implement distributed qr (#30)

lib/orchpy/arrays/dist/__init__.py

@@ -1,2 +1,2 @@
-import random
+import random, linalg
 from core import *

lib/orchpy/arrays/dist/core.py

@@ -4,16 +4,16 @@ import arrays.single as single
 import orchpy as op
 
 __all__ = ["BLOCK_SIZE", "DistArray", "assemble", "zeros", "ones", "copy",
-           "eye", "triu", "tril", "blockwise_dot", "dot", "block_column", "block_row"]
+           "eye", "triu", "tril", "blockwise_dot", "dot", "transpose", "add", "subtract", "eye2", "numpy_to_dist", "subblocks"]
 
 BLOCK_SIZE = 10
 
 class DistArray(object):
-  def construct(self, shape, objrefs):
+  def construct(self, shape, objrefs=None):
     self.shape = shape
-    self.objrefs = objrefs
     self.ndim = len(shape)
     self.num_blocks = [int(np.ceil(1.0 * a / BLOCK_SIZE)) for a in self.shape]
+    self.objrefs = objrefs if objrefs is not None else np.empty(self.num_blocks, dtype=object)
     if self.num_blocks != list(self.objrefs.shape):
       raise Exception("The fields `num_blocks` and `objrefs` are inconsistent, `num_blocks` is {} and `objrefs` has shape {}".format(self.num_blocks, list(self.objrefs.shape)))
 
@@ -24,20 +24,20 @@ class DistArray(object):
   def serialize(self):
     return (self.shape, self.objrefs)
 
-  def __init__(self):
-    self.shape = None
-    self.objrefs = None
+  def __init__(self, shape=None):
+    if shape is not None:
+      self.construct(shape)
 
   @staticmethod
   def compute_block_lower(index, shape):
-    # TODO(rkn): Check that the entries of index are in the correct range.
-    # TODO(rkn): Check that len(index) == len(shape).
+    if len(index) != len(shape):
+      raise Exception("The fields `index` and `shape` must have the same length, but `index` is {} and `shape` is {}.".format(index, shape))
     return [elem * BLOCK_SIZE for elem in index]
 
   @staticmethod
   def compute_block_upper(index, shape):
-    # TODO(rkn): Check that the entries of index are in the correct range.
-    # TODO(rkn): Check that len(index) == len(shape).
+    if len(index) != len(shape):
+      raise Exception("The fields `index` and `shape` must have the same length, but `index` is {} and `shape` is {}.".format(index, shape))
     upper = []
     for i in range(len(shape)):
       upper.append(min((index[i] + 1) * BLOCK_SIZE, shape[i]))
@@ -73,82 +73,87 @@ class DistArray(object):
 def assemble(a):
   return a.assemble()
 
+# TODO(rkn): what should we call this method
+@op.distributed([np.ndarray], [DistArray])
+def numpy_to_dist(a):
+  result = DistArray(a.shape)
+  for index in np.ndindex(*result.num_blocks):
+    lower = DistArray.compute_block_lower(index, a.shape)
+    upper = DistArray.compute_block_upper(index, a.shape)
+    result.objrefs[index] = op.push(a[[slice(l, u) for (l, u) in zip(lower, upper)]])
+  return result
+
 @op.distributed([List[int], str], [DistArray])
 def zeros(shape, dtype_name):
-  num_blocks = DistArray.compute_num_blocks(shape)
-  objrefs = np.empty(num_blocks, dtype=object)
-  for index in np.ndindex(*num_blocks):
-    objrefs[index] = single.zeros(DistArray.compute_block_shape(index, shape), dtype_name)
-  result = DistArray()
-  result.construct(shape, objrefs)
+  result = DistArray(shape)
+  for index in np.ndindex(*result.num_blocks):
+    result.objrefs[index] = single.zeros(DistArray.compute_block_shape(index, shape), dtype_name)
   return result
 
 @op.distributed([List[int], str], [DistArray])
 def ones(shape, dtype_name):
-  num_blocks = DistArray.compute_num_blocks(shape)
-  objrefs = np.empty(num_blocks, dtype=object)
-  for index in np.ndindex(*num_blocks):
-    objrefs[index] = single.ones(DistArray.compute_block_shape(index, shape), dtype_name)
-  result = DistArray()
-  result.construct(shape, objrefs)
+  result = DistArray(shape)
+  for index in np.ndindex(*result.num_blocks):
+    result.objrefs[index] = single.ones(DistArray.compute_block_shape(index, shape), dtype_name)
   return result
 
