[Fluid Clean] remove paddle.fluid.layers.nn.reduce_all,reduce_any (#48269)

python/paddle/fluid/contrib/mixed_precision/decorator.py

@@ -460,7 +460,7 @@ class OptimizerWithMixedPrecision:
         if self._is_distributed or self._use_pure_fp16:
             with self._train_program._optimized_guard([]):
                 all_infs = layers.concat(found_infs)
-                found_inf = layers.reduce_any(all_infs)
+                found_inf = paddle.any(all_infs)
 
         return found_inf
 

python/paddle/fluid/layers/nn.py

@@ -71,8 +71,6 @@ __all__ = [
     'softmax',
     'pool2d',
     'batch_norm',
-    'reduce_all',
-    'reduce_any',
     'dropout',
     'split',
     'ctc_greedy_decoder',
@@ -2504,137 +2502,6 @@ def reduce_sum(input, dim=None, keep_dim=False, name=None):
     return out
 
 
-def reduce_all(input, dim=None, keep_dim=False, name=None):
-    """
-
-    This OP computes the ``logical and`` of tensor elements over the given dimension, and output the result.
-
-    Args:
-        input (Tensor): the input tensor, it's data type should be `bool`.
-        dim (list|int|optional): The dimension along which the logical and is computed.
-            If :attr:`None`, compute the logical and 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]`. The default value is None.
-        keep_dim (bool): 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. The default value is False.
-        name(str|None): A name for this layer(optional). If set None, the layer
-                       will be named automatically. The default value is None.
-
-    Returns:
-        Tensor, the output data type is bool. : The reduced tensor variable with ``logical and`` in given dims.
-
-    Examples:
-        .. code-block:: python
-
-            import paddle
-            import paddle.fluid as fluid
-            import paddle.fluid.layers as layers
-            import numpy as np
-
-            # x is a bool Tensor variable with following elements:
-            #    [[True, False]
-            #     [True, True]]
-            x = fluid.layers.assign(np.array([[1, 0], [1, 1]], dtype='int32'))
-            x = fluid.layers.cast(x, 'bool')
-
-            out = fluid.layers.reduce_all(x)  # False
-            out = fluid.layers.reduce_all(x, dim=0)  # [True, False]
-            out = fluid.layers.reduce_all(x, dim=-1)  # [False, True]
-            # keep_dim=False, x.shape=(2,2), out.shape=(2,)
-
-            out = fluid.layers.reduce_all(x, dim=1, keep_dim=True)  # [[False], [True]]
-            # keep_dim=True, x.shape=(2,2), out.shape=(2,1)
-
-    """
-    if dim is not None and not isinstance(dim, list):
-        dim = [dim]
-
-    if in_dygraph_mode():
-        return _C_ops.all(input, dim if dim is not None else [], keep_dim)
-
-    check_variable_and_dtype(input, 'input', ('bool'), 'reduce_all')
-    helper = LayerHelper('reduce_all', **locals())
-    out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
-    helper.append_op(
-        type='reduce_all',
-        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_any(input, dim=None, keep_dim=False, name=None):
-    """
-    This OP computes the ``logical or`` of tensor elements over the given dimension, and output the result.
-
-    Args:
-        input (Tensor): the input tensor, it's data type should be `bool`.
-        dim (list|int|optional): The dimension along which the logical and is computed.
-            If :attr:`None`, compute the logical and 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]`. The default value is None.
-        keep_dim (bool): 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. The default value is False.
-        name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
-
-    Returns:
-        Tensor, the output data type is bool. : The reduced tensor variable with ``logical or`` in given dims.
-
-    Examples:
-        .. code-block:: python
-
-            import paddle
-            import paddle.fluid as fluid
-            import paddle.fluid.layers as layers
-            import numpy as np
-
-            # x is a bool Tensor variable with following elements:
-            #    [[True, False]
-            #     [False, False]]
-            x = fluid.layers.assign(np.array([[1, 0], [0, 0]], dtype='int32'))
-            x = fluid.layers.cast(x, 'bool')
-
-            out = fluid.layers.reduce_any(x)  # True
-            out = fluid.layers.reduce_any(x, dim=0)  # [True, False]
-            out = fluid.layers.reduce_any(x, dim=-1)  # [True, False]
-            # keep_dim=False, x.shape=(2,2), out.shape=(2,)
-
-            out = fluid.layers.reduce_any(x, dim=1,
-                                     keep_dim=True)  # [[True], [False]]
-            # keep_dim=True, x.shape=(2,2), out.shape=(2,1)
-
-    """
-    check_variable_and_dtype(input, 'input', ('bool'), 'reduce_any')
-    helper = LayerHelper('reduce_any', **locals())
-    out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
-    if dim is not None and not isinstance(dim, list):
-        dim = [dim]
-    helper.append_op(
-        type='reduce_any',
-        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 split(input, num_or_sections, dim=-1, name=None):
     """
     Split the input tensor into multiple sub-Tensors.

