diff --git a/python/paddle/fluid/tests/unittests/test_einsum_0d_tensor.py b/python/paddle/fluid/tests/unittests/test_einsum_0d_tensor.py
new file mode 100644
index 0000000000000000000000000000000000000000..a401b1300bfd6164aa2f0cd6eec16bf15d500dcd
--- /dev/null
+++ b/python/paddle/fluid/tests/unittests/test_einsum_0d_tensor.py
@@ -0,0 +1,197 @@
+# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+import unittest
+
+import numpy as np
+from numpy.testing import assert_allclose
+
+import paddle
+
+os.environ['NVIDIA_TF32_OVERRIDE'] = "0"
+
+
+class Test0DCase0(unittest.TestCase):
+    def setUp(self):
+        paddle.disable_static()
+
+    def tearDown(self):
+        paddle.enable_static()
+
+    def test_func(self):
+        x = paddle.rand([])
+        x.stop_gradient = False
+        y = paddle.rand([])
+        y.stop_gradient = False
+        z = paddle.einsum("...,...->...", x, y)
+        assert_allclose(
+            z.numpy(),
+            np.einsum('...,...->...', x.numpy(), y.numpy()),
+            atol=1e-6,
+        )
+        z.mean().backward()
+        assert z.shape == []
+        assert x.grad.shape == []
+        assert y.grad.shape == []
+
+
+class Test0DCase1(Test0DCase0):
+    def test_func(self):
+        x = paddle.rand([])
+        x.stop_gradient = False
+        y = paddle.rand([2, 2])
+        y.stop_gradient = False
+        z = paddle.einsum("...,ij->...", x, y)
+        assert_allclose(
+            z.numpy(), np.einsum('...,ij->...', x.numpy(), y.numpy()), atol=1e-6
+        )
+        z.mean().backward()
+        assert z.shape == []
+        assert x.grad.shape == []
+        assert y.grad.shape == [2, 2]
+
+
+class Test0DCase2(Test0DCase0):
+    def test_func(self):
+        x = paddle.rand([2, 2])
+        x.stop_gradient = False
+        y = paddle.rand([2, 2])
+        y.stop_gradient = False
+        z = paddle.einsum("ij,ij->", x, y)
+        assert_allclose(
+            z.numpy(), np.einsum('ij,ij->', x.numpy(), y.numpy()), atol=1e-6
+        )
+        z.mean().backward()
+        assert z.shape == []
+        assert x.grad.shape == [2, 2]
+        assert y.grad.shape == [2, 2]
+
+
+class Test0DCase3(Test0DCase0):
+    def test_func(self):
+        x = paddle.rand([2, 2])
+        x.stop_gradient = True
+        y = paddle.rand([2, 2])
+        y.stop_gradient = False
+        z = paddle.einsum("ij,ij->", x, y)
+        assert_allclose(
+            z.numpy(), np.einsum('ij,ij->', x.numpy(), y.numpy()), atol=1e-6
+        )
+        z.mean().backward()
+        assert z.shape == []
+        assert x.grad is None
+        assert y.grad.shape == [2, 2]
+
+
+class Test0DCase4(Test0DCase0):
+    def test_func(self):
+        x = paddle.rand([])
+        x.stop_gradient = False
+        z = paddle.einsum("...->...", x)
+        assert_allclose(z.numpy(), np.einsum('...->...', x.numpy()), atol=1e-6)
+        z.mean().backward()
+        assert z.shape == []
+        assert x.grad.shape == []
+        assert x.grad.numpy() == 1.0
+
+
+class Test0DCase5(Test0DCase0):
+    def test_func(self):
+        x = paddle.rand([2, 2])
+        x.stop_gradient = False
+        y = paddle.rand([2, 2])
+        y.stop_gradient = False
+        z = paddle.einsum("i...j, i...j->...", x, y)
+        assert_allclose(
+            z.numpy(),
+            np.einsum('i...j, i...j->...', x.numpy(), y.numpy()),
+            atol=1e-6,
+        )
+        z.mean().backward()
+        assert z.shape == []
+        assert x.grad.shape == [2, 2]
+        assert y.grad.shape == [2, 2]
+
+
+class Test0DCase6(Test0DCase0):
+    def test_func(self):
+        x = paddle.rand([2, 2])
+        x.stop_gradient = False
+        z = paddle.einsum("ij->", x)
+        assert_allclose(z.numpy(), np.einsum('ij->', x.numpy()), atol=1e-6)
+        z.mean().backward()
+        assert z.shape == []
+        assert x.grad.shape == [2, 2]
+
+
+class Test0DCase7(Test0DCase0):
+    def test_func(self):
+        """
+        3 operands.
+        """
+        x = paddle.rand([2, 2])
+        y = paddle.rand([])
+        z = paddle.rand([])
+        x.stop_gradient = False
+        y.stop_gradient = False
+        z.stop_gradient = False
+        o = paddle.einsum("ij...,...,...->...", x, y, z)
+        assert_allclose(
+            o.numpy(),
+            np.einsum("ij...,...,...->...", x.numpy(), y.numpy(), z.numpy()),
+            atol=1e-6,
+        )
+        o.mean().backward()
+        assert o.shape == []
+        assert x.grad.shape == [2, 2]
+        assert y.grad.shape == []
+        assert z.grad.shape == []
+
+
+class Test0DCase8(Test0DCase0):
+    def test_func(self):
+        """
+        3 operands.
+        """
+        x = paddle.rand([2, 2])
+        y = paddle.rand([])
+        z = paddle.rand([])
+        e = paddle.rand([3, 1])
+        x.stop_gradient = False
+        y.stop_gradient = False
+        z.stop_gradient = False
+        e.stop_gradient = False
+        o = paddle.einsum("ij...,...,..., km->...", x, y, z, e)
+        assert_allclose(
+            o.numpy(),
+            np.einsum(
+                "ij...,...,...,km->...",
+                x.numpy(),
+                y.numpy(),
+                z.numpy(),
+                e.numpy(),
+            ),
+            atol=1e-6,
+        )
+        o.mean().backward()
+        assert o.shape == []
+        assert x.grad.shape == [2, 2]
+        assert y.grad.shape == []
+        assert z.grad.shape == []
+        assert e.grad.shape == [3, 1]
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/python/paddle/tensor/einsum.py b/python/paddle/tensor/einsum.py
index 4a69cd76304bbcb10ca9d59da0744f92b44dc380..b98a3094cafa6beac0a256184a9862967f7b229f 100644
--- a/python/paddle/tensor/einsum.py
+++ b/python/paddle/tensor/einsum.py
@@ -59,9 +59,7 @@ def parse_op_labels(labelstr, operand):
         labelstr.replace('...', '', 1).find('.') == -1
     ), "Invalid equation: `.` is found outside of an ellipsis."
 
