手把手教你用PyTorch从零训练自己的大模型（下）

Article Summary

Building and Training a Large Language Model (LLM) with PyTorch from Scratch

The article details a step-by-step guide on how to build and train a large language model using PyTorch. It covers the architecture components such as the feedforward network, layer normalization, add-and-norm, encoder and decoder blocks, and projection layer. The process involves implementing these components in classes and defining their forward pass methods.

Step 6: Feedforward Network, Layer Normalization, and Add-and-Norm

• Feedforward Network: Utilizes a two-layer linear transformation to capture the features of embedding vectors, with ReLU activation and dropout to introduce non-linearity and mitigate overfitting.
• Layer Normalization: Ensures an even distribution of embedding vector values across the network for stable learning, incorporating learnable gamma and beta parameters.
• Add-and-Norm: Combines skip connections and layer normalization to preserve early layer features for deep network use and reduce gradient vanishing during backpropagation. Applied twice in the encoder and thrice in the decoder.

Step 7: Encoder Block and Encoder

• Encoder Block: Central to the encoder, featuring multi-head attention mechanisms and the feedforward network, along with two add-and-norm units to regulate the information flow. The structure is repeated six times for enhanced learning.
• Encoder: Assembles a sequence of encoder blocks, ensuring smooth information flow and high-quality encoding output, laying the foundation for subsequent decoding.

Step 8: Decoder Block, Decoder, and Projection Layer

• Decoder Block: Comprises masked multi-head attention, standard multi-head attention, and a feedforward network, along with three add-and-norm units to optimize information processing. Repeated six times to enhance decoding capabilities.
• Decoder: Builds upon decoder blocks, stacking them to facilitate continuous information processing and feature extraction, culminating in decoder output.
• Projection Layer: Transforms the final decoder output through a linear layer and a softmax function to produce a probability distribution over the vocabulary, selecting the highest probability token as the prediction.

Step 9: Assembling the Transformer Model

The Transformer model is assembled by initializing instances of all component classes, defining encoding, decoding, and projecting functions to handle encoding tasks, generate decoder outputs, and map outputs to the vocabulary for predictions, respectively.

Step 10: Training and Validating the LLM

The model training is executed using a DataLoader constructed earlier. GPU training is recommended due to the size of the dataset, with checkpointing after each epoch. Validation uses a smaller DataLoader and relies on the encoder output calculated once, appending newly generated tokens to the decoder input until the [SEP] token is predicted.

Step 11: Constructing and Testing the Model for New Translation Tasks

The "malaygpt" function is designed for English to Malay translation, taking user input and producing the corresponding translation. The function processes input text, encodes it, and iteratively predicts tokens until the [SEP] token is reached, returning the translated text.

Recommended Reading

The book "PyTorch Deep Learning in Practice" is recommended for Python programmers interested in deep learning, providing practical guidance on building neural networks with PyTorch without requiring previous experience with the framework.