[MRG] POT numpy/torch/jax backends (#249)

* add numpy and torch backends

* stat sets on functions

* proper import

* install recent torch on windows

* install recent torch on windows

* now testing all functions in backedn

* add jax backedn

* clenaup windowds

* proper convert for jax backedn

* pep8

* try again windows tests

* test jax conversion

* try proper widows tests

* emd fuction ses backedn

* better test partial OT

* proper tests to_numpy and teplate Backend

* pep8

* pep8 x2

* feaking sinkhorn works with torch

* sinkhorn2 compatible

* working ot.emd2

* important detach

* it should work

* jax autodiff emd

* pep8

* no tast same for jax

* new independat tests per backedn

* freaking pep8

* add tests for gradients

* deprecate ot.gpu

* worging dist function

* working dist

* dist done in backedn

* not in

* remove indexing

* change accuacy for jax

* first pull backend

* projection simplex

* projection simplex

* projection simplex

* projection simplex no ci

* projection simplex no ci

* projection simplex no ci

* pep8

* add backedn discusion to quickstart guide

* projection simplex no ci

* projection simplex no ci

* projection simplex no ci

* pep8 + better doc

* proper links

* corect doctest

* big debug documentation

* doctest again

* doctest again bis

* doctest again ter (last one or i kill myself)

* backend test + doc proj simplex

* correction test_utils

* correction test_utils

* correction cumsum

* correction flip

* correction flip v2

* more debug

* more debug

* more debug + pep8

* pep8

* argh

* proj_simplex

* backedn works for sort

* proj simplex

* jax sucks

* update doc

* Update test/test_utils.py

Co-authored-by: Alexandre Gramfort <alexandre.gramfort@m4x.org>

* Update docs/source/quickstart.rst

Co-authored-by: Alexandre Gramfort <alexandre.gramfort@m4x.org>

* Update docs/source/quickstart.rst

Co-authored-by: Alexandre Gramfort <alexandre.gramfort@m4x.org>

* Update docs/source/quickstart.rst

Co-authored-by: Alexandre Gramfort <alexandre.gramfort@m4x.org>

* Update docs/source/readme.rst

Co-authored-by: Alexandre Gramfort <alexandre.gramfort@m4x.org>

* Update test/test_utils.py

Co-authored-by: Alexandre Gramfort <alexandre.gramfort@m4x.org>

* Update ot/utils.py

Co-authored-by: Alexandre Gramfort <alexandre.gramfort@m4x.org>

* Update docs/source/readme.rst

Co-authored-by: Alexandre Gramfort <alexandre.gramfort@m4x.org>

* Update ot/lp/__init__.py

Co-authored-by: Alexandre Gramfort <alexandre.gramfort@m4x.org>

* begin comment alex

* comment alex part 2

* optimize test gromov

* proj_simplex on vectors

* add awesome gradient decsnt example on the weights

* pep98 of course

* proof read example by alex

* pep8 again

* encoding oos in translation

* correct legend

Co-authored-by: Nicolas Courty <ncourty@irisa.fr>
Co-authored-by: Alexandre Gramfort <alexandre.gramfort@m4x.org>

1 files changed, 74 insertions, 0 deletions

diff --git a/test/test_bregman.py b/test/test_bregman.py
index 1ebd21f..7c5162a 100644
--- a/test/test_bregman.py
+++ b/test/test_bregman.py
@@ -9,6 +9,10 @@ import numpy as np
 import pytest
 
 import ot
+from ot.backend import get_backend_list
+from ot.backend import torch
+
+backend_list = get_backend_list()
 
 
 def test_sinkhorn():
@@ -30,6 +34,76 @@ def test_sinkhorn():
         u, G.sum(0), atol=1e-05)  # cf convergence sinkhorn
 
 
+@pytest.mark.parametrize('nx', backend_list)
+def test_sinkhorn_backends(nx):
+    n_samples = 100
+    n_features = 2
+    rng = np.random.RandomState(0)
+
+    x = rng.randn(n_samples, n_features)
+    y = rng.randn(n_samples, n_features)
+    a = ot.utils.unif(n_samples)
+
+    M = ot.dist(x, y)
+
+    G = ot.sinkhorn(a, a, M, 1)
+
+    ab = nx.from_numpy(a)
+    Mb = nx.from_numpy(M)
+
+    Gb = ot.sinkhorn(ab, ab, Mb, 1)
+
+    np.allclose(G, nx.to_numpy(Gb))
+
+
+@pytest.mark.parametrize('nx', backend_list)
+def test_sinkhorn2_backends(nx):
+    n_samples = 100
+    n_features = 2
+    rng = np.random.RandomState(0)
+
+    x = rng.randn(n_samples, n_features)
+    y = rng.randn(n_samples, n_features)
+    a = ot.utils.unif(n_samples)
+
+    M = ot.dist(x, y)
+
+    G = ot.sinkhorn(a, a, M, 1)
+
+    ab = nx.from_numpy(a)
+    Mb = nx.from_numpy(M)
+
+    Gb = ot.sinkhorn2(ab, ab, Mb, 1)
+
+    np.allclose(G, nx.to_numpy(Gb))
+
+
+def test_sinkhorn2_gradients():
+    n_samples = 100
+    n_features = 2
+    rng = np.random.RandomState(0)
+
+    x = rng.randn(n_samples, n_features)
+    y = rng.randn(n_samples, n_features)
+    a = ot.utils.unif(n_samples)
+
+    M = ot.dist(x, y)
+
+    if torch:
+
+        a1 = torch.tensor(a, requires_grad=True)
+        b1 = torch.tensor(a, requires_grad=True)
+        M1 = torch.tensor(M, requires_grad=True)
+
+        val = ot.sinkhorn2(a1, b1, M1, 1)
+
+        val.backward()
+
+        assert a1.shape == a1.grad.shape
+        assert b1.shape == b1.grad.shape
+        assert M1.shape == M1.grad.shape
+
+
 def test_sinkhorn_empty():
     # test sinkhorn
     n = 100