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[model, ops] feat: shared-prefix training for Qwen3.5 - #1266
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- 🪄 Fix CodeRabbit comments on this PR
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Review comments at
@veomni/models/transformers/qwen3_5/qwen3_5_gpu_patch_gen_config.py:
- Around line 672-673: Before `shared_prefix_plan.attention_kv` compacts
shared-prefix inputs in both attention overrides, ensure the configured backend
supports variable-length attention; otherwise use the packed path or reject the
incompatible backend. Apply this at
veomni/models/transformers/qwen3_5/qwen3_5_gpu_patch_gen_config.py:672-673 and
veomni/models/transformers/qwen3_5/qwen3_5_npu_patch_gen_config.py:503-504.
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.github/workflows/gpu_unit_tests.yml.github/workflows/npu_unit_tests.ymldocs/examples/qwen3_5.mdtests/models/test_qwen3_5_shared_prefix.pytests/models/test_qwen3_5_shared_prefix_accelerator.pyveomni/models/transformers/qwen3_5/qwen3_5_gpu_patch_gen_config.pyveomni/models/transformers/qwen3_5/qwen3_5_npu_patch_gen_config.pyveomni/models/transformers/qwen3_5/shared_prefix.pyveomni/ops/kernels/gated_delta_rule/_ascend/triton/utils.py
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Hi @Coach257 @qcm1, friendly ping when you have a moment! These three PRs are decoupled and ready for review:
All automated checks and CodeRabbit suggestions have been addressed, and local tests pass cleanly. Could you please help review or approve the CI workflows when you have time? Thanks! |
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Heads-up on ordering: #1142 ( What changes in #1142:
How to register and call an op: For this PR specifically:
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Thanks for the heads-up and the clear migration guide @Coach257! Makes complete sense. We will keep an eye on #1142, and as soon as it lands on
Appreciate the review and guidance! |
| plan = build_shared_prefix_plan( | ||
| input_ids, | ||
| kwargs["cu_seq_lens_q"], | ||
| position_ids=position_ids, |
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[P1] When position_ids=None, grouping ignores positions, but TextModel later creates global packed positions. Later rollout suffixes then attend to the representative prefix at a different RoPE offset, silently changing logits and gradients. Skip sharing without validated positions, or construct consistent per-sequence positions before building the plan.
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Fixed in ec4b26bd. When position_ids is None in shared-prefix training, we now construct consistent per-sequence positions [0, 1, ..., len-1] and pass them to self.model so TextModel does not generate global packed arange positions and RoPE offsets match across sequences. Added regression tests in tests/models/test_qwen3_5_shared_prefix.py.
| ) | ||
| elif shared_prefix_plan is not None: | ||
| # Modification: two varlen calls; suffixes start from the prefix state at its last chunk boundary. | ||
| core_attn_out = shared_prefix_plan.gated_delta_rule( |
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[P1] npu_ascendc also reaches this branch, but its backward discards dht and returns dh0=None. Shared-prefix execution passes the prefix's final state into suffix calls, so this silently drops suffix gradients through that state. Reject npu_ascendc here or fall back to ordinary packed execution.
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Fixed in ec4b26bd. We now explicitly reject the npu_ascendc backend with an actionable NotImplementedError when shared_prefix_plan is active, noting that it discards dht and returns dh0=None, and advising to use the 'npu' (Triton) backend.
| labels = labels[None] | ||
| kwargs["shift_labels"] = labels | ||
| else: | ||
| hidden_states = shared_prefix_plan.expand(hidden_states) |
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[P2] With output_hidden_states=True, the decoder captures tensors of length compact_length, but this only expands the local LM-head input and outputs.hidden_states is returned unchanged. Consumers aligning hidden states with the original tokens or labels now get incompatible lengths. Expand the captured hidden-state tensors with the plan before returning them.
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Fixed in ec4b26bd. When output_hidden_states=True, outputs.hidden_states is now expanded with shared_prefix_plan.expand(h) before returning so consumers receive full packed-length tensors. Added a unit test in test_expand_output_hidden_states.
