prototype_index.rst

PyTorch Prototype Recipes
---------------------------------------------
Prototype features are not available as part of binary distributions like PyPI or Conda (except maybe behind run-time flags). To test these features we would, depending on the feature, recommend building from master or using the nightly wheels that are made available on `pytorch.org <https://pytorch.org>`_.

*Level of commitment*: We are committing to gathering high bandwidth feedback only on these features. Based on this feedback and potential further engagement between community members, we as a community will decide if we want to upgrade the level of commitment or to fail fast.


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.. Add prototype tutorial cards below this line

.. Quantization

.. customcarditem::
   :header: FX Graph Mode Quantization User Guide
   :card_description: Learn about FX Graph Mode Quantization.
   :image: ../_static/img/thumbnails/cropped/generic-pytorch-logo.png
   :link: ../prototype/fx_graph_mode_quant_guide.html
   :tags: FX,Quantization

.. customcarditem::
   :header: FX Graph Mode Post Training Dynamic Quantization
   :card_description: Learn how to do post training dynamic quantization in graph mode based on torch.fx.
   :image: ../_static/img/thumbnails/cropped/generic-pytorch-logo.png
   :link: ../prototype/fx_graph_mode_ptq_dynamic.html
   :tags: FX,Quantization

.. customcarditem::
   :header: FX Graph Mode Post Training Static Quantization
   :card_description: Learn how to do post training static quantization in graph mode based on torch.fx.
   :image: ../_static/img/thumbnails/cropped/generic-pytorch-logo.png
   :link: ../prototype/fx_graph_mode_ptq_static.html
   :tags: FX,Quantization

.. customcarditem::
   :header: Graph Mode Dynamic Quantization on BERT
   :card_description: Learn how to do post training dynamic quantization with graph mode quantization on BERT models.
   :image: ../_static/img/thumbnails/cropped/graph-mode-dynamic-bert.png
   :link: ../prototype/graph_mode_dynamic_bert_tutorial.html
   :tags: Text,Quantization

.. customcarditem::
   :header: PyTorch Numeric Suite Tutorial
   :card_description: Learn how to use the PyTorch Numeric Suite to support quantization debugging efforts.
   :image: ../_static/img/thumbnails/cropped/generic-pytorch-logo.png
   :link: ../prototype/numeric_suite_tutorial.html
   :tags: Debugging,Quantization

.. customcarditem::
   :header: How to Write a Quantizer for PyTorch 2 Export Quantization
   :card_description: Learn how to implement a Quantizer for PT2 Export Quantization
   :image: ../_static/img/thumbnails/cropped/generic-pytorch-logo.png
   :link: ../prototype/pt2e_quantizer.html
   :tags: Quantization

.. customcarditem::
   :header: PyTorch 2 Export Post Training Quantization
   :card_description: Learn how to use Post Training Quantization in PyTorch 2 Export.
   :image: ../_static/img/thumbnails/cropped/generic-pytorch-logo.png
   :link: ../prototype/pt2e_quant_ptq.html
   :tags: Quantization

.. customcarditem::
   :header: PyTorch 2 Export Quantization-Aware Training
   :card_description: Learn how to use Quantization-Aware-Training in PyTorch 2 Export.
   :image: ../_static/img/thumbnails/cropped/generic-pytorch-logo.png
   :link: ../prototype/pt2e_quant_qat.html
   :tags: Quantization

.. customcarditem::
   :header: PyTorch 2 Export Quantization with X86 Backend through Inductor
   :card_description: Learn how to use PT2 Export Quantization with X86 Backend through Inductor.
   :image: ../_static/img/thumbnails/cropped/generic-pytorch-logo.png
   :link: ../prototype/pt2e_quant_x86_inductor.html
   :tags: Quantization

.. Sparsity

.. customcarditem::
   :header: (prototype) Accelerating BERT with semi-structured (2:4) sparsity
   :card_description: Prune BERT to be 2:4 sparse and accelerate for inference.
   :image: _static/img/thumbnails/cropped/generic-pytorch-logo.png
   :link: prototype/semi_structured_sparse.html
   :tags: Model-Optimiziation

.. Mobile

.. customcarditem::
   :header: Use iOS GPU in PyTorch
   :card_description: Learn how to run your models on iOS GPU.
   :image: ../_static/img/thumbnails/cropped/ios.png
   :link: ../prototype/ios_gpu_workflow.html
   :tags: Mobile

.. customcarditem::
   :header: Convert MobileNetV2 to NNAPI
   :card_description: Learn how to prepare a computer vision model to use Android’s Neural Networks API (NNAPI).
   :image: ../_static/img/thumbnails/cropped/android.png
   :link: ../prototype/nnapi_mobilenetv2.html
   :tags: Mobile

.. customcarditem::
   :header: PyTorch Vulkan Backend User Workflow
   :card_description: Learn how to use the Vulkan backend on mobile GPUs.
   :image: ../_static/img/thumbnails/cropped/android.png
   :link: ../prototype/vulkan_workflow.html
   :tags: Mobile

