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Application Kernel for Containers
https://gvisor.dev
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NV50_P2P allocation class unimplemented in nvproxy #9827

Open thundergolfer opened 10 months ago

thundergolfer commented 10 months ago

Description

When running multi-GPU training on A100s applications can attempt to use the unimplemented NV50_P2P allocation class.

This presents as a 'mapping of buffer object failed' error. 

torch.distributed.DistBackendError: NCCL error in: ../torch/csrc/distributed/c10d/ProcessGroupNCCL.cpp:1331, unhandled cuda error (run with NCCL_DEBUG=INFO for details), NCCL version 2.18.1
ncclUnhandledCudaError: Call to CUDA function failed.
Last error:
Cuda failure 'mapping of buffer object failed'

I've take a look at the implementation of this allocation class and unfortunately it's non-trivial: https://github.dev/NVIDIA/open-gpu-kernel-modules/blob/4c29105335610933e744f4ab2524ea63fc39edaf/src/common/sdk/nvidia/inc/class/cl503b.h#L57

Opening this as a tracking issue.

Steps to reproduce

Dockerfile

This Dockerfile runs but I wasn't able to use it to reproduce the issue because I couldn't get an on-demand multi-GPU A100 VM in GCP 😓. 

FROM nvidia/cuda:12.2.0-devel-ubuntu20.04

RUN apt-get update && apt-get install --yes python3 python3-distutils clang wget vim
RUN wget https://bootstrap.pypa.io/get-pip.py
RUN python3 get-pip.py
RUN python3 -m pip install clang~=10.0.1 # must match version of `clang` installed above.
RUN python3 -m pip install --ignore-installed torch torchvision lightning numpy memory_profiler

COPY <<EOF repro.py
print("Hello from inside container.")
import psutil
current_process = psutil.Process()
parent_process = current_process.parent()
print(f"Processes: {current_process=} {parent_process=}")

import time

import torch
import torch.nn as nn
import torch.nn.functional as F
import lightning as L

from memory_profiler import profile

from torchvision.datasets import CIFAR100
from torchvision import transforms
from torchvision import models
from torch.utils.data import DataLoader

class MagixNet(L.LightningModule):
    def __init__(self, nbr_cat):
        super().__init__()

        module = models.resnet50(weights=models.ResNet50_Weights.DEFAULT)
        module.fc = nn.Linear(2048, nbr_cat)

        self.module = module

    def forward(self, x):
        return self.module(x)

    def training_step(self, batch, batch_idx):
        x, y = batch
        y_hat = self(x)
        loss = F.cross_entropy(y_hat, y)
        return loss

    def configure_optimizers(self):
        return torch.optim.Adam(self.parameters(), lr=0.02)

def prepare_data():

    pipeline = transforms.Compose([
        transforms.Resize((224, 224)),
        transforms.ToTensor(),
    ])

    train_ds = CIFAR100('data', train=True, download=True, transform=pipeline)
    train_dl = DataLoader(train_ds, batch_size=128, num_workers=4)

    val_ds = CIFAR100('data', train=False, download=True, transform=pipeline)
    val_dl = DataLoader(val_ds, batch_size=128, num_workers=4)

    return train_dl, val_dl

torch.set_float32_matmul_precision('medium')
train_dl, val_dl = prepare_data()
model = MagixNet(100)
trainer = L.Trainer(max_epochs=1, strategy="ddp_notebook")

start  = time.time()
trainer.fit(model, train_dl, val_dl)
time.sleep(20)
end = time.time()

training_duration =  end - start

print(f"Training duration (seconds): {training_duration}")
EOF

ENTRYPOINT ["python3", "repro.py"]

On Modal this script reproduces the issue most of the time. I was able to observe the unknown allocation class logline by running this script with debug logs enabled and then jumping onto the worker it ran on to read them.

import modal

stub = modal.Stub("repro-mod-1860")

# TODO: pin these deps for a better repro
DEPS = [
    "torch",
    "torchvision",
    "lightning",
    "numpy",
    "memory_profiler",
]

runtime = modal.Image.debian_slim().pip_install(*DEPS)

with runtime.run_inside():

    print("Hello from inside container.")
    import psutil
    current_process = psutil.Process()
    parent_process = current_process.parent()
    print(f"Processes: {current_process=} {parent_process=}")

    import time

    import torch
    import torch.nn as nn
    import torch.nn.functional as F
    import lightning as L

    from memory_profiler import profile

    from torchvision.datasets import CIFAR100
    from torchvision import transforms
    from torchvision import models
    from torch.utils.data import DataLoader

    class MagixNet(L.LightningModule):
        def __init__(self, nbr_cat):
            super().__init__()

            module = models.resnet50(weights=models.ResNet50_Weights.DEFAULT)
            module.fc = nn.Linear(2048, nbr_cat)

            self.module = module

        def forward(self, x):
            return self.module(x)

        def training_step(self, batch, batch_idx):
            x, y = batch
            y_hat = self(x)
            loss = F.cross_entropy(y_hat, y)
            return loss

        def configure_optimizers(self):
            return torch.optim.Adam(self.parameters(), lr=0.02)

def prepare_data():

    pipeline = transforms.Compose([
        transforms.Resize((224, 224)),
        transforms.ToTensor(),
    ])

    train_ds = CIFAR100('data', train=True, download=True, transform=pipeline)
    train_dl = DataLoader(train_ds, batch_size=128, num_workers=4)

    val_ds = CIFAR100('data', train=False, download=True, transform=pipeline)
    val_dl = DataLoader(val_ds, batch_size=128, num_workers=4)

    return train_dl, val_dl

@stub.function(image=runtime, gpu=modal.gpu.A100(count=2), timeout=86400)
def train():
    torch.set_float32_matmul_precision('medium')
    train_dl, val_dl = prepare_data()
    model = MagixNet(100)
    trainer = L.Trainer(max_epochs=1, strategy="ddp_notebook")

    start  = time.time()
    trainer.fit(model, train_dl, val_dl)
    time.sleep(20)
    end = time.time()

    training_duration =  end - start

    print(f"Training duration (seconds): {training_duration}")

@stub.local_entrypoint()
def main():
    train.remote()

runsc version

runsc version release-20230717.0-12-g0244c8c19fb7
spec: 1.1.0-rc.1

docker version (if using docker)

No response

uname

No response

runsc debug logs (if available)

Omitted. Logs show unknown allocation class for 0x0000503b, which is NV50_P2P.

ayushr2 commented 10 months ago

Let me know if https://github.com/google/gvisor/pull/9828 solves this issue. Do you know if this will repro on T4 as well? Hard to get a hold of A100. But I will try again tomorrow.

ayushr2 commented 10 months ago

I tried running the above mentioned Docker image on an A100 with runsc, it segfaults and crashes with a different error:

Traceback (most recent call last):
  File "repro.py", line 65, in <module>
    trainer.fit(model, train_dl, val_dl)
  File "/usr/local/lib/python3.8/dist-packages/lightning/pytorch/trainer/trainer.py", line 544, in fit
    call._call_and_handle_interrupt(
  File "/usr/local/lib/python3.8/dist-packages/lightning/pytorch/trainer/call.py", line 43, in _call_and_handle_interrupt
    return trainer.strategy.launcher.launch(trainer_fn, *args, trainer=trainer, **kwargs)
  File "/usr/local/lib/python3.8/dist-packages/lightning/pytorch/strategies/launchers/multiprocessing.py", line 141, in launch
    while not process_context.join():
  File "/usr/local/lib/python3.8/dist-packages/torch/multiprocessing/spawn.py", line 145, in join
    raise ProcessExitedException(
torch.multiprocessing.spawn.ProcessExitedException: process 0 terminated with signal SIGSEGV

The boot logs show that a different set of ioctls are not implemented:

$ cat /tmp/logs/runsc.log.20231220-181102.011243.boot.txt | grep "nvproxy: unknown"
W1220 18:11:35.603862   18656 frontend.go:683] [  15:  15] nvproxy: unknown allocation class 0x0000cb33
W1220 18:11:35.616556   18656 frontend.go:521] [  15:  15] nvproxy: unknown control command 0x2080182b (paramsSize=20)
ayushr2 commented 10 months ago

What Nvidia driver version is being used at Modal? I was testing on 525.105.17.

ayushr2 commented 10 months ago

0x0000cb33 allocation class is NV_CONFIDENTIAL_COMPUTE, which was only added in 535.43.02.
So I tested again with 535.54.03 driver, and the above mentioned unknown ioctls dissapeared. However, the segfault persists. This does not happen with runc. I can reproduce this segfault on a T4 GPU too.

