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提交 0ebdb42f authored 作者: abergeron's avatar abergeron
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Merge pull request #2509 from nouiz/tests

Tests
上级 b5cca42e 2cb04fea
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正在显示 包含 61 行增加 和 65 行删除
theano/sandbox/cuda/tests/test_opt.py
浏览文件 @ 0ebdb42f
......@@ -16,14 +16,14 @@ from theano.tests import unittest_tools as utt
import theano.sandbox.cuda as cuda
if cuda.cuda_available == False:
if not cuda.cuda_available:
raise SkipTest('Optional package cuda disabled')
from theano.sandbox.cuda import basic_ops
from theano.sandbox.cuda.type import CudaNdarrayType
from theano.scalar.basic_scipy import erfinv
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_without_gpu = theano.compile.mode.get_mode('FAST_RUN').excluding('gpu')
else:
......@@ -34,14 +34,14 @@ else:
def test_no_shared_var_graph():
"""Test that the InputToGpuOptimizer optimizer make graph that don't have shared variable compiled too.
"""
a=tensor.fmatrix()
b=tensor.fmatrix()
f = theano.function([a,b],[a+b], mode=mode_with_gpu)
a = tensor.fmatrix()
b = tensor.fmatrix()
f = theano.function([a, b], [a + b], mode=mode_with_gpu)
l = f.maker.fgraph.toposort()
assert len(l)==4
assert numpy.any(isinstance(x.op,cuda.GpuElemwise) for x in l)
assert numpy.any(isinstance(x.op,cuda.GpuFromHost) for x in l)
assert numpy.any(isinstance(x.op,cuda.HostFromGpu) for x in l)
assert len(l) == 4
assert numpy.any(isinstance(x.op, cuda.GpuElemwise) for x in l)
assert numpy.any(isinstance(x.op, cuda.GpuFromHost) for x in l)
assert numpy.any(isinstance(x.op, cuda.HostFromGpu) for x in l)
def test_local_assert():
......@@ -66,8 +66,6 @@ def test_int_pow():
op_names = [n.op.__class__.__name__ for n in f.maker.fgraph.toposort()]
assert op_names == ['GpuElemwise', 'GpuCAReduce', 'HostFromGpu']
#theano.printing.debugprint(f)
def test_gpualloc():
'''
......@@ -95,12 +93,12 @@ class Test_local_elemwise_alloc(test_opt.Test_local_elemwise_alloc):
super(Test_local_elemwise_alloc, self).setUp()
self.fast_run_mode = mode_with_gpu
#self.vec = tensor.vector('vec', dtype=dtype)
#self.mat = tensor.matrix('mat', dtype=dtype)
#self.tens = tensor.tensor3('tens', dtype=dtype)
# self.vec = tensor.vector('vec', dtype=dtype)
# self.mat = tensor.matrix('mat', dtype=dtype)
# self.tens = tensor.tensor3('tens', dtype=dtype)
#self.alloc_wo_dep = basic_ops.gpu_alloc(self.vec, 2, 2)
#self.alloc_w_dep = basic_ops.gpu_alloc(self.vec, *self.mat.shape)
# self.alloc_wo_dep = basic_ops.gpu_alloc(self.vec, 2, 2)
# self.alloc_w_dep = basic_ops.gpu_alloc(self.vec, *self.mat.shape)
self.alloc_wo_dep = basic_ops.gpu_alloc(self.vec, 2, 2)
self.alloc_w_dep = basic_ops.gpu_alloc(self.vec, *self.mat.shape)
......@@ -166,7 +164,7 @@ def test_alloc_memset_0():
def test_gpuspecifyshape():
x = cuda.shared_constructor(numpy.ones(3,dtype='float32'), 'x')
x = cuda.shared_constructor(numpy.ones(3, dtype='float32'), 'x')
m = theano.tensor.specify_shape(x + numpy.float32(1), (3,))
f = theano.function([], updates=[(x, m * numpy.float32(2))],
mode=mode_with_gpu)
......@@ -174,41 +172,44 @@ def test_gpuspecifyshape():
