ViperEkura/AstrAI
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32 Commits
Author SHA1 Message Date
ViperEkura cf4f5ab9f6 feat: add persistent DataLoader workers 
- Keep training workers alive between epochs when enabled.
- Avoid invalid prefetch settings for single-process loading.
 2026-08-09 11:38:50 +08:00
ViperEkura 1c7369f293 feat: add MoE auxiliary loss metrics 
- Propagates MoE load-balancing loss through model outputs
- Logs task, auxiliary, and weighted losses across strategies
- Computes only explicitly requested callback metrics
- Preserves tensor compute_loss API and adds regression tests
 2026-08-02 06:30:43 +08:00
QueenAmish 04899a2b15 Make Nora+NAdamW the default optimizer 2026-07-31 23:16:39 +08:00
ViperEkura 22cf798d81 feat: add field and model validators to config classes 
- TrainConfig: enum validators (strategy, parallel_mode, backend, start_method, compile_mode), positive/non-negative/range validators, model_validator requiring reward_model_fn for online RL strategies
- AutoRegressiveLMConfig/EncoderConfig: attn_type, ffn_type enum validators
- ProcessingConfig: packing_strategy, truncation_mode enums, positive int validators
- OutputConfig: storage_format, position_ids_mode enum validators
 2026-07-30 08:41:14 +08:00
ViperEkura 164be9708b refactor: migrate config system to Pydantic dataclasses 
- Replace hand-rolled BaseConfig (from_dict/to_dict/_coerce/_unwrap_optional) with pydantic.dataclasses
- from_dict now uses cls(**d), to_dict uses dataclasses.asdict + json.dumps filter
- TrainConfig: required fields are now truly required (no default=None), delete manual validate()/__post_init__
- Remove dead required() helper and metadata={'help': ...} annotations
- Fix gradient_checkpointing_modules type from List[str] to List[type]
- Add pydantic>=2.0 as direct dependency in pyproject.toml
- Add numpy-style Parameters docstrings to all config classes
- Enable use_attribute_docstrings in BaseConfig for schema generation
- LoRAConfig also migrated to pydantic dataclass
 2026-07-30 08:25:32 +08:00
ViperEkura 8150ab6c32 feat: add torch.compile CLI option for training 
- Add --compile flag (default/reduce-overhead/max-autotune)
- Apply torch.compile in _before_wrap before DDP/FSDP wrapping
- Profiling shows MFU 85.5% -> 88.5% (+3%), time -3.2%, memory -7.9%
 2026-07-29 22:06:51 +08:00
ViperEkura d9a0c72149 feat: store metric logs inside each checkpoint dir, remove log_dir config 2026-07-28 00:22:29 +08:00
ViperEkura 754624acf0 feat: add online rollout framework for RL strategies 
- RolloutRunner: generate + score responses with cached re-rollout trigger
- BaseStrategy.__call__ switches online/offline via runner injection
- GRPO/DPO implement prepare_from_rollout; aliases online_grpo/online_dpo
- TrainConfig + train.py add rollout params and CLI flags
- Tests cover generate_responses, RolloutRunner cache, shared __call__
 2026-07-20 03:49:56 +08:00
ViperEkura e5f9b1a3a9 fix: default max_grad_norm to 1.0 and drop None branch 2026-07-20 01:08:13 +08:00
ViperEkura d7695b40e3 feat: make max_grad_norm optional (None disables clipping) 
- TrainConfig.max_grad_norm defaults to None
- executor.clip_grad_norm returns grad norm without clipping when None
- train.py --max_grad_norm defaults to None
 2026-07-19 00:08:18 +08:00
ViperEkura a74e5b91a3 feat: add record-mode to Store for DPO/GRPO 
