Fix minimun minimum (#60920)

Author: co63oc

python/paddle/sparse/creation.py

@@ -155,7 +155,7 @@ def sparse_coo_tensor(
             shape = list(shape)
             if shape < min_shape:
                 raise ValueError(
-                    f"the minimun shape required is {min_shape}, but get {shape}"
+                    f"the minimum shape required is {min_shape}, but get {shape}"
                 )
             if len(shape) != sparse_dim + dense_dim:
                 raise ValueError(
@@ -188,7 +188,7 @@ def sparse_csr_tensor(
     r"""
     Constructs a sparse ``paddle.Tensor`` in CSR(Compressed Sparse Row) format according to the
     ``crows``, ``cols`` and ``values``.
-    Currently, the crows and cols of each batch must be incrementd.
+    Currently, the crows and cols of each batch must be incremented.
 
     Args:
         crows(list|tuple|ndarray|Tensor): 1-D array, each element in the rows represents the

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

@@ -72,7 +72,7 @@ class Softmax(Layer):
         Only support axis=-1 for SparseCsrTensor, which is faster when read data
         by row (axis=-1).
 
-    Transform x to dense matix, and :math:`i` is row index, :math:`j` is column index.
+    Transform x to dense matrix, and :math:`i` is row index, :math:`j` is column index.
     If axis=-1, We have:
 
     .. math::

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

@@ -238,7 +238,7 @@ def extra_repr(self):
 
 class Conv3D(_Conv3D):
     r"""
-    **Sparse Convlution3d Layer**
+    **Sparse Convolution3d Layer**
     The 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
@@ -267,11 +267,11 @@ class Conv3D(_Conv3D):
         stride(int|list|tuple, optional): The stride size. If stride is a list/tuple, it must
             contain three integers, (stride_D, stride_H, stride_W). Otherwise, the
             stride_D = stride_H = stride_W = stride. The default value is 1.
-        padding(int|str|tuple|list, optional): The padding size. Padding coule be in one of the following forms.
+        padding(int|str|tuple|list, optional): The padding size. Padding couple be in one of the following forms.
             1. a string in ['valid', 'same'].
-            2. an int, which means each spartial dimension(depth, height, width) is zero paded by size of `padding`
-            3. a list[int] or tuple[int] whose length is the number of spartial dimensions, which contains the amount of padding on each side for each spartial dimension. It has the form [pad_d1, pad_d2, ...].
-            4. a list[int] or tuple[int] whose length is 2 * number of spartial dimensions. It has the form  [pad_before, pad_after, pad_before, pad_after, ...] for all spartial dimensions.
+            2. an int, which means each spatial dimension(depth, height, width) is zero padded by size of `padding`
+            3. a list[int] or tuple[int] whose length is the number of spatial dimensions, which contains the amount of padding on each side for each spatial dimension. It has the form [pad_d1, pad_d2, ...].
+            4. a list[int] or tuple[int] whose length is 2 * number of spatial dimensions. It has the form  [pad_before, pad_after, pad_before, pad_after, ...] for all spatial dimensions.
             5. a list or tuple of pairs of ints. It has the form [[pad_before, pad_after], [pad_before, pad_after], ...]. Note that, the batch dimension and channel dimension are also included. Each pair of integers correspond to the amount of padding for a dimension of the input. Padding in batch dimension and channel dimension should be [0, 0] or (0, 0).
             The default value is 0.
         dilation(int|list|tuple, optional): The dilation size. If dilation is a list/tuple, it must
@@ -373,7 +373,7 @@ def __init__(
 
 class Conv2D(_Conv2D):
     r"""
-    **Sparse Convlution2d Layer**
+    **Sparse Convolution2d Layer**
 
     The Sparse convolution2d layer calculates the output based on the input, filter
     and strides, paddings, dilations, groups parameters. Input(Input) and
@@ -403,12 +403,12 @@ class Conv2D(_Conv2D):
         stride(int|list|tuple, optional): The stride size. If stride is a list/tuple, it must
             contain three integers, (stride_H, stride_W). Otherwise, the
             stride_H = stride_W = stride. The default value is 1.
-        padding(int|str|tuple|list, optional): The padding size. Padding coule be in one of the following forms.
+        padding(int|str|tuple|list, optional): The padding size. Padding couple be in one of the following forms.
 
