【不兼容升级】移除paddle.nn.functional.diag_embed，添加paddle.diag_embed并支持作为类方法 (P…

…addlePaddle#58223) * remove_diag_embed_to_creation * remove_diag_embed_to_creation * Update __init__.py * Incompatible upgrade * fix_bugs * add_deprecated * remove diag_embed from __all__
jiahy0825 · Oct 26, 2023 · f5fdb34 · f5fdb34
1 parent 734cd84
commit f5fdb34
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Showing 10 changed files with 147 additions and 182 deletions.
diff --git a/python/paddle/__init__.py b/python/paddle/__init__.py
@@ -71,7 +71,6 @@
 Tensor.__qualname__ = 'Tensor'
 
 import paddle.distributed.fleet  # noqa: F401
-
 from paddle import (  # noqa: F401
     distributed,
     sysconfig,
@@ -113,6 +112,7 @@
     create_parameter,
     to_tensor,
     diag,
+    diag_embed,
     diagflat,
     eye,
     linspace,
@@ -568,6 +568,7 @@
     'subtract',
     'diag',
     'diagflat',
+    'diag_embed',
     'isnan',
     'scatter_nd_add',
     'unstack',

diff --git a/python/paddle/nn/__init__.py b/python/paddle/nn/__init__.py
@@ -164,48 +164,6 @@
 from . import initializer  # noqa: F401
 from . import quant  # noqa: F401
 
-# TODO: remove 'diag_embed', 'remove_weight_norm', 'weight_norm' months later.
-from paddle.utils import deprecated
-
-
-@deprecated(
-    since="2.0.0",
-    update_to="paddle.nn.functional.diag_embed",
-    level=1,
-    reason="diag_embed in paddle.nn will be removed in future",
-)
-def diag_embed(*args):
-    '''
-    alias name of paddle.nn.functional.diag_embed
-    '''
-    return functional.diag_embed(*args)
-
-
-@deprecated(
-    since="2.0.0",
-    update_to="paddle.nn.utils.remove_weight_norm",
-    level=1,
-    reason="remove_weight_norm in paddle.nn will be removed in future",
-)
-def remove_weight_norm(*args):
-    '''
-    alias name of paddle.nn.utils.remove_weight_norm
-    '''
-    return utils.remove_weight_norm(*args)
-
-
-@deprecated(
-    since="2.0.0",
-    update_to="paddle.nn.utils.weight_norm",
-    level=1,
-    reason="weight_norm in paddle.nn will be removed in future",
-)
-def weight_norm(*args):
-    '''
-    alias name of paddle.nn.utils.weight_norm
-    '''
-    return utils.weight_norm(*args)
-
 
 __all__ = [
     'BatchNorm',

diff --git a/python/paddle/nn/functional/__init__.py b/python/paddle/nn/functional/__init__.py
@@ -183,7 +183,6 @@
     'log_softmax',
     'glu',
     'gumbel_softmax',
-    'diag_embed',
     'sequence_mask',
     'dropout',
     'dropout2d',

diff --git a/python/paddle/nn/functional/extension.py b/python/paddle/nn/functional/extension.py
@@ -14,141 +14,26 @@
 
 # TODO: define the extention functions
 
-import numpy as np
 
-from paddle import _C_ops, _legacy_C_ops, in_dynamic_mode
+from paddle import _C_ops, _legacy_C_ops, in_dynamic_mode, tensor
+from paddle.utils import deprecated
 
-from ...base.data_feeder import (
-    check_dtype,
-    check_type,
-    check_variable_and_dtype,
-)
+from ...base.data_feeder import check_type, check_variable_and_dtype
 from ...base.layer_helper import LayerHelper
 from ...common_ops_import import Variable
 from ...framework import convert_np_dtype_to_dtype_, core
-from ...tensor.creation import assign
 
