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提交 a2503274 authored 作者: Chiheb Trabelsi's avatar Chiheb Trabelsi
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test_basic_ops.py has been modified in order to respect the flake8 style.

上级 6d8bd41f
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正在显示 包含 126 行增加 和 114 行删除
theano/sandbox/cuda/tests/test_basic_ops.py
浏览文件 @ a2503274
...@@ -9,19 +9,20 @@ import numpy ...@@ -9,19 +9,20 @@ import numpy
from six.moves import xrange from six.moves import xrange
import theano import theano
import theano.tensor as T import theano.tensor as T
# Skip test if cuda_ndarray is not available.
from nose.plugins.skip import SkipTest
from nose.tools import assert_raises
import theano.sandbox.cuda as cuda_ndarray
if cuda_ndarray.cuda_available == False:
raise SkipTest('Optional package cuda disabled')
import theano.sandbox.cuda as tcn import theano.sandbox.cuda as tcn
import theano.sandbox.cuda as cuda import theano.sandbox.cuda as cuda
import theano.sandbox.cuda.basic_ops as B import theano.sandbox.cuda.basic_ops as B
from theano.tensor.basic import _allclose from theano.tensor.basic import _allclose
from theano.tests import unittest_tools as utt from theano.tests import unittest_tools as utt
import theano.tensor.tests.test_basic
import theano.tensor.tests.test_subtensor
import theano.tensor.tests.test_sharedvar
# Skip test if cuda_ndarray is not available.
from nose.plugins.skip import SkipTest
import theano.sandbox.cuda as cuda_ndarray
if cuda_ndarray.cuda_available is False:
raise SkipTest('Optional package cuda disabled')
if theano.config.mode == 'FAST_COMPILE': if theano.config.mode == 'FAST_COMPILE':
mode_with_gpu = theano.compile.mode.get_mode('FAST_RUN').including('gpu') mode_with_gpu = theano.compile.mode.get_mode('FAST_RUN').including('gpu')
...@@ -75,8 +76,8 @@ def test_careduce(): ...@@ -75,8 +76,8 @@ def test_careduce():
# The following 2 cases could work if the scalar_op.c_code work with float* dtype. # The following 2 cases could work if the scalar_op.c_code work with float* dtype.
# Currently we have this error: # Currently we have this error:
# error: invalid operands of types 'npy_float32' and 'npy_float32' to binary 'operator&' # error: invalid operands of types 'npy_float32' and 'npy_float32' to binary 'operator&'
#(theano.scalar.and_, tensor.elemwise.CAReduce), # (theano.scalar.and_, tensor.elemwise.CAReduce),
#(theano.scalar.or_, tensor.elemwise.CAReduce), # (theano.scalar.or_, tensor.elemwise.CAReduce),
]: ]:
for shape, pattern in [((1, 1), (1,)), for shape, pattern in [((1, 1), (1,)),
((1, 0), (1,)), ((1, 0), (1,)),
...@@ -113,7 +114,7 @@ def test_careduce(): ...@@ -113,7 +114,7 @@ def test_careduce():
((4100, 4, 3), [2]), ((5, 4100, 3), [2]), ((5, 4, 4100), [2]), # 001 ((4100, 4, 3), [2]), ((5, 4100, 3), [2]), ((5, 4, 4100), [2]), # 001
((4100, 4, 3), [0, 1]), ((5, 4100, 3), [0, 1]), ((5, 4, 4100), [0, 1]), # 110 ((4100, 4, 3), [0, 1]), ((5, 4100, 3), [0, 1]), ((5, 4, 4100), [0, 1]), # 110
((4100, 4, 3), [1, 2]), ((5, 4100, 3), [1, 2]), ((5, 4, 4100), [1, 2]), # 011 ((4100, 4, 3), [1, 2]), ((5, 4100, 3), [1, 2]), ((5, 4, 4100), [1, 2]), # 011
((4100,4,3),[0,2]),((5,4100,3),[0,2]),((5,4,4100),[0,2]), ((4100, 4, 3), [0, 2]), ((5, 4100, 3), [0, 2]), ((5, 4, 4100), [0, 2]),
((4100, 4, 3), [0, 1, 2]), ((5, 4100, 3), [0, 1, 2]), ((5, 4, 4100), [0, 1, 2]), # 111 ((4100, 4, 3), [0, 1, 2]), ((5, 4100, 3), [0, 1, 2]), ((5, 4, 4100), [0, 1, 2]), # 111
((65, 4, 3), [0, 1, 2]), ((5, 65, 3), [0, 1, 2]), ((5, 4, 65), [0, 1, 2]), # 111 ((65, 4, 3), [0, 1, 2]), ((5, 65, 3), [0, 1, 2]), ((5, 4, 65), [0, 1, 2]), # 111
