Port logical op and clip op to nn

test=release/1.0.0

paddle/fluid/API.spec

@@ -177,6 +177,12 @@ paddle.fluid.layers.gaussian_random_batch_size_like ArgSpec(args=['input', 'shap
 paddle.fluid.layers.sum ArgSpec(args=['x', 'use_mkldnn'], varargs=None, keywords=None, defaults=(False,))
 paddle.fluid.layers.slice ArgSpec(args=['input', 'axes', 'starts', 'ends'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.layers.shape ArgSpec(args=['input'], varargs=None, keywords=None, defaults=None)
+paddle.fluid.layers.logical_and ArgSpec(args=['x', 'y', 'out', 'name'], varargs=None, keywords=None, defaults=(None, None))
+paddle.fluid.layers.logical_or ArgSpec(args=['x', 'y', 'out', 'name'], varargs=None, keywords=None, defaults=(None, None))
+paddle.fluid.layers.logical_xor ArgSpec(args=['x', 'y', 'out', 'name'], varargs=None, keywords=None, defaults=(None, None))
+paddle.fluid.layers.logical_not ArgSpec(args=['x', 'out', 'name'], varargs=None, keywords=None, defaults=(None, None))
+paddle.fluid.layers.clip ArgSpec(args=['x', 'min', 'max', 'name'], varargs=None, keywords=None, defaults=(None,))
+paddle.fluid.layers.clip_by_norm ArgSpec(args=['x', 'max_norm', 'name'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.layers.data ArgSpec(args=['name', 'shape', 'append_batch_size', 'dtype', 'lod_level', 'type', 'stop_gradient'], varargs=None, keywords=None, defaults=(True, 'float32', 0, VarType.LOD_TENSOR, True))
 paddle.fluid.layers.open_files ArgSpec(args=['filenames', 'shapes', 'lod_levels', 'dtypes', 'thread_num', 'buffer_size', 'pass_num', 'is_test'], varargs=None, keywords=None, defaults=(None, None, 1, None))
 paddle.fluid.layers.read_file ArgSpec(args=['reader'], varargs=None, keywords=None, defaults=None)
@@ -242,12 +248,6 @@ paddle.fluid.layers.is_empty ArgSpec(args=['x', 'cond'], varargs=None, keywords=
 paddle.fluid.layers.mean ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
 paddle.fluid.layers.mul ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
 paddle.fluid.layers.sigmoid_cross_entropy_with_logits ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
-paddle.fluid.layers.clip ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
-paddle.fluid.layers.clip_by_norm ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
-paddle.fluid.layers.logical_and ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
-paddle.fluid.layers.logical_or ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
-paddle.fluid.layers.logical_xor ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
-paddle.fluid.layers.logical_not ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
 paddle.fluid.layers.maxout ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
 paddle.fluid.layers.sigmoid ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.layers.logsigmoid ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))

python/paddle/fluid/layers/control_flow.py

@@ -21,7 +21,7 @@ from .. import core
 from ..framework import Program, Variable, Operator
 from ..layer_helper import LayerHelper, unique_name
 from ..initializer import force_init_on_cpu
-from .ops import logical_and, logical_not, logical_or
+from .nn import logical_and, logical_not, logical_or
 import numpy
 import warnings
 import six

python/paddle/fluid/layers/nn.py

@@ -51,7 +51,9 @@ __all__ = [
     'expand', 'sequence_concat', 'scale', 'elementwise_add', 'elementwise_div',
     'elementwise_sub', 'elementwise_mul', 'elementwise_max', 'elementwise_min',
     'elementwise_pow', 'uniform_random_batch_size_like', 'gaussian_random',
-    'sampling_id', 'gaussian_random_batch_size_like', 'sum', 'slice', 'shape'
+    'sampling_id', 'gaussian_random_batch_size_like', 'sum', 'slice', 'shape',
+    'logical_and', 'logical_or', 'logical_xor', 'logical_not', 'clip',
+    'clip_by_norm'
 ]
 
 
@@ -6775,3 +6777,165 @@ for func in [
             "act (basestring|None): Activation applied to the output.",
             "name (basestring|None): Name of the output."
         ])
+
+
+def _logical_op(op_name, x, y, out=None, name=None, binary_op=True):
+    helper = LayerHelper(op_name, **locals())
+
+    if binary_op:
+        assert x.dtype == y.dtype
+
+    if out is None:
+        if name is None:
+            out = helper.create_tmp_variable(dtype=x.dtype)
+        else:
+            out = helper.create_variable(
+                name=name, dtype=x.dtype, persistable=False)
+
+    if binary_op:
+        helper.append_op(
+            type=op_name, inputs={"X": x,
+                                  "Y": y}, outputs={"Out": out})
+    else:
+        helper.append_op(type=op_name, inputs={"X": x}, outputs={"Out": out})
+
+    return out
+
+
+@templatedoc()
+def logical_and(x, y, out=None, name=None):
+    """
+    ${comment}
+
+    Args:
+        x(${x_type}): ${x_comment}
+        y(${y_type}): ${y_comment}
+        out(Tensor): Output tensor of logical operation.
+        name(basestring|None): Name of the output.
+
+    Returns:
+        out(${out_type}): ${out_comment}
+    """
+
+    return _logical_op(
+        op_name="logical_and", x=x, y=y, name=name, out=out, binary_op=True)
+
+
+@templatedoc()
+def logical_or(x, y, out=None, name=None):
+    """
+    ${comment}
+
+    Args:
+        x(${x_type}): ${x_comment}
+        y(${y_type}): ${y_comment}
+        out(Tensor): Output tensor of logical operation.
+        name(basestring|None): Name of the output.
+
+    Returns:
+        out(${out_type}): ${out_comment}
+    """
+
+    return _logical_op(
+        op_name="logical_or", x=x, y=y, name=name, out=out, binary_op=True)
+
+
+@templatedoc()
+def logical_xor(x, y, out=None, name=None):
+    """
+    ${comment}
+
+    Args:
+        x(${x_type}): ${x_comment}
+        y(${y_type}): ${y_comment}
+        out(Tensor): Output tensor of logical operation.
+        name(basestring|None): Name of the output.
+
+    Returns:
+        out(${out_type}): ${out_comment}
+    """
+
+    return _logical_op(
+        op_name="logical_xor", x=x, y=y, name=name, out=out, binary_op=True)
+
+
+@templatedoc()
+def logical_not(x, out=None, name=None):
+    """
+    ${comment}
+
+    Args:
+        x(${x_type}): ${x_comment}
+        out(Tensor): Output tensor of logical operation.
+        name(basestring|None): Name of the output.
+
+    Returns:
+        out(${out_type}): ${out_comment}
+    """
+
+    return _logical_op(
+        op_name="logical_not", x=x, y=None, name=name, out=out, binary_op=False)
+
+
+@templatedoc()
+def clip(x, min, max, name=None):
+    """
+    ${comment}
+
+    Args:
+        x(${x_type}): ${x_comment}
+        min(${min_type}): ${min_comment}
+        max(${max_type}): ${max_comment}
+        name(basestring|None): Name of the output.
+
+    Returns:
+        out(${out_type}): ${out_comment}
+    """
+
+    helper = LayerHelper("clip", **locals())
+
+    if name is None:
+        out = helper.create_tmp_variable(dtype=x.dtype)
+    else:
+        out = helper.create_variable(
+            name=name, dtype=x.dtype, persistable=False)
+
+    helper.append_op(
+        type="clip",
+        inputs={"X": x},
+        attrs={"min": min,
+               "max": max},
+        outputs={"Out": out})
+
+    return out
+
+
+@templatedoc()
+def clip_by_norm(x, max_norm, name=None):
+    """
+    ${comment}
+
+    Args:
+        x(${x_type}): ${x_comment}
+        max_norm(${max_norm_type}): ${max_norm_comment}
+        name(basestring|None): Name of the output.
+
+    Returns:
+        out(${out_type}): ${out_comment}
+    """
+
+    helper = LayerHelper("clip_by_norm", **locals())
+
+    if name is None:
+        out = helper.create_tmp_variable(dtype=x.dtype)
+    else:
+        out = helper.create_variable(
+            name=name, dtype=x.dtype, persistable=False)
+
+    helper.append_op(
+        type="clip_by_norm",
+        inputs={"X": x},
+        attrs={"max_norm": max_norm},
+        outputs={"Out": out})
+
+    return out

python/paddle/fluid/layers/ops.py

@@ -39,12 +39,6 @@ __all__ = [
     'mean',
     'mul',
     'sigmoid_cross_entropy_with_logits',
-    'clip',
-    'clip_by_norm',
-    'logical_and',
-    'logical_or',
-    'logical_xor',
-    'logical_not',
     'maxout',
 ]