Add some double/triple grad kernel yaml file (#42361)

* add double yaml

* add inline func

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

@@ -22,17 +22,12 @@ import os
 ### Global Variables ###
 ########################
 ops_to_fill_zero_for_empty_grads = set([
-    "split_grad",
-    "rnn_grad",
-    "matmul_double_grad",
-    "matmul_triple_grad",
-    "sigmoid_double_grad",
-    "sigmoid_triple_grad",
-    "add_double_grad",
-    "add_triple_grad",
-    "multiply_double_grad",
-    "multiply_triple_grad",
-    "conv2d_grad_grad",
+    "split_grad", "rnn_grad", "matmul_double_grad", "matmul_triple_grad",
+    "sigmoid_double_grad", "sigmoid_triple_grad", "add_double_grad",
+    "add_triple_grad", "multiply_double_grad", "multiply_triple_grad",
+    "conv2d_grad_grad", "batch_norm_double_grad", "tanh_double_grad",
+    "tanh_triple_grad", "subtract_double_grad", "divide_double_grad",
+    "log_double_grad", "elu_double_grad"
 ])
 
 # For API dispatch used at python-level

paddle/phi/api/lib/kernel_dispatch.h

@@ -96,8 +96,7 @@ struct KernelKeyParser : ArgsIterator<KernelKeyParser> {
 
   // TODO(chenweihang): deal with multiple diff input Tensors
   // TODO(chenweihang): add global device guard method to set backend
-  void operator()(const Tensor& x) {
-    const phi::TensorBase& tensor = *x.impl();
+  inline void AssignKernelKeySet(const phi::TensorBase& tensor) {
     key_set.backend_set =
         key_set.backend_set | detail::GetTensorBackendSet(tensor);
     // TODO(chenweihang): select multi layout and dtype
@@ -110,6 +109,8 @@ struct KernelKeyParser : ArgsIterator<KernelKeyParser> {
     }
   }
 
+  void operator()(const Tensor& x) { AssignKernelKeySet(*x.impl()); }
+
   void operator()(const std::vector<Tensor>& x) {
     const phi::TensorBase& tensor = *x.at(0).impl();
     key_set.backend_set =
@@ -119,6 +120,13 @@ struct KernelKeyParser : ArgsIterator<KernelKeyParser> {
     key_set.dtype = tensor.dtype();
   }
 
+  void operator()(const paddle::optional<const Tensor&> x) {
+    if (x.get_ptr() != nullptr) {
+      const phi::TensorBase& tensor = *(x.get_ptr()->impl());
+      AssignKernelKeySet(tensor);
+    }
+  }
+
   // skip other type args, these args don't used in kernel selection
   template <typename T>
   void operator()(const T& x) {

paddle/phi/kernels/activation_grad_kernel.h

@@ -82,18 +82,18 @@ void ReluDoubleGradKernel(const Context& dev_ctx,
 template <typename T, typename Context>
 void TanhDoubleGradKernel(const Context& dev_ctx,
                           const DenseTensor& out,
-                          const DenseTensor& ddx,
                           const DenseTensor& dout,
+                          const DenseTensor& ddx,
                           DenseTensor* dout_new,
                           DenseTensor* ddout);
 
 template <typename T, typename Context>
 void TanhTripleGradKernel(const Context& dev_ctx,
                           const DenseTensor& out,
-                          const DenseTensor& ddx,
                           const DenseTensor& dout,
-                          const DenseTensor& d_ddout,
+                          const DenseTensor& ddx,
                           const DenseTensor& d_dout_new,
+                          const DenseTensor& d_ddout,
                           DenseTensor* d_out_new,
                           DenseTensor* d_dout,
                           DenseTensor* d_ddx);

paddle/phi/kernels/batch_norm_grad_kernel.h

@@ -66,16 +66,16 @@ void BatchNormGradKernel(const Context& dev_ctx,
 
 template <typename T, typename Context>
 void BatchNormDoubleGradKernel(const Context& dev_ctx,
-                               const DenseTensor& x_grad_grad,
-                               const DenseTensor& scale_grad_grad,
-                               const DenseTensor& bias_grad_grad,
-                               const DenseTensor& y_grad,
                                const DenseTensor& x,
                                const DenseTensor& scale,
-                               const DenseTensor& saved_mean,
-                               const DenseTensor& saved_variance,
                                paddle::optional<const DenseTensor&> mean,
                                paddle::optional<const DenseTensor&> variance,
+                               const DenseTensor& saved_mean,
+                               const DenseTensor& saved_variance,
+                               const DenseTensor& y_grad,
+                               const DenseTensor& x_grad_grad,
+                               const DenseTensor& scale_grad_grad,
+                               const DenseTensor& bias_grad_grad,
                                float momentum,
                                float epsilon,
                                const std::string& data_layout,

