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未验证 提交 13add823 编写于 作者: zhouweiwei2014's avatar zhouweiwei2014 提交者： GitHub
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Unify sparse api in paddle.incubate (#43122)

上级 664758fa
隐藏空白更改
内联 并排
Showing with 196 addition and 165 deletion
python/paddle/__init__.py
浏览文件 @ 13add823
......@@ -75,7 +75,6 @@ import paddle.onnx # noqa: F401
import paddle.reader # noqa: F401
import paddle.static # noqa: F401
import paddle.vision # noqa: F401
import paddle.sparse # noqa: F401
from .tensor.attribute import is_complex # noqa: F401
from .tensor.attribute import is_integer # noqa: F401
......
python/paddle/fluid/dygraph/varbase_patch_methods.py
浏览文件 @ 13add823
......@@ -902,7 +902,7 @@ def monkey_patch_varbase():
indices = [[0, 0, 1, 2, 2], [1, 3, 2, 0, 1]]
values = [1, 2, 3, 4, 5]
dense_shape = [3, 4]
sparse_x = paddle.sparse.sparse_coo_tensor(paddle.to_tensor(indices, dtype='int32'), paddle.to_tensor(values, dtype='float32'), shape=dense_shape)
sparse_x = paddle.incubate.sparse.sparse_coo_tensor(paddle.to_tensor(indices, dtype='int32'), paddle.to_tensor(values, dtype='float32'), shape=dense_shape)
print(sparse_x.values())
#[1, 2, 3, 4, 5]
"""
......@@ -932,7 +932,7 @@ def monkey_patch_varbase():
indices = [[0, 0, 1, 2, 2], [1, 3, 2, 0, 1]]
values = [1, 2, 3, 4, 5]
dense_shape = [3, 4]
sparse_x = paddle.sparse.sparse_coo_tensor(paddle.to_tensor(indices, dtype='int64'), paddle.to_tensor(values, dtype='float32'), shape=dense_shape)
sparse_x = paddle.incubate.sparse.sparse_coo_tensor(paddle.to_tensor(indices, dtype='int64'), paddle.to_tensor(values, dtype='float32'), shape=dense_shape)
dense_x = sparse_x.to_dense()
#[[0., 1., 0., 2.],
# [0., 0., 3., 0.],
......
python/paddle/fluid/tests/unittests/test_sparse_conv_op.py
浏览文件 @ 13add823
......@@ -41,7 +41,7 @@ class TestSparseConv(unittest.TestCase):
correct_out_values = [[5], [11]]
sparse_input = core.eager.sparse_coo_tensor(indices, values,
dense_shape, False)
out = paddle.sparse.functional.conv3d(
out = paddle.incubate.sparse.nn.functional.conv3d(
sparse_input,
dense_kernel,
bias=paddle.to_tensor(
......@@ -61,10 +61,11 @@ class TestSparseConv(unittest.TestCase):
indices = paddle.to_tensor(indices, dtype='int32')
values = paddle.to_tensor(values, dtype='float32')
dense_shape = [1, 1, 3, 4, 1]
sparse_x = paddle.sparse.sparse_coo_tensor(
sparse_x = paddle.incubate.sparse.sparse_coo_tensor(
indices, values, dense_shape, stop_gradient=True)
weight = paddle.randn((1, 3, 3, 1, 1), dtype='float32')
y = paddle.sparse.functional.subm_conv3d(sparse_x, weight)
y = paddle.incubate.sparse.nn.functional.subm_conv3d(sparse_x,
weight)
assert np.array_equal(sparse_x.indices().numpy(),
y.indices().numpy())
......@@ -78,16 +79,16 @@ class TestSparseConv(unittest.TestCase):
values = paddle.to_tensor(values, dtype='float32')
dense_shape = [1, 1, 3, 4, 1]
correct_out_values = [[4], [10]]
sparse_input = paddle.sparse.sparse_coo_tensor(indices, values,
dense_shape, False)
sparse_input = paddle.incubate.sparse.sparse_coo_tensor(
indices, values, dense_shape, False)
sparse_conv3d = paddle.sparse.Conv3D(
sparse_conv3d = paddle.incubate.sparse.nn.Conv3D(
1, 1, (1, 3, 3), data_format='NDHWC')
sparse_out = sparse_conv3d(sparse_input)
#test errors
with self.assertRaises(ValueError):
#Currently, only support data_format='NDHWC'
conv3d = paddle.sparse.SubmConv3D(
conv3d = paddle.incubate.sparse.nn.SubmConv3D(
1, 1, (1, 3, 3), data_format='NCDHW')
def test_SubmConv3D(self):
......@@ -98,10 +99,10 @@ class TestSparseConv(unittest.TestCase):
values = paddle.to_tensor(values, dtype='float32')
dense_shape = [1, 1, 3, 4, 1]
correct_out_values = [[4], [10]]
sparse_input = paddle.sparse.sparse_coo_tensor(indices, values,
dense_shape, False)
sparse_input = paddle.incubate.sparse.sparse_coo_tensor(
indices, values, dense_shape, False)
subm_conv3d = paddle.sparse.SubmConv3D(
subm_conv3d = paddle.incubate.sparse.nn.SubmConv3D(
1, 1, (1, 3, 3), data_format='NDHWC')
# test extra_repr
print(subm_conv3d.extra_repr())
......@@ -113,5 +114,5 @@ class TestSparseConv(unittest.TestCase):
#test errors
with self.assertRaises(ValueError):
#Currently, only support data_format='NDHWC'
conv3d = paddle.sparse.SubmConv3D(
conv3d = paddle.incubate.sparse.nn.SubmConv3D(
1, 1, (1, 3, 3), data_format='NCDHW')
python/paddle/fluid/tests/unittests/test_sparse_norm_op.py
浏览文件 @ 13add823
......@@ -38,7 +38,7 @@ class TestSparseBatchNorm(unittest.TestCase):
dense_x2 = copy.deepcopy(dense_x)
dense_x2.stop_gradient = False
sparse_x = dense_x2.to_sparse_coo(sparse_dim)
sparse_batch_norm = paddle.sparse.BatchNorm(channels)
sparse_batch_norm = paddle.incubate.sparse.nn.BatchNorm(channels)
# set same params
sparse_batch_norm._mean.set_value(batch_norm._mean)
sparse_batch_norm._variance.set_value(batch_norm._variance)
......@@ -66,7 +66,7 @@ class TestSparseBatchNorm(unittest.TestCase):
shape = [2, 3, 6, 6, 3]
x = paddle.randn(shape)
sparse_x = x.to_sparse_coo(4)
sparse_batch_norm = paddle.sparse.BatchNorm(
sparse_batch_norm = paddle.incubate.sparse.nn.BatchNorm(
3, data_format='NCDHW')
sparse_batch_norm(sparse_x)
......@@ -77,7 +77,7 @@ class TestSparseBatchNorm(unittest.TestCase):
x_data = paddle.randn((1, 6, 6, 6, channels)).astype('float32')
dense_x = paddle.to_tensor(x_data)
sparse_x = dense_x.to_sparse_coo(4)
batch_norm = paddle.sparse.BatchNorm(channels)
batch_norm = paddle.incubate.sparse.nn.BatchNorm(channels)
batch_norm_out = batch_norm(sparse_x)
print(batch_norm_out.shape)
# [1, 6, 6, 6, 3]
......
python/paddle/fluid/tests/unittests/test_sparse_pooling_op.py
浏览文件 @ 13add823
......@@ -47,7 +47,7 @@ class TestMaxPool3DFunc(unittest.TestCase):
self.setUp()
self.dense_x.stop_gradient = False
sparse_x = self.dense_x.to_sparse_coo(4)
sparse_out = paddle.sparse.functional.max_pool3d(
sparse_out = paddle.incubate.sparse.nn.functional.max_pool3d(
sparse_x,
self.kernel_sizes,
stride=self.strides,
......@@ -104,7 +104,7 @@ class TestMaxPool3DAPI(unittest.TestCase):
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.MaxPool3D(
max_pool3d = paddle.incubate.sparse.nn.MaxPool3D(
kernel_size=3, data_format='NDHWC')
out = max_pool3d(sparse_x)
out = out.to_dense()
......
python/paddle/fluid/tests/unittests/test_sparse_unary_op.py
浏览文件 @ 13add823
......@@ -67,15 +67,15 @@ class TestSparseUnary(unittest.TestCase):
x,
lambda x: x.to_sparse_coo(sparse_dim),
paddle.nn.ReLU(),
paddle.sparse.ReLU(),
paddle.incubate.sparse.nn.ReLU(),
True, )
self.compare_with_dense(
x,
lambda x: x.to_sparse_csr(),
paddle.nn.ReLU(),
paddle.sparse.ReLU(),
paddle.incubate.sparse.nn.ReLU(),
False, )
self.assert_raises_on_dense_tensor(paddle.sparse.ReLU())
self.assert_raises_on_dense_tensor(paddle.incubate.sparse.nn.ReLU())
def test_sparse_sqrt(self):
x = [[0, 16, 0, 0], [0, 0, 0, 0], [0, 4, 2, 0]]
......@@ -84,15 +84,15 @@ class TestSparseUnary(unittest.TestCase):
x,
lambda x: x.to_sparse_coo(sparse_dim),
paddle.sqrt,
paddle.sparse.sqrt,
paddle.incubate.sparse.sqrt,
True, )
self.compare_with_dense(
x,
lambda x: x.to_sparse_csr(),
paddle.sqrt,
paddle.sparse.sqrt,
paddle.incubate.sparse.sqrt,
False, )
self.assert_raises_on_dense_tensor(paddle.sparse.sqrt)
self.assert_raises_on_dense_tensor(paddle.incubate.sparse.sqrt)
def test_sparse_sin(self):
x = [[0, 16, 0, 0], [0, 0, 0, 0], [0, 4, 2, 0]]
......@@ -101,15 +101,15 @@ class TestSparseUnary(unittest.TestCase):
x,
lambda x: x.to_sparse_coo(sparse_dim),
paddle.sin,
paddle.sparse.sin,
paddle.incubate.sparse.sin,
True, )
self.compare_with_dense(
x,
lambda x: x.to_sparse_csr(),
paddle.sin,
paddle.sparse.sin,
paddle.incubate.sparse.sin,
False, )
self.assert_raises_on_dense_tensor(paddle.sparse.sin)
self.assert_raises_on_dense_tensor(paddle.incubate.sparse.sin)
def test_sparse_tanh(self):
x = [[0, 16, 0, 0], [0, 0, 0, 0], [0, -4, 2, 0]]
......@@ -118,15 +118,15 @@ class TestSparseUnary(unittest.TestCase):
x,
lambda x: x.to_sparse_coo(sparse_dim),
paddle.tanh,
paddle.sparse.tanh,
paddle.incubate.sparse.tanh,
True, )
self.compare_with_dense(
x,
lambda x: x.to_sparse_csr(),
paddle.tanh,
paddle.sparse.tanh,
paddle.incubate.sparse.tanh,
False, )
self.assert_raises_on_dense_tensor(paddle.sparse.tanh)
self.assert_raises_on_dense_tensor(paddle.incubate.sparse.tanh)
if __name__ == "__main__":
......
python/paddle/fluid/tests/unittests/test_sparse_utils_op.py
浏览文件 @ 13add823
......@@ -30,7 +30,7 @@ class TestSparseCreate(unittest.TestCase):
dense_shape = [3, 4]
dense_indices = paddle.to_tensor(indices)
dense_elements = paddle.to_tensor(values, dtype='float32')
coo = paddle.sparse.sparse_coo_tensor(
coo = paddle.incubate.sparse.sparse_coo_tensor(
dense_indices, dense_elements, dense_shape, stop_gradient=False)
# test the to_string.py
print(coo)
......@@ -42,7 +42,8 @@ class TestSparseCreate(unittest.TestCase):
indices = [[0, 1, 2], [1, 2, 0]]
values = [1.0, 2.0, 3.0]
dense_shape = [3, 3]
coo = paddle.sparse.sparse_coo_tensor(indices, values, dense_shape)
coo = paddle.incubate.sparse.sparse_coo_tensor(indices, values,
dense_shape)
assert np.array_equal(indices, coo.indices().numpy())
assert np.array_equal(values, coo.values().numpy())
......@@ -56,7 +57,7 @@ class TestSparseCreate(unittest.TestCase):
dense_cols = paddle.to_tensor(cols)
dense_elements = paddle.to_tensor(values, dtype='float32')
stop_gradient = False
csr = paddle.sparse.sparse_csr_tensor(
csr = paddle.incubate.sparse.sparse_csr_tensor(
dense_crows,
dense_cols,
dense_elements,
......@@ -69,8 +70,8 @@ class TestSparseCreate(unittest.TestCase):
cols = [1, 3, 2, 0, 1]
values = [1, 2, 3, 4, 5]
dense_shape = [3, 4]
csr = paddle.sparse.sparse_csr_tensor(crows, cols, values,
dense_shape)
csr = paddle.incubate.sparse.sparse_csr_tensor(crows, cols, values,
dense_shape)
# test the to_string.py
print(csr)
assert np.array_equal(crows, csr.crows().numpy())
......@@ -83,7 +84,7 @@ class TestSparseCreate(unittest.TestCase):
indices = [[0, 1], [0, 1]]
values = [1.0, 2.0]
dense_shape = [2, 2]
coo = paddle.sparse.sparse_coo_tensor(
coo = paddle.incubate.sparse.sparse_coo_tensor(
indices, values, dense_shape, place=place)
assert coo.place.is_cpu_place()
assert coo.values().place.is_cpu_place()
......@@ -92,7 +93,7 @@ class TestSparseCreate(unittest.TestCase):
crows = [0, 2, 3, 5]
cols = [1, 3, 2, 0, 1]
values = [1.0, 2.0, 3.0, 4.0, 5.0]
csr = paddle.sparse.sparse_csr_tensor(
csr = paddle.incubate.sparse.sparse_csr_tensor(
crows, cols, values, [3, 5], place=place)
assert csr.place.is_cpu_place()
assert csr.crows().place.is_cpu_place()
......@@ -106,14 +107,14 @@ class TestSparseCreate(unittest.TestCase):
dense_shape = [2, 2]
indices = paddle.to_tensor(indices, dtype='int32')
values = paddle.to_tensor(values, dtype='float32')
coo = paddle.sparse.sparse_coo_tensor(
coo = paddle.incubate.sparse.sparse_coo_tensor(
indices, values, dense_shape, dtype='float64')
assert coo.dtype == paddle.float64
crows = [0, 2, 3, 5]
cols = [1, 3, 2, 0, 1]
values = [1.0, 2.0, 3.0, 4.0, 5.0]
csr = paddle.sparse.sparse_csr_tensor(
csr = paddle.incubate.sparse.sparse_csr_tensor(
crows, cols, values, [3, 5], dtype='float16')
assert csr.dtype == paddle.float16
......@@ -123,7 +124,7 @@ class TestSparseCreate(unittest.TestCase):
values = [1.0, 2.0]
indices = paddle.to_tensor(indices, dtype='int32')
values = paddle.to_tensor(values, dtype='float32')
coo = paddle.sparse.sparse_coo_tensor(indices, values)
coo = paddle.incubate.sparse.sparse_coo_tensor(indices, values)
assert [2, 2] == coo.shape
......@@ -140,7 +141,7 @@ class TestSparseConvert(unittest.TestCase):
#test to_sparse_coo_grad backward
out_grad_indices = [[0, 1], [0, 1]]
out_grad_values = [2.0, 3.0]
out_grad = paddle.sparse.sparse_coo_tensor(
out_grad = paddle.incubate.sparse.sparse_coo_tensor(
paddle.to_tensor(out_grad_indices),
paddle.to_tensor(out_grad_values),
shape=out.shape,
......@@ -153,7 +154,7 @@ class TestSparseConvert(unittest.TestCase):
with _test_eager_guard():
indices = [[0, 0, 1, 2, 2], [1, 3, 2, 0, 1]]