Here is the relevant logs from the segfault (looks like a null pointer dereference):

Boot logs ``` D1220 18:48:20.286367 31376 task_run.go:312] [ 15: 32] Unhandled user fault: addr=0 ip=7eb1f7de9ddb access=r-- sig=11 err=bad address D1220 18:48:20.286470 31376 task_log.go:87] [ 15: 32] Registers: D1220 18:48:20.286506 31376 task_log.go:94] [ 15: 32] Cs = 0000000000000033 D1220 18:48:20.286513 31376 task_log.go:94] [ 15: 32] Ds = 0000000000000000 D1220 18:48:20.286519 31376 task_log.go:94] [ 15: 32] Eflags = 0000000000010246 D1220 18:48:20.286524 31376 task_log.go:94] [ 15: 32] Es = 0000000000000000 D1220 18:48:20.286529 31376 task_log.go:94] [ 15: 32] Fs = 0000000000000000 D1220 18:48:20.286534 31376 task_log.go:94] [ 15: 32] Fs_base = 00007eb110c00700 D1220 18:48:20.286539 31376 task_log.go:94] [ 15: 32] Gs = 0000000000000000 D1220 18:48:20.286543 31376 task_log.go:94] [ 15: 32] Gs_base = 0000000000000000 D1220 18:48:20.286548 31376 task_log.go:94] [ 15: 32] Orig_rax = ffffffffffffffff D1220 18:48:20.286552 31376 task_log.go:94] [ 15: 32] R10 = 0000000000000000 D1220 18:48:20.286558 31376 task_log.go:94] [ 15: 32] R11 = 0000000000000000 D1220 18:48:20.286562 31376 task_log.go:94] [ 15: 32] R12 = 0000000000000000 D1220 18:48:20.286566 31376 task_log.go:94] [ 15: 32] R13 = 00007eb0b9d39460 D1220 18:48:20.286570 31376 task_log.go:94] [ 15: 32] R14 = 000000000e39cae0 D1220 18:48:20.286574 31376 task_log.go:94] [ 15: 32] R15 = 0000000000000000 D1220 18:48:20.286579 31376 task_log.go:94] [ 15: 32] R8 = 0000000000000000 D1220 18:48:20.286583 31376 task_log.go:94] [ 15: 32] R9 = 0000000000000001 D1220 18:48:20.286587 31376 task_log.go:94] [ 15: 32] Rax = 0000000000000000 D1220 18:48:20.286591 31376 task_log.go:94] [ 15: 32] Rbp = 0000000000000000 D1220 18:48:20.286595 31376 task_log.go:94] [ 15: 32] Rbx = 00007eb110bdeec0 D1220 18:48:20.286600 31376 task_log.go:94] [ 15: 32] Rcx = 0000000000000001 D1220 18:48:20.286604 31376 task_log.go:94] [ 15: 32] Rdi = 0000000000000000 D1220 18:48:20.286608 31376 task_log.go:94] [ 15: 32] Rdx = 0000000000000000 D1220 18:48:20.286612 31376 task_log.go:94] [ 15: 32] Rip = 00007eb1f7de9ddb D1220 18:48:20.286616 31376 task_log.go:94] [ 15: 32] Rsi = 00007eb0b9d394f0 D1220 18:48:20.286620 31376 task_log.go:94] [ 15: 32] Rsp = 00007eb110bdee90 D1220 18:48:20.286624 31376 task_log.go:94] [ 15: 32] Ss = 000000000000002b D1220 18:48:20.286628 31376 task_log.go:111] [ 15: 32] Stack: D1220 18:48:20.286642 31376 task_log.go:128] [ 15: 32] 7eb110bdee90: c0 ee bd 10 b1 7e 00 00 0c 00 00 00 00 00 00 00 D1220 18:48:20.286649 31376 task_log.go:128] [ 15: 32] 7eb110bdeea0: 00 00 00 00 00 00 00 00 de 37 48 55 b1 7e 00 00 D1220 18:48:20.286655 31376 task_log.go:128] [ 15: 32] 7eb110bdeeb0: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 D1220 18:48:20.286661 31376 task_log.go:128] [ 15: 32] 7eb110bdeec0: 34 30 38 34 66 34 35 65 37 33 61 61 00 00 00 00 D1220 18:48:20.286667 31376 task_log.go:128] [ 15: 32] 7eb110bdeed0: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 D1220 18:48:20.286672 31376 task_log.go:128] [ 15: 32] 7eb110bdeee0: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 D1220 18:48:20.286678 31376 task_log.go:128] [ 15: 32] 7eb110bdeef0: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 D1220 18:48:20.286683 31376 task_log.go:128] [ 15: 32] 7eb110bdef00: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 D1220 18:48:20.286688 31376 task_log.go:128] [ 15: 32] 7eb110bdef10: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 D1220 18:48:20.286693 31376 task_log.go:128] [ 15: 32] 7eb110bdef20: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 D1220 18:48:20.286698 31376 task_log.go:128] [ 15: 32] 7eb110bdef30: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 D1220 18:48:20.286703 31376 task_log.go:128] [ 15: 32] 7eb110bdef40: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 D1220 18:48:20.286708 31376 task_log.go:128] [ 15: 32] 7eb110bdef50: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 D1220 18:48:20.286713 31376 task_log.go:128] [ 15: 32] 7eb110bdef60: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 D1220 18:48:20.286718 31376 task_log.go:128] [ 15: 32] 7eb110bdef70: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 D1220 18:48:20.286724 31376 task_log.go:128] [ 15: 32] 7eb110bdef80: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 D1220 18:48:20.286729 31376 task_log.go:128] [ 15: 32] 7eb110bdef90: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 D1220 18:48:20.286734 31376 task_log.go:128] [ 15: 32] 7eb110bdefa0: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 D1220 18:48:20.286740 31376 task_log.go:128] [ 15: 32] 7eb110bdefb0: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 D1220 18:48:20.286745 31376 task_log.go:128] [ 15: 32] 7eb110bdefc0: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 D1220 18:48:20.286749 31376 task_log.go:128] [ 15: 32] 7eb110bdefd0: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 D1220 18:48:20.286755 31376 task_log.go:128] [ 15: 32] 7eb110bdefe0: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 D1220 18:48:20.286760 31376 task_log.go:128] [ 15: 32] 7eb110bdeff0: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 D1220 18:48:20.286767 31376 task_log.go:128] [ 15: 32] 7eb110bdf000: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 D1220 18:48:20.286772 31376 task_log.go:128] [ 15: 32] 7eb110bdf010: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 D1220 18:48:20.286777 31376 task_log.go:128] [ 15: 32] 7eb110bdf020: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 D1220 18:48:20.286782 31376 task_log.go:128] [ 15: 32] 7eb110bdf030: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 D1220 18:48:20.286787 31376 task_log.go:128] [ 15: 32] 7eb110bdf040: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 D1220 18:48:20.286792 31376 task_log.go:128] [ 15: 32] 7eb110bdf050: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 D1220 18:48:20.286797 31376 task_log.go:128] [ 15: 32] 7eb110bdf060: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 D1220 18:48:20.286802 31376 task_log.go:128] [ 15: 32] 7eb110bdf070: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 D1220 18:48:20.286806 31376 task_log.go:128] [ 15: 32] 7eb110bdf080: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 D1220 18:48:20.286811 31376 task_log.go:128] [ 15: 32] 7eb110bdf090: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 D1220 18:48:20.286816 31376 task_log.go:128] [ 15: 32] 7eb110bdf0a0: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 D1220 18:48:20.286824 31376 task_log.go:128] [ 15: 32] 7eb110bdf0b0: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 D1220 18:48:20.286829 31376 task_log.go:128] [ 15: 32] 7eb110bdf0c0: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 D1220 18:48:20.286834 31376 task_log.go:128] [ 15: 32] 7eb110bdf0d0: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 D1220 18:48:20.286839 