paddle/phi/kernels/cpu/batch_norm_grad_kernel.cc

@@ -341,16 +341,16 @@ void BatchNormGradKernel(const Context& dev_ctx,
 
 template <typename T, typename Context>
 void BatchNormDoubleGradKernel(const Context& ctx,
-                               const DenseTensor& x_grad_grad,
-                               const DenseTensor& scale_grad_grad,
-                               const DenseTensor& bias_grad_grad,
-                               const DenseTensor& y_grad,
                                const DenseTensor& x,
                                const DenseTensor& scale,
-                               const DenseTensor& saved_mean,
-                               const DenseTensor& saved_variance,
                                paddle::optional<const DenseTensor&> mean,
                                paddle::optional<const DenseTensor&> variance,
+                               const DenseTensor& saved_mean,
+                               const DenseTensor& saved_variance,
+                               const DenseTensor& y_grad,
+                               const DenseTensor& x_grad_grad,
+                               const DenseTensor& scale_grad_grad,
+                               const DenseTensor& bias_grad_grad,
                                float momentum,
                                float epsilon,
                                const std::string& data_layout_str,

paddle/phi/kernels/cpu/elementwise_subtract_grad_kernel.cc

@@ -38,9 +38,9 @@ void SubtractGradKernel(const Context& dev_ctx,
 template <typename T, typename Context>
 void SubtractDoubleGradKernel(const Context& dev_ctx,
                               const DenseTensor& y,
+                              const DenseTensor& dout,
                               paddle::optional<const DenseTensor&> ddx,
                               paddle::optional<const DenseTensor&> ddy,
-                              const DenseTensor& dout,
                               int axis,
                               DenseTensor* ddout) {
   phi::SubtractDoubleGradImpl<T>(dev_ctx, y, ddx, ddy, dout, axis, ddout);

paddle/phi/kernels/elementwise_subtract_grad_kernel.h

@@ -30,9 +30,9 @@ void SubtractGradKernel(const Context& dev_ctx,
 template <typename T, typename Context>
 void SubtractDoubleGradKernel(const Context& dev_ctx,
                               const DenseTensor& y,
+                              const DenseTensor& dout,
                               paddle::optional<const DenseTensor&> ddx,
                               paddle::optional<const DenseTensor&> ddy,
-                              const DenseTensor& dout,
                               int axis,
                               DenseTensor* ddout);
 

paddle/phi/kernels/gpu/batch_norm_grad_kernel.cu

@@ -908,16 +908,16 @@ void BatchNormGradKernel(const Context &dev_ctx,
 
 template <typename T, typename Context>
 void BatchNormDoubleGradKernel(const Context &ctx,
-                               const DenseTensor &x_grad_grad,
-                               const DenseTensor &scale_grad_grad,
-                               const DenseTensor &bias_grad_grad,
-                               const DenseTensor &y_grad,
                                const DenseTensor &x,
                                const DenseTensor &scale,
-                               const DenseTensor &saved_mean,
-                               const DenseTensor &saved_variance,
                                paddle::optional<const DenseTensor &> mean,
                                paddle::optional<const DenseTensor &> variance,
+                               const DenseTensor &saved_mean,
+                               const DenseTensor &saved_variance,
+                               const DenseTensor &y_grad,
+                               const DenseTensor &x_grad_grad,
+                               const DenseTensor &scale_grad_grad,
+                               const DenseTensor &bias_grad_grad,
                                float momentum,
                                float epsilon,
                                const std::string &data_layout_str,

paddle/phi/kernels/gpu/elementwise_subtract_grad_kernel.cu

@@ -46,9 +46,9 @@ void SubtractGradKernel(const Context& dev_ctx,
 template <typename T, typename Context>
 void SubtractDoubleGradKernel(const Context& dev_ctx,
                               const DenseTensor& y,
+                              const DenseTensor& dout,
                               paddle::optional<const DenseTensor&> ddx,
                               paddle::optional<const DenseTensor&> ddy,
-                              const DenseTensor& dout,
                               int axis,
                               DenseTensor* ddout) {
   phi::SubtractDoubleGradImpl<T>(dev_ctx, y, ddx, ddy, dout, axis, ddout);

