The growing demand for Large Language Models (LLMs) in applications such as
content generation, intelligent chatbots, and sentiment analysis poses
considerable challenges for LLM service providers. To efficiently use GPU
resources and boost throughput, batching multiple requests has emerged as a
popular paradigm; to further speed up batching, LLM quantization techniques
reduce memory consumption and increase computing capacity. However, prevalent
quantization schemes (e.g., 8-bit weight-activation quantization) cannot fully
leverage the capabilities of modern GPUs, such as 4-bit integer operators,
resulting in sub-optimal performance.
To maximize LLMs' serving throughput, we introduce Atom, a low-bit
quantization method that achieves high throughput improvements with negligible
accuracy loss. Atom significantly boosts serving throughput by using low-bit
operators and considerably reduces memory consumption via low-bit quantization.
It attains high accuracy by applying a novel mixed-precision and fine-grained
quantization process. We evaluate Atom on 4-bit weight-activation quantization
setups in the serving context. Atom improves end-to-end throughput by up to
$7.73\times$ compared to the FP16 and by $2.53\times$ compared to INT8
quantization, while maintaining the same latency target.

Atom 是一种低位量化方法，通过使用低位算子和低位量化显著提高 serving 吞吐量以及减少内存消耗，同时保持相同的延迟目标。

Atom：高效准确的 LLM 服务器的低比特量化

Atom: Low-bit Quantization for Efficient and Accurate LLM Serving

Language-guided human motion synthesis has been a challenging task due to the
inherent complexity and diversity of human behaviors. Previous methods face
limitations in generalization to novel actions, often resulting in unrealistic
or incoherent motion sequences. In this paper, we propose ATOM (ATomic mOtion
Modeling) to mitigate this problem, by decomposing actions into atomic actions,
and employing a curriculum learning strategy to learn atomic action
composition. First, we disentangle complex human motions into a set of atomic
actions during learning, and then assemble novel actions using the learned
atomic actions, which offers better adaptability to new actions. Moreover, we
introduce a curriculum learning training strategy that leverages masked motion
modeling with a gradual increase in the mask ratio, and thus facilitates atomic
action assembly. This approach mitigates the overfitting problem commonly
encountered in previous methods while enforcing the model to learn better
motion representations. We demonstrate the effectiveness of ATOM through
extensive experiments, including text-to-motion and action-to-motion synthesis
tasks. We further illustrate its superiority in synthesizing plausible and
coherent text-guided human motion sequences.

ATOM 通过将动作分解为原子动作，并采用课程学习策略来组合原子动作，从而更好地适应新动作。同时引入了渐进式的蒙版运动建模的课程学习训练策略，有效缓解了过拟合问题，并提高了运动表示的学习质量。通过广泛的实验，包括文本到运动和动作到运动合成任务，证明了 ATOM 的有效性，并展示了它在合成可行和连贯的文本指导人体动作序列方面的优势。