...@@ -127,15 +128,15 @@ def test_careduce(): ...@@ -127,15 +128,15 @@ def test_careduce():
((4100, 4, 3, 2), [2, 3]), ((4, 4100, 3, 2), [2, 3]), ((4, 3, 4100, 2), [2, 3]), ((4, 3, 2, 4100), [2, 3]), # 0011 ((4100, 4, 3, 2), [2, 3]), ((4, 4100, 3, 2), [2, 3]), ((4, 3, 4100, 2), [2, 3]), ((4, 3, 2, 4100), [2, 3]), # 0011
((4100, 4, 3, 2), [1, 3]), ((4, 4100, 3, 2), [1, 3]), ((4, 3, 4100, 2), [1, 3]), ((4, 3, 2, 4100), [1, 3]), # 0101 ((4100, 4, 3, 2), [1, 3]), ((4, 4100, 3, 2), [1, 3]), ((4, 3, 4100, 2), [1, 3]), ((4, 3, 2, 4100), [1, 3]), # 0101
((4100, 4, 3, 2), [1, 2]), ((4, 4100, 3, 2), [1, 2]), ((4, 3, 4100, 2), [1, 2]), ((4, 3, 2, 4100), [1, 2]), # 0110 ((4100, 4, 3, 2), [1, 2]), ((4, 4100, 3, 2), [1, 2]), ((4, 3, 4100, 2), [1, 2]), ((4, 3, 2, 4100), [1, 2]), # 0110
((4100,4,3,2),[0,3]),((4,4100,3,2),[0,3]),((4,3,4100,2),[0,3]),((4,3,2,4100),[0,3]),#1001 ((4100, 4, 3, 2), [0, 3]), ((4, 4100, 3, 2), [0, 3]), ((4, 3, 4100, 2), [0, 3]), ((4, 3, 2, 4100), [0, 3]), # 1001
# ((4100,4,3,2),[0,2]),((4,4100,3,2),[0,2]),((4,3,4100,2),[0,2]),((4,3,2,4100),[0,2]),#1010 not implemented # ((4100,4,3,2),[0,2]),((4,4100,3,2),[0,2]),((4,3,4100,2),[0,2]),((4,3,2,4100),[0,2]),#1010 not implemented
((4100, 4, 3, 2), [0, 1]), ((4, 4100, 3, 2), [0, 1]), ((4, 3, 4100, 2), [0, 1]), ((4, 3, 2, 4100), [0, 1]), # 1100 ((4100, 4, 3, 2), [0, 1]), ((4, 4100, 3, 2), [0, 1]), ((4, 3, 4100, 2), [0, 1]), ((4, 3, 2, 4100), [0, 1]), # 1100
# reduce over 3d # reduce over 3d
# 3d not tested: 1101, 1110, 1111 # 3d not tested: 1101, 1110, 1111
((4100,4,3,2),[0,1,3]),((4,4100,3,2),[0,1,3]),((4,3,4100,2),[0,1,3]),((4,3,2,4100),[0,1,3]),#1101 ((4100, 4, 3, 2), [0, 1, 3]), ((4, 4100, 3, 2), [0, 1, 3]), ((4, 3, 4100, 2), [0, 1, 3]), ((4, 3, 2, 4100), [0, 1, 3]), # 1101
((4100, 4, 3, 2), [0, 1, 2]), ((4, 4100, 3, 2), [0, 1, 2]), ((4, 3, 4100, 2), [0, 1, 2]), ((4, 3, 2, 4100), [0, 1, 2]), # 1110 ((4100, 4, 3, 2), [0, 1, 2]), ((4, 4100, 3, 2), [0, 1, 2]), ((4, 3, 4100, 2), [0, 1, 2]), ((4, 3, 2, 4100), [0, 1, 2]), # 1110
((4100, 4, 3, 2), [0, 2, 3]), ((4, 4100, 3, 2), [0, 2, 3]), ((4, 3, 4100, 2), [0, 2, 3]), # ((4,3,2,4100),[0,2,3]),#1011 ((4100, 4, 3, 2), [0, 2, 3]), ((4, 4100, 3, 2), [0, 2, 3]), ((4, 3, 4100, 2), [0, 2, 3]), # ((4, 3, 2, 4100), [0, 2, 3]), # 1011
((4100, 4, 3, 2), [1, 2, 3]), ((4, 4100, 3, 2), [1, 2, 3]), ((4, 3, 4100, 2), [1, 2, 3]), ((4, 3, 2, 4100), [1, 2, 3]), # 0111 ((4100, 4, 3, 2), [1, 2, 3]), ((4, 4100, 3, 2), [1, 2, 3]), ((4, 3, 4100, 2), [1, 2, 3]), ((4, 3, 2, 4100), [1, 2, 3]), # 0111
((65, 4, 3, 2), [1, 2, 3]), ((4, 65, 3, 2), [1, 2, 3]), ((4, 3, 65, 2), [1, 2, 3]), ((4, 3, 2, 65), [1, 2, 3]), # 0111 ((65, 4, 3, 2), [1, 2, 3]), ((4, 65, 3, 2), [1, 2, 3]), ((4, 3, 65, 2), [1, 2, 3]), ((4, 3, 2, 65), [1, 2, 3]), # 0111
...@@ -148,26 +149,26 @@ def test_careduce(): ...@@ -148,26 +149,26 @@ def test_careduce():
]: ]:
op = careduce_op(scalar_op, axis=pattern) op = careduce_op(scalar_op, axis=pattern)
pat = tensor_pattern_to_gpu_pattern(shape, pattern) tensor_pattern_to_gpu_pattern(shape, pattern)
a = tensor.TensorType('float32', (False,) * len(shape))() a = tensor.TensorType('float32', (False,) * len(shape))()
b = op(a*a) b = op(a * a)
val = numpy.random.rand(numpy.prod(shape)).reshape(shape) val = numpy.random.rand(numpy.prod(shape)).reshape(shape)
# val = numpy.ones(shape) # val = numpy.ones(shape)
# val = numpy.arange(numpy.prod(shape)).reshape(shape) # val = numpy.arange(numpy.prod(shape)).reshape(shape)
val = theano._asarray(val, dtype='float32') val = theano._asarray(val, dtype='float32')
f = theano.function([a], b, mode=mode_with_gpu) f = theano.function([a], b, mode=mode_with_gpu)
f2 = theano.function([a], b, mode=mode_without_gpu) f2 = theano.function([a], b, mode=mode_without_gpu)
assert tcn.GpuCAReduce in [x.op.__class__ assert tcn.GpuCAReduce in [
for x in f.maker.fgraph.toposort()], ( x.op.__class__ for x in f.maker.fgraph.toposort()], (
scalar_op, shape, pattern) scalar_op, shape, pattern)
if tcn.GpuElemwise in [x.op.__class__ if(tcn.GpuElemwise in [
for x in f.maker.fgraph.toposort()]: x.op.__class__ for x in f.maker.fgraph.toposort()]):
assert tcn.GpuReshape in [x.op.__class__ assert tcn.GpuReshape in [
for x in f.maker.fgraph.toposort()] x.op.__class__ for x in f.maker.fgraph.toposort()]
assert op.__class__ in [x.op.__class__ assert op.__class__ in [
for x in f2.maker.fgraph.toposort()], ( x.op.__class__ for x in f2.maker.fgraph.toposort()], (
scalar_op, shape, pattern) scalar_op, shape, pattern)
f_caused_value_error = False f_caused_value_error = False
try: try:
f_out = f(val) f_out = f(val)
...@@ -176,8 +177,9 @@ def test_careduce(): ...@@ -176,8 +177,9 @@ def test_careduce():
f_caused_value_error = True f_caused_value_error = True
except NotImplementedError: except NotImplementedError:
if (numpy.prod(shape) == 0 and if (numpy.prod(shape) == 0 and
getattr(scalar_op, 'identity', None) != 0): getattr(
continue scalar_op, 'identity', None) != 0):
continue
raise raise
f2_caused_value_error = False f2_caused_value_error = False
...@@ -208,45 +210,49 @@ def test_careduce(): ...@@ -208,45 +210,49 @@ def test_careduce():
# example in debug mode with unittests.rseed=9275 # example in debug mode with unittests.rseed=9275
orig_rtol = theano.tensor.basic.float32_rtol orig_rtol = theano.tensor.basic.float32_rtol
theano.tensor.basic.float32_rtol = 2e-5 theano.tensor.basic.float32_rtol = 2e-5
assert _allclose(f_out, f2_out), ('shape', shape, assert _allclose(f_out, f2_out), (
'pattern', pattern, 'shape',
scalar_op, shape,
sum([shape[i] for i in pattern]), 'pattern',
f2(val), f(val), val) pattern, scalar_op,
sum([shape[i] for i in pattern]),
f2(val), f(val), val)
finally: finally:
theano.tensor.basic.float32_rtol = orig_rtol theano.tensor.basic.float32_rtol = orig_rtol
# test with dimshuffle # test with dimshuffle
# we shuffle the 2 outer dims. # we shuffle the 2 outer dims.
for shape, pattern in [ # ((5,),[0]), # for shape, pattern in [((5,), [0]),
((5, 4), [0, 1]), ((5, 4), [0]), for shape, pattern in [((5, 4), [0, 1]), ((5, 4), [0]),
((5, 4, 3), [0]), ((5, 4, 3), [0, 1]), ((5, 4, 3), [2]), ((5, 4, 3), [0, 1, 2]), ((5, 4, 3), [0]), ((5, 4, 3), [0, 1]),
((5, 4, 3, 2), [0, 1, 2, 3]), ((5, 4, 3, 2), [0, 2, 3]), ((5, 4, 3), [2]), ((5, 4, 3), [0, 1, 2]),
((128, 1, 3, 3), [0, 1, 2, 3]), ((5, 4, 3, 2), [0, 1, 2, 3]),
]: ((5, 4, 3, 2), [0, 2, 3]),
((128, 1, 3, 3), [0, 1, 2, 3]), ]:
op = careduce_op(scalar_op, axis=pattern) op = careduce_op(scalar_op, axis=pattern)
pat = tensor_pattern_to_gpu_pattern(shape, pattern) tensor_pattern_to_gpu_pattern(shape, pattern)
a = tensor.TensorType('float32', (False,) * len(shape))() a = tensor.TensorType('float32', (False,) * len(shape))()
dim_pattern = list(range(len(shape))) dim_pattern = list(range(len(shape)))
dim_pattern[0] = 1 dim_pattern[0] = 1
dim_pattern[1] = 0 dim_pattern[1] = 0
a = a.dimshuffle(dim_pattern) a = a.dimshuffle(dim_pattern)
b = op(a*a) b = op(a * a)
val = numpy.random.rand(numpy.prod(shape)).reshape(shape) val = numpy.random.rand(numpy.prod(shape)).reshape(shape)
# val = numpy.ones(shape) # val = numpy.ones(shape)
# val = numpy.arange(numpy.prod(shape)).reshape(shape) # val = numpy.arange(numpy.prod(shape)).reshape(shape)
val = theano._asarray(val, dtype='float32') val = theano._asarray(val, dtype='float32')
f = theano.function([a], b, mode=mode_with_gpu) f = theano.function([a], b, mode=mode_with_gpu)
f2 = theano.function([a], b, mode=mode_without_gpu) f2 = theano.function([a], b, mode=mode_without_gpu)
assert tcn.GpuCAReduce in [x.op.__class__ assert tcn.GpuCAReduce in [
for x in f.maker.fgraph.toposort()], ( x.op.__class__ for x in f.maker.fgraph.toposort()], (
scalar_op, shape, pattern) scalar_op, shape, pattern)
assert tcn.GpuElemwise not in [x.op.__class__ assert tcn.GpuElemwise not in [
for x in f.maker.fgraph.toposort()] x.op.__class__ for x in f.maker.fgraph.toposort()]
assert op.__class__ in [x.op.__class__ assert op.__class__ in [
for x in f2.maker.fgraph.toposort()], ( x.op.__class__ for x in f2.maker.fgraph.toposort()], (
scalar_op, shape, pattern) scalar_op, shape, pattern)
assert _allclose(f2(val), f(val)), ('shape', shape, assert _allclose(f2(val), f(val)), ('shape', shape,
'pattern', pattern, 'pattern', pattern,
scalar_op, scalar_op,
...@@ -258,16 +264,15 @@ def test_careduce(): ...@@ -258,16 +264,15 @@ def test_careduce():
((5, 4, 3), [0]), ((5, 4, 3), [0, 1]), ((5, 4, 3), [0]), ((5, 4, 3), [0, 1]),
((5, 4, 3), [2]), ((5, 4, 3), [0, 1, 2]), ((5, 4, 3), [2]), ((5, 4, 3), [0, 1, 2]),
((5, 4, 3, 2), [0, 1, 2, 3]), ((5, 4, 3, 2), [0, 2, 3]), ((5, 4, 3, 2), [0, 1, 2, 3]), ((5, 4, 3, 2), [0, 2, 3]),
((128, 1, 3, 3), [0, 1, 2, 3]), ((128, 1, 3, 3), [0, 1, 2, 3]), ]:
]:
op = careduce_op(scalar_op, axis=pattern) op = careduce_op(scalar_op, axis=pattern)
pat = tensor_pattern_to_gpu_pattern(shape, pattern) tensor_pattern_to_gpu_pattern(shape, pattern)
shape = numpy.asarray(shape) * 2 shape = numpy.asarray(shape) * 2
a = tensor.TensorType('float32', (False,) * len(shape))() a = tensor.TensorType('float32', (False,) * len(shape))()
a2 = tcn.CudaNdarrayType((False,) * len(shape))() a2 = tcn.CudaNdarrayType((False,) * len(shape))()
b = op(a*a) b = op(a * a)
b2 = op(a2*a2) b2 = op(a2 * a2)
val = numpy.random.rand(numpy.prod(shape)).reshape(shape) val = numpy.random.rand(numpy.prod(shape)).reshape(shape)
# val = numpy.ones(shape) # val = numpy.ones(shape)
# val = numpy.arange(numpy.prod(shape)).reshape(shape) # val = numpy.arange(numpy.prod(shape)).reshape(shape)
...@@ -287,14 +292,14 @@ def test_careduce(): ...@@ -287,14 +292,14 @@ def test_careduce():
val2 = val2[::2, ::2, ::2, ::2] val2 = val2[::2, ::2, ::2, ::2]
f = theano.function([a], b, mode=mode_without_gpu) f = theano.function([a], b, mode=mode_without_gpu)
f2 = theano.function([a2], b2, mode=mode_with_gpu) f2 = theano.function([a2], b2, mode=mode_with_gpu)
assert tcn.GpuCAReduce in [x.op.__class__ assert tcn.GpuCAReduce in [
for x in f2.maker.fgraph.toposort()], ( x.op.__class__ for x in f2.maker.fgraph.toposort()], (
scalar_op, shape, pattern) scalar_op, shape, pattern)
assert tcn.GpuElemwise not in [x.op.__class__ assert tcn.GpuElemwise not in [x.op.__class__
for x in f.maker.fgraph.toposort()] for x in f.maker.fgraph.toposort()]
assert op.__class__ in [x.op.__class__ assert op.__class__ in [x.op.__class__
for x in f.maker.fgraph.toposort()], ( for x in f.maker.fgraph.toposort()], (
scalar_op, shape, pattern) scalar_op, shape, pattern)
assert _allclose(f2(val2), f(val)), ('shape', shape, assert _allclose(f2(val2), f(val)), ('shape', shape,
'pattern', pattern, 'pattern', pattern,
sum([shape[i] for i in pattern])) sum([shape[i] for i in pattern]))
...@@ -374,8 +379,10 @@ def test_reshape(): ...@@ -374,8 +379,10 @@ def test_reshape():
