Sharding Models: Slicing Giants into Dancing Fairies
In the sacred scrolls of Computer Science, there is a golden proverb:
“If a task is too heavy for one, divide it among many.”
And so, when today’s Large Language Models — veritable Titans — grew so large they could no longer fit into the humble GPU memory of mere mortals, the ancient art of Sharding was reborn.
Pull up a chair, sip some masala chai, and let us embark upon the enchanting journey of How to use Sharding in Models. 🚀
🌟 What is Sharding?
Imagine you baked a cake so gigantic that no single plate could hold it.
Would you weep? Nay! You would cut it into slices and pass it around.
Sharding is exactly this:
Splitting a large model across multiple devices (or nodes or GPUs) so that each holds a small, manageable piece.
Each shard holds a part of the model’s parameters, and together, they whisper secrets to each other across the network to behave like one single mighty model.
🛕 Why Shard a Model?
- Memory Constraints: Your model is thicc (scientific term), and your GPU can’t hold it alone.
- Faster Training: Many hands make light work.
- Scalability: Tomorrow’s models will have trillions of parameters. You either shard, or you get left behind, weeping in 8GB VRAM.
🛡️ Ways to Shard Models
Now, in the royal courts of machine learning, there are different schools of sharding:
1. Tensor Sharding
Cut the tensors themselves. Slice that giant matrix horizontally, vertically, diagonally — like a ninja slicing watermelons.
Example:
- GPU 1 holds the first half of a weight matrix.
- GPU 2 holds the second half.
Used in DeepSpeed and Megatron-LM.
Poetry in code:
# Pseudo code: Partition a tensor
tensor = torch.randn(1000, 1000)
shard1, shard2 = tensor.chunk(2, dim=0)2. Layer Sharding (aka Pipeline Parallelism)
Ah, the assembly line of dreams!
Each device holds a different layer (or set of layers) of the model.
Like a relay race: GPU 1 computes Layer 1, hands the baton to GPU 2 for Layer 2, and so forth.
Poetry in code:
# Example with PyTorch PipelineParallel
from torch.distributed.pipeline.sync import Pipe
model = nn.Sequential(layer1, layer2, layer3, layer4)
model = Pipe(model, chunks=8)3. Expert Sharding (Mixture of Experts)
This is like having a room full of wise old men, but only a few are consulted at a time.
Only parts of the model (“experts”) are active for each input.
Popular in models like Switch Transformer.
Efficiency: You shard everything, but you don’t wake everyone up unless needed. (Let the lazy geniuses nap.)
🛠️ How to Implement Sharding — A Ritual of Three Steps
Step 1: Pick a Framework
Several enchanted tools exist:
- DeepSpeed (Microsoft’s battle-tested sharding spellbook)
- FairScale (Meta’s gift to mankind)
- FSDP (Fully Sharded Data Parallelism from PyTorch itself)
Each has its own charm and cost.
Step 2: Wrap Your Model Carefully
Depending on the framework, you usually just “wrap” your model.
For example, in FSDP:
import torch.distributed.fsdp as fsdp
model = MyLargeModel()
sharded_model = fsdp.FullyShardedDataParallel(model)It’s like tucking a giant into a perfectly tailored suit!
Step 3: Training and Saving Models
Training proceeds like normal — except now, under the hood, weights fly like carrier pigeons from GPU to GPU!
Saving needs special care too. Often, you’ll “gather” shards to save a single monolithic checkpoint or save shards individually.
⚡ Quick Tips from the Wise
- Network is the new bottleneck: Sharding means more communication!
Invest in a good network stack — or your GPUs will sit around like bored cows. - Balance your shards: Uneven sharding leads to sad GPUs. Like uneven marriages — someone’s doing all the work.
- Checkpoint wisely: Sharded checkpoints can become messy if not managed properly. Use your framework’s recommended methods.
- Test with small models first: A cracked pot leaks wisdom before the whole dam bursts.
🧙 Final Words: Sharding — Ancient, Yet Modern
In a world where models grow fatter than feasting kings, sharding is the humble carpenter’s knife — carving giants into manageable heroes.
So go forth, young magus, and slice those models like an ancient master chef! 🍰
The AI future awaits those who dare to cut smart and stitch wisely.