1.7.0 release

New features

• Update to PyTorch 1.9 #2887
• A StreamingMCMC class for high-dimensional Bayesian inference using NUTS or HMC, thanks to @mtsokol #2857 .
  StreamingMCMC is a drop-in replacement for MCMC that avoids storing samples during inference by streamingly computing statistics such as mean, variance, and r_hat. You can define your own statistics using the pyro.ops.streaming module by either composing existing statistics or defining your own subclass of StreamingStats #2856 .
• Make poutine.reparam compatible with initialization logic in autoguides and MCMC #2876 . Previously you needed to manually transform the value in init_to_value() when using a reparametrizer. In Pyro 1.7 you can specify a single init_to_value() output that should work regardless of whether your model is transformed by a reparametrizer. Note this involves a major refactoring of the Reparam interface, namely replacing .call() with .apply(). If you have defined custom reparametrizers using .__call__() you should refactor them before the next Pyro release.
• Add an AutoStructured guide with flexible distributions, sparse flexible dependencies among latent variables, and support for reparametrization #2812 . This autoguide is somewhere between AutoNormal and AutoIAFNormal+NeuTraReparam. Like AutoNormal this guide is interpretable and structured. Like NeuTraReparam this guide is flexible and can be used to improve geometry for subsequent inference via HMC or NUTS.
• New directional distributions thanks to @OlaRonning
  • SinSkewedToroidal #2826
  • SinBivariateVonMises #2821
• New distributions over genetic and amino acid sequences, thanks to @EWeinstein #2728
  (see https://www.biorxiv.org/content/10.1101/2020.07.31.231381v2)
• New distributions with exponential tails
  • Logistic #2895
  • SkewLogistic #2874
  • AsymmetricLaplace #2850
  • SoftLaplace #2791
  • SoftAsymmetricLaplace #2872
• Add AutoGuideList.quantiles() thanks to @vitkl #2896
• Allow saving a subset of variables in MCMC via the save_params option, which can save memory #2793
• Add a sample option to pyro.contrib.funsor.infer_discrete #2789

New tutorials

• A predictive and deterministic tutorial thanks to @mtsokol #2852
• ProfileHMM with MuE by @EWeinstein #2728
• Factor MuE and Probabilistic PCA by @EWeinstein #2728
• Add @ordabayevy's trick to the enumeration tutorial #2892
• Enhance the LDA tutorial #2790

Bug fixes

• Fix poutine.do to avoid duplicate entries in cond_indep_stack #2846
• Fix infer.csis to ignore unused gradients, thanks to @fshipy #2828
• Catch NAN values in TraceENUM_ELBO grads #2782

Breaking changes

• Simplify param names in callable optim configs #2814
• Switch to softplus transform when using autoguide scales, thanks to experiments performed by @vitkl #2823

Ceanup

• Start using mypy for type checking, thanks to @kamathhrishi #2853 #2858
• Start using black code formatter #2891