LogoLogo
Continuum WebsiteContinuum ApplicationsContinuum KnowledgeAxolotl Platform
  • TensorRT-LLM
  • The TensorRT-LLM Process
  • Performance
  • Virtual Machine Creation
  • CUDA Introduction
    • CUDA Architecture
    • Stream Multiprocessors: The Heart of GPU Computing
    • Pre Installation
    • Compatibility Assessment
    • NVCC: The NVIDIA CUDA Compiler
    • Installing Cuda
    • Installing the NVIDIA Container Toolkit
    • CUDA and bandwidth
    • Tensor Cores
  • Building TensorRT-LLM
    • Building from Source
    • TensorRT-LLM Dockerfile
      • Base Image
      • install_base.sh
      • install_cmake.sh
      • install_tensorrt.sh
      • install_pytorch.sh
      • requirements.txt
      • build_wheel.py
      • setup.py
      • Docker Makefile
      • Persistence
      • Running with persistent volumes
  • TensorRT-LLM Architecture and Process
    • The TensorRT-LLM process
    • INetworkDefinition
    • Model Definition
    • Compilation
    • Runtime Engine
    • Weight Bindings
    • Model Configuration
  • TensorRT-LLM build workflow
    • TensorRT-LLM build workflow - process
  • CUDA Graphs
    • Experimentation with CUDA Graphs
  • TensorRT-LLM Libraries
    • tensorrt_llm folders
    • tensorrt_llm/builder.py
    • tensorrt_llm/network.py
    • tensorrt_llm/module.py
    • top_model_mixin.py
    • trt-llm build command
    • trtllm-build CLI configurations
  • LLama2 installation
    • Converting Checkpoints
      • Checkpoint List - Arguments
      • Examples of running the convert_checkpoint.py script
      • convert_checkpoint examples
      • Checkpoint Script Arguments
      • checkpoint configuration file
      • run_convert_checkpoint.py script
    • LLama2 Files Analysis
    • TensorRT-LLM Build Engine Process
    • TensorRT-LLM Build Process Documentation
    • Build arguments
    • trtllm build configuration file
    • Run the buildconfig file
    • Analysis of the output from build.py
    • LLama3 configurations
    • Proposed checkpoint config file for LLama3
    • Proposed build config file for LLama3
    • run.py for inference
    • Using the models - running Llama
    • generate_int8 function
    • summarize.py script in Llama folder
    • Compiling LLama Models
  • Tasks
  • LLama Model Directory
    • llama/model.py
    • llama/utils.py
    • llama/weight.py
    • llama/convert.py
    • PreTrainedModel class
    • LlamaForCausalLM class
    • PretrainedConfig class
  • TensorRT-LLM Tutorial
  • Tutorial 2 - get inference going
  • examples/run.py
  • examples/utils.py
  • examples/summarize.py
  • The Python API
    • Layers
    • Functionals
    • functional.py
    • tensorrt_llm.functional.embedding
    • tensorrt_llm.functional.gpt_attention
    • tensorrt_llm.functional.layer_norm
    • tensorrt_llm.functional.rms_norm
    • Model
    • Quantization
    • Runtime
    • Runtime Process
  • Transformer Architecture
    • Attention Mechanism
    • Multi Head Attention
    • Positional Encoding
    • Scaled dot-product attention
    • Layer Normalisation
    • Activation Functions
    • Residual Connections
    • Position Wise Feed-Forward Layer
    • Transformer Feed-Forward Layers Are Key-Value Memories
    • KV Cache
      • Efficient Streaming Language Models with Attention Sinks
      • Input QKV tensor
    • General Notes on Model Architecture
  • Best Practices for Tuning the Performance of TensorRT-LLM
    • Optimisation Techniques
    • Batch Manager
    • Alibi
    • Relative Attention Bias
    • Beam Search
    • Rotary Positional Embedding (RoPE)
    • Numerical Precision
    • FP8 Formats for Deep Learning
  • Graph Rewriting
  • Reducing Activation Recomputation in Large Transformer Models
  • Efficient Large-Scale Language Model Training on GPU Clusters Using Megatron-LM
  • Numerical Position
  • TensorRT Models
  • Bloom
    • Huggingface Bloom Documentation
  • Runtime
  • Graph Rewriting (GW) module
  • FasterTransfomer Library
  • Dual ABI issues
  • Phi 2.0
  • ONNX
  • Message Passing Interface (MPI)
  • NVIDIA Nsight Systems: A Comprehensive Guide for TensorRT-LLM and Triton Inference Server
  • NCCL
Powered by GitBook
LogoLogo

Continuum - Accelerated Artificial Intelligence

  • Continuum Website
  • Axolotl Platform

Copyright Continuum Labs - 2023

On this page

Was this helpful?

Efficient Large-Scale Language Model Training on GPU Clusters Using Megatron-LM

PreviousReducing Activation Recomputation in Large Transformer ModelsNextNumerical Position

Last updated 1 year ago

Was this helpful?

The paper titled "Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism" by Mohammad Shoeybi et al., presents significant advancements in training extremely large transformer models for natural language processing (NLP) applications. Here's a detailed analysis of this paper:Introduction and Motivation

The motivation behind the research is the need to effectively train large transformer models, which have shown promising results in various NLP tasks but are hindered by memory constraints when scaled up. Traditional model training approaches struggle with the immense size of these models due to the memory requirements of popular optimization algorithms like ADAM.

Core Contributions and Methodology

  • Model Parallel Approach: The paper introduces a simple yet efficient intra-layer model parallelism technique that does not require custom compilers or major library changes. It is implemented within the PyTorch framework with minimal modifications.

  • Scalability: Demonstrating the scalability of their approach, the authors successfully trained models up to 8.3 billion parameters using 512 GPUs. This setup achieved a significant 76% scaling efficiency, which is a substantial improvement over existing methods.

  • Optimization of BERT and GPT-2 Models: They explored modifications in the architecture, such as adjustments to layer normalization and residual connections, to prevent performance degradation as model size increases.

  • State-of-the-Art Results: Their models achieved top results on several benchmarks: WikiText103 for perplexity, LAMBADA for cloze-style prediction accuracy, and RACE for reading comprehension.

Results

The experiments demonstrated that their model parallelism approach not only allows the training of significantly larger models but also enhances their performance on various benchmarks. For instance, the modified BERT models showed improved performance on downstream tasks as the model size increased, attributed to the strategic placement of layer normalization.

Implications

This paper's findings are crucial for the field of AI and machine learning, especially in scaling NLP models. The ability to train larger models more efficiently could lead to more advanced AI systems capable of understanding and generating human language with higher accuracy.

Conclusion

The "Megatron-LM" framework marks a pivotal advancement in NLP model training. By enabling the training of multi-billion parameter models without extensive hardware requirements, it opens new possibilities for research and application in AI. The techniques introduced serve as a foundation for further research into efficient training methods for large-scale AI models.

For more detailed insights and to access their code, you can visit their GitHub repository: .

Megatron-LM GitHub
Megatron-LM: Training Multi-Billion Parameter Language Models...arXiv.org
Efficient Large-Scale Language Model Training on GPU Clusters Using Megatron-LM
Logo