made summing up consistent if MPI is not used

nifty6/minimization/metric_gaussian_kl.py

@@ -233,10 +233,10 @@ class MetricGaussianKL(Energy):
         # This is a deterministic implementation of MPI allreduce in the sense
         # that it takes into account that floating point operations are not
         # associative.
-        res = None
         if self._comm is None:
-            for o in obj:
-                res = o if res is None else res + o
+            who = np.zeros(self._n_samples, dtype=np.int32)
+            rank = 0
+            vals = obj
         else:
             ntask = self._comm.Get_size()
             rank = self._comm.Get_rank()
@@ -252,29 +252,31 @@ class MetricGaussianKL(Energy):
             who = np.zeros(len(vals), dtype=np.int32)
             for t, (l,h) in enumerate(rank_lo_hi):
                 who[l:h] = t
-            def add2(v, w, rank):
-                #Note that communication only happens if rank in w
-                if len(v) == 1:
-                    return v[0]
-                if rank == w[0]:
-                    if w[0] == w[1]:
-                        return v[0]+v[1]
-                    self._comm.send(v[0], dest=w[1])
-                    return None
-                if rank == w[1]:
-                    return self._comm.recv(source=w[0]) + v[1] 
+        def add2(v, w, rank):
+            #Note that communication only happens if rank in w
+            if len(v) == 1:
+                return v[0]
+            if rank == w[0]:
+                if w[0] == w[1]:
+                    return v[0]+v[1]
+                self._comm.send(v[0], dest=w[1])
+                return None
+            if rank == w[1]:
+                return self._comm.recv(source=w[0]) + v[1] 
 
-            while len(vals) > 1:
-                new_vals = []
-                new_who = []
-                for j in range((len(vals)+1)//2):
-                    w = who[2*j:2*j+2]
-                    nv = add2(vals[2*j:2*j+2],w, rank)
-                    new_vals += [nv]
-                    new_who += [w[-1]]
-                vals = new_vals
-                who = new_who
-            res = self._comm.bcast(vals[0], root=who[0])
+        while len(vals) > 1:
+            new_vals = []
+            new_who = []
+            for j in range((len(vals)+1)//2):
+                w = who[2*j:2*j+2]
+                nv = add2(vals[2*j:2*j+2],w, rank)
+                new_vals += [nv]
+                new_who += [w[-1]]
+            vals = new_vals
+            who = new_who
+        if self._comm is None:
+            return vals[0]
+        res = self._comm.bcast(vals[0], root=who[0])
         return res