[cherry-pick 2.4] remove incubate of all paddle sparse api (#47183)

python/paddle/__init__.py

@@ -82,6 +82,7 @@
 import paddle.vision  # noqa: F401
 import paddle.audio  # noqa: F401
 import paddle.geometric  # noqa: F401
+import paddle.sparse
 
 from .tensor.attribute import is_complex  # noqa: F401
 from .tensor.attribute import is_integer  # noqa: F401

python/paddle/fluid/dygraph/varbase_patch_methods.py

@@ -919,7 +919,7 @@ def values(self):
                     indices = [[0, 0, 1, 2, 2], [1, 3, 2, 0, 1]]
                     values = [1, 2, 3, 4, 5]
                     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())
                     #[1, 2, 3, 4, 5]
         """
@@ -944,7 +944,7 @@ def to_dense(self):
                     indices = [[0, 0, 1, 2, 2], [1, 3, 2, 0, 1]]
                     values = [1, 2, 3, 4, 5]
                     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()
                     #[[0., 1., 0., 2.],
                     # [0., 0., 3., 0.],

python/paddle/fluid/tests/unittests/test_sparse_addmm_op.py

@@ -64,7 +64,7 @@ def check_result(self, input_shape, x_shape, y_shape, format):
         sp_x.stop_gradient = False
         sp_y = origin_y.detach()
         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(),
                                    dense_out.numpy(),

python/paddle/fluid/tests/unittests/test_sparse_conv_op.py

@@ -19,7 +19,7 @@
 from paddle import _C_ops, _legacy_C_ops
 from paddle.fluid import core
 from paddle.fluid.framework import _test_eager_guard
-import paddle.incubate.sparse as sparse
+import paddle.sparse as sparse
 
 
 class TestSparseConv(unittest.TestCase):
@@ -44,17 +44,17 @@ def test_conv3d(self):
             correct_out_values = [[5], [11]]
             sparse_input = core.eager.sparse_coo_tensor(indices, values,
                                                         dense_shape, False)
-            out = paddle.incubate.sparse.nn.functional.conv3d(
-                sparse_input,
-                dense_kernel,
-                bias=paddle.to_tensor(bias, dtype='float32'),
-                stride=strides,
-                padding=paddings,
-                dilation=dilations,
-                groups=1,
-                data_format="NDHWC")
+            out = paddle.sparse.nn.functional.conv3d(sparse_input,
+                                                     dense_kernel,
+                                                     bias=paddle.to_tensor(
+                                                         bias, dtype='float32'),
+                                                     stride=strides,
+                                                     padding=paddings,
+                                                     dilation=dilations,
+                                                     groups=1,
+                                                     data_format="NDHWC")
             out.backward(out)
-            out = paddle.incubate.sparse.coalesce(out)
+            out = paddle.sparse.coalesce(out)
             assert np.array_equal(correct_out_values, out.values().numpy())
 
     def test_subm_conv3d(self):
@@ -64,11 +64,14 @@ def test_subm_conv3d(self):
             indices = paddle.to_tensor(indices, dtype='int32')
             values = paddle.to_tensor(values, dtype='float32')
             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')
-            y = paddle.incubate.sparse.nn.functional.subm_conv3d(
-                sparse_x, weight, key='subm_conv')
+            y = paddle.sparse.nn.functional.subm_conv3d(sparse_x,
+                                                        weight,
+                                                        key='subm_conv')
             assert np.array_equal(sparse_x.indices().numpy(),
                                   y.indices().numpy())
 
@@ -82,17 +85,20 @@ def test_Conv3D(self):
             values = paddle.to_tensor(values, dtype='float32')
             dense_shape = [1, 1, 3, 4, 1]
             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)
 
-            sparse_conv3d = paddle.incubate.sparse.nn.Conv3D(
-                1, 1, (1, 3, 3), data_format='NDHWC')
+            sparse_conv3d = paddle.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.incubate.sparse.nn.SubmConv3D(
-                    1, 1, (1, 3, 3), data_format='NCDHW', key='subm_conv')
+                conv3d = paddle.sparse.nn.SubmConv3D(1,
+                                                     1, (1, 3, 3),
+                                                     data_format='NCDHW',
+                                                     key='subm_conv')
 
     def test_SubmConv3D(self):
         with _test_eager_guard():
@@ -102,11 +108,13 @@ def test_SubmConv3D(self):
             values = paddle.to_tensor(values, dtype='float32')
             dense_shape = [1, 1, 3, 4, 1]
             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)
 
