The exponential growth of large language models poses significant challenges in terms of deployment costs and environmental impact due to high energy consumption. In response to these challenges, this paper introduces BitNet, a scalable and stable 1-bit Transformer architecture designed for large language models. By introducing BitLinear as a replacement for the traditional nn.Linear layer, BitNet aims to train with 1-bit weights from scratch, significantly reducing the memory footprint and energy consumption while maintaining competitive performance.
The rapid growth of large language models has brought about significant advancements across various tasks. However, hosting these models comes at a high cost, primarily due to the steep inference costs and energy consumption. As these models grow larger, the memory bandwidth required for accessing and processing model parameters becomes a bottleneck, limiting inference performance. Moreover, the inter-device communication overhead in distributed systems or multi-device platforms significantly impacts inference latency and energy consumption.
Model quantization emerges as a promising solution to this problem by significantly reducing the memory footprint and computational cost of large-scale models while preserving competitive performance. Existing quantization approaches for large language models are predominantly post-training, making them easy to apply without altering the training pipeline. However, this approach often results in significant accuracy loss, especially at lower precision levels, since the models are not optimized for quantized representations during training.
Quantization-aware training represents another approach, typically yielding better accuracy as models are trained to account for reduced precision from the outset. This method also allows for continued training or fine-tuning, essential for large language models. The challenge, however, lies in optimization, as models become more difficult to converge with reduced precision. Furthermore, it is uncertain whether quantization-aware training adheres to the scaling laws of neural language models.
This work focuses on binarization, the extreme case of quantization applied to large language models. Unlike previous studies on binarized neural networks that primarily involved convolutional neural networks, this research delves into binarized Transformers, catering specifically to the unique needs of large language models. BitNet leverages low-precision binary weights and quantized activations while maintaining high precision for optimizer states and gradients during training, ensuring scalability and stability for efficiently handling large language models.
BitLinear plays a crucial role in BitNet's architecture, enabling the training of 1-bit weights. Here's a detailed breakdown of its implementation:
Weights are binarized to +1 or -1 using the signum function, with weights centralized to zero mean before binarization to enhance the layer's capacity within a constrained numerical range. This binarization process can be formulated as \( W = \text{Sign}(W - \alpha) \), where \( \alpha \) is the mean of the weight matrix \( W \).
Activations are quantized to b-bit precision using absmax quantization, scaling activations into the range \([-Q_b, Q_b]\) (where \( Q_b = 2^{b-1} \)) by multiplying with \( Q_b \) and dividing by the absolute maximum of the input matrix. For activations before non-linear functions like ReLU, they are scaled to the range \([0, Q_b]\) by subtracting the minimum of the inputs, ensuring all values are non-negative.
With the quantized weights and activations, matrix multiplication is carried out as \( y = Wx \), maintaining the output variance by employing a LayerNorm function before the activation quantization. This ensures the output variance \( \text{Var}(y) \) is approximately equal to \( \text{E}[LN(x)^2] = 1 \), akin to the full-precision counterpart.
The BitLinear layer is formulated as \( y = W \times \text{Quant}(\text{LN}(x)) \times \frac{\beta\gamma}{Q_b} \), where \( \beta \) and \( \gamma \) are scaling factors applied to dequantize the output activations back to the original precision.
This novel approach to implementing BitLinear within BitNet signifies a major stride towards achieving scalable and efficient large language models, promising significant reductions in memory and energy requirements while maintaining competitive performance levels.
Model parallelism is a technique used to scale up large language models by partitioning the matrix multiplication across multiple devices. It allows for the distribution of computational workload and memory usage, enabling the training and inference of models that are too large to fit on a single device.
In model parallelism, the weight matrices and activations are divided along a specific dimension, usually the partition dimension, and each partition is assigned to a different device. This allows for parallel computation of matrix multiplication, reducing the overall training and inference time.
However, a prerequisite for existing model parallelism approaches is that the tensors must be independent along the partition dimension. This means that the computations within each partition should not depend on the values from other partitions. In the context of BitNet, the parameters α, β, γ, and η are calculated from the entire weight matrix or activation matrix, which breaks the independence requirement.
One solution to this problem is to introduce an all-reduce operation for each parameter. This operation collects the values from all partitions, performs a reduction (e.g., sum or mean), and broadcasts the result back to all partitions. However, as the model becomes deeper and the number of parameters increases, the amount of synchronization grows, leading to a significant slowdown in the forward pass.
To address this issue, BitNet proposes a simple yet effective approach called Group Quantization and Normalization. The idea is to divide the weights and activations into groups and estimate the parameters independently for each group. This way, the parameters can be calculated locally within each partition, eliminating the need for additional communication.
Here's how Group Quantization works:
For a weight matrix W ∈ ℝ^(n×m), divide it into G groups along the partition dimension, where each group has a size of (n/G) × m.
Estimate the parameters α and β independently for each group:
β_g = (G/nm) × ||W^(g)||_1 where W^(g) denotes the g-th group of the weight matrix.
Similarly, for the activation matrix x ∈ ℝ^(n×m), divide it into G groups and calculate the parameters γ and η for each group:
η_g = min_ij x_ij^(g)
Apply Group Normalization to compute the mean and variance for each group independently:
By employing Group Quantization and Normalization, BitNet enables efficient model parallelism without the need for additional communication. Each partition can calculate the parameters locally, and the normalization is performed within each group independently. This approach allows BitNet to scale to large language models while maintaining computational efficiency.
The combination of model parallelism and Group Quantization and Normalization in BitNet provides a powerful framework for training and deploying large-scale language models. It allows for the distribution of workload across multiple devices while minimizing the communication overhead, enabling the development of highly efficient and scalable models.
-BitNet: Scaling 1-bit Transformers for Large Language Models
-BayJarvis Implementaion:BitNet Transformer
Created 2024-03-09T20:50:52-08:00, updated 2024-03-09T21:18:59-08:00 · History · Edit