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Inline comments:
Review comments at
@veomni/models/transformers/qwen3_5/qwen3_5_gpu_patch_gen_config.py:
- Around line 1942-1947: Before deriving position_ids from cu_seq_lens_q,
validate that the packed metadata is present and raise a clear error explaining
that shared-prefix training requires it; ensure the later read of cu_seq_lens_q
uses the same validated input.
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⛔ Files ignored due to path filters: 4
veomni/models/transformers/qwen3_5/generated/patched_modeling_qwen3_5_gpu.diffis excluded by!**/generated/**,!**/*.diff,!veomni/models/transformers/**/generated/**veomni/models/transformers/qwen3_5/generated/patched_modeling_qwen3_5_gpu.pyis excluded by!**/generated/**,!veomni/models/transformers/**/generated/**veomni/models/transformers/qwen3_5/generated/patched_modeling_qwen3_5_npu.diffis excluded by!**/generated/**,!**/*.diff,!veomni/models/transformers/**/generated/**veomni/models/transformers/qwen3_5/generated/patched_modeling_qwen3_5_npu.pyis excluded by!**/generated/**,!veomni/models/transformers/**/generated/**
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tests/models/test_qwen3_5_shared_prefix.pyveomni/models/transformers/qwen3_5/qwen3_5_gpu_patch_gen_config.pyveomni/models/transformers/qwen3_5/qwen3_5_npu_patch_gen_config.pyveomni/models/transformers/qwen3_5/shared_prefix.py
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- Implement shared-prefix training for Qwen3.5 and Qwen3.5 MoE on GPU and NPU - Adapt GatedDeltaNet and Attention layers to VeomniOp interface (ByteDance-Seed#1142) - Fork recurrent state for GatedDeltaNet at prefix boundary - Gather key/value states for Attention across prefix and suffix - Add shared prefix plan layout and compaction utilities - Add comprehensive unit tests and documentation
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Hi @Coach257, Following up on your earlier note: now that #1142 has landed, this PR has been rebased onto latest
Ready for review when you have a moment. Thanks! |
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Reviewed the shared-prefix training design and implementation after the rebase onto #1142. The chunk-aligned GDN split (aligned + tail) and varlen attention/conv1d formulations are very clean. Left a few inline comments on edge cases around lm_rows deduplication, attention_mask compaction, and guard placement.
| if shared_prefix_plan is not None: | ||
| if labels is not None and kwargs.get("return_log_probs") and kwargs.get("teacher_topk_ids") is None: | ||
| shift_labels = kwargs.pop("shift_labels", None) | ||
| if shift_labels is None: | ||
| shift_labels = F.pad(labels[..., 1:], (0, 1), value=IGNORE_INDEX) # noqa: F821 | ||
| lm_rows, labels, lm_inverse = shared_prefix_plan.lm_rows(shift_labels) | ||
| hidden_states = hidden_states.index_select(1, lm_rows) | ||
| labels = labels[None] | ||
| kwargs["shift_labels"] = labels | ||
| else: | ||
| hidden_states = shared_prefix_plan.expand(hidden_states) | ||
| # Only compute necessary logits, and do not upcast them to float if we are not computing the loss | ||
| slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep | ||
| hidden_states = hidden_states[:, slice_indices, :] |
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lm_rows deduplication changes scalar loss when shared-prefix tokens have valid labels (!= IGNORE_INDEX), and conflicts with non-zero logits_to_keep
- When
return_log_probs=True,hidden_statesandshift_labelsare deduplicated to lengthU = len(lm_rows)before callingself.loss_function(...), and onlyfused_linear_aux.log_probs/fused_linear_aux.entropyare expanded back vialm_inverseon lines 1954–1955. If all prompt tokens haveshift_labels == IGNORE_INDEX (-100), every valid suffix token has a distinctexpand_indexsolossis unaffected. However, if any shared-prefix position has a valid label (!= -100) — e.g. multi-turn / SFT prefixes, or the test cases intest_qwen3_5_shared_prefix.py:261andtest_qwen3_5_shared_prefix_accelerator.py:72where all labels are valid — each deduplicated prefix token is weighted1xinstead ofnxin the mean reduction, silently changingoutput.loss(the tests currently mask this by backwardingout.log_probs.sum() + out.loss * 0). Consider either restricting deduplication insidelm_rowstoIGNORE_INDEXrows / prompt-masked prefixes, or recomputinglossfrom the expandedlog_probs. - Line 1936 (
hidden_states = hidden_states[:, slice_indices, :]) still sliceshidden_statesbylogits_to_keepafterlm_rowsdeduplication whilekwargs["shift_labels"]stays at lengthU, which will raise a shape mismatch inloss_functionif a caller passes a non-zerologits_to_keep.