# Test zero dimensions are allowed # Test zero dimensions are allowed
x = T.vector('x') x = T.vector('x')
f_reshp = theano.function([x], x.reshape((0,100)), mode=mode_with_gpu) f_reshp = theano.function(
assert f_reshp(numpy.ndarray((0,), dtype='float32')).shape == (0,100) [x], x.reshape((0, 100)), mode=mode_with_gpu)
assert f_reshp(
numpy.ndarray((0, ), dtype='float32')).shape == (0, 100)
def test_alloc_empty(): def test_alloc_empty():
...@@ -406,7 +413,7 @@ def test_elemwise_empty(): ...@@ -406,7 +413,7 @@ def test_elemwise_empty():
b = tensor.fmatrix() b = tensor.fmatrix()
f = pfunc([b], [], updates=[(a, a + b)], mode=mode_with_gpu) f = pfunc([b], [], updates=[(a, a + b)], mode=mode_with_gpu)
f2 = pfunc([b], [], updates=[(a, a + b)], mode=mode_without_gpu) pfunc([b], [], updates=[(a, a + b)], mode=mode_without_gpu)
a0 = a.get_value() * 1.0 a0 = a.get_value() * 1.0
f(numpy.ones((0, 0), dtype='float32')) f(numpy.ones((0, 0), dtype='float32'))
...@@ -424,8 +431,9 @@ def test_elemwise0(): ...@@ -424,8 +431,9 @@ def test_elemwise0():
f = pfunc([b], [], updates=[(a, a + b)], mode=mode_with_gpu) f = pfunc([b], [], updates=[(a, a + b)], mode=mode_with_gpu)
# check that we work inplace. # check that we work inplace.
assert (list(f.maker.fgraph.toposort()[1].op.destroy_map.items()) assert (list(
== [(0, [0])]) f.maker.fgraph.toposort()[1].op.destroy_map.items()) == [
(0, [0])])
a0 = a.get_value() * 1.0 a0 = a.get_value() * 1.0
f(numpy.ones((4, 4), dtype='float32')) f(numpy.ones((4, 4), dtype='float32'))
...@@ -495,7 +503,8 @@ def test_elemwise2(): ...@@ -495,7 +503,8 @@ def test_elemwise2():
dtype='float32'), 'a') dtype='float32'), 'a')
b = tensor.Tensor(dtype='float32', broadcastable=[0] * len(shape))() b = tensor.Tensor(dtype='float32', broadcastable=[0] * len(shape))()
f = pfunc([b], [], updates=[(a, (a + b).dimshuffle([2, 0, 3, 1]) * f = pfunc([b], [], updates=[(a, (a + b).dimshuffle([2, 0, 3, 1]) *
tensor.exp(b ** a).dimshuffle([2, 0, 3, 1]))], mode=mode_with_gpu) tensor.exp(b ** a).dimshuffle([2, 0, 3, 1]))],
mode=mode_with_gpu)
has_elemwise = False has_elemwise = False
for i, node in enumerate(f.maker.fgraph.toposort()): for i, node in enumerate(f.maker.fgraph.toposort()):
has_elemwise = has_elemwise or isinstance(node.op, tensor.Elemwise) has_elemwise = has_elemwise or isinstance(node.op, tensor.Elemwise)
...@@ -585,10 +594,11 @@ def test_elemwise_composite_float64(): ...@@ -585,10 +594,11 @@ def test_elemwise_composite_float64():
return l return l
for mode in [mode_with_gpu, mode_with_gpu.excluding('gpu_after_fusion'), for mode in [mode_with_gpu, mode_with_gpu.excluding('gpu_after_fusion'),
mode_with_gpu.excluding('elemwise_fusion')]: mode_with_gpu.excluding('elemwise_fusion')]:
f = pfunc([a, b], f = pfunc(
tensor.cast(tensor.lt(tensor.cast(a, 'float64') ** 2, [a, b],
b), tensor.cast(
'float32'), mode=mode) tensor.lt(tensor.cast(a, 'float64') ** 2, b), 'float32'),
mode=mode)
out = f(av, bv) out = f(av, bv)
assert numpy.all(out == ((av ** 2) < bv)) assert numpy.all(out == ((av ** 2) < bv))
...@@ -648,11 +658,11 @@ def speed_elemwise_collapse(): ...@@ -648,11 +658,11 @@ def speed_elemwise_collapse():
v = theano._asarray(numpy.random.rand(*shape), dtype='float32') v = theano._asarray(numpy.random.rand(*shape), dtype='float32')
v = v[:, ::2, :, :] v = v[:, ::2, :, :]
v = cuda_ndarray.CudaNdarray(v) v = cuda_ndarray.CudaNdarray(v)
t1 = time.time() time.time()
for i in range(100): for i in range(100):
# let debugmode catch errors # let debugmode catch errors
f(v) f(v)
t2 = time.time() time.time()
