add add_n for the 0d tensor (#49854)

paddle/phi/infermeta/multiary.cc

@@ -297,7 +297,7 @@ void AddNInferMeta(const std::vector<const MetaTensor*>& x,
   if (N == 1) {
     VLOG(3) << "Warning: SumOp have only one input, may waste memory";
   }
-
+  bool is_all_0d_tensor = true;
   phi::DDim in_dim({0});
   for (size_t i = 0; i < x.size(); ++i) {
     auto x_dim = x[i]->dims();
@@ -313,6 +313,7 @@ void AddNInferMeta(const std::vector<const MetaTensor*>& x,
     if (x_dim.size() == 0) {
       continue;
     }
+    is_all_0d_tensor = false;
     if (phi::product(in_dim) == 0) {
       in_dim = x_dim;
     } else {
@@ -360,7 +361,11 @@ void AddNInferMeta(const std::vector<const MetaTensor*>& x,
       }
     }
   }
-  out->set_dims(in_dim);
+  if (is_all_0d_tensor) {
+    out->set_dims(make_ddim({}));
+  } else {
+    out->set_dims(in_dim);
+  }
   out->share_lod(*x[0]);
 }
 

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

@@ -900,6 +900,31 @@ class TestSundryAPI(unittest.TestCase):
         np.testing.assert_array_equal(out3_1.numpy(), out3_2.numpy())
         np.testing.assert_array_equal(out3_2.numpy(), np.asarray(1))
 
+    def test_add_n(self):
+        x1 = paddle.rand([])
+        x1.stop_gradient = False
+        x2 = paddle.rand([])
+        x2.stop_gradient = False
+        x3 = paddle.rand([])
+        x3.stop_gradient = False
+
+        out1 = paddle.add_n(x1)
+        out2 = paddle.add_n([x2, x3])
+
+        out1.backward()
+        out2.backward()
+
+        self.assertEqual(x1.grad.shape, [])
+        self.assertTrue(x1.grad.numpy() == 1)
+        self.assertEqual(x2.grad.shape, [])
+        self.assertTrue(x2.grad.numpy() == 1)
+        self.assertEqual(x3.grad.shape, [])
+        self.assertTrue(x3.grad.numpy() == 1)
+        self.assertEqual(out1.shape, [])
+        self.assertEqual(out1.grad.shape, [])
+        self.assertEqual(out2.shape, [])
+        self.assertEqual(out2.grad.shape, [])
+
     def test_reshape_list(self):
         x = paddle.rand([])
         x.stop_gradient = False
@@ -1534,6 +1559,46 @@ class TestSundryAPIStatic(unittest.TestCase):
         np.testing.assert_array_equal(out3_1, out3_2)
         np.testing.assert_array_equal(out3_2, np.asarray(1))
 
+    @prog_scope()
+    def test_add_n(self):
+        x1 = paddle.rand([])
+        x1.stop_gradient = False
+        x2 = paddle.rand([])
+        x2.stop_gradient = False
+        x3 = paddle.rand([])
+        x3.stop_gradient = False
+
+        out1 = paddle.add_n(x1)
+        out2 = paddle.add_n([x2, x3])
+
+        paddle.static.append_backward(out1.sum())
+        paddle.static.append_backward(out2.sum())
+
+        prog = paddle.static.default_main_program()
+        block = prog.global_block()
+        res = self.exe.run(
+            prog,
+            fetch_list=[
+                out1,
+                out2,
+                x1.grad_name,
+                x2.grad_name,
+                x3.grad_name,
+                out1.grad_name,
+                out2.grad_name,
+            ],
+        )
+        self.assertEqual(res[0].shape, ())
+        self.assertEqual(res[1].shape, ())
+        self.assertEqual(res[2].shape, ())
+        self.assertEqual(res[2], 1)
+        self.assertEqual(res[3].shape, ())
+        self.assertEqual(res[3], 1)
+        self.assertEqual(res[4].shape, ())
+        self.assertEqual(res[4], 1)
+        self.assertEqual(res[5].shape, ())
+        self.assertEqual(res[6].shape, ())
+
     @prog_scope()
     def test_reshape_list(self):
         x1 = paddle.rand([])

python/paddle/fluid/tests/unittests/xpu/test_zero_dim_tensor_xpu.py

@@ -592,6 +592,28 @@ class TestSundryAPI(unittest.TestCase):
         np.testing.assert_array_equal(out3_1.numpy(), out3_2.numpy())
         np.testing.assert_array_equal(out3_2.numpy(), np.asarray(1))
 
+    def test_add_n(self):
+        x1 = paddle.rand([])
+        x1.stop_gradient = False
+        x2 = paddle.rand([])
+        x2.stop_gradient = False
+        x3 = paddle.rand([])
+        x3.stop_gradient = False
+
+        out1 = paddle.add_n(x1)
+        out2 = paddle.add_n([x2, x3])
+
+        out1.retain_grads()
+        out2.retain_grads()
+
+        out1.backward()
+        out2.backward()
+
+        self.assertEqual(out1.shape, [])
+        self.assertEqual(out1.grad.shape, [])
+        self.assertEqual(out2.shape, [])
+        self.assertEqual(out2.grad.shape, [])
+
     def test_reshape_list(self):
         x = paddle.rand([])
         x.stop_gradient = False