Case Study: How To Scale NLP Research with Distributed PyTorch ?

Problem Statement: Our deep learning R&D team needs to train large state-of-the-art models like BERT and GPT-3 for natural language research. These models have billions of parameters and require massive compute for training.

With models of this size, standard single GPU training is not feasible. Training can take weeks or months on a GPU server. We need ways to accelerate the training by leveraging our existing GPU clusters.

Some solutions are:

Data Parallelism with Horovod:

We can use Horovod to efficiently parallelize training across multiple servers and GPUs. Each GPU trains on a shard of the data simultaneously and gradients are synchronized via ring-allreduce. This allows us to scale model size and batch size while reducing training time.

Pipeline Parallelism:

For extremely large models that don't fit on a single GPU, we can use pipeline parallelism to partition the model layers across different GPUs. This enables training models that are too large for a single GPU.

Profiling:

Using profiling tools like PyTorch profiler, we can analyze the performance of parallel training. Profiling helps identify bottlenecks like slow gradient synchronization or load imbalance between GPUs so we can optimize configurations.

Data Parallelism with Horovod

When training large deep learning models like BERT or GPT-3 with billions of parameters, using multiple GPUs is critical to reduce training time from weeks to days.

For example, training GPT-3 took 3-4 days on 256 to 288 GPUs using Horovod data parallelism and mixed precision to parallelize across GPUs and speed up computation.

# Train GPT-3 on GPU cluster
import horovod.torch as hvd 

hvd.init() # initialize Horovod

# Wrap optimizer with DistributedOptimizer
optimizer = hvd.DistributedOptimizer(optimizer) 

# Train model
for batch in batches:
  output = model(batch)
  loss = loss_fn(output)
  loss.backward()
  optimizer.step() 

# Scale learning rate by gpu count 
lr = base_lr * hvd.size() 
hvd.broadcast_parameters(model.state_dict(), root_rank=0)

Pipeline Parallelism

For models like Megatron-LM with over 8 billion parameters, pipeline parallelism is needed to partition the model across GPUs, since the full model cannot fit on one GPU.

# Split Megatron-LM into stages
stage1 = EmbeddingLayers() 
stage2 = EncoderLayers()
stage3 = DecoderLayers()

# Place stages on different GPUs
stage1.cuda(0) 
stage2.cuda(1)
stage3.cuda(2)

# Forward pass sequentially through pipeline
x = sample_data
x = stage1(x) 
x = stage2(x)
output = stage3(x)

This enables training huge models that wouldn't fit otherwise by splitting across GPU pipeline.

Profiling

Profiling helps discover optimal configurations - like finding the best pipeline stage split or batch size. E.g:

# Profile training loop on GPUs
with torch.profiler.profile(activities=[torch.profiler.ProfilerActivity.CPU]) as prof:

  for batch in batches:
    output = model(batch)
    loss = loss_fn(output)
    optimizer.step()

# Analyze profiler output
print(prof.key_averages().table(sort_by="cpu_time_total"))

By leveraging these distributed training techniques, we can train massive models faster on our existing infrastructure to advance our natural language research.