refactor: Address some issues with enums and overhaul documentation

Signed-off-by: Naren Dasan <[email protected]>
Signed-off-by: Naren Dasan <[email protected]>

ci: Adding typos pre-commit

Signed-off-by: Naren Dasan <[email protected]>
Signed-off-by: Naren Dasan <[email protected]>

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

CODE_OF_CONDUCT.md

@@ -6,7 +6,7 @@ In the interest of fostering an open and welcoming environment, we as
 contributors and maintainers pledge to make participation in our project and
 our community a harassment-free experience for everyone, regardless of age, body
 size, disability, ethnicity, sex characteristics, gender identity and expression,
-level of experience, education, socio-economic status, nationality, personal
+level of experience, education, socioeconomic status, nationality, personal
 appearance, race, religion, or sexual identity and orientation.
 
 ## Our Standards

CONTRIBUTING.md

@@ -4,9 +4,9 @@
 
 Do try to fill an issue with your feature or bug before filling a PR (op support is generally an exception as long as you provide tests to prove functionality). There is also a backlog (https://github.com/pytorch/TensorRT/issues) of issues which are tagged with the area of focus, a coarse priority level and whether the issue may be accessible to new contributors. Let us know if you are interested in working on a issue. We are happy to provide guidance and mentorship for new contributors. Though note, there is no claiming of issues, we prefer getting working code quickly vs. addressing concerns about "wasted work".
 
-#### Development enviornment
+#### Development environment
 
-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 development dependencies can be installed via the `requirements-dev.txt` file.
 
 #### Communication
 
@@ -27,7 +27,7 @@ We use the PyTorch Slack for communication about core development, integration w
 
 - Avoid introducing unnecessary complexity into existing code so that maintainability and readability are preserved
 
-- Try to avoid commiting commented out code
+- Try to avoid committing commented out code
 
 - Minimize warnings (and no errors) from the compiler
 

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/RELEASE_CHECKLIST.md

@@ -9,7 +9,7 @@ While Torch-TensorRT is in alpha, patch versions are bumped sequentially on brea
 In beta Torch-TensorRT will get a minor version bump on breaking changes, or upgrade to the next version of PyTorch, patch version will be incremented based on significant bug fixes, or siginficant new functionality in the compiler.
 
 Once Torch-TensorRT hits version 1.0.0, major versions are bumped on breaking API changes, breaking changes or significant new functionality in the compiler
-will result in a minor version bump and sigificant bug fixes will result in a patch version change.
+will result in a minor version bump and significant bug fixes will result in a patch version change.
 
 ## Steps to Packaging a Release
 
@@ -50,7 +50,7 @@ will result in a minor version bump and sigificant bug fixes will result in a pa
             - `[3, 1920, 1080]` (P2)
     - Batch Sizes: 1, 4, 8, 16, 32
     - Frameworks: PyTorch, Torch-TensorRT, ONNX + TRT
-        - If any models do not convert to ONNX / TRT, that is fine. Mark them as failling / no result
+        - If any models do not convert to ONNX / TRT, that is fine. Mark them as failing / no result
     - Devices:
         - A100 (P0)
         - A30 / A30 MIG (P1) (same batches as T4

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/conversion.rst

@@ -3,7 +3,7 @@
 Conversion Phase
 ==================
 
-Once the graph has be simplified to a form thats easy to convert, we then set up a conversion context
+Once the graph has be simplified to a form that's easy to convert, we then set up a conversion context
 to manage the construction of a TensorRT ``INetworkDefinition`` from the blocks nodes. The conversion context
 records the set of converted nodes, block inputs and outputs and other information about the conversion
 of the graph. This data is then used to help converters link together layers and also hold build time