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a2ffd07 | 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 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 | #!/bin/bash
#PBS -N visualize_sae_features
#PBS -l select=1:ngpus=8
#PBS -l walltime=12:00:00
#PBS -q ic102
#PBS -P 71001002
#PBS -j oe
# =============================================================================
# PBS Job File: Visualize SAE Features (LLaVA, multi-GPU)
#
# Runs training/visualize_multilayer_sae_features.py to produce HTML reports
# for each specified feature.
#
# DATA_MODE choices:
# toilet (default) — pbcong/bathroom-toilet positives + CC3M negatives.
# Needs IMAGE_FOLDER. Set HF_DATASET + SPLIT if you want
# CC3M captions for negatives in caption mode.
# cc3m — full CC3M via HF_DATASET + LOCAL_VAL_PATH.
# coco — COCO via HF_DATASET + LOCAL_VAL_PATH.
# folder — plain image folder via DATA_DIR.
#
# CAPTION_MODE choices:
# generated (default) — model generates caption; teacher-forced re-pass
# collects residual activations.
# caption — use stored caption (CC3M txt / COCO sentences /
# pbcong/bathroom-toilet caption field).
#
# To submit:
# qsub training/scripts/run_visualize_multilayer_sae_features.pbs
#
# Override variables before submission:
# DATA_MODE=cc3m HF_DATASET=pixparse/cc3m-wds \
# LOCAL_VAL_PATH=/path/to/cc3m/train \
# FEATURE_IDS="0 1 42" \
# qsub training/scripts/run_visualize_multilayer_sae_features.pbs
# =============================================================================
cd ${PBS_O_WORKDIR}
# Load CUDA
module load cuda/12.6.2
export CUDA_HOME="${CUDA_HOME:-$(dirname $(dirname $(which nvcc 2>/dev/null)))}"
# Activate environment
source "${HOME}/scratch/testing/multilayer-sae/venv/bin/activate"
# Ensure paths
export PATH=$HOME/.local/bin:$PATH
export HF_HOME="${HF_HOME:-${HOME}/scratch/hf_home}"
export PYTHONPATH="${PBS_O_WORKDIR}:${PYTHONPATH:-}"
if [ -f "${PBS_O_WORKDIR}/.env" ]; then
set -a
source "${PBS_O_WORKDIR}/.env"
set +a
fi
# Create log directory
mkdir -p logs/visualize
# =============================================================================
# Hyperparameters — override via env before qsub
# =============================================================================
# ── GPU ───────────────────────────────────────────────────────────────────────
export NUM_GPUS="${NUM_GPUS:-8}"
# ── Model & SAE ───────────────────────────────────────────────────────────────
export SAE_CKPT="${SAE_CKPT:-training/multilayer_sae_ckpt/last.ckpt}"
export MODEL_NAME="${MODEL_NAME:-llava-hf/llava-1.5-7b-hf}"
export DTYPE="${DTYPE:-float16}"
# ── Data mode ─────────────────────────────────────────────────────────────────
export DATA_MODE="${DATA_MODE:-toilet}" # toilet | cc3m | coco | folder
export CAPTION_MODE="${CAPTION_MODE:-generated}" # generated | caption
export NUM_WORKERS="${NUM_WORKERS:-16}"
# Mode 1:
# ── Data — toilet ─────────────────────────────────────────────────────────────
export IMAGE_FOLDER="${IMAGE_FOLDER:-/home/users/ntu/cong045/scratch/testing/hallucination/CC3M-Dataset/cc3m_images/train}"
export OBJECT_MODE="${OBJECT_MODE:-toilet}" # toilet | bathroom | both
export NUM_NEGATIVES="${NUM_NEGATIVES:-10000}"
# Mode 2 + 3:
# ── Data — CC3M / COCO / folder ───────────────
export HF_DATASET="${HF_DATASET:-}" # e.g. pixparse/cc3m-wds | yerevann/coco-karpathy
export LOCAL_VAL_PATH="${LOCAL_VAL_PATH:-}" # local image root for HF dataset
export DATA_DIR="${DATA_DIR:-}" # [folder mode] plain image directory
export SPLIT="${SPLIT:-train}"
# ── Hook point (REQUIRED) ─────────────────────────────────────────────────────
export HOOK_POINT="${HOOK_POINT:-model.language_model.layers.19.hook_resid_post}"
# ── Feature selection (REQUIRED) ──────────────────────────────────────────────
export FEATURE_IDS="${FEATURE_IDS:-49199 18633 6307 52414 879 29368 55162 41137 39220 8437}"
# ── Processing ────────────────────────────────────────────────────────────────
export BATCH_SIZE="${BATCH_SIZE:-4}"
export SAE_BATCH="${SAE_BATCH:-4096}"
export THRESHOLD="${THRESHOLD:-1e-3}"
export MAX_BATCHES="${MAX_BATCHES:-}"
export MAX_NEW_TOKENS="${MAX_NEW_TOKENS:-128}"
# ── Visualisation ─────────────────────────────────────────────────────────────
export OUTPUT_DIR="${OUTPUT_DIR:-training/visualize_features}"
export TOP_IMAGES="${TOP_IMAGES:-20}"
export TOP_TEXTS="${TOP_TEXTS:-20}"
export BUFFER="${BUFFER:-10}"
LOGFILE="logs/visualize/visualize_sae_features_${PBS_JOBID}.log"
# =============================================================================
# Validation
# =============================================================================
if [ ! -f "${SAE_CKPT}" ]; then
echo "Error: SAE checkpoint not found: ${SAE_CKPT}" >&2
exit 1
fi
case "${DATA_MODE}" in
toilet)
if [ ! -d "${IMAGE_FOLDER}" ]; then
echo "Error: IMAGE_FOLDER not found: ${IMAGE_FOLDER}" >&2
exit 1
fi
;;
cc3m|coco)
if [ -z "${HF_DATASET}" ]; then
echo "Error: DATA_MODE=${DATA_MODE} requires HF_DATASET." >&2
exit 1
fi
;;
folder)
if [ ! -d "${DATA_DIR}" ]; then
echo "Error: DATA_DIR not found: ${DATA_DIR}" >&2
exit 1
fi
;;
*)
echo "Error: Unknown DATA_MODE=${DATA_MODE}. Choose: toilet | cc3m | coco | folder" >&2
exit 1
;;
esac
if [ -z "${HOOK_POINT}" ]; then
echo "Error: HOOK_POINT must be set." >&2
exit 1
fi
if [ -z "${FEATURE_IDS}" ]; then
echo "Error: FEATURE_IDS must be set (e.g. FEATURE_IDS=\"0 1 42\")." >&2
exit 1
fi
# =============================================================================
# Setup
# =============================================================================
echo "========================================" | tee -a "${LOGFILE}"
echo "Job: Visualize SAE Features" | tee -a "${LOGFILE}"
echo "Job ID: ${PBS_JOBID}" | tee -a "${LOGFILE}"
echo "Node: $(hostname)" | tee -a "${LOGFILE}"
echo "Started: $(date)" | tee -a "${LOGFILE}"
echo "sae_ckpt: ${SAE_CKPT}" | tee -a "${LOGFILE}"
echo "model_name: ${MODEL_NAME}" | tee -a "${LOGFILE}"
echo "data_mode: ${DATA_MODE}" | tee -a "${LOGFILE}"
echo "caption_mode: ${CAPTION_MODE}" | tee -a "${LOGFILE}"
