sweep

name	sweep
description	Prepare and run a KV-cache compression sweep. Loads sweep configuration, validates prerequisites, and provides the exact commands needed. Use before starting any GPU experiment.

Sweep Preparation and Execution

Before running a sweep, verify prerequisites and provide the correct commands.

Steps

1. Check environment:

   • Run uv run python -c "import torch; print(f'CUDA: {torch.cuda.is_available()}, MPS: {torch.backends.mps.is_available()}')" to verify GPU access
   • Run make check to ensure code is clean

2. Check existing results:

   • Count files: find results -name "*_$1p.json" 2>/dev/null | wc -l
   • List what's done: find results -name "*_$1p.json" -exec basename {} \;
   • Check checkpoints: find results -name "*.ckpt.jsonl" -exec wc -l {} \;

3. Validate model access:

   • Check if model is cached: ls ~/.cache/huggingface/hub/models--$(echo "$0" | tr '/' '--')/ 2>/dev/null
   • If not cached, suggest: bash scripts/download_models.sh

4. Provide the sweep command:

   uv run kvguard sweep \
     --num-prompts ${1:-500} \
     --model "${0:-Qwen/Qwen2.5-7B-Instruct}" \
     --output-dir results \
     --max-new-tokens 512
   

5. For the full Phase 2 pipeline (both models + train + eval):

   nohup bash scripts/run_phase2.sh &
   # Monitor with: bash scripts/check_status.sh
   

Sweep configuration

16 configs per model:

• Baseline: none @ 0.0
• StreamingLLM: 0.25, 0.5, 0.625, 0.75, 0.875
• SnapKV: 0.25, 0.5, 0.625, 0.75, 0.875
• ObservedAttention: 0.25, 0.5, 0.625, 0.75, 0.875

Expected output: results/{press}/{ModelShort}_{ratio}_{num_prompts}p.json

Checkpoint files: results/{press}/{ModelShort}_{ratio}.ckpt.jsonl (auto-resume on restart)

Time estimates (5090 GPU)

• 500 prompts x 16 configs ≈ 12-18h per model
• Full Phase 2 (2 models) ≈ 48-60h total including train/eval