[WIP] Update version in to 0.8.1dev and release file (#323)

* update delease file and add it to doc ith mystè_parser

* typos in release file

* update PR template

4 files changed, 38 insertions, 474 deletions

diff --git a/.github/PULL_REQUEST_TEMPLATE.md b/.github/PULL_REQUEST_TEMPLATE.md
index 7cfe4e6..f2c6606 100644
--- a/.github/PULL_REQUEST_TEMPLATE.md
+++ b/.github/PULL_REQUEST_TEMPLATE.md
@@ -1,10 +1,6 @@
 ## Types of changes
 <!--- What types of changes does your code introduce? Put an `x` in all the boxes that apply: -->
 
-- [ ] Docs change / refactoring / dependency upgrade
-- [ ] Bug fix (non-breaking change which fixes an issue)
-- [ ] New feature (non-breaking change which adds functionality)
-- [ ] Breaking change (fix or feature that would cause existing functionality to change)
 
 
 ## Motivation and context / Related issue
@@ -13,16 +9,19 @@
 <!--- (we recommend to have an existing issue for each pull request) -->
 
 
+
 ## How has this been tested (if it applies)
 <!--- Please describe here how your modifications have been tested. -->
 
 
-## Checklist
+
+## PR checklist
 <!-- - Go over all the following points, and put an `x` in all the boxes that apply. -->
 <!--- If you're unsure about any of these, don't hesitate to ask. We're here to help! -->
 
-- [ ] The documentation is up-to-date with the changes I made.
 - [ ] I have read the [**CONTRIBUTING**](CONTRIBUTING.md) document.
-- [ ] All tests passed, and additional code has been covered with new tests.
+- [ ] The documentation is up-to-date with the changes I made (check build artifacts).
+- [ ] All tests passed, and additional code has been **covered with new tests**.
+- [ ] I have added the PR and Issue fix to the [**RELEASES.md**](RELEASES.md) file.
 
 <!--- In any case, don't hesitate to join and ask questions if you need on slack (https://pot-toolbox.slack.com/), gitter (https://gitter.im/PythonOT/community), or the mailing list (https://mail.python.org/mm3/mailman3/lists/pot.python.org/). -->
diff --git a/RELEASES.md b/RELEASES.md
index 6eb1502..6d4f565 100644
--- a/RELEASES.md
+++ b/RELEASES.md
@@ -1,6 +1,34 @@
 # Releases
 
 
+## 0.8.1dev
+*December 2021*
+
+This release fixes several bugs and introduce two new backends: Cupy
+and Tensorflow. Note that teh tensorflow backend will work only when tensorflow
+has enabled the Numpy behavior (that's for transpose that is not by default in
+tensorflow). We also introduce a simple benchmark on CPU GPU for te sinkhorn
+solver that will be provided in teh documentation.
+
+As always we want to that the contributors who helped mak POT better (and bug free).
+
+#### New features
+
+- New benchmark for sinkhorn solver on CPU/GPU and between backends (PR #316)
+- New tensorflow backend (PR #316)
+- New Cupy backend (PR #315)
+- Documentation always up-to-date with README, RELEASES, CONTRIBUTING and
+  CODE_OF_CONDUCT files (PR #316, PR #322).
+
+#### Closed issues
+
+- Fix bug  in `ot.dist` function when non euclidean distance (Issue #305, PR #306)
+- Fix gradient scaling for functions using `nx.set_gradients` (Issue #309, PR
+  #310)
+- Fix bug in generalized Conditional gradient solver and SinkhornL1L2 (Issue
+  #311, PR #313)
+- Fix log error in `gromov_barycenters` (Issue #317, PR #3018)
+
 ## 0.8.0
 *November 2021*
 
diff --git a/docs/source/releases.rst b/docs/source/releases.rst
index aa06105..8250a4d 100644
--- a/docs/source/releases.rst
+++ b/docs/source/releases.rst
@@ -1,469 +1,6 @@
 Releases
 ========
 
