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The following commit(s) were added to refs/heads/unity by this push:
new 4b59ee6e9d [Unity] FuseOps skipping PrimValues (#14687)
4b59ee6e9d is described below
commit 4b59ee6e9db8269c09096569b9c3ceb81a789ec5
Author: Ruihang Lai <[email protected]>
AuthorDate: Fri Apr 21 02:49:43 2023 -0400
[Unity] FuseOps skipping PrimValues (#14687)
Previously, FuseOps does not take PrimValue into account and will error
on PrimValues for a sanity check inside. This PR supports FuseOps with
PrimValues so that FuseOps will skip the PrimValues - no need to handle
them in a special way.
One regression test is added.
---
src/relax/transform/fuse_ops.cc | 4 +++-
tests/python/relax/test_transform_fuse_ops.py | 24 ++++++++++++++++++++++++
2 files changed, 27 insertions(+), 1 deletion(-)
diff --git a/src/relax/transform/fuse_ops.cc b/src/relax/transform/fuse_ops.cc
index c9c36bfcd8..f50a578954 100644
--- a/src/relax/transform/fuse_ops.cc
+++ b/src/relax/transform/fuse_ops.cc
@@ -253,7 +253,9 @@ class GraphCreator : public ExprVisitor {
IndexedForwardGraph::Node* leaf_node = nullptr;
if (it != graph_.node_map.end()) {
leaf_node = it->second;
- } else if (leaf_expr->IsInstance<ConstantNode>() ||
leaf_expr->IsInstance<ShapeExprNode>()) {
+ } else if (leaf_expr->IsInstance<ConstantNode>() ||
leaf_expr->IsInstance<ShapeExprNode>() ||
+ leaf_expr->IsInstance<PrimValueNode>() ||
leaf_expr->IsInstance<StringImmNode>() ||
+ leaf_expr->IsInstance<DataTypeImmNode>()) {
leaf_node = CreateNode(leaf_expr.get());
// Since we never fuse constants, the pattern of the constant is set to
`kOpaque`.
SetNodePattern(leaf_node, OpPatternKind::kOpaque);
diff --git a/tests/python/relax/test_transform_fuse_ops.py
b/tests/python/relax/test_transform_fuse_ops.py
index 285a78a30e..1a4af26bd8 100644
--- a/tests/python/relax/test_transform_fuse_ops.py
+++ b/tests/python/relax/test_transform_fuse_ops.py
@@ -1387,5 +1387,29 @@ def test_shape_expr_arg():
_check(Before, Expected)
+def test_skipping_primvalue():
+ @I.ir_module
+ class Module:
+ @R.function
+ def main(inp: R.Tensor((2, 2), dtype="float32")) -> R.Tensor((2, 2),
dtype="float32"):
+ with R.dataflow():
+ lv = R.call_packed(
+ "my_func1", inp, R.prim_value(0), sinfo_args=[R.Tensor((2,
2), dtype="float32")]
+ )
+ lv1 = R.call_packed(
+ "my_func2", lv, R.str("str"), sinfo_args=[R.Tensor((2, 2),
dtype="float32")]
+ )
+ gv = R.call_packed(
+ "my_func3",
+ lv1,
+ R.dtype("float32"),
+ sinfo_args=[R.Tensor((2, 2), dtype="float32")],
+ )
+ R.output(gv)
+ return gv
+
+ _check(Module, Module)
+
+
if __name__ == "__main__":
tvm.testing.main()