wassname/ray
1
0
Fork 0
mirror of https://github.com/wassname/ray.git synced 2026-07-11 17:59:28 +08:00
Code Issues Packages Projects Releases Wiki Activity

Allow remote decorator to be used with no parentheses.

Browse Source
This commit is contained in:
Robert Nishihara
2016-08-30 15:14:02 -07:00
parent ce4e5ec544
commit fb7ccef493
22 changed files with 193 additions and 175 deletions
Download Patch File Download Diff File Expand all files Collapse all files
lib/python/ray/array/distributed/core.py
+14 -14
View File
@@ -65,12 +65,12 @@ class DistArray(object):
a = self.assemble()
return a[sliced]
@ray.remote()
@ray.remote
def assemble(a):
return a.assemble()
# TODO(rkn): what should we call this method
@ray.remote()
@ray.remote
def numpy_to_dist(a):
result = DistArray(a.shape)
for index in np.ndindex(*result.num_blocks):
@@ -79,28 +79,28 @@ def numpy_to_dist(a):
result.objectids[index] = ray.put(a[[slice(l, u) for (l, u) in zip(lower, upper)]])
return result
@ray.remote()
@ray.remote
def zeros(shape, dtype_name="float"):
result = DistArray(shape)
for index in np.ndindex(*result.num_blocks):
result.objectids[index] = ra.zeros.remote(DistArray.compute_block_shape(index, shape), dtype_name=dtype_name)
return result
@ray.remote()
@ray.remote
def ones(shape, dtype_name="float"):
result = DistArray(shape)
for index in np.ndindex(*result.num_blocks):
result.objectids[index] = ra.ones.remote(DistArray.compute_block_shape(index, shape), dtype_name=dtype_name)
return result
@ray.remote()
@ray.remote
def copy(a):
result = DistArray(a.shape)
for index in np.ndindex(*result.num_blocks):
result.objectids[index] = a.objectids[index] # We don't need to actually copy the objects because cluster-level objects are assumed to be immutable.
return result
@ray.remote()
@ray.remote
def eye(dim1, dim2=-1, dtype_name="float"):
dim2 = dim1 if dim2 == -1 else dim2
shape = [dim1, dim2]
@@ -113,7 +113,7 @@ def eye(dim1, dim2=-1, dtype_name="float"):
result.objectids[i, j] = ra.zeros.remote(block_shape, dtype_name=dtype_name)
return result
@ray.remote()
@ray.remote
def triu(a):
if a.ndim != 2:
raise Exception("Input must have 2 dimensions, but a.ndim is " + str(a.ndim))
@@ -127,7 +127,7 @@ def triu(a):
result.objectids[i, j] = ra.zeros_like.remote(a.objectids[i, j])
return result
@ray.remote()
@ray.remote
def tril(a):
if a.ndim != 2:
raise Exception("Input must have 2 dimensions, but a.ndim is " + str(a.ndim))
@@ -141,7 +141,7 @@ def tril(a):
result.objectids[i, j] = ra.zeros_like.remote(a.objectids[i, j])
return result
@ray.remote()
@ray.remote
def blockwise_dot(*matrices):
n = len(matrices)
if n % 2 != 0:
@@ -152,7 +152,7 @@ def blockwise_dot(*matrices):
result += np.dot(matrices[i], matrices[n / 2 + i])
return result
@ray.remote()
@ray.remote
def dot(a, b):
if a.ndim != 2:
raise Exception("dot expects its arguments to be 2-dimensional, but a.ndim = {}.".format(a.ndim))
@@ -167,7 +167,7 @@ def dot(a, b):
result.objectids[i, j] = blockwise_dot.remote(*args)
return result
@ray.remote()
@ray.remote
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,
@@ -197,7 +197,7 @@ def subblocks(a, *ranges):
result.objectids[index] = a.objectids[tuple([ranges[i][index[i]] for i in range(a.ndim)])]
return result
@ray.remote()
@ray.remote
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))
@@ -208,7 +208,7 @@ def transpose(a):
return result
# TODO(rkn): support broadcasting?
@ray.remote()
@ray.remote
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))
@@ -218,7 +218,7 @@ def add(x1, x2):
