distributed -> remote (#82)

lib/python/arrays/dist/core.py

@@ -69,12 +69,12 @@ class DistArray(object):
     a = self.assemble()
     return a[sliced]
 
-@halo.distributed([DistArray], [np.ndarray])
+@halo.remote([DistArray], [np.ndarray])
 def assemble(a):
   return a.assemble()
 
 # TODO(rkn): what should we call this method
-@halo.distributed([np.ndarray], [DistArray])
+@halo.remote([np.ndarray], [DistArray])
 def numpy_to_dist(a):
   result = DistArray(a.shape)
   for index in np.ndindex(*result.num_blocks):
@@ -83,28 +83,28 @@ def numpy_to_dist(a):
     result.objrefs[index] = halo.push(a[[slice(l, u) for (l, u) in zip(lower, upper)]])
   return result
 
-@halo.distributed([List[int], str], [DistArray])
+@halo.remote([List[int], str], [DistArray])
 def zeros(shape, dtype_name="float"):
   result = DistArray(shape)
   for index in np.ndindex(*result.num_blocks):
     result.objrefs[index] = single.zeros(DistArray.compute_block_shape(index, shape), dtype_name=dtype_name)
   return result
 
-@halo.distributed([List[int], str], [DistArray])
+@halo.remote([List[int], str], [DistArray])
 def ones(shape, dtype_name="float"):
   result = DistArray(shape)
   for index in np.ndindex(*result.num_blocks):
     result.objrefs[index] = single.ones(DistArray.compute_block_shape(index, shape), dtype_name=dtype_name)
   return result
 
-@halo.distributed([DistArray], [DistArray])
+@halo.remote([DistArray], [DistArray])
 def copy(a):
   result = DistArray(a.shape)
   for index in np.ndindex(*result.num_blocks):
     result.objrefs[index] = a.objrefs[index] # We don't need to actually copy the objects because cluster-level objects are assumed to be immutable.
   return result
 
-@halo.distributed([int, int, str], [DistArray])
+@halo.remote([int, int, str], [DistArray])
 def eye(dim1, dim2=-1, dtype_name="float"):
   dim2 = dim1 if dim2 == -1 else dim2
   shape = [dim1, dim2]
@@ -117,7 +117,7 @@ def eye(dim1, dim2=-1, dtype_name="float"):
       result.objrefs[i, j] = single.zeros(block_shape, dtype_name=dtype_name)
   return result
 
-@halo.distributed([DistArray], [DistArray])
+@halo.remote([DistArray], [DistArray])
 def triu(a):
   if a.ndim != 2:
     raise Exception("Input must have 2 dimensions, but a.ndim is " + str(a.ndim))
@@ -131,7 +131,7 @@ def triu(a):
       result.objrefs[i, j] = single.zeros_like(a.objrefs[i, j])
   return result
 
-@halo.distributed([DistArray], [DistArray])
+@halo.remote([DistArray], [DistArray])
 def tril(a):
   if a.ndim != 2:
     raise Exception("Input must have 2 dimensions, but a.ndim is " + str(a.ndim))
@@ -145,7 +145,7 @@ def tril(a):
       result.objrefs[i, j] = single.zeros_like(a.objrefs[i, j])
   return result
 
-@halo.distributed([np.ndarray, None], [np.ndarray])
+@halo.remote([np.ndarray, None], [np.ndarray])
 def blockwise_dot(*matrices):
   n = len(matrices)
   if n % 2 != 0:
@@ -156,7 +156,7 @@ def blockwise_dot(*matrices):
     result += np.dot(matrices[i], matrices[n / 2 + i])
   return result
 
-@halo.distributed([DistArray, DistArray], [DistArray])
+@halo.remote([DistArray, DistArray], [DistArray])
 def dot(a, b):
   if a.ndim != 2:
     raise Exception("dot expects its arguments to be 2-dimensional, but a.ndim = {}.".format(a.ndim))
@@ -172,7 +172,7 @@ def dot(a, b):
   return result
 
 # This is not in numpy, should we expose this?
-@halo.distributed([DistArray, List[int], None], [DistArray])
+@halo.remote([DistArray, List[int], None], [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,
@@ -203,7 +203,7 @@ def subblocks(a, *ranges):
     result.objrefs[index] = a.objrefs[tuple([ranges[i][index[i]] for i in range(a.ndim)])]
   return result
 
-@halo.distributed([DistArray], [DistArray])
+@halo.remote([DistArray], [DistArray])
 def transpose(a):
   if a.ndim != 2:
     raise Exception("transpose expects its argument to be 2-dimensional, but a.ndim = {}, a.shape = {}.".format(a.ndim, a.shape))
@@ -214,7 +214,7 @@ def transpose(a):
   return result
 
