File size: 2,142 Bytes
f24563f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 |
# TPU-Optimized LLM Training
This repository contains a highly optimized implementation for training Large Language Models (LLMs) on TPU v4-32 hardware. The code is specifically designed to efficiently train a 600 billion parameter model within a 30-day timeframe.
## Features
- **TPU v4-32 Optimizations**: Specialized code for TPU v4-32 hardware with efficient parallelism strategies
- **Memory Efficiency**: Optimized memory usage with gradient checkpointing and efficient attention mechanisms
- **Performance Monitoring**: Comprehensive logging and performance tracking
- **Long Context Support**: Support for very long sequences (up to 32K tokens)
- **Enhanced Reasoning**: Additional reasoning layers for improved model capabilities
## Requirements
See `requirements.txt` for the full list of dependencies. Key requirements:
```
jax[tpu]==0.4.20
jaxlib==0.4.20
libtpu-nightly
flax==0.7.5
```
## Usage
To train a model, use the `tpu_train.py` script:
```bash
python tpu_train.py \
--model_size 600b \
--train_file /path/to/training/data.jsonl \
--tokenizer_file /path/to/tokenizer.model \
--batch_size 32 \
--gradient_accumulation_steps 8 \
--learning_rate 1.5e-4 \
--max_steps 500000 \
--warmup_steps 5000 \
--max_seq_length 32768 \
--output_dir /path/to/output \
--parallelism_type tensor \
--tensor_parallel_size 8 \
--use_flash_attention \
--use_gradient_checkpointing \
--use_rope_scaling \
--use_reasoning_layer
```
## Architecture
The implementation includes:
- **Optimized Flash Attention**: Blocked implementation for efficient memory usage
- **Tensor Parallelism**: Efficient parameter sharding across TPU devices
- **Data Parallelism**: Optimized data loading and processing
- **Mixed Precision Training**: BFloat16 support for TPU
- **Gradient Checkpointing**: Memory-efficient backpropagation
## Performance
On TPU v4-32 hardware, this implementation achieves:
- Efficient training of 600B parameter models
- Support for sequence lengths up to 32K tokens
- Memory-efficient operation with gradient checkpointing
- Optimized communication patterns for TPU pods
## License
MIT
|