31376 task_log.go:128] [ 15: 32] 7eb110bdf0e0: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 D1220 18:48:20.286844 31376 task_log.go:128] [ 15: 32] 7eb110bdf0f0: 00 00 00 00 00 00 00 00 bc 8c 2b ba b1 7e 00 00 D1220 18:48:20.286849 31376 task_log.go:128] [ 15: 32] 7eb110bdf100: 00 00 00 00 00 00 00 00 e7 03 00 00 00 00 00 00 D1220 18:48:20.286854 31376 task_log.go:128] [ 15: 32] 7eb110bdf110: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 D1220 18:48:20.286859 31376 task_log.go:128] [ 15: 32] 7eb110bdf120: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 D1220 18:48:20.286864 31376 task_log.go:128] [ 15: 32] 7eb110bdf130: 60 0b 00 c4 b0 7e 00 00 00 00 00 00 00 00 00 00 D1220 18:48:20.286868 31376 task_log.go:128] [ 15: 32] 7eb110bdf140: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 D1220 18:48:20.286873 31376 task_log.go:128] [ 15: 32] 7eb110bdf150: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 D1220 18:48:20.286878 31376 task_log.go:128] [ 15: 32] 7eb110bdf160: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 D1220 18:48:20.286883 31376 task_log.go:128] [ 15: 32] 7eb110bdf170: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 D1220 18:48:20.286888 31376 task_log.go:128] [ 15: 32] 7eb110bdf180: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 D1220 18:48:20.286892 31376 task_log.go:128] [ 15: 32] 7eb110bdf190: 00 00 00 00 00 00 00 00 49 a0 56 55 b1 7e 00 00 D1220 18:48:20.286897 31376 task_log.go:128] [ 15: 32] 7eb110bdf1a0: 00 00 00 00 00 00 00 00 d7 1a 52 55 b1 7e 00 00 D1220 18:48:20.286902 31376 task_log.go:128] [ 15: 32] 7eb110bdf1b0: 80 00 00 00 00 00 00 00 77 f3 4f 55 b1 7e 00 00 D1220 18:48:20.286907 31376 task_log.go:128] [ 15: 32] 7eb110bdf1c0: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 D1220 18:48:20.286912 31376 task_log.go:128] [ 15: 32] 7eb110bdf1d0: 00 00 00 00 00 00 00 00 80 14 f9 0d 00 00 00 00 D1220 18:48:20.286917 31376 task_log.go:128] [ 15: 32] 7eb110bdf1e0: a0 c7 ed 00 00 00 00 00 64 94 d3 b9 b0 7e 00 00 D1220 18:48:20.286923 31376 task_log.go:128] [ 15: 32] 7eb110bdf1f0: 80 00 00 00 00 00 00 00 a0 c6 53 55 b1 7e 00 00 D1220 18:48:20.286927 31376 task_log.go:128] [ 15: 32] 7eb110bdf200: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 D1220 18:48:20.286932 31376 task_log.go:128] [ 15: 32] 7eb110bdf210: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 D1220 18:48:20.286937 31376 task_log.go:128] [ 15: 32] 7eb110bdf220: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 D1220 18:48:20.286942 31376 task_log.go:128] [ 15: 32] 7eb110bdf230: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 D1220 18:48:20.286947 31376 task_log.go:128] [ 15: 32] 7eb110bdf240: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 D1220 18:48:20.286951 31376 task_log.go:128] [ 15: 32] 7eb110bdf250: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 D1220 18:48:20.286957 31376 task_log.go:128] [ 15: 32] 7eb110bdf260: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 D1220 18:48:20.286962 31376 task_log.go:128] [ 15: 32] 7eb110bdf270: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 D1220 18:48:20.286968 31376 task_log.go:128] [ 15: 32] 7eb110bdf280: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 D1220 18:48:20.286972 31376 task_log.go:149] [ 15: 32] Code: D1220 18:48:20.286978 31376 task_log.go:167] [ 15: 32] 7eb1f7de9d90: 4d 00 00 02 00 00 e9 50 ff ff ff 0f 1f 44 00 00 D1220 18:48:20.286984 31376 task_log.go:167] [ 15: 32] 7eb1f7de9da0: 48 8b 1d c9 a0 16 00 8b 7d 70 64 44 8b 23 e8 cd D1220 18:48:20.286989 31376 task_log.go:167] [ 15: 32] 7eb1f7de9db0: df 08 00 64 44 89 23 85 c0 75 d4 e9 2b ff ff ff D1220 18:48:20.286998 31376 task_log.go:167] [ 15: 32] 7eb1f7de9dc0: e8 9b bd 00 00 e9 fd fe ff ff e8 c1 de 0a 00 90 D1220 18:48:20.287003 31376 task_log.go:167] [ 15: 32] 7eb1f7de9dd0: f3 0f 1e fa 41 54 55 48 89 fd 53 8b 07 f6 c4 20 D1220 18:48:20.287008 31376 task_log.go:167] [ 15: 32] 7eb1f7de9de0: 0f 85 ea 00 00 00 89 c2 81 e2 00 80 00 00 0f 84 D1220 18:48:20.287012 31376 task_log.go:167] [ 15: 32] 7eb1f7de9df0: e9 00 00 00 c1 e0 1a c1 f8 1f 41 89 c4 85 d2 75 D1220 18:48:20.287017 31376 task_log.go:167] [ 15: 32] 7eb1f7de9e00: 11 48 8b bd 88 00 00 00 83 6f 04 01 0f 84 3e 01 D1220 18:48:20.287021 31376 task_log.go:71] [ 15: 32] Mappings: VMAs: 00060000-00065000 r-xp 00000000 00:00 0 [usertrap] 00400000-00423000 r--p 00000000 00:19 23 /usr/bin/python3.8 00423000-006b5000 r-xp 00023000 00:19 23 /usr/bin/python3.8 006b5000-008ef000 r--p 002b5000 00:19 23 /usr/bin/python3.8 008ef000-008f0000 r--p 004ee000 00:19 23 /usr/bin/python3.8 008f0000-00937000 rw-p 004ef000 00:19 23 /usr/bin/python3.8 00937000-0095a000 rw-p 00000000 00:00 0 0095a000-14799000 rw-p 00000000 00:00 0 [heap] 200000000-200400000 ---p 00000000 00:00 0 200400000-200600000 rw-s 00000000 00:14 20 /dev/nvidia0 200600000-203e00000 rw-s 00000000 00:14 18 /dev/nvidiactl 203e00000-205c00000 ---p 00000000 00:00 0 205c00000-205e00000 rw-s 00000000 00:14 18 /dev/nvidiactl 205e00000-206000000 rw-s 00000000 00:14 18 /dev/nvidiactl 206000000-206200000 rw-s 00000000 00:14 18 /dev/nvidiactl 206200000-206400000 rw-s 206200000 00:14 19 /dev/nvidia-uvm 206400000-206600000 rw-s 00000000 00:14 18 /dev/nvidiactl 206600000-206800000 ---p 00000000 00:00 0 206800000-206a00000 rw-s 00000000 00:14 18 /dev/nvidiactl 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1159b000-11c00000 r--p 39400000 *pgalloc.MemoryFile 11c00000-11da4000 r--p 20ac8000 *pgalloc.MemoryFile 11da4000-12000000 r--p 39000000 *pgalloc.MemoryFile 12000000-121ac000 rw-p 22815000 *pgalloc.MemoryFile 121ac000-12a00000 r--p 38600000 *pgalloc.MemoryFile 12a00000-12aaf000 r--p 20e18000 *pgalloc.MemoryFile 12aaf000-12e00000 r--p 38200000 *pgalloc.MemoryFile 12e00000-12eb1000 r--p 20ec7000 *pgalloc.MemoryFile 12eb1000-13200000 r--p 37e00000 *pgalloc.MemoryFile 13200000-132ba000 r--p 20f78000 *pgalloc.MemoryFile 132ba000-13a00000 r--p 37600000 *pgalloc.MemoryFile 13a00000-13bbd000 r--p 21032000 *pgalloc.MemoryFile 13bbd000-13e00000 r--p 37200000 *pgalloc.MemoryFile 13e00000-13fc0000 rw-p 22ac1000 *pgalloc.MemoryFile 13fc0000-14000000 r--p 36a00000 *pgalloc.MemoryFile 14000000-14200000 rw-p 28c00000 *pgalloc.MemoryFile 14200000-14600000 rw-p 29400000 *pgalloc.MemoryFile 14600000-14799000 rw-p 24197000 *pgalloc.MemoryFile 200400000-200600000 rw-s 00000000 *nvproxy.frontendFDMemmapFile 200600000-203e00000 rw-s 00000000 *nvproxy.frontendFDMemmapFile 205c00000-205e00000 rw-s 00000000 *nvproxy.frontendFDMemmapFile 205e00000-206000000 rw-s 00000000 *nvproxy.frontendFDMemmapFile 206000000-206200000 rw-s 00000000 *nvproxy.frontendFDMemmapFile 206200000-206400000 rw-s 206200000 *nvproxy.uvmFDMemmapFile 206400000-206600000 rw-s 00000000 *nvproxy.frontendFDMemmapFile 206800000-206a00000 r--s 00000000 *nvproxy.frontendFDMemmapFile 7eb0b8000000-7eb0b810f000 rw-p 35eec000 *pgalloc.MemoryFile 7eb0b810f000-7eb0b811c000 rw-p 37040000 *pgalloc.MemoryFile 7eb0b811c000-7eb0b811f000 