[api move] cvm

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

@@ -182,6 +182,46 @@ def test_type():
 
             self.assertRaises(TypeError, test_type)
 
+    def test_cvm(self):
+        inp = np.ones([10, 10], dtype='float32')
+        arr = [[0.6931472, -1.904654e-09, 1, 1, 1, 1, 1, 1, 1, 1]] * 10
+        cvm1 = np.array(arr, dtype='float32')
+        cvm2 = np.ones([10, 8], dtype='float32')
+        show_clk = np.ones([10, 2], dtype='float32')
+        with self.static_graph():
+            x = paddle.static.data(
+                name='data',
+                shape=[10, 10],
+                dtype='float32',
+            )
+            u = paddle.static.data(
+                name='show_click',
+                shape=[10, 2],
+                dtype='float32',
+            )
+            no_cvm = paddle.static.nn.continuous_value_model(x, u, True)
+            static_ret1 = self.get_static_graph_result(
+                feed={'data': inp, 'show_click': show_clk},
+                fetch_list=[no_cvm],
+            )[0]
+        with self.static_graph():
+            x = paddle.static.data(
+                name='data',
+                shape=[10, 10],
+                dtype='float32',
+            )
+            u = paddle.static.data(
+                name='show_click',
+                shape=[10, 2],
+                dtype='float32',
+            )
+            cvm = paddle.static.nn.continuous_value_model(x, u, False)
+            static_ret2 = self.get_static_graph_result(
+                feed={'data': inp, 'show_click': show_clk}, fetch_list=[cvm]
+            )[0]
+        np.testing.assert_allclose(static_ret1, cvm1, rtol=1e-5, atol=1e-06)
+        np.testing.assert_allclose(static_ret2, cvm2, rtol=1e-5, atol=1e-06)
+
     def test_Flatten(self):
         inp = np.ones([3, 4, 4, 5], dtype='float32')
         with self.static_graph():

python/paddle/static/nn/__init__.py

@@ -16,6 +16,7 @@
 from .common import batch_norm  # noqa: F401
 from .common import instance_norm  # noqa: F401
 from .common import data_norm  # noqa: F401
+from .common import continuous_value_model  # noqa: F401
 from .common import group_norm  # noqa: F401
 from .common import deform_conv2d  # noqa: F401
 from .common import conv3d  # noqa: F401

python/paddle/static/nn/common.py

@@ -321,6 +321,55 @@ def instance_norm(
     return instance_norm_out
 
 
+@static_only
+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
+          import paddle
+          input = paddle.static.data(name="input", shape=[64, 1], dtype="int64")
+          label = paddle.static.data(name="label", shape=[64, 1], dtype="int64")
+          w0 = paddle.full(shape=(100, 1), fill_value=2).astype(paddle.float32)
+          embed = paddle.nn.functional.embedding(
+                            input,
+                            w0)
+          ones = paddle.full_like(label, 1, dtype="int64")
+          show_clk = paddle.cast(paddle.concat([ones, label], axis=1), dtype='float32')
+          show_clk.stop_gradient = True
+          input_with_cvm = paddle.static.nn.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
+
+
 @static_only
 def data_norm(
     input,