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Addressed in commit 8f42fc0e:
- When
lm_inverseis active and scalarlossis computed,lossis recomputed from the expandedlog_probs(-fused_linear_aux.log_probs[valid_mask].sum() / valid_mask.sum().clamp_min(1)), preserving exact mathematical equivalence with packed training where each rollout's prompt tokens contribute to the loss mean. - Slicing by
logits_to_keepis now guarded byif lm_inverse is None:, andlm_rowsdeduplication is bypassed iflogits_to_keep > 0(falling back to standard expansion to preserve sequence order).
| if shared_prefix_plan is not None: | ||
| if return_mtp_context or use_cache: | ||
| raise NotImplementedError("Shared-prefix training supports neither MTP nor KV cache.") | ||
| inputs_embeds = shared_prefix_plan.compact(inputs_embeds) | ||
| position_ids = shared_prefix_plan.compact(position_ids, dim=-1) | ||
| for key in ("cu_seq_lens_q", "cu_seq_lens_k", "max_length_q", "max_length_k"): | ||
| kwargs.pop(key, None) | ||
|
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Clear attention_mask = None (and guard past_key_values is not None) when shared_prefix_plan is not None
inputs_embedsandposition_idsare compacted from[1, T, ...]to[1, C, ...], andcu_seq_lens_q/kare popped fromkwargs, butattention_mask(shape[1, T]) is left untouched and passed intocreate_causal_maskandcreate_recurrent_attention_maskon lines 582–593:- When
attention_maskis all-ones[1, T], bothcreate_causal_maskandcreate_recurrent_attention_maskexecutetorch.all(attention_mask == 1), causing two unnecessary GPU-to-CPU scalar syncs (aten::_local_scalar_dense) per micro-step. - Under
torch.compile(is_tracing(attention_mask)) or ifattention_maskcontains any0,create_recurrent_attention_maskslicesattention_mask[:, -C:]from the[1, T]packed mask (misaligning it with compacthidden_statesinapply_mask_to_padding_states), andcreate_causal_maskreturns a[1, T]mask that mismatches compactQ(C) / gatheredK.
Settingattention_mask = Nonein this block (and inqwen3_5_npu_patch_gen_config.py:556-563) avoids both the host syncs and the shape mismatch.
- When
- Consider checking
if return_mtp_context or use_cache or past_key_values is not None:on line 569, sinceQwen3_5Attention.forwardupdatespast_key_valueswheneverpast_key_values is not Noneeven ifuse_cache=False.
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Addressed in commit 8f42fc0e:
- Set
attention_mask = NoneinTextModel.forwardunderif shared_prefix_plan is not None:(in both GPU and NPU configs) to prevent host-CPU synchronization increate_causal_mask/create_recurrent_attention_maskand avoid mask shape mismatches. - Expanded the guard to
if return_mtp_context or use_cache or past_key_values is not None:in both configs, with unit test coverage intest_text_model_past_key_values_raises_not_implemented.
| if shared_prefix_plan is not None and get_parallel_state().ulysses_enabled: | ||
| raise NotImplementedError("Shared-prefix training does not support Ulysses sequence parallelism yet.") |
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Move the sequence-parallelism guard before build_shared_prefix_plan / compact
Checking get_parallel_state().ulysses_enabled inside Qwen3_5GatedDeltaNet.forward is too late: when SP (get_parallel_state().sp_enabled) is enabled, SequenceParallelCollator slices input_ids to T // sp_size while cu_seq_lens_q retains the full packed length T (and even if input_ids is unsliced, Qwen3_5Model.forward:1482 slices inputs_embeds to T // sp_size). As a result, build_shared_prefix_plan in Qwen3_5ForConditionalGeneration.forward or shared_prefix_plan.compact(inputs_embeds) in Qwen3_5TextModel.forward will fail with an IndexError before reaching any GatedDeltaNet layer. Moving if get_parallel_state().sp_enabled: raise NotImplementedError(...) to Qwen3_5ForConditionalGeneration.forward (around line 1869) and Qwen3_5TextModel.forward (line 569) ensures a clean error.
