[Eager] refactor general_grad and fix some bugs (#44611)

* refactor general_grad and fix some bugs

* add TODO: support prune logic deeper

paddle/fluid/eager/grad_node_info.h

@@ -253,6 +253,19 @@ class GradNodeBase {
    * **/
   inline bool GradientHooksRegistered() { return !gradient_hooks_.empty(); }
 
+  std::map<int64_t, std::tuple<size_t, size_t, std::shared_ptr<TensorHook>>>
+  GetGradientHookFuntions() {
+    VLOG(6) << "GetGradientHookFuntions ";
+    return gradient_hooks_;
+  }
+
+  void SetGradientHookFuntions(
+      std::map<int64_t, std::tuple<size_t, size_t, std::shared_ptr<TensorHook>>>
+          hooks) {
+    VLOG(6) << "SetGradientHookFuntions ";
+    gradient_hooks_ = hooks;
+  }
+
   paddle::small_vector<std::vector<paddle::experimental::Tensor>,
                        kSlotSmallVectorSize>
   ApplyGradientHooks(

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

@@ -166,6 +166,46 @@ class TestEagerGrad(TestCase):
             self.func_simple_example_eager_grad_duplicate_output()
         self.func_simple_example_eager_grad_duplicate_output()
 
+    def test_simple_example_eager_two_grad_output(self):
+        with _test_eager_guard():
+            x1 = paddle.to_tensor([1.0, 2.0])
+            x1.stop_gradient = False
+            x2 = paddle.to_tensor([1.0, 2.0])
+            x2.stop_gradient = False
+            out1 = x1 * 2
+            out2 = x2 * 2
+
+            dout2_record_by_hook = []
+
+            def record_hook(grad):
+                dout2_record_by_hook.append(grad)
+
+            out2.register_hook(record_hook)
+
+            out3 = paddle.multiply(out1, out2)
+            out4 = paddle.mean(out3)
+            egr_dout2, egr_dout3 = paddle.grad([out4], [out2, out3])
+
+            self.assertTrue(
+                np.array_equal(dout2_record_by_hook[0].numpy(),
+                               np.array([1., 2.])))
+
+        x1 = paddle.to_tensor([1.0, 2.0])
+        x1.stop_gradient = False
+        x2 = paddle.to_tensor([1.0, 2.0])
+        x2.stop_gradient = False
+        out1 = x1 * 2
+        out2 = x2 * 2
+
+        out3 = paddle.multiply(out1, out2)
+        out4 = paddle.mean(out3)
+        dout2, dout3 = paddle.grad([out4], [out2, out3])
+
+        self.assertEqual(dout2.stop_gradient, egr_dout2.stop_gradient)
+        self.assertEqual(dout3.stop_gradient, egr_dout3.stop_gradient)
+        self.assertTrue(np.array_equal(dout2.numpy(), egr_dout2.numpy()))
+        self.assertTrue(np.array_equal(dout3.numpy(), egr_dout3.numpy()))
+
 
 class TestDygraphDoubleGrad(TestCase):