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提交 1ac6cb77 authored 作者: Marc-Alexandre Cote's avatar Marc-Alexandre Cote
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Refactore unit test on array strides for GPUCumsum

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theano/sandbox/cuda/tests/test_extra_ops.py
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...@@ -17,7 +17,7 @@ from theano import tensor as T ...@@ -17,7 +17,7 @@ from theano import tensor as T
import numpy as np import numpy as np
import theano import theano
from theano.tensor.extra_ops import cumsum, CumsumOp from theano.tensor.extra_ops import cumsum, CumsumOp
import itertools
class TestGpuCumsum(theano.tensor.tests.test_extra_ops.TestCumsumOp): class TestGpuCumsum(theano.tensor.tests.test_extra_ops.TestCumsumOp):
mode = mode_with_gpu mode = mode_with_gpu
...@@ -44,128 +44,63 @@ class TestGpuCumsum(theano.tensor.tests.test_extra_ops.TestCumsumOp): ...@@ -44,128 +44,63 @@ class TestGpuCumsum(theano.tensor.tests.test_extra_ops.TestCumsumOp):
def test_Strides1D(self): def test_Strides1D(self):
x = T.fvector('x') x = T.fvector('x')
# Stepped strides for axis in [0, None]:
f = theano.function([x], cumsum(x[::2]), mode=self.mode) a = np.random.random((42,)).astype("float32")
assert [n for n in f.maker.fgraph.toposort() cumsum_function = theano.function([x], cumsum(x, axis=axis), mode=self.mode)
if isinstance(n.op, GpuCumsum)]
a = np.random.randint(10, size=(42,)).astype("float32")
assert np.allclose(np.cumsum(a[::2]), f(a))
# Alternative stepped strides slicings = [slice(None, None, None), # Normal strides
f = theano.function([x], cumsum(x), mode=self.mode) slice(None, None, 2), # Stepped strides
assert [n for n in f.maker.fgraph.toposort() slice(None, None, -1), # Negative strides
if isinstance(n.op, GpuCumsum)] ]
a = np.random.randint(10, size=(42,)).astype("float32")
assert np.allclose(np.cumsum(a[::2]), f(a[::2]))
# Negative strides # Cartesian product of all slicings to test.
f = theano.function([x], cumsum(x[::-1]), mode=self.mode) for slicing in itertools.product(slicings, repeat=x.ndim):
f = theano.function([x], cumsum(x[slicing], axis=axis), mode=self.mode)
assert [n for n in f.maker.fgraph.toposort() assert [n for n in f.maker.fgraph.toposort()
if isinstance(n.op, GpuCumsum)] if isinstance(n.op, GpuCumsum)]
a = np.random.randint(10, size=(42,)).astype("float32") assert np.allclose(np.cumsum(a[slicing], axis=axis), f(a))
assert np.allclose(np.cumsum(a[::-1]), f(a)) assert np.allclose(np.cumsum(a[slicing], axis=axis), cumsum_function(a[slicing]))
def test_Strides2D(self): def test_Strides2D(self):
x = T.fmatrix('x') x = T.fmatrix('x')
for axis in [0, 1, None]: for axis in [0, 1, None]:
a = np.random.random((42, 30)).astype("float32") a = np.random.random((42, 30)).astype("float32")
cumsum_function = theano.function([x], cumsum(x, axis=axis), mode=self.mode)
# Stepped strides along axis=0 slicings = [slice(None, None, None), # Normal strides
f = theano.function([x], cumsum(x[::2], axis=axis), mode=self.mode) slice(None, None, 2), # Stepped strides
assert [n for n in f.maker.fgraph.toposort() slice(None, None, -1), # Negative strides
if isinstance(n.op, GpuCumsum)] ]
assert np.allclose(np.cumsum(a[::2], axis=axis), f(a))
# Stepped strides along axis=1
f = theano.function([x], cumsum(x[:, ::2], axis=axis), mode=self.mode)
assert [n for n in f.maker.fgraph.toposort()
if isinstance(n.op, GpuCumsum)]
assert np.allclose(np.cumsum(a[:, ::2], axis=axis), f(a))
# Alternative stepped strides along axis=0
f = theano.function([x], cumsum(x), mode=self.mode)
assert [n for n in f.maker.fgraph.toposort()
if isinstance(n.op, GpuCumsum)]
assert np.allclose(np.cumsum(a[::2]), f(a[::2]))
# Alternative stepped strides along axis=1
f = theano.function([x], cumsum(x), mode=self.mode)
assert [n for n in f.maker.fgraph.toposort()
if isinstance(n.op, GpuCumsum)]
assert np.allclose(np.cumsum(a[:, ::2]), f(a[:, ::2]))
# Negative strides along axis=0
f = theano.function([x], cumsum(x[::-1], axis=axis), mode=self.mode)
assert [n for n in f.maker.fgraph.toposort()
if isinstance(n.op, GpuCumsum)]
assert np.allclose(np.cumsum(a[::-1], axis=axis), f(a))
