Skip to content

P
pytensor
提交 e0e5b3b8 authored 作者: Brandon T. Willard's avatar Brandon T. Willard 提交者： Thomas Wiecki
浏览文件
操作

Put math Ops in theano.tensor.math and array Ops in theano.tensor.basic

上级 2b06fa16
全部展开 隐藏空白字符变更
内嵌 并排
正在显示 包含 358 行增加 和 269 行删除
.github/workflows/test.yml
浏览文件 @ e0e5b3b8
......@@ -59,8 +59,8 @@ jobs:
float32: [0]
part:
- "tests --ignore=tests/tensor --ignore=tests/sparse --ignore=tests/tensor/nnet"
- "tests/tensor tests/sparse --ignore=tests/tensor/test_basic.py --ignore=tests/tensor/test_basic_scipy.py --ignore=tests/tensor/test_inplace.py --ignore=tests/tensor/test_elemwise.py --ignore=tests/tensor/test_opt.py --ignore=tests/tensor/nnet"
- "tests/tensor/test_basic.py tests/tensor/test_basic_scipy.py tests/tensor/test_inplace.py"
- "tests/tensor tests/sparse --ignore=tests/tensor/test_basic.py --ignore=tests/tensor/test_math.py --ignore=tests/tensor/test_basic_scipy.py --ignore=tests/tensor/test_inplace.py --ignore=tests/tensor/test_elemwise.py --ignore=tests/tensor/test_opt.py --ignore=tests/tensor/nnet"
- "tests/tensor/test_basic.py tests/tensor/test_math.py tests/tensor/test_basic_scipy.py tests/tensor/test_inplace.py"
- "tests/tensor/test_elemwise.py tests/tensor/test_opt.py"
- "tests/tensor/nnet --ignore-glob='*/test_abstract_conv.py'"
- "tests/tensor/nnet/test_abstract_conv.py"
......
doc/library/gradient.txt
浏览文件 @ e0e5b3b8
......@@ -36,7 +36,7 @@ List of Implemented R op
See the :ref:`gradient tutorial <tutcomputinggrads>` for the R op documentation.
list of ops that support R-op:
* with test [Most is tests/tensor/test_rop.py]
* with test
* SpecifyShape
* MaxAndArgmax
* Subtensor
......
doc/library/printing.txt
浏览文件 @ e0e5b3b8
......@@ -51,10 +51,11 @@ Theano also provides :func:`theano.printing.pydotprint` that creates a png image
1) The first is :func:`theano.pp`.
>>> from theano import pp, tensor as tt
>>> from theano import pp, grad,
>>> from theano import tensor as tt
>>> x = tt.dscalar('x')
>>> y = x ** 2
>>> gy = tt.grad(y, x)
>>> gy = grad(y, x)
>>> pp(gy) # print out the gradient prior to optimization
'((fill((x ** TensorConstant{2}), TensorConstant{1.0}) * TensorConstant{2}) * (x ** (TensorConstant{2} - TensorConstant{1})))'
>>> f = function([x], gy)
......
tests/compile/function/test_function.py
浏览文件 @ e0e5b3b8
......@@ -3,6 +3,7 @@ import pickle
import re
import shutil
import tempfile
from collections import OrderedDict
import numpy as np
import pytest
......@@ -10,11 +11,14 @@ import pytest
import theano
from theano.compile.function import function, function_dump
from theano.compile.io import In
from theano.configdefaults import config
from theano.tensor.math import sum as tt_sum
from theano.tensor.type import (
bscalar,
bvector,
dscalar,
dvector,
fmatrix,
fscalar,
fvector,
vector,
......@@ -22,6 +26,9 @@ from theano.tensor.type import (
)
floatX = "float32"
def test_function_dump():
v = vector()
fct1 = function([v], v + 1)
......@@ -195,7 +202,7 @@ class TestFunctionIn:
f(0, 0.1, 0)
# If allow_downcast is None, it should work iff floatX=float32
if theano.config.floatX == "float32":
if config.floatX == "float32":
assert np.allclose(f(0, 0, 0.1), 0.1)
else:
with pytest.raises(TypeError):
......@@ -229,3 +236,68 @@ class TestFunctionIn:
# If allow_downcast is None, like False
with pytest.raises(TypeError):
f(z, z, [0.1])
def test_pickle_unpickle_with_reoptimization():
mode = config.mode
if mode in ["DEBUG_MODE", "DebugMode"]:
mode = "FAST_RUN"
x1 = fmatrix("x1")
x2 = fmatrix("x2")
x3 = theano.shared(np.ones((10, 10), dtype=floatX))
x4 = theano.shared(np.ones((10, 10), dtype=floatX))
y = tt_sum(tt_sum(tt_sum(x1 ** 2 + x2) + x3) + x4)
updates = OrderedDict()
updates[x3] = x3 + 1
updates[x4] = x4 + 1
f = theano.function([x1, x2], y, updates=updates, mode=mode)
# now pickle the compiled theano fn
string_pkl = pickle.dumps(f, -1)
in1 = np.ones((10, 10), dtype=floatX)
in2 = np.ones((10, 10), dtype=floatX)
# test unpickle with optimization
default = config.reoptimize_unpickled_function
try:
# the default is True
config.reoptimize_unpickled_function = True
f_ = pickle.loads(string_pkl)
assert f(in1, in2) == f_(in1, in2)
finally:
config.reoptimize_unpickled_function = default
def test_pickle_unpickle_without_reoptimization():
mode = config.mode
if mode in ["DEBUG_MODE", "DebugMode"]:
mode = "FAST_RUN"
x1 = fmatrix("x1")
x2 = fmatrix("x2")
x3 = theano.shared(np.ones((10, 10), dtype=floatX))
x4 = theano.shared(np.ones((10, 10), dtype=floatX))
y = tt_sum(tt_sum(tt_sum(x1 ** 2 + x2) + x3) + x4)
updates = OrderedDict()
updates[x3] = x3 + 1
updates[x4] = x4 + 1
f = theano.function([x1, x2], y, updates=updates, mode=mode)
# now pickle the compiled theano fn
string_pkl = pickle.dumps(f, -1)
# compute f value
in1 = np.ones((10, 10), dtype=floatX)
in2 = np.ones((10, 10), dtype=floatX)
# test unpickle without optimization
default = config.reoptimize_unpickled_function
try:
# the default is True
config.reoptimize_unpickled_function = False
f_ = pickle.loads(string_pkl)
assert f(in1, in2) == f_(in1, in2)
finally:
config.reoptimize_unpickled_function = default
tests/compile/function/test_pfunc.py
浏览文件 @ e0e5b3b8
......@@ -8,6 +8,7 @@ from theano.compile.io import In
from theano.compile.sharedvalue import shared
from theano.configdefaults import config
from theano.misc.safe_asarray import _asarray
from theano.tensor.math import sum as tt_sum
from theano.tensor.type import (
bscalar,
bvector,
......@@ -92,7 +93,7 @@ class TestPfunc:
with pytest.raises(
TypeError, match=r"^Cannot use a shared variable \(w\) as explicit input"
):
pfunc([w], tt.sum(w * w))
pfunc([w], tt_sum(w * w))
def test_default_container(self):
# Ensure it is possible to (implicitly) use a shared variable in a
......@@ -101,7 +102,7 @@ class TestPfunc:
rng = np.random.RandomState(1827)
w_init = rng.rand(5)
w = shared(w_init.copy(), "w")
reg = tt.sum(w * w)
reg = tt_sum(w * w)
f = pfunc([], reg)
assert f() == np.sum(w_init * w_init)
......
tests/compile/function/test_types.py
浏览文件 @ e0e5b3b8
import copy
import os
import pickle
import time
......@@ -16,11 +17,15 @@ from theano.configdefaults import config
from theano.graph.basic import Constant
from theano.graph.fg import MissingInputError
from theano.graph.opt import OpKeyOptimizer, PatternSub
from theano.tensor.math import dot
from theano.tensor.math import sum as tt_sum
from theano.tensor.math import tanh
from theano.tensor.type import (
dmatrix,
dscalar,
dscalars,
dvector,
fmatrix,
fscalar,
iscalar,
matrix,
......@@ -317,7 +322,7 @@ class TestFunction:
# SharedVariable for tests, one of them has update
y = theano.shared(value=1)
z = theano.shared(value=2)
out = tt.tanh((x + y + 2) / (x + z - 0.2) ** 2)
out = tanh((x + y + 2) / (x + z - 0.2) ** 2)
# Test for different linkers
for mode in ["FAST_RUN", "FAST_COMPILE"]:
......@@ -426,7 +431,7 @@ class TestFunction:
x = vector("x")
y = vector("y")
# this formular has no sense but for a test
out = (tt.sum(x) - y) ** 2
out = (tt_sum(x) - y) ** 2
train = theano.function(
[i],
out,
......@@ -921,7 +926,7 @@ class TestPicklefunction:
f = function(
[a, x, s, xm, sm],
((a.T.T) * (tt.dot(xm, (sm.T.T.T)) + x).T * (x / x) + s),
((a.T.T) * (dot(xm, (sm.T.T.T)) + x).T * (x / x) + s),
)
old_default_mode = config.mode
old_default_opt = config.optimizer
......@@ -1061,7 +1066,7 @@ class TestPicklefunction:
x = matrix()
y = theano.shared(b)
f = theano.function([x], tt.dot(x, y))
f = theano.function([x], dot(x, y))
from io import BytesIO
......@@ -1175,7 +1180,7 @@ def test_sync_update():
target=tests.gpuarray.config.test_ctx_name,
)
updates = [(w, w + np.asarray(0.001, "float32") * tt.dot(x, x))]
updates = [(w, w + np.asarray(0.001, "float32") * dot(x, x))]
f = theano.function([], updates=updates, mode=tests.gpuarray.config.mode_with_gpu)
assert len(f.maker.fgraph.apply_nodes) == 1
......@@ -1221,3 +1226,40 @@ def test_sync_update():
d1 = t_1 - t_0
d2 = t_2 - t_1
assert d1 > d2, (d1, d2)
def test_FunctionMaker_cache_optimizations():
opt_db_file = os.path.join(config.compiledir, "optimized_graphs.pkl")
if os.path.exists(opt_db_file):
os.remove(opt_db_file)
floatX = "float32"
mode = config.mode
if mode in ["DEBUG_MODE", "DebugMode"]:
mode = "FAST_RUN"
graph_db_file = os.path.join(config.compiledir, "optimized_graphs.pkl")
assert not os.path.exists(graph_db_file)
with config.change_flags(cache_optimizations=True):
a = fmatrix("a")
b = fmatrix("b")
c = theano.shared(np.ones((10, 10), dtype=floatX))
d = theano.shared(np.ones((10, 10), dtype=floatX))
e = tt_sum(tt_sum(tt_sum(a ** 2 + b) + c) + d)
f1 = theano.function([a, b], e, mode=mode)
