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DiNTS forward() breaks the torch.compile graph on every topology branch

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@nilapate がすでに取り組んでいます。

2026年10月1日 から。

  • #9145 @nilapate による — オープン

評価

難易度
4/5
見積もり時間
3〜5日
初心者へのやさしさ
68/100
issue の種類
バグ
明瞭さ
明確に書かれている
活発さ
活発
技術スタック
python, pytorch
領域
machine-learning, performance

調査の方向性

Start in monai/networks/nets/dints.py, focusing on DiNTS.forward and TopologyInstance.forward and their node_a/arch_code_a control flow. Run the provided TorchDynamo reproducer first, then verify that the topology behavior is preserved and compilation reaches the stated 1 graph with 0 breaks for the sample configuration.

索引モデルが issue の本文から書いたものです。

説明

Describe the bug

DiNTS.forward and TopologyInstance.forward pick which cells to run by indexing the node_a / arch_code_a tensors inside Python control flow:

# monai/networks/nets/dints.py
if self.node_a[0][d]: ...
elif self.node_a[blk_idx + 1][res_idx]: ...
for res_idx, activation in enumerate(self.arch_code_a[blk_idx].data): ...

Each is a data-dependent tensor read, so TorchDynamo cannot constant-fold the branch and splits the graph instead: a 6-block / 3-depth DiNTS compiles into 14 graphs with 13 breaks. These flags are fixed once a model is deployed, so the breaks are pure overhead — they block fusion across the network and scale with num_blocks, so deeper searched architectures fragment further.

To Reproduce

  1. Install MONAI from dev, with a torch 2.x providing TorchDynamo.

  2. Run:

    import torch, torch._dynamo as dyn
from monai.networks.nets.dints import DiNTS, TopologyInstance

grid = dict(channel_mul=0.2, num_blocks=6, num_depths=3,
            use_downsample=True, spatial_dims=3, device="cpu")
m = DiNTS(dints_space=TopologyInstance(**grid), in_channels=1,
          num_classes=2, spatial_dims=3, use_downsample=True).eval()

expl = dyn.explain(m)(torch.randn(1, 1, 32, 32, 32))
print(expl.graph_count, expl.graph_break_count)

  3. Observed: 14 13. (expl.break_reasons is not populated on this torch version, so only the counts are available.)

Expected behavior

The topology flags are constant for the lifetime of a deployed model, so the branches should be constant-foldable and the network should compile to 1 0 — one graph, no breaks.
Screenshots

N/A — the reproducer output above is the full evidence.

Environment

MONAI version: 1.6.1rc0+8.g860506514
MONAI rev id : 860506514fb1fba41db1578e0eeacfa952583a13   (dev)
Pytorch version: 2.12.1+rocm7.1
Numpy version: 2.5.2

Reproduced on CPU. The graph breaks are introduced at Dynamo trace time, so they are independent of
the accelerator and of the torch build — this is not platform-specific.

python -c "import monai; monai.config.print_debug_info()" 
================================
Printing MONAI config...
================================
MONAI version: 1.6.1rc0+8.g860506514
Numpy version: 2.5.2
Pytorch version: 2.12.1+rocm7.1
MONAI flags: HAS_EXT=False, USE_COMPILED=False, USE_META_DICT=False
MONAI rev id: 860506514fb1fba41db1578e0eeacfa952583a13
MONAI __file__: /home/user/upstream_monai/upstream-fork-monai/monai/__init__.py

Optional dependencies:
Pytorch Ignite version: 0.5.5
ITK version: NOT INSTALLED or UNKNOWN VERSION.
Nibabel version: 5.4.2
scikit-image version: 0.26.0
scipy version: 1.18.1
Pillow version: 12.3.0
Tensorboard version: NOT INSTALLED or UNKNOWN VERSION.
gdown version: NOT INSTALLED or UNKNOWN VERSION.
TorchVision version: 0.27.1+rocm7.1
tqdm version: 4.70.1
lmdb version: NOT INSTALLED or UNKNOWN VERSION.
psutil version: NOT INSTALLED or UNKNOWN VERSION.
pandas version: NOT INSTALLED or UNKNOWN VERSION.
einops version: 0.8.2
transformers version: NOT INSTALLED or UNKNOWN VERSION.
mlflow version: NOT INSTALLED or UNKNOWN VERSION.
pynrrd version: NOT INSTALLED or UNKNOWN VERSION.
clearml version: NOT INSTALLED or UNKNOWN VERSION.

================================
Printing system config...
================================
`psutil` required for `print_system_info`

================================
Printing GPU config...
================================
Num GPUs: 1
Has CUDA: True
CUDA version: None
cuDNN enabled: True
NVIDIA_TF32_OVERRIDE: None
TORCH_ALLOW_TF32_CUBLAS_OVERRIDE: None
cuDNN version: 3005001
Current device: 0
Library compiled for CUDA architectures: ['gfx900', 'gfx906', 'gfx908', 'gfx90a', 'gfx942', 'gfx1030', 'gfx1100', 'gfx1101', 'gfx1102', 'gfx1200', 'gfx1201', 'gfx950', 'gfx1150', 'gfx1151']
GPU 0 Name: AMD Instinct MI300X
GPU 0 Is integrated: False
GPU 0 Is multi GPU board: False
GPU 0 Multi processor count: 304
GPU 0 Total memory (GB): 192.0
GPU 0 CUDA capability (maj.min): 9.4
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