[Prim] Add stack_double_grad (#63161)

* add stack_double_grad composite API * add TestStackDoubleGradCheck

Author: HydrogenSulfate

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

@@ -57,6 +57,7 @@
  "conv3d_double_grad",
  "depthwise_conv2d_grad_grad",
  "concat_double_grad",
+ "stack_double_grad",
  "expand_grad",
  "argsort_grad",
  "eigh_grad",

paddle/phi/api/yaml/backward.yaml

@@ -2375,6 +2375,12 @@
  inplace : (out_grad -> x_grad)
  backward: squeeze_double_grad
 
+- backward_op : stack_double_grad
+ forward : stack_grad (Tensor[] x, Tensor grad_out, int axis=0) -> Tensor[](grad_x)
+ args : (Tensor[] grad_x_grad, int axis = 0)
+ output : Tensor(grad_out_grad)
+ invoke : stack(grad_x_grad, axis)
+
 - backward_op : stack_grad
  forward : stack (Tensor[] x, int axis) -> Tensor(out)
  args : (Tensor[] x, Tensor out_grad, int axis)
@@ -2389,6 +2395,7 @@
  data_type : out_grad
  no_need_buffer : x
  composite : stack_grad(x, out_grad, axis, x_grad)
+ backward: stack_double_grad
 
 - backward_op : stanh_grad
  forward : stanh(Tensor x, float scale_a, float scale_b) -> Tensor(out)

paddle/phi/api/yaml/op_compat.yaml

@@ -3225,7 +3225,7 @@
  outputs : [xshape]
 
 - op : stack
- backward : stack_grad
+ backward : stack_grad, stack_double_grad
  inputs :
  x : X
  outputs :

test/legacy_test/test_nn_grad.py

@@ -405,7 +405,6 @@ def concat_wrapper(self, x):
  @prog_scope()
  def func(self, place):
  x_shape = [2, 3, 4, 5]
- pad = [1, 1, 1, 1]
  dtype = np.float64
 
  x1 = paddle.static.data('x', x_shape, dtype)
@@ -437,6 +436,45 @@ def test_grad(self):
  self.func(p)
 
 
+class TestStackDoubleGradCheck(unittest.TestCase):
+ def stack_wrapper(self, x):
+ return paddle.stack(x, axis=1)
+
+ @test_with_pir_api
+ @prog_scope()
+ def func(self, place):
+ x_shape = [2, 3, 4, 5]
+ dtype = np.float64
+
+ x1 = paddle.static.data('x', x_shape, dtype)
+ x2 = paddle.static.data('x', x_shape, dtype)
+ x1.persistable = True
+ x1.stop_gradient = False
+ x2.persistable = True
+ x2.stop_gradient = False
+ out = paddle.stack([x1, x2], axis=0)
+ x2_arr = np.random.uniform(-1, 1, x_shape).astype(dtype)
+ x1_arr = np.random.uniform(-1, 1, x_shape).astype(dtype)
+
+ gradient_checker.double_grad_check(
+ [x1, x2], out, x_init=[x1_arr, x2_arr], place=place
+ )
+ gradient_checker.double_grad_check_for_dygraph(
+ self.stack_wrapper,
+ [x1, x2],
+ out,
+ x_init=[x1_arr, x2_arr],
+ place=place,
+ )
+
+ def test_grad(self):
+ places = [base.CPUPlace()]
+ if core.is_compiled_with_cuda():
+ places.append(base.CUDAPlace(0))
+ for p in places:
+ self.func(p)
+
+
 class TestAvgPool2DDoubleGradCheckCase1(unittest.TestCase):
  @test_with_pir_api
  @prog_scope()