paddle/phi/kernels/impl/activation_grad_impl.h

@@ -152,8 +152,8 @@ void LeakyReluDoubleGradKernel(const Context& dev_ctx,
 template <typename T, typename Context>
 void TanhDoubleGradKernel(const Context& dev_ctx,
                           const DenseTensor& out,
-                          const DenseTensor& ddx,
                           const DenseTensor& dout,
+                          const DenseTensor& ddx,
                           DenseTensor* dout_new,
                           DenseTensor* ddout) {
   if (dout_new) {
@@ -171,10 +171,10 @@ void TanhDoubleGradKernel(const Context& dev_ctx,
 template <typename T, typename Context>
 void TanhTripleGradKernel(const Context& dev_ctx,
                           const DenseTensor& out,
-                          const DenseTensor& ddx,
                           const DenseTensor& dout,
-                          const DenseTensor& d_ddout,
+                          const DenseTensor& ddx,
                           const DenseTensor& d_dout_new,
+                          const DenseTensor& d_ddout,
                           DenseTensor* d_out_new,
                           DenseTensor* d_dout,
                           DenseTensor* d_ddx) {

paddle/phi/ops/compat/activation_sig.cc

@@ -121,13 +121,13 @@ KernelSignature ReluDoubleGradOpArgumentMapping(
 KernelSignature TanhDoubleGradOpArgumentMapping(
     const ArgumentMappingContext& ctx) {
   return KernelSignature(
-      "tanh_double_grad", {"Out", "DDX", "DOut"}, {}, {"DOutNew", "DDOut"});
+      "tanh_double_grad", {"Out", "DOut", "DDX"}, {}, {"DOutNew", "DDOut"});
 }
 
 KernelSignature TanhTripleGradOpArgumentMapping(
     const ArgumentMappingContext& ctx) {
   return KernelSignature("tanh_triple_grad",
-                         {"Out", "DDX", "DOut", "D_DDOut", "D_DOut_New"},
+                         {"Out", "DOut", "DDX", "D_DOut_New", "D_DDOut"},
                          {},
                          {"D_OutNew", "D_DOut", "D_DDx"});
 }

paddle/phi/ops/compat/batch_norm_sig.cc

@@ -82,16 +82,16 @@ KernelSignature BatchNormGradOpArgumentMapping(
 KernelSignature BatchNormGradGradOpArgumentMapping(
     const ArgumentMappingContext& ctx) {
   return KernelSignature("batch_norm_grad_grad",
-                         {"DDX",
-                          "DDScale",
-                          "DDBias",
-                          "DY",
-                          "X",
+                         {"X",
                           "Scale",
+                          "Mean",
+                          "Variance",
                           "SavedMean",
                           "SavedVariance",
-                          "Mean",
-                          "Variance"},
+                          "DY",
+                          "DDX",
+                          "DDScale",
+                          "DDBias"},
                          {"momentum",
                           "epsilon",
                           "data_layout",

paddle/phi/ops/compat/elementwise_sig.cc

@@ -133,7 +133,7 @@ KernelSignature ElementwiseSubGradOpArgumentMapping(
 KernelSignature ElementwiseSubDoubleGradOpArgumentMapping(
     const ArgumentMappingContext& ctx) {
   return KernelSignature(
-      "subtract_double_grad", {"Y", "DDX", "DDY", "DOut"}, {"axis"}, {"DDOut"});
+      "subtract_double_grad", {"Y", "DOut", "DDX", "DDY"}, {"axis"}, {"DDOut"});
 }
 
 KernelSignature ElementwiseDivGradOpArgumentMapping(

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

@@ -560,7 +560,10 @@ def get_static_double_grad(x,
     # so, they are also the input of second-order backward.
     x += y_grads
     x_init += dy_init
-    y = dx
+
+    # filter None in dx for DX/DY may be None in kernel
+    filted_dx = [dxi for dxi in dx if dxi is not None]
+    y = filted_dx
 
     # check input arguments
     x = _as_list(x)
@@ -619,6 +622,7 @@ def get_static_double_grad(x,
 def get_eager_double_grad(func,
                           x_init=None,
                           dy_init=None,
+                          place=None,
                           return_mid_result=False):
     """
     Get Double Grad result of dygraph.
@@ -627,6 +631,7 @@ def get_eager_double_grad(func,
         func: A wrapped dygraph function that its logic is equal to static program
         x_init (numpy.array|list[numpy.array]|None): the init value for input x.
         dy_init (numpy.array|list[numpy.array]|None): the init value for gradient of output.
+        place (fluid.CPUPlace or fluid.CUDAPlace): the device.
         return_mid_result (bool): A flag that controls the return content.
     Returns:
         If 'return_mid_result' set True. 
@@ -635,6 +640,10 @@ def get_eager_double_grad(func,
         If 'return_mid_result' set False. 
         A list of numpy array that stores second derivative result calulated by dygraph.
     """
+    if isinstance(place, fluid.CPUPlace):
+        paddle.set_device("cpu")
+    if isinstance(place, fluid.CUDAPlace):
+        paddle.set_device("gpu")
     inputs = []
     dys = []
     for x in x_init:
@@ -648,7 +657,12 @@ def get_eager_double_grad(func,
     # calculate first derivative
     outputs = func(inputs)
     d_inputs = paddle.grad(
-        outputs=outputs, inputs=inputs, grad_outputs=dys, create_graph=True)
+        outputs=outputs,
+        inputs=inputs,
+        grad_outputs=dys,
+        create_graph=True,
+        allow_unused=True)
+    d_inputs = [d_input for d_input in d_inputs if d_input is not None]
 