# FIXME: We can do much better about testing this.
assert os.path.exists(graph_db_file)
m = fmatrix("x1")
n = fmatrix("x2")
p = theano.shared(np.ones((10, 10), dtype=floatX))
q = theano.shared(np.ones((10, 10), dtype=floatX))
j = tt_sum(tt_sum(tt_sum(m ** 2 + n) + p) + q)
f2 = theano.function([m, n], j, mode=mode)
in1 = np.ones((10, 10), dtype=floatX)
in2 = np.ones((10, 10), dtype=floatX)
assert f1(in1, in2) == f2(in1, in2)
tests/compile/test_builders.py
浏览文件 @ e0e5b3b8
......@@ -4,7 +4,6 @@ import numpy as np
import pytest
import theano
import theano.tensor as tt
from tests import unittest_tools
from theano import shared
from theano.compile.builders import OpFromGraph
......@@ -12,6 +11,9 @@ from theano.compile.function import function
from theano.configdefaults import config
from theano.gradient import DisconnectedType, Rop, grad
from theano.graph.null_type import NullType
from theano.tensor.math import dot, exp
from theano.tensor.math import round as tt_round
from theano.tensor.math import sum as tt_sum
from theano.tensor.nnet import sigmoid
from theano.tensor.random.utils import RandomStream
from theano.tensor.type import TensorType, matrices, matrix, scalar, vector, vectors
......@@ -43,7 +45,7 @@ class TestOpFromGraph(unittest_tools.InferShapeTester):
)
def test_size_changes(self, cls_ofg):
x, y, z = matrices("xyz")
e = tt.dot(x, y)
e = dot(x, y)
op = cls_ofg([x, y], [e])
f = op(x, op(y, z))
fn = function([x, y, z], f)
......@@ -65,7 +67,7 @@ class TestOpFromGraph(unittest_tools.InferShapeTester):
e = x + y * z
op = cls_ofg([x, y, z], [e])
f = op(x, y, z)
f = f - grad(tt.sum(f), y)
f = f - grad(tt_sum(f), y)
fn = function([x, y, z], f)
xv = np.ones((2, 2), dtype=config.floatX)
yv = np.ones((2, 2), dtype=config.floatX) * 3
......@@ -80,8 +82,8 @@ class TestOpFromGraph(unittest_tools.InferShapeTester):
e = x + y * z
op = cls_ofg([x, y, z], [e])
f = op(x, y, z)
f = f - grad(tt.sum(f), y)
f = f - grad(tt.sum(f), y)
f = f - grad(tt_sum(f), y)
f = f - grad(tt_sum(f), y)
fn = function([x, y, z], f)
xv = np.ones((2, 2), dtype=config.floatX)
yv = np.ones((2, 2), dtype=config.floatX) * 3
......@@ -117,7 +119,7 @@ class TestOpFromGraph(unittest_tools.InferShapeTester):
e = x + y * z + s
op = cls_ofg([x, y, z], [e])
f = op(x, y, z)
f = f - grad(tt.sum(f), y)
f = f - grad(tt_sum(f), y)
fn = function([x, y, z], f)
xv = np.ones((2, 2), dtype=config.floatX)
yv = np.ones((2, 2), dtype=config.floatX) * 3
......@@ -126,7 +128,7 @@ class TestOpFromGraph(unittest_tools.InferShapeTester):
# grad again the shared variable
f = op(x, y, z)
f = f - grad(tt.sum(f), s)
f = f - grad(tt_sum(f), s)
fn = function([x, y, z], f)
assert np.allclose(15.0 + s.get_value(), fn(xv, yv, zv))
......@@ -150,7 +152,7 @@ class TestOpFromGraph(unittest_tools.InferShapeTester):
# single override case (function or OfG instance)
xx, yy = vector("xx"), vector("yy")
for op in [op_mul, op_mul2]:
zz = tt.sum(op(xx, yy))
zz = tt_sum(op(xx, yy))
dx, dy = grad(zz, [xx, yy])
fn = function([xx, yy], [dx, dy])
xv = np.random.rand(16).astype(config.floatX)
......@@ -176,7 +178,7 @@ class TestOpFromGraph(unittest_tools.InferShapeTester):
[x, w, b], [x * w + b], grad_overrides=[go1, go2, "default"]
)
xx, ww, bb = vector("xx"), vector("yy"), vector("bb")
zz = tt.sum(op_linear(xx, ww, bb))
zz = tt_sum(op_linear(xx, ww, bb))
dx, dw, db = grad(zz, [xx, ww, bb])
fn = function([xx, ww, bb], [dx, dw, db])
xv = np.random.rand(16).astype(config.floatX)
......@@ -193,7 +195,7 @@ class TestOpFromGraph(unittest_tools.InferShapeTester):
[x * w + b],
grad_overrides=[go1, NullType()(), DisconnectedType()()],
)
zz2 = tt.sum(op_linear2(xx, ww, bb))
zz2 = tt_sum(op_linear2(xx, ww, bb))
dx2, dw2, db2 = grad(
zz2,
[xx, ww, bb],
......@@ -211,7 +213,7 @@ class TestOpFromGraph(unittest_tools.InferShapeTester):
)
def test_lop_override(self, cls_ofg):
x = vector()
y = 1.0 / (1.0 + tt.exp(-x))
y = 1.0 / (1.0 + exp(-x))
def lop_ov(inps, outs, grads):
(y_,) = outs
......@@ -222,12 +224,12 @@ class TestOpFromGraph(unittest_tools.InferShapeTester):
op_lop_ov = cls_ofg([x, y_, dedy], [2.0 * y_ * (1.0 - y_) * dedy])
xx = vector()
yy1 = tt.sum(sigmoid(xx))
yy1 = tt_sum(sigmoid(xx))
gyy1 = 2.0 * grad(yy1, xx)
for ov in [lop_ov, op_lop_ov]:
op = cls_ofg([x], [y], lop_overrides=ov)
yy2 = tt.sum(op(xx))
yy2 = tt_sum(op(xx))
gyy2 = grad(yy2, xx)
fn = function([xx], [gyy1, gyy2])
......@@ -241,7 +243,7 @@ class TestOpFromGraph(unittest_tools.InferShapeTester):
def test_rop(self, cls_ofg):
a = vector()
M = matrix()
b = tt.dot(a, M)
b = dot(a, M)
op_matmul = cls_ofg([a, M], [b])
x = vector()
W = matrix()
......@@ -295,7 +297,7 @@ class TestOpFromGraph(unittest_tools.InferShapeTester):
del x
# but we know how to backpropagate for x for some reasons
# and we don't care about the gradient wrt y.
return y + tt.round(y)
return y + tt_round(y)
def f1_back(inputs, output_gradients):
return [output_gradients[0], theano.gradient.disconnected_type()]
......
tests/compile/test_debugmode.py
浏览文件 @ e0e5b3b8
......@@ -13,6 +13,7 @@ from theano.graph.op import COp, Op
from theano.graph.opt import local_optimizer
from theano.graph.optdb import EquilibriumDB
from theano.graph.toolbox import BadOptimization
from theano.tensor.math import add, dot, log
from theano.tensor.type import TensorType, dvector, fmatrix, fvector, vector
......@@ -227,9 +228,9 @@ def test_badthunkoutput():
def test_badoptimization():
@local_optimizer([tt.add])
@local_optimizer([add])
def insert_broken_add(fgraph, node):
if node.op == tt.add:
if node.op == add:
return [off_by_half(*node.inputs)]
return False
......@@ -253,18 +254,18 @@ def test_badoptimization():
def test_badoptimization_opt_err():
# This variant of test_badoptimization() replace the working code
# with a new apply node that will raise an error.
@local_optimizer([tt.add])
@local_optimizer([add])
def insert_bigger_b_add(fgraph, node):
if node.op == tt.add:
if node.op == add:
inputs = list(node.inputs)
if inputs[-1].owner is None:
inputs[-1] = tt.concatenate((inputs[-1], inputs[-1]))
return [node.op(*inputs)]
return False
@local_optimizer([tt.add])
@local_optimizer([add])
def insert_bad_dtype(fgraph, node):
if node.op == tt.add:
if node.op == add:
inputs = list(node.inputs)
if inputs[-1].owner is None:
......@@ -316,9 +317,9 @@ def test_stochasticoptimization():
last_time_replaced = [False]
@local_optimizer([tt.add])
@local_optimizer([add])
def insert_broken_add_sometimes(fgraph, node):
if node.op == tt.add:
if node.op == add:
last_time_replaced[0] = not last_time_replaced[0]
if last_time_replaced[0]:
return [off_by_half(*node.inputs)]
......@@ -334,7 +335,7 @@ def test_stochasticoptimization():
with pytest.raises(debugmode.StochasticOrder):
theano.function(
[a, b],
tt.add(a, b),
add(a, b),
mode=debugmode.DebugMode(
optimizer=opt,
check_c_code=True,
......@@ -559,7 +560,7 @@ class TestCheckIsfinite:
def test_check_isfinite(self):
x = vector()
f = theano.function([x], (x + 2) * 5, mode="DEBUG_MODE")
g = theano.function([x], tt.log(x), mode="DEBUG_MODE")
g = theano.function([x], log(x), mode="DEBUG_MODE")
# this should work
f(np.log([3, 4, 5]).astype(config.floatX))
......@@ -736,7 +737,7 @@ class TestPreallocatedOutput:
b = fmatrix("b")
z = BrokenCImplementationAdd()(a, b)
# In this test, we do not want z to be an output of the graph.
out = tt.dot(z, np.eye(7))
out = dot(z, np.eye(7))
a_val = self.rng.randn(7, 7).astype("float32")
b_val = self.rng.randn(7, 7).astype("float32")
......
