[MRG] Tests with types/device on sliced/bregman/gromov functions (#303)

* First draft : making pytest use gpu for torch testing

* bug solve

* Revert "bug solve"

This reverts commit 29b013abd162f8693128f26d8129186b79923609.

* Revert "First draft : making pytest use gpu for torch testing"

This reverts commit 2778175bcc338016c704efa4187d132fe5162e3a.

* sliced

* sliced

* ot 1dsolver

* bregman

* better print

* jax works with sinkhorn, sinkhorn_log and sinkhornn_stabilized, no need to skip them

* gromov & entropic gromov

6 files changed, 191 insertions, 22 deletions

diff --git a/test/conftest.py b/test/conftest.py
index 876b525..987d98e 100644
--- a/test/conftest.py
+++ b/test/conftest.py
@@ -11,6 +11,7 @@ import functools
 
 if jax:
     from jax.config import config
+    config.update("jax_enable_x64", True)
 
 backend_list = get_backend_list()
 
@@ -18,16 +19,25 @@ backend_list = get_backend_list()
 @pytest.fixture(params=backend_list)
 def nx(request):
     backend = request.param
-    if backend.__name__ == "jax":
-        config.update("jax_enable_x64", True)
 
     yield backend
 
-    if backend.__name__ == "jax":
-        config.update("jax_enable_x64", False)
-
 
 def skip_arg(arg, value, reason=None, getter=lambda x: x):
+    if isinstance(arg, tuple) or isinstance(arg, list):
+        n = len(arg)
+    else:
+        arg = (arg, )
+        n = 1
+    if n != 1 and (isinstance(value, tuple) or isinstance(value, list)):
+        pass
+    else:
+        value = (value, )
+    if isinstance(getter, tuple) or isinstance(value, list):
+        pass
+    else:
+        getter = [getter] * n
+
     if reason is None:
         reason = f"Param {arg} should be skipped for value {value}"
 
@@ -35,7 +45,10 @@ def skip_arg(arg, value, reason=None, getter=lambda x: x):
 
         @functools.wraps(function)
         def wrapped(*args, **kwargs):
-            if arg in kwargs.keys() and getter(kwargs[arg]) == value:
+            if all(
+                arg[i] in kwargs.keys() and getter[i](kwargs[arg[i]]) == value[i]
+                for i in range(n)
+            ):
                 pytest.skip(reason)
             return function(*args, **kwargs)
 
diff --git a/test/test_1d_solver.py b/test/test_1d_solver.py
index 77b1234..cb85cb9 100644
--- a/test/test_1d_solver.py
+++ b/test/test_1d_solver.py
@@ -85,7 +85,6 @@ def test_wasserstein_1d(nx):
     np.testing.assert_almost_equal(100 * res[0], res[1], decimal=4)
 
 
-@pytest.mark.parametrize('nx', backend_list)
 def test_wasserstein_1d_type_devices(nx):
 
     rng = np.random.RandomState(0)
@@ -98,8 +97,7 @@ def test_wasserstein_1d_type_devices(nx):
     rho_v /= rho_v.sum()
 
     for tp in nx.__type_list__:
-
-        print(tp.dtype)
+        print(nx.dtype_device(tp))
 
         xb = nx.from_numpy(x, type_as=tp)
         rho_ub = nx.from_numpy(rho_u, type_as=tp)
@@ -107,8 +105,7 @@ def test_wasserstein_1d_type_devices(nx):
 
         res = wasserstein_1d(xb, xb, rho_ub, rho_vb, p=1)
 
-        if not str(nx) == 'numpy':
-            assert res.dtype == xb.dtype
+        nx.assert_same_dtype_device(xb, res)
 
 
 def test_emd_1d_emd2_1d():
@@ -162,17 +159,14 @@ def test_emd1d_type_devices(nx):
     rho_v /= rho_v.sum()
 
     for tp in nx.__type_list__:
-
-        print(tp.dtype)
+        print(nx.dtype_device(tp))
 
         xb = nx.from_numpy(x, type_as=tp)
         rho_ub = nx.from_numpy(rho_u, type_as=tp)
         rho_vb = nx.from_numpy(rho_v, type_as=tp)
 
         emd = ot.emd_1d(xb, xb, rho_ub, rho_vb)
-
         emd2 = ot.emd2_1d(xb, xb, rho_ub, rho_vb)
 
