Proper way to call torch.distributed.send/recv

heisenberg_bonker: <p><strong>In the send/recv code below, only choice=2 works (when using the default group). Why don’t the other two options work?</strong></p>
<p>`CUDA_LAUNCH_BLOCKING=1 uv run torchrun --nproc-per-node=4 mini_pp.py 0/1/2` to launch the code</p>
<pre data-code-wrap="python"><code class="lang-python">import os
import sys

import torch
from torch.distributed.device_mesh import DeviceMesh, init_device_mesh


def init_distributed():

    # Initializes the distributed backend
    # which will take care of sychronizing nodes/GPUs
    dist_url = "env://"  # default

    # only works with torch.distributed.launch // torch.run
    rank = int(os.environ["RANK"])
    world_size = int(os.environ["WORLD_SIZE"])
    local_rank = int(os.environ["LOCAL_RANK"])

    # this will make all .cuda() calls work properly
    torch.cuda.set_device(local_rank)

    torch.distributed.init_process_group(
        backend="nccl", init_method=dist_url, world_size=world_size, rank=rank,
        device_id=torch.device(f"cuda:{torch.cuda.current_device()}"),
    )

    # synchronizes all the threads to reach this point before moving on
    torch.distributed.barrier()
    return world_size, rank, local_rank


def demo():
    choice = int(sys.argv[1])
    world_size, rank, local_rank = init_distributed()
    device_mesh: DeviceMesh = init_device_mesh(
        'cuda',
        (world_size // 2, 2),
        mesh_dim_names=('pp', 'tp'),
    )

    pp_mesh: DeviceMesh = device_mesh["pp"]
    pp_group = pp_mesh.get_group()
    # tp_mesh: DeviceMesh = device_mesh["tp"]
    # tp_group = tp_mesh.get_group()

    pp_rank = pp_group.rank()
    # pp_size = pp_group.size()

    device = torch.device(f"cuda:{torch.cuda.current_device()}")
    dtype = torch.bfloat16
    shape = (3, 1024)

    if choice == 0:  # get stuck
        if pp_rank == 0:
            hidden_states = torch.randn(shape, device=device, dtype=dtype)
            torch.distributed.send(hidden_states, group_dst=pp_rank + 1, group=pp_group)
            print(f"send {torch.distributed.get_global_rank(pp_group, pp_rank)}-&gt;{torch.distributed.get_global_rank(pp_group, pp_rank + 1)}")
        else:
            hidden_states = torch.empty(shape, device=device, dtype=dtype)
            torch.distributed.recv(hidden_states, group_src=pp_rank - 1, group=pp_group)
            print(f"recv {torch.distributed.get_global_rank(pp_group, pp_rank - 1)}-&gt;{torch.distributed.get_global_rank(pp_group, pp_rank)}")
    elif choice == 1:  # get stuck too
        if pp_rank == 0:
            hidden_states = torch.randn(shape, device=device, dtype=dtype)
            group_dst = torch.distributed.get_global_rank(pp_group, pp_rank + 1)
            torch.distributed.send(hidden_states, dst=group_dst, group=pp_group)
            print(f"send {torch.distributed.get_global_rank(pp_group, pp_rank)}-&gt;{torch.distributed.get_global_rank(pp_group, pp_rank + 1)}")
        else:
            hidden_states = torch.empty(shape, device=device, dtype=dtype)
            group_src = torch.distributed.get_global_rank(pp_group, pp_rank - 1)
            torch.distributed.recv(hidden_states, src=group_src, group=pp_group)
            print(f"recv {torch.distributed.get_global_rank(pp_group, pp_rank - 1)}-&gt;{torch.distributed.get_global_rank(pp_group, pp_rank)}")
    else:  # works
        if pp_rank == 0:
            hidden_states = torch.randn(shape, device=device, dtype=dtype)
            group_dst = torch.distributed.get_global_rank(pp_group, pp_rank + 1)
            torch.distributed.send(hidden_states, dst=group_dst)
            print(f"send {torch.distributed.get_global_rank(pp_group, pp_rank)}-&gt;{torch.distributed.get_global_rank(pp_group, pp_rank + 1)}")
        else:
            hidden_states = torch.empty(shape, device=device, dtype=dtype)
            group_src = torch.distributed.get_global_rank(pp_group, pp_rank - 1)
            torch.distributed.recv(hidden_states, src=group_src)
            print(f"recv {torch.distributed.get_global_rank(pp_group, pp_rank - 1)}-&gt;{torch.distributed.get_global_rank(pp_group, pp_rank)}")

    torch.distributed.barrier()
    print(f"rank {rank}, pp_rank {pp_rank}, hidden_states: {hidden_states}", flush=True)


if __name__ == "__main__":
    demo()

</code></pre>

H-Huang: <p>I just tried all 3 options on my machine locally and they all worked.</p>
<p>Are you hitting this when doing this across nodes? My guess is that there is a hang on nccl initialization. To figure out where the hang is happening, I would run your script and then use py-spy to inspect the callstack each process is hung on.</p>

heisenberg_bonker: <p>my env</p>
<pre><code class="lang-auto">
Collecting environment information...
PyTorch version: 2.8.0+cu128
Is debug build: False
CUDA used to build PyTorch: 12.8
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.5 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version: Could not collect
CMake version: version 3.22.1
Libc version: glibc-2.35

Python version: 3.12.3 | packaged by Anaconda, Inc. | (main, May  6 2024, 19:46:43) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-5.15.0-91-generic-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration:
GPU 0: NVIDIA L20
GPU 1: NVIDIA L20
GPU 2: NVIDIA L20
GPU 3: NVIDIA L20

Nvidia driver version: 580.65.06
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.9.8.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.8.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn.so.9.8.0
/usr/lib/x86_64-linux-gnu/libcudnn_engines_precompiled.so.9.8.0
/usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.8.0
/usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.8.0
/usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.8.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.8.0
Is XPU available: False
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

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):                             180
On-line CPU(s) list:                0-179
Vendor ID:                          GenuineIntel
Model name:                         Intel(R) Xeon(R) Platinum 8457C
CPU family:                         6
Model:                              143
Thread(s) per core:                 2
Core(s) per socket:                 45
Socket(s):                          2
Stepping:                           8
BogoMIPS:                           5200.00
Flags:                              fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch cpuid_fault invpcid_single ssbd ibrs ibpb stibp ibrs_enhanced fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves avx_vnni avx512_bf16 wbnoinvd arat avx512vbmi umip pku ospke waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq la57 rdpid cldemote movdiri movdir64b fsrm md_clear serialize tsxldtrk arch_lbr amx_bf16 avx512_fp16 amx_tile amx_int8 arch_capabilities
Hypervisor vendor:                  KVM
Virtualization type:                full
L1d cache:                          4.2 MiB (90 instances)
L1i cache:                          2.8 MiB (90 instances)
L2 cache:                           180 MiB (90 instances)
L3 cache:                           195 MiB (2 instances)
NUMA node(s):                       2
NUMA node0 CPU(s):                  0-89
NUMA node1 CPU(s):                  90-179
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:      Unknown: No mitigations
Vulnerability Retbleed:             Not affected
Vulnerability Spec rstack overflow: Not affected
Vulnerability Spec store bypass:    Mitigation; Speculative Store Bypass disabled via prctl and seccomp
Vulnerability Spectre v1:           Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:           Mitigation; Enhanced IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS SW sequence
Vulnerability Srbds:                Not affected
Vulnerability Tsx async abort:      Mitigation; TSX disabled

Versions of relevant libraries:
[pip3] numpy==2.3.2
[pip3] nvidia-cublas-cu12==12.8.4.1
[pip3] nvidia-cuda-cupti-cu12==12.8.90
[pip3] nvidia-cuda-nvrtc-cu12==12.8.93
[pip3] nvidia-cuda-runtime-cu12==12.8.90
[pip3] nvidia-cudnn-cu12==9.10.2.21
[pip3] nvidia-cufft-cu12==11.3.3.83
[pip3] nvidia-curand-cu12==10.3.9.90
[pip3] nvidia-cusolver-cu12==11.7.3.90
[pip3] nvidia-cusparse-cu12==12.5.8.93
[pip3] nvidia-cusparselt-cu12==0.7.1
[pip3] nvidia-nccl-cu12==2.27.3
[pip3] nvidia-nvjitlink-cu12==12.8.93
[pip3] nvidia-nvtx-cu12==12.8.90
[pip3] torch==2.8.0+cu128
[pip3] torchvision==0.23.0+cu128
[pip3] triton==3.4.0
[conda] numpy                     2.3.2                    pypi_0    pypi
[conda] nvidia-cublas-cu12        12.8.4.1                 pypi_0    pypi
[conda] nvidia-cuda-cupti-cu12    12.8.90                  pypi_0    pypi
[conda] nvidia-cuda-nvrtc-cu12    12.8.93                  pypi_0    pypi
[conda] nvidia-cuda-runtime-cu12  12.8.90                  pypi_0    pypi
[conda] nvidia-cudnn-cu12         9.10.2.21                pypi_0    pypi
[conda] nvidia-cufft-cu12         11.3.3.83                pypi_0    pypi
[conda] nvidia-curand-cu12        10.3.9.90                pypi_0    pypi
[conda] nvidia-cusolver-cu12      11.7.3.90                pypi_0    pypi
[conda] nvidia-cusparse-cu12      12.5.8.93                pypi_0    pypi
[conda] nvidia-cusparselt-cu12    0.7.1                    pypi_0    pypi
[conda] nvidia-nccl-cu12          2.27.3                   pypi_0    pypi
[conda] nvidia-nvjitlink-cu12     12.8.93                  pypi_0    pypi
[conda] nvidia-nvtx-cu12          12.8.90                  pypi_0    pypi
[conda] torch                     2.8.0+cu128              pypi_0    pypi
[conda] torchvision               0.23.0+cu128             pypi_0    pypi
[conda] triton                    3.4.0                    pypi_0    pypi
</code></pre>