assert not numpy.any([isinstance(x.op, cuda.HostFromGpu) for x in l])
def test_softmax():
x = tensor.fmatrix()
f = theano.function([x],tensor.nnet.nnet.Softmax()(x), mode=mode_with_gpu)
f2 = theano.function([x],tensor.nnet.nnet.Softmax()(x), mode=mode_without_gpu)
assert isinstance(f.maker.fgraph.toposort()[1].op,cuda.nnet.GpuSoftmax)
xv=numpy.random.rand(7,8).astype('float32')
assert numpy.allclose(f(xv),f2(xv))
f = theano.function([x], tensor.nnet.nnet.Softmax()(x),
mode=mode_with_gpu.excluding('cudnn'))
f2 = theano.function([x], tensor.nnet.nnet.Softmax()(x),
mode=mode_without_gpu)
assert isinstance(f.maker.fgraph.toposort()[1].op, cuda.nnet.GpuSoftmax)
xv = numpy.random.rand(7, 8).astype('float32')
assert numpy.allclose(f(xv), f2(xv))
def test_softmax_with_bias():
x = tensor.fmatrix()
b = tensor.fvector()
f = theano.function([x,b],tensor.nnet.nnet.SoftmaxWithBias()(x,b), mode=mode_with_gpu)
f2 = theano.function([x,b],tensor.nnet.nnet.SoftmaxWithBias()(x,b), mode=mode_without_gpu)
assert isinstance(f.maker.fgraph.toposort()[2].op,cuda.nnet.GpuSoftmaxWithBias)
xv=numpy.random.rand(7,8).astype('float32')
bv=numpy.random.rand(8).astype('float32')
assert numpy.allclose(f(xv,bv),f2(xv,bv))
f = theano.function([x, b], tensor.nnet.nnet.SoftmaxWithBias()(x, b),
mode=mode_with_gpu)
f2 = theano.function([x, b], tensor.nnet.nnet.SoftmaxWithBias()(x, b),
mode=mode_without_gpu)
assert isinstance(f.maker.fgraph.toposort()[2].op,
cuda.nnet.GpuSoftmaxWithBias)
xv = numpy.random.rand(7, 8).astype('float32')
bv = numpy.random.rand(8).astype('float32')
assert numpy.allclose(f(xv, bv), f2(xv, bv))
def test_opt_gpujoin_onlyajoin():
# from a bug in normal sampling
_a = numpy.asarray([[1,2],[3,4]],dtype='float32')
_b = numpy.asarray([[5,6,7],[8,9,10]],dtype='float32')
_a = numpy.asarray([[1, 2], [3, 4]], dtype='float32')
_b = numpy.asarray([[5, 6, 7], [8, 9, 10]], dtype='float32')
a = cuda.shared_constructor(_a)
b = cuda.shared_constructor(_b)
c = tensor.join(1,a,b)
c = tensor.join(1, a, b)
f = theano.function([], c, mode=mode_with_gpu)
#theano.printing.debugprint(f)
f()
graph_nodes = f.maker.fgraph.toposort()
......@@ -216,35 +217,32 @@ def test_opt_gpujoin_onlyajoin():
assert isinstance(graph_nodes[-1].op, cuda.HostFromGpu)
assert isinstance(graph_nodes[-2].op, cuda.GpuJoin)
assert numpy.all(f() == numpy.concatenate([_a,_b], axis=1))
assert numpy.all(f() == numpy.concatenate([_a, _b], axis=1))
def test_opt_gpujoin_joinvectors_elemwise_then_minusone():
# from a bug in gpu normal sampling
_a = numpy.asarray([1,2,3,4],dtype='float32')
_b = numpy.asarray([5,6,7,8],dtype='float32')
_a = numpy.asarray([1, 2, 3, 4], dtype='float32')
_b = numpy.asarray([5, 6, 7, 8], dtype='float32')
a = cuda.shared_constructor(_a)
b = cuda.shared_constructor(_b)
a_prime = tensor.cos(a)
b_prime = tensor.sin(b)
c = tensor.join(0,a_prime,b_prime)
c = tensor.join(0, a_prime, b_prime)
d = c[:-1]
f = theano.function([], d, mode=mode_with_gpu)
#theano.printing.debugprint(f)
graph_nodes = f.maker.fgraph.toposort()
assert isinstance(graph_nodes[-1].op, cuda.HostFromGpu)
assert isinstance(graph_nodes[-2].op, cuda.GpuSubtensor)
assert isinstance(graph_nodes[-3].op, cuda.GpuJoin)
concat = numpy.concatenate([numpy.cos(_a),numpy.sin(_b)],axis=1)
concat = numpy.concatenate([numpy.cos(_a), numpy.sin(_b)], axis=1)
concat = concat[:-1]
assert numpy.allclose(numpy.asarray(f()), concat)
......@@ -312,19 +310,20 @@ def test_local_split():
# Check equality
assert all([(cpu == gpu).all() for cpu, gpu in zip(cpu_res, gpu_res)])
def test_print_op():
""" Test that print ops don't block gpu optimization"""
b = tensor.fmatrix()
f = theano.function([b],theano.printing.Print()(b)*2, mode=mode_with_gpu)
#theano.printing.debugprint(f)
#print f.maker.fgraph.toposort()
f = theano.function([b], theano.printing.Print()(b)*2, mode=mode_with_gpu)
# theano.printing.debugprint(f)
# print f.maker.fgraph.toposort()
#[GpuFromHost(<TensorType(float32, matrix)>), <theano.printing.Print object at 0x3581210>(GpuFromHost.0), GpuElemwise{mul}(CudaNdarray{[[ 2.]]}, <theano.printing.Print object at 0x3581210>.0), HostFromGpu(GpuElemwise{mul}.0)]
topo = f.maker.fgraph.toposort()
assert topo[0].op == cuda.gpu_from_host
assert isinstance(topo[1].op, theano.printing.Print)
assert isinstance(topo[2].op, cuda.GpuElemwise)
assert topo[3].op == cuda.host_from_gpu
f(numpy.random.random((5,5)).astype('float32'))
f(numpy.random.random((5, 5)).astype('float32'))
def test_huge_elemwise_fusion():
......@@ -348,14 +347,11 @@ def test_huge_elemwise_fusion():
f = pfunc(vars, [reduce(operator.sub, vars)], mode=mode_with_gpu)
topo = f.maker.fgraph.toposort()
#theano.printing.debugprint(f)
#for i, node in enumerate(topo):
# print >> sys.stdout, i, node
assert len(topo) == len_topo
assert sum([isinstance(node.op, cuda.GpuElemwise) for node in topo]) == 2
assert isinstance(topo[-3].op.scalar_op, theano.scalar.basic.Sub)
assert isinstance(topo[-2].op.scalar_op, theano.scalar.basic.Composite)
#let debugmode catch errors
# let debugmode catch errors
gen = lambda: theano._asarray(numpy.random.rand(*shape), dtype='float32')
f(*[gen() for i in range(nb_in)])
......@@ -368,11 +364,10 @@ def test_huge_elemwise_fusion():
f = pfunc(vars, [vars[0] - vars[1] - vars[2] - vars[3] - vars[4] -
vars[5] - vars[6]], mode=mode_with_gpu)
topo = f.maker.fgraph.toposort()
#theano.printing.debugprint(f)
assert len(topo) == 1
assert sum([isinstance(node.op, cuda.GpuElemwise) for node in topo]) == 0
assert sum([isinstance(node.op, tensor.Elemwise) for node in topo]) == 1
#let debugmode catch errors
# let debugmode catch errors
gen = lambda: theano._asarray(numpy.random.rand(*shape), dtype='float32')
f(gen(), gen(), gen(), gen(), gen(), gen(), gen())
......@@ -402,14 +397,14 @@ def test_huge_elemwise_fusion():
out = cuda.gpu_from_host(out)
f = pfunc([], [out], mode=mode_with_gpu)
topo = f.maker.fgraph.toposort()
#print shape, nb_var, use_tan, len(topo)
# print shape, nb_var, use_tan, len(topo)
assert (sum([isinstance(node.op, cuda.GpuElemwise)
for node in topo]) == len(topo) or
(nb_var == 1 and use_tan == False))
(nb_var == 1 and use_tan is False))
assert sum([isinstance(node.op, tensor.Elemwise)
for node in topo]) == 0
#let debugmode catch errors
# let debugmode catch errors
f()
......@@ -428,7 +423,6 @@ def test_local_gpu_elemwise_0():