- Store gains fetch_record/num_records alongside stream fetch/__len__
- save_bin/load_bin support per-record offsets via record_keys param
- H5Store/MmapStore/JsonlStore all support dual stream+record access
- DPODataset/GRPODataset use fetch_record, no cross-record concat
- dpo_collate_fn + collate_fn wired through TrainConfig
- fixes attention context leakage in DPO from windowed concatenation
 2026-07-18 21:02:29 +08:00
ViperEkura 70c0e5de90 refactor: merge validation into MetricCallback, simplify progress bar to optimizer steps 
- Remove separate ValidationCallback, merge into MetricCallback
- Progress bar now tracks optimizer steps instead of micro-steps
- Remove unused log_interval config field and CLI flag
- Fix validation all_reduce: use SUM(loss, count) instead of AVG
- Simplify metric logging: always log every optimizer step
- Add grad_norm display to progress bar
 2026-07-03 21:43:08 +08:00
ViperEkura aabb0d83e9 refactor : replace iteration with consumed_samples 
- Replace context.iteration with consumed_samples (global sample count)
- Add optimizer_step property derived from consumed_samples
- Checkpoint meta.json stores consumed_samples, drops iteration
- CLI --start_batch renamed to --start_samples (per-rank samples)
- Checkpoint dir naming: epoch_X_step_Y instead of epoch_X_iter_Y
- Metric log entries use step and consumed_samples fields
- Backward compat removed (old iteration checkpoints unsupported)
 2026-06-30 18:42:42 +08:00
ViperEkura 44579ea6dc refactor : metric 日志改为以 optimizer step 为单位，默认每步记录 
- log_interval 默认 100 -> 1，语义从 batch iteration 改为 optimizer step
- step 指标从 on_batch_end 移到 on_optimizer_step，不受梯度累积影响
- JSONL 条目新增 step 字段，保留 iter
- flush 落盘仍在 on_batch_end
 2026-06-30 15:12:31 +08:00
ViperEkura 0f1fcb079f refactor : grad_norm 指标简化，clip_grad_norm 移至 executor 
- metrics 默认加入 grad_norm，移除 grad_std/max/min/mean/nan_num
- grad_norm 默认返回总 L2 范数，per_param=True 返回各参数范数
- clip_grad_norm 从 callback 移至 BaseExecutor/FSDPExecutor
- FSDPExecutor 覆盖为 model.clip_grad_norm_() 保证分布式正确
- ctx_get_grad_norm 改为读取 context.grad_norm
 2026-06-30 14:59:43 +08:00
ViperEkura fec376b0dd fix : 修复策略相关文件的类型注解与抽象方法体 
- 修复 strategy.py 单元素 Union 与缺失的参数/返回类型注解
- 修复 train_context.py 8 个 default=None 字段缺 Optional 标记
- 修复 sample.py/packing.py/position_id.py 方法缺参数及返回类型注解
- 修复 factory.py _resolve_type/list_registered 缺类型注解
- 修复 train_config.py 裸 dict/list 缺泛型参数
- abstractmethod body 从 ... 改为 raise NotImplementedError
- feat : checkpoint meta.json 保存 TrainConfig 超参供人工查阅
 2026-06-14 16:20:10 +08:00
ViperEkura 445378667f feat : NEFTune 噪声注入 + label_smoothing 默认值修正 
- Embedding.forward 训练时注入 randn 噪声，缩放系数 neftune_noise_alpha / sqrt(seq_len)
- TrainConfig.neftune_alpha 通过 config 传递（默认 0=关闭）
- TrainContextBuilder 将 config.neftune_alpha 写入 embed_tokens
- --neftune_alpha CLI 参数（典型值 5.0）
- label_smoothing 默认值 0.05 -> 0.0
 2026-06-11 15:32:43 +08:00
ViperEkura 9fe2121743 feat : TrainConfig 支持 val_split 从训练集自动切分验证集 
- val_split 比例从 dataset 中划出验证集，用 random_seed 固定随机切分
- 若 val_dataset 已显式设置则跳过自动切分
 2026-06-02 20:33:40 +08:00
ViperEkura 4145d35e3c refactor: 检查点加载重构，路径替代对象传递 
- model: nn.Module -> model_fn 工厂函数，spawn 边界只传字符串
- Trainer.train(resume_dir=path) — Checkpoint 不再通过 pickle 传递
- TrainContextBuilder.with_resume_dir(path) — 自动检测 meta.json 分流 resume/from-scratch
- CheckpointCallback: 拆分 state_dict 收集（全 rank）与磁盘写入（rank-0），修复 FSDP 死锁
