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未验证 提交 50d4fa54 编写于 作者: zhouweiwei2014's avatar zhouweiwei2014 提交者： GitHub
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[cherry-pick 2.4] remove incubate of all paddle sparse api (#47183)

上级 396427a7
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Showing with 312 addition and 256 deletion
python/paddle/__init__.py
浏览文件 @ 50d4fa54
...@@ -82,6 +82,7 @@ import paddle.static # noqa: F401 ...@@ -82,6 +82,7 @@ import paddle.static # noqa: F401
import paddle.vision # noqa: F401 import paddle.vision # noqa: F401
import paddle.audio # noqa: F401 import paddle.audio # noqa: F401
import paddle.geometric # noqa: F401 import paddle.geometric # noqa: F401
import paddle.sparse
from .tensor.attribute import is_complex # noqa: F401 from .tensor.attribute import is_complex # noqa: F401
from .tensor.attribute import is_integer # noqa: F401 from .tensor.attribute import is_integer # noqa: F401
......
python/paddle/fluid/dygraph/varbase_patch_methods.py
浏览文件 @ 50d4fa54
...@@ -919,7 +919,7 @@ def monkey_patch_varbase(): ...@@ -919,7 +919,7 @@ def monkey_patch_varbase():
indices = [[0, 0, 1, 2, 2], [1, 3, 2, 0, 1]] indices = [[0, 0, 1, 2, 2], [1, 3, 2, 0, 1]]
values = [1, 2, 3, 4, 5] values = [1, 2, 3, 4, 5]
dense_shape = [3, 4] dense_shape = [3, 4]
sparse_x = paddle.incubate.sparse.sparse_coo_tensor(paddle.to_tensor(indices, dtype='int32'), paddle.to_tensor(values, dtype='float32'), shape=dense_shape) sparse_x = paddle.sparse.sparse_coo_tensor(paddle.to_tensor(indices, dtype='int32'), paddle.to_tensor(values, dtype='float32'), shape=dense_shape)
print(sparse_x.values()) print(sparse_x.values())
#[1, 2, 3, 4, 5] #[1, 2, 3, 4, 5]
""" """
...@@ -944,7 +944,7 @@ def monkey_patch_varbase(): ...@@ -944,7 +944,7 @@ def monkey_patch_varbase():
indices = [[0, 0, 1, 2, 2], [1, 3, 2, 0, 1]] indices = [[0, 0, 1, 2, 2], [1, 3, 2, 0, 1]]
values = [1, 2, 3, 4, 5] values = [1, 2, 3, 4, 5]
dense_shape = [3, 4] dense_shape = [3, 4]
sparse_x = paddle.incubate.sparse.sparse_coo_tensor(paddle.to_tensor(indices, dtype='int64'), paddle.to_tensor(values, dtype='float32'), shape=dense_shape) sparse_x = paddle.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() dense_x = sparse_x.to_dense()
#[[0., 1., 0., 2.], #[[0., 1., 0., 2.],
# [0., 0., 3., 0.], # [0., 0., 3., 0.],
......
python/paddle/fluid/tests/unittests/test_sparse_addmm_op.py
浏览文件 @ 50d4fa54
...@@ -64,7 +64,7 @@ class TestAddmm(unittest.TestCase): ...@@ -64,7 +64,7 @@ class TestAddmm(unittest.TestCase):
sp_x.stop_gradient = False sp_x.stop_gradient = False
sp_y = origin_y.detach() sp_y = origin_y.detach()
sp_y.stop_gradient = False sp_y.stop_gradient = False
sp_out = paddle.incubate.sparse.addmm(sp_input, sp_x, sp_y, 3.0, 2.0) sp_out = paddle.sparse.addmm(sp_input, sp_x, sp_y, 3.0, 2.0)
np.testing.assert_allclose(sp_out.numpy(), np.testing.assert_allclose(sp_out.numpy(),
dense_out.numpy(), dense_out.numpy(),
......
python/paddle/fluid/tests/unittests/test_sparse_conv_op.py
浏览文件 @ 50d4fa54
...@@ -19,7 +19,7 @@ import paddle ...@@ -19,7 +19,7 @@ import paddle
from paddle import _C_ops, _legacy_C_ops from paddle import _C_ops, _legacy_C_ops
from paddle.fluid import core from paddle.fluid import core
from paddle.fluid.framework import _test_eager_guard from paddle.fluid.framework import _test_eager_guard
import paddle.incubate.sparse as sparse import paddle.sparse as sparse
class TestSparseConv(unittest.TestCase): class TestSparseConv(unittest.TestCase):
...@@ -44,17 +44,17 @@ class TestSparseConv(unittest.TestCase): ...@@ -44,17 +44,17 @@ class TestSparseConv(unittest.TestCase):
correct_out_values = [[5], [11]] correct_out_values = [[5], [11]]
sparse_input = core.eager.sparse_coo_tensor(indices, values, sparse_input = core.eager.sparse_coo_tensor(indices, values,
dense_shape, False) dense_shape, False)
out = paddle.incubate.sparse.nn.functional.conv3d( out = paddle.sparse.nn.functional.conv3d(sparse_input,
sparse_input, dense_kernel,
dense_kernel, bias=paddle.to_tensor(
bias=paddle.to_tensor(bias, dtype='float32'), bias, dtype='float32'),
stride=strides, stride=strides,
padding=paddings, padding=paddings,
dilation=dilations, dilation=dilations,
groups=1, groups=1,
data_format="NDHWC") data_format="NDHWC")
out.backward(out) out.backward(out)
out = paddle.incubate.sparse.coalesce(out) out = paddle.sparse.coalesce(out)
assert np.array_equal(correct_out_values, out.values().numpy()) assert np.array_equal(correct_out_values, out.values().numpy())
def test_subm_conv3d(self): def test_subm_conv3d(self):
...@@ -64,11 +64,14 @@ class TestSparseConv(unittest.TestCase): ...@@ -64,11 +64,14 @@ class TestSparseConv(unittest.TestCase):
indices = paddle.to_tensor(indices, dtype='int32') indices = paddle.to_tensor(indices, dtype='int32')
values = paddle.to_tensor(values, dtype='float32') values = paddle.to_tensor(values, dtype='float32')
dense_shape = [1, 1, 3, 4, 1] dense_shape = [1, 1, 3, 4, 1]
sparse_x = paddle.incubate.sparse.sparse_coo_tensor( sparse_x = paddle.sparse.sparse_coo_tensor(indices,
indices, values, dense_shape, stop_gradient=True) values,
dense_shape,
stop_gradient=True)
weight = paddle.randn((1, 3, 3, 1, 1), dtype='float32') weight = paddle.randn((1, 3, 3, 1, 1), dtype='float32')
y = paddle.incubate.sparse.nn.functional.subm_conv3d( y = paddle.sparse.nn.functional.subm_conv3d(sparse_x,
sparse_x, weight, key='subm_conv') weight,
key='subm_conv')
assert np.array_equal(sparse_x.indices().numpy(), assert np.array_equal(sparse_x.indices().numpy(),
y.indices().numpy()) y.indices().numpy())
...@@ -82,17 +85,20 @@ class TestSparseConv(unittest.TestCase): ...@@ -82,17 +85,20 @@ class TestSparseConv(unittest.TestCase):
values = paddle.to_tensor(values, dtype='float32') values = paddle.to_tensor(values, dtype='float32')
dense_shape = [1, 1, 3, 4, 1] dense_shape = [1, 1, 3, 4, 1]
correct_out_values = [[4], [10]] correct_out_values = [[4], [10]]
sparse_input = paddle.incubate.sparse.sparse_coo_tensor( sparse_input = paddle.sparse.sparse_coo_tensor(
indices, values, dense_shape, False) indices, values, dense_shape, False)
sparse_conv3d = paddle.incubate.sparse.nn.Conv3D( sparse_conv3d = paddle.sparse.nn.Conv3D(1,
1, 1, (1, 3, 3), data_format='NDHWC') 1, (1, 3, 3),
data_format='NDHWC')
sparse_out = sparse_conv3d(sparse_input) sparse_out = sparse_conv3d(sparse_input)
#test errors #test errors
with self.assertRaises(ValueError): with self.assertRaises(ValueError):
#Currently, only support data_format='NDHWC' #Currently, only support data_format='NDHWC'
conv3d = paddle.incubate.sparse.nn.SubmConv3D( conv3d = paddle.sparse.nn.SubmConv3D(1,
1, 1, (1, 3, 3), data_format='NCDHW', key='subm_conv') 1, (1, 3, 3),
data_format='NCDHW',
key='subm_conv')
def test_SubmConv3D(self): def test_SubmConv3D(self):
with _test_eager_guard(): with _test_eager_guard():
...@@ -102,11 +108,13 @@ class TestSparseConv(unittest.TestCase): ...@@ -102,11 +108,13 @@ class TestSparseConv(unittest.TestCase):
values = paddle.to_tensor(values, dtype='float32') values = paddle.to_tensor(values, dtype='float32')
dense_shape = [1, 1, 3, 4, 1] dense_shape = [1, 1, 3, 4, 1]
correct_out_values = [[4], [10]] correct_out_values = [[4], [10]]
sparse_input = paddle.incubate.sparse.sparse_coo_tensor( sparse_input = paddle.sparse.sparse_coo_tensor(
indices, values, dense_shape, False) indices, values, dense_shape, False)
subm_conv3d = paddle.incubate.sparse.nn.SubmConv3D( subm_conv3d = paddle.sparse.nn.SubmConv3D(1,
1, 1, (1, 3, 3), data_format='NDHWC', key='subm_conv') 1, (1, 3, 3),
data_format='NDHWC',
key='subm_conv')
# test extra_repr # test extra_repr
print(subm_conv3d.extra_repr()) print(subm_conv3d.extra_repr())
...@@ -117,8 +125,10 @@ class TestSparseConv(unittest.TestCase): ...@@ -117,8 +125,10 @@ class TestSparseConv(unittest.TestCase):
#test errors #test errors
with self.assertRaises(ValueError): with self.assertRaises(ValueError):
#Currently, only support data_format='NDHWC' #Currently, only support data_format='NDHWC'
conv3d = paddle.incubate.sparse.nn.SubmConv3D( conv3d = paddle.sparse.nn.SubmConv3D(1,
1, 1, (1, 3, 3), data_format='NCDHW', key='subm_conv') 1, (1, 3, 3),
data_format='NCDHW',
key='subm_conv')
def test_Conv3D_bias(self): def test_Conv3D_bias(self):
with _test_eager_guard(): with _test_eager_guard():
...@@ -128,10 +138,7 @@ class TestSparseConv(unittest.TestCase): ...@@ -128,10 +138,7 @@ class TestSparseConv(unittest.TestCase):
sp_x = x.to_sparse_coo(4) sp_x = x.to_sparse_coo(4)
conv3d = paddle.nn.Conv3D(3, 2, 3, data_format='NDHWC') conv3d = paddle.nn.Conv3D(3, 2, 3, data_format='NDHWC')
sp_conv3d = paddle.incubate.sparse.nn.Conv3D(3, sp_conv3d = paddle.sparse.nn.Conv3D(3, 2, 3, data_format='NDHWC')
2,
3,
data_format='NDHWC')
sp_conv3d.weight.set_value( sp_conv3d.weight.set_value(
paddle.to_tensor(conv3d.weight.numpy().transpose(2, 3, 4, 1, paddle.to_tensor(conv3d.weight.numpy().transpose(2, 3, 4, 1,
0))) 0)))
......
python/paddle/fluid/tests/unittests/test_sparse_elementwise_op.py
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...@@ -18,20 +18,20 @@ from operator import __add__, __sub__, __mul__, __truediv__ ...@@ -18,20 +18,20 @@ from operator import __add__, __sub__, __mul__, __truediv__
import numpy as np import numpy as np
import paddle import paddle
import paddle.incubate.sparse as sparse import paddle.sparse as sparse
op_list = [__add__, __sub__, __mul__, __truediv__] op_list = [__add__, __sub__, __mul__, __truediv__]
def get_actual_res(x, y, op): def get_actual_res(x, y, op):
if op == __add__: if op == __add__:
res = paddle.incubate.sparse.add(x, y) res = paddle.sparse.add(x, y)
elif op == __sub__: elif op == __sub__:
res = paddle.incubate.sparse.subtract(x, y) res = paddle.sparse.subtract(x, y)
elif op == __mul__: elif op == __mul__:
res = paddle.incubate.sparse.multiply(x, y) res = paddle.sparse.multiply(x, y)
elif op == __truediv__: elif op == __truediv__:
res = paddle.incubate.sparse.divide(x, y) res = paddle.sparse.divide(x, y)
else: else:
raise ValueError("unsupported op") raise ValueError("unsupported op")
return res return res
......
python/paddle/fluid/tests/unittests/test_sparse_fused_attention_op.py
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...@@ -92,7 +92,7 @@ class TestSparseAttentionAPI1(unittest.TestCase): ...@@ -92,7 +92,7 @@ class TestSparseAttentionAPI1(unittest.TestCase):
output = paddle.matmul(softmax, value) output = paddle.matmul(softmax, value)
output.backward() output.backward()
output_sp = paddle.incubate.sparse.nn.functional.attention( output_sp = paddle.sparse.nn.functional.attention(
query_sp, key_sp, value_sp, sp_mask, kp_mask, attn_mask) query_sp, key_sp, value_sp, sp_mask, kp_mask, attn_mask)
output_sp.backward() output_sp.backward()
else: else:
...@@ -103,7 +103,7 @@ class TestSparseAttentionAPI1(unittest.TestCase): ...@@ -103,7 +103,7 @@ class TestSparseAttentionAPI1(unittest.TestCase):
output = paddle.matmul(softmax, value) output = paddle.matmul(softmax, value)
output.backward() output.backward()
output_sp = paddle.incubate.sparse.nn.functional.attention( output_sp = paddle.sparse.nn.functional.attention(
query_sp, key_sp, value_sp, sp_mask) query_sp, key_sp, value_sp, sp_mask)
output_sp.backward() output_sp.backward()
......
