added case of numpy arrays for frozen Tensor attributes or TensorRT ITensors which are ouput Tensors of previous nodes, correspoding to edges/output Tensors of intermediate operations in the graph.
+The function is provided arguments as the native PyTorch op would be provided with the added case of numpy arrays for frozen Tensor attributes or TensorRT ITensors which are output Tensors of previous nodes, correspoding to edges/output Tensors of intermediate operations in the graph.
 To determine the types expected as well as the return type of the converter, look at the definition of the op being converted. In the case of ``aten`` operations, this file will be the source of truth: https://github.com/pytorch/pytorch/blob/main/aten/src/ATen/native/native_functions.yaml
 Since many converters a developer may write are a composition of lower level operators, instead of needing to implement the converter in raw TensorRT, the ``torch_tensorrt.dynamo.conversion.impl`` subpackage contains many implementations of operations that can be chained to create a TensorRT subgraph.
 
@@ -53,7 +53,7 @@ Capability Validation
 
 There are some converters which have special cases to be accounted for. In those cases, one should use ``capability_validators`` to register the converter using ``@dynamo_tensorrt_converter``
 We illustrate this through ``torch.ops.aten.embedding.default``. It has parameters - ``scale_grad_by_freq`` and ``sparse`` which are not currently supported by the implementation.
-In such cases we can write validator ``embedding_param_validator`` which implements that given those paramters the converter is not supported and register the converter by
+In such cases we can write validator ``embedding_param_validator`` which implements that given those parameters the converter is not supported and register the converter by
 
 
 Type Contract
@@ -105,7 +105,7 @@ Some operations do not produce TensorRT subgraphs as a side-effect. These are te
 Operator Decomposition
 -----------------------
 
-There are some converters which can be decomposed into suboperations in PyTorch and need not have seperate converter registration.
+There are some converters which can be decomposed into suboperations in PyTorch and need not have separate converter registration.
 Such converters can be implemented via a decomposition
 
 Example: ``addmm``

docsrc/contributors/lowering.rst

@@ -35,7 +35,7 @@ Eliminate Exeception Or Pass Pattern
 
     `Torch-TensorRT/core/lowering/passes/exception_elimination.cpp <https://github.com/pytorch/TensorRT/blob/master/core/lowering/passes/exception_elimination.cpp>`_
 
-A common pattern in scripted modules are dimension gaurds which will throw execptions if
+A common pattern in scripted modules are dimension guards which will throw execptions if
 the input dimension is not what was expected.
 
 .. code-block:: none
@@ -50,7 +50,7 @@ the input dimension is not what was expected.
 
 Since we are resolving all of this at compile time and there are no execptions in the TensorRT graph, we just remove it.
 
-Eliminate Redundant Gaurds
+Eliminate Redundant Guards
 ***************************************
 
     `torch/csrc/jit/passes/guard_elimination.h <https://github.com/pytorch/pytorch/blob/master/torch/csrc/jit/passes/guard_elimination.h>`_
@@ -63,7 +63,7 @@ Freeze Module
 
     `torch/csrc/jit/passes/freeze_module.h <https://github.com/pytorch/pytorch/blob/master/torch/csrc/jit/passes/freeze_module.h>`_
 
-Freeze attributes and inline constants and modules. Propogates constants in the graph.
+Freeze attributes and inline constants and modules. Propagates constants in the graph.
 
 Fuse AddMM Branches
 ***************************************
@@ -103,7 +103,7 @@ Fuse Flatten Linear
 
     `Torch-TensorRT/core/lowering/passes/fuse_flatten_linear.cpp <https://github.com/pytorch/TensorRT/blob/master/core/lowering/passes/fuse_flatten_linear.cpp>`_
 
-TensorRT implicity flattens input layers into fully connected layers when they are higher than 1D. So when there is a
+TensorRT implicitly flattens input layers into fully connected layers when they are higher than 1D. So when there is a
 ``aten::flatten`` -> ``aten::linear`` pattern we remove the ``aten::flatten``.
 
 Lower Graph
@@ -147,7 +147,7 @@ Places delimiting nodes around module calls pre freezing to signify where in the
 
 Looks for delimiters then marks all nodes between the delimiters to tell partitioning to run them in PyTorch
 
-Peephole Optimze
+Peephole Optimize
 ***************************************
 
     `torch/csrc/jit/passes/peephole_optimze.h <https://github.com/pytorch/pytorch/blob/master/torch/csrc/jit/passes/ppeephole_optimze.h>`_
@@ -204,7 +204,7 @@ Unroll Loops
 
     `torch/csrc/jit/passes/loop_unrolling.h <https://github.com/pytorch/pytorch/blob/master/torch/csrc/jit/passes/loop_unrolling.h>`_
 
-Unrolls the operations of compatable loops (e.g. sufficently short) so that you only have to go through the loop once.
+Unrolls the operations of compatible loops (e.g. sufficently short) so that you only have to go through the loop once.
 
 Replace Tile with Repeat
 ***************************************

docsrc/contributors/runtime.rst

@@ -6,7 +6,7 @@ Runtime Phase
 The Runtime phase is responsible for constructing self standing TorchScript graphs with embedded TensorRT engines and serving as the runtime
 when these engines are called. The main interface accepts a serialized TensorRT engine. The execution phase
 will deserialize and wrap this engine in a class which maintains a execution context for each engine
-and some metadata about its inputs and outputs and is compatable with the TorchScript interpreter so that
+and some metadata about its inputs and outputs and is compatible with the TorchScript interpreter so that
 it can be moved around and used like other TorchScript IValues. The engine is run by providing it and inputs
 to the ``tensorrt::execute_engine`` operator which will take the engine and its inputs and return the results of engine exeuction.