4.1,易用性:从训练到推理的无缝衔接 pipeline 4.2,开源模型的 latency 加速效果(可复现) 4.3,提高吞吐量并降低大型 Transformer 模型的推理成本 4.4,DeepSpeed 量化对降低推理成本和提高量化模型精度的影响 参考资料 我的自制大模型推理框架课程介绍 框架亮点:基于 Triton + PyTorch 开发的
模型变得越来越大,单卡都无法支持一个模型训练的时候,就会使用模型并行的方法,模型并行又分为流水线并行(Pipeline Parallelism)和张量并行(Tensor Parallelism),其中流水线并行指的是将模型的每一层拆开分布到不同GPU。当模型大到单层模型都无...
`"zero_optimization": {"offload_optimizer": {"device": "cpu", "pin_memory": true}}`。 截至本文完稿时(2024/10/14),Pytorch对deepspeed的支持主要在ZeRO上,在PP和TP上有限。 4. DeepSpeed在Accelerate中的实现: Accelerate库提供了一个简单的接口来集成DeepSpeed,使得在PyTorch中进行分布式训练变得更加容易。
docker run -d -t --network=host --gpus all --privileged --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 --name megatron-deepspeed -v /etc/localtime:/etc/localtime -v /root/.ssh:/root/.ssh nvcr.io/nvidia/pytorch:21.10-py3 3.执行以下命令,进入容器终端。 docker exec -it meg...
Build Pipeline Status DescriptionStatus NVIDIA AMD CPU Intel Gaudi Intel XPU PyTorch Nightly Integrations Misc Huawei Ascend NPU Installation The quickest way to get started with DeepSpeed is via pip, this will install the latest release of DeepSpeed which is not tied to specific PyTorch or CUDA ...
前向传播API与PyTorch兼容,不需要进行任何更改。 反向传播 通过在模型引擎上直接调用 backward(loss) 来进行反向传播。 代码语言:javascript 代码运行次数:0 运行 AI代码解释 def backward_step(optimizer, model, lm_loss, args, timers): """Backward step.""" # Total loss. loss = lm_loss # Backward pas...
Pipeline communications are implemented using broadcast collectives between groups of size 2. Starting with PyTorch 1.8+, the bundled NCCL version also supports send/recv, and so I am preparing to release a new backend that uses send/recv when available. Other collectives include AllReduce for grad...
docker run-d-t--network=host--gpus all--privileged--ipc=host--ulimit memlock=-1--ulimit stack=67108864--name megatron-deepspeed-v/etc/localtime:/etc/localtime-v/root/.ssh:/root/.ssh nvcr.io/nvidia/pytorch:21.10-py3 1. 执行以下命令,进入容器终端。
A full pipeline to finetune ChatGLM LLM with LoRA and RLHF on consumer hardware. Implementation of RLHF (Reinforcement Learning with Human Feedback) on top of the ChatGLM architecture. Basically ChatGPT but with ChatGLM pytorch llama gpt lora finetune ppo peft deepspeed llm chatgpt rlhf rewa...
Figure 6: The largest models can be trained using default PyTorch and ZeRO-Offload on a single GPU. The key technology behind ZeRO-Offload is our new capability to offload optimizer states and gradients onto CPU memory, building on top of ZeRO-2. This approach allows ZeRO-Offload to minimize...