-            subm_conv3d = paddle.incubate.sparse.nn.SubmConv3D(
-                1, 1, (1, 3, 3), data_format='NDHWC', key='subm_conv')
+            subm_conv3d = paddle.sparse.nn.SubmConv3D(1,
+                                                      1, (1, 3, 3),
+                                                      data_format='NDHWC',
+                                                      key='subm_conv')
             # test extra_repr
             print(subm_conv3d.extra_repr())
 
@@ -117,8 +125,10 @@ def test_SubmConv3D(self):
             #test errors
             with self.assertRaises(ValueError):
                 #Currently, only support data_format='NDHWC'
-                conv3d = paddle.incubate.sparse.nn.SubmConv3D(
-                    1, 1, (1, 3, 3), data_format='NCDHW', key='subm_conv')
+                conv3d = paddle.sparse.nn.SubmConv3D(1,
+                                                     1, (1, 3, 3),
+                                                     data_format='NCDHW',
+                                                     key='subm_conv')
 
     def test_Conv3D_bias(self):
         with _test_eager_guard():
@@ -128,10 +138,7 @@ def test_Conv3D_bias(self):
             sp_x = x.to_sparse_coo(4)
             conv3d = paddle.nn.Conv3D(3, 2, 3, data_format='NDHWC')
 
-            sp_conv3d = paddle.incubate.sparse.nn.Conv3D(3,
-                                                         2,
-                                                         3,
-                                                         data_format='NDHWC')
+            sp_conv3d = paddle.sparse.nn.Conv3D(3, 2, 3, data_format='NDHWC')
             sp_conv3d.weight.set_value(
                 paddle.to_tensor(conv3d.weight.numpy().transpose(2, 3, 4, 1,
                                                                  0)))

python/paddle/fluid/tests/unittests/test_sparse_elementwise_op.py

@@ -18,20 +18,20 @@
 
 import numpy as np
 import paddle
-import paddle.incubate.sparse as sparse
+import paddle.sparse as sparse
 
 op_list = [__add__, __sub__, __mul__, __truediv__]
 
 
 def get_actual_res(x, y, op):
     if op == __add__:
-        res = paddle.incubate.sparse.add(x, y)
+        res = paddle.sparse.add(x, y)
     elif op == __sub__:
-        res = paddle.incubate.sparse.subtract(x, y)
+        res = paddle.sparse.subtract(x, y)
     elif op == __mul__:
-        res = paddle.incubate.sparse.multiply(x, y)
+        res = paddle.sparse.multiply(x, y)
     elif op == __truediv__:
-        res = paddle.incubate.sparse.divide(x, y)
+        res = paddle.sparse.divide(x, y)
     else:
         raise ValueError("unsupported op")
     return res

python/paddle/fluid/tests/unittests/test_sparse_fused_attention_op.py

@@ -92,7 +92,7 @@ def test_dygraph(self):
                 output = paddle.matmul(softmax, value)
                 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)
                 output_sp.backward()
             else:
@@ -103,7 +103,7 @@ def test_dygraph(self):
                 output = paddle.matmul(softmax, value)
                 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)
                 output_sp.backward()
 

python/paddle/fluid/tests/unittests/test_sparse_is_same_shape.py

@@ -16,12 +16,12 @@
 import unittest
 
 import paddle
-from paddle.incubate.sparse.binary import is_same_shape
+from paddle.sparse.binary import is_same_shape
 
 
 class TestSparseIsSameShapeAPI(unittest.TestCase):
     """
-    test paddle.incubate.sparse.is_same_shape
+    test paddle.sparse.is_same_shape
     """
 
     def setUp(self):

python/paddle/fluid/tests/unittests/test_sparse_matmul_op.py

@@ -58,7 +58,7 @@ def check_result(self, x_shape, y_shape, format):
         sp_x.stop_gradient = False
         sp_y = origin_y.detach()
         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(),
                                    dense_out.numpy(),
@@ -107,7 +107,7 @@ def test_masked_matmul_2d(self):
         x = paddle.to_tensor(np_x, 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()
-        out = paddle.incubate.sparse.masked_matmul(x, y, mask)
+        out = paddle.sparse.masked_matmul(x, y, mask)
 
         np.testing.assert_allclose(np_out.indptr,
                                    out.crows().numpy(),
@@ -145,7 +145,7 @@ def test_masked_matmul_3d(self):
         sp_x.stop_gradient = False
         sp_y = origin_y.detach()
         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()
 
         np.testing.assert_allclose(sp_out.numpy(),

python/paddle/fluid/tests/unittests/test_sparse_model.py

@@ -15,8 +15,7 @@
 import unittest
 import numpy as np
 import paddle
-from paddle.incubate import sparse
-from paddle.incubate.sparse import nn
+from paddle.sparse import nn
 from paddle.fluid.framework import _test_eager_guard
 
