Galvatron

Galvatron : A Dynamic Mixture-Based Framework in Training Multi-Trillion Parameter Language Models

Abstract

in 2639 lines of code We present "Optimus-Megatron", a novel dynamic framework designed to optimize the training of large-scale language models with billions of parameters integrated with Megatron-DeepSpeed. The framework leverages dynamic parallelism, an AI-driven system that automates the selection and adjustment of parallelism strategies—such as data parallelism, tensor model parallelism, and pipeline parallelism. These strategies are tailored to the specific requirements of model architecture, dataset size, and hardware constraints. By eliminating the need for manual configurations, Optimus-Megatron simplifies the training process and significantly enhances computational efficiency.

Example of the dynamic parallelism integrated with megatron

from megatron.core.strategy_selector import DynamicStrategySelector
from megatron.core.model_profiler import ModelProfiler
from megatron.core.hardware_profiler import HardwareProfiler
from megatron.core.parallelism_manager import ParallelismManager

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Introduction

The rapid development of natural language processing (NLP) has brought large-scale language models (LLMs) like GPT and BERT to the forefront. Despite their transformative capabilities, training these models is notoriously resource-intensive, requiring substantial computational power and memory. Popular frameworks such as Megatron-LM and DeepSpeed offer support for distributed training but depend on static, manually-configured parallelism strategies, making them both complex and less efficient for diverse hardware setups and dynamic workloads.

To address these challenges, we introduce "Optimus-Megatron", a framework built around dynamic parallelism. Unlike static approaches, this framework dynamically optimizes and adjusts parallelism strategies based on real-time training metrics, hardware configurations, and model characteristics. Specifically, the contributions of this work include:

• Automation of Parallelism Selection: Automatically identifying the optimal combination of data, model, and pipeline parallelism strategies.
• Dynamic Adjustment During Training: Adapting strategies in real time as training progresses.
• Improved Usability: Simplifying distributed training for users by abstracting technical complexities into a user-friendly interface.

By addressing the limitations of existing static frameworks, Optimus-Megatron paves the way for faster, more scalable training of next-generation LLMs.

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Dynamic Parallelism Framework

Core Concepts

The Optimus-Megatron framework departs from traditional static parallelism configurations by introducing a dynamic hybrid parallelism model. This approach optimizes training efficiency by intelligently switching or combining parallelism strategies at different stages of training.

1. Layer-Specific Optimization:

   • Certain layers in large transformer architectures benefit more from specific parallelism strategies:
     • Tensor Model Parallelism: Best for computation-heavy layers, such as self-attention.
     • Data Parallelism: More efficient for lightweight layers like feed-forward networks, as it leverages statistical advantages from larger batch sizes.

2. Dynamic Switching During Training:

   • Parallelism strategies are dynamically adapted based on:
     • Model Characteristics: Layer size, computational demand, and memory footprint.
     • Hardware Constraints: GPU memory capacity, interconnect bandwidth, and available processing units.
     • Training Phase: Early training may emphasize throughput (e.g., pipeline parallelism), while later phases prioritize convergence and stability (e.g., data parallelism).

3. Concurrent Strategy Application:

   • For LLMs such as GPT, multiple parallelism strategies are applied simultaneously:
     • Pipeline Parallelism splits layers across GPUs.
     • Data Parallelism distributes batch data across GPUs within pipeline stages.
     • Model Parallelism partitions operations within individual layers.

from megatron.core import ParallelismManager

# Initialize the manager
manager = ParallelismManager(
    model=model,
    dataset=dataset,
    batch_size=initial_batch_size,
    enable_dynamic_adaptation=True
)

# Initialize the framework
manager.initialize()

# During training
for batch in dataloader:
    # Train step
    loss = train_step(batch)
    
    # Update parallelism manager with metrics
    manager.step({
        'throughput': samples_per_second,
        'convergence_rate': loss_improvement,
        'communication_overhead': communication_time,
        'load_imbalance': load_imbalance_ratio
    })

# Cleanup
manager.cleanup()

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Architecture of Optimus-Megatron

Key Components

1. Hardware Profiler:

   • Automatically detects the system's computational resources, including GPU count, memory, and interconnect type (e.g., PCIe or NVLink).

