PraljakReps commented 9 months ago

Hello,

I am having issues with using deepspeed (stage 2) for 2 node configuration with 8 A100 GPUs. I followed https://github.com/argonne-lcf/GettingStarted/tree/master/DataScience/DeepSpeed, but I am using pytorch lightning instead for implementing DeepSpeed https://lightning.ai/docs/pytorch/stable/advanced/model_parallel/deepspeed.html.

I am finding that I have no problems with training a model with 4 GPUs over 1 node using DeepSpeed(stage=2) with pytorch lightning; however, when I use 2 nodes with 8 total gpus, it appears that the 2nd node is stalling, and the code freezes at the final GPU rank on the first node.

I tried to see if this was reproducible with the boring_model.py, which is Pytorch Lightning's minimalist code script for reproducible error messages... I ended up with similar issues.

Here is the boring_model.py: https://gist.github.com/PraljakReps/d699f5d16af00e35cf4c8b8abfb09b6c

Using the trainer found in the above python script, I tried three configurations.

(works fine) gpus=4 and num_nodes=1
(error; second node hangs) gpus=4 and num_nodes=2
(error; second node hangs) mpiexec , gpus=4, and num_nodes=2

Depending on your pytorch lightning version (see below for my virtual env.), the trainer should look like this to reproduce my errors.

Initialize a trainer

trainer = pl.Trainer(
    gpus=4,
    max_epochs=1,
    num_nodes=2,
    precision=16,
    strategy="deepspeed_stage_2",
    callbacks=[lr_monitor]
)

i followed this link too when running mpiexec command: https://docs.alcf.anl.gov/polaris/data-science-workflows/frameworks/deepspeed/#:~:text=DeepSpeed.%20The%20base%20conda%20environment,cloning%20the%20base%20environment%20can

Thus, the code that I ran is the following:

NHOSTS=$(wc -l < "${PBS_NODEFILE}")
NGPU_PER_HOST=$(nvidia-smi -L | wc -l)
NGPUS="$((${NHOSTS}*${NGPU_PER_HOST}))"
mpiexec \
  --verbose \
  --envall \
  -n "${NGPUS}" \
  --ppn "${NGPU_PER_HOST}" \
  --hostfile="${PBS_NODEFILE}" \
  python \
  boring_model.py

and I am still getting the issue where the second node hangs...

Note: I am entering a compute node interactively with the following command:

qsub -I -l select=2:ngpus=4 -l filesystems=home:eagle -l walltime=1:00:00 -q debug -A <account> Is there a way to run deepspeed over multiple-nodes on Polaris for the boring_model.py with pytorch lightning as a test case? Of course, my main goal is to conduct multi-node training for my research project, but I think success running of boring_model.py with pytorch-lightning+deepspeed is a easy case.

Software packages:

pytorch-lightning==1.9.5
torch==2.0.1
torchmetrics==1.2.0
lightning-bolts==0.7.0
deepspeed==0.12.5
python==3.8.18

saforem2 commented 9 months ago

@PraljakReps I came across this (from our own @coreyjadams 😂 just 3 weeks ago) that might be helpful

https://github.com/Lightning-AI/pytorch-lightning/issues/19086

saforem2 commented 9 months ago

but I've been having trouble getting things to launch / work correctly on more than one node of Polaris.

If you just need DeepSpeed support, but can do without PyTorch Lightning, you can try:

you should then be able to

$ module load conda/2023-10-04 ; conda activate base
$ # cd /path/to/your/project/
$ # and make virtual environment in your project dir:
$ mkdir -p venvs/polaris/2023-10-04
$ python3 -m venv venvs/polaris/2023-10-04 --system-site-packages
$ source venvs/polaris/2023-10-04/bin/activate
$ # install [`ezpz`](https://github.com/saforem2/ezpz)
$ # (a little python library I've been working on)
$ python3 -m pip install "git+https://github.com/saforem2/ezpz"
$ # determine available GPUs
$ NHOSTS=$(wc -l < "${PBS_NODEFILE}")
$ NGPU_PER_HOST=$(nvidia-smi -L | wc -l)
$ NGPUS="$((${NHOSTS}*${NGPU_PER_HOST}))"
$ mpiexec \
    --verbose \
    --envall \
    -n "${NGPUS}" \
    --ppn "${NGPU_PER_HOST}" \
    --hostfile="${PBS_NODEFILE}" \
    python3 \
    deepspeed_test.py

where deepspeed_test.py contains:

"""
deepspeed_test.py
"""
import os
from pathlib import Path
import deepspeed

from ezpz import setup_torch, get_world_size

import torch
from torch.utils.data import DataLoader  # , Dataset
from pl_bolts.datasets import RandomDataset

