comaniac commented on a change in pull request #6655:
URL: https://github.com/apache/incubator-tvm/pull/6655#discussion_r504078734
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File path: tests/python/relay/test_pass_annotate_target.py
##########
@@ -327,6 +327,42 @@ def after():
assert tvm.ir.structural_equal(expected, result)
+def test_tuple_two_targets():
+ """Tests whether the TupleNode is promoted to previously annotatated
operation or is excluded."""
+ target_relu = "relu_target"
+ target_maximum = "maximum_target"
+ target_default = "default"
+
+ @tvm.ir.register_op_attr("nn.relu", "target." + target_relu)
+ def relu(attrs, args): # pylint: disable=unused-variable
+ return True
+
+ @tvm.ir.register_op_attr("maximum", "target." + target_maximum)
+ def maximum(attrs, args): # pylint: disable=unused-variable
+ return True
+
+ def before():
+ a = relay.var("a", shape=(10, 5))
+ b = relay.var("b", shape=(10, 5))
+ r = relay.nn.relu(b)
+ t1 = relay.Tuple((r, r))
+ r2 = relay.nn.relu(t1)
+ m = relay.maximum(a, b)
+ t2 = relay.Tuple((m, r2))
+ f = relay.Function([a, b], t2)
+ return tvm.IRModule.from_expr(f)
+
+ for default_tuples, parts in [(True, 3), (False, 2)]:
+ result = before()
+ result = transform.AnnotateTarget([target_relu],
default_tuples)(result)
+ result = transform.AnnotateTarget([target_maximum], True)(result)
Review comment:
List of targets serves for the different purpose. List of target allows
us to have a whole picture during the annotation. We can determine which target
an op should be annotated by considering the region size, for example. As a
result, I personally prefer to have a list of targets instead of multiple runs
if we could. However, given the scenario you provided, which two targets
require different graphs, it seems inevitable.
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