merge with new gpu stuff

4 files changed, 275 insertions, 144 deletions

diff --git a/ot/gpu/__init__.py b/ot/gpu/__init__.py
index ed6dcc4..213578c 100644
--- a/ot/gpu/__init__.py
+++ b/ot/gpu/__init__.py
@@ -1,17 +1,37 @@
 # -*- coding: utf-8 -*-
+"""
 
-from . import bregman
-from . import da
-from .bregman import sinkhorn
+This module provides GPU implementation for several OT solvers and utility 
+functions. The GPU backend in handled by `cupy 
+<https://cupy.chainer.org/>`_.
+
+By default, the functions in this module accept and return numpy arrays 
+in order to proide drop-in replacement for the other POT function but
+the transfer between CPU en GPU comes with a significant overhead.
+
+In order to get the best erformances, we recommend to given only cupy 
+arrays to the functions and desactivate the conversion to numpy of the 
+result of the function with parameter ``to_numpy=False``.
+
+"""
 
 # Author: Remi Flamary <remi.flamary@unice.fr>
 #         Leo Gautheron <https://github.com/aje>
 #
 # License: MIT License
 
-import warnings
+from . import bregman
+from . import da
+from .bregman import sinkhorn
+from .da import sinkhorn_lpl1_mm
+
+from . import utils
+from .utils import dist, to_gpu, to_np
+
+
+
+
 
-warnings.warn("the ot.gpu module is deprecated because cudamat in no longer maintained", DeprecationWarning,
-              stacklevel=2)
+__all__ = ["utils", "dist", "sinkhorn",
+           "sinkhorn_lpl1_mm", 'bregman', 'da', 'to_gpu', 'to_np']
 
-__all__ = ["bregman", "da", "sinkhorn"]
diff --git a/ot/gpu/bregman.py b/ot/gpu/bregman.py
index 47939c4..978b307 100644
--- a/ot/gpu/bregman.py
+++ b/ot/gpu/bregman.py
@@ -8,14 +8,18 @@ Bregman projections for regularized OT with GPU
 #
 # License: MIT License
 
-import numpy as np
-import cudamat
+import cupy as np  # np used for matrix computation
+import cupy as cp  # cp used for cupy specific operations
+from . import utils
 
 
-def sinkhorn(a, b, M_GPU, reg, numItermax=1000, stopThr=1e-9, verbose=False,
-                log=False, returnAsGPU=False):
-    r"""
-    Solve the entropic regularization optimal transport problem on GPU
+def sinkhorn_knopp(a, b, M, reg, numItermax=1000, stopThr=1e-9,
+                   verbose=False, log=False, to_numpy=True, **kwargs):
+    """
+    Solve the entropic regularization optimal transport on GPU
+
+    If the input matrix are in numpy format, they will be uploaded to the
+    GPU first which can incur significant time overhead.
 
     The function solves the following optimization problem:
 
@@ -40,9 +44,10 @@ def sinkhorn(a, b, M_GPU, reg, numItermax=1000, stopThr=1e-9, verbose=False,
     ----------
     a : np.ndarray (ns,)
         samples weights in the source domain
-    b : np.ndarray (nt,)
-        samples in the target domain
-    M_GPU : cudamat.CUDAMatrix (ns,nt)
+    b : np.ndarray (nt,) or np.ndarray (nt,nbb)
+        samples in the target domain, compute sinkhorn with multiple targets
+        and fixed M if b is a matrix (return OT loss + dual variables in log)
+    M : np.ndarray (ns,nt)
         loss matrix
     reg : float
         Regularization term >0
@@ -54,8 +59,9 @@ def sinkhorn(a, b, M_GPU, reg, numItermax=1000, stopThr=1e-9, verbose=False,
         Print information along iterations
     log : bool, optional
         record log if True
-    returnAsGPU : bool, optional
-        return the OT matrix as a cudamat.CUDAMatrix
+    to_numpy : boolean, optional (default True)
+        If true convert back the GPU array result to numpy format.
+
 