-        assert emd.dtype == xb.dtype
-        if not str(nx) == 'numpy':
-            assert emd2.dtype == xb.dtype
+        nx.assert_same_dtype_device(xb, emd)
+        nx.assert_same_dtype_device(xb, emd2)
diff --git a/test/test_bregman.py b/test/test_bregman.py
index edfe9c3..830052d 100644
--- a/test/test_bregman.py
+++ b/test/test_bregman.py
@@ -278,6 +278,51 @@ def test_sinkhorn_variants(nx):
     np.testing.assert_allclose(G0, G_green, atol=1e-5)
 
 
+@pytest.mark.parametrize("method", ["sinkhorn", "sinkhorn_stabilized",
+                                    "sinkhorn_epsilon_scaling",
+                                    "greenkhorn",
+                                    "sinkhorn_log"])
+@pytest.skip_arg(("nx", "method"), ("jax", "sinkhorn_epsilon_scaling"), reason="jax does not support sinkhorn_epsilon_scaling", getter=str)
+@pytest.skip_arg(("nx", "method"), ("jax", "greenkhorn"), reason="jax does not support greenkhorn", getter=str)
+def test_sinkhorn_variants_dtype_device(nx, method):
+    n = 100
+
+    x = np.random.randn(n, 2)
+    u = ot.utils.unif(n)
+
+    M = ot.dist(x, x)
+
+    for tp in nx.__type_list__:
+        print(nx.dtype_device(tp))
+
+        ub = nx.from_numpy(u, type_as=tp)
+        Mb = nx.from_numpy(M, type_as=tp)
+
+        Gb = ot.sinkhorn(ub, ub, Mb, 1, method=method, stopThr=1e-10)
+
+        nx.assert_same_dtype_device(Mb, Gb)
+
+
+@pytest.mark.parametrize("method", ["sinkhorn", "sinkhorn_stabilized", "sinkhorn_log"])
+def test_sinkhorn2_variants_dtype_device(nx, method):
+    n = 100
+
+    x = np.random.randn(n, 2)
+    u = ot.utils.unif(n)
+
+    M = ot.dist(x, x)
+
+    for tp in nx.__type_list__:
+        print(nx.dtype_device(tp))
+
+        ub = nx.from_numpy(u, type_as=tp)
+        Mb = nx.from_numpy(M, type_as=tp)
+
+        lossb = ot.sinkhorn2(ub, ub, Mb, 1, method=method, stopThr=1e-10)
+
+        nx.assert_same_dtype_device(Mb, lossb)
+
+
 @pytest.skip_backend("jax")
 def test_sinkhorn_variants_multi_b(nx):
     # test sinkhorn
diff --git a/test/test_gromov.py b/test/test_gromov.py
index bcbcc3a..c4bc04c 100644
--- a/test/test_gromov.py
+++ b/test/test_gromov.py
@@ -75,6 +75,41 @@ def test_gromov(nx):
         q, Gb.sum(0), atol=1e-04)  # cf convergence gromov

 

 

+def test_gromov_dtype_device(nx):

+    # setup

+    n_samples = 50  # nb samples

+

+    mu_s = np.array([0, 0])

+    cov_s = np.array([[1, 0], [0, 1]])

+

+    xs = ot.datasets.make_2D_samples_gauss(n_samples, mu_s, cov_s, random_state=4)

+

+    xt = xs[::-1].copy()

+

+    p = ot.unif(n_samples)

+    q = ot.unif(n_samples)

+

+    C1 = ot.dist(xs, xs)

+    C2 = ot.dist(xt, xt)

+

+    C1 /= C1.max()

+    C2 /= C2.max()

+

+    for tp in nx.__type_list__:

+        print(nx.dtype_device(tp))

+

+        C1b = nx.from_numpy(C1, type_as=tp)

+        C2b = nx.from_numpy(C2, type_as=tp)

+        pb = nx.from_numpy(p, type_as=tp)

+        qb = nx.from_numpy(q, type_as=tp)

+

+        Gb = ot.gromov.gromov_wasserstein(C1b, C2b, pb, qb, 'square_loss', verbose=True)

+        gw_valb = ot.gromov.gromov_wasserstein2(C1b, C2b, pb, qb, 'kl_loss', log=False)

+

+        nx.assert_same_dtype_device(C1b, Gb)

+        nx.assert_same_dtype_device(C1b, gw_valb)

+

+

 def test_gromov2_gradients():

     n_samples = 50  # nb samples

 

@@ -168,6 +203,46 @@ def test_entropic_gromov(nx):
         q, Gb.sum(0), atol=1e-04)  # cf convergence gromov

 

 

+@pytest.skip_backend("jax", reason="test very slow with jax backend")

+def test_entropic_gromov_dtype_device(nx):

+    # setup

+    n_samples = 50  # nb samples

+

+    mu_s = np.array([0, 0])

