pytorch · Olivia-liu · Sep 16, 2024 · Sep 16, 2024
diff --git a/docs/source/concepts.md b/docs/source/concepts.md
@@ -283,7 +283,7 @@ Techniques for performing computations and memory accesses on tensors with lower
 
 The ExecuTorch runtime executes models on edge devices. It is responsible for program initialization, program execution and, optionally, destruction (releasing backend owned resources).
 
-## [Developer Tools](./sdk-overview.md)
+## [Developer Tools](./devtools-overview.md)
 
 A collection of tools users need to profile, debug and visualize programs that are running with ExecuTorch.
 

diff --git a/docs/source/devtools-overview.md b/docs/source/devtools-overview.md
@@ -0,0 +1,44 @@
+# Introduction to the ExecuTorch Developer Tools
+
+ExecuTorch has been designed with [productivity](./intro-overview.md) as one of its core objectives and the ExecuTorch Developer Tools enable this through the comprehensive suite of tools it provides users to help them profile, debug, and visualize models that they have onboarded onto ExecuTorch.
+
+All the components of the Developer Tools have been designed from the ground up with deep integration in both the export process and the runtime. This enables us to provide unique features such as linking back operator execution in the runtime to the line of code in the original eager model that this operator originated from.
+
+## Developer Tools Features
+
+The ExecuTorch Developer Tools support the following features:
+
+- **BundledProgram** is a utility tool for exporting the model bundled with a sample set of (representative) inputs and expected outputs, so that during runtime users can validate that the actual output is in fact the same as the expected output.
+- **Profiling** models with operator level breakdown of performance stats
+    - Linking back operator performance stats to source code and module hierarchy
+    - Model loading and execution time
+- **Delegate Integration** - Surfacing performance details from delegate backends
+    - Link back delegate operator execution to the nodes they represent in the edge dialect graph (and subsequently linking back to source code and module hierarchy)
+- **Debugging** - Intermediate outputs and output quality analysis
+- **Visualization** - Coming soon
+
+## Fundamental components of the Developer Tools
+
+In order to fully understand and leverage the power of the Developer Tools in this section, the fundamental components that power the Developer Tools will be detailed.
+
+### ETRecord
+ETRecord (ExecuTorch Record) is an artifact generated during the export process that stores the graphs and other metadata that is critical for the Developer Tools to be able to link back the performance/debug data sourced from the runtime to the source code of the eager model.
+
+To draw a rough equivalence to conventional software development ETRecord can be considered as the binary built with debug symbols that is used for debugging in GNU Project debugger (gdb).
+
+More details are available in the [ETRecord documentation](sdk-etrecord.rst) on how to generate and store an ETRecord.
+
+### ETDump
+ETDump (ExecuTorch Dump) is the binary blob that is generated by the runtime after running a model. Similarly as above, to draw a rough equivalence to conventional software development, ETDump can be considered as the coredump of ExecuTorch, but in this case within ETDump we store all the performance and debug data that was generated by the runtime during model execution.
+
+```{note}
+If you only care about looking at the raw performance data without linking back to source code and other extensive features, an ETDump alone will be enough to leverage the basic features of the Developer Tools. For the full experience, it is recommended that the users also generate an ETRecord.
+```
+
+More details are available in the [ETDump documentation](sdk-etdump.md) on how to generate and store an ETDump from the runtime.
+
+
+### Inspector APIs
+The Inspector Python APIs are the main user enrty point into the Developer Tools. They join the data sourced from ETDump and ETRecord to give users access to all the performance and debug data sourced from the runtime along with linkage back to eager model source code and module hierarchy in an easy to use API.
+
+More details are available in the [Inspector API documentation](sdk-inspector.rst) on how to use the Inspector APIs.
diff --git a/docs/source/devtools-tutorial.md b/docs/source/devtools-tutorial.md
@@ -0,0 +1,3 @@
+## Developer Tools Usage Tutorial
+
+Please refer to the [Developer Tools tutorial](./tutorials/devtools-integration-tutorial) for a walkthrough on how to profile a model in ExecuTorch using the Developer Tools.
diff --git a/docs/source/getting-started-architecture.md b/docs/source/getting-started-architecture.md
@@ -89,6 +89,6 @@ _Executor_ is the entry point to load the program and execute it. The execution
 
 ## Developer Tools
 
-It should be efficient for users to go from research to production using the flow above. Productivity is essentially important, for users to author, optimize and deploy their models. We provide [ExecuTorch Developer Tools](./sdk-overview.md) to improve productivity. The Developer Tools are not in the diagram. Instead it's a tool set that covers the developer workflow in all three phases.
+It should be efficient for users to go from research to production using the flow above. Productivity is essentially important, for users to author, optimize and deploy their models. We provide [ExecuTorch Developer Tools](./devtools-overview.md) to improve productivity. The Developer Tools are not in the diagram. Instead it's a tool set that covers the developer workflow in all three phases.
 