2. Model Profiler:

   • Analyzes the architecture, including parameter count, layer-wise complexity, and resource demands.
   • Identifies optimal layers for tensor model parallelism and pipeline placement.

3. Dataset Profiler:

   • Evaluates the size and structure of the dataset, including batch size requirements.

4. Dynamic Strategy Selector:

   • Synthesizes insights from hardware, model, and dataset profiling to generate an optimized initial configuration.
   • Dynamically adjusts parallelism strategies during training using real-time metrics such as GPU utilization and memory consumption.

5. Framework Integration:

   • Built atop existing platforms like Megatron-LM and DeepSpeed, the framework introduces a dynamic optimization layer that enhances their static designs.

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Methodology

The Optimus-Megatron workflow consists of three main phases:

1. Discovery Phase (Initialization):

   • Profile hardware, model architecture, and dataset properties to create an initial parallelism configuration tailored to the system.

2. Monitoring Phase (Training Execution):

   • Begin training with the selected strategies while collecting runtime metrics such as GPU utilization, memory overhead, and data throughput.

3. Optimization Phase (Dynamic Adjustment):

   • Analyze runtime data to identify performance bottlenecks or inefficiencies.
   • Reconfigure parallelism strategies on the fly to address emerging issues and improve overall performance.

Sample code of the Megatron core (dynamic parallelism training) integrated inside the training.py of Megatron-DeepSpeed

def setup_model_and_optimizer(model_provider_func,
                            model_type,
                            no_wd_decay_cond=None,
                            scale_lr_cond=None,
                            lr_mult=1.0):
    """Setup model and optimizer with dynamic parallelism support."""
    args = get_args()

    # Initialize model
    model = get_model(model_provider_func, model_type)

    # Initialize profilers and managers
    model_profiler = ModelProfiler(model)
    hardware_profiler = HardwareProfiler()
    parallelism_manager = ParallelismManager()

    # Initialize dynamic strategy selector with profilers
    if not hasattr(model, 'strategy_selector'):
        model.strategy_selector = DynamicStrategySelector(
            model,
            model_profiler=model_profiler,
            hardware_profiler=hardware_profiler,
            parallelism_manager=parallelism_manager
        )

    # Setup optimizer with parallel state awareness
    optimizer = get_megatron_optimizer(model, no_wd_decay_cond, scale_lr_cond, lr_mult)
    lr_scheduler = get_learning_rate_scheduler(optimizer)

    if args.deepspeed:
        print_rank_0("DeepSpeed is enabled.")
        model, optimizer, _, lr_scheduler = deepspeed.initialize(
            model=model[0],
            optimizer=optimizer,
            args=args,
            lr_scheduler=lr_scheduler,
            mpu=mpu,
            dist_init_required=False  # We've already initialized parallel groups
        )
        model = [model]
    
    return model, optimizer, lr_scheduler

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Evaluation and Proof of Concept

To validate the effectiveness of Optimus-Megatron, we conducted experiments with large transformer models. The framework dynamically optimized training workflows by considering three primary factors:

1. Model Characteristics:

   • Parameter count, computational complexity, and memory demands of individual layers.

2. Dataset Scale:

   • Size of the training dataset and batch sizes.

3. Hardware Resources:

   • Number of GPUs, per-GPU memory, and interconnect bandwidth.

By integrating profiling data from these dimensions, Optimus-Megatron demonstrated consistent performance improvements compared to static configurations. For example, in tests with transformer-based models, the dynamic approach reduced training time by 20–30% while maintaining or improving model convergence.

