add reverse yaml

paddle/phi/api/yaml/legacy_api.yaml

@@ -1802,6 +1802,24 @@
   intermediate : xshape
   backward: reshape_grad
 
+- api : reverse
+  args : (Tensor x, int[] axis)
+  output : Tensor
+  infer_meta :
+    func : ReverseInferMeta
+  kernel :
+    func : reverse
+  backward : reverse_grad
+
+- api : reverse_array
+  args : (Tensor[] x, int[] axis)
+  output : Tensor[]{x.size()}
+  infer_meta :
+    func : ReverseArrayInferMeta
+  kernel :
+    func : reverse_array
+  backward : reverse_array_grad
+
 - api : roi_align
   args : (Tensor x, Tensor boxes, Tensor boxes_num, int pooled_height, int pooled_width, float spatial_scale, int sampling_ratio, bool aligned)
   output : Tensor

paddle/phi/api/yaml/legacy_backward.yaml

@@ -1708,6 +1708,23 @@
   backward : reshape_double_grad
   inplace : (out_grad -> x_grad)
 
+- backward_api : reverse_array_grad
+  forward : reverse_array (Tensor[] x, int[] axis) -> Tensor[](out)
+  args : (Tensor[] out_grad, int[] axis)
+  output : Tensor[](x_grad){out_grad.size()}
+  infer_meta :
+    func : ReverseArrayInferMeta
+  kernel :
+    func : reverse
+
+- backward_api : reverse_grad
+  forward : reverse (Tensor x, int[] axis) -> Tensor(out)
+  args : (Tensor out_grad, int[] axis)
+  output : Tensor(x_grad)
+  infer_meta :
+    func : ReverseInferMeta
+  invoke : reverse(out_grad, axis)
+
 - backward_api : roi_align_grad
   forward : roi_align (Tensor x, Tensor boxes, Tensor boxes_num, int pooled_height, int pooled_width, float spatial_scale, int sampling_ratio, bool aligned) -> Tensor(out)
   args : (Tensor x, Tensor boxes, Tensor boxes_num, Tensor out_grad, int pooled_height, int pooled_width, float spatial_scale, int sampling_ratio, bool aligned)

paddle/phi/infermeta/unary.cc

@@ -2165,6 +2165,25 @@ void ReverseInferMeta(const MetaTensor& x,
   out->share_meta(x);
 }
 
+void ReverseArrayInferMeta(const std::vector<const phi::MetaTensor*>& x,
+                           const std::vector<int>& axis,
+                           std::vector<phi::MetaTensor*> out) {
+  PADDLE_ENFORCE_EQ(
+      axis.size(),
+      1,
+      phi::errors::InvalidArgument(
+          "The size of axis must be 1 when the Input(X) is LoDTensorArray, "
+          "but received %d.",
+          axis.size()));
+  PADDLE_ENFORCE_EQ(
+      axis[0],
+      0,
+      phi::errors::InvalidArgument("The value of axis should be 1 when "
+                                   "the Input(X) is LoDTensorArray, "
+                                   "but received %d.",
+                                   axis[0]));
+}
+
 void RollInferMeta(const MetaTensor& x,
                    const IntArray& shifts,
                    const std::vector<int64_t>& axis,

paddle/phi/infermeta/unary.h

@@ -298,6 +298,10 @@ void ReverseInferMeta(const MetaTensor& x,
                       const std::vector<int>& axis,
                       MetaTensor* out);
 
+void ReverseArrayInferMeta(const std::vector<const phi::MetaTensor*>& x,
+                           const std::vector<int>& axis,
+                           std::vector<phi::MetaTensor*> out);
+
 void RollInferMeta(const MetaTensor& x,
                    const IntArray& shifts,
                    const std::vector<int64_t>& axis,

python/paddle/fluid/layers/tensor.py

@@ -1271,6 +1271,11 @@ def reverse(x, axis):
     check_type(axis, 'axis', (int, tuple, list), 'reverse')
     if isinstance(axis, int):
         axis = [axis]
+    if in_dygraph_mode():
+        if x.type == core.VarDesc.VarType.LOD_TENSOR_ARRAY:
+            return _C_ops.final_state_reverse_array(x, axis)
+        else:
+            return _C_ops.final_state_reverse(x, axis)
     helper = LayerHelper("reverse", **locals())
     out = helper.create_variable_for_type_inference(dtype=x.dtype)
     helper.append_op(type='reverse',

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

@@ -31,6 +31,7 @@ def initTestCase(self):
     def setUp(self):
         self.initTestCase()
         self.op_type = "reverse"
+        self.python_api = fluid.layers.reverse
         self.inputs = {"X": self.x}
         self.attrs = {'axis': self.axis}
         out = self.x
@@ -39,10 +40,10 @@ def setUp(self):
         self.outputs = {'Out': out}
 
     def test_check_output(self):
-        self.check_output()
+        self.check_output(check_eager=True)
 
     def test_check_grad(self):
-        self.check_grad(['X'], 'Out')
+        self.check_grad(['X'], 'Out', check_eager=True)
 
 
 class TestCase0(TestReverseOp):