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Don't constant fold Quantize/DequantizeLinear nodes by default #2713

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Merged
titaiwangms merged 4 commits into from
Dec 3, 2025
Merged

Don't constant fold Quantize/DequantizeLinear nodes by default #2713

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e6c62a1
refactor FoldConstantsPass default rules handling
ruro Nov 21, 2025
a377a0b
don't constant fold Quantize/DequantizeLinear nodes by default
ruro Nov 21, 2025
4e44470
Merge branch 'main' into onnx_dont_constant_fold_qdq
titaiwangms Dec 1, 2025
0d6a91b
Merge branch 'main' into onnx_dont_constant_fold_qdq
titaiwangms Dec 3, 2025
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25 changes: 18 additions & 7 deletions onnxscript/optimizer/_constant_folding.py
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Original file line number Diff line number Diff line change
Expand Up @@ -26,6 +26,14 @@
import onnxscript.utils.utils as utils
from onnxscript.ir import _tape

DEFAULT_CONSTANT_FOLD_BLACKLIST = [
# ConstantOfShape is preserved to avoid increasing model size unnecessarily
"ConstantOfShape",
# Quantize/DequantizeLinear are preserved to keep the quantization info
"QuantizeLinear",
"DequantizeLinear",
]

DEFAULT_CONSTANT_FOLD_INPUT_SIZE_LIMIT = 8192

DEFAULT_CONSTANT_FOLD_OUTPUT_SIZE_LIMIT = 512 * 512
Expand Down Expand Up @@ -1226,14 +1234,17 @@ def process_node(self, node: ir.Node, is_function: bool) -> Replacement | 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
# - Nodes in the DEFAULT_CONSTANT_FOLD_BLACKLIST list are not folded
# - 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,
)
return None
for op_type in DEFAULT_CONSTANT_FOLD_BLACKLIST:
if _is_onnx_op(node, op_type):
logger.info(
"Skipping constant folding for node %r because "
"%s is preserved by default",
node.name,
op_type,
)
return None

input_tensors = [x.const_value if x is not None else None for x in node.inputs]
large_inputs = [
Expand Down
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