     Returns
     -------
@@ -88,60 +94,78 @@ def sinkhorn(a, b, M_GPU, reg, numItermax=1000, stopThr=1e-9, verbose=False,
     ot.optim.cg : General regularized OT
 
     """
+
+    a = cp.asarray(a)
+    b = cp.asarray(b)
+    M = cp.asarray(M)
+
+    if len(a) == 0:
+        a = np.ones((M.shape[0],)) / M.shape[0]
+    if len(b) == 0:
+        b = np.ones((M.shape[1],)) / M.shape[1]
+
     # init data
     Nini = len(a)
     Nfin = len(b)
 
+    if len(b.shape) > 1:
+        nbb = b.shape[1]
+    else:
+        nbb = 0
+
     if log:
         log = {'err': []}
 
     # we assume that no distances are null except those of the diagonal of
     # distances
-    u = (np.ones(Nini) / Nini).reshape((Nini, 1))
-    u_GPU = cudamat.CUDAMatrix(u)
-    a_GPU = cudamat.CUDAMatrix(a.reshape((Nini, 1)))
-    ones_GPU = cudamat.empty(u_GPU.shape).assign(1)
-    v = (np.ones(Nfin) / Nfin).reshape((Nfin, 1))
-    v_GPU = cudamat.CUDAMatrix(v)
-    b_GPU = cudamat.CUDAMatrix(b.reshape((Nfin, 1)))
-
-    M_GPU.divide(-reg)
+    if nbb:
+        u = np.ones((Nini, nbb)) / Nini
+        v = np.ones((Nfin, nbb)) / Nfin
+    else:
+        u = np.ones(Nini) / Nini
+        v = np.ones(Nfin) / Nfin
 
-    K_GPU = cudamat.exp(M_GPU)
+    # print(reg)
 
-    ones_GPU.divide(a_GPU, target=a_GPU)
-    Kp_GPU = cudamat.empty(K_GPU.shape)
-    K_GPU.mult_by_col(a_GPU, target=Kp_GPU)
+    # 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)
 
-    tmp_GPU = cudamat.empty(K_GPU.shape)
+    # print(np.min(K))
+    tmp2 = np.empty(b.shape, dtype=M.dtype)
 
+    Kp = (1 / a).reshape(-1, 1) * K
     cpt = 0
     err = 1
     while (err > stopThr and cpt < numItermax):
-        uprev_GPU = u_GPU.copy()
-        vprev_GPU = v_GPU.copy()
+        uprev = u
+        vprev = v
 
-        KtransposeU_GPU = K_GPU.transpose().dot(u_GPU)
-        b_GPU.divide(KtransposeU_GPU, target=v_GPU)
-        ones_GPU.divide(Kp_GPU.dot(v_GPU), target=u_GPU)
+        KtransposeU = np.dot(K.T, u)
+        v = np.divide(b, KtransposeU)
+        u = 1. / np.dot(Kp, v)
 
-        if (np.any(KtransposeU_GPU.asarray() == 0) or
-                not u_GPU.allfinite() or not v_GPU.allfinite()):
+        if (np.any(KtransposeU == 0) or
+                np.any(np.isnan(u)) or np.any(np.isnan(v)) or
+                np.any(np.isinf(u)) or np.any(np.isinf(v))):
             # we have reached the machine precision
             # come back to previous solution and quit loop
             print('Warning: numerical errors at iteration', cpt)
-            u_GPU = uprev_GPU.copy()
-            v_GPU = vprev_GPU.copy()
+            u = uprev
+            v = vprev
             break
         if cpt % 10 == 0:
             # we can speed up the process by checking for the error only all
             # the 10th iterations
-            K_GPU.mult_by_col(u_GPU, target=tmp_GPU)
-            tmp_GPU.mult_by_row(v_GPU.transpose(), target=tmp_GPU)
-
-            bcopy_GPU = b_GPU.copy().transpose()
-            bcopy_GPU.add_sums(tmp_GPU, axis=0, beta=-1)
-            err = bcopy_GPU.euclid_norm()**2
+            if nbb:
+                err = np.sum((u - uprev)**2) / np.sum((u)**2) + \
+                    np.sum((v - vprev)**2) / np.sum((v)**2)
+            else:
+                # compute right marginal tmp2= (diag(u)Kdiag(v))^T1
+                tmp2 = np.sum(u[:, None] * K * v[None, :], 0)
+                #tmp2=np.einsum('i,ij,j->j', u, K, v)
+                err = np.linalg.norm(tmp2 - b)**2  # violation of marginal
             if log:
                 log['err'].append(err)
 
