Next Steps for Training Decision

Next Steps for Training Decision | Generated by AI

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Question: What should I do next after reviewing the Nanochat 760M MI300X setup document I uploaded?

Answer:

Based on the detailed breakdown in your uploaded file, here’s a structured next-step plan:

1. Decide on Training Strategy

You have two options:

Option A: Continue with current SDPA setup

Current MFU is ~27%, training will take ~62 hours for 29,000 steps.
Pros: Stable, fully compatible with MI300X.
Cons: Slower throughput than potential max.

Option B: Attempt speed optimizations

Consider these carefully:
1. ROCm Flash Attention – potentially boost MFU to 40–50%.
  - Risk: May fail to compile.
2. FP8 Precision – ~2x throughput gain.
  - Requires PyTorch compiled against ROCm 7.2+.
3. Increase device_batch_size – reduce gradient accumulation overhead.
  - Risk: SDPA memory could cause OOM errors.
4. Multi-GPU training – scale throughput linearly.
5. Increase total batch size – fewer steps, same tokens.

Recommendation: If reliability is your priority, stick to current setup (Option A). If you want to experiment and can handle potential build issues, try Option B selectively.

2. Prepare Training

Verify dataset availability: ~/.cache/nanochat/base_data_climbmix/
Ensure tokenizer is trained: ~/.cache/nanochat/tokenizer/
Decide on whether to resume or restart:
- Resume: ./run_mi300x_d24_pretrain.sh --resume-from-step=XXXX
- Full run: ./run_mi300x_d24.sh

3. Run Pretraining

Start monitoring logs:

tail -f /root/nanochat/run_mi300x_d24.log

Monitor metrics: loss, tokens/sec, MFU.
Ensure you’re logging checkpoints for safety every 5000 steps.

4. Post-Training Pipeline

After base pretraining:

Evaluate base model with DCLM CORE benchmark.
Run SFT on chat data (identity_conversations.jsonl).
Evaluate SFT model with chat benchmarks.

Launch interactive chat:

python -m scripts.chat_cli -p 'Hello!'
python -m scripts.chat_web

5. Optional Improvements for Future Runs

ROCm flash attention and FP8 for throughput.
Multi-GPU for larger scale.
Experiment with batch sizes and sliding window attention if using flash-attn.

Suggested Immediate Next Step

Decide if you will stick with the current setup or optimize.
If sticking with current setup, launch the full pretrain and monitor logs.
If optimizing, choose one improvement at a time (e.g., attempt ROCm flash-attn) and verify stability.

You can start by confirming your priority: reliability vs speed. That will dictate whether you run run_mi300x_d24.sh now or experiment with the performance tweaks.

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