diff --git a/paddle/phi/kernels/impl/matmul_grad_kernel_impl.h b/paddle/phi/kernels/impl/matmul_grad_kernel_impl.h
index 5b02a5e88a2829..a08f2fe7d95910 100644
--- a/paddle/phi/kernels/impl/matmul_grad_kernel_impl.h
+++ b/paddle/phi/kernels/impl/matmul_grad_kernel_impl.h
@@ -241,6 +241,9 @@ void MatmulGradKernel(const Context& dev_ctx,
         dev_ctx, phi::IntArray(common::vectorize(x.dims())), 0, dx);
     return;
   }
+  if (!transpose_x && transpose_y && y.dims().size() < 2) {
+    transpose_y = false;
+  }
   // get dims
   std::vector<std::int64_t> x_dims = common::vectorize(x.dims());
   std::vector<std::int64_t> y_dims = common::vectorize(y.dims());
diff --git a/paddle/phi/kernels/xpu/matmul_grad_kernel.cc b/paddle/phi/kernels/xpu/matmul_grad_kernel.cc
index 33fcab2308ec41..349a72277e7aa0 100644
--- a/paddle/phi/kernels/xpu/matmul_grad_kernel.cc
+++ b/paddle/phi/kernels/xpu/matmul_grad_kernel.cc
@@ -37,6 +37,9 @@ void MatmulGradKernel(const Context& dev_ctx,
     dev_ctx.template Alloc<T>(dy);
   }
 
+  if (!transpose_x && transpose_y && y.dims().size() < 2) {
+    transpose_y = false;
+  }
   const XPUType* dout_ptr = reinterpret_cast<const XPUType*>(dout.data<T>());
   const XPUType* x_ptr = reinterpret_cast<const XPUType*>(x.data<T>());
   const XPUType* y_ptr = reinterpret_cast<const XPUType*>(y.data<T>());
diff --git a/test/legacy_test/test_matmul_v2_op.py b/test/legacy_test/test_matmul_v2_op.py
index b3c25f7cd7397a..2f0d9425000461 100644
--- a/test/legacy_test/test_matmul_v2_op.py
+++ b/test/legacy_test/test_matmul_v2_op.py
@@ -978,6 +978,50 @@ def init_input_output(self):
         self.y = np.random.random((1, 3, 3, 2))
 
 
+class TestMatMulOp_trans_y(TestMatMulV2Op):
+    # y is 1-D and trans_y is True
+    def config(self):
+        self.x_shape = (2, 100)
+        self.y_shape = (100,)
+        self.trans_x = False
+        self.trans_y = True
+
+    def init_kernel_type(self):
+        self.dtype = "float32" if core.is_compiled_with_rocm() else "float64"
+
+    def setUp(self):
+        self.init_kernel_type()
+        self.config()
+        self.op_type = "matmul_v2"
+        self.python_api = paddle.tensor.matmul
+        self.public_python_api = paddle.tensor.matmul
+        x = np.random.random(self.x_shape).astype(self.dtype)
+        y = np.random.random(self.y_shape).astype(self.dtype)
+        # -0.1 ~ 0.1
+        x = -0.1 + 0.2 * x
+        y = -0.1 + 0.2 * y
+        result = reference_matmul(x, y, self.trans_x, self.trans_y)
+        result = result.astype(self.dtype)
+        self.inputs = {
+            'X': x,
+            'Y': y,
+        }
+        self.attrs = {'trans_x': self.trans_x, 'trans_y': self.trans_y}
+        self.outputs = {'Out': result}
+
+    def test_check_output(self):
+        self.check_output(
+            check_pir=True,
+        )
+
+    def test_check_grad(self):
+        self.check_grad(
+            ['X', 'Y'],
+            'Out',
+            check_pir=True,
+        )
+
+
 if __name__ == "__main__":
     paddle.enable_static()
     unittest.main()