-    # Check shape. Note, in Paddle a tensor rank is always nonzero
     ndims = len(operand.shape)
-    assert ndims > 0
 
     full_labelstr = labelstr.replace('...', '.' * (ndims - len(labelstr) + 3))
 
@@ -743,20 +741,25 @@ def parse_fake_shape(equation, operands, labels):
     list of shape
 
     """
+    origin_labels = map(lambda x: x.strip(), equation.split(','))
     shaped = collections.namedtuple('shaped', ['shape'])
 
-    def fake_shape(label, op):
+    def fake_shape(ori_label, label, op):
+        """
+        1. ori_label is the original labels, not aligned by '....'
+        2. if the '...' is evalulated to empty list, there is no '.' in label
+        """
         assert len(op.shape) == len(label), (
             "length of shape and length of label must be the same, but received %d != %d"
             % (len(op.shape), len(label))
         )
         fakes = [s for i, (l, s) in enumerate(zip(label, op.shape)) if l != '.']
         fakes = list(map(abs, fakes))  # make -1 -> 1
-        if '.' in label:
-            fakes.insert(label.index('.'), 1)
+        if '.' in ori_label:
+            fakes.insert(ori_label.index('.'), 1)
         return shaped(fakes)
 
-    out = list(map(fake_shape, labels, operands))
+    out = list(map(fake_shape, origin_labels, labels, operands))
     return out
 
 
@@ -782,7 +785,7 @@ def gen_equation_for_opteinsum(lhs, rhs):
             if c not in used:
                 return c
         raise ValueError(
-            "You have used all `a` - `z`, there can't find a unused for einsum optimization"
+            "You have used all `a` - `z`, there can't find a unused char for einsum optimization"
         )
 
     cnt = collections.Counter(lhs)