echo "image_folder: ${IMAGE_FOLDER}" | tee -a "${LOGFILE}"
echo "toilet_mode: ${OBJECT_MODE}" | tee -a "${LOGFILE}"
echo "hf_dataset: ${HF_DATASET}" | tee -a "${LOGFILE}"
echo "hook_point: ${HOOK_POINT}" | tee -a "${LOGFILE}"
echo "feature_ids: ${FEATURE_IDS}" | tee -a "${LOGFILE}"
echo "batch_size: ${BATCH_SIZE}" | tee -a "${LOGFILE}"
echo "dtype: ${DTYPE}" | tee -a "${LOGFILE}"
echo "output_dir: ${OUTPUT_DIR}" | tee -a "${LOGFILE}"
echo "========================================" | tee -a "${LOGFILE}"
# GPU check
echo "" | tee -a "${LOGFILE}"
echo "GPU Info:" | tee -a "${LOGFILE}"
nvidia-smi --query-gpu=index,name,memory.total --format=csv | tee -a "${LOGFILE}"
# =============================================================================
# Build argument list
# =============================================================================
ARGS=(
--sae_ckpt "${SAE_CKPT}"
--model_name "${MODEL_NAME}"
--dtype "${DTYPE}"
--data_mode "${DATA_MODE}"
--caption_mode "${CAPTION_MODE}"
--num_workers "${NUM_WORKERS}"
--hook_point "${HOOK_POINT}"
--output_dir "${OUTPUT_DIR}"
--feature_ids ${FEATURE_IDS}
--batch_size "${BATCH_SIZE}"
--sae_batch "${SAE_BATCH}"
--threshold "${THRESHOLD}"
--max_new_tokens "${MAX_NEW_TOKENS}"
--top_images "${TOP_IMAGES}"
--top_texts "${TOP_TEXTS}"
--buffer "${BUFFER}"
--split "${SPLIT}"
)
# ── Data-mode-specific args ──────────────────────────────────────────────────
case "${DATA_MODE}" in
toilet)
ARGS+=(
--image_folder "${IMAGE_FOLDER}"
--object_mode "${OBJECT_MODE}"
--num_negatives "${NUM_NEGATIVES}"
)
# Optionally pass CC3M HF dataset for negative caption lookup
if [ -n "${HF_DATASET}" ]; then
ARGS+=(--hf_dataset "${HF_DATASET}")
fi
;;
cc3m|coco)
ARGS+=(--hf_dataset "${HF_DATASET}")
if [ -n "${LOCAL_VAL_PATH}" ]; then
ARGS+=(--local_val_path "${LOCAL_VAL_PATH}")
fi
;;
folder)
ARGS+=(--data_dir "${DATA_DIR}")
;;
esac
if [ -n "${MAX_BATCHES}" ]; then
ARGS+=(--max_batches "${MAX_BATCHES}")
fi
# =============================================================================
# Run
# =============================================================================
echo "" | tee -a "${LOGFILE}"
if [ "${NUM_GPUS}" -gt 1 ]; then
echo "Launching with torchrun on ${NUM_GPUS} GPUs..." | tee -a "${LOGFILE}"
torchrun --nproc_per_node="${NUM_GPUS}" -m training.visualize_multilayer_sae_features "${ARGS[@]}" \
2>&1 | tee -a "${LOGFILE}"
else
echo "Launching single-GPU mode..." | tee -a "${LOGFILE}"
python -m training.visualize_multilayer_sae_features "${ARGS[@]}" \
2>&1 | tee -a "${LOGFILE}"
fi
EXIT_CODE=${PIPESTATUS[0]}
echo "" | tee -a "${LOGFILE}"
echo "========================================" | tee -a "${LOGFILE}"
if [ $EXIT_CODE -eq 0 ]; then
echo "Status: SUCCESS" | tee -a "${LOGFILE}"
else
echo "Status: FAILED (exit code $EXIT_CODE)" | tee -a "${LOGFILE}"
fi
echo "Completed: $(date)" | tee -a "${LOGFILE}"
echo "========================================" | tee -a "${LOGFILE}"
exit $EXIT_CODE
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