 __all__ = []
 
 
+@deprecated(
+    since="2.5.2",
+    update_to="paddle.diag_embed",
+    level=1,
+    reason="diag_embed in paddle.nn.functional will be removed in future",
+)
 def diag_embed(input, offset=0, dim1=-2, dim2=-1):
-    """
-    Creates a tensor whose diagonals of certain 2D planes (specified by dim1 and dim2)
-    are filled by ``input``. By default, a 2D plane formed by the last two dimensions
-    of the returned tensor will be selected.
-
-    The argument ``offset`` determines which diagonal is generated:
-
-    - If offset = 0, it is the main diagonal.
-    - If offset > 0, it is above the main diagonal.
-    - If offset < 0, it is below the main diagonal.
-
-    Args:
-        input(Tensor|numpy.ndarray): The input tensor. Must be at least 1-dimensional. The input data type should be float32, float64, int32, int64.
-        offset(int, optional): Which diagonal to consider. Default: 0 (main diagonal).
-        dim1(int, optional): The first dimension with respect to which to take diagonal. Default: -2.
-        dim2(int, optional): The second dimension with respect to which to take diagonal. Default: -1.
-
-    Returns:
-        Tensor, the output data type is the same as input data type.
-
-    Examples:
-        .. code-block:: python
-
-            >>> import paddle
-            >>> import paddle.nn.functional as F
-
-            >>> diag_embed_input = paddle.arange(6)
-
-            >>> diag_embed_output1 = F.diag_embed(diag_embed_input)
-            >>> print(diag_embed_output1)
-            Tensor(shape=[6, 6], dtype=int64, place=Place(cpu), stop_gradient=True,
-            [[0, 0, 0, 0, 0, 0],
-             [0, 1, 0, 0, 0, 0],
-             [0, 0, 2, 0, 0, 0],
-             [0, 0, 0, 3, 0, 0],
-             [0, 0, 0, 0, 4, 0],
-             [0, 0, 0, 0, 0, 5]])
-
-            >>> diag_embed_output2 = F.diag_embed(diag_embed_input, offset=-1, dim1=0,dim2=1 )
-            >>> print(diag_embed_output2)
-            Tensor(shape=[7, 7], dtype=int64, place=Place(cpu), stop_gradient=True,
-            [[0, 0, 0, 0, 0, 0, 0],
-             [0, 0, 0, 0, 0, 0, 0],
-             [0, 1, 0, 0, 0, 0, 0],
-             [0, 0, 2, 0, 0, 0, 0],
-             [0, 0, 0, 3, 0, 0, 0],
-             [0, 0, 0, 0, 4, 0, 0],
-             [0, 0, 0, 0, 0, 5, 0]])
-
-            >>> diag_embed_input_2dim = paddle.reshape(diag_embed_input,[2,3])
-            >>> print(diag_embed_input_2dim)
-            Tensor(shape=[2, 3], dtype=int64, place=Place(cpu), stop_gradient=True,
-            [[0, 1, 2],
-            [3, 4, 5]])
-            >>> diag_embed_output3 = F.diag_embed(diag_embed_input_2dim,offset= 0, dim1=0, dim2=2 )
-            >>> print(diag_embed_output3)
-            Tensor(shape=[3, 2, 3], dtype=int64, place=Place(cpu), stop_gradient=True,
-            [[[0, 0, 0],
-              [3, 0, 0]],
-             [[0, 1, 0],
-              [0, 4, 0]],
-             [[0, 0, 2],
-              [0, 0, 5]]])
-    """
-    if not isinstance(input, Variable):
-        input = assign(input)
-
-    if in_dynamic_mode():
-        return _C_ops.diag_embed(input, offset, dim1, dim2)
-
-    inputs = {'Input': [input]}
-    attrs = {'offset': offset, 'dim1': dim1, 'dim2': dim2}
-
-    def __check_input(input, offset, dim1, dim2):
-        check_dtype(
-            input.dtype,
-            'Input',
-            ['int32', 'int64', 'float16', 'float32', 'float64'],
-            'diag_embed',
-        )
-
-        input_shape = list(input.shape)
-        assert len(input_shape) >= 1, (
-            "Input must be at least 1-dimensional, "
-            "But received Input's dimensional: %s.\n" % len(input_shape)
-        )
-
-        assert np.abs(dim1) <= len(input_shape), (
-            "Dim1 is out of range (expected to be in range of [%d, %d], but got %d).\n"
-            % (-(len(input_shape) + 1), len(input_shape), dim1)
-        )
-
-        assert np.abs(dim2) <= len(input_shape), (
-            "Dim2 is out of range (expected to be in range of [%d, %d], but got %d).\n"
-            % (-(len(input_shape) + 1), len(input_shape), dim2)
-        )
-
-        dim1_ = dim1 if dim1 >= 0 else len(input_shape) + dim1 + 1
-        dim2_ = dim2 if dim2 >= 0 else len(input_shape) + dim2 + 1
-        assert dim1_ != dim2_, (
-            "dim1 and dim2 cannot be the same dimension."
-            "But received dim1 = %d, dim2 = %d\n" % (dim1, dim2)
-        )
-
-    __check_input(input, offset, dim1, dim2)
-    helper = LayerHelper("diag_embed", **locals())
-
-    out = helper.create_variable_for_type_inference(dtype=input.dtype)
-
-    helper.append_op(
-        type='diag_embed',
-        inputs={'Input': [input]},
-        attrs={'offset': offset, 'dim1': dim1, 'dim2': dim2},
-        outputs={'Out': [out]},
-    )
-    out.stop_gradient = True
-    return out
+    return tensor.diag_embed(input, offset, dim1, dim2)
 