rw-p 35ffb000 *pgalloc.MemoryFile 7eb0b811f000-7eb0b8133000 rw-p 3704d000 *pgalloc.MemoryFile 7eb0b8133000-7eb0b8134000 rw-p 35ffe000 *pgalloc.MemoryFile 7eb0b8134000-7eb0b814b000 rw-p 37061000 *pgalloc.MemoryFile 7eb0b814b000-7eb0b814c000 rw-p 35fff000 *pgalloc.MemoryFile 7eb0b814c000-7eb0b81df000 rw-p 37078000 *pgalloc.MemoryFile 7eb0b81df000-7eb0b8200000 rw-p 3e523000 *pgalloc.MemoryFile 7eb0b8200000-7eb0b82d2000 rw-p 3710b000 *pgalloc.MemoryFile 7eb0b82d2000-7eb0b82fa000 rw-p 37443000 *pgalloc.MemoryFile 7eb0b82fa000-7eb0b8311000 rw-p 371dd000 *pgalloc.MemoryFile 7eb0b8311000-7eb0b832d000 rw-p 3746b000 *pgalloc.MemoryFile 7eb0b832d000-7eb0b8330000 rw-p 371f4000 *pgalloc.MemoryFile 7eb0b8330000-7eb0b8381000 rw-p 37487000 *pgalloc.MemoryFile 7eb0b8381000-7eb0b8384000 rw-p 371f7000 *pgalloc.MemoryFile 7eb0b8384000-7eb0b8398000 rw-p 374d8000 *pgalloc.MemoryFile 7eb0b8398000-7eb0b839b000 rw-p 371fa000 *pgalloc.MemoryFile 7eb0b839b000-7eb0b83c5000 rw-p 374ec000 *pgalloc.MemoryFile 7eb0b83c5000-7eb0b83c7000 rw-p 371fd000 *pgalloc.MemoryFile 7eb0b83c7000-7eb0b83cb000 rw-p 37516000 *pgalloc.MemoryFile 7eb0b83cb000-7eb0b83cc000 rw-p 371ff000 *pgalloc.MemoryFile 7eb0b83cc000-7eb0b83ea000 rw-p 3751a000 *pgalloc.MemoryFile 7eb0b83ea000-7eb0b8400000 rw-p 3e50d000 *pgalloc.MemoryFile 7eb0b8400000-7eb0b84be000 rw-p 37538000 *pgalloc.MemoryFile 7eb0b84be000-7eb0b84cb000 rw-p 37d46000 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7eb11ae0e000-7eb11ae0f000 rw-s 00000000 *nvproxy.frontendFDMemmapFile 7eb11ae0f000-7eb11ae10000 rw-s 00000000 *nvproxy.frontendFDMemmapFile 7eb11ae10000-7eb11ae11000 rw-s 00000000 *nvproxy.frontendFDMemmapFile 7eb11ae11000-7eb11ae12000 rw-s 00000000 *nvproxy.frontendFDMemmapFile 7eb11ae12000-7eb11ae13000 rw-s 00000000 *nvproxy.frontendFDMemmapFile 7eb11ae13000-7eb11ae14000 rw-s 00000000 *nvproxy.frontendFDMemmapFile 7eb11b800000-7eb11ba00000 rw-p 36400000 *pgalloc.MemoryFile 7eb11ba00000-7eb11ba01000 rw-p 1b3ff000 *pgalloc.MemoryFile 7eb11c200000-7eb11c400000 rw-p 36600000 *pgalloc.MemoryFile 7eb11c400000-7eb11c401000 rw-p 1b3fc000 *pgalloc.MemoryFile 7eb11cc00000-7eb11ce00000 rw-p 36800000 *pgalloc.MemoryFile 7eb11ce00000-7eb11ce01000 rw-p 1abfe000 *pgalloc.MemoryFile 7eb123000000-7eb123182000 r-xs 00000000 *gofer.dentryPlatformFile 7eb123382000-7eb1233dc000 r--p 21588000 *pgalloc.MemoryFile 7eb1233dc000-7eb123400000 r--p 3a8e5000 *pgalloc.MemoryFile 7eb123400000-7eb123e00000 r--p 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*pgalloc.MemoryFile 7eb1f7db1000-7eb1f7dca000 r-xs 00049000 *gofer.dentryPlatformFile 7eb1f7dca000-7eb1f7dcb000 r-xp 10392000 *pgalloc.MemoryFile 7eb1f7dcb000-7eb1f7e45000 r-xs 00063000 *gofer.dentryPlatformFile 7eb1f7e45000-7eb1f7e46000 r-xp 0063c000 *pgalloc.MemoryFile 7eb1f7e46000-7eb1f7e47000 r-xp 003a2000 *pgalloc.MemoryFile 7eb1f7e47000-7eb1f7e4a000 r-xs 000df000 *gofer.dentryPlatformFile 7eb1f7e4a000-7eb1f7e4b000 r-xp 00981000 *pgalloc.MemoryFile 7eb1f7e4b000-7eb1f7e4c000 r-xs 000e3000 *gofer.dentryPlatformFile 7eb1f7e4c000-7eb1f7e4d000 r-xp 004aa000 *pgalloc.MemoryFile 7eb1f7e4d000-7eb1f7e6a000 r-xs 000e5000 *gofer.dentryPlatformFile 7eb1f7e6a000-7eb1f7e6b000 r-xp 0021f000 *pgalloc.MemoryFile 7eb1f7e6b000-7eb1f7e75000 r-xs 00103000 *gofer.dentryPlatformFile 7eb1f7e75000-7eb1f7e76000 r-xp 00296000 *pgalloc.MemoryFile 7eb1f7e76000-7eb1f7e77000 r-xp 10ade000 *pgalloc.MemoryFile 7eb1f7e77000-7eb1f7e78000 r-xp 00298000 *pgalloc.MemoryFile 7eb1f7e7a000-7eb1f7e7b000 r-xp 0e7b9000 *pgalloc.MemoryFile 7eb1f7e7b000-7eb1f7e7c000 r-xp 1abff000 *pgalloc.MemoryFile 7eb1f7e7c000-7eb1f7e7d000 r-xp 00273000 *pgalloc.MemoryFile 7eb1f7e7d000-7eb1f7e80000 r-xs 00115000 *gofer.dentryPlatformFile 7eb1f7e80000-7eb1f7e81000 r-xp 00297000 *pgalloc.MemoryFile 7eb1f7e81000-7eb1f7e87000 r-xs 00119000 *gofer.dentryPlatformFile 7eb1f7e87000-7eb1f7e88000 r-xp 006c1000 *pgalloc.MemoryFile 7eb1f7e88000-7eb1f7e89000 r-xp 10add000 *pgalloc.MemoryFile 7eb1f7e89000-7eb1f7f02000 r-xs 00121000 *gofer.dentryPlatformFile 7eb1f7f02000-7eb1f7f50000 r--s 0019a000 *gofer.dentryPlatformFile 7eb1f7f50000-7eb1f7f5a000 rw-p 106f9000 *pgalloc.MemoryFile 7eb1f7f5e000-7eb1f7f60000 r--p 00757000 *pgalloc.MemoryFile 7eb1f7f60000-7eb1f7f62000 rw-p 1a1fe000 *pgalloc.MemoryFile 7eb1f7f62000-7eb1f7f63000 r--s 00002000 *pgalloc.MemoryFile 7eb1f7f63000-7eb1f7f64000 r-xs 00000000 *pgalloc.MemoryFile 7eb1f7f64000-7eb1f7f65000 r-xp 1b7f3000 *pgalloc.MemoryFile 7eb1f7f65000-7eb1f7f66000 r--p 0015e000 *pgalloc.MemoryFile 7eb1f7f66000-7eb1f7f81000 r-xs 00001000 *gofer.dentryPlatformFile 7eb1f7f81000-7eb1f7f82000 r-xp 00229000 *pgalloc.MemoryFile 7eb1f7f82000-7eb1f7f83000 r-xp 00228000 *pgalloc.MemoryFile 7eb1f7f83000-7eb1f7f84000 r-xp 0022d000 *pgalloc.MemoryFile 7eb1f7f84000-7eb1f7f85000 r-xp 0022a000 *pgalloc.MemoryFile 7eb1f7f85000-7eb1f7f88000 r-xs 00020000 *gofer.dentryPlatformFile 7eb1f7f88000-7eb1f7f89000 r-xp 0015f000 *pgalloc.MemoryFile 7eb1f7f89000-7eb1f7f91000 r--p 00160000 *pgalloc.MemoryFile 7eb1f7f92000-7eb1f7f94000 rw-p 00232000 *pgalloc.MemoryFile 7eb1f7f94000-7eb1f7f95000 rw-p 10ae6000 *pgalloc.MemoryFile 7ed1a4000000-7ed1a40ad000 r--p 0016a000 *pgalloc.MemoryFile 7ed1a40ad000-7ed1a40af000 rw-p 1abfc000 *pgalloc.MemoryFile 7ed1a40af000-7ed1a40b1000 rw-p 1abfa000 *pgalloc.MemoryFile 7ed1a40b1000-7ed1a40b3000 rw-p 10a61000 *pgalloc.MemoryFile 7ed1a40b3000-7ed1a40b4000 rw-p 10a60000 *pgalloc.MemoryFile 7ed1a40b4000-7ed1a40b6000 rw-p 1039d000 *pgalloc.MemoryFile 7ed1a40b6000-7ed1a40b8000 r--p 00220000 *pgalloc.MemoryFile D1220 18:48:20.291737 31376 task_log.go:73] [ 15: 32] FDTable: fd:46 => name pipe:[8] fd:48 => name socket:[28] fd:2 => name host:[3] fd:17 => name pipe:[7] fd:20 => name /dev/nvidiactl fd:23 => name /dev/nvidia0 fd:41 => name /dev/nvidia0 fd:29 => name /dev/nvidia0 fd:35 => name /dev/nvidia0 fd:43 => name /dev/nvidia0 fd:1 => name host:[2] fd:3 => name /dev/nvidiactl fd:7 => name /dev/nvidia0 fd:9 => name pipe:[3] fd:16 => name pipe:[7] fd:49 => name socket:[35] fd:30 => name /dev/nvidia0 fd:32 => name /dev/nvidia0 fd:47 => name socket:[34] fd:10 => name pipe:[4] fd:13 => name pipe:[5] fd:22 => name /dev/nvidia0 fd:24 => name /dev/nvidia0 fd:28 => name /dev/nvidia0 fd:4 => name /dev/nvidia0 fd:8 => name pipe:[3] fd:19 => name socket:[20] fd:39 => name /dev/nvidia0 fd:6 => name /dev/nvidia0 fd:15 => name socket:[18] fd:42 => name /dev/nvidia0 fd:50 => name socket:[36] fd:40 => name /dev/nvidia0 fd:44 => name /dev/nvidia0 fd:45 => name pipe:[8] fd:0 => name host:[1] fd:21 => name /dev/nvidia-uvm fd:27 => name anon_inode:[eventfd] fd:31 => name /dev/nvidia0 fd:36 => name anon_inode:[eventfd] fd:38 => name /dev/nvidia0 fd:11 => name pipe:[4] fd:12 => name /dev/null fd:14 => name pipe:[6] fd:18 => name socket:[19] fd:33 => name /dev/nvidia0 fd:5 => name /dev/nvidia-uvm fd:25 => name anon_inode:[eventfd] fd:26 => name socket:[22] fd:34 => name /dev/nvidia0 fd:37 => name /dev/nvidia0 ```
ayushr2 commented 10 months ago

