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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:
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
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