@@ -150,20 +174,32 @@ def sinkhorn(a, b, M_GPU, reg, numItermax=1000, stopThr=1e-9, verbose=False,
                     print(
                         '{:5s}|{:12s}'.format('It.', 'Err') + '\n' + '-' * 19)
                 print('{:5d}|{:8e}|'.format(cpt, err))
-        cpt += 1
-    if log:
-        log['u'] = u_GPU.asarray()
-        log['v'] = v_GPU.asarray()
-
-    K_GPU.mult_by_col(u_GPU, target=K_GPU)
-    K_GPU.mult_by_row(v_GPU.transpose(), target=K_GPU)
-
-    if returnAsGPU:
-        res = K_GPU
-    else:
-        res = K_GPU.asarray()
-
+        cpt = cpt + 1
     if log:
-        return res, log
-    else:
-        return res
+        log['u'] = u
+        log['v'] = v
+
+    if nbb:  # return only loss
+        #res = np.einsum('ik,ij,jk,ij->k', u, K, v, M) (explodes cupy memory)
+        res = np.empty(nbb)
+        for i in range(nbb):
+            res[i] = np.sum(u[:, None, i] * (K * M) * v[None, :, i])
+        if to_numpy:
+            res = utils.to_np(res)
+        if log:
+            return res, log
+        else:
+            return res
+
+    else:  # return OT matrix
+        res = u.reshape((-1, 1)) * K * v.reshape((1, -1))
+        if to_numpy:
+            res = utils.to_np(res)
+        if log:
+            return res, log
+        else:
+            return res
+
+
+# define sinkhorn as sinkhorn_knopp
+sinkhorn = sinkhorn_knopp
diff --git a/ot/gpu/da.py b/ot/gpu/da.py
index ac3f8d7..f5e7daa 100644
--- a/ot/gpu/da.py
+++ b/ot/gpu/da.py
@@ -11,79 +11,30 @@ Domain adaptation with optimal transport with GPU implementation
 # License: MIT License
 
 
-import numpy as np
-#from ..utils import unif
+import cupy as np  # np used for matrix computation
+import cupy as cp  # cp used for cupy specific operations
+import numpy as npp
+from . import utils
+
 from .bregman import sinkhorn
-import cudamat
 
 
-def pairwiseEuclideanGPU(a, b, returnAsGPU=False, squared=False):
+def sinkhorn_lpl1_mm(a, labels_a, b, M, reg, eta=0.1, numItermax=10,
+                     numInnerItermax=200, stopInnerThr=1e-9, verbose=False,
+                     log=False, to_numpy=True):
     """
-    Compute the pairwise euclidean distance between matrices a and b.
-
-
-    Parameters
-    ----------
-    a : np.ndarray (n, f)
-        first matrice
-    b : np.ndarray (m, f)
-        second matrice
-    returnAsGPU : boolean, optional (default False)
-        if True, returns cudamat matrix still on GPU, else return np.ndarray
-    squared : boolean, optional (default False)
-        if True, return squared euclidean distance matrice
+    Solve the entropic regularization optimal transport problem with nonconvex
+    group lasso regularization on GPU
 
+    If the input matrix are in numpy format, they will be uploaded to the
+    GPU first which can incur significant time overhead.
 