tests/compile/test_misc.py
浏览文件 @ e0e5b3b8
import numpy as np
from theano import tensor as tt
from theano.compile.function.pfunc import pfunc
from theano.compile.sharedvalue import shared
from theano.gradient import grad
from theano.tensor.math import dot
from theano.tensor.math import sum as tt_sum
from theano.tensor.nnet import sigmoid
from theano.tensor.type import dvector
......@@ -33,9 +34,9 @@ class NNet:
self.w2 = shared(np.zeros((n_output, n_hidden)), "w2")
# print self.lr.type
self.hidden = sigmoid(tt.dot(self.w1, self.input))
self.output = tt.dot(self.w2, self.hidden)
self.cost = tt.sum((self.output - self.target) ** 2)
self.hidden = sigmoid(dot(self.w1, self.input))
self.output = dot(self.w2, self.hidden)
self.cost = tt_sum((self.output - self.target) ** 2)
self.sgd_updates = {
self.w1: self.w1 - self.lr * grad(self.cost, self.w1),
......
tests/compile/test_mode.py
浏览文件 @ e0e5b3b8
import pytest
import theano
import theano.tensor as tt
from theano.compile.mode import AddFeatureOptimizer, Mode
from theano.graph.toolbox import NoOutputFromInplace
from theano.tensor.math import dot, tanh
from theano.tensor.type import matrix
......@@ -13,8 +13,8 @@ from theano.tensor.type import matrix
def test_no_output_from_implace():
x = matrix()
y = matrix()
a = tt.dot(x, y)
b = tt.tanh(a)
a = dot(x, y)
b = tanh(a)
# Ensure that the elemwise op that produces the output is inplace when
# using a mode that does not include the optimization
......
tests/compile/test_nanguardmode.py
浏览文件 @ e0e5b3b8
......@@ -10,6 +10,7 @@ import pytest
import theano
import theano.tensor as tt
from theano.compile.nanguardmode import NanGuardMode
from theano.tensor.math import dot
from theano.tensor.type import matrix, tensor3
......@@ -19,7 +20,7 @@ def test_NanGuardMode():
# the abnormalties.
x = matrix()
w = theano.shared(np.random.randn(5, 7).astype(theano.config.floatX))
y = tt.dot(x, w)
y = dot(x, w)
fun = theano.function(
[x], y, mode=NanGuardMode(nan_is_error=True, inf_is_error=True)
......
tests/disturb_mem.py deleted 100644 → 0
浏览文件 @ 2b06fa16
from datetime import datetime
__authors__ = "Ian Goodfellow"
__credits__ = ["Ian Goodfellow"]
__license__ = "3-clause BSD"
__maintainer__ = "Ian Goodfellow"
__email__ = "goodfeli@iro"
def disturb_mem():
# Allocate a time-dependent amount of objects to increase
# chances of subsequently objects' ids changing from run
# to run. This is useful for exposing issues that cause
# non-deterministic behavior due to dependence on memory
# addresses, like iterating over a dict or a set.
global l
now = datetime.now()
ms = now.microsecond
ms = int(ms)
n = ms % 1000
m = ms // 1000
l = [[0] * m for i in range(n)]
tests/diverse_tests.py
浏览文件 @ e0e5b3b8
......@@ -8,9 +8,10 @@
import numpy as np
import numpy.random
import theano.tensor as tt
from tests import unittest_tools as utt
from theano import config, function, shared
from theano.gradient import grad
from theano.tensor.math import dot, exp, log
from theano.tensor.type import matrix, vector
......@@ -35,11 +36,11 @@ class TestScipy:
b = shared(np.zeros(()))
# Construct Theano expression graph
p_1 = 1 / (1 + tt.exp(-tt.dot(x, w) - b))
xent = -y * tt.log(p_1) - (1 - y) * tt.log(1 - p_1)
p_1 = 1 / (1 + exp(-dot(x, w) - b))
xent = -y * log(p_1) - (1 - y) * log(1 - p_1)
prediction = p_1 > 0.5
cost = xent.mean() + 0.01 * (w ** 2).sum()
gw, gb = tt.grad(cost, [w, b])
gw, gb = grad(cost, [w, b])
# Compile expressions to functions
train = function(
......
tests/gpuarray/rnn_support.py
浏览文件 @ e0e5b3b8
import numpy as np
import theano
import theano.tensor as tt
from theano.tensor import nnet
from theano.tensor.math import dot, tanh
class Model:
......@@ -127,15 +127,15 @@ class GRU(Layer):
def step(inp, s_prev):
i_t = nnet.sigmoid(
tt.dot(inp, self.W_i) + tt.dot(s_prev, self.R_i) + self.b_wi + self.b_ru
dot(inp, self.W_i) + dot(s_prev, self.R_i) + self.b_wi + self.b_ru
)
r_t = nnet.sigmoid(
tt.dot(inp, self.W_r) + tt.dot(s_prev, self.R_r) + self.b_wr + self.b_rr
dot(inp, self.W_r) + dot(s_prev, self.R_r) + self.b_wr + self.b_rr
)
h_hat_t = tt.tanh(
tt.dot(inp, self.W_h)
+ (r_t * (tt.dot(s_prev, self.R_h) + self.b_rh))
h_hat_t = tanh(
dot(inp, self.W_h)
+ (r_t * (dot(s_prev, self.R_h) + self.b_rh))
+ self.b_wh
)
......@@ -231,20 +231,20 @@ class LSTM(Layer):
def step(x_t, h_tm1, c_tm1):
i_t = nnet.sigmoid(
tt.dot(x_t, self.W_i) + tt.dot(h_tm1, self.R_i) + self.b_wi + self.b_ri
dot(x_t, self.W_i) + dot(h_tm1, self.R_i) + self.b_wi + self.b_ri
)
f_t = nnet.sigmoid(
tt.dot(x_t, self.W_f) + tt.dot(h_tm1, self.R_f) + self.b_wf + self.b_rf
dot(x_t, self.W_f) + dot(h_tm1, self.R_f) + self.b_wf + self.b_rf
)
o_t = nnet.sigmoid(
tt.dot(x_t, self.W_o) + tt.dot(h_tm1, self.R_o) + self.b_ro + self.b_wo
dot(x_t, self.W_o) + dot(h_tm1, self.R_o) + self.b_ro + self.b_wo
)
c_hat_t = tt.tanh(
tt.dot(x_t, self.W_c) + tt.dot(h_tm1, self.R_c) + self.b_wc + self.b_rc
c_hat_t = tanh(
dot(x_t, self.W_c) + dot(h_tm1, self.R_c) + self.b_wc + self.b_rc
)
c_t = f_t * c_tm1 + i_t * c_hat_t
h_t = o_t * tt.tanh(c_t)
h_t = o_t * tanh(c_t)
return h_t, c_t
......@@ -276,7 +276,7 @@ class FC(Layer):
self.b = bias_weights((output_dim,), param_list=self.params, name=name + ".b")
def output(self):
return tt.dot(self.X, self.W) + self.b
return dot(self.X, self.W) + self.b
class WrapperLayer(Layer):
......
tests/gpuarray/test_blas.py
浏览文件 @ e0e5b3b8
......@@ -7,7 +7,6 @@ from tests import unittest_tools as utt
from tests.gpuarray.config import mode_with_gpu, test_ctx_name
from tests.gpuarray.test_basic_ops import makeTester, rand
from tests.tensor.test_blas import BaseGemv, TestGer
from theano import tensor as tt
from theano.configdefaults import config
from theano.gpuarray import gpuarray_shared_constructor
from theano.gpuarray.blas import (
......@@ -22,7 +21,15 @@ from theano.gpuarray.blas import (
gpuger_inplace,
gpuger_no_inplace,
)
from theano.tensor.blas import BatchedDot, _dot22, gemm_inplace, gemv, gemv_inplace
from theano.tensor.blas import (
BatchedDot,
_dot22,
batched_dot,
gemm_inplace,
gemv,
gemv_inplace,
)
from theano.tensor.math import dot
from theano.tensor.type import matrix, tensor, tensor3, vector
......@@ -197,7 +204,7 @@ class TestGpuGemmBatchStrided:
# Reported in https://github.com/Theano/Theano/issues/5730
x = tensor3()
y = tensor3()
z = tt.batched_dot(x, y[:, 0, :, np.newaxis])
z = batched_dot(x, y[:, 0, :, np.newaxis])
f = theano.function([x, y], z, mode=mode_with_gpu)
x_num = np.arange(32 * 19 * 600, dtype=config.floatX).reshape((32, 19, 600))
y_num = np.arange(7 * 32 * 600, dtype=config.floatX).reshape((32, 7, 600))
......@@ -270,6 +277,6 @@ def test_gemv_dot_strides():
yv = rand(5, 1)
x = gpuarray_shared_constructor(xv)
y = gpuarray_shared_constructor(yv, broadcastable=(False, True))
f = theano.function([], tt.dot(x, y[::-1]), mode=mode_with_gpu)
f = theano.function([], dot(x, y[::-1]), mode=mode_with_gpu)
out = f()
utt.assert_allclose(out, np.dot(xv, yv[::-1]))
tests/gpuarray/test_ctc.py
浏览文件 @ e0e5b3b8
......@@ -3,11 +3,12 @@ import pytest
import theano
import theano.gpuarray
import theano.tensor as tt
from tests import unittest_tools as utt
from tests.gpuarray.config import mode_with_gpu, mode_without_gpu
from tests.tensor.nnet.test_ctc import setup_ctc_case, setup_grad_case, setup_torch_case
from theano.gpuarray.ctc import GpuConnectionistTemporalClassification, gpu_ctc
from theano.gradient import grad
from theano.tensor.math import mean
from theano.tensor.nnet.ctc import (
ConnectionistTemporalClassification,
ctc,
......@@ -50,7 +51,7 @@ class TestCTC:
outputs = [cpu_ctc_cost]
if compute_grad:
# Symbolic gradient of CTC cost
cpu_ctc_grad = tt.grad(tt.mean(cpu_ctc_cost), activations)
cpu_ctc_grad = grad(mean(cpu_ctc_cost), activations)
outputs += [cpu_ctc_grad]
return theano.function([], outputs, mode=mode)
......@@ -59,7 +60,7 @@ class TestCTC:
outputs = [gpu_ctc_cost]
if compute_grad:
# Symbolic gradient of CTC cost
gpu_ctc_grad = tt.grad(tt.mean(gpu_ctc_cost), activations)
gpu_ctc_grad = grad(mean(gpu_ctc_cost), activations)
outputs += [gpu_ctc_grad]
return theano.function([], outputs, mode=mode_with_gpu)
......
