Fixes for IR optimizer (#1865)

A few fixes needed to make the IR based optimizer work for the HF
benchmark.

Removed the changes relating to SymbolicTensor hash issue, and what's
remaining are pure bug fixes.

onnxscript/optimizer/_constant_folding.py

@@ -348,7 +348,7 @@ def sequence_construct(node: ir.Node, op, state: OptimizerState) -> ReturnValue:
 def concat_from_sequence(node: ir.Node, op, state: OptimizerState) -> ReturnValue:
     input = node.inputs[0]
     inputs = state.get_sym_value(input)
-    if any(x is None for x in inputs):
+    if inputs is None or any(x is None for x in inputs):
         return None
     new_axis = _get_int_attribute(node, "new_axis", 0)
     axis = _get_int_attribute(node, "axis", None)

onnxscript/optimizer/_inliner.py

@@ -62,7 +62,7 @@ def clone_value(self, value: ir.Value) -> ir.Value | None:
         if value in self._value_map:
             return self._value_map[value]
         # If the value is not in the value map, it must be a graph input.
-        assert value.producer() is not None, f"Value {value} has no entry in the value map"
+        assert value.producer() is None, f"Value {value} has no entry in the value map"
         new_value = ir.Value(
             name=value.name,
             type=value.type,
@@ -90,8 +90,17 @@ def clone_attr(self, key: str, attr: ir.Attr | ir.RefAttr) -> ir.Attr | ir.RefAt
                 )
             return attr
         assert isinstance(attr, ir.RefAttr)
-        if key in self._attr_map:
-            return self._attr_map[key]
+        ref_attr_name = attr.ref_attr_name
+        if ref_attr_name in self._attr_map:
+            ref_attr = self._attr_map[ref_attr_name]
+            if isinstance(ref_attr, ir.Attr):
+                return ir.Attr(
+                    key, ref_attr.type, ref_attr.value, doc_string=ref_attr.doc_string
+                )
+            assert isinstance(ref_attr, ir.RefAttr)
+            return ir.RefAttr(
+                key, ref_attr.ref_attr_name, ref_attr.type, doc_string=ref_attr.doc_string
+            )
         # Note that if a function has an attribute-parameter X, and a call (node) to the function
         # has no attribute X, all references to X in nodes inside the function body will be
         # removed. This is just the ONNX representation of optional-attributes.
@@ -142,10 +151,13 @@ def clone_graph(self, graph: ir.Graph) -> ir.Graph:
         input_values = [self.clone_value(v) for v in graph.inputs]
         nodes = [self.clone_node(node) for node in graph]
         initializers = [self.clone_value(init) for init in graph.initializers.values()]
+        output_values = [
+            self.clone_value(v) for v in graph.outputs
+        ]  # Looks up already cloned values
 
         return ir.Graph(
             input_values,  # type: ignore
-            graph.outputs,
+            output_values,  # type: ignore
             nodes=nodes,
             initializers=initializers,  # type: ignore
             doc_string=graph.doc_string,