python/paddle/fluid/tests/unittests/test_sparse_is_same_shape.py
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...@@ -16,12 +16,12 @@ from __future__ import print_function ...@@ -16,12 +16,12 @@ from __future__ import print_function
import unittest import unittest
import paddle import paddle
from paddle.incubate.sparse.binary import is_same_shape from paddle.sparse.binary import is_same_shape
class TestSparseIsSameShapeAPI(unittest.TestCase): class TestSparseIsSameShapeAPI(unittest.TestCase):
""" """
test paddle.incubate.sparse.is_same_shape test paddle.sparse.is_same_shape
""" """
def setUp(self): def setUp(self):
......
python/paddle/fluid/tests/unittests/test_sparse_matmul_op.py
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...@@ -58,7 +58,7 @@ class TestMatmul(unittest.TestCase): ...@@ -58,7 +58,7 @@ class TestMatmul(unittest.TestCase):
sp_x.stop_gradient = False sp_x.stop_gradient = False
sp_y = origin_y.detach() sp_y = origin_y.detach()
sp_y.stop_gradient = False sp_y.stop_gradient = False
sp_out = paddle.incubate.sparse.matmul(sp_x, sp_y) sp_out = paddle.sparse.matmul(sp_x, sp_y)
np.testing.assert_allclose(sp_out.numpy(), np.testing.assert_allclose(sp_out.numpy(),
dense_out.numpy(), dense_out.numpy(),
...@@ -107,7 +107,7 @@ class TestMaskedMatmul(unittest.TestCase): ...@@ -107,7 +107,7 @@ class TestMaskedMatmul(unittest.TestCase):
x = paddle.to_tensor(np_x, stop_gradient=False) x = paddle.to_tensor(np_x, stop_gradient=False)
y = paddle.to_tensor(np_y, stop_gradient=False) y = paddle.to_tensor(np_y, stop_gradient=False)
mask = paddle.to_tensor(np.ones([10, 6]) * np_mask).to_sparse_csr() mask = paddle.to_tensor(np.ones([10, 6]) * np_mask).to_sparse_csr()
out = paddle.incubate.sparse.masked_matmul(x, y, mask) out = paddle.sparse.masked_matmul(x, y, mask)
np.testing.assert_allclose(np_out.indptr, np.testing.assert_allclose(np_out.indptr,
out.crows().numpy(), out.crows().numpy(),
...@@ -145,7 +145,7 @@ class TestMaskedMatmul(unittest.TestCase): ...@@ -145,7 +145,7 @@ class TestMaskedMatmul(unittest.TestCase):
sp_x.stop_gradient = False sp_x.stop_gradient = False
sp_y = origin_y.detach() sp_y = origin_y.detach()
sp_y.stop_gradient = False sp_y.stop_gradient = False
sp_out = paddle.incubate.sparse.matmul(sp_x, sp_y) sp_out = paddle.sparse.matmul(sp_x, sp_y)
sp_out.backward() sp_out.backward()
np.testing.assert_allclose(sp_out.numpy(), np.testing.assert_allclose(sp_out.numpy(),
......
python/paddle/fluid/tests/unittests/test_sparse_model.py
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...@@ -15,8 +15,7 @@ ...@@ -15,8 +15,7 @@
import unittest import unittest
import numpy as np import numpy as np
import paddle import paddle
from paddle.incubate import sparse from paddle.sparse import nn
from paddle.incubate.sparse import nn
from paddle.fluid.framework import _test_eager_guard from paddle.fluid.framework import _test_eager_guard
...@@ -26,10 +25,10 @@ class TestGradientAdd(unittest.TestCase): ...@@ -26,10 +25,10 @@ class TestGradientAdd(unittest.TestCase):
indentity = sp_x indentity = sp_x
out = nn.functional.relu(sp_x) out = nn.functional.relu(sp_x)
values = out.values() + indentity.values() values = out.values() + indentity.values()
out = sparse.sparse_coo_tensor(out.indices(), out = paddle.sparse.sparse_coo_tensor(out.indices(),
values, values,
shape=out.shape, shape=out.shape,
stop_gradient=out.stop_gradient) stop_gradient=out.stop_gradient)
return out return out
def dense(self, x): def dense(self, x):
......
python/paddle/fluid/tests/unittests/test_sparse_mv_op.py
浏览文件 @ 50d4fa54
...@@ -61,7 +61,7 @@ class TestCsrMv(unittest.TestCase): ...@@ -61,7 +61,7 @@ class TestCsrMv(unittest.TestCase):
sp_x.stop_gradient = False sp_x.stop_gradient = False
sp_vec = origin_vec.detach() sp_vec = origin_vec.detach()
sp_vec.stop_gradient = False sp_vec.stop_gradient = False
sp_out = paddle.incubate.sparse.mv(sp_x, sp_vec) sp_out = paddle.sparse.mv(sp_x, sp_vec)
sp_out.backward() sp_out.backward()
np.testing.assert_allclose(sp_out.numpy(), np.testing.assert_allclose(sp_out.numpy(),
...@@ -99,7 +99,7 @@ class TestCooMv(unittest.TestCase): ...@@ -99,7 +99,7 @@ class TestCooMv(unittest.TestCase):
sp_x.stop_gradient = False sp_x.stop_gradient = False
sp_vec = origin_vec.detach() sp_vec = origin_vec.detach()
sp_vec.stop_gradient = False sp_vec.stop_gradient = False
sp_out = paddle.incubate.sparse.mv(sp_x, sp_vec) sp_out = paddle.sparse.mv(sp_x, sp_vec)
sp_out.backward() sp_out.backward()
np.testing.assert_allclose(sp_out.numpy(), np.testing.assert_allclose(sp_out.numpy(),
......
python/paddle/fluid/tests/unittests/test_sparse_norm_op.py
浏览文件 @ 50d4fa54
...@@ -16,7 +16,7 @@ from __future__ import print_function ...@@ -16,7 +16,7 @@ from __future__ import print_function
import unittest import unittest
import numpy as np import numpy as np
import paddle import paddle
from paddle.incubate.sparse import nn from paddle.sparse import nn
import paddle.fluid as fluid import paddle.fluid as fluid
import copy import copy
...@@ -40,7 +40,7 @@ class TestSparseBatchNorm(unittest.TestCase): ...@@ -40,7 +40,7 @@ class TestSparseBatchNorm(unittest.TestCase):
dense_x2 = copy.deepcopy(dense_x) dense_x2 = copy.deepcopy(dense_x)
dense_x2.stop_gradient = False dense_x2.stop_gradient = False
sparse_x = dense_x2.to_sparse_coo(sparse_dim) sparse_x = dense_x2.to_sparse_coo(sparse_dim)
sparse_batch_norm = paddle.incubate.sparse.nn.BatchNorm(channels) sparse_batch_norm = paddle.sparse.nn.BatchNorm(channels)
# set same params # set same params
sparse_batch_norm._mean.set_value(batch_norm._mean) sparse_batch_norm._mean.set_value(batch_norm._mean)
sparse_batch_norm._variance.set_value(batch_norm._variance) sparse_batch_norm._variance.set_value(batch_norm._variance)
...@@ -66,8 +66,8 @@ class TestSparseBatchNorm(unittest.TestCase): ...@@ -66,8 +66,8 @@ class TestSparseBatchNorm(unittest.TestCase):
shape = [2, 3, 6, 6, 3] shape = [2, 3, 6, 6, 3]
x = paddle.randn(shape) x = paddle.randn(shape)
sparse_x = x.to_sparse_coo(4) sparse_x = x.to_sparse_coo(4)
sparse_batch_norm = paddle.incubate.sparse.nn.BatchNorm( sparse_batch_norm = paddle.sparse.nn.BatchNorm(3,
3, data_format='NCDHW') data_format='NCDHW')
sparse_batch_norm(sparse_x) sparse_batch_norm(sparse_x)
def test2(self): def test2(self):
...@@ -76,7 +76,7 @@ class TestSparseBatchNorm(unittest.TestCase): ...@@ -76,7 +76,7 @@ class TestSparseBatchNorm(unittest.TestCase):
x_data = paddle.randn((1, 6, 6, 6, channels)).astype('float32') x_data = paddle.randn((1, 6, 6, 6, channels)).astype('float32')
dense_x = paddle.to_tensor(x_data) dense_x = paddle.to_tensor(x_data)
sparse_x = dense_x.to_sparse_coo(4) sparse_x = dense_x.to_sparse_coo(4)
batch_norm = paddle.incubate.sparse.nn.BatchNorm(channels) batch_norm = paddle.sparse.nn.BatchNorm(channels)
batch_norm_out = batch_norm(sparse_x) batch_norm_out = batch_norm(sparse_x)
dense_bn = paddle.nn.BatchNorm1D(channels) dense_bn = paddle.nn.BatchNorm1D(channels)
dense_x = dense_x.reshape((-1, dense_x.shape[-1])) dense_x = dense_x.reshape((-1, dense_x.shape[-1]))
......
python/paddle/fluid/tests/unittests/test_sparse_pooling_op.py
浏览文件 @ 50d4fa54
...@@ -48,7 +48,7 @@ class TestMaxPool3DFunc(unittest.TestCase): ...@@ -48,7 +48,7 @@ class TestMaxPool3DFunc(unittest.TestCase):
self.setUp() self.setUp()
self.dense_x.stop_gradient = False self.dense_x.stop_gradient = False
sparse_x = self.dense_x.to_sparse_coo(4) sparse_x = self.dense_x.to_sparse_coo(4)
sparse_out = paddle.incubate.sparse.nn.functional.max_pool3d( sparse_out = paddle.sparse.nn.functional.max_pool3d(
sparse_x, sparse_x,
self.kernel_sizes, self.kernel_sizes,
stride=self.strides, stride=self.strides,
...@@ -109,8 +109,8 @@ class TestMaxPool3DAPI(unittest.TestCase): ...@@ -109,8 +109,8 @@ class TestMaxPool3DAPI(unittest.TestCase):
with _test_eager_guard(): with _test_eager_guard():
dense_x = paddle.randn((2, 3, 6, 6, 3)) dense_x = paddle.randn((2, 3, 6, 6, 3))
sparse_x = dense_x.to_sparse_coo(4) sparse_x = dense_x.to_sparse_coo(4)
max_pool3d = paddle.incubate.sparse.nn.MaxPool3D( max_pool3d = paddle.sparse.nn.MaxPool3D(kernel_size=3,
kernel_size=3, data_format='NDHWC') data_format='NDHWC')
out = max_pool3d(sparse_x) out = max_pool3d(sparse_x)
out = out.to_dense() out = out.to_dense()
......
python/paddle/fluid/tests/unittests/test_sparse_reshape_op.py
浏览文件 @ 50d4fa54
...@@ -19,7 +19,7 @@ import unittest ...@@ -19,7 +19,7 @@ import unittest
class TestReshape(unittest.TestCase): class TestReshape(unittest.TestCase):
""" """
Test the API paddle.incubate.sparse.reshape on some sparse tensors. Test the API paddle.sparse.reshape on some sparse tensors.
x: sparse, out: sparse x: sparse, out: sparse
""" """
...@@ -31,7 +31,7 @@ class TestReshape(unittest.TestCase): ...@@ -31,7 +31,7 @@ class TestReshape(unittest.TestCase):
Transform a sparse tensor with shape "x_shape" to Transform a sparse tensor with shape "x_shape" to
a sparse tensor with shape "new_shape". a sparse tensor with shape "new_shape".
Compare the output of paddle.reshape and the output of Compare the output of paddle.reshape and the output of
paddle.incubate.sparse.reshape. paddle.sparse.reshape.
""" """
mask = np.random.randint(0, 2, x_shape) mask = np.random.randint(0, 2, x_shape)
np_x = np.random.randint(-100, 100, x_shape) * mask np_x = np.random.randint(-100, 100, x_shape) * mask
...@@ -49,7 +49,7 @@ class TestReshape(unittest.TestCase): ...@@ -49,7 +49,7 @@ class TestReshape(unittest.TestCase):
sp_x = paddle.to_tensor(np_x, sp_x = paddle.to_tensor(np_x,
place=paddle.CPUPlace()).to_sparse_csr() place=paddle.CPUPlace()).to_sparse_csr()
sp_x.stop_gradient = False sp_x.stop_gradient = False
sp_out = paddle.incubate.sparse.reshape(sp_x, new_shape) sp_out = paddle.sparse.reshape(sp_x, new_shape)
np.testing.assert_allclose(sp_out.to_dense().numpy(), np.testing.assert_allclose(sp_out.to_dense().numpy(),
dense_out.numpy(), dense_out.numpy(),
...@@ -75,7 +75,7 @@ class TestReshape(unittest.TestCase): ...@@ -75,7 +75,7 @@ class TestReshape(unittest.TestCase):
sp_x = paddle.to_tensor( sp_x = paddle.to_tensor(
np_x, place=paddle.CUDAPlace(0)).to_sparse_csr() np_x, place=paddle.CUDAPlace(0)).to_sparse_csr()
sp_x.stop_gradient = False sp_x.stop_gradient = False
sp_out = paddle.incubate.sparse.reshape(sp_x, new_shape) sp_out = paddle.sparse.reshape(sp_x, new_shape)
np.testing.assert_allclose(sp_out.to_dense().numpy(), np.testing.assert_allclose(sp_out.to_dense().numpy(),
dense_out.numpy(), dense_out.numpy(),
......
python/paddle/fluid/tests/unittests/test_sparse_softmax_op.py
浏览文件 @ 50d4fa54
...@@ -48,7 +48,7 @@ class TestCsrSoftmax(unittest.TestCase): ...@@ -48,7 +48,7 @@ class TestCsrSoftmax(unittest.TestCase):
np_out = np.concatenate([np_out, x_exp / x_exp_sum]) np_out = np.concatenate([np_out, x_exp / x_exp_sum])
csr = paddle.to_tensor(np_x, stop_gradient=False).to_sparse_csr() csr = paddle.to_tensor(np_x, stop_gradient=False).to_sparse_csr()
m = paddle.incubate.sparse.nn.Softmax() m = paddle.sparse.nn.Softmax()
out = m(csr) out = m(csr)
np.testing.assert_allclose(out.crows().numpy(), np.testing.assert_allclose(out.crows().numpy(),
np_csr.indptr, np_csr.indptr,
...@@ -105,7 +105,7 @@ class TestCsrSoftmax(unittest.TestCase): ...@@ -105,7 +105,7 @@ class TestCsrSoftmax(unittest.TestCase):
np_out = np.concatenate([np_out, x_exp / x_exp_sum]) np_out = np.concatenate([np_out, x_exp / x_exp_sum])
csr = paddle.to_tensor(np_x, stop_gradient=False).to_sparse_csr() csr = paddle.to_tensor(np_x, stop_gradient=False).to_sparse_csr()
m = paddle.incubate.sparse.nn.Softmax() m = paddle.sparse.nn.Softmax()
out = m(csr) out = m(csr)
np.testing.assert_allclose(out.values().numpy(), np_out, rtol=1e-05) np.testing.assert_allclose(out.values().numpy(), np_out, rtol=1e-05)
......
