[Pass] Graph extractor pass

onnxscript/ir/_convenience.py

@@ -12,6 +12,8 @@
     "convert_attribute",
     "convert_attributes",
     "replace_all_uses_with",
+    "create_value_mapping",
+    "replace_nodes_and_values",
 ]
 
 import typing

onnxscript/ir/convenience.py

@@ -9,11 +9,13 @@
     "convert_attributes",
     "replace_all_uses_with",
     "replace_nodes_and_values",
+    "create_value_mapping",
 ]
 
 from onnxscript.ir._convenience import (
     convert_attribute,
     convert_attributes,
+    create_value_mapping,
     replace_all_uses_with,
     replace_nodes_and_values,
 )

onnxscript/ir/passes/_pass_infra.py

@@ -70,12 +70,31 @@ class PassBase(abc.ABC):
 
     Class attributes:
         in_place: Whether the pass modifies the model in place.
+        destructive: Whether the pass will destroy the input model when ``in_place=False``.
     """
 
     in_place: bool = True
+    destructive: bool = False
 
     def __call__(self, model: ir.Model) -> PassResult:
-        return self.call(model)
+        # Check preconditions
+        try:
+            self.requires(model)
+        except PreconditionError:
+            raise
+        except Exception as e:
+            raise PreconditionError("Pre-condition failed") from e
+
+        result = self.call(model)
+
+        # Check postconditions
+        try:
+            self.ensures(model)
+        except PostconditionError:
+            raise
+        except Exception as e:
+            raise PostconditionError("Post-condition failed") from e
+        return result
 
     @abc.abstractmethod
     def call(self, model: ir.Model) -> PassResult:
@@ -111,12 +130,10 @@ class PassManager:
     def __init__(
         self,
         passes: Sequence[PassBase],
-        check_invariants: bool = False,
         steps: int = 1,
     ):
         # TODO(justinchuby): Implement constraints
         self.passes = list(passes)
-        self.check_invariants = check_invariants
         self.steps = steps
 
     def __call__(self, model: ir.Model) -> PassResult:
@@ -137,17 +154,10 @@ def _run_one_step(self, model: ir.Model, step: int) -> PassResult:
         modified = False
         for i, pass_ in enumerate(self.passes):
             logger.debug("Running the %s-th pass '%s', (step %s)", i, pass_, step)
-
-            # 1. Check preconditions
-            if self.check_invariants:
-                try:
-                    pass_.requires(model)
-                except Exception as e:
-                    raise PreconditionError(f"Pre-condition failed for {pass_}") from e
-
-            # 2. Run the pass
             try:
                 pass_result = pass_(model)
+            except (PreconditionError, PostconditionError):
+                raise
             except Exception as e:
                 prev_pass_names = [str(p) for p in self.passes[:i]]
                 raise PassError(
@@ -163,10 +173,4 @@ def _run_one_step(self, model: ir.Model, step: int) -> PassResult:
             model = pass_result.model
             modified = modified or pass_result.modified
 
-            # 3. Check postconditions
-            if self.check_invariants:
-                try:
-                    pass_.ensures(model)
-                except Exception as e:
-                    raise PostconditionError(f"Post-condition failed for {pass_}") from e
         return PassResult(model, modified)

