convert ot.gpu to cupy

Author: rflamary

ot/gpu/__init__.py

@@ -4,9 +4,13 @@
 from . import da
 from .bregman import sinkhorn
 
+from . import utils
+from .utils import dist, to_gpu, to_np
+
+
 # Author: Remi Flamary <[email protected]>
 #         Leo Gautheron <https://github.com/aje>
 #
 # License: MIT License
 
-__all__ = ["bregman", "da", "sinkhorn"]
+__all__ = ["utils", "dist", "sinkhorn"]

ot/gpu/bregman.py

@@ -8,14 +8,16 @@
 #
 # 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 problem and return the OT matrix
 
     The function solves the following optimization problem:
 
@@ -40,9 +42,10 @@ 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 +57,7 @@ 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
+
 
     Returns
     -------
@@ -88,60 +90,78 @@ def sinkhorn(a, b, M_GPU, reg, numItermax=1000, stopThr=1e-9, verbose=False,
     ot.optim.cg : General regularized OT
 
     """
+
+    a = cp.asarray(a, dtype=np.float64)
+    b = cp.asarray(b, dtype=np.float64)
+    M = cp.asarray(M, dtype=np.float64)
+
+    if len(a) == 0:
+        a = np.ones((M.shape[0],), dtype=np.float64) / M.shape[0]
+    if len(b) == 0:
+        b = np.ones((M.shape[1],), dtype=np.float64) / 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 +170,31 @@ 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