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Llama 3 Performance Optimization: From 70B to 405B Parameters using NVIDIA GPUs on VALDI

Introduction

The release of Llama 3 70B on April 18, 2024, marked a significant milestone in the evolution of large language models (LLMs). This state-of-the-art model has demonstrated impressive capabilities across various NLP tasks. Now, with the release of Llama 3.1 405B we're on the cusp of a new era in AI.

In this guide, we'll dive deep into performance optimization techniques for Llama 3 models by comparing the 70B and upcoming 400B versions across different NVIDIA GPUs. Whether you're an AI researcher, a machine learning engineer, or a developer working on NLP applications, this post will provide valuable insights to help you maximize the potential of these powerful models.

Understanding Model Scaling: From 70B to 405B Parameters

Before we get into performance optimization, let's explore what it means to scale a language model from 70 billion to 405 billion parameters.

The resource needs increase dramatically as models increase in size, requiring massive computing power, longer training times, and higher energy consumption. Memory constraints become a significant hurdle needing advanced techniques like model parallelism or sharding. Data requirements expand considerably, demanding vastly larger and more diverse datasets to prevent overfitting. Optimization becomes more complex, with trickier loss domains and potential instability during training. Inference latency increases, potentially limiting real-time applications. Hardware limitations may necessitate specialized equipment like TPUs or large GPU clusters. Finally, there's the challenge of diminishing returns, where performance improvements may not scale linearly with the increase in model size. Addressing these challenges requires a combination of cutting-edge hardware, advanced software techniques, and careful planning and optimization strategies.

Technical Challenges

  1. Memory Management: Efficiently handling the memory requirements of a 405B parameter model is a significant challenge.
  2. Computational Complexity: Training and inference times can increase substantially without proper optimization.
  3. Distributed Computing: Effective distribution of model computation across multiple GPUs or nodes becomes crucial.

Performance Optimization Techniques

Let's explore key techniques for optimizing the performance of large language models like Llama 3.

1. Model Parallelism

For models as large as Llama 3.1 405B, model parallelism becomes essential. Testing code on a single GPU is useful for verifying functionality, however, for training and inference at production scale you must rely on parallel computing. Model parallelism allows you to perform ML operations across a number of GPUs by splitting the work up into discrete pieces, then combining the results. Here's a simple example using PyTorch:

import torch.distributed as dist
from torch.nn.parallel import DistributedDataParallel as DDP

# ...Your ParallelModel Class or Type..

def run(rank, world_size):
    setup(rank, world_size)

    # Get GPU information
    gpu_name = torch.cuda.get_device_name(rank)

    # Create model and move it to GPU with id rank
    model = ParallelModel().to(rank)

    # Wrap the model with DistributedDataParallel
    ddp_model = DDP(model, device_ids=[rank])


if __name__ == "__main__":
    world_size = torch.cuda.device_count()
    mp.spawn(run, args=(world_size,), nprocs=world_size, join=True)

2. Mixed Precision Training

Utilizing mixed precision can significantly reduce memory usage and increase training speed. Many frameworks use 32-bit floats, however, using 16-bit floats on hardware that supports it can produce the same accuracy during training. Using half-precision during the process can save memory overhead and also processing time during training [1].

import torch
# Creates once at the beginning of training
scaler = torch.cuda.amp.GradScaler()

for data, label in data_iter:
   optimizer.zero_grad()
   # Casts operations to mixed precision
   with torch.cuda.amp.autocast():
      loss = model(data)

   # Scales the loss, and calls backward()
   # to create scaled gradients
   scaler.scale(loss).backward()

   # Unscales gradients and calls
   # or skips optimizer.step()
   scaler.step(optimizer)

   # Updates the scale for next iteration
   scaler.update()

3. Gradient Checkpointing

Gradient checkpointing can help manage memory usage during training by adding a moderate increase in compute time. Instead of holding the entire state in memory, a checkpoint can be created where the intermediate state of the nodes is re-computed during backpropagation, instead of simply being referenced from memory. Some analysis shows memory is reduced to O(sqrt(n)) with only a single additional pass [2]. Checkpointing typically benefits models where the activation structure is more complex.

from torch.utils.checkpoint import checkpoint

# Block Class and data 

class ComplexModel(nn.Module):
   def __init__(self, size, num_blocks, use_checkpoint=False):
       super().__init__()
       self.blocks = nn.ModuleList([ComplexBlock(size) for _ in range(num_blocks)])
       self.use_checkpoint = use_checkpoint

   def forward(self, x):
       for block in self.blocks:
           if self.use_checkpoint:
               x = checkpoint(block, x, use_reentrant=False)
           else:
               x = block(x)
       return x

4. Efficient Attention Mechanisms

Attention mechanisms work by weighting the input query (or context) to the keys that are identified within the input query. Implementing efficient attention mechanisms like Flash Attention can significantly speed up computation:

class FlashAttention(nn.Module):
    def __init__(self, embed_dim, num_heads):
        super().__init__()
        self.embed_dim = embed_dim
        self.num_heads = num_heads
        self.head_dim = embed_dim // num_heads
        self.qkv_proj = nn.Linear(embed_dim, 3 * embed_dim)
        self.out_proj = nn.Linear(embed_dim, embed_dim)

    def forward(self, x):
        qkv = self.qkv_proj(x)
        q, k, v = qkv.chunk(3, dim=-1)
        q = q.view(q.shape[0], q.shape[1], self.num_heads, self.head_dim).transpose(1, 2)
        k = k.view(k.shape[0], k.shape[1], self.num_heads, self.head_dim).transpose(1, 2)
        v = v.view(v.shape[0], v.shape[1], self.num_heads, self.head_dim).transpose(1, 2)
        context = flash_attn_func(q, k, v)
        context = context.transpose(1, 2).contiguous().view(x.shape[0], x.shape[1], self.embed_dim)
        return self.out_proj(context)

Benchmarking Results

We conducted extensive benchmarks of Llama 3.x across NVIDIA H100 GPUs. We implemented a custom script to measure Tokens Per Second (TPS) throughput. We expect to enhance the testing approach over-time, but here are our initial findings:

NVIDIA H100 80G

Model TPS Peak GPU Memory (MB) Avg GPU Utilization (%)
Meta-Llama-3-8B 36.9631457 10069.125 23.5
Meta-Llama-3-8B 37.10334192 10069.125 23
Meta-Llama-3-8B 37.14281194 10069.125 23.5
Meta-Llama-3-8B 37.13641446 10069.125 23
Meta-Llama-3-8B 36.90376236 10069.125 22
Meta-Llama-3-70B 14.96634636 68811.125 47
Meta-Llama-3-70B 14.96532571 68811.125 47
Meta-Llama-3-70B 14.96652259 68811.125 47.5
Meta-Llama-3-70B 14.97096115 68811.125 47.5
Meta-Llama-3-70B 14.92738074 68811.125 48
Meta-Llama-3.1-8B 36.81009765 10069.125 22.5
Meta-Llama-3.1-8B 36.9252028 10069.125 23
Meta-Llama-3.1-8B 37.2084815 10069.125 24.5
Meta-Llama-3.1-8B 37.19781406 10069.125 23.5
Meta-Llama-3.1-8B 36.77084981 10069.125 22.5
Meta-Llama-3.1-70B 39.96497617 10069.125 24.5
Meta-Llama-3.1-70B 40.02926996 10069.125 24.5
Meta-Llama-3.1-70B 39.81242247 10069.125 22.5
Meta-Llama-3.1-70B 37.32692827 10069.125 22
Meta-Llama-3.1-70B 37.05194818 10069.125 22.5

In our initial testing we saw that LLama3.1 has a slightly higher TPS with default configurations, though the models are very similar in memory and processor usage with PyTorch. As expected, the inference is heavily hardware-bound, specifically memory and I/O bandwidth. We’ll be publishing more tests in the coming days on 3.1/405 and updating with parallel processing and inference engines.

Inference Optimization for Llama 3.1 405B

With the release of Llama 3.1 405B, here are some general strategies to review for efficient inference:

  1. Quantization: Int8 or even Int4 quantization can significantly reduce model size and inference time. Quantization helps us reduce the model size by substituting lower precision values in the computations between the activation nodes in the network. Repeated batches of data pumped through the network during training allow us to find a balance between reducing the precision (thus saving space) and measuring impact to the output quality of the network.
  2. Tensor Parallelism: Distributing tensor computations across multiple GPUs can help manage the increased model size. Matrix multiplication - and other linear algebra operations - often lend themselves to being split apart into discrete operations that can be processed on separate GPUs, then results combined and processed.
  3. KV Cache Optimization: Efficient key-value cache management is crucial for fast autoregressive inference. Autoregressive(AR) models work by predicting the next work or token in a sequence based upon the tokens that have come before. Experimental cache techniques to store recent or important tokens in a previous sequence help AR models make better predictions. Promising work is being done around sharing caches across attention layers [2].

Here's a sample code snippet for int8 quantization using PyTorch:

quantized_model = torch.quantization.quantize_dynamic(
   model, {nn.Linear}, dtype=torch.qint8
)

# Get the size of the quantized model
quantized_size = get_model_size(quantized_model)

# Run the quantized model with the same input data
output_int8 = quantized_model(input_data)

Conclusion

As we transition from Llama 3.1 70B to 405B, the potential for groundbreaking NLP applications grows exponentially. However, harnessing this power requires sophisticated optimization techniques and careful hardware considerations.

Key takeaways:

  1. Model parallelism and efficient memory management are crucial for handling 405B parameters.
  2. Mixed precision training and gradient checkpointing can significantly improve performance.
  3. Preparing for Llama 3.1 405B involves rethinking inference strategies, with a focus on quantization and parallelism.

By implementing these optimization techniques, developers and researchers can push the boundaries of what's possible with large language models like Llama 3.1.

Next Steps

Stay tuned for our upcoming posts where we'll dive deeper into:

  1. Distributed training strategies for Llama 3.1 405B
  2. Advanced quantization techniques for sub-100ms inference
  3. Real-world case studies: Deploying Llama 3.x in production environments

Don't miss out on these cutting-edge insights! Follow us on X @valdilabs.

Try It Yourself

Want to experiment with Llama 3.1 optimization? Check out our GPU marketplace to rent high-performance NVIDIA GPUs. Sign up now and start optimizing in minutes!

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Keywords: Llama 3.1, 70B model, 405B model, NVIDIA GPU, performance optimization, model parallelism, mixed precision training, gradient checkpointing, efficient attention, quantization, inference optimization, NLP, large language models

refs:

https://research.character.ai/optimizing-inference/

https://mlcommons.org/2024/03/mlperf-llama2-70b/

[1] https://pytorch.org/blog/accelerating-training-on-nvidia-gpus-with-pytorch-automatic-mixed-precision/

[2] https://github.com/cybertronai/gradient-checkpointing?tab=readme-ov-file

[3] https://arxiv.org/abs/2405.12981?ref=research.character.ai