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Comparison with Existing Solutions

Feature	Optimus-Megatron	Megatron-LM / DeepSpeed
Parallelism Strategy Selection	Dynamic (AI-driven)	Static (manual)
Runtime Adaptation	Yes	No
User Interface	Simplified (train())	Complex (manual tuning)
Scalability Across Hardware	High	Moderate

Key Differences: Dynamic Parallelism vs. Hybrid Parallelism

Aspect	Hybrid Parallelism	Dynamic Parallelism
Definition	Combines static configurations of multiple parallelism techniques (data, tensor, and pipeline) to optimize resource utilization during training.	Uses dynamic, real-time adjustment of parallelism techniques based on runtime metrics (e.g., GPU utilization, memory, and training phase).
Configuration	Parallelism strategies (e.g., number of pipeline stages, tensor splits) are fixed before training begins and do not change during training.	Parallelism strategies are adaptively chosen and adjusted during training based on model size, dataset size, hardware, and runtime behavior.
Adaptability	Non-adaptive: Hybrid parallelism is designed once and applied throughout training, irrespective of changing conditions.	Adaptive: Strategies are re-evaluated and adjusted during training, enabling better utilization and performance as conditions change.
User Interaction	Requires users to manually configure each type of parallelism (e.g., setting pipeline stages, tensor groups, and data-parallel replicas).	Automates the process: Users provide the model and dataset, and the system dynamically selects and optimizes the best parallelism strategies.
AI Integration	Does not involve AI; relies on heuristic-based decisions or manual tuning for parallelism configuration.	Incorporates AI-driven decision-making (e.g., reinforcement learning or neural networks) to predict and optimize parallelism dynamically.
Scope of Application	Focused on distributing computations efficiently across available hardware using pre-determined strategies.	Targets real-time optimization and adaptation of strategies, making it flexible across diverse models, datasets, and hardware setups.
Complexity for the User	Higher complexity: Requires users to understand the model architecture and parallelism strategies to configure them manually.	Low complexity: Abstracts the details, providing a plug-and-play experience (e.g., train(model, data)).
Efficiency	Efficiency is limited by static decisions that might not be optimal for all stages of training.	Aims for higher efficiency by dynamically responding to bottlenecks, resource constraints, and training progress.

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Specific Innovations in Optimus-Megatron

1. Dynamic Switching During Training:

   • Hybrid parallelism strategies remain fixed throughout training. For example, if a model starts with data parallelism and pipeline parallelism, it will stick with this configuration until training ends.
   • In dynamic parallelism, your framework monitors runtime metrics (e.g., memory usage, compute utilization) and dynamically switches strategies as needed. For example:
     • Start with model parallelism for large layers during initial iterations.
     • Switch to data parallelism for later stages when gradients dominate memory usage.

2. Layer-Specific Optimization:

   • Hybrid parallelism typically applies a consistent strategy across all layers.
   • Your approach uses layer-specific optimization:
     • Heavy computation layers (e.g., attention heads in transformers) might use tensor parallelism.
     • Lightweight or final layers might use data parallelism.
     • This layer-wise customization is performed dynamically, potentially improving memory and computational efficiency.

3. Real-Time Adaptation:

   • Hybrid parallelism assumes static hardware availability and model behavior throughout training.
   • Your framework adapts to:
     • Hardware changes (e.g., GPUs becoming unavailable or resource bottlenecks during training).
     • Different phases of training (e.g., convergence stability in later stages).

4. AI-Driven Optimization:

   • Hybrid parallelism relies on fixed configurations, typically requiring human expertise to optimize.
   • Your research proposes AI-driven dynamic decisions:
     • Use reinforcement learning or a trained neural network to predict the best parallelism strategy for each layer, batch, or training phase.

5. Simplified User Experience:

   • In hybrid parallelism, users need to define and configure multiple parameters:
     • Number of pipeline stages.
     • Size of tensor-parallel groups.
     • Number of data-parallel replicas.
   • Your framework abstracts these details:
     • Users simply provide the model and dataset, and the system handles the rest, making it more accessible for researchers and practitioners.

