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[trainer, ops] fix: make channel loss capture lifecycle-safe - #1276
#1276 → #1006. Merge #1276 first. Its current head ee0e95065963 is already an ancestor of #1006 (482877158c09). The dependent-only review scope, after normalizing both sides to current main, is 3 files, +376/−17.
After #1276 lands, sync #1006 with the actual upstream main before final landing. Squash can create ancestry/add-add conflicts; preserve both main and the dependent implementation/tests, then recheck the combined tree. Do not strip prerequisite code in advance or publish synthetic merge commits.
Dry-run evidence and integration instructions: portable CPU regression 350 passed / 39 hardware skips, lint and all 31 patchgen configurations passed. This is not native accelerator requalification.
Channel-loss observation must not retain tensors across failed forwards, change the training objective or desynchronize SP ranks. This PR scopes detached per-token loss capture to the actual forward lifecycle, reuses chunk-CE observations where supported, and makes observer failure coordination explicit.
Review scope:12 files, +3854/-207. Framework implementation is +1644/-124; tests are +2176/-79. The central callback is deliberately tested across normal, failed, packed and distributed lifecycles. Review it in three parts:
Capture ownership/cleanup and BaseTrainer/DPO lifecycle hooks.
Packed source alignment and rank-consistent preflight before payload collectives.
Detached observer reduction/metric emission, plus chunk-CE saved-hook/offload interaction.
The observer never contributes to the returned loss. Successful and observer-error paths retain main loss/gradient semantics; true forward failures discard pending observations without observer collectives or masking the original exception. Detached CE still needs chunk-sized vocabulary workspace; sampling/cache release do not make it allocation-free.
#1004 supplies related chunk-CE/offload tests. Merge this core before #1006; its compact composition counters are an additional three-file increment rather than another implementation of this core. Native distributed/offload qualification remains distinct from CPU regression evidence.
Summary by CodeRabbit
New Features
Channel-level loss reporting supports packed sequences, sequence-parallel layouts, and validated synthetic tail padding.
Step totals include sample, input-token, and label-token counts.
Chunked cross-entropy can provide per-token loss observations, including with logits biases or alternate labels.
Bug Fixes
Improved validation and error handling for missing observations, metadata issues, and distributed loss reductions.
Loss reporting coordinates strict-mode capture and reduction failures across ranks.
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Reviewing files that changed from the base of the PR and between 05f950b and e37bfee.
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scripts/ci/accelerator/README.md
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Walkthrough
The change adds detached per-token loss capture to chunked cross-entropy and expands channel-loss aggregation for packed sequences, sequence-parallel metadata, and sample and input-token totals. Strict capture and distributed reduction coordinate failures across ranks. Tests and GPU/NPU suite entries cover these paths.
Changes: Channel-loss observation and distributed aggregation
Layer / File(s)
Summary
Capture and reuse chunk losses veomni/ops/kernels/loss/cross_entropy_loss/standard/chunk_loss.py, veomni/trainer/callbacks/channel_loss_callback.py, tests/ops/loss/test_cross_entropy_loss.py, tests/ops/loss/test_chunk_loss_offload.py, tests/trainer/test_channel_loss_callback.py
Chunked cross-entropy can send detached per-token losses to an active callback consumer. The callback can reuse those losses and supports logits biases and shifted labels. Tests compare loss, gradient, and observation results across capture and offload cases.
Align packed sequences and aggregate data totals veomni/trainer/callbacks/channel_loss_callback.py, tests/trainer/test_channel_loss_callback.py
The callback extracts packed-sequence and router-mask metadata, resolves sequence-parallel spans, and aggregates loss segments. Results can include sample and input-token totals. Tests cover headwise padding, empty segments, metadata validation, and aggregation.
Capture finalization exchanges status and observations across ranks. Strict-mode checks synchronize missing observations, reduction errors, and source-name conflicts. Trainer and distributed tests exercise dispatch and failure cases; GPU and NPU suites include the callback test module.
sequenceDiagram
participant ChannelLossComputer
participant chunk_loss
participant ChunkLossConsumer
ChannelLossComputer->>chunk_loss: Run cross-entropy during active capture
chunk_loss->>ChunkLossConsumer: Send concatenated detached token losses
ChunkLossConsumer->>ChannelLossComputer: Record the observation
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Possibly related issues
ByteDance-Seed/VeOmni#1073: The issue describes runtime-owned, per-module loss dispatch; this PR uses the migrated instance-local loss wrapper for channel-loss handling.
Possibly related PRs
ByteDance-Seed/VeOmni#1006: Adds source-qualified sample and token metrics using the channel-loss observation and alignment paths changed here.
ByteDance-Seed/VeOmni#1351: Changes runner selection and scheduling for the GPU and NPU suites registered here.
Suggested labels:ci, doc, ascend
Suggested reviewers:coach257, zx55
Merge Risk: Minimal · previous review e37bf
No concrete current-head failure or merge-blocking risk is established.