heisenberg_bonker: <p>The same issue occurs on H20 and H100 GPUs.</p>
<h2><a name="p-475510-h20-1" class="anchor" href="#p-475510-h20-1"></a>H20</h2>
<pre><code class="lang-auto">Collecting environment information...
PyTorch version: 2.8.0+cu128
Is debug build: False
CUDA used to build PyTorch: 12.8
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.5 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version: Could not collect
CMake version: version 3.22.1
Libc version: glibc-2.35

Python version: 3.12.3 | packaged by Anaconda, Inc. | (main, May  6 2024, 19:46:43) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-5.15.0-130-generic-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration:
GPU 0: NVIDIA H20
GPU 1: NVIDIA H20
GPU 2: NVIDIA H20
GPU 3: NVIDIA H20

Nvidia driver version: 580.65.06
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.9.8.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.8.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn.so.9.8.0
/usr/lib/x86_64-linux-gnu/libcudnn_engines_precompiled.so.9.8.0
/usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.8.0
/usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.8.0
/usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.8.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.8.0
Is XPU available: False
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

CPU:
Architecture:                         x86_64
CPU op-mode(s):                       32-bit, 64-bit
Address sizes:                        52 bits physical, 48 bits virtual
Byte Order:                           Little Endian
CPU(s):                               128
On-line CPU(s) list:                  0-127
Vendor ID:                            AuthenticAMD
Model name:                           AMD EPYC 9K84 96-Core Processor
CPU family:                           25
Model:                                17
Thread(s) per core:                   2
Core(s) per socket:                   32
Socket(s):                            2
Stepping:                             0
BogoMIPS:                             5200.08
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 nopl nonstop_tsc cpuid extd_apicid amd_dcm tsc_known_freq pni pclmulqdq monitor ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw topoext perfctr_core invpcid_single ibpb vmmcall fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 avx512_bf16 clzero xsaveerptr wbnoinvd arat avx512vbmi umip avx512_vbmi2 vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq rdpid fsrm
Hypervisor vendor:                    KVM
Virtualization type:                  full
L1d cache:                            2 MiB (64 instances)
L1i cache:                            2 MiB (64 instances)
L2 cache:                             64 MiB (64 instances)
L3 cache:                             256 MiB (8 instances)
NUMA node(s):                         2
NUMA node0 CPU(s):                    0-63
NUMA node1 CPU(s):                    64-127
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 Reg file data sampling: Not affected
Vulnerability Retbleed:               Not affected
Vulnerability Spec rstack overflow:   Mitigation; safe RET, no microcode
Vulnerability Spec store bypass:      Vulnerable
Vulnerability Spectre v1:             Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:             Mitigation; Retpolines; IBPB conditional; STIBP disabled; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected
Vulnerability Srbds:                  Not affected
Vulnerability Tsx async abort:        Not affected

