[DoubleGrad] Enabled test_imperative_star_gan_with_gradient_penalty.py under eager mode (#41730)

* [DoubleGrad] Enabled double grad test cases in eager_mode for test_imperative_double_grad

* Fixed elementwise issue

* Addressed CI failures

* [DoubleGrad] Enabled test_imperative_triple_grad test cases under eager_mode

* [DoubleGrad] Enabled test_autograd_functional_dynamic.py under eager mode

* Enabled more test cases

* [DoubleGrad] Enabled test_imperative_star_gan_with_gradient_penalty.py under eager mode

* Adjusted test_imperative_star_gan_with_gradient_penalty.py

paddle/fluid/eager/auto_code_generator/final_state_generator/codegen_utils.py

@@ -32,6 +32,7 @@ ops_to_fill_zero_for_empty_grads = set([
     "add_triple_grad",
     "multiply_double_grad",
     "multiply_triple_grad",
+    "conv2d_grad_grad",
 ])
 
 # For API dispatch used at python-level

paddle/fluid/eager/tensor_wrapper.h

@@ -118,7 +118,6 @@ class TensorWrapper {
         p_ab_autograd_meta->SetGradNode(new_grad_node);
       }
       recovered_tensor.set_autograd_meta(p_ab_autograd_meta);
-
       return recovered_tensor;
     }
   }

paddle/phi/kernels/conv_grad_grad_kernel.h

@@ -20,11 +20,11 @@ namespace phi {
 
 template <typename T, typename Context>
 void ConvGradGradKernel(const Context& dev_ctx,
-                        paddle::optional<const DenseTensor&> input_grad_grad,
-                        paddle::optional<const DenseTensor&> filter_grad_grad,
-                        const DenseTensor& out_grad,
                         const DenseTensor& input,
                         const DenseTensor& filter,
+                        const DenseTensor& out_grad,
+                        paddle::optional<const DenseTensor&> input_grad_grad,
+                        paddle::optional<const DenseTensor&> filter_grad_grad,
                         const std::vector<int>& strides,
                         const std::vector<int>& paddings,
                         const std::string& paddding_algorithm,
@@ -34,9 +34,9 @@ void ConvGradGradKernel(const Context& dev_ctx,
                         bool use_addto,
                         int workspace_size_MB,
                         bool exhaustive_search,
-                        DenseTensor* out_grad_grad,
                         DenseTensor* input_grad,
-                        DenseTensor* filter_grad);
+                        DenseTensor* filter_grad,
+                        DenseTensor* out_grad_grad);
 
 template <typename T, typename Context>
 void Conv3DGradGradKernel(const Context& dev_ctx,

paddle/phi/kernels/cpu/conv_grad_grad_kernel.cc

@@ -39,11 +39,11 @@ void Conv3DGradGradKernel(const Context& ctx,
                           DenseTensor* input_grad,
                           DenseTensor* filter_grad) {
   ConvGradGradKernel<T>(ctx,
-                        input_grad_grad,
-                        filter_grad_grad,
-                        out_grad,
                         input,
                         filter,
+                        out_grad,
+                        input_grad_grad,
+                        filter_grad_grad,
                         strides,
                         paddings_t,
                         padding_algorithm,
@@ -53,9 +53,9 @@ void Conv3DGradGradKernel(const Context& ctx,
                         use_addto,
                         workspace_size_MB,
                         exhaustive_search_t,
-                        out_grad_grad,
                         input_grad,
-                        filter_grad);
+                        filter_grad,
+                        out_grad_grad);
 }
 
 }  // namespace phi

paddle/phi/kernels/gpudnn/conv_grad_grad_kernel.cu

@@ -44,11 +44,11 @@ namespace phi {
 template <typename T, typename Context>
 void ConvCudnnGradGradKernel(
     const Context& ctx,
-    paddle::optional<const DenseTensor&> input_grad_grad,
-    paddle::optional<const DenseTensor&> filter_grad_grad,
-    const DenseTensor& out_grad,
     const DenseTensor& input,
     const DenseTensor& filter,
+    const DenseTensor& out_grad,
+    paddle::optional<const DenseTensor&> input_grad_grad,
+    paddle::optional<const DenseTensor&> filter_grad_grad,
     const std::vector<int>& strides,
     const std::vector<int>& paddings_t,
     const std::string& padding_algorithm,
@@ -58,9 +58,9 @@ void ConvCudnnGradGradKernel(
     bool use_addto,
     int workspace_size_MB,
     bool exhaustive_search_t,
-    DenseTensor* out_grad_grad,
     DenseTensor* input_grad,
-    DenseTensor* filter_grad) {
+    DenseTensor* filter_grad,
+    DenseTensor* out_grad_grad) {
   auto X = &input;
   auto W = &filter;
   auto dO = &out_grad;
@@ -689,11 +689,11 @@ void DepthwiseConvCudnnGradGradKernel(
     DenseTensor* input_grad,
     DenseTensor* filter_grad) {
   ConvCudnnGradGradKernel<T>(ctx,
-                             input_grad_grad,
-                             filter_grad_grad,
-                             out_grad,
                              input,
                              filter,
+                             out_grad,
+                             input_grad_grad,
+                             filter_grad_grad,
                              strides,
                              paddings_t,
                              padding_algorithm,
@@ -703,9 +703,9 @@ void DepthwiseConvCudnnGradGradKernel(
                              use_addto,
                              workspace_size_MB,
                              exhaustive_search_t,
-                             out_grad_grad,
                              input_grad,
-                             filter_grad);
+                             filter_grad,
+                             out_grad_grad);
 }
 