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Addressed in commit 8f42fc0e:
Moved the sequence parallelism check if get_parallel_state().sp_enabled: raise NotImplementedError(...) early into Qwen3_5ForConditionalGeneration.forward and Qwen3_5TextModel.forward, cleanly failing before sequence slicing or plan construction. Also updated the linear attention guard to check sp_enabled and added unit test coverage in test_forward_sp_enabled_raises_not_implemented.
| if self.veomni_chunk_gated_delta_rule.impl == "fla_npu": | ||
| raise NotImplementedError( | ||
| "Shared-prefix training requires gradient propagation through recurrent state, " | ||
| "which the 'fla_npu' backend does not support (it discards dht and returns dh0=None). " | ||
| "Use the 'triton' backend on NPU, or unset text_config.shared_prefix_training." | ||
| ) |
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Also reject self.veomni_chunk_gated_delta_rule.impl == "eager" on NPU
The GPU config (qwen3_5_gpu_patch_gen_config.py:469) guards against self.veomni_chunk_gated_delta_rule.impl == "eager" because eager ignores cu_seqlens in shared_prefix_plan.gated_delta_rule. We should include the same impl == "eager" check here on NPU alongside "fla_npu".
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Addressed in commit 8f42fc0e:
Added if self.veomni_chunk_gated_delta_rule.impl == "eager": raise RuntimeError(...) to the NPU GDN forward path alongside "fla_npu", with unit test coverage in test_npu_gated_delta_rule_eager_raises_runtime_error.
| torch_dtype="bfloat16", | ||
| init_device=DEVICE, | ||
| ops_implementation=OpsImplementationConfig( | ||
| attn_implementation="veomni_flash_attention_2_with_sp", |
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Nit: "veomni_flash_attention_2_with_sp" is a deprecated alias after #1142; prefer the canonical name "veomni_flash_attention_2":
| attn_implementation="veomni_flash_attention_2_with_sp", | |
| attn_implementation="veomni_flash_attention_2", |
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Updated to attn_implementation="veomni_flash_attention_2" in commit 8f42fc0e.
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Hi @Coach257, Thank you for the thorough and insightful review! All 5 items have been addressed in commit
Re-ran |
…ck, and loss reduction
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What does this PR do?
In GRPO-style RL a micro-batch carries
nrollouts of one prompt, each packed asprompt + response,so packed training computes every prompt
ntimes. Withtext_config.shared_prefix_training: true, theQwen3.5 model detects packed sequences that share a token prefix, runs the decoder stack on a compact
row in which each shared prefix appears once, and restores the packed layout for the outputs. Callers
see identical shapes; gradients from every rollout sum into the shared prefix.
Both modeling paths are supported: NPU with the vendored Triton kernels, GPU with
flash-linear-attention. On Qwen3.5-4B an actor train step is 2.92-5.41x faster,
update_actor2.7-3.3x,and actor peak memory roughly halves, at an identical configuration with upstream verl. Nothing is
required from the trainer beyond keeping a prompt's rollouts in one micro-batch, which is configuration
only.
Related work this follows rather than reinvents: tree training in AReaL, its GDN extension HARTS
(areal-project/AReaL#1765) and OpenPipe ART, specialised here to the two-level tree a rollout group forms.
Checklist Before Starting
this repository. Adjacent Ascend GDN work: [ops, docs, ci] fix: support Ascend 910B4-1 GDR training #966, [ops, ci] fix: mask padded GDR dot lanes on Ascend #970, [dist] feat: add lossless headwise GDN context parallelism #1087 (same vendored
_ascend/tritonkernels).[{modules}] {type}: {description}:[model, ops] feat: shared-prefix training for Qwen3.5Test
Two new tests, both listed in
gpu_unit_tests.ymlandnpu_unit_tests.yml:tests/models/test_qwen3_5_shared_prefix.py— CPU, 25 cases against float64 reference kernels(convolution, gated delta rule, attention, LM rows, index layouts, kernel-call counts, grouping).
Listed in
gpu_unit_tests.ymlandnpu_unit_tests.yml.tests/models/test_qwen3_5_shared_prefix_accelerator.py— toy config on the accelerator present,feature off vs on within twice the packed-training noise floor, plus an assertion that the shared
path actually ran. It differs across the two paths only in the device it builds on and the GDN
backends it asks for, so it is one test rather than two. Listed in both
gpu_unit_tests.ymlandnpu_unit_tests.yml; skips without an accelerator. Verified passing on both: NPU withcausal_conv1d (npu) / chunk_gated_delta_rule (npu) / rms_norm_gated (npu), GPU with theflabackends.
Validation beyond CI, on a real Qwen3.5-4B (Ascend 910B2C, CANN 9.1.1, torch_npu 2.10.0), feature on vs
off through a verl-style fused log-prob call with gradient checkpointing. The floor is the difference
between running the same sequences one per call and packed together — what differing kernel shapes
produce by themselves:
Training is unchanged. One epoch of GRPO on the public DocQA-RL-1.6K (16 prompts per step, 77 steps),
same data and order, feature off vs on:
Both arms ran the same 77 steps of the same data in the same order, both at micro batch 8, on two
cards of the same type, each with an empty Triton cache. Reward, gradient norm, policy loss, response
length and the rollout-vs-training KL follow the same trajectories and the two arms finish at the same
reward (0.760 vs 0.761). Entropy runs lower on the shared-prefix arm throughout, the gap opening
around step 10. Two independent RL runs drift apart regardless of seeding — weights differ by
bf16-level noise after the first update and the sampled responses follow — so with one run per arm
this is not separable from run-to-run variation; an earlier pair had the gap in the other direction.
Performance. Model level, one card, train step (forward + backward), gradient checkpointing, median
of 5 with spread under 1%. The bound is the token ratio
n(P+R) / (P+nR). The same five shapes on bothdevices — Ascend 910B2C with the
npubackends, NVIDIA H20 withfla:Efficiency is 0.81-0.88 across a 4x range of prompt lengths, and the two devices agree to within a few
percent at every shape they share — so the speedup is close to
0.85 x boundwhatever the bound is.On GPU, peak memory on the same five shapes goes 19.7 -> 17.9, 21.8 -> 18.1, 29.3 -> 19.4,
29.3 -> 18.5 and 32.4 -> 19.7 GiB.
End to end, upstream verl
8718ca3unmodified, 8 cards, GRPO on the public DocQA-RL-1.6K,n=4, 16 prompts per step. The first three rows are medians over steps 2-10 on a warm Tritoncache; the fourth is a full epoch from cold:
Every pair is the same configuration on both arms, same machine, back to back; only
shared_prefix_trainingdiffers. A micro batch must hold whole rollout groups — multiples ofn— or agroup is split and only part of it shares a prefix, which is the third row: at micro batch 2 with
n=4just two rollouts share, the bound halves to 1.92x, and the measured 1.81x is 94% of it.
Rollout (~19-24 s) and weight sync are outside the feature's reach, which is why the step speedup is
smaller than the
update_actorspeedup.The same end-to-end comparison on 8 H20s, one epoch, medians over steps 2-77:
update_actorold_log_probThe step ratio here is a floor. For the same drift reason, this shared arm settled on longer answers
(498 tokens against 135), which makes its generation slower — the one part of a step the feature does
not touch. A 20-step pair whose arms stayed close (314 against 290) gave 2.09x on the step and 3.33x on
update_actor.The training side is unaffected, because the prompt dominates the packed length: the arms process
355,954 against 377,929 tokens per step, 1.06x apart. Per million tokens processed,
update_actoris59.22 -> 20.41 s (2.90x) and
old_log_prob16.41 -> 6.01 s (2.73x). Both arms learn: reward0.647 -> 0.719 and 0.668 -> 0.738.
