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[Speculative Decoding] LiLiCorr: offline/streaming training and an optional calibration loss - #2589
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Included review availability: This review used your included allowance. Your plan provides up to 12 included reviews per hour; 10 remain after this review. 📝 WalkthroughWalkthroughLiLiCorr now supports offline training with reconstructed target logits and an optional candidate-distribution calibration loss. DFlash conversion retains the draft’s RoPE value when the target value is unset. Offline logit reconstruction casts captured hidden states to the language-model head’s weight dtype. ChangesLiLiCorr training
DFlash RoPE conversion
Priority: ⬇️ Low Estimated code review effort: 3 (Moderate) | ~25 minutes Change: Feature Sequence Diagram(s)sequenceDiagram
participant OfflineBatch
participant HFDFlashModel
participant DFlashBaseModelOutput
participant HFLiLiCorrModel
OfflineBatch->>HFDFlashModel: Provide captured final hidden states
HFDFlashModel->>DFlashBaseModelOutput: Reconstruct target logits when needed
DFlashBaseModelOutput->>HFLiLiCorrModel: Supply target logits
HFLiLiCorrModel->>HFLiLiCorrModel: Compute target-dependent loss terms
Suggested reviewers: Merge Risk: ⚪ Minimal · up to This change adds offline LiLiCorr training, an opt-in calibration loss that is off by default, and a fix for DFlash conversion when the target RoPE value is unset. No merge-blocking risk is evident, and online behavior is described as unchanged. 🚥 Pre-merge checks | ✅ 6✅ Passed checks (6 passed)
✨ Finishing Touches🧪 Generate unit tests (beta)
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Review comments at @CHANGELOG.rst:
- Line 93: Update the DFlash conversion changelog entry to state the user-facing
outcome: conversion succeeds for NoPE targets with an unset rope_theta. Remove
the explanation of how the issue occurred.
Review comments at @tests/unit/torch/speculative/plugins/test_hf_lilicorr.py:
- Line 327: Update the test’s _converted model setup to select all vocabulary
candidates, ensuring ground-truth tokens are covered before asserting positive
lilicorr_calibration at positive w_cal values.
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CHANGELOG.rstmodelopt/torch/speculative/config.pymodelopt/torch/speculative/plugins/hf_dflash.pymodelopt/torch/speculative/plugins/hf_lilicorr.pymodelopt_recipes/general/speculative_decoding/lilicorr.yamltests/unit/torch/speculative/plugins/test_hf_dflash.pytests/unit/torch/speculative/plugins/test_hf_lilicorr.py
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Codecov Report✅ All modified and coverable lines are covered by tests. Additional details and impacted files@@ Coverage Diff @@
## main #2589 +/- ##
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+ Coverage 69.19% 78.42% +9.23%
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Files 620 618 -2
Lines 69588 69610 +22
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+ Hits 48150 54591 +6441
+ Misses 21438 15019 -6419
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Reviewed with the Super-3.5 offline/streaming path in mind, since that's the configuration I've been running DFlash2 in. Checked out and ran the two test files locally: 118 pass. The One substantive concern, on the path this PR exists to enable. When That 3.6x looks harmless, but the materialized side is linear in vocabulary while the consumed side is independent of it. On the shipped LiLiCorr recipe ( Since the motivation is explicitly "targets too large to replicate per rank, where streaming is the only mode", this seems worth avoiding: the candidate ids are already in hand, so Two smaller things:
Minor, on observability: The NoPE fix and the |
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Thanks @yeyu-nvidia for the careful review and for running the probe. I pushed 50d6976, which addresses three of the four points. For the first one I'd like to keep the current behavior, for the reasons below. Full-vocabulary reconstruction. On this path the reconstructed tensor isn't the dominant cost. With the shipped recipe (
So the reconstruction is about 12% of the logits-sized buffers on this path. The column gather also costs more than it looks. dtype cast. Added. Offline forward test. Added KL for the logged value. Switched. |
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| out_hiddens = _maybe_apply_base_final_norm(out_hiddens, d, base_model_norm) | ||
| logits = base_model_lm_head(out_hiddens) | ||
| # A producer can store the hidden states in a wider dtype than the target's weights. | ||
| logits = base_model_lm_head(out_hiddens.to(base_model_lm_head.weight.dtype)) |
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[SUGGESTION] The dtype cast lands after _maybe_apply_base_final_norm, so the base final norm still runs in the producer's dtype against the target's weights.
This closes the lm_head matmul but not the norm immediately above it. HF RMSNorm survives it by accident — it upcasts to fp32 internally and self.weight * hidden.to(input_dtype) type-promotes bf16×fp64 — so the shipped Llama/Qwen path works. A final norm that does not upcast internally (an nn.LayerNorm, which goes straight into F.layer_norm with a bf16 weight against an fp32/fp64 input) still raises the same dtype error this fix targets, and base_model_norm_path is resolved by probing candidate paths rather than by asserting an RMSNorm.
There's also a small fidelity point: normalizing in fp64 and then truncating diverges from the online path, which norms in the target's dtype. Casting first makes offline bit-for-bit closer to online and makes the guarantee uniform across both modules:
out_hiddens = out_hiddens.to(base_model_lm_head.weight.dtype)
out_hiddens = _maybe_apply_base_final_norm(out_hiddens, d, base_model_norm)
logits = base_model_lm_head(out_hiddens)Not blocking — the configurations that ship today work either way.
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^ This nit-picking make sense to me, PTAL @mrusanovsky thanks!
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Done. The hidden state is now cast to the target's dtype before the final norm, so a pre-norm capture is normalized in the same dtype as online. The offline parity test also covers a pre-norm capture now.
Thanks!
| # The objective is the DFlash block loss plus three weighted terms: | ||
| # loss = dflash_loss + w_ce*CE + w_margin*hinge + w_pen*penalty |
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[SUGGESTION] This header is now the only LiLiCorr doc surface that still describes a three-term objective.
config.py, the hf_lilicorr.py module docstring, and the modify() log line all gained w_cal, but the published recipe still says "three weighted terms" with the pre-w_cal formula, and the file never mentions dflash_lilicorr_w_cal anywhere — including in the weights block around line 107 where w_ce/w_margin/w_pen are all spelled out explicitly.
That matters more here than in a normal comment-drift case: the all-or-nothing validation forces users to set the other three by hand, so this file is where people go to learn the knob names. A reader following the recipe has no way to discover the new term. Worth updating the formula and adding a commented-out dflash_lilicorr_w_cal: 0.0 next to the other weights so the opt-in is discoverable.
| cal_loss = ce_loss.new_zeros(()) | ||
| if self.dflash_lilicorr_w_cal > 0.0: | ||
| cal_loss = self._calibration_loss( | ||
| node_potentials=node_potentials, | ||
| candidate_ids=candidate_ids, | ||
| anchor_positions=anchor_positions, | ||
| target_logits=target_logits, | ||
| supervised=supervised, | ||
| denominator=denominator, | ||
| ) | ||
| loss = loss + self.dflash_lilicorr_w_cal * cal_loss |
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[SUGGESTION] With both w_pen > 0 and w_cal > 0, _candidate_target_logits runs twice on identical inputs.
Both branches pass the same candidate_ids / anchor_positions / target_logits, so the position arithmetic, the [blocks, slots, k] advanced-index gather, and the .float() copy are all repeated. The result is detach()ed teacher data, so hoisting it is behavior-preserving:
candidate_target_logits = None
if self.dflash_lilicorr_w_pen > 0.0 or self.dflash_lilicorr_w_cal > 0.0:
candidate_target_logits = self._candidate_target_logits(
candidate_ids=candidate_ids,
anchor_positions=anchor_positions,
target_logits=target_logits,
requested_by="dflash_lilicorr_w_pen/_w_cal",
)then have _distractor_penalty / _calibration_loss take the gathered tensor instead of re-deriving it.
Low priority on two counts: the two terms are documented as alternatives so both-on is an unusual config, and the gather is only ~61k elements on the shipped recipe. The nicer part is that it would make the two terms visibly share one teacher tensor rather than two independently-derived ones. If you keep it as-is, requested_by stays more precise in the error message, which is a fair trade.
