feat(assignment01): 自动评测配置 grading/ - #1
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配合 seminars-infra-judger（AOI runner）使用： - student_files.txt: 评测取用的学生文件白名单，tests/ 等一律用官方版本 - cuda_targets.txt: CUDA 计分目标与权重（退出码定通过，saxpy 单独按 7 个 测试点给部分分，权重 3） - pytest_flags: 选做题（1.6/7.8/Bonus）不计入自动评分 - grade_cuda.sh: 产出 cuda_report.json（pass/fail/compile_error/not_attempted） - README.md: 评测流程、计分范围与调整方式 概念题/填表题无判测载体，不自动计分。 Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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配合 seminars-infra-judger（AOI runner，基于 lfs-auto-grader 的单机 Docker 评测机）使用的 assignment01 自动评测配置。
内容
student_files.txt— 评测取用的学生文件白名单（9 个 kernels + 12 个 CUDA 文件）；白名单之外（tests/、conftest.py、Makefile、judge_saxpy.sh等）一律用官方版本，防止改测试作弊cuda_targets.txt— CUDA 计分目标与权重：常规题权重 1，压轴 3.5 reduce 权重 2；判定沿用common.h约定（退出码只表达正确性）pytest_flags— 选做题（1.6 simt_sim、7.8 triton softmax、Bonus matmul）默认不计入自动评分grade_cuda.sh— 逐目标编译运行，产出cuda_report.json（pass/fail/compile_error/not_attempted四态）；压轴 2.9 saxpy 走现有judge_saxpy.sh，7 个测试点给部分分，权重 3README.md— 评测流程、计分范围与调整方式评测流程
评测容器以官方 assignment01 为基准建工作树 → 白名单覆盖学生文件 →
WMHPC_GRADING=1跑pytest --json-report→grade_cuda.sh→ judger 的wmhpc1adapter 加权合并两份报告计分。概念题/填表题无判测载体，不自动计分。评分范围与权重后续只需改本目录，不需要动 judger 或重建镜像。
待讨论
pytest_flags一行）🤖 Generated with Claude Code