Security architecture risk: Low · up to 8f61a
The change appears bounded to training observability, with no demonstrated expansion of access or authority. Confidence is limited by unconfirmed distributed failure and recovery behavior.
Retained concerns
No architecture-level concerns identified.
Security review details
Security Blast Radius
inferred — The new descriptor preflight couples sampled successful forwards across the initialized default process group, including when sequence parallelism is disabled. Its failure-containment scope is therefore the participating distributed job, not merely one source or sequence-parallel subgroup.
Trust Boundaries and Controls
observed — The inspected dispatch does not grant arbitrary callback authority: it checks instance identity against the registered callback list. Distributed descriptor validation checks rank ordering, world size, enabled agreement, errors, and exact sequence-parallel group membership before group-specific payload work.
Resilience and Maintainability Implications
inferred — A failed forward preserves its exception and skips observation collectives, while successful peers can reach preflight. Safe job-wide termination consequently depends on external failure supervision or collective timeout. The base already lacked rank-local forward recovery before backward, so this is an unresolved containment dependency rather than an established PR-introduced vulnerability.
Hardening Proposals
proposed — Qualify the supported distributed launcher with an asymmetric forward-failure scenario: one rank raises while peers finish forward. Establish bounded whole-job termination without requiring the failed rank to enter another collective.
Pre-merge checks: 4 · 1
Failed checks: 1 warning
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The title clearly identifies the trainer and operations scope and accurately summarizes the main change: making channel-loss capture safe across its lifecycle.
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Full details: Docstring Coverage
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Docstring coverage is 5.42% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Documentation-only edits and deletions within surviving functions are included; unchanged diff context and fully deleted functions are excluded. Analyzed 166 functions across 5 files. (1 skipped: 1 unsupported.)
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Actionable comments posted: 1
🪄 Fix CodeRabbit comments on this PR
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Inline comments:
Review comments at @veomni/trainer/callbacks/channel_loss_callback.py:
- Around line 425-426: Update `capture()` to detect when its body exits with an
exception; on that path, clear the pending capture state without calling
`_finalize_capture()`, preserving the original exception. Add a regression test
that verifies a forward exception does not invoke `dist.all_gather_object`
during capture finalization.
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Reviewing files that changed from the base of the PR and between 6438cbe and 90eeb11.
📒 Files selected for processing (10)
.github/workflows/gpu_unit_tests.yml
.github/workflows/npu_unit_tests.yml
docs/usage/arguments.md
tests/trainer/test_channel_loss_callback.py
veomni/arguments/arguments_types.py
veomni/ops/kernels/cross_entropy/chunk_loss.py
veomni/trainer/base.py
veomni/trainer/callbacks/channel_loss_callback.py
veomni/trainer/text_dpo_trainer.py
veomni/utils/loss_observer.py
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Heads-up on ordering: #1142 ([BREAKING][ops, model] refactor: replace OpSlot with VeomniOp) is planned to land before this PR, so please rebase onto main after it merges and build the kernel changes here on the new op registry.
Every kernel is a row registered with register_op(op, variant, impl, ..., requirement=..., requires=(...)), keyed by (op, variant, impl, device). The device is chosen with GpuKernelRequirement / NpuKernelRequirement / MluKernelRequirement.
Modeling code holds a handle: VeomniOp(op, variant, resolve_op_impl("<field>_implementation")). It does not read config fields or bind slots.
Kernels live under veomni/ops/kernels/<op>/<variant>/<impl>.py, with the rows in veomni/ops/kernels/<op>/__init__.py. Many files moved.
veomni/ops/kernels/cross_entropy/chunk_loss.py moved to veomni/ops/kernels/loss/cross_entropy_loss/standard/chunk_loss.py (row cross_entropy_loss/standard/chunk_loss).
The dispatch in veomni/ops/kernels/cross_entropy/__init__.py is replaced by register_op rows in veomni/ops/kernels/loss/__init__.py. Models reach it through ForCausalLMLoss with a VeomniOp.
tests/ops/test_chunk_loss.py is now tests/ops/loss/test_cross_entropy_loss.py.
@Coach257 The migration is complete on current main: ede4cf64e42a0ed2865468d317896a83d6781a52 uses VeomniOp with an instance-local loss wrapper and forward-scoped token-loss capture. It does not restore OpSlot or patch the global op registry. Chunk-loss observation reuses the existing projection and returns the original scalar loss/gradient; explicit shift_labels is supported. CPU and distributed Gloo regressions pass. Native full-training/checkpoint acceptance is still pending, so this is not a claim of complete NPU qualification.
Add a CUDA parameter so the GPU workflow tests the offload path.
DEVICES lists only cpu and npu. The GPU workflow runs tests/ops, but on that runner only the CPU case executes. The CPU case does not test the real behavior of custom_save_on_cpu. On CPU, the "offload" moves tensors from CPU to CPU. The test never checks that a tensor moves from the device to the host and back, and it never checks that vocabulary logits stay out of the saved set under device memory. The change is device-sensitive, so the CUDA path needs the same coverage as the NPU path.
Generalize the device == "npu" source/restore assertions at Lines 65-67 to device != "cpu".
As per path instructions: "Flag tests ... that only run on one hardware path when the change is device-sensitive."
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Review comment at @tests/ops/loss/test_chunk_loss_offload.py at line 14:
Add CUDA to the DEVICES parameter with a skip condition based on
torch.cuda.is_available(), and generalize the device-sensitive source/restore
assertions in the relevant test from device == "npu" to device != "cpu" so both
CUDA and NPU exercise offload behavior.
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Nitpick comments:
Review comments at @tests/ops/loss/test_chunk_loss_offload.py:
- Line 14: Add CUDA to the DEVICES parameter with a skip condition based on
torch.cuda.is_available(), and generalize the device-sensitive source/restore
assertions in the relevant test from device == "npu" to device != "cpu" so both
CUDA and NPU exercise offload behavior.
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Added the CUDA parameter to the registered chunk-loss offload matrix and generalized the source/restore assertions to the selected accelerator. This exact test change is also carried by #1006.
24 CPU tests passed; 48 CUDA/NPU cases skipped here. Independent review and make quality passed. No GPU/NPU tests were executed locally; native CI approval/results remain outstanding.
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@YLlllllllllll I couldn't produce a complete response to this request. Please try again.
🧠 Learnings used
Learnt from: CR
Repo: ByteDance-Seed/VeOmni PR: 0
File: .agents/knowledge/architecture.md:0-0
Timestamp: 2026-10-20T06:47:22.784Z
Learning: Applies to tests/** : Applicability reference:
AGENTS.md: # VeOmni Development Guide
## Context Loading
On session start, read the following:
- `.agents/knowledge/architecture.md` — module map, trainer hierarchy, data flow
Source excerpt:
# VeOmni Architecture Overview
## Testing
### Test Commands by Change Area
**A passing local run does not mean CI runs it.**
`.github/workflows/gpu_unit_tests.yml` and `npu_unit_tests.yml` enumerate most
test files one by one. Only `tests/data` runs as a whole directory in both
workflows; `tests/ops` and `tests/parallel/context_parallel` run wholesale on
GPU only (the NPU job enumerates three named ops files). The e2e paths belong
to `gpu_e2e_test.yml` / `npu_e2e_test.yml` instead. A new file anywhere else is
invisible to CI until it is added to the workflow that owns its path, usually
both unit workflows. See `.agents/knowledge/testing.md` before adding a test.
Learnt from: CR
Repo: ByteDance-Seed/VeOmni PR: 0
File: .agents/knowledge/testing.md:0-0
Timestamp: 2026-10-20T06:47:39.978Z
Learning: Applicability reference:
AGENTS.md: # VeOmni Development Guide
## Context Loading
Read on demand:
- `.agents/knowledge/testing.md` — how CI selects tests, and how to decide whether a change needs one; read before adding or wiring a test
Source excerpt:
# Test Wiring
## Guidance
- Prefer the cheapest tier that can fail for the right reason: CPU unit test over
single-GPU test over multi-GPU test over e2e training.
Updated in b29ca8bfcdf8909fcb0467e730dccbd6c5951839 and 008ee1f705fd60af0ea4cd4407ea096954931a9d.
The previous head e0c82821 passed GPU unit tests and GPU/NPU E2E, but NPU unit tests failed in the BF16 offload parity case: FP32 scalar loss differed by one ULP (9.5367431640625e-07). No tolerance was relaxed.
Native exact offload comparisons now enable deterministic algorithms for the test module, restoring previous mode/warn-only settings afterward. This is a candidate fix; the observed failure's cause and resolution still require native CI rerun.
Current main's full/factored logits_bias support is retained with all gradients and chunk-loss observations.
Eight new native NPU cases compare unobserved, observed, and observed-plus-offload loss/all gradients and token observations, across FP32/BF16, recompute and full/factored bias, with zero tolerance.
Additional integration review found that the eager fallback channel metric omitted the new logits bias. This is now fixed using detached chunked projection and the same FP32 addition order. Training loss/gradients were unaffected. Thirty-four regressions cover eager/chunk_loss, full/factored bias, raw/shifted targets, FP32/BF16, strict/non-strict mode and explicit rejection of unsupported backends; all have CPU launch plans.
Validation at latest head: 239 CPU tests passed, 71 skipped, formatting/lint passed, independent review safe. The eager regressions reproduced 16 failures before the fix and pass afterward. Native NPU execution remains pending, including the original failing case and new coverage. Please run current-head accelerator CI before acceptance.
Latest head ee0e9506596327ba67fd1576770b627de4a643b9 also includes the new Gloo CI deadline repair: process-group timeouts60s and total worker deadline120s, with all assertions and cleanup unchanged. Both real Gloo regressions passed (18.20s); independent review safe. The earlier full-suite counts remain the evidence for the unchanged functional code.