On ~10.8k-token prompts the baseline cannot reach a micro batch that holds a whole group at all: it OOMs
at micro batch 4 and 8 on a 910B2C (57.4 GiB allocated, +2.3 GiB requested inside
update_actor), so itis stuck at micro batch 2. Shared prefix runs micro batch 8 there in 25.5 GB. That is a memory result,
not a speed one, but it is what decides whether the configuration is reachable — and the low-memory arm
is also the busy one: actor MFU as verl accounts it (it does not know about the dedup, so this is
effective MFU) goes 0.15 -> 0.40 at ~5.8k / micro 8, and 0.07 -> 0.36 between the two arms' best feasible
settings at ~10.8k.
API and Usage Example
One config field, default off, set in the
config.jsonof the model directory the trainer loads.Detection is internal to the patched modeling, so a trainer driving VeOmni needs no flag of its own
and no adapter code:
{ "text_config": { "shared_prefix_training": true } }From verl, with no verl change — point
hf_config_pathat a copy of the config directory carrying thefield (weights still load from
model.path):A group split across micro-batches is still correct, only slower.
Design & Code Changes
Token-wise work (projections, norms, MLP, gates) runs unchanged on the compact row. Three mixers consume
the plan, each a fixed number of kernel calls per layer regardless of group count or
n:its state; the remaining tail (< 64 tokens) is replayed in front of every suffix, which starts from
that state. Exporting state only at chunk boundaries keeps the chunk partition identical to unshared
training.
kernel_size - 1prefix rowsas look-back context, whose outputs are dropped. No conv state is passed, so the state-carrying
convolution backward is not used.
[prefix | suffix]keys, bottom-right causal.
(compact row, next-token label)pair is scored once and theper-token
log_probs/entropyare expanded back.cu_seq_lens_qand identical position ids. Candidates sharing ahead are sorted and split to maximise tokens saved, so prompts that share a system prompt stay
separate groups.
Changes:
veomni/models/transformers/qwen3_5/shared_prefix.py(new): the plan, the three mixer wrappers andthe LM-row deduplication.
qwen3_5_npu_patch_gen_config.py: TextModel compacts its inputs, DecoderLayer passes the plan through,GatedDeltaNet branches at the conv and delta-rule calls, and
Qwen3_5Attention.forwardgains anoverride (it had none).
qwen3_5_gpu_patch_gen_config.py: the sharedForConditionalGeneration.forwardbuilds the plan anddeduplicates the LM-head rows, where the text-only guard lives; and, as on NPU, TextModel compacts,
DecoderLayer passes the plan through, GatedDeltaNet branches at the conv and delta-rule calls and
Qwen3_5Attention.forwardgains an override.generated/: regenerated by patchgen, never hand-edited. The NPU patch config imports from the GPUone and
Qwen3_5Moereuses the GatedDeltaNet patches from both throughname_map, so the changereaches four generated files. The MoE plumbing is inert: nothing there builds a plan, so
shared_prefix_planis alwaysNoneand shared-prefix training stays unsupported for MoE.docs/examples/qwen3_5.md: a Shared-prefix training section.veomni/ops/kernels/gated_delta_rule/_ascend/triton/utils.py:prepare_chunk_indices/prepare_lens/
prepare_chunk_offsetsare tensor-identity caches of size 3 / 1 / 1. With three cu_seqlens layoutsused in turn per layer they thrash and every sub-kernel pays a host sync (
.tolist()). Raised to16 / 8 / 16; plan-path GDN forward 11.2 -> 9.4 ms at 6.9k prefix with
n=4.Limitations, each enforced with a raise or a silent fall-back to the ordinary path:
pixel_values/pixel_values_videosare present, because placeholdertoken ids are shared across different media.
need a multi-round plan.
n; below roughly 1k-token prefixes the per-call overhead dominates.Checklist Before Submitting
ruff check,ruff format) and regeneratedgenerated/with patchgendocs/examples/qwen3_5.md— what it does,the config field, what a caller has to get right about micro-batch grouping, and the limitations above
tasks/training scripts moved or renamedgpu_unit_tests.ymlandnpu_unit_tests.ymlruff check/ruff format --check(0.13.2),patchgen --check,tests/special_sanity/check_device_api_usage.pyontasks,veomniandtestsSummary by CodeRabbit