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 `_. *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. .. raw:: html
.. 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 .. customcarditem:: :header: PyTorch 2 Export Quantization for OpenVINO torch.compile Backend :card_description: Learn how to use PT2 Export Quantization with OpenVINO torch.compile Backend. :image: ../_static/img/thumbnails/cropped/generic-pytorch-logo.png :link: ../prototype/openvino_quantizer.html :tags: Quantization .. customcarditem:: :header: PyTorch 2 Export Quantization with Intel GPU Backend through Inductor :card_description: Learn how to use PT2 Export Quantization with Intel GPU Backend through Inductor. :image: _static/img/thumbnails/cropped/pytorch-logo.png :link: ../prototype/pt2e_quant_xpu_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 .. customcarditem:: :header: Inductor Windows CPU Tutorial :card_description: Speed up your models with Inductor On Windows CPU :image: ../_static/img/thumbnails/cropped/generic-pytorch-logo.png :link: ../prototype/inductor_windows.html :tags: Model-Optimization .. customcarditem:: :header: Use max-autotune compilation on CPU to gain additional performance boost :card_description: Tutorial for max-autotune mode on CPU to gain additional performance boost :image: ../_static/img/thumbnails/cropped/generic-pytorch-logo.png :link: ../prototype/max_autotune_on_CPU_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 .. customcarditem:: :header: Context Parallel Tutorial :card_description: Parallelize the attention computation along sequence dimension :image: ../_static/img/thumbnails/cropped/generic-pytorch-logo.png :link: ../prototype/context_parallel.html :tags: Distributed, Context Parallel .. Integration .. customcarditem:: :header: Out-of-tree extension autoloading in Python :card_description: Learn how to improve the seamless integration of out-of-tree extension with PyTorch based on the autoloading mechanism. :image: ../_static/img/thumbnails/cropped/generic-pytorch-logo.png :link: ../prototype/python_extension_autoload.html :tags: Extending-PyTorch, Frontend-APIs .. GPUDirect Storage .. customcarditem:: :header: (prototype) Using GPUDirect Storage :card_description: Learn how to use GPUDirect Storage in PyTorch. :image: ../_static/img/thumbnails/cropped/generic-pytorch-logo.png :link: ../prototype/gpu_direct_storage.html :tags: GPUDirect-Storage .. End of tutorial card section .. raw:: html
.. ----------------------------------------- .. Page TOC .. ----------------------------------------- .. toctree:: :hidden: prototype/context_parallel.html 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/inductor_windows.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 prototype/python_extension_autoload.html prototype/max_autotune_CPU_with_gemm_template_tutorial.html