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rm unittest eager guard tests part20 sparse_mv2split #48879

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Dec 15, 2022
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rm unittest eager guard tests part20 sparse_mv2split #48879

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127 changes: 62 additions & 65 deletions python/paddle/fluid/tests/unittests/test_sparse_mv_op.py
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Original file line number Diff line number Diff line change
Expand Up @@ -19,7 +19,6 @@
import numpy as np

import paddle
from paddle.fluid.framework import _test_eager_guard

paddle.seed(100)

Expand All @@ -43,38 +42,37 @@ def get_cuda_version():
class TestCsrMv(unittest.TestCase):
# x: csr-matrix, y: dense-vec, out: dense-vec
def test_mv(self):
with _test_eager_guard():
paddle.set_default_dtype('float64')
origin_x = paddle.rand([64, 32])
mask = paddle.randint(0, 2, [64, 32])
origin_x = origin_x * mask
origin_vec = paddle.rand([32])

dense_x = origin_x.detach()
dense_x.stop_gradient = False
dense_vec = origin_vec.detach()
dense_vec.stop_gradient = False
dense_out = paddle.mv(dense_x, dense_vec)
dense_out.backward()

sp_x = origin_x.detach().to_sparse_csr()
sp_x.stop_gradient = False
sp_vec = origin_vec.detach()
sp_vec.stop_gradient = False
sp_out = paddle.sparse.mv(sp_x, sp_vec)
sp_out.backward()

np.testing.assert_allclose(
sp_out.numpy(), dense_out.numpy(), rtol=1e-05
)
np.testing.assert_allclose(
sp_x.grad.to_dense().numpy(),
(dense_x.grad * mask).numpy(),
rtol=1e-05,
)
np.testing.assert_allclose(
sp_vec.grad.numpy(), dense_vec.grad.numpy(), rtol=1e-05
)
paddle.set_default_dtype('float64')
origin_x = paddle.rand([64, 32])
mask = paddle.randint(0, 2, [64, 32])
origin_x = origin_x * mask
origin_vec = paddle.rand([32])

dense_x = origin_x.detach()
dense_x.stop_gradient = False
dense_vec = origin_vec.detach()
dense_vec.stop_gradient = False
dense_out = paddle.mv(dense_x, dense_vec)
dense_out.backward()

sp_x = origin_x.detach().to_sparse_csr()
sp_x.stop_gradient = False
sp_vec = origin_vec.detach()
sp_vec.stop_gradient = False
sp_out = paddle.sparse.mv(sp_x, sp_vec)
sp_out.backward()

np.testing.assert_allclose(
sp_out.numpy(), dense_out.numpy(), rtol=1e-05
)
np.testing.assert_allclose(
sp_x.grad.to_dense().numpy(),
(dense_x.grad * mask).numpy(),
rtol=1e-05,
)
np.testing.assert_allclose(
sp_vec.grad.numpy(), dense_vec.grad.numpy(), rtol=1e-05
)


@unittest.skipIf(
Expand All @@ -84,38 +82,37 @@ def test_mv(self):
class TestCooMv(unittest.TestCase):
# x: csr-matrix, y: dense-vec, out: dense-vec
def test_mv(self):
with _test_eager_guard():
paddle.set_default_dtype('float64')
origin_x = paddle.rand([64, 32])
mask = paddle.randint(0, 2, [64, 32])
origin_x = origin_x * mask
origin_vec = paddle.rand([32])

dense_x = origin_x.detach()
dense_x.stop_gradient = False
dense_vec = origin_vec.detach()
dense_vec.stop_gradient = False
dense_out = paddle.mv(dense_x, dense_vec)
dense_out.backward()

sp_x = origin_x.detach().to_sparse_coo(sparse_dim=2)
sp_x.stop_gradient = False
sp_vec = origin_vec.detach()
sp_vec.stop_gradient = False
sp_out = paddle.sparse.mv(sp_x, sp_vec)
sp_out.backward()

np.testing.assert_allclose(
sp_out.numpy(), dense_out.numpy(), rtol=1e-05
)
np.testing.assert_allclose(
sp_x.grad.to_dense().numpy(),
(dense_x.grad * mask).numpy(),
rtol=1e-05,
)
np.testing.assert_allclose(
sp_vec.grad.numpy(), dense_vec.grad.numpy(), rtol=1e-05
)
paddle.set_default_dtype('float64')
origin_x = paddle.rand([64, 32])
mask = paddle.randint(0, 2, [64, 32])
origin_x = origin_x * mask
origin_vec = paddle.rand([32])

