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<h2>2D Parallelism using PyTorch in Ray</h2>
<p>
Last time we already covered
<a href="/tp-pytorch">Tensor Parallelism</a> using PyTorch Distributed
Elastic and little bit of Pipeline Parallelism, but did you know that
you can combine Tensor Parallelism and Pipeline Parallelism in the same
parallelism? Actually we got up to 4D! Tensor Parallelism + Pipeline
Parallelism + Data Parallelism + Context Parallelism, TP + PP + DP + CP!
But this blog we will only cover TP and PP.
</p>
<p>
As we know, Tensor Parallelism split the weights either Row-Wise or
Column-Wise to N GPUs and Pipeline Parallelism split hidden layers to N
GPUs,
</p>
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Tensor Parallelism (2 GPUs)
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Pipeline Parallelism (2 GPUs)
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<p>
We can combine TP and PP to become a single parallelism, called 2D
Parallelism. Assumed I have a deep learning model with 4 hidden layers,
and each hidden layer got a linear layer, to make the model fit into 2D
Parallelism,
</p>
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<p>
- GPU 0 take hidden layers 0-1, this is a PP for hidden layers 0-1, and
GPU 0 TP with GPU 1 to shard the weights, this can be done using
`torch.distributed.new_group`. This required 2 GPUs.
</p>
<p>
- Output from hidden layers 0-1 in GPU 0 and will pass to GPU 2, and GPU
2 PP hidden layers 2-3. GPU 2 TP with GPU 3 to shard the weights. Also
required to create new group using `torch.distributed.new_group`. This
required 2 GPUs.
</p>
<p>
- The number of GPUs required is, M PP x N TP, if M = 2 and N = 2, we
need 4 GPUs. 1 PP 2 TP means, all hidden layers inside the same GPU 0,
but the weights sharded with GPU 1, so it required 2 GPUs.
</p>
<p>
- Because the hidden layers are split across M devices, and each weight
is sharded by N, GPU memory is saved by a factor of M x N!
</p>
<p>- This 2D Parallelism communication groups are like below,</p>
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<text x="60" y="290" font-family="Arial" font-size="20">GPU 2</text>
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TP Group: [0, 1]
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TP Group: [2, 3]
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PP Group: [0, 2]
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<p>
- TP Group: [0, 1] is the TP communication group for GPU 0 and GPU 1, PP
Group: [0, 2] is the PP communication group for GPU 0 and GPU 2, and TP
Group: [2, 3] is the TP communication group for GPU 2 and GPU 3.
</p>
<h3>PyTorch in Ray</h3>
<p>
For distributed framework we decided to use
<a href="https://www.ray.io/">Ray</a>
<b>(Please sponsor us something)</b> because we do not have a node with
4 GPUs, but we got 2 nodes with each 2 GPUs, so we connect those nodes
using Ray inside Tailscale VPN.
</p>
<p>
Why Ray? Ray is cool, nice UI, and the important parts are,
<b>node auto discovery and automatic distributed execution</b>.
</p>
<p>
What does means by node auto discovery and automatic distributed
execution? actually Torch Elastic Distributed support multi-nodes
natively, you must set rendezvous backend,
<a
href="https://pytorch.org/docs/stable/elastic/run.html#note-on-rendezvous-backend"
>https://pytorch.org/docs/stable/elastic/run.html#note-on-rendezvous-backend</a
>
</p>
<pre>
```bash
torchrun
--nnodes=$NUM_NODES
--nproc-per-node=$NUM_TRAINERS
--rdzv-id=$JOB_ID
--rdzv-backend=c10d
--rdzv-endpoint=$HOST_NODE_ADDR
YOUR_TRAINING_SCRIPT.py
```</pre
>
<p>- `$NUM_NODES` must set equal to the size of nodes.</p>
<p>- `$NUM_TRAINERS` must set equal to the size of GPUs.</p>
<p>
- `$JOB_ID` can set any ID, if you have multiple jobs, you must set
different ID.
</p>
<p>
- `$HOST_NODE_ADDR` is the first node or the fastest node you have, and
it will elect as host.
</p>
<p>
Now we have 2 nodes and each node got 2 GPUs, with IPs `100.93.25.29`
and `100.92.17.27`, so to run using torchrun,
</p>
<p>In `100.93.25.29`,</p>
<pre>
```bash
torchrun \
--nnodes=2 --nproc_per_node=2 \
--rdzv_id=1234 --rdzv_backend=c10d --rdzv_endpoint=100.93.25.29:29500 train.py
```</pre
>
<p>And in `100.92.17.27`, you have to run the same thing,</p>
<pre>
```bash
torchrun \
--nnodes=2 --nproc_per_node=2 \
--rdzv_id=1234 --rdzv_backend=c10d --rdzv_endpoint=100.93.25.29:29500 train.py
```</pre
>
<p>
Which is tedious, and each nodes must have the same script plus you must
know the head of IP address! Or maybe you saw someone run using Slurm
before,
</p>
<pre>
```bash
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)
srun torchrun \
--nnodes 2 \
--nproc_per_node 2 \
--rdzv_id 1234 \
--rdzv_backend c10d \
--rdzv_endpoint $head_node_ip:29500 \
train.py
```</pre
>
<p>
Slurm also run the script for the entire nodes register in Slurm, but in
other to build a Slurm cluster,
</p>
<pre>
```bash
# /etc/slurm-llnl/slurm.conf
ClusterName=my_cluster
ControlMachine=100.93.25.29
# extra configs
NodeName=node1 NodeAddr=100.93.25.29 RealMemory=32000 Sockets=1 CoresPerSocket=4 ThreadsPerCore=2 Gres=gpu:2
NodeName=node2 NodeAddr=100.92.17.27 RealMemory=32000 Sockets=1 CoresPerSocket=4 ThreadsPerCore=2 Gres=gpu:2
PartitionName=debug Nodes=node1,node2 Default=YES MaxTime=INFINITE State=UP
```</pre
>
<p>
You need to put the config for the all nodes available, and as you can
see, you have to mention all the IP nodes!
</p>
<p>
But in Ray, you do not have to do all of that, you just run the script
anywhere as long the script connected to the Ray head and Ray will
automatically distribute the script to another nodes.
</p>
<p>In head node 100.93.25.29, you have to run the Ray head mode,</p>
<pre>
```bash
ray start --head --node-ip-address=100.93.25.29 --port=6379 --dashboard-host=0.0.0.0
```</pre
>
<p>After that other nodes just connect using,</p>
<pre>
```bash
ray start --address=100.93.25.29:6379
```</pre
>
<p>Done! The cluster looks like below,</p>
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Tailscale VPN
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