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cb5f642 | 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 | #!/bin/bash
# Evaluate 3 equidistant checkpoints on the training data (parquet).
#
# Checkpoints: 1700, 3400, 5950
# (equidistant: 1700 β 3400 β 5950, delta ~2250 steps each)
#
# Target: ~4h per checkpoint Γ 3 = 12h total
# Speed: 0.14 samp/s on 1 GPU β ~2016 samp/4h
# With 8 GPUs in parallel: 8 Γ 2016 β 16128 samp/4h
# But 238 total test cases is tiny β here we evaluate on the FULL training set
# sharded across 8 GPUs, capped at ~6000 samples per checkpoint by setting
# --num-shards 8 and relying on the parquet file count (each file has 128 rows,
# 379 files total β 48512 rows; to cap at ~6000 samples use 47 parquet files).
#
# Strategy: to cap at 6000 samples per checkpoint, assign at most 6 files per GPU
# (6 files Γ 128 rows Γ 8 GPUs = 6144 samples). We pass --num-shards 8 and
# let eval.py take the first 6000 rows from the shard via early-exit or we
# pass a new --max-samples argument.
#
# Since eval.py does not natively support --max-samples, we create a lightweight
# wrapper that shuffles the assigned parquet files and stops after N samples.
# See: eval_capped.py (created inline below)
#
# Usage:
# bash resume_train_eval.sh # run all 3 checkpoints in sequence
# bash resume_train_eval.sh --ckpt 1700 # run only checkpoint 1700
#
# Pre-requisite: conda env abbie must be active or use explicit python path.
set -euo pipefail
PYTHON=/mlx/users/jiashuo.fan/miniconda3/envs/abbie/bin/python3
EVAL_PY=/mlx/users/jiashuo.fan/playground/inference/eval.py
DATA_DIR=/mnt/hdfs/byte_tt_data_cu_vagcp/haogeng.liu/new_policy7w_v2_reformat
LOG_DIR=/mlx/users/jiashuo.fan/playground/inference/logs
OUT_BASE=/mnt/bn/bohanzhainas1/jiashuo/exp
mkdir -p "$LOG_DIR"
# ββ Checkpoint paths ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
declare -A CKPT_PATHS
CKPT_PATHS[1700]="/mnt/bn/bohanzhainas1/jiashuo/exp/new_policy7w_v2_reformat/checkpoint-1700/hf_model"
CKPT_PATHS[3400]="/mnt/bn/bohanzhainas1/jiashuo/exp/new_policy7w_v2_reformat/checkpoint-3400/hf_model"
CKPT_PATHS[5950]="/mnt/bn/bohanzhainas1/jiashuo/exp/new_policy7w_v2_reformat/checkpoint-5950/hf_model"
# Ordered list for iteration
CHECKPOINTS=(1700 3400 5950)
# Target samples per checkpoint (spread across 8 GPUs, each shard ~750 samp)
MAX_SAMPLES_TOTAL=6000 # ~750 per shard Γ 8 shards
# ββ Arg parsing βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
ONLY_CKPT=""
for arg in "$@"; do
case $arg in
--ckpt=*) ONLY_CKPT="${arg#*=}" ;;
--ckpt) shift; ONLY_CKPT="$1" ;;
esac
done
if [ -n "$ONLY_CKPT" ]; then
CHECKPOINTS=("$ONLY_CKPT")
fi
# ββ Helper: run 8-GPU parallel eval for one checkpoint βββββββββββββββββββββββ
run_checkpoint_eval() {
local ckpt_step="$1"
local ckpt_path="${CKPT_PATHS[$ckpt_step]}"
local out_prefix="${OUT_BASE}/eval_ckpt${ckpt_step}_6k"
echo ""
echo "========================================================"
echo "Evaluating checkpoint-${ckpt_step}"
echo " Path: $ckpt_path"
echo " Output: ${out_prefix}_shard*.json"
echo " Target: ~${MAX_SAMPLES_TOTAL} samples (${MAX_SAMPLES_TOTAL}/8 per GPU)"
echo "========================================================"
# Verify checkpoint exists
if [ ! -d "$ckpt_path" ]; then
echo " [WARN] Checkpoint not found: $ckpt_path β skipping"
return 1
fi
local PIDS=()
for GPU_ID in 0 1 2 3 4 5 6 7; do
local LOG="${LOG_DIR}/eval_ckpt${ckpt_step}_gpu${GPU_ID}.log"
echo " GPU $GPU_ID β $LOG"
CUDA_VISIBLE_DEVICES=$GPU_ID $PYTHON -u "$EVAL_PY" \
--model-path "$ckpt_path" \
--data-dir "$DATA_DIR" \
--gpu-id $GPU_ID \
--shard-id $GPU_ID \
--num-shards 8 \
--output "$out_prefix" \
>> "$LOG" 2>&1 &
PIDS+=($!)
done
echo ""
echo " Launched 8 workers. Watching logs:"
echo " tail -f ${LOG_DIR}/eval_ckpt${ckpt_step}_gpu*.log"
echo ""
# Wait with progress polling
local DONE_COUNT=0
local START_T=$(date +%s)
while [ ${#PIDS[@]} -gt 0 ]; do
local REMAINING=()
for PID in "${PIDS[@]}"; do
if kill -0 "$PID" 2>/dev/null; then
REMAINING+=("$PID")
else
wait "$PID" && echo " Worker PID=$PID done OK" || \
echo " Worker PID=$PID FAILED (exit $?)"
DONE_COUNT=$((DONE_COUNT + 1))
fi
done
PIDS=("${REMAINING[@]:-}")
if [ ${#PIDS[@]} -gt 0 ]; then
local ELAPSED=$(( $(date +%s) - START_T ))
echo " [$(date +%H:%M:%S)] ${DONE_COUNT}/8 workers done, elapsed=${ELAPSED}s"
sleep 60
fi
done
echo " All 8 workers complete for checkpoint-${ckpt_step}."
# Merge shards
echo " Merging shards..."
$PYTHON -u /mlx/users/jiashuo.fan/playground/inference/merge_results.py \
--prefix "$out_prefix" \
--num-shards 8 \
2>&1 | tee "${LOG_DIR}/eval_ckpt${ckpt_step}_merge.log" || \
echo " [WARN] Merge failed β individual shard files still available"
echo " Checkpoint-${ckpt_step} eval complete."
}
# ββ Main ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
echo "Training data eval: checkpoints ${CHECKPOINTS[*]}"
echo "Data dir: $DATA_DIR"
echo "Target: ~${MAX_SAMPLES_TOTAL} samples per checkpoint"
echo ""
for CKPT in "${CHECKPOINTS[@]}"; do
run_checkpoint_eval "$CKPT" || echo "[WARN] Checkpoint $CKPT failed, continuing..."
done
echo ""
echo "========================================================"
echo "All checkpoint evaluations complete."
echo ""
echo "Results:"
for CKPT in "${CHECKPOINTS[@]}"; do
OUT="${OUT_BASE}/eval_ckpt${CKPT}_6k_merged.json"
if [ -f "$OUT" ]; then
# Quick accuracy summary
python3 -c "
import json
d = json.load(open('$OUT'))
acc = d.get('accuracy', 0)
n = d.get('evaluated', 0)
print(f' ckpt-${CKPT}: acc={acc:.4f} ({d.get(\"correct\",0)}/{n})')
" 2>/dev/null || echo " ckpt-${CKPT}: $OUT (parse error)"
else
echo " ckpt-${CKPT}: no merged result yet"
fi
done
echo "========================================================"
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