IR-based inliner (#1829)

Add utility to inline all functions in a model.

Still TODO:

* Some edge cases to be considered for renaming and avoiding conflicts,
especially with subgraphs.
* Must ensure no variable capture happens (part of above renaming).
* Test renaming of node names.

Fixes #1769

---------

Signed-off-by: Ganesan Ramalingam <[email protected]>

onnxscript/optimizer/_inliner.py

@@ -0,0 +1,298 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+"""Implementation of an inliner for onnxscript.ir"""
+
+from __future__ import annotations
+
+from collections import defaultdict
+from typing import Iterable, Sequence, Tuple
+
+import onnxscript.ir as ir
+import onnxscript.ir.convenience as ir_convenience
+
+# A replacement for a node specifies a list of nodes that replaces the original node,
+# and a list of values that replaces the original node's outputs.
+
+NodeReplacement = Tuple[Sequence[ir.Node], Sequence[ir.Value]]
+
+# A call stack is a list of identifiers of call sites, where the first element is the
+# outermost call site, and the last element is the innermost call site. This is used
+# primarily for generating unique names for values in the inlined functions.
+CallSiteId = str
+CallStack = list[CallSiteId]
+
+
+def _make_unique_name(name: str, callstack: CallStack, used_names: set[str]) -> str:
+    """Generate a unique name from a name, calling-context, and set of used names.
+
+    When a value X in a function is inlined into a graph, we rename X by adding a prefix
+    representing the call-stack of the function. This should typically avoid name clashes.
+    If there is a name clash, even after this, we add a numeric suffix to the name to make
+    it unique. We use the same strategy to make node names unique.
+    """
+    prefix = "_".join(callstack)
+    name = prefix + "_" + name
+    candidate = name
+    i = 1
+    while candidate in used_names:
+        i += 1
+        candidate = f"{name}_{i}"
+    used_names.add(candidate)
+    return candidate
+
+
+class _CopyReplace:
+    """Utilities for creating a copy of IR objects with substitutions for attributes/input values."""
+
+    def __init__(
+        self,
+        inliner: _Inliner,
+        attr_map: dict[str, ir.Attr | ir.RefAttr],
+        value_map: dict[ir.Value, ir.Value | None],
+        metadata_props: dict[str, str],
+        call_stack: CallStack,
+    ) -> None:
+        self._inliner = inliner
+        self._value_map = value_map
+        self._attr_map = attr_map
+        self._metadata_props = metadata_props
+        self._call_stack = call_stack
+
+    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"
+        new_value = ir.Value(
+            name=value.name,
+            type=value.type,
+            shape=value.shape,
+            doc_string=value.doc_string,
+            const_value=value.const_value,
+        )
+        self._value_map[value] = new_value
+        return new_value
+
+    def clone_optional_value(self, value: ir.Value | None) -> ir.Value | None:
+        if value is None:
+            return None
+        return self.clone_value(value)
+
+    def clone_attr(self, key: str, attr: ir.Attr | ir.RefAttr) -> ir.Attr | ir.RefAttr | None:
+        if isinstance(attr, ir.Attr):
+            if attr.type == ir.AttributeType.GRAPH:
+                graph = self.clone_graph(attr.value)
+                return ir.Attr(key, ir.AttributeType.GRAPH, graph, doc_string=attr.doc_string)
+            elif attr.type == ir.AttributeType.GRAPHS:
+                graphs = [self.clone_graph(graph) for graph in attr.value]
+                return ir.Attr(
+                    key, ir.AttributeType.GRAPHS, graphs, doc_string=attr.doc_string
+                )
+            return attr
+        assert isinstance(attr, ir.RefAttr)
+        if key in self._attr_map:
+            return self._attr_map[key]
+        # 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.
+        return None
+
+    def clone_node(self, node: ir.Node) -> ir.Node:
+        new_inputs = [self.clone_optional_value(input) for input in node.inputs]
+        new_attributes = [
+            new_value
+            for key, value in node.attributes.items()
+            if (new_value := self.clone_attr(key, value)) is not None
+        ]
+        new_name = node.name
+        if new_name is not None:
+            new_name = _make_unique_name(
+                new_name, self._call_stack, self._inliner.used_node_names
+            )
+
+        new_metadata = {**self._metadata_props, **node.metadata_props}
+        # TODO: For now, node metadata overrides callnode metadata if there is a conflict.
+        # Do we need to preserve both?
+
+        new_node = ir.Node(
+            node.domain,
+            node.op_type,
+            new_inputs,
+            new_attributes,
+            overload=node.overload,
+            num_outputs=len(node.outputs),
+            graph=None,
+            name=new_name,
+            doc_string=node.doc_string,
+            metadata_props=new_metadata,
+        )
+        new_outputs = new_node.outputs
+        for i, output in enumerate(node.outputs):
+            self._value_map[output] = new_outputs[i]
+            old_name = output.name if output.name is not None else f"output_{i}"
+            new_outputs[i].name = _make_unique_name(
+                old_name, self._call_stack, self._inliner.used_value_names
+            )
+
+        self._inliner.node_context[new_node] = self._call_stack
+
+        return new_node
+
+    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()]
+
+        return ir.Graph(
+            input_values,  # type: ignore
+            graph.outputs,
+            nodes=nodes,
+            initializers=initializers,  # type: ignore
+            doc_string=graph.doc_string,
+            opset_imports=graph.opset_imports,
+            name=graph.name,
+            metadata_props=graph.metadata_props,
+        )
+
+
+def _abbreviate(
+    function_ids: Iterable[ir.OperatorIdentifier],
+) -> dict[ir.OperatorIdentifier, str]:
+    """Create a short unambiguous abbreviation for all function ids."""