def speed_elemwise_collapse2(): def speed_elemwise_collapse2():
...@@ -672,11 +682,11 @@ def speed_elemwise_collapse2(): ...@@ -672,11 +682,11 @@ def speed_elemwise_collapse2():
v = theano._asarray(numpy.random.rand(*shape), dtype='float32') v = theano._asarray(numpy.random.rand(*shape), dtype='float32')
v = v[:, :, :, ::2] v = v[:, :, :, ::2]
v = cuda_ndarray.CudaNdarray(v) v = cuda_ndarray.CudaNdarray(v)
t1 = time.time() time.time()
for i in range(100): for i in range(100):
# let debugmode catch errors # let debugmode catch errors
f(v) f(v)
t2 = time.time() time.time()
def test_elemwise_collapse(): def test_elemwise_collapse():
...@@ -848,8 +858,8 @@ def test_hostfromgpu_shape_i(): ...@@ -848,8 +858,8 @@ def test_hostfromgpu_shape_i():
ca = theano.sandbox.cuda.var.CudaNdarrayType((False, False))() ca = theano.sandbox.cuda.var.CudaNdarrayType((False, False))()
av = numpy.asarray(numpy.random.rand(5, 4), dtype='float32') av = numpy.asarray(numpy.random.rand(5, 4), dtype='float32')
cv = cuda.CudaNdarray(numpy.asarray(numpy.random.rand(5, 4), cv = cuda.CudaNdarray(numpy.asarray(
dtype='float32')) numpy.random.rand(5, 4), dtype='float32'))
f = theano.function([a], cuda.basic_ops.gpu_from_host(a), mode=m) f = theano.function([a], cuda.basic_ops.gpu_from_host(a), mode=m)
assert cuda.basic_ops.gpu_from_host in [x.op assert cuda.basic_ops.gpu_from_host in [x.op
...@@ -880,7 +890,7 @@ def test_gpujoin_assert_cndas(): ...@@ -880,7 +890,7 @@ def test_gpujoin_assert_cndas():
a = theano.shared(_a) a = theano.shared(_a)
try: try:
c = cuda.basic_ops.gpu_join(1, a) cuda.basic_ops.gpu_join(1, a)
# can't "assert False" here, as we want the assertion # can't "assert False" here, as we want the assertion
# error from gpu_join # error from gpu_join
except TypeError: except TypeError:
...@@ -921,13 +931,18 @@ def test_gpujoin_gpualloc(): ...@@ -921,13 +931,18 @@ def test_gpujoin_gpualloc():
b = T.fmatrix('b') b = T.fmatrix('b')
b_val = numpy.asarray(numpy.random.rand(3, 5), dtype='float32') b_val = numpy.asarray(numpy.random.rand(3, 5), dtype='float32')
f = theano.function([a, b], T.join(0, T.zeros_like(a), T.ones_like(b)) + 4, f = theano.function(
mode=mode_without_gpu) [a, b],
f_gpu = theano.function([a, b], T.join(0, T.zeros_like(a), T.ones_like(b)), T.join(0, T.zeros_like(a), T.ones_like(b)) + 4,
mode=mode_with_gpu) mode=mode_without_gpu)
f_gpu2 = theano.function([a, b], T.join(0, T.zeros_like(a), f_gpu = theano.function(
T.ones_like(b)) + 4, [a, b],
mode=mode_with_gpu) T.join(0, T.zeros_like(a), T.ones_like(b)),
mode=mode_with_gpu)
f_gpu2 = theano.function(
[a, b],
T.join(0, T.zeros_like(a), T.ones_like(b)) + 4,
mode=mode_with_gpu)
assert sum([node.op == T.alloc for node in f.maker.fgraph.toposort()]) == 2 assert sum([node.op == T.alloc for node in f.maker.fgraph.toposort()]) == 2
assert sum([node.op == T.join for node in f.maker.fgraph.toposort()]) == 1 assert sum([node.op == T.join for node in f.maker.fgraph.toposort()]) == 1
...@@ -963,9 +978,6 @@ def test_gpualloc_output_to_gpu(): ...@@ -963,9 +978,6 @@ def test_gpualloc_output_to_gpu():
assert numpy.allclose(f(5), f_gpu(5)) assert numpy.allclose(f(5), f_gpu(5))
import theano.tensor.tests.test_basic
class TestAlloc(theano.tensor.tests.test_basic.TestAlloc): class TestAlloc(theano.tensor.tests.test_basic.TestAlloc):
dtype = "float32" dtype = "float32"
mode = mode_with_gpu mode = mode_with_gpu
...@@ -987,7 +999,6 @@ class T_Join_and_Split(theano.tensor.tests.test_basic.T_Join_and_Split): ...@@ -987,7 +999,6 @@ class T_Join_and_Split(theano.tensor.tests.test_basic.T_Join_and_Split):
