diff --git a/paddle/phi/infermeta/unary.cc b/paddle/phi/infermeta/unary.cc
index 8a3c33a4d6c72..3b3202c291725 100644
--- a/paddle/phi/infermeta/unary.cc
+++ b/paddle/phi/infermeta/unary.cc
@@ -4648,6 +4648,7 @@ void UniqueRawInferMeta(const MetaTensor& x,
     if (axis_value < 0) {
       axis_value += x.dims().size();
     }
+
     PADDLE_ENFORCE_LT(
         axis_value,
         x.dims().size(),
@@ -4655,6 +4656,14 @@ void UniqueRawInferMeta(const MetaTensor& x,
                                      "the dimension size(%d) of x.",
                                      axis_value,
                                      x.dims().size()));
+    PADDLE_ENFORCE_GE(
+        axis_value,
+        0,
+        phi::errors::InvalidArgument(
+            "The axis(%d) + rank(x) (%d) should be greater than or equal to 0.",
+            axis_value,
+            -x.dims().size()));
+
     auto out_dims = x.dims();
     out_dims[axis_value] = -1;
     out->set_dims(out_dims);
diff --git a/python/paddle/fluid/tests/unittests/test_unique.py b/python/paddle/fluid/tests/unittests/test_unique.py
index 9183c1bd676bb..b3ae10a6c335e 100644
--- a/python/paddle/fluid/tests/unittests/test_unique.py
+++ b/python/paddle/fluid/tests/unittests/test_unique.py
@@ -190,6 +190,32 @@ def init_config(self):
         }
 
 
+class TestUniqueOpAxisNeg(TestUniqueOp):
+    def init_config(self):
+        self.inputs = {'X': np.random.random((6, 1, 8)).astype('float64')}
+        unique, indices, inverse, counts = np.unique(
+            self.inputs['X'],
+            return_index=True,
+            return_inverse=True,
+            return_counts=True,
+            axis=-1,
+        )
+        self.attrs = {
+            'dtype': int(core.VarDesc.VarType.INT32),
+            "return_index": True,
+            "return_inverse": True,
+            "return_counts": True,
+            "axis": [-1],
+            "is_sorted": True,
+        }
+        self.outputs = {
+            'Out': unique,
+            'Indices': indices,
+            "Index": inverse,
+            "Counts": counts,
+        }
+
+
 class TestUniqueOpAxis1(TestUniqueOp):
     def init_config(self):
         self.inputs = {'X': np.random.random((3, 8, 8)).astype('float64')}