python/paddle/fluid/tests/unittests/test_sparse_transpose_op.py
浏览文件 @ 50d4fa54
...@@ -35,7 +35,7 @@ class TestTranspose(unittest.TestCase): ...@@ -35,7 +35,7 @@ class TestTranspose(unittest.TestCase):
else: else:
sp_x = origin_x.detach().to_sparse_csr() sp_x = origin_x.detach().to_sparse_csr()
sp_x.stop_gradient = False sp_x.stop_gradient = False
sp_out = paddle.incubate.sparse.transpose(sp_x, dims) sp_out = paddle.sparse.transpose(sp_x, dims)
np.testing.assert_allclose(sp_out.to_dense().numpy(), np.testing.assert_allclose(sp_out.to_dense().numpy(),
dense_out.numpy(), dense_out.numpy(),
......
python/paddle/fluid/tests/unittests/test_sparse_unary_op.py
浏览文件 @ 50d4fa54
...@@ -90,84 +90,79 @@ class TestSparseUnary(unittest.TestCase): ...@@ -90,84 +90,79 @@ class TestSparseUnary(unittest.TestCase):
self.check_result(dense_func, sparse_func, 'csr', attr1, attr2) self.check_result(dense_func, sparse_func, 'csr', attr1, attr2)
def test_sparse_sin(self): def test_sparse_sin(self):
self.compare_with_dense(paddle.sin, paddle.incubate.sparse.sin) self.compare_with_dense(paddle.sin, paddle.sparse.sin)
def test_sparse_tan(self): def test_sparse_tan(self):
self.compare_with_dense(paddle.tan, paddle.incubate.sparse.tan) self.compare_with_dense(paddle.tan, paddle.sparse.tan)
def test_sparse_asin(self): def test_sparse_asin(self):
self.compare_with_dense(paddle.asin, paddle.incubate.sparse.asin) self.compare_with_dense(paddle.asin, paddle.sparse.asin)
def test_sparse_atan(self): def test_sparse_atan(self):
self.compare_with_dense(paddle.atan, paddle.incubate.sparse.atan) self.compare_with_dense(paddle.atan, paddle.sparse.atan)
def test_sparse_sinh(self): def test_sparse_sinh(self):
self.compare_with_dense(paddle.sinh, paddle.incubate.sparse.sinh) self.compare_with_dense(paddle.sinh, paddle.sparse.sinh)
def test_sparse_tanh(self): def test_sparse_tanh(self):
self.compare_with_dense(paddle.tanh, paddle.incubate.sparse.tanh) self.compare_with_dense(paddle.tanh, paddle.sparse.tanh)
def test_sparse_asinh(self): def test_sparse_asinh(self):
self.compare_with_dense(paddle.asinh, paddle.incubate.sparse.asinh) self.compare_with_dense(paddle.asinh, paddle.sparse.asinh)
def test_sparse_atanh(self): def test_sparse_atanh(self):
self.compare_with_dense(paddle.atanh, paddle.incubate.sparse.atanh) self.compare_with_dense(paddle.atanh, paddle.sparse.atanh)
def test_sparse_sqrt(self): def test_sparse_sqrt(self):
self.compare_with_dense(paddle.sqrt, paddle.incubate.sparse.sqrt) self.compare_with_dense(paddle.sqrt, paddle.sparse.sqrt)
def test_sparse_square(self): def test_sparse_square(self):
self.compare_with_dense(paddle.square, paddle.incubate.sparse.square) self.compare_with_dense(paddle.square, paddle.sparse.square)
def test_sparse_log1p(self): def test_sparse_log1p(self):
self.compare_with_dense(paddle.log1p, paddle.incubate.sparse.log1p) self.compare_with_dense(paddle.log1p, paddle.sparse.log1p)
def test_sparse_relu(self): def test_sparse_relu(self):
self.compare_with_dense(paddle.nn.ReLU(), self.compare_with_dense(paddle.nn.ReLU(), paddle.sparse.nn.ReLU())
paddle.incubate.sparse.nn.ReLU())
def test_sparse_relu6(self): def test_sparse_relu6(self):
self.compare_with_dense(paddle.nn.ReLU6(), self.compare_with_dense(paddle.nn.ReLU6(), paddle.sparse.nn.ReLU6())
paddle.incubate.sparse.nn.ReLU6())
def test_sparse_leaky_relu(self): def test_sparse_leaky_relu(self):
self.compare_with_dense(paddle.nn.LeakyReLU(0.1), self.compare_with_dense(paddle.nn.LeakyReLU(0.1),
paddle.incubate.sparse.nn.LeakyReLU(0.1)) paddle.sparse.nn.LeakyReLU(0.1))
def test_sparse_abs(self): def test_sparse_abs(self):
self.compare_with_dense(paddle.abs, paddle.incubate.sparse.abs) self.compare_with_dense(paddle.abs, paddle.sparse.abs)
def test_sparse_expm1(self): def test_sparse_expm1(self):
self.compare_with_dense(paddle.expm1, paddle.incubate.sparse.expm1) self.compare_with_dense(paddle.expm1, paddle.sparse.expm1)
def test_sparse_deg2rad(self): def test_sparse_deg2rad(self):
self.compare_with_dense(paddle.deg2rad, paddle.incubate.sparse.deg2rad) self.compare_with_dense(paddle.deg2rad, paddle.sparse.deg2rad)
def test_sparse_rad2deg(self): def test_sparse_rad2deg(self):
self.compare_with_dense(paddle.rad2deg, paddle.incubate.sparse.rad2deg) self.compare_with_dense(paddle.rad2deg, paddle.sparse.rad2deg)
def test_sparse_neg(self): def test_sparse_neg(self):
self.compare_with_dense(paddle.neg, paddle.incubate.sparse.neg) self.compare_with_dense(paddle.neg, paddle.sparse.neg)
def test_sparse_pow(self): def test_sparse_pow(self):
self.compare_with_dense_one_attr(paddle.pow, paddle.incubate.sparse.pow, self.compare_with_dense_one_attr(paddle.pow, paddle.sparse.pow, 3)
3)
def test_sparse_mul_scalar(self): def test_sparse_mul_scalar(self):
self.compare_with_dense_one_attr(paddle.Tensor.__mul__, self.compare_with_dense_one_attr(paddle.Tensor.__mul__,
paddle.incubate.sparse.multiply, 3) paddle.sparse.multiply, 3)
def test_sparse_div_scalar(self): def test_sparse_div_scalar(self):
self.compare_with_dense_one_attr(paddle.Tensor.__div__, self.compare_with_dense_one_attr(paddle.Tensor.__div__,
paddle.incubate.sparse.divide, 2) paddle.sparse.divide, 2)
def test_sparse_cast(self): def test_sparse_cast(self):
self.compare_with_dense_two_attr(paddle.cast, self.compare_with_dense_two_attr(paddle.cast, paddle.sparse.cast,
paddle.incubate.sparse.cast, 'int32', 'int32', 'float32')
'float32') self.compare_with_dense_two_attr(paddle.cast, paddle.sparse.cast,
self.compare_with_dense_two_attr(paddle.cast, 'int32', 'float64')
paddle.incubate.sparse.cast, 'int32',
'float64')
if __name__ == "__main__": if __name__ == "__main__":
......
python/paddle/fluid/tests/unittests/test_sparse_utils_op.py
浏览文件 @ 50d4fa54
...@@ -16,7 +16,6 @@ from __future__ import print_function ...@@ -16,7 +16,6 @@ from __future__ import print_function
import unittest import unittest
import numpy as np import numpy as np
import paddle import paddle
from paddle.incubate import sparse
import paddle.fluid as fluid import paddle.fluid as fluid
import paddle.fluid.core as core import paddle.fluid.core as core
from paddle.fluid.framework import _test_eager_guard from paddle.fluid.framework import _test_eager_guard
...@@ -33,10 +32,10 @@ class TestSparseCreate(unittest.TestCase): ...@@ -33,10 +32,10 @@ class TestSparseCreate(unittest.TestCase):
dense_shape = [3, 4] dense_shape = [3, 4]
dense_indices = paddle.to_tensor(indices) dense_indices = paddle.to_tensor(indices)
dense_elements = paddle.to_tensor(values, dtype='float32') dense_elements = paddle.to_tensor(values, dtype='float32')
coo = paddle.incubate.sparse.sparse_coo_tensor(dense_indices, coo = paddle.sparse.sparse_coo_tensor(dense_indices,
dense_elements, dense_elements,
dense_shape, dense_shape,
stop_gradient=False) stop_gradient=False)
# test the to_string.py # test the to_string.py
assert np.array_equal(indices, coo.indices().numpy()) assert np.array_equal(indices, coo.indices().numpy())
assert np.array_equal(values, coo.values().numpy()) assert np.array_equal(values, coo.values().numpy())
...@@ -46,8 +45,7 @@ class TestSparseCreate(unittest.TestCase): ...@@ -46,8 +45,7 @@ class TestSparseCreate(unittest.TestCase):
indices = [[0, 1, 2], [1, 2, 0]] indices = [[0, 1, 2], [1, 2, 0]]
values = [1.0, 2.0, 3.0] values = [1.0, 2.0, 3.0]
dense_shape = [3, 3] dense_shape = [3, 3]
coo = paddle.incubate.sparse.sparse_coo_tensor( coo = paddle.sparse.sparse_coo_tensor(indices, values, dense_shape)
indices, values, dense_shape)
assert np.array_equal(3, coo.nnz()) assert np.array_equal(3, coo.nnz())
assert np.array_equal(indices, coo.indices().numpy()) assert np.array_equal(indices, coo.indices().numpy())
assert np.array_equal(values, coo.values().numpy()) assert np.array_equal(values, coo.values().numpy())
...@@ -62,12 +60,11 @@ class TestSparseCreate(unittest.TestCase): ...@@ -62,12 +60,11 @@ class TestSparseCreate(unittest.TestCase):
dense_cols = paddle.to_tensor(cols) dense_cols = paddle.to_tensor(cols)
dense_elements = paddle.to_tensor(values, dtype='float32') dense_elements = paddle.to_tensor(values, dtype='float32')
stop_gradient = False stop_gradient = False
csr = paddle.incubate.sparse.sparse_csr_tensor( csr = paddle.sparse.sparse_csr_tensor(dense_crows,
dense_crows, dense_cols,
dense_cols, dense_elements,
dense_elements, dense_shape,
dense_shape, stop_gradient=stop_gradient)
stop_gradient=stop_gradient)
def test_create_csr_by_np(self): def test_create_csr_by_np(self):
with _test_eager_guard(): with _test_eager_guard():
...@@ -75,8 +72,8 @@ class TestSparseCreate(unittest.TestCase): ...@@ -75,8 +72,8 @@ class TestSparseCreate(unittest.TestCase):
cols = [1, 3, 2, 0, 1] cols = [1, 3, 2, 0, 1]
values = [1, 2, 3, 4, 5] values = [1, 2, 3, 4, 5]
dense_shape = [3, 4] dense_shape = [3, 4]
csr = paddle.incubate.sparse.sparse_csr_tensor( csr = paddle.sparse.sparse_csr_tensor(crows, cols, values,
crows, cols, values, dense_shape) dense_shape)
# test the to_string.py # test the to_string.py
assert np.array_equal(5, csr.nnz()) assert np.array_equal(5, csr.nnz())
assert np.array_equal(crows, csr.crows().numpy()) assert np.array_equal(crows, csr.crows().numpy())
...@@ -89,10 +86,10 @@ class TestSparseCreate(unittest.TestCase): ...@@ -89,10 +86,10 @@ class TestSparseCreate(unittest.TestCase):
indices = [[0, 1], [0, 1]] indices = [[0, 1], [0, 1]]
values = [1.0, 2.0] values = [1.0, 2.0]
dense_shape = [2, 2] dense_shape = [2, 2]
coo = paddle.incubate.sparse.sparse_coo_tensor(indices, coo = paddle.sparse.sparse_coo_tensor(indices,
values, values,
dense_shape, dense_shape,
place=place) place=place)
assert coo.place.is_cpu_place() assert coo.place.is_cpu_place()
assert coo.values().place.is_cpu_place() assert coo.values().place.is_cpu_place()
assert coo.indices().place.is_cpu_place() assert coo.indices().place.is_cpu_place()
...@@ -100,10 +97,10 @@ class TestSparseCreate(unittest.TestCase): ...@@ -100,10 +97,10 @@ class TestSparseCreate(unittest.TestCase):
crows = [0, 2, 3, 5] crows = [0, 2, 3, 5]
cols = [1, 3, 2, 0, 1] cols = [1, 3, 2, 0, 1]
values = [1.0, 2.0, 3.0, 4.0, 5.0] values = [1.0, 2.0, 3.0, 4.0, 5.0]
csr = paddle.incubate.sparse.sparse_csr_tensor(crows, csr = paddle.sparse.sparse_csr_tensor(crows,
cols, cols,
values, [3, 5], values, [3, 5],
place=place) place=place)
assert csr.place.is_cpu_place() assert csr.place.is_cpu_place()
assert csr.crows().place.is_cpu_place() assert csr.crows().place.is_cpu_place()
assert csr.cols().place.is_cpu_place() assert csr.cols().place.is_cpu_place()
...@@ -116,19 +113,19 @@ class TestSparseCreate(unittest.TestCase): ...@@ -116,19 +113,19 @@ class TestSparseCreate(unittest.TestCase):
dense_shape = [2, 2] dense_shape = [2, 2]
indices = paddle.to_tensor(indices, dtype='int32') indices = paddle.to_tensor(indices, dtype='int32')
values = paddle.to_tensor(values, dtype='float32') values = paddle.to_tensor(values, dtype='float32')
coo = paddle.incubate.sparse.sparse_coo_tensor(indices, coo = paddle.sparse.sparse_coo_tensor(indices,
values, values,
dense_shape, dense_shape,
dtype='float64') dtype='float64')
assert coo.dtype == paddle.float64 assert coo.dtype == paddle.float64
crows = [0, 2, 3, 5] crows = [0, 2, 3, 5]
cols = [1, 3, 2, 0, 1] cols = [1, 3, 2, 0, 1]
values = [1.0, 2.0, 3.0, 4.0, 5.0] values = [1.0, 2.0, 3.0, 4.0, 5.0]
csr = paddle.incubate.sparse.sparse_csr_tensor(crows, csr = paddle.sparse.sparse_csr_tensor(crows,
cols, cols,
values, [3, 5], values, [3, 5],
dtype='float16') dtype='float16')
assert csr.dtype == paddle.float16 assert csr.dtype == paddle.float16
def test_create_coo_no_shape(self): def test_create_coo_no_shape(self):
...@@ -137,7 +134,7 @@ class TestSparseCreate(unittest.TestCase): ...@@ -137,7 +134,7 @@ class TestSparseCreate(unittest.TestCase):
values = [1.0, 2.0] values = [1.0, 2.0]
indices = paddle.to_tensor(indices, dtype='int32') indices = paddle.to_tensor(indices, dtype='int32')
values = paddle.to_tensor(values, dtype='float32') values = paddle.to_tensor(values, dtype='float32')
