Add tensorflow unique op

python/tvm/relay/frontend/tensorflow.py

@@ -2295,6 +2295,18 @@ def _impl(inputs, attr, params, mod):
     return _impl
 
 
+def _unique():
+    def _impl(inputs, attr, params, mod):
+        assert len(inputs) == 1
+        x = inputs[0]
+        [output, indices, counts, num_uniq] = _op.unique(x)
+        output_sliced = _op.strided_slice(
+            output, begin=[0], end=num_uniq, slice_mode="size"
+        )
+        return [output_sliced, indices]
+
+    return _impl
+
 # compatible operators that do NOT require any conversion.
 _identity_list = []
 
@@ -2471,6 +2483,7 @@ def _impl(inputs, attr, params, mod):
     "TopKV2": _topk(),
     "Transpose": _transpose(),
     "TruncateMod": _elemwise("mod"),
+    "Unique": _unique(),
     "Unpack": _unpack(),
     "UnravelIndex": _unravel_index(),
     "Where": _where(),

tests/python/frontend/tensorflow/test_forward.py

@@ -4686,5 +4686,39 @@ def lstm_cell():
             tvm.testing.assert_allclose(tf_output[i], tvm_output[i], atol=1e-5, rtol=1e-5)
 
 
+#######################################################################
+# Unique
+# ------------
+def _test_unique(
+    n,
+    dtype,
+    is_dyn
+):
+    """ One iteration of a Stridedslice """
+
+    tf.reset_default_graph()
+    np_data = np.random.randint(100, size=n).astype(dtype)
+    with tf.Graph().as_default():
+        if is_dyn:
+            in_data = tf.placeholder(dtype, [n], name="in_data")
+        else:
+            in_data = tf.constant(np_data, dtype, name="in_data")
+        tf.unique(in_data)
+        if is_dyn:
+            compare_tf_with_tvm(np_data, "in_data:0", [
+                                "Unique:0", "Unique:1"], mode="vm")
+        else:
+            compare_tf_with_tvm(None, "", ["Unique:0", "Unique:1"])
+
+
+def test_forward_unique():
+    """test Unique"""
+
+    for dtype in ["int32", "int64"]:
+        for is_dyn in [False, True]:
+            _test_unique(50, dtype, is_dyn)
+            _test_unique(100, dtype, is_dyn)
+
+
 if __name__ == "__main__":
     pytest.main([__file__])

tests/python/relay/test_op_level6.py

@@ -151,15 +151,15 @@ def calc_unique(data):
         num_uniq = np.array([len(uniq)]).astype("int32")
         return uniq, inverse, counts, num_uniq
 
-    def verify_unique(len, dtype, is_dyn=False):
+    def verify_unique(n, dtype, is_dyn=False):
         if is_dyn:
             x = relay.var("x", relay.TensorType([relay.Any()], dtype))
         else:
-            x = relay.var("x", relay.TensorType([len], dtype))
+            x = relay.var("x", relay.TensorType([n], dtype))
         outs = relay.unique(x)
         outs = outs.astuple()
         func = relay.Function([x], outs)
-        x_data = np.random.randint(100, size=len).astype(dtype)
+        x_data = np.random.randint(100, size=n).astype(dtype)
 
         if is_dyn:
             backends = ["vm", "debug"]