PaddlePaddle · Echo-Nie · Aug 13, 2026
diff --git a/swe-paddle/tasks/PaddlePaddle__Paddle-60417/README.md b/swe-paddle/tasks/PaddlePaddle__Paddle-60417/README.md
@@ -0,0 +1,45 @@
+# PaddlePaddle__Paddle-60417
+
+This directory converts Paddle PR #60417 into a SWE-Paddle community task candidate.
+
+## Source
+
+| Field | Value |
+| --- | --- |
+| Repo | `PaddlePaddle/Paddle` |
+| PR | [60417](https://github.com/PaddlePaddle/Paddle/pull/60417) |
+| PR title | `[auto config] Resume from history csv file` |
+| Base commit | `e4b39bb56a4e55213383e96daf262f4f72c1811d` |
+| Merged at | `2023-12-29T07:40:45Z` |
+| Task type | `feature_enhancement` |
+| Resource | CPU |
+
+## Summary
+
+Allow distributed auto-tuner runs to reuse completed configurations from a history CSV and continue with only the unfinished work.
+
+## Why This Is A Good SWE-Paddle Candidate
+
+- Interrupted tuning runs are a practical distributed-training problem, and the wasted work is directly observable.
+- The change coordinates CSV value restoration, configuration matching, recorder updates, and launch control rather than adding an isolated special case.
+- The source PR contains no test changes, so the candidate adds focused behavior tests that execute the checkout's real `AutoTuner` and `launch()` control flow.
+- The tests prove that a resumed task does not start a training controller while preserving the existing search path.
+- Verification is deterministic on CPU and requires no Paddle wheel overlay, GPU, network, training data, or subprocess timing.
+
+## Files
+
+- `proposal.md`: candidate proposal for maintainer triage.
+- `instruction.md`: self-contained problem statement for the coding agent.
+- `solution/code.patch`: exact production patch from the merged commit.
+- `tests/test.patch`: focused behavior tests for history resume and launch reuse.
+- `tests/test.sh`: minimal target test command.
+- `environment/README.md`: environment notes for reproduction.
+- `README.md`: task overview and verification entrypoint.
+
+## Verification
+
+```bash
+bash tests/test.sh
+```
+
+Expected behavior: applying `tests/test.patch` to `base_commit` should preserve the existing search case but fail the history-resume cases; applying both `tests/test.patch` and `solution/code.patch` should pass all target tests.
diff --git a/swe-paddle/tasks/PaddlePaddle__Paddle-60417/environment/README.md b/swe-paddle/tasks/PaddlePaddle__Paddle-60417/environment/README.md
@@ -0,0 +1,27 @@
+# Environment Notes
+
+This candidate is part of the SWE-Paddle community task set.
+
+## Expected Environment
+
+- Repository: `PaddlePaddle/Paddle`
+- Base commit: `e4b39bb56a4e55213383e96daf262f4f72c1811d`
+- Resource: CPU
+- GPU required: no
+- Build path: no Paddle source build or installed-wheel overlay is required; the tests execute the checkout Python modules with controlled dependencies.
+
+## Run Order
+
+1. Check out `PaddlePaddle/Paddle` at the base commit.
+2. Apply `tests/test.patch`.
+3. Run `bash tests/test.sh`; the target behavior should fail before the fix.
+4. Apply `solution/code.patch`.
+5. Run `bash tests/test.sh` again; the target behavior should pass after the gold patch.
+
+## Minimal Test Command
+
+```bash
+bash tests/test.sh
+```
+
+The verifier is responsible for deriving stable F2P and P2P node IDs from repeated runs.