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Claude review passed — no blocking issues found. LGTM
Findings: CRITICAL 0 · IMPORTANT 0 · SUGGESTION 4
Full-coverage review (8 files changed, all reviewed: modelopt/torch/speculative/{config.py,plugins/hf_dflash.py,plugins/hf_lilicorr.py,plugins/modeling_dflash.py}, the recipe, both test files, CHANGELOG.rst). Scope was the plain /claude review trigger, so no restriction applied.
What I verified
Calibration loss math. KL(p|K || q) is correct: non-negative with a floor at 0, the p log p half is constant because _candidate_target_logits detaches, so the gradient w.r.t. the head potentials is exactly the cross-entropy's — the switch from CE to KL is a pure reporting change, matching the claim in the reply to @yeyu-nvidia. Both the target logits and the node potentials are upcast to fp32 before any softmax, the supervised mask and denominator match what L_ce and L_pen use, and cal_loss = ce_loss.new_zeros(()) keeps the metric present with the right dtype/device when the term is off.
Target-logit dataflow, online vs offline. Traced end to end. _candidate_target_logits indexes [B, S, V] in both modes, and the shapes agree: online populates logits=raw_outputs.logits unconditionally (so w_cal is not accidentally gated on dflash_self_logit_distillation — LiLiCorr reads base_outputs.logits directly at hf_lilicorr.py:256, not the gated base_logits parameter at hf_dflash.py:1154), and offline reconstructs the same shape. Positions are clamped to target_seq_len - 1, so the unsupervised slots masked out downstream cannot index out of bounds. I also checked the streaming producer: hf_streaming_dataset.py:595 indexes the plane axis, not the position axis, so it yields the full [seq, hidden] the reconstruction needs — streaming is genuinely wired up, not just offline.
Mode/state and backward compatibility. dflash_lilicorr_w_cal is a new DFlashConfig field with default=0.0, ge=0.0, allow_inf_nan=False, so existing modelopt_state round-trips to the off value with no migration needed and no key renames. HFDFlashModel._needs_base_logits returns bool(self.dflash_self_logit_distillation), byte-for-byte the prior need_logits argument, so EAGLE and DSpark (which passes need_logits=True unconditionally on its own path) are untouched. super()._needs_base_logits through the property works as intended. The all-or-nothing validation correctly admits w_cal as the sole nonzero weight, so the reranker still receives gradient and cannot be exported randomly initialized.
Export. No train/serve RoPE divergence from the NoPE fix: _get_rope_theta in hf_spec_export.py already treats a None base value as absent and falls back to the draft's own theta (_get_rope_theta(base_config, _get_rope_theta(draft_config, 1000000.0))), which is exactly the value the draft now trains with. I also confirmed offline conversion deletes only the base layers — base_model_norm_path and base_model_lm_head_path survive — so the reconstruction path has the modules it needs.
Tests. test_offline_forward_matches_online is a real end-to-end check, not a property assertion: the eval-mode pass-through at hf_dflash.py:1005 makes output_hidden_states=True return the base model's hidden states, hidden_states[-1] is post-final-norm for Llama (consistent with base_hidden_prenorm defaulting falsy in the hand-built dict), and asserting on the penalty and calibration terms exercises both consumers. Together with the float64 storage it covers the gap @yeyu-nvidia raised. CodeRabbit's earlier note about candidate coverage is addressed — the test uses topk=VOCAB_SIZE with a comment explaining why.
Suggestions (none blocking)
modeling_dflash.py:153— the dtype cast sits after_maybe_apply_base_final_norm, so the final norm still runs in the producer's dtype. HFRMSNormsurvives by upcasting internally, but a norm that doesn't (nn.LayerNorm) would still hit the error this fix targets. Casting first also makes offline numerically match online.hf_dflash.py:469— thebase_val is Noneskip is justified forrope_thetabut applies torope_type/rope_interleavedtoo, suppressing the override warning for them. Not a live bug (I traced both: inert in the draft rotary and in the exporter); the guard is just broader than the invariant it documents.lilicorr.yaml:11-12— now the only LiLiCorr doc surface still describing a three-term objective, and it never namesdflash_lilicorr_w_cal, so the opt-in isn't discoverable from the published recipe.hf_lilicorr.py:451-461—_candidate_target_logitsis computed twice when bothw_penandw_calare positive.