 # TODO(rkn): support broadcasting?
-@halo.distributed([DistArray, DistArray], [DistArray])
+@halo.remote([DistArray, DistArray], [DistArray])
 def add(x1, x2):
   if x1.shape != x2.shape:
     raise Exception("add expects arguments `x1` and `x2` to have the same shape, but x1.shape = {}, and x2.shape = {}.".format(x1.shape, x2.shape))
@@ -224,7 +224,7 @@ def add(x1, x2):
   return result
 
 # TODO(rkn): support broadcasting?
-@halo.distributed([DistArray, DistArray], [DistArray])
+@halo.remote([DistArray, DistArray], [DistArray])
 def subtract(x1, x2):
   if x1.shape != x2.shape:
     raise Exception("subtract expects arguments `x1` and `x2` to have the same shape, but x1.shape = {}, and x2.shape = {}.".format(x1.shape, x2.shape))

lib/python/arrays/dist/linalg.py

@@ -8,7 +8,7 @@ from core import *
 
 __all__ = ["tsqr", "modified_lu", "tsqr_hr", "qr"]
 
-@halo.distributed([DistArray], [DistArray, np.ndarray])
+@halo.remote([DistArray], [DistArray, np.ndarray])
 def tsqr(a):
   """
   arguments:
@@ -80,7 +80,7 @@ def tsqr(a):
   return q_result, r
 
 # TODO(rkn): This is unoptimized, we really want a block version of this.
-@halo.distributed([DistArray], [DistArray, np.ndarray, np.ndarray])
+@halo.remote([DistArray], [DistArray, np.ndarray, np.ndarray])
 def modified_lu(q):
   """
   Algorithm 5 from http://www.eecs.berkeley.edu/Pubs/TechRpts/2013/EECS-2013-175.pdf
@@ -110,19 +110,19 @@ def modified_lu(q):
   U = np.triu(q_work)[:b, :]
   return numpy_to_dist(halo.push(L)), U, S # TODO(rkn): get rid of push and pull
 
-@halo.distributed([np.ndarray, np.ndarray, np.ndarray, int], [np.ndarray, np.ndarray])
+@halo.remote([np.ndarray, np.ndarray, np.ndarray, int], [np.ndarray, np.ndarray])
 def tsqr_hr_helper1(u, s, y_top_block, b):
   y_top = y_top_block[:b, :b]
   s_full = np.diag(s)
   t = -1 * np.dot(u, np.dot(s_full, np.linalg.inv(y_top).T))
   return t, y_top
 
-@halo.distributed([np.ndarray, np.ndarray], [np.ndarray])
+@halo.remote([np.ndarray, np.ndarray], [np.ndarray])
 def tsqr_hr_helper2(s, r_temp):
   s_full = np.diag(s)
   return np.dot(s_full, r_temp)
 
-@halo.distributed([DistArray], [DistArray, np.ndarray, np.ndarray, np.ndarray])
+@halo.remote([DistArray], [DistArray, np.ndarray, np.ndarray, np.ndarray])
 def tsqr_hr(a):
   """Algorithm 6 from http://www.eecs.berkeley.edu/Pubs/TechRpts/2013/EECS-2013-175.pdf"""
   q, r_temp = tsqr(a)
@@ -132,15 +132,15 @@ def tsqr_hr(a):
   r = tsqr_hr_helper2(s, r_temp)
   return y, t, y_top, r
 
-@halo.distributed([np.ndarray, np.ndarray, np.ndarray, np.ndarray], [np.ndarray])
+@halo.remote([np.ndarray, np.ndarray, np.ndarray, np.ndarray], [np.ndarray])
 def qr_helper1(a_rc, y_ri, t, W_c):
   return a_rc - np.dot(y_ri, np.dot(t.T, W_c))
 
-@halo.distributed([np.ndarray, np.ndarray], [np.ndarray])
+@halo.remote([np.ndarray, np.ndarray], [np.ndarray])
 def qr_helper2(y_ri, a_rc):
   return np.dot(y_ri.T, a_rc)
 
-@halo.distributed([DistArray], [DistArray, DistArray])
+@halo.remote([DistArray], [DistArray, DistArray])
 def qr(a):
   """Algorithm 7 from http://www.eecs.berkeley.edu/Pubs/TechRpts/2013/EECS-2013-175.pdf"""
   m, n = a.shape[0], a.shape[1]

lib/python/arrays/dist/random.py

@@ -6,7 +6,7 @@ import halo
 
 from core import *
 
-@halo.distributed([List[int]], [DistArray])
+@halo.remote([List[int]], [DistArray])
 def normal(shape):
   num_blocks = DistArray.compute_num_blocks(shape)
   objrefs = np.empty(num_blocks, dtype=object)