 @op.distributed([DistArray], [DistArray])
 def copy(a):
-  num_blocks = DistArray.compute_num_blocks(a.shape)
-  objrefs = np.empty(num_blocks, dtype=object)
-  for index in np.ndindex(*num_blocks):
-    objrefs[index] = single.copy(a.objrefs[index])
-  result = DistArray()
-  result.construct(a.shape, objrefs)
+  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
 
 @op.distributed([int, str], [DistArray])
 def eye(dim, dtype_name):
   shape = [dim, dim]
-  num_blocks = DistArray.compute_num_blocks(shape)
-  objrefs = np.empty(num_blocks, dtype=object)
-  for (i, j) in np.ndindex(*num_blocks):
+  result = DistArray(shape)
+  for (i, j) in np.ndindex(*result.num_blocks):
     if i == j:
-      objrefs[i, j] = single.eye(DistArray.compute_block_shape([i, j], shape)[0], dtype_name)
+      result.objrefs[i, j] = single.eye(DistArray.compute_block_shape([i, j], shape)[0], dtype_name)
     else:
-      objrefs[i, j] = single.zeros(DistArray.compute_block_shape([i, j], shape), dtype_name)
-  result = DistArray()
-  result.construct(shape, objrefs)
+      result.objrefs[i, j] = single.zeros(DistArray.compute_block_shape([i, j], shape), dtype_name)
+  return result
+
+# TODO(rkn): Support optional arguments so that we can make this part of eye.
+@op.distributed([int, int, str], [DistArray])
+def eye2(dim1, dim2, dtype_name):
+  shape = [dim1, dim2]
+  result = DistArray(shape)
+  for (i, j) in np.ndindex(*result.num_blocks):
+    block_shape = DistArray.compute_block_shape([i, j], shape)
+    if i == j:
+      result.objrefs[i, j] = single.eye2(block_shape[0], block_shape[1], dtype_name)
+    else:
+      result.objrefs[i, j] = single.zeros(block_shape, dtype_name)
   return result
 
 @op.distributed([DistArray], [DistArray])
 def triu(a):
   if a.ndim != 2:
     raise Exception("Input must have 2 dimensions, but a.ndim is " + str(a.ndim))
-  objrefs = np.empty(a.num_blocks, dtype=object)
-  for i in range(a.num_blocks[0]):
-    for j in range(a.num_blocks[1]):
-      if i < j:
-        objrefs[i, j] = single.copy(a.objrefs[i, j])
-      elif i == j:
-        objrefs[i, j] = single.triu(a.objrefs[i, j])
-      else:
-        objrefs[i, j] = single.zeros_like(a.objrefs[i, j])
-  result = DistArray()
-  result.construct(a.shape, objrefs)
+  result = DistArray(a.shape)
+  for (i, j) in np.ndindex(*result.num_blocks):
+    if i < j:
+      result.objrefs[i, j] = single.copy(a.objrefs[i, j])
+    elif i == j:
+      result.objrefs[i, j] = single.triu(a.objrefs[i, j])
+    else:
+      result.objrefs[i, j] = single.zeros_like(a.objrefs[i, j])
   return result
 
 @op.distributed([DistArray], [DistArray])
 def tril(a):
   if a.ndim != 2:
     raise Exception("Input must have 2 dimensions, but a.ndim is " + str(a.ndim))
-  objrefs = np.empty(a.num_blocks, dtype=object)
-  for i in range(a.num_blocks[0]):
-    for j in range(a.num_blocks[1]):
-      if i > j:
-        objrefs[i, j] = single.copy(a.objrefs[i, j])
-      elif i == j:
-        objrefs[i, j] = single.tril(a.objrefs[i, j])
-      else:
-        objrefs[i, j] = single.zeros_like(a.objrefs[i, j])
-  result = DistArray()
-  result.construct(a.shape, objrefs)
+  result = DistArray(a.shape)
+  for (i, j) in np.ndindex(*result.num_blocks):
+    if i > j:
+      result.objrefs[i, j] = single.copy(a.objrefs[i, j])
+    elif i == j:
+      result.objrefs[i, j] = single.tril(a.objrefs[i, j])
+    else:
+      result.objrefs[i, j] = single.zeros_like(a.objrefs[i, j])
   return result
 