 template <typename T, typename Context>
@@ -729,11 +729,11 @@ void Conv3DCudnnGradGradKernel(
     DenseTensor* input_grad,
     DenseTensor* filter_grad) {
   ConvCudnnGradGradKernel<T>(ctx,
-                             input_grad_grad,
-                             filter_grad_grad,
-                             out_grad,
                              input,
                              filter,
+                             out_grad,
+                             input_grad_grad,
+                             filter_grad_grad,
                              strides,
                              paddings_t,
                              padding_algorithm,
@@ -743,9 +743,9 @@ void Conv3DCudnnGradGradKernel(
                              use_addto,
                              workspace_size_MB,
                              exhaustive_search_t,
-                             out_grad_grad,
                              input_grad,
-                             filter_grad);
+                             filter_grad,
+                             out_grad_grad);
 }
 
 }  // namespace phi

paddle/phi/kernels/impl/conv_grad_grad_kernel_impl.h

@@ -26,11 +26,11 @@ namespace phi {
 
 template <typename T, typename Context>
 void ConvGradGradKernel(const Context& dev_ctx,
-                        paddle::optional<const DenseTensor&> input_grad_grad,
-                        paddle::optional<const DenseTensor&> filter_grad_grad,
-                        const DenseTensor& out_grad,
                         const DenseTensor& input,
                         const DenseTensor& filter,
+                        const DenseTensor& out_grad,
+                        paddle::optional<const DenseTensor&> input_grad_grad,
+                        paddle::optional<const DenseTensor&> filter_grad_grad,
                         const std::vector<int>& strides_t,
                         const std::vector<int>& paddings_t,
                         const std::string& padding_algorithm,
@@ -40,9 +40,9 @@ void ConvGradGradKernel(const Context& dev_ctx,
                         bool use_addto,
                         int workspace_size_MB,
                         bool exhaustive_search,
-                        DenseTensor* out_grad_grad,
                         DenseTensor* input_grad,
-                        DenseTensor* filter_grad) {
+                        DenseTensor* filter_grad,
+                        DenseTensor* out_grad_grad) {
   const DenseTensor* X = &input;
   const DenseTensor* dY = &out_grad;
   const DenseTensor* ddX = input_grad_grad.get_ptr();

paddle/phi/ops/compat/conv2d_sig.cc

@@ -62,7 +62,7 @@ KernelSignature Conv2dGradOpArgumentMapping(const ArgumentMappingContext& ctx) {
 KernelSignature Conv2dDoubleGradOpArgumentMapping(
     const ArgumentMappingContext& ctx) {
   return KernelSignature("conv2d_grad_grad",
-                         {"DDInput", "DDFilter", "DOutput", "Input", "Filter"},
+                         {"Input", "Filter", "DOutput", "DDInput", "DDFilter"},
                          {"strides",
                           "paddings",
                           "padding_algorithm",
@@ -72,7 +72,7 @@ KernelSignature Conv2dDoubleGradOpArgumentMapping(
                           "use_addto",
                           "workspace_size_MB",
                           "exhaustive_search"},
-                         {"DDOutput", "DInput", "DFilter"});
+                         {"DInput", "DFilter", "DDOutput"});
 }
 
 }  // namespace phi

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

@@ -590,6 +590,46 @@ class TestStarGANWithGradientPenalty(unittest.TestCase):
         if fluid.is_compiled_with_cuda():
             self.place_test(fluid.CUDAPlace(0))
 
