File size: 3,586 Bytes
48c96cf | 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 | #!/usr/bin/env bash
# Strawman baseline for the rebuttal: vanilla LoRA, no curriculum, no thought
# tokens, single-shot whole-puzzle prediction. SFT followed by GRPO.
#
# Same model (Qwen2.5-1.5B-Instruct), same LoRA (r=32, α=64, dropout=0.05),
# same JSONL data file, same Qwen chat template wrapping as the cell-policy
# experiments. The ONLY differences from the cell-policy baseline are:
# - no per-cell expansion (one example per puzzle)
# - no stage_i / curriculum
# - no multi_value_oversample, no exact_match_bonus / cardinality penalties
# - reward = number of correct values out of 20 + whole-solve bonus
set -euo pipefail
ROOT=/home/ubuntu/curriculum_cot
SCRIPT=${ROOT}/_runs/simple_baseline_sudoku_train.py
PYTHON_BIN=/opt/pytorch/bin/python
TRAIN_JSONL=${ROOT}/data/sudoku_t3_20empty_value_qwen_text_stage1_train.jsonl
EVAL_JSONL=${ROOT}/data/sudoku_t3_20empty_value_qwen_text_stage1_eval.jsonl
SWEEP_ROOT=${ROOT}/_runs/strawman_baseline_$(date +%Y%m%d_%H%M%S)
mkdir -p "${SWEEP_ROOT}"
echo "${SWEEP_ROOT}" > "${ROOT}/_runs/current_strawman_sweep_dir"
echo "SWEEP_ROOT=${SWEEP_ROOT}"
export TOKENIZERS_PARALLELISM=false
export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True
export HF_HOME="${ROOT}/.hf_cache"
export TRANSFORMERS_CACHE="${ROOT}/.hf_cache"
export WANDB_MODE=offline
run_pipeline() {
local gpu="$1" tag="$2" sft_lr="$3" grpo_lr="$4" sft_max="$5" grpo_max="$6"
local out=${SWEEP_ROOT}/${tag}
mkdir -p "${out}"
local log=${out}/pipeline.log
: > "${log}"
(
export CUDA_VISIBLE_DEVICES="${gpu}"
echo "[$(date +%H:%M:%S)] === ${tag} on GPU ${gpu}: SFT lr=${sft_lr} max_steps=${sft_max} ===" >> "${log}"
"${PYTHON_BIN}" -u "${SCRIPT}" \
--phase sft \
--train_jsonl "${TRAIN_JSONL}" \
--eval_jsonl "${EVAL_JSONL}" \
--output_dir "${out}/sft" \
--learning_rate "${sft_lr}" \
--max_steps "${sft_max}" \
--per_device_train_batch_size 8 \
--gradient_accumulation_steps 2 \
--num_epochs 8 \
--logging_steps 25 \
--save_steps 200 \
--eval_rows 100 \
--max_completion_length 96 \
--max_prompt_length 1024 \
--lora_r 32 --lora_alpha 64 --lora_dropout 0.05 \
--seed 0 \
>> "${log}" 2>&1
echo "[$(date +%H:%M:%S)] === ${tag} on GPU ${gpu}: GRPO lr=${grpo_lr} max_steps=${grpo_max} ===" >> "${log}"
"${PYTHON_BIN}" -u "${SCRIPT}" \
--phase grpo \
--init_adapter_dir "${out}/sft/final" \
--train_jsonl "${TRAIN_JSONL}" \
--eval_jsonl "${EVAL_JSONL}" \
--output_dir "${out}/grpo" \
--learning_rate "${grpo_lr}" \
--max_steps "${grpo_max}" \
--per_device_train_batch_size 4 \
--gradient_accumulation_steps 2 \
--num_generations 8 \
--beta 0.0 \
--temperature 1.0 \
--num_epochs 50 \
--logging_steps 25 \
--save_steps 200 \
--eval_rows 100 \
--max_completion_length 96 \
--max_prompt_length 1024 \
--lora_r 32 --lora_alpha 64 --lora_dropout 0.05 \
--seed 0 \
>> "${log}" 2>&1
echo "[$(date +%H:%M:%S)] === ${tag} DONE ===" >> "${log}"
) >/dev/null 2>&1 &
local pid=$!
echo "$pid $gpu $tag" >> "${SWEEP_ROOT}/PIDS.txt"
disown $pid 2>/dev/null || true
printf 'GPU %s -> %s pid=%s log=%s\n' "$gpu" "$tag" "$pid" "$log"
}
# 2 variants on GPUs 0,1: explore SFT LR (5e-5 and 1e-4) — same GRPO LR (5e-6).
run_pipeline 0 strawman_a_sft5e5_grpo5e6 5e-5 5e-6 2000 1500
run_pipeline 1 strawman_b_sft1e4_grpo5e6 1e-4 5e-6 2000 1500
echo
echo "=== launched ==="
cat "${SWEEP_ROOT}/PIDS.txt"
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