[Fluid Clean] Remove paddle.fluid.layers.nn.reduce_max/min (#48236)

python/paddle/fluid/layers/distributions.py

@@ -530,9 +530,9 @@ class Categorical(Distribution):
         """
         check_type(other, 'other', Categorical, 'kl_divergence')
 
-        logits = self.logits - nn.reduce_max(self.logits, dim=-1, keep_dim=True)
-        other_logits = other.logits - nn.reduce_max(
-            other.logits, dim=-1, keep_dim=True
+        logits = self.logits - paddle.max(self.logits, axis=-1, keepdim=True)
+        other_logits = other.logits - paddle.max(
+            other.logits, axis=-1, keepdim=True
         )
         e_logits = paddle.exp(logits)
         other_e_logits = paddle.exp(other_logits)
@@ -554,7 +554,7 @@ class Categorical(Distribution):
           Variable: Shannon entropy of Categorical distribution. The data type is float32.
 
         """
-        logits = self.logits - nn.reduce_max(self.logits, dim=-1, keep_dim=True)
+        logits = self.logits - paddle.max(self.logits, axis=-1, keepdim=True)
         e_logits = paddle.exp(logits)
         z = nn.reduce_sum(e_logits, dim=-1, keep_dim=True)
         prob = e_logits / z

python/paddle/fluid/layers/nn.py

@@ -79,8 +79,6 @@ __all__ = [
     'data_norm',
     'reduce_sum',
     'reduce_mean',
-    'reduce_max',
-    'reduce_min',
     'reduce_all',
     'reduce_any',
     'dropout',
@@ -190,7 +188,7 @@ OP_NAMEMAPPING = {
 
 def _get_reduce_dim(dim, input):
     """
-    Internal function for reduce_sum, reduce_mean, reduce_max, reduce_min, reduce_prod.
+    Internal function for reduce_sum, reduce_mean, reduce_prod.
     It computes the attribute reduce_all value based on axis.
     """
     if dim is not None and not isinstance(dim, list):
@@ -3938,150 +3936,6 @@ def reduce_mean(input, dim=None, keep_dim=False, name=None):
     return paddle.mean(x=input, axis=dim, keepdim=keep_dim, name=name)
 
