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

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@itsrayeedscodespace ya está trabajando en esto.

Desde el 12/11/2026.

  • #3205 de @itsrayeedscodespace — abierto

Evaluación

Dificultad
3/5
Tiempo estimado
1-2 días
Aptitud para principiantes
35/100
Tipo de issue
Error
Claridad
Bien especificado
Estado de actividad
Estancado
Stack tecnológico
python
Área
backend-api-design

Línea de trabajo

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.

Escrito por el modelo de indexación a partir del texto del issue.

Descripción

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.

Lenguaje dominante
Python
Estrellas
456
Forks
159
Merge medio
3 d 5 h
PR fusionados (30 d)
23

Preparar el entorno

Primeros pasos

  1. Lee el issue completo y luego la guía de contribución del proyecto.
  2. Comenta en el issue que vas a ocuparte — evita que dos personas hagan lo mismo.
  3. Haz un fork del repositorio y trabaja en una rama.
  4. Abre un pull request que haga referencia al número del issue.

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