Update constant folding behavior for large tensors (#2488)

Suggested by #2466, I
updated the constant folder logic to allow

**Constant folding customization:**

* Replaced the `always_fold_ops` parameter with a `should_fold` callable
that determines on a per-node basis whether folding should occur. This
allows users to specify more complex folding policies and makes the API
more explicit. (`FoldConstantsPass`, `fold_constants`)
[[1]](diffhunk://#diff-99f13fcef1aa8c8c81fd51b7a71477572a86c91ed0dc8e618e18644d70fdf8b5L902-R904)
[[2]](diffhunk://#diff-99f13fcef1aa8c8c81fd51b7a71477572a86c91ed0dc8e618e18644d70fdf8b5L913-R918)
[[3]](diffhunk://#diff-99f13fcef1aa8c8c81fd51b7a71477572a86c91ed0dc8e618e18644d70fdf8b5L1248-R1268)
[[4]](diffhunk://#diff-99f13fcef1aa8c8c81fd51b7a71477572a86c91ed0dc8e618e18644d70fdf8b5L1263-R1285)
[[5]](diffhunk://#diff-99f13fcef1aa8c8c81fd51b7a71477572a86c91ed0dc8e618e18644d70fdf8b5L1276-R1295)

**Logging and diagnostics improvements:**

* Upgraded logging throughout the folding process to provide more
informative messages, including reasons for skipping nodes (e.g.,
control flow, non-deterministic ops, large inputs, or graph inputs) and
explicit logging when `should_fold` returns a decision.
[[1]](diffhunk://#diff-99f13fcef1aa8c8c81fd51b7a71477572a86c91ed0dc8e618e18644d70fdf8b5L964-R958)
[[2]](diffhunk://#diff-99f13fcef1aa8c8c81fd51b7a71477572a86c91ed0dc8e618e18644d70fdf8b5L990-R984)
[[3]](diffhunk://#diff-99f13fcef1aa8c8c81fd51b7a71477572a86c91ed0dc8e618e18644d70fdf8b5L1075-R1141)

**Code cleanup and minor fixes:**

* Removed the unused `_update_type` function.

Fix #2466

cc @iksnagreb

---------

Signed-off-by: Justin Chu <justinchuby@users.noreply.github.com>
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>

Author: justinchuby

onnxscript/optimizer/_constant_folding.py

@@ -9,7 +9,7 @@
 import logging
 import math
 import typing
-from typing import Any, Callable, Collection, Iterable, Sequence, Union
+from typing import Any, Callable, Iterable, Sequence, Union
 
 import numpy as np
 import onnx
@@ -34,6 +34,13 @@
     }
 )
 
+# A list of ops to always fold regardless of their input size limits, as long as
+# they are the single consumer of the large input tensors
+_DEFAULT_ALWAYS_FOLD_OPS = frozenset(
+    {
+        ("", "Transpose"),
+    }
+)
 
 logger = logging.getLogger(__name__)
 
@@ -332,12 +339,6 @@ def _get_output(node: ir.Node, index: int) -> ir.Value | None:
     return None
 
 
-def _update_type(value: ir.Value, type: ir.TypeProtocol | None) -> None:
-    if type is not None:
-        # TODO: merge types
-        value.type = type
-
-
 def _get_input_element_type(node: ir.Node, index: int) -> int:
     input = _get_input(node, index)
     if input is not None and input.type is not None:
@@ -899,9 +900,10 @@ class FoldConstantsPass(ir.passes.InPlacePass):
         shape_inference: Whether to perform shape inference.
         input_size_limit: Maximum size of input tensors to fold.
         output_size_limit: Maximum size of output tensors to fold.
-        always_fold_ops: Collection of op types that should always be folded.
-            For ops from the default opset, only op_type is neede (e.g. "Transpose"),
-            otherwise specify the domain with ``{domain}::{op_type}``.
+        should_fold: An optional function that takes a node and returns True if
+            the node should be considered for folding.
+            The function should return True/False value to indicate if this particular
+            node should be folded, or None to use the default folding rules.
     """
 
     def __init__(
@@ -910,18 +912,12 @@ def __init__(
         shape_inference: bool,
         input_size_limit: int,
         output_size_limit: int,
-        always_fold_ops: Collection[str] = frozenset(["Transpose"]),
+        should_fold: Callable[[ir.Node], bool | None] = lambda node: None,
     ) -> None:
         self.shape_inference = shape_inference
         self.input_size_limit = input_size_limit
         self.output_size_limit = output_size_limit
-        ops = []
-        for name in always_fold_ops:
-            domain, op_type = name.split("::", 1) if "::" in name else ("", name)
-            if domain == "ai.onnx":
-                domain = ""
-            ops.append((domain, op_type))
-        self.always_fold_ops: frozenset[tuple[str, str]] = frozenset(ops)
+        self.should_fold = should_fold
 
