We are excited to announce the release of PyTorch 1.10. This release is composed of over 3,400 commits since 1.9, made by 426 contributors. We want to sincerely thank our community for continuously improving PyTorch.
PyTorch 1.10 updates are focused on improving training and performance of PyTorch, and developer usability. The full release notes are available here. Highlights include:
- CUDA Graphs APIs are integrated to reduce CPU overheads for CUDA workloads.
- Several frontend APIs such as FX, torch.special, and nn.Module Parametrization, have moved from beta to stable.
- Support for automatic fusion in JIT Compiler expands to CPUs in addition to GPUs.
- Android NNAPI support is now available in beta.
Along with 1.10, we are also releasing major updates to the PyTorch libraries, which you can read about in this blog post.
Frontend APIs
(Stable) Python code transformations with FX
FX provides a Pythonic platform for transforming and lowering PyTorch programs. It is a toolkit for pass writers to facilitate Python-to-Python transformation of functions and nn.Module instances. This toolkit aims to support a subset of Python language semantics—rather than the whole Python language—to facilitate ease of implementation of transforms. With 1.10, FX is moving to stable.
You can learn more about FX in the official documentation and GitHub examples of program transformations implemented using torch.fx.
(Stable) torch.special
A torch.special module, analogous to SciPy’s special module, is now available in stable. The module has 30 operations, including gamma, Bessel, and (Gauss) error functions.
Refer to this documentation for more details.
(Stable) nn.Module Parametrization
nn.Module parametrizaton, a feature that allows users to parametrize any parameter or buffer of an nn.Module without modifying the nn.Module itself, is available in stable. This release adds weight normalization (weight_norm), orthogonal parametrization (matrix constraints and part of pruning) and more flexibility when creating your own parametrization.
Refer to this tutorial and the general documentation for more details.
(Beta) CUDA Graphs APIs Integration
PyTorch now integrates CUDA Graphs APIs to reduce CPU overheads for CUDA workloads.
CUDA Graphs greatly reduce the CPU overhead for CPU-bound cuda workloads and thus improve performance by increasing GPU utilization. For distributed workloads, CUDA Graphs also reduce jitter, and since parallel workloads have to wait for the slowest worker, reducing jitter improves overall parallel efficiency.
Integration allows seamless interop between the parts of the network captured by cuda graphs, and parts of the network that cannot be captured due to graph limitations.
Read the note for more details and examples, and refer to the general documentation for additional information.
[Beta] Conjugate View
PyTorch’s conjugation for complex tensors (torch.conj()) is now a constant time operation, and returns a view of the input tensor with a conjugate bit set as can be seen by calling torch.is_conj() . This has already been leveraged in various other PyTorch operations like matrix multiplication, dot product etc., to fuse conjugation with the operation leading to significant performance gain and memory savings on both CPU and CUDA.
Distributed Training
Distributed Training Releases Now in Stable
In 1.10, there are a number of features that are moving from beta to stable in the distributed package:
- (Stable) Remote Module: This feature allows users to operate a module on a remote worker like using a local module, where the RPCs are transparent to the user. Refer to this documentation for more details.
- (Stable) DDP Communication Hook: This feature allows users to override how DDP synchronizes gradients across processes. Refer to this documentation for more details.
- (Stable) ZeroRedundancyOptimizer: This feature can be used in conjunction with DistributedDataParallel to reduce the size of per-process optimizer states. With this stable release, it now can handle uneven inputs to different data-parallel workers. Check out this tutorial. We also improved the parameter partition algorithm to better balance memory and computation overhead across processes. Refer to this documentation and this tutorial to learn more.
Performance Optimization and Tooling
[Beta] Profile-directed typing in TorchScript
TorchScript has a hard requirement for source code to have type annotations in order for compilation to be successful. For a long time, it was only possible to add missing or incorrect type annotations through trial and error (i.e., by fixing the type-checking errors generated by torch.jit.script one by one), which was inefficient and time consuming.
Now, we have enabled profile directed typing for torch.jit.script by leveraging existing tools like MonkeyType, which makes the process much easier, faster, and more efficient. For more details, refer to the documentation.
(Beta) CPU Fusion
In PyTorch 1.10, we’ve added an LLVM-based JIT compiler for CPUs that can fuse together sequences of torch library calls to improve performance. While we’ve had this capability for some time on GPUs, this release is the first time we’ve brought compilation to the CPU.
You can check out a few performance results for yourself in this Colab notebook.
(Beta) PyTorch Profiler
The objective of PyTorch Profiler is to target the execution steps that are the most costly in time and/or memory, and visualize the workload distribution between GPUs and CPUs. PyTorch 1.10 includes the following key features:
- Enhanced Memory View: This helps you understand your memory usage better. This tool will help you avoid Out of Memory errors by showing active memory allocations at various points of your program run.
- Enhanced Automated Recommendations: This helps provide automated performance recommendations to help optimize your model. The tools recommend changes to batch size, TensorCore, memory reduction technologies, etc.
- Enhanced Kernel View: Additional columns show grid and block sizes as well as shared memory usage and registers per thread.
- Distributed Training: Gloo is now supported for distributed training jobs.
- Correlate Operators in the Forward & Backward Pass: This helps map the operators found in the forward pass to the backward pass, and vice versa, in a trace view.
- TensorCore: This tool shows the Tensor Core (TC) usage and provides recommendations for data scientists and framework developers.
- NVTX: Support for NVTX markers was ported from the legacy autograd profiler.
- Support for profiling on mobile devices: The PyTorch profiler now has better integration with TorchScript and mobile backends, enabling trace collection for mobile workloads.
Refer to this documentation for details. Check out this tutorial to learn how to get started with this feature.
PyTorch Mobile
(Beta) Android NNAPI Support in Beta
Last year we released prototype support for Android’s Neural Networks API (NNAPI). NNAPI allows Android apps to run computationally intensive neural networks on the most powerful and efficient parts of the chips that power mobile phones, including GPUs (Graphics Processing Units) and NPUs (specialized Neural Processing Units).
Since the prototype we’ve added more op coverage, added support for load-time flexible shapes and ability to run the model on the host for testing. Try out this feature using the tutorial.
Additionally, Transfer Learning steps have been added to Object Detection examples. Check out this GitHub page to learn more. Please provide your feedback or ask questions on the forum. You can also check out this presentation to get an overview.
Thanks for reading. If you’re interested in these updates and want to join the PyTorch community, we encourage you to join the discussion forums and open GitHub issues. To get the latest news from PyTorch, follow us on Twitter, Medium, YouTube, and LinkedIn.
Cheers! Team PyTorch