BACKEND = 'deepspeed'  # also tried with DDP

RANK = setup_torch(backend=BACKEND)
WORLD_SIZE = get_world_size()
GPUS_PER_NODE = torch.cuda.device_count()
NODE_ID = RANK % GPUS_PER_NODE
NUM_NODES = WORLD_SIZE // GPUS_PER_NODE

NUM_SAMPLES = 10000
train = RandomDataset(32, NUM_SAMPLES)
train = DataLoader(train, batch_size=32)
val = RandomDataset(32, NUM_SAMPLES)
val = DataLoader(val, batch_size=32)
test = RandomDataset(32, NUM_SAMPLES)
test = DataLoader(test, batch_size=32)

def main():
    from ezpz import load_ds_config
    from .boring_model import BoringModel
    model = BoringModel()
    # lr_monitor = LearningRateMonitor(logging_interval='step')
    # ---- Couldn't get the `pl.Trainer` to work ---------------
    # Initialize a trainer
    # trainer = pl.Trainer(
    #     accelerator='gpu',
    #     strategy='deepspeed',
    #     # strategy="deepspeed_stage_2",
    #     # devices=WORLD_SIZE,
    #     gpus=GPUS_PER_NODE,
    #     max_epochs=1,
    #     num_nodes=NUM_NODES,
    #     # progress_bar_refresh_rate=20,
    #     precision=16,
    #     callbacks=[lr_monitor]
    # )
    # ---- Couldn't get the `pl.Trainer` to work ---------------
    if RANK == 0:
        print(f'{model=}')
    optimizer_, _ = model.configure_optimizers()
    optimizer = optimizer_[0]

    HERE = Path(os.path.abspath(__file__)).parent
    ds_config_path = HERE / 'ds_config.yaml'
    ds_config = load_ds_config(ds_config_path)
    model_engine, optimizer, *_ = deepspeed.initialize(
        model=model,
        config=ds_config,
        optimizer=optimizer,
        model_parameters=model.parameters(),
    )

    # Train loop
    device = model_engine.local_rank
    for step, x in enumerate(train):
        x = x.to(device)
        y = model_engine(x)
        loss = model_engine.loss(x, y)
        model_engine.backward(loss)
        model_engine.step()
        if RANK == 0:
            print(f'{step=}, {loss.item()=}')

if __name__ == '__main__':
    main()

and ds_config.yaml has:

---
dump_state: false
gradient_accumulation_steps: 1
wall_clock_breakdown: true
train_micro_batch_size_per_gpu: 1
fp16:
  enabled: false
  # min_loss_scale: 0
# bf16:
#   enabled: false
...

🤷🏻‍♂️ happy to chat more if you have any questions about anything

saforem2 commented 9 months ago

in the above ^ boring_model.py contains:

"""
boring_model.py
"""
import torch
from pytorch_lightning import LightningModule

class BoringModel(LightningModule):

    def __init__(self):
        super().__init__()
        self.layer = torch.nn.Linear(32, 2)

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

    def loss(self, batch, prediction):
        # An arbitrary loss to have a loss that updates the model weights
        # during `Trainer.fit` calls
        return torch.nn.functional.mse_loss(
            prediction,
            torch.ones_like(prediction)
        )

    def training_step(self, batch, batch_idx):
        output = self.layer(batch)
        loss = self.loss(batch, output)
        return {"loss": loss}

    def training_step_end(self, training_step_outputs):
        return training_step_outputs

    def training_epoch_end(self, outputs) -> None:
        torch.stack([x["loss"] for x in outputs]).mean()

    def validation_step(self, batch, batch_idx):
        output = self.layer(batch)
        loss = self.loss(batch, output)
        return {"x": loss}

    def validation_epoch_end(self, outputs) -> None:
        torch.stack([x['x'] for x in outputs]).mean()

    def test_step(self, batch, batch_idx):
        output = self.layer(batch)
        loss = self.loss(batch, output)
        self.log('fake_test_acc', loss)
        return {"y": loss}

    def test_epoch_end(self, outputs) -> None:
        torch.stack([x["y"] for x in outputs]).mean()

    def configure_optimizers(self):
        optimizer = torch.optim.SGD(self.layer.parameters(), lr=0.1)
        lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=1)
        return [optimizer], [lr_scheduler]

PraljakReps commented 9 months ago

Thanks @saforem2! The code works using deepspeed boilerplate without pl.trainer.

For now, I don't mind using DeepSpeed basic boiler plate, but ideally, I would like to get trainer.fit() to work since there is a lot of advantages of pytorch_lightning (e.g. checkpointing, tracking metrics, etc).

Do you recall the error message when implementing pl.trainer(...) and trainer.fit(...) ?

argonne-lcf / GettingStarted

Issues with DeepSpeed using Pytorch Lightning #20

Initialize a trainer

Software packages: