Add conj pixel shuffle yaml (#41499)

* ad conj flip yaml

* add flip conj pixel shuffle

paddle/fluid/operators/pixel_shuffle_op.cc

@@ -82,42 +82,6 @@ class PixelShuffleGradMaker : public framework::SingleGradOpMaker<T> {
 class PixelShuffleGradOp : public framework::OperatorWithKernel {
  public:
   using framework::OperatorWithKernel::OperatorWithKernel;
-
-  void InferShape(framework::InferShapeContext* ctx) const override {
-    PADDLE_ENFORCE_EQ(
-        ctx->HasInput(framework::GradVarName("Out")), true,
-        platform::errors::NotFound("Input(Out@Grad) should not be null"));
-    PADDLE_ENFORCE_EQ(
-        ctx->HasOutput(framework::GradVarName("X")), true,
-        platform::errors::NotFound("Output(X@Grad) should not be null"));
-
-    auto do_dims = ctx->GetInputDim(framework::GradVarName("Out"));
-    PADDLE_ENFORCE_EQ(do_dims.size(), 4,
-                      platform::errors::InvalidArgument(
-                          "Input should be a 4-D tensor of format [N, C, H, W] "
-                          "or [N, H, W, C], but got %u.",
-                          do_dims.size()));
-
-    auto upscale_factor = ctx->Attrs().Get<int>("upscale_factor");
-
-    const std::string data_format =
-        ctx->Attrs().Get<std::string>("data_format");
-    const bool channel_last = (data_format == "NHWC");
-
-    auto dx_dims = do_dims;
-    dx_dims[0] = do_dims[0];
-
-    if (!channel_last) {
-      dx_dims[1] = do_dims[1] * (upscale_factor * upscale_factor);
-      dx_dims[2] = do_dims[2] / upscale_factor;
-      dx_dims[3] = do_dims[3] / upscale_factor;
-    } else {
-      dx_dims[1] = do_dims[1] / upscale_factor;
-      dx_dims[2] = do_dims[2] / upscale_factor;
-      dx_dims[3] = do_dims[3] * (upscale_factor * upscale_factor);
-    }
-    ctx->SetOutputDim(framework::GradVarName("X"), dx_dims);
-  }
 };
 
 }  // namespace operators
@@ -132,7 +96,11 @@ REGISTER_OPERATOR(pixel_shuffle, ops::PixelShuffleOp, ops::PixelShuffleOpMaker,
                   ops::PixelShuffleGradMaker<paddle::imperative::OpBase>,
                   PixelShuffleInferShapeFunctor);
 
-REGISTER_OPERATOR(pixel_shuffle_grad, ops::PixelShuffleGradOp);
+DECLARE_INFER_SHAPE_FUNCTOR(pixel_shuffle_grad,
+                            PixelShuffleGradInferShapeFunctor,
+                            PD_INFER_META(phi::PixelShuffleGradInferMeta));
+REGISTER_OPERATOR(pixel_shuffle_grad, ops::PixelShuffleGradOp,
+                  PixelShuffleGradInferShapeFunctor);
 
 REGISTER_OP_VERSION(pixel_shuffle)
     .AddCheckpoint(

paddle/phi/infermeta/unary.cc

@@ -1315,6 +1315,36 @@ void PixelShuffleInferMeta(const MetaTensor& x,
   out->set_dims(output_dims);
 }
 