 
 def sequence_mask(x, maxlen=None, dtype='int64', name=None):

diff --git a/python/paddle/tensor/__init__.py b/python/paddle/tensor/__init__.py
@@ -24,6 +24,7 @@
 from .creation import to_tensor  # noqa: F401
 from .creation import diag  # noqa: F401
 from .creation import diagflat  # noqa: F401
+from .creation import diag_embed  # noqa: F401
 from .creation import eye  # noqa: F401
 from .creation import linspace  # noqa: F401
 from .creation import fill_constant  # noqa: F401
@@ -694,6 +695,7 @@
     'i1e',
     'polygamma',
     'polygamma_',
+    'diag_embed',
     'atan2',
     'diagflat',
     'multinomial',

diff --git a/python/paddle/tensor/creation.py b/python/paddle/tensor/creation.py
@@ -1674,6 +1674,125 @@ def meshgrid(*args, **kwargs):
         return out
 
 
+def diag_embed(input, offset=0, dim1=-2, dim2=-1):
+    """
+    Creates a tensor whose diagonals of certain 2D planes (specified by dim1 and dim2)
+    are filled by ``input``. By default, a 2D plane formed by the last two dimensions
+    of the returned tensor will be selected.
+
+    The argument ``offset`` determines which diagonal is generated:
+
+    - If offset = 0, it is the main diagonal.
+    - If offset > 0, it is above the main diagonal.
+    - If offset < 0, it is below the main diagonal.
+
+    Args:
+        input(Tensor|numpy.ndarray): The input tensor. Must be at least 1-dimensional. The input data type should be float32, float64, int32, int64.
+        offset(int, optional): Which diagonal to consider. Default: 0 (main diagonal).
+        dim1(int, optional): The first dimension with respect to which to take diagonal. Default: -2.
+        dim2(int, optional): The second dimension with respect to which to take diagonal. Default: -1.
+
+    Returns:
+        Tensor, the output data type is the same as input data type.
+
+    Examples:
+        .. code-block:: python
+
+            >>> import paddle
+
+            >>> diag_embed_input = paddle.arange(6)
+
+            >>> diag_embed_output1 = paddle.diag_embed(diag_embed_input)
+            >>> print(diag_embed_output1)
+            Tensor(shape=[6, 6], dtype=int64, place=Place(cpu), stop_gradient=True,
+            [[0, 0, 0, 0, 0, 0],
+             [0, 1, 0, 0, 0, 0],
+             [0, 0, 2, 0, 0, 0],
+             [0, 0, 0, 3, 0, 0],
+             [0, 0, 0, 0, 4, 0],
+             [0, 0, 0, 0, 0, 5]])
+
+            >>> diag_embed_output2 = paddle.diag_embed(diag_embed_input, offset=-1, dim1=0,dim2=1 )
+            >>> print(diag_embed_output2)
+            Tensor(shape=[7, 7], dtype=int64, place=Place(cpu), stop_gradient=True,
+            [[0, 0, 0, 0, 0, 0, 0],
+             [0, 0, 0, 0, 0, 0, 0],
+             [0, 1, 0, 0, 0, 0, 0],
+             [0, 0, 2, 0, 0, 0, 0],
+             [0, 0, 0, 3, 0, 0, 0],