Versions of relevant libraries:
[pip3] numpy==2.3.2
[pip3] nvidia-cublas-cu12==12.8.4.1
[pip3] nvidia-cuda-cupti-cu12==12.8.90
[pip3] nvidia-cuda-nvrtc-cu12==12.8.93
[pip3] nvidia-cuda-runtime-cu12==12.8.90
[pip3] nvidia-cudnn-cu12==9.10.2.21
[pip3] nvidia-cufft-cu12==11.3.3.83
[pip3] nvidia-curand-cu12==10.3.9.90
[pip3] nvidia-cusolver-cu12==11.7.3.90
[pip3] nvidia-cusparse-cu12==12.5.8.93
[pip3] nvidia-cusparselt-cu12==0.7.1
[pip3] nvidia-nccl-cu12==2.27.3
[pip3] nvidia-nvjitlink-cu12==12.8.93
[pip3] nvidia-nvtx-cu12==12.8.90
[pip3] torch==2.8.0+cu128
[pip3] torchvision==0.23.0+cu128
[pip3] triton==3.4.0
[conda] numpy                     2.3.2                    pypi_0    pypi
[conda] nvidia-cublas-cu12        12.8.4.1                 pypi_0    pypi
[conda] nvidia-cuda-cupti-cu12    12.8.90                  pypi_0    pypi
[conda] nvidia-cuda-nvrtc-cu12    12.8.93                  pypi_0    pypi
[conda] nvidia-cuda-runtime-cu12  12.8.90                  pypi_0    pypi
[conda] nvidia-cudnn-cu12         9.10.2.21                pypi_0    pypi
[conda] nvidia-cufft-cu12         11.3.3.83                pypi_0    pypi
[conda] nvidia-curand-cu12        10.3.9.90                pypi_0    pypi
[conda] nvidia-cusolver-cu12      11.7.3.90                pypi_0    pypi
[conda] nvidia-cusparse-cu12      12.5.8.93                pypi_0    pypi
[conda] nvidia-cusparselt-cu12    0.7.1                    pypi_0    pypi
[conda] nvidia-nccl-cu12          2.27.3                   pypi_0    pypi
[conda] nvidia-nvjitlink-cu12     12.8.93                  pypi_0    pypi
[conda] nvidia-nvtx-cu12          12.8.90                  pypi_0    pypi
[conda] torch                     2.8.0+cu128              pypi_0    pypi
[conda] torchvision               0.23.0+cu128             pypi_0    pypi
[conda] triton                    3.4.0                    pypi_0    pypi
</code></pre>
<h2><a name="p-475510-h100-2" class="anchor" href="#p-475510-h100-2"></a>H100</h2>
<pre><code class="lang-auto">Collecting environment information...

CUDA runtime version: 12.8.93
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration:
GPU 0: NVIDIA H100 80GB HBM3
GPU 1: NVIDIA H100 80GB HBM3
GPU 2: NVIDIA H100 80GB HBM3
GPU 3: NVIDIA H100 80GB HBM3

Nvidia driver version: 570.172.08
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.9.8.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.8.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn.so.9.8.0
/usr/lib/x86_64-linux-gnu/libcudnn_engines_precompiled.so.9.8.0
/usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.8.0
/usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.8.0
/usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.8.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.8.0
Is XPU available: False
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

CPU:
Architecture:                            x86_64
CPU op-mode(s):                          32-bit, 64-bit
Address sizes:                           46 bits physical, 57 bits virtual
Byte Order:                              Little Endian
CPU(s):                                  208
On-line CPU(s) list:                     0-207
Vendor ID:                               GenuineIntel
Model name:                              Intel(R) Xeon(R) Platinum 8470
CPU family:                              6
Model:                                   143
Thread(s) per core:                      2
Core(s) per socket:                      52
Socket(s):                               2
Stepping:                                8
BogoMIPS:                                4000.00
Flags:                                   fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cat_l2 cdp_l3 invpcid_single intel_ppin cdp_l2 ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb intel_pt avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local split_lock_detect avx_vnni avx512_bf16 wbnoinvd dtherm ida arat pln pts avx512vbmi umip pku ospke waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq la57 rdpid bus_lock_detect cldemote movdiri movdir64b enqcmd fsrm md_clear serialize tsxldtrk pconfig arch_lbr amx_bf16 avx512_fp16 amx_tile amx_int8 flush_l1d arch_capabilities
Virtualization:                          VT-x
L1d cache:                               4.9 MiB (104 instances)
L1i cache:                               3.3 MiB (104 instances)
L2 cache:                                208 MiB (104 instances)
L3 cache:                                210 MiB (2 instances)
NUMA node(s):                            2
NUMA node0 CPU(s):                       0-51,104-155
NUMA node1 CPU(s):                       52-103,156-207
Vulnerability Gather data sampling:      Not affected
Vulnerability Indirect target selection: 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 Reg file data sampling:    Not affected
Vulnerability Retbleed:                  Not affected
Vulnerability Spec rstack overflow:      Not affected
Vulnerability Spec store bypass:         Mitigation; Speculative Store Bypass disabled via prctl and seccomp
Vulnerability Spectre v1:                Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:                Mitigation; Enhanced / Automatic IBRS; IBPB disabled; PBRSB-eIBRS SW sequence; BHI BHI_DIS_S
Vulnerability Srbds:                     Not affected
Vulnerability Tsx async abort:           Not affected

