Big-GCVAE

Big GCVAE: decision-making with adaptive transformer model for failure root cause analysis in semiconductor industry

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Abstract

Pre-trained Language Models (LMs) have gained significant attention in the field of Natural Language
Processing (NLP), particularly for tasks such as text summarization, generation, and question answering.
The success of LMs can be attributed to the attention mechanism introduced in Transformer models,
which have surpassed the performance of traditional Recurrent Neural Network Models (e.g., LSTM)
in modeling sequential data. In this paper, we harness the power of the attention mechanism in pre-trained
causal language models for the downstream task of Failure Analysis Triplet Generation (FATG), which
 involves generating a sequence of text-encoded steps for analyzing  defective components in the
semiconductor industry. In particular, we perform extensive comparative analysis of various transformer
 models for the FATG task is performed to find that BERT-GPT-2 Transformer (Big GCVAE),
 finetuned on a proposed Generalized-Controllable Variational AutoEncoder loss (GCVAE) exhibits
superior performance compared to other transformer models. Specifically, we observe that fine-tuning
 the Transformer style BERT-GPT2 on the GCVAE loss yields a smooth and
interpretable latent representation with high quality generative performance across all scoring
metrics.

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How to use

• Clone repo: git clone https://github.com/FA4-0/Big-GCVAE
• Run training and evaluation on example data either using Bash Slurm file on HCP or:

  python pretrainer_gcvae.py \
        --model_type_enc bert-base-uncased \
        --model_name_or_path_enc bert-base-uncased \
        --model_type_dec gpt2-medium \
        --model_name_or_path_dec gpt2-medium \
        --do_eval \
        --max_seq_length 128 \
        --per_gpu_train_batch_size 1 \
        --learning_rate 5e-5 \
        --num_train_epochs 1.0 \
        --output_dir result/ \
        --eval_dir evaluation/ \
        --overwrite_output_dir \
        --length_weighted_loss \
        --fp16 \
        --fp16_opt_level O2 \
        --gradient_accumulation_steps 1 \
        --seed 42 \
        --do_lower_case \
        --encoder_decoder_sep \
        --warmup_steps 100 \
        --logging_steps 100 \
        --save_steps 100 \
        --evaluate_during_training \
        --adam_epsilon 1e-8 \
        --weight_decay 0.05 \
        --max_grad_norm 1.0 \
        --return_token_type_ids \
        --dim_target_kl 1.0 \
        --ratio_zero .5 \
        --ratio_increase .25 \
       	--temperature 1.9 \
       	--top_p 0.95 \
       	--top_k 10 \
       	--num_interpolation_steps 1 \
       	--use_variational_loss \
       	--vae_model_name gcvae \
       	--mmd_type mmd \
        --beta 5.0 \
        --gamma 500.0 \
        --init_kld 1 \
        --init_bce 0.01 \
        --init_mmd 0.01 \
        --max_steps -1

Results

• Evaluation loss:

Model	Evaluation	Reconstruction	KL divergence
	loss	loss
Big VAE	1.10	128.34	6.49
Big ControlVAE	1.18	1.10	9.85
Big GCVAE†	1.18	1.09	8.23
Big GCVAE‡	1.11	1.09	3.80

Performance evaluation of Big GCVAE models and 	its derivatives. Both Big GCVAE† (Maximum Mean Discrepancy) and 
Big GCVAE‡ (Squared Mahalanobis) have the lowest reconstruction loss compared to Big VAE (Li et al., 2020).

• Evaluation metrics:

Model	BLEU-1	BLEU-3	MET.	ROUGE-1	ROUGE-L	LESE-1	LESE-3
				F1-score	F1-score	F1-score	F1-score
GPT2-M	21.26	15.47	26.74	29.15	26.56	19.41	8.55
Big VAE	21.87	16.15	26.64	31.23	28.73	21.46	9.57
Big ControlVAE	22.25	16.38	27.10	31.55	28.89	21.54	9.58
Big GCVAE†	22.09	16.25	27.01	31.17	28.58	21.36	9.50
Big GCVAE‡	22.53	17.00	27.63	31.71	29.08	21.79	9.70

Model comparison on BLEU (Papineni et al., 2002), ROUGE-1 & L (Lin, 2004), METEOR (MET.) (Banerjee
and Lavie, 2005) and LESE (Ezukwoke et al., 2022b). Higher values (in bold-blue) is preferred for 
all metric except Lev-n	(average n-gram Levenshstein distance). Big GCVAE‡ performs better across
all evaluation metric. Observe the approximately 3-point increase in performance of the generative
strength for ROUGE-1 and LESE-1 and a comparable increase for the triplet evaluations.

• Latent representation

2D Latent representation space (top) and t-SNE Embedding (bottom). Observe the quality of clusters in the latent
space for Big GCVAE† (best), Big VAE (second best) and Big GCVAE‡ (less fuzzy). The latent space of Big ControlVAE
is the most fuzzy with overlapping cluster of densities in t-SNE Embedding space.

Citation

@article{Ezukwoke2024,
 author = {Ezukwoke, Kenneth and Hoayek, Anis and Batton-Hubert, Mireille and Boucher, Xavier and Gounet, Pascal and Adrian, J{\'e}r{\^o}me},
 title = {Big GCVAE: decision-making with adaptive transformer model for failure root cause analysis in semiconductor industry},
 journal = {Journal of Intelligent Manufacturing},
 year = {2024},
 month = apr,
 day = {02},
 issn = {1572-8145},
 doi = {10.1007/s10845-024-02346-x},
 url = {https://doi.org/10.1007/s10845-024-02346-x}
}