[Sparse] fix doc (#46967)

python/paddle/sparse/binary.py

@@ -239,11 +239,9 @@ def add(x, y, name=None):
     ..  code-block:: python
 
         import paddle
-        from paddle.fluid.framework import _test_eager_guard
 
         paddle.device.set_device("cpu")
 
-        with _test_eager_guard():
         x = paddle.to_tensor([[0, -1, 0, 2], [0, 0, -3, 0], [4, 5, 0, 0]], 'float32')
         y = paddle.to_tensor([[0, 0, 0, -2], [0, 2, -3, 0], [2, 3, 4, 8]], 'float32')
         sparse_x = x.to_sparse_csr()
@@ -295,11 +293,9 @@ def subtract(x, y, name=None):
     ..  code-block:: python
 
         import paddle
-        from paddle.fluid.framework import _test_eager_guard
 
         paddle.device.set_device("cpu")
 
-        with _test_eager_guard():
         x = paddle.to_tensor([[0, -1, 0, 2], [0, 0, -3, 0], [4, 5, 0, 0]], 'float32')
         y = paddle.to_tensor([[0, 0, 0, -2], [0, 2, -3, 0], [2, 3, 4, 8]], 'float32')
         sparse_x = x.to_sparse_csr()
@@ -340,11 +336,9 @@ def multiply(x, y, name=None):
     ..  code-block:: python
 
         import paddle
-        from paddle.fluid.framework import _test_eager_guard
 
         paddle.device.set_device("cpu")
 
-        with _test_eager_guard():
         x = paddle.to_tensor([[0, -1, 0, 2], [0, 0, -3, 0], [4, 5, 0, 0]], 'float32')
         y = paddle.to_tensor([[0, 0, 0, -2], [0, 2, -3, 0], [2, 3, 4, 8]], 'float32')
         sparse_x = x.to_sparse_csr()
@@ -388,11 +382,9 @@ def divide(x, y, name=None):
     ..  code-block:: python
 
         import paddle
-        from paddle.fluid.framework import _test_eager_guard
 
         paddle.device.set_device("cpu")
 
-        with _test_eager_guard():
         x = paddle.to_tensor([[0, -1, 0, 2], [0, 0, -3, 0], [4, 5, 0, 0]], 'float32')
         y = paddle.to_tensor([[0, 0, 0, -2], [0, 2, -3, 0], [2, 3, 4, 8]], 'float32')
         sparse_x = x.to_sparse_csr()

python/paddle/sparse/creation.py

@@ -98,9 +98,7 @@ def sparse_coo_tensor(
     .. code-block:: python
 
         import paddle
-        from paddle.fluid.framework import _test_eager_guard
 
-        with _test_eager_guard():
         indices = [[0, 1, 2], [1, 2, 0]]
         values = [1.0, 2.0, 3.0]
         dense_shape = [3, 3]
@@ -218,9 +216,7 @@ def sparse_csr_tensor(
     .. code-block:: python
 
         import paddle
-        from paddle.fluid.framework import _test_eager_guard
 
-        with _test_eager_guard():
         crows = [0, 2, 3, 5]
         cols = [1, 3, 2, 0, 1]
         values = [1, 2, 3, 4, 5]

python/paddle/sparse/nn/functional/conv.py

@@ -149,36 +149,17 @@ def conv3d(
     * :math:`b`: Bias value, a 1-D tensor with shape [M].
     * :math:`Out`: Output value, the shape of :math:`Out` and :math:`X` may be different.
 