+    cov_s = np.array([[1, 0], [0, 1]])

+

+    xs = ot.datasets.make_2D_samples_gauss(n_samples, mu_s, cov_s, random_state=42)

+

+    xt = xs[::-1].copy()

+

+    p = ot.unif(n_samples)

+    q = ot.unif(n_samples)

+

+    C1 = ot.dist(xs, xs)

+    C2 = ot.dist(xt, xt)

+

+    C1 /= C1.max()

+    C2 /= C2.max()

+

+    for tp in nx.__type_list__:

+        print(nx.dtype_device(tp))

+

+        C1b = nx.from_numpy(C1, type_as=tp)

+        C2b = nx.from_numpy(C2, type_as=tp)

+        pb = nx.from_numpy(p, type_as=tp)

+        qb = nx.from_numpy(q, type_as=tp)

+

+        Gb = ot.gromov.entropic_gromov_wasserstein(

+            C1b, C2b, pb, qb, 'square_loss', epsilon=5e-4, verbose=True

+        )

+        gw_valb = ot.gromov.entropic_gromov_wasserstein2(

+            C1b, C2b, pb, qb, 'square_loss', epsilon=5e-4, verbose=True

+        )

+

+        nx.assert_same_dtype_device(C1b, Gb)

+        nx.assert_same_dtype_device(C1b, gw_valb)

+

+

 def test_pointwise_gromov(nx):

     n_samples = 50  # nb samples

 

diff --git a/test/test_ot.py b/test/test_ot.py
index dc3930a..92f26a7 100644
--- a/test/test_ot.py
+++ b/test/test_ot.py
@@ -88,8 +88,7 @@ def test_emd_emd2_types_devices(nx):
     M = ot.dist(x, y)
 
     for tp in nx.__type_list__:
-
-        print(tp.dtype)
+        print(nx.dtype_device(tp))
 
         ab = nx.from_numpy(a, type_as=tp)
         Mb = nx.from_numpy(M, type_as=tp)
@@ -98,9 +97,8 @@ def test_emd_emd2_types_devices(nx):
 
         w = ot.emd2(ab, ab, Mb)
 
-        assert Gb.dtype == Mb.dtype
-        if not str(nx) == 'numpy':
-            assert w.dtype == Mb.dtype
+        nx.assert_same_dtype_device(Mb, Gb)
+        nx.assert_same_dtype_device(Mb, w)
 
 
 def test_emd2_gradients():
diff --git a/test/test_sliced.py b/test/test_sliced.py
index 0bd74ec..245202c 100644
--- a/test/test_sliced.py
+++ b/test/test_sliced.py
@@ -139,6 +139,28 @@ def test_sliced_backend(nx):
     assert np.allclose(val0, valb)
 
 
+def test_sliced_backend_type_devices(nx):
+    n = 100
+    rng = np.random.RandomState(0)
+
+    x = rng.randn(n, 2)
+    y = rng.randn(2 * n, 2)
+
+    P = rng.randn(2, 20)
+    P = P / np.sqrt((P**2).sum(0, keepdims=True))
+
+    for tp in nx.__type_list__:
+        print(nx.dtype_device(tp))
+
+        xb = nx.from_numpy(x, type_as=tp)
+        yb = nx.from_numpy(y, type_as=tp)
+        Pb = nx.from_numpy(P, type_as=tp)
+
+        valb = ot.sliced_wasserstein_distance(xb, yb, projections=Pb)
+
+        nx.assert_same_dtype_device(xb, valb)
+
+
 def test_max_sliced_backend(nx):
 
     n = 100
@@ -167,3 +189,25 @@ def test_max_sliced_backend(nx):
     valb = nx.to_numpy(ot.max_sliced_wasserstein_distance(xb, yb, projections=Pb))
 
     assert np.allclose(val0, valb)
+
+
+def test_max_sliced_backend_type_devices(nx):
+    n = 100
+    rng = np.random.RandomState(0)
+
+    x = rng.randn(n, 2)
+    y = rng.randn(2 * n, 2)
+
+    P = rng.randn(2, 20)
+    P = P / np.sqrt((P**2).sum(0, keepdims=True))
+
+    for tp in nx.__type_list__:
+        print(nx.dtype_device(tp))
+
+        xb = nx.from_numpy(x, type_as=tp)
+        yb = nx.from_numpy(y, type_as=tp)
+        Pb = nx.from_numpy(P, type_as=tp)
+
+        valb = ot.max_sliced_wasserstein_distance(xb, yb, projections=Pb)
+
+        nx.assert_same_dtype_device(xb, valb)