values = [1.0, 2.0, 3.0, 4.0, 5.0]
sparse_x = paddle.sparse.sparse_coo_tensor(
sparse_x = paddle.incubate.sparse.sparse_coo_tensor(
paddle.to_tensor(indices),
paddle.to_tensor(values),
shape=[3, 4],
......@@ -169,7 +170,7 @@ class TestSparseConvert(unittest.TestCase):
sparse_x.grad.values().numpy())
paddle.device.set_device("cpu")
sparse_x_cpu = paddle.sparse.sparse_coo_tensor(
sparse_x_cpu = paddle.incubate.sparse.sparse_coo_tensor(
paddle.to_tensor(indices),
paddle.to_tensor(values),
shape=[3, 4],
......@@ -198,7 +199,7 @@ class TestSparseConvert(unittest.TestCase):
with _test_eager_guard():
indices = [[0, 0, 1, 2, 2], [1, 3, 2, 0, 1]]
values = [1.0, 2.0, 3.0, 4.0, 5.0]
sparse_x = paddle.sparse.sparse_coo_tensor(
sparse_x = paddle.incubate.sparse.sparse_coo_tensor(
paddle.to_tensor(indices),
paddle.to_tensor(values),
shape=[3, 4],
......@@ -211,7 +212,7 @@ class TestSparseConvert(unittest.TestCase):
indices = [[0, 0, 1, 2, 2], [1, 3, 2, 0, 1]]
values = [[1.0, 1.0], [2.0, 2.0], [3.0, 3.0], [4.0, 4.0],
[5.0, 5.0]]
sparse_x = paddle.sparse.sparse_coo_tensor(
sparse_x = paddle.incubate.sparse.sparse_coo_tensor(
paddle.to_tensor(indices),
paddle.to_tensor(values),
shape=[3, 4, 2],
......@@ -234,13 +235,13 @@ class TestSparseConvert(unittest.TestCase):
indices = paddle.to_tensor(indices, dtype='int32')
values = paddle.to_tensor(
values, dtype='float32', stop_gradient=False)
sparse_x = paddle.sparse.sparse_coo_tensor(
sparse_x = paddle.incubate.sparse.sparse_coo_tensor(
indices, values, shape=[2, 2], stop_gradient=False)
grad_indices = [[0, 1], [1, 1]]
grad_values = [2, 3]
grad_indices = paddle.to_tensor(grad_indices, dtype='int32')
grad_values = paddle.to_tensor(grad_values, dtype='float32')
sparse_out_grad = paddle.sparse.sparse_coo_tensor(
sparse_out_grad = paddle.incubate.sparse.sparse_coo_tensor(
grad_indices, grad_values, shape=[2, 2])
sparse_x.backward(sparse_out_grad)
correct_values_grad = [0, 3]
......@@ -251,11 +252,11 @@ class TestSparseConvert(unittest.TestCase):
values = [[1, 1], [2, 2]]
values = paddle.to_tensor(
values, dtype='float32', stop_gradient=False)
sparse_x = paddle.sparse.sparse_coo_tensor(
sparse_x = paddle.incubate.sparse.sparse_coo_tensor(
indices, values, shape=[2, 2, 2], stop_gradient=False)
grad_values = [[2, 2], [3, 3]]
grad_values = paddle.to_tensor(grad_values, dtype='float32')
sparse_out_grad = paddle.sparse.sparse_coo_tensor(
sparse_out_grad = paddle.incubate.sparse.sparse_coo_tensor(
grad_indices, grad_values, shape=[2, 2, 2])
sparse_x.backward(sparse_out_grad)
correct_values_grad = [[0, 0], [3, 3]]
......@@ -273,7 +274,8 @@ class TestSparseConvert(unittest.TestCase):
values = [1.0, 2.0, 3.0]
indices = paddle.to_tensor(indices, dtype='int32')
values = paddle.to_tensor(values, dtype='float32')
sparse_x = paddle.sparse.sparse_coo_tensor(indices, values)
sparse_x = paddle.incubate.sparse.sparse_coo_tensor(indices,
values)
indices_sorted = [[0, 1], [1, 0]]
values_sorted = [5.0, 1.0]
assert np.array_equal(indices_sorted,
......@@ -284,7 +286,8 @@ class TestSparseConvert(unittest.TestCase):
# test the non-zero values is a vector
values = [[1.0, 1.0], [2.0, 2.0], [3.0, 3.0]]
values = paddle.to_tensor(values, dtype='float32')
sparse_x = paddle.sparse.sparse_coo_tensor(indices, values)
sparse_x = paddle.incubate.sparse.sparse_coo_tensor(indices,
values)
values_sorted = [[5.0, 5.0], [1.0, 1.0]]
assert np.array_equal(indices_sorted,
sparse_x.indices().numpy())
......@@ -300,7 +303,7 @@ class TestCooError(unittest.TestCase):
values = [1, 2]
# 1. the shape too small
dense_shape = [2, 2]
sparse_x = paddle.sparse.sparse_coo_tensor(
