GraphTuner supports relay.module as input (#3434)

python/tvm/autotvm/graph_tuner/base_graph_tuner.py

@@ -141,6 +141,9 @@ class BaseGraphTuner(object):
             self._logger.propagate = False
 
         # Generate workload and schedule dictionaries.
+        if isinstance(graph, relay.Module):
+            graph = graph[graph.entry_func]
+
         if isinstance(graph, relay.expr.Function):
             node_dict = {}
             graph = bind_inputs(graph, input_shapes, dtype)

tests/python/unittest/test_graph_tuner_core.py

@@ -159,6 +159,8 @@ def test_DPTuner_run():
     target_ops = [relay.nn.conv2d]
 
     g, records, ltf_records, ltf_keys, tasks = _create_data(target, dshape, dtype, layout)
+    mod = relay.module.Module()
+    mod[mod.entry_func] = g
     costs = [0.02, 0.02, 0.045]
     config_list = []
     cfg_dict = {"i": -1,
@@ -190,7 +192,7 @@ def test_DPTuner_run():
         ms_output = MeasureResult(costs=(cost,), error_no=0, all_cost=-1, timestamp=-1)
         records.append((ms_input, ms_output))
 
-    executor = DPTuner(g, {"data": dshape}, records, target_ops, target, log_file=log_file)
+    executor = DPTuner(mod, {"data": dshape}, records, target_ops, target, log_file=log_file)
     executor.benchmark_layout_transform(layout_records=ltf_records, infer_layout=True)
     executor.run()
     out = [record[0].config for record in executor.get_optimal_records()]