tests/gpuarray/test_dnn.py
浏览文件 @ e0e5b3b8
......@@ -29,6 +29,21 @@ from theano.configdefaults import SUPPORTED_DNN_CONV_ALGO_FWD
from theano.gpuarray import dnn
from theano.gpuarray.basic_ops import GpuAllocEmpty
from theano.gpuarray.type import GpuArrayType, gpuarray_shared_constructor
from theano.tensor.math import (
ceil,
clip,
dot,
floor,
inv,
log,
max_and_argmax,
mean,
minimum,
mod,
prod,
sqrt,
)
from theano.tensor.math import sum as tt_sum
from theano.tensor.nnet import (
LogSoftmax,
Softmax,
......@@ -660,7 +675,7 @@ def test_pooling_opt_arbitrary_dimensions():
for mode in modes:
out_pool = Pool(ndim=len(ws), mode=mode, ignore_border=True)(input, ws)
out_pool_grad = theano.grad(tt.sum(out_pool), wrt=input)
out_pool_grad = theano.grad(tt_sum(out_pool), wrt=input)
out = [out_pool, out_pool_grad]
# run on GPU
......@@ -714,7 +729,7 @@ def test_pooling_empty_batch():
d = f(np.random.rand(*img_shp).astype("float32"))
assert d.shape == (0, 5, 3, 4)
g = theano.grad(tt.sum(o), wrt=img)
g = theano.grad(tt_sum(o), wrt=img)
f = theano.function([img], g, mode=mode_with_gpu)
d = f(np.random.rand(*img_shp).astype("float32"))
# Not sure what to assert, it should just pass, that's all.
......@@ -1539,7 +1554,7 @@ class TestSoftMax(test_nnet.TestSoftMax):
# more recent. Don't test if the cuDNN version is too old.
x = tensor4()
softmax_out = dnn.GpuDnnSoftmax("accurate", "channel")(x)
log_out = tt.log(tt.as_tensor_variable(softmax_out))
log_out = log(tt.as_tensor_variable(softmax_out))
f = theano.function([x], log_out, mode=mode_with_gpu)
......@@ -1600,7 +1615,7 @@ class TestSoftMax(test_nnet.TestSoftMax):
utt.assert_allclose(f(inp), f_ref(inp))
# Build the first graph and ensure that the optimization is applied
log_softmax_out = tt.log(Softmax()(x))
log_softmax_out = log(Softmax()(x))
f = theano.function([x], log_softmax_out, mode=mode_with_gpu)
dnn_softmax_nodes = [
......@@ -1776,7 +1791,7 @@ def test_dnn_reduction_error():
vecT = vector(dtype=theano.config.floatX)
outputT = tt.alloc(2.0 * vecT, 5, vecT.shape[0])
outputSummedT = tt.sum(tt.transpose(outputT), axis=1)
outputSummedT = tt_sum(tt.transpose(outputT), axis=1)
f3 = theano.function(inputs=[vecT], outputs=outputSummedT)
output = f3(vec)
......@@ -1785,7 +1800,7 @@ def test_dnn_reduction_error():
def dnn_maxargmax(nd, idtype, axis):
inp = TensorType(idtype, (False,) * nd)()
res = tt.max_and_argmax(inp, axis=axis)
res = max_and_argmax(inp, axis=axis)
f = theano.function([inp], res, mode=mode_with_gpu)
assert any(
isinstance(n.op, dnn.GpuDnnReduction) for n in f.maker.fgraph.apply_nodes
......@@ -1871,10 +1886,10 @@ def test_dnn_batchnorm_train():
axes = (0,) + tuple(range(2, ndim))
x_mean_ref = x.mean(axis=axes, keepdims=True)
x_var_ref = x.var(axis=axes, keepdims=True)
x_invstd_ref = tt.inv(tt.sqrt(x_var_ref + eps))
x_invstd_ref = inv(sqrt(x_var_ref + eps))
scale_ref = tt.addbroadcast(scale, *axes)
bias_ref = tt.addbroadcast(bias, *axes)
m = tt.cast(tt.prod(x.shape) / tt.prod(scale.shape), theano.config.floatX)
m = tt.cast(prod(x.shape) / prod(scale.shape), theano.config.floatX)
out_ref = (x - x_mean_ref) * (scale_ref * x_invstd_ref) + bias_ref
out_running_mean_ref = (
running_mean * (1 - running_average_factor)
......@@ -2239,7 +2254,7 @@ def test_batchnorm_inference():
scale_ref, bias_ref, mean_ref, var_ref = (
tt.addbroadcast(t, *axes) for t in (scale, bias, mean, var)
)
out_ref = (x - mean_ref) * (scale_ref / tt.sqrt(var_ref + eps)) + bias_ref
out_ref = (x - mean_ref) * (scale_ref / sqrt(var_ref + eps)) + bias_ref
# backward pass
dy = vartype("dy")
grads_gpu = theano.grad(
......@@ -2482,9 +2497,9 @@ def test_dnn_rnn_gru():
def funcs(out, params, hy=None):
cost = 0
if out:
cost += tt.mean((Y - out) ** 2)
cost += mean((Y - out) ** 2)
if hy:
cost += tt.mean(hy ** 2)
cost += mean(hy ** 2)
grad = theano.grad(cost, [X, h0] + params)
grad_fn = theano.function(
[X, Y, h0], grad, mode=mode_with_gpu, on_unused_input="ignore"
......@@ -2580,9 +2595,9 @@ def test_dnn_rnn_gru_bidi():
def funcs(out, params, hy=None):
cost = 0
if out:
cost += tt.mean((Y - out) ** 2)
cost += mean((Y - out) ** 2)
if hy:
cost += tt.mean(hy ** 2)
cost += mean(hy ** 2)
grad = theano.grad(cost, [X, h0] + params)
grad_fn = theano.function(
[X, Y, h0], grad, mode=mode_with_gpu, on_unused_input="ignore"
......@@ -2652,7 +2667,7 @@ def test_dnn_rnn_lstm():
def funcs(out, params):
fn = theano.function([X, h0, c0], out, mode=mode_with_gpu)
cost = tt.mean((Y - out) ** 2)
cost = mean((Y - out) ** 2)
grad = theano.grad(cost, [X, h0, c0] + params)
grad_fn = theano.function([X, Y, h0, c0], grad, mode=mode_with_gpu)
return fn, grad_fn
......@@ -2737,7 +2752,7 @@ def test_dnn_rnn_lstm_grad_c():
p[:] = layer_params[j].get_value(borrow=True, return_internal_type=True)
def funcs(out, params):
cost = tt.mean((CY - out) ** 2)
cost = mean((CY - out) ** 2)
grad = theano.grad(cost, [X, h0, c0] + params)
grad_fn = theano.function([X, CY, h0, c0], grad, mode=mode_with_gpu)
return grad_fn
......@@ -2817,11 +2832,11 @@ def test_dnn_spatialtf():
theta = reshape(theta, (-1, 2, 3))
# grid of (x_t, y_t, 1), eq (1) in ref [1]
out_height = tt.cast(tt.ceil(height * scale_height), "int64")
out_width = tt.cast(tt.ceil(width * scale_width), "int64")
out_height = tt.cast(ceil(height * scale_height), "int64")
out_width = tt.cast(ceil(width * scale_width), "int64")
grid = _meshgrid(out_height, out_width)
# transform a x (x_t, y_t, 1)^t -> (x_s, y_s)
t_g = tt.dot(theta, grid)
t_g = dot(theta, grid)
x_s = t_g[:, 0]
y_s = t_g[:, 1]
x_s_flat = x_s.flatten()
......@@ -2852,29 +2867,29 @@ def test_dnn_spatialtf():
# obtain indices of the 2x2 pixel neighborhood surrounding the coordinates;
# we need those in floatX for interpolation and in int64 for indexing.
x0_f = tt.floor(x)
y0_f = tt.floor(y)
x0_f = floor(x)
y0_f = floor(y)
x1_f = x0_f + 1
y1_f = y0_f + 1
# for indexing, we need to take care of the border mode for outside pixels.
if border_mode == "nearest":
x0 = tt.clip(x0_f, 0, width_f - 1)
x1 = tt.clip(x1_f, 0, width_f - 1)
y0 = tt.clip(y0_f, 0, height_f - 1)
y1 = tt.clip(y1_f, 0, height_f - 1)
x0 = clip(x0_f, 0, width_f - 1)
x1 = clip(x1_f, 0, width_f - 1)
y0 = clip(y0_f, 0, height_f - 1)
y1 = clip(y1_f, 0, height_f - 1)
elif border_mode == "mirror":
w = 2 * (width_f - 1)
x0 = tt.minimum(x0_f % w, -x0_f % w)
x1 = tt.minimum(x1_f % w, -x1_f % w)
x0 = minimum(x0_f % w, -x0_f % w)
x1 = minimum(x1_f % w, -x1_f % w)
h = 2 * (height_f - 1)
y0 = tt.minimum(y0_f % h, -y0_f % h)
y1 = tt.minimum(y1_f % h, -y1_f % h)
y0 = minimum(y0_f % h, -y0_f % h)
y1 = minimum(y1_f % h, -y1_f % h)
elif border_mode == "wrap":
x0 = tt.mod(x0_f, width_f)
x1 = tt.mod(x1_f, width_f)
y0 = tt.mod(y0_f, height_f)
y1 = tt.mod(y1_f, height_f)
x0 = mod(x0_f, width_f)
x1 = mod(x1_f, width_f)
y0 = mod(y0_f, height_f)
y1 = mod(y1_f, height_f)
else:
raise ValueError(
"border_mode must be one of " "'nearest', 'mirror', 'wrap'"
......@@ -2908,7 +2923,7 @@ def test_dnn_spatialtf():
wb = ((x1_f - x) * (y - y0_f)).dimshuffle(0, "x")
wc = ((x - x0_f) * (y1_f - y)).dimshuffle(0, "x")
wd = ((x - x0_f) * (y - y0_f)).dimshuffle(0, "x")
output = tt.sum([wa * Ia, wb * Ib, wc * Ic, wd * Id], axis=0)
output = tt_sum([wa * Ia, wb * Ib, wc * Ic, wd * Id], axis=0)
return output
def _linspace(start, stop, num):
......@@ -2930,12 +2945,8 @@ def test_dnn_spatialtf():
# Note: If the image size is known at layer construction time, we could
# compute the meshgrid offline in numpy instead of doing it dynamically
# in Theano. However, it hardly affected performance when we tried.
x_t = tt.dot(
tt.ones((height, 1)), _linspace(-1.0, 1.0, width).dimshuffle("x", 0)
)
y_t = tt.dot(
_linspace(-1.0, 1.0, height).dimshuffle(0, "x"), tt.ones((1, width))
)
x_t = dot(tt.ones((height, 1)), _linspace(-1.0, 1.0, width).dimshuffle("x", 0))
y_t = dot(_linspace(-1.0, 1.0, height).dimshuffle(0, "x"), tt.ones((1, width)))
x_t_flat = x_t.reshape((1, -1))
y_t_flat = y_t.reshape((1, -1))
......@@ -3019,7 +3030,7 @@ def test_dnn_spatialtf_grad():
theta = tensor3("theta")
out = dnn.dnn_spatialtf(inputs, theta, scale_height=0.25, scale_width=0.75)
out_mean = tt.mean(out)
out_mean = mean(out)
mean_gi = theano.grad(out_mean, [inputs])
mean_gt = theano.grad(out_mean, [theta])
......