coo = paddle.incubate.sparse.sparse_coo_tensor(indices, values) coo = paddle.sparse.sparse_coo_tensor(indices, values)
assert [2, 2] == coo.shape assert [2, 2] == coo.shape
...@@ -155,7 +152,7 @@ class TestSparseConvert(unittest.TestCase): ...@@ -155,7 +152,7 @@ class TestSparseConvert(unittest.TestCase):
#test to_sparse_coo_grad backward #test to_sparse_coo_grad backward
out_grad_indices = [[0, 1], [0, 1]] out_grad_indices = [[0, 1], [0, 1]]
out_grad_values = [2.0, 3.0] out_grad_values = [2.0, 3.0]
out_grad = paddle.incubate.sparse.sparse_coo_tensor( out_grad = paddle.sparse.sparse_coo_tensor(
paddle.to_tensor(out_grad_indices), paddle.to_tensor(out_grad_indices),
paddle.to_tensor(out_grad_values), paddle.to_tensor(out_grad_values),
shape=out.shape, shape=out.shape,
...@@ -171,7 +168,7 @@ class TestSparseConvert(unittest.TestCase): ...@@ -171,7 +168,7 @@ class TestSparseConvert(unittest.TestCase):
values = [1.0, 2.0, 3.0, 4.0, 5.0] values = [1.0, 2.0, 3.0, 4.0, 5.0]
indices_dtypes = ['int32', 'int64'] indices_dtypes = ['int32', 'int64']
for indices_dtype in indices_dtypes: for indices_dtype in indices_dtypes:
sparse_x = paddle.incubate.sparse.sparse_coo_tensor( sparse_x = paddle.sparse.sparse_coo_tensor(
paddle.to_tensor(indices, dtype=indices_dtype), paddle.to_tensor(indices, dtype=indices_dtype),
paddle.to_tensor(values), paddle.to_tensor(values),
shape=[3, 4], shape=[3, 4],
...@@ -187,7 +184,7 @@ class TestSparseConvert(unittest.TestCase): ...@@ -187,7 +184,7 @@ class TestSparseConvert(unittest.TestCase):
sparse_x.grad.values().numpy()) sparse_x.grad.values().numpy())
paddle.device.set_device("cpu") paddle.device.set_device("cpu")
sparse_x_cpu = paddle.incubate.sparse.sparse_coo_tensor( sparse_x_cpu = paddle.sparse.sparse_coo_tensor(
paddle.to_tensor(indices, dtype=indices_dtype), paddle.to_tensor(indices, dtype=indices_dtype),
paddle.to_tensor(values), paddle.to_tensor(values),
shape=[3, 4], shape=[3, 4],
...@@ -218,7 +215,7 @@ class TestSparseConvert(unittest.TestCase): ...@@ -218,7 +215,7 @@ class TestSparseConvert(unittest.TestCase):
with _test_eager_guard(): with _test_eager_guard():
indices = [[0, 0, 1, 2, 2], [1, 3, 2, 0, 1]] indices = [[0, 0, 1, 2, 2], [1, 3, 2, 0, 1]]
values = [1.0, 2.0, 3.0, 4.0, 5.0] values = [1.0, 2.0, 3.0, 4.0, 5.0]
sparse_x = paddle.incubate.sparse.sparse_coo_tensor( sparse_x = paddle.sparse.sparse_coo_tensor(
paddle.to_tensor(indices), paddle.to_tensor(indices),
paddle.to_tensor(values), paddle.to_tensor(values),
shape=[3, 4], shape=[3, 4],
...@@ -231,7 +228,7 @@ class TestSparseConvert(unittest.TestCase): ...@@ -231,7 +228,7 @@ class TestSparseConvert(unittest.TestCase):
indices = [[0, 0, 1, 2, 2], [1, 3, 2, 0, 1]] 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], values = [[1.0, 1.0], [2.0, 2.0], [3.0, 3.0], [4.0, 4.0],
[5.0, 5.0]] [5.0, 5.0]]
sparse_x = paddle.incubate.sparse.sparse_coo_tensor( sparse_x = paddle.sparse.sparse_coo_tensor(
paddle.to_tensor(indices), paddle.to_tensor(indices),
paddle.to_tensor(values), paddle.to_tensor(values),
shape=[3, 4, 2], shape=[3, 4, 2],
...@@ -256,13 +253,13 @@ class TestSparseConvert(unittest.TestCase): ...@@ -256,13 +253,13 @@ class TestSparseConvert(unittest.TestCase):
values = paddle.to_tensor(values, values = paddle.to_tensor(values,
dtype='float32', dtype='float32',
stop_gradient=False) stop_gradient=False)
sparse_x = paddle.incubate.sparse.sparse_coo_tensor( sparse_x = paddle.sparse.sparse_coo_tensor(
indices, values, shape=[2, 2], stop_gradient=False) indices, values, shape=[2, 2], stop_gradient=False)
grad_indices = [[0, 1], [1, 1]] grad_indices = [[0, 1], [1, 1]]
grad_values = [2, 3] grad_values = [2, 3]
grad_indices = paddle.to_tensor(grad_indices, dtype='int32') grad_indices = paddle.to_tensor(grad_indices, dtype='int32')
grad_values = paddle.to_tensor(grad_values, dtype='float32') grad_values = paddle.to_tensor(grad_values, dtype='float32')
sparse_out_grad = paddle.incubate.sparse.sparse_coo_tensor( sparse_out_grad = paddle.sparse.sparse_coo_tensor(
grad_indices, grad_values, shape=[2, 2]) grad_indices, grad_values, shape=[2, 2])
sparse_x.backward(sparse_out_grad) sparse_x.backward(sparse_out_grad)
correct_values_grad = [0, 3] correct_values_grad = [0, 3]
...@@ -274,11 +271,11 @@ class TestSparseConvert(unittest.TestCase): ...@@ -274,11 +271,11 @@ class TestSparseConvert(unittest.TestCase):
values = paddle.to_tensor(values, values = paddle.to_tensor(values,
dtype='float32', dtype='float32',
stop_gradient=False) stop_gradient=False)
sparse_x = paddle.incubate.sparse.sparse_coo_tensor( sparse_x = paddle.sparse.sparse_coo_tensor(
indices, values, shape=[2, 2, 2], stop_gradient=False) indices, values, shape=[2, 2, 2], stop_gradient=False)
grad_values = [[2, 2], [3, 3]] grad_values = [[2, 2], [3, 3]]
grad_values = paddle.to_tensor(grad_values, dtype='float32') grad_values = paddle.to_tensor(grad_values, dtype='float32')
sparse_out_grad = paddle.incubate.sparse.sparse_coo_tensor( sparse_out_grad = paddle.sparse.sparse_coo_tensor(
grad_indices, grad_values, shape=[2, 2, 2]) grad_indices, grad_values, shape=[2, 2, 2])
sparse_x.backward(sparse_out_grad) sparse_x.backward(sparse_out_grad)
correct_values_grad = [[0, 0], [3, 3]] correct_values_grad = [[0, 0], [3, 3]]
...@@ -296,9 +293,8 @@ class TestSparseConvert(unittest.TestCase): ...@@ -296,9 +293,8 @@ class TestSparseConvert(unittest.TestCase):
values = [1.0, 2.0, 3.0] values = [1.0, 2.0, 3.0]
indices = paddle.to_tensor(indices, dtype='int32') indices = paddle.to_tensor(indices, dtype='int32')
values = paddle.to_tensor(values, dtype='float32') values = paddle.to_tensor(values, dtype='float32')
sparse_x = paddle.incubate.sparse.sparse_coo_tensor( sparse_x = paddle.sparse.sparse_coo_tensor(indices, values)
indices, values) sparse_x = paddle.sparse.coalesce(sparse_x)
sparse_x = paddle.incubate.sparse.coalesce(sparse_x)
indices_sorted = [[0, 1], [1, 0]] indices_sorted = [[0, 1], [1, 0]]
values_sorted = [5.0, 1.0] values_sorted = [5.0, 1.0]
assert np.array_equal(indices_sorted, assert np.array_equal(indices_sorted,
...@@ -309,9 +305,8 @@ class TestSparseConvert(unittest.TestCase): ...@@ -309,9 +305,8 @@ class TestSparseConvert(unittest.TestCase):
# test the non-zero values is a vector # test the non-zero values is a vector
values = [[1.0, 1.0], [2.0, 2.0], [3.0, 3.0]] values = [[1.0, 1.0], [2.0, 2.0], [3.0, 3.0]]
values = paddle.to_tensor(values, dtype='float32') values = paddle.to_tensor(values, dtype='float32')
sparse_x = paddle.incubate.sparse.sparse_coo_tensor( sparse_x = paddle.sparse.sparse_coo_tensor(indices, values)
indices, values) sparse_x = paddle.sparse.coalesce(sparse_x)
sparse_x = paddle.incubate.sparse.coalesce(sparse_x)
values_sorted = [[5.0, 5.0], [1.0, 1.0]] values_sorted = [[5.0, 5.0], [1.0, 1.0]]
assert np.array_equal(indices_sorted, assert np.array_equal(indices_sorted,
sparse_x.indices().numpy()) sparse_x.indices().numpy())
...@@ -365,8 +360,9 @@ class TestCooError(unittest.TestCase): ...@@ -365,8 +360,9 @@ class TestCooError(unittest.TestCase):
values = [1, 2] values = [1, 2]
# 1. the shape too small # 1. the shape too small
dense_shape = [2, 2] dense_shape = [2, 2]
sparse_x = paddle.incubate.sparse.sparse_coo_tensor( sparse_x = paddle.sparse.sparse_coo_tensor(indices,
indices, values, shape=dense_shape) values,
shape=dense_shape)
def test_same_nnz(self): def test_same_nnz(self):
with _test_eager_guard(): with _test_eager_guard():
...@@ -374,8 +370,7 @@ class TestCooError(unittest.TestCase): ...@@ -374,8 +370,7 @@ class TestCooError(unittest.TestCase):
# 2. test the nnz of indices must same as nnz of values # 2. test the nnz of indices must same as nnz of values
indices = [[1, 2], [1, 0]] indices = [[1, 2], [1, 0]]
values = [1, 2, 3] values = [1, 2, 3]
sparse_x = paddle.incubate.sparse.sparse_coo_tensor( sparse_x = paddle.sparse.sparse_coo_tensor(indices, values)
indices, values)
def test_same_dimensions(self): def test_same_dimensions(self):
with _test_eager_guard(): with _test_eager_guard():
...@@ -383,17 +378,16 @@ class TestCooError(unittest.TestCase): ...@@ -383,17 +378,16 @@ class TestCooError(unittest.TestCase):
indices = [[1, 2], [1, 0]] indices = [[1, 2], [1, 0]]
values = [1, 2, 3] values = [1, 2, 3]
shape = [2, 3, 4] shape = [2, 3, 4]
sparse_x = paddle.incubate.sparse.sparse_coo_tensor(indices, sparse_x = paddle.sparse.sparse_coo_tensor(indices,
values, values,
shape=shape) shape=shape)
def test_indices_dtype(self): def test_indices_dtype(self):
with _test_eager_guard(): with _test_eager_guard():
with self.assertRaises(TypeError): with self.assertRaises(TypeError):
indices = [[1.0, 2.0], [0, 1]] indices = [[1.0, 2.0], [0, 1]]
values = [1, 2] values = [1, 2]
sparse_x = paddle.incubate.sparse.sparse_coo_tensor( sparse_x = paddle.sparse.sparse_coo_tensor(indices, values)
indices, values)
class TestCsrError(unittest.TestCase): class TestCsrError(unittest.TestCase):
...@@ -405,7 +399,7 @@ class TestCsrError(unittest.TestCase): ...@@ -405,7 +399,7 @@ class TestCsrError(unittest.TestCase):
cols = [0, 1, 2] cols = [0, 1, 2]
values = [1, 2, 3] values = [1, 2, 3]
shape = [3] shape = [3]
sparse_x = paddle.incubate.sparse.sparse_csr_tensor( sparse_x = paddle.sparse.sparse_csr_tensor(
crows, cols, values, shape) crows, cols, values, shape)
def test_dimension2(self): def test_dimension2(self):
...@@ -415,7 +409,7 @@ class TestCsrError(unittest.TestCase): ...@@ -415,7 +409,7 @@ class TestCsrError(unittest.TestCase):
cols = [0, 1, 2] cols = [0, 1, 2]
values = [1, 2, 3] values = [1, 2, 3]
shape = [3, 3, 3, 3] shape = [3, 3, 3, 3]
sparse_x = paddle.incubate.sparse.sparse_csr_tensor( sparse_x = paddle.sparse.sparse_csr_tensor(
crows, cols, values, shape) crows, cols, values, shape)
def test_same_shape1(self): def test_same_shape1(self):
...@@ -425,7 +419,7 @@ class TestCsrError(unittest.TestCase): ...@@ -425,7 +419,7 @@ class TestCsrError(unittest.TestCase):
cols = [0, 1, 2, 3] cols = [0, 1, 2, 3]
values = [1, 2, 3] values = [1, 2, 3]
shape = [3, 4] shape = [3, 4]
sparse_x = paddle.incubate.sparse.sparse_csr_tensor( sparse_x = paddle.sparse.sparse_csr_tensor(
crows, cols, values, shape) crows, cols, values, shape)
def test_same_shape2(self): def test_same_shape2(self):
...@@ -435,7 +429,7 @@ class TestCsrError(unittest.TestCase): ...@@ -435,7 +429,7 @@ class TestCsrError(unittest.TestCase):
cols = [0, 1, 2, 3] cols = [0, 1, 2, 3]
values = [1, 2, 3, 4] values = [1, 2, 3, 4]
shape = [3, 4] shape = [3, 4]
sparse_x = paddle.incubate.sparse.sparse_csr_tensor( sparse_x = paddle.sparse.sparse_csr_tensor(
crows, cols, values, shape) crows, cols, values, shape)
def test_same_shape3(self): def test_same_shape3(self):
...@@ -445,7 +439,7 @@ class TestCsrError(unittest.TestCase): ...@@ -445,7 +439,7 @@ class TestCsrError(unittest.TestCase):
cols = [0, 1, 2, 3, 0, 1, 2] cols = [0, 1, 2, 3, 0, 1, 2]
values = [1, 2, 3, 4, 0, 1, 2] values = [1, 2, 3, 4, 0, 1, 2]
shape = [2, 3, 4] shape = [2, 3, 4]
sparse_x = paddle.incubate.sparse.sparse_csr_tensor( sparse_x = paddle.sparse.sparse_csr_tensor(
crows, cols, values, shape) crows, cols, values, shape)
def test_crows_first_value(self): def test_crows_first_value(self):
...@@ -455,7 +449,7 @@ class TestCsrError(unittest.TestCase): ...@@ -455,7 +449,7 @@ class TestCsrError(unittest.TestCase):
cols = [0, 1, 2] cols = [0, 1, 2]
values = [1, 2, 3] values = [1, 2, 3]
shape = [3, 4] shape = [3, 4]
sparse_x = paddle.incubate.sparse.sparse_csr_tensor( sparse_x = paddle.sparse.sparse_csr_tensor(
crows, cols, values, shape) crows, cols, values, shape)
def test_dtype(self): def test_dtype(self):
...@@ -465,7 +459,7 @@ class TestCsrError(unittest.TestCase): ...@@ -465,7 +459,7 @@ class TestCsrError(unittest.TestCase):
cols = [0, 1, 2] cols = [0, 1, 2]
values = [1, 2, 3] values = [1, 2, 3]
shape = [3] shape = [3]
sparse_x = paddle.incubate.sparse.sparse_csr_tensor( sparse_x = paddle.sparse.sparse_csr_tensor(
crows, cols, values, shape) crows, cols, values, shape)
......