Risk
Low. The new term is off by default, the offline/online equivalence is now pinned by a test, and the two bug fixes are narrow with no export-side fallout. The one behavioral widening — LiLiCorr no longer refusing offline — fails loudly rather than silently if the saved data lacks base_model_hidden_states.
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lgtm, approve to unblock
…l calibration loss - LiLiCorr no longer refuses offline/streaming training. HFDFlashModel gains a _needs_base_logits property; LiLiCorr overrides it when a term reads the target's logits, and the offline path reconstructs them from the captured final hidden state. - Add dflash_lilicorr_w_cal (default 0.0, off): a cross-entropy from the target's distribution renormalized over each slot's candidates onto the reranker's candidate distribution, usable in place of the distractor penalty. The penalty's candidate-logit gather moves into a helper both terms share. - Fix DFlash conversion on a target whose config declares rope_theta but leaves it None (e.g. a NoPE model): the draft keeps its own RoPE base instead of inheriting None. Signed-off-by: mrusanovsky <mrusanovsky@nvidia.com>
…alibration loss test Signed-off-by: mrusanovsky <mrusanovsky@nvidia.com>
…calibration as a KL Cast the captured final hidden state to the lm_head weight dtype before reconstructing target logits offline, cover the offline path with a forward test that matches the online loss, and report the calibration term as KL(p|K || q), whose gradient is identical to the cross-entropy's. Signed-off-by: mrusanovsky <mrusanovsky@nvidia.com>
Offline reconstruction now casts to the target's dtype before re-applying the final norm, so a pre-norm capture is normalized in the same dtype online training uses. The offline parity test also covers a pre-norm capture. Signed-off-by: mrusanovsky <mrusanovsky@nvidia.com>
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Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Signed-off-by: h-guo18 <67671475+h-guo18@users.noreply.github.com>
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@mrusanovsky Merged main into this branch to pick up the CI fixes (#2666 for tf_min, #2649 for the gpt-oss example). Please |
…s reads it, family-wide DSpark already projected its teacher this way; the rest of the DFlash family did not. DFlash's self-logit distillation (on by default) rebuilt [B, seq, vocab] base logits every step -- offline/streaming by running the whole captured final hidden through lm_head, online through the CausalLM forward -- then gathered the N * block_size rows it uses out of them. LiLiCorr's distractor penalty read k candidate logits per slot out of that same full tensor. Now there is one way in: - DFlashBaseModelOutput carries the base final hidden (and whether it was captured before the final norm), never logits built from it; from_offline_dict loses need_logits, defer_lm_head and base_model_lm_head. - HFDFlashModel._base_outputs builds it for the whole family: replayed offline, and online through the inner model only, as DSpark and Domino already did, so text batches never run the base lm_head. Multimodal batches still need the top-level forward and keep its logits as the teacher, since hidden_states[-1] is not lm_head's input on every transformers version (Qwen3-VL on 4.57). - HFDFlashModel._teacher_logits gathers the requested rows, re-applies the base final norm when the capture was pre-norm, and projects just those rows -- or, given token_ids, just those vocab entries, which is all LiLiCorr's penalty needs. DFlash KD (and DFlash2 through the shared loss), DSpark and LiLiCorr all read the teacher through it; _compute_loss takes it as a DFlashBaseModelOutput instead of a logits tensor. Domino reads no base distribution and is unchanged. One behavior change: online, the teacher used to be the CausalLM forward's logits, which for Gemma-family bases include final_logit_softcapping; it is now lm_head on the final hidden, which is what offline/streaming training and DSpark have always used. Bases without softcapping get the same teacher up to matmul rounding. Tested: tests/unit/torch/speculative 369 passed on transformers 5.12 and the affected files 156 passed on 4.57; the speculative GPU tests 15 passed. New tests pin the teacher against the full-sequence projection (post- and pre-norm capture, token_ids, handed-over logits) and that a training step never feeds lm_head the whole sequence; each fails when its property is broken. On JHB, Gemma-4-E4B DSpark streaming for 80 steps against the same config before this commit: fwd+bwd 374.7 ms