tests/gpuarray/test_elemwise.py
浏览文件 @ e0e5b3b8
......@@ -29,6 +29,7 @@ from theano.gpuarray.elemwise import (
from theano.gpuarray.type import GpuArrayType, get_context, gpuarray_shared_constructor
from theano.link.basic import PerformLinker
from theano.link.c.basic import CLinker
from theano.tensor.math import erfcinv, erfinv, mul, tanh
from theano.tensor.type import bvector, float_dtypes, fmatrix, fvector, vector
......@@ -131,7 +132,7 @@ class TestMathErrorFunctions:
def test_elemwise_erfinv(self):
for dtype in self.dtypes:
vec = vector(dtype=dtype)
output = tt.erfinv(vec)
output = erfinv(vec)
f_host = theano.function(
[vec],
output,
......@@ -166,7 +167,7 @@ class TestMathErrorFunctions:
def test_elemwise_erfcinv(self):
for dtype in self.dtypes:
vec = vector(dtype=dtype)
output = tt.erfcinv(vec)
output = erfcinv(vec)
f_host = theano.function(
[vec],
output,
......@@ -205,7 +206,7 @@ class TestFloat16:
x = vector(dtype="float16")
y = fvector()
cz = tt.tanh(x + tt.cast(y, "float16"))
cz = tanh(x + tt.cast(y, "float16"))
o = (
cz
- cz ** 2
......@@ -223,7 +224,7 @@ class TestFloat16:
y = vector(dtype="float16")
z = vector(dtype="float16")
o = tt.switch(v, tt.mul(w, x, y), z)
o = tt.switch(v, mul(w, x, y), z)
theano.function([v, w, x, y, z], o, mode=mode_with_gpu)
def test_cast_float16(self):
......
tests/gpuarray/test_extra_ops.py
浏览文件 @ e0e5b3b8
......@@ -5,7 +5,7 @@ import numpy as np
import pytest
import theano
import theano.tensor as tt
import theano.tensor.math as tm
from tests import unittest_tools as utt
from tests.gpuarray.config import mode_with_gpu, test_ctx_name
from tests.tensor.test_extra_ops import TestCumOp
......@@ -30,13 +30,13 @@ class TestGpuCumOp(TestCumOp):
# The CPU implementation is not so accurate, which throws out DebugMode.
# Since propagating .tag.values_eq_approx to the output of every
# GpuFromHost seems overkill, we just relax the rtol for these tests
self.old_rtol = tt.float32_rtol
tt.float32_rtol *= 2
self.old_rtol = tm.float32_rtol
tm.float32_rtol *= 2
def teardown_method(self):
super().teardown_method()
# Restore rtol
tt.float32_rtol = self.old_rtol
tm.float32_rtol = self.old_rtol
@pytest.mark.skipif(
theano.config.floatX != "float32",
......
tests/gpuarray/test_nnet.py
浏览文件 @ e0e5b3b8
......@@ -10,6 +10,8 @@ from theano.gpuarray.nnet import (
GpuSoftmax,
GpuSoftmaxWithBias,
)
from theano.gradient import grad
from theano.tensor.math import argmax, log, mean
from theano.tensor.nnet import crossentropy_softmax_1hot_with_bias_dx
from theano.tensor.type import fmatrix, fvector, lvector, matrix, vector
......@@ -51,9 +53,9 @@ def test_GpuCrossentropySoftmaxArgmax1HotWithBias():
dot_value = np.asarray(np.dot(xx, W_values), dtype="float32")
del W_values
p_y_given_x = theano.tensor.nnet.softmax(dot_result + b)
y_pred = tt.argmax(p_y_given_x, axis=-1)
loss = -tt.mean(tt.log(p_y_given_x)[tt.arange(y.shape[0]), y])
dW = tt.grad(loss, dot_result)
y_pred = argmax(p_y_given_x, axis=-1)
loss = -mean(log(p_y_given_x)[tt.arange(y.shape[0]), y])
dW = grad(loss, dot_result)
classify = theano.function(
inputs=[y, b, dot_result], outputs=[loss, y_pred, dW], mode=mode_without_gpu
)
......
tests/gpuarray/test_opt.py
浏览文件 @ e0e5b3b8
......@@ -35,6 +35,8 @@ from theano.gpuarray.subtensor import GpuSubtensor
from theano.gpuarray.type import GpuArrayType, get_context, gpuarray_shared_constructor
from theano.graph.opt import check_stack_trace
from theano.tensor.basic import Alloc, AllocEmpty, Rebroadcast
from theano.tensor.blas import batched_dot
from theano.tensor.math import dot, eq, exp, gt, tanh
from theano.tensor.nnet import abstract_conv
from theano.tensor.type import (
TensorType,
......@@ -80,7 +82,7 @@ def _check_stack_trace(thing):
def test_local_assert():
x = fmatrix()
a = assert_op(x, tt.eq(x, 0).any())
a = assert_op(x, eq(x, 0).any())
f = theano.function([x], a, mode=mode_with_gpu)
topo = f.maker.fgraph.toposort()
a_op = [n for n in topo if isinstance(n.op, Assert)]
......@@ -90,7 +92,7 @@ def test_local_assert():
def test_local_remove_all_assert():
x = fmatrix()
a = assert_op(x, tt.eq(x, 0).any())
a = assert_op(x, eq(x, 0).any())
# By default `unsafe` should not be there
f = theano.function([x], a, mode=mode_with_gpu.excluding("unsafe"))
......@@ -398,7 +400,7 @@ def test_pdbbreakpoint_op():
# Create a function composed of a breakpoint followed by
# some computation
condition = tt.gt(b.sum(), 0)
condition = gt(b.sum(), 0)
b_monitored = PdbBreakpoint(name="TestBreakpoint")(condition, b)
output = b_monitored ** 2
......@@ -645,7 +647,7 @@ def test_not_useless_scalar_gpuelemwise():
X = fmatrix()
x = np.random.randn(32, 32).astype(np.float32)
m1 = theano.shared(np.random.randn(32, 32).astype(np.float32))
loss = (X - tt.dot(X, m1)).norm(L=2)
loss = (X - dot(X, m1)).norm(L=2)
lr = theano.shared(np.asarray(0.001, dtype=np.float32))
grad = theano.grad(loss, m1)
......@@ -673,7 +675,7 @@ def test_local_assert_no_cpu_op():
rng = np.random.RandomState(utt.fetch_seed())
m = rng.uniform(-1, 1, (10, 10)).astype("float32")
ms = gpuarray_shared_constructor(m, name="m_shared")
out = tt.tanh(ms).dot(ms.T)
out = tanh(ms).dot(ms.T)
mode_local_assert = mode_with_gpu.including("assert_no_cpu_op")
mode_local_assert = mode_local_assert.excluding("local_gpua_elemwise")
......@@ -691,7 +693,7 @@ def test_no_complex():
width_var = cscalar()
freq_var = fscalar()
signal_var = fscalar()
stft_out = tt.exp(width_var * freq_var) * signal_var
stft_out = exp(width_var * freq_var) * signal_var
f = theano.function([width_var, freq_var, signal_var], stft_out, mode=mode_with_gpu)
assert _check_stack_trace(f)
......@@ -726,7 +728,7 @@ def test_gpu_solve_not_inplace():
A = fmatrix()
b = fmatrix()
s = slinalg.solve(A, b)
o = tt.dot(A, s)
o = dot(A, s)
f_cpu = theano.function([A, b], o, mode_without_gpu)
f_gpu = theano.function([A, b], o, mode=mode_with_gpu)
count_not_inplace = len(
......@@ -795,8 +797,8 @@ def test_local_gpua_advanced_incsubtensor():
target = ftensor4()
y = target.dimshuffle(1, 0, 2, 3).flatten(ndim=1)
w = tt.ones_like(y)
w = theano.tensor.subtensor.set_subtensor(w[tt.eq(y, 1.0).nonzero()], 100)
w = theano.tensor.subtensor.set_subtensor(w[tt.eq(y, -1.0).nonzero()], 0)
w = theano.tensor.subtensor.set_subtensor(w[eq(y, 1.0).nonzero()], 100)
w = theano.tensor.subtensor.set_subtensor(w[eq(y, -1.0).nonzero()], 0)
f = theano.function([target], w)
assert _check_stack_trace(f)
......@@ -823,7 +825,7 @@ def test_batched_dot_lifter():
y = TensorType(broadcastable=[s == 1 for s in y_val.shape], dtype=y_val.dtype)(
"y"
)
z = tt.batched_dot(x, y)
z = batched_dot(x, y)
f = theano.function([x, y], z, mode=mode_with_gpu)
f(x_val, y_val)
assert check_stack_trace(f, ops_to_check="all")
......
tests/gpuarray/test_reduction.py
浏览文件 @ e0e5b3b8
......@@ -11,6 +11,8 @@ from tests.gpuarray.test_basic_ops import rand_gpuarray
from theano.gpuarray import GpuArrayType
from theano.gpuarray.dnn import GpuDnnReduction
from theano.gpuarray.reduction import GpuMaxAndArgmax
from theano.tensor.math import argmax
from theano.tensor.math import max as tt_max
# Number of values to be used in test tensors (except with 0-D tensors!).
......@@ -113,7 +115,7 @@ class BaseTest:
M = self.get_host_tensor()
f = theano.function(
[M],
[tt.max(M, axis=axis), tt.argmax(M, axis=axis)],
[tt_max(M, axis=axis), argmax(M, axis=axis)],
name="shape:" + str(test_tensor.shape) + "/axis:" + str(axis) + "/HOST",
mode=mode_without_gpu,
)
......@@ -128,7 +130,7 @@ class BaseTest:
M = self.get_gpu_tensor()
f = theano.function(
[M],
[tt.max(M, axis=axis), tt.argmax(M, axis=axis)],
[tt_max(M, axis=axis), argmax(M, axis=axis)],
name="shape:" + str(test_gpu_tensor.shape) + "/axis:" + str(axis) + "/GPU",
mode=mode_with_gpu,
)
......