     # calcluate second derivative
     inputs = inputs + dys
@@ -663,15 +677,20 @@ def get_eager_double_grad(func,
         ddy = paddle.ones(shape=d_input.shape, dtype=d_input.dtype)
         ddy.stop_gradient = False
         ddys.append(ddy)
+
     dd_inputs = paddle.grad(
         outputs=d_inputs,
         inputs=inputs,
         grad_outputs=ddys,
-        create_graph=create_graph)
+        create_graph=create_graph,
+        allow_unused=True)
+
     if return_mid_result:
         return dd_inputs, inputs + ddys
     else:
-        return [dd_input.numpy() for dd_input in dd_inputs]
+        return [
+            dd_input.numpy() for dd_input in dd_inputs if dd_input is not None
+        ]
 
 
 def double_grad_check_for_dygraph(func,
@@ -693,7 +712,6 @@ def double_grad_check_for_dygraph(func,
         y (Variable|list[Variable]): output variables to the program.
         x_init (numpy.array|list[numpy.array]|None): the init value for input x.
         place (fluid.CPUPlace or fluid.CUDAPlace): the device.
-        eps (float): perturbation for finite differences.
         atol (float): absolute tolerance.
         rtol (float): relative tolerance.
         raise_exception (bool): whether to raise an exception if
@@ -722,19 +740,25 @@ def double_grad_check_for_dygraph(func,
 
     paddle.disable_static()
     with _test_eager_guard():
-        eager_double_grad = get_eager_double_grad(func, x_init, y_grads_init)
+        eager_double_grad = get_eager_double_grad(func, x_init, y_grads_init,
+                                                  place)
     paddle.enable_static()
 
     static_double_grad = get_static_double_grad(x, y, x_init, y_grads_init,
                                                 place)
 
+    if len(static_double_grad) != len(eager_double_grad):
+        msg = "The output grad tensor's number of static graph is different with dygraph, " \
+            "please check the python api unit test used."
+        raise RuntimeError(msg)
+
     for i in six.moves.xrange(len(static_double_grad)):
         if not np.allclose(static_double_grad[i], eager_double_grad[i], rtol,
                            atol):
-            msg = 'Check eager double result fail. Mismatch between static_graph double grad %s ' \
-                'and eager double grad %s on %s,\n' \
+            msg = 'Check eager double result fail. Mismatch between static_graph double grad ' \
+                'and eager double grad on %s, the output double grad tensor\'s index is : %d \n' \
                 'static:%s\n eager:%s\n' \
-                % (static_double_grad[i].name, eager_double_grad[i].name, str(place), static_double_grad[i], eager_double_grad[i])
+                % (str(place), i, static_double_grad[i], eager_double_grad[i])
             return fail_test(msg)
 
 
@@ -794,6 +818,7 @@ def get_static_triple_grad(x,
 def get_eager_triple_grad(func,
                           x_init=None,
                           dy_init=None,
+                          place=None,
                           return_mid_result=False):
     """
     Get triple Grad result of dygraph.
@@ -802,12 +827,13 @@ def get_eager_triple_grad(func,
         func: A wrapped dygraph function that its logic is equal to static program
         x_init (numpy.array|list[numpy.array]|None): the init value for input x.
         dy_init (numpy.array|list[numpy.array]|None): the init value for gradient of output.
+        place (fluid.CPUPlace or fluid.CUDAPlace): the device.
         return_mid_result (list[Tensor], list[Tensor]): If set True, the 
     Returns:
         A list of numpy array that stores second derivative result calulated by dygraph
     """
     dd_y, dd_x = get_eager_double_grad(
-        func, x_init, dy_init, return_mid_result=True)
+        func, x_init, dy_init, place, return_mid_result=True)
 