-    Returns
-    -------
-    c : (n x m) np.ndarray or cudamat.CUDAMatrix
-        pairwise euclidean distance distance matrix
-    """
-    # a is shape (n, f) and b shape (m, f). Return matrix c of shape (n, m).
-    # First compute in c_GPU the squared euclidean distance. And return its
-    # square root. At each cell [i,j] of c, we want to have
-    # sum{k in range(f)} ( (a[i,k] - b[j,k])^2 ). We know that
-    # (a-b)^2 = a^2 -2ab +b^2. Thus we want to have in each cell of c:
-    # sum{k in range(f)} ( a[i,k]^2 -2a[i,k]b[j,k] +b[j,k]^2).
-
-    a_GPU = cudamat.CUDAMatrix(a)
-    b_GPU = cudamat.CUDAMatrix(b)
-
-    # Multiply a by b transpose to obtain in each cell [i,j] of c the
-    # value sum{k in range(f)} ( a[i,k]b[j,k] )
-    c_GPU = cudamat.dot(a_GPU, b_GPU.transpose())
-    # multiply by -2 to have sum{k in range(f)} ( -2a[i,k]b[j,k] )
-    c_GPU.mult(-2)
-
-    # Compute the vectors of the sum of squared elements.
-    a_GPU = cudamat.pow(a_GPU, 2).sum(axis=1)
-    b_GPU = cudamat.pow(b_GPU, 2).sum(axis=1)
-
-    # Add the vectors in each columns (respectivly rows) of c.
-    # sum{k in range(f)} ( a[i,k]^2 -2a[i,k]b[j,k] )
-    c_GPU.add_col_vec(a_GPU)
-    # sum{k in range(f)} ( a[i,k]^2 -2a[i,k]b[j,k] +b[j,k]^2)
-    c_GPU.add_row_vec(b_GPU.transpose())
-
-    if not squared:
-        c_GPU = cudamat.sqrt(c_GPU)
-
-    if returnAsGPU:
-        return c_GPU
-    else:
-        return c_GPU.asarray()
-
-
-def sinkhorn_lpl1_mm(a, labels_a, b, M_GPU, reg, eta=0.1, numItermax=10,
-                     numInnerItermax=200, stopInnerThr=1e-9,
-                     verbose=False, log=False):
-    """
-    Solve the entropic regularization optimal transport problem with nonconvex group lasso regularization
 
     The function solves the following optimization problem:
 
     .. math::
-        \gamma = arg\min_\gamma <\gamma,M>_F + reg\cdot\Omega_e(\gamma)+ \eta \Omega_g(\gamma)
+        \gamma = arg\min_\gamma <\gamma,M>_F + reg\cdot\Omega_e(\gamma)
+        + \eta \Omega_g(\gamma)
 
         s.t. \gamma 1 = a
 
@@ -93,11 +44,16 @@ def sinkhorn_lpl1_mm(a, labels_a, b, M_GPU, reg, eta=0.1, numItermax=10,
     where :
 
     - M is the (ns,nt) metric cost matrix
-    - :math:`\Omega_e` is the entropic regularization term :math:`\Omega_e(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})`
-    - :math:`\Omega_g` is the group lasso  regulaization term :math:`\Omega_g(\gamma)=\sum_{i,c} \|\gamma_{i,\mathcal{I}_c}\|^{1/2}_1`   where  :math:`\mathcal{I}_c` are the index of samples from class c in the source domain.
+    - :math:`\Omega_e` is the entropic regularization term
+        :math:`\Omega_e(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})`
+    - :math:`\Omega_g` is the group lasso  regulaization term
+      :math:`\Omega_g(\gamma)=\sum_{i,c} \|\gamma_{i,\mathcal{I}_c}\|^{1/2}_1`
+      where  :math:`\mathcal{I}_c` are the index of samples from class c
+      in the source domain.
     - a and b are source and target weights (sum to 1)
 