RIP = 0x7eb1f7de9ddb

VMAs:
...
7eb1f7d68000-7eb1f7d8a000 r--p 00000000 00:19 34                         /usr/lib/x86_64-linux-gnu/libc-2.31.so
7eb1f7d8a000-7eb1f7f02000 r-xp 00022000 00:19 34                         /usr/lib/x86_64-linux-gnu/libc-2.31.so

So we need to look at offset 0x00022000 + (0x7eb1f7de9ddb - 0x7eb1f7d8a000) = 0x81ddb in /usr/lib/x86_64-linux-gnu/libc-2.31.so.

ayushr2 commented 10 months ago

Using objdump -d:

   81dbd:       48 89 44 24 28          mov    %rax,0x28(%rsp)
   81dc2:       8b 44 24 24             mov    0x24(%rsp),%eax
   81dc6:       48 8d 35 1d 42 11 00    lea    0x11421d(%rip),%rsi        # 195fea <_libc_intl_domainname@@GLIBC_2.2.5+0x1012>
   81dcd:       4c 89 ef                mov    %r13,%rdi
   81dd0:       89 c2                   mov    %eax,%edx
   81dd2:       83 c0 01                add    $0x1,%eax
   81dd5:       89 44 24 24             mov    %eax,0x24(%rsp)
   81dd9:       31 c0                   xor    %eax,%eax
   81ddb:       e8 50 1e fd ff          callq  53c30 <fprintf@@GLIBC_2.2.5>      <----- HERE
   81de0:       64 8b 04 25 18 00 00    mov    %fs:0x18,%eax
   81de7:       00
ayushr2 commented 10 months ago

@nixprime pointed out that I was looking at the objdump of the wrong file. He figured out the actual fault instruction:

# objdump -d /usr/lib/x86_64-linux-gnu/libc-2.31.so | less
...
0000000000081dd0 <_IO_fclose@@GLIBC_2.2.5>:
   81dd0:       f3 0f 1e fa             endbr64 
   81dd4:       41 54                   push   %r12
   81dd6:       55                      push   %rbp
   81dd7:       48 89 fd                mov    %rdi,%rbp
   81dda:       53                      push   %rbx
   81ddb:       8b 07                   mov    (%rdi),%eax    <--- here

Per logs %rdi = 0, so the segfault lines up. He found out that if libnccl.so.2:getHostHash() fails to open /proc/sys/kernel/random/boot_id, then it calls fclose(NULL) and takes SIGSEGV.

https://github.com/google/gvisor/pull/9833 fixes this issue, but now we get a different exception:

Epoch 0:   0%|          | 0/391 [00:00<?, ?it/s] ERROR: Unexpected bus error encountered in worker. This might be caused by insufficient shared memory (shm).
ERROR: Unexpected bus error encountered in worker. This might be caused by insufficient shared memory (shm).
ERROR: Unexpected bus error encountered in worker. This might be caused by insufficient shared memory (shm).
ERROR: Unexpected bus error encountered in worker. This might be caused by insufficient shared memory (shm).
Traceback (most recent call last):
  File "repro.py", line 65, in <module>
    trainer.fit(model, train_dl, val_dl)
  File "/usr/local/lib/python3.8/dist-packages/lightning/pytorch/trainer/trainer.py", line 544, in fit
    call._call_and_handle_interrupt(
  File "/usr/local/lib/python3.8/dist-packages/lightning/pytorch/trainer/call.py", line 43, in _call_and_handle_interrupt
    return trainer.strategy.launcher.launch(trainer_fn, *args, trainer=trainer, **kwargs)
  File "/usr/local/lib/python3.8/dist-packages/lightning/pytorch/strategies/launchers/multiprocessing.py", line 141, in launch
    while not process_context.join():
  File "/usr/local/lib/python3.8/dist-packages/torch/multiprocessing/spawn.py", line 163, in join
    raise ProcessRaisedException(msg, error_index, failed_process.pid)
torch.multiprocessing.spawn.ProcessRaisedException: 