             1. a string in ['valid', 'same'].
-            2. an int, which means each spartial dimension(height, width) is zero paded by size of `padding`
-            3. a list[int] or tuple[int] whose length is the number of spartial dimensions, which contains the amount of padding on each side for each spartial dimension. It has the form [pad_d1, pad_d2, ...].
-            4. a list[int] or tuple[int] whose length is 2 * number of spartial dimensions. It has the form  [pad_before, pad_after, pad_before, pad_after, ...] for all spartial dimensions.
+            2. an int, which means each spatial dimension(height, width) is zero padded by size of `padding`
+            3. a list[int] or tuple[int] whose length is the number of spatial dimensions, which contains the amount of padding on each side for each spatial dimension. It has the form [pad_d1, pad_d2, ...].
+            4. a list[int] or tuple[int] whose length is 2 * number of spatial dimensions. It has the form  [pad_before, pad_after, pad_before, pad_after, ...] for all spatial dimensions.
             5. a list or tuple of pairs of ints. It has the form [[pad_before, pad_after], [pad_before, pad_after], ...].
 
             Note that, the batch dimension and channel dimension are also included. Each pair of integers correspond to the amount of padding for a dimension of the input. Padding in batch dimension and channel dimension should be [0, 0] or (0, 0).
@@ -508,7 +508,7 @@ def __init__(
 
 class SubmConv3D(_Conv3D):
     r"""
-    **Submanifold Sparse Convlution3d Layer**
+    **Submanifold Sparse Convolution3d 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
@@ -537,11 +537,11 @@ class SubmConv3D(_Conv3D):
         stride(int|list|tuple, optional): The stride size. If stride is a list/tuple, it must
             contain three integers, (stride_D, stride_H, stride_W). Otherwise, the
             stride_D = stride_H = stride_W = stride. The default value is 1.
-        padding(int|str|tuple|list, optional): The padding size. Padding coule be in one of the following forms.
+        padding(int|str|tuple|list, optional): The padding size. Padding couple be in one of the following forms.
             1. a string in ['valid', 'same'].
-            2. an int, which means each spartial dimension(depth, height, width) is zero paded by size of `padding`
-            3. a list[int] or tuple[int] whose length is the number of spartial dimensions, which contains the amount of padding on each side for each spartial dimension. It has the form [pad_d1, pad_d2, ...].
-            4. a list[int] or tuple[int] whose length is 2 * number of spartial dimensions. It has the form  [pad_before, pad_after, pad_before, pad_after, ...] for all spartial dimensions.
+            2. an int, which means each spatial dimension(depth, height, width) is zero padded by size of `padding`
+            3. a list[int] or tuple[int] whose length is the number of spatial dimensions, which contains the amount of padding on each side for each spatial dimension. It has the form [pad_d1, pad_d2, ...].
+            4. a list[int] or tuple[int] whose length is 2 * number of spatial dimensions. It has the form  [pad_before, pad_after, pad_before, pad_after, ...] for all spatial dimensions.
             5. a list or tuple of pairs of ints. It has the form [[pad_before, pad_after], [pad_before, pad_after], ...]. Note that, the batch dimension and channel dimension are also included. Each pair of integers correspond to the amount of padding for a dimension of the input. Padding in batch dimension and channel dimension should be [0, 0] or (0, 0).
             The default value is 0.
         dilation(int|list|tuple, optional): The dilation size. If dilation is a list/tuple, it must
@@ -648,7 +648,7 @@ def __init__(
 
 class SubmConv2D(_Conv2D):
     r"""
-    **Submanifold Sparse Convlution2d Layer**
+    **Submanifold Sparse Convolution2d Layer**
 
     The submanifold sparse convolution2d layer calculates the output based on the input, filter
     and strides, paddings, dilations, groups parameters. Input(Input) and
@@ -678,12 +678,12 @@ class SubmConv2D(_Conv2D):
         stride(int|list|tuple, optional): The stride size. If stride is a list/tuple, it must
             contain two integers, (stride_H, stride_W). Otherwise, the
             stride_H = stride_W = stride. The default value is 1.
-        padding(int|str|tuple|list, optional): The padding size. Padding coule be in one of the following forms.
+        padding(int|str|tuple|list, optional): The padding size. Padding couple be in one of the following forms.
 
