Files
ti_coding_agent_probe/scripts/swift_train_common.sh
2026-06-24 23:26:14 +08:00

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#!/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="${LOGGING_STEPS:-1}"
NUM_EPOCHS="${NUM_EPOCHS:-1}"
WARMUP_RATIO="${WARMUP_RATIO:-0.1}"
LR_SCHEDULER_TYPE="${LR_SCHEDULER_TYPE:-cosine}"
LORA_RANK="${LORA_RANK:-32}"
DEFAULT_PER_DEVICE_BATCH_SIZE="${DEFAULT_PER_DEVICE_BATCH_SIZE:-1}"
DEFAULT_GRAD_ACCUM_STEPS="${DEFAULT_GRAD_ACCUM_STEPS:-1}"
DEFAULT_EVAL_BATCH_SIZE="${DEFAULT_EVAL_BATCH_SIZE:-1}"
env_key() {
printf '%s' "$1" | tr '[:lower:]-' '[:upper:]_' | sed 's/[^A-Z0-9_]/_/g'
}
env_or_default() {
local name="$1"
local fallback="$2"
if [[ -n "${!name:-}" ]]; then
printf '%s' "${!name}"
else
printf '%s' "${fallback}"
fi
}
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"
local run_key
run_key="$(env_key "${run_name}")"
local default_lr
if [[ "${train_type}" == "lora" ]]; then
default_lr="${LORA_LEARNING_RATE:-5e-5}"
else
default_lr="${FULL_LEARNING_RATE:-1e-5}"
fi
local learning_rate
learning_rate="$(env_or_default "${run_key}_LEARNING_RATE" "${LEARNING_RATE:-${default_lr}}")"
local type_key
type_key="$(env_key "${train_type}")"
local type_bsz_var="${type_key}_PER_DEVICE_BATCH_SIZE"
local type_accum_var="${type_key}_GRAD_ACCUM_STEPS"
local per_device_batch_size
local grad_accum_steps
local eval_batch_size
per_device_batch_size="$(env_or_default "${run_key}_PER_DEVICE_BATCH_SIZE" "${PER_DEVICE_BATCH_SIZE:-${!type_bsz_var:-${DEFAULT_PER_DEVICE_BATCH_SIZE}}}")"
grad_accum_steps="$(env_or_default "${run_key}_GRAD_ACCUM_STEPS" "${GRAD_ACCUM_STEPS:-${!type_accum_var:-${DEFAULT_GRAD_ACCUM_STEPS}}}")"
eval_batch_size="$(env_or_default "${run_key}_EVAL_BATCH_SIZE" "${EVAL_PER_DEVICE_BATCH_SIZE:-${DEFAULT_EVAL_BATCH_SIZE}}")"
require_file "${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 "${per_device_batch_size}"
--per_device_eval_batch_size "${eval_batch_size}"
--gradient_accumulation_steps "${grad_accum_steps}"
--learning_rate "${learning_rate}"
--warmup_ratio "${WARMUP_RATIO}"
--lr_scheduler_type "${LR_SCHEDULER_TYPE}"
--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}"
}