 
-def reduce_max(input, dim=None, keep_dim=False, name=None):
-    """
-
-    Computes the maximum of tensor elements over the given dimension.
-
-    Args:
-        input (Variable): The input variable which is a Tensor, the data type is float32,
-            float64, int32, int64.
-        dim (list|int, optional): The dimension along which the maximum is computed.
-            If :attr:`None`, compute the maximum over all elements of
-            :attr:`input` and return a Tensor variable with a single element,
-            otherwise must be in the range :math:`[-rank(input), rank(input))`.
-            If :math:`dim[i] < 0`, the dimension to reduce is :math:`rank + dim[i]`.
-        keep_dim (bool, optional): Whether to reserve the reduced dimension in the
-            output Tensor. The result tensor will have one fewer dimension
-            than the :attr:`input` unless :attr:`keep_dim` is true, default
-            value is False.
-        name(str, optional): The default value is None.  Normally there is no need for
-            user to set this property.  For more information, please refer to :ref:`api_guide_Name`
-
-    Returns:
-        Variable: Tensor, results of maximum on the specified dim of input tensor,
-        it's data type is the same as input's Tensor.
-
-    Examples:
-        .. code-block:: python
-
-            import paddle.fluid as fluid
-            import paddle
-            paddle.enable_static()
-            # x is a Tensor variable with following elements:
-            #    [[0.2, 0.3, 0.5, 0.9]
-            #     [0.1, 0.2, 0.6, 0.7]]
-            # Each example is followed by the corresponding output tensor.
-            x = fluid.data(name='x', shape=[2, 4], dtype='float32')
-            fluid.layers.reduce_max(x)  # [0.9]
-            fluid.layers.reduce_max(x, dim=0)  # [0.2, 0.3, 0.6, 0.9]
-            fluid.layers.reduce_max(x, dim=-1)  # [0.9, 0.7]
-            fluid.layers.reduce_max(x, dim=1, keep_dim=True)  # [[0.9], [0.7]]
-
-            # y is a Tensor variable with shape [2, 2, 2] and elements as below:
-            #      [[[1.0, 2.0], [3.0, 4.0]],
-            #      [[5.0, 6.0], [7.0, 8.0]]]
-            # Each example is followed by the corresponding output tensor.
-            y = fluid.data(name='y', shape=[2, 2, 2], dtype='float32')
-            fluid.layers.reduce_max(y, dim=[1, 2]) # [4.0, 8.0]
-            fluid.layers.reduce_max(y, dim=[0, 1]) # [7.0, 8.0]
-    """
-    helper = LayerHelper('reduce_max', **locals())
-    out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
-
-    if dim is not None and not isinstance(dim, list):
-        dim = [dim]
-
-    if in_dygraph_mode():
-        return _C_ops.max(input, dim if dim is not None else [], keep_dim)
-
-    helper.append_op(
-        type='reduce_max',
-        inputs={'X': input},
-        outputs={'Out': out},
-        attrs={
-            'dim': dim if dim is not None and dim != [] else [0],
-            'keep_dim': keep_dim,
-            'reduce_all': True
-            if dim is None or dim == [] or len(dim) == len(input.shape)
-            else False,
-        },
-    )
-    return out
-
-
-def reduce_min(input, dim=None, keep_dim=False, name=None):
-    """
-
-    Computes the minimum of tensor elements over the given dimension.
-
-    Args:
-        input (Variable): The input variable which is a Tensor, the data type is float32,
-            float64, int32, int64.
-        dim (list|int, optional): The dimensions along which the minimum is computed.
-            If :attr:`None`, compute the minimum over all elements of
-            :attr:`input` and return a Tensor variable with a single element,
-            otherwise must be in the range :math:`[-rank(input), rank(input))`.
-            If :math:`dim[i] < 0`, the dimension to reduce is :math:`rank + dim[i]`.
-        keep_dim (bool, optional): Whether to reserve the reduced dimension in the
-            output Tensor. The result tensor will have one fewer dimension
-            than the :attr:`input` unless :attr:`keep_dim` is true, default
-            value is False.
-        name(str, optional): The default value is None.  Normally there is no need for
-            user to set this property.  For more information, please refer to :ref:`api_guide_Name`
-
-    Returns:
-        Variable: Tensor, result of minimum on the specified dim of input tensor,
-        it's data type is the same as input's Tensor.
-
-    Examples:
-        .. code-block:: python
-
-            import paddle.fluid as fluid
-            import paddle
-            paddle.enable_static()
-
-            # x is a Tensor variable with following elements:
-            #    [[0.2, 0.3, 0.5, 0.9]
-            #     [0.1, 0.2, 0.6, 0.7]]
-            # Each example is followed by the corresponding output tensor.
-            x = fluid.data(name='x', shape=[2, 4], dtype='float32')
-            fluid.layers.reduce_min(x)  # [0.1]
-            fluid.layers.reduce_min(x, dim=0)  # [0.1, 0.2, 0.5, 0.7]
-            fluid.layers.reduce_min(x, dim=-1)  # [0.2, 0.1]
-            fluid.layers.reduce_min(x, dim=1, keep_dim=True)  # [[0.2], [0.1]]
-
-            # y is a Tensor variable with shape [2, 2, 2] and elements as below:
-            #      [[[1.0, 2.0], [3.0, 4.0]],
-            #      [[5.0, 6.0], [7.0, 8.0]]]
-            # Each example is followed by the corresponding output tensor.
-            y = fluid.data(name='y', shape=[2, 2, 2], dtype='float32')
-            fluid.layers.reduce_min(y, dim=[1, 2]) # [1.0, 5.0]
-            fluid.layers.reduce_min(y, dim=[0, 1]) # [1.0, 2.0]
-    """
-    helper = LayerHelper('reduce_min', **locals())
-    out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
-    if dim is not None and not isinstance(dim, list):
-        dim = [dim]
-
-    if in_dygraph_mode():
-        return _C_ops.min(input, dim if dim is not None else [], keep_dim)
-
-    helper.append_op(
-        type='reduce_min',
-        inputs={'X': input},
-        outputs={'Out': out},
-        attrs={
-            'dim': dim if dim is not None and dim != [] else [0],
-            'keep_dim': keep_dim,
-            'reduce_all': True
-            if dim is None or dim == [] or len(dim) == len(input.shape)
-            else False,
-        },
-    )
-    return out
-
-
 def reduce_all(input, dim=None, keep_dim=False, name=None):
     """
 