-- Process 0 terminated with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py", line 1132, in _try_get_data
    data = self._data_queue.get(timeout=timeout)
  File "/usr/lib/python3.8/multiprocessing/queues.py", line 107, in get
    if not self._poll(timeout):
  File "/usr/lib/python3.8/multiprocessing/connection.py", line 257, in poll
    return self._poll(timeout)
  File "/usr/lib/python3.8/multiprocessing/connection.py", line 424, in _poll
    r = wait([self], timeout)
  File "/usr/lib/python3.8/multiprocessing/connection.py", line 931, in wait
    ready = selector.select(timeout)
  File "/usr/lib/python3.8/selectors.py", line 415, in select
    fd_event_list = self._selector.poll(timeout)
  File "/usr/local/lib/python3.8/dist-packages/torch/utils/data/_utils/signal_handling.py", line 66, in handler
    _error_if_any_worker_fails()
RuntimeError: DataLoader worker (pid 36) is killed by signal: Bus error. It is possible that dataloader's workers are out of shared memory. Please try to raise your shared memory limit.
ayushr2 commented 10 months ago

The logs also show an unimplemented control command (NV2080_CTRL_CMD_NVLINK_GET_NVLINK_CAPS). I added support for it in https://github.com/google/gvisor/pull/9835. But it did not fix the issue.

The logs also shows 4 user faults happening at the same instruction:

D1221 15:38:39.022730  228800 task_run.go:312] [  60:  60] Unhandled user fault: addr=7ea232582000 ip=7ea3c2f95963 access=r-- sig=11 err=BusError: no space left on device
...
VMAs:
...
7ea3c2e0a000-7ea3c2e2c000 r--p 00000000 00:19 34                         /usr/lib/x86_64-linux-gnu/libc-2.31.so
7ea3c2e2c000-7ea3c2fa4000 r-xp 00022000 00:19 34                         /usr/lib/x86_64-linux-gnu/libc-2.31.so

So need to look at (0x7ea3c2f95963 - 0x7ea3c2e2c000) + 0x22000 = 0x18b963 offset in /usr/lib/x86_64-linux-gnu/libc-2.31.so.

root@2b2350fada82:/# objdump -d /usr/lib/x86_64-linux-gnu/libc-2.31.so | less
...
  18b927:       c5 fe 6f 06             vmovdqu (%rsi),%ymm0
  18b92b:       c5 fe 6f 4c 16 e0       vmovdqu -0x20(%rsi,%rdx,1),%ymm1
  18b931:       c5 fe 7f 07             vmovdqu %ymm0,(%rdi)
  18b935:       c5 fe 7f 4c 17 e0       vmovdqu %ymm1,-0x20(%rdi,%rdx,1)
  18b93b:       c5 f8 77                vzeroupper 
  18b93e:       c3                      retq   
  18b93f:       48 3b 15 72 5b 06 00    cmp    0x65b72(%rip),%rdx        # 1f14b8 <mallwatch@@GLIBC_2.2.5+0x8>
  18b946:       0f 83 25 01 00 00       jae    18ba71 <__nss_database_lookup@GLIBC_2.2.5+0x283f1>
  18b94c:       48 39 f7                cmp    %rsi,%rdi
  18b94f:       72 0f                   jb     18b960 <__nss_database_lookup@GLIBC_2.2.5+0x282e0>
  18b951:       74 12                   je     18b965 <__nss_database_lookup@GLIBC_2.2.5+0x282e5>
  18b953:       4c 8d 0c 16             lea    (%rsi,%rdx,1),%r9
  18b957:       4c 39 cf                cmp    %r9,%rdi
  18b95a:       0f 82 c5 01 00 00       jb     18bb25 <__nss_database_lookup@GLIBC_2.2.5+0x284a5>
  18b960:       48 89 d1                mov    %rdx,%rcx
  18b963:       f3 a4                   rep movsb %ds:(%rsi),%es:(%rdi)   <--- here
thundergolfer commented 10 months ago

Our driver version is still 525.60.13.

Sorry I couldn't get a multi-GPU A100 VM to test the repro myself. Looks like it's thrown up a lot of things! Internally we populate boot_id so makes sense that I didn't see that running the Modal script.

ayushr2 commented 10 months ago

Do you know if the A100 GPU has 40GB memory or 80GB?

ayushr2 commented 10 months ago

So the BusError: no space left on device error on page fault in gVisor happens because we are hitting tmpfs size limit for /dev/shm. The OCI spec shows that /dev/shm has a limit of 67108864 bytes:

...
    {
      "destination": "/dev/shm",
      "type": "tmpfs",
      "source": "/run/containerd/io.containerd.runtime.v2.task/moby/bfb43b3231cbb1f7e60e78f81eab2f25d80f48e802cb5d29185979785746b814/shm",
      "options": [
        "nosuid",
        "noexec",
        "nodev",
        "mode=1777",
        "size=67108864"
      ]
    },
...

Some investigation showed that we were over allocating in tmpfs on page faults. See: https://github.com/google/gvisor/blob/149350e5c428ae9b4ca8f2e2b7960071708113dd/pkg/sentry/fsimpl/tmpfs/regular_file.go#L300-L310

We were trying to allocate optional range if possible. Many times, optional was much bigger than required range. MemoryManager.getPMAsInternalLocked() passes a larger optional range if the PMA gap is larger than the requested range length. Allocating the entire PMA gap can prevent future page faults. But in this case, it is causing us to hit tmpfs size limits really quickly.

So I updated tmpfs to only allocate required range on page faults in #9839. Maybe it helped, but we are still hitting the same error. Need to investigate if tmpfs is overallocating in some other place, or not releasing pages, or there is some page accounting bug.

ayushr2 commented 10 months ago

Strace logs show an interesting pattern:

I1223 19:14:31.881425  937434 strace.go:564] [  15:  15] python3 E lstat(0x7ef4b30c5190 /dev/shm/ZVh4Mj, 0x7ef4b30c50a0)
I1223 19:14:31.881442  937434 strace.go:602] [  15:  15] python3 X lstat(0x7ef4b30c5190 /dev/shm/ZVh4Mj, 0x7ef4b30c50a0) = 0 (0x0) errno=2 (no such file or directory) (4.447µs)
I1223 19:14:31.881462  937434 strace.go:570] [  15:  15] python3 E openat(AT_FDCWD /, 0x7ef4b30c5190 /dev/shm/ZVh4Mj, O_RDWR|O_CREAT|O_EXCL, 0o600)
I1223 19:14:31.881486  937434 strace.go:608] [  15:  15] python3 X openat(AT_FDCWD /, 0x7ef4b30c5190 /dev/shm/ZVh4Mj, O_RDWR|O_CREAT|O_EXCL, 0o600) = 73 (0x49) (11.537µs)
I1223 19:14:31.881529  937434 strace.go:567] [  15:  15] python3 E write(0x49 /dev/shm/ZVh4Mj, 0x7ef4b30c5240 "\xff\xff\xff\x7f\x00\x00\x00\x00\x80\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00", 0x20)
I1223 19:14:31.881556  937434 strace.go:605] [  15:  15] python3 X write(0x49 /dev/shm/ZVh4Mj, ..., 0x20) = 32 (0x20) (14.519µs)
I1223 19:14:31.881587  937434 strace.go:576] [  15:  15] python3 E mmap(0x0, 0x20, PROT_READ|PROT_WRITE, MAP_SHARED, 0x49 /dev/shm/ZVh4Mj, 0x0)
I1223 19:14:31.881609  937434 strace.go:614] [  15:  15] python3 X mmap(0x0, 0x20, PROT_READ|PROT_WRITE, MAP_SHARED, 0x49 /dev/shm/ZVh4Mj, 0x0) = 139497752580096 (0x7edf59fd5000) (9.119µs)
I1223 19:14:31.881649  937434 strace.go:564] [  15:  15] python3 E link(0x7ef4b30c5190 /dev/shm/ZVh4Mj, 0x7ef4b30c51b0 /dev/shm/sem.mp-uq5k_1mc)
I1223 19:14:31.881670  937434 strace.go:602] [  15:  15] python3 X link(0x7ef4b30c5190 /dev/shm/ZVh4Mj, 0x7ef4b30c51b0 /dev/shm/sem.mp-uq5k_1mc) = 0 (0x0) (9.127µs)
I1223 19:14:31.881685  937434 strace.go:564] [  15:  15] python3 E fstat(0x49 /dev/shm/ZVh4Mj, 0x7ef4b30c50b0)
I1223 19:14:31.881708  937434 strace.go:602] [  15:  15] python3 X fstat(0x49 /dev/shm/ZVh4Mj, 0x7ef4b30c50b0 {dev=24, ino=27, mode=S_IFREG|0o600, nlink=2, uid=0, gid=0, rdev=0, size=32, blksize=4096, blocks=1, atime=2023-12-23 19:14:31.88147723 +0000 UTC, mtime=2023-12-23 19:14:31.881553145 +0000 UTC, ctime=2023-12-23 19:14:31.881553145 +0000 UTC}) = 0 (0x0) (2.177µs)
I1223 19:14:31.882240  937434 strace.go:561] [  15:  15] python3 E unlink(0x7ef4b30c5190 /dev/shm/ZVh4Mj)
I1223 19:14:31.882362  937434 strace.go:599] [  15:  15] python3 X unlink(0x7ef4b30c5190 /dev/shm/ZVh4Mj) = 0 (0x0) (12.97µs)
I1223 19:14:31.882448  937434 strace.go:561] [  15:  15] python3 E close(0x49 /dev/shm/ZVh4Mj (deleted))
I1223 19:14:31.882459  937434 strace.go:599] [  15:  15] python3 X close(0x49 /dev/shm/ZVh4Mj (deleted)) = 0 (0x0) (2.197µs)
I1223 19:14:31.882599  937434 strace.go:561] [  15:  15] python3 E unlink(0x7ef4b30c5270 /dev/shm/sem.mp-uq5k_1mc)
I1223 19:14:31.882633  937434 strace.go:599] [  15:  15] python3 X unlink(0x7ef4b30c5270 /dev/shm/sem.mp-uq5k_1mc) = 0 (0x0) (13.497µs)

