clean fluid task: transfer logical_and api (#48341)

python/paddle/fluid/dygraph/dygraph_to_static/convert_operators.py

@@ -36,7 +36,6 @@ from paddle.fluid.layers import (
 from paddle.fluid.layers import (
     cast,
     control_flow,
-    logical_and,
     nn,
 )
 from paddle.fluid.layers.control_flow import (
@@ -233,7 +232,7 @@ def convert_logical_and(x_func, y_func):
 def _run_paddle_logical_and(x, y):
     x = cast_bool_if_necessary(x)
     y = cast_bool_if_necessary(y)
-    return logical_and(x, y)
+    return paddle.logical_and(x, y)
 
 
 def _run_py_logical_and(x_func, y_func):

python/paddle/fluid/layers/control_flow.py

@@ -27,7 +27,6 @@ from ..framework import (
     in_dygraph_mode,
 )
 from ..layer_helper import LayerHelper, unique_name
-from .nn import logical_and
 from .utils import (
     assert_same_structure,
     map_structure,
@@ -3278,12 +3277,12 @@ class Switch:
         else:
             pre_cond_num = len(self.pre_not_conditions)
             pre_not_cond = self.pre_not_conditions[pre_cond_num - 1]
-            new_not_cond = logical_and(
+            new_not_cond = paddle.logical_and(
                 x=pre_not_cond, y=paddle.logical_not(x=condition)
             )
             self.pre_not_conditions.append(new_not_cond)
             cond_block = ConditionalBlock(
-                [logical_and(x=pre_not_cond, y=condition)],
+                [paddle.logical_and(x=pre_not_cond, y=condition)],
                 is_scalar_condition=True,
             )
 

python/paddle/fluid/layers/nn.py

@@ -124,7 +124,6 @@ __all__ = [
     'strided_slice',
     'shape',
     'size',
-    'logical_and',
     'clip',
     'clip_by_norm',
     'mean',
@@ -8435,46 +8434,6 @@ def _logical_op(op_name, x, y, out=None, name=None, binary_op=True):
     return out
 
 
-def logical_and(x, y, out=None, name=None):
-    r"""
-
-    ``logical_and`` operator computes element-wise logical AND on ``x`` and ``y``, and returns ``out``. ``out`` is N-dim boolean ``Tensor``.
-    Each element of ``out`` is calculated by
-
-    .. math::
-
-        out = x \&\& y
-
-    .. note::
-        ``paddle.logical_and`` supports broadcasting. If you want know more about broadcasting, please refer to :ref:`user_guide_broadcasting`.
-
-    Args:
-        x (Tensor): the input tensor, it's data type should be one of bool, int8, int16, in32, in64, float32, float64.
-        y (Tensor): the input tensor, it's data type should be one of bool, int8, int16, in32, in64, float32, float64.
-        out(Tensor): The ``Tensor`` that specifies the output of the operator, which can be any ``Tensor`` that has been created in the program. The default value is None, and a new ``Tensor`` will be created to save the output.
-        name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
-
-    Returns:
-        N-D Tensor. A location into which the result is stored. It's dimension equals with ``x``.
-
-    Examples:
-        .. code-block:: python
-
-            import paddle
-
-            x = paddle.to_tensor([True])
-            y = paddle.to_tensor([True, False, True, False])
-            res = paddle.logical_and(x, y)
-            print(res) # [True False True False]
-    """
-    if in_dygraph_mode():
-        return _C_ops.logical_and(x, y)
-
-    return _logical_op(
-        op_name="logical_and", x=x, y=y, name=name, out=out, binary_op=True
-    )
-
-
 @templatedoc()
 def clip(x, min, max, name=None):
     """

python/paddle/fluid/layers/rnn.py

@@ -1720,7 +1720,7 @@ def _dynamic_decode_declarative(
                 states_arrays,
             )
         if max_step_num is not None:
-            control_flow.logical_and(
+            paddle.logical_and(
                 paddle.logical_not(nn.reduce_all(global_finished)),
                 control_flow.less_equal(step_idx, max_step_num),
                 cond,

python/paddle/fluid/tests/book/test_machine_translation.py

@@ -166,7 +166,7 @@ def decoder_decode(context, is_sparse):
         # source sentences have ended.
         length_cond = pd.less_than(x=counter, y=array_len)
         finish_cond = paddle.logical_not(pd.is_empty(x=selected_ids))
-        pd.logical_and(x=length_cond, y=finish_cond, out=cond)
+        paddle.logical_and(x=length_cond, y=finish_cond, out=cond)
 
     translation_ids, translation_scores = pd.beam_search_decode(
         ids=ids_array, scores=scores_array, beam_size=beam_size, end_id=10

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

@@ -1863,7 +1863,7 @@ def fast_decode(
                 layers.assign(pre_caches[i]["v"], caches[i]["v"])
             length_cond = layers.less_than(x=step_idx, y=max_len)
             finish_cond = paddle.logical_not(layers.is_empty(x=selected_ids))
-            layers.logical_and(x=length_cond, y=finish_cond, out=cond)
+            paddle.logical_and(x=length_cond, y=finish_cond, out=cond)
 
         finished_ids, finished_scores = layers.beam_search_decode(
             ids, scores, beam_size=beam_size, end_id=eos_idx

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

@@ -351,9 +351,9 @@ def bmn_loss_func(
         gt_iou_map = fluid.layers.elementwise_mul(gt_iou_map, mask)
 
         u_hmask = fluid.layers.cast(x=gt_iou_map > 0.7, dtype=DATATYPE)
-        u_mmask = fluid.layers.logical_and(gt_iou_map <= 0.7, gt_iou_map > 0.3)
+        u_mmask = paddle.logical_and(gt_iou_map <= 0.7, gt_iou_map > 0.3)
         u_mmask = fluid.layers.cast(x=u_mmask, dtype=DATATYPE)
-        u_lmask = fluid.layers.logical_and(gt_iou_map <= 0.3, gt_iou_map >= 0.0)
+        u_lmask = paddle.logical_and(gt_iou_map <= 0.3, gt_iou_map >= 0.0)
         u_lmask = fluid.layers.cast(x=u_lmask, dtype=DATATYPE)
         u_lmask = fluid.layers.elementwise_mul(u_lmask, mask)
 

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

@@ -32,7 +32,7 @@ class TestLogicalAnd(IPUOpTest):
         return False
 
     def set_test_op(self):
-        self.op = paddle.fluid.layers.logical_and
+        self.op = paddle.logical_and
 
     def set_op_attrs(self):
         self.attrs = {}