         self._opset_imports: dict[str, int] = {}
         self._counts: dict[str, int] = {}
@@ -961,7 +957,7 @@ def get_type(value: ir.Value) -> onnx.TypeProto | None:
         input_data = {k: v for k, v in input_data.items() if v is not None}
         if any(t is None for t in input_types.values()):
             logger.debug(
-                "Skipping shape inference for node %s due to missing input type.",
+                "Skipping shape inference for node %r due to missing input type.",
                 node.name,
             )
         else:
@@ -987,7 +983,7 @@ def get_type(value: ir.Value) -> onnx.TypeProto | None:
                         output.type = ir.serde.deserialize_type_proto_for_type(inferred_type)
             except Exception as e:
                 logger.debug(
-                    "Skipping shape inference for node %s due to exception: %s",
+                    "Skipping shape inference for node %r due to exception: %s",
                     node.name,
                     e,
                 )
@@ -1072,62 +1068,102 @@ def process_node(self, node: ir.Node) -> Replacement | None:
                     output = [output]
                 return Replacement(output, context.nodes)
 
-        if _is_control_flow_op(node) or _is_non_deterministic_op(node):
+        if _is_control_flow_op(node):
+            logger.info(
+                "Skipping constant folding for control flow op %r (%s::%s) because it is not supported yet",
+                node.name,
+                node.domain,
+                node.op_type,
+            )
+
+            return None
+
+        if _is_non_deterministic_op(node):
+            logger.info(
+                "Skipping constant folding for non-deterministic op %r (%s::%s)",
+                node.name,
+                node.domain,
+                node.op_type,
+            )
             return None
 
         if _is_onnx_op(node, "Constant"):
             _process_constant_node(node)
             return None
 
         if any(x.is_graph_input() for x in node.inputs if x is not None):
-            # Do not fold any graph inputs to preserve graph signature
+            logger.info(
+                "Skipping constant folding for node %r because it is graph input to preserve graph signature",
+                node.name,
+            )
             return None
 
         # Ensure all node inputs are constants
         if any(x.const_value is None for x in node.inputs if x is not None):
-            if logger.isEnabledFor(logging.DEBUG):
-                logger.debug(
-                    "Skipping constant folding for node %s because it has non-constant inputs",
-                    node,
-                    [x.name for x in node.inputs if x is not None],
-                )
             return None
 
-        input_tensors = [x.const_value if x is not None else None for x in node.inputs]
-        if any(
-            tensor.size > self.input_size_limit
-            for tensor in input_tensors
-            if tensor is not None
-        ):
-            if (node.domain, node.op_type) in self.always_fold_ops and all(
-                len(input.consumers()) == 1 for input in node.inputs if input is not None
-            ):
-                # If the op is in always_fold_ops and all inputs are used only by this node,
-                # we can still fold it even if the input size exceeds the limit.
-                logger.debug(
-                    "Folding large constant for node %s because it is in the always_fold_ops list",
-                    node,
+        should_fold = self.should_fold(node)
+
+        if should_fold is False:
+            logger.info(
+                "Skipping constant folding for node %r because should_fold returned False",
+                node.name,
+            )
+            return None
+
+        elif should_fold is None:
+            # Use default rules to decide whether to fold the node:
+            # - ConstantOfShape is preserved to avoid increasing model size unnecessarily
+            # - If the any tensor input size exceeds the input_size_limit, skip folding the node
+            if _is_onnx_op(node, "ConstantOfShape"):
+                logger.info(
+                    "Skipping constant folding for node %r because ConstantOfShape is preserved by default",
+                    node.name,
                 )
-            else:
-                # Skip folding large tensors
-                if logger.isEnabledFor(logging.DEBUG):
-                    input_sizes = [
-                        tensor.size for tensor in input_tensors if tensor is not None
-                    ]
-                    logger.debug(
-                        "Skipping constant folding for node %s due to large input size: %s",
-                        node,
-                        input_sizes,
-                    )
                 return None
 