self.shared = cuda.shared_constructor self.shared = cuda.shared_constructor
import theano.tensor.tests.test_subtensor
# This is to don't duplicate test. # This is to don't duplicate test.
...@@ -1026,7 +1037,7 @@ class T_subtensor(theano.tensor.tests.test_subtensor.T_subtensor): ...@@ -1026,7 +1037,7 @@ class T_subtensor(theano.tensor.tests.test_subtensor.T_subtensor):
# version when we should. Users should not use it. # version when we should. Users should not use it.
for shape, idx, fast in [((70000,), range(70000), True), for shape, idx, fast in [((70000,), range(70000), True),
((70000, 5), range(70000), True), ((70000, 5), range(70000), True),
((70000, 5), numpy.zeros((0,), 'int64'), ((70000, 5), numpy.zeros((0,), 'int64'),
True), True),
((70000, 2, 3), range(70000), True), ((70000, 2, 3), range(70000), True),
((1025, 1025), [5, 10], True), ((1025, 1025), [5, 10], True),
...@@ -1035,7 +1046,7 @@ class T_subtensor(theano.tensor.tests.test_subtensor.T_subtensor): ...@@ -1035,7 +1046,7 @@ class T_subtensor(theano.tensor.tests.test_subtensor.T_subtensor):
((3, 10, 68000), [1, 2], True), ((3, 10, 68000), [1, 2], True),
((3, 69000, 11), [1, 2], True), ((3, 69000, 11), [1, 2], True),
# much memory, will be disabled if needed # much memory, will be disabled if needed
((2*10e7,), [-1, 199999999], True), ((2 * 10e7,), [-1, 199999999], True),
((4, 5), [2, 3], True), ((4, 5), [2, 3], True),
((4, 2, 3), [0, 3], True), ((4, 2, 3), [0, 3], True),
((4, 2, 3), [3, 3, 1, 1, 2, ((4, 2, 3), [3, 3, 1, 1, 2,
...@@ -1047,8 +1058,7 @@ class T_subtensor(theano.tensor.tests.test_subtensor.T_subtensor): ...@@ -1047,8 +1058,7 @@ class T_subtensor(theano.tensor.tests.test_subtensor.T_subtensor):
# optimized for that case. # optimized for that case.
((4, 4, 2, 3), [3, 3, 1, 1, 2, 2, 0, 0, ((4, 4, 2, 3), [3, 3, 1, 1, 2, 2, 0, 0,
-1, -2, -3, -4], False), -1, -2, -3, -4], False),
((1, 10), [0, 0], True), ((1, 10), [0, 0], True), ]:
]:
# If there is not enough memory on the GPU, skip the test # If there is not enough memory on the GPU, skip the test
size_needed = numpy.prod(shape) * (4 + 1) size_needed = numpy.prod(shape) * (4 + 1)
if isinstance(theano.compile.get_default_mode(), if isinstance(theano.compile.get_default_mode(),
...@@ -1106,13 +1116,14 @@ def test_advinc_subtensor1(): ...@@ -1106,13 +1116,14 @@ def test_advinc_subtensor1():
rep[[0, 2]] += yval rep[[0, 2]] += yval
utt.assert_allclose(rval, rep) utt.assert_allclose(rval, rep)
def test_advset_subtensor1(): def test_advset_subtensor1():
""" Test GPU version of set_subtensor on vectors (uses GpuAdvancedIncSubtensor1) """ """ Test GPU version of set_subtensor on vectors (uses GpuAdvancedIncSubtensor1) """
shp = (10,) shp = (10,)
shared = cuda.shared_constructor shared = cuda.shared_constructor
xval = numpy.arange(shp[0], dtype='float32').reshape(shp) + 1 xval = numpy.arange(shp[0], dtype='float32').reshape(shp) + 1
idxs = numpy.array([0,2,5,7,3], dtype='int32') idxs = numpy.array([0, 2, 5, 7, 3], dtype='int32')
yval = numpy.ones(len(idxs), dtype='float32')*10 yval = numpy.ones(len(idxs), dtype='float32') * 10
x = shared(xval, name='x') x = shared(xval, name='x')
y = T.tensor(dtype='float32', broadcastable=(False,) * len(shp), name='y') y = T.tensor(dtype='float32', broadcastable=(False,) * len(shp), name='y')
expr = T.advanced_set_subtensor1(x, y, idxs) expr = T.advanced_set_subtensor1(x, y, idxs)
...@@ -1124,13 +1135,14 @@ def test_advset_subtensor1(): ...@@ -1124,13 +1135,14 @@ def test_advset_subtensor1():
rep[idxs] = yval rep[idxs] = yval
utt.assert_allclose(rval, rep) utt.assert_allclose(rval, rep)
def test_advset_subtensor1_2d(): def test_advset_subtensor1_2d():
""" Test GPU version of set_subtensor on matrices (uses GpuAdvancedIncSubtensor1_dev20 if compute capability >= 2.0) """ """ Test GPU version of set_subtensor on matrices (uses GpuAdvancedIncSubtensor1_dev20 if compute capability >= 2.0) """
shp = (10,5) shp = (10, 5)
shared = cuda.shared_constructor shared = cuda.shared_constructor
xval = numpy.arange(numpy.prod(shp), dtype='float32').reshape(shp) + 1 xval = numpy.arange(numpy.prod(shp), dtype='float32').reshape(shp) + 1
idxs = numpy.array([0,2,5,7,3], dtype='int32') idxs = numpy.array([0, 2, 5, 7, 3], dtype='int32')
