import torch
import torch.nn.functional as F
from torch.utils.data import Dataset, DataLoader
from torch.utils.data.distributed import DistributedSampler
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.distributed import init_process_group, destroy_process_group

import argparse
import os
import socket


class MyTrainDataset(Dataset):
    def __init__(self, size):
        self.size = size
        self.data = [(torch.rand(20), torch.rand(1)) for _ in range(size)]

    def __len__(self):
        return self.size

    def __getitem__(self, index):
        return self.data[index]


def ddp_setup():
    torch.cuda.set_device(int(os.environ["LOCAL_RANK"]))
    init_process_group(backend="nccl")


class Trainer:
    def __init__(
        self,
        model: torch.nn.Module,
        train_data: DataLoader,
        optimizer: torch.optim.Optimizer,
        save_every: int,
        snapshot_path: str,
    ) -> None:
        self.local_rank = int(os.environ["LOCAL_RANK"])
        self.global_rank = int(os.environ["RANK"])
        self.model = model.to(self.local_rank)
        self.train_data = train_data
        self.optimizer = optimizer
        self.save_every = save_every
        self.epochs_run = 0
        self.snapshot_path = snapshot_path
        if os.path.exists(snapshot_path):
            print("Loading snapshot")
            self._load_snapshot(snapshot_path)

        self.model = DDP(self.model, device_ids=[self.local_rank])

    def _load_snapshot(self, snapshot_path):
        loc = f"cuda:{self.local_rank}"
        snapshot = torch.load(snapshot_path, map_location=loc)
        self.model.load_state_dict(snapshot["MODEL_STATE"])
        self.epochs_run = snapshot["EPOCHS_RUN"]
        print(f"Resuming training from snapshot at Epoch {self.epochs_run}")

    def _run_batch(self, source, targets):
        self.optimizer.zero_grad()
        output = self.model(source)
        loss = F.cross_entropy(output, targets)
        loss.backward()
        self.optimizer.step()

    def _run_epoch(self, epoch):
        b_sz = len(next(iter(self.train_data))[0])
        print(f"[{socket.gethostname()}] [GPU{self.global_rank}] Epoch {epoch} | Batchsize: {b_sz}"
              f" | Steps: {len(self.train_data)}")
        self.train_data.sampler.set_epoch(epoch)
        for source, targets in self.train_data:
            source = source.to(self.local_rank)
            targets = targets.to(self.local_rank)
            self._run_batch(source, targets)

    def _save_snapshot(self, epoch):
        snapshot = {
            "MODEL_STATE": self.model.module.state_dict(),
            "EPOCHS_RUN": epoch,
        }
        torch.save(snapshot, self.snapshot_path)
        print(f"Epoch {epoch} | Training snapshot saved at {self.snapshot_path}")

    def train(self, max_epochs: int):
        for epoch in range(self.epochs_run, max_epochs):
            self._run_epoch(epoch)
            if self.local_rank == 0 and epoch % self.save_every == 0:
                self._save_snapshot(epoch)


def load_train_objs():
    train_set = MyTrainDataset(2048)  # load your dataset
    model = torch.nn.Linear(20, 1)  # load your model
    optimizer = torch.optim.SGD(model.parameters(), lr=1e-3)
    return train_set, model, optimizer


def prepare_dataloader(dataset: Dataset, batch_size: int):
    return DataLoader(
        dataset,
        batch_size=batch_size,
        pin_memory=True,
        shuffle=False,
        sampler=DistributedSampler(dataset)
    )


def main(save_every: int, total_epochs: int, batch_size: int, snapshot_path: str = "snapshot.pt"):
    ddp_setup()
    dataset, model, optimizer = load_train_objs()
    train_data = prepare_dataloader(dataset, batch_size)
    trainer = Trainer(model, train_data, optimizer, save_every, snapshot_path)
    trainer.train(total_epochs)
    destroy_process_group()


