2.2 inference pipeline 的效果分析 使用transformers 默认的 pipeline 进行 6B GPT-J 文本生成任务,并测量延时性能 importosfromtimeimportperf_counterimportnumpyasnpfromtransformersimportpipeline,set_seeddefmeasure_pipeline_latency(generator,prompt,max_length,num_return_sequences):latencies=[]# warm upfor_inran...
Pipeline 并行 1.2DeepSpeed Inferenceby Microsoft 对于Transformer layer,可分为以下4个主要部分: Input Layer-Norm plus Query, Key, and Value GeMMs and their bias adds. Transform plus Attention. Intermediate FF, Layer-Norm, Bias-add, Residual, and Gaussian Error Linear Unit (GELU). Bias-add plus ...
Model Parallelism。模型并行,包括Tensor Parallelism和Pipeline Parallelism。Model Parallelism解决的是单张卡放不下一个完整模型权重的问题,每张显卡只放部分参数。一般来说,会按照层进行划分参数,按层划分一般叫Pipeline Parallelism。如果模型的一层如果都装不下了,同一个模型层内拆分开...
通过深入研究 Megatron-Core 的代码和关键技术点,我们可以更好地理解如何在大规模计算环境中高效地训练语言模型。 core主要包含datasets、models、transformer、fusion、distributed、tensor_parallel、pipline_parallel、inference子目录,我们分为数据集、模型结构、并行策略和推理四个模块来解读。 4.1 数据集构造 Megatron在da...
Nipun Kwatra, Bhargav S. Gulavani, Ramachandran Ramjee September 2023 PDF 下载BibTex Large Language Model (LLM) inference consists of two distinct phases – prefill phase which processes the input prompt and decode phase which generates output tokens autoregressively...
In the most recent round ofMLPerf Inference 4.1, we made our first-ever submission with theBlackwell platform. It delivered 4x more performance than the previous generation. This submission was also the first-ever MLPerf submission to use FP4 precision. Narrower precision formats, like FP4, reduces...
4 Inference 首先请参考2.2 Model Architecture中,关于基本推理过程,KV Cache,GQA部分的内容,同时请参考3.2 SFT中关于PagedAttention的介绍。 4.1 Parallelism Parallelism,LLM分布式训练推理的一部分,包括Data Parallelism和Model Parallelism,本节做一些介绍。同样涉及到OS的一些概念。
Furthermore, uniform batches in Sarathi-Serve ameliorate the imbalance between iterations resulting in minimal pipeline bubbles. Our techniques yield significant improvements in inference performance across models and hardware under tail latency constraints. For Mistral-7B on sing...
model by layers. Here we use two-way pipeline parallelism to shard the model across the two nodes, and eight-way tensor parallelism to shard the model across the eight GPUs on each node. Below are the relevant TRT-LLM functions. Feel free to modify them as requi...
and they can be memory- and compute-intensive during inference (a recurring cost). The most popularlarge language models (LLMs)today can reach tens to hundreds of billions of parameters in size and, depending on the use case, may require ingesting long inputs (or contexts), which can also...