ScratchFormers

implementing transformers from scratch.

Attention is all you need.

Modules

• einops starter

• attentions

  • multi-head causal attention
  • multi-head cross attention
  • multi-head grouped query attention (torch + einops)

• positional embeddings

  • rotary positional embeddings (RoPE)

• Low-Rank Adaptation (LoRA)

  • implementing LoRA based on this wonderful tutorial by Sebastian Raschka
  • finetuning LoRA adapted deberta-v3-base on IMDb dataset

• KV Cache

  • implemented KV Cache that supports RoPE
  • Works and verified with Llama (RoPE + GQA)

Models

• LlaMA

  • for process, check building_llama_complete.ipynb
  • model implementation
  • inference (used SmolLM2-135M-Instruct which is based on LlaMA architecture but super small) code kaggle
  • super cool resource: LLMs From Scratch by Sebastian Raschka
  • added KV Caching support: llama_with_kv_caching.ipynb

• simple Vision Transformer

  • for process, check building_ViT.ipynb
  • model implementation
  • used mean pooling instead of [class] token

• GPT2

  • for process, check buildingGPT2.ipynb
  • model implementation
  • built in such a way that it supports loading pretrained openAI/huggingface weights gpt2-load-via-hf.ipynb
  • for my own custom trained causal LM, checkout shakespeareGPT which is although a bit more like GPT-1.

• OpenAI CLIP

  • implemented ViT-B/32 variant
  • for process, check building_clip.ipynb
  • inference req: install clip for tokenization and preprocessing: pip install git+https://github.com/openai/CLIP.git
  • model implementation
  • zero-shot inference code
  • built in such a way that it supports loading pretrained openAI weights and IT WORKS!!!
  • My lighter implementation of this using existing image and language models trained on Flickr8k dataset is available here: liteCLIP

• Encoder Decoder Transformer

  • for process, check building_encoder-decoder.ipynb
  • model implementation
  • src_mask for encoder is optional but is nice to have since it is used to mask out the pad tokens so attention is not considered for those tokens.
  • used learned embeddings for position instead of sin/cos as per the OG.
  • I trained a model for multilingual machine translation.
    • Translates english to hindi and telugu.
    • change: single encoder & decoder embedding layer since I used a single tokenizer.
    • for the code and results check: shreydan/multilingual-translation

• BERT - MLM

  • for process of masked language modeling, check masked-language-modeling.ipynb
  • model implementation
  • simplification: for pre-training no use of [CLS] & [SEP] tokens since I only built the model for masked language modeling and not for next sentence prediction.
  • I trained an entire model on the wikipedia dataset, more info in shreydan/masked-language-modeling repo.
  • once, pretrained the MLM head can be replaced with any other downstream task head.

• ViT MAE

  • Paper: Masked autoencoders are scalable vision learners
  • model implementation
  • for process, check: building-vitmae.ipynb
  • Quite reliant on the original code released by authors.
  • Only simplification: No [CLS] token so used mean pooling
  • The model can be trained 2 ways:
    • For pretraining: the decoder can be thrown away and the encoder can be used for downstream tasks
    • For visualization: can be used to reconstruct masked images.
  • I trained a smaller model for reconstruction visualization: ViTMAE on Animals Dataset

• UNETR

  • 3D segmentation model for medical domain
  • Transformer based architecture, more info
  • process: building_unetr

Requirements

einops
torch
torchvision
numpy
matplotlib
pandas

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Psalm 46:1