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Arm/TOSA: allow int64 through when the spec declares the int64 extension

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#24,095 4 comentarios 0 reacciones 0 asignados Ver en GitHub

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@abm-77 ya está trabajando en esto.

Desde el 23/10/2026.

  • #24128 de @abm-77 — abierto

Evaluación

Dificultad
4/5
Tiempo estimado
3-5 días
Aptitud para principiantes
54/100
Tipo de issue
Error
Claridad
Bien especificado
Estado de actividad
Activo
Stack tecnológico
python, pytorch
Área
backend, machine-learning

Línea de trabajo

Start by reading backends/arm/tosa/mapping.py and locating map_dtype, then trace the three int64 cast passes and the node validators and visitor dtype lists named in the issue. Check how each path accesses the TOSA specification and compare lowering under TOSA-1.0+INT and TOSA-1.1+INT+int64. Done means int64 is preserved and serializes with the extension enabled, while the existing int32 casting behavior remains for specs without it.

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

Descripción

bug module: arm partner: arm
🐛 Describe the bug

TosaSpecification accepts TOSA-1.1+INT+int64 and support_extension("int64") returns True, but this is not honored downstream. The Arm backend narrows int64 to int32 anyway, and then refuses to serialize the int64.

Repro, an embedding with an int64 index, lowered under both specs:

import traceback
import torch
from torch.export import export
from torchao.quantization.pt2e.quantize_pt2e import convert_pt2e, prepare_pt2e

from executorch.backends.arm.quantizer.arm_quantizer import (
    TOSAQuantizer, get_symmetric_quantization_config,
)
from executorch.backends.arm.tosa import TosaSpecification
from executorch.backends.arm.tosa.compile_spec import TosaCompileSpec
from executorch.backends.arm.tosa.partitioner import TOSAPartitioner
from executorch.exir import to_edge_transform_and_lower


class Embed(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.emb = torch.nn.Embedding(10, 3)

    def forward(self, idx):
        return self.emb(idx)


def run(spec_str):
    print(f"=== {spec_str} ===", flush=True)
    inputs = (torch.tensor([[1, 2, 3, 4]], dtype=torch.int64),)
    tosa_spec = TosaSpecification.create_from_string(spec_str)
    print(f"support_extension('int64') = {tosa_spec.support_extension('int64')}")

    quantizer = TOSAQuantizer(tosa_spec)
    quantizer.set_global(get_symmetric_quantization_config())

    prepared = prepare_pt2e(
        export(Embed().eval(), inputs).module(check_guards=False), quantizer
    )
    prepared(*inputs)
    converted = convert_pt2e(prepared)

    try:
        to_edge_transform_and_lower(
            export(converted, inputs),
            partitioner=[TOSAPartitioner(TosaCompileSpec(spec_str))],
        )
        print("lowered OK")
    except Exception:
        traceback.print_exc()


for s in ("TOSA-1.0+INT", "TOSA-1.1+INT+int64"):
    run(s)

Under TOSA-1.0+INT:

support_extension('int64') = False
WARNING insert_int32_casts_after_int64_placeholders: Inserting a casting node
  _to_dim_order_copy_default after idx to cast int64 placeholder to int32 for idx
lowered OK

Under TOSA-1.1+INT+int64:

support_extension('int64') = True
WARNING insert_int32_casts_after_int64_placeholders: Inserting a casting node
  _to_dim_order_copy_default after idx to cast int64 placeholder to int32 for idx
  File "executorch/backends/arm/tosa/mapping.py", line 98, in map_dtype
    raise ValueError(f"Unsupported type: {data_type}")
ValueError: Unsupported type: torch.int64

The above exception was the direct cause of the following exception:

File "executorch/backends/arm/process_node.py", line 188, in process_inputs
ValueError: Failed processing input placeholder: idx. Is the original torch function supported?

InsertInt32CastsAfterInt64PlaceholdersPass is constructed without a spec. It therefore cannot tell whether int64 is allowed, and rewrites the placeholder before serialization runs. Under TOSA-1.0+INT
that is intended and lowering succeeds. The model still ends up computing in 32 bits, with only a log warning.

UNSUPPORTED_DTYPES in backends/arm/tosa/mapping.py is a module-level tuple that lists torch.int64. map_dtype's only argument is a dtype, so there is no spec available to check against. TOSA 1.1 added
the int64 extension after this code was written.

Proposed changes:

  1. map_dtype to take the spec and allow int64 when the spec declares the extension.
  2. InsertInt32CastsAfterInt64PlaceholdersPass, ConvertInt64ConstOpsToInt32Pass and ConvertInt64OutputOpsToInt32Pass to skip on the same condition.
  3. Node validators and the per-visitor dtype lists to accept int64 on the same condition.

Related: #12957 proposes sharing the int64 cast pass across backends, on the basis that the TOSA backend requires int32 inputs. That assumption does not hold with the int64 extension enabled. A
shared pass would need the same spec check.

Happy to put up PRs if this approach sounds right.

Versions
Versions
  PyTorch version: 2.14.0+cu130
  OS: Rocky Linux 9.8 (Blue Onyx) (x86_64)
  GCC version: (GCC) 11.5.0 20240719 (Red Hat 11.5.0-14)
  Libc version: glibc-2.34
  Python version: 3.11.13 (64-bit runtime)
  Python platform: Linux-5.14.0-611.54.1.el9_7.x86_64-x86_64-with-glibc2.34

  Versions of relevant libraries:
  [pip3] executorch==1.5.1
  [pip3] numpy==2.1.3
  [pip3] pytorch_tokenizers==1.5.0
  [pip3] torch==2.14.0
  [pip3] torchao==0.18.0

cc @digantdesai @freddan80 @per @zingo @oscarandersson8218 @mansnils @Sebastian-Larsson @robell @rascani

Lenguaje dominante
Python
Estrellas
5.1k
Forks
1.2k
Merge medio
1 d 22 h
PR fusionados (30 d)
684

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