sparse_x = paddle.incubate.sparse.sparse_coo_tensor(
indices, values, shape=dense_shape)
def test_same_nnz(self):
......@@ -309,7 +312,8 @@ class TestCooError(unittest.TestCase):
# 2. test the nnz of indices must same as nnz of values
indices = [[1, 2], [1, 0]]
values = [1, 2, 3]
sparse_x = paddle.sparse.sparse_coo_tensor(indices, values)
sparse_x = paddle.incubate.sparse.sparse_coo_tensor(indices,
values)
def test_same_dimensions(self):
with _test_eager_guard():
......@@ -317,7 +321,7 @@ class TestCooError(unittest.TestCase):
indices = [[1, 2], [1, 0]]
values = [1, 2, 3]
shape = [2, 3, 4]
sparse_x = paddle.sparse.sparse_coo_tensor(
sparse_x = paddle.incubate.sparse.sparse_coo_tensor(
indices, values, shape=shape)
def test_indices_dtype(self):
......@@ -325,7 +329,8 @@ class TestCooError(unittest.TestCase):
with self.assertRaises(TypeError):
indices = [[1.0, 2.0], [0, 1]]
values = [1, 2]
sparse_x = paddle.sparse.sparse_coo_tensor(indices, values)
sparse_x = paddle.incubate.sparse.sparse_coo_tensor(indices,
values)
class TestCsrError(unittest.TestCase):
......@@ -336,8 +341,8 @@ class TestCsrError(unittest.TestCase):
cols = [0, 1, 2]
values = [1, 2, 3]
shape = [3]
sparse_x = paddle.sparse.sparse_csr_tensor(crows, cols, values,
shape)
sparse_x = paddle.incubate.sparse.sparse_csr_tensor(
crows, cols, values, shape)
def test_dimension2(self):
with _test_eager_guard():
......@@ -346,8 +351,8 @@ class TestCsrError(unittest.TestCase):
cols = [0, 1, 2]
values = [1, 2, 3]
shape = [3, 3, 3, 3]
sparse_x = paddle.sparse.sparse_csr_tensor(crows, cols, values,
shape)
sparse_x = paddle.incubate.sparse.sparse_csr_tensor(
crows, cols, values, shape)
def test_same_shape1(self):
with _test_eager_guard():
......@@ -356,8 +361,8 @@ class TestCsrError(unittest.TestCase):
cols = [0, 1, 2, 3]
values = [1, 2, 3]
shape = [3, 4]
sparse_x = paddle.sparse.sparse_csr_tensor(crows, cols, values,
shape)
sparse_x = paddle.incubate.sparse.sparse_csr_tensor(
crows, cols, values, shape)
def test_same_shape2(self):
with _test_eager_guard():
......@@ -366,8 +371,8 @@ class TestCsrError(unittest.TestCase):
cols = [0, 1, 2, 3]
values = [1, 2, 3, 4]
shape = [3, 4]
sparse_x = paddle.sparse.sparse_csr_tensor(crows, cols, values,
shape)
sparse_x = paddle.incubate.sparse.sparse_csr_tensor(
crows, cols, values, shape)
def test_same_shape3(self):
with _test_eager_guard():
......@@ -376,8 +381,8 @@ class TestCsrError(unittest.TestCase):
cols = [0, 1, 2, 3, 0, 1, 2]
values = [1, 2, 3, 4, 0, 1, 2]
shape = [2, 3, 4]
sparse_x = paddle.sparse.sparse_csr_tensor(crows, cols, values,
shape)
sparse_x = paddle.incubate.sparse.sparse_csr_tensor(
crows, cols, values, shape)
def test_crows_first_value(self):
with _test_eager_guard():
......@@ -386,8 +391,8 @@ class TestCsrError(unittest.TestCase):
cols = [0, 1, 2]
values = [1, 2, 3]
shape = [3, 4]
sparse_x = paddle.sparse.sparse_csr_tensor(crows, cols, values,
shape)
sparse_x = paddle.incubate.sparse.sparse_csr_tensor(
crows, cols, values, shape)
def test_dtype(self):
with _test_eager_guard():
......@@ -396,8 +401,8 @@ class TestCsrError(unittest.TestCase):
cols = [0, 1, 2]
values = [1, 2, 3]
shape = [3]
sparse_x = paddle.sparse.sparse_csr_tensor(crows, cols, values,
shape)
sparse_x = paddle.incubate.sparse.sparse_csr_tensor(
crows, cols, values, shape)
if __name__ == "__main__":
......
python/paddle/incubate/__init__.py
浏览文件 @ 13add823
......@@ -30,6 +30,7 @@ from .tensor import segment_min
from .passes import fuse_resnet_unit_pass
import paddle.incubate.autograd
import paddle.incubate.autotune
import paddle.incubate.sparse
from . import nn #noqa: F401
from . import asp #noqa: F401
......