tests/gpuarray/test_scan.py
浏览文件 @ e0e5b3b8
......@@ -12,6 +12,8 @@ from theano.gpuarray.elemwise import GpuElemwise
from theano.scan.basic import scan
from theano.scan.checkpoints import scan_checkpoints
from theano.scan.op import Scan
from theano.tensor.math import dot
from theano.tensor.math import sum as tt_sum
from theano.tensor.type import fscalar, ftensor3, fvector, iscalar, vector
......@@ -574,10 +576,10 @@ class ScanGpuTests:
nparams = [U, V, W]
# Build the forward pass
l1_base = tt.dot(xin, U)
l1_base = dot(xin, U)
def scan_l(baseline, last_step):
return baseline + tt.dot(last_step, V)
return baseline + dot(last_step, V)
zero_output = tt.alloc(np.asarray(0.0, dtype="float32"), mb_size, n_hid)
......@@ -588,10 +590,10 @@ class ScanGpuTests:
mode=self.mode_with_gpu_nodebug,
)
l2_out = tt.dot(l1_out, W)
l2_out = dot(l1_out, W)
# Compute the cost and take the gradient wrt params
cost = tt.sum((l2_out - yout) ** 2)
cost = tt_sum((l2_out - yout) ** 2)
grads = theano.grad(cost, nparams)
updates = list(zip(nparams, (n - g for n, g in zip(nparams, grads))))
......
tests/gpuarray/test_subtensor.py
浏览文件 @ e0e5b3b8
......@@ -5,7 +5,6 @@ from tests import unittest_tools as utt
from tests.gpuarray.config import mode_with_gpu, test_ctx_name
from tests.tensor.test_basic import TestAllocDiag
from tests.tensor.test_subtensor import TestAdvancedSubtensor, TestSubtensor
from theano import tensor as tt
from theano.compile import DeepCopyOp
from theano.gpuarray.basic_ops import GpuContiguous, GpuFromHost, HostFromGpu
from theano.gpuarray.elemwise import GpuDimShuffle
......@@ -22,6 +21,7 @@ from theano.gpuarray.subtensor import (
)
from theano.gpuarray.type import gpuarray_shared_constructor
from theano.tensor.basic import AllocDiag, ExtractDiag
from theano.tensor.math import sum as tt_sum
from theano.tensor.subtensor import advanced_inc_subtensor1, inc_subtensor
from theano.tensor.type import ivectors, matrix, tensor, tensor4, vector
......@@ -395,7 +395,7 @@ class TestGpuAllocDiag(TestAllocDiag):
# offset = 0 case:
mtx_x = GpuAllocDiag()(x)
sum_mtx_x = tt.sum(mtx_x)
sum_mtx_x = tt_sum(mtx_x)
grad_x = theano.grad(sum_mtx_x, x)
grad_mtx_x = theano.grad(sum_mtx_x, mtx_x)
......@@ -409,7 +409,7 @@ class TestGpuAllocDiag(TestAllocDiag):
# offset > 0 case:
mtx_x = GpuAllocDiag(2)(x)
sum_mtx_x = tt.sum(mtx_x)
sum_mtx_x = tt_sum(mtx_x)
grad_x = theano.grad(sum_mtx_x, x)
grad_mtx_x = theano.grad(sum_mtx_x, mtx_x)
......@@ -423,7 +423,7 @@ class TestGpuAllocDiag(TestAllocDiag):
# offset < 0 case:
mtx_x = GpuAllocDiag(-3)(x)
sum_mtx_x = tt.sum(mtx_x)
sum_mtx_x = tt_sum(mtx_x)
grad_x = theano.grad(sum_mtx_x, x)
grad_mtx_x = theano.grad(sum_mtx_x, mtx_x)
......
tests/graph/test_basic.py
浏览文件 @ e0e5b3b8
......@@ -25,6 +25,7 @@ from theano.graph.basic import (
)
from theano.graph.op import Op
from theano.graph.type import Type
from theano.tensor.math import max_and_argmax
from theano.tensor.type import TensorType, iscalars, matrix, scalars
from theano.tensor.type_other import NoneConst
from theano.tensor.var import TensorVariable
......@@ -337,8 +338,8 @@ def test_equal_computations():
assert equal_computations([NoneConst], [NoneConst])
m = matrix()
max_argmax1 = tt.max_and_argmax(m)
max_argmax2 = tt.max_and_argmax(m)
max_argmax1 = max_and_argmax(m)
max_argmax2 = max_and_argmax(m)
assert equal_computations(max_argmax1, max_argmax2)
......
tests/graph/test_compute_test_value.py
浏览文件 @ e0e5b3b8
......@@ -11,7 +11,7 @@ from theano.graph import utils
from theano.graph.basic import Apply
from theano.graph.op import COp, Op
from theano.graph.type import Type
from theano.tensor.basic import _allclose
from theano.tensor.math import _allclose, dot
from theano.tensor.type import fmatrix, iscalar, matrix, vector
......@@ -84,7 +84,7 @@ class TestComputeTestValue:
y.tag.test_value = np.random.rand(4, 5).astype(config.floatX)
# should work
z = tt.dot(x, y)
z = dot(x, y)
assert hasattr(z.tag, "test_value")
f = theano.function([x, y], z)
assert _allclose(f(x.tag.test_value, y.tag.test_value), z.tag.test_value)
......@@ -92,7 +92,7 @@ class TestComputeTestValue:
# this test should fail
y.tag.test_value = np.random.rand(6, 5).astype(config.floatX)
with pytest.raises(ValueError):
tt.dot(x, y)
dot(x, y)
def test_compute_flag(self):
x = matrix("x")
......@@ -101,18 +101,18 @@ class TestComputeTestValue:
# should skip computation of test value
with config.change_flags(compute_test_value="off"):
z = tt.dot(x, y)
z = dot(x, y)
assert not hasattr(z.tag, "test_value")
# should fail when asked by user
with pytest.raises(ValueError), config.change_flags(compute_test_value="raise"):
tt.dot(x, y)
dot(x, y)
# test that a warning is raised if required
with warnings.catch_warnings(), config.change_flags(compute_test_value="warn"):
warnings.simplefilter("error", UserWarning)
with pytest.raises(UserWarning):
tt.dot(x, y)
dot(x, y)
def test_string_var(self):
x = matrix("x")
......@@ -123,13 +123,13 @@ class TestComputeTestValue:
z = theano.shared(np.random.rand(5, 6).astype(config.floatX))
# should work
out = tt.dot(tt.dot(x, y), z)
out = dot(dot(x, y), z)
assert hasattr(out.tag, "test_value")
tf = theano.function([x, y], out)
assert _allclose(tf(x.tag.test_value, y.tag.test_value), out.tag.test_value)
def f(x, y, z):
return tt.dot(tt.dot(x, y), z)
return dot(dot(x, y), z)
# this test should fail
z.set_value(np.random.rand(7, 6).astype(config.floatX))
......@@ -142,7 +142,7 @@ class TestComputeTestValue:
y = theano.shared(np.random.rand(4, 6).astype(config.floatX), "y")
# should work
z = tt.dot(x, y)
z = dot(x, y)
assert hasattr(z.tag, "test_value")
f = theano.function([x], z)
assert _allclose(f(x.tag.test_value), z.tag.test_value)
......@@ -150,14 +150,14 @@ class TestComputeTestValue:
# this test should fail
y.set_value(np.random.rand(5, 6).astype(config.floatX))
with pytest.raises(ValueError):
tt.dot(x, y)
dot(x, y)
def test_ndarray(self):
x = np.random.rand(2, 3).astype(config.floatX)
y = theano.shared(np.random.rand(3, 6).astype(config.floatX), "y")
# should work
z = tt.dot(x, y)
z = dot(x, y)
assert hasattr(z.tag, "test_value")
f = theano.function([], z)
assert _allclose(f(), z.tag.test_value)
......@@ -165,7 +165,7 @@ class TestComputeTestValue:
# this test should fail
x = np.random.rand(2, 4).astype(config.floatX)
with pytest.raises(ValueError):
tt.dot(x, y)
dot(x, y)
def test_empty_elemwise(self):
x = theano.shared(np.random.rand(0, 6).astype(config.floatX), "x")
......@@ -181,7 +181,7 @@ class TestComputeTestValue:
y = theano.shared(np.random.rand(3, 6).astype(config.floatX), "y")
# should work
z = tt.dot(x, y)
z = dot(x, y)
assert hasattr(z.tag, "test_value")
f = theano.function([], z)
assert _allclose(f(), z.tag.test_value)
......@@ -189,7 +189,7 @@ class TestComputeTestValue:
# this test should fail
x = tt.constant(np.random.rand(2, 4), dtype=config.floatX)
with pytest.raises(ValueError):
tt.dot(x, y)
dot(x, y)
def test_incorrect_type(self):
......@@ -242,7 +242,7 @@ class TestComputeTestValue:
A.tag.test_value = np.random.rand(5, 3).astype(config.floatX)
def fx(prior_result, A):
return tt.dot(prior_result, A)
return dot(prior_result, A)
with pytest.raises(ValueError) as e:
theano.scan(fn=fx, outputs_info=tt.ones_like(A), non_sequences=A, n_steps=k)
......@@ -260,7 +260,7 @@ class TestComputeTestValue:
A.tag.test_value = np.random.rand(5, 3).astype(config.floatX)
def fx(prior_result, A):
return tt.dot(prior_result, A)
return dot(prior_result, A)
with pytest.raises(ValueError):
theano.scan(
......