yval = numpy.ones((len(idxs), shp[1]), dtype='float32')*10 yval = numpy.ones((len(idxs), shp[1]), dtype='float32') * 10
x = shared(xval, name='x') x = shared(xval, name='x')
y = T.tensor(dtype='float32', broadcastable=(False,) * len(shp), name='y') y = T.tensor(dtype='float32', broadcastable=(False,) * len(shp), name='y')
expr = T.advanced_set_subtensor1(x, y, idxs) expr = T.advanced_set_subtensor1(x, y, idxs)
...@@ -1142,37 +1154,38 @@ def test_advset_subtensor1_2d(): ...@@ -1142,37 +1154,38 @@ def test_advset_subtensor1_2d():
rep[idxs] = yval rep[idxs] = yval
utt.assert_allclose(rval, rep) utt.assert_allclose(rval, rep)
def test_inc_subtensor(): def test_inc_subtensor():
shared = cuda.shared_constructor cuda.shared_constructor
#shared = tensor.shared # shared = tensor.shared
x, y = T.fmatrices('x', 'y') x, y = T.fmatrices('x', 'y')
xval = numpy.asarray([[1, 2, 3], [4, 5, 6], [7, 8, 9]], xval = numpy.asarray(
dtype='float32') [[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype='float32')
yval = numpy.asarray([[10, 10, 10], [10, 10, 10], [10, 10, 10]], yval = numpy.asarray(
dtype='float32') [[10, 10, 10], [10, 10, 10], [10, 10, 10]], dtype='float32')
expr = T.inc_subtensor(x[:, 1:3], y[:, 1:3]) expr = T.inc_subtensor(x[:, 1:3], y[:, 1:3])
f = theano.function([x, y], expr, mode=mode_with_gpu) f = theano.function([x, y], expr, mode=mode_with_gpu)
assert sum([isinstance(node.op, cuda.GpuIncSubtensor) and assert sum([isinstance(node.op, cuda.GpuIncSubtensor) and
node.op.set_instead_of_inc == False node.op.set_instead_of_inc is False
for node in f.maker.fgraph.toposort()]) == 1 for node in f.maker.fgraph.toposort()]) == 1
utt.assert_allclose(f(xval, yval), [[1., 12., 13.], utt.assert_allclose(f(xval, yval), [[1., 12., 13.],
[4., 15., 16.], [7., 18., 19.]]) [4., 15., 16.], [7., 18., 19.]])
def test_set_subtensor(): def test_set_subtensor():
shared = cuda.shared_constructor cuda.shared_constructor
#shared = tensor.shared # shared = tensor.shared
x, y = T.fmatrices('x', 'y') x, y = T.fmatrices('x', 'y')
xval = numpy.asarray([[1, 2, 3], [4, 5, 6], [7, 8, 9]], xval = numpy.asarray(
dtype='float32') [[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype='float32')
yval = numpy.asarray([[10, 10, 10], [10, 10, 10], [10, 10, 10]], yval = numpy.asarray(
dtype='float32') [[10, 10, 10], [10, 10, 10], [10, 10, 10]], dtype='float32')
expr = T.set_subtensor(x[:, 1:3], y[:, 1:3]) expr = T.set_subtensor(x[:, 1:3], y[:, 1:3])
f = theano.function([x, y], expr, mode=mode_with_gpu) f = theano.function([x, y], expr, mode=mode_with_gpu)
assert sum([isinstance(node.op, cuda.GpuIncSubtensor) and assert sum([isinstance(node.op, cuda.GpuIncSubtensor) and
node.op.set_instead_of_inc == True node.op.set_instead_of_inc is True
for node in f.maker.fgraph.toposort()]) == 1 for node in f.maker.fgraph.toposort()]) == 1
f(xval, yval) f(xval, yval)
...@@ -1191,7 +1204,7 @@ def test_many_arg_elemwise(): ...@@ -1191,7 +1204,7 @@ def test_many_arg_elemwise():
for arg in xrange(0, num_args)] for arg in xrange(0, num_args)]
symb_args = [theano.tensor.TensorType('float32', symb_args = [theano.tensor.TensorType('float32',
(False,)*nb_dim)() (False,) * nb_dim)()
for arg in xrange(0, num_args)] for arg in xrange(0, num_args)]
outputs = [] outputs = []
...@@ -1313,7 +1326,6 @@ class test_size(unittest.TestCase): ...@@ -1313,7 +1326,6 @@ class test_size(unittest.TestCase):
assert y.size == theano.function([], x.size)() assert y.size == theano.function([], x.size)()
import theano.tensor.tests.test_sharedvar
# This test the case when the shared constructor view an CudaNdarray as input # This test the case when the shared constructor view an CudaNdarray as input
test_shared_options = theano.tensor.tests.test_sharedvar.makeSharedTester( test_shared_options = theano.tensor.tests.test_sharedvar.makeSharedTester(
shared_constructor_=tcn.shared_constructor, shared_constructor_=tcn.shared_constructor,
...@@ -1374,7 +1386,7 @@ def speed_reduce10(): ...@@ -1374,7 +1386,7 @@ def speed_reduce10():
if __name__ == '__main__': if __name__ == '__main__':
#test_many_arg_elemwise() # test_many_arg_elemwise()
#test_gpujoin_assert_cndas() # test_gpujoin_assert_cndas()
test_advset_subtensor1() test_advset_subtensor1()
test_advset_subtensor1_2d() test_advset_subtensor1_2d()
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