+
+    def id_abbreviation(id: ir.OperatorIdentifier) -> str:
+        """Create a short unambiguous abbreviation for a function id."""
+        domain, name, overload = id
+        # Omit the domain, if it remains unambiguous after omitting it.
+        if any(x[0] != domain and x[1] == name and x[2] == overload for x in function_ids):
+            short_domain = domain + "_"
+        else:
+            short_domain = ""
+        if overload != "":
+            return short_domain + name + "_" + overload
+        return short_domain + name
+
+    return {id: id_abbreviation(id) for id in function_ids}
+
+
+class _Inliner:
+    def __init__(self, model: ir.Model) -> None:
+        self._functions = model.functions
+        self._function_id_abbreviations = _abbreviate(self._functions.keys())
+        self._opset_imports = model.opset_imports
+        self.used_value_names: set[str] = set()
+        self.used_node_names: set[str] = set()
+        self.node_context: dict[ir.Node, CallStack] = {}
+
+    def _instantiate_call(self, node: ir.Node, call_site_id: CallSiteId) -> NodeReplacement:
+        id = node.op_identifier()
+        function = self._functions[id]
+
+        # check opset compatibility and update the opset imports
+        for key, value in function.opset_imports.items():
+            if key not in self._opset_imports:
+                self._opset_imports[key] = value
+            elif self._opset_imports[key] != value:
+                raise ValueError(
+                    f"Opset mismatch: {key} {self._opset_imports[key]} != {value}"
+                )
+
+        # Identify substitutions for both inputs and attributes of the function:
+        attributes: dict[str, ir.Attr | ir.RefAttr] = node.attributes
+        default_attr_values = {
+            attr.name: attr
+            for attr in function.attributes.values()
+            if attr.name not in attributes and attr.value is not None
+        }
+        if default_attr_values:
+            attributes = {**attributes, **default_attr_values}
+        if any(
+            attr.type == ir.AttributeType.GRAPH or attr.type == ir.AttributeType.GRAPHS
+            for attr in attributes.values()
+        ):
+            raise ValueError(
+                "Inliner does not support graph attribute parameters to functions"
+            )
+
+        if len(node.inputs) > len(function.inputs):
+            raise ValueError(f"Input mismatch: {len(node.inputs)} > {len(function.inputs)}")
+        value_map = {}
+        for i, input in enumerate(node.inputs):
+            value_map[function.inputs[i]] = input
+        for i in range(len(node.inputs), len(function.inputs)):
+            value_map[function.inputs[i]] = None
+
+        # Identify call-stack for node, used to generate unique names.
+        call_stack = self.node_context.get(node, [])
+        call_stack.append(call_site_id)
+
+        cloner = _CopyReplace(self, attributes, value_map, node.metadata_props, call_stack)
+
+        # iterate over the nodes in the function, creating a copy of each node
+        # and replacing inputs with the corresponding values in the value map.
+        # Update the value map with the new values.
+
+        nodes = [cloner.clone_node(node) for node in function]
+        output_values = [value_map[output] for output in function.outputs]
+        return nodes, output_values  # type: ignore
+
+    def inline_calls_in(self, graph: ir.Graph) -> None:
+        for input in graph.inputs:
+            if input.name is not None:
+                self.used_value_names.add(input.name)
+        for initializer in graph.initializers:
+            self.used_value_names.add(initializer)
+
+        # Pre-processing:
+        # * Count the number of times each function is called in the graph.
+        #   This is used for disambiguating names of values in the inlined functions.
+        # * And identify names of values that are used in the graph.
+        id_count: dict[ir.OperatorIdentifier, int] = defaultdict(int)
+        for node in graph:
+            if node.name:
+                self.used_node_names.add(node.name)
+            id = node.op_identifier()
+            if id in self._functions:
+                id_count[id] += 1
+            for output in node.outputs:
+                if output.name is not None:
+                    self.used_value_names.add(output.name)
+        next_id: dict[ir.OperatorIdentifier, int] = defaultdict(int)
+        for node in graph:
+            id = node.op_identifier()
+            if id in self._functions:
+                # If there are multiple calls to same function, we use a prefix to disambiguate
+                # the different call-sites:
+                if id_count[id] > 1:
+                    call_site_prefix = f"_{next_id[id]}"
+                    next_id[id] += 1
+                else:
+                    call_site_prefix = ""
+                call_site = node.name or (
+                    self._function_id_abbreviations[id] + call_site_prefix
+                )
+                nodes, values = self._instantiate_call(node, call_site)
+                ir_convenience.replace_nodes_and_values(
+                    graph,
+                    insertion_point=node,
+                    old_nodes=[node],
+                    new_nodes=nodes,
+                    old_values=node.outputs,
+                    new_values=values,
+                )
+            else:
+                for attr in node.attributes.values():
+                    if not isinstance(attr, ir.Attr):
+                        continue
+                    if attr.type == ir.AttributeType.GRAPH:
+                        self.inline_calls_in(attr.value)
+                    elif attr.type == ir.AttributeType.GRAPHS:
+                        for graph in attr.value:
+                            self.inline_calls_in(graph)
+
+
+def inline(model: ir.Model) -> None:
+    """Inline all function calls (recursively) in the model."""
+    inliner = _Inliner(model)
+    inliner.inline_calls_in(model.graph)
+    model.functions.clear()