     # calcluate third derivative
     dddys = []
@@ -839,7 +865,6 @@ def triple_grad_check_for_dygraph(func,
         y (Variable|list[Variable]): output variables to the program.
         x_init (numpy.array|list[numpy.array]|None): the init value for input x.
         place (fluid.CPUPlace or fluid.CUDAPlace): the device.
-        eps (float): perturbation for finite differences.
         atol (float): absolute tolerance.
         rtol (float): relative tolerance.
         raise_exception (bool): whether to raise an exception if
@@ -868,17 +893,23 @@ def triple_grad_check_for_dygraph(func,
 
     paddle.disable_static()
     with _test_eager_guard():
-        eager_triple_grad = get_eager_triple_grad(func, x_init, y_grads_init)
+        eager_triple_grad = get_eager_triple_grad(func, x_init, y_grads_init,
+                                                  place)
     paddle.enable_static()
 
     static_triple_grad = get_static_triple_grad(x, y, x_init, y_grads_init,
                                                 place)
 
+    if len(static_triple_grad) != len(eager_triple_grad):
+        msg = "The output grad tensor's number of static graph is different with dygraph, " \
+            "please check the python api unit test used."
+        raise RuntimeError(msg)
+
     for i in six.moves.xrange(len(static_triple_grad)):
         if not np.allclose(static_triple_grad[i], eager_triple_grad[i], rtol,
                            atol):
-            msg = 'Check eager double result fail. Mismatch between static_graph double grad %s ' \
-                'and eager double grad %s on %s,\n' \
+            msg = 'Check eager double result fail. Mismatch between static_graph double grad ' \
+                'and eager double grad on %s, the output double grad tensor\'s index is : %d \n' \
                 'static:%s\n eager:%s\n' \
-                % (static_triple_grad[i].name, eager_triple_grad[i].name, str(place), static_triple_grad[i], eager_triple_grad[i])
+                % (str(place), i, static_triple_grad[i], eager_triple_grad[i])
             return fail_test(msg)

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

@@ -52,6 +52,9 @@ class TestSigmoidTripleGradCheck(unittest.TestCase):
 
 
 class TestSigmoidDoubleGradCheck(unittest.TestCase):
+    def sigmoid_wrapper(self, x):
+        return fluid.layers.sigmoid(x[0])
+
     @prog_scope()
     def func(self, place):
         shape = [2, 3, 7, 9]
@@ -64,6 +67,8 @@ class TestSigmoidDoubleGradCheck(unittest.TestCase):
         x_arr[np.abs(x_arr) < 0.005] = 0.002
         gradient_checker.double_grad_check(
             [x], y, x_init=x_arr, place=place, eps=eps)
+        gradient_checker.double_grad_check_for_dygraph(
+            self.sigmoid_wrapper, [x], y, x_init=x_arr, place=place)
 
     def test_grad(self):
         paddle.enable_static()
@@ -75,6 +80,9 @@ class TestSigmoidDoubleGradCheck(unittest.TestCase):
 
 
 class TestTanhTripleGradCheck(unittest.TestCase):
+    def tanh_wrapper(self, x):
+        return paddle.tanh(x[0])
+
     @prog_scope()
     def func(self, place):
         shape = [2, 3, 7, 9]
@@ -87,6 +95,8 @@ class TestTanhTripleGradCheck(unittest.TestCase):
         x_arr[np.abs(x_arr) < 0.005] = 0.002
         gradient_checker.triple_grad_check(
             [x], y, x_init=x_arr, place=place, eps=eps)
+        gradient_checker.triple_grad_check_for_dygraph(
+            self.tanh_wrapper, [x], y, x_init=x_arr, place=place)
 
     def test_grad(self):
         paddle.enable_static()
@@ -98,6 +108,9 @@ class TestTanhTripleGradCheck(unittest.TestCase):
 
 
 class TestTanhDoubleGradCheck(unittest.TestCase):
+    def tanh_wrapper(self, x):
+        return paddle.tanh(x[0])
+
     @prog_scope()
     def func(self, place):
         shape = [2, 3, 7, 9]
@@ -110,6 +123,8 @@ class TestTanhDoubleGradCheck(unittest.TestCase):
         x_arr[np.abs(x_arr) < 0.005] = 0.002
         gradient_checker.double_grad_check(
             [x], y, x_init=x_arr, place=place, eps=eps)
+        gradient_checker.double_grad_check_for_dygraph(
+            self.tanh_wrapper, [x], y, x_init=x_arr, place=place)
 
     def test_grad(self):
         paddle.enable_static()
@@ -173,6 +188,9 @@ class TestLeakyReluDoubleGradCheck(unittest.TestCase):
 