python/paddle/fluid/tests/unittests/dygraph_to_static/test_sentiment.py

@@ -52,7 +52,7 @@ class SimpleConvPool(fluid.dygraph.Layer):
 
     def forward(self, inputs):
         x = paddle.tanh(self._conv2d(inputs))
-        x = fluid.layers.reduce_max(x, dim=-1)
+        x = paddle.max(x, axis=-1)
         x = paddle.reshape(x, shape=[self.batch_size, -1])
         return x
 
@@ -194,7 +194,7 @@ class GRU(fluid.dygraph.Layer):
         emb = paddle.reshape(emb, shape=[self.batch_size, -1, self.hid_dim])
         fc_1 = self._fc1(emb)
         gru_hidden = self._gru(fc_1)
-        gru_hidden = fluid.layers.reduce_max(gru_hidden, dim=1)
+        gru_hidden = paddle.max(gru_hidden, axis=1)
         tanh_1 = paddle.tanh(gru_hidden)
         fc_2 = self._fc2(tanh_1)
         prediction = self._fc_prediction(fc_2)
@@ -254,7 +254,7 @@ class BiGRU(fluid.dygraph.Layer):
         encoded_vector = fluid.layers.concat(
             input=[gru_forward_tanh, gru_backward_tanh], axis=2
         )
-        encoded_vector = fluid.layers.reduce_max(encoded_vector, dim=1)
+        encoded_vector = paddle.max(encoded_vector, axis=1)
         fc_2 = self._fc2(encoded_vector)
         prediction = self._fc_prediction(fc_2)
         # TODO(Aurelius84): Uncomment the following codes when we support return variable-length vars.

python/paddle/fluid/tests/unittests/dygraph_to_static/test_tsm.py

@@ -208,7 +208,7 @@ class TSM_ResNet(fluid.dygraph.Layer):
         y = self.pool2d_avg(y)
         y = fluid.layers.dropout(y, dropout_prob=0.5)
         y = paddle.reshape(y, [-1, self.seg_num, y.shape[1]])
-        y = fluid.layers.reduce_mean(y, dim=1)
+        y = paddle.mean(y, axis=1)
         y = paddle.reshape(y, shape=[-1, 2048])
         y = self.out(y)
         return y

python/paddle/fluid/tests/unittests/ipu/test_reduce_x_op_ipu.py

@@ -123,12 +123,12 @@ class TestMean(IPUOpTest):
 
 class TestMax(TestMean):
     def set_test_op(self):
-        self.op = paddle.fluid.layers.reduce_max
+        self.op = paddle.max
 
 
 class TestMin(TestMean):
     def set_test_op(self):
-        self.op = paddle.fluid.layers.reduce_min
+        self.op = paddle.min
 
 
 class TestSum(TestMean):

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

@@ -70,7 +70,7 @@ class TestAssertOp(unittest.TestCase):
     def test_assert_summary(self):
         def net_func():
             x = layers.fill_constant(shape=[10], dtype='float32', value=2.0)
-            condition = layers.reduce_max(x) < 1.0
+            condition = paddle.max(x) < 1.0
             layers.Assert(condition, (x,), 5)
 
         print("test_assert_summary")
@@ -80,7 +80,7 @@ class TestAssertOp(unittest.TestCase):
     def test_assert_summary_greater_than_size(self):
         def net_func():
             x = layers.fill_constant(shape=[2, 3], dtype='float32', value=2.0)
-            condition = layers.reduce_max(x) < 1.0
+            condition = paddle.max(x) < 1.0
             layers.Assert(condition, [x], 10, name="test")
 
         print("test_assert_summary_greater_than_size")