 During the program preparation and execution, users can use the ExecuTorch Developer Tools to profile, debug, or visualize the program. Since the end-to-end flow is within the PyTorch ecosystem, users can correlate and display performance data along with graph visualization as well as direct references to the program source code and model hierarchy. We consider this to be a critical component for quickly iterating and lowering PyTorch programs to edge devices and environments.
diff --git a/docs/source/index.rst b/docs/source/index.rst
@@ -94,7 +94,7 @@ Topics in this section will help you get started with ExecuTorch.
    tutorials/export-to-executorch-tutorial
    running-a-model-cpp-tutorial
    extension-module
-   tutorials/sdk-integration-tutorial
+   tutorials/devtools-integration-tutorial
    apple-runtime
    demo-apps-ios
    demo-apps-android
@@ -196,15 +196,15 @@ Topics in this section will help you get started with ExecuTorch.
    :caption: Developer Tools
    :hidden:
 
-   sdk-overview
+   devtools-overview
    sdk-bundled-io
    sdk-etrecord
    sdk-etdump
    sdk-profiling
    sdk-debugging
    sdk-inspector
    sdk-delegate-integration
-   sdk-tutorial
+   devtools-tutorial
 
 .. toctree::
    :glob:
@@ -247,7 +247,7 @@ ExecuTorch tutorials.
    :header: Using the ExecuTorch Developer Tools to Profile a Model
    :card_description: A tutorial for using the ExecuTorch Developer Tools to profile and analyze a model with linkage back to source code.
    :image: _static/img/generic-pytorch-logo.png
-   :link: tutorials/sdk-integration-tutorial.html
+   :link: tutorials/devtools-integration-tutorial.html
    :tags: devtools
 
 .. customcarditem::

diff --git a/docs/source/native-delegates-executorch-xnnpack-delegate.md b/docs/source/native-delegates-executorch-xnnpack-delegate.md
@@ -74,7 +74,7 @@ Since weight packing creates an extra copy of the weights inside XNNPACK, We fre
 When executing the XNNPACK subgraphs, we prepare the tensor inputs and outputs and feed them to the XNNPACK runtime graph. After executing the runtime graph, the output pointers are filled with the computed tensors.
 
 #### **Profiling**
-We have enabled basic profiling for XNNPACK delegate that can be enabled with the following compiler flag `-DENABLE_XNNPACK_PROFILING`. With ExecuTorch's Developer Tools integration, you can also now use the Developer Tools to profile the model. You can follow the steps in [Using the ExecuTorch Developer Tools to Profile a Model](./tutorials/sdk-integration-tutorial) on how to profile ExecuTorch models and use Developer Tools' Inspector API to view XNNPACK's internal profiling information.
+We have enabled basic profiling for XNNPACK delegate that can be enabled with the following compiler flag `-DENABLE_XNNPACK_PROFILING`. With ExecuTorch's Developer Tools integration, you can also now use the Developer Tools to profile the model. You can follow the steps in [Using the ExecuTorch Developer Tools to Profile a Model](./tutorials/devtools-integration-tutorial) on how to profile ExecuTorch models and use Developer Tools' Inspector API to view XNNPACK's internal profiling information.
 
 
 [comment]: <> (TODO: Refactor quantizer to a more official quantization doc)

diff --git a/docs/source/sdk-inspector.rst b/docs/source/sdk-inspector.rst
@@ -17,7 +17,7 @@ APIs:
 * By accessing the `public attributes <#inspector-attributes>`__ of the ``Inspector``, ``EventBlock``, and ``Event`` classes.
 * By using a `CLI <#cli>`__ tool for basic functionalities.
 
-Please refer to the `e2e use case doc <tutorials/sdk-integration-tutorial.html>`__ get an understanding of how to use these in a real world example.
+Please refer to the `e2e use case doc <tutorials/devtools-integration-tutorial.html>`__ get an understanding of how to use these in a real world example.
 