 
 class TestELUDoubleGradCheck(unittest.TestCase):
+    def elu_wrapper(self, x):
+        return paddle.nn.functional.elu(x[0], alpha=0.2)
+
     @prog_scope()
     def func(self, place):
         shape = [2, 4, 4, 4]
@@ -189,6 +207,8 @@ class TestELUDoubleGradCheck(unittest.TestCase):
         x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
         gradient_checker.double_grad_check(
             [x], y, x_init=x_arr, place=place, eps=eps)
+        gradient_checker.double_grad_check_for_dygraph(
+            self.elu_wrapper, [x], y, x_init=x_arr, place=place)
 
     def test_grad(self):
         paddle.enable_static()

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

@@ -139,6 +139,9 @@ class TestElementwiseAddBroadcastDoubleGradCheck(unittest.TestCase):
 
 
 class TestElementwiseSubDoubleGradCheck(unittest.TestCase):
+    def subtract_wrapper(self, x):
+        return paddle.subtract(x[0], x[1])
+
     @prog_scope()
     def func(self, place):
         # the shape of input variable should be clearly specified, not inlcude -1.
@@ -156,6 +159,11 @@ class TestElementwiseSubDoubleGradCheck(unittest.TestCase):
 
         gradient_checker.double_grad_check(
             [x, y], out, x_init=[x_arr, y_arr], place=place, eps=eps)
+        gradient_checker.double_grad_check_for_dygraph(
+            self.subtract_wrapper, [x, y],
+            out,
+            x_init=[x_arr, y_arr],
+            place=place)
 
     def test_grad(self):
         paddle.enable_static()
@@ -195,6 +203,9 @@ class TestElementwiseSubBroadcastDoubleGradCheck(unittest.TestCase):
 
 
 class TestElementwiseDivDoubleGradCheck(unittest.TestCase):
+    def divide_wrapper(self, x):
+        return paddle.divide(x[0], x[1])
+
     @prog_scope()
     def func(self, place):
         # the shape of input variable should be clearly specified, not inlcude -1.
@@ -213,6 +224,12 @@ class TestElementwiseDivDoubleGradCheck(unittest.TestCase):
 
         gradient_checker.double_grad_check(
             [x, y], out, x_init=[x_arr, y_arr], place=place, eps=eps, atol=1e-3)
+        gradient_checker.double_grad_check_for_dygraph(
+            self.divide_wrapper, [x, y],
+            out,
+            x_init=[x_arr, y_arr],
+            place=place,
+            atol=1e-3)
 
     def test_grad(self):
         paddle.enable_static()

python/paddle/nn/functional/activation.py

@@ -112,7 +112,10 @@ def elu(x, alpha=1.0, name=None):
             #  [ 1.          15.6      ]]
     """
 
-    if in_dynamic_mode():
+    if in_dygraph_mode():
+        return _C_ops.final_state_elu(x, alpha)
+
+    if _in_legacy_dygraph():
         return _C_ops.elu(x, 'alpha', alpha)
 
     check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'], 'elu')

python/paddle/utils/code_gen/api.yaml

@@ -466,6 +466,7 @@
     func : DeformableConvInferMeta
   kernel :
     func : deformable_conv
+    data_type : x
   optional : mask
   backward : deformable_conv_grad
 
@@ -546,6 +547,7 @@
     func : DropoutInferMeta
   kernel :
     func : dropout
+    data_type : x
   optional : seed_tensor
   backward : dropout_grad
 
@@ -1065,6 +1067,7 @@
     func : LayerNormInferMeta
   kernel :
     func : layer_norm
+    data_type : x
   backward : layer_norm_grad
   optional : scale, bias
 
@@ -1608,6 +1611,7 @@
     func : PsroiPoolInferMeta
   kernel :
     func : psroi_pool
+    data_type : x
   optional : boxes_num
   backward : psroi_pool_grad
 
@@ -1713,6 +1717,7 @@
     func : RoiAlignInferMeta
   kernel :
     func : roi_align
+    data_type : x
   optional : boxes_num
   backward : roi_align_grad
 