vs 373.3 / 376.7, mean loss over steps 21-45 2.6541 vs 2.6543 / 2.6545, and the loss spikes this config shows at steps 46-55 land on the same steps within the controls' range. Rebased over #2589, which let LiLiCorr train offline by rebuilding full-sequence target logits whenever _needs_base_logits says a term reads them. Here the target is read per row instead: _candidate_target_logits, which #2589 shares between the distractor penalty and its new calibration term, projects just the candidates through _teacher_logits; offline, _needs_base_logits now only makes the entry fail early when the batch carries neither logits nor the final hidden; and #2589's cast of a wider-dtype captured hidden to lm_head's dtype moves into _teacher_logits, still ahead of the final norm. #2670 dropped the Qwen3-VL position-id workaround, so the multimodal branch passes position_ids through as main does. Signed-off-by: h-guo18 <67671475+h-guo18@users.noreply.github.com> Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
…her per row offline only Narrows 576b312 to offline/streaming training, where the full-sequence logits were ours to avoid: from_offline_dict used to run the whole captured final hidden through lm_head. That part stays -- the hidden is carried unprojected and _teacher_logits projects just the rows a loss reads -- and online training goes back to the base forward each model had before: - DFlash (and with it DFlash2 and LiLiCorr) runs the CausalLM forward again and hands its logits over as the teacher, which _teacher_logits now only gathers from. - DSpark keeps its own inner-model call with lm_head applied per row, as before. - LiLiCorr reads its candidate logits through _teacher_logits, which online just gathers them from the handed-over logits; offline (possible since #2589) it projects only those candidates from the captured hidden. Gone with it: the shared online helper (_base_outputs), and the one behavior change 576b312 made -- the online KD teacher again includes Gemma's final_logit_softcapping, as before. Tested: tests/unit/torch/speculative 368 passed on transformers 5.12 and the affected files 238 passed on 4.57; the speculative GPU tests 15 passed. The offline full-sequence test fails when _teacher_logits is made to project the whole sequence. The streaming path is the one JHB job 738406 validated for 576b312 and is unchanged here. Signed-off-by: h-guo18 <67671475+h-guo18@users.noreply.github.com> Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
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What does this PR do?
Type of change: new feature + bug fix
HFLiLiCorrModel.modify()refuseddflash_offlinebecause the distractor penalty reads the target's logits. The offline pathcan already reconstruct those from the captured final hidden state (self-logit distillation
and DSpark use it), so the refusal is replaced by a
_needs_base_logitsproperty onHFDFlashModel, which LiLiCorr overrides when a term reads the target distribution. Thismatters for targets too large to replicate per rank, where streaming is the only mode.
dflash_lilicorr_w_cal(default0.0, off). An optional cross-entropy from the target'sdistribution renormalized over each slot's
kcandidates onto the reranker's candidatedistribution, usable in place of the distractor penalty. The penalty's candidate-logit
gather moves into a helper both terms share.
rope_thetabut leaves itNone(e.g. a NoPE model). The enforced RoPE inheritance wrote
Noneonto the draft, which failedwhen the draft rotary was built. The draft now keeps its own RoPE base.
Usage
Testing
CPU unit tests (
test_hf_lilicorr.py,test_hf_dflash.py, 118 pass):w_cal = 0is a no-op andw_cal > 0enters the loss with its absolute weight;distribution already matches the target's;
rope_theta=Noneconverts and the draft rotary builds (fails withoutthe fix).
Offline training on Qwen3-8B: 200 steps on hidden states saved with
compute_hidden_states_hf.py --aux-layers dflash, with no target model loaded. The targetlogits the calibration loss needs are rebuilt from the saved final hidden state.
Calibration vs. penalty, Qwen3-8B, both from scratch on the published LiLiCorr recipe,
global batch 64, 10k steps, differing only in the loss weights (
0.25 / 0 / 0.25vs0.05 / 0 / 0.45). Output throughput (tokens/s), SGLang, both drafters served with the sameserving config on the same H100 node, back to back in one allocation per benchmark; T=1 is
the mean of 3 sampling seeds:
The term is opt-in and off by default.
Before your PR is "Ready for review"
w_caldefaults to 0; online behavior unchanged)CONTRIBUTING.md: N/ASummary by CodeRabbit