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Actionable comments posted: 1
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Inline comments:
Review comments at @tests/trainer/test_channel_loss_callback.py:
- Around line 2820-2832: Increase the Gloo worker deadline in _run_gloo_workers
from 30 seconds to 120 seconds, and increase the file-store process-group
timeout from 15 seconds to 60 seconds at both affected test setups. Keep the
existing worker and rendezvous behavior unchanged.
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@YLlllllllllll Thanks for the work here. Looking at the full diff (+3.8k lines across 12 files), the current implementation is much heavier than necessary for the core goal (computing detached per-token CE alongside scalar loss in chunk_loss without a second LM-head projection) and introduces several framework changes we'd prefer to avoid.
Could we simplify this PR along the following lines?
Avoid modifying framework/trainer files and adding veomni/utils/loss_observer.py:
Please revert the changes to veomni/trainer/base.py, veomni/trainer/text_dpo_trainer.py, veomni/arguments/arguments_types.py, and docs/usage/arguments.md, and remove veomni/utils/loss_observer.py.
Use a simple global state in channel_loss_callback.py (similar to moe_monitor.py / moe_router_replay.py):
Instead of ContextVar taps and error-reporting callbacks in a separate loss_observer.py module, define a lightweight module-level state/buffer directly in veomni/trainer/callbacks/channel_loss_callback.py (e.g. a flag indicating whether the current step/forward should record per-token CE loss, plus a slot holding the detached per-token loss tensor).
ChannelLossCallback / ChannelLossComputer simply toggles this flag on for sampled forwards, reads & clears the recorded per-token CE tensor afterward, and leaves it off on non-sampled steps.
Keep chunk_loss.py minimal:
In veomni/ops/kernels/loss/cross_entropy_loss/standard/chunk_loss.py, import the global state from channel_loss_callback.py and check whether per-token CE recording is enabled.
When disabled, run the existing path unchanged; when enabled, compute the detached per-chunk token CE (F.cross_entropy(..., reduction="none")) during the chunk loop and write the concatenated detached tensor to the global state, without introducing _ChunkObserver or has_aux=True plumbing.
Drop the extra state-machine complexity in channel_loss_callback.py:
The ~1,500 lines of SP preflight (all_gather_object), _SPCaptureDescriptor, _PendingSPObservation, and release_cache machinery are over-engineered for a diagnostic callback. Please keep channel_loss_callback.py focused on consuming the recorded per-token loss.
@Coach257 Thanks for the specific simplification direction. I agree the core sampled chunk-loss path should reuse its existing projection and remain small; I am working through a reduced version and will provide the resulting diff and CPU parity evidence.
Two existing behaviors need care while removing the current machinery:
With multi-source data, the meter currently consumes ds_idx and source_name before the channel callback runs. Reverting the base callback-order change without relocating this handling makes source-channel metrics disappear. I am checking whether to put that integration in the dependent [trainer] feat: add compact per-source data metrics #1006 instead, keeping this PR focused on the chunk-loss path.
On an SP group, a rank-local missing or invalid observation must not cause peers to enter an unmatched conditional collective. The existing two-/four-rank Gloo regressions exercise that situation. I am looking for a substantially smaller unconditional coordination scheme that preserves the valid packed/CP numerical results and avoids hanging on asymmetric observations.
I will keep the actual training loss/gradients unchanged and distinguish CPU proof from pending native NPU CI. If the intended contract instead excludes multi-source or asymmetric-observation handling from this PR, please let me know; either way I will make the scope explicit in the PR.
@Coach257@FoolPlayer Updated #1276 in 55ecbf5576c630b27457956bd483d2bfec23aec5, after merging current upstream 029929bfb3b2 normally.
Restored the four framework/argument/doc files to current main, removed veomni/utils/loss_observer.py and release_cache from this PR, and moved scoped detached-token capture to a lightweight module-level thread-local buffer. chunk_loss reuses the main projection, without _ChunkObserver or has_aux plumbing.
Removed the previous descriptor/pending-payload state machines. I retained one small WORLD metadata/status exchange and one per-SP-group detached payload exchange: the existing mixed packed/SP paths need to agree before entering subgroup collectives when a rank lacks metadata or observation. WORLD exchanges bounded source fingerprints and counts, not token/segment payloads. The strict path also synchronizes any reconstruction failure before backward and optimizer updates. Training-forward or communication failures still rely on the distributed launcher to tear down peers.
The diff is now +3,296/-184, still larger than the minimal capture change because it contains packed/SP alignment and correctness tests. If you prefer to move those existing safeguards into [trainer] feat: add compact per-source data metrics #1006 or another follow-up, I am happy to split further; I did not remove them without an equivalent behavior.
CPU: callback and chunk-loss coverage passed, 96 FP32/BF16 loss/gradient comparisons against upstream were exact; real 2-/4-process Gloo covered packed loss/gradient parity, SP-group isolation and rank-local failures; a real BaseTrainer.train_step test verifies strict observation failure cannot update the optimizer or scheduler. No NPU/HCCL or full training claim for this head; current accelerator CI still needs approval.
The dependent #1006 is being updated to preserve the multisource metadata ordering and data counters; that integration is not yet claimed complete.
