[PHI] channel_shuffle add yaml (#49808)

paddle/phi/api/yaml/legacy_backward.yaml

@@ -194,6 +194,15 @@
   invoke : cast (out_grad, x.dtype())
   no_need_buffer : x
 
+- backward_op : channel_shuffle_grad
+  forward : channel_shuffle (Tensor x, int groups, str data_format="NCHW") -> Tensor(out)
+  args : (Tensor out_grad, int groups, str data_format="NCHW")
+  output : Tensor(x_grad)
+  infer_meta :
+    func : ChannelShuffleGradInferMeta
+  kernel :
+    func : channel_shuffle_grad
+
 - backward_op : concat_double_grad
   forward : concat_grad (Tensor[] x, Tensor grad_out, Scalar axis) -> Tensor[](grad_x)
   args : (Tensor[] grad_x_grad, Scalar axis = 0)

paddle/phi/api/yaml/legacy_ops.yaml

@@ -338,6 +338,15 @@
     data_type : x
   backward : cast_grad
 
+- op : channel_shuffle
+  args : (Tensor x, int groups, str data_format="NCHW")
+  output : Tensor(out)
+  infer_meta :
+    func : ChannelShuffleInferMeta
+  kernel :
+    func : channel_shuffle
+  backward : channel_shuffle_grad
+
 - op : check_finite_and_unscale_
   args : (Tensor[] x, Tensor scale, Tensor input_found_infinite)
   output : Tensor[](out){x.size()}, Tensor(output_found_infinite)

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

@@ -47,6 +47,7 @@ class TestChannelShuffleOp(OpTest):
         self.op_type = "channel_shuffle"
         self.init_data_format()
         n, c, h, w = 2, 9, 4, 4
+        self.python_api = paddle.nn.functional.channel_shuffle
 
         if self.format == "NCHW":
             shape = [n, c, h, w]
@@ -66,10 +67,10 @@ class TestChannelShuffleOp(OpTest):
         self.format = "NCHW"
 
     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 TestChannelLast(TestChannelShuffleOp):

python/paddle/nn/functional/vision.py

@@ -516,9 +516,7 @@ def channel_shuffle(x, groups, data_format="NCHW", name=None):
         )
 
     if in_dygraph_mode():
-        return _legacy_C_ops.channel_shuffle(
-            x, "groups", groups, "data_format", data_format
-        )
+        return _C_ops.channel_shuffle(x, groups, data_format)
 
     helper = LayerHelper("channel_shuffle", **locals())
     check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'channel_shuffle')