fix: Update file and reference naming for new API

docsrc/index.rst

@@ -70,9 +70,9 @@ Tutorials
 
    tutorials/serving_torch_tensorrt_with_triton
    tutorials/notebooks
-   tutorials/_rendered_examples/dynamo/dynamo_compile_resnet_example
-   tutorials/_rendered_examples/dynamo/dynamo_compile_transformers_example
-   tutorials/_rendered_examples/dynamo/dynamo_compile_advanced_usage
+   tutorials/_rendered_examples/dynamo/torch_compile_resnet_example
+   tutorials/_rendered_examples/dynamo/torch_compile_transformers_example
+   tutorials/_rendered_examples/dynamo/torch_compile_advanced_usage
 
 Python API Documenation
 ------------------------

examples/dynamo/README.rst

@@ -1,11 +1,11 @@
-.. _dynamo_compile:
+.. _torch_compile:
 
 Dynamo / ``torch.compile``
 ----------------------------
 
 Torch-TensorRT provides a backend for the new ``torch.compile`` API released in PyTorch 2.0. In the following examples we describe
 a number of ways you can leverage this backend to accelerate inference.
 
-* :ref:`dynamo_compile_resnet`: Compiling a ResNet model using the Dynamo Compile Frontend for ``torch_tensorrt.compile``
+* :ref:`torch_compile_resnet`: Compiling a ResNet model using the Torch Compile Frontend for ``torch_tensorrt.compile``
 * :ref:`torch_compile_transformer`: Compiling a Transformer model using ``torch.compile``
-* :ref:`dynamo_compile_advanced_usage`: Advanced usage including making a custom backend to use directly with the ``torch.compile`` API
+* :ref:`torch_compile_advanced_usage`: Advanced usage including making a custom backend to use directly with the ``torch.compile`` API

examples/dynamo/dynamo_compile_advanced_usage.py → examples/dynamo/torch_compile_advanced_usage.py

@@ -1,7 +1,7 @@
 """
-.. _dynamo_compile_advanced_usage:
+.. _torch_compile_advanced_usage:
 
-Dynamo Compile Advanced Usage
+Torch Compile Advanced Usage
 ======================================================
 
 This interactive script is intended as an overview of the process by which `torch_tensorrt.dynamo.compile` works, and how it integrates with the new `torch.compile` API."""
@@ -11,6 +11,7 @@
 # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 import torch
+import torch_tensorrt
 
 # %%
 

examples/dynamo/dynamo_compile_resnet_example.py → examples/dynamo/torch_compile_resnet_example.py

@@ -1,10 +1,10 @@
 """
-.. _dynamo_compile_resnet:
+.. _torch_compile_resnet:
 
-Compiling ResNet using the Torch-TensorRT Dyanmo Frontend
+Compiling ResNet using the Torch-TensorRT Dynamo Backend
 ==========================================================
 
-This interactive script is intended as a sample of the `torch_tensorrt.compile` workflow with `torch.compile` on a ResNet model."""
+This interactive script is intended as a sample of the Torch-TensorRT workflow with `torch.compile` on a ResNet model."""
 
 # %%
 # Imports and Model Definition
@@ -57,8 +57,8 @@
 )
 
 # %%
-# Equivalently, we could have run the above via the convenience frontend, as so:
-# `torch_tensorrt.compile(model, ir="dynamo_compile", inputs=inputs, ...)`
+# Equivalently, we could have run the above via the torch.compile frontend, as so:
+# `optimized_model = torch.compile(model, backend="torch_tensorrt", options={"enabled_precisions": enabled_precisions, ...}); optimized_model(*inputs)`
 
 # %%
 # Inference

examples/dynamo/dynamo_compile_transformers_example.py → examples/dynamo/torch_compile_transformers_example.py

@@ -4,7 +4,7 @@
 Compiling a Transformer using torch.compile and TensorRT
 ==============================================================
 
-This interactive script is intended as a sample of the `torch_tensorrt.compile` 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 transformer-based model."""
 
 # %%
 # Imports and Model Definition
@@ -45,24 +45,29 @@
 torch_executed_ops = {}
 
 # %%
-# Compilation with `torch_tensorrt.compile`
+# Compilation with `torch.compile`
 # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
+# Define backend compilation keyword arguments
+compilation_kwargs = {
+    "enabled_precisions": enabled_precisions,
+    "debug": debug,
+    "workspace_size": workspace_size,
+    "min_block_size": min_block_size,
+    "torch_executed_ops": torch_executed_ops,
+}
+
 # Build and compile the model with torch.compile, using Torch-TensorRT backend
-optimized_model = torch_tensorrt.compile(
+optimized_model = torch.compile(
     model,
-    ir="torch_compile",
-    inputs=inputs,
-    enabled_precisions=enabled_precisions,
-    debug=debug,
-    workspace_size=workspace_size,
-    min_block_size=min_block_size,
-    torch_executed_ops=torch_executed_ops,
+    backend="torch_tensorrt",
+    options=compilation_kwargs,
 )
+optimized_model(*inputs)
 
 # %%
 # Equivalently, we could have run the above via the convenience frontend, as so:
-# `torch_tensorrt.compile(model, ir="dynamo_compile", inputs=inputs, ...)`
+# `torch_tensorrt.compile(model, ir="torch_compile", inputs=inputs, **compilation_kwargs)`
 
 # %%
 # Inference