TransFusion: End-to-End Transformer Acceleration via Graph Fusion and Pipelining
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it