Add TopK to ONNX Frontend

python/tvm/relay/frontend/onnx.py

@@ -1470,6 +1470,22 @@ def _impl_v9(cls, inputs, attr, params):
         output = AttrCvt(op_name='argwhere')(inputs, attr, params)
         return _op.transpose(output, axes=(1, 0))
 
+class TopK(OnnxOpConverter):
+    """Operator converter for TopK
+    """
+    @classmethod
+    def _impl_v1(cls, inputs, attr, params):
+        if len(inputs) != 2:
+            raise ValueError("Expect 2 input only")
+        axis = attr.get("axis", -1)
+        largest = attr.get("largest", 1)
+
+        if largest == 0:
+            raise ValueError("TVM only supports finding TopK largest elements")
+
+        K = int(infer_value(inputs[1], params).asnumpy()[0])
+
+        return _op.topk(inputs[0], k=K, axis=axis)
 
 # compatible operators that do NOT require any conversion.
 _identity_list = []
@@ -1573,8 +1589,11 @@ def _get_convert_map(opset):
         'ReduceProd': ReduceProd.get_converter(opset),
         # 'ReduceProd'
         # 'ReduceLogSumExp'
+
+        #defs/sorting
         'ArgMax': ArgMax.get_converter(opset),
         'ArgMin': ArgMin.get_converter(opset),
+        'TopK': TopK.get_converter(opset),
 
         # defs/tensor
         'Cast': Cast.get_converter(opset),

tests/python/frontend/onnx/test_forward.py

@@ -2330,6 +2330,43 @@ def verify_nonzero(indata, outdata, dtype):
     result = np.array((np.nonzero(input_data)))  # expected output [[0, 1, 2, 2], [0, 1, 0, 1]]
     verify_nonzero(input_data, result, dtype=np.int64)
 
+def test_topk():
+    def verify_topk(input_dims, K, axis=-1):
+        output_dims = list(input_dims)
+        output_dims[axis] = K
+
+        node = helper.make_node('TopK',
+                                inputs=['X', 'K'],
+                                outputs=['Values', 'Indicies'],
+                                axis=axis)
+
+        graph = helper.make_graph([node],
+                                  "topk_test",
+                                  inputs=[helper.make_tensor_value_info("X", TensorProto.FLOAT, list(input_dims)),
+                                          helper.make_tensor_value_info("K", TensorProto.INT64, [1,])],
+                                  initializer=[helper.make_tensor("K", TensorProto.INT64, [1], [K])],
+                                  outputs=[helper.make_tensor_value_info("Values", TensorProto.FLOAT, output_dims), 
+                                           helper.make_tensor_value_info("Indicies", TensorProto.INT64, output_dims)])
+
+        model = helper.make_model(graph, producer_name='topk_test')
+
+        indata = np.random.uniform(-10, 10, input_dims).astype(np.float32)
+        onnx_out = get_onnxruntime_output(model, [indata, k])
+
+        for target, ctx in [('llvm', tvm.cpu())]:
+            tvm_out = get_tvm_output(model, indata, target, ctx, [output_dims, output_dims], 
+                    output_dtype=['float32', 'int64'])
+            tvm.testing.assert_allclose(onnx_out, tvm_out, rtol=1e-05, atol=1e-05)
+
+    for n in [12, 32]:
+        for shape in [[n], [n, n], [n, n, n]]:
+            for k in [1, 5, 10]:
+                verify_topk(shape, k)
+
+        verify_topk([n, n, n], 5, 0)
+        verify_topk([n, n, n], 5, 1)
+        verify_topk([n, n, n], 5, 2)
+
 
 if __name__ == '__main__':
     test_flatten()
@@ -2392,3 +2429,4 @@ def verify_nonzero(indata, outdata, dtype):
     test_lstm()
     test_resize()
     test_nonzero()
+    test_topk()