Add op support table

Author: GregoryComer

.gitignore

@@ -62,7 +62,6 @@ xcuserdata/
 /include/
 /share/
 /version.py
-*.csv
 *_etdump
 
 # Android

backends/xnnpack/README.md

@@ -134,4 +134,4 @@ create an issue on [github](https://www.github.com/pytorch/executorch/issues).
 ## See Also
 For more information about the XNNPACK Backend, please check out the following resources:
 - [XNNPACK Backend](https://pytorch.org/executorch/main/backends-xnnpack)
-- [XNNPACK Backend Internals](https://pytorch.org/executorch/main/backend-delegates-xnnpack-reference)
+- [XNNPACK Backend Internals](https://pytorch.org/executorch/main/backends/xnnpack/backend-delegates-xnnpack-reference)

docs/source/backends-overview.md

@@ -18,20 +18,20 @@ Backends are the bridge between your exported model and the hardware it runs on.
 
 ## Choosing a Backend
 
-| Backend                                       | Platform(s)         | Hardware Type | Typical Use Case                |
-|-----------------------------------------------|---------------------|---------------|---------------------------------|
-| [XNNPACK](backends/xnnpack/xnnpack-overview)              | All                 | CPU           | General-purpose, fallback       |
-| [Core ML](backends-coreml)                    | iOS, macOS          | NPU/GPU       | Apple devices, high performance |
-| [Metal Performance Shaders](backends-mps)     | iOS, macOS          | GPU           | Apple GPU acceleration          |
-| [Vulkan ](backends-vulkan)                    | Android             | GPU           | Android GPU acceleration        |
-| [Qualcomm](backends-qualcomm)                 | Android             | NPU           | Qualcomm SoCs                   |
-| [MediaTek](backends-mediatek)                 | Android             | NPU           | MediaTek SoCs                   |
-| [ARM EthosU](backends-arm-ethos-u)            | Embedded            | NPU           | ARM MCUs                        |
-| [ARM VGF](backends-arm-vgf)                   | Android             | NPU           | ARM platforms                   |
-| [OpenVINO](build-run-openvino)                | Embedded            | CPU/GPU/NPU   | Intel  SoCs                     |
-| [NXP](backends-nxp)                           | Embedded            | NPU           | NXP SoCs                        |
-| [Cadence](backends-cadence)                   | Embedded            | DSP           | DSP-optimized workloads         |
-| [Samsung Exynos](backends-samsung-exynos)     | Android             | NPU           | Samsung Socs                    |
+| Backend                                                   | Platform(s)         | Hardware Type | Typical Use Case                |
+|-----------------------------------------------------------|---------------------|---------------|---------------------------------|
+| [XNNPACK](/backends/xnnpack/xnnpack-overview)             | All                 | CPU           | General-purpose, fallback       |
+| [Core ML](backends-coreml)                                | iOS, macOS          | NPU/GPU       | Apple devices, high performance |
+| [Metal Performance Shaders](backends-mps)                 | iOS, macOS          | GPU           | Apple GPU acceleration          |
+| [Vulkan ](backends-vulkan)                                | Android             | GPU           | Android GPU acceleration        |
+| [Qualcomm](backends-qualcomm)                             | Android             | NPU           | Qualcomm SoCs                   |
+| [MediaTek](backends-mediatek)                             | Android             | NPU           | MediaTek SoCs                   |
+| [ARM EthosU](backends-arm-ethos-u)                        | Embedded            | NPU           | ARM MCUs                        |
+| [ARM VGF](backends-arm-vgf)                               | Android             | NPU           | ARM platforms                   |
+| [OpenVINO](build-run-openvino)                            | Embedded            | CPU/GPU/NPU   | Intel  SoCs                     |
+| [NXP](backends-nxp)                                       | Embedded            | NPU           | NXP SoCs                        |
+| [Cadence](backends-cadence)                               | Embedded            | DSP           | DSP-optimized workloads         |
+| [Samsung Exynos](backends-samsung-exynos)                 | Android             | NPU           | Samsung Socs                    |
 
 **Tip:** For best performance, export a `.pte` file for each backend you plan to support.
 

docs/source/backends/template/README.md

@@ -0,0 +1,53 @@
+# Backend Documentation Template
+
+This template provides a standardized structure and starting point for backend documentation. It is intended to provide a uniform experience for users while allowing for backends to customize their documentation as needed.
+
+## Template Structure
+
+The template includes the following files:
+
+### Required Pages
+
+- `backend-overview.md` - Main backend overview and introduction
+
+### Recommended Pages
+
+- `backend-quantization.md` - Quantization support and API documentation
+- `backend-partitioner.md` - Partitioner API reference
+- `op-support.csv` - Operator support data in CSV format
+
+### Optional Pages (and Subsections)
+
+- `backend-troubleshooting.md` - Common issues and troubleshooting guide
+- `backend-op-support.rst` - Operator support documentation (RST format)
+- `backend-arch-internals.md` - Architecture and internals documentation
+- `tutorials/backend-tutorials.md` - Tutorial sub-section
+  - Use this sub-section to provide tutorials for your backend.
+  - Tutorials should explain how a user can accomplish a task, in a step by step manner.
+  - Some examples might include:
+     - An end to end example of lowering and running a model on a specific platform.
+- `tutorials/backend-guides.md` - Guides sub-section
+  - Use this sub-section to provide guides or how-tos for backend-specific functionality.
+  - Guides should focus on providing information and building conceptual understanding, rather than giving step by step directions.
+  - Some examples might include:
+    - LLM attention management / static attention
+    - Performance optimization guide
+
+## Using the Template
+
+To use this template for a new backend:
+
+1. Copy the entire `template` directory contents to your backend's documentation directory
+2. Rename files to match your backend name (e.g., `backend-overview.md` → `mybackend-overview.md`)
+3. Populate the content for your backend.
+
+### Additional Customization
+
+You may need to:
+- Add backend-specific sections to any file
+- Remove sections that don't apply to your backend
+- Update the operator support CSV with your backend's supported operators
+- Add backend-specific images or diagrams
+- Update cross-references and links
+
+Try to keep the landing page (`backend-overview.md`) simple and straigtforward. Use the child pages and sections to provide more detailed information.

docs/source/backends/template/backend-arch-internals.md

@@ -0,0 +1,8 @@
+# {BACKEND_NAME} Architecture and Internals
+
+This page covers internal implementation details of the backend, and is mainly aimed at contributors and heavy power users. This is an optional page for each backend and has no set structure.
+
+Some topics to consider:
+ * High-level design of the backend
+ * Details on the lowering flow
+ * Internal debugging tools and techniques

docs/source/backends/template/backend-op-support.rst

@@ -0,0 +1,13 @@
+================
+Operator Support
+================
+
+This page lists the operators supported by the {BACKEND_NAME} backend. Operators are the building blocks of the ML model. See `IRs <https://docs.pytorch.org/docs/stable/torch.compiler_ir.html>`_ for more information on the PyTorch operator set.
+
+{OPERATOR_SUPPORT_NOTES}
+
+.. csv-table:: Operator Support
+   :file: op-support.csv
+   :header-rows: 1
+   :widths: 20 15 30 30
+   :align: center

docs/source/backend-template.md renamed to docs/source/backends/template/backend-overview.md

@@ -4,7 +4,7 @@ Provide a brief overview/description of the backend. At a high-level, what does
 
 ## Features
 
-List high-level features of backend, such as general operator and hardware support.
+List high-level features of backend, such as operator and hardware support.
 
 ## Target Requirements
 
@@ -18,27 +18,32 @@ What software and hardware is needed to create a .PTE file targeting this backen
 
 This section describes the steps users need to take in order to generate a .PTE targeting this backend. Include a full code sample for exporting and lowering a model to this backend. Make sure relevant imports for the backend partitioner are included.
 