python/paddle/sparse/layer/__init__.py → python/paddle/incubate/sparse/__init__.py
浏览文件 @ 13add823
......@@ -12,10 +12,19 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from .unary import ReLU
from .norm import BatchNorm
from .conv import Conv3D
from .conv import SubmConv3D
from .pooling import MaxPool3D
from .creation import sparse_coo_tensor
from .creation import sparse_csr_tensor
__all__ = []
from .unary import sqrt
from .unary import sin
from .unary import tanh
from . import nn
__all__ = [
'sparse_coo_tensor',
'sparse_csr_tensor',
'sqrt',
'sin',
'tanh',
]
python/paddle/sparse/creation.py → python/paddle/incubate/sparse/creation.py
浏览文件 @ 13add823
......@@ -14,11 +14,10 @@
import paddle
from paddle import _C_ops
from ..framework import core, dygraph_only
from ..framework import _current_expected_place, _get_paddle_place
from ..tensor import to_tensor
from ..tensor import max
from ..fluid.data_feeder import check_variable_and_dtype, check_type, check_dtype, convert_dtype
from paddle.fluid.framework import core, dygraph_only
from paddle.fluid.framework import _current_expected_place, _get_paddle_place
from paddle.tensor import to_tensor, max
from paddle.fluid.data_feeder import check_variable_and_dtype, check_type, check_dtype, convert_dtype
import numpy as np
......@@ -112,7 +111,7 @@ def sparse_coo_tensor(indices,
indices = [[0, 1, 2], [1, 2, 0]]
values = [1.0, 2.0, 3.0]
dense_shape = [3, 3]
coo = paddle.sparse.sparse_coo_tensor(indices, values, dense_shape)
coo = paddle.incubate.sparse.sparse_coo_tensor(indices, values, dense_shape)
# print(coo)
# Tensor(shape=[2, 3], dtype=paddle.float32, place=Place(gpu:0), stop_gradient=True,
# indices=[[0, 1, 2],
......@@ -222,7 +221,7 @@ def sparse_csr_tensor(crows,
cols = [1, 3, 2, 0, 1]
values = [1, 2, 3, 4, 5]
dense_shape = [3, 4]
csr = paddle.sparse.sparse_csr_tensor(crows, cols, values, dense_shape)
csr = paddle.incubate.sparse.sparse_csr_tensor(crows, cols, values, dense_shape)
# print(csr)
# Tensor(shape=[3, 4], dtype=paddle.int64, place=Place(gpu:0), stop_gradient=True,
# crows=[0, 2, 3, 5],
......
python/paddle/sparse/__init__.py → python/paddle/incubate/sparse/nn/__init__.py
浏览文件 @ 13add823
......@@ -12,21 +12,18 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from .creation import sparse_coo_tensor
from .creation import sparse_csr_tensor
from .layer import ReLU
from .layer import BatchNorm
from . import functional
from .layer import Conv3D
from .layer import SubmConv3D
from .layer import MaxPool3D
from .functional import sqrt
from .functional import sin
from .functional import tanh
from .layer.activation import ReLU
from .layer.norm import BatchNorm
from .layer.conv import Conv3D
from .layer.conv import SubmConv3D
from .layer.pooling import MaxPool3D
__all__ = [
'sparse_coo_tensor', 'sparse_csr_tensor', 'ReLU', 'Conv3D', 'SubmConv3D',
'BatchNorm', 'MaxPool3D', 'sqrt', 'sin', 'tanh'
'ReLU',
'BatchNorm',
'Conv3D',
'SubmConv3D',
'MaxPool3D',
]
python/paddle/sparse/functional/__init__.py → python/paddle/incubate/sparse/nn/functional/__init__.py
浏览文件 @ 13add823
......@@ -12,12 +12,14 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from .unary import relu # noqa: F401
from .unary import tanh # noqa: F401
from .unary import sqrt # noqa: F401
from .unary import sin # noqa: F401
from .conv import conv3d # noqa: F401
from .conv import subm_conv3d # noqa: F401
from .pooling import max_pool3d # noqa: F401
from .activation import relu # noqa: F401
__all__ = ['relu', 'tanh', 'conv3d', 'subm_conv3d', 'max_pool3d', 'sqrt', 'sin']
__all__ = [
'conv3d',
'subm_conv3d',
'max_pool3d',
'relu',
]
python/paddle/incubate/sparse/nn/functional/activation.py 0 → 100644
浏览文件 @ 13add823
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
__all__ = []
from paddle import _C_ops, in_dynamic_mode
def relu(x, name=None):
"""
sparse relu activation, requiring x to be a sparse coo or sparse csr tensor.
.. math::
out = max(x, 0)
Parameters:
x (Tensor): The input Sparse Tensor with data type float32, float64.
name (str, optional): Name for the operation (optional, default is None).
For more information, please refer to :ref:`api_guide_Name`.
Returns:
A Sparse Tensor with the same data type and shape as ``x`` .
