config):super(SwitchMLP,self).__init__()args=get_args()self.router=torch.nn.Linear(args.hidden_size,args.num_experts)# 初始化 router 权重self.expert_parallel_size=mpu.get_expert_model_parallel_world_size()# 获取当前 EP 进程组的 world sizeself.sequence_parallel=config.sequence...
virtual_pipeline_model_parallel_size is not None: assert config.num_layers % config.virtual_pipeline_model_parallel_size == 0, \ 'num_layers_per_stage must be divisible by ' \ 'virtual_pipeline_model_parallel_size' assert args.model_type != ModelType.encoder_and_decoder # Number of ...
Megatron (1,2, and3) is a large, powerful transformer developed by the Applied Deep Learning Research team at NVIDIA. This repository is for ongoing research related to training large transformer language models at scale. We developed efficient, model-parallel (tensor,sequence, andpipeline), and...
InstructRetro (Wang et al., 2023b) further scales up the size of Retro to 48B, featuring the largest LLM pretrained with retrieval (as of December 2023). The obtained foundation model, Retro 48B, largely outperforms the GPT counterpart in terms of perplexity. With instruction tuning on Retro...
This repository comprises two essential components:Megatron-LMandMegatron-Core. Megatron-LM serves as a research-oriented framework leveraging Megatron-Core for large language model (LLM) training. Megatron-Core, on the other hand, is a library of GPU optimized training techniques that comes with for...
Megatron-Core是NVIDIA推出的一个成熟且轻量级的大规模LLM训练框架,它包含了训练大规模LLM模型所需的所有关键技术,例如各类模型并行的支持、算子优化、通信优化、显存优化以及FP8低精度训练等。Megatron-Core不仅继承了前代Megatron-LM的优秀特性,还在代码质量、稳定性、功能丰富性和测试覆盖率上进行了全面提升。更重要的是...
Megatron-Core是NVIDIA推出的一个成熟且轻量级的大规模LLM训练框架,它包含了训练大规模LLM模型所需的所有关键技术,例如各类模型并行的支持、算子优化、通信优化、显存优化以及FP8低精度训练等。Megatron-Core不仅继承了前代Megatron-LM的优秀特性,还在代码质量、稳定性、功能丰富性和测试覆盖率上进行了全面提升。更重要的是...
level efficiency. By abstracting these GPU optimized techniques into composable and modular APIs, Megatron Core allows full flexibility for developers and model researchers to train custom transformers at-scale and easily facilitate developing their own LLM framework on NVIDIA accelerated computing ...
该模型集成了预训练的语音编码器、语音适配器、LLM和流式语音解码器,能够在不进行语音转录的情况下直接生成文本和语音响应,显著提升了用户体验。实验结果显示,LLaMA-Omni的响应延迟低至226ms,具有创新性和实用性。 48 1 1 ModelScope内容运营小助手 | 2月前 | 数据可视化 Swift 小钢炮进化,MiniCPM 3.0 开源!
defload_checkpoint(model,optimizer,lr_scheduler,args):"""Load a model checkpoint."""iteration,release=get_checkpoint_iteration(args)ifargs.deepspeed:checkpoint_name,sd=model.load_checkpoint(args.load,iteration)ifcheckpoint_nameisNone:ifmpu.get_data_parallel_rank()==0:print("Unable t...