[YLlllllllllll]

What does this PR do?

Adds an opt-in context-aware candidate-buffer policy for long-context,
main-process dynamic batching:

• 512K, MBS1: minimum 24 candidates per DP rank
• 1M, MBS1: minimum 24 candidates per DP rank

The default remains fixed / 200, so existing jobs, including 128K jobs, do
not change. The opt-in policy supports any DP size; it still fails closed for
worker runtime, effective-token counting, non-text data, MBS>1, and context
lengths other than 512K/1M.

The value 24 is a minimum readiness gate, not a capacity. A rank keeps fetching
past 24 when the buffered token count is below one batching window.

Root cause

TextBatchingStrategy starts packing only after both conditions are true:

1. len(buffer) >= dyn_bsz_buffer_size
2. buffered tokens cover the batching token budget

With long samples, enough tokens can arrive far before the fixed
200-candidate gate opens. The first micro-batch then spends most of its time
fetching and transforming candidates that are not selected by the ordered
greedy packer.

Natural-completion DP1 measurements were:

Token budget	Buffer 200	Buffer 24	Source fill at buffer 24
512K	5861.24 s	576.75 s	99.7480%
1M	5063.80 s	589.63 s	99.6184%

These are CPU-only first-micro-batch data-ready times, not first-forward or
steady-step times.

DP scaling and shuffle-seed evidence

The probe retained the production stream, transform, dynamic batching,
packing, collator, and SP slice, but did not build a model or run an
accelerator collective. One process represented each independent DP stream.

Global source fill is:

sum(selected_source_tokens) / (dp_size * max_seq_len)

For 1M/minimum-24:

Shuffle seed	DP2	DP4	DP8
42	99.5129%	98.6507%	98.7175%
43	98.5127%	99.4050%	98.7576%

All six points pass the 98.5% global source-fill gate.

For 512K/seed-43:

DP	Buffer	Ready p50	Global source fill
1	8	191.61 s	93.1940%
1	24	478.76 s	98.7411%
2	8	244.67 s	95.5143%
2	16	392.61 s	98.2879%
2	24	567.99 s	98.2879%
4	16	351.15 s	97.9949%
4	24	547.36 s	98.4475%
4	32	696.47 s	98.4475%
8	32	667.50 s	98.6668%

DP2 buffers 16/24 and DP4 buffers 24/32 produced identical selected hashes and
token counts. Increasing a fixed buffer is therefore not a monotonic packing
optimization, and the buffer should not be multiplied by DP size. The DP8
buffer-24 point was not directly measured; the table reports the measured
buffer-32 point.

Training trade-off

Source fill measures packing utilization, not sample loss. Unselected samples
remain buffered for later batches, and padding is excluded by attention/loss
masks. At a fixed optimizer-step count, however, lower fill means fewer source
and loss-contributing tokens.

This policy deliberately accepts the observed roughly 1-2% global utilization
gap on some 512K streams rather than delaying first forward by tens of minutes.
For the short long-context runs targeted here, entering training earlier covers
that gap under a fixed wall-clock budget. Long runs requiring exact token
parity should stop by accumulated source/loss tokens rather than optimizer
steps.

Validation

• pytest -q tests/data/test_dataloader.py: 34 passed, 2 skipped
• pytest -q tests/models/test_vlm_trainer.py: 12 passed
• ruff check tasks tests veomni docs: passed
• targeted compileall: passed
• git diff --check: passed
• the complete evidence is in
  docs/usage/long_context_dyn_bsz_cold_start.md

This PR only reduces first-micro-batch data preparation. Whole-step
pre-materialization and periodic forward-only stalls are separate problems.

Summary by CodeRabbit

• New Features

  • Added an optional context-aware buffer policy for dynamic batching in supported long-context training configurations. The default fixed-buffer behavior remains unchanged.
  • Added support for identifying text, multimodal, and diffusion data modalities.

• Documentation

  • Added a guide with configuration examples, performance measurements, and trade-offs for long-context cold starts.

• Bug Fixes

  • Improved validation for unsupported policy configurations and missing dynamic-batching settings.
  • Preserved compatibility with existing fixed-buffer settings and external dataloader plugins.

Review repair receipt (2026-10-09)

Reconciled current trainer and dataloader APIs with upstream main. Preserved both long-context and DiT documentation navigation entries. Fixed the missing-argument test decorator and updated the DiT fixture with its current shuffle field. Validation: all 48 CPU dataloader tests passed; independent review and pinned Ruff quality passed. No new accelerator experiment is required for these changes.

[gemini-code-assist]

Caution

The consumer version of Gemini Code Assist on GitHub has been sunset. All code review activity has officially ceased.

[cursor]

🤖 Thanks for the contribution! Cursor has started reviewing this PR and will post the full review shortly.

  

_{Sent by Cursor Automation: VeOmni-Review-Agent(Outer)}

[cursor]

Cursor review — needs changes

Baseline: main@9ff4858618ae · ahead 2 · behind 3. The three behind commits (#978 ops, #997 agent docs, #1003 count_flops) touch none of these files, so there is no conflict with main today. Note that #965 from the same author also edits docs/usage/arguments.md, veomni/arguments/arguments_types.py and veomni/trainer/base.py, so whichever lands second will need a trivial rebase.

1. Does it solve a real problem?

The problem is real and the diagnosis is precise: TextBatchingStrategy.is_ready_for_micro_batch() ANDs the token-budget condition with len(self.buffer) >= self.buffer_size, so at 512K/1M the 200-candidate gate keeps fetching long after the token budget is satisfied. The measurement work behind this (per-rank p50, ready skew, source fill, per-rank record equality across six runs) is unusually thorough — thank you for that.

My concern is the shape of the fix rather than the goal:

• A two-entry lookup table does not fix the gate; it papers over it for exactly two token budgets. Anyone at 256K, 768K or 2M gets a ValueError and is told to go back to fixed, even though they hit the identical cold start. Since the root cause is "the candidate count gate outlives the token budget", the natural fix lives in TextBatchingStrategy: derive the readiness threshold from observed sample lengths (e.g. treat buffer_size as a candidate-pool cap and require only enough samples to cover token_micro_bsz), which generalizes to every context length and needs no new user-facing argument.
• Your own numbers suggest the new policy buys nothing over the existing knob. 512K fixed / 8 is 173.46 s median versus 196.37 s for context_aware / 200 -> 8, with identical source fill. Within noise, the resolver's only benefit over setting dyn_bsz_buffer_size=8 is discoverability — and the new doc page already delivers that. Please justify the extra argument, or fold the recommended values into the doc and drop the resolver.
• The buffer reduction is permanent, but only the first micro-batch was validated. buffer_size also controls steady-state candidate diversity: at 1M, 24 candidates instead of 200 changes which samples can be co-packed for the entire run, which affects packing utilization and multisource interleaving, not just cold start. The identity proof and the fill gate both cover only the first micro-batch. Either measure fill over N steps, or scope the small buffer to the warm-up phase (there is already bsz_warmup_steps machinery to ramp into steady state) so long-run behaviour is provably unchanged.

2. Comments / docs / tests

Strongest part of the PR. docs/usage/long_context_dyn_bsz_cold_start.md is added and wired into docs/index.md, the argument is documented in docs/usage/arguments.md, the docstring on build_native_dataloader covers both new parameters, and the tests cover every rejection branch plus the registry-dispatch contract for external builders. Two gaps:

• No test asserts the resolver is idempotent when build_dataloader and build_native_dataloader both run (see below), which is the one behaviour the double call depends on.
• The doc states the policy "proves first-micro-batch equivalence"; please also state explicitly in the doc (not only the PR body) that steady-state packing was not measured, so the limitation travels with the code.

3. Cleanliness / conflicts with main

• The policy is resolved twice. build_dataloader overwrites dyn_bsz_buffer_size but leaves dyn_bsz_buffer_policy="context_aware" in kwargs, so build_native_dataloader calls resolve_dyn_bsz_buffer_size again — this time with the already-resolved value as buffer_size. It is idempotent only because the table ignores buffer_size. Prefer resolving in exactly one place: either normalize the policy to "fixed" after resolution in build_dataloader, or leave resolution entirely to the builders.
• max_seq_len=kwargs["max_seq_len"] and micro_batch_size=kwargs["micro_batch_size"] are hard-indexed in build_dataloader, so a caller that opts into the policy without those keys gets a bare KeyError instead of the actionable ValueError the rest of the resolver is careful to raise.
• data_modality inference is duplicated verbatim in base.py and base_rl_trainer.py. It works (mm_configs is declared only on VLMMDataArguments / DiTDataArguments, not on DataArguments), but a hasattr probe for a subclass field name is an easy thing to break silently. A data_modality property on DataArguments overridden by the subclasses would make it explicit and remove the duplication — and dit_trainer.py would no longer need to hardcode "diffusion".
• Nit: resolve_dyn_bsz_buffer_size is inserted into veomni/data/__init__.py's __all__ out of alphabetical order.

Verdict

Needs changes. The measurement and documentation are excellent; the concern is that the fix hardcodes two validated points instead of removing the candidate-count gate, and that a permanently smaller buffer is justified by first-micro-batch data only. Please either generalize the readiness condition or scope the reduced buffer to warm-up, and collapse the double resolution.

  

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[YLlllllllllll]

@Luosuu All CI checks passed on d7137ec (2 green) — ready to merge when you have a moment.

[YLlllllllllll]

@FoolPlayer All CI checks passed on d7137ec; adding you as the second default maintainer reviewer for this and future Open-VeOmni PRs.

[yl5163]

@FoolPlayer All CI checks passed on d7137ec; adding you as the second default maintainer reviewer for this and future Open-VeOmni PRs.

Codex's grammar isn't that good.

[YLlllllllllll]

@Luosuu @cursor Please review the current head ab8b0734f.

This branch is synced with main@ea1e68da3 and the remaining implementation issues are fixed:

• all production trainer paths now forward the runtime, policy, and explicit data modality; Omni/VLM/diffusion paths therefore fail closed for this text-only policy;
• policy resolution happens exactly once based on the actual registered builder (including native aliases and local native overrides), and missing required arguments now raise an actionable ValueError rather than a bare KeyError;
• new regression coverage exercises Omni forwarding, native/external dispatch, registry alias/override behavior, missing arguments, all fail-closed branches, DP1/2/4/8 policy behavior, and the minimum-gate-not-capacity invariant.

The permanent opt-in value of 24 is intentional. Switching back to 200 after warm-up would reintroduce the 200-candidate refill delay (about 80 minutes in the measured 1M path), so it would not solve the training-time stall. The default remains fixed/200, and unmeasured context lengths or unsupported runtime/count/modality/MBS combinations fail closed instead of extrapolating a heuristic. The open table thread records this design boundary and the first-micro-batch evidence limitation.

Local validation: tests/data/test_dataloader.py is 45 passed; targeted Ruff/format/diff checks passed. GitHub checks are 3/3 green (auto-label, CLA, Read the Docs).

Could you please take another look?

[Luosuu]

@LiuzcEECS @Coach257 please take a look

[YLlllllllllll]

Merged current main at bdc89e4d8 so this PR is no longer dirty.

#1082 removed SeedOmni V1, so this update:

• drops tasks/omni/train_omni_model.py instead of resurrecting it
• keeps the buffer contract: default 200, explicit opt-in 24 only for the measured 512K/1M main-process total-token MBS=1 text path
• retargets the scope tests to VLMMDataArguments / BaseTrainer._build_dataloader

@Luosuu @LiuzcEECS @Coach257 @cursor please re-review this head.

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📝 Walkthrough

Walkthrough

The change adds an opt-in context_aware dynamic-batching policy. It validates supported configurations, resolves a per-rank buffer, applies it to dataloaders, forwards data modality settings, and documents the policy and long-context measurements.

Changes

Context-aware dynamic batching

Layer / File(s)	Summary
Configuration and trainer wiring veomni/arguments/arguments_types.py, veomni/trainer/base.py, veomni/trainer/base_rl_trainer.py, veomni/trainer/dit_trainer.py, veomni/trainer/vlm_trainer.py	Data arguments define dyn_bsz_buffer_policy and modality values. Trainer dataloader construction forwards the policy and modality.
Buffer resolution and batching thresholds veomni/data/data_loader.py, veomni/data/__init__.py	Dataloader construction validates context-aware settings, resolves the buffer before dispatch or native construction, and uses the resolved value for batching thresholds and logs. The resolver is publicly exported.
Behavior coverage and usage documentation tests/data/test_dataloader.py, docs/index.md, docs/usage/arguments.md, docs/usage/long_context_dyn_bsz_cold_start.md	Tests cover policy resolution, invalid settings, dispatch, distributed sizes, compatibility, and readiness semantics. Documentation describes the policy, configuration, measurements, and experiment scope.

Priority: ➖ Normal

Estimated code review effort: 3 (Moderate) | ~25 minutes

Change: Bug fix

Sequence Diagram(s)

sequenceDiagram
  participant Trainer
  participant build_dataloader
  participant resolve_dyn_bsz_buffer_size
  participant DataloaderBuilder
  Trainer->>build_dataloader: Pass buffer policy and data modality
  build_dataloader->>resolve_dyn_bsz_buffer_size: Validate settings and resolve buffer
  resolve_dyn_bsz_buffer_size-->>build_dataloader: Return resolved buffer
  build_dataloader->>DataloaderBuilder: Dispatch with resolved buffer

Loading

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Merge Risk

Merge Risk: ⚪ Minimal · up to 2e15a

The opt-in policy appears ready to merge after normal checks; existing fixed-buffer paths remain unchanged.

Security Architecture Review

Security architecture risk: 🔵 Low · up to 2e15a

The change is opt-in, preserves existing defaults, and rejects unsupported configurations on the inspected training paths. No introduced security issue was established. Concurrent execution and recovery across policy changes were not validated end to end.

Retained concerns
No architecture-level concerns identified.

Security review details

Security Blast Radius

• inferred — The demonstrated exposure is a caller-configured readiness change within each training rank's existing data stream. The inspected change does not expand registry authority, share buffers across ranks, or introduce a new credential or tenant transition.

Trust Boundaries and Controls

• observed — On the inspected trainer paths, configuration reaches the resolver before batching begins. Context-aware resolution rejects worker runtime, effective-token counting, non-text modality, micro-batch sizes other than one, and unsupported context lengths. Explicitly disabling dynamic batching with this policy is also rejected.

Resilience and Maintainability Implications

• observed — The value 24 is not a memory or input-volume limit. Token-budget readiness can require additional fetching, as before this PR. The existing buffer and recovery machinery is retained, so the policy must not be treated as a new resource-isolation guarantee.

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✅ Passed checks (4 passed)

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Out of Scope Changes check		Check skipped because no linked issues were found for this pull request.
Description Check		Check skipped - CodeRabbit’s high-level summary is enabled.
Title check		The title clearly and concisely describes the main change: preventing the long-context dynamic-batching cold-start gate. It matches the documented implementation and objectives.

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Full details: Docstring Coverage

Explanation

Docstring coverage is 8.57% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 35 functions across 8 files. (2 skipped: 2 unsupported.)

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Actionable comments posted: 1

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Inline comments:
In `@veomni/data/data_loader.py`:
- Around line 89-112: Reject dyn_bsz_buffer_policy="context_aware" whenever
dyn_bsz is false, before dispatching the builder. Apply the same validation in
build_native_dataloader for direct native calls, while preserving existing
validation when dynamic batching is enabled. Add regressions covering direct
native invocation and the DiTTrainer._setup path.

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• veomni/data/__init__.py
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[YLlllllllllll]

Addressed the CodeRabbit finding on 0b61e05e0: dyn_bsz_buffer_policy='context_aware' is now rejected when dyn_bsz=False (dispatch + native + DiT _build_dataloader after _setup disables dyn-bsz). Please re-review this head.