Add TV (total-variation) loss option for the MTP draft head#56
Open
ElliotXinqiWang wants to merge 1 commit into
Open
Add TV (total-variation) loss option for the MTP draft head#56ElliotXinqiWang wants to merge 1 commit into
ElliotXinqiWang wants to merge 1 commit into
Conversation
MTP draft heads are trained with cross-entropy, but for speculative decoding the
rejection-sampling acceptance rate is accept_rate = 1 - d_TV(p_main, q_draft), so
total variation is the *direct* training target (CE only bounds it via Pinsker).
This adds an opt-in TV loss for the MTP head:
- TransformerConfig.mtp_loss_type ('ce' default | 'tv') + mtp_tv_chunk (int),
auto-exposed as --mtp-loss-type / --mtp-tv-chunk via ArgumentGroupFactory.
- GPTModel MTP loss: when mtp_loss_type=='tv', compute per-token
1 - sum_v min(softmax(p_main).detach(), softmax(q_draft)); p_main is the main
model's next-token distribution (detached; hidden states rolled to align with
the MTP target). Drop-in for the CE branch -> reuses MTPLossAutoScaler path.
- mtp_tv_chunk>0 chunks the vocab-sum over the sequence dim and gradient-
checkpoints each chunk, so backward memory is ~1 chunk instead of O(T*V),
letting TV fit alongside full-backbone joint training.
Limitation: TV needs the full vocab per rank for sum_v min(p,q), so it requires
tensor-model-parallel size 1 (raises NotImplementedError for TP>1).
Ref: "Breaking Entropy Bounds: Accelerating RL Training via MTP with Rejection
Sampling" (Bebop).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.This suggestion is invalid because no changes were made to the code.Suggestions cannot be applied while the pull request is closed.Suggestions cannot be applied while viewing a subset of changes.Only one suggestion per line can be applied in a batch.Add this suggestion to a batch that can be applied as a single commit.Applying suggestions on deleted lines is not supported.You must change the existing code in this line in order to create a valid suggestion.Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be applied on multi-line comments.Suggestions cannot be applied while the pull request is queued to merge.Suggestion cannot be applied right now. Please check back later.
PR: Add TV (total-variation) loss option for the MTP draft head
Repo:
radixark/Megatron-LMBase:miles-main@36ceb4fc0Branch:feat/mtp-tv-lossFiles:
megatron/core/models/gpt/gpt_model.py(+106),megatron/core/transformer/transformer_config.py(+13)Motivation
The MTP draft head is trained with cross-entropy. But for speculative decoding the
rejection-sampling acceptance rate is
accept_rate = 1 - d_TV(p_main, q_draft), sototal variation is the direct objective; CE only bounds TV via Pinsker. Optimizing TV
directly heals the draft head faster (esp. when the main model has drifted from the head's
training distribution — e.g. an official MTP head grafted onto an SFT'd / RL'd main model).
Ref: "Breaking Entropy Bounds: Accelerating RL Training via MTP with Rejection Sampling" (Bebop).
What it adds
TransformerConfig.mtp_loss_type('ce'default |'tv') andmtp_tv_chunk: int = 0.Auto-exposed as
--mtp-loss-type/--mtp-tv-chunk(ArgumentGroupFactory) and mapped backvia
core_transformer_config_from_args— no manual arg wiring.GPTModel: whenmtp_loss_type == 'tv', the MTP-loss branch computes per token1 - Σ_v min(softmax(p_main).detach(), softmax(q_draft)).p_mainis the main model'snext-token distribution (fully detached; main hidden states are rolled to align with the
MTP target, mirroring the existing label shift). Produces
mtp_lossin the same[b, s]layout as the CE branch, so the existing
MTPLossAutoScaler/ scaling / logging path isunchanged — it's a drop-in alternative to the CE branch.
mtp_tv_chunk > 0chunks the vocab-sum over the sequence dim and gradient-checkpointseach chunk (recomputes softmax in backward), so backward memory is ~one chunk instead of
O(T·V) — this is what lets TV fit alongside full-backbone (joint) training.
Limitation
TV needs the full vocabulary per rank for
Σ_v min(p,q), so it requirestensor-model-parallel size 1 (raises
NotImplementedErrorfor TP>1). Vocab-parallel TV(all-reduce of the min-overlap) is possible future work.
Validation
Trained the Qwen3.5-35B-A3B grafted MTP head with
--mtp-loss-type tv --mtp-tv-chunk 512(freeze + joint), 300 steps each, on the miles RL stack;
mtp_loss(= mean TV) ~0.13–0.15,no OOM with chunking, spec accept-rate tracked as expected. CE path unchanged (default).
Notes for reviewers
mtp_loss_type='ce').--mtp-tv-chunkonly affects the TV path; ignored for CE.reset_argcould surface--mtp-loss-typeexplicitly.