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Example Scenario

Hybrid Parallelism:

• A 24-layer transformer model is split as follows:
  • Pipeline Parallelism: Split into 4 pipeline stages across 8 GPUs.
  • Tensor Parallelism: Each pipeline stage splits large layers across 2 GPUs.
  • Data Parallelism: Replicated twice (16 GPUs total).
• These configurations are decided before training starts and remain unchanged throughout.

Dynamic Parallelism :

• The same 24-layer model begins with:
  • Model Parallelism for attention-heavy layers during early training iterations to optimize memory usage.
  • Pipeline Parallelism dynamically adjusted from 4 stages to 3 stages mid-training due to imbalances in stage compute times.
  • Data Parallelism becomes the dominant strategy in later iterations as larger batch sizes stabilize convergence.

The system monitors runtime metrics, detects bottlenecks (e.g., communication overhead, idle GPUs), and dynamically adjusts configurations to ensure maximum efficiency.

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Key Advantages of Optimus-Megatron Over Hybrid Parallelism

1. Dynamic Adaptation:

   • Handles real-world training variability (e.g., hardware resource changes, varying layer behavior) better than static hybrid configurations.

2. Higher Resource Utilization:

   • By dynamically reallocating workloads, your system avoids underutilization of GPUs, common in hybrid parallelism when configurations are suboptimal.

3. Automation:

   • Makes large-scale distributed training more accessible by eliminating the need for manual configuration.

4. Optimized for All Training Stages:

   • Adapts to the evolving demands of training, whereas hybrid parallelism is optimized only for the initial conditions.

5. Future-Proof:

   • AI-driven decisions make your framework adaptable to future hardware architectures and increasingly complex models.

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Conclusion

Optimus-Megatron introduces a dynamic and adaptive framework for training large-scale language models, addressing the inefficiencies and usability challenges of current static approaches. By automating the selection and adjustment of parallelism strategies, it not only accelerates training but also lowers the barrier for users without advanced expertise in distributed systems.

Future work will expand the framework's capabilities with more granular AI-driven decision-making and optimization techniques, enabling even greater scalability and efficiency for training increasingly complex models. This innovation represents a critical step toward democratizing LLM training in resource-constrained environments.

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Acknowledgments

The development of Optimus-Megatron was supported by advancements in distributed systems research, building upon existing frameworks like Megatron-LM and DeepSpeed, which provided a robust foundation for this work.

Papers and Implementations on Combined Techniques

• Microsoft's DeepSpeed:

  • DeepSpeed offers a seamless integration of various parallelism strategies.
  • Provides APIs to easily apply hybrid parallelism without extensive changes to the model code.
  • Title: "ZeRO: Memory Optimizations Toward Training Trillion Parameter Models".
  • Techniques Used: Combines ZeRO with data and model parallelism.
  • Novelty: Significant memory optimizations and scalability for massive models.

• NVIDIA's Implementation:

  • NVIDIA's Megatron-LM framework leverages model parallelism within a node (splitting the model across GPUs in a server) and data parallelism across nodes.
  • Uses Ring All-Reduce algorithms to optimize gradient aggregation.
  • Title: "Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism".
  • Techniques Used: Combines intra-layer model parallelism with data parallelism.
  • Novelty: Focus on transformers and optimizing intra-layer parallelism.
  • Summary: - Megatron-LM is a framework developed by NVIDIA for training large Transformer-based language models with billions of parameters. - It employs both model parallelism and data parallelism to efficiently utilize multiple GPUs. - Model Parallelism: The model's layers are partitioned across GPUs to handle the enormous size. - Data Parallelism: The data is distributed across multiple GPU groups to accelerate training.

• PipeDream:

  • Title: "PipeDream: Generalized Pipeline Parallelism for DNN Training".
  • Techniques Used: Combines pipeline parallelism with replication of parameters to minimize staleness.
  • Novelty: Optimized pipeline efficiency.