Versions of relevant libraries:
[pip3] numpy==2.1.2
[pip3] nvidia-cublas-cu12==12.8.3.14
[pip3] nvidia-cuda-cupti-cu12==12.8.57
[pip3] nvidia-cuda-nvrtc-cu12==12.8.61
[pip3] nvidia-cuda-runtime-cu12==12.8.57
[pip3] nvidia-cudnn-cu12==9.7.1.26
[pip3] nvidia-cufft-cu12==11.3.3.41
[pip3] nvidia-curand-cu12==10.3.9.55
[pip3] nvidia-cusolver-cu12==11.7.2.55
[pip3] nvidia-cusparse-cu12==12.5.7.53
[pip3] nvidia-cusparselt-cu12==0.6.3
[pip3] nvidia-nccl-cu12==2.26.2
[pip3] nvidia-nvjitlink-cu12==12.8.61
[pip3] nvidia-nvtx-cu12==12.8.55
[pip3] torch==2.7.1+cu128
[pip3] torchaudio==2.7.1+cu128
[pip3] torchvision==0.22.1+cu128
[pip3] triton==3.3.1
[conda] numpy                     2.1.2                    pypi_0    pypi
[conda] nvidia-cublas-cu12        12.8.3.14                pypi_0    pypi
[conda] nvidia-cuda-cupti-cu12    12.8.57                  pypi_0    pypi
[conda] nvidia-cuda-nvrtc-cu12    12.8.61                  pypi_0    pypi
[conda] nvidia-cuda-runtime-cu12  12.8.57                  pypi_0    pypi
[conda] nvidia-cudnn-cu12         9.7.1.26                 pypi_0    pypi
[conda] nvidia-cufft-cu12         11.3.3.41                pypi_0    pypi
[conda] nvidia-curand-cu12        10.3.9.55                pypi_0    pypi
[conda] nvidia-cusolver-cu12      11.7.2.55                pypi_0    pypi
[conda] nvidia-cusparse-cu12      12.5.7.53                pypi_0    pypi
[conda] nvidia-cusparselt-cu12    0.6.3                    pypi_0    pypi
[conda] nvidia-nccl-cu12          2.26.2                   pypi_0    pypi
[conda] nvidia-nvjitlink-cu12     12.8.61                  pypi_0    pypi
[conda] nvidia-nvtx-cu12          12.8.55                  pypi_0    pypi
[conda] torch                     2.7.1+cu128              pypi_0    pypi
[conda] torchaudio                2.7.1+cu128              pypi_0    pypi
[conda] torchvision               0.22.1+cu128             pypi_0    pypi
[conda] triton                    3.3.1                    pypi_0    pypi
</code></pre>

heisenberg_bonker: <p>for l20 case, two ranks block at isend/irecv</p>
<p><div class="lightbox-wrapper"><a class="lightbox" href="https://discuss.pytorch.org/uploads/default/original/3X/2/e/2e3ab31f45b185403cb6ac3ed22eee192e39abe3.png" data-download-href="https://discuss.pytorch.org/uploads/default/2e3ab31f45b185403cb6ac3ed22eee192e39abe3" title="image"><img src="https://discuss.pytorch.org/uploads/default/optimized/3X/2/e/2e3ab31f45b185403cb6ac3ed22eee192e39abe3_2_493x500.png" alt="image" data-base62-sha1="6AXJ5co86yqPnkfcUykPB4SPL0v" width="493" height="500" srcset="https://discuss.pytorch.org/uploads/default/optimized/3X/2/e/2e3ab31f45b185403cb6ac3ed22eee192e39abe3_2_493x500.png, https://discuss.pytorch.org/uploads/default/optimized/3X/2/e/2e3ab31f45b185403cb6ac3ed22eee192e39abe3_2_739x750.png 1.5x, https://discuss.pytorch.org/uploads/default/optimized/3X/2/e/2e3ab31f45b185403cb6ac3ed22eee192e39abe3_2_986x1000.png 2x" data-dominant-color="0B0D0B"><div class="meta"><svg class="fa d-icon d-icon-far-image svg-icon" aria-hidden="true"><use href="#far-image"></use></svg><span class="filename">image</span><span class="informations">1284×1300 179 KB</span><svg class="fa d-icon d-icon-discourse-expand svg-icon" aria-hidden="true"><use href="#discourse-expand"></use></svg></div></a></div></p>