pytorch/pytorch
LocalTensorMode: all_to_all_single crashes with uneven split sizes
Closed
#177,371 opened on Mar 13, 2026
bot-triagedgood first issueoncall: distributedoncall: distributed infraptd-bot-triaged
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Description
🐛 Describe the bug
Description
LocalTensorMode's all_to_all_single implementation crashes when using uneven split sizes. It tries to reshape a tensor to an incompatible shape.
Reproduction
import torch
import torch.distributed as dist
from torch.distributed._local_tensor import LocalTensor, LocalTensorMode
from torch.distributed._local_tensor._testing import FakeStore
WORLD_SIZE = 3
store = FakeStore()
dist.init_process_group("fake", rank=0, world_size=WORLD_SIZE, store=store)
pg = dist.new_group(range(WORLD_SIZE), backend="fake")
mode = LocalTensorMode(list(range(WORLD_SIZE)))
with mode:
x = mode.rank_map(
lambda r: torch.arange(24, dtype=torch.float).reshape(6, 4) + r * 100
)
output_split_sizes = [3, 2, 1]
input_split_sizes = [2, 2, 2]
output = x.new_empty([sum(output_split_sizes)] + list(x.shape[1:]))
dist.all_to_all_single(
output,
x,
output_split_sizes=output_split_sizes,
input_split_sizes=input_split_sizes,
group=pg,
)
Error
RuntimeError: shape '[3, 4]' is invalid for input of size 8
at torch/distributed/_local_tensor/_c10d.py in _local_functional_all_to_all_single, where it attempts:
if split_tensor.size() != output_section.size():
split_tensor = split_tensor.view(output_section.size())
Expected Behavior
all_to_all_single with uneven split sizes should work correctly in LocalTensorMode, simulating the data exchange across ranks the same way dist.all_to_all (list-based) does.
### Versions
Collecting environment information...
PyTorch version: 2.10.0a0+git366b450
Is debug build: False
CUDA used to build PyTorch: 12.4
ROCM used to build PyTorch: N/A
OS: CentOS Stream 9 (x86_64)
GCC version: (GCC) 11.5.0 20240719 (Red Hat 11.5.0-14)
Clang version: Could not collect
CMake version: version 3.30.2
Libc version: glibc-2.34
Python version: 3.12.0 | packaged by conda-forge | (main, Oct 3 2023, 08:43:22) [GCC 12.3.0] (64-bit runtime)
Python platform: Linux-6.4.3-0_fbk20_zion_2830_g3e5ab162667d-x86_64-with-glibc2.34
Is CUDA available: True
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to:
GPU models and configuration:
GPU 0: NVIDIA H100
GPU 1: NVIDIA H100
GPU 2: NVIDIA H100
GPU 3: NVIDIA H100
GPU 4: NVIDIA H100
GPU 5: NVIDIA H100
GPU 6: NVIDIA H100
GPU 7: NVIDIA H100
Nvidia driver version: 535.183.06
cuDNN version: Probably one of the following:
/usr/lib64/libcudnn.so.8.9.7
/usr/lib64/libcudnn_adv_infer.so.8.9.7
/usr/lib64/libcudnn_adv_train.so.8.9.7
/usr/lib64/libcudnn_cnn_infer.so.8.9.7
/usr/lib64/libcudnn_cnn_train.so.8.9.7
/usr/lib64/libcudnn_ops_infer.so.8.9.7
/usr/lib64/libcudnn_ops_train.so.8.9.7
Is XPU available: False
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True
Caching allocator config: N/A
CPU:
Architecture: x86_64
CPU op-mode(s): 32-bit, 64-bit
Address sizes: 52 bits physical, 57 bits virtual
Byte Order: Little Endian
CPU(s): 384
On-line CPU(s) list: 0-383
Vendor ID: AuthenticAMD
Model name: AMD EPYC 9654 96-Core Processor
CPU family: 25
Model: 17
Thread(s) per core: 2
Core(s) per socket: 96
Socket(s): 2
Stepping: 1
Frequency boost: enabled
CPU(s) scaling MHz: 98%
CPU max MHz: 3707.8120
CPU min MHz: 1500.0000
BogoMIPS: 4792.43
Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good amd_lbr_v2 nopl nonstop_tsc cpuid extd_apicid aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 invpcid_single hw_pstate ssbd mba perfmon_v2 ibrs ibpb stibp ibrs_enhanced vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local avx512_bf16 clzero irperf xsaveerptr rdpru wbnoinvd amd_ppin cppc arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif x2avic v_spec_ctrl vnmi avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq rdpid overflow_recov succor smca fsrm flush_l1d
Virtualization: AMD-V
L1d cache: 6 MiB (192 instances)
L1i cache: 6 MiB (192 instances)
L2 cache: 192 MiB (192 instances)
L3 cache: 768 MiB (24 instances)
NUMA node(s): 2
NUMA node0 CPU(s): 0-95,192-287
NUMA node1 CPU(s): 96-191,288-383
Vulnerability Gather data sampling: Not affected
Vulnerability Itlb multihit: Not affected
Vulnerability L1tf: Not affected
Vulnerability Mds: Not affected
Vulnerability Meltdown: Not affected
Vulnerability Mmio stale data: Not affected
Vulnerability Retbleed: Not affected
Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl
Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2: Vulnerable: eIBRS with unprivileged eBPF
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Not affected
Versions of relevant libraries:
[pip3] mypy-extensions==1.0.0
[pip3] ncclx_trainer_context==0.0.0
[pip3] numpy==1.26.4
[pip3] optree==0.17.0
[pip3] torch==2.10.0a0+git366b450
[pip3] torchaudio==2.6.0a0+ea5de17
[pip3] torchcodec==0.5.0a0
[pip3] torchcomms==0.1.0
[pip3] torchcomms_mccl==0.0.1
[pip3] torchdata==0.11.0
[pip3] torchvision==0.25.0a0+617079d
[pip3] triton==3.4.0+git3f1f55e0
[conda] mkl 2024.0.0 ha957f24_49657 conda-forge
[conda] mkl-include 2024.0.0 ha957f24_49657 conda-forge
[conda] nccl 2.27.100 0a78bfb4_0 https://interncache-all.fbcdn.net/manifold/conda_pkgs/tree/channels/81ce7a030675bdca3f03a350097bf7d3f36ec17e128c39b33ee935ea145c2989_64b7fb40243ea
[conda] ncclx-trainer-context 0.0.0 pypi_0 pypi
[conda] numpy 1.26.4 py312h2809609_0 main
[conda] numpy-base 1.26.4 py312he1a6c75_0 main
[conda] optree 0.17.0 pypi_0 pypi
[conda] pytorch 2.10.0 366b4507_0 https://interncache-all.fbcdn.net/manifold/conda_pkgs/tree/channels/81ce7a030675bdca3f03a350097bf7d3f36ec17e128c39b33ee935ea145c2989_64b7fb40243ea
[conda] tbb 2021.8.0 hf52228f_0 conda-forge
[conda] torchaudio 2.5.0a0 ea5de177_0 https://interncache-all.fbcdn.net/manifold/conda_pkgs/tree/channels/81ce7a030675bdca3f03a350097bf7d3f36ec17e128c39b33ee935ea145c2989_64b7fb40243ea
[conda] torchcodec trunk cfd1d4a0_0 https://interncache-all.fbcdn.net/manifold/conda_pkgs/tree/channels/81ce7a030675bdca3f03a350097bf7d3f36ec17e128c39b33ee935ea145c2989_64b7fb40243ea
[conda] torchcomms trunk 0a78bfb4_0 https://interncache-all.fbcdn.net/manifold/conda_pkgs/tree/channels/81ce7a030675bdca3f03a350097bf7d3f36ec17e128c39b33ee935ea145c2989_64b7fb40243ea
[conda] torchdata 0.11.0 pypi_0 pypi
[conda] torchvision 0.20.0a0 617079d9_0 https://interncache-all.fbcdn.net/manifold/conda_pkgs/tree/channels/81ce7a030675bdca3f03a350097bf7d3f36ec17e128c39b33ee935ea145c2989_64b7fb40243ea
[conda] triton 3.5.0 3f1f55e0_0 https://interncache-all.fbcdn.net/manifold/conda_pkgs/tree/channels/81ce7a030675bdca3f03a350097bf7d3f36ec17e128c39b33ee935ea145c2989_64b7fb40243ea
cc @awgu @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k @pragupta @msaroufim @dcci @aditvenk @weifengpy @xmfan