 
 Inspector Methods

diff --git a/docs/source/sdk-overview.md b/docs/source/sdk-overview.md
@@ -1,44 +1,3 @@
 # Introduction to the ExecuTorch Developer Tools
 
-ExecuTorch has been designed with [productivity](./intro-overview.md) as one of its core objectives and the ExecuTorch Developer Tools enable this through the comprehensive suite of tools it provides users to help them profile, debug, and visualize models that they have onboarded onto ExecuTorch.
-
-All the components of the Developer Tools have been designed from the ground up with deep integration in both the export process and the runtime. This enables us to provide unique features such as linking back operator execution in the runtime to the line of code in the original eager model that this operator originated from.
-
-## Developer Tools Features
-
-The ExecuTorch Developer Tools support the following features:
-
-- **BundledProgram** is a utility tool for exporting the model bundled with a sample set of (representative) inputs and expected outputs, so that during runtime users can validate that the actual output is in fact the same as the expected output.
-- **Profiling** models with operator level breakdown of performance stats
-    - Linking back operator performance stats to source code and module hierarchy
-    - Model loading and execution time
-- **Delegate Integration** - Surfacing performance details from delegate backends
-    - Link back delegate operator execution to the nodes they represent in the edge dialect graph (and subsequently linking back to source code and module hierarchy)
-- **Debugging** - Intermediate outputs and output quality analysis
-- **Visualization** - Coming soon
-
-## Fundamental components of the Developer Tools
-
-In order to fully understand and leverage the power of the Developer Tools in this section, the fundamental components that power the Developer Tools will be detailed.
-
-### ETRecord
-ETRecord (ExecuTorch Record) is an artifact generated during the export process that stores the graphs and other metadata that is critical for the Developer Tools to be able to link back the performance/debug data sourced from the runtime to the source code of the eager model.
-
-To draw a rough equivalence to conventional software development ETRecord can be considered as the binary built with debug symbols that is used for debugging in GNU Project debugger (gdb).
-
-More details are available in the [ETRecord documentation](sdk-etrecord.rst) on how to generate and store an ETRecord.
-
-### ETDump
-ETDump (ExecuTorch Dump) is the binary blob that is generated by the runtime after running a model. Similarly as above, to draw a rough equivalence to conventional software development, ETDump can be considered as the coredump of ExecuTorch, but in this case within ETDump we store all the performance and debug data that was generated by the runtime during model execution.
-
-```{note}
-If you only care about looking at the raw performance data without linking back to source code and other extensive features, an ETDump alone will be enough to leverage the basic features of the Developer Tools. For the full experience, it is recommended that the users also generate an ETRecord.
-```
-
-More details are available in the [ETDump documentation](sdk-etdump.md) on how to generate and store an ETDump from the runtime.
-
-
-### Inspector APIs
-The Inspector Python APIs are the main user enrty point into the Developer Tools. They join the data sourced from ETDump and ETRecord to give users access to all the performance and debug data sourced from the runtime along with linkage back to eager model source code and module hierarchy in an easy to use API.
-
-More details are available in the [Inspector API documentation](sdk-inspector.rst) on how to use the Inspector APIs.
+Please update your link to <https://pytorch.org/executorch/main/devtools-overview.html>. This URL will be deleted after v0.4.0.
diff --git a/docs/source/sdk-profiling.md b/docs/source/sdk-profiling.md
@@ -20,4 +20,4 @@ We provide access to all the profiling data via the Python [Inspector API](./sdk
     - Through the Inspector API, users can do a wide range of analysis varying from printing out performance details to doing more finer granular calculation on module level.
 
 
-Please refer to the [Developer Tools tutorial](./tutorials/sdk-integration-tutorial.rst) for a step-by-step walkthrough of the above process on a sample model.
+Please refer to the [Developer Tools tutorial](./tutorials/devtools-integration-tutorial.rst) for a step-by-step walkthrough of the above process on a sample model.
diff --git a/docs/source/sdk-tutorial.md b/docs/source/sdk-tutorial.md
@@ -1,3 +1,3 @@
 ## Developer Tools Usage Tutorial
 
-Please refer to the [Developer Tools tutorial](./tutorials/sdk-integration-tutorial) for a walkthrough on how to profile a model in ExecuTorch using the Developer Tools.
+Please update your link to <https://pytorch.org/executorch/main/devtools-tutorial.html>. This URL will be deleted after v0.4.0.