-0.8.0
------
-
-*November 2021*
-
-This new stable release introduces several important features.
-
-First we now have an OpenMP compatible exact ot solver in ``ot.emd``.
-The OpenMP version is used when the parameter ``numThreads`` is greater
-than one and can lead to nice speedups on multi-core machines.
-
-| Second we have introduced a backend mechanism that allows to use
-  standard POT function seamlessly on Numpy, Pytorch and Jax arrays.
-  Other backends are coming but right now POT can be used seamlessly for
-  training neural networks in Pytorch. Notably we propose the first
-  differentiable computation of the exact OT loss with ``ot.emd2`` (can
-  be differentiated w.r.t. both cost matrix and sample weights), but
-  also for the classical Sinkhorn loss with ``ot.sinkhorn2``, the
-  Wasserstein distance in 1D with ``ot.wasserstein_1d``, sliced
-  Wasserstein with ``ot.sliced_wasserstein_distance`` and
-  Gromov-Wasserstein with ``ot.gromov_wasserstein2``. Examples of how
-  this new feature can be used are now available in the documentation
-  where the Pytorch backend is used to estimate a `minimal Wasserstein
-  estimator <https://PythonOT.github.io/auto_examples/backends/plot_unmix_optim_torch.html>`__,
-  a `Generative Network
-  (GAN) <https://PythonOT.github.io/auto_examples/backends/plot_wass2_gan_torch.html>`__,
-  for a `sliced Wasserstein gradient
-  flow <https://PythonOT.github.io/auto_examples/backends/plot_sliced_wass_grad_flow_pytorch.html>`__
-  and `optimizing the Gromov-Wassersein
-  distance <https://PythonOT.github.io/auto_examples/backends/plot_optim_gromov_pytorch.html>`__.
-  Note that the Jax backend is still in early development and quite slow
-  at the moment, we strongly recommend for Jax users to use the `OTT
-  toolbox <https://github.com/google-research/ott>`__ when possible.
-| As a result of this new feature, the old ``ot.gpu`` submodule is now
-  deprecated since GPU implementations can be done using GPU arrays on
-  the torch backends.
-
-Other novel features include implementation for `Sampled Gromov
-Wasserstein and Pointwise Gromov
-Wasserstein <https://PythonOT.github.io/auto_examples/gromov/plot_gromov.html#compute-gw-with-a-scalable-stochastic-method-with-any-loss-function>`__,
-Sinkhorn in log space with ``method='sinkhorn_log'``, `Projection Robust
-Wasserstein <https://PythonOT.github.io/gen_modules/ot.dr.html?highlight=robust#ot.dr.projection_robust_wasserstein>`__,
-ans `deviased Sinkorn
-barycenters <https://PythonOT.github.ioauto_examples/barycenters/plot_debiased_barycenter.html>`__.
-
-This release will also simplify the installation process. We have now a
-``pyproject.toml`` that defines the build dependency and POT should now
-build even when cython is not installed yet. Also we now provide
-pe-compiled wheels for linux ``aarch64`` that is used on Raspberry PI
-and android phones and for MacOS on ARM processors.
-
-Finally POT was accepted for publication in the Journal of Machine
-Learning Research (JMLR) open source software track and we ask the POT
-users to cite `this
-paper <https://www.jmlr.org/papers/v22/20-451.html>`__ from now on. The
-documentation has been improved in particular by adding a "Why OT?"
-section to the quick start guide and several new examples illustrating
-the new features. The documentation now has two version : the stable
-version https://pythonot.github.io/ corresponding to the last release
-and the master version https://pythonot.github.io/master that
-corresponds to the current master branch on GitHub.
-
-As usual, we want to thank all the POT contributors (now 37 people have
-contributed to the toolbox). But for this release we thank in particular
-Nathan Cassereau and Kamel Guerda from the AI support team at
-`IDRIS <http://www.idris.fr/>`__ for their support to the development of
-the backend and OpenMP implementations.
-
-New features
-^^^^^^^^^^^^
-
--  OpenMP support for exact OT solvers (PR #260)