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Run the observer + bias + offload parity test on CUDA as well as NPU.
test_npu_chunk_observer_bias_offload_parity is the only test that combines the chunk observer, both logits-bias forms, and activation offload/recompute on an accelerator. It runs only on NPU.
The observer adds a detached per-chunk CE inside the grad_and_value loop on device tensors, so the change is device-sensitive. On GPU, nothing checks this combination. The general offload test does not pass observed or bias_kind.
Parametrize the test over the existing DEVICES list, excluding cpu if needed. The NPU deterministic fixture can stay as it is.
As per path instructions: "Flag tests ... that only run on one hardware path when the change is device-sensitive."
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Review comment at @tests/ops/loss/test_chunk_loss_offload.py around lines 122 -
141:
Update test_npu_chunk_observer_bias_offload_parity to run across the existing
accelerator devices in DEVICES, excluding CPU, instead of only on NPU; remove
the NPU-only skip and use the parametrized device for each _run call. Rename the
test to reflect that it covers accelerators rather than only NPU.
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Nitpick comments:
Review comments at @tests/ops/loss/test_chunk_loss_offload.py:
- Around line 122-141: Update test_npu_chunk_observer_bias_offload_parity to run
across the existing accelerator devices in DEVICES, excluding CPU, instead of
only on NPU; remove the NPU-only skip and use the parametrized device for each
_run call. Rename the test to reflect that it covers accelerators rather than
only NPU.
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Follow-up in 05f950b025641c1107fb2a46df1e410261ea2e7d addresses the GPU coverage suggestion in review 5662674766: the observer + bias + activation-offload/checkpoint test now covers CPU, CUDA, and NPU while preserving exact loss, gradient, and detached-token checks. Local CPU: 8 passed; CUDA/NPU: 16 skipped on this host, so accelerator results remain for CI. CI inventory validation confirms the CPU cases require zero cards and accelerator cases one card.
The channel-loss CI entry was also moved within each suite, preserving every entry, so this PR and #982 now merge together without a conflict; both channel-loss and token-accounting test selections survive the combination.
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Actionable comments posted: 1
🪄 Fix comments: Fix CodeRabbit comments on this PR
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Inline comments:
Review comments at @scripts/ci/accelerator/suites/gpu.json:
- Around line 13-18: Update the accelerator suite documentation or configuration
examples to include qwen38-channel_loss_callback-unit under both GPU and NPU
coverage; extend the existing channel-loss callback description so it documents
this CI suite.
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scripts/ci/accelerator/suites/gpu.json
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tests/ops/loss/test_chunk_loss_offload.py
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Updated to e37bfee43afa55b67d3387f0d7619c3966b86cbd with the CI documentation requested in the review. Documentation only; no runtime, test selection, packing, SP or precision changes. The dependent #1006597fa2eeaf76 and this parent merge without conflicts, preserving both the documented suite and #1006 implementation/tests.
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@YLlllllllllll Thanks for reverting the trainer/config files and removing veomni/utils/loss_observer.py. However, the PR is still +3,300 lines because channel_loss_callback.py (+1,277 lines) and its test file (+1,914 lines) still contain large amounts of dead code and over-engineered distributed coordination that have no practical effect in real training.
I left inline comments on the specific blocks explaining why they are unnecessary and what to delete vs. keep. With those removed, the actual production diff across chunk_loss.py and channel_loss_callback.py should be well under ~100 lines.
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Remove error-callback plumbing (report_chunk_loss_observer_error / capture_failed) and sys.modules reflection:
Line 50 just computed _eager.cross_entropy_from_logits(logits, labels_flat, ignore_index=ignore_index, ...) (F.cross_entropy(..., reduction="sum")) on the exact same logits and labels_flat. Calling F.cross_entropy(logits.detach(), ..., reduction="none") immediately afterward on the same inputs cannot fail if the main loss succeeded, so capture_failed and report_chunk_loss_observer_error have no real-world trigger.
Please directly import the module-level capture state from veomni.trainer.callbacks.channel_loss_callback (there is no circular import), append the detached per-chunk token loss when enabled, and store torch.cat(token_chunks, dim=split_dim).detach() at the end of forward.
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Simplify _CHUNK_LOSS_CAPTURE to a plain global flag + tensor slot:
This is still the callback/error-handler abstraction from loss_observer.py. Since training is single-threaded per rank and on_error is unnecessary, please replace capture_chunk_loss_per_token(consumer, on_error) / get_chunk_loss_consumer() / report_chunk_loss_observer_error() with a simple module-level flag and tensor slot (matching moe_monitor.py / moe_router_replay.py).
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Delete the SP preflight / topology / pickle serialization helpers (lines 93–240) and _finalize_capture state machine (lines 391–613):
Why this has no practical effect: In real distributed training, all ranks load the same config (strict is identical across ranks), execute the same model forward, and ranks in the same SP group slice the exact same micro-batch (so source_ids and loss-hook call counts are identical within an SP group). If a rank crashes during forward, torchrun tears down the job.