- serialization: load_torch 支持 broadcast，消除 _load_extra/_load_torch_broadcast
- optimizer/scheduler 恢复逻辑内联到 build()，在 executor.prepare() 之后执行
- pyproject.toml: ruff exclude build/ 避免 CI 扫描构建产物
 2026-05-27 20:15:29 +08:00
ViperEkura a4688021bf feat: 新增LoRA微调模块 
- LoRALinear基于register_parameter托管base weight，state_dict路径不变
- inject_lora/merge_lora/save_lora/load_lora完备封装
- 24个单元测试覆盖注入、合并、存取、边界场景
 2026-05-25 20:15:31 +08:00
ViperEkura 7df6eb9211 feat: 新增FSDP并行后端 
- FSDPExecutor通过**fsdp_kwargs直传FSDP参数
- unwrap_model同时支持DDP和FSDP
- parallel_mode新增fsdp选项
 2026-05-25 19:43:14 +08:00
ViperEkura 3ab4f237e5 refactor: 重构训练后端为 Executor 模式 
- backend.py → executor.py，BaseTrainingBackend → BaseExecutor
- 新增 NoneExecutor（单卡）和 DDPExecutor（DDP，world_size=1 自动降级）
- 新增 GradientState 分离梯度同步状态，AccumOptimizer/AccumScheduler 包裹拦截
- 新增 astrai/protocols.py：OptimizerProtocol/SchedulerProtocol 结构子类型
- TrainContext.backend → executor，TrainConfig 移除 parallel_wrapper/state_dict_fn，新增 parallel_mode/executor_kwargs
- 训练循环用 accumulate() 包裹，on_optimizer_step 命名约定=gate
- scripts/tools/train.py 移除 ddp_wrap/prepare_checkpoint，新增 --parallel_mode
 2026-05-24 20:35:44 +08:00
ViperEkura 45479b5731 feat: metric 参数通过 TrainConfig 传递 
- TrainConfig 新增 log_dir/log_interval/metrics 配置字段

- metric_logger 调用改用 **kwargs 传递，BaseFactory.create 自动过滤
 2026-05-19 17:50:24 +08:00
ViperEkura 2c2697390d feat: 新增 GradientCheckpointingCallback 
- TrainConfig.gradient_checkpointing_modules 指定模块类型
- apply 递归遍历，兼容 DDP，不硬编码模型结构
- modules=None 时静默跳过，零开销
 2026-05-17 18:21:05 +08:00
ViperEkura 42a391f0fb feat: 训练中新增验证循环 
- TrainConfig 添加 val_dataset/val_step 字段
- TrainContext 添加 val_dataloader/val_loss 字段
- 新增 ValidationCallback 按 step 触发验证 + 训练结束时验证
- ProgressBar/MetricLogger 支持 val_loss 展示与记录
 2026-05-17 16:12:42 +08:00
ViperEkura 8f1b32f2b6 fix: 移除多余 request 参数并增强 tokenizer 健壮性 
- 路由和 _get_engine 不再需要 request 参数，直接引用模块级 app
- from_pretrained 增加文件完整性校验，缺 tokenizer.json 则抛 FileNotFoundError
- 移除 from_pretrained 中未使用的 **kwargs
 2026-05-17 12:52:18 +08:00
ViperEkura c241a5dcef refactor: 优化并行训练配置与启动管理 
- 配置新增 start_method 支持 spawn/fork/forkserver 选择
- 启动方式 mp.spawn 改为 mp.start_processes，支持 daemon=True
- validate() 改为基于 metadata 的反射式校验，不再硬编码字段列表
- CLI 新增 --start_method 参数
 2026-05-17 12:33:10 +08:00
ViperEkura f91bfee33e refactor: Config序列化统一BaseConfig基类 
- 新增astrai/config/base.py，提供to_dict/from_dict基类
- 统一命名：load/save → from_file/to_file
- Checkpoint.meta合并训练配置到meta.json
- sys.stderr.warn → warnings.warn
- from_file改为classmethod
 2026-05-16 22:06:39 +08:00
ViperEkura d7a7f570ed refactor: 训练循环改为两重迭代并统一参数命名 
- 训练循环从三重(epoch→batched→batch)改为二重(epoch→batch)
- batch_size → batch_per_device, accumulation_steps → grad_accum_steps
- scheduler 移入 step block 对齐 optimizer 更新步
- GradientClippingCallback 改用 on_step_begin 避免零梯度裁剪
- 移除 _train_impl 误导性的 -> Checkpoint 标注
- total_steps 修除为向下取整并精简为一行
- warmup_steps 改为 warmup_ratio (默认0.05)
 2026-05-16 21:27:35 +08:00
ViperEkura b98c9cefdc refactor: 移除 device_ids 参数设计，统一通过 CUDA_VISIBLE_DEVICES 控制 GPU 分配；更新 README 训练示例 
- setup.py: 移除 device_ids 参数，setup_parallel 直接用 rank 作为设备索引
- train_config.py: 移除 device_ids 字段
- trainer.py: 不再传递 device_ids
- train.py: ddp_wrap 用 get_rank() 直接取值
- README.md, README-zh-CN.md: 训练示例改为多行命令风格，去掉参数表格
 2026-05-09 14:55:43 +08:00
ViperEkura 0852b852f8 refactor: 优化参数传递，清理导入样式 2026-04-03 22:06:32 +08:00
ViperEkura 2e009cf59a chore: 更新项目名称 2026-03-31 09:34:11 +08:00