Datasets:

zeyu12
/

eval

#!/bin/bash
# =============================================================================
# EvalPlus 代码评测 自包含脚本 (HumanEval+ / MBPP+, vLLM 后端, greedy pass@1)
# 由 code_eval/run_evalplus.sh 抽取, 路径改为本目录相对, 可独立搬运。
# 官方项目: https://github.com/evalplus/evalplus (已装在集群 vllm conda 环境)
#
# 数据: ./data/ (HumanEvalPlus-v0.1.10.jsonl, MbppPlus-v0.2.0.jsonl);
#       运行时若 ~/.cache/evalplus 缺数据, 自动从 ./data 离线补齐 (不联网也能跑)
#
# 用法:
#   sbatch --export=ALL,MODEL=/data/icto/mc56499/model/Qwen2.5-Coder-3B \
#       /data/icto/mc56499/eval/code/run_evalplus.sh          # pro6000 2卡
#   sbatch --export=ALL,MODEL=... -p gpu-l40s run_evalplus.sh # 换分区
#   bash run_evalplus.sh [/模型路径] [TP]                      # srun/交互调试
#
# 产物: <本目录>/evalplus_results/
#   samples: evalplus_results/<dataset>/<model>_vllm_temp_0.0.jsonl
#   报告:    同目录 *_eval_results.json (含 pass@1 base/plus)
# =============================================================================
#SBATCH --job-name              code_eval
#SBATCH --partition             gpu-pro6000
#SBATCH --nodes                 1
#SBATCH --tasks-per-node        1
#SBATCH --time                  06:00:00
#SBATCH --mem                   128G
#SBATCH --cpus-per-task         16
#SBATCH --gres                  gpu:2
#SBATCH --output                /data/icto/mc56499/eval/logs/code_eval-%j.out
#SBATCH --error                 /data/icto/mc56499/eval/logs/code_eval-%j.err

set -e

SCRIPT_DIR=$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)
cd "$SCRIPT_DIR"
mkdir -p /data/icto/mc56499/eval/logs

# 环境防护: IDE/TRAE 终端提交时继承的 LD_LIBRARY_PATH 污染 (旧 libstdc++)
unset LD_LIBRARY_PATH LD_PRELOAD
module load nvidia
module load cuda/13.0.2
source /data/icto/mc56499/miniconda3/etc/profile.d/conda.sh
conda activate vllm        # evalplus.* 入口脚本安装在此环境

export PYTHONUNBUFFERED=1
export TOKENIZERS_PARALLELISM=false
export HF_HOME=/data/icto/mc56499/hf_cache
export HF_HUB_CACHE="$HF_HOME"

# ====== 离线数据种子: ~/.cache/evalplus 缺数据时从 ./data 补齐 ======
mkdir -p "$HOME/.cache/evalplus"
for f in data/*.jsonl; do
    [ -f "$f" ] || continue
    dst="$HOME/.cache/evalplus/$(basename "$f")"
    if [ ! -f "$dst" ]; then
        cp "$f" "$dst"
        echo "已补齐 evalplus 数据: $dst"
    fi
done

# ====== 模型与并行度 ======
MODEL_PATH=${MODEL:-${1:-}}
if [ -z "$MODEL_PATH" ]; then
    echo "用法: sbatch --export=ALL,MODEL=/path/to/model run_evalplus.sh"
    echo "  或: bash run_evalplus.sh /path/to/model [TP]"
    exit 1
fi
TP=${2:-auto}
if [ "$TP" = "auto" ] || [ -z "$TP" ]; then
    TP=$(nvidia-smi -L 2>/dev/null | wc -l)
fi
[ "$TP" -lt 1 ] && TP=1

ROOT="$SCRIPT_DIR/evalplus_results"
PARALLEL=16   # 评测进程数 (代码沙箱执行)

echo "========================================================"
echo "=== EvalPlus 代码评测: $(basename "$MODEL_PATH") ==="
echo "=== 数据集: HumanEval+ / MBPP+   GPU: $TP 张 ==="
echo "=== 输出: $ROOT ==="
echo "=== 开始: $(date) ==="
echo "========================================================"
mkdir -p "$ROOT"

for DATASET in humaneval mbpp; do
    echo ""
    echo "===== [EvalPlus] dataset=$DATASET model=$MODEL_PATH tp=$TP ====="
    evalplus.evaluate \
        --model "$MODEL_PATH" \
        --dataset "$DATASET" \
        --backend vllm \
        --tp "$TP" \
        --greedy \
        --force_base_prompt \
        --root "$ROOT" \
        --parallel "$PARALLEL"
done

# ====== 结果汇总 ======
echo ""
echo "===== EvalPlus pass@1 汇总 ====="
for f in $(find "$ROOT" -name "*_eval_results.json" | sort); do
    echo "--- $f"
    python - "$f" <<'EOF'
import json, sys
with open(sys.argv[1]) as f:
    r = json.load(f)
tasks = r["eval"].values()
base = sum(1 for samples in tasks if any(s["base_status"] == "pass" for s in samples))
plus = sum(1 for samples in r["eval"].values() if any(
    s["base_status"] == "pass" and s["plus_status"] == "pass" for s in samples))
n = len(r["eval"])
print(f"  pass@1 (base tests): {base / n:.4f}  ({base}/{n})")
print(f"  pass@1 (base+extra): {plus / n:.4f}  ({plus}/{n})")
EOF
done
echo ""
echo "===== DONE: $(date '+%F %T') ====="

