Notebook for the code snippets in Mamba: A shallow dive
This code is setting up and executing a training process for a ๐ mamba using PyTorch, Hugging Face's Transformers, Accelerate, and W&B (Weights & Biases). Here's a breakdown of the key components and steps:
-
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
) frommamba_ssm.models.mixer_seq_simple
. -
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. -
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: Theforward
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.
- Model Path and Loading: The model is loaded from a specified path using the
-
Token Embeddings Update: The token embeddings in the model are resized to accommodate additional tokens and special tokens added to the tokenizer.
-
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.
-
Data Collation: A
collate
function is defined to transform a list of tokenized elements into a format suitable for batch processing during training. -
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.
-
Trainer Setup: A
Trainer
object is created with the model, tokenizer, training arguments, data collator, and datasets for training and evaluation. -
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.