 @op.distributed([np.ndarray, None], [np.ndarray])
@@ -171,34 +176,70 @@ def dot(a, b):
   if a.shape[1] != b.shape[0]:
     raise Exception("dot expects a.shape[1] to equal b.shape[0], but a.shape = {} and b.shape = {}.".format(a.shape, b.shape))
   shape = [a.shape[0], b.shape[1]]
-  num_blocks = DistArray.compute_num_blocks(shape)
-  objrefs = np.empty(num_blocks, dtype=object)
-  for i in range(num_blocks[0]):
-    for j in range(num_blocks[1]):
-      args = list(a.objrefs[i, :]) + list(b.objrefs[:, j])
-      objrefs[i, j] = blockwise_dot(*args)
-  result = DistArray()
-  result.construct(shape, objrefs)
+  result = DistArray(shape)
+  for (i, j) in np.ndindex(*result.num_blocks):
+    args = list(a.objrefs[i, :]) + list(b.objrefs[:, j])
+    result.objrefs[i, j] = blockwise_dot(*args)
   return result
 
 # This is not in numpy, should we expose this?
-@op.distributed([DistArray], [DistArray])
-def block_column(a, col):
-  if a.ndim != 2:
-    raise Exception("block_column expects its argument to be 2-dimensional, but a.ndim = {}, a.shape = {}.".format(a.ndim, a.shape))
-  top_block_shape = DistArray.compute_block_shape([0, col])
-  shape = [a.shape[0], top_block_shape[1]]
-  result = DistArray()
-  result.construct(shape, a.objrefs[:, col])
+@op.distributed([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,
+      subblocks(a, [0, 1], [2, 4])
+  will produce a DistArray whose objrefs are
+      [[a.objrefs[0, 2], a.objrefs[0, 4]],
+       [a.objrefs[1, 2], a.objrefs[1, 4]]]
+  We allow the user to pass in an empty list [] to indicate the full range.
+  """
+  ranges = list(ranges)
+  if len(ranges) != a.ndim:
+    raise Exception("sub_blocks expects to receive a number of ranges equal to a.ndim, but it received {} ranges and a.ndim = {}.".format(len(ranges), a.ndim))
+  for i in range(len(ranges)):
+    if ranges[i] == []: # We allow the user to pass in an empty list to indicate the full range
+      ranges[i] = range(a.num_blocks[i])
+    if not np.alltrue(ranges[i] == np.sort(ranges[i])):
+      raise Exception("Ranges passed to sub_blocks must be sorted, but the {}th range is {}.".format(i, ranges[i]))
+    if ranges[i][0] < 0:
+      raise Exception("Values in the ranges passed to sub_blocks must be at least 0, but the {}th range is {}.".format(i, ranges[i]))
+    if ranges[i][-1] >= a.num_blocks[i]:
+        raise Exception("Values in the ranges passed to sub_blocks must be less than the relevant number of blocks, but the {}th range is {}, and a.num_blocks = {}.".format(i, ranges[i], a.num_blocks))
+  last_index = [r[-1] for r in ranges]
+  last_block_shape = DistArray.compute_block_shape(last_index, a.shape)
+  shape = [(len(ranges[i]) - 1) * BLOCK_SIZE + last_block_shape[i] for i in range(a.ndim)]
+  result = DistArray(shape)
+  for index in np.ndindex(*result.num_blocks):
+    print tuple([ranges[i][index[i]] for i in range(a.ndim)])
+    result.objrefs[index] = a.objrefs[tuple([ranges[i][index[i]] for i in range(a.ndim)])]
   return result
 
-# This is not in numpy, should we expose this?
 @op.distributed([DistArray], [DistArray])
-def block_row(a, row):
+def transpose(a):
   if a.ndim != 2:
-    raise Exception("block_row expects its argument to be 2-dimensional, but a.ndim = {}, a.shape = {}.".format(a.ndim, a.shape))
-  left_block_shape = DistArray.compute_block_shape([row, 0])
-  shape = [left_block_shape[0], a.shape[1]]
-  result = DistArray()
-  result.construct(shape, a.objrefs[row, :])
+    raise Exception("transpose expects its argument to be 2-dimensional, but a.ndim = {}, a.shape = {}.".format(a.ndim, a.shape))
+  result = DistArray([a.shape[1], a.shape[0]])
+  for i in range(result.num_blocks[0]):
+    for j in range(result.num_blocks[1]):
+      result.objrefs[i, j] = single.transpose(a.objrefs[j, i])
+  return result
+
+# TODO(rkn): support broadcasting?
+@op.distributed([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))
+  result = DistArray(x1.shape)
+  for index in np.ndindex(*result.num_blocks):
+    result.objrefs[index] = single.add(x1.objrefs[index], x2.objrefs[index])
+  return result
+
+# TODO(rkn): support broadcasting?
+@op.distributed([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))
+  result = DistArray(x1.shape)
+  for index in np.ndindex(*result.num_blocks):
+    result.objrefs[index] = single.subtract(x1.objrefs[index], x2.objrefs[index])
   return result