--  Backend for running POT in numpy/torch + exact solver (PR #249)
--  Backend implementation of most functions in ``ot.bregman`` (PR #280)
--  Backend implementation of most functions in ``ot.optim`` (PR #282)
--  Backend implementation of most functions in ``ot.gromov`` (PR #294,
-   PR #302)
--  Test for arrays of different type and device (CPU/GPU) (PR #304,
-   #303)
--  Implementation of Sinkhorn in log space with
-   ``method='sinkhorn_log'`` (PR #290)
--  Implementation of regularization path for L2 Unbalanced OT (PR #274)
--  Implementation of Projection Robust Wasserstein (PR #267)
--  Implementation of Debiased Sinkhorn Barycenters (PR #291)
--  Implementation of Sampled Gromov Wasserstein and Pointwise Gromov
-   Wasserstein (PR #275)
--  Add ``pyproject.toml`` and build POT without installing cython first
-   (PR #293)
--  Lazy implementation in log space for sinkhorn on samples (PR #259)
--  Documentation cleanup (PR #298)
--  Two up-to-date documentations `for stable
-   release <https://PythonOT.github.io/>`__ and for `master
-   branch <https://pythonot.github.io/master/>`__.
--  Building wheels on ARM for Raspberry PI and smartphones (PR #238)
--  Update build wheels to new version and new pythons (PR #236, #253)
--  Implementation of sliced Wasserstein distance (Issue #202, PR #203)
--  Add minimal build to CI and perform pep8 test separately (PR #210)
--  Speedup of tests and return run time (PR #262)
--  Add "Why OT" discussion to the documentation (PR #220)
--  New introductory example to discrete OT in the documentation (PR
-   #191)
--  Add templates for Issues/PR on Github (PR#181)
-
-Closed issues
-^^^^^^^^^^^^^
-
--  Debug Memory leak in GAN example (#254)
--  DEbug GPU bug (Issue #284, #287, PR #288)
--  set\_gradients method for JAX backend (PR #278)
--  Quicker GAN example for CircleCI build (PR #258)
--  Better formatting in Readme (PR #234)
--  Debug CI tests (PR #240, #241, #242)
--  Bug in Partial OT solver dummy points (PR #215)
--  Bug when Armijo linesearch (Issue #184, #198, #281, PR #189, #199,
-   #286)
--  Bug Barycenter Sinkhorn (Issue 134, PR #195)
--  Infeasible solution in exact OT (Issues #126,#93, PR #217)
--  Doc for SUpport Barycenters (Issue #200, PR #201)
--  Fix labels transport in BaseTransport (Issue #207, PR #208)
--  Bug in ``emd_1d``, non respected bounds (Issue #169, PR #170)
--  Removed Python 2.7 support and update codecov file (PR #178)
--  Add normalization for WDA and test it (PR #172, #296)
--  Cleanup code for new version of ``flake8`` (PR #176)
--  Fixed requirements in ``setup.py`` (PR #174)
--  Removed specific MacOS flags (PR #175)
-
-0.7.0
------
-
-*May 2020*
-
-This is the new stable release for POT. We made a lot of changes in the
-documentation and added several new features such as Partial OT,
-Unbalanced and Multi Sources OT Domain Adaptation and several bug fixes.
-One important change is that we have created the GitHub organization
-`PythonOT <https://github.com/PythonOT>`__ that now owns the main POT
-repository https://github.com/PythonOT/POT and the repository for the
-new documentation is now hosted at https://PythonOT.github.io/.
-
-This is the first release where the Python 2.7 tests have been removed.
-Most of the toolbox should still work but we do not offer support for
-Python 2.7 and will close related Issues.
-
-A lot of changes have been done to the documentation that is now hosted
-on https://PythonOT.github.io/ instead of readthedocs. It was a hard
-choice but readthedocs did not allow us to run sphinx-gallery to update
-our beautiful examples and it was a huge amount of work to maintain. The
-documentation is now automatically compiled and updated on merge. We
-also removed the notebooks from the repository for space reason and also
-because they are all available in the `example
-gallery <auto_examples/index.html>`__. Note
-that now the output of the documentation build for each commit in the PR