 
@@ -26,10 +25,10 @@ def sparse(self, sp_x):
         indentity = sp_x
         out = nn.functional.relu(sp_x)
         values = out.values() + indentity.values()
-        out = sparse.sparse_coo_tensor(out.indices(),
-                                       values,
-                                       shape=out.shape,
-                                       stop_gradient=out.stop_gradient)
+        out = paddle.sparse.sparse_coo_tensor(out.indices(),
+                                              values,
+                                              shape=out.shape,
+                                              stop_gradient=out.stop_gradient)
         return out
 
     def dense(self, x):

python/paddle/fluid/tests/unittests/test_sparse_mv_op.py

@@ -61,7 +61,7 @@ def test_mv(self):
             sp_x.stop_gradient = False
             sp_vec = origin_vec.detach()
             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()
 
             np.testing.assert_allclose(sp_out.numpy(),
@@ -99,7 +99,7 @@ def test_mv(self):
             sp_x.stop_gradient = False
             sp_vec = origin_vec.detach()
             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()
 
             np.testing.assert_allclose(sp_out.numpy(),

python/paddle/fluid/tests/unittests/test_sparse_norm_op.py

@@ -16,7 +16,7 @@
 import unittest
 import numpy as np
 import paddle
-from paddle.incubate.sparse import nn
+from paddle.sparse import nn
 import paddle.fluid as fluid
 import copy
 
@@ -40,7 +40,7 @@ def test(self):
         dense_x2 = copy.deepcopy(dense_x)
         dense_x2.stop_gradient = False
         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
         sparse_batch_norm._mean.set_value(batch_norm._mean)
         sparse_batch_norm._variance.set_value(batch_norm._variance)
@@ -66,8 +66,8 @@ def test_error_layout(self):
             shape = [2, 3, 6, 6, 3]
             x = paddle.randn(shape)
             sparse_x = x.to_sparse_coo(4)
-            sparse_batch_norm = paddle.incubate.sparse.nn.BatchNorm(
-                3, data_format='NCDHW')
+            sparse_batch_norm = paddle.sparse.nn.BatchNorm(3,
+                                                           data_format='NCDHW')
             sparse_batch_norm(sparse_x)
 
     def test2(self):
@@ -76,7 +76,7 @@ def test2(self):
         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.incubate.sparse.nn.BatchNorm(channels)
+        batch_norm = paddle.sparse.nn.BatchNorm(channels)
         batch_norm_out = batch_norm(sparse_x)
         dense_bn = paddle.nn.BatchNorm1D(channels)
         dense_x = dense_x.reshape((-1, dense_x.shape[-1]))

python/paddle/fluid/tests/unittests/test_sparse_pooling_op.py

@@ -48,7 +48,7 @@ def test(self):
             self.setUp()
             self.dense_x.stop_gradient = False
             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,
                 self.kernel_sizes,
                 stride=self.strides,
@@ -109,8 +109,8 @@ def test(self):
         with _test_eager_guard():
             dense_x = paddle.randn((2, 3, 6, 6, 3))
             sparse_x = dense_x.to_sparse_coo(4)
-            max_pool3d = paddle.incubate.sparse.nn.MaxPool3D(
-                kernel_size=3, data_format='NDHWC')
+            max_pool3d = paddle.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_reshape_op.py

@@ -19,7 +19,7 @@
 
 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
     """
 
@@ -31,7 +31,7 @@ def check_result(self, x_shape, new_shape, format):
         Transform a sparse tensor with shape "x_shape" to
         a sparse tensor with shape "new_shape".
         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)
         np_x = np.random.randint(-100, 100, x_shape) * mask
@@ -49,7 +49,7 @@ def check_result(self, x_shape, new_shape, format):
             sp_x = paddle.to_tensor(np_x,
                                     place=paddle.CPUPlace()).to_sparse_csr()
         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(),
                                    dense_out.numpy(),
@@ -75,7 +75,7 @@ def check_result(self, x_shape, new_shape, format):
                 sp_x = paddle.to_tensor(
                     np_x, place=paddle.CUDAPlace(0)).to_sparse_csr()
             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(),
                                        dense_out.numpy(),

python/paddle/fluid/tests/unittests/test_sparse_softmax_op.py

@@ -48,7 +48,7 @@ def test_softmax2d(self):
                 np_out = np.concatenate([np_out, x_exp / x_exp_sum])
 
             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)
             np.testing.assert_allclose(out.crows().numpy(),
                                        np_csr.indptr,
@@ -105,7 +105,7 @@ def test_softmax3d(self):
                     np_out = np.concatenate([np_out, x_exp / x_exp_sum])
 
             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)
             np.testing.assert_allclose(out.values().numpy(), np_out, rtol=1e-05)