lib/orchpy/arrays/dist/linalg.py

@@ -0,0 +1,192 @@
+from typing import List
+
+import numpy as np
+import arrays.single as single
+import orchpy as op
+
+from core import *
+
+__all__ = ["tsqr", "modified_lu", "tsqr_hr", "qr"]
+
+@op.distributed([DistArray], [DistArray, np.ndarray])
+def tsqr(a):
+  """
+  arguments:
+    a: a distributed matrix
+  Suppose that
+    a.shape == (M, N)
+    K == min(M, N)
+  return values:
+    q: DistArray, if q_full = op.context.pull(DistArray, q).assemble(), then
+      q_full.shape == (M, K)
+      np.allclose(np.dot(q_full.T, q_full), np.eye(K)) == True
+    r: np.ndarray, if r_val = op.context.pull(np.ndarray, r), then
+      r_val.shape == (K, N)
+      np.allclose(r, np.triu(r)) == True
+  """
+  if len(a.shape) != 2:
+    raise Exception("tsqr requires len(a.shape) == 2, but a.shape is {}".format(a.shape))
+  if a.num_blocks[1] != 1:
+    raise Exception("tsqr requires a.num_blocks[1] == 1, but a.num_blocks is {}".format(a.num_blocks))
+
+  num_blocks = a.num_blocks[0]
+  K = int(np.ceil(np.log2(num_blocks))) + 1
+  q_tree = np.empty((num_blocks, K), dtype=object)
+  current_rs = []
+  for i in range(num_blocks):
+    block = a.objrefs[i, 0]
+    q, r = single.linalg.qr(block)
+    q_tree[i, 0] = q
+    current_rs.append(r)
+  for j in range(1, K):
+    new_rs = []
+    for i in range(int(np.ceil(1.0 * len(current_rs) / 2))):
+      stacked_rs = single.vstack(*current_rs[(2 * i):(2 * i + 2)])
+      q, r = single.linalg.qr(stacked_rs)
+      q_tree[i, j] = q
+      new_rs.append(r)
+    current_rs = new_rs
+  assert len(current_rs) == 1, "len(current_rs) = " + str(len(current_rs))
+
+  q_result = DistArray()
+
+  # handle the special case in which the whole DistArray "a" fits in one block
+  # and has fewer rows than columns, this is a bit ugly so think about how to
+  # remove it
+  if a.shape[0] >= a.shape[1]:
+    q_shape = a.shape
+  else:
+    q_shape = [a.shape[0], a.shape[0]]
+  q_num_blocks = DistArray.compute_num_blocks(q_shape)
+  q_result = DistArray()
+  q_objrefs = np.empty(q_num_blocks, dtype=object)
+  q_result.construct(q_shape, q_objrefs)
+
+  # reconstruct output
+  for i in range(num_blocks):
+    q_block_current = q_tree[i, 0]
+    ith_index = i
+    for j in range(1, K):
+      if np.mod(ith_index, 2) == 0:
+        lower = [0, 0]
+        upper = [a.shape[1], BLOCK_SIZE]
+      else:
+        lower = [a.shape[1], 0]
+        upper = [2 * a.shape[1], BLOCK_SIZE]
+      ith_index /= 2
+      q_block_current = single.dot(q_block_current, single.subarray(q_tree[ith_index, j], lower, upper))
+    q_result.objrefs[i] = q_block_current
+  r = current_rs[0]
+  return q_result, r
+
+# TODO(rkn): This is unoptimized, we really want a block version of this.
+@op.distributed([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
+  takes a matrix q with orthonormal columns, returns l, u, s such that q - s = l * u
+  arguments:
+    q: a two dimensional orthonormal q
+  return values:
+    l: lower triangular
+    u: upper triangular
+    s: a diagonal matrix represented by its diagonal
+  """
+  q = q.assemble()
+  m, b = q.shape[0], q.shape[1]
+  S = np.zeros(b)
+
+  q_work = np.copy(q)
+
+  for i in range(b):
+    S[i] = -1 * np.sign(q_work[i, i])