• FairScale (PyTorch):

  • Title: "FairScale: PyTorch Extensions for Large Scale and Efficient Training".
  • Techniques Used: Implements Sharded Data Parallel and Gradient Accumulation.
  • Novelty: Reduces redundancy in memory for efficient training.

• ZeRO: Memory Optimizations Toward Training Trillion Parameter Models

  • Authors: Samyam Rajbhandari, Jeff Rasley, et al.
  • Summary:
    • ZeRO (Zero Redundancy Optimizer) is a memory optimization technique developed by Microsoft Research.
    • It partitions the model states (optimizer states, gradients, and parameters) across data-parallel processes to reduce memory redundancy.
    • Hybrid Approach: Combines data parallelism with model state sharding to enable training of extremely large models.
  • Reference:
    • ZeRO Paper (arXiv:1910.02054)

• PipeDream: Generalized Pipeline Parallelism for DNN Training

  • Authors: Deepak Narayanan, Aaron Harlap, et al.
  • Summary:
    • Introduces pipeline parallelism, a form of model parallelism where different layers of the model are placed on different GPUs, and micro-batches of data are pipelined through them.
    • Combines pipeline parallelism with data parallelism to improve resource utilization and throughput.
  • Reference:
    • PipeDream Paper (Proceedings of SOSP 2019)

• DeepSpeed: Extreme-Scale Model Training for Everyone

  • Authors: Microsoft DeepSpeed Team
  • Summary:
    • DeepSpeed is a deep learning optimization library that enables efficient training of models with over a trillion parameters.
    • It uses a combination of data parallelism, model parallelism, and pipeline parallelism.
    • Incorporates ZeRO and other optimization techniques to minimize memory usage and maximize training speed.
  • Reference:
    • DeepSpeed Documentation
    • DeepSpeed Paper (arXiv:1910.02054)

• FairScale: A General Purpose Modular PyTorch Library for High Performance and Large Scale Training

  • Authors: Facebook AI Research (FAIR) Team
  • Summary:
    • FairScale provides a collection of optimization tools for large-scale training in PyTorch.
    • Supports Sharded Data Parallel (SDP) and Fully Sharded Data Parallel (FSDP) which combine data and model parallelism.
  • Reference:
    • FairScale GitHub Repository
    • Blog Post on FairScale

References

1. Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet Classification with Deep Convolutional Neural Networks. Advances in Neural Information Processing Systems.
2. Dean, J., et al. (2012). Large Scale Distributed Deep Networks. Advances in Neural Information Processing Systems.
3. Shoeybi, M., et al. (2019). Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism. arXiv preprint arXiv:1909.08053.
4. Huang, Y., et al. (2019). GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism. Advances in Neural Information Processing Systems.
5. Narayanan, D., Santhanam, K., Harlap, A., Phanishayee, A., Seshadri, V., & Zaharia, M. (2019). PipeDream: Generalized Pipeline Parallelism for DNN Training. ACM Symposium on Operating Systems Principles.
6. Rajbhandari, S., et al. (2020). ZeRO: Memory Optimization Towards Training A Trillion Parameter Models. arXiv preprint arXiv:1910.02054.
7. Ren, S., et al. (2021). ZeRO-Infinity: Breaking the GPU Memory Wall for Extreme Scale Deep Learning. arXiv preprint arXiv:2104.07857.
8. Chen, T., Xu, B., Zhang, C., & Guestrin, C. (2016). Training Deep Nets with Sublinear Memory Cost. arXiv preprint arXiv:1604.06174.
9. "Efficient Large-Scale Language Model Training on GPU Clusters Using Megatron-LM" by Shoeybi et al.
   • Link

• "Scale ML Model Training to Thousands of GPUs with PyTorch and Azure Machine Learning" by Microsoft.
  • Link

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