diff --git a/paddle/phi/kernels/xpu/top_k_kernel.cc b/paddle/phi/kernels/xpu/top_k_kernel.cc
index 1ed20b0ddf22d655e015c48d6858ef8005ac69d5..fca852a086d506d7092a1202722bc5c2c0613854 100644
--- a/paddle/phi/kernels/xpu/top_k_kernel.cc
+++ b/paddle/phi/kernels/xpu/top_k_kernel.cc
@@ -43,12 +43,6 @@ void TopkKernel(const Context& dev_ctx,
       errors::External(
           "XPU API does not support unsorted topk operation currently."
           " Operator will be supported in future update."));
-  PADDLE_ENFORCE_EQ(
-      largest,
-      true,
-      errors::External(
-          "XPU API does not support smallest topk operation currently."
-          " Operator will be supported in future update."));
   if (in_dims.size() == 0) {
     int r = xpu::copy<XPUType>(dev_ctx.x_context(),
                                reinterpret_cast<const XPUType*>(x.data<T>()),
@@ -77,7 +71,8 @@ void TopkKernel(const Context& dev_ctx,
                                       indices_int_data,
                                       row,
                                       col,
-                                      k);
+                                      k,
+                                      largest);
     PADDLE_ENFORCE_XDNN_SUCCESS(r, "sorted_topk");
 
     r = xpu::cast<int32_t, int64_t>(dev_ctx.x_context(),
@@ -140,7 +135,8 @@ void TopkKernel(const Context& dev_ctx,
         trans_idx_int32_data,
         row,
         col,
-        k);
+        k,
+        largest);
     PADDLE_ENFORCE_XDNN_SUCCESS(r, "sorted_topk");
 
     r = xpu::cast<int32_t, int64_t>(dev_ctx.x_context(),
diff --git a/python/paddle/fluid/tests/unittests/xpu/test_top_k_v2_op_xpu.py b/python/paddle/fluid/tests/unittests/xpu/test_top_k_v2_op_xpu.py
index 3ddfc115ae7f18a5d4b02511d7bb2e93757e28bd..eaad7001928faf2919979a1996f51fe62a939e55 100644
--- a/python/paddle/fluid/tests/unittests/xpu/test_top_k_v2_op_xpu.py
+++ b/python/paddle/fluid/tests/unittests/xpu/test_top_k_v2_op_xpu.py
@@ -193,6 +193,102 @@ class XPUTestTopKV2Op(XPUOpTestWrapper):
             self.largest = True
             self.input_data_shape = (10, 10, 5)
 
+    class TestTopkSmallestOp1(TestTopkOp):
+        def init_args(self):
+            self.k = 3
+            self.axis = 1
+            self.largest = False
+            # too many values for fp16 will lead to failure in random_unique_float function
+            if self.dtype == np.float16:
+                self.input_data_shape = (100, 55)
+            else:
+                self.input_data_shape = (100, 155)
+
+    class TestTopkSmallestOp2(TestTopkOp):
+        def init_args(self):
+            self.k = 3
+            self.axis = 1
+            self.largest = False
+            self.input_data_shape = (10, 10, 5)
+
+    class TestTopkSmallestOp3(TestTopkOp):
+        def init_args(self):
+            self.k = 5
+            self.axis = 1
+            self.largest = False
+            self.input_data_shape = (10, 10, 5)
+
+    class TestTopkSmallestOp4(TestTopkOp):
+        def init_args(self):
+            self.k = 1
+            self.axis = 1
+            self.largest = False
+            self.input_data_shape = (10, 10, 5)
+
+    class TestTopkSmallestOp5(TestTopkOp):
+        def init_args(self):
+            self.k = 3
+            self.axis = 2
+            self.largest = False
+            self.input_data_shape = (10, 10, 5)
+
+    class TestTopkSmallestOp6(TestTopkOp):
+        def init_args(self):
+            self.k = 5
+            self.axis = 1
+            self.largest = False
+            # too many values for fp16 will lead to failure in random_unique_float function
+            if self.dtype == np.float16:
+                self.input_data_shape = (8, 32, 32)
+            else:
+                self.input_data_shape = (8, 32, 64)
+
+    class TestTopkSmallestOp7(TestTopkOp):
+        def init_args(self):
+            self.k = 10
+            self.axis = 2
+            self.largest = False
+            self.input_data_shape = (8, 5, 10, 16)
+
+    class TestTopkSmallestOp8(TestTopkOp):
+        def init_args(self):
+            self.k = 1
+            self.axis = 1
+            self.largest = False
+            # too many values for fp16 will lead to failure in random_unique_float function
+            if self.dtype == np.float16:
+                self.input_data_shape = (8, 32, 32)
+            else:
+                self.input_data_shape = (8, 32, 64)
+
+    class TestTopkSmallestOp9(TestTopkOp):
+        def init_args(self):
+            self.k = 3
+            self.axis = 1
+            self.largest = False
+            self.input_data_shape = (10, 10, 5)
+
+    class TestTopkSmallestOp10(TestTopkOp):
+        def init_args(self):
+            self.k = 3
+            self.axis = 1
+            self.largest = False
+            self.input_data_shape = (10, 10, 5)
+
+    class TestTopkSmallestOp11(TestTopkOp):
+        def init_args(self):
+            self.k = 5
+            self.axis = 1
+            self.largest = False
+            self.input_data_shape = (10, 10, 5)
+
+    class TestTopkSmallestOp12(TestTopkOp):
+        def init_args(self):
+            self.k = 1
+            self.axis = 1
+            self.largest = False
+            self.input_data_shape = (10, 10, 5)
+
 
 support_types = get_xpu_op_support_types('top_k_v2')
 for stype in support_types: