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fix(reward): guard clevr_count_70k_reward_fn against scoring failures (#1430) * fix(reward): score non-string answers in clevr_count_70k_reward_fn clevr_count_70k_reward_fn did not str()-coerce its inputs or guard against errors, unlike the sibling reward fns (gsm8k, geometry3k). A non-string answer (e.g. an int) made ans.strip() raise AttributeError, which WorkflowExecutor catches and uses to reject the whole trajectory instead of scoring the sample — and even a matching completion was lost rather than scored 1.0. Coerce completions and answer to str and wrap the body in try/except, matching the sibling reward fns. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> * fix(example): apply the same guard to the clevr GRPO example reward fn The example defines its own clevr_count_70k_reward_fn (referenced via workflow_kwargs) that still had the old logic. Mirror the built-in fix: str-coerce completions/answer and guard, so a non-string answer is scored rather than raising and dropping the trajectory. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> --------- Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com> | 2 个月前 | |
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 | |
refactor(trainer): move trainer modules from experimental to areal/trainer (#896) * refactor(trainer): move trainer modules from experimental to areal/trainer Move trainer-related modules to establish a cleaner architecture: - Move PPOTrainer and SFTTrainer from areal/experimental/trainer/ to areal/trainer/ - Move PPO actor/critic from areal/engine/ppo/ to areal/trainer/ppo/ - Move SFT lm_engine from areal/engine/sft/ to areal/trainer/sft/ - Move RW engine from areal/engine/rw/ to areal/trainer/rw/ - Export PPOTrainer and SFTTrainer from top-level areal package This refactoring separates training algorithm concerns (trainer/) from backend infrastructure (engine/), making the codebase more modular. The trainers can now be imported directly via `from areal import PPOTrainer`. Updates all imports across examples, tests, docs, and internal modules. * minor fix test * fix * fix missing links | 6 个月前 | |
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 | |
feat(workflow): support multimodal agent trajectories (#1606) * feat(workflow): support multimodal agent trajectories Prepare image prompts with the Hugging Face processor and preserve vision tensors and token type IDs so exported agent trajectories remain trainable. Key changes: - Support processor-backed Chat Completions and Responses prompts - Export multimodal training fields across interaction chains - Add the Geometry3K agent workflow and focused regression tests * fix: scope multimodal agent workflow to sglang Add a standalone Geometry3K Chat Completions agent and a dedicated SGLang example while preserving the existing VisionRLVR entry point. Reject unsafe inline image inputs and missing multimodal processors, with focused unit coverage for request and image validation. * fix(experimental): narrow inline image validation Keep the review fix limited to rejecting remote image URLs and avoid introducing custom decoding or resource limits. * fix(experimental): preserve tokenizer-only image forwarding Keep backend-managed image URLs working for tokenizer-only clients while requiring a local processor for trainable v1 multimodal trajectories. Scope remote URL rejection to the local processor path so the shared client does not regress v2 inference. * fix(experimental): decode inline images from memory Prevent local multimodal preprocessing from interpreting request strings as filesystem paths. Validate base64 input and construct images from in-memory bytes before invoking the Hugging Face image helper. * test(experimental): align tokenizer mock with transformers 5 * fix: enforce multimodal rollout backend boundaries Keep the SGLang tokenizer configuration advisory while preventing strict multimodal agent trajectories from entering the unsupported vLLM path. Key changes: - Warn when SGLang multimodal rollout may reprocess expanded tokens - Reject strict multimodal agent requests on the vLLM backend - Cover both OpenAI request paths and SGLang launch behavior Refs: #1606 | 11 天前 | |
feat(workflow): support multimodal agent trajectories (#1606) * feat(workflow): support multimodal agent trajectories Prepare image prompts with the Hugging Face processor and preserve vision tensors and token type IDs so exported agent trajectories remain trainable. Key changes: - Support processor-backed Chat Completions and Responses prompts - Export multimodal training fields across interaction chains - Add the Geometry3K agent workflow and focused regression tests * fix: scope multimodal agent workflow to sglang Add a standalone Geometry3K Chat Completions agent and a dedicated SGLang example while preserving the existing VisionRLVR entry point. Reject unsafe inline image inputs and missing multimodal processors, with focused unit coverage for request and image validation. * fix(experimental): narrow inline image validation Keep the review fix limited to rejecting remote image URLs and avoid introducing custom decoding or resource limits. * fix(experimental): preserve tokenizer-only image forwarding Keep backend-managed image URLs working for tokenizer-only clients while requiring a local processor for trainable v1 multimodal trajectories. Scope remote URL rejection to the local processor path so the shared client does not regress v2 inference. * fix(experimental): decode inline images from memory Prevent local multimodal preprocessing from interpreting request strings as filesystem paths. Validate base64 input and construct images from in-memory bytes before invoking the Hugging Face image helper. * test(experimental): align tokenizer mock with transformers 5 * fix: enforce multimodal rollout backend boundaries Keep the SGLang tokenizer configuration advisory while preventing strict multimodal agent trajectories from entering the unsupported vLLM path. Key changes: - Warn when SGLang multimodal rollout may reprocess expanded tokens - Reject strict multimodal agent requests on the vLLM backend - Cover both OpenAI request paths and SGLang launch behavior Refs: #1606 | 11 天前 | |
refactor(trainer): move trainer modules from experimental to areal/trainer (#896) * refactor(trainer): move trainer modules from experimental to areal/trainer Move trainer-related modules to establish a cleaner architecture: - Move PPOTrainer and SFTTrainer from areal/experimental/trainer/ to areal/trainer/ - Move PPO actor/critic from areal/engine/ppo/ to areal/trainer/ppo/ - Move SFT lm_engine from areal/engine/sft/ to areal/trainer/sft/ - Move RW engine from areal/engine/rw/ to areal/trainer/rw/ - Export PPOTrainer and SFTTrainer from top-level areal package This refactoring separates training algorithm concerns (trainer/) from backend infrastructure (engine/), making the codebase more modular. The trainers can now be imported directly via `from areal import PPOTrainer`. Updates all imports across examples, tests, docs, and internal modules. * minor fix test * fix * fix missing links | 6 个月前 | |
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 | |
feat(megatron): Qwen3.5 dense + MoE training/inference support via megatron-bridge (#1384) * feat(engine): delegate megatron live weight sync to bridge.export_hf_weights Add use_bridge_for_update_weights flag that routes the live weight update path through megatron-bridge.export_hf_weights instead of the hand-rolled convert_to_hf registry. Required for new model families (e.g. Qwen3.5) that don't have a registry entry. The bridge handles TP/EP/PP gather and HF layout transformation internally; AReaL keeps the bucketed broadcast loop unchanged. FP8 and LoRA paths fall back to the registry automatically. Also fix a latent device-context bug in _load_model_from_hf: megatron-bridge builds shard-index tensors via torch.arange() under the caller's `with self.device:` context, putting them on CUDA while HF weights are loaded on CPU. The resulting indexing error trips ChunkedMapping for any model with GDN/Mamba conv1d weights (e.g. Qwen3.5). Force CPU as the factory-op default just around the bridge.load_hf_weights call. Key changes: - New MegatronEngineConfig.use_bridge_for_update_weights flag - Refactor _update_weights_from_distributed into dispatch + _update_weights_via_registry helper - New _update_weights_via_bridge streams from bridge.export_hf_weights and reuses the bucket broadcast loop - Wrap bridge.load_hf_weights in `with torch.device("cpu"):` to prevent CUDA index / CPU tensor mismatch in ChunkedMapping * test(megatron): add Qwen3.5 distributed test scaffolding Add 1-GPU smoke + 5 multi-GPU tests (TP=2, PP=2, PP+VPP=2, DP=2 grad_norm invariance, DCP save/load) mirroring the Qwen3 dense set. All Qwen3.5 tests route through bridge_type=megatron-bridge because its GDN hybrid attention is only handled by megatron-bridge's model definitions (mbridge would substring-match qwen3 and emit wrong shapes). NOTE: these tests currently fail at engine.forward because megatron-core's GDN layer raises NotImplementedError on packed (THD) sequences. A follow-up will add a BSHD path mirroring verl's data_format switch; until then these tests document the expected coverage and act as a regression target. Key changes: - Add qwen3_5 to MODEL_PATHS in run_megatron_engine_distributed.py - Re-key MODEL_PATHS from areal.utils.testing_utils canonical paths so local-path overrides propagate from a single source - Wire bridge_type via _MODEL_BRIDGE_OVERRIDES (qwen3_5 → megatron-bridge) - Six test_qwen3_5_* tests in test_megatron_engine_distributed.py * feat(engine): add BSHD padded forward path and megatron-bridge patches for Qwen3.5 Qwen3.5's GDN (Gated Delta Net) layers reject packed (THD) sequences in megatron-core. Add a BSHD path that reconstructs [B, S] padded input from cu_seqlens inside packed_context_parallel_forward, mirroring the existing VLM 2D-reconstruction logic but for text-only models. Also add runtime monkey-patch for megatron-bridge PR #3143 (MTP shadow embedding missing word_embeddings attribute under sequence_parallel + tied embeddings). The patch lazily restores the attribute from the closure before _postprocess runs, avoiding the need to replace the full forward method. Key changes: - New MegatronEngineConfig.use_padded_seq flag (BSHD mode) - Generalize VLM 2D-reconstruction path in packed_context_parallel_forward to also fire on use_padded_seq=True for non-VLM models - CP>1 guard for use_padded_seq (same constraint VLM has) - New megatron_bridge_patches.py with PR #3143 workaround - New train_hf_save_load test_type in runner (replaces DCP for SSM models whose flattened_range tensors are unsupported by mcore DCP) - Qwen3.5 tests: 1-GPU, TP=2, PP=2, HF save/load all pass; VPP and grad_norm_mb_invariance skipped with documented reasons * fix(engine): register Qwen3.5 as vision model and fix non-contiguous broadcast Add qwen3_5 and qwen3_5_moe to VALID_VISION_MODELS so the engine loads the HF processor and passes pixel_values / image_grid_thw through the VLM forward path. Qwen3.5's base architecture (Qwen3_5ForConditionalGeneration) is inherently multimodal — there is no separate qwen3_5_vl model_type. Also fix a ValueError in _update_weights_via_bridge where bridge.export_hf_weights yields non-contiguous tensor views (from QKV split / gate-up chunk) that NCCL broadcast rejects. Call .contiguous() before bucketing. * feat(vllm): add gdn_prefill_backend to avoid FlashInfer GDN hang Qwen3.5 and other GDN hybrids default to vLLM's FlashInfer GDN prefill kernel, which hangs on SM90 — a runtime mbarrier deadlock (flashinfer #2623/#3329) and a JIT-compile deadlock (vLLM #41865/#39287), surfacing as shm_broadcast stall -> sample_tokens timeout -> EngineDeadError. Expose gdn_prefill_backend so configs can set "triton" (stable Triton/FLA kernel). None default emits no flag, so non-GDN models are unaffected. * test(megatron): add Qwen3.5-MoE expert-parallel + HF save/load tests Qwen3.5-35B-A3B megatron coverage via megatron-bridge, both running on 4 GPUs: - test_qwen3_5_moe_expert_parallel: PP2/TP2/EP2 forward + cross-rank logprob consistency. CP is unavailable for the GDN series (Megatron-LM #4043) and the full-attention layers cap TP<=2, so ranks are filled with PP at EP=2. - test_qwen3_5_moe_hf_save_load: save -> zero -> load -> compare round-trip validating MoE expert-weight conversion (TEGroupedLinear weight0..N + GLU linear_fc1 stride-2). The train step is skipped (_MODEL_SAVELOAD_SKIP_TRAIN) since a 35B-A3B optimizer state does not fit; the loaded HF weights are already non-trivial. The megatron-vs-FSDP logit comparison is skipped for this model (_MODEL_SKIP_FSDP_COMPARE): AReaL's FSDP engine materializes the full fp32 35B per rank on load, which cannot fit. * docs(example): add Qwen3.5-2B megatron geometry3k GRPO config * docs(engine): clarify megatron-bridge patch docstring + document gdn_prefill_backend choices * refactor(engine): auto-derive padded-seq layout from model type The padded (BSHD) vs packed (THD) forward layout is a hard architectural property of the model -- GDN/SSM kernels (the Qwen3.5 family) reject packed sequences -- not a user tunable. Exposing it as the `use_padded_seq` config field let it be mis-set and risked silent correctness or crash issues. Derive it from `model_type` instead so the layout can never disagree with the architecture. Also surface a startup warning when `use_bridge_for_update_weights=True` but a fallback condition (non-megatron-bridge, FP8/quantized, or LoRA) silently routes live weight sync through the registry path, so the effective behavior is visible in logs. Key changes: - Add requires_padded_seq(model_type) helper in engine/core/model.py - Derive self.use_padded_seq from model_type in MegatronEngine.initialize - Remove use_padded_seq from MegatronEngineConfig and regenerate CLI docs - Warn once when bridge weight-sync falls back to the registry path - Drop the test-runner override map and example yaml flag Refs: #1384 | 2 个月前 | |
