metadata

title: Pipeline Parallelism Schedule Visualizer
emoji: 📊
colorFrom: indigo
colorTo: blue
sdk: docker
app_file: app.py
pinned: false
suggested_hardware: cpu-basic
suggested_storage: small
header: default

Pipeline Parallelism Schedule Visualizer

An interactive visualization tool for exploring different pipeline parallelism scheduling strategies in large language models.

Features

Visualize multiple scheduling strategies for pipeline parallelism
Adjust parameters like number of devices, stages, and batches
Compare execution timelines between different strategies
Explore operation timings and their effects on performance

Supported Strategies

1F1B (One-Forward-One-Backward)
1F1B with Interleaved Placement
1F1B with Overlapped Operations
1F1B with Interleaved Placement and Overlapped Operations
Zero-Bubble 1 Pipeline (ZB1P)
Dual Pipeline (DualPipe)

Usage

Simply adjust the parameters and select the strategies you want to compare, then click "Generate Schedule" to visualize the results.

Deployment

This app is deployed on Hugging Face Spaces using Dash.

Overview

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

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, Zero-Bubble, etc.)
Visualize the execution schedule on multiple devices
Compare different strategies for efficiency

Features

Supported Pipeline Strategies:
- 1F1B (One-Forward-One-Backward)
- Interleaved 1F1B
- Zero-Bubble 1F1B (ZB-1P)
- 1F1B with computation-communication overlap
- Interleaved 1F1B with computation-communication overlap
- DualPipe (Bidirectional pipeline parallelism with full forward-backward overlap)
Visualization:
- Interactive visualization dashboard using Plotly/Dash
Configuration:
- Configurable simulation parameters through Hydra
- Customizable stage latency and communication costs

Installation

This project uses uv for dependency management.

Setup uv if not installed on your computer:

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

Running the Interactive Server

To visualize schedules interactively:

uv run src/server.py

This will start a Dash server (usually on http://127.0.0.1:8050/). Open this URL in your web browser.

You can then adjust parameters like the number of devices, stages, batches, operation times, and select different scheduling strategies to see the resulting pipeline visualization.

Running from Command Line

Running for 1F1B strategy:

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

Running for interleaved 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 DualPipe strategy:

uv run python main.py strategy=dualpipe num_devices=8 num_stages=8 num_batches=20

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.

Override Specific Parameters

You can override specific parameters at runtime:

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

Use DualPipe as an example, you can manually set different time for forward/backward/backward_D/backward_W/overlapped_forward_backward:

uv run python main.py strategy=dualpipe num_devices=8 num_stages=8 num_batches=32 op_times.forward=1.0 op_times.backward=2.0 op_times.backward_D=1.0 op_times.backward_W=1.0 op_times.overlapped_forward_backward=2.5

Using Different Configuration Files

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

Recommended Approach

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

Run with your desired configuration using the --config-name flag:
```
uv run python main.py --config-name=model_A
```

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

References

PipeDream: Fast and Efficient Pipeline Parallel DNN Training. arxiv
Efficient Large-Scale Language Model Training on GPU Clusters Using Megatron-LM. arxiv
Zero Bubble Pipeline Parallelism. arxiv
Communication-Computation Overlap in MoE Training with 1F1B Pipeline Parallelism. 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.