python/paddle/fluid/layers/rnn.py

@@ -1481,7 +1481,7 @@ def _dynamic_decode_imperative(
         initial_states,
         initial_finished,
     )
-    cond = paddle.logical_not((nn.reduce_all(initial_finished)))
+    cond = paddle.logical_not((paddle.all(initial_finished)))
     sequence_lengths = tensor.cast(paddle.zeros_like(initial_finished), "int64")
     outputs = None
 
@@ -1539,7 +1539,7 @@ def _dynamic_decode_imperative(
         control_flow.increment(x=step_idx_tensor, value=1.0, in_place=True)
         step_idx += 1
 
-        cond = paddle.logical_not(nn.reduce_all(finished))
+        cond = paddle.logical_not(paddle.all(finished))
         if max_step_num is not None and step_idx > max_step_num:
             break
 
@@ -1589,7 +1589,7 @@ def _dynamic_decode_declarative(
     global_finished.stop_gradient = True
     step_idx = tensor.fill_constant(shape=[1], dtype="int64", value=0)
 
-    cond = paddle.logical_not((nn.reduce_all(initial_finished)))
+    cond = paddle.logical_not((paddle.all(initial_finished)))
     if max_step_num is not None:
         max_step_num = tensor.fill_constant(
             shape=[1], dtype="int64", value=max_step_num
@@ -1720,12 +1720,12 @@ def _dynamic_decode_declarative(
             )
         if max_step_num is not None:
             paddle.logical_and(
-                paddle.logical_not(nn.reduce_all(global_finished)),
+                paddle.logical_not(paddle.all(global_finished)),
                 paddle.less_equal(step_idx, max_step_num),
                 cond,
             )
         else:
-            paddle.logical_not(nn.reduce_all(global_finished), cond)
+            paddle.logical_not(paddle.all(global_finished), cond)
 
     final_outputs = map_structure(
         lambda array: tensor.tensor_array_to_tensor(

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

@@ -873,7 +873,7 @@ class Transformer(Layer):
             predict_ids.append(token_indices)
             parent_ids.append(beam_indices)
 
-            if layers.reduce_all(finished).numpy():
+            if paddle.all(finished).numpy():
                 break
 
         predict_ids = paddle.stack(predict_ids, axis=0)

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

@@ -180,12 +180,12 @@ class TestAll(TestMean):
         self.fetch_list = [out.name]
 
     def set_test_op(self):
-        self.op = paddle.fluid.layers.reduce_all
+        self.op = paddle.all
 
 
 class TestAny(TestAll):
     def set_test_op(self):
-        self.op = paddle.fluid.layers.reduce_any
+        self.op = paddle.any
 
 
 if __name__ == "__main__":

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

@@ -505,12 +505,12 @@ class TestAllOpError(unittest.TestCase):
         with program_guard(Program(), Program()):
             # The input type of reduce_all_op must be Variable.
             input1 = 12
-            self.assertRaises(TypeError, fluid.layers.reduce_all, input1)
+            self.assertRaises(TypeError, paddle.all, input1)
             # The input dtype of reduce_all_op must be bool.
             input2 = fluid.layers.data(
                 name='input2', shape=[12, 10], dtype="int32"
             )
-            self.assertRaises(TypeError, fluid.layers.reduce_all, input2)
+            self.assertRaises(TypeError, paddle.all, input2)
 
 
 class TestAnyOp(OpTest):
@@ -622,12 +622,12 @@ class TestAnyOpError(unittest.TestCase):
         with program_guard(Program(), Program()):
             # The input type of reduce_any_op must be Variable.
             input1 = 12
-            self.assertRaises(TypeError, fluid.layers.reduce_any, input1)
+            self.assertRaises(TypeError, paddle.any, input1)
             # The input dtype of reduce_any_op must be bool.
             input2 = fluid.layers.data(
                 name='input2', shape=[12, 10], dtype="int32"
             )
-            self.assertRaises(TypeError, fluid.layers.reduce_any, input2)
+            self.assertRaises(TypeError, paddle.any, input2)
 
 
 class Test1DReduce(OpTest):

python/paddle/jit/dy2static/convert_operators.py

@@ -27,8 +27,6 @@ from paddle.fluid.layers import (
 from paddle.fluid.layers import (
     assign,
     fill_constant,
-    reduce_all,
-    reduce_any,
 )
 from paddle.fluid.layers import (
     cast,
@@ -651,7 +649,7 @@ def convert_shape_compare(left, *args):
         def reduce_compare(x, op_str, y):
             element_wise_result = eval("x " + op_str + " y")
             if op_str == "!=":
-                return reduce_any(element_wise_result)
+                return paddle.any(element_wise_result)
             elif (
                 op_str == "is"
                 or op_str == "is not"
@@ -660,7 +658,7 @@ def convert_shape_compare(left, *args):
             ):
                 return element_wise_result
             else:
-                return reduce_all(element_wise_result)
+                return paddle.all(element_wise_result)
 
         final_result = reduce_compare(left, args[0], args[1])
         for i in range(1, num_cmp):