feat: add MTP training with speculative decoding rollout (#1659) * feat: add MTP training with NEXTN speculative decoding rollout Train the built-in MTP head jointly with GRPO/SFT on Megatron and use it during rollout through SGLang NEXTN speculative decoding. Online weight synchronization updates both the target model and the built-in MTP draft runner, ensuring that speculative decoding uses the latest RL-trained MTP weights instead of a stale initialization. MTP is trained through Megatron-Core's auxiliary cross-entropy path with a configurable loss scaling factor of `0.1` by default. AReaL supplies independent MTP label and loss-mask channels while keeping the main forward path logits-based. Shared output weights, backbone hidden states, and embedding inputs are detached from the MTP loss graph, so the backbone receives only the policy/SFT gradient while the MTP-specific parameters learn from future-token supervision. For packed THD training with context parallelism, AReaL applies the same per-sequence zigzag CP split and rank-local repacking to MTP labels and loss masks as it does to input IDs. Megatron-Core's CP-aware rolling then aligns future-token targets across CP ranks without crossing packed sequence boundaries. For online rollout, a focused compatibility bridge for `sglang==0.5.10.post1` receives each distributed weight bucket once and applies it to both the built-in MTP draft runner and the target runner. It supports both SGLang speculative worker layouts, handles SGLang's internal `NEXTN`-to-`EAGLE` normalization, and leaves external EAGLE draft models untouched. Draft-weight CPU backup can be enabled so the server remains available while updated weights arrive online. End-to-end validation was performed with Qwen3.5-2B on Geometry3K GRPO. The training-side MTP weights changed during optimization, and the same updated tensors were loaded into all SGLang draft runners. Key changes: - Add `enable_mtp_training` and `mtp_loss_scaling_factor` to the Megatron engine configuration. MTP training implies retaining the model's MTP layers and is incompatible with `lm_head_loss_chunk_size`. - Feed independent MTP labels and loss masks through Megatron forward passes while preserving the main logits-based loss path. - Patch Megatron-Core `GPTModel` and Megatron-Bridge `Qwen3VLGPTModel` forwarding so Qwen3.5 text and multimodal batches can train MTP. - Align MTP labels and masks with padded or packed execution layouts and prevent targets from crossing sequence or padding boundaries. - Support MTP training with `CP > 1` for wrapper-owned packed THD by applying the same per-sequence zigzag split and rank-local repacking to input IDs, MTP labels, and MTP loss masks. - Reuse Megatron-Core's packed, CP-aware rolling semantics to align future-token supervision across CP ranks. - Isolate MTP gradients from shared output weights, embeddings, and backbone hidden states. - Report the auxiliary `mtp_loss` in training statistics. - Add SGLang speculative-decoding configuration passthrough for `NEXTN`, speculative steps, EAGLE top-k, draft-token count, external draft-model path, and draft-weight CPU backup. - Add an SGLang distributed weight-update bridge that updates both target and built-in MTP draft runners from the same received tensors. - Support both SGLang Spec v1 and Spec v2 draft-runner layouts, while failing fast for unsupported SGLang versions, missing draft runners, unsupported load formats, and inference pipeline parallelism. - Record `rollout/spec_accept_rate` and `rollout/spec_accept_length` from SGLang response metadata. - Add a Qwen3.5-2B Geometry3K GRPO example with MTP training and NEXTN rollout enabled. - Add unit coverage for padded and packed MTP label/mask layouts, CP zigzag alignment, multimodal forwarding, NEXTN/EAGLE routing, Spec v1/v2 compatibility, and draft/target online weight updates. - Regenerate the English and Chinese CLI reference documentation. Current limitations: - MTP training with `CP > 1` is supported only for wrapper-owned packed THD. Padded BSHD, VLM, and model-owned THD execution still require `CP=1`. - The current gradient-isolation implementation supports a single MTP prediction layer. Multi-layer MTP