             1. a string in ['valid', 'same'].
-            2. an int, which means each spartial dimension(depth, height, width) is zero paded by size of `padding`
-            3. a list[int] or tuple[int] whose length is the number of spartial dimensions, which contains the amount of padding on each side for each spartial dimension. It has the form [pad_d1, pad_d2, ...].
-            4. a list[int] or tuple[int] whose length is 2 * number of spartial dimensions. It has the form  [pad_before, pad_after, pad_before, pad_after, ...] for all spartial dimensions.
+            2. an int, which means each spatial dimension(depth, height, width) is zero padded by size of `padding`
+            3. a list[int] or tuple[int] whose length is the number of spatial dimensions, which contains the amount of padding on each side for each spatial dimension. It has the form [pad_d1, pad_d2, ...].
+            4. a list[int] or tuple[int] whose length is 2 * number of spatial dimensions. It has the form  [pad_before, pad_after, pad_before, pad_after, ...] for all spatial dimensions.
             5. a list or tuple of pairs of ints. It has the form [[pad_before, pad_after], [pad_before, pad_after], ...].
 
             Note that, the batch dimension and channel dimension are also included. Each pair of integers correspond to the amount of padding for a dimension of the input. Padding in batch dimension and channel dimension should be [0, 0] or (0, 0).

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

@@ -260,7 +260,7 @@ class SyncBatchNorm(paddle.nn.SyncBatchNorm):
         epsilon(float, optional): The small value added to the variance to prevent division by zero. Default: 1e-5.
         momentum(float, optional): The value used for the moving_mean and moving_var computation. Default: 0.9.
         weight_attr(ParamAttr|bool, optional): The parameter attribute for Parameter `scale`
-             of this layer. If it is set to None or one attribute of ParamAttr, this layerr
+             of this layer. If it is set to None or one attribute of ParamAttr, this layer
              will create ParamAttr as param_attr. If the Initializer of the param_attr
              is not set, the parameter is initialized with Xavier. If it is set to False,
              this layer will not have trainable scale parameter. Default: None.

python/paddle/sparse/unary.py

@@ -953,7 +953,7 @@ def slice(x, axes, starts, ends, name=None):
     ``starts`` or ``ends`` such as :math:`-i`, it represents the reverse position of
     the axis :math:`i-1` (here 0 is the initial position).
     If the value passed to ``starts`` or ``ends`` is greater than the number of elements
-    in the dimenstion (n), it represents n.
+    in the dimension (n), it represents n.
     For slicing to the end of a dimension with unknown size, it is recommended to pass
     in INT_MAX. The size of ``axes`` must be equal to ``starts`` and ``ends``.
 
@@ -1039,7 +1039,7 @@ def pca_lowrank(x, q=None, center=True, niter=2, name=None):
 
     Args:
         x (Tensor): The input tensor. Its shape should be `[N, M]`,
-            N and M can be arbitraty positive number.
+            N and M can be arbitrary positive number.
             The data type of x should be float32 or float64.
         q (int, optional): a slightly overestimated rank of :math:`X`.
             Default value is :math:`q=min(6,N,M)`.

python/paddle/static/input.py

@@ -207,7 +207,7 @@ class InputSpec:
     def __init__(self, shape, dtype='float32', name=None, stop_gradient=False):
         # replace `None` in shape  with -1
         self.shape = self._verify(shape)
-        # convert dtype into united represention
+        # convert dtype into united representation
         if dtype is not None:
             if isinstance(dtype, (np.dtype, str)):
                 dtype = convert_np_dtype_to_dtype_(dtype)

python/paddle/static/io.py

@@ -197,7 +197,7 @@ def normalize_program(program, feed_vars, fetch_vars, **kwargs):
         feed_vars(Tensor | list[Tensor]): Variables needed by inference.
         fetch_vars(Tensor | list[Tensor]): Variables returned by inference.
         kwargs: Supported keys including ``skip_prune_program``.
-            - skip_prune_program(bool): whether to skip prunning program. Defaults to False.
+            - skip_prune_program(bool): whether to skip pruning program. Defaults to False.
 
