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GGitHubfeat(api): add unified RejectionSamplingConfig for async training (#1088)

bc9f0098创建于 4月20日历史提交

文件	最后提交记录	最后更新时间
config.py	refactor(api): migrate allocation_mode to per-engine backend fields (#1044) * refactor(api): migrate allocation_mode to per-engine backend fields Replace the centralized `allocation_mode` string with explicit `backend` fields on `TrainEngineConfig` and `InferenceEngineConfig`. Each engine now owns its own backend+parallelism spec (e.g. `fsdp:d4`, `sglang:d4t2`), eliminating implicit auto-backend selection and the shared `AllocationMode` object. Key changes: - Add `backend` field to TrainEngineConfig and InferenceEngineConfig - Add `ModelAllocation.from_str()` for single-component parsing - Remove `AllocationMode` public export (replaced by `ModelAllocation`) - Rename internal `AllocationMode` to `_AllocationMode` for SPMD launcher backward compatibility with FutureWarning - Remove auto-backend selection — explicit backend prefix is now required - Controllers (`TrainController`, `RolloutController`) parse `backend` directly instead of receiving `alloc_mode` from trainers - `WeightUpdateMeta.alloc_mode` replaced by `gen_allocation` (single `ModelAllocation`) - Add `RWTrainer` and `ArchonRWEngine` for reward model training - Remove `get_model_update_meta()` helper (logic moved to trainers) - Update all YAML configs, examples, docs (EN+ZH), and tests BREAKING CHANGE: `AllocationMode` is removed from public API. Users must migrate to per-engine `backend` fields. SPMD launchers emit deprecation warnings. * chore(ci): fix backend specifier for vlm sft test * fix: fix bare dims for actor backends * chore(docs): fix reminder for bare allocation dims	5 个月前
config.yaml	feat(api): add unified RejectionSamplingConfig for async training (#1088) Replace behave_imp_weight_cap/behave_imp_weight_mode with unified RejectionSamplingConfig supporting multiple metrics (ratio, kl_k1, kl_k2, kl_k3), levels (token/sequence), and actions (mask/clamp). Key changes: - Add RejectionSamplingConfig dataclass with comprehensive validation - Implement apply_rejection_sampling for 1D packed and 2D padded formats - Fix loss denominator scaling bug in mask mode (save count before filtering) - Use geometric mean for sequence-level ratio aggregation (matching GSPO) - Broadcast sequence-level geometric mean as uniform behave_imp_weight - Warn when use_decoupled_loss=True but rejection_sampling is None - Update ppo_actor_loss_fn and grpo_loss_fn to use new config - Migrate 40 example configs to new rejection_sampling field - Add 43 unit tests covering all modes, metrics, and edge cases Refs: #1052	4 个月前
train_agents.py	refactor(api): migrate allocation_mode to per-engine backend fields (#1044) * refactor(api): migrate allocation_mode to per-engine backend fields Replace the centralized `allocation_mode` string with explicit `backend` fields on `TrainEngineConfig` and `InferenceEngineConfig`. Each engine now owns its own backend+parallelism spec (e.g. `fsdp:d4`, `sglang:d4t2`), eliminating implicit auto-backend selection and the shared `AllocationMode` object. Key changes: - Add `backend` field to TrainEngineConfig and InferenceEngineConfig - Add `ModelAllocation.from_str()` for single-component parsing - Remove `AllocationMode` public export (replaced by `ModelAllocation`) - Rename internal `AllocationMode` to `_AllocationMode` for SPMD launcher backward compatibility with FutureWarning - Remove auto-backend selection — explicit backend prefix is now required - Controllers (`TrainController`, `RolloutController`) parse `backend` directly instead of receiving `alloc_mode` from trainers - `WeightUpdateMeta.alloc_mode` replaced by `gen_allocation` (single `ModelAllocation`) - Add `RWTrainer` and `ArchonRWEngine` for reward model training - Remove `get_model_update_meta()` helper (logic moved to trainers) - Update all YAML configs, examples, docs (EN+ZH), and tests BREAKING CHANGE: `AllocationMode` is removed from public API. Users must migrate to per-engine `backend` fields. SPMD launchers emit deprecation warnings. * chore(ci): fix backend specifier for vlm sft test * fix: fix bare dims for actor backends * chore(docs): fix reminder for bare allocation dims	5 个月前