chore: doc updates (#3238)

docsrc/index.rst

@@ -54,6 +54,11 @@ User Guide
    tutorials/_rendered_examples/dynamo/vgg16_ptq
    tutorials/_rendered_examples/dynamo/engine_caching_example
    tutorials/_rendered_examples/dynamo/refit_engine_example
+   tutorials/serving_torch_tensorrt_with_triton
+   tutorials/_rendered_examples/dynamo/torch_export_cudagraphs
+   tutorials/_rendered_examples/dynamo/converter_overloading
+   tutorials/_rendered_examples/dynamo/custom_kernel_plugins
+   tutorials/_rendered_examples/dynamo/mutable_torchtrt_module_example
 
 Dynamo Frontend
 ----------------
@@ -99,30 +104,28 @@ FX Frontend
 
    fx/getting_started_with_fx_path
 
-Tutorials
+Model Zoo
 ------------
-* :ref:`torch_tensorrt_tutorials`
-* :ref:`serving_torch_tensorrt_with_triton`
+* :ref:`torch_compile_resnet`
+* :ref:`torch_compile_transformer`
+* :ref:`torch_compile_stable_diffusion`
+* :ref:`torch_export_gpt2`
+* :ref:`torch_export_llama2`
 * :ref:`notebooks`
 
 .. toctree::
-   :caption: Tutorials
+   :caption: Model Zoo
    :maxdepth: 3
    :hidden:
-
-   tutorials/serving_torch_tensorrt_with_triton
-   tutorials/notebooks
+
    tutorials/_rendered_examples/dynamo/torch_compile_resnet_example
    tutorials/_rendered_examples/dynamo/torch_compile_transformers_example
    tutorials/_rendered_examples/dynamo/torch_compile_stable_diffusion
-   tutorials/_rendered_examples/dynamo/torch_export_cudagraphs
-   tutorials/_rendered_examples/dynamo/converter_overloading
-   tutorials/_rendered_examples/dynamo/custom_kernel_plugins
    tutorials/_rendered_examples/distributed_inference/data_parallel_gpt2
    tutorials/_rendered_examples/distributed_inference/data_parallel_stable_diffusion
-   tutorials/_rendered_examples/dynamo/mutable_torchtrt_module_example
    tutorials/_rendered_examples/dynamo/torch_export_gpt2
    tutorials/_rendered_examples/dynamo/torch_export_llama2
+   tutorials/notebooks
 
 Python API Documentation
 ------------------------

docsrc/tutorials/notebooks.rst

@@ -1,10 +1,9 @@
 .. _notebooks:
 
-Example notebooks
+Legacy notebooks
 ===================
 
-There exists a number of notebooks which cover specific using specific features and models
-with Torch-TensorRT
+There exists a number of notebooks which demonstrate different model conversions / features / frontends available within Torch-TensorRT
 
 Notebooks
 ------------

examples/README.rst

@@ -1,7 +1,4 @@
 .. _torch_tensorrt_tutorials:
 
 Torch-TensorRT Tutorials
-===========================
-
-The user guide covers the basic concepts and usage of Torch-TensorRT.
-We also provide a number of tutorials to explore specific usecases and advanced concepts
+===========================

examples/dynamo/README.rst

@@ -1,10 +1,6 @@
-.. _torch_compile:
+.. _torch_tensorrt_examples:
 
-Torch-TensorRT Examples
-====================================
-
-Please refer to the following examples which demonstrate the usage of different features of Torch-TensorRT. We also provide
-examples of Torch-TensorRT compilation of select computer vision and language models.
+Here we provide examples of Torch-TensorRT compilation of popular computer vision and language models.
 
 Dependencies
 ------------------------------------
@@ -16,18 +12,6 @@ Please install the following external dependencies (assuming you already have co
     pip install -r requirements.txt
 
 
-Compiler Features
-------------------------------------
-* :ref:`torch_compile_advanced_usage`: Advanced usage including making a custom backend to use directly with the ``torch.compile`` API
-* :ref:`torch_export_cudagraphs`: Using the Cudagraphs integration with `ir="dynamo"`
-* :ref:`converter_overloading`: How to write custom converters and overload existing ones
-* :ref:`custom_kernel_plugins`: Creating a plugin to use a custom kernel inside TensorRT engines
-* :ref:`refit_engine_example`: Refitting a compiled TensorRT Graph Module with updated weights
-* :ref:`mutable_torchtrt_module_example`: Compile, use, and modify TensorRT Graph Module with MutableTorchTensorRTModule
-* :ref:`vgg16_fp8_ptq`: Compiling a VGG16 model with FP8 and PTQ using ``torch.compile``
-* :ref:`engine_caching_example`: Utilizing engine caching to speed up compilation times
-* :ref:`engine_caching_bert_example`: Demonstrating engine caching on BERT
-
 Model Zoo
 ------------------------------------
 * :ref:`torch_compile_resnet`: Compiling a ResNet model using the Torch Compile Frontend for ``torch_tensorrt.compile``

examples/dynamo/torch_compile_resnet_example.py

@@ -1,7 +1,7 @@
 """
 .. _torch_compile_resnet:
 
