对于大规模数据集,尽管与最新的state-of-the-art DataComp-B/16相比并非最佳,但作者与几项现有工作相比仍取得了一些具有竞争力的结果。 4.2.3 Inference Speed. 为了评估推理速度,作者在CPU(Intel(R)-Xeon(R)-Silver-4314-CPU@2.40GHz...
· A new transformer engine enables H100 to deliver up to 9x faster AI training and up to 30x faster AI inference speedups on large language models compared to the prior generation NVIDIA A100 GPU. · Improved features for spatial and temporal data locality and asynchronous execution enable appli...
批量推理(Batch Inference):通过将多个输入样本一起输入模型进行推理,可以减少推理的总时间。这可以通过将多个样本组织成批量并同时进行计算来实现。 Beam Search:在解码阶段使用Beam Search算法,它可以在保持一定的解码质量的同时减少搜索空间。通过限制搜索宽度,可以降低计算量。 剪枝(Pruning):通过移除不必要的计算,如...
Problem torch.compile() shows an impressive ~2x speed-up for this code repo, but when applying to huggingface transformers there is barely no speed-up. I want to understand why, and then figure out how TorchInductor can also benefit HF m...
rather compelling direction. We propose to reframe the standard greedy autoregressive decoding of MT with a parallel formulation leveraging Jacobi and Gauss-Seidel fixed-point iteration methods for fast inference. This formulation allows to speed up existing models without training or modifications while ...
On the architecture level, we recognize that the transition down process (encompassing FPS and kNN operations) constitutes 71.77% of the total inference time, PTrAcc++ proposes an integrated FPS-kNN architecture to select error-driven k neighbors, reducing repeated memory accesses and distance re...
To speed up inference, non-autoregressive (NAR) methods, e.g. single-step NAR, were designed, to enable parallel generation. However, due to an independence assumption within the output tokens, performance of single-step NAR is inferior to that of AR models, especially with a large-scale ...
Now that we have a basic Transformer layer, let’s use Transformer Engine to speed up the training. [6]: importtransformer_engine.pytorchaste TE provides a set of PyTorch modules that can be used to build Transformer layers. The simplest of the provided modules are theLinearandLayerNormlaye...
GPUs are used for two important machine learning tasks—training and inference. These have somewhat different requirements: At training time, the parameters of the model are constantly being updated, and these updates need to be communicated to the GPUs. Additional state, such as momentum terms for...
论文名称:LeViT: a Vision Transformer in ConvNet's Clothing for Faster Inference 论文地址: 19.1 LeViT原理分析: 本文的目的是在DeiT的基础上减小视觉Transformer在不同设备上的推理时间 (inference speed),包含的设备包括了具有高并行计算能力的GPU,常规的CPU以及移动设备常用的ARM处理器。