Efficient 1D Grouped Convolution for PyTorch a Case Study: Fast On-Device Fine-Tuning for SqueezeBERT
Bibliographic record
Abstract
Grouped convolution has been observed to be an effective approximation for convolution in many DNN applications. For example, SqueezeBERT, which is a light and fast BERT language processing model, utilizes 1D grouped convolutions. Though SqueezeBERT is well-optimized for inference on edge devices, it suffers from poor memory management during fine-tuning (training). This results in longer fine-tuning time on resource-limited GPUs compared to the original BERT model, BERT-base, despite being specifically designed for edge devices. We study this behavior and show that this poor memory management originates from the use of 1D grouped convolutions in SqueezeBERT. We re-implement 1D grouped convolutions using fully-connected layers, addressing the poor memory allocation and data locality of 1D grouped convolutions. We show that our method is well-suited for edge devices with limited memory; further, it has a negligible effect on inference speed. When utilizing our method, we observe a 42 % reduction in fine-tuning time for SqueezeBERT on edge devices.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".