-is available to check that the doc builds correctly before merging which
-was not possible with readthedocs.
-
-The CI framework has also been changed with a move from Travis to Github
-Action which allows to get faster tests on Windows, MacOS and Linux. We
-also now report our coverage on
-`Codecov.io <https://codecov.io/gh/PythonOT/POT>`__ and we have a
-reasonable 92% coverage. We also now generate wheels for a number of OS
-and Python versions at each merge in the master branch. They are
-available as outputs of this
-`action <https://github.com/PythonOT/POT/actions?query=workflow%3A%22Build+dist+and+wheels%22>`__.
-This will allow simpler multi-platform releases from now on.
-
-In terms of new features we now have `OTDA Classes for unbalanced
-OT <https://pythonot.github.io/gen_modules/ot.da.html#ot.da.UnbalancedSinkhornTransport>`__,
-a new Domain adaptation class form `multi domain problems
-(JCPOT) <auto_examples/domain-adaptation/plot_otda_jcpot.html#sphx-glr-auto-examples-domain-adaptation-plot-otda-jcpot-py>`__,
-and several solvers to solve the `Partial Optimal
-Transport <auto_examples/unbalanced-partial/plot_partial_wass_and_gromov.html#sphx-glr-auto-examples-unbalanced-partial-plot-partial-wass-and-gromov-py>`__
-problems.
-
-This release is also the moment to thank all the POT contributors (old
-and new) for helping making POT such a nice toolbox. A lot of changes
-(also in the API) are coming for the next versions.
-
-Features
-^^^^^^^^
-
--  New documentation on https://PythonOT.github.io/ (PR #160, PR #143,
-   PR #144)
--  Documentation build on CircleCI with sphinx-gallery (PR #145,PR #146,
-   #155)
--  Run sphinx gallery in CI (PR #146)
--  Remove notebooks from repo because available in doc (PR #156)
--  Build wheels in CI (#157)
--  Move from travis to GitHub Action for Windows, MacOS and Linux (PR
-   #148, PR #150)
--  Partial Optimal Transport (PR#141 and PR #142)
--  Laplace regularized OTDA (PR #140)
--  Multi source DA with target shift (PR #137)
--  Screenkhorn algorithm (PR #121)
-
-Closed issues
-^^^^^^^^^^^^^
-
--  Add JMLR paper to teh readme ad Mathieu Blondel to the Acknoledgments
-   (PR #231, #232)
--  Bug in Unbalanced OT example (Issue #127)
--  Clean Cython output when calling setup.py clean (Issue #122)
--  Various Macosx compilation problems (Issue #113, Issue #118, PR#130)
--  EMD dimension mismatch (Issue #114, Fixed in PR #116)
--  2D barycenter bug for non square images (Issue #124, fixed in PR
-   #132)
--  Bad value in EMD 1D (Issue #138, fixed in PR #139)
--  Log bugs for Gromov-Wassertein solver (Issue #107, fixed in PR #108)
--  Weight issues in barycenter function (PR #106)
-
-0.6.0
------
-
-*July 2019*
-
-This is the first official stable release of POT and this means a jump
-to 0.6! The library has been used in the wild for a while now and we
-have reached a state where a lot of fundamental OT solvers are available
-and tested. It has been quite stable in the last months but kept the
-beta flag in its Pypi classifiers until now.
-
-Note that this release will be the last one supporting officially Python
-2.7 (See https://python3statement.org/ for more reasons). For next
-release we will keep the travis tests for Python 2 but will make them
-non necessary for merge in 2020.
-
-The features are never complete in a toolbox designed for solving
-mathematical problems and research but with the new contributions we now
-implement algorithms and solvers from 24 scientific papers (listed in
-the README.md file). New features include a direct implementation of the
-`empirical Sinkhorn
-divergence <all.html#ot.bregman.empirical_sinkhorn_divergence>`__,
-a new efficient (Cython implementation) solver for `EMD in
-1D <all.html#ot.lp.emd_1d>`__ and
-corresponding `Wasserstein
-1D <all.html#ot.lp.wasserstein_1d>`__.
-We now also have implementations for `Unbalanced
-OT <auto_examples/plot_UOT_1D.html>`__
-and a solver for `Unbalanced OT