Harmful side effect: To guard against synthetic unit-test scenarios where ranks have mismatched configs/topologies or single-rank observer errors, _finalize_capture runs up to 3 dist.all_gather_object collectives (including WORLD-wide all_gather_object + pickle.dumps + sha256 hashing) on every sampled micro-step, adding cluster-wide CPU synchronization overhead.
It also duplicates the existing _reduce_sp implementation (leaving _reduce_sp at lines 1304–1409 dead in normal capture() because defer_sp=True is hardcoded at line 807).
Please delete lines 93–240 and 391–613 and keep main's existing capture(), compute_side_channel(), and _reduce_sp() flow.
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Delete dead step_data_totals / include_data_stats code (here and at lines 267, 664, 893, 1011, 1104–1110, 1131, 1197–1203, 1293–1299, 1310, 1359–1369, 1399–1401, 1453–1455, 1473–1475): include_data_stats=False is hardcoded at line 893, and self.step_data_totals is never read or logged anywhere in this PR. Please remove all include_data_stats and step_data_totals branches from this PR.
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Keep this part:
Reusing the captured per-token loss from chunk_loss, falling back to shift_labels when labels is None, and forwarding logits_bias / logits_bias_weight to _eager_chunked (lines 929–984) are the core valuable changes in this file and should be kept.
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@Coach257 Updated in cd7c3f53d3ae01d67b957b6a049ba9ddace25259. I kept the chunk-loss per-token reuse, shift_labels fallback, and logits_bias / logits_bias_weight forwarding you identified. Source provenance and capture lifecycle now fail closed on ambiguous packing and asynchronous observations without changing the training loss/gradient; DPO uses the model's expanded per-token outputs.
Validation on this exact code: 146 CPU tests and 52 localhost Gloo multi-process tests passed, plus make quality. These are dry runs, not NPU validation; Ascend/HCCL results are still pending. Could you take another look when convenient?
The tip was rewritten solely to correct the commit author for the CLA; its code tree and test evidence are unchanged.
The fork CI workflows are action_required; please approve the NPU/GPU and format jobs if appropriate.
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Delete _synchronize_strict_failure / _synchronize_missing_observation / _raise_if_strict_post_collective_failure:
Adding extra world-wide dist.all_reduce collectives in on_step_begin and on_step_end just to synchronize strict=True exceptions across ranks only serves synthetic tests where a single rank is manually fed invalid metadata. Please revert on_step_begin, _require_source_ids, on_step_end, _reduce_step_totals, and _register_sources to main.
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Simplify _rank_local_packed_source_spans to ~20 lines:
Supporting rank-local cu_seq_lens_q for Headwise CP (where per-sample CP padding has position_ids == 0) is useful, but lines 2120–2226 spend ~110 lines cross-validating packed_global_cu_seqlens, packed_global_physical_seq_len, and ((length + 2*sp - 1) // (2*sp)) * (2*sp) to 'prove' collator invariants. Those tensors are produced by our own collator in the same process. If rank-local cu_seq_lens_q ends at seq_len, simply build the zip(points[:-1], points[1:]) spans (dropping the trailing span if len(spans) == source_count + 1 and tail_padding_length > 0) without the extra packed_global_* plumbing.
@Coach257 Thank you for the detailed review. I am removing the WORLD-wide preflight, serialized observer payload, dead data counters, and the related synthetic-only machinery from #1276, while retaining chunk CE reuse, label fallback, bias and packed-CP alignment.
One small safety check appears necessary even with identical configs: with strict=False, a rank-local observer exception or absent source metadata can make one SP rank skip the existing _reduce_sp while a peer enters its collectives. An independent review reproduced the issue. I am testing a single small SP-group tensor status exchange at the shared successful-forward capture boundary, before the existing mainline _reduce_sp; it checks failure, observation count, source order and packed-layout agreement. No WORLD preflight or custom serialized full-observation payload is proposed. Mainline _reduce_sp still exchanges small metadata via all_gather_object (which serializes objects), followed by its existing tensor collectives. Forward and collective failures still propagate to the launcher. Observer-only CE failures are isolated so they do not change the training loss.
The capture flag/slot is kept on the op using a scoped ContextVar so standalone op imports do not load the trainer and concurrent/nested calls cannot leak the state. CPU loss/gradient and 2-/4-rank Gloo checks are in progress; the #1006 dependent branch also needs integration before I can claim the stack is ready. Would this narrow SP safety guard be acceptable?
Update: please hold review/merge of f61a94f briefly. Dependent #1006's original two-rank Gloo parity exposed a valid Ulysses layout with different rank-local CU boundaries, which the new guard incorrectly rejects. I have reproduced the regression and am testing a follow-up that validates rank-local CU against the replicated global spans, accepts both contiguous and headwise CP geometries when proven, and preserves the original Ulysses case. This is a channel-loss observation-path issue; no conclusion about native accelerator testing is implied. I will push and post the exact new heads after both branches pass review.
@Coach257 Thank you for the detailed review. While simplifying the observer I found a correctness boundary that needs your input before this can be considered merge-ready:
PackingCollator currently derives Flash CU from position_ids == 0 after concatenation; it does not retain the original per-feature source lengths, and it only emits tail_padding_length for a positive tail. Thus position-only segmentation cannot distinguish a missing empty source from a padding or an internal position reset. A reproduced two-rank example reports [empty=2, real=0] where the actual counts are [0, 2]; this changes metrics, not training loss/gradients.
For CP a global CU alone also does not identify whether a shard is contiguous or headwise; mixed layouts can pass zero-boundary checks while reporting incorrect per-source loss. Our current safety candidate skips or errors when the necessary provenance cannot be established. That protects precision but affects valid standard global-CU-only CP and, without an explicit zero-tail declaration, normal Ulysses batches.
A robust solution appears to require a separate channel-loss source-boundary contract emitted by the packing producer before samples are concatenated (including explicit tail length and local slicing layout), with DPO pair boundaries handled separately. That is a broader change than simply removing the old guards. Would you prefer that producer contract to be included here or developed as a follow-up? I am keeping the previous hold on merging this PR while validating the smallest safe option.
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[P2] This assumes every rank-local CU uses headwise CP geometry. A valid two-rank contiguous Ulysses split of global CU [0,3,6,8] has local CUs [0,3,4,4] and [0,0,2,4], which this check rejects. Distinguish contiguous and headwise layouts, and update the later layout digest to accept legitimate rank-local differences; otherwise valid metrics are skipped or strict training aborts.
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[P2] The reported Ulysses rejection is fixed: _ulysses_local_span_lengths now validates rank-specific contiguous intersections, and the layout signature uses shared global CU metadata. The new ulysses_local_cu regression covers the reported boundaries.
@YLlllllllllll Please stop adding defensive machinery for hypothetical corner cases that do not exist in VeOmni's training runtime. The latest update (cd7c3f53d) expanded the PR back to +3,158 lines across 13 files and re-modified veomni/data/data_collator.py, veomni/trainer/base.py, veomni/trainer/vlm_trainer.py, veomni/trainer/text_dpo_trainer.py, and docs/usage/arguments.md.
All of the following hypothetical mechanisms (and their corresponding synthetic unit tests) are non-goals and must be completely removed:
Asyncio tasks / multi-threaded concurrent capture on the same ChannelLossComputer (asyncio.current_task(), threading.get_ident(), _capture_lock, _capture_state_lock, _capture_owner, _capture_epoch, _ACTIVE_CHANNEL_LOSS_EPOCH, _foreign_capture_errors, _is_owner):
PyTorch distributed training in VeOmni is strictly synchronous and single-threaded per rank. Forward passes never spawn background asyncio tasks or child threads that call loss_function across steps. Delete all thread/task ownership locks, epoch tokens, and all test_*_async_child_* / test_foreign_* / test_*_concurrent_* tests.
Zero-length (0-token) samples, single samples that reset position_ids to 0 mid-sample, user datasets with a key literally named "_veomni_channel_loss_source_provenance", and metadata_collate_func hooks that secretly .flip(-1) or mutate input_ids/labels are not real scenarios. Cloning input_ids/labels/position_ids/attention_mask before every metadata hook and comparing them with torch.equal adds unnecessary overhead to the collator.
Revert veomni/data/data_collator.py, veomni/trainer/base.py, veomni/trainer/vlm_trainer.py, veomni/trainer/text_dpo_trainer.py, and docs/usage/arguments.md completely to main.
Cross-rank SHA-256 hashing, SP preflight all_gather, geometry_candidates bitmasks, and WORLD all_reduce in _finish_capture / _require_source_ids:
All ranks in an SP group receive the exact same micro-batch from the dataloader and run the exact same model forward and config. Do not hash source_ids / input_mask with sha256, do not run preflight status/geometry all_gathers in _finish_capture, and do not add dist.all_reduce in _require_source_ids or _finish_capture. Keep main's existing capture(), compute_side_channel(), and _reduce_sp() flow.
Delete all of these validation helpers. If rank-local cu_seqlens_list_q / cu_seq_lens_q is present and points[-1] == seq_len (with len(spans) == len(source_ids) or len(spans) == len(source_ids) + 1 when tail_padding_length > 0), slice by those local spans; otherwise fall back directly to main's existing position_ids == 0 logic.
Expected scope of this PR
Untouched (identical to main):veomni/data/data_collator.py, veomni/trainer/base.py, veomni/trainer/vlm_trainer.py, veomni/trainer/text_dpo_trainer.py, veomni/arguments/arguments_types.py, docs/usage/arguments.md.
veomni/ops/kernels/loss/cross_entropy_loss/standard/chunk_loss.py (~25 lines): When capture is enabled, compute detached per-chunk F.cross_entropy(..., reduction="none") and store the concatenated tensor in the module-level slot.
@Coach257 Thanks for spelling out the desired scope. I agree the async-task/thread ownership machinery and the synthetic cases around it are not justified by the synchronous trainer contract; I will remove them and reduce the PR.
