implement varargs (#83)

* implement varargs

* clean up varargs

lib/python/arrays/dist/core.py

@@ -145,7 +145,7 @@ def tril(a):
       result.objrefs[i, j] = single.zeros_like(a.objrefs[i, j])
   return result
 
-@halo.remote([np.ndarray, None], [np.ndarray])
+@halo.remote([np.ndarray], [np.ndarray])
 def blockwise_dot(*matrices):
   n = len(matrices)
   if n % 2 != 0:
@@ -171,8 +171,7 @@ def dot(a, b):
     result.objrefs[i, j] = blockwise_dot(*args)
   return result
 
-# This is not in numpy, should we expose this?
-@halo.remote([DistArray, List[int], None], [DistArray])
+@halo.remote([DistArray, List[int]], [DistArray])
 def subblocks(a, *ranges):
   """
   This function produces a distributed array from a subset of the blocks in the `a`. The result and `a` will have the same number of dimensions.For example,

lib/python/arrays/single/core.py

@@ -26,18 +26,15 @@ def eye(N, M=-1, k=0, dtype_name="float"):
 def dot(a, b):
   return np.dot(a, b)
 
-# TODO(rkn): My preferred signature would have been
-# @halo.remote([List[np.ndarray]], [np.ndarray]) but that currently doesn't
-# work because that would expect a list of ndarrays not a list of ObjRefs
-@halo.remote([np.ndarray, None], [np.ndarray])
+@halo.remote([np.ndarray], [np.ndarray])
 def vstack(*xs):
   return np.vstack(xs)
 
-@halo.remote([np.ndarray, None], [np.ndarray])
+@halo.remote([np.ndarray], [np.ndarray])
 def hstack(*xs):
   return np.hstack(xs)
 
-# TODO(rkn): this doesn't parallel the numpy API, but we can't really slice an ObjRef, think about this
+# TODO(rkn): instead of this, consider implementing slicing
 @halo.remote([np.ndarray, List[int], List[int]], [np.ndarray])
 def subarray(a, lower_indices, upper_indices): # TODO(rkn): be consistent about using "index" versus "indices"
   return a[[slice(l, u) for (l, u) in zip(lower_indices, upper_indices)]]
@@ -71,7 +68,7 @@ def add(x1, x2):
 def subtract(x1, x2):
   return np.subtract(x1, x2)
 
-@halo.remote([int, np.ndarray, None], [np.ndarray])
+@halo.remote([int, np.ndarray], [np.ndarray])
 def sum(axis, *xs):
   return np.sum(xs, axis=axis)
 

lib/python/arrays/single/linalg.py

@@ -83,6 +83,6 @@ def cond(x):
 def matrix_rank(M):
   return np.linalg.matrix_rank(M)
 
-@halo.remote([np.ndarray, None], [np.ndarray])
-def multi_dot(a):
+@halo.remote([np.ndarray], [np.ndarray])
+def multi_dot(*a):
   raise NotImplementedError