@@ -1723,6 +1728,7 @@
     func : RoiPoolInferMeta
   kernel :
     func : roi_pool
+    data_type : x
   optional : boxes_num
   intermediate : arg_max
   backward : roi_pool_grad

python/paddle/utils/code_gen/backward.yaml

@@ -152,6 +152,18 @@
   kernel :
     func : atanh_grad
 
+- backward_api : batch_norm_double_grad
+  forward : batch_norm_grad (Tensor x, Tensor scale, Tensor bias, Tensor out_mean, Tensor out_variance, Tensor saved_mean, Tensor saved_variance, Tensor reserve_space, Tensor grad_out, float momentum, float epsilon, str data_layout, bool is_test, bool use_global_stats, bool trainable_statistics, bool fuse_with_relu) -> Tensor(grad_x), Tensor(grad_scale), Tensor(grad_bias)
+  args : (Tensor x, Tensor scale, Tensor out_mean, Tensor out_variance, Tensor saved_mean, Tensor saved_variance, Tensor grad_out,  Tensor grad_x_grad, Tensor grad_scale_grad, Tensor grad_bias_grad, float momentum, float epsilon, str data_layout, bool is_test, bool use_global_stats, bool trainable_statistics, bool fuse_with_relu)
+  output : Tensor(x_grad), Tensor(scale_grad), Tensor(grad_out_grad)
+  infer_meta :
+    func : GeneralTernaryGradInferMeta
+    param : [x, scale, x]
+  kernel :
+    func : batch_norm_grad_grad
+    data_type : x
+  optional : out_mean, out_variance
+
 - backward_api : batch_norm_grad
   forward : batch_norm (Tensor x, Tensor scale, Tensor bias, Tensor mean, Tensor variance, float momentum, float epsilon, str data_layout, bool is_test, bool use_global_stats, bool trainable_statistics, bool fuse_with_relu) -> Tensor(out), Tensor(mean_out), Tensor(variance_out), Tensor(saved_mean), Tensor(saved_variance), Tensor(reserve_space)
   args : (Tensor x, Tensor scale, Tensor bias, Tensor mean_out, Tensor variance_out, Tensor saved_mean, Tensor saved_variance, Tensor reserve_space, Tensor out_grad, float momentum, float epsilon, str data_layout, bool is_test, bool use_global_stats, bool trainable_statistics, bool fuse_with_relu)
@@ -163,6 +175,7 @@
     func : batch_norm_grad
     data_type : out_grad
   optional : mean_out, variance_out, reserve_space
+  backward : batch_norm_double_grad
 
 - backward_api : bce_loss_grad
   forward : bce_loss (Tensor input, Tensor label) -> Tensor(out)
@@ -362,6 +375,7 @@
     func : DeformableConvGradInferMeta
   kernel :
     func : deformable_conv_grad
+    data_type : x
   optional : mask
 
 - backward_api : depthwise_conv2d_transpose_grad
@@ -414,6 +428,18 @@
   kernel :
     func : dist_grad
 
+- backward_api : divide_double_grad
+  forward : divide_grad (Tensor x, Tensor y, Tensor out, Tensor grad_out, int axis = -1) -> Tensor(grad_x), Tensor(grad_y)
+  args : (Tensor y, Tensor out, Tensor grad_x, Tensor grad_x_grad, Tensor grad_y_grad, int axis = -1)
+  output : Tensor(y_grad), Tensor(out_grad), Tensor(grad_out_grad)
+  infer_meta :
+    func : GeneralTernaryGradInferMeta
+    param : [y, grad_x, grad_x]
+  kernel :
+    func : divide_double_grad
+    data_type : out
+  optional : grad_x_grad, grad_y_grad
+
 - backward_api : divide_grad
   forward : divide (Tensor x, Tensor y) -> Tensor(out)
   args : (Tensor x, Tensor y, Tensor out, Tensor out_grad, int axis = -1)
@@ -423,6 +449,7 @@
     param : [x, y]
   kernel :
     func : divide_grad
+  backward : divide_double_grad
 
 - backward_api : dropout_grad
   forward : dropout (Tensor x, Tensor seed_tensor, float p, bool is_test, str mode, int seed, bool fix_seed) -> Tensor(out), Tensor(mask)
@@ -455,6 +482,16 @@
   kernel :
     func : elementwise_pow_grad
 
+- backward_api : elu_double_grad
+  forward : elu_grad (Tensor x, Tensor out, Tensor grad_out, float alpha)-> Tensor(grad_x)
+  args : (Tensor x, Tensor grad_out, Tensor grad_x_grad, float alpha)
+  output : Tensor(x_grad), Tensor(grad_out_grad)
+  infer_meta :
+    func : GeneralBinaryGradInferMeta
+    param : [x, x]
+  kernel :
+    func : elu_double_grad
+
 - backward_api : elu_grad
   forward : elu (Tensor x, float alpha) -> Tensor(out)
   args : (Tensor x, Tensor out, Tensor out_grad, float alpha)
@@ -464,6 +501,7 @@
     param : [x]
   kernel :
     func : elu_grad
+  backward : elu_double_grad
 