python/paddle/incubate/__init__.py
浏览文件 @ 50d4fa54
...@@ -31,7 +31,6 @@ from .passes import fuse_resnet_unit_pass ...@@ -31,7 +31,6 @@ from .passes import fuse_resnet_unit_pass
from . import autograd #noqa: F401 from . import autograd #noqa: F401
from . import autotune #noqa: F401 from . import autotune #noqa: F401
from . import sparse #noqa: F401
from . import nn #noqa: F401 from . import nn #noqa: F401
from . import asp #noqa: F401 from . import asp #noqa: F401
......
python/paddle/incubate/sparse/__init__.py → python/paddle/sparse/__init__.py
浏览文件 @ 50d4fa54
...@@ -51,9 +51,36 @@ from .multiary import addmm ...@@ -51,9 +51,36 @@ from .multiary import addmm
from . import nn from . import nn
__all__ = [ __all__ = [
'sparse_coo_tensor', 'sparse_csr_tensor', 'sin', 'tan', 'asin', 'atan', 'sparse_coo_tensor',
'sinh', 'tanh', 'asinh', 'atanh', 'sqrt', 'square', 'log1p', 'abs', 'pow', 'sparse_csr_tensor',
'cast', 'neg', 'deg2rad', 'rad2deg', 'expm1', 'mv', 'matmul', 'sin',
'masked_matmul', 'addmm', 'add', 'subtract', 'transpose', 'multiply', 'tan',
'divide', 'coalesce', 'is_same_shape', 'reshape' 'asin',
'atan',
'sinh',
'tanh',
'asinh',
'atanh',
'sqrt',
'square',
'log1p',
'abs',
'pow',
'cast',
'neg',
'deg2rad',
'rad2deg',
'expm1',
'mv',
'matmul',
'masked_matmul',
'addmm',
'add',
'subtract',
'transpose',
'multiply',
'divide',
'coalesce',
'is_same_shape',
'reshape',
] ]
python/paddle/incubate/sparse/binary.py → python/paddle/sparse/binary.py
浏览文件 @ 50d4fa54
...@@ -64,19 +64,20 @@ def matmul(x, y, name=None): ...@@ -64,19 +64,20 @@ def matmul(x, y, name=None):
.. code-block:: python .. code-block:: python
# required: gpu
import paddle import paddle
# csr @ dense -> dense # csr @ dense -> dense
crows = [0, 1, 2, 3] crows = [0, 1, 2, 3]
cols = [1, 2, 0] cols = [1, 2, 0]
values = [1., 2., 3.] values = [1., 2., 3.]
csr = paddle.incubate.sparse.sparse_csr_tensor(crows, cols, values, [3, 3]) csr = paddle.sparse.sparse_csr_tensor(crows, cols, values, [3, 3])
# Tensor(shape=[3, 3], dtype=paddle.float32, place=Place(gpu:0), stop_gradient=True, # Tensor(shape=[3, 3], dtype=paddle.float32, place=Place(gpu:0), stop_gradient=True,
# crows=[0, 1, 2, 3], # crows=[0, 1, 2, 3],
# cols=[1, 2, 0], # cols=[1, 2, 0],
# values=[1., 2., 3.]) # values=[1., 2., 3.])
dense = paddle.ones([3, 2]) dense = paddle.ones([3, 2])
out = paddle.incubate.sparse.matmul(csr, dense) out = paddle.sparse.matmul(csr, dense)
# Tensor(shape=[3, 2], dtype=float32, place=Place(gpu:0), stop_gradient=True, # Tensor(shape=[3, 2], dtype=float32, place=Place(gpu:0), stop_gradient=True,
# [[1., 1.], # [[1., 1.],
# [2., 2.], # [2., 2.],
...@@ -85,13 +86,13 @@ def matmul(x, y, name=None): ...@@ -85,13 +86,13 @@ def matmul(x, y, name=None):
# coo @ dense -> dense # coo @ dense -> dense
indices = [[0, 1, 2], [1, 2, 0]] indices = [[0, 1, 2], [1, 2, 0]]
values = [1., 2., 3.] values = [1., 2., 3.]
coo = paddle.incubate.sparse.sparse_coo_tensor(indices, values, [3, 3]) coo = paddle.sparse.sparse_coo_tensor(indices, values, [3, 3])
# Tensor(shape=[3, 3], dtype=paddle.float32, place=Place(gpu:0), stop_gradient=True, # Tensor(shape=[3, 3], dtype=paddle.float32, place=Place(gpu:0), stop_gradient=True,
# indices=[[0, 1, 2], # indices=[[0, 1, 2],
# [1, 2, 0]], # [1, 2, 0]],
# values=[1., 2., 3.]) # values=[1., 2., 3.])
dense = paddle.ones([3, 2]) dense = paddle.ones([3, 2])
out = paddle.incubate.sparse.matmul(coo, dense) out = paddle.sparse.matmul(coo, dense)
# Tensor(shape=[3, 2], dtype=float32, place=Place(gpu:0), stop_gradient=True, # Tensor(shape=[3, 2], dtype=float32, place=Place(gpu:0), stop_gradient=True,
# [[1., 1.], # [[1., 1.],
# [2., 2.], # [2., 2.],
...@@ -133,6 +134,7 @@ def masked_matmul(x, y, mask, name=None): ...@@ -133,6 +134,7 @@ def masked_matmul(x, y, mask, name=None):
.. code-block:: python .. code-block:: python
# required: gpu
import paddle import paddle
paddle.seed(100) paddle.seed(100)
...@@ -141,7 +143,7 @@ def masked_matmul(x, y, mask, name=None): ...@@ -141,7 +143,7 @@ def masked_matmul(x, y, mask, name=None):
cols = [1, 3, 2, 0, 1] cols = [1, 3, 2, 0, 1]
values = [1., 2., 3., 4., 5.] values = [1., 2., 3., 4., 5.]
dense_shape = [3, 4] dense_shape = [3, 4]
mask = paddle.incubate.sparse.sparse_csr_tensor(crows, cols, values, dense_shape) mask = paddle.sparse.sparse_csr_tensor(crows, cols, values, dense_shape)
# Tensor(shape=[3, 4], dtype=paddle.float32, place=Place(gpu:0), stop_gradient=True, # Tensor(shape=[3, 4], dtype=paddle.float32, place=Place(gpu:0), stop_gradient=True,
# crows=[0, 2, 3, 5], # crows=[0, 2, 3, 5],
# cols=[1, 3, 2, 0, 1], # cols=[1, 3, 2, 0, 1],
...@@ -150,7 +152,7 @@ def masked_matmul(x, y, mask, name=None): ...@@ -150,7 +152,7 @@ def masked_matmul(x, y, mask, name=None):
x = paddle.rand([3, 5]) x = paddle.rand([3, 5])
y = paddle.rand([5, 4]) y = paddle.rand([5, 4])
out = paddle.incubate.sparse.masked_matmul(x, y, mask) out = paddle.sparse.masked_matmul(x, y, mask)
# Tensor(shape=[3, 4], dtype=paddle.float32, place=Place(gpu:0), stop_gradient=True, # Tensor(shape=[3, 4], dtype=paddle.float32, place=Place(gpu:0), stop_gradient=True,
# crows=[0, 2, 3, 5], # crows=[0, 2, 3, 5],
# cols=[1, 3, 2, 0, 1], # cols=[1, 3, 2, 0, 1],
...@@ -191,6 +193,7 @@ def mv(x, vec, name=None): ...@@ -191,6 +193,7 @@ def mv(x, vec, name=None):
.. code-block:: python .. code-block:: python
# required: gpu
import paddle import paddle
from paddle.fluid.framework import _test_eager_guard from paddle.fluid.framework import _test_eager_guard
paddle.seed(100) paddle.seed(100)
...@@ -201,14 +204,14 @@ def mv(x, vec, name=None): ...@@ -201,14 +204,14 @@ def mv(x, vec, name=None):
cols = [1, 3, 2, 0, 1] cols = [1, 3, 2, 0, 1]
values = [1., 2., 3., 4., 5.] values = [1., 2., 3., 4., 5.]
dense_shape = [3, 4] dense_shape = [3, 4]
csr = paddle.incubate.sparse.sparse_csr_tensor(crows, cols, values, dense_shape) csr = paddle.sparse.sparse_csr_tensor(crows, cols, values, dense_shape)
# Tensor(shape=[3, 4], dtype=paddle.float32, place=Place(gpu:0), stop_gradient=True, # Tensor(shape=[3, 4], dtype=paddle.float32, place=Place(gpu:0), stop_gradient=True,
# crows=[0, 2, 3, 5], # crows=[0, 2, 3, 5],
# cols=[1, 3, 2, 0, 1], # cols=[1, 3, 2, 0, 1],
# values=[1., 2., 3., 4., 5.]) # values=[1., 2., 3., 4., 5.])
vec = paddle.randn([4]) vec = paddle.randn([4])
out = paddle.incubate.sparse.mv(csr, vec) out = paddle.sparse.mv(csr, vec)
# Tensor(shape=[3], dtype=float32, place=Place(gpu:0), stop_gradient=True, # Tensor(shape=[3], dtype=float32, place=Place(gpu:0), stop_gradient=True,
# [-3.85499096, -2.42975140, -1.75087738]) # [-3.85499096, -2.42975140, -1.75087738])
...@@ -247,7 +250,7 @@ def add(x, y, name=None): ...@@ -247,7 +250,7 @@ def add(x, y, name=None):
y = paddle.to_tensor([[0, 0, 0, -2], [0, 2, -3, 0], [2, 3, 4, 8]], 'float32') y = paddle.to_tensor([[0, 0, 0, -2], [0, 2, -3, 0], [2, 3, 4, 8]], 'float32')
sparse_x = x.to_sparse_csr() sparse_x = x.to_sparse_csr()
sparse_y = y.to_sparse_csr() sparse_y = y.to_sparse_csr()
sparse_z = paddle.incubate.sparse.add(sparse_x, sparse_y) sparse_z = paddle.sparse.add(sparse_x, sparse_y)
print(sparse_z.to_dense()) print(sparse_z.to_dense())
# [[ 0., -1., 0., 0.], # [[ 0., -1., 0., 0.],
...@@ -304,7 +307,7 @@ def subtract(x, y, name=None): ...@@ -304,7 +307,7 @@ def subtract(x, y, name=None):
y = paddle.to_tensor([[0, 0, 0, -2], [0, 2, -3, 0], [2, 3, 4, 8]], 'float32') y = paddle.to_tensor([[0, 0, 0, -2], [0, 2, -3, 0], [2, 3, 4, 8]], 'float32')
sparse_x = x.to_sparse_csr() sparse_x = x.to_sparse_csr()
sparse_y = y.to_sparse_csr() sparse_y = y.to_sparse_csr()
sparse_z = paddle.incubate.sparse.subtract(sparse_x, sparse_y) sparse_z = paddle.sparse.subtract(sparse_x, sparse_y)
print(sparse_z.to_dense()) print(sparse_z.to_dense())
# [[ 0., -1., 0., 4.], # [[ 0., -1., 0., 4.],
...@@ -349,7 +352,7 @@ def multiply(x, y, name=None): ...@@ -349,7 +352,7 @@ def multiply(x, y, name=None):
y = paddle.to_tensor([[0, 0, 0, -2], [0, 2, -3, 0], [2, 3, 4, 8]], 'float32') y = paddle.to_tensor([[0, 0, 0, -2], [0, 2, -3, 0], [2, 3, 4, 8]], 'float32')
sparse_x = x.to_sparse_csr() sparse_x = x.to_sparse_csr()
sparse_y = y.to_sparse_csr() sparse_y = y.to_sparse_csr()
sparse_z = paddle.incubate.sparse.multiply(sparse_x, sparse_y) sparse_z = paddle.sparse.multiply(sparse_x, sparse_y)
print(sparse_z.to_dense()) print(sparse_z.to_dense())
# [[ 0., 0., 0., -4.], # [[ 0., 0., 0., -4.],
...@@ -397,7 +400,7 @@ def divide(x, y, name=None): ...@@ -397,7 +400,7 @@ def divide(x, y, name=None):
y = paddle.to_tensor([[0, 0, 0, -2], [0, 2, -3, 0], [2, 3, 4, 8]], 'float32') y = paddle.to_tensor([[0, 0, 0, -2], [0, 2, -3, 0], [2, 3, 4, 8]], 'float32')
sparse_x = x.to_sparse_csr() sparse_x = x.to_sparse_csr()
sparse_y = y.to_sparse_csr() sparse_y = y.to_sparse_csr()
sparse_z = paddle.incubate.sparse.divide(sparse_x, sparse_y) sparse_z = paddle.sparse.divide(sparse_x, sparse_y)
print(sparse_z.to_dense()) print(sparse_z.to_dense())
# [[ nan , -inf. , nan , -1. ], # [[ nan , -inf. , nan , -1. ],
...@@ -440,9 +443,9 @@ def is_same_shape(x, y): ...@@ -440,9 +443,9 @@ def is_same_shape(x, y):
y = y.to_sparse_csr() y = y.to_sparse_csr()
z = paddle.rand([2, 5]) z = paddle.rand([2, 5])
paddle.incubate.sparse.is_same_shape(x, y) paddle.sparse.is_same_shape(x, y)
# True # True
paddle.incubate.sparse.is_same_shape(x, z) paddle.sparse.is_same_shape(x, z)
# False # False
""" """
......
python/paddle/incubate/sparse/creation.py → python/paddle/sparse/creation.py
浏览文件 @ 50d4fa54
...@@ -113,7 +113,7 @@ def sparse_coo_tensor(indices, ...@@ -113,7 +113,7 @@ def sparse_coo_tensor(indices,
indices = [[0, 1, 2], [1, 2, 0]] indices = [[0, 1, 2], [1, 2, 0]]
values = [1.0, 2.0, 3.0] values = [1.0, 2.0, 3.0]
dense_shape = [3, 3] dense_shape = [3, 3]
coo = paddle.incubate.sparse.sparse_coo_tensor(indices, values, dense_shape) coo = paddle.sparse.sparse_coo_tensor(indices, values, dense_shape)
# print(coo) # print(coo)
# Tensor(shape=[2, 3], dtype=paddle.float32, place=Place(gpu:0), stop_gradient=True, # Tensor(shape=[2, 3], dtype=paddle.float32, place=Place(gpu:0), stop_gradient=True,
# indices=[[0, 1, 2], # indices=[[0, 1, 2],
...@@ -240,7 +240,7 @@ def sparse_csr_tensor(crows, ...@@ -240,7 +240,7 @@ def sparse_csr_tensor(crows,
cols = [1, 3, 2, 0, 1] cols = [1, 3, 2, 0, 1]
values = [1, 2, 3, 4, 5] values = [1, 2, 3, 4, 5]
dense_shape = [3, 4] dense_shape = [3, 4]
csr = paddle.incubate.sparse.sparse_csr_tensor(crows, cols, values, dense_shape) csr = paddle.sparse.sparse_csr_tensor(crows, cols, values, dense_shape)
# print(csr) # print(csr)
# Tensor(shape=[3, 4], dtype=paddle.int64, place=Place(gpu:0), stop_gradient=True, # Tensor(shape=[3, 4], dtype=paddle.int64, place=Place(gpu:0), stop_gradient=True,
# crows=[0, 2, 3, 5], # crows=[0, 2, 3, 5],
......
python/paddle/incubate/sparse/multiary.py → python/paddle/sparse/multiary.py
浏览文件 @ 50d4fa54
...@@ -58,6 +58,7 @@ def addmm(input, x, y, beta=1.0, alpha=1.0, name=None): ...@@ -58,6 +58,7 @@ def addmm(input, x, y, beta=1.0, alpha=1.0, name=None):
.. code-block:: python .. code-block:: python
# required: gpu
import paddle import paddle
# dense + csr @ dense -> dense # dense + csr @ dense -> dense
...@@ -65,17 +66,17 @@ def addmm(input, x, y, beta=1.0, alpha=1.0, name=None): ...@@ -65,17 +66,17 @@ def addmm(input, x, y, beta=1.0, alpha=1.0, name=None):
crows = [0, 1, 2, 3] crows = [0, 1, 2, 3]
cols = [1, 2, 0] cols = [1, 2, 0]
values = [1., 2., 3.] values = [1., 2., 3.]
x = paddle.incubate.sparse.sparse_csr_tensor(crows, cols, values, [3, 3]) x = paddle.sparse.sparse_csr_tensor(crows, cols, values, [3, 3])
y = paddle.rand([3, 2]) y = paddle.rand([3, 2])
out = paddle.incubate.sparse.addmm(input, x, y, 3.0, 2.0) out = paddle.sparse.addmm(input, x, y, 3.0, 2.0)
# dense + coo @ dense -> dense # dense + coo @ dense -> dense
input = paddle.rand([3, 2]) input = paddle.rand([3, 2])
indices = [[0, 1, 2], [1, 2, 0]] indices = [[0, 1, 2], [1, 2, 0]]
values = [1., 2., 3.] values = [1., 2., 3.]
x = paddle.incubate.sparse.sparse_coo_tensor(indices, values, [3, 3]) x = paddle.sparse.sparse_coo_tensor(indices, values, [3, 3])
y = paddle.rand([3, 2]) y = paddle.rand([3, 2])
out = paddle.incubate.sparse.addmm(input, x, y, 3.0, 2.0) out = paddle.sparse.addmm(input, x, y, 3.0, 2.0)
""" """
return _C_ops.sparse_addmm(input, x, y, alpha, beta) return _C_ops.sparse_addmm(input, x, y, alpha, beta)
python/paddle/incubate/sparse/nn/functional/activation.py → python/paddle/sparse/nn/functional/activation.py
浏览文件 @ 50d4fa54
...@@ -43,7 +43,7 @@ def relu(x, name=None): ...@@ -43,7 +43,7 @@ def relu(x, name=None):
dense_x = paddle.to_tensor([-2., 0., 1.]) dense_x = paddle.to_tensor([-2., 0., 1.])
sparse_x = dense_x.to_sparse_coo(1) sparse_x = dense_x.to_sparse_coo(1)
out = paddle.incubate.sparse.nn.functional.relu(sparse_x) out = paddle.sparse.nn.functional.relu(sparse_x)
# [0., 0., 1.] # [0., 0., 1.]
""" """
if in_dynamic_mode(): if in_dynamic_mode():
...@@ -104,7 +104,7 @@ def softmax(x, axis=-1, name=None): ...@@ -104,7 +104,7 @@ def softmax(x, axis=-1, name=None):
# values=[0.96823406, 0.19722934, 0.94373937, 0.02060066, 0.71456372, # values=[0.96823406, 0.19722934, 0.94373937, 0.02060066, 0.71456372,
# 0.98275049]) # 0.98275049])
out = paddle.incubate.sparse.nn.functional.softmax(csr) out = paddle.sparse.nn.functional.softmax(csr)
# Tensor(shape=[3, 4], dtype=paddle.float64, place=Place(gpu:0), stop_gradient=True, # Tensor(shape=[3, 4], dtype=paddle.float64, place=Place(gpu:0), stop_gradient=True,
# crows=[0, 2, 5, 6], # crows=[0, 2, 5, 6],
# cols=[2, 3, 0, 2, 3, 3], # cols=[2, 3, 0, 2, 3, 3],
...@@ -139,7 +139,7 @@ def relu6(x, name=None): ...@@ -139,7 +139,7 @@ def relu6(x, name=None):
dense_x = paddle.to_tensor([-2., 0., 8.]) dense_x = paddle.to_tensor([-2., 0., 8.])
sparse_x = dense_x.to_sparse_coo(1) sparse_x = dense_x.to_sparse_coo(1)
out = paddle.incubate.sparse.nn.functional.relu6(sparse_x) out = paddle.sparse.nn.functional.relu6(sparse_x)
""" """
return _C_ops.sparse_relu6(x, 6.0) return _C_ops.sparse_relu6(x, 6.0)
...@@ -175,6 +175,6 @@ def leaky_relu(x, negative_slope=0.01, name=None): ...@@ -175,6 +175,6 @@ def leaky_relu(x, negative_slope=0.01, name=None):
dense_x = paddle.to_tensor([-2., 0., 5.]) dense_x = paddle.to_tensor([-2., 0., 5.])
sparse_x = dense_x.to_sparse_coo(1) sparse_x = dense_x.to_sparse_coo(1)
out = paddle.incubate.sparse.nn.functional.leaky_relu(sparse_x, 0.5) out = paddle.sparse.nn.functional.leaky_relu(sparse_x, 0.5)
""" """
return _C_ops.sparse_leaky_relu(x, negative_slope) return _C_ops.sparse_leaky_relu(x, negative_slope)
python/paddle/incubate/sparse/nn/functional/conv.py → python/paddle/sparse/nn/functional/conv.py
浏览文件 @ 50d4fa54
...@@ -203,9 +203,9 @@ def conv3d(x, ...@@ -203,9 +203,9 @@ def conv3d(x,
indices = paddle.to_tensor(indices, dtype='int32') indices = paddle.to_tensor(indices, dtype='int32')
values = paddle.to_tensor(values, dtype='float32') values = paddle.to_tensor(values, dtype='float32')
dense_shape = [1, 1, 3, 4, 1] dense_shape = [1, 1, 3, 4, 1]
sparse_x = paddle.incubate.sparse.sparse_coo_tensor(indices, values, dense_shape, stop_gradient=True) sparse_x = paddle.sparse.sparse_coo_tensor(indices, values, dense_shape, stop_gradient=True)
weight = paddle.randn((1, 3, 3, 1, 1), dtype='float32') weight = paddle.randn((1, 3, 3, 1, 1), dtype='float32')
y = paddle.incubate.sparse.nn.functional.conv3d(sparse_x, weight) y = paddle.sparse.nn.functional.conv3d(sparse_x, weight)
print(y.shape) print(y.shape)
# (1, 1, 1, 2, 1) # (1, 1, 1, 2, 1)
""" """
...@@ -323,9 +323,9 @@ def subm_conv3d(x, ...@@ -323,9 +323,9 @@ def subm_conv3d(x,
indices = paddle.to_tensor(indices, dtype='int32') indices = paddle.to_tensor(indices, dtype='int32')
values = paddle.to_tensor(values, dtype='float32') values = paddle.to_tensor(values, dtype='float32')
dense_shape = [1, 1, 3, 4, 1] dense_shape = [1, 1, 3, 4, 1]
sparse_x = paddle.incubate.sparse.sparse_coo_tensor(indices, values, dense_shape, stop_gradient=True) sparse_x = paddle.sparse.sparse_coo_tensor(indices, values, dense_shape, stop_gradient=True)
weight = paddle.randn((1, 3, 3, 1, 1), dtype='float32') weight = paddle.randn((1, 3, 3, 1, 1), dtype='float32')
y = paddle.incubate.sparse.nn.functional.subm_conv3d(sparse_x, weight) y = paddle.sparse.nn.functional.subm_conv3d(sparse_x, weight)
print(y.shape) print(y.shape)
#(1, 1, 3, 4, 1) #(1, 1, 3, 4, 1)
""" """
......
python/paddle/incubate/sparse/nn/functional/pooling.py → python/paddle/sparse/nn/functional/pooling.py
浏览文件 @ 50d4fa54
...@@ -70,7 +70,7 @@ def max_pool3d(x, ...@@ -70,7 +70,7 @@ def max_pool3d(x,
kernel_sizes = [3, 3, 3] kernel_sizes = [3, 3, 3]
paddings = [0, 0, 0] paddings = [0, 0, 0]
strides = [1, 1, 1] strides = [1, 1, 1]
out = paddle.incubate.sparse.nn.functional.max_pool3d(sparse_x, kernel_sizes, stride=strides, padding=paddings) out = paddle.sparse.nn.functional.max_pool3d(sparse_x, kernel_sizes, stride=strides, padding=paddings)
#[1, 2, 2, 2, 3] #[1, 2, 2, 2, 3]
""" """
......
python/paddle/incubate/sparse/nn/functional/transformer.py → python/paddle/sparse/nn/functional/transformer.py
浏览文件 @ 50d4fa54
...@@ -63,7 +63,8 @@ def attention(query, ...@@ -63,7 +63,8 @@ def attention(query,
Examples: Examples:
.. code-block:: python .. code-block:: python
# required: gpu
import paddle import paddle
batch_size = 16 batch_size = 16
...@@ -85,7 +86,7 @@ def attention(query, ...@@ -85,7 +86,7 @@ def attention(query,
kp_mask = paddle.randint(0, 2, [batch_size, seq_len]) kp_mask = paddle.randint(0, 2, [batch_size, seq_len])
attn_mask = paddle.randint(0, 2, [seq_len, seq_len]) attn_mask = paddle.randint(0, 2, [seq_len, seq_len])
output = paddle.incubate.sparse.nn.functional.attention(query, key, value, sp_mask, kp_mask, attn_mask) output = paddle.sparse.nn.functional.attention(query, key, value, sp_mask, kp_mask, attn_mask)
output.backward() output.backward()
""" """
return _C_ops.sparse_fused_attention(query, key, value, sparse_mask, return _C_ops.sparse_fused_attention(query, key, value, sparse_mask,
......
