story-snippets.ipynb

Notebook for the code snippets in Mamba: A shallow dive

finetune.py

This code is setting up and executing a training process for a 🐍 mamba using PyTorch, Hugging Face's Transformers, Accelerate, and W&B (Weights & Biases). Here's a breakdown of the key components and steps:

1. Imports and Initial Setup: The script imports necessary libraries and modules such as PyTorch, Transformers, Accelerate, and W&B. It also imports a specific model (MambaLMHeadModel) from mamba_ssm.models.mixer_seq_simple.

2. Accelerator and W&B Initialization: The Accelerator is initialized for distributed and mixed precision training, and W&B (a tool for tracking and visualizing machine learning experiments) is initialized in a disabled mode.

3. Model Loading and Modification:

   • Model Path and Loading: The model is loaded from a specified path using the MambaLMHeadModel.from_pretrained method.
   • Monkey Patching forward Method: The forward method of the model is modified (monkey patched) to include a custom loss function using cross-entropy loss. This modification allows the model to compute loss if labels are provided.
   • Tokenizer Initialization: A tokenizer (AutoTokenizer) is loaded and configured with padding and end-of-sequence tokens.

4. Token Embeddings Update: The token embeddings in the model are resized to accommodate additional tokens and special tokens added to the tokenizer.

5. Dataset Preparation:

   • Loading Dataset: A dataset is loaded from a specified source.
   • Tokenization: The dataset is tokenized using the prepared tokenizer. The tokenize function processes text into a format suitable for the model.
   • Multithreading: Tokenization is done in a multithreaded manner using os.cpu_count() to utilize all available CPU cores.

6. Data Collation: A collate function is defined to transform a list of tokenized elements into a format suitable for batch processing during training.

7. Training Configuration:

   • Batch Size, Gradient Accumulation, and Learning Rate: Key hyperparameters for training, such as batch size, gradient accumulation steps, and learning rate, are set.
   • TrainingArguments: The TrainingArguments class from Hugging Face is used to configure various aspects of training like batch sizes, logging, evaluation strategy, and learning rate scheduler.

8. Trainer Setup: A Trainer object is created with the model, tokenizer, training arguments, data collator, and datasets for training and evaluation.

9. Model Training: Finally, the model is trained using the Trainer.train() method.

Overall, the script is a comprehensive setup for training a large language model (MambaLMHeadModel) with specific configurations and customizations for tokenization, data collation, and training arguments.