-Compiling ResNet using the Torch-TensorRT `torch.compile` Backend
+Compiling ResNet with dynamic shapes using the `torch.compile` backend
 ==========================================================
 
 This interactive script is intended as a sample of the Torch-TensorRT workflow with `torch.compile` on a ResNet model."""

examples/dynamo/torch_compile_stable_diffusion.py

@@ -1,7 +1,7 @@
 """
 .. _torch_compile_stable_diffusion:
 
-Torch Compile Stable Diffusion
+Compiling Stable Diffusion model using the `torch.compile` backend
 ======================================================
 
 This interactive script is intended as a sample of the Torch-TensorRT workflow with `torch.compile` on a Stable Diffusion model. A sample output is featured below:

examples/dynamo/torch_compile_transformers_example.py

@@ -1,10 +1,10 @@
 """
 .. _torch_compile_transformer:
 
-Compiling a Transformer using torch.compile and TensorRT
+Compiling BERT using the `torch.compile` backend
 ==============================================================
 
-This interactive script is intended as a sample of the Torch-TensorRT workflow with `torch.compile` on a transformer-based model."""
+This interactive script is intended as a sample of the Torch-TensorRT workflow with `torch.compile` on a BERT model."""
 
 # %%
 # Imports and Model Definition

examples/dynamo/torch_export_gpt2.py

@@ -1,10 +1,10 @@
 """
 .. _torch_export_gpt2:
 
-Compiling GPT2 using the Torch-TensorRT with dynamo backend
+Compiling GPT2 using the dynamo backend
 ==========================================================
 
-This interactive script is intended as a sample of the Torch-TensorRT workflow with dynamo backend on a GPT2 model."""
+This script illustrates Torch-TensorRT workflow with dynamo backend on popular GPT2 model."""
 
 # %%
 # Imports and Model Definition
@@ -88,13 +88,10 @@
     tokenizer.decode(trt_gen_tokens[0], skip_special_tokens=True),
 )
 
-# %%
-# The output sentences should look like
-# =============================
-# Pytorch model generated text:  What is parallel programming ?
+# Prompt : What is parallel programming ?
 
-# The parallel programming paradigm is a set of programming languages that are designed to be used in parallel. The main difference between parallel programming and parallel programming is that
 # =============================
-# TensorRT model generated text:  What is parallel programming ?
+# Pytorch model generated text: The parallel programming paradigm is a set of programming languages that are designed to be used in parallel. The main difference between parallel programming and parallel programming is that
 
-# The parallel programming paradigm is a set of programming languages that are designed to be used in parallel. The main difference between parallel programming and parallel programming is that
+# =============================
+# TensorRT model generated text: The parallel programming paradigm is a set of programming languages that are designed to be used in parallel. The main difference between parallel programming and parallel programming is that

examples/dynamo/torch_export_llama2.py

@@ -1,10 +1,10 @@
 """
 .. _torch_export_llama2:
 
-Compiling Llama2 using the Torch-TensorRT with dynamo backend
+Compiling Llama2 using the dynamo backend
 ==========================================================
 
-This interactive script is intended as a sample of the Torch-TensorRT workflow with dynamo backend on a Llama2 model."""
+This script illustrates Torch-TensorRT workflow with dynamo backend on popular Llama2 model."""
 
 # %%
 # Imports and Model Definition
@@ -91,9 +91,11 @@
     )[0],
 )
 
-# %%
-# The output sentences should look like
+
+# Prompt : What is dynamic programming?
+
 # =============================
-# Pytorch model generated text:  Dynamic programming is an algorithmic technique used to solve complex problems by breaking them down into smaller subproblems, solving each subproblem only once, and
+# Pytorch model generated text: Dynamic programming is an algorithmic technique used to solve complex problems by breaking them down into smaller subproblems, solving each subproblem only once, and
+
 # =============================
-# TensorRT model generated text:  Dynamic programming is an algorithmic technique used to solve complex problems by breaking them down into smaller subproblems, solving each subproblem only once, and
+# TensorRT model generated text: Dynamic programming is an algorithmic technique used to solve complex problems by breaking them down into smaller subproblems, solving each subproblem only once, and