-barycenters <auto_examples/plot_UOT_barycenter_1D.html>`__.
-A new variant of Gromov-Wasserstein divergence called `Fused
-Gromov-Wasserstein <all.html?highlight=fused_#ot.gromov.fused_gromov_wasserstein>`__
-has been also contributed with exemples of use on `structured
-data <auto_examples/plot_fgw.html>`__
-and computing `barycenters of labeld
-graphs <auto_examples/plot_barycenter_fgw.html>`__.
-
-A lot of work has been done on the documentation with several new
-examples corresponding to the new features and a lot of corrections for
-the docstrings. But the most visible change is a new `quick start
-guide <quickstart.html>`__ for POT
-that gives several pointers about which function or classes allow to
-solve which specific OT problem. When possible a link is provided to
-relevant examples.
-
-We will also provide with this release some pre-compiled Python wheels
-for Linux 64bit on github and pip. This will simplify the install
-process that before required a C compiler and numpy/cython already
-installed.
-
-Finally we would like to acknowledge and thank the numerous contributors
-of POT that has helped in the past build the foundation and are still
-contributing to bring new features and solvers to the library.
-
-Features
-^^^^^^^^
-
--  Add compiled manylinux 64bits wheels to pip releases (PR #91)
--  Add quick start guide (PR #88)
--  Make doctest work on travis (PR #90)
--  Update documentation (PR #79, PR #84)
--  Solver for EMD in 1D (PR #89)
--  Solvers for regularized unbalanced OT (PR #87, PR#99)
--  Solver for Fused Gromov-Wasserstein (PR #86)
--  Add empirical Sinkhorn and empirical Sinkhorn divergences (PR #80)
-
-Closed issues
-^^^^^^^^^^^^^
-
--  Issue #59 fail when using "pip install POT" (new details in doc+
-   hopefully wheels)
--  Issue #85 Cannot run gpu modules
--  Issue #75 Greenkhorn do not return log (solved in PR #76)
--  Issue #82 Gromov-Wasserstein fails when the cost matrices are
-   slightly different
--  Issue #72 Macosx build problem
-
-0.5.0
------
-
-*Sep 2018*
-
-POT is 2 years old! This release brings numerous new features to the
-toolbox as listed below but also several bug correction.
-
-| Among the new features, we can highlight a `non-regularized
-  Gromov-Wasserstein
-  solver <auto_examples/plot_gromov.html>`__,
-  a new `greedy variant of
-  sinkhorn <all.html#ot.bregman.greenkhorn>`__,
-| `non-regularized <all.html#ot.lp.barycenter>`__,
-  `convolutional
-  (2D) <auto_examples/plot_convolutional_barycenter.html>`__
-  and `free
-  support <auto_examples/plot_free_support_barycenter.html>`__
-  Wasserstein barycenters and
-  `smooth <https://github.com/rflamary/POT/blob/prV0.5/notebooks/plot_OT_1D_smooth.html>`__
-  and
-  `stochastic <all.html#ot.stochastic.sgd_entropic_regularization>`__
-  implementation of entropic OT.
-
-POT 0.5 also comes with a rewriting of ot.gpu using the cupy framework
-instead of the unmaintained cudamat. Note that while we tried to keed
-changes to the minimum, the OTDA classes were deprecated. If you are
-happy with the cudamat implementation, we recommend you stay with stable
-release 0.4 for now.
-
-The code quality has also improved with 92% code coverage in tests that
-is now printed to the log in the Travis builds. The documentation has
-also been greatly improved with new modules and examples/notebooks.
-
-This new release is so full of new stuff and corrections thanks to the
-old and new POT contributors (you can see the list in the
-`readme <https://github.com/rflamary/POT/blob/master/README.md>`__).
-
-Features
-^^^^^^^^
-
--  Add non regularized Gromov-Wasserstein solver (PR #41)
--  Linear OT mapping between empirical distributions and 90% test
-   coverage (PR #42)
--  Add log parameter in class EMDTransport and SinkhornLpL1Transport (PR
-   #44)
--  Add Markdown format for Pipy (PR #45)
--  Test for Python 3.5 and 3.6 on Travis (PR #46)
--  Non regularized Wasserstein barycenter with scipy linear solver
-   and/or cvxopt (PR #47)
