[MRG] Gromov_Wasserstein2 not performing backward properly on GPU (#352)

* Resolves gromov wasserstein backward bug

* release file updated

Author: ncassereau

RELEASES.md

@@ -18,6 +18,9 @@
 - Fix bug in instantiating an `autograd` function `ValFunction` (Issue #337, 
   PR #338)
 - Fix POT ABI compatibility with old and new numpy (Issue #346, PR #349)
+- Fix bug where gromov_wasserstein2 does not perform backpropagation with CUDA
+  tensors (Issue #351, PR #352)
+
 
 ## 0.8.1.0
 *December 2021*

ot/gromov.py

@@ -546,8 +546,10 @@ def df(G):
     gw = log_gw['gw_dist']
 
     if loss_fun == 'square_loss':
-        gC1 = nx.from_numpy(2 * C1 * (p[:, None] * p[None, :]) - 2 * T.dot(C2).dot(T.T))
-        gC2 = nx.from_numpy(2 * C2 * (q[:, None] * q[None, :]) - 2 * T.T.dot(C1).dot(T))
+        gC1 = 2 * C1 * (p[:, None] * p[None, :]) - 2 * T.dot(C2).dot(T.T)
+        gC2 = 2 * C2 * (q[:, None] * q[None, :]) - 2 * T.T.dot(C1).dot(T)
+        gC1 = nx.from_numpy(gC1, type_as=C10)
+        gC2 = nx.from_numpy(gC2, type_as=C10)
         gw = nx.set_gradients(gw, (p0, q0, C10, C20),
                               (log_gw['u'], log_gw['v'], gC1, gC2))
 
@@ -786,8 +788,10 @@ def df(G):
     log_fgw['T'] = T0
 
     if loss_fun == 'square_loss':
-        gC1 = nx.from_numpy(2 * C1 * (p[:, None] * p[None, :]) - 2 * T.dot(C2).dot(T.T))
-        gC2 = nx.from_numpy(2 * C2 * (q[:, None] * q[None, :]) - 2 * T.T.dot(C1).dot(T))
+        gC1 = 2 * C1 * (p[:, None] * p[None, :]) - 2 * T.dot(C2).dot(T.T)
+        gC2 = 2 * C2 * (q[:, None] * q[None, :]) - 2 * T.T.dot(C1).dot(T)
+        gC1 = nx.from_numpy(gC1, type_as=C10)
+        gC2 = nx.from_numpy(gC2, type_as=C10)
         fgw_dist = nx.set_gradients(fgw_dist, (p0, q0, C10, C20, M0),
                                     (log_fgw['u'], log_fgw['v'], alpha * gC1, alpha * gC2, (1 - alpha) * T0))
 

test/test_gromov.py

@@ -181,19 +181,24 @@ def test_gromov2_gradients():
 
     if torch:
 
-        p1 = torch.tensor(p, requires_grad=True)
-        q1 = torch.tensor(q, requires_grad=True)
-        C11 = torch.tensor(C1, requires_grad=True)
-        C12 = torch.tensor(C2, requires_grad=True)
+        devices = [torch.device("cpu")]
+        if torch.cuda.is_available():
+            devices.append(torch.device("cuda"))
+        for device in devices:
+            p1 = torch.tensor(p, requires_grad=True, device=device)
+            q1 = torch.tensor(q, requires_grad=True, device=device)
+            C11 = torch.tensor(C1, requires_grad=True, device=device)
+            C12 = torch.tensor(C2, requires_grad=True, device=device)
 
-        val = ot.gromov_wasserstein2(C11, C12, p1, q1)
+            val = ot.gromov_wasserstein2(C11, C12, p1, q1)
 
-        val.backward()
+            val.backward()
 
-        assert q1.shape == q1.grad.shape
-        assert p1.shape == p1.grad.shape
-        assert C11.shape == C11.grad.shape
-        assert C12.shape == C12.grad.shape
+            assert val.device == p1.device
+            assert q1.shape == q1.grad.shape
+            assert p1.shape == p1.grad.shape
+            assert C11.shape == C11.grad.shape
+            assert C12.shape == C12.grad.shape
 
 
 @pytest.skip_backend("jax", reason="test very slow with jax backend")
@@ -636,21 +641,26 @@ def test_fgw2_gradients():
 
     if torch:
 
-        p1 = torch.tensor(p, requires_grad=True)
-        q1 = torch.tensor(q, requires_grad=True)
-        C11 = torch.tensor(C1, requires_grad=True)
-        C12 = torch.tensor(C2, requires_grad=True)
-        M1 = torch.tensor(M, requires_grad=True)
-
-        val = ot.fused_gromov_wasserstein2(M1, C11, C12, p1, q1)
-
-        val.backward()
-
-        assert q1.shape == q1.grad.shape
-        assert p1.shape == p1.grad.shape
-        assert C11.shape == C11.grad.shape
-        assert C12.shape == C12.grad.shape
-        assert M1.shape == M1.grad.shape
+        devices = [torch.device("cpu")]
+        if torch.cuda.is_available():
+            devices.append(torch.device("cuda"))
+        for device in devices:
+            p1 = torch.tensor(p, requires_grad=True, device=device)
+            q1 = torch.tensor(q, requires_grad=True, device=device)
+            C11 = torch.tensor(C1, requires_grad=True, device=device)
+            C12 = torch.tensor(C2, requires_grad=True, device=device)
+            M1 = torch.tensor(M, requires_grad=True, device=device)
+
+            val = ot.fused_gromov_wasserstein2(M1, C11, C12, p1, q1)
+
+            val.backward()
+
+            assert val.device == p1.device
+            assert q1.shape == q1.grad.shape
+            assert p1.shape == p1.grad.shape
+            assert C11.shape == C11.grad.shape
+            assert C12.shape == C12.grad.shape
+            assert M1.shape == M1.grad.shape
 
 
 def test_fgw_barycenter(nx):