# Due to optimization order, this composite is created when all
# the op are on the gpu.
f = theano.function([a, b, c], [a + b + c], mode=mode_with_gpu)
#theano.printing.debugprint(f)
topo = f.maker.fgraph.toposort()
assert sum(isinstance(node.op, cuda.GpuElemwise) for node in topo) == 1
assert sum(isinstance(node.op, tensor.Elemwise) for node in topo) == 1
......@@ -442,7 +436,6 @@ def test_local_gpu_elemwise_0():
out_s = theano.scalar.Composite([a_s, b_s, c_s], [a_s + b_s + c_s])
out_op = tensor.Elemwise(out_s)
f = theano.function([a, b, c], [out_op(a, b, c)], mode=mode_with_gpu)
#theano.printing.debugprint(f)
topo = f.maker.fgraph.toposort()
assert sum(isinstance(node.op, cuda.GpuElemwise) for node in topo) == 1
assert sum(isinstance(node.op, tensor.Elemwise) for node in topo) == 1
......@@ -462,7 +455,7 @@ def test_elemwise_fusion():
print >> sys.stdout, i, node
assert len(topo) == 4
assert isinstance(topo[2].op.scalar_op, theano.scalar.basic.Composite)
#let debugmode catch errors
# let debugmode catch errors
f(theano._asarray(numpy.random.rand(*shape), dtype='float32'),
theano._asarray(numpy.random.rand(*shape), dtype='float32'))
......@@ -479,6 +472,7 @@ class TestIfElse(theano.tests.test_ifelse.test_ifelse):
def get_ifelse(self, n):
return theano.ifelse.IfElse(n, gpu=True, as_view=True)
def test_incsubtensor_mixed():
# This catches a bug that occurred when incrementing
......@@ -491,14 +485,14 @@ def test_incsubtensor_mixed():
# fail.
X = tensor.fmatrix()
Y = tensor.dmatrix()
Z = tensor.inc_subtensor(X[0:1,0:1],Y)
f = theano.function([X,Y], Z, mode=mode_with_gpu)
Z = tensor.inc_subtensor(X[0:1, 0:1], Y)
f = theano.function([X, Y], Z, mode=mode_with_gpu)
packed, = f.maker.fgraph.inputs[1].clients
client, idx = packed
print client
assert isinstance(client.op, tensor.Elemwise)
assert isinstance(client.op.scalar_op, theano.scalar.Cast)
packed ,= client.outputs[0].clients
packed, = client.outputs[0].clients
client, idx = packed
assert isinstance(client.op, cuda.GpuFromHost)
......@@ -507,11 +501,13 @@ def test_erfinvgpu():
""" Test that local_gpu_elemwise_0 replaces Erfinv with ErfinvGPU """
x = tensor.fmatrix()
f = theano.function([x], tensor.Elemwise(erfinv)(x), mode=mode_with_gpu)
f2 = theano.function([x], tensor.Elemwise(erfinv)(x), mode=mode_without_gpu)
f2 = theano.function([x], tensor.Elemwise(erfinv)(x),
mode=mode_without_gpu)
assert isinstance(f.maker.fgraph.toposort()[1].op, cuda.GpuElemwise)
assert isinstance(f.maker.fgraph.toposort()[1].op.scalar_op, cuda.elemwise.ErfinvGPU)
xv=numpy.random.rand(7,8).astype('float32')
assert numpy.allclose(f(xv),f2(xv))
assert isinstance(f.maker.fgraph.toposort()[1].op.scalar_op,
cuda.elemwise.ErfinvGPU)
xv = numpy.random.rand(7, 8).astype('float32')
assert numpy.allclose(f(xv), f2(xv))
def test_local_gpu_dot_to_dot22dot():
......
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