[Relay] Change Default "opt_level" of Sequantial from 2 to 0 (#8634)

python/tvm/ir/transform.py

@@ -199,7 +199,7 @@ class Sequential(Pass):
         The list of passes that the sequential pass is dependent on.
     """
 
-    def __init__(self, passes=None, opt_level=2, name="sequential", required=None):
+    def __init__(self, passes=None, opt_level=0, name="sequential", required=None):
         passes = passes if passes else []
         if not isinstance(passes, (list, tuple)):
             raise TypeError("passes must be a list of Pass objects.")

src/ir/transform.cc

@@ -435,7 +435,7 @@ Sequential::Sequential(tvm::Array<Pass> passes, PassInfo pass_info) {
 Sequential::Sequential(tvm::Array<Pass> passes, String name) {
   auto n = make_object<SequentialNode>();
   n->passes = std::move(passes);
-  PassInfo pass_info = PassInfo(2, std::move(name), {});
+  PassInfo pass_info = PassInfo(0, std::move(name), {});
   n->pass_info = std::move(pass_info);
   data_ = std::move(n);
 }

tests/python/relay/test_pass_manager.py

@@ -507,6 +507,34 @@ def expected():
     assert tvm.ir.structural_equal(zz, zexpected)
 
 
+def test_nested_sequential_with_scoping():
+    def before():
+        x = relay.var("x", shape=(1, 16, 16, 16), dtype="float32")
+        w = relay.var("w", shape=(32, 16, 3, 3), dtype="float32")
+        y = relay.nn.conv2d(x, w, padding=(1, 1))
+        y = relay.reshape(y, newshape=(1, 16, -1))
+        y = relay.reshape(y, newshape=(4, 8, -1, 16))
+        y = relay.reverse_reshape(y, newshape=(32, 0, -1))
+        return tvm.IRModule.from_expr(y)
+
+    def expected():
+        x = relay.var("x", shape=(1, 16, 16, 16), dtype="float32")
+        w = relay.var("w", shape=(32, 16, 3, 3), dtype="float32")
+        y = relay.nn.conv2d(x, w, padding=(1, 1))
+        y = relay.reshape(y, newshape=(32, 16, 16))
+        return tvm.IRModule.from_expr(y)
+
+    z = before()
+    passes = [
+        tvm.transform.Sequential([relay.transform.SimplifyExpr()]),
+    ]
+    with tvm.transform.PassContext(opt_level=1):
+        zz = tvm.transform.Sequential(passes)(z)
+
+    expected = relay.transform.InferType()(expected())
+    assert tvm.ir.structural_equal(zz, expected)
+
+
 def test_print_ir(capfd):
     shape = (1, 2, 3)
     tp = relay.TensorType(shape, "float32")