-    Example:
-
-        - Input:
-
-          Input shape: :math:`(N, D_{in}, H_{in}, W_{in}, C_{in})`
-
-          Filter shape: :math:`(D_f, H_f, W_f, C_{in}, C_{out})`
-
-        - Output:
-          Output shape: :math:`(N, D_{out}, H_{out}, W_{out}, C_{out})`
-
-        Where
-
-        ..  math::
-
-            D_{out}&= \\frac{(D_{in} + 2 * paddings[0] - (dilations[0] * (D_f - 1) + 1))}{strides[0]} + 1 \\\\
-            H_{out}&= \\frac{(H_{in} + 2 * paddings[1] - (dilations[1] * (H_f - 1) + 1))}{strides[1]} + 1 \\\\
-            W_{out}&= \\frac{(W_{in} + 2 * paddings[2] - (dilations[2] * (W_f - 1) + 1))}{strides[2]} + 1
-
     Args:
         x (Tensor): The input is 5-D SparseCooTensor with shape [N, D, H, W, C], the data
             type of input is float16 or float32 or float64.
         weight (Tensor): The convolution kernel, a Tensor with shape [kD, kH, kW, C/g, M],
             where M is the number of filters(output channels), g is the number of groups,
             kD, kH, kW are the filter's depth, height and width respectively.
-        bias (Tensor, optional): The bias, a Tensor of shape [M, ], currently, only support bias is None.
-        stride (int|list|tuple): The stride size. It means the stride in convolution. If stride is a
+        bias (Tensor, optional): The bias, a Tensor of shape [M].
+        stride (int|list|tuple, optional): The stride size. It means the stride in convolution. If stride is a
             list/tuple, it must contain three integers, (stride_depth, stride_height, stride_width).
             Otherwise, stride_depth = stride_height = stride_width = stride. Default: stride = 1.
-        padding (string|int|list|tuple): The padding size. It means the number of zero-paddings
+        padding (string|int|list|tuple, optional): The padding size. It means the number of zero-paddings
             on both sides for each dimension. If `padding` is a string, either 'VALID' or
             'SAME' which is the padding algorithm. If padding size is a tuple or list,
             it could be in three forms: `[pad_depth, pad_height, pad_width]` or
@@ -188,11 +169,11 @@ def conv3d(
             when `data_format` is `"NDHWC"`, `padding` can be in the form
             `[[0,0], [pad_depth_front, pad_depth_back], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right], [0,0]]`.
             Default: padding = 0.
-        dilation (int|list|tuple): The dilation size. It means the spacing between the kernel points.
+        dilation (int|list|tuple, optional): The dilation size. It means the spacing between the kernel points.
             If dilation is a list/tuple, it must contain three integers, (dilation_depth, dilation_height,
             dilation_width). Otherwise, dilation_depth = dilation_height = dilation_width = dilation.
             Default: dilation = 1.
-        groups (int): The groups number of the Conv3D Layer. According to grouped
+        groups (int, optional): The groups number of the Conv3D Layer. According to grouped
             convolution in Alex Krizhevsky's Deep CNN paper: when group=2,
             the first half of the filters is only connected to the first half
             of the input channels, while the second half of the filters is only
@@ -201,7 +182,7 @@ def conv3d(
             will be consistent with that of the input. An optional string from: `"NCDHW"`, `"NDHWC"`.
             The default is `"NDHWC"`. When it is `"NDHWC"`, the data is stored in the order of:
             `[batch_size, input_depth, input_height, input_width, input_channels]`.
-        name(str|None): For detailed information, please refer
+        name(str, optional): For detailed information, please refer
            to :ref:`api_guide_Name`. Usually name is no need to set and
            None by default.
 
@@ -212,9 +193,7 @@ def conv3d(
         .. code-block:: python
 
             import paddle
-            from paddle.fluid.framework import _test_eager_guard
 
-            with _test_eager_guard():
             indices = [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 1, 2], [1, 3, 2, 3]]
             values = [[1], [2], [3], [4]]
             indices = paddle.to_tensor(indices, dtype='int32')
@@ -277,33 +256,14 @@ def subm_conv3d(
     * :math:`b`: Bias value, a 1-D tensor with shape [M].
     * :math:`Out`: Output value, the shape of :math:`Out` and :math:`X` may be different.
 
