Have you seen the interactive explanation of the Transformer?

It's a really cool visualization of how the architecture works.

https://poloclub.github.io/transformer-explainer/

One of the best resources for SQL performance:

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Exporting the model from PyTorch to the universal ONNX format for independent inference 🚀

Deploying PyTorch models in production often requires installing a large framework and relying on a Python environment. The ONNX (Open Neural Network Exchange) format converts the computation graph into an intermediate binary format suitable for running on any device and programming language. We will export the PyTorch neural network and run its inference using the lightweight ONNX Runtime. 🛠

To export and run the neural network, we will install the PyTorch framework, the ONNX library, and the cross-platform ONNX Runtime engine.

pip install torch onnx onnxruntime

The packages for converting and high-performance execution of graphs have been successfully installed. ✅

We will write a Python script that creates a test PyTorch model, exports it to a .onnx file, and immediately performs a verification of the output.

import torch, torch.nn as nn, onnxruntime as ort, numpy as np

model = nn.Sequential(nn.Linear(10, 5), nn.ReLU())
x = torch.randn(1, 10)
torch.onnx.export(model, x, "model.onnx", input_names=["input"], output_names=["output"])

session = ort.InferenceSession("model.onnx")
res = session.run(None, {"input": x.numpy()})
print("ONNX Output shape:", res[0].shape)

The model graph has been successfully serialized into a binary file, and the runtime performed the prediction without involving PyTorch. 📦

# verification (checks the correctness and structure of the saved ONNX model)

python3 -c "import onnx; model = onnx.load('model.onnx'); onnx.checker.check_model(model); print('ONNX Model Status: Valid')"

Expected output: ONNX Model Status: Valid

# cleanup (deletes the generated model file and cleans up binaries)

rm -f model.onnx

Converting neural networks to ONNX allows you to decouple inference from Python and run models in C++, Rust, Go, or directly in a web browser. Be sure to specify the names of the input and output tensors when exporting to simplify integration with the service. 💻

#PyTorch #ONNX #MachineLearning #DeepLearning #AI #DevOps

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One of the key moments when I truly understood how transformers work: 🧠✨

"Stop thinking of a transformer as a conveyor belt, where each layer transforms the output of the previous one."

"Start thinking of it as a residual flow." 🌊

Each block in a transformer has a residual connection that adds the input of the block to its output:

x' = f(x) + x

Because of this addition, the attention mechanism or MLP within the layer – the function f in the formula above – actually does NOT transform the input. Instead, it calculates the information that needs to be ADDED to the input before passing it to the next block! ➕

Imagine the main part of the transformer as a shared whiteboard. 📝 Each block reads what it needs from it and adds its own notes. All changes are additions.

Furthermore, layers can exchange information over distances. A block in the first layer can write information, and a block in the fifth layer can read it, even though there is no direct connection between them. 🔗

I owe these ideas to an older article by Anthropic about the architecture of transformers:
transformer-circuits.pub/2021/framework

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