There is one concrete DPO boundary that needs a separate owner before reverting the trainer changes: process_dpo_example concatenates chosen/rejected in one feature and resets positions between them. _build_dpo_labels_list also masks each segment's last position. For a two-token chosen and two-token rejected sequence with fully supervised labels, a single global CE shift counts the chosen-to-rejected boundary, whereas the actual DPO mask excludes it. Models with shared-prefix compaction also need observation after per-token outputs are expanded.
I propose making #1276 the narrow chunk-loss reuse / shift-label / bias improvement you described, and keeping the minimal DPO/source-boundary correctness work in #1006 (or a separate prerequisite if you prefer). For SP, a detached observer failure on one rank can leave peers in a collective even though training forward succeeded; can we retain a small SP-scoped status agreement, without per-tensor hashing or the geometry/async machinery? I will keep both PRs on hold for native validation while reducing the implementation.
@Coach257 I narrowed #1276 to c31ba0e34b4a8c0bb24583894d653ed33aa91a61, based on current main 3382d42: 8 files, +934/-11, mostly tests. The collator, base/VLM/DPO trainers, argument types and usage docs are identical to main. The implementation retains chunk per-token CE reuse, shift_labels fallback, and full/factored bias handling; task/thread ownership, provenance cloning and geometry machinery are removed from this PR.
On this exact code I ran the quality gate, 124 focused CPU tests and a two-rank Gloo capture-failure/fallback regression. Loss-operator CPU coverage was 86 passed; 79 device-dependent cases skipped on the CPU host. An independent code review also covered the final tree. NPU/Ascend CI is still pending and the PR stays on hold for native acceptance.
This PR intentionally does not claim to fix main's pre-existing source/DPO attribution and SP observer-error coordination boundaries. I am keeping the DPO/source work in #1006 while aligning its dependency with this smaller #1276; #1006 still needs separate review and native validation.
A follow-up preserves the previous observer extension interface: only explicitly declared extension keywords are assumed usable, so an old override that accepts only **kwargs cannot silently drop a non-null bias; it skips metrics or raises in strict mode. Public observer exceptions retain main's existing propagation behavior.
The newly approved NPU unit workflow reached this PR's CPU Gloo regression and failed before starting workers: run 40498256274 declared nproc=1 for a test that starts two processes. It was not an NPU numerical failure.
Fixed in 9760fe5c4580d82e3ffb182007b616f4d7d4b96b: the test now declares LaunchSpec(device="cpu", nproc=2). The production code is unchanged. I collected the case with the CI inventory plugin, verified its two-process plan, and ran it through the actual CI batch/lease plugin on CPU: 1 passed. The previous-head NPU e2e workflow subsequently completed successfully: 23 passed, 6 skipped, including a two-card toy Qwen4-Exp training smoke (seq_len 64, PLE2/EP2/SP2). It does not establish the chunk-loss-specific native gate or full-model training. The new head requires its own CI result.
@Coach257@FoolPlayer Thanks for the reviews. The Ulysses local-CU issue is fixed, and the current head 9760fe5c4580d82e3ffb182007b616f4d7d4b96b differs from the previously reviewed implementation only in the CPU/Gloo test process reservation. The latest-head NPU unit and e2e workflows still show action_required; could you please approve those runs and, if the outstanding review threads are satisfied, resolve them? The earlier-head e2e result does not substitute for this head's native gate.
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Coach257
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Landing order
#1276 → #1006. Merge #1276 first. Its current head
ee0e95065963is already an ancestor of #1006 (482877158c09). The dependent-only review scope, after normalizing both sides to current main, is 3 files, +376/−17.After #1276 lands, sync #1006 with the actual upstream main before final landing. Squash can create ancestry/add-add conflicts; preserve both main and the dependent implementation/tests, then recheck the combined tree. Do not strip prerequisite code in advance or publish synthetic merge commits.
Dry-run evidence and integration instructions: portable CPU regression 350 passed / 39 hardware skips, lint and all 31 patchgen configurations passed. This is not native accelerator requalification.
Channel-loss observation must not retain tensors across failed forwards, change the training objective or desynchronize SP ranks. This PR scopes detached per-token loss capture to the actual forward lifecycle, reuses chunk-CE observations where supported, and makes observer failure coordination explicit.
Review scope:12 files, +3854/-207. Framework implementation is +1644/-124; tests are +2176/-79. The central callback is deliberately tested across normal, failed, packed and distributed lifecycles. Review it in three parts:
The observer never contributes to the returned loss. Successful and observer-error paths retain main loss/gradient semantics; true forward failures discard pending observations without observer collectives or masking the original exception. Detached CE still needs chunk-sized vocabulary workspace; sampling/cache release do not make it allocation-free.
#1004 supplies related chunk-CE/offload tests. Merge this core before #1006; its compact composition counters are an additional three-file increment rather than another implementation of this core. Native distributed/offload qualification remains distinct from CPU regression evidence.
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