python/paddle/incubate/sparse/nn/layer/activation.py → python/paddle/sparse/nn/layer/activation.py
浏览文件 @ 50d4fa54
...@@ -41,7 +41,7 @@ class ReLU(Layer): ...@@ -41,7 +41,7 @@ class ReLU(Layer):
dense_x = paddle.to_tensor([-2., 0., 1.]) dense_x = paddle.to_tensor([-2., 0., 1.])
sparse_x = dense_x.to_sparse_coo(1) sparse_x = dense_x.to_sparse_coo(1)
relu = paddle.incubate.sparse.nn.ReLU() relu = paddle.sparse.nn.ReLU()
out = relu(sparse_x) out = relu(sparse_x)
# [0., 0., 1.] # [0., 0., 1.]
""" """
...@@ -102,7 +102,7 @@ class Softmax(Layer): ...@@ -102,7 +102,7 @@ class Softmax(Layer):
# values=[0.96823406, 0.19722934, 0.94373937, 0.02060066, 0.71456372, # values=[0.96823406, 0.19722934, 0.94373937, 0.02060066, 0.71456372,
# 0.98275049]) # 0.98275049])
softmax = paddle.incubate.sparse.nn.Softmax() softmax = paddle.sparse.nn.Softmax()
out = softmax(csr) out = softmax(csr)
# Tensor(shape=[3, 4], dtype=paddle.float64, place=Place(gpu:0), stop_gradient=True, # Tensor(shape=[3, 4], dtype=paddle.float64, place=Place(gpu:0), stop_gradient=True,
# crows=[0, 2, 5, 6], # crows=[0, 2, 5, 6],
...@@ -147,7 +147,7 @@ class ReLU6(Layer): ...@@ -147,7 +147,7 @@ class ReLU6(Layer):
dense_x = paddle.to_tensor([-2., 0., 8.]) dense_x = paddle.to_tensor([-2., 0., 8.])
sparse_x = dense_x.to_sparse_coo(1) sparse_x = dense_x.to_sparse_coo(1)
relu6 = paddle.incubate.sparse.nn.ReLU6() relu6 = paddle.sparse.nn.ReLU6()
out = relu6(sparse_x) out = relu6(sparse_x)
""" """
...@@ -194,7 +194,7 @@ class LeakyReLU(Layer): ...@@ -194,7 +194,7 @@ class LeakyReLU(Layer):
dense_x = paddle.to_tensor([-2., 0., 5.]) dense_x = paddle.to_tensor([-2., 0., 5.])
sparse_x = dense_x.to_sparse_coo(1) sparse_x = dense_x.to_sparse_coo(1)
leaky_relu = paddle.incubate.sparse.nn.LeakyReLU(0.5) leaky_relu = paddle.sparse.nn.LeakyReLU(0.5)
out = leaky_relu(sparse_x) out = leaky_relu(sparse_x)
""" """
......
python/paddle/incubate/sparse/nn/layer/conv.py → python/paddle/sparse/nn/layer/conv.py
浏览文件 @ 50d4fa54
...@@ -216,8 +216,8 @@ class Conv3D(_Conv3D): ...@@ -216,8 +216,8 @@ class Conv3D(_Conv3D):
indices = paddle.to_tensor(indices, dtype='int32') indices = paddle.to_tensor(indices, dtype='int32')
values = paddle.to_tensor(values, dtype='float32') values = paddle.to_tensor(values, dtype='float32')
dense_shape = [1, 1, 3, 4, 1] dense_shape = [1, 1, 3, 4, 1]
sparse_x = paddle.incubate.sparse.sparse_coo_tensor(indices, values, dense_shape, stop_gradient=True) sparse_x = paddle.sparse.sparse_coo_tensor(indices, values, dense_shape, stop_gradient=True)
conv = paddle.incubate.sparse.nn.Conv3D(1, 1, (1, 3, 3)) conv = paddle.sparse.nn.Conv3D(1, 1, (1, 3, 3))
y = conv(sparse_x) y = conv(sparse_x)
print(y.shape) print(y.shape)
# (1, 1, 1, 2, 1) # (1, 1, 1, 2, 1)
...@@ -353,8 +353,8 @@ class SubmConv3D(_Conv3D): ...@@ -353,8 +353,8 @@ class SubmConv3D(_Conv3D):
dense_shape = [1, 1, 3, 4, 1] dense_shape = [1, 1, 3, 4, 1]
indices = paddle.to_tensor(indices, dtype='int32') indices = paddle.to_tensor(indices, dtype='int32')
values = paddle.to_tensor(values, dtype='float32') values = paddle.to_tensor(values, dtype='float32')
sparse_x = paddle.incubate.sparse.sparse_coo_tensor(indices, values, dense_shape, stop_gradient=True) sparse_x = paddle.sparse.sparse_coo_tensor(indices, values, dense_shape, stop_gradient=True)
subm_conv = paddle.incubate.sparse.nn.SubmConv3D(1, 1, (1, 3, 3)) subm_conv = paddle.sparse.nn.SubmConv3D(1, 1, (1, 3, 3))
y = subm_conv(sparse_x) y = subm_conv(sparse_x)
print(y.shape) print(y.shape)
# (1, 1, 3, 4, 1) # (1, 1, 3, 4, 1)
......
python/paddle/incubate/sparse/nn/layer/norm.py → python/paddle/sparse/nn/layer/norm.py
浏览文件 @ 50d4fa54
...@@ -102,7 +102,7 @@ class BatchNorm(paddle.nn.BatchNorm1D): ...@@ -102,7 +102,7 @@ class BatchNorm(paddle.nn.BatchNorm1D):
x_data = paddle.randn((1, 6, 6, 6, channels)).astype('float32') x_data = paddle.randn((1, 6, 6, 6, channels)).astype('float32')
dense_x = paddle.to_tensor(x_data) dense_x = paddle.to_tensor(x_data)
sparse_x = dense_x.to_sparse_coo(4) sparse_x = dense_x.to_sparse_coo(4)
batch_norm = paddle.incubate.sparse.nn.BatchNorm(channels) batch_norm = paddle.sparse.nn.BatchNorm(channels)
batch_norm_out = batch_norm(sparse_x) batch_norm_out = batch_norm(sparse_x)
print(batch_norm_out.shape) print(batch_norm_out.shape)
# [1, 6, 6, 6, 3] # [1, 6, 6, 6, 3]
...@@ -154,7 +154,7 @@ class BatchNorm(paddle.nn.BatchNorm1D): ...@@ -154,7 +154,7 @@ class BatchNorm(paddle.nn.BatchNorm1D):
data_format='NC', data_format='NC',
use_global_stats=self._use_global_stats) use_global_stats=self._use_global_stats)
return paddle.incubate.sparse.sparse_coo_tensor( return paddle.sparse.sparse_coo_tensor(
input.indices(), input.indices(),
batch_norm_out, batch_norm_out,
shape=input.shape, shape=input.shape,
...@@ -237,7 +237,7 @@ class SyncBatchNorm(paddle.nn.SyncBatchNorm): ...@@ -237,7 +237,7 @@ class SyncBatchNorm(paddle.nn.SyncBatchNorm):
# required: gpu # required: gpu
import paddle import paddle
import paddle.incubate.sparse.nn as nn import paddle.sparse.nn as nn
import numpy as np import numpy as np
x = np.array([[[[0.3, 0.4], [0.3, 0.07]], [[0.83, 0.37], [0.18, 0.93]]]]).astype('float32') x = np.array([[[[0.3, 0.4], [0.3, 0.07]], [[0.83, 0.37], [0.18, 0.93]]]]).astype('float32')
...@@ -274,13 +274,15 @@ class SyncBatchNorm(paddle.nn.SyncBatchNorm): ...@@ -274,13 +274,15 @@ class SyncBatchNorm(paddle.nn.SyncBatchNorm):
assert x.is_sparse_coo( assert x.is_sparse_coo(
), "SyncBatchNorm only support SparseTensor in COO format." ), "SyncBatchNorm only support SparseTensor in COO format."
out = super(SyncBatchNorm, self).forward(x.values()) out = super(SyncBatchNorm, self).forward(x.values())
return paddle.incubate.sparse.sparse_coo_tensor( return paddle.sparse.sparse_coo_tensor(x.indices(),
x.indices(), out, shape=x.shape, stop_gradient=x.stop_gradient) out,
shape=x.shape,
stop_gradient=x.stop_gradient)
@classmethod @classmethod
def convert_sync_batchnorm(cls, layer): def convert_sync_batchnorm(cls, layer):
r""" r"""
Helper function to convert :class: `paddle.incubate.sparse.nn.BatchNorm` layers in the model to :class: `paddle.incubate.sparse.nn.SyncBatchNorm` layers. Helper function to convert :class: `paddle.sparse.nn.BatchNorm` layers in the model to :class: `paddle.sparse.nn.SyncBatchNorm` layers.
Parameters: Parameters:
layer(paddle.nn.Layer): model containing one or more `BatchNorm` layers. layer(paddle.nn.Layer): model containing one or more `BatchNorm` layers.
...@@ -293,7 +295,7 @@ class SyncBatchNorm(paddle.nn.SyncBatchNorm): ...@@ -293,7 +295,7 @@ class SyncBatchNorm(paddle.nn.SyncBatchNorm):
.. code-block:: python .. code-block:: python
import paddle import paddle
import paddle.incubate.sparse.nn as nn import paddle.sparse.nn as nn
model = paddle.nn.Sequential(nn.Conv3D(3, 5, 3), nn.BatchNorm(5)) model = paddle.nn.Sequential(nn.Conv3D(3, 5, 3), nn.BatchNorm(5))
sync_model = nn.SyncBatchNorm.convert_sync_batchnorm(model) sync_model = nn.SyncBatchNorm.convert_sync_batchnorm(model)
......
python/paddle/incubate/sparse/nn/layer/pooling.py → python/paddle/sparse/nn/layer/pooling.py
浏览文件 @ 50d4fa54
...@@ -66,7 +66,7 @@ class MaxPool3D(Layer): ...@@ -66,7 +66,7 @@ class MaxPool3D(Layer):
with _test_eager_guard(): with _test_eager_guard():
dense_x = paddle.randn((2, 3, 6, 6, 3)) dense_x = paddle.randn((2, 3, 6, 6, 3))
sparse_x = dense_x.to_sparse_coo(4) sparse_x = dense_x.to_sparse_coo(4)
max_pool3d = paddle.incubate.sparse.nn.MaxPool3D( max_pool3d = paddle.sparse.nn.MaxPool3D(
kernel_size=3, data_format='NDHWC') kernel_size=3, data_format='NDHWC')
out = max_pool3d(sparse_x) out = max_pool3d(sparse_x)
#shape=[2, 1, 2, 2, 3] #shape=[2, 1, 2, 2, 3]
......
python/paddle/incubate/sparse/unary.py → python/paddle/sparse/unary.py
浏览文件 @ 50d4fa54
...@@ -53,7 +53,7 @@ def sin(x, name=None): ...@@ -53,7 +53,7 @@ def sin(x, name=None):
dense_x = paddle.to_tensor([-2., 0., 1.]) dense_x = paddle.to_tensor([-2., 0., 1.])
sparse_x = dense_x.to_sparse_coo(1) sparse_x = dense_x.to_sparse_coo(1)
out = paddle.incubate.sparse.sin(sparse_x) out = paddle.sparse.sin(sparse_x)
""" """
return _C_ops.sparse_sin(x) return _C_ops.sparse_sin(x)
...@@ -83,7 +83,7 @@ def tan(x, name=None): ...@@ -83,7 +83,7 @@ def tan(x, name=None):
dense_x = paddle.to_tensor([-2., 0., 1.]) dense_x = paddle.to_tensor([-2., 0., 1.])
sparse_x = dense_x.to_sparse_coo(1) sparse_x = dense_x.to_sparse_coo(1)
out = paddle.incubate.sparse.tan(sparse_x) out = paddle.sparse.tan(sparse_x)
""" """
return _C_ops.sparse_tan(x) return _C_ops.sparse_tan(x)
...@@ -113,7 +113,7 @@ def asin(x, name=None): ...@@ -113,7 +113,7 @@ def asin(x, name=None):
dense_x = paddle.to_tensor([-2., 0., 1.]) dense_x = paddle.to_tensor([-2., 0., 1.])
sparse_x = dense_x.to_sparse_coo(1) sparse_x = dense_x.to_sparse_coo(1)
out = paddle.incubate.sparse.asin(sparse_x) out = paddle.sparse.asin(sparse_x)
""" """
return _C_ops.sparse_asin(x) return _C_ops.sparse_asin(x)
...@@ -144,7 +144,7 @@ def transpose(x, perm, name=None): ...@@ -144,7 +144,7 @@ def transpose(x, perm, name=None):
dense_x = paddle.to_tensor([[-2., 0.], [1., 2.]]) dense_x = paddle.to_tensor([[-2., 0.], [1., 2.]])
sparse_x = dense_x.to_sparse_coo(1) sparse_x = dense_x.to_sparse_coo(1)
out = paddle.incubate.sparse.transpose(sparse_x, [1, 0]) out = paddle.sparse.transpose(sparse_x, [1, 0])
""" """
return _C_ops.sparse_transpose(x, perm) return _C_ops.sparse_transpose(x, perm)
...@@ -174,7 +174,7 @@ def atan(x, name=None): ...@@ -174,7 +174,7 @@ def atan(x, name=None):
dense_x = paddle.to_tensor([-2., 0., 1.]) dense_x = paddle.to_tensor([-2., 0., 1.])
sparse_x = dense_x.to_sparse_coo(1) sparse_x = dense_x.to_sparse_coo(1)
out = paddle.incubate.sparse.atan(sparse_x) out = paddle.sparse.atan(sparse_x)
""" """
return _C_ops.sparse_atan(x) return _C_ops.sparse_atan(x)
...@@ -204,7 +204,7 @@ def sinh(x, name=None): ...@@ -204,7 +204,7 @@ def sinh(x, name=None):
dense_x = paddle.to_tensor([-2., 0., 1.]) dense_x = paddle.to_tensor([-2., 0., 1.])
