TransFusion: End-to-End Transformer Acceleration via Graph Fusion and Pipelining
Bibliographic record
Abstract
Transformer acceleration has increasingly emphasized local fusion within isolated submodules, such as multi-head attention (MHA) and softmax.However, as Transformer models continue to scale in both depth and context length, such fragmented optimizations fail to address end-to-end inefficiencies across the full encoder/decoder stack.This paper presents TransFusion, a comprehensive framework for end-to-end Transformer layers, including QKV projections, MHA, LayerNorm, and FFN, as structured Einsum Cascades, enabling precise modelling of data dependencies and execution order.TransFusion introduces DPipe, a unified graph-based scheduler that partitions the Einsum-centric directed acyclic graph (DAG) and applies latency-aware pipelining across hardware hierarchies using dynamic programming (DP).To enable scalable execution under strict memory budgets, TransFusion integrates TileSeek, a Monte Carlo Tree Search (MCTS)-based tiling search algorithm that balances buffer reuse and system constraints.Evaluated across both cloud and edge architecture, TransFusion achieves up to an average of 1.6× speedup on cloud and 2.2× on edge over the prior state-of-the-art, FuseMax, by jointly optimizing inter-layer data reuse, intra-layer pipelining, and operator scheduling.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".