-    Example:
-
-        - Input:
-
-          Input shape: :math:`(N, D_{in}, H_{in}, W_{in}, C_{in})`
-
-          Filter shape: :math:`(D_f, H_f, W_f, C_{in}, C_{out})`
-
-        - Output:
-          Output shape: :math:`(N, D_{out}, H_{out}, W_{out}, C_{out})`
-
-        Where
-
-        ..  math::
-
-            D_{out}&= \\frac{(D_{in} + 2 * paddings[0] - (dilations[0] * (D_f - 1) + 1))}{strides[0]} + 1 \\\\
-            H_{out}&= \\frac{(H_{in} + 2 * paddings[1] - (dilations[1] * (H_f - 1) + 1))}{strides[1]} + 1 \\\\
-            W_{out}&= \\frac{(W_{in} + 2 * paddings[2] - (dilations[2] * (W_f - 1) + 1))}{strides[2]} + 1
-
     Args:
         x (Tensor): The input is 5-D SparseCooTensor with shape [N, D, H, W, C], the data
             type of input is float16 or float32 or float64.
         weight (Tensor): The convolution kernel, a Tensor with shape [kD, kH, kW, C/g, M],
             where M is the number of filters(output channels), g is the number of groups,
             kD, kH, kW are the filter's depth, height and width respectively.
-        bias (Tensor, optional): The bias, a Tensor of shape [M, ], currently, only support bias is None.
-        stride (int|list|tuple): The stride size. It means the stride in convolution. If stride is a
+        bias (Tensor, optional): The bias, a Tensor of shape [M].
+        stride (int|list|tuple, optional): The stride size. It means the stride in convolution. If stride is a
             list/tuple, it must contain three integers, (stride_depth, stride_height, stride_width).
             Otherwise, stride_depth = stride_height = stride_width = stride. Default: stride = 1.
         padding (string|int|list|tuple): The padding size. It means the number of zero-paddings
@@ -316,11 +276,11 @@ def subm_conv3d(
             when `data_format` is `"NHWC"`, `padding` can be in the form
             `[[0,0], [pad_depth_front, pad_depth_back], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right], [0,0]]`.
             Default: padding = 0.
-        dilation (int|list|tuple): The dilation size. It means the spacing between the kernel points.
+        dilation (int|list|tuple, optional): The dilation size. It means the spacing between the kernel points.
             If dilation is a list/tuple, it must contain three integers, (dilation_depth, dilation_height,
             dilation_width). Otherwise, dilation_depth = dilation_height = dilation_width = dilation.
             Default: dilation = 1.
-        groups (int): The groups number of the Conv3D Layer. According to grouped
+        groups (int, optional): The groups number of the Conv3D Layer. According to grouped
             convolution in Alex Krizhevsky's Deep CNN paper: when group=2,
             the first half of the filters is only connected to the first half
             of the input channels, while the second half of the filters is only
@@ -333,7 +293,7 @@ def subm_conv3d(
             the definition and role of rulebook refers to
             https://pdfs.semanticscholar.org/5125/a16039cabc6320c908a4764f32596e018ad3.pdf. The
             default value is None.
-        name(str|None): For detailed information, please refer
+        name(str, optional): For detailed information, please refer
            to :ref:`api_guide_Name`. Usually name is no need to set and
            None by default.
 
@@ -345,9 +305,7 @@ def subm_conv3d(
         .. code-block:: python
 
             import paddle
-            from paddle.fluid.framework import _test_eager_guard
 
-            with _test_eager_guard():
             indices = [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 1, 2], [1, 3, 2, 3]]
             values = [[1], [2], [3], [4]]
             indices = paddle.to_tensor(indices, dtype='int32')

python/paddle/sparse/nn/functional/pooling.py

@@ -39,18 +39,18 @@ def max_pool3d(
             is a tuple or list, it must contain three integers,
             (kernel_size_Depth, kernel_size_Height, kernel_size_Width).
             Otherwise, the pool kernel size will be the cube of an int.
-        stride (int|list|tuple): The pool stride size. If pool stride size is a tuple or list,
+        stride (int|list|tuple, optional): The pool stride size. If pool stride size is a tuple or list,
             it must contain three integers, [stride_Depth, stride_Height, stride_Width).
             Otherwise, the pool stride size will be a cube of an int.
-        padding (string|int|list|tuple): The padding size. Padding could be in one of the following forms.
+        padding (string|int|list|tuple, optional): The padding size. Padding could be in one of the following forms.
             1. A string in ['valid', 'same'].
             2. An int, which means the feature map is zero padded by size of `padding` on every sides.
             3. A list[int] or tuple(int) whose length is 3, [pad_depth, pad_height, pad_weight] whose value means the padding size of each dimension.
             4. A list[int] or tuple(int) whose length is 6. [pad_depth_front, pad_depth_back, pad_height_top, pad_height_bottom, pad_width_left, pad_width_right] whose value means the padding size of each side.
             5. A list or tuple of pairs of integers. It has the form [[pad_before, pad_after], [pad_before, pad_after], ...]. Note that, the batch dimension and channel dimension should be [0,0] or (0,0).
             The default value is 0.
-        ceil_mode (bool): ${ceil_mode_comment}
-        data_format (string): The data format of the input and output data. An optional string from: `"NCDHW"`, `"NDHWC"`.
+        ceil_mode (bool, optional): ${ceil_mode_comment}
+        data_format (string, optional): The data format of the input and output data. An optional string from: `"NCDHW"`, `"NDHWC"`.
                         The default is `"NCDHW"`. When it is `"NCDHW"`, the data is stored in the order of:
                         `[batch_size, input_channels, input_depth, input_height, input_width]`. Currently only support `"NDHWC"` .
         name(str, optional): For detailed information, please refer
@@ -64,9 +64,7 @@ def max_pool3d(
         .. code-block:: python
 