# Negative strides along axis=1 # Cartesian product of all slicings to test.
f = theano.function([x], cumsum(x[:, ::-1], axis=axis), mode=self.mode) for slicing in itertools.product(slicings, repeat=x.ndim):
f = theano.function([x], cumsum(x[slicing], axis=axis), mode=self.mode)
assert [n for n in f.maker.fgraph.toposort() assert [n for n in f.maker.fgraph.toposort()
if isinstance(n.op, GpuCumsum)] if isinstance(n.op, GpuCumsum)]
assert np.allclose(np.cumsum(a[:, ::-1], axis=axis), f(a)) assert np.allclose(np.cumsum(a[slicing], axis=axis), f(a))
assert np.allclose(np.cumsum(a[slicing], axis=axis), cumsum_function(a[slicing]))
def test_Strides3D(self): def test_Strides3D(self):
x = T.ftensor3('x') x = T.ftensor3('x')
for axis in [0, 1, 2, None]: for axis in [0, 1, 2, None]:
a = np.random.random((42, 30, 25)).astype("float32") a = np.random.random((42, 30, 25)).astype("float32")
cumsum_function = theano.function([x], cumsum(x, axis=axis), mode=self.mode)
# Stepped strides along axis=0 slicings = [slice(None, None, None), # Normal strides
f = theano.function([x], cumsum(x[::2], axis=axis), mode=self.mode) slice(None, None, 2), # Stepped strides
assert [n for n in f.maker.fgraph.toposort() slice(None, None, -1), # Negative strides
if isinstance(n.op, GpuCumsum)] ]
assert np.allclose(np.cumsum(a[::2], axis=axis), f(a))
# Stepped strides along axis=1 # Cartesian product of all slicings to test.
f = theano.function([x], cumsum(x[:, ::2], axis=axis), mode=self.mode) for slicing in itertools.product(slicings, repeat=x.ndim):
f = theano.function([x], cumsum(x[slicing], axis=axis), mode=self.mode)
assert [n for n in f.maker.fgraph.toposort() assert [n for n in f.maker.fgraph.toposort()
if isinstance(n.op, GpuCumsum)] if isinstance(n.op, GpuCumsum)]
assert np.allclose(np.cumsum(a[:, ::2], axis=axis), f(a)) assert np.allclose(np.cumsum(a[slicing], axis=axis), f(a))
assert np.allclose(np.cumsum(a[slicing], axis=axis), cumsum_function(a[slicing]))
# Stepped strides along axis=2
f = theano.function([x], cumsum(x[:, :, ::2], axis=axis), mode=self.mode)
assert [n for n in f.maker.fgraph.toposort()
if isinstance(n.op, GpuCumsum)]
assert np.allclose(np.cumsum(a[:, :, ::2], axis=axis), f(a))
# Alternative stepped strides along axis=0
f = theano.function([x], cumsum(x), mode=self.mode)
assert [n for n in f.maker.fgraph.toposort()
if isinstance(n.op, GpuCumsum)]
assert np.allclose(np.cumsum(a[::2]), f(a[::2]))
# Alternative stepped strides along axis=1
f = theano.function([x], cumsum(x), mode=self.mode)
assert [n for n in f.maker.fgraph.toposort()
if isinstance(n.op, GpuCumsum)]
assert np.allclose(np.cumsum(a[:, ::2]), f(a[:, ::2]))
# Alternative stepped strides along axis=2
f = theano.function([x], cumsum(x), mode=self.mode)
assert [n for n in f.maker.fgraph.toposort()
if isinstance(n.op, GpuCumsum)]
assert np.allclose(np.cumsum(a[:, :, ::2]), f(a[:, :, ::2]))
# Negative strides along axis=0
f = theano.function([x], cumsum(x[::-1], axis=axis), mode=self.mode)
assert [n for n in f.maker.fgraph.toposort()
if isinstance(n.op, GpuCumsum)]
assert np.allclose(np.cumsum(a[::-1], axis=axis), f(a))
# Negative strides along axis=1
f = theano.function([x], cumsum(x[:, ::-1], axis=axis), mode=self.mode)
assert [n for n in f.maker.fgraph.toposort()
if isinstance(n.op, GpuCumsum)]
assert np.allclose(np.cumsum(a[:, ::-1], axis=axis), f(a))
# Negative strides along axis=2
f = theano.function([x], cumsum(x[:, :, ::-1], axis=axis), mode=self.mode)
assert [n for n in f.maker.fgraph.toposort()
if isinstance(n.op, GpuCumsum)]
assert np.allclose(np.cumsum(a[:, :, ::-1], axis=axis), f(a))
def test_GpuCumsum1D(self): def test_GpuCumsum1D(self):
block_max_size = self.max_threads_dim0 * 2 block_max_size = self.max_threads_dim0 * 2
......
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