模型量化(quantization)指的是用更少的bit表示模型参数,从而减少模型的大小,加速推理过程的技术。 一种常见的量化方式是线性量化(linear quantization),也叫仿射量化(affine quantization)。其实就是按比例将tensor(一般为fp32)放缩到 2bitwidth 的范围内,比如8bit等。我们很容易给出量化公式: r=s(q−z) 其中,...
scale &zero_point& bit-width 非对称量化定义了三个参数:scale / zero-point / bit-width scale和zero-point这两个参数用于映射float32至int8,最终的quantzation space范围取决于bit-width位宽。 scale通常是一个浮点数,用于映射float值到quantization space,同时明确了量化过程中的step-size zero_point通常是一个...
After being deployed on a platform, MEBQAT allows the (meta-)trained model to be quantized to any candidate bitwidth with minimal inference accuracy drop. Moreover, in a few-shot learning scenario, MEBQAT can also adapt a model to any bitwidth as well as any unseen target classes by ...
e.g., 8-bit fixed-point integer. We use a simple-yet-effective quantization method refer to [20] for both weights and activations. Specifically, given full-precision weights\({\theta }\)and the quantization precisionk, we quantize\({\theta }\)to\({\theta _q}\)in...
3.3 Hot-Swap Bit-Width Adjustment 摘要(Abstract) 对目标位宽进行改进,在量化精度调整时,需要对量化模型进行量化或者最小化量化噪声,给实际应用造成不便。在这项工作中,我们提出了一个方法来训练一个支持不同位宽(例如,从8位到1位)的所有量化模型,以满足在线量化位宽调整。它是热插拔的,可以通过多尺度量化为不同...
An acceleration library that supports arbitrary bit-width combinatorial quantization operations - bytedance/ABQ-LLM
The symmetric uniform quantization Q is the most common method [2], which is formulated as: Xq=Q(X,s)=clip(round(Xs),−2k−1,2k−1−1)where k is the bit-width of quantization (in general, k = 8), and s is a quantization parameter named scale factor. The dequantization of...
Bit-width flexibility:Hardware must support various precision levels, such as int4/2/1 for weights and FP16/8 or int8 for activations, along with their combinations. This flexibility is crucial for accommodating diverse model architectures and use cases. ...
1、Experimental results also show that for every quantization width, every scale in time and every scale in space, 3D-PCW not only achieves higher compression rate but also costs less time than 3D-ESCOT.───实验数据也表明,对于每个量化幅度和每个空间及时间分辨率,3D-PCW不仅获得了比3D-ESCOT更高...
与混合精度训练不同，MPQ关注的是如何动态调整各层的量化策略。一篇论文通过可微分神经架构搜索，利用Gumbel-Softmax等技术，结合准确率和硬件成本设计目标函数。实验主要与其他三值神经网络进行对比，虽然简单但实现复杂，效果良好。另一篇HAQ论文则采用强化学习，智能体根据模型状态调整bitwidth，确保硬件成本在...