if __name__ == "__main__":
    device_count = torch.cuda.device_count()
    hostname = socket.gethostname()
    device_str = ("[" + hostname + "] PyTorch Version: " + str(torch.__version__) + "\n" +
                  "Torch Distributed: " + str(torch.distributed.is_available()) + "\n" +
                  "Cuda Available: " + str(torch.cuda.is_available()) + "\n" +
                  "Cuda Version: " + str(torch.version.cuda) + "\n" +
                  "ArchList: " + "\n" + str(torch.cuda.get_arch_list()) + "\n" +
                  "NCCL: Version: " + str(torch.cuda.nccl.version()) + "\n" +
                  "Device Count: " + str(device_count))
    print(device_str)

    for device_id in range(0, device_count):
        device = torch.device("cuda:" + str(device_id))
        major, minor = torch.cuda.get_device_capability(device)
        gpu_str = ("[" + hostname + "] Device ID: " + str(device_id) +
                   " Device Name: " + str(torch.cuda.get_device_name(device_id)) + "\n" +
                   "  CUDA compute capability: " + str(major) + "." + str(minor) + "\n" +
                   "  Properties: " + str(torch.cuda.get_device_properties(device_id)) + "\n" +
                   "  NCCL: Available: " + str(torch.cuda.nccl.is_available(torch.rand(1, device=device))))
        print(gpu_str)

    parser = argparse.ArgumentParser(description='simple distributed training job')
    parser.add_argument('total_epochs', type=int, help='Total epochs to train the model')
    parser.add_argument('save_every', type=int, help='How often to save a snapshot')
    parser.add_argument('--batch_size', default=32, type=int, help='Input batch size on each device (default: 32)')
    args = parser.parse_args()

    main(args.save_every, args.total_epochs, args.batch_size)

#!/bin/bash

#SBATCH --job-name=pyt-multi-node
#SBATCH --account=<project-name>
#SBATCH --time=5:00
#SBATCH --nodes=<num-of-nodes>
#SBATCH --ntasks-per-node=1
#SBATCH --gpus-per-task=<num-of-gpus-per-node>
#SBATCH --cpus-per-task=4
#SBATCH --mail-type=ALL
#SBATCH --mail-user=<email_address>
#SBATCH --output=pyt_multi_node_%A.out
#SBATCH --partition=<gpu-partition>

export OMP_NUM_THREADS=1
# Uncomment for NCCL related logging.
# export NCCL_DEBUG=INFO

echo "SLURM_JOB_ID:" $SLURM_JOB_ID
echo "SLURM_JOB_NUM_NODES:" $SLURM_JOB_NUM_NODES
echo "SLURM_JOB_NODELIST:" $SLURM_JOB_NODELIST
echo "- - - - - - - - - - - -"
echo "SLURM_GPUS:" $SLURM_GPUS
echo "SLURM_GPUS_PER_NODE" $SLURM_GPUS_PER_NODE
echo "SLURM_GPUS_ON_NODE:" $SLURM_GPUS_ON_NODE
echo "SLURM_JOB_GPUS:" $SLURM_JOB_GPUS
echo "CUDA_VISIBLE_DEVICES:" $CUDA_VISIBLE_DEVICES
echo "- - - - - - - - - - - -"

# Environment Variable used to set a random port to potentially allow different jobs to 
# run on the same GPU device.
export MASTER_PORT=$(expr 10000 + $(echo -n $SLURM_JOBID | tail -c 4))
echo "MASTER_PORT="$MASTER_PORT

# List GPU devices allocated on head node.
nvidia-smi -L

module purge
module load miniconda3/24.3.0
conda activate <path-to-conda-environmnet>

nodes=( $( scontrol show hostnames $SLURM_JOB_NODELIST ) )
nodes_array=($nodes)
head_node=${nodes_array[0]}
head_node_ip=$(srun --nodes=1 --ntasks=1 -w "$head_node" hostname --ip-address)
echo "Node IP: "$head_node_ip

srun torchrun \
--nnodes "$SLURM_JOB_NUM_NODES" \
--nproc_per_node "$SLURM_GPUS_ON_NODE" \
--rdzv_id $RANDOM \
--rdzv_backend c10d \
--rdzv_endpoint $head_node_ip:$MASTER_PORT \
multi_node.py 50 10

echo "Done."