+void PixelShuffleGradInferMeta(const MetaTensor& out_grad,
+                               int upscale_factor,
+                               const std::string& data_format,
+                               MetaTensor* x_grad) {
+  auto do_dims = out_grad.dims();
+  PADDLE_ENFORCE_EQ(do_dims.size(),
+                    4,
+                    phi::errors::InvalidArgument(
+                        "Input should be a 4-D tensor of format [N, C, H, W] "
+                        "or [N, H, W, C], but got %u.",
+                        do_dims.size()));
+
+  const bool channel_last = (data_format == "NHWC");
+
+  auto dx_dims = do_dims;
+  dx_dims[0] = do_dims[0];
+
+  if (!channel_last) {
+    dx_dims[1] = do_dims[1] * (upscale_factor * upscale_factor);
+    dx_dims[2] = do_dims[2] / upscale_factor;
+    dx_dims[3] = do_dims[3] / upscale_factor;
+  } else {
+    dx_dims[1] = do_dims[1] / upscale_factor;
+    dx_dims[2] = do_dims[2] / upscale_factor;
+    dx_dims[3] = do_dims[3] * (upscale_factor * upscale_factor);
+  }
+  x_grad->set_dims(dx_dims);
+  x_grad->set_dtype(out_grad.dtype());
+}
+
 void PNormInferMeta(const MetaTensor& x,
                     float porder,
                     int axis,

paddle/phi/infermeta/unary.h

@@ -200,6 +200,11 @@ void PixelShuffleInferMeta(const MetaTensor& x,
                            const std::string& data_format,
                            MetaTensor* out);
 
+void PixelShuffleGradInferMeta(const MetaTensor& out_grad,
+                               int upscale_factor,
+                               const std::string& data_format,
+                               MetaTensor* x_grad);
+
 void PNormInferMeta(const MetaTensor& x,
                     float porder,
                     int axis,

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

@@ -32,6 +32,7 @@
 class TestConjOp(OpTest):
     def setUp(self):
         self.op_type = "conj"
+        self.python_api = paddle.tensor.conj
         self.init_dtype_type()
         self.init_input_output()
         self.init_grad_input_output()
@@ -53,14 +54,15 @@ def init_grad_input_output(self):
         self.grad_in = np.conj(self.grad_out)
 
     def test_check_output(self):
-        self.check_output()
+        self.check_output(check_eager=True)
 
     def test_check_grad_normal(self):
         self.check_grad(
             ['X'],
             'Out',
             user_defined_grads=[self.grad_in],
-            user_defined_grad_outputs=[self.grad_out])
+            user_defined_grad_outputs=[self.grad_out],
+            check_eager=True)
 
 
 class TestComplexConjOp(unittest.TestCase):

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

@@ -67,6 +67,7 @@ def test_dygraph(self):
 class TestFlipOp(OpTest):
     def setUp(self):
         self.op_type = 'flip'
+        self.python_api = paddle.tensor.flip
         self.init_test_case()
         self.inputs = {'X': np.random.random(self.in_shape).astype('float64')}
         self.init_attrs()
@@ -76,10 +77,10 @@ def init_attrs(self):
         self.attrs = {"axis": self.axis}
 
     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)
 
     def init_test_case(self):
         self.in_shape = (6, 4, 2, 3)
@@ -131,4 +132,5 @@ def init_test_case(self):
 
 
 if __name__ == "__main__":
+    paddle.enable_static()
     unittest.main()

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

@@ -52,6 +52,7 @@ def pixel_shuffle_np(x, up_factor, data_format="NCHW"):
 class TestPixelShuffleOp(OpTest):
     def setUp(self):
         self.op_type = "pixel_shuffle"
+        self.python_api = paddle.nn.functional.pixel_shuffle
         self.init_data_format()
         n, c, h, w = 2, 9, 4, 4
 
@@ -73,10 +74,10 @@ def init_data_format(self):
         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(TestPixelShuffleOp):
@@ -220,4 +221,5 @@ def error_data_format_layer():
 
 
 if __name__ == '__main__':
+    paddle.enable_static()
     unittest.main()

python/paddle/tensor/manipulation.py

@@ -458,6 +458,10 @@ def flip(x, axis, name=None):
     """
     if isinstance(axis, int):
         axis = [axis]
+
+    if in_dygraph_mode():
+        return _C_ops.final_state_flip(x, axis)
+
     if paddle.in_dynamic_mode():
         return _C_ops.flip(x, "axis", axis)
 

python/paddle/tensor/math.py

@@ -3349,6 +3349,9 @@ def conj(x, name=None):
           #        [(4-4j), (5-5j), (6-6j)]])
 