+             [0, 0, 0, 0, 4, 0, 0],
+             [0, 0, 0, 0, 0, 5, 0]])
+
+            >>> diag_embed_input_2dim = paddle.reshape(diag_embed_input,[2,3])
+            >>> print(diag_embed_input_2dim)
+            Tensor(shape=[2, 3], dtype=int64, place=Place(cpu), stop_gradient=True,
+            [[0, 1, 2],
+            [3, 4, 5]])
+            >>> diag_embed_output3 = paddle.diag_embed(diag_embed_input_2dim,offset= 0, dim1=0, dim2=2 )
+            >>> print(diag_embed_output3)
+            Tensor(shape=[3, 2, 3], dtype=int64, place=Place(cpu), stop_gradient=True,
+            [[[0, 0, 0],
+              [3, 0, 0]],
+             [[0, 1, 0],
+              [0, 4, 0]],
+             [[0, 0, 2],
+              [0, 0, 5]]])
+    """
+    if not isinstance(input, Variable):
+        input = assign(input)
+
+    if in_dynamic_mode():
+        return _C_ops.diag_embed(input, offset, dim1, dim2)
+
+    inputs = {'Input': [input]}
+    attrs = {'offset': offset, 'dim1': dim1, 'dim2': dim2}
+
+    def __check_input(input, offset, dim1, dim2):
+        check_dtype(
+            input.dtype,
+            'Input',
+            ['int32', 'int64', 'float16', 'float32', 'float64'],
+            'diag_embed',
+        )
+
+        input_shape = list(input.shape)
+        assert len(input_shape) >= 1, (
+            "Input must be at least 1-dimensional, "
+            "But received Input's dimensional: %s.\n" % len(input_shape)
+        )
+
+        assert np.abs(dim1) <= len(input_shape), (
+            "Dim1 is out of range (expected to be in range of [%d, %d], but got %d).\n"
+            % (-(len(input_shape) + 1), len(input_shape), dim1)
+        )
+
+        assert np.abs(dim2) <= len(input_shape), (
+            "Dim2 is out of range (expected to be in range of [%d, %d], but got %d).\n"
+            % (-(len(input_shape) + 1), len(input_shape), dim2)
+        )
+
+        dim1_ = dim1 if dim1 >= 0 else len(input_shape) + dim1 + 1
+        dim2_ = dim2 if dim2 >= 0 else len(input_shape) + dim2 + 1
+        assert dim1_ != dim2_, (
+            "dim1 and dim2 cannot be the same dimension."
+            "But received dim1 = %d, dim2 = %d\n" % (dim1, dim2)
+        )
+
+    __check_input(input, offset, dim1, dim2)
+    helper = LayerHelper("diag_embed", **locals())
+
+    out = helper.create_variable_for_type_inference(dtype=input.dtype)
+
+    helper.append_op(
+        type='diag_embed',
+        inputs={'Input': [input]},
+        attrs={'offset': offset, 'dim1': dim1, 'dim2': dim2},
+        outputs={'Out': [out]},
+    )
+    out.stop_gradient = True
+    return out
+
+
 def diagflat(x, offset=0, name=None):
     """
     If ``x`` is a vector (1-D tensor), a 2-D square tensor with the elements of ``x`` as the diagonal is returned.