BitNet

PyTorch Implementation of the linear methods and model from the paper "BitNet: Scaling 1-bit Transformers for Large Language Models"

Paper link:

BitLinear = tensor -> layernorm -> Binarize -> abs max quantization -> dequant

"The implementation of the BitNet architecture is quite simple, requiring only the replacement of linear projections (i.e., nn.Linear in PyTorch) in the Transformer. " -- BitNet is really easy to implement just swap out the linears with the BitLinear modules!

NEWS

BitNet Transformer has been trained using the train.py file that trains on enwiki8 a small 1gb dataset of wikipedia: HERE IS THE LINK
New Iteration 🔥 There is an all-new iteration from the paper "The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits", we're implementing it now. Join the Agora discord and contribute! Join Here
New Optimizations The first BitLinear has been optimized and we now have a Bit Attention BitMGQA That implements BitLinear into the attention mechanism. Multi Grouped Query Attention is also widely recognized as the best attention for its fast decoding and long context handling, thanks to Frank for his easy to use implementation!

Appreciation

Dimitry, Nullonix for analysis and code review and revision
Vyom, for providing 4080 to train!

Installation

pip install bitnet

Usage:

`BitLinear`

Example of the BitLinear layer which is the main innovation of the paper!

import torch

from bitnet import BitLinear

# Input
x = torch.randn(10, 512)

# BitLinear layer
layer = BitLinear(512, 400)

# Output
y = layer(x)

print(y)

`BitNetTransformer`

Fully implemented Transformer as described in the diagram with MHA, and BitFeedforwards
Can be utilized not just for text but for images and maybe even video or audio processing
Complete with residuals and skip connections for gradient flow

# Import the necessary libraries
import torch
from bitnet import BitNetTransformer

# Create a random tensor of integers
x = torch.randint(0, 20000, (1, 1024))

# Initialize the BitNetTransformer model
bitnet = BitNetTransformer(
    num_tokens=20000,  # Number of unique tokens in the input
    dim=1024,  # Dimension of the input and output embeddings
    depth=6,  # Number of transformer layers
    heads=8,  # Number of attention heads
    ff_mult=4,  # Multiplier for the hidden dimension in the feed-forward network
)

# Pass the tensor through the transformer model
logits = bitnet(x)

# Print the shape of the output
print(logits)

`BitAttention`

This Attention has been modified to use BitLinear instead of the default linear projection. It's also using Multi-Grouped Query Attention instead of regular multi-head attention for faster decoding and longer context handling.

import torch
from bitnet import BitMGQA

# Create a random tensor of shape (1, 10, 512)
x = torch.randn(1, 10, 512)

# Create an instance of the BitMGQA model with input size 512, 8 attention heads, and 4 layers
gqa = BitMGQA(512, 8, 4)

# Pass the input tensor through the BitMGQA model and get the output and attention weights
out, _ = gqa(x, x, x, need_weights=True)

# Print the shapes of the output tensor and attention tensor
print(out)

`BitFeedForward`

Feedforward as shown in the diagram with BitLinear and a GELU:
Linear -> GELU -> Linear
You can add dropouts, or layernorms, or other layers for a better ffn

import torch
from bitnet import BitFeedForward

# Create a random input tensor of shape (10, 512)
x = torch.randn(10, 512)

# Create an instance of the BitFeedForward class with the following parameters:
# - input_dim: 512
# - hidden_dim: 512
# - num_layers: 4
# - swish: True (use Swish activation function)
# - post_act_ln: True (apply Layer Normalization after each activation)
# - dropout: 0.1 (apply dropout with a probability of 0.1)
ff = BitFeedForward(512, 512, 4, swish=True, post_act_ln=True, dropout=0.1)

# Apply the BitFeedForward network to the input tensor x
y = ff(x)

# Print the shape of the output tensor y
print(y)  # torch.Size([10, 512])

Inference

from bitnet import BitNetInference

bitnet = BitNetInference()
bitnet.load_model("../model_checkpoint.pth")  # Download model
output_str = bitnet.generate("The dog jumped over the ", 512)
print(output_str)

Huggingface Usage

import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer

from bitnet import replace_linears_in_hf

# Load a model from Hugging Face's Transformers
model_name = "bert-base-uncased"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

# Replace Linear layers with BitLinear
replace_linears_in_hf(model)

# Example text to classify
text = "Replace this with your text"
inputs = tokenizer(
    text, return_tensors="pt", padding=True, truncation=True, max_length=512
)

# Perform inference
model.eval()  # Set the model to evaluation mode
with torch.no_grad():
    outputs = model(**inputs)
    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
    print(predictions)

# Process predictions
predicted_class_id = predictions.argmax().item()
print(f"Predicted class ID: {predicted_class_id}")

# Optionally, map the predicted class ID to a label, if you know the classification labels
# labels = ["Label 1", "Label 2", ...]  # Define your labels corresponding to the model's classes
# print(f"Predicted label: {labels[predicted_class_id]}")

License

MIT

Citation

@misc{2310.11453,
Author = {Hongyu Wang and Shuming Ma and Li Dong and Shaohan Huang and Huaijie Wang and Lingxiao Ma and Fan Yang and Ruiping Wang and Yi Wu and Furu Wei},
Title = {BitNet: Scaling 1-bit Transformers for Large Language Models},
Year = {2023},
Eprint = {arXiv:2310.11453},
}

sreev / BitNet