     """
+    if in_dygraph_mode():
+        return _C_ops.final_state_conj(x)
+
     if paddle.in_dynamic_mode():
         return _C_ops.conj(x)
 

python/paddle/utils/code_gen/api.yaml

@@ -345,6 +345,7 @@
     func : UnchangedInferMeta
   kernel :
     func : conj
+  backward : conj_grad
 
 - api : conv2d
   args : (Tensor input, Tensor filter, int[] strides, int[] paddings, str paddding_algorithm, int groups, int[] dilations, str data_format, bool use_addto, int workspace_size_MB, bool exhaustive_search)
@@ -659,6 +660,7 @@
     func : FlipInferMeta
   kernel :
     func : flip
+  backward : flip_grad
 
 - api : floor
   args : (Tensor x)
@@ -1430,7 +1432,7 @@
     func : PixelShuffleInferMeta
   kernel :
     func : pixel_shuffle
-  # backward : pixel_shuffle_grad
+  backward : pixel_shuffle_grad
 
 # poisson  // no need grad
 - api : poisson

python/paddle/utils/code_gen/backward.yaml

@@ -208,6 +208,16 @@
   output : Tensor[](x_grad)
   invoke : concat_grad_impl(x, out_grad, axis)
 
+- backward_api : conj_grad
+  forward : conj (Tensor x) -> Tensor(out)
+  args : (Tensor out_grad)
+  output : Tensor(x_grad)
+  infer_meta :
+    func : UnchangedInferMeta
+    param: [out_grad]
+  kernel :
+    func : conj
+
 - backward_api : conv2d_grad
   forward : conv2d (Tensor input, Tensor filter, int[] strides, int[] paddings, str paddding_algorithm, int groups, int[] dilations, str data_format, bool use_addto, int workspace_size_MB, bool exhaustive_search) -> Tensor(out)
   args : (Tensor input, Tensor filter, Tensor out_grad,  int[] strides, int[] paddings, str paddding_algorithm, int groups, int[] dilations, str data_format, bool use_addto, int workspace_size_MB, bool exhaustive_search)
@@ -456,6 +466,16 @@
     backend: out_grad
     layout: out_grad
 
+- backward_api : flip_grad
+  forward : flip (Tensor x, int[] axis) -> Tensor(out)
+  args : (Tensor out_grad, int[] axis)
+  output : Tensor(x_grad)
+  infer_meta :
+    func : UnchangedInferMeta
+    param: [out_grad]
+  kernel :
+    func : flip
+
 - backward_api : floor_grad
   forward : floor(Tensor x) -> Tensor(out)
   args : (Tensor out_grad)
@@ -1010,6 +1030,15 @@
   kernel :
     func : pad3d_grad
 
+- backward_api : pixel_shuffle_grad
+  forward : pixel_shuffle (Tensor x, int upscale_factor, str data_format) -> Tensor(out)
+  args : (Tensor out_grad, int upscale_factor, str data_format)
+  output : Tensor(x_grad)
+  infer_meta :
+    func : PixelShuffleGradInferMeta
+  kernel :
+    func : pixel_shuffle_grad
+
 - backward_api : pool2d_grad
   forward : pool2d(Tensor x, int[] kernel_size, int[] strides, int[] paddings, bool ceil_mode, bool exclusive, str data_format, str pooling_type, bool global_pooling, bool adaptive, str padding_algorithm) -> Tensor(out)
   args : (Tensor x, Tensor out, Tensor out_grad, int[] kernel_size, int[] strides, int[] paddings, bool ceil_mode, bool exclusive, str data_format, str pooling_type, bool global_pooling, bool adaptive, str padding_algorithm)