These files are created in /dev/shm, written to, then mmap(2)-ed, then hard linked, then deleted (both the original path and the link path). The pages are referenced from the mmap(2)-ed VMA. In fact, there are 94 such deleted /dev/shm files that still have an active VMA due to mmap(2). How does the application plan on munmap(2)-ing these VMAs?

thundergolfer commented 10 months ago

Hey, just got back from PTO. Thanks again for your investigation. Looks like I should incorporate the related fixes into our runtime and see what I get with the repro program.

EtiennePerot commented 10 months ago

I added a modified version of the Dockerfile above to https://github.com/google/gvisor/blob/e6a42ae59450fe561cb37eb23ca14e5202067637/images/gpu/pytorch/, but currently disabled since the bug isn't fixed yet, and because this Python script runs forever when it does work (with runc).

Can you assist with making it run for just a small amount of time under runc? I tried reducing the model size and using fast_dev_run=True but it doesn't seem to have any effect. If we can limit it to a few seconds of test time under runc, then we can turn it into a permanent regression test for gVisor.

nixprime commented 10 months ago

I think /dev/shm is just undersized; each /dev/shm/torch_* file is ~77MB. Oddly, with /dev/shm set to a larger size (and with #9828 applied), the test passes under runsc but still hangs under runc:

$ sudo docker run --gpus all --runtime=runsc --shm-size=128g sha256:9ecb6090c8fae027985f0b7bf5be27ec4438a0ac6d6ba966bafea55f0fd3d772
Hello from inside container.
Processes: current_process=psutil.Process(pid=1, name='python3', status='running', started='19:13:03') parent_process=None
Downloading https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz to data/cifar-100-python.tar.gz
100%|██████████| 169001437/169001437 [00:02<00:00, 72318989.98it/s]Extracting data/cifar-100-python.tar.gz to data
Files already downloaded and verified

Downloading: "https://download.pytorch.org/models/resnet50-11ad3fa6.pth" to /root/.cache/torch/hub/checkpoints/resnet50-11ad3fa6.pth
100%|██████████| 97.8M/97.8M [00:00<00:00, 112MB/s] 
GPU available: True (cuda), used: True
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs
/usr/local/lib/python3.8/dist-packages/lightning/pytorch/trainer/connectors/logger_connector/logger_connector.py:67: Starting from v1.9.0, `tensorboardX` has been removed as a dependency of the `lightning.pytorch` package, due to potential conflicts with other packages in the ML ecosystem. For this reason, `logger=True` will use `CSVLogger` as the default logger, unless the `tensorboard` or `tensorboardX` packages are found. Please `pip install lightning[extra]` or one of them to enable TensorBoard support by default
/usr/local/lib/python3.8/dist-packages/lightning/pytorch/trainer/configuration_validator.py:72: You passed in a `val_dataloader` but have no `validation_step`. Skipping val loop.
Initializing distributed: GLOBAL_RANK: 0, MEMBER: 1/1
----------------------------------------------------------------------------------------------------
distributed_backend=nccl
All distributed processes registered. Starting with 1 processes
----------------------------------------------------------------------------------------------------

Missing logger folder: /lightning_logs
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]

  | Name   | Type   | Params
----------------------------------
0 | module | ResNet | 23.7 M
----------------------------------
23.7 M    Trainable params
0         Non-trainable params
23.7 M    Total params
94.852    Total estimated model params size (MB)
Epoch 0: 100%|██████████| 391/391 [07:46<00:00,  0.84it/s, v_num=0]`Trainer.fit` stopped: `max_epochs=1` reached.
Epoch 0: 100%|██████████| 391/391 [07:47<00:00,  0.84it/s, v_num=0]
Training duration (seconds): 491.89744663238525
ayushr2 commented 10 months ago

Per Jamie's findings, the reproducer should now work with gVisor (after increasing /dev/shm size limit).

github-actions[bot] commented 10 months ago

There are TODOs still referencing this issue:

  1. test/gpu/pytorch_test.go:57: Don't skip this once the
  2. images/gpu/pytorch/issue_9827.py:87: Make this not take forever.

Search TODO

thundergolfer commented 10 months ago

Thanks for all your great help here! Still meaning to look into https://github.com/google/gvisor/blob/HEAD/images/gpu/pytorch/issue_9827.py#L87

luiscape commented 10 months ago

We tested this in A100s with 40 and 80GB and the merged fix works.

thundergolfer commented 6 months ago

Revisting the currently skipped test added during this issue's investigation. Could use your guidance @ayushr2 on how to get debug logs enabled for test executions. I've tried the typical way (daemon.json and reloading docker) but that has not done anything. I read through the test code a bit and didn't see anything.

I'm running the test on an A100 80GB SXM4 server and getting SIGSEV: 

ubuntu@207-211-185-116:~/gvisor$ make test TARGETS="//test/gpu:pytorch_test"
--- TAG default
--- DOCKER BUILD
gvisor-builder-7a84ed41-x86_64
sha256:0c0586de8611f38f0fd8041a6c3aaebc65685cc4ac32b8742bec947a12224c89
--- DOCKER RUN
gvisor-bazel-7a84ed41-x86_64
1a4bf151325dd1c159f5338141e21e4de689b5e8000641adcca48d86398d35da
--- TEST //test/gpu:pytorch_test
Another command holds the client lock:
pid=139701
owner=client
cwd=/home/ubuntu/gvisor

Waiting for it to complete...
Another command (pid=139701) is running. Waiting for it to complete on the server (server_pid=139703)...
Loading:
Loading:
Loading: 1 packages loaded
Analyzing: target //test/gpu:pytorch_test (2 packages loaded, 0 targets configured)
Analyzing: target //test/gpu:pytorch_test (63 packages loaded, 767 targets configured)
Analyzing: target //test/gpu:pytorch_test (84 packages loaded, 11204 targets configured)
Analyzing: target //test/gpu:pytorch_test (435 packages loaded, 15706 targets configured)
INFO: Analyzed target //test/gpu:pytorch_test (440 packages loaded, 17542 targets configured).
INFO: Found 1 test target...
[2 / 25] [Prepa] BazelWorkspaceStatusAction stable-status.txt
[1,605 / 1,606] Testing //test/gpu:pytorch_test; 0s linux-sandbox
[1,605 / 1,606] Testing //test/gpu:pytorch_test; 11s linux-sandbox
FAIL: //test/gpu:pytorch_test (see /home/ubuntu/.cache/bazel/_bazel_ubuntu/9cf5d2f37e613aac6d73fb8ae06b7c50/execroot/__main__/bazel-out/k8-fastbuild/testlogs/test/gpu/pytorch_test/test.log)
[1,605 / 1,606] 1 / 1 tests, 1 failed; Testing //test/gpu:pytorch_test; 38s linux-sandbox
INFO: From Testing //test/gpu:pytorch_test:
==================== Test output for //test/gpu:pytorch_test:
--- FAIL: TestIssue9827 (21.07s)
    pytorch_test.go:60: Failed: container returned non-zero status: 1, msg: ""
        Container output:

        ==========
        == CUDA ==
        ==========

        CUDA Version 12.2.0

        Container image Copyright (c) 2016-2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved.

