Add SWIFT coding agent probe experiment scripts

scripts/swift_train_common.sh

@@ -0,0 +1,88 @@
+#!/usr/bin/env bash
+set -euo pipefail
+
+ROOT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)"
+cd "${ROOT_DIR}"
+
+export http_proxy="${http_proxy:-http://100.72.0.101:8888}"
+export https_proxy="${https_proxy:-http://100.72.0.101:8888}"
+export HTTP_PROXY="${HTTP_PROXY:-${http_proxy}}"
+export HTTPS_PROXY="${HTTPS_PROXY:-${https_proxy}}"
+export HF_ENDPOINT="${HF_ENDPOINT:-https://hf-mirror.com}"
+export TOKENIZERS_PARALLELISM="${TOKENIZERS_PARALLELISM:-false}"
+
+if [[ -f .venv/bin/activate ]]; then
+  source .venv/bin/activate
+elif [[ "${DRY_RUN:-0}" != "1" ]]; then
+  echo "Missing .venv. Run ./scripts/setup_env.sh first." >&2
+  exit 2
+fi
+mkdir -p outputs runs logs
+
+TRAIN_JSONL="${TRAIN_JSONL:-data/processed/training_probe/train.jsonl}"
+VAL_JSONL="${VAL_JSONL:-data/processed/training_probe/validation.jsonl}"
+MAX_LENGTH="${MAX_LENGTH:-262144}"
+SAVE_STEPS="${SAVE_STEPS:-1000}"
+EVAL_STEPS="${EVAL_STEPS:-1000}"
+LOGGING_STEPS="${LOGGING_STEPS:-1}"
+GRAD_ACCUM_STEPS="${GRAD_ACCUM_STEPS:-1}"
+PER_DEVICE_BATCH_SIZE="${PER_DEVICE_BATCH_SIZE:-1}"
+NUM_EPOCHS="${NUM_EPOCHS:-1}"
+LEARNING_RATE="${LEARNING_RATE:-1e-5}"
+WARMUP_RATIO="${WARMUP_RATIO:-0.1}"
+LORA_RANK="${LORA_RANK:-32}"
+
+require_file() {
+  if [[ ! -f "$1" ]]; then
+    echo "Missing required file: $1" >&2
+    exit 2
+  fi
+}
+
+run_swift_train() {
+  local model_path="$1"
+  local train_type="$2"
+  local run_name="$3"
+  local output_dir="outputs/${run_name}"
+  local tb_dir="runs/${run_name}"
+  local log_file="logs/${run_name}.log"
+
+  require_file "${TRAIN_JSONL}"
+  require_file "${VAL_JSONL}"
+  mkdir -p "${output_dir}" "${tb_dir}" logs
+
+  local cmd=(
+    swift sft
+    --model "${model_path}"
+    --dataset "${TRAIN_JSONL}"
+    --val_dataset "${VAL_JSONL}"
+    --train_type "${train_type}"
+    --torch_dtype bfloat16
+    --num_train_epochs "${NUM_EPOCHS}"
+    --per_device_train_batch_size "${PER_DEVICE_BATCH_SIZE}"
+    --per_device_eval_batch_size 1
+    --gradient_accumulation_steps "${GRAD_ACCUM_STEPS}"
+    --learning_rate "${LEARNING_RATE}"
+    --warmup_ratio "${WARMUP_RATIO}"
+    --max_length "${MAX_LENGTH}"
+    --save_steps "${SAVE_STEPS}"
+    --eval_steps "${EVAL_STEPS}"
+    --logging_steps "${LOGGING_STEPS}"
+    --report_to tensorboard
+    --logging_dir "${tb_dir}"
+    --output_dir "${output_dir}"
+    --save_total_limit "${SAVE_TOTAL_LIMIT:-3}"
+    --dataloader_num_workers "${DATALOADER_NUM_WORKERS:-4}"
+  )
+
+  if [[ "${train_type}" == "lora" ]]; then
+    cmd+=(--lora_rank "${LORA_RANK}")
+  fi
+
+  printf '%q ' "${cmd[@]}" | tee "${log_file}.cmd"
+  echo
+  if [[ "${DRY_RUN:-0}" == "1" ]]; then
+    return 0
+  fi
+  "${cmd[@]}" 2>&1 | tee "${log_file}"
+}