     Returns:
         Program: Normalized/Optimized program.
@@ -211,7 +211,7 @@ def normalize_program(program, feed_vars, fetch_vars, **kwargs):
 
             >>> path_prefix = "./infer_model"
 
-            # User defined network, here a softmax regession example
+            # User defined network, here a softmax regression example
             >>> image = paddle.static.data(name='img', shape=[None, 28, 28], dtype='float32')
             >>> label = paddle.static.data(name='label', shape=[None, 1], dtype='int64')
             >>> predict = paddle.static.nn.fc(image, 10, activation='softmax')
@@ -283,10 +283,10 @@ def normalize_program(program, feed_vars, fetch_vars, **kwargs):
                 # remove backward block
                 copy_program.blocks.pop(backward_block_id)
                 # update attrs ``blocks``
-                reserverd_blocks = []
+                reserved_blocks = []
                 for block_id in sub_blocks_ids[:-1]:
-                    reserverd_blocks.append(copy_program.block(block_id))
-                op._update_desc_attr("blocks", reserverd_blocks)
+                    reserved_blocks.append(copy_program.block(block_id))
+                op._update_desc_attr("blocks", reserved_blocks)
 
     for idx in remove_op_idx[::-1]:
         global_block._remove_op(idx)
@@ -332,7 +332,7 @@ def serialize_program(feed_vars, fetch_vars, **kwargs):
 
             >>> path_prefix = "./infer_model"
 
-            # User defined network, here a softmax regession example
+            # User defined network, here a softmax regression example
             >>> image = paddle.static.data(name='img', shape=[None, 28, 28], dtype='float32')
             >>> label = paddle.static.data(name='label', shape=[None, 1], dtype='int64')
             >>> predict = paddle.static.nn.fc(image, 10, activation='softmax')
@@ -391,7 +391,7 @@ def serialize_persistables(feed_vars, fetch_vars, executor, **kwargs):
 
             >>> path_prefix = "./infer_model"
 
-            # User defined network, here a softmax regession example
+            # User defined network, here a softmax regression example
             >>> image = paddle.static.data(name='img', shape=[None, 28, 28], dtype='float32')
             >>> label = paddle.static.data(name='label', shape=[None, 1], dtype='int64')
             >>> predict = paddle.static.nn.fc(image, 10, activation='softmax')
@@ -431,7 +431,7 @@ def _serialize_persistables(program, executor):
             "variables in your model to save"
         )
         return None
-    # create a new program and clone persitable vars to it
+    # create a new program and clone persistable vars to it
     save_program = Program()
     save_block = save_program.global_block()
     save_var_map = {}
@@ -543,7 +543,7 @@ def save_inference_model(
 
             >>> path_prefix = "./infer_model"
 
-            # User defined network, here a softmax regession example
+            # User defined network, here a softmax regression example
             >>> image = paddle.static.data(name='img', shape=[None, 28, 28], dtype='float32')
             >>> label = paddle.static.data(name='label', shape=[None, 1], dtype='int64')
             >>> predict = paddle.static.nn.fc(image, 10, activation='softmax')
@@ -644,7 +644,7 @@ def deserialize_program(data):
 
             >>> path_prefix = "./infer_model"
 
-            # User defined network, here a softmax regession example
+            # User defined network, here a softmax regression example
             >>> image = paddle.static.data(name='img', shape=[None, 28, 28], dtype='float32')
             >>> label = paddle.static.data(name='label', shape=[None, 1], dtype='int64')
             >>> predict = paddle.static.nn.fc(image, 10, activation='softmax')
@@ -693,7 +693,7 @@ def deserialize_persistables(program, data, executor):
 
             >>> path_prefix = "./infer_model"
 
-            # User defined network, here a softmax regession example
+            # User defined network, here a softmax regression example
             >>> image = paddle.static.data(name='img', shape=[None, 28, 28], dtype='float32')
             >>> label = paddle.static.data(name='label', shape=[None, 1], dtype='int64')
             >>> predict = paddle.static.nn.fc(image, 10, activation='softmax')
@@ -1620,7 +1620,7 @@ def load(program, model_path, executor=None, var_list=None):
             program_var_list = program.list_vars()
             program_var_name_set = {var.name for var in program_var_list}
 
-            # check all the variable inlcuded in program
+            # check all the variable included in program
             for var in var_list:
                 if var.name not in program_var_name_set:
                     raise LookupError(
@@ -1804,7 +1804,7 @@ def set_program_state(program, state_dict):
             unused_para_list.append(k)
     if len(unused_para_list) > 0:
         warnings.warn(
-            "This list is not set, Because of Paramerter not found in program. There are: {}".format(
+            "This list is not set, Because of Parameter not found in program. There are: {}".format(
                 " ".join(unused_para_list)
             )
         )