--  Rename dataset functions to be more sklearn compliant (PR #49)
--  Smooth and sparse Optimal transport implementation with entropic and
-   quadratic regularization (PR #50)
--  Stochastic OT in the dual and semi-dual (PR #52 and PR #62)
--  Free support barycenters (PR #56)
--  Speed-up Sinkhorn function (PR #57 and PR #58)
--  Add convolutional Wassersein barycenters for 2D images (PR #64)
--  Add Greedy Sinkhorn variant (Greenkhorn) (PR #66)
--  Big ot.gpu update with cupy implementation (instead of un-maintained
-   cudamat) (PR #67)
-
-Deprecation
-^^^^^^^^^^^
-
-Deprecated OTDA Classes were removed from ot.da and ot.gpu for version
-0.5 (PR #48 and PR #67). The deprecation message has been for a year
-here since 0.4 and it is time to pull the plug.
-
-Closed issues
-^^^^^^^^^^^^^
-
--  Issue #35 : remove import plot from ot/\ **init**.py (See PR #41)
--  Issue #43 : Unusable parameter log for EMDTransport (See PR #44)
--  Issue #55 : UnicodeDecodeError: 'ascii' while installing with pip
-
-0.4
----
-
-*15 Sep 2017*
-
-This release contains a lot of contribution from new contributors.
-
-Features
-^^^^^^^^
-
--  Automatic notebooks and doc update (PR #27)
--  Add gromov Wasserstein solver and Gromov Barycenters (PR #23)
--  emd and emd2 can now return dual variables and have max\_iter (PR #29
-   and PR #25)
--  New domain adaptation classes compatible with scikit-learn (PR #22)
--  Proper tests with pytest on travis (PR #19)
--  PEP 8 tests (PR #13)
-
-Closed issues
-^^^^^^^^^^^^^
-
--  emd convergence problem du to fixed max iterations (#24)
--  Semi supervised DA error (#26)
-
-0.3.1
------
-
-*11 Jul 2017*
-
--  Correct bug in emd on windows
-
-0.3
----
-
-*7 Jul 2017*
-
--  emd\* and sinkhorn\* are now performed in parallel for multiple
-   target distributions
--  emd and sinkhorn are for OT matrix computation
--  emd2 and sinkhorn2 are for OT loss computation
--  new notebooks for emd computation and Wasserstein Discriminant
-   Analysis
--  relocate notebooks
--  update documentation
--  clean\_zeros(a,b,M) for removimg zeros in sparse distributions
--  GPU implementations for sinkhorn and group lasso regularization
-
-V0.2
-----
-
-*7 Apr 2017*
-
--  New dimensionality reduction method (WDA)
--  Efficient method emd2 returns only tarnsport (in paralell if several
-   histograms given)
-
-0.1.11
-------
-
-*5 Jan 2017*
-
--  Add sphinx gallery for better documentation
--  Small efficiency tweak in sinkhorn
--  Add simple tic() toc() functions for timing
-
-0.1.10
-------
-
-*7 Nov 2016* \* numerical stabilization for sinkhorn (log domain and
-epsilon scaling)
-
-0.1.9
------
-
-*4 Nov 2016*
-
--  Update classes and examples for domain adaptation
--  Joint OT matrix and mapping estimation
-
-0.1.7
------
-
-*31 Oct 2016*
-
--  Original Domain adaptation classes
-
-0.1.3
------
-
--  pipy works
-
-First pre-release
------------------
-
-*28 Oct 2016*
-
-It provides the following solvers: \* OT solver for the linear program/
-Earth Movers Distance. \* Entropic regularization OT solver with
-Sinkhorn Knopp Algorithm. \* Bregman projections for Wasserstein
-barycenter [3] and unmixing. \* Optimal transport for domain adaptation
-with group lasso regularization \* Conditional gradient and Generalized
-conditional gradient for regularized OT.
-
-Some demonstrations (both in Python and Jupyter Notebook format) are
-available in the examples folder.
+.. include:: ../../RELEASES.md
+    :parser: myst_parser.sphinx_
+    :start-line: 3
diff --git a/ot/__init__.py b/ot/__init__.py
index b6dc2b4..55972ea 100644
--- a/ot/__init__.py
+++ b/ot/__init__.py
@@ -50,7 +50,7 @@ from .gromov import (gromov_wasserstein, gromov_wasserstein2,
 # utils functions
 from .utils import dist, unif, tic, toc, toq
 
-__version__ = "0.8.0"
+__version__ = "0.8.1dev"
 
 __all__ = ['emd', 'emd2', 'emd_1d', 'sinkhorn', 'sinkhorn2', 'utils',
            'datasets', 'bregman', 'lp', 'tic', 'toc', 'toq', 'gromov',