diff --git a/swe-paddle/tasks/PaddlePaddle__Paddle-60417/instruction.md b/swe-paddle/tasks/PaddlePaddle__Paddle-60417/instruction.md
@@ -0,0 +1,23 @@
+# auto-tuner 中断后无法从历史记录继续运行
+
+## 详细描述
+
+使用分布式 auto-tuner 进行调优时，任务会因为机器重启、运行超时或人工停止而中断。重新启动 auto-tuner 后，即使已经保留了上一次运行生成的历史 CSV，程序仍会从第一个配置重新开始，导致已经完成的训练任务被重复执行。
+
+应该让 auto-tuner 支持从指定的历史 CSV 继续调优：对于历史记录中已经完成的配置，直接复用原有结果；对于尚未完成的配置，继续正常运行。这样可以减少重复训练，缩短中断后恢复调优所需的时间。
+
+当历史文件不存在、内容为空或没有匹配到当前配置时，auto-tuner 按照原有流程正常运行。
+
+## 验收说明
+
+* 可以读取指定的历史 CSV，并正确恢复其中已经完成的配置和指标。
+* 当前配置在历史记录中已经完成时，直接复用原有结果，不再重复启动训练。
+* 当前配置没有历史记录时，继续执行正常的调优任务。
+* 使用历史文件恢复时应保留原始文件，避免覆盖已有记录。
+* 未配置历史文件或文件不存在时，现有 auto-tuner 行为保持不变。
+
+## 技术要求
+
+* 熟悉 Python 文件读写及 CSV 数据处理。
+* 熟悉 PaddlePaddle 分布式 launch 流程。
+* 了解 auto-tuner 的配置搜索与历史记录机制。
diff --git a/swe-paddle/tasks/PaddlePaddle__Paddle-60417/proposal.md b/swe-paddle/tasks/PaddlePaddle__Paddle-60417/proposal.md
@@ -0,0 +1,56 @@
+# Task Proposal: PaddlePaddle__Paddle-60417
+
+## 1. 来源信息
+
+- Instance ID：`PaddlePaddle__Paddle-60417`
+- PR 链接：https://github.com/PaddlePaddle/Paddle/pull/60417
+- PR 标题：`[auto config] Resume from history csv file`
+- `base_commit`：`e4b39bb56a4e55213383e96daf262f4f72c1811d`
+- merged 时间：`2023-12-29T07:40:45Z`
+- 你的身份：熟悉该模块的 contributor
+- 后续联系人：TBD
+
+## 2. 问题一句话
+
+auto-tuner 重新启动后无法复用历史 CSV 中已经完成的配置，会重复运行全部调优任务。
+
+## 3. 为什么适合作为 SWE-Paddle 样本
+
+- **真实性**：长时间分布式调优可能被中断，恢复后重复运行已完成任务会直接增加训练成本。
+- **代表性**：任务同时涉及历史数据读取、配置匹配、结果记录和 launch 调度，是典型的断点恢复问题。
+- **边界清楚**：修改只服务于 auto-tuner 从历史 CSV 恢复，不改变训练脚本、搜索算法或分布式执行后端。
+- **非平凡性**：实现需要恢复 CSV 中的真实数据类型，正确识别同一配置，并在不启动控制器的情况下把历史结果接回现有记录与调度流程。
+- **环境友好性**：测试使用受控的 CPU doubles 隔离真实训练进程，但执行 checkout 中的 `AutoTuner` 和完整 `launch()` 控制流。
+
+## 4. 任务类型和标签
+
+- 任务类型：`feature_enhancement`
+- 执行后端：`cpu`
+- 设备范围：`cpu_only`
+- 模块标签：`[distributed, auto-tuner, launch, resume]`
+
+## 5. 验证思路
+
+- 目标测试命令：`bash tests/test.sh`
+- 目标测试文件：`test/swe_test/test_auto_tuner_resume.py`
+- 修复前预期：既有搜索与 history list 行为通过；历史 CSV 读取、配置匹配和 launch 跳过已完成任务三个目标场景失败。
+- 修复后预期：CSV 值恢复为可用类型并生成副本，配置按历史顺序匹配，命中的任务写入 recorder 且不启动训练控制器；既有搜索行为保持通过。
+- P2P 候选：`test_existing_search_and_history_flow_is_unchanged`。
+- F2P 候选：`test_resume_history_loads_values_and_preserves_a_copy`、`test_resume_lookup_returns_first_matching_configuration`、`test_launch_reuses_history_without_starting_training`。
+
+## 6. 环境与资源
+
+- 资源需求：CPU
+- Paddle 来源：`PaddlePaddle/Paddle` source checkout at `base_commit`
+- 是否能提供 Docker：暂无
+- patch 类型：Python-only
+- 环境建议：测试通过 `importlib` 直接执行 checkout 中的目标模块，并使用 controlled doubles 隔离搜索器、记录器和训练控制器；无需安装或覆盖 Paddle wheel，也无需启动分布式进程。
+- 最小测试命令：`bash tests/test.sh`
+- 是否有 oracle 日志：由 SWE-Paddle verifier 结果另行维护
+
+## 7. 风险自查
+
+- 泄露风险：任务说明只描述中断恢复的用户行为，没有给出 Gold patch 的方法名、分支位置或具体代码结构。
+- 环境风险：测试只依赖 Python 标准库和 pytest，直接读取 checkout 源码，不受已安装 Paddle wheel 版本影响。
+- flaky 风险：不启动训练进程、不访问 GPU、网络或外部数据集，历史文件全部在 pytest 临时目录中生成。
+- 拆分风险：CSV 恢复、配置匹配和跳过重复训练共同构成同一个断点恢复流程，来源 PR 没有混入其他问题。
diff --git a/swe-paddle/tasks/PaddlePaddle__Paddle-60417/solution/code.patch b/swe-paddle/tasks/PaddlePaddle__Paddle-60417/solution/code.patch
@@ -0,0 +1,188 @@
+diff --git a/python/paddle/distributed/auto_tuner/tuner.py b/python/paddle/distributed/auto_tuner/tuner.py
+index b3b6cbf3cdc5289faa7076eff8101d5f20893ba8..6a6a0ba4e082ff246aec90864f81324a597a0e17 100644
+--- a/python/paddle/distributed/auto_tuner/tuner.py
++++ b/python/paddle/distributed/auto_tuner/tuner.py
+@@ -12,6 +12,8 @@
+ # See the License for the specific language governing permissions and
+ # limitations under the License.
+
++import csv
++import os
+
+ from .utils import default_candidates, gbs_default_candidates
+
+@@ -54,6 +56,8 @@ class AutoTuner:
+             raise NotImplementedError()
+
+         self.history_cfgs = []
++        self.resume_cfgs = []
++        self.tuner_cfg = tuner_cfg
+