-    The algorithm used for solving the problem is the generalised conditional gradient as proposed in  [5]_ [7]_
+    The algorithm used for solving the problem is the generalised conditional
+    gradient as proposed in  [5]_ [7]_
 
 
     Parameters
@@ -108,7 +64,7 @@ def sinkhorn_lpl1_mm(a, labels_a, b, M_GPU, reg, eta=0.1, numItermax=10,
         labels of samples in the source domain
     b : np.ndarray (nt,)
         samples weights in the target domain
-    M_GPU : cudamat.CUDAMatrix (ns,nt)
+    M : np.ndarray (ns,nt)
         loss matrix
     reg : float
         Regularization term for entropic regularization >0
@@ -124,6 +80,8 @@ def sinkhorn_lpl1_mm(a, labels_a, b, M_GPU, reg, eta=0.1, numItermax=10,
         Print information along iterations
     log : bool, optional
         record log if True
+    to_numpy : boolean, optional (default True)
+        If true convert back the GPU array result to numpy format.
 
 
     Returns
@@ -137,8 +95,13 @@ def sinkhorn_lpl1_mm(a, labels_a, b, M_GPU, reg, eta=0.1, numItermax=10,
     References
     ----------
 
-    .. [5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, "Optimal Transport for Domain Adaptation," in IEEE Transactions on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1
-    .. [7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). Generalized conditional gradient: analysis of convergence and applications. arXiv preprint arXiv:1510.06567.
+    .. [5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy,
+       "Optimal Transport for Domain Adaptation," in IEEE
+       Transactions on Pattern Analysis and Machine Intelligence ,
+       vol.PP, no.99, pp.1-1
+    .. [7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015).
+       Generalized conditional gradient: analysis of convergence
+       and applications. arXiv preprint arXiv:1510.06567.
 
     See Also
     --------
@@ -147,29 +110,30 @@ def sinkhorn_lpl1_mm(a, labels_a, b, M_GPU, reg, eta=0.1, numItermax=10,
     ot.optim.cg : General regularized OT
 
     """
+
+    a, labels_a, b, M = utils.to_gpu(a, labels_a, b, M)
+
     p = 0.5
     epsilon = 1e-3
-    Nfin = len(b)
 
     indices_labels = []
-    classes = np.unique(labels_a)
+    labels_a2 = cp.asnumpy(labels_a)
+    classes = npp.unique(labels_a2)
     for c in classes:
-        idxc, = np.where(labels_a == c)
-        indices_labels.append(cudamat.CUDAMatrix(idxc.reshape(1, -1)))
+        idxc, = utils.to_gpu(npp.where(labels_a2 == c))
+        indices_labels.append(idxc)
 
-    Mreg_GPU = cudamat.empty(M_GPU.shape)
-    W_GPU = cudamat.empty(M_GPU.shape).assign(0)
+    W = np.zeros(M.shape)
 
     for cpt in range(numItermax):
-        Mreg_GPU.assign(M_GPU)
-        Mreg_GPU.add_mult(W_GPU, eta)
-        transp_GPU = sinkhorn(a, b, Mreg_GPU, reg, numItermax=numInnerItermax,
-                              stopThr=stopInnerThr, returnAsGPU=True)
+        Mreg = M + eta * W
+        transp = sinkhorn(a, b, Mreg, reg, numItermax=numInnerItermax,
+                          stopThr=stopInnerThr, to_numpy=False)
         # the transport has been computed. Check if classes are really
         # separated
-        W_GPU.assign(1)
-        W_GPU = W_GPU.transpose()
+        W = np.ones(M.shape)
         for (i, c) in enumerate(classes):
+<<<<<<< HEAD
             (_, nbRow) = indices_labels[i].shape
             tmpC_GPU = cudamat.empty((Nfin, nbRow)).assign(0)
             transp_GPU.transpose().select_columns(indices_labels[i], tmpC_GPU)
@@ -184,3 +148,13 @@ def sinkhorn_lpl1_mm(a, labels_a, b, M_GPU, reg, eta=0.1, numItermax=10,
         W_GPU = W_GPU.transpose()
 