+    def place_test(self, place):
+        cfg = Config(place, False)
+
+        dataset = create_mnist_dataset(cfg)
+        dataset = paddle.reader.cache(dataset)
+
+        fluid_dygraph_loss = []
+        with fluid.dygraph.guard(cfg.place):
+            fluid_dygraph_model = DyGraphTrainModel(cfg)
+            for batch_id, (image_real, label_org,
+                           label_trg) in enumerate(dataset()):
+                loss = fluid_dygraph_model.run(image_real, label_org, label_trg)
+                fluid_dygraph_loss.append(loss)
+
+        eager_dygraph_loss = []
+        with _test_eager_guard():
+            with fluid.dygraph.guard(cfg.place):
+                eager_dygraph_model = DyGraphTrainModel(cfg)
+                for batch_id, (image_real, label_org,
+                               label_trg) in enumerate(dataset()):
+                    loss = eager_dygraph_model.run(image_real, label_org,
+                                                   label_trg)
+                    eager_dygraph_loss.append(loss)
+
+        for (g_loss_f, d_loss_f), (g_loss_e, d_loss_e) in zip(
+                fluid_dygraph_loss, eager_dygraph_loss):
+            self.assertEqual(g_loss_f, g_loss_e)
+            self.assertEqual(d_loss_f, d_loss_e)
+
+    def test_all_cases(self):
+        self.func_main()
+
+
+class TestStarGANWithGradientPenaltyLegacy(unittest.TestCase):
+    def func_main(self):
+        self.place_test(fluid.CPUPlace())
+
+        if fluid.is_compiled_with_cuda():
+            self.place_test(fluid.CUDAPlace(0))
+
     def place_test(self, place):
         cfg = Config(place)
 
@@ -617,7 +657,6 @@ class TestStarGANWithGradientPenalty(unittest.TestCase):
             self.assertEqual(d_loss_s, d_loss_d)
 
     def test_all_cases(self):
-        if _in_legacy_dygraph():
         self.func_main()
 
 

python/paddle/nn/functional/activation.py

@@ -434,7 +434,10 @@ def leaky_relu(x, negative_slope=0.01, name=None):
             out = F.leaky_relu(x) # [-0.02, 0., 1.]
 
     """
-    if in_dynamic_mode():
+    if in_dygraph_mode():
+        return _C_ops.final_state_leaky_relu(x, negative_slope)
+
+    if _in_legacy_dygraph():
         return _C_ops.leaky_relu(x, 'alpha', negative_slope)
 
     check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'],

python/paddle/utils/code_gen/backward.yaml

@@ -261,6 +261,19 @@
   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)
   output : Tensor(input_grad), Tensor(filter_grad)
   invoke : conv2d_grad_impl(input, filter, out_grad,  strides, paddings, paddding_algorithm, groups, dilations, data_format, use_addto, workspace_size_MB, exhaustive_search)
+  backward : conv2d_grad_grad
+
+- backward_api : conv2d_grad_grad
+  forward : conv2d_grad (Tensor input, Tensor filter, Tensor grad_out,  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(grad_input), Tensor(grad_filter)
+  args : (Tensor input, Tensor filter, Tensor grad_out, Tensor grad_input_grad, Tensor grad_filter_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)
+  output : Tensor(input_grad), Tensor(filter_grad), Tensor(grad_out_grad)
+  infer_meta :
+    func : GeneralTernaryGradInferMeta
+    param: [input, filter, grad_out]
+  kernel :
+    func : conv2d_grad_grad
+    use_cudnn : true
+  optional : grad_input_grad, grad_filter_grad
 
 - backward_api : conv2d_transpose_grad
   forward : conv2d_transpose(Tensor x, Tensor filter, int[] strides, int[] paddings, int[] output_padding, int[] output_size, str padding_algorithm, int groups, int[] dilations, str data_format) -> Tensor(out)
@@ -755,6 +768,16 @@
     data_type : out_grad
   optional : scale, bias
 
+- backward_api : leaky_relu_double_grad
+  forward : leaky_relu_grad (Tensor x, Tensor grad_out, float alpha) -> Tensor(grad_x)
+  args : (Tensor x, Tensor grad_x_grad, float alpha)
+  output : Tensor(grad_out_grad)
+  infer_meta :
+    func : UnchangedInferMeta
+    param : [grad_x_grad]
+  kernel :
+    func : leaky_relu_double_grad
+
 - backward_api : leaky_relu_grad
   forward : leaky_relu (Tensor x, float alpha) -> Tensor(out)
   args : (Tensor x, Tensor out_grad, float alpha)
@@ -764,6 +787,7 @@
     param : [x]
   kernel :
     func : leaky_relu_grad
+  backward : leaky_relu_double_grad
 
 - backward_api : lerp_grad
   forward : lerp (Tensor x, Tensor y, Tensor weight) -> Tensor(out)