diff --git a/test/test_network_ops/test_sort_without_indices.py b/test/test_network_ops/test_sort_without_indices.py
new file mode 100644
index 0000000000000000000000000000000000000000..12f552940c798d3d1e1160e0d0cf303e5f5bb4de
--- /dev/null
+++ b/test/test_network_ops/test_sort_without_indices.py
@@ -0,0 +1,88 @@
+# Copyright (c) 2020 Huawei Technologies Co., Ltd
+# Copyright (c) 2019, Facebook CORPORATION. 
+# All rights reserved.
+#
+# Licensed under the BSD 3-Clause License  (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# https://opensource.org/licenses/BSD-3-Clause
+#
+# 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 torch
+import torch_npu
+import numpy as np
+from torch.nn import functional as F
+
+from torch_npu.testing.common_utils import TestCase, run_tests
+from torch_npu.testing.common_device_type import Dtypes, instantiate_device_type_tests
+from torch_npu.testing.util_test import create_common_tensor, test_2args_broadcast, create_dtype_tensor, UT_FAST_MODE
+
+class TestSortWithoutIndices(TestCase):
+    def cpu_default_op_exec(self, input1):
+        output, _ = torch.sort(input1)
+        output = output.to(torch.float16).numpy()
+        return output
+    
+    def npu_default_op_exec(self, input1):
+        output = torch_npu.npu_sort_v2(input1)
+        output = output.to("cpu")
+        output = output.numpy()
+        return output
+    
+    def cpu_op_exec(self, input1, descending):
+        output, _ = torch.sort(input1, descending=descending)
+        output = output.to(torch.float16).numpy()
+        return output
+    
+    def npu_op_exec(self, input1, descending):
+        output = torch_npu.npu_sort_v2(input1, descending=descending)
+        output = output.to("cpu")
+        output = output.numpy()
+        return output
+    
+    def test_sort_v2_shape_format(self, device):
+        shape_format = [
+                [[np.float16, 0, (1, 5000)]],
+                [[np.float16, 0, (1, 50000)]],
+                [[np.float16, 0, (1, 289600)], False],
+                [[np.float16, 0, (1, 409600)], True]
+        ]
+
+        for item in shape_format:
+            cpu_input1, npu_input1 = create_common_tensor(item[0], -100, 100)
+            if len(item) == 1:
+                cpu_output = self.cpu_default_op_exec(cpu_input1.to(torch.float))
+                npu_output = self.npu_default_op_exec(npu_input1)
+            else:
+                cpu_output = self.cpu_op_exec(cpu_input1.to(torch.float), item[1])
+                npu_output = self.npu_op_exec(npu_input1, item[1])
+            self.assertRtolEqual(cpu_output, npu_output)
+    
+    def test_sort_v2_shape_format_big_range(self, device):
+        shape_format = [
+                [[np.float16, 0, (1, 5000)]],
+                [[np.float16, 0, (1, 50000)]],
+                [[np.float16, 0, (1, 289600)], False],
+                [[np.float16, 0, (1, 409600)], True]
+        ]
+
+        for item in shape_format:
+            cpu_input1, npu_input1 = create_common_tensor(item[0], -60000, 60000)
+            if len(item) == 1:
+                cpu_output = self.cpu_default_op_exec(cpu_input1.to(torch.float))
+                npu_output = self.npu_default_op_exec(npu_input1)
+            else:
+                cpu_output = self.cpu_op_exec(cpu_input1.to(torch.float), item[1])
+                npu_output = self.npu_op_exec(npu_input1, item[1])
+            self.assertRtolEqual(cpu_output, npu_output)
+
+
+instantiate_device_type_tests(TestSortWithoutIndices, globals(), except_for="cpu")
+if __name__ == "__main__":
+    run_tests()
\ No newline at end of file
diff --git a/torch_npu/SortWithoutIndicesKernelNpu.cpp b/torch_npu/SortWithoutIndicesKernelNpu.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..3ee7d10cb6873128441946b38de2ff382fe20c58
--- /dev/null
+++ b/torch_npu/SortWithoutIndicesKernelNpu.cpp
@@ -0,0 +1,52 @@
+// Copyright (c) 2020 Huawei Technologies Co., Ltd
+// Copyright (c) 2019, Facebook CORPORATION.
+// All rights reserved.
+//
+// Licensed under the BSD 3-Clause License  (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+// https://opensource.org/licenses/BSD-3-Clause
+//
+// 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.
+
+#include "torch_npu/csrc/framework/utils/OpAdapter.h"
+#include "torch_npu/csrc/aten/NPUNativeFunctions.h"
+
+namespace at_npu {
+namespace native {
+at::Tensor& NPUNativeFunctions::npu_sort_v2_out(
+    const at::Tensor& self,
+    int64_t dim,
+    bool descending,
+    at::Tensor& result) {
+  OpCommand cmd;
+  cmd.Name("SortV2")
+      .Input(self)
+      .Output(result)
+      .Attr("axis", dim)
+      .Attr("descending", descending)
+      .Run();
+  
+  return result;
+}
+
+at::Tensor NPUNativeFunctions::npu_sort_v2(
+    const at::Tensor& self,
+    int64_t dim,
+    bool descending) {
+  auto outputSize = input_same_output_size(self);
+
+  at::Tensor result = OpPreparation::ApplyTensor(self);
+  
+  npu_sort_v2_out(self, dim, descending, result);
+  
+  return result;
+}
+
+} // namespace native
+} // namespace at_npu
\ No newline at end of file
diff --git a/torch_npu/csrc/aten/npu_native_functions.yaml b/torch_npu/csrc/aten/npu_native_functions.yaml
index c2624df5c34a5feb2e077fe8689c5c5959bcb26e..c95089e0be685fff0e17894c7202e052cec7fb54 100644
--- a/torch_npu/csrc/aten/npu_native_functions.yaml
+++ b/torch_npu/csrc/aten/npu_native_functions.yaml
@@ -1914,6 +1914,9 @@ custom:
     variants: function, method
   - func: npu_softmax_cross_entropy_with_logits_backward(Tensor grad, Tensor self, Tensor labels) -> Tensor
     variants: function, method
+  - func: npu_sort_v2.out(Tensor self, int dim=-1, bool descending=False, *, Tensor(a!) out) -> Tensor(a!)
+  - func: npu_sort_v2(Tensor self, int dim=-1, bool descending=False) -> Tensor
+
 custom_autograd:
   - func: npu_convolution(Tensor input, Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] dilation, int groups) -> Tensor
-  - func: npu_convolution_transpose(Tensor input, Tensor weight, Tensor? bias, int[] padding, int[] output_padding, int[] stride, int[] dilation, int groups) -> Tensor
+  - func: npu_convolution_transpose(Tensor input, Tensor weight, Tensor? bias, int[] padding, int[] output_padding, int[] stride, int[] dilation, int groups) -> Tensor
\ No newline at end of file