[MRG] Make partial_wasserstein, partial_wasserstein2 and entropic_partial_wasserstein work with backend (#449)

* add test of partial_wasserstein with torch tensors

* WIP: differentiable ot.partial.partial_wasserstein

* change test of torch partial

* make partial_wasserstein2 work with torch

* test backward through ot.partial.partial_wasserstein2

* add test of entropic_partial_wasserstein with torch tensors

* make entropic_partial_wasserstein work with torch tensors

* add test of backward through entropic_partial_wasserstein

* rm unused import

* test partial_wasserstein with all backends

* tests of partial fcts: check if torch is available

* partial: check if marginals are empty arrays

* add tests when marginals are empty arrays and/or m=None

* add PR to RELEASES.md

---------

Co-authored-by: Antoine Collas <[email protected]>
Co-authored-by: Rémi Flamary <[email protected]>

Author: antoinecollas

RELEASES.md

@@ -14,7 +14,7 @@
 - Added Generalized Wasserstein Barycenter solver + example (PR #372), fixed graphical details on the example (PR #376)
 - Added Free Support Sinkhorn Barycenter + example (PR #387)
 - New API for OT solver using function `ot.solve` (PR #388)
-- Backend version of `ot.partial` and `ot.smooth` (PR #388)
+- Backend version of `ot.partial` and `ot.smooth` (PR #388 and #449)
 - Added argument for warmstart of dual potentials in Sinkhorn-based methods in `ot.bregman` (PR #437)
 - Add parameters method in `ot.da.SinkhornTransport` (PR #440)
 - `ot.dr` now uses the new Pymanopt API and POT is compatible with current Pymanopt (PR #443)

ot/partial.py

@@ -120,7 +120,7 @@ def partial_wasserstein_lagrange(a, b, M, reg_m=None, nb_dummies=1, log=False,
 
     nx = get_backend(a, b, M)
 
-    if nx.sum(a) > 1 or nx.sum(b) > 1:
+    if nx.sum(a) > 1 + 1e-15 or nx.sum(b) > 1 + 1e-15:  # 1e-15 for numerical errors
         raise ValueError("Problem infeasible. Check that a and b are in the "
                          "simplex")
 
@@ -270,36 +270,43 @@ def partial_wasserstein(a, b, M, m=None, nb_dummies=1, log=False, **kwargs):
 
     nx = get_backend(a, b, M)
 
+    dim_a, dim_b = M.shape
+    if len(a) == 0:
+        a = nx.ones(dim_a, type_as=a) / dim_a
+    if len(b) == 0:
+        b = nx.ones(dim_b, type_as=b) / dim_b
+
     if m is None:
         return partial_wasserstein_lagrange(a, b, M, log=log, **kwargs)
     elif m < 0:
         raise ValueError("Problem infeasible. Parameter m should be greater"
                          " than 0.")
-    elif m > nx.min((nx.sum(a), nx.sum(b))):
+    elif m > nx.min(nx.stack((nx.sum(a), nx.sum(b)))):
         raise ValueError("Problem infeasible. Parameter m should lower or"
                          " equal than min(|a|_1, |b|_1).")
 
-    a0, b0, M0 = a, b, M
-    # convert to humpy
-    a, b, M = nx.to_numpy(a, b, M)
-
-    b_extended = np.append(b, [(np.sum(a) - m) / nb_dummies] * nb_dummies)
-    a_extended = np.append(a, [(np.sum(b) - m) / nb_dummies] * nb_dummies)
-    M_extended = np.zeros((len(a_extended), len(b_extended)))
-    M_extended[-nb_dummies:, -nb_dummies:] = np.max(M) * 2
-    M_extended[:len(a), :len(b)] = M
+    b_extension = nx.ones(nb_dummies, type_as=b) * (nx.sum(a) - m) / nb_dummies
+    b_extended = nx.concatenate((b, b_extension))
+    a_extension = nx.ones(nb_dummies, type_as=a) * (nx.sum(b) - m) / nb_dummies
+    a_extended = nx.concatenate((a, a_extension))
+    M_extension = nx.ones((nb_dummies, nb_dummies), type_as=M) * nx.max(M) * 2
+    M_extended = nx.concatenate(
+        (nx.concatenate((M, nx.zeros((M.shape[0], M_extension.shape[1]))), axis=1),
+         nx.concatenate((nx.zeros((M_extension.shape[0], M.shape[1])), M_extension), axis=1)),
+        axis=0
+    )
 
     gamma, log_emd = emd(a_extended, b_extended, M_extended, log=True,
                          **kwargs)
 
-    gamma = nx.from_numpy(gamma[:len(a), :len(b)], type_as=M)
+    gamma = gamma[:len(a), :len(b)]
 
     if log_emd['warning'] is not None:
         raise ValueError("Error in the EMD resolution: try to increase the"
                          " number of dummy points")
-    log_emd['partial_w_dist'] = nx.sum(M0 * gamma)
-    log_emd['u'] = nx.from_numpy(log_emd['u'][:len(a)], type_as=a0)
-    log_emd['v'] = nx.from_numpy(log_emd['v'][:len(b)], type_as=b0)
+    log_emd['partial_w_dist'] = nx.sum(M * gamma)
+    log_emd['u'] = log_emd['u'][:len(a)]
+    log_emd['v'] = log_emd['v'][:len(b)]
 
     if log:
         return gamma, log_emd
@@ -389,14 +396,18 @@ def partial_wasserstein2(a, b, M, m=None, nb_dummies=1, log=False, **kwargs):
         NeurIPS.
     """
 
+    a, b, M = list_to_array(a, b, M)
+
+    nx = get_backend(a, b, M)
+
     partial_gw, log_w = partial_wasserstein(a, b, M, m, nb_dummies, log=True,
                                             **kwargs)
     log_w['T'] = partial_gw
 
     if log:
-        return np.sum(partial_gw * M), log_w
+        return nx.sum(partial_gw * M), log_w
     else:
-        return np.sum(partial_gw * M)
+        return nx.sum(partial_gw * M)
 
 
 def gwgrad_partial(C1, C2, T):
@@ -838,60 +849,64 @@ def entropic_partial_wasserstein(a, b, M, reg, m=None, numItermax=1000,
     ot.partial.partial_wasserstein: exact Partial Wasserstein
     """
 
-    a = np.asarray(a, dtype=np.float64)
-    b = np.asarray(b, dtype=np.float64)
-    M = np.asarray(M, dtype=np.float64)
+    a, b, M = list_to_array(a, b, M)
+
+    nx = get_backend(a, b, M)
 
     dim_a, dim_b = M.shape
-    dx = np.ones(dim_a, dtype=np.float64)
-    dy = np.ones(dim_b, dtype=np.float64)
+    dx = nx.ones(dim_a, type_as=a)
+    dy = nx.ones(dim_b, type_as=b)
 
     if len(a) == 0:
-        a = np.ones(dim_a, dtype=np.float64) / dim_a
+        a = nx.ones(dim_a, type_as=a) / dim_a
     if len(b) == 0:
-        b = np.ones(dim_b, dtype=np.float64) / dim_b
+        b = nx.ones(dim_b, type_as=b) / dim_b
 
     if m is None:
-        m = np.min((np.sum(a), np.sum(b))) * 1.0
+        m = nx.min(nx.stack((nx.sum(a), nx.sum(b)))) * 1.0
     if m < 0:
         raise ValueError("Problem infeasible. Parameter m should be greater"
                          " than 0.")
-    if m > np.min((np.sum(a), np.sum(b))):
+    if m > nx.min(nx.stack((nx.sum(a), nx.sum(b)))):
         raise ValueError("Problem infeasible. Parameter m should lower or"
                          " equal than min(|a|_1, |b|_1).")
 
     log_e = {'err': []}
 
-    # Next 3 lines equivalent to K=np.exp(-M/reg), but faster to compute
-    K = np.empty(M.shape, dtype=M.dtype)
-    np.divide(M, -reg, out=K)
-    np.exp(K, out=K)
-    np.multiply(K, m / np.sum(K), out=K)
+    if type(a) == type(b) == type(M) == np.ndarray:
+        # Next 3 lines equivalent to K=nx.exp(-M/reg), but faster to compute
+        K = np.empty(M.shape, dtype=M.dtype)
+        np.divide(M, -reg, out=K)
+        np.exp(K, out=K)
+        np.multiply(K, m / np.sum(K), out=K)
+    else:
+        K = nx.exp(-M / reg)
+        K = K * m / nx.sum(K)
 
     err, cpt = 1, 0
-    q1 = np.ones(K.shape)
-    q2 = np.ones(K.shape)
-    q3 = np.ones(K.shape)
+    q1 = nx.ones(K.shape, type_as=K)
+    q2 = nx.ones(K.shape, type_as=K)
+    q3 = nx.ones(K.shape, type_as=K)
 
     while (err > stopThr and cpt < numItermax):
         Kprev = K
         K = K * q1
-        K1 = np.dot(np.diag(np.minimum(a / np.sum(K, axis=1), dx)), K)
+        K1 = nx.dot(nx.diag(nx.minimum(a / nx.sum(K, axis=1), dx)), K)
         q1 = q1 * Kprev / K1
         K1prev = K1
         K1 = K1 * q2
-        K2 = np.dot(K1, np.diag(np.minimum(b / np.sum(K1, axis=0), dy)))
+        K2 = nx.dot(K1, nx.diag(nx.minimum(b / nx.sum(K1, axis=0), dy)))
         q2 = q2 * K1prev / K2
         K2prev = K2
         K2 = K2 * q3
-        K = K2 * (m / np.sum(K2))
+        K = K2 * (m / nx.sum(K2))
         q3 = q3 * K2prev / K
 
-        if np.any(np.isnan(K)) or np.any(np.isinf(K)):
+        if nx.any(nx.isnan(K)) or nx.any(nx.isinf(K)):
             print('Warning: numerical errors at iteration', cpt)
             break
         if cpt % 10 == 0:
-            err = np.linalg.norm(Kprev - K)
+            err = nx.norm(Kprev - K)
             if log:
                 log_e['err'].append(err)
             if verbose:
@@ -901,7 +916,7 @@ def entropic_partial_wasserstein(a, b, M, reg, m=None, numItermax=1000,
                 print('{:5d}|{:8e}|'.format(cpt, err))
 
         cpt = cpt + 1
-    log_e['partial_w_dist'] = np.sum(M * K)
+    log_e['partial_w_dist'] = nx.sum(M * K)
     if log:
         return K, log_e
     else:

test/test_partial.py

@@ -8,6 +8,7 @@
 import numpy as np
 import scipy as sp
 import ot
+from ot.backend import to_numpy, torch
 import pytest
 
 
@@ -82,7 +83,7 @@ def test_partial_wasserstein_lagrange():
     w0, log0 = ot.partial.partial_wasserstein_lagrange(p, q, M, 100, log=True)
 
 
-def test_partial_wasserstein():
+def test_partial_wasserstein(nx):
 
     n_samples = 20  # nb samples (gaussian)
     n_noise = 20  # nb of samples (noise)
@@ -102,25 +103,20 @@ def test_partial_wasserstein():
 
     m = 0.5
 
+    p, q, M = nx.from_numpy(p, q, M)
+
     w0, log0 = ot.partial.partial_wasserstein(p, q, M, m=m, log=True)
-    w, log = ot.partial.entropic_partial_wasserstein(p, q, M, reg=1, m=m,
-                                                     log=True, verbose=True)
+    w, log = ot.partial.entropic_partial_wasserstein(p, q, M, reg=1, m=m, log=True, verbose=True)
 
     # check constraints
-    np.testing.assert_equal(
-        w0.sum(1) - p <= 1e-5, [True] * len(p))  # cf convergence wasserstein
-    np.testing.assert_equal(
-        w0.sum(0) - q <= 1e-5, [True] * len(q))  # cf convergence wasserstein
-    np.testing.assert_equal(
-        w.sum(1) - p <= 1e-5, [True] * len(p))  # cf convergence wasserstein
-    np.testing.assert_equal(
-        w.sum(0) - q <= 1e-5, [True] * len(q))  # cf convergence wasserstein
+    np.testing.assert_equal(to_numpy(nx.sum(w0, axis=1) - p) <= 1e-5, [True] * len(p))
+    np.testing.assert_equal(to_numpy(nx.sum(w0, axis=0) - q) <= 1e-5, [True] * len(q))
+    np.testing.assert_equal(to_numpy(nx.sum(w0, axis=1) - p) <= 1e-5, [True] * len(p))
+    np.testing.assert_equal(to_numpy(nx.sum(w0, axis=0) - q) <= 1e-5, [True] * len(q))
 
     # check transported mass
-    np.testing.assert_allclose(
-        np.sum(w0), m, atol=1e-04)
-    np.testing.assert_allclose(
-        np.sum(w), m, atol=1e-04)
+    np.testing.assert_allclose(np.sum(to_numpy(w0)), m, atol=1e-04)
+    np.testing.assert_allclose(np.sum(to_numpy(w)), m, atol=1e-04)
 
     w0, log0 = ot.partial.partial_wasserstein2(p, q, M, m=m, log=True)
     w0_val = ot.partial.partial_wasserstein2(p, q, M, m=m, log=False)
@@ -130,12 +126,87 @@ def test_partial_wasserstein():
     np.testing.assert_allclose(w0, w0_val, atol=1e-1, rtol=1e-1)
 
     # check constraints
-    np.testing.assert_equal(
-        G.sum(1) - p <= 1e-5, [True] * len(p))  # cf convergence wasserstein
-    np.testing.assert_equal(
-        G.sum(0) - q <= 1e-5, [True] * len(q))  # cf convergence wasserstein
-    np.testing.assert_allclose(
-        np.sum(G), m, atol=1e-04)
+    np.testing.assert_equal(to_numpy(nx.sum(G, axis=1) - p) <= 1e-5, [True] * len(p))
+    np.testing.assert_equal(to_numpy(nx.sum(G, axis=0) - q) <= 1e-5, [True] * len(q))
+    np.testing.assert_allclose(np.sum(to_numpy(G)), m, atol=1e-04)
+
+    empty_array = nx.zeros(0, type_as=M)
+    w = ot.partial.partial_wasserstein(empty_array, empty_array, M=M, m=None)
+
+    # check constraints
+    np.testing.assert_equal(to_numpy(nx.sum(w, axis=1) - p) <= 1e-5, [True] * len(p))
+    np.testing.assert_equal(to_numpy(nx.sum(w, axis=0) - q) <= 1e-5, [True] * len(q))
+    np.testing.assert_equal(to_numpy(nx.sum(w, axis=1) - p) <= 1e-5, [True] * len(p))
+    np.testing.assert_equal(to_numpy(nx.sum(w, axis=0) - q) <= 1e-5, [True] * len(q))
+
+    # check transported mass
+    np.testing.assert_allclose(np.sum(to_numpy(w)), 1, atol=1e-04)
+
+    w0 = ot.partial.entropic_partial_wasserstein(empty_array, empty_array, M=M, reg=10, m=None)
+
+    # check constraints
+    np.testing.assert_equal(to_numpy(nx.sum(w0, axis=1) - p) <= 1e-5, [True] * len(p))
+    np.testing.assert_equal(to_numpy(nx.sum(w0, axis=0) - q) <= 1e-5, [True] * len(q))
+    np.testing.assert_equal(to_numpy(nx.sum(w0, axis=1) - p) <= 1e-5, [True] * len(p))
+    np.testing.assert_equal(to_numpy(nx.sum(w0, axis=0) - q) <= 1e-5, [True] * len(q))
+
+    # check transported mass
+    np.testing.assert_allclose(np.sum(to_numpy(w0)), 1, atol=1e-04)
+
+
+def test_partial_wasserstein2_gradient():
+    if torch:
+        n_samples = 40
+
+        mu = np.array([0, 0])
+        cov = np.array([[1, 0], [0, 2]])
+
+        xs = ot.datasets.make_2D_samples_gauss(n_samples, mu, cov)
+        xt = ot.datasets.make_2D_samples_gauss(n_samples, mu, cov)
+
+        M = torch.tensor(ot.dist(xs, xt), requires_grad=True, dtype=torch.float64)
+
+        p = torch.tensor(ot.unif(n_samples), dtype=torch.float64)
+        q = torch.tensor(ot.unif(n_samples), dtype=torch.float64)
+
+        m = 0.5
+
+        w, log = ot.partial.partial_wasserstein2(p, q, M, m=m, log=True)
+
+        w.backward()
+
+        assert M.grad is not None
+        assert M.grad.shape == M.shape
+
+
+def test_entropic_partial_wasserstein_gradient():
+    if torch:
+        n_samples = 40
+
+        mu = np.array([0, 0])
+        cov = np.array([[1, 0], [0, 2]])
+
+        xs = ot.datasets.make_2D_samples_gauss(n_samples, mu, cov)
+        xt = ot.datasets.make_2D_samples_gauss(n_samples, mu, cov)
+
+        M = torch.tensor(ot.dist(xs, xt), requires_grad=True, dtype=torch.float64)
+
+        p = torch.tensor(ot.unif(n_samples), requires_grad=True, dtype=torch.float64)
+        q = torch.tensor(ot.unif(n_samples), requires_grad=True, dtype=torch.float64)
+
+        m = 0.5
+        reg = 1
+
+        _, log = ot.partial.entropic_partial_wasserstein(p, q, M, m=m, reg=reg, log=True)
+
+        log['partial_w_dist'].backward()
+
+        assert M.grad is not None
+        assert p.grad is not None
+        assert q.grad is not None
+        assert M.grad.shape == M.shape
+        assert p.grad.shape == p.shape
+        assert q.grad.shape == q.shape
 
 
 def test_partial_gromov_wasserstein():