BERT-base uncased model fine-tuned on SQuAD v1
This model was created using the nn_pruning python library: the linear layers contains 26.0% of the original weights.
The model contains 42.0% of the original weights overall (the embeddings account for a significant part of the model, and they are not pruned by this method).
With a simple resizing of the linear matrices it ran 2.44x as fast as the original model on the evaluation. This is possible because the pruning method lead to structured matrices: to visualize them, hover below on the plot to see the non-zero/zero parts of each matrix.
In terms of accuracy, its F1 is 87.71, compared with 88.5 for the original model, a F1 drop of 0.79.
Fine-Pruning details
This model was fine-tuned from the HuggingFace model checkpoint on SQuAD1.1, and distilled from the model csarron/bert-base-uncased-squad-v1 This model is case-insensitive: it does not make a difference between english and English.
A side-effect of the block pruning is that some of the attention heads are completely removed: 80 heads were removed on a total of 144 (55.6%). Here is a detailed view on how the remaining heads are distributed in the network after pruning.
Details of the SQuAD1.1 dataset
| Dataset | Split | # samples |
|---|---|---|
| SQuAD1.1 | train | 90.6K |
| SQuAD1.1 | eval | 11.1k |
Fine-tuning
-
Python:
3.8.5 -
Machine specs:
Memory: 64 GiB
GPUs: 1 GeForce GTX 3090, with 24GiB memory
GPU driver: 455.23.05, CUDA: 11.1
Results
Pytorch model file size: 355MB (original BERT: 420MB)
| Metric | # Value | # Original (Table 2) | Variation |
|---|---|---|---|
| EM | 80.03 | 80.8 | -0.77 |
| F1 | 87.71 | 88.5 | -0.79 |
Example Usage
Install nn_pruning: it contains the optimization script, which just pack the linear layers into smaller ones by removing empty rows/columns.
pip install nn_pruning
Then you can use the transformers library almost as usual: you just have to call optimize_model when the pipeline has loaded.
from transformers import pipeline
from nn_pruning.inference_model_patcher import optimize_model
qa_pipeline = pipeline(
"question-answering",
model="madlag/bert-base-uncased-squadv1-x2.44-f87.7-d26-hybrid-filled-v1",
tokenizer="madlag/bert-base-uncased-squadv1-x2.44-f87.7-d26-hybrid-filled-v1"
)
print("/home/lagunas/devel/hf/nn_pruning/nn_pruning/analysis/tmp_finetune parameters: 189.0M")
print(f"Parameters count (includes only head pruning, not feed forward pruning)={int(qa_pipeline.model.num_parameters() / 1E6)}M")
qa_pipeline.model = optimize_model(qa_pipeline.model, "dense")
print(f"Parameters count after complete optimization={int(qa_pipeline.model.num_parameters() / 1E6)}M")
predictions = qa_pipeline({
'context': "Frédéric François Chopin, born Fryderyk Franciszek Chopin (1 March 1810 – 17 October 1849), was a Polish composer and virtuoso pianist of the Romantic era who wrote primarily for solo piano.",
'question': "Who is Frederic Chopin?",
})
print("Predictions", predictions)