	#!/bin/bash
	# =============================================================================
	# EvalPlus 代码评测自包含脚本 (HumanEval+ / MBPP+, vLLM 后端, greedy pass@1)
	# 由 code_eval/run_evalplus.sh 抽取, 路径改为本目录相对, 可独立搬运。
	# 官方项目: https://github.com/evalplus/evalplus (已装在集群 vllm conda 环境)
	#
	# 数据: ./data/ (HumanEvalPlus-v0.1.10.jsonl, MbppPlus-v0.2.0.jsonl);
	# 运行时若 ~/.cache/evalplus 缺数据, 自动从 ./data 离线补齐 (不联网也能跑)
	#
	# 用法:
	# sbatch --export=ALL,MODEL=/data/icto/mc56499/model/Qwen2.5-Coder-3B \
	# /data/icto/mc56499/eval/code/run_evalplus.sh # pro6000 2卡
	# sbatch --export=ALL,MODEL=... -p gpu-l40s run_evalplus.sh # 换分区
	# bash run_evalplus.sh [/模型路径] [TP] # srun/交互调试
	#
	# 产物: <本目录>/evalplus_results/
	# samples: evalplus_results/<dataset>/<model>_vllm_temp_0.0.jsonl
	# 报告: 同目录 *_eval_results.json (含 pass@1 base/plus)
	# =============================================================================
	#SBATCH --job-name code_eval
	#SBATCH --partition gpu-pro6000
	#SBATCH --nodes 1
	#SBATCH --tasks-per-node 1
	#SBATCH --time 06:00:00
	#SBATCH --mem 128G
	#SBATCH --cpus-per-task 16
	#SBATCH --gres gpu:2
	#SBATCH --output /data/icto/mc56499/eval/logs/code_eval-%j.out
	#SBATCH --error /data/icto/mc56499/eval/logs/code_eval-%j.err

	set -e

	SCRIPT_DIR=$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)
	cd "$SCRIPT_DIR"
	mkdir -p /data/icto/mc56499/eval/logs

	# 环境防护: IDE/TRAE 终端提交时继承的 LD_LIBRARY_PATH 污染 (旧 libstdc++)
	unset LD_LIBRARY_PATH LD_PRELOAD
	module load nvidia
	module load cuda/13.0.2
	source /data/icto/mc56499/miniconda3/etc/profile.d/conda.sh
	conda activate vllm # evalplus.* 入口脚本安装在此环境

	export PYTHONUNBUFFERED=1
	export TOKENIZERS_PARALLELISM=false
	export HF_HOME=/data/icto/mc56499/hf_cache
	export HF_HUB_CACHE="$HF_HOME"

	# ====== 离线数据种子: ~/.cache/evalplus 缺数据时从 ./data 补齐 ======
	mkdir -p "$HOME/.cache/evalplus"
	for f in data/*.jsonl; do
	[ -f "$f" ] \|\| continue
	dst="$HOME/.cache/evalplus/$(basename "$f")"
	if [ ! -f "$dst" ]; then
	cp "$f" "$dst"
	echo "已补齐 evalplus 数据: $dst"
	fi
	done

	# ====== 模型与并行度 ======
	MODEL_PATH=${MODEL:-${1:-}}
	if [ -z "$MODEL_PATH" ]; then
	echo "用法: sbatch --export=ALL,MODEL=/path/to/model run_evalplus.sh"
	echo " 或: bash run_evalplus.sh /path/to/model [TP]"
	exit 1
	fi
	TP=${2:-auto}
	if [ "$TP" = "auto" ] \|\| [ -z "$TP" ]; then
	TP=$(nvidia-smi -L 2>/dev/null \| wc -l)
	fi
	[ "$TP" -lt 1 ] && TP=1

	ROOT="$SCRIPT_DIR/evalplus_results"
	PARALLEL=16 # 评测进程数 (代码沙箱执行)

	echo "========================================================"
	echo "=== EvalPlus 代码评测: $(basename "$MODEL_PATH") ==="
	echo "=== 数据集: HumanEval+ / MBPP+ GPU: $TP 张 ==="
	echo "=== 输出: $ROOT ==="
	echo "=== 开始: $(date) ==="
	echo "========================================================"
	mkdir -p "$ROOT"

	for DATASET in humaneval mbpp; do
	echo ""
	echo "===== [EvalPlus] dataset=$DATASET model=$MODEL_PATH tp=$TP ====="
	evalplus.evaluate \
	--model "$MODEL_PATH" \
	--dataset "$DATASET" \
	--backend vllm \
	--tp "$TP" \
	--greedy \
	--force_base_prompt \
	--root "$ROOT" \
	--parallel "$PARALLEL"
	done

	# ====== 结果汇总 ======
	echo ""
	echo "===== EvalPlus pass@1 汇总 ====="
	for f in $(find "$ROOT" -name "*_eval_results.json" \| sort); do
	echo "--- $f"
	python - "$f" <<'EOF'
	import json, sys
	with open(sys.argv[1]) as f:
	r = json.load(f)
	tasks = r["eval"].values()
	base = sum(1 for samples in tasks if any(s["base_status"] == "pass" for s in samples))
	plus = sum(1 for samples in r["eval"].values() if any(
	s["base_status"] == "pass" and s["plus_status"] == "pass" for s in samples))
	n = len(r["eval"])
	print(f" pass@1 (base tests): {base / n:.4f} ({base}/{n})")
	print(f" pass@1 (base+extra): {plus / n:.4f} ({plus}/{n})")
	EOF
	done
	echo ""
	echo "===== DONE: $(date '+%F %T') ====="