Discussion on TPU and GPU Best Sharding Methods in JAX with EasyDeL.

[erfanzar]

In this thread, we aim to discuss and document the best practices for sharding configurations on TPUs and GPUs, specifically tailored for libraries like EasyDeL, which leverages sharding methods such as Data Parallel (DP), Fully Sharded Data Parallel (FSDP), Tensor Parallel (TP), and Sequence Parallel (SP). EasyDeL also allows for custom sharding methods to be defined via axis annotations.

Importance of Sharding Configurations

Sharding configurations can significantly impact training speed, efficiency, and model performance, especially on hardware like TPUs and GPUs. By fine-tuning the sharding strategies, developers and researchers can maximize throughput, reduce memory overhead, and balance workloads across multiple devices.

Sharding Methods Overview

1. Data Parallel (DP): Distributes data across multiple devices, each handling a subset of the input batch.
2. Fully Sharded Data Parallel (FSDP): Splits model parameters across devices, enabling efficient memory usage.
3. Tensor Parallel (TP): Divides tensor computations (e.g., matrix multiplications) across devices.
4. Sequence Parallel (SP): Breaks down sequences and assigns them to different devices, particularly useful for long sequence models (often combined with FSDP).
5. Custom Shardings: EasyDeL supports custom sharding methods, allowing users to define axes and strategies tailored to their needs.

TPU and GPU Sharding Observations

Below are some insights into how different sharding methods perform on TPUs and GPUs based on experimentation and metrics:

TPU Observations

• Tensor Parallel (TP): TP is often faster on TPUs due to their high interconnect bandwidth and specialized architecture, but it uses more memory compared to other methods.
• Fully Sharded Data Parallel (FSDP): While slower than TP, FSDP optimizes memory usage by distributing parameters across all TPU cores.
• Sequence Parallel (SP): SP shines in scenarios involving long-context models, where sequences are distributed across devices for better efficiency.

GPU Observations

• For GPUs, operations that rely heavily on “take” or “slice update” methods perform better with Data Parallel (DP), as it efficiently handles high memory bandwidth demands.
• TP and FSDP can be combined to achieve a balance between memory optimization and computational throughput, depending on the workload.

Best Practices

1. TPU Specific:

   • Use FSDP for memory-intensive models with large parameters.
   • Prefer TP for computationally heavy operations to maximize throughput.
   • Leverage SP for models with long sequences to improve efficiency.

2. GPU Specific:

   • Prioritize DP for “take” or “slice update” operations.
   • Combine FSDP and TP for balanced memory and compute optimization.

Proposed Next Steps

• Discussion Topic: Open this thread to collect insights, experiments, and edge cases from the community.
• Documentation: Based on the discussions, we will create a comprehensive guide to TPU and GPU sharding configurations and their best practices.
• Reference Implementations: Share reproducible code snippets using EasyDeL to demonstrate effective sharding strategies.

Feel free to share your configurations, metrics, and observations!

[salrowili]

Great effort. Thank you for creating such an amazing repo and for this detailed introduction. i will start sharing my best practice for TPUv4-8 , TPUv-32, and TPUv4-64 once the 0.1dev release fixed. This way our examples will be consistence.