README.md

Pipeline Parallelism Emulation

This project provides tools for emulating and visualizing pipeline parallelism strategies used in large language model training.

Overview

Pipeline parallelism is a technique used to train large models by partitioning the model across multiple devices and processing data in a pipelined fashion. This project allows you to:

• Simulate different pipeline parallelism strategies (1F1B, Interleaved)
• Visualize the execution schedule on multiple devices
• Compare different strategies for efficiency

Features

• Supported Pipeline Stragegies:
  • 1F1B
  • Interleaved 1F1B
• Visualization:
  • Interactive visualization dashboard using Plotly/Dash
• Config:
  • Configurable simulation parameters through Hydra
  • Each stage

Installation

This project uses uv for dependency management.

Setup uv if not installed in your computer:

# On macOS and Linux.
curl -LsSf https://astral.sh/uv/install.sh | sh

Usage

Running for 1F1B strategy:

uv run python main.py strategy=1f1b num_devices=4 num_stages=4 num_batches=8

Running for interleave strategy:

uv run python main.py strategy=interleave num_devices=4 num_stages=8 num_batches=8

Running for ZB-1P strategy:

uv run python main.py strategy=zb1p num_devices=4 num_stages=4 num_batches=8

Running for 1F1B-batch-overlap strategy:

uv run python main.py strategy=1f1b_overlap num_devices=4 num_stages=4 num_batches=8

Running for 1F1B-interleave-overlap strategy:

uv run python main.py strategy=1f1b_interleave_overlap num_devices=4 num_stages=8 num_batches=8

Configuration

The default configuration is in conf/config.yaml. You can override any parameter on the command line or create configuration groups for different scenarios.

Using Different Configuration Files

You can use different configuration files with Hydra in several ways:

Recommended Approach

1. Create multiple configuration files in the conf directory for different use cases:

   conf/
   ├── config.yaml     # Default configuration
   └── model_A.yaml    # Create your own config with stage-specific latency for performance projection.
   

2. Run with your desired configuration using the --config-name flag:

   uv run python main.py --config-name=model_A
   

Override Specific Parameters

You can also override specific parameters at runtime:

uv run python main.py op_times.forward=0.5 op_times.backward=1.0 num_batches=6

Project Structure

PP-Emulation/
├── conf/                   # Hydra configuration files
│   └── config.yaml         # Default configuration
├── src/                    # Source code
│   ├── __init__.py         # Package initialization
│   ├── execution_model.py  # Schedule execution models
│   ├── strategies.py       # Pipeline parallelism strategies
│   └── visualizer.py       # Visualization utilities
├── main.py                 # Main entry point
├── pyproject.toml          # Project metadata and dependencies
└── README.md               # This file

Refences

1. PipeDream: Fast and Efficient Pipeline Parallel DNN Training. arxiv
2. Efficient Large-Scale Language Model Training on GPU Clusters Using Megatron-LM. arxiv
3. Zero Bubble Pipeline Parallelism arxiv
4. 基于1F1B的MoE A2A通信计算Overlap blog

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.