Examples:
.. code-block:: python
import paddle
from paddle.fluid.framework import _test_eager_guard
with _test_eager_guard():
dense_x = paddle.to_tensor([-2, 0, 1], dtype='float32')
sparse_x = dense_x.to_sparse_coo(1)
out = paddle.incubate.sparse.nn.functional.relu(sparse_x)
"""
assert in_dynamic_mode(), "Currently, Sparse API only support dynamic mode"
if x.is_sparse_coo() or x.is_sparse_csr():
return _C_ops.final_state_sparse_relu(x)
else:
raise ValueError(
"Currently, sparse.relu only support the input of SparseCooTensor or SparseCsrTensor"
)
python/paddle/sparse/functional/conv.py → python/paddle/incubate/sparse/nn/functional/conv.py
浏览文件 @ 13add823
......@@ -15,9 +15,9 @@
__all__ = []
from paddle import _C_ops, in_dynamic_mode
from ...fluid.layers.utils import convert_to_list
from ...fluid.layers.nn import elementwise_add
from .. import sparse_coo_tensor
from paddle.fluid.layers.utils import convert_to_list
from paddle.fluid.layers.nn import elementwise_add
from ...creation import sparse_coo_tensor
from paddle.nn.functional.conv import _update_padding_nd
......@@ -180,9 +180,9 @@ def conv3d(x,
indices = paddle.to_tensor(indices, dtype='int32')
values = paddle.to_tensor(values, dtype='float32')
dense_shape = [1, 1, 3, 4, 1]
sparse_x = paddle.sparse.sparse_coo_tensor(indices, values, dense_shape, stop_gradient=True)
sparse_x = paddle.incubate.sparse.sparse_coo_tensor(indices, values, dense_shape, stop_gradient=True)
weight = paddle.randn((1, 3, 3, 1, 1), dtype='float32')
y = paddle.sparse.functional.conv3d(sparse_x, weight)
y = paddle.incubate.sparse.nn.functional.conv3d(sparse_x, weight)
print(y.shape)
# (1, 1, 1, 2, 1)
"""
......@@ -295,9 +295,9 @@ def subm_conv3d(x,
indices = paddle.to_tensor(indices, dtype='int32')
values = paddle.to_tensor(values, dtype='float32')
dense_shape = [1, 1, 3, 4, 1]
sparse_x = paddle.sparse.sparse_coo_tensor(indices, values, dense_shape, stop_gradient=True)
sparse_x = paddle.incubate.sparse.sparse_coo_tensor(indices, values, dense_shape, stop_gradient=True)
weight = paddle.randn((1, 3, 3, 1, 1), dtype='float32')
y = paddle.sparse.functional.subm_conv3d(sparse_x, weight)
y = paddle.incubate.sparse.nn.functional.subm_conv3d(sparse_x, weight)
print(y.shape)
#(1, 1, 3, 4, 1)
"""
......
python/paddle/sparse/functional/pooling.py → python/paddle/incubate/sparse/nn/functional/pooling.py
浏览文件 @ 13add823
......@@ -12,7 +12,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from ...fluid.layers import utils
from paddle.fluid.layers import utils
from paddle import _C_ops, in_dynamic_mode
from paddle.nn.functional.pooling import _update_padding_nd
......@@ -70,7 +70,7 @@ def max_pool3d(x,
kernel_sizes = [3, 3, 3]
paddings = [0, 0, 0]
strides = [1, 1, 1]
out = paddle.sparse.functional.max_pool3d(sparse_x, kernel_sizes, stride=strides, padding=paddings)
out = paddle.incubate.sparse.nn.functional.max_pool3d(sparse_x, kernel_sizes, stride=strides, padding=paddings)
#[1, 2, 2, 2, 3]
"""
......
python/paddle/sparse/layer/unary.py → python/paddle/incubate/sparse/nn/layer/activation.py
浏览文件 @ 13add823
......@@ -44,7 +44,7 @@ class ReLU(Layer):
dense_x = paddle.to_tensor(x, dtype='float32')
sparse_dim = 2
sparse_x = dense_x.to_sparse_coo(sparse_dim)
relu = paddle.sparse.ReLU()
relu = paddle.incubate.sparse.nn.ReLU()
out = relu(sparse_x)
#out.values: [0., 2., 0., 4., 5.]
"""
......
python/paddle/sparse/layer/conv.py → python/paddle/incubate/sparse/nn/layer/conv.py
浏览文件 @ 13add823
......@@ -16,8 +16,8 @@ import numpy as np
from .. import functional as F
from paddle.nn import Layer
from paddle.nn.initializer import Normal
from ..functional.conv import _update_padding_nd
from ...fluid.layers import utils
from paddle.nn.functional.conv import _update_padding_nd
from paddle.fluid.layers import utils
__all__ = []
......@@ -213,8 +213,8 @@ class Conv3D(_Conv3D):
indices = paddle.to_tensor(indices, dtype='int32')
values = paddle.to_tensor(values, dtype='float32')
dense_shape = [1, 1, 3, 4, 1]
sparse_x = paddle.sparse.sparse_coo_tensor(indices, values, dense_shape, stop_gradient=True)
conv = paddle.sparse.Conv3D(1, 1, (1, 3, 3))
sparse_x = paddle.incubate.sparse.sparse_coo_tensor(indices, values, dense_shape, stop_gradient=True)
conv = paddle.incubate.sparse.nn.Conv3D(1, 1, (1, 3, 3))
y = conv(sparse_x)
print(y.shape)
# (1, 1, 1, 2, 1)
......@@ -346,8 +346,8 @@ class SubmConv3D(_Conv3D):
dense_shape = [1, 1, 3, 4, 1]
indices = paddle.to_tensor(indices, dtype='int32')
values = paddle.to_tensor(values, dtype='float32')
sparse_x = paddle.sparse.sparse_coo_tensor(indices, values, dense_shape, stop_gradient=True)
subm_conv = paddle.sparse.SubmConv3D(1, 1, (1, 3, 3))
sparse_x = paddle.incubate.sparse.sparse_coo_tensor(indices, values, dense_shape, stop_gradient=True)
subm_conv = paddle.incubate.sparse.nn.SubmConv3D(1, 1, (1, 3, 3))
y = subm_conv(sparse_x)
print(y.shape)
# (1, 1, 3, 4, 1)
......