onnxscript/ir/passes/common/graph_extration.py

@@ -0,0 +1,136 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+"""Passes for extracting subgraphs from a graph."""
+
+from __future__ import annotations
+
+import itertools
+
+__all__ = [
+    "ExtractGraphPass",
+]
+
+import logging
+from collections.abc import Collection
+
+from onnxscript import ir
+
+logger = logging.getLogger(__name__)
+
+
+def _find_subgraph_bounded_by_values(
+    graph: ir.Graph, inputs: Collection[ir.Value], outputs: Collection[ir.Value]
+) -> tuple[list[ir.Node], list[ir.Value]]:
+    """Finds the subgraph bounded by the given inputs and outputs.
+
+    Args:
+        graph: The graph to search.
+        inputs: The inputs to the subgraph.
+        outputs: The outputs of the subgraph.
+
+    Returns:
+        A list of nodes in the subgraph and the initializers used.
+    """
+    all_nodes = []
+    value_stack: list[ir.Value] = [*outputs]
+    visited_nodes: set[ir.Node] = set()
+    visited_values: set[ir.Value] = set()
+    initializers = []
+    while value_stack:
+        value = value_stack.pop()
+        if value in visited_values:
+            continue
+        if value.name in graph.initializers:
+            # Record the initializer
+            assert value.const_value is not None
+            initializers.append(value)
+        visited_values.add(value)
+        if (node := value.producer()) is not None:
+            if node not in visited_nodes:
+                visited_nodes.add(node)
+                all_nodes.append(node)
+                for input in node.inputs:
+                    if input not in visited_values and input is not None:
+                        value_stack.append(input)
+    return all_nodes, initializers
+
+
+class ExtractGraphPass(ir.passes.PassBase):
+    """This pass performs shape inference on the graph."""
+
+    # This pass does not modify the model in place
+    in_place = False
+    # This pass destroys the input model
+    destructive = True
+
+    def __init__(self, *, input_names: Collection[str], output_names: Collection[str]) -> None:
+        """Extracts sub-model from an ONNX model.
+
+        The sub-model is defined by the names of the input and output tensors *exactly*.
+
+        Args:
+            input_names: The names of the inputs to extract.
+            output_names: The names of the outputs to extract.
+        """
+        super().__init__()
+        self.input_names = input_names
+        self.output_names = output_names
+
+    def requires(self, model: ir.Model) -> None:
+        # All inputs and outputs can be found in the model
+        values = ir.convenience.create_value_mapping(model.graph)
+        input_names_not_found = sorted(set(self.input_names) - set(values.keys()))
+        if input_names_not_found:
+            raise ir.passes.PreconditionError(
+                f"Input names not found in the model: {input_names_not_found}"
+            )
+        output_names_not_found = sorted(set(self.output_names) - set(values.keys()))
+        if output_names_not_found:
+            raise ir.passes.PreconditionError(
+                f"Output names not found in the model: {output_names_not_found}"
+            )
+
+        # All inputs and outputs must have type and shape
+        for name in itertools.chain(self.input_names, self.output_names):
+            value = values[name]
+            if value.type is None:
+                raise ir.passes.PreconditionError(
+                    f"Value {name} does not have a type: {value}. "
+                    "Consider setting its type or running shape inference first."
+                )
+            if value.shape is None:
+                raise ir.passes.PreconditionError(
+                    f"Value {name} does not have a shape: {value}. "
+                    "Consider setting its shape or running shape inference first."
+                )
+
+    def call(self, model: ir.Model) -> ir.passes.PassResult:
+        values = ir.convenience.create_value_mapping(model.graph)
+        inputs = [values[name] for name in self.input_names]
+        outputs = [values[name] for name in self.output_names]
+        extracted_nodes, initializers = _find_subgraph_bounded_by_values(
+            model.graph, inputs, outputs
+        )
+        # Create a graph with the extracted nodes
+        model.graph.remove(extracted_nodes)
+        new_model = ir.Model(
+            ir.Graph(
+                inputs,
+                outputs,
+                nodes=extracted_nodes,
+                initializers=initializers,
+                doc_string=model.graph.doc_string,
+                opset_imports=model.graph.opset_imports,
+                name=model.graph.name,
+                metadata_props=model.graph.metadata_props,
+            ),
+            ir_version=model.ir_version,
+            producer_name=model.producer_name,
+            producer_version=model.producer_version,
+            domain=model.domain,
+            model_version=model.model_version,
+            doc_string=model.doc_string,
+            functions=tuple(model.functions.values()),
+            meta_data_props=model.metadata_props,
+        )
+        return ir.passes.PassResult(new_model, modified=True)