[Zero-Dim]Paddle.t support 0d tensor (#49880)

* support paddle.t 0d tensor

* fix paddle.t test case

* merge from develop

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

@@ -1345,6 +1345,17 @@ class TestSundryAPI(unittest.TestCase):
         self.assertEqual(x.grad.shape, [])
         self.assertEqual(x.grad.numpy(), 1)
 
+    def test_t(self):
+        x = paddle.full([], 2.0)
+        x.stop_gradient = False
+        x.retain_grads()
+        out = paddle.t(x)
+        out.retain_grads()
+        out.backward()
+        self.assertEqual(out.shape, [])
+        self.assertEqual(out.grad.shape, [])
+        self.assertEqual(x.grad.shape, [])
+
 
 class TestSundryAPIStatic(unittest.TestCase):
     def setUp(self):
@@ -2080,6 +2091,21 @@ class TestSundryAPIStatic(unittest.TestCase):
         self.assertEqual(res[3].shape, ())
         self.assertEqual(res[3], 1)
 
+    @prog_scope()
+    def test_t(self):
+        x = paddle.full([], 2.0)
+        x.stop_gradient = False
+        out = paddle.t(x)
+        paddle.static.append_backward(out.sum())
+        prog = paddle.static.default_main_program()
+        res = self.exe.run(
+            prog, feed={}, fetch_list=[out, out.grad_name, x.grad_name]
+        )
+
+        self.assertEqual(res[0].shape, ())
+        self.assertEqual(res[1].shape, ())
+        self.assertEqual(res[2].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):

python/paddle/tensor/linalg.py

@@ -1296,7 +1296,7 @@ def t(input, name=None):
             "tensor.transpose() instead." % len(input.shape)
         )
     if in_dygraph_mode():
-        if len(input.shape) == 1:
+        if len(input.shape) <= 1:
             return input
         # 2-D tensor
         perm = [1, 0]
@@ -1313,7 +1313,7 @@ def t(input, name=None):
         helper = LayerHelper('t', **locals())
         out = helper.create_variable_for_type_inference(input.dtype)
         input_shape = helper.create_variable_for_type_inference(input.dtype)
-        if len(input.shape) == 1:
+        if len(input.shape) <= 1:
             out = input
         else:
             helper.append_op(