sparse_x = dense_x.to_sparse_coo(1) sparse_x = dense_x.to_sparse_coo(1)
out = paddle.incubate.sparse.sinh(sparse_x) out = paddle.sparse.sinh(sparse_x)
""" """
return _C_ops.sparse_sinh(x) return _C_ops.sparse_sinh(x)
...@@ -234,7 +234,7 @@ def asinh(x, name=None): ...@@ -234,7 +234,7 @@ def asinh(x, name=None):
dense_x = paddle.to_tensor([-2., 0., 1.]) dense_x = paddle.to_tensor([-2., 0., 1.])
sparse_x = dense_x.to_sparse_coo(1) sparse_x = dense_x.to_sparse_coo(1)
out = paddle.incubate.sparse.asinh(sparse_x) out = paddle.sparse.asinh(sparse_x)
""" """
return _C_ops.sparse_asinh(x) return _C_ops.sparse_asinh(x)
...@@ -264,7 +264,7 @@ def atanh(x, name=None): ...@@ -264,7 +264,7 @@ def atanh(x, name=None):
dense_x = paddle.to_tensor([-2., 0., 1.]) dense_x = paddle.to_tensor([-2., 0., 1.])
sparse_x = dense_x.to_sparse_coo(1) sparse_x = dense_x.to_sparse_coo(1)
out = paddle.incubate.sparse.atanh(sparse_x) out = paddle.sparse.atanh(sparse_x)
""" """
return _C_ops.sparse_atanh(x) return _C_ops.sparse_atanh(x)
...@@ -294,7 +294,7 @@ def tanh(x, name=None): ...@@ -294,7 +294,7 @@ def tanh(x, name=None):
dense_x = paddle.to_tensor([-2., 0., 1.]) dense_x = paddle.to_tensor([-2., 0., 1.])
sparse_x = dense_x.to_sparse_coo(1) sparse_x = dense_x.to_sparse_coo(1)
out = paddle.incubate.sparse.tanh(sparse_x) out = paddle.sparse.tanh(sparse_x)
""" """
return _C_ops.sparse_tanh(x) return _C_ops.sparse_tanh(x)
...@@ -324,7 +324,7 @@ def square(x, name=None): ...@@ -324,7 +324,7 @@ def square(x, name=None):
dense_x = paddle.to_tensor([-2., 0., 1.]) dense_x = paddle.to_tensor([-2., 0., 1.])
sparse_x = dense_x.to_sparse_coo(1) sparse_x = dense_x.to_sparse_coo(1)
out = paddle.incubate.sparse.square(sparse_x) out = paddle.sparse.square(sparse_x)
""" """
return _C_ops.sparse_square(x) return _C_ops.sparse_square(x)
...@@ -354,7 +354,7 @@ def sqrt(x, name=None): ...@@ -354,7 +354,7 @@ def sqrt(x, name=None):
dense_x = paddle.to_tensor([-2., 0., 1.]) dense_x = paddle.to_tensor([-2., 0., 1.])
sparse_x = dense_x.to_sparse_coo(1) sparse_x = dense_x.to_sparse_coo(1)
out = paddle.incubate.sparse.sqrt(sparse_x) out = paddle.sparse.sqrt(sparse_x)
""" """
return _C_ops.sparse_sqrt(x) return _C_ops.sparse_sqrt(x)
...@@ -384,7 +384,7 @@ def log1p(x, name=None): ...@@ -384,7 +384,7 @@ def log1p(x, name=None):
dense_x = paddle.to_tensor([-2, 0, 1], dtype='float32') dense_x = paddle.to_tensor([-2, 0, 1], dtype='float32')
sparse_x = dense_x.to_sparse_coo(1) sparse_x = dense_x.to_sparse_coo(1)
out = paddle.incubate.sparse.log1p(sparse_x) out = paddle.sparse.log1p(sparse_x)
""" """
return _C_ops.sparse_log1p(x) return _C_ops.sparse_log1p(x)
...@@ -415,7 +415,7 @@ def cast(x, index_dtype=None, value_dtype=None, name=None): ...@@ -415,7 +415,7 @@ def cast(x, index_dtype=None, value_dtype=None, name=None):
dense_x = paddle.to_tensor([-2, 0, 1]) dense_x = paddle.to_tensor([-2, 0, 1])
sparse_x = dense_x.to_sparse_coo(1) sparse_x = dense_x.to_sparse_coo(1)
out = paddle.incubate.sparse.cast(sparse_x, 'int32', 'float64') out = paddle.sparse.cast(sparse_x, 'int32', 'float64')
""" """
if index_dtype and not isinstance(index_dtype, core.VarDesc.VarType): if index_dtype and not isinstance(index_dtype, core.VarDesc.VarType):
...@@ -450,7 +450,7 @@ def pow(x, factor, name=None): ...@@ -450,7 +450,7 @@ def pow(x, factor, name=None):
dense_x = paddle.to_tensor([-2, 0, 3], dtype='float32') dense_x = paddle.to_tensor([-2, 0, 3], dtype='float32')
sparse_x = dense_x.to_sparse_coo(1) sparse_x = dense_x.to_sparse_coo(1)
out = paddle.incubate.sparse.pow(sparse_x, 2) out = paddle.sparse.pow(sparse_x, 2)
""" """
return _C_ops.sparse_pow(x, float(factor)) return _C_ops.sparse_pow(x, float(factor))
...@@ -480,7 +480,7 @@ def neg(x, name=None): ...@@ -480,7 +480,7 @@ def neg(x, name=None):
dense_x = paddle.to_tensor([-2, 0, 3], dtype='float32') dense_x = paddle.to_tensor([-2, 0, 3], dtype='float32')
sparse_x = dense_x.to_sparse_coo(1) sparse_x = dense_x.to_sparse_coo(1)
out = paddle.incubate.sparse.neg(sparse_x) out = paddle.sparse.neg(sparse_x)
""" """
return _C_ops.sparse_scale(x, -1.0, 0.0, True) return _C_ops.sparse_scale(x, -1.0, 0.0, True)
...@@ -510,7 +510,7 @@ def abs(x, name=None): ...@@ -510,7 +510,7 @@ def abs(x, name=None):
dense_x = paddle.to_tensor([-2, 0, 3], dtype='float32') dense_x = paddle.to_tensor([-2, 0, 3], dtype='float32')
sparse_x = dense_x.to_sparse_coo(1) sparse_x = dense_x.to_sparse_coo(1)
out = paddle.incubate.sparse.abs(sparse_x) out = paddle.sparse.abs(sparse_x)
""" """
return _C_ops.sparse_abs(x) return _C_ops.sparse_abs(x)
...@@ -532,12 +532,10 @@ def coalesce(x): ...@@ -532,12 +532,10 @@ def coalesce(x):
import paddle import paddle
from paddle.incubate import sparse
indices = [[0, 0, 1], [1, 1, 2]] indices = [[0, 0, 1], [1, 1, 2]]
values = [1.0, 2.0, 3.0] values = [1.0, 2.0, 3.0]
sp_x = sparse.sparse_coo_tensor(indices, values) sp_x = paddle.sparse.sparse_coo_tensor(indices, values)
sp_x = sparse.coalesce(sp_x) sp_x = paddle.sparse.coalesce(sp_x)
print(sp_x.indices()) print(sp_x.indices())
#[[0, 1], [1, 2]] #[[0, 1], [1, 2]]
print(sp_x.values()) print(sp_x.values())
...@@ -571,7 +569,7 @@ def rad2deg(x, name=None): ...@@ -571,7 +569,7 @@ def rad2deg(x, name=None):
dense_x = paddle.to_tensor([3.142, 0., -3.142]) dense_x = paddle.to_tensor([3.142, 0., -3.142])
sparse_x = dense_x.to_sparse_coo(1) sparse_x = dense_x.to_sparse_coo(1)
out = paddle.incubate.sparse.rad2deg(sparse_x) out = paddle.sparse.rad2deg(sparse_x)
""" """
if x.dtype in _int_dtype_: if x.dtype in _int_dtype_:
...@@ -604,7 +602,7 @@ def deg2rad(x, name=None): ...@@ -604,7 +602,7 @@ def deg2rad(x, name=None):
dense_x = paddle.to_tensor([-180, 0, 180]) dense_x = paddle.to_tensor([-180, 0, 180])
sparse_x = dense_x.to_sparse_coo(1) sparse_x = dense_x.to_sparse_coo(1)
out = paddle.incubate.sparse.deg2rad(sparse_x) out = paddle.sparse.deg2rad(sparse_x)
""" """
if x.dtype in _int_dtype_: if x.dtype in _int_dtype_:
...@@ -636,7 +634,7 @@ def expm1(x, name=None): ...@@ -636,7 +634,7 @@ def expm1(x, name=None):
dense_x = paddle.to_tensor([-2., 0., 1.]) dense_x = paddle.to_tensor([-2., 0., 1.])
sparse_x = dense_x.to_sparse_coo(1) sparse_x = dense_x.to_sparse_coo(1)
out = paddle.incubate.sparse.expm1(sparse_x) out = paddle.sparse.expm1(sparse_x)
""" """
return _C_ops.sparse_expm1(x) return _C_ops.sparse_expm1(x)
...@@ -689,7 +687,7 @@ def reshape(x, shape, name=None): ...@@ -689,7 +687,7 @@ def reshape(x, shape, name=None):
sp_x = dense_x.to_sparse_coo(len(x_shape)) sp_x = dense_x.to_sparse_coo(len(x_shape))
else: else:
sp_x = dense_x.to_sparse_csr() sp_x = dense_x.to_sparse_csr()
sp_out = paddle.incubate.sparse.reshape(sp_x, new_shape) sp_out = paddle.sparse.reshape(sp_x, new_shape)
print(sp_out) print(sp_out)
# the shape of sp_out is [1, 2, 2, 3, 3] # the shape of sp_out is [1, 2, 2, 3, 3]
......
python/setup.py.in
浏览文件 @ 50d4fa54
...@@ -283,7 +283,6 @@ packages=['paddle', ...@@ -283,7 +283,6 @@ packages=['paddle',
'paddle.incubate.tensor', 'paddle.incubate.tensor',
'paddle.incubate.multiprocessing', 'paddle.incubate.multiprocessing',
'paddle.incubate.nn', 'paddle.incubate.nn',
'paddle.incubate.sparse',
'paddle.incubate.asp', 'paddle.incubate.asp',
'paddle.incubate.passes', 'paddle.incubate.passes',
'paddle.distribution', 'paddle.distribution',
...@@ -370,8 +369,8 @@ packages=['paddle', ...@@ -370,8 +369,8 @@ packages=['paddle',
'paddle.vision.transforms', 'paddle.vision.transforms',
'paddle.vision.datasets', 'paddle.vision.datasets',
'paddle.audio', 'paddle.audio',
'paddle.audio.functional', 'paddle.audio.functional',
'paddle.audio.features', 'paddle.audio.features',
'paddle.text', 'paddle.text',
'paddle.text.datasets', 'paddle.text.datasets',
'paddle.incubate', 'paddle.incubate',
...@@ -385,10 +384,10 @@ packages=['paddle', ...@@ -385,10 +384,10 @@ packages=['paddle',
'paddle.incubate.distributed.models', 'paddle.incubate.distributed.models',
'paddle.incubate.distributed.models.moe', 'paddle.incubate.distributed.models.moe',
'paddle.incubate.distributed.models.moe.gate', 'paddle.incubate.distributed.models.moe.gate',
'paddle.incubate.sparse', 'paddle.sparse',
'paddle.incubate.sparse.nn', 'paddle.sparse.nn',
'paddle.incubate.sparse.nn.layer', 'paddle.sparse.nn.layer',
'paddle.incubate.sparse.nn.functional', 'paddle.sparse.nn.functional',
'paddle.incubate.xpu', 'paddle.incubate.xpu',
'paddle.io', 'paddle.io',
'paddle.optimizer', 'paddle.optimizer',
......
tools/print_signatures.py
浏览文件 @ 50d4fa54
...@@ -224,17 +224,47 @@ def process_module(m, attr="__all__"): ...@@ -224,17 +224,47 @@ def process_module(m, attr="__all__"):
def check_public_api(): def check_public_api():
import paddle import paddle
modulelist = [ #npqa modulelist = [ #npqa
paddle, paddle.amp, paddle.nn, paddle.nn.functional, paddle,
paddle.nn.initializer, paddle.nn.utils, paddle.static, paddle.static.nn, paddle.amp,
paddle.io, paddle.jit, paddle.metric, paddle.distribution, paddle.nn,
paddle.optimizer, paddle.optimizer.lr, paddle.regularizer, paddle.text, paddle.nn.functional,
paddle.utils, paddle.utils.download, paddle.utils.profiler, paddle.nn.initializer,
paddle.utils.cpp_extension, paddle.sysconfig, paddle.vision, paddle.nn.utils,
paddle.vision.datasets, paddle.vision.models, paddle.vision.transforms, paddle.static,
paddle.vision.ops, paddle.distributed, paddle.distributed.fleet, paddle.static.nn,
paddle.distributed.fleet.utils, paddle.distributed.parallel, paddle.io,
paddle.distributed.utils, paddle.callbacks, paddle.hub, paddle.autograd, paddle.jit,
paddle.incubate, paddle.inference, paddle.onnx, paddle.device paddle.metric,
paddle.distribution,
paddle.optimizer,
paddle.optimizer.lr,
paddle.regularizer,
paddle.text,
paddle.utils,
paddle.utils.download,
paddle.utils.profiler,
paddle.utils.cpp_extension,
paddle.sysconfig,
paddle.vision,
paddle.vision.datasets,
paddle.vision.models,
paddle.vision.transforms,
paddle.vision.ops,
paddle.distributed,
paddle.distributed.fleet,
paddle.distributed.fleet.utils,
paddle.distributed.parallel,
paddle.distributed.utils,
paddle.callbacks,
paddle.hub,
paddle.autograd,
paddle.incubate,
paddle.inference,
paddle.onnx,
paddle.device,
paddle.sparse,
paddle.sparse.nn,
paddle.sparse.nn.functional,
] ]
apinum = 0 apinum = 0
......
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