wangyu
b4be3983b7
Hardware analysis:
4x RTX 5090 32GB without NVLink is fully sufficient.
PCIe 5.0 all-reduce overhead <1% of step time for MacBERT-large (340M params).
BF16 mixed precision gives ~2x throughput vs FP32 on 5090.
Module B (Detector) — full 4-GPU DDP via Accelerate:
- DistributedSampler with per-epoch shuffling (correct DDP data split)
- BF16 autocast via accelerator.mixed_precision
- Gradient accumulation handled by accelerator.accumulate()
- Only rank-0 saves checkpoints and logs to wandb
- accelerator.gather_for_metrics() for correct multi-GPU validation
- per_gpu_batch_size=32, effective_batch = 32×4 = 128
Module C (Intervention) — hybrid parallel strategy:
- Stage 1 (BC warm-up): all 4 GPUs via Accelerate DDP
TensorDataset broadcast from rank-0 to all processes
- Stage 2 (PPO): GPU-0 only — env-agent loop is inherently sequential
- Detector preprocessing: distributed across all 4 GPUs via shard split
+ all_gather_object to collect results on rank-0
Configs updated:
detector_config.yaml: per_gpu_batch_size=32, gradient_accumulation_steps=1,
mixed_precision=bf16, num_workers=4
intervention_config.yaml: BC per_gpu_batch_size=256, PPO batch_size=256
Launch scripts added:
scripts/run_detector.sh — single command: 4-GPU detector training
scripts/run_intervention.sh — single command: hybrid BC+PPO training
scripts/run_full_pipeline.sh — end-to-end pipeline steps 1-5
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
38 lines
1.1 KiB
Bash
Executable File
38 lines
1.1 KiB
Bash
Executable File
#!/bin/bash
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# Train Module C (Intervention Policy) on 4x RTX 5090.
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#
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# Stage 1 — Behavior Cloning: all 4 GPUs (DDP, BF16)
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# Stage 2 — PPO fine-tuning: GPU-0 only (inherently sequential)
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#
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# Usage:
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# bash scripts/run_intervention.sh
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# bash scripts/run_intervention.sh data/processed/train.jsonl
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set -e
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TRAIN_DATA="${1:-data/processed/train.jsonl}"
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CONFIG="configs/intervention_config.yaml"
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NUM_GPUS=4
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echo "=============================================="
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echo " CompanionGuard-RL — Module C: Intervention"
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echo " Stage 1 (BC) : ${NUM_GPUS}x GPU (DDP, BF16)"
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echo " Stage 2 (PPO) : GPU-0 only"
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echo " Config : ${CONFIG}"
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echo " Train data : ${TRAIN_DATA}"
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echo "=============================================="
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ACTUAL_GPUS=$(nvidia-smi --query-gpu=name --format=csv,noheader | wc -l)
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if [ "$ACTUAL_GPUS" -lt "$NUM_GPUS" ]; then
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echo "[WARN] Expected ${NUM_GPUS} GPUs, found ${ACTUAL_GPUS}. Adjusting."
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NUM_GPUS=$ACTUAL_GPUS
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fi
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accelerate launch \
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--num_processes=${NUM_GPUS} \
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--mixed_precision=bf16 \
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--multi_gpu \
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scripts/train_intervention.py \
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--config ${CONFIG} \
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--train-data ${TRAIN_DATA}
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