python/paddle/sparse/layer/norm.py → python/paddle/incubate/sparse/nn/layer/norm.py
浏览文件 @ 13add823
......@@ -100,7 +100,7 @@ class BatchNorm(paddle.nn.BatchNorm1D):
x_data = paddle.randn((1, 6, 6, 6, channels)).astype('float32')
dense_x = paddle.to_tensor(x_data)
sparse_x = dense_x.to_sparse_coo(4)
batch_norm = paddle.sparse.BatchNorm(channels)
batch_norm = paddle.incubate.sparse.nn.BatchNorm(channels)
batch_norm_out = batch_norm(sparse_x)
print(batch_norm_out.shape)
# [1, 6, 6, 6, 3]
......@@ -153,7 +153,7 @@ class BatchNorm(paddle.nn.BatchNorm1D):
data_format='NC',
use_global_stats=self._use_global_stats)
return paddle.sparse.sparse_coo_tensor(
return paddle.incubate.sparse.sparse_coo_tensor(
input.indices(),
batch_norm_out,
shape=input.shape,
......
python/paddle/sparse/layer/pooling.py → python/paddle/incubate/sparse/nn/layer/pooling.py
浏览文件 @ 13add823
......@@ -66,7 +66,7 @@ class MaxPool3D(Layer):
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.MaxPool3D(
max_pool3d = paddle.incubate.sparse.nn.MaxPool3D(
kernel_size=3, data_format='NDHWC')
out = max_pool3d(sparse_x)
#shape=[2, 1, 2, 2, 3]
......
python/paddle/sparse/functional/unary.py → python/paddle/incubate/sparse/unary.py
浏览文件 @ 13add823
......@@ -17,44 +17,6 @@ __all__ = []
from paddle import _C_ops, in_dynamic_mode
def relu(x, name=None):
"""
sparse relu activation, requiring x to be a sparse coo or sparse csr tensor.
.. math::
out = max(x, 0)
Parameters:
x (Tensor): The input Sparse Tensor with data type float32, float64.
name (str, optional): Name for the operation (optional, default is None).
For more information, please refer to :ref:`api_guide_Name`.
Returns:
A Sparse Tensor with the same data type and shape as ``x`` .
Examples:
.. code-block:: python
import paddle
from paddle.fluid.framework import _test_eager_guard
with _test_eager_guard():
dense_x = paddle.to_tensor([-2, 0, 1], dtype='float32')
sparse_x = dense_x.to_sparse_coo(1)
out = paddle.sparse.functional.relu(sparse_x)
"""
assert in_dynamic_mode(), "Currently, Sparse API only support dynamic mode"
if x.is_sparse_coo() or x.is_sparse_csr():
return _C_ops.final_state_sparse_relu(x)
else:
raise ValueError(
"Currently, sparse.relu only support the input of SparseCooTensor or SparseCsrTensor"
)
def tanh(x, name=None):
"""
sparse tanh activation, requiring x to be a sparse coo or sparse csr tensor.
......@@ -80,7 +42,7 @@ def tanh(x, name=None):
with _test_eager_guard():
dense_x = paddle.to_tensor([-2, 0, 1], dtype='float32')
sparse_x = dense_x.to_sparse_coo(1)
out = paddle.sparse.tanh(sparse_x)
out = paddle.incubate.sparse.tanh(sparse_x)
"""
assert in_dynamic_mode(), "Currently, Sparse API only support dynamic mode"
......@@ -118,7 +80,7 @@ def sqrt(x, name=None):
with _test_eager_guard():
dense_x = paddle.to_tensor([4, 0, 1], dtype='float32')
sparse_x = dense_x.to_sparse_coo(1)
out = paddle.sparse.sqrt(sparse_x)
out = paddle.incubate.sparse.sqrt(sparse_x)
"""
assert in_dynamic_mode(), "Currently, Sparse API only support dynamic mode"
......@@ -156,7 +118,7 @@ def sin(x, name=None):
with _test_eager_guard():
dense_x = paddle.to_tensor([-2, 0, 3], dtype='float32')
sparse_x = dense_x.to_sparse_coo(1)
out = paddle.sparse.sin(sparse_x)
out = paddle.incubate.sparse.sin(sparse_x)
"""
assert in_dynamic_mode(), "Currently, Sparse API only support dynamic mode"
......
python/setup.py.in
浏览文件 @ 13add823
......@@ -374,6 +374,10 @@ packages=['paddle',
'paddle.incubate.distributed.models',
'paddle.incubate.distributed.models.moe',
'paddle.incubate.distributed.models.moe.gate',
'paddle.incubate.sparse',
'paddle.incubate.sparse.nn',
'paddle.incubate.sparse.nn.layer',
'paddle.incubate.sparse.nn.functional',
'paddle.io',
'paddle.optimizer',
'paddle.nn',
......@@ -394,9 +398,6 @@ packages=['paddle',
'paddle.device.cuda',
'paddle.version',
'paddle.profiler',
'paddle.sparse',
'paddle.sparse.layer',
'paddle.sparse.functional',
]
with open('@PADDLE_SOURCE_DIR@/python/requirements.txt') as f:
......
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