tests/graph/test_graph_opt_caching.py deleted 100644 → 0
浏览文件 @ 2b06fa16
import os
import numpy as np
import theano
import theano.tensor as tt
from theano.tensor.type import fmatrix
def test_graph_opt_caching():
opt_db_file = os.path.join(theano.config.compiledir, "optimized_graphs.pkl")
if os.path.exists(opt_db_file):
os.remove(opt_db_file)
floatX = "float32"
mode = theano.config.mode
if mode in ["DEBUG_MODE", "DebugMode"]:
mode = "FAST_RUN"
with theano.config.change_flags(cache_optimizations=True):
a = fmatrix("a")
b = fmatrix("b")
c = theano.shared(np.ones((10, 10), dtype=floatX))
d = theano.shared(np.ones((10, 10), dtype=floatX))
e = tt.sum(tt.sum(tt.sum(a ** 2 + b) + c) + d)
f1 = theano.function([a, b], e, mode=mode)
m = fmatrix("x1")
n = fmatrix("x2")
p = theano.shared(np.ones((10, 10), dtype=floatX))
q = theano.shared(np.ones((10, 10), dtype=floatX))
j = tt.sum(tt.sum(tt.sum(m ** 2 + n) + p) + q)
f2 = theano.function([m, n], j, mode=mode)
in1 = np.ones((10, 10), dtype=floatX)
in2 = np.ones((10, 10), dtype=floatX)
assert f1(in1, in2) == f2(in1, in2)
tests/graph/test_op.py
浏览文件 @ e0e5b3b8
......@@ -11,6 +11,7 @@ from theano.graph.basic import Apply, Variable
from theano.graph.op import COp, Op
from theano.graph.type import Generic, Type
from theano.graph.utils import MethodNotDefined, TestValueError
from theano.tensor.math import log
from theano.tensor.type import dmatrix, vector
......@@ -289,7 +290,7 @@ def test_test_value_shared():
@config.change_flags(compute_test_value="raise")
def test_test_value_op():
x = tt.log(np.ones((5, 5)))
x = log(np.ones((5, 5)))
v = op.get_test_value(x)
assert np.allclose(v, np.zeros((5, 5)))
......
tests/graph/test_opt.py
浏览文件 @ e0e5b3b8
import pytest
import theano.tensor as tt
from tests.graph.utils import (
MyType,
MyVariable,
......@@ -30,6 +29,7 @@ from theano.graph.opt import (
pre_greedy_local_optimizer,
theano,
)
from theano.tensor.math import dot
from theano.tensor.opt import constant_folding
from theano.tensor.subtensor import AdvancedSubtensor
from theano.tensor.type import matrix
......@@ -332,7 +332,7 @@ class TestMergeOptimizer:
# Merge two nodes, one has assert, the other not.
x1 = matrix("x1")
x2 = matrix("x2")
e = tt.dot(x1, x2) + tt.dot(assert_op(x1, (x1 > x2).all()), x2)
e = dot(x1, x2) + dot(assert_op(x1, (x1 > x2).all()), x2)
g = FunctionGraph([x1, x2], [e])
MergeOptimizer().optimize(g)
strg = theano.printing.debugprint(g, file="str")
......@@ -354,7 +354,7 @@ class TestMergeOptimizer:
# with the same conditions.
x1 = matrix("x1")
x2 = matrix("x2")
e = tt.dot(assert_op(x1, (x1 > x2).all()), x2) + tt.dot(
e = dot(assert_op(x1, (x1 > x2).all()), x2) + dot(
assert_op(x1, (x1 > x2).all()), x2
)
g = FunctionGraph([x1, x2], [e])
......@@ -381,7 +381,7 @@ class TestMergeOptimizer:
x1 = matrix("x1")
x2 = matrix("x2")
x3 = matrix("x3")
e = tt.dot(assert_op(x1, (x1 > x3).all()), x2) + tt.dot(
e = dot(assert_op(x1, (x1 > x3).all()), x2) + dot(
assert_op(x1, (x1 > x2).all()), x2
)
g = FunctionGraph([x1, x2, x3], [e])
......@@ -426,7 +426,7 @@ class TestMergeOptimizer:
x1 = matrix("x1")
x2 = matrix("x2")
x3 = matrix("x3")
e = tt.dot(assert_op(x1, (x1 > x3).all()), x2) + tt.dot(
e = dot(assert_op(x1, (x1 > x3).all()), x2) + dot(
x1, assert_op(x2, (x2 > x3).all())
)
g = FunctionGraph([x1, x2, x3], [e])
......@@ -457,7 +457,7 @@ class TestMergeOptimizer:
x1 = matrix("x1")
x2 = matrix("x2")
x3 = matrix("x3")
e = tt.dot(x1, assert_op(x2, (x2 > x3).all())) + tt.dot(
e = dot(x1, assert_op(x2, (x2 > x3).all())) + dot(
assert_op(x1, (x1 > x3).all()), x2
)
g = FunctionGraph([x1, x2, x3], [e])
......
tests/graph/test_sched.py
浏览文件 @ e0e5b3b8
from theano import tensor as tt
from theano.graph.basic import io_toposort
from theano.graph.sched import (
_toposort,
......@@ -7,6 +6,7 @@ from theano.graph.sched import (
reverse_dict,
sort_apply_nodes,
)
from theano.tensor.math import dot
from theano.tensor.type import matrix
from theano.utils import cmp
......@@ -15,7 +15,7 @@ def test_dependence():
dependence = make_dependence_cmp()
x = matrix("x")
y = tt.dot(x * 2, x + 1)
y = dot(x * 2, x + 1)
nodes = io_toposort([x], [y])
for a, b in zip(nodes[:-1], nodes[1:]):
......@@ -24,7 +24,7 @@ def test_dependence():
def test_sort_apply_nodes():
x = matrix("x")
y = tt.dot(x * 2, x + 1)
y = dot(x * 2, x + 1)
def str_cmp(a, b):
return cmp(str(a), str(b)) # lexicographical sort
......
tests/graph/test_toolbox.py
浏览文件 @ e0e5b3b8
from theano import tensor as tt
from theano.graph.basic import Apply, Variable
from theano.graph.fg import FunctionGraph
from theano.graph.op import Op
from theano.graph.toolbox import NodeFinder, is_same_graph
from theano.graph.type import Type
from theano.tensor.math import neg
from theano.tensor.type import vectors
......@@ -122,8 +122,8 @@ class TestIsSameGraph:
({y: x}, True),
),
),
(x, tt.neg(x), (({}, False),)),
(x, tt.neg(y), (({}, False),)),
(x, neg(x), (({}, False),)),
(x, neg(y), (({}, False),)),
]
)
......
tests/link/test_jax.py
浏览文件 @ e0e5b3b8
差异被折叠。
tests/link/test_vm.py
浏览文件 @ e0e5b3b8
......@@ -5,18 +5,20 @@ import time
import numpy as np
import pytest
import theano
from theano import function
from theano import tensor as tt
from theano.compile.function import function
from theano.compile.io import In
from theano.compile.mode import Mode
from theano.compile.mode import Mode, get_mode
from theano.compile.sharedvalue import shared
from theano.configdefaults import config
from theano.graph.basic import Apply
from theano.graph.op import Op
from theano.ifelse import ifelse
from theano.link.c.basic import OpWiseCLinker
from theano.link.c.exceptions import MissingGXX
from theano.link.vm import Loop, VMLinker
from theano.tensor.math import cosh, sin, tanh
from theano.tensor.type import dvector, lscalar, scalar, scalars, vector, vectors
from theano.tensor.var import TensorConstant
class TestCallbacks:
......@@ -59,7 +61,7 @@ def test_c_thunks():
a = scalars("a")
b, c = vectors("bc")
cases = [False]
if theano.config.cxx:
if config.cxx:
cases.append(True)
for c_thunks in cases:
f = function(
......@@ -76,7 +78,7 @@ def test_c_thunks():
@pytest.mark.skipif(
not theano.config.cxx, reason="G++ not available, so we need to skip this test."
not config.cxx, reason="G++ not available, so we need to skip this test."
)
def test_speed():
def build_graph(x, depth=5):
......@@ -94,7 +96,7 @@ def test_speed():
def time_numpy():
steps_a = 5
steps_b = 100
x = np.asarray([2.0, 3.0], dtype=theano.config.floatX)
x = np.asarray([2.0, 3.0], dtype=config.floatX)
numpy_version(x, steps_a)
t0 = time.time()
......@@ -134,12 +136,10 @@ def test_speed():
print(f"{name} takes {1000 * (t_b - t_a) / (steps_b - steps_a):f} s/Kop")
from theano.link.c.basic import OpWiseCLinker
time_linker("c|py", OpWiseCLinker)
time_linker("vmLinker", VMLinker)
time_linker("vmLinker_nogc", lambda: VMLinker(allow_gc=False))
if theano.config.cxx:
if config.cxx:
time_linker("vmLinker_CLOOP", lambda: VMLinker(allow_gc=False, use_cloop=True))
time_numpy()
......@@ -179,7 +179,7 @@ def test_speed_lazy():
time_linker("vmLinker", VMLinker)
time_linker("vmLinker_nogc", lambda: VMLinker(allow_gc=False))
if theano.config.cxx:
if config.cxx:
time_linker("vmLinker_C", lambda: VMLinker(allow_gc=False, use_cloop=True))
......@@ -189,7 +189,7 @@ def test_partial_function():
def check_partial_function(linker_name):
x = scalar("input")
y = x ** 2
f = theano.function(
f = function(
[x], [y + 7, y - 9, y / 14.0], mode=Mode(optimizer=None, linker=linker_name)
)
......@@ -198,19 +198,19 @@ def test_partial_function():
utt.assert_allclose(f(5), np.array([32.0, 16.0, 1.7857142857142858]))
check_partial_function(VMLinker(allow_partial_eval=True, use_cloop=False))
if not theano.config.cxx:
if not config.cxx:
pytest.skip("Need cxx for this test")
check_partial_function("cvm")
@pytest.mark.skipif(
not theano.config.cxx, reason="G++ not available, so we need to skip this test."
not config.cxx, reason="G++ not available, so we need to skip this test."
)
def test_partial_function_with_output_keys():
def check_partial_function_output_keys(linker_name):
x = scalar("input")
y = 3 * x
f = theano.function(
f = function(
[x], {"a": y * 5, "b": y - 7}, mode=Mode(optimizer=None, linker=linker_name)
)
......@@ -223,19 +223,19 @@ def test_partial_function_with_output_keys():
@pytest.mark.skipif(
not theano.config.cxx, reason="G++ not available, so we need to skip this test."
not config.cxx, reason="G++ not available, so we need to skip this test."