-### Partitioner API
+## Runtime Integration
 
-What options, if any, does the partitioner take? Are there any other export-time configurations that can be applied? Document each option.
+This section is intended to tell the user all of the steps they'll need to take to be able to run a .PTE file on-device that is targeting the given backend.
+- What CMake targets should they link to?
+- How is this backend compiled from source?
+- Is the backend bundled by default in iOS and/or Android pre-built libraries?
 
-### Quantization
+## Reference
 
-What quantization schemes does this backend support? Consider including the following, as appropriate.
-- What operators are supported?
-- Number of bits?
-- Static vs dynamic activations?
-- Weight only vs activations + weights?
-- Symmetric vs asymmetric weights?
-- Per-tensor, per-chanel, group/blockwise?
+**→{doc}`backend-troubleshooting` — Debug common issues.**
 
-If using a PT2E quantizer, document how to initialize the quantizer and all relevant configs and options.
+**→{doc}`backend-partitioner` — Partitioner options.**
 
-Include a code snippet demonstrating how to perform quantization for this backend. Document, or link to, a description of the parameters that the user can specify.
+**→{doc}`backend-quantization` — Supported quantization schemes.**
 
-## Runtime Integration
+**→{doc}`backend-op-support` — Supported operators.**
 
-This section is intended to tell the user all of the steps they'll need to take to be able to run a .PTE file on-device that is targeting the given backend.
-- What CMake targets should they link to?
-- How is this backend compiled from source?
-- Is the backend bundled by default in iOS and/or Android pre-built libraries?
+**→{doc}`backend-arch-internals` — Backend internals.**
+
+```{toctree}
+:maxdepth: 2
+:hidden:
+:caption: {BACKEND} Backend
+
+backend-troubleshooting
+backend-partitioner
+backend-quantization
+backend-op-support
+backend-arch-internals

docs/source/backends/template/backend-partitioner.md

@@ -0,0 +1,9 @@
+# {BACKEND_NAME} Partitioner API
+
+Document the partitioner API for the backend, including configuration options and compile specs.
+
+ * `option1` - description of the option and values.
+ * `option2`
+ * ...
+
+...

docs/source/backends/template/backend-quantization.md

@@ -0,0 +1,31 @@
+# {BACKEND_NAME} Quantization
+
+Document quantization schemes and flows for the backend. This should include a description of each scheme and a code example to perform quantization. Example sections for PT2E and quantize_ are included below, to be replaced with details for the target backend.
+
+For each supported quantization scheme, include the following:
+ * What is the quantization scheme?
+   * How are weights quantized?
+   * How are activations quantized? Static or dynamic?
+   * How many bits?
+   * What is the granularity? Per-tensor, per-channel, group/block-wise?
+ * What are the steps to quantize a model with this scheme?
+ * Include a code sample.
+ * If the quantization flow only supports a small set of operators - for example, linear only - note this.
+
+### Supported Quantization Schemes
+The {BACKEND_NAME} delegate supports the following quantization schemes:
+
+- {QUANTIZATION_SCHEME_1}
+- {QUANTIZATION_SCHEME_2}
+
+### {QUANTIZATION_METHOD_1} using the PT2E Flow
+
+[Description]
+
+[Code Sample]
+
+### LLM Quantization with quantize_
+
+[Description]
+
+[Code Sample]

docs/source/backends/template/backend-troubleshooting.md

@@ -0,0 +1,15 @@
+# {BACKEND_NAME} Troubleshooting
+
+This page describes common issues that you may encounter when using the {BACKEND_NAME} backend and how to debug and resolve them.
+
+## {COMMON_ISSUE_1}
+
+{ISSUE_DESCRIPTION_1}
+
+{SOLUTION_STEPS_1}
+
+## {COMMON_ISSUE_2}
+
+{ISSUE_DESCRIPTION_2}
+
+{SOLUTION_STEPS_2}