+    q_work[i, i] -= S[i]
+    q_work[(i + 1):m, i] /= q_work[i, i] # scale ith column of L by diagonal element
+    q_work[(i + 1):m, (i + 1):b] -= np.outer(q_work[(i + 1):m, i], q_work[i, (i + 1):b]) # perform Schur complement update
+
+  L = np.tril(q_work)
+  for i in range(b):
+    L[i, i] = 1
+  U = np.triu(q_work)[:b, :]
+  return numpy_to_dist(op.push(L)), U, S # TODO(rkn): get rid of push and pull
+
+@op.distributed([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
+
+@op.distributed([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)
+
+@op.distributed([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)
+  y, u, s = modified_lu(q)
+  y_blocked = op.pull(y)
+  t, y_top = tsqr_hr_helper1(u, s, y_blocked.objrefs[0, 0], a.shape[1])
+  r = tsqr_hr_helper2(s, r_temp)
+  return y, t, y_top, r
+
+@op.distributed([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))
+
+@op.distributed([np.ndarray, np.ndarray], [np.ndarray])
+def qr_helper2(y_ri, a_rc):
+  return np.dot(y_ri.T, a_rc)
+
+@op.distributed([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]
+  k = min(m, n)
+
+  # we will store our scratch work in a_work
+  a_work = DistArray()
+  a_work.construct(a.shape, np.copy(a.objrefs))
+
+  result_dtype = np.linalg.qr(op.pull(a.objrefs[0, 0]))[0].dtype.name
+  r_res = op.pull(zeros([k, n], result_dtype)) # TODO(rkn): It would be preferable not to pull this right after creating it.
+  y_res = op.pull(zeros([m, k], result_dtype)) # TODO(rkn): It would be preferable not to pull this right after creating it.
+  Ts = []
+
+  for i in range(min(a.num_blocks[0], a.num_blocks[1])): # this differs from the paper, which says "for i in range(a.num_blocks[1])", but that doesn't seem to make any sense when a.num_blocks[1] > a.num_blocks[0]
+    sub_dist_array = subblocks(a_work, range(i, a_work.num_blocks[0]), [i])
+    y, t, _, R = tsqr_hr(sub_dist_array)
+    y_val = op.pull(y)
+
+    for j in range(i, a.num_blocks[0]):
+      y_res.objrefs[j, i] = y_val.objrefs[j - i, 0]
+    if a.shape[0] > a.shape[1]:
+      # in this case, R needs to be square
+      R_shape = op.pull(single.shape(R))
+      eye_temp = single.eye2(R_shape[1], R_shape[0], result_dtype)
+      r_res.objrefs[i, i] = single.dot(eye_temp, R)
+    else:
+      r_res.objrefs[i, i] = R
+    Ts.append(numpy_to_dist(t))
+
+    for c in range(i + 1, a.num_blocks[1]):
+      W_rcs = []
+      for r in range(i, a.num_blocks[0]):
+        y_ri = y_val.objrefs[r - i, 0]
+        W_rcs.append(qr_helper2(y_ri, a_work.objrefs[r, c]))
+      W_c = single.sum(0, *W_rcs)
+      for r in range(i, a.num_blocks[0]):
+        y_ri = y_val.objrefs[r - i, 0]
+        A_rc = qr_helper1(a_work.objrefs[r, c], y_ri, t, W_c)
+        a_work.objrefs[r, c] = A_rc
+      r_res.objrefs[i, c] = a_work.objrefs[i, c]
+
+  # construct q_res from Ys and Ts
+  q = eye2(m, k, result_dtype)
+  for i in range(len(Ts))[::-1]:
+    y_col_block = subblocks(y_res, [], [i])
+    q = subtract(q, dot(y_col_block, dot(Ts[i], dot(transpose(y_col_block), q))))
+
+  return q, r_res

lib/orchpy/arrays/single/__init__.py

@@ -1,2 +1,2 @@
 import random, linalg
-from core import zeros, zeros_like, ones, eye, dot, vstack, hstack, subarray, copy, tril, triu
+from core import *

lib/orchpy/arrays/single/core.py

@@ -2,6 +2,8 @@ from typing import List
 import numpy as np
 import orchpy as op
 
+__all__ = ["zeros", "zeros_like", "ones", "eye", "dot", "vstack", "hstack", "subarray", "copy", "tril", "triu", "diag", "transpose", "add", "subtract", "eye2", "sum", "shape"]
+
 @op.distributed([List[int], str], [np.ndarray])
 def zeros(shape, dtype_name):
   return np.zeros(shape, dtype=np.dtype(dtype_name))
@@ -18,6 +20,11 @@ def ones(shape, dtype_name):
 def eye(dim, dtype_name):
   return np.eye(dim, dtype=np.dtype(dtype_name))
 
+# TODO(rkn): This should be part of eye
+@op.distributed([int, int, str], [np.ndarray])
+def eye2(dim1, dim2, dtype_name):
+  return np.eye(dim1, dim2, dtype=np.dtype(dtype_name))
+
 @op.distributed([np.ndarray, np.ndarray], [np.ndarray])
 def dot(a, b):
   return np.dot(a, b)
@@ -49,3 +56,27 @@ def tril(a):
 @op.distributed([np.ndarray], [np.ndarray])
 def triu(a):
   return np.triu(a)
+
+@op.distributed([np.ndarray], [np.ndarray])
+def diag(a):
+  return np.diag(a)
+
+@op.distributed([np.ndarray], [np.ndarray])
+def transpose(a):
+  return np.transpose(a)
+
+@op.distributed([np.ndarray, np.ndarray], [np.ndarray])
+def add(x1, x2):
+  return np.add(x1, x2)
+
+@op.distributed([np.ndarray, np.ndarray], [np.ndarray])
+def subtract(x1, x2):
+  return np.subtract(x1, x2)
+
+@op.distributed([int, np.ndarray, None], [np.ndarray])
+def sum(axis, *xs):
+  return np.sum(xs, axis=axis)
+
+@op.distributed([np.ndarray], [tuple])
+def shape(a):
+  return np.shape(a)

lib/orchpy/arrays/single/linalg.py

@@ -86,39 +86,3 @@ def matrix_rank(M):
 @op.distributed([np.ndarray, None], [np.ndarray])
 def multi_dot(a):
   raise NotImplementedError
-
-
-
-# This isn't in numpy, should we expose it?
-@op.distributed([np.ndarray], [np.ndarray, np.ndarray, np.ndarray])
-def modified_lu(q):
-  """
-  Algorithm 5 from http://www.eecs.berkeley.edu/Pubs/TechRpts/2013/EECS-2013-175.pdf
-  takes a matrix q with orthonormal columns, returns l, u, s such that q - s = l * u
-  arguments:
-    q: a two dimensional orthonormal q
-  return values:
-    l: lower triangular
-    u: upper triangular
-    s: a diagonal matrix represented by its diagonal
-  """
-  m, b = q.shape[0], q.shape[1]
-  S = np.zeros(b)
-
-  q_work = np.copy(q)
-
-  for i in range(b):
-    S[i] = -1 * np.sign(q_work[i, i])
-    q_work[i, i] -= S[i]
-
-    # scale ith column of L by diagonal element
-    q_work[(i + 1):m, i] /= q_work[i, i]
-
-    # perform Schur complement update
-    q_work[(i + 1):m, (i + 1):b] -= np.outer(q_work[(i + 1):m, i], q_work[i, (i + 1):b])
-
-  L = np.tril(q_work)
-  for i in range(b):
-    L[i, i] = 1
-  U = np.triu(q_work)[:b, :]
-  return L, U, S

lib/orchpy/orchpy/worker.py

@@ -99,10 +99,10 @@ def distributed(arg_types, return_types, worker=global_worker):
   def distributed_decorator(func):
     def func_executor(arguments):
       """This is what gets executed remotely on a worker after a distributed function is scheduled by the scheduler."""
-      print "Calling function {} with arguments {}".format(func.__name__, arguments)
+      print "Calling function {}".format(func.__name__)
       result = func(*arguments)
       check_return_values(func_call, result) # throws an exception if result is invalid
-      print "Finished executing function {} with arguments {}".format(func.__name__, arguments)
+      print "Finished executing function {}".format(func.__name__)
       return result
     def func_call(*args):
       """This is what gets run immediately when a worker calls a distributed function."""