Cherry pick 0 d output

paddle/fluid/operators/matrix_rank_op.cc

@@ -27,7 +27,7 @@ namespace detail {
 static DDim CheckAndGetOutputDim(const DDim& dim_x) {
   auto x_vec = phi::vectorize(dim_x);
   if (x_vec.size() == 2) {
-    return phi::make_ddim({1});
+    return phi::make_ddim({});
   }
   x_vec.erase(x_vec.end() - 2, x_vec.end());
   return phi::make_ddim(x_vec);

paddle/phi/infermeta/binary.cc

@@ -72,7 +72,7 @@ static void BinarySameInputDimsCheck(const MetaTensor& x,
 static DDim CheckAndGetOutputDim(const DDim& dim_x) {
   auto x_vec = phi::vectorize(dim_x);
   if (x_vec.size() == 2) {
-    return phi::make_ddim({1});
+    return phi::make_ddim({});
   }
   x_vec.erase(x_vec.end() - 2, x_vec.end());
   return phi::make_ddim(x_vec);

paddle/phi/infermeta/multiary.cc

@@ -2344,7 +2344,7 @@ void MultiDotInferMeta(const std::vector<const MetaTensor*>& x,
   // If the last tensor is 1D of size n view it as a column vector (n, 1)
   if (last_dim.size() == 1) {
     last_dim = phi::make_ddim({static_cast<int>(last_dim[0]), 1});
-    out_dim = is_vector ? phi::make_ddim({1}) : phi::make_ddim({first_dim[0]});
+    out_dim = is_vector ? phi::make_ddim({}) : phi::make_ddim({first_dim[0]});
   } else {
     out_dim = is_vector ? phi::make_ddim({last_dim[1]})
                         : phi::make_ddim({first_dim[0], last_dim[1]});

paddle/phi/infermeta/unary.cc

@@ -38,7 +38,7 @@ namespace detail {
 static DDim CheckAndGetOutputDim(const DDim& dim_x) {
   auto x_vec = phi::vectorize(dim_x);
   if (x_vec.size() == 2) {
-    return phi::make_ddim({1});
+    return phi::make_ddim({});
   }
   x_vec.erase(x_vec.end() - 2, x_vec.end());
   return phi::make_ddim(x_vec);

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

@@ -2221,6 +2221,23 @@ def test_linalg_slogdet(self):
         self.assertTrue(out1.shape, [2, 3])
         self.assertTrue(x1.grad.shape, [3, 3, 3])
 
+    def test_multi_dot(self):
+        a = paddle.randn([4])
+        a.stop_gradient = False
+        b = paddle.randn([4, 5])
+        b.stop_gradient = False
+        c = paddle.randn([5])
+        c.stop_gradient = False
+
+        out = paddle.linalg.multi_dot([a, b, c])
+        out.retain_grads()
+        out.backward()
+
+        self.assertTrue(out.shape, [])
+        self.assertTrue(a.grad.shape, [4])
+        self.assertTrue(b.grad.shape, [4, 5])
+        self.assertTrue(c.grad.shape, [5])
+
 
 class TestSundryAPIStatic(unittest.TestCase):
     def setUp(self):
@@ -3900,6 +3917,19 @@ def test_linalg_slogdet(self):
         self.assertEqual(res[0].shape, (2, 3))
         self.assertEqual(res[1].shape, (3, 3, 3))
 
+    def test_multi_dot(self):
+        a = paddle.randn([4])
+        a.stop_gradient = False
+        b = paddle.randn([4, 5])
+        b.stop_gradient = False
+        c = paddle.randn([5])
+        c.stop_gradient = False
+
+        out = paddle.linalg.multi_dot([a, b, c])
+        prog = paddle.static.default_main_program()
+        res = self.exe.run(prog, fetch_list=[out])
+        self.assertTrue(res[0].shape, [])
+
 
 # Use to test API whose zero-dim input tensors don't have grad and not need to test backward in OpTest.
 class TestNoBackwardAPI(unittest.TestCase):
@@ -4325,6 +4355,39 @@ def test_unique(self):
         self.assertEqual(res[2].shape, (1,))
         self.assertEqual(res[3].shape, (1,))
 
+    def test_matrix_rank(self):
+        x = paddle.eye(10)
+        x.stop_gradient = False
+        out = paddle.linalg.matrix_rank(x)
+
+        self.assertTrue(out.shape, [])
+        np.testing.assert_equal(out, np.array(10))
+
+        c = paddle.ones(shape=[3, 4, 5])
+        c.stop_gradient = False
+        out_c = paddle.linalg.matrix_rank(c)
+        self.assertTrue(out_c.shape, [3])
+        np.testing.assert_equal(out_c, np.array([1, 1, 1]))
+
+    def test_static_matrix_rank(self):
+        # 2D : OUTPUT 0D
+        x = paddle.eye(10)
+        x.stop_gradient = False
+        out = paddle.linalg.matrix_rank(x)
+        prog = paddle.static.default_main_program()
+        self.exe.run(paddle.static.default_startup_program())
+        res = self.exe.run(prog, fetch_list=[out])
+        self.assertTrue(res[0].shape, [])
+
+        # 3D : OUTPUT 1D
+        c = paddle.ones(shape=[3, 4, 5])
+        c.stop_gradient = False
+        out_c = paddle.linalg.matrix_rank(c)
+        prog = paddle.static.default_main_program()
+        self.exe.run(paddle.static.default_startup_program())
+        res = self.exe.run(prog, fetch_list=[out_c])
+        self.assertTrue(res[0].shape, [3])
+
 
 unary_apis_with_complex_input = [
     paddle.real,