File size: 4,912 Bytes
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 | #!/bin/bash
# =============================================================================
# integrated_gradient_reverse.sh — toilet→bathroom causal influence map via
# INTEGRATED GRADIENT of the toilet probe (REVERSED direction).
#
# Steer = toilet probe, readout = bathroom probe. Images: toilet-only (toilet=1,
# bathroom=0). Steered direction at layer l = integrated gradient of score_toilet_l
# along base→image, base = mean caption-token residual of NEGATIVE-label validation
# images. Step = ALPHA*‖h_l‖*ĝ plus a 'full' panel h'=h+(x−b). Δ bathroom score read
# at every layer l'>=l. Averaged → heatmap.
#
# Tunables (override inline, e.g. DEVICE_ID=2 IG_STEPS=64 bash <script>):
# DEVICE_ID GPU id (default 0)
# DTYPE bfloat16 | float16 | float32 (default bfloat16)
# BATH_PROBE steered-direction checkpoint = TOILET probe in reverse
# TOILET_PROBE measured-readout checkpoint = BATHROOM probe in reverse
# IMAGE_FOLDER folder of images
# SAMPLES_JSON samples.json path
# BASE_PROMPT prompt key in samples.json
# STEER_NAME display name of steered concept (plots) (default toilet)
# READOUT_NAME display name of readout concept (plots) (default bathroom)
# NEG_JSONL {"train":[ids],"validation":[ids]} negative stems
# BASELINE_SPLIT which NEG_JSONL split for the baseline (default validation)
# BASELINE_NUM cap on negatives for baseline mean (default 1000; 0=all)
# IG_STEPS Riemann steps for the IG path base→image (default 32)
# QUESTION prompt question
# NUM_IMAGES cap on images; 0 = ALL toilet-only (default 0)
# ALPHAS steps as FRACTION of ‖h_l‖ (default "0.05 0.1 0.2 0.4 0.8")
# HOOK_TYPE pre | mid | post (default post)
# MAX_NEW_TOKENS caption generation length (default 64)
# MAX_SEQ_TOKENS caption tokens kept for the probe (default 64)
# FORCED_TEXT forced ASSISTANT answer (default "In this image, there is a")
# OUT / OUT_JSON output heatmap PNG / matrix JSON
# =============================================================================
export HF_HOME="/data/caotue/hf_cache"
export HF_DATASETS_CACHE="/data/caotue/hf_cache/datasets"
export TORCH_HOME="/data/caotue/torch_cache"
export TMPDIR="/data/caotue/tmp"
DEVICE_ID="${DEVICE_ID:-0}"
DTYPE="${DTYPE:-bfloat16}"
# REVERSED: BATH_PROBE = toilet probe (steer), TOILET_PROBE = bathroom probe (readout); 4variant
BATH_PROBE="${BATH_PROBE:-/data/caotue/latent_probes/seqprobes_4variant_toilet/post/seqprobe.pth}"
TOILET_PROBE="${TOILET_PROBE:-/data/caotue/latent_probes/seqprobes_4variant_bathroom/post/seqprobe.pth}"
IMAGE_FOLDER="${IMAGE_FOLDER:-/data/caotue/CC3M-Dataset/cc3m_images}"
SAMPLES_JSON="${SAMPLES_JSON:-mechanistic_interp/toilet_bathroom/samples.json}"
BASE_PROMPT="${BASE_PROMPT:-Describe this image.}"
STEER_NAME="${STEER_NAME:-toilet}"
READOUT_NAME="${READOUT_NAME:-bathroom}"
# Integrated-gradient baseline (mean NEGATIVE-label validation residual).
NEG_JSONL="${NEG_JSONL:-mechanistic_interp/neg_cc3m_5k.json}"
BASELINE_SPLIT="${BASELINE_SPLIT:-validation}"
BASELINE_NUM="${BASELINE_NUM:-1000}"
IG_STEPS="${IG_STEPS:-32}"
BASE_MENTIONS="${BASE_MENTIONS:-any}"
QUESTION="${QUESTION:-Describe this image.}"
FORCED_TEXT="${FORCED_TEXT:-In this image, there is a}"
NUM_IMAGES="${NUM_IMAGES:-0}"
ALPHAS="${ALPHAS:-0.05 0.1 0.2 0.4 0.8}"
HOOK_TYPE="${HOOK_TYPE:-post}"
MAX_NEW_TOKENS="${MAX_NEW_TOKENS:-64}"
MAX_SEQ_TOKENS="${MAX_SEQ_TOKENS:-64}"
OUT="${OUT:-mechanistic_interp/graph/integrated_gradient_toilet2bath.png}"
OUT_JSON="${OUT_JSON:-mechanistic_interp/graph/integrated_gradient_toilet2bath.json}"
REPO_ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")/../.." && pwd)"
cd "${REPO_ROOT}"
export PYTHONPATH="$(cd .. && pwd):$(pwd):${PYTHONPATH:-}"
ARGS=(
--device_id "${DEVICE_ID}"
--dtype "${DTYPE}"
--bath_probe "${BATH_PROBE}"
--toilet_probe "${TOILET_PROBE}"
--image_folder "${IMAGE_FOLDER}"
--samples_json "${SAMPLES_JSON}"
--base_prompt "${BASE_PROMPT}"
--base_mentions "${BASE_MENTIONS}"
--question "${QUESTION}"
--num_images "${NUM_IMAGES}"
--neg_jsonl "${NEG_JSONL}"
--baseline_split "${BASELINE_SPLIT}"
--baseline_num "${BASELINE_NUM}"
--ig_steps "${IG_STEPS}"
--alphas ${ALPHAS}
--hook_type "${HOOK_TYPE}"
--max_new_tokens "${MAX_NEW_TOKENS}"
--max_seq_tokens "${MAX_SEQ_TOKENS}"
--steer_name "${STEER_NAME}"
--readout_name "${READOUT_NAME}"
--out "${OUT}"
--out_json "${OUT_JSON}"
)
if [ -n "${FORCED_TEXT}" ]; then
ARGS+=(--forced_text "${FORCED_TEXT}")
fi
python -m mechanistic_interp.integrated_gradient_reverse "${ARGS[@]}" "$@"
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