.. customcarditem::
   :header: Tracing-based Selective Build Mobile Interpreter in Android and iOS
   :card_description: Learn how to optimize the mobile interpreter size with a tracing-based selective build.
   :image: ../_static/img/thumbnails/cropped/mobile.png
   :link: ../prototype/tracing_based_selective_build.html
   :tags: Mobile

.. customcarditem::
   :header: Convert Mobilenetv2 to Core ML
   :card_description: Learn how to prepare a computer vision model to use the PyTorch Core ML mobile backend.
   :image: ../_static/img/thumbnails/cropped/ios.png
   :link: ../prototype/ios_coreml_workflow.html
   :tags: Mobile

.. Modules

.. customcarditem::
   :header: Skipping Module Parameter Initialization in PyTorch 1.10
   :card_description: Describes skipping parameter initialization during module construction in PyTorch 1.10, avoiding wasted computation.
   :image: ../_static/img/thumbnails/cropped/generic-pytorch-logo.png
   :link: ../prototype/skip_param_init.html
   :tags: Modules

.. TorchScript

.. customcarditem::
   :header: Model Freezing in TorchScript
   :card_description: Freezing is the process of inlining Pytorch module parameters and attributes values into the TorchScript internal representation.
   :image: ../_static/img/thumbnails/cropped/generic-pytorch-logo.png
   :link: ../prototype/torchscript_freezing.html
   :tags: TorchScript

.. vmap

.. customcarditem::
   :header: Using torch.vmap
   :card_description: Learn about torch.vmap, an autovectorizer for PyTorch operations.
   :image: ../_static/img/thumbnails/cropped/generic-pytorch-logo.png
   :link: ../prototype/vmap_recipe.html
   :tags: vmap

.. NestedTensor

.. customcarditem::
   :header: Nested Tensor
   :card_description: Learn about nested tensors, the new way to batch heterogeneous-length data
   :image: ../_static/img/thumbnails/cropped/generic-pytorch-logo.png
   :link: ../prototype/nestedtensor.html
   :tags: NestedTensor

.. MaskedTensor

.. customcarditem::
   :header: MaskedTensor Overview
   :card_description: Learn about masked tensors, the source of truth for specified and unspecified values
   :image: ../_static/img/thumbnails/cropped/generic-pytorch-logo.png
   :link: ../prototype/maskedtensor_overview.html
   :tags: MaskedTensor

.. customcarditem::
   :header: Masked Tensor Sparsity
   :card_description: Learn about how to leverage sparse layouts (e.g. COO and CSR) in MaskedTensor
   :image: ../_static/img/thumbnails/cropped/generic-pytorch-logo.png
   :link: ../prototype/maskedtensor_sparsity.html
   :tags: MaskedTensor

.. customcarditem::
   :header: Masked Tensor Advanced Semantics
   :card_description: Learn more about Masked Tensor's advanced semantics (reductions and comparing vs. NumPy's MaskedArray)
   :image: ../_static/img/thumbnails/cropped/generic-pytorch-logo.png
   :link: ../prototype/maskedtensor_advanced_semantics.html
   :tags: MaskedTensor

.. customcarditem::
   :header: MaskedTensor: Simplifying Adagrad Sparse Semantics
   :card_description: See a showcase on how masked tensors can enable sparse semantics and provide for a cleaner dev experience
   :image: ../_static/img/thumbnails/cropped/generic-pytorch-logo.png
   :link: ../prototype/maskedtensor_adagrad.html
   :tags: MaskedTensor

.. Model-Optimization

.. customcarditem::
   :header: Inductor Cpp Wrapper Tutorial
   :card_description: Speed up your models with Inductor Cpp Wrapper
   :image: ../_static/img/thumbnails/cropped/generic-pytorch-logo.png
   :link: ../prototype/inductor_cpp_wrapper_tutorial.html
   :tags: Model-Optimization

.. Distributed
.. customcarditem::
   :header: Flight Recorder Tutorial
   :card_description: Debug stuck jobs easily with Flight Recorder
   :image: ../_static/img/thumbnails/cropped/generic-pytorch-logo.png
   :link: ../prototype/flight_recorder_tutorial.html
   :tags: Distributed, Debugging, FlightRecorder

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.. Page TOC
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.. toctree::
   :hidden:

   prototype/fx_graph_mode_quant_guide.html
   prototype/fx_graph_mode_ptq_dynamic.html
   prototype/fx_graph_mode_ptq_static.html
   prototype/flight_recorder_tutorial.html
   prototype/graph_mode_dynamic_bert_tutorial.html
   prototype/inductor_cpp_wrapper_tutorial.html
   prototype/pt2e_quantizer.html
   prototype/pt2e_quant_ptq.html
   prototype/pt2e_quant_qat.html
   prototype/ios_gpu_workflow.html
   prototype/nnapi_mobilenetv2.html
   prototype/tracing_based_selective_build.html
   prototype/ios_coreml_workflow.html
   prototype/numeric_suite_tutorial.html
   prototype/torchscript_freezing.html
   prototype/vmap_recipe.html
   prototype/vulkan_workflow.html
   prototype/nestedtensor.html
   prototype/maskedtensor_overview.html
   prototype/maskedtensor_sparsity.html
   prototype/maskedtensor_advanced_semantics.html
   prototype/maskedtensor_adagrad.html