Add ONNX torch_topk pnnx regression test

Author: vlordier

tools/pnnx/tests/onnx/CMakeLists.txt

@@ -191,6 +191,7 @@ pnnx_onnx_add_test(torch_split)
 pnnx_onnx_add_test(torch_squeeze)
 pnnx_onnx_add_test(torch_stack)
 pnnx_onnx_add_test(torch_sum)
+pnnx_onnx_add_test(torch_topk)
 pnnx_onnx_add_test(torch_transpose)
 pnnx_onnx_add_test(torch_unbind)
 pnnx_onnx_add_test(torch_unsqueeze)

tools/pnnx/tests/onnx/test_torch_topk.py

@@ -0,0 +1,61 @@
+# Copyright 2026 Tencent
+# SPDX-License-Identifier: BSD-3-Clause
+
+import torch
+import torch.nn as nn
+
+
+class Model(nn.Module):
+    def __init__(self):
+        super(Model, self).__init__()
+
+    def forward(self, x, y, z):
+        x_values, x_indices = torch.topk(
+            x, 2, dim=1, largest=True, sorted=True
+        )
+        y_values, y_indices = torch.topk(
+            y, 4, dim=3, largest=False, sorted=True
+        )
+        z_values, z_indices = torch.topk(
+            z, 3, dim=0, largest=True, sorted=True
+        )
+        return x_values, x_indices, y_values, y_indices, z_values, z_indices
+
+
+def test():
+    net = Model()
+    net.eval()
+
+    torch.manual_seed(0)
+    x = torch.rand(1, 3, 16)
+    y = torch.rand(1, 5, 9, 11)
+    z = torch.rand(14, 8, 5, 9, 10)
+
+    a = net(x, y, z)
+
+    # export onnx
+    torch.onnx.export(net, (x, y, z), "test_torch_topk.onnx")
+
+    # onnx to pnnx
+    import os
+
+    os.system(
+        "../../src/pnnx test_torch_topk.onnx "
+        "inputshape=[1,3,16],[1,5,9,11],[14,8,5,9,10]"
+    )
+
+    # pnnx inference
+    import test_torch_topk_pnnx
+    b = test_torch_topk_pnnx.test_inference()
+
+    for a0, b0 in zip(a, b):
+        if not torch.equal(a0, b0):
+            return False
+    return True
+
+
+if __name__ == "__main__":
+    if test():
+        exit(0)
+    else:
+        exit(1)