 - backward_api : erf_grad
   forward : erf (Tensor x) -> Tensor(out)
@@ -633,6 +671,7 @@
     param : [x]
   kernel :
     func : graph_send_recv_grad
+    data_type : out_grad
   optional: out, dst_count
 
 - backward_api : gumbel_softmax_grad
@@ -1287,6 +1326,7 @@
     param : [x]
   kernel :
     func : psroi_pool_grad
+    data_type : x
   optional : boxes_num
 
 # output is optional
@@ -1381,6 +1421,7 @@
     param : [x]
   kernel :
     func : roi_align_grad
+    data_type : boxes
   optional : boxes_num
 
 - backward_api : roi_pool_grad
@@ -1392,6 +1433,7 @@
     param : [x]
   kernel :
     func : roi_pool_grad
+    data_type : x
   optional : boxes_num
 
 - backward_api : roll_grad
@@ -1498,7 +1540,7 @@
     func : UnchangedInferMeta
     param : [x]
   kernel :
-    func : sigmoid_cross_entropy_with_logits_grad 
+    func : sigmoid_cross_entropy_with_logits_grad
 
 - backward_api : sigmoid_double_grad
   forward : sigmoid_grad (Tensor out, Tensor fwd_grad_out) -> Tensor(grad_x)
@@ -1654,6 +1696,18 @@
     func : strided_slice_grad
   no_need_buffer : x
 
+- backward_api : subtract_double_grad
+  forward : subtract_grad (Tensor x, Tensor y, Tensor grad_out, int axis = -1) -> Tensor(grad_x), Tensor(grad_y)
+  args : (Tensor y, Tensor grad_out, Tensor grad_x_grad, Tensor grad_y_grad, int axis = -1)
+  output : Tensor(grad_out_grad)
+  infer_meta :
+    func : UnchangedInferMeta
+    param : [grad_out]
+  kernel :
+    func : subtract_double_grad
+  optional : grad_x_grad, grad_y_grad
+  no_need_buffer : y, grad_out
+
 - backward_api : subtract_grad
   forward : subtract (Tensor x, Tensor y) -> Tensor(out)
   args : (Tensor x, Tensor y, Tensor out_grad, int axis = -1)
@@ -1664,6 +1718,7 @@
   kernel :
     func : subtract_grad
   no_need_buffer : x, y
+  backward : subtract_double_grad
 
 - backward_api : sum_double_grad
   forward : sum_grad (Tensor x, Tensor grad_out, int64_t[] dims, bool keep_dim, bool reduce_all=false) -> Tensor(grad_x)
@@ -1720,6 +1775,17 @@
   kernel :
     func : tan_grad
 
+- backward_api : tanh_double_grad
+  forward : tanh_grad (Tensor out, Tensor grad_out) -> Tensor(grad_x)
+  args : (Tensor out, Tensor grad_out, Tensor grad_x_grad)
+  output : Tensor(out_grad), Tensor(grad_out_grad)
+  infer_meta :
+    func : GeneralBinaryGradInferMeta
+    param : [out, out]
+  kernel :
+    func : tanh_double_grad
+  backward : tanh_triple_grad
+
 - backward_api : tanh_grad
   forward : tanh (Tensor x) -> Tensor(out)
   args : (Tensor out, Tensor out_grad)
@@ -1729,6 +1795,7 @@
     param : [out]
   kernel :
     func : tanh_grad
+  backward : tanh_double_grad
 
 - backward_api : tanh_shrink_grad
   forward : tanh_shrink (Tensor x) -> Tensor(out)
@@ -1740,6 +1807,16 @@
   kernel :
     func : tanh_shrink_grad
 
+- backward_api : tanh_triple_grad
+  forward : tanh_double_grad (Tensor out, Tensor grad_out_forward, Tensor grad_x_grad_forward) -> Tensor(grad_out_new), Tensor(grad_out_grad)
+  args : (Tensor out, Tensor grad_out_forward, Tensor grad_x_grad_forward, Tensor grad_out_new_grad, Tensor grad_out_grad_grad)
+  output : Tensor(out_grad), Tensor(grad_out_forward_grad), Tensor(grad_x_grad_forward_grad)
+  infer_meta :
+    func : GeneralTernaryGradInferMeta
+    param : [out, out, grad_x_grad_forward]
+  kernel :
+    func : tanh_triple_grad
+
 - backward_api : thresholded_relu_grad
   forward : thresholded_relu (Tensor x, float threshold) -> Tensor(out)
   args : (Tensor x, Tensor out_grad, float threshold)