CastCast rewrite changes rounding for DOUBLE inputs by removing the FLOAT intermediate

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Dal 12/11/2026.

  • #3205 di @itsrayeedscodespace — aperta

Valutazione

Difficoltà
3/5
Tempo stimato
1-2 giorni
Idoneità per principianti
35/100
Tipo di issue
Bug
Chiarezza
Specificata chiaramente
Stato di attività
Ferma
Stack tecnologico
python
Ambito
backend-api-design

Direzione di ricerca

Find the CastCast rewrite rule in onnxscript.rewriter.rules.common and read how it determines whether the intermediate cast can be removed. Add focused tests for the reported DOUBLE rounding boundary and for supported mixed precision source types, then run the relevant rewrite tests. Done means the rewrite preserves both casts for DOUBLE or unknown source types while still collapsing the supported combinations; an open linked pull request already addresses this issue.

Scritto dal modello di indicizzazione a partire dal testo della issue.

Descrizione

Description

The CastCast rewrite checks the intermediate/output dtype pair but ignores the source dtype. It currently treats both Cast(Cast(X, FLOAT), FLOAT16) and Cast(Cast(X, FLOAT), BFLOAT16) as equivalent to a direct cast for every source type.

That is not true for wider sources: rounding to FLOAT can land exactly on a lower-precision midpoint, so the second rounding can differ from a direct cast. This silently changes valid model outputs.

Reproduction

Reproduced locally on main f51083c, Python 3.12.16, macOS arm64, using the ONNX reference evaluator:

import numpy as np
import onnx
import onnx.reference
import onnx_ir as ir
from onnxscript.rewriter.rules.common import _basic_rules

h = onnx.helper
model = h.make_model(
    h.make_graph(
        [
            h.make_node("Cast", ["X"], ["M"], to=onnx.TensorProto.FLOAT),
            h.make_node("Cast", ["M"], ["Y"], to=onnx.TensorProto.FLOAT16),
        ],
        "rounding",
        [h.make_tensor_value_info("X", onnx.TensorProto.DOUBLE, [1])],
        [h.make_tensor_value_info("Y", onnx.TensorProto.FLOAT16, [1])],
    ),
    opset_imports=[h.make_opsetid("", 18)],
    ir_version=10,
)
rewritten = ir.from_proto(model)
print(_basic_rules.cast_cast_rule.apply_to_model(rewritten))  # 1
feeds = {"X": np.array([1.00048828125 + 2**-30], dtype=np.float64)}
for candidate in (model, ir.to_proto(rewritten)):
    y = onnx.reference.ReferenceEvaluator(candidate).run(None, feeds)[0]
    print(y, y.view(np.uint16))

Original: [1.] [15360]
Rewritten: [1.001] [15361]

NumPy independently gives the same distinction for x.astype(np.float32).astype(np.float16) versus x.astype(np.float16).

This is semantic correctness evidence from the reference evaluator, not a GPU/performance claim. In my local ONNX Runtime CPU check, both graphs returned the lower value; that runtime result does not independently demonstrate this rewrite's numerical difference.

Proposed fix

Restrict elimination to source dtypes for which widening to FLOAT is exact, retaining the intended mixed-precision cases (FLOAT, FLOAT16 and BFLOAT16). Preserve both casts for DOUBLE, unproven source types, or missing source-type information. Add exact-result tests around rounding boundaries plus positive tests that the supported mixed-precision combinations still collapse.

I checked open PRs and searched issues/PRs for CastCast and cast rounding/precision; I did not find a competing fix for this source-type guard.

Lingua principale
Python
Stelle
456
Fork
159
Merge medio
3g 5h
PR unite (30g)
23

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