gradient propagation is not supported. - The SGLang distributed MTP update bridge requires inference pipeline parallel size `1`. - The compatibility bridge is intentionally pinned to `sglang==0.5.10.post1` because it relies on version-specific internal weight-update and draft-runner APIs. * test: use HttpGenerationResult in rollout version race test * fix(engine): align MTP masks and detach untied output weights Keep next-token-aligned masks unchanged before MCore's per-layer roll, and detach internal output-layer weights for untied models. Co-authored-by: Cursor <cursoragent@cursor.com> * fix(engine): unify Megatron main and auxiliary loss scaling Let MCore apply the optimizer loss scale to both the main backward path and separately seeded MTP/MoE auxiliary gradients. This prevents FP16 optimizer unscaling from suppressing auxiliary updates. Co-authored-by: Cursor <cursoragent@cursor.com> * fix(models): reject unsupported multilayer MTP training Fail before model construction when MTP training requests more than one prediction layer, whose gradients are not fully supported yet. Co-authored-by: Cursor <cursoragent@cursor.com> --------- Co-authored-by: huaqingyuan <huaqingyuan@tencent.com> Co-authored-by: Cursor <cursoragent@cursor.com> | 22 小时前 | |
feat: support qwen35 awex colocate weight update (#1620) Enable Qwen3.5 Dense and MoE registry recovery for AWEX colocated weight updates and provide a two-node Megatron/SGLang Geometry3K configuration. | 16 天前 | |
feat(vlm): add Qwen3.6 LoRA GRPO training support for 27B and 35B-A3B (#1444) - Add VLM geometry3k GRPO configs for Qwen3.6-27B (dense) and 35B-A3B (MoE) - Add sft_train_batch to FSDPPPOActor for areal-mint SFT - Fix GPU group separation for actor and rollout (avoid colocation) - Fix max_tokens handling in sglang_remote - Add vlm_math_agent and train.py examples | 1 个月前 | |
feat(vlm): add Qwen3.6 LoRA GRPO training support for 27B and 35B-A3B (#1444) - Add VLM geometry3k GRPO configs for Qwen3.6-27B (dense) and 35B-A3B (MoE) - Add sft_train_batch to FSDPPPOActor for areal-mint SFT - Fix GPU group separation for actor and rollout (avoid colocation) - Fix max_tokens handling in sglang_remote - Add vlm_math_agent and train.py examples | 1 个月前 | |
feat(megatron): make MTP head opt-in to support Qwen3.6 MoE RL (#1403) * feat(megatron): make MTP head opt-in to support Qwen3.6 MoE RL Qwen3.6 MoE checkpoints store their MTP (multi-token-prediction) experts in a fused layout that megatron-bridge 0.4.x cannot export, so update_weights crashes with "Object must exist on at least one PP rank" at step 1. MTP is an inference-only head, not part of the RL objective and unused by the rollout, so it should not be built for RL training. Add MegatronEngineConfig.enable_mtp (default False). In make_mcore_model (megatron-bridge path), drop MTP (provider.mtp_num_layers=None) when the model has one and enable_mtp is False; raise if enable_mtp is True but the model has no MTP head. Set True for SFT or bridge builds that support the model's MTP format. Refs: #1398 * docs(examples): add Qwen3.6-35B-A3B megatron geometry3k GRPO config GRPO recipe for Qwen3.6-35B-A3B on geometry3k with the megatron actor (megatron-bridge) + vLLM rollout, demonstrating the new megatron.enable_mtp=False switch (MTP dropped for RL). Refs: #1398 * docs(megatron): refine enable_mtp help + Qwen3.6 example (review) Address PR #1403 review: - Scope the enable_mtp help to bridge_type=megatron-bridge (the flag is a no-op on the mbridge/registry paths) and drop the unwired SFT claim. - Add PYTORCH_ALLOC_CONF=expandable_segments to the Qwen3.6 example to avoid 35B-MoE OOM. Refs: #1398 * fix(engine): keep non-MTP weights in HF export when MTP head is dropped With enable_mtp=False the bridge export yields no mtp.* tensors, and megatron-bridge's save_generator with strict=True silently skips every source shard containing an MTP key -- discarding the non-MTP weights packed in those shards (Qwen3.6-35B lost lm_head + 2 layers; 27B lost ~16 layers) while rebuilding a consistent-looking index. Pass strict=False to save_hf_pretrained when the MTP head was dropped so incomplete shards are written with all present keys (only mtp.* is absent, as intended), zero the MTP layer counts in the exported config.json so external loaders do not fabricate an MTP head over missing weights, and rebuild the safetensors index from the shard files' actual contents (the bridge's strict=False path leaves ghost mtp.* entries and a stale metadata.total_size). Refs: #1398 | 2 个月前 |
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