)
def test_partial_function_with_updates():
def check_updates(linker_name):
x = lscalar("input")
y = theano.shared(np.asarray(1, "int64"), name="global")
f = theano.function(
y = shared(np.asarray(1, "int64"), name="global")
f = function(
[x],
[x, x + 34],
updates=[(y, x + 1)],
mode=Mode(optimizer=None, linker=linker_name),
)
g = theano.function(
g = function(
[x],
[x - 6],
updates=[(y, y + 3)],
......@@ -253,12 +253,12 @@ def test_partial_function_with_updates():
def test_allow_gc_cvm():
mode = theano.config.mode
mode = config.mode
if mode in ["DEBUG_MODE", "DebugMode"]:
mode = "FAST_RUN"
v = vector()
f = theano.function([v], v + 1, mode=mode)
f = function([v], v + 1, mode=mode)
f([1])
n = list(f.maker.fgraph.apply_nodes)[0].outputs[0]
......@@ -289,7 +289,7 @@ if run_memory_usage_tests:
x = vector()
z = x
for d in range(10):
z = tt.sin(-z + 1)
z = sin(-z + 1)
f = function([x], z, mode=Mode(optimizer=None, linker="cvm"))
if not i % 100:
......@@ -347,7 +347,7 @@ if run_memory_usage_tests:
def build_graph(x, depth=5):
z = x
for d in range(depth):
z = tt.sin(-z + 1)
z = sin(-z + 1)
return z
def time_linker(name, linker):
......@@ -391,35 +391,33 @@ def test_vm_gc():
x = vector()
p = RunOnce()(x)
mode = Mode(linker=VMLinker(lazy=True))
f = theano.function([In(x, mutable=True)], [p + 1, p + 2], mode=mode)
f = function([In(x, mutable=True)], [p + 1, p + 2], mode=mode)
f([1, 2, 3])
p = RunOnce()(x)
pp = p + p
f = theano.function([x], [pp + pp], mode=mode)
f = function([x], [pp + pp], mode=mode)
f([1, 2, 3])
def test_reallocation():
x = scalar("x")
y = scalar("y")
z = tt.tanh(3 * x + y) + tt.cosh(x + 5 * y)
z = tanh(3 * x + y) + cosh(x + 5 * y)
# The functinality is currently implement for non lazy and non c VM only.
for linker in [
VMLinker(allow_gc=False, lazy=False, use_cloop=False),
VMLinker(allow_gc=True, lazy=False, use_cloop=False),
]:
m = theano.compile.get_mode(Mode(linker=linker))
m = get_mode(Mode(linker=linker))
m = m.excluding("fusion", "inplace")
f = theano.function([x, y], z, name="test_reduce_memory", mode=m)
f = function([x, y], z, name="test_reduce_memory", mode=m)
output = f(1, 2)
assert output
storage_map = f.fn.storage_map
def check_storage(storage_map):
from theano.tensor.var import TensorConstant
for i in storage_map:
if not isinstance(i, TensorConstant):
keys_copy = list(storage_map.keys())[:]
......@@ -434,7 +432,7 @@ def test_reallocation():
@pytest.mark.skipif(
not theano.config.cxx, reason="G++ not available, so we need to skip this test."
not config.cxx, reason="G++ not available, so we need to skip this test."
)
def test_no_recycling():
x = vector()
......@@ -446,15 +444,15 @@ def test_no_recycling():
]:
mode = Mode(optimizer="fast_compile", linker=lnk)
f = theano.function([x], x + 1, mode=mode)
f2 = theano.function([x], (x + 1) * 2, mode=mode)
f = function([x], x + 1, mode=mode)
f2 = function([x], (x + 1) * 2, mode=mode)
m1 = f.fn.thunks[0].thunk.module
m2 = f2.fn.thunks[0].thunk.module
assert m1 is m2
@pytest.mark.skipif(
not theano.config.cxx, reason="G++ not available, so we need to skip this test."
not config.cxx, reason="G++ not available, so we need to skip this test."
)
def test_VMLinker_make_vm_cvm():
# We don't want this at module level, since CXX might not be present
......
tests/record.py
浏览文件 @ e0e5b3b8
......@@ -6,7 +6,6 @@ from theano.printing import hex_digest, min_informative_str
__authors__ = ["PyMC Team", "Ian Goodfellow"]
__credits__ = ["PyMC Team", "Ian Goodfellow"]
class MismatchError(Exception):
......
tests/sandbox/linalg/test_linalg.py
浏览文件 @ e0e5b3b8
......@@ -19,8 +19,8 @@ from theano.sandbox.linalg.ops import (
solve,
spectral_radius_bound,
)
from theano.tensor.basic import _allclose
from theano.tensor.elemwise import DimShuffle
from theano.tensor.math import _allclose
from theano.tensor.nlinalg import MatrixInverse
from theano.tensor.type import dmatrix, matrix, vector
......
tests/scan/test_basic.py
浏览文件 @ e0e5b3b8
This source diff could not be displayed because it is too large. You can view the blob instead.
tests/scan/test_opt.py
浏览文件 @ e0e5b3b8
差异被折叠。
tests/sparse/test_basic.py
浏览文件 @ e0e5b3b8
差异被折叠。
tests/sparse/test_opt.py
浏览文件 @ e0e5b3b8
差异被折叠。
tests/tensor/test_basic.py
浏览文件 @ e0e5b3b8
差异被折叠。
tests/tensor/test_blas.py
浏览文件 @ e0e5b3b8
差异被折叠。
tests/tensor/test_math.py 0 → 100644
浏览文件 @ e0e5b3b8
差异被折叠。
tests/tensor/test_misc.py
浏览文件 @ e0e5b3b8
差异被折叠。
tests/tensor/test_mlp.py
浏览文件 @ e0e5b3b8
差异被折叠。
tests/tensor/test_opt.py
浏览文件 @ e0e5b3b8
差异被折叠。
tests/tensor/test_shape.py
浏览文件 @ e0e5b3b8
差异被折叠。
tests/tensor/test_type.py
浏览文件 @ e0e5b3b8
差异被折叠。
tests/test_2nd_order_grads.py deleted 100644 → 0
浏览文件 @ 2b06fa16
差异被折叠。
tests/test_breakpoint.py
浏览文件 @ e0e5b3b8
差异被折叠。
tests/test_determinism.py
浏览文件 @ e0e5b3b8
差异被折叠。
tests/test_gradient.py
浏览文件 @ e0e5b3b8
差异被折叠。
tests/test_ifelse.py
浏览文件 @ e0e5b3b8
差异被折叠。
tests/test_rop.py
浏览文件 @ e0e5b3b8
差异被折叠。
tests/unittest_tools.py
浏览文件 @ e0e5b3b8
差异被折叠。
theano/breakpoint.py
浏览文件 @ e0e5b3b8
差异被折叠。
theano/configdefaults.py
浏览文件 @ e0e5b3b8
差异被折叠。
theano/gpuarray/ctc.py
浏览文件 @ e0e5b3b8
差异被折叠。
theano/gpuarray/dnn.py
浏览文件 @ e0e5b3b8
差异被折叠。
theano/gpuarray/dnn_opt.py
浏览文件 @ e0e5b3b8
差异被折叠。
theano/gpuarray/fft.py
浏览文件 @ e0e5b3b8
差异被折叠。
theano/gpuarray/linalg.py
浏览文件 @ e0e5b3b8
差异被折叠。
theano/gpuarray/opt.py
浏览文件 @ e0e5b3b8
差异被折叠。
theano/gpuarray/type.py
浏览文件 @ e0e5b3b8
差异被折叠。
theano/graph/basic.py
浏览文件 @ e0e5b3b8
差异被折叠。
theano/graph/sched.py
浏览文件 @ e0e5b3b8
差异被折叠。
theano/ifelse.py
浏览文件 @ e0e5b3b8
差异被折叠。
theano/misc/check_blas.py
浏览文件 @ e0e5b3b8
差异被折叠。
theano/sandbox/rng_mrg.py
浏览文件 @ e0e5b3b8
差异被折叠。
theano/scan/basic.py
浏览文件 @ e0e5b3b8
差异被折叠。
theano/scan/checkpoints.py
浏览文件 @ e0e5b3b8
差异被折叠。
theano/scan/op.py
浏览文件 @ e0e5b3b8
差异被折叠。
theano/scan/opt.py
浏览文件 @ e0e5b3b8
差异被折叠。
theano/sparse/basic.py
浏览文件 @ e0e5b3b8
差异被折叠。
theano/sparse/opt.py
浏览文件 @ e0e5b3b8
差异被折叠。
theano/tensor/__init__.py
浏览文件 @ e0e5b3b8
差异被折叠。
theano/tensor/basic.py
浏览文件 @ e0e5b3b8
差异被折叠。
theano/tensor/blas.py
浏览文件 @ e0e5b3b8
差异被折叠。
theano/tensor/elemwise.py
浏览文件 @ e0e5b3b8
差异被折叠。
theano/tensor/extra_ops.py
浏览文件 @ e0e5b3b8
差异被折叠。
theano/tensor/fft.py
浏览文件 @ e0e5b3b8
差异被折叠。
theano/tensor/fourier.py
浏览文件 @ e0e5b3b8
差异被折叠。
theano/tensor/math.py 0 → 100644
浏览文件 @ e0e5b3b8
差异被折叠。
theano/tensor/nlinalg.py
浏览文件 @ e0e5b3b8
差异被折叠。
theano/tensor/nnet/bn.py
浏览文件 @ e0e5b3b8
差异被折叠。
theano/tensor/nnet/conv.py
浏览文件 @ e0e5b3b8
差异被折叠。
theano/tensor/nnet/sigm.py
浏览文件 @ e0e5b3b8
差异被折叠。
theano/tensor/opt.py
浏览文件 @ e0e5b3b8
差异被折叠。
theano/tensor/random/op.py
浏览文件 @ e0e5b3b8
差异被折叠。
theano/tensor/shape.py
浏览文件 @ e0e5b3b8
差异被折叠。
theano/tensor/slinalg.py
浏览文件 @ e0e5b3b8
差异被折叠。
theano/tensor/sort.py
浏览文件 @ e0e5b3b8
差异被折叠。
theano/tensor/subtensor.py
浏览文件 @ e0e5b3b8
差异被折叠。
theano/tensor/type.py
浏览文件 @ e0e5b3b8
差异被折叠。
theano/tensor/var.py
浏览文件 @ e0e5b3b8
差异被折叠。
Markdown 格式
0% 或
您添加了 0 人 到此讨论。请谨慎行事。
请先完成此评论的编辑！
请 注册 或者 后发表评论