return result
# TODO(rkn): support broadcasting?
@ray.remote()
@ray.remote
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/ray/array/distributed/linalg.py
+3 -3
View File
@@ -112,7 +112,7 @@ def tsqr_hr_helper1(u, s, y_top_block, b):
t = -1 * np.dot(u, np.dot(s_full, np.linalg.inv(y_top).T))
return t, y_top
@ray.remote()
@ray.remote
def tsqr_hr_helper2(s, r_temp):
s_full = np.diag(s)
return np.dot(s_full, r_temp)
@@ -127,11 +127,11 @@ def tsqr_hr(a):
r = tsqr_hr_helper2.remote(s, r_temp)
return y, t, y_top, r
@ray.remote()
@ray.remote
def qr_helper1(a_rc, y_ri, t, W_c):
return a_rc - np.dot(y_ri, np.dot(t.T, W_c))
@ray.remote()
@ray.remote
def qr_helper2(y_ri, a_rc):
return np.dot(y_ri.T, a_rc)
lib/python/ray/array/distributed/random.py
+1 -1
View File
@@ -4,7 +4,7 @@ import ray
from core import *
@ray.remote()
@ray.remote
def normal(shape):
num_blocks = DistArray.compute_num_blocks(shape)
objectids = np.empty(num_blocks, dtype=object)
lib/python/ray/array/remote/core.py
+18 -18
View File
@@ -3,80 +3,80 @@ import ray
__all__ = ["zeros", "zeros_like", "ones", "eye", "dot", "vstack", "hstack", "subarray", "copy", "tril", "triu", "diag", "transpose", "add", "subtract", "sum", "shape", "sum_list"]
@ray.remote()
@ray.remote
def zeros(shape, dtype_name="float", order="C"):
return np.zeros(shape, dtype=np.dtype(dtype_name), order=order)
@ray.remote()
@ray.remote
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)
@ray.remote()
@ray.remote
def ones(shape, dtype_name="float", order="C"):
return np.ones(shape, dtype=np.dtype(dtype_name), order=order)
@ray.remote()
@ray.remote
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))
@ray.remote()
@ray.remote
def dot(a, b):
return np.dot(a, b)
@ray.remote()
@ray.remote
def vstack(*xs):
return np.vstack(xs)
@ray.remote()
@ray.remote
def hstack(*xs):
return np.hstack(xs)
# TODO(rkn): instead of this, consider implementing slicing
@ray.remote()
@ray.remote
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)]]
@ray.remote()
@ray.remote
def copy(a, order="K"):
return np.copy(a, order=order)
@ray.remote()
@ray.remote
def tril(m, k=0):
return np.tril(m, k=k)
@ray.remote()
@ray.remote
def triu(m, k=0):
return np.triu(m, k=k)
@ray.remote()
@ray.remote
def diag(v, k=0):
return np.diag(v, k=k)
@ray.remote()
@ray.remote
def transpose(a, axes=[]):
axes = None if axes == [] else axes
return np.transpose(a, axes=axes)
@ray.remote()
@ray.remote
def add(x1, x2):
return np.add(x1, x2)
@ray.remote()
@ray.remote
def subtract(x1, x2):
return np.subtract(x1, x2)
@ray.remote()
@ray.remote
def sum(x, axis=-1):
return np.sum(x, axis=axis if axis != -1 else None)
@ray.remote()
@ray.remote
def shape(a):
return np.shape(a)
# We use Any to allow different numerical types as well as numpy arrays.
# TODO(rkn):this isn't in the numpy API, so be careful about exposing this.
@ray.remote()
@ray.remote
def sum_list(*xs):
return np.sum(xs, axis=0)
lib/python/ray/array/remote/linalg.py
+13 -13
View File
@@ -6,11 +6,11 @@ __all__ = ["matrix_power", "solve", "tensorsolve", "tensorinv", "inv",
"svd", "eig", "eigh", "lstsq", "norm", "qr", "cond", "matrix_rank",
"LinAlgError", "multi_dot"]
@ray.remote()
@ray.remote
def matrix_power(M, n):
return np.linalg.matrix_power(M, n)
@ray.remote()
@ray.remote
def solve(a, b):
return np.linalg.solve(a, b)
@@ -22,31 +22,31 @@ def tensorsolve(a):
def tensorinv(a):
raise NotImplementedError
@ray.remote()
@ray.remote
def inv(a):
return np.linalg.inv(a)
@ray.remote()
@ray.remote
def cholesky(a):
return np.linalg.cholesky(a)
@ray.remote()
@ray.remote
def eigvals(a):
return np.linalg.eigvals(a)
@ray.remote()
@ray.remote
def eigvalsh(a):
raise NotImplementedError
@ray.remote()
@ray.remote
def pinv(a):
return np.linalg.pinv(a)
@ray.remote()
@ray.remote
def slogdet(a):
raise NotImplementedError
@ray.remote()
@ray.remote
def det(a):
return np.linalg.det(a)
@@ -66,7 +66,7 @@ def eigh(a):
def lstsq(a, b):
return np.linalg.lstsq(a)
@ray.remote()
@ray.remote
def norm(x):
return np.linalg.norm(x)
@@ -74,14 +74,14 @@ def norm(x):
def qr(a):
return np.linalg.qr(a)
@ray.remote()
@ray.remote
def cond(x):
return np.linalg.cond(x)
@ray.remote()
@ray.remote
def matrix_rank(M):
return np.linalg.matrix_rank(M)
@ray.remote()
@ray.remote
def multi_dot(*a):
raise NotImplementedError
lib/python/ray/array/remote/random.py
+1 -1
View File
@@ -1,6 +1,6 @@
import numpy as np
import ray
@ray.remote()
@ray.remote
def normal(shape):
return np.random.normal(size=shape)