8000 Fix minor grammatical corrections by aakashapoorv · Pull Request #2779 · pytorch/TensorRT · GitHub
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Fix minor grammatical corrections #2779

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Apr 26, 2024
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Fix minor grammatical corrections #2779

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8 changes: 4 additions & 4 deletions README.md
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Expand Up @@ -5,13 +5,13 @@

> Ahead of Time (AOT) compiling for PyTorch JIT and FX

Torch-TensorRT is a compiler for PyTorch/TorchScript/FX, targeting NVIDIA GPUs via NVIDIA's TensorRT Deep Learning Optimizer and Runtime. Unlike PyTorch's Just-In-Time (JIT) compiler, Torch-TensorRT is an Ahead-of-Time (AOT) compiler, meaning that before you deploy your TorchScript code, you go through an explicit compile step to convert a standard TorchScript or FX program into an module targeting a TensorRT engine. Torch-TensorRT operates as a PyTorch extention and compiles modules that integrate into the JIT runtime seamlessly. After compilation using the optimized graph should feel no different than running a TorchScript module. You also have access to TensorRT's suite of configurations at compile time, so you are able to specify operating precision (FP32/FP16/INT8) and other settings for your module.
Torch-TensorRT is a compiler for PyTorch/TorchScript/FX, targeting NVIDIA GPUs via NVIDIA's TensorRT Deep Learning Optimizer and Runtime. Unlike PyTorch's Just-In-Time (JIT) compiler, Torch-TensorRT is an Ahead-of-Time (AOT) compiler, meaning that before you deploy your TorchScript code, you go through an explicit compile step to convert a standard TorchScript or FX program into an module targeting a TensorRT engine. Torch-TensorRT operates as a PyTorch extension and compiles modules that integrate into the JIT runtime seamlessly. After compilation using the optimized graph should feel no different than running a TorchScript module. You also have access to TensorRT's suite of configurations at compile time, so you are able to specify operating precision (FP32/FP16/INT8) and other settings for your module.

Resources:
- [Documentation](https://nvidia.github.io/Torch-TensorRT/)
- [FX path Documentation](https://github.com/pytorch/TensorRT/blob/master/docsrc/tutorials/getting_started_with_fx_path.rst)
- [Torch-TensorRT Explained in 2 minutes!](https://www.youtube.com/watch?v=TU5BMU6iYZ0&ab_channel=NVIDIADeveloper)
- [Comprehensive Discusion (GTC Event)](https://www.nvidia.com/en-us/on-demand/session/gtcfall21-a31107/)
- [Comprehensive Discussion (GTC Event)](https://www.nvidia.com/en-us/on-demand/session/gtcfall21-a31107/)
- [Pre-built Docker Container](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch). To use this container, make an NGC account and sign in to NVIDIA's registry with an API key. Refer to [this guide](https://docs.nvidia.com/ngc/ngc-catalog-user-guide/index.html#registering-activating-ngc-account) for the same.

## NVIDIA NGC Container
Expand Down Expand Up @@ -44,7 +44,7 @@ If you would like to build outside a docker container, please follow the section
#include "torch_tensorrt/torch_tensorrt.h"

...
// Set input datatypes. Allowerd options torch::{kFloat, kHalf, kChar, kInt32, kBool}
// Set input datatypes. Allowed options torch::{kFloat, kHalf, kChar, kInt32, kBool}
// Size of input_dtypes should match number of inputs to the network.
// If input_dtypes is not set, default precision follows traditional PyT / TRT rules
auto input = torch_tensorrt::Input(dims, torch::kHalf);
Expand Down Expand Up @@ -306,7 +306,7 @@ Supported Python versions:

### In Torch-TensorRT?

Thanks for wanting to contribute! There are two main ways to handle supporting a new op. Either you can write a converter for the op from scratch and register it in the NodeConverterRegistry or if you can map the op to a set of ops that already have converters you can write a graph rewrite pass which will replace your new op with an equivalent subgraph of supported ops. Its preferred to use graph rewriting because then we do not need to maintain a large library of op converters. Also do look at the various op support trackers in the [issues](https://github.com/pytorch/TensorRT/issues) for information on the support status of various operators.
Thanks for wanting to contribute! There are two main ways to handle supporting a new op. Either you can write a converter for the op from scratch and register it in the NodeConverterRegistry or if you can map the op to a set of ops that already have converters you can write a graph rewrite pass which will replace your new op with an equivalent subgraph of supported ops. It's preferred to use graph rewriting because then we do not need to maintain a large library of op converters. Also do look at the various op support trackers in the [issues](https://github.com/pytorch/TensorRT/issues) for information on the support status of various operators.

### In my application?

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