Tensor parallelism on LM-head of inference on 4 devices 需要注意的是,这里最后的collective 采用的是 all-gather 而非之前的 all-reduce。 2.5 CrossEntropyLoss 的并行 前边讨论的主要是推理过程中的并行过程,如果在训练过程中还需要考虑loss的并行,为此笔者曾专门进行过详细讨论,有兴趣可参考下文,本文在此不予...
out=DeepSpeed is a machine learning framework for deep learning models. It is designed to be easy to use and flexible. DeepSpeed is a Python library that provides a high-level API for training and inference on deep learning models. DeepSpeed is a Python library that provides a high-level API...
TensorRT-LLM usestensor parallelism, a type of model parallelism in which individual weight matrices are split across devices. This enables efficient inference at scale—with each model running in parallel across multiple GPUs connected through NVLink and across multiple servers—without develo...
[6] 大模型推理优化技术-KV Cache:https://zhuanlan.zhihu.com/p/700197845 [7] Benchmarking LLM Inference Backends: vLLM, LMDeploy, MLC-LLM, TensorRT-LLM, and TGIhttps://bentoml.com/blog/benchmarking-llm-inference-backends [8] Iteration batching (a.k.a. continuous batching) to increase LLM...
--tensor-parallel-size是用于分布式推理的参数,设置为一就是单卡推理,也就是8卡推理(ollama的在文末),单节点多卡推理是说一台机子上有多个GPU推理,多节点多卡推理是说多个机子多GPU推理。 下面参数影响篇幅有限,具体就不再详细说明了。 ▲ Vllm几个参数影响并发性能表 ...
edited Describe the bug Incorrect vLLM tensor-parallel-size calculated by auto-scheduling and causes inference engine error Steps to reproduce In an A800x4 environment, try to deploy ModelScope/OpenGVLab/InternVL2_5-78B-AWQ with --trust-remote-code, --quantization=awq. The auto schedule will ...
tensor_parallel.tp_size instead [2023-05-17 0840,881] [INFO] [logging.pylog_dist] [Rank 0] quantize_bits = 8 mlp_extra_grouping = False, quantize_groups = 1 Detected CUDA files, patching ldflags Emitting ninja build file /root/.cache/torch_extensions/py38_cu117/transformer_inference/...
通过这种机制,可以使得极大减少模型在执行复杂采样算法(例如parallel sampling和beam search)的内存开销,提升推理吞吐性能。 4.2. KV Cache KV Cache是LLM推理优化里的一个常用技术,可以在不影响计算精度的情况下,通过空间换时间的办法,提高推理性能。KV Cache发生在多个token生成的步骤中,并且只发生在Decoder-only模型中...
Figure 3b is an example of two-way tensor parallelism in the self-attention layer. The multiple attention heads are parallel by nature and can be split across devices. Sequence parallelism Tensor parallelism has limitations, as it requires layers to be divided into independent, manageable blocks. ...
多年前搞大数据,因为单节点无力存储和计算PB级别的数据,所以hadoop这种分布式存储和计算框架是标配!如今搞大模型,仍然需要对大量样本数据做计算,因为涉及矩阵运算,单机单卡运算效率太低,也涉及到分布式计算了,大模型时代的分布式pre-train和Inference框架就有现成的—deepspeed!