[Fluid Clean] remove fluid.layers.continuous_value_model (#48509)

python/paddle/fluid/layers/nn.py

@@ -102,7 +102,6 @@ __all__ = [
     'bilinear_tensor_product',
     'merge_selected_rows',
     'get_tensor_from_selected_rows',
-    'continuous_value_model',
     'unfold',
     'deformable_roi_pooling',
     'shard_index',
@@ -5563,63 +5562,6 @@ def get_tensor_from_selected_rows(x, name=None):
     return out
 
 
-def continuous_value_model(input, cvm, use_cvm=True):
-    r"""
-
-    **continuous_value_model layers**
-
-    Now, this OP is used in CTR project to remove or dispose show and click value in :attr:`input`.
-
-    :attr:`input` is an embedding vector including show and click value, whose shape is :math:`[N, D]` (N is batch size. D is `2 + embedding dim` ).
-    Show and click at first two dims of embedding vector D.
-    If :attr:`use_cvm` is True, it will calculate :math:`log(show)` and :math:`log(click)` , and output shape is :math:`[N, D]` .
-    If :attr:`use_cvm` is False, it will remove show and click from :attr:`input` , and output shape is :math:`[N, D - 2]` .
-    :attr:`cvm` is show_click info, whose shape is :math:`[N, 2]` .
-
-    Args:
-        input (Variable): The input variable. A 2-D LoDTensor with shape :math:`[N, D]` , where N is the batch size, D is `2 + the embedding dim` . `lod level = 1` .
-        A Tensor with type float32, float64.
-        cvm (Variable): Show and click variable. A 2-D Tensor with shape :math:`[N, 2]` , where N is the batch size, 2 is show and click.
-        A Tensor with type float32, float64.
-        use_cvm  (bool):  Use show_click or not. if use, the output dim is the same as input.
-                          if not use, the output dim is `input dim - 2` (remove show and click)
-
-    Returns:
-
-        Variable: A 2-D LodTensor with shape :math:`[N, M]` . if :attr:`use_cvm` = True, M is equal to input dim D. if False, M is equal to `D - 2`. \
-        A Tensor with same type as input.
-
-    Examples:
-
-        .. code-block:: python
-
-          import paddle.fluid as fluid
-          input = fluid.data(name="input", shape=[64, 1], dtype="int64")
-          label = fluid.data(name="label", shape=[64, 1], dtype="int64")
-          embed = fluid.layers.embedding(
-                            input=input,
-                            size=[100, 11],
-                            dtype='float32')
-          ones = fluid.layers.fill_constant_batch_size_like(input=label, shape=[-1, 1], dtype="int64", value=1)
-          show_clk = fluid.layers.cast(fluid.layers.concat([ones, label], axis=1), dtype='float32')
-          show_clk.stop_gradient = True
-          input_with_cvm = fluid.layers.continuous_value_model(embed, show_clk, True)
-
-    """
-    helper = LayerHelper('cvm', **locals())
-    out = helper.create_variable(dtype=input.dtype)
-    check_variable_and_dtype(
-        input, 'input', ['float16', 'float32', 'float64'], 'cvm'
-    )
-    helper.append_op(
-        type='cvm',
-        inputs={'X': [input], 'CVM': [cvm]},
-        outputs={'Y': [out]},
-        attrs={"use_cvm": use_cvm},
-    )
-    return out
-
-
 def unfold(x, kernel_sizes, strides=1, paddings=0, dilations=1, name=None):
     r"""
 

python/paddle/static/nn/__init__.py

@@ -40,7 +40,6 @@ from ...fluid.layers import while_loop  # noqa: F401
 
 from ...fluid.input import embedding  # noqa: F401
 from ...fluid.contrib.layers import sparse_embedding  # noqa: F401
-from ...fluid.layers import continuous_value_model  # noqa: F401
 from ...fluid.layers import StaticRNN  # noqa: F401
 
 from ...fluid.layers.sequence_lod import sequence_conv  # noqa: F401