change filenames and directory structure to use halo (#81)

lib/python/arrays/single/__init__.py

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+import random, linalg
+from core import *

lib/python/arrays/single/core.py

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+from typing import List
+import numpy as np
+import halo
+
+__all__ = ["zeros", "zeros_like", "ones", "eye", "dot", "vstack", "hstack", "subarray", "copy", "tril", "triu", "diag", "transpose", "add", "subtract", "sum", "shape"]
+
+@halo.distributed([List[int], str, str], [np.ndarray])
+def zeros(shape, dtype_name="float", order="C"):
+  return np.zeros(shape, dtype=np.dtype(dtype_name), order=order)
+
+@halo.distributed([np.ndarray, str, str, bool], [np.ndarray])
+def zeros_like(a, dtype_name="None", order="K", subok=True):
+  dtype_val = None if dtype_name == "None" else np.dtype(dtype_name)
+  return np.zeros_like(a, dtype=dtype_val, order=order, subok=subok)
+
+@halo.distributed([List[int], str, str], [np.ndarray])
+def ones(shape, dtype_name="float", order="C"):
+  return np.ones(shape, dtype=np.dtype(dtype_name), order=order)
+
+@halo.distributed([int, int, int, str], [np.ndarray])
+def eye(N, M=-1, k=0, dtype_name="float"):
+  M = N if M == -1 else M
+  return np.eye(N, M=M, k=k, dtype=np.dtype(dtype_name))
+
+@halo.distributed([np.ndarray, np.ndarray], [np.ndarray])
+def dot(a, b):
+  return np.dot(a, b)
+
+# TODO(rkn): My preferred signature would have been
+# @halo.distributed([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.distributed([np.ndarray, None], [np.ndarray])
+def vstack(*xs):
+  return np.vstack(xs)
+
+@halo.distributed([np.ndarray, None], [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
+@halo.distributed([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)]]
+
+@halo.distributed([np.ndarray, str], [np.ndarray])
+def copy(a, order="K"):
+  return np.copy(a, order=order)
+
+@halo.distributed([np.ndarray, int], [np.ndarray])
+def tril(m, k=0):
+  return np.tril(m, k=k)
+
+@halo.distributed([np.ndarray, int], [np.ndarray])
+def triu(m, k=0):
+  return np.triu(m, k=k)
+
+@halo.distributed([np.ndarray, int], [np.ndarray])
+def diag(v, k=0):
+  return np.diag(v, k=k)
+
+@halo.distributed([np.ndarray, List[int]], [np.ndarray])
+def transpose(a, axes=[]):
+  axes = None if axes == [] else axes
+  return np.transpose(a, axes=axes)
+
+@halo.distributed([np.ndarray, np.ndarray], [np.ndarray])
+def add(x1, x2):
+  return np.add(x1, x2)
+
+@halo.distributed([np.ndarray, np.ndarray], [np.ndarray])
+def subtract(x1, x2):
+  return np.subtract(x1, x2)
+
+@halo.distributed([int, np.ndarray, None], [np.ndarray])
+def sum(axis, *xs):
+  return np.sum(xs, axis=axis)
+
+@halo.distributed([np.ndarray], [tuple])
+def shape(a):
+  return np.shape(a)

lib/python/arrays/single/linalg.py

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+from typing import List
+import numpy as np
+import halo
+
+__all__ = ["matrix_power", "solve", "tensorsolve", "tensorinv", "inv",
+           "cholesky", "eigvals", "eigvalsh", "pinv", "slogdet", "det",
+           "svd", "eig", "eigh", "lstsq", "norm", "qr", "cond", "matrix_rank",
+           "LinAlgError", "multi_dot"]
+
+@halo.distributed([np.ndarray, int], [np.ndarray])
+def matrix_power(M, n):
+  return np.linalg.matrix_power(M, n)
+
+@halo.distributed([np.ndarray, np.ndarray], [np.ndarray])
+def solve(a, b):
+  return np.linalg.solve(a, b)
+
+@halo.distributed([np.ndarray], [np.ndarray, np.ndarray])
+def tensorsolve(a):
+  raise NotImplementedError
+
+@halo.distributed([np.ndarray], [np.ndarray, np.ndarray])
+def tensorinv(a):
+  raise NotImplementedError
+
+@halo.distributed([np.ndarray], [np.ndarray])
+def inv(a):
+  return np.linalg.inv(a)
+
+@halo.distributed([np.ndarray], [np.ndarray])
+def cholesky(a):
+  return np.linalg.cholesky(a)
+
+@halo.distributed([np.ndarray], [np.ndarray])
+def eigvals(a):
+  return np.linalg.eigvals(a)
+
+@halo.distributed([np.ndarray], [np.ndarray])
+def eigvalsh(a):
+  raise NotImplementedError
+
+@halo.distributed([np.ndarray], [np.ndarray])
+def pinv(a):
+  return np.linalg.pinv(a)
+
+@halo.distributed([np.ndarray], [int])
+def slogdet(a):
+  raise NotImplementedError
+
+@halo.distributed([np.ndarray], [float])
+def det(a):
+  return np.linalg.det(a)
+
+@halo.distributed([np.ndarray], [np.ndarray, np.ndarray, np.ndarray])
+def svd(a):
+  return np.linalg.svd(a)
+
+@halo.distributed([np.ndarray], [np.ndarray, np.ndarray])
+def eig(a):
+  return np.linalg.eig(a)
+
+@halo.distributed([np.ndarray], [np.ndarray, np.ndarray])
+def eigh(a):
+  return np.linalg.eigh(a)
+
+@halo.distributed([np.ndarray], [np.ndarray, np.ndarray, int, np.ndarray])
+def lstsq(a, b):
+  return np.linalg.lstsq(a)
+
+@halo.distributed([np.ndarray], [float])
+def norm(x):
+  return np.linalg.norm(x)
+
+@halo.distributed([np.ndarray], [np.ndarray, np.ndarray])
+def qr(a):
+  return np.linalg.qr(a)
+
+@halo.distributed([np.ndarray], [float])
+def cond(x):
+  return np.linalg.cond(x)
+
+@halo.distributed([np.ndarray], [int])
+def matrix_rank(M):
+  return np.linalg.matrix_rank(M)
+
+@halo.distributed([np.ndarray, None], [np.ndarray])
+def multi_dot(a):
+  raise NotImplementedError

lib/python/arrays/single/random.py

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+from typing import List
+import numpy as np
+import halo
+
+@halo.distributed([List[int]], [np.ndarray])
+def normal(shape):
+  return np.random.normal(size=shape)