+            input_tensors = [x.const_value if x is not None else None for x in node.inputs]
+            large_inputs = [
+                tensor is not None and tensor.size > self.input_size_limit
+                for tensor in input_tensors
+            ]
+            if any(large_inputs):
+                # Decide whether to fold large constants
+                assert len(node.inputs) == len(large_inputs)
+                if (node.domain, node.op_type) in _DEFAULT_ALWAYS_FOLD_OPS and all(
+                    len(input.consumers()) == 1 or (not is_large)
+                    for input, is_large in zip(node.inputs, large_inputs)
+                    if input is not None
+                ):
+                    # If the op is in _DEFAULT_ALWAYS_FOLD_OPS and all large inputs are used only by this node,
+                    # we can still fold it even if the input size exceeds the limit
+                    pass
+                else:
+                    # Skip folding large tensors
+                    if logger.isEnabledFor(logging.INFO):
+                        input_sizes = [
+                            tensor.size for tensor in input_tensors if tensor is not None
+                        ]
+                        logger.info(
+                            "Skipping constant folding for node %r due to large input sizes: %s",
+                            node,
+                            input_sizes,
+                        )
+                    return None
+        else:
+            logger.info(
+                "Constant folding node %r because should_fold returned True",
+                node.name,
+            )
+
         input_values = [_get_numpy_value(x) for x in node.inputs]
 
         def convert(av):
             if av.type == ir.AttributeType.TENSOR:
                 return ir.serde.serialize_tensor(av.value)
             return av.value
 
+        # TODO(justinchuby): We should find a way to avoid serializing tensors every time we want to evaluate a node
         attr_values = {name: convert(attr) for name, attr in node.attributes.items()}
         outputs = _reference_evaluator.evaluate(
             node.domain, node.op_type, version, *input_values, **attr_values
@@ -1137,7 +1173,7 @@ def convert(av):
             return None
         if len(node.outputs) == 1 and not isinstance(outputs, (tuple, list)):
             replacement = self.new_constant(node, outputs)
-            if _is_onnx_op(node, "ConstantOfShape") or replacement is None:
+            if replacement is None:
                 return None
             return Replacement(replacement.outputs, [replacement])
         else:
@@ -1245,7 +1281,7 @@ def fold_constants(
     onnx_shape_inference: bool = False,
     input_size_limit: int = DEFAULT_CONSTANT_FOLD_INPUT_SIZE_LIMIT,
     output_size_limit: int = DEFAULT_CONSTANT_FOLD_OUTPUT_SIZE_LIMIT,
-    always_fold_ops: Collection[str] = frozenset(["Transpose"]),
+    should_fold: Callable[[ir.Node], bool | None] = lambda node: None,
 ) -> FoldConstantsResult:
     """
     Applies constant folding optimization to the model.
@@ -1260,10 +1296,9 @@ def fold_constants(
         output_size_limit: The maximum size of output tensors
             that can be stored after constant folding. Defaults to
             `DEFAULT_CONSTANT_FOLD_OUTPUT_SIZE_LIMIT`.
-        always_fold_ops: A collection of op types that should always be folded,
-            regardless of their input or output sizes. For ops from the default opset,
-            only op_type is neede (e.g. "Transpose"), otherwise specify the domain
-            with ``{domain}::{op_type}``.
+        should_fold: An optional function that takes a node and returns True if
+            the node should be considered for folding, False if it should not be folded,
+            or None to use the default rules. Defaults to a function that always returns None.
 
     Returns:
         An instance of `FoldConstantsResult`.
@@ -1273,6 +1308,6 @@ def fold_constants(
         shape_inference=onnx_shape_inference,
         input_size_limit=input_size_limit,
         output_size_limit=output_size_limit,
-        always_fold_ops=always_fold_ops,
+        should_fold=should_fold,
     )
     return folder_pass(model)  # type: ignore[return-value]

onnxscript/optimizer/_constant_folding_test.py

@@ -581,6 +581,33 @@ def test_transpose_is_always_folded(self):
         ops = [node.op_type for node in optimized.graph]
         self.assertEqual(ops, ["Constant"])
 
+    def test_node_is_folded_if_specified_as_should_fold(self):
+        model_text = """
+            <ir_version: 10, opset_import: [ "" : 20]>
+            agraph (float[M, 256] x) => (float[42, 42] z)
+            <int64[2] w = {42, 42}>
+            {
+                z = ConstantOfShape <value: tensor = int64[1] {1}> (w)
+            }
+        """
+        model = ir.from_onnx_text(model_text)
+
+        # ConstantOfShape is not folded by default
+        optimized = self._fold(model)
+        ops = [node.op_type for node in optimized.graph]
+        self.assertEqual(ops, ["ConstantOfShape"])
+
+        # But ConstantOfShape is folded when specified in should_fold
+        optimized = self._fold(
+            model, should_fold=lambda node: node.op_type == "ConstantOfShape" or None
+        )
+        ops = [node.op_type for node in optimized.graph]
+        self.assertEqual(ops, ["Constant"])
+        np.testing.assert_array_equal(
+            optimized.graph.node(0).attributes["value"].as_tensor().numpy(),
+            np.ones((42, 42), dtype=np.int64),
+        )
+
     def test_multi_graph_identity_output_preserves_output_name(self):
         model = """
             <ir_version: 10, opset_import: ["" : 20]>