+     def search_once(self):
+         """Return a new task config."""
+@@ -67,3 +71,76 @@ class AutoTuner:
+     def add_cfg(self, cfg):
+         """Add cfg into history cfgs"""
+         self.history_cfgs.append(cfg)
++
++    def resume_form_history(self, history_csv_path="./history.csv"):
++        """Resume form history csv file"""
++        # The breakpoint resume function does not start when the resume csv file does not exist.
++        if not os.path.exists(history_csv_path):
++            return
++        resume_csv_path = os.path.join(
++            os.path.dirname(history_csv_path),
++            f'{os.path.basename(history_csv_path).split(".")[0]}_copy.csv',
++        )
++        with open(history_csv_path, "r") as fread:
++            reader = csv.reader(fread)
++            data_list = list(reader)
++            with open(resume_csv_path, "w") as fwrite:
++                writer = csv.writer(fwrite)
++                for row in data_list:
++                    writer.writerow(row)
++        # chang str type to real type
++        for row in data_list:
++            for i, value in enumerate(row):
++                try:
++                    row[i] = int(value)
++                except ValueError:
++                    try:
++                        row[i] = float(value)
++                    except ValueError:
++                        pass
++
++        data_dict = []
++        keys = data_list[0]
++        values = data_list[1:]
++        for val in values:
++            val = [x if x != '' else None for x in val]
++            val = [True if x == 'True' else x for x in val]
++            val = [False if x == 'False' else x for x in val]
++            dictionary = dict(zip(keys, val))
++            time_val = -1
++            target_key = self.tuner_cfg["metric_cfg"]["name"]
++            if dictionary[target_key]:
++                time_val = dictionary[target_key]
++            dictionary["time"] = time_val
++            data_dict.append(dictionary)
++        self.resume_cfgs = data_dict
++
++    def get_cfg_from_resume(self, cur_cfg):
++        """Get cfg from resume cfgs"""
++        keys_to_compare = [
++            'mp_degree',
++            'sharding_degree',
++            'pp_degree',
++            'dp_degree',
++            'sharding_stage',
++            'micro_batch_size',
++            'vpp_degree',
++            'use_recompute',
++            'recompute_granularity',
++            'num_gpus',
++            'nodes',
++            'global_batch_size',
++            'sharding_overlap',
++            'acc_steps',
++        ]
++        for cfg in self.resume_cfgs:
++            ret_is_same = True
++            for key in keys_to_compare:
++                if not cfg.get(key) and not cur_cfg.get(key):
++                    continue
++                else:
++                    is_same = str(cfg.get(key)) == str(cur_cfg.get(key))
++                ret_is_same = ret_is_same and is_same
++            if ret_is_same:
++                return cfg
++        return None
+diff --git a/python/paddle/distributed/launch/main.py b/python/paddle/distributed/launch/main.py
+index 0869ac7bbfcd956ba1396390098865ce49e3f2c9..40caf7f223677b548cc0185644f26819a449dd78 100644
+--- a/python/paddle/distributed/launch/main.py
++++ b/python/paddle/distributed/launch/main.py
+@@ -587,6 +587,10 @@ def launch():
+         logger.info(
+             f"Launch {len(auto_tuner.algo.all_tasks)} tasks by auto tuner: "
+         )
++        resume_csv_file_path = tuner_cfg.get(
++            "resume_csv_file_path", history_file_path
++        )
++        auto_tuner.resume_form_history(resume_csv_file_path)
+         cur_cfg = auto_tuner.search_once()
+         auto_tuner.add_cfg(cur_cfg)
+         assert cur_cfg is not None, "No config can run."