dense_x = origin_x.detach()
dense_x.stop_gradient = False
dense_vec = origin_vec.detach()
dense_vec.stop_gradient = False
dense_out = paddle.mv(dense_x, dense_vec)
dense_out.backward()

sp_x = origin_x.detach().to_sparse_coo(sparse_dim=2)
sp_x.stop_gradient = False
sp_vec = origin_vec.detach()
sp_vec.stop_gradient = False
sp_out = paddle.sparse.mv(sp_x, sp_vec)
sp_out.backward()

np.testing.assert_allclose(
sp_out.numpy(), dense_out.numpy(), rtol=1e-05
)
np.testing.assert_allclose(
sp_x.grad.to_dense().numpy(),
(dense_x.grad * mask).numpy(),
rtol=1e-05,
)
np.testing.assert_allclose(
sp_vec.grad.numpy(), dense_vec.grad.numpy(), rtol=1e-05
)


if __name__ == "__main__":
Expand Down
77 changes: 37 additions & 40 deletions python/paddle/fluid/tests/unittests/test_sparse_pooling_op.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -18,7 +18,6 @@
import numpy as np

import paddle
from paddle.fluid.framework import _test_eager_guard


class TestMaxPool3DFunc(unittest.TestCase):
Expand All @@ -42,32 +41,31 @@ def setUp(self):
self.setPadding()

def test(self):
with _test_eager_guard():
self.setUp()
self.dense_x.stop_gradient = False
sparse_x = self.dense_x.to_sparse_coo(4)
sparse_out = paddle.sparse.nn.functional.max_pool3d(
sparse_x,
self.kernel_sizes,
stride=self.strides,
padding=self.paddings,
)
out = sparse_out.to_dense()
out.backward(out)

dense_x = copy.deepcopy(self.dense_x)
dense_out = paddle.nn.functional.max_pool3d(
dense_x,
self.kernel_sizes,
stride=self.strides,
padding=self.paddings,
data_format='NDHWC',
)
dense_out.backward(dense_out)

# compare with dense
assert np.allclose(dense_out.numpy(), out.numpy())
assert np.allclose(dense_x.grad.numpy(), self.dense_x.grad.numpy())
self.setUp()
self.dense_x.stop_gradient = False
sparse_x = self.dense_x.to_sparse_coo(4)
sparse_out = paddle.sparse.nn.functional.max_pool3d(
sparse_x,
self.kernel_sizes,
stride=self.strides,
padding=self.paddings,
)
out = sparse_out.to_dense()
out.backward(out)

dense_x = copy.deepcopy(self.dense_x)
dense_out = paddle.nn.functional.max_pool3d(
dense_x,
self.kernel_sizes,
stride=self.strides,
padding=self.paddings,
data_format='NDHWC',
)
dense_out.backward(dense_out)

# compare with dense
assert np.allclose(dense_out.numpy(), out.numpy())
assert np.allclose(dense_x.grad.numpy(), self.dense_x.grad.numpy())


class TestStride(TestMaxPool3DFunc):
Expand Down Expand Up @@ -102,19 +100,18 @@ def setInput(self):

class TestMaxPool3DAPI(unittest.TestCase):
def test(self):
with _test_eager_guard():
dense_x = paddle.randn((2, 3, 6, 6, 3))
sparse_x = dense_x.to_sparse_coo(4)
max_pool3d = paddle.sparse.nn.MaxPool3D(
kernel_size=3, data_format='NDHWC'
)
out = max_pool3d(sparse_x)
out = out.to_dense()

dense_out = paddle.nn.functional.max_pool3d(
dense_x, 3, data_format='NDHWC'
)
assert np.allclose(dense_out.numpy(), out.numpy())
dense_x = paddle.randn((2, 3, 6, 6, 3))
sparse_x = dense_x.to_sparse_coo(4)
max_pool3d = paddle.sparse.nn.MaxPool3D(
kernel_size=3, data_format='NDHWC'
)
out = max_pool3d(sparse_x)
out = out.to_dense()

dense_out = paddle.nn.functional.max_pool3d(
dense_x, 3, data_format='NDHWC'
)
assert np.allclose(dense_out.numpy(), out.numpy())


if __name__ == "__main__":
Expand Down
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