             import paddle
-            from paddle.fluid.framework import _test_eager_guard
 
-            with _test_eager_guard():
             dense_x = paddle.randn((1, 4, 4, 4, 3))
             sparse_x = dense_x.to_sparse_coo(4)
             kernel_sizes = [3, 3, 3]

python/paddle/sparse/nn/layer/conv.py

@@ -221,9 +221,7 @@ class Conv3D(_Conv3D):
         .. code-block:: python
 
           import paddle
-          from paddle.fluid.framework import _test_eager_guard
 
-          with _test_eager_guard():
           indices = [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 1, 2], [1, 3, 2, 3]]
           values = [[1], [2], [3], [4]]
           indices = paddle.to_tensor(indices, dtype='int32')
@@ -269,8 +267,8 @@ class Conv3D(_Conv3D):
 
 class SubmConv3D(_Conv3D):
     r"""
-    **Sparse Submanifold Convlution3d Layer**
-    The Sparse submanifold convolution3d layer calculates the output based on the input, filter
+    **Submanifold Sparse Convlution3d Layer**
+    The submanifold sparse convolution3d layer calculates the output based on the input, filter
     and strides, paddings, dilations, groups parameters. Input(Input) and
     Output(Output) are multidimensional SparseCooTensors with a shape of
     :math:`[N, D, H, W, C]` . Where N is batch size, C is the number of
@@ -281,7 +279,7 @@ class SubmConv3D(_Conv3D):
 
     ..  math::
 
-        Out =(W \ast X + b
+        Out = W \ast X + b
 
     In the above equation:
 
@@ -362,9 +360,7 @@ class SubmConv3D(_Conv3D):
         .. code-block:: python
 
           import paddle
-          from paddle.fluid.framework import _test_eager_guard
 
-          with _test_eager_guard():
           indices = [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 1, 2], [1, 3, 2, 3]]
           values = [[1], [2], [3], [4]]
           dense_shape = [1, 1, 3, 4, 1]

python/paddle/sparse/nn/layer/norm.py

@@ -83,9 +83,7 @@ class BatchNorm(paddle.nn.BatchNorm1D):
         .. code-block:: python
 
           import paddle
-          from paddle.fluid.framework import _test_eager_guard
 
-          with _test_eager_guard():
           paddle.seed(123)
           channels = 3
           x_data = paddle.randn((1, 6, 6, 6, channels)).astype('float32')
@@ -270,6 +268,8 @@ class SyncBatchNorm(paddle.nn.SyncBatchNorm):
              will create ParamAttr as bias_attr. If the Initializer of the bias_attr
              is not set, the bias is initialized zero. If it is set to False, this layer will not
              have trainable bias parameter. Default: None.
+        data_format(str, optional): Specify the input data format, may be "NCHW". Default "NCHW".
+        name(str, optional): Name for the BatchNorm, default is None. For more information, please refer to :ref:`api_guide_Name`..
 
     Shapes:
         input: Tensor that the dimension from 2 to 5.
@@ -282,10 +282,8 @@ class SyncBatchNorm(paddle.nn.SyncBatchNorm):
           # required: gpu
           import paddle
           import paddle.sparse.nn as nn
-          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 = paddle.to_tensor(x)
+          x = paddle.to_tensor([[[[0.3, 0.4], [0.3, 0.07]], [[0.83, 0.37], [0.18, 0.93]]]], dtype='float32')
           x = x.to_sparse_coo(len(x.shape)-1)
 
           if paddle.is_compiled_with_cuda():

python/paddle/sparse/nn/layer/pooling.py

@@ -61,9 +61,7 @@ class MaxPool3D(Layer):
         .. code-block:: python
 
             import paddle
-            from paddle.fluid.framework import _test_eager_guard
 
-            with _test_eager_guard():
             dense_x = paddle.randn((2, 3, 6, 6, 3))
             sparse_x = dense_x.to_sparse_coo(4)
             max_pool3d = paddle.sparse.nn.MaxPool3D(

python/paddle/sparse/unary.py

@@ -521,12 +521,14 @@ def abs(x, name=None):
 
 
 @dygraph_only
-def coalesce(x):
+def coalesce(x, name=None):
     r"""
     the coalesced operator include sorted and merge, after coalesced, the indices of x is sorted and unique.
 
     Parameters:
         x (Tensor): the input SparseCooTensor.
+        name (str, optional): Name for the operation (optional, default is None).
+            For more information, please refer to :ref:`api_guide_Name`.
 
     Returns:
         Tensor: return the SparseCooTensor after coalesced.