Revolutionizing Large Language Models with 1-Bit Transformers: BitLinear and BitNet b1.58
Introduction:
Large language models have shown impressive results in natural language processing tasks
BitNet uses a novel linear layer called BitLinear to train 1-bit weights from scratch, enabling significant reductions in memory footprint and energy consumption compared to traditional full-precision Transformer architectures.
The results show that BitNet achieves competitive performance on language modeling tasks while outperforming state-of-the-art quantization methods and significantly reducing memory and computation requirements.
The scaling law observed for BitNet is similar to that of full-precision Transformers, suggesting that it can be effectively scaled to larger model sizes while maintaining efficiency and performance benefits.
A scaling law is a mathematical relationship that describes how a particular quantity changes as a function of size or scale. In the context of machine learning models, scaling laws refer to how the performance of a model improves as its size (e.g., number of parameters) increases. Specifically, a scaling law can help us understand how much improvement we can expect from increasing the size of a model, and how much more resources (e.g., computation, data) will be required to achieve that improvement. In the case of BitNet, the authors observe a scaling law similar to that of full-precision Transformers, suggesting that the performance of BitNet can be improved by increasing its size, while still maintaining its efficiency advantages.
Motivation for using 1-bit Transformers :
The motivation behind using 1-bit Transformers for large language models is to address the challenges posed by the increasing size of these models, such as high energy consumption and deployment costs. by using 1-bit weights, the memory footprint and computation requirements of the model can be significantly reduced, leading to more efficient training and inference. In addition, 1-bit Transformers have been shown to perform competitively with full-precision models on a range of natural language processing tasks, making them an attractive alternative for scaling up language models to handle large amounts of data.
Differences between BitNet and traditional Transformer architectures
BitNet differs from traditional Transformer architectures in several ways. The most significant difference is the use of 1-bit weights in the model, which are trained from scratch using the proposed BitLinear layer.
This enables BitNet to achieve significant reductions in memory footprint and energy consumption
BitNet uses a modified version of the traditional Transformer architecture that is designed to be more stable and scalable when using 1-bit weights. This includes the use of layer normalization before and after each sub-layer, as well as the use of a learnable scaling factor for the 1-bit weights , these modifications allow BitNet to achieve competitive performance with traditional Transformer architectures while using significantly less memory and computation.
BitLinear for training 1-bit weights from scratch
BitLinear is a novel linear layer proposed in the BitNet paper that enables training of 1-bit weights from scratch. It is a drop-in replacement for the traditional linear layer used in Transformer architectures, and is designed to work with binary weights.
BitLinear uses a binary weight matrix and a real-valued scaling factor to compute the output of the linear layer.
During the forward pass, the input is first multiplied by the binary weight matrix, and then the scaling factor is applied to the result.
During the backward pass, the gradients are computed with respect to the real-valued scaling factor and the binary weight matrix. By using BitLinear, the weights in the model can be binarized during training, which significantly reduces the memory footprint and computation requirements of the model.
BitLinear implementation (PyTorch) :
class BitLinear(nn.Linear):
def __init__(self, in_features, out_features, bias=True, num_groups=1):
super(BitLinear, self).__init__(in_features, out_features, bias)
self.num_groups = num_groups
self.eps = 1e-5
def ste_binarize(self, x):
# Apply the sign function for binarization
binarized_x = torch.sign(x)
# Use STE: during backward pass, we bypass the binarization
binarized_x = (binarized_x - x).detach() + x
return binarized_x
def binarize_weights_groupwise(self):
# Divide weights into groups
group_size = self.weight.shape[0] // self.num_groups
binarized_weights = torch.zeros_like(self.weight)
for g in range(self.num_groups):
start_idx = g * group_size
end_idx = (g + 1) * group_size
weight_group = self.weight[start_idx:end_idx]
# Binarize each group using STE
alpha_g = weight_group.mean()
binarized_weights[start_idx:end_idx] = self.ste_binarize(
weight_group - alpha_g
)
return binarized_weights
def quantize_activations_groupwise(self, x, b=8):
Q_b = 2 ** (b - 1)
# Divide activations into groups
group_size = x.shape[0] // self.num_groups
quantized_x = torch.zeros_like(x)
for g in range(self.num_groups):
start_idx = g * group_size
end_idx = (g + 1) * group_size
activation_group = x[start_idx:end_idx]
# Quantize each group
gamma_g = activation_group.abs().max()
quantized_x[start_idx:end_idx] = torch.clamp(
activation_group * Q_b / (gamma_g + self.eps),
-Q_b + self.eps,
Q_b - self.eps,
)
return quantized_x
def forward(self, input):
# Binarize weights (group-wise) using STE
binarized_weights = self.binarize_weights_groupwise()
# Normal linear transformation with binarized weights
output = torch.nn.functional.linear(input, binarized_weights, self.bias)
# Quantize activations group-wise
output = self.quantize_activations_groupwise(output)
return output
领英推荐
BitNet lib :
pip install bitnet
import torch
from bitnet import BitLinear
x = torch.randn(10, 512)
layer = BitLinear(512, 400)
y = layer(x)
Performance and efficiency of BitNet
BitNet outperforms state-of-the-art quantization methods and significantly reduces memory footprint and energy consumption compared to full-precision Transformer baselines, while maintaining competitive performance in language modeling tasks.