        This container image and its contents are governed by the NVIDIA Deep Learning Container License.
        By pulling and using the container, you accept the terms and conditions of this license:
        https://developer.nvidia.com/ngc/nvidia-deep-learning-container-license

        A copy of this license is made available in this container at /NGC-DL-CONTAINER-LICENSE for your convenience.

        GPU available: True (cuda), used: True
        TPU available: False, using: 0 TPU cores
        IPU available: False, using: 0 IPUs
        HPU available: False, using: 0 HPUs
        /usr/local/lib/python3.8/dist-packages/lightning/pytorch/trainer/connectors/logger_connector/logger_connector.py:75: Starting from v1.9.0, `tensorboardX` has been removed as a dependency of the `lightning.pytorch` package, due to potential conflicts with other packages in the ML ecosystem. For this reason, `logger=True` will use `CSVLogger` as the default logger, unless the `tensorboard` or `tensorboardX` packages are found. Please `pip install lightning[extra]` or one of them to enable TensorBoard support by default
        Processes: current_process=psutil.Process(pid=1, name='python3', status='running', started='02:53:39') parent_process=None
        /usr/local/lib/python3.8/dist-packages/lightning/pytorch/trainer/configuration_validator.py:72: You passed in a `val_dataloader` but have no `validation_step`. Skipping val loop.
        Initializing distributed: GLOBAL_RANK: 0, MEMBER: 1/8
        /usr/local/lib/python3.8/dist-packages/lightning/pytorch/trainer/configuration_validator.py:72: You passed in a `val_dataloader` but have no `validation_step`. Skipping val loop.
        Initializing distributed: GLOBAL_RANK: 2, MEMBER: 3/8
        Missing logger folder: /lightning_logs
        /usr/local/lib/python3.8/dist-packages/lightning/pytorch/trainer/configuration_validator.py:72: You passed in a `val_dataloader` but have no `validation_step`. Skipping val loop.
        Initializing distributed: GLOBAL_RANK: 7, MEMBER: 8/8
        Missing logger folder: /lightning_logs
        /usr/local/lib/python3.8/dist-packages/lightning/pytorch/trainer/configuration_validator.py:72: You passed in a `val_dataloader` but have no `validation_step`. Skipping val loop.
        Initializing distributed: GLOBAL_RANK: 6, MEMBER: 7/8
        Missing logger folder: /lightning_logs
        /usr/local/lib/python3.8/dist-packages/lightning/pytorch/trainer/configuration_validator.py:72: You passed in a `val_dataloader` but have no `validation_step`. Skipping val loop.
        Initializing distributed: GLOBAL_RANK: 4, MEMBER: 5/8
        Missing logger folder: /lightning_logs
        /usr/local/lib/python3.8/dist-packages/lightning/pytorch/trainer/configuration_validator.py:72: You passed in a `val_dataloader` but have no `validation_step`. Skipping val loop.
        Initializing distributed: GLOBAL_RANK: 3, MEMBER: 4/8
        Missing logger folder: /lightning_logs
        /usr/local/lib/python3.8/dist-packages/lightning/pytorch/trainer/configuration_validator.py:72: You passed in a `val_dataloader` but have no `validation_step`. Skipping val loop.
        Initializing distributed: GLOBAL_RANK: 1, MEMBER: 2/8
        Missing logger folder: /lightning_logs
        /usr/local/lib/python3.8/dist-packages/lightning/pytorch/trainer/configuration_validator.py:72: You passed in a `val_dataloader` but have no `validation_step`. Skipping val loop.
        Initializing distributed: GLOBAL_RANK: 5, MEMBER: 6/8
        Missing logger folder: /lightning_logs
        ----------------------------------------------------------------------------------------------------
        distributed_backend=nccl
        All distributed processes registered. Starting with 8 processes
        ----------------------------------------------------------------------------------------------------

        Missing logger folder: /lightning_logs
        LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]
        LOCAL_RANK: 4 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]
        LOCAL_RANK: 3 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]
        LOCAL_RANK: 6 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]
        LOCAL_RANK: 2 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]
        LOCAL_RANK: 7 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]
        LOCAL_RANK: 5 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]
        LOCAL_RANK: 1 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]

          | Name   | Type   | Params
        ----------------------------------
        0 | module | ResNet | 23.7 M
        ----------------------------------
        23.7 M    Trainable params
        0         Non-trainable params
        23.7 M    Total params
        94.852    Total estimated model params size (MB)
        Traceback (most recent call last):
          File "/issue_9827.py", line 88, in <module>
            trainer.fit(model, train_dl, val_dl)
          File "/usr/local/lib/python3.8/dist-packages/lightning/pytorch/trainer/trainer.py", line 544, in fit
            call._call_and_handle_interrupt(
          File "/usr/local/lib/python3.8/dist-packages/lightning/pytorch/trainer/call.py", line 43, in _call_and_handle_interrupt
            return trainer.strategy.launcher.launch(trainer_fn, *args, trainer=trainer, **kwargs)
          File "/usr/local/lib/python3.8/dist-packages/lightning/pytorch/strategies/launchers/multiprocessing.py", line 144, in launch
            while not process_context.join():
          File "/usr/local/lib/python3.8/dist-packages/torch/multiprocessing/spawn.py", line 140, in join
            raise ProcessExitedException(
        torch.multiprocessing.spawn.ProcessExitedException: process 0 terminated with signal SIGSEGV
FAIL
================================================================================
Target //test/gpu:pytorch_test up-to-date:
  bazel-bin/test/gpu/pytorch_test_/pytorch_test
INFO: Elapsed time: 46.666s, Critical Path: 39.11s
INFO: 2 processes: 1 internal, 1 linux-sandbox.
INFO: Build completed, 1 test FAILED, 2 total actions
//test/gpu:pytorch_test                                                  FAILED in 38.6s
  /home/ubuntu/.cache/bazel/_bazel_ubuntu/9cf5d2f37e613aac6d73fb8ae06b7c50/execroot/__main__/bazel-out/k8-fastbuild/testlogs/test/gpu/pytorch_test/test.log

Executed 1 out of 1 test: 1 fails locally.
INFO: Build completed, 1 test FAILED, 2 total actions
INFO: Build Event Protocol files produced successfully.
make: *** [Makefile:64: test] Error 3
ayushr2 commented 6 months ago

how to get debug logs enabled for test executions

I usually look at the Makefile and Buildkite configuration to see how the make command is being used. In this case, we have a Makefile target named gpu-all-tests: https://github.com/google/gvisor/blob/a5b10b7dd04c309680b9c4c69080a7aae0f87074/Makefile#L304-L310

And see how this is invoked in Buildkite: https://github.com/google/gvisor/blob/a5b10b7dd04c309680b9c4c69080a7aae0f87074/.buildkite/pipeline.yaml#L180-L185

So for your usecase, maybe comment out Line 307-309 in the Makefile and invoke make gpu-all-tests RUNTIME_ARGS="--debug". Note that the flag --debug-log is automatically added in https://github.com/google/gvisor/blob/a5b10b7dd04c309680b9c4c69080a7aae0f87074/Makefile#L141. The Makefile will reload the Docker daemon and everything.

To find the debug logs, see what --debug-log flag was used in /etc/docker/daemon.json and you will find all logs there. Hope that's helpful.

thundergolfer commented 6 months ago

Thanks a lot 🙏