+@@ -658,6 +662,73 @@ def launch():
+             )
+             logger.info(f"Launch task: job_id {task_job_id}, log_dir {log_dir}")
+
++            cur_resume_cfg = auto_tuner.get_cfg_from_resume(cur_cfg)
++            if cur_resume_cfg:
++                cur_cfg = cur_resume_cfg
++                cur_cfg['job_id'] = job_id
++                auto_tuner.history_cfgs.pop(-1)
++                auto_tuner.add_cfg(cur_cfg)
++                recorder.add_cfg(**cur_cfg)
++                cur_best_cfgs, err = recorder.get_best(
++                    metric=tuner_cfg['metric_cfg']['name'],
++                    direction=tuner_cfg['metric_cfg']['OptimizationDirection'],
++                )
++                if not err:
++                    ctx.logger.info(f"Current best config: {cur_best_cfgs}")
++                    logger.info(f"Current best config: {cur_best_cfgs}")
++                else:
++                    ctx.logger.info(
++                        "Get best config failed. Currently no config can be run."
++                    )
++                    logger.info(
++                        "Get best config failed. Currently no config can be run."
++                    )
++                if (
++                    "sharding_overlap" in cur_cfg
++                    and cur_cfg["sharding_overlap"]
++                ):
++                    add_overlap_performance(
++                        cur_cfg, tuner_cfg, recorder.history
++                    )
++
++                if cur_cfg["error_info"]:
++                    error_task_nums += 1
++                error_info = cur_cfg["error_info"]
++                task_nums = len(auto_tuner.algo.all_tasks)
++                cur_task_id = auto_tuner.algo.idx
++                ctx.logger.info(
++                    "Auto Tuner Schedule: [{}/{}], Pruned nums {}, Error nums {}, Error info {}, Remaining time {} min".format(
++                        cur_task_id,
++                        task_nums,
++                        cur_task_id - job_id,
++                        error_task_nums,
++                        error_info,
++                        round(
++                            (task_nums - cur_task_id) * max_time_per_task / 60,
++                            2,
++                        ),
++                    )
++                )
++                logger.info(
++                    "Auto Tuner Schedule: [{}/{}], Pruned nums {}, Error nums {}, Error info {}, Remaining time {} min".format(
++                        cur_task_id,
++                        task_nums,
++                        cur_task_id - job_id,
++                        error_task_nums,
++                        error_info,
++                        round(
++                            (task_nums - cur_task_id) * max_time_per_task / 60,
++                            2,
++                        ),
++                    )
++                )
++                recorder.store_history(history_file_path)
++                # generate a new config
++                new_cfg = auto_tuner.search_once()
++                cur_cfg = copy.deepcopy(new_cfg)
++                auto_tuner.add_cfg(cur_cfg)
++                continue
++
+             # in single dp estimation scene, just some nodes not all nodes run
+             ctx = gen_new_ctx(ctx, cur_cfg, tuner_cfg)
+             actual_nnodes = int(ctx.args.nnodes.split(":")[0])