     return transp_GPU.asarray()
+=======
+            majs = np.sum(transp[indices_labels[i]], axis=0)
+            majs = p * ((majs + epsilon)**(p - 1))
+            W[indices_labels[i]] = majs
+
+    if to_numpy:
+        return utils.to_np(transp)
+    else:
+        return transp
+>>>>>>> master
diff --git a/ot/gpu/utils.py b/ot/gpu/utils.py
new file mode 100644
index 0000000..41e168a
--- /dev/null
+++ b/ot/gpu/utils.py
@@ -0,0 +1,101 @@
+# -*- coding: utf-8 -*-
+"""
+Utility functions for GPU
+"""
+
+# Author: Remi Flamary <remi.flamary@unice.fr>
+#         Nicolas Courty <ncourty@irisa.fr>
+#         Leo Gautheron <https://github.com/aje>
+#
+# License: MIT License
+
+import cupy as np  # np used for matrix computation
+import cupy as cp  # cp used for cupy specific operations
+
+
+def euclidean_distances(a, b, squared=False, to_numpy=True):
+    """
+    Compute the pairwise euclidean distance between matrices a and b.
+
+    If the input matrix are in numpy format, they will be uploaded to the
+    GPU first which can incur significant time overhead.
+
+    Parameters
+    ----------
+    a : np.ndarray (n, f)
+        first matrix
+    b : np.ndarray (m, f)
+        second matrix
+    to_numpy : boolean, optional (default True)
+        If true convert back the GPU array result to numpy format.
+    squared : boolean, optional (default False)
+        if True, return squared euclidean distance matrix
+
+    Returns
+    -------
+    c : (n x m) np.ndarray or cupy.ndarray
+        pairwise euclidean distance distance matrix
+    """
+
+    a, b = to_gpu(a, b)
+
+    a2 = np.sum(np.square(a), 1)
+    b2 = np.sum(np.square(b), 1)
+
+    c = -2 * np.dot(a, b.T)
+    c += a2[:, None]
+    c += b2[None, :]
+
+    if not squared:
+        np.sqrt(c, out=c)
+    if to_numpy:
+        return to_np(c)
+    else:
+        return c
+
+
+def dist(x1, x2=None, metric='sqeuclidean', to_numpy=True):
+    """Compute distance between samples in x1 and x2 on gpu
+
+    Parameters
+    ----------
+
+    x1 : np.array (n1,d)
+        matrix with n1 samples of size d
+    x2 : np.array (n2,d), optional
+        matrix with n2 samples of size d (if None then x2=x1)
+    metric : str
+        Metric from 'sqeuclidean', 'euclidean',
+
+
+    Returns
+    -------
+
+    M : np.array (n1,n2)
+        distance matrix computed with given metric
+
+    """
+    if x2 is None:
+        x2 = x1
+    if metric == "sqeuclidean":
+        return euclidean_distances(x1, x2, squared=True, to_numpy=to_numpy)
+    elif metric == "euclidean":
+        return euclidean_distances(x1, x2, squared=False, to_numpy=to_numpy)
+    else:
+        raise NotImplementedError
+
+
+def to_gpu(*args):
+    """ Upload numpy arrays to GPU and return them"""
+    if len(args) > 1:
+        return (cp.asarray(x) for x in args)
+    else:
+        return cp.asarray(args[0])
+
+
+def to_np(*args):
+    """ convert GPU arras to numpy and return them"""
+    if len(args) > 1:
+        return (cp.asnumpy(x) for x in args)
+    else:
+        return cp.asnumpy(args[0])