In terms of performance, BitNet achieves perplexity scores that are comparable to or better than those of full-precision Transformers on several benchmark datasets.
PPL stands for Perplexity, which is a commonly used evaluation metric in natural language processing tasks such as language modeling. Perplexity measures how well a language model predicts a sample of text. A lower perplexity score indicates better performance, meaning that the model is better at predicting the next word in a sentence. Mathematically, perplexity is defined as the inverse of the geometric mean of the probability assigned to each word in a test set.
Scaling law for BitNet
The scaling law observed for BitNet is similar to that of full-precision Transformers. As the model size increases, the performance of BitNet improves at a predictable rate, following a power law. However, the rate of improvement is slightly slower for BitNet compared to full-precision Transformers. The scaling law for BitNet suggests that it can be effectively scaled to larger model sizes while maintaining efficiency and performance benefits.
Cost-Effectiveness of BitNet b1.58
BitNet b1.58 is a Large Language Model (LLM) variant that uses ternary weights {-1, 0, 1} for all of its parameters. This is in contrast to traditional LLMs, which typically use full-precision floating-point weights represented in 16 or 32 bits. By using ternary weights, BitNet b1.58 significantly reduces the memory and computational requirements for inference, while still maintaining the same level of performance as full-precision LLMs.
The ternary weights in BitNet b1.58 are learned during training using a technique called quantization. During training, the weights are updated using full-precision gradients, but are then quantized to ternary values {-1, 0, 1} before being used for the next training step. This process is repeated until the model converges.
One of the key benefits of using ternary weights is that it simplifies the mathematical operations required for inference. In traditional LLMs, inference involves multiplying and accumulating large matrices of floating-point values, which is computationally expensive and power-hungry.
In BitNet b1.58, the ternary weights allow for simpler and more efficient matrix operations, such as binary XNOR and bitcount operations. This enables faster inference and reduced energy consumption, making it feasible to deploy LLMs on low-power and resource-constrained devices.
Another advantage of BitNet b1.58 is that it defines a new scaling law and recipe for training high-performance and cost-effective LLMs. Traditional LLMs require large amounts of data and computational resources to train, which can be expensive and time-consuming. BitNet b1.58 demonstrates that it is possible to train LLMs using less data and fewer resources, while still achieving the same level of performance as full-precision models.
BitNet b1.58 enables a new computation paradigm for designing specific hardware optimized for 1-bit LLMs. The simplified matrix operations used in BitNet b1.58 can be implemented more efficiently in hardware, allowing for the design of specialized accelerators that are optimized for low-power and high-throughput inference of 1-bit LLMs. This opens up new opportunities for deploying LLMs in a wide range of applications, from edge devices to data centers.
In conclusion, the use of 1-bit Transformers in large language models presents a promising solution to address the challenges of high energy consumption and deployment costs. The BitLinear layer is a key component of these models, enabling the use of binary weights and quantized activations to significantly reduce memory footprint and computation requirements. The provided code demonstrates an implementation of the BitLinear layer using group-wise binarization and quantization. BitNet b1.58 is an example of a large language model that utilizes this technology, achieving cost-effectiveness in terms of latency, memory, throughput, and energy consumption while maintaining competitive performance with full-precision models.
Sources :
By Kirouane Ayoub