pipeline_model_parallel_size(必选,默认为1):表示一个pipeline模型并行通信组中的GPU卡数,pipeline并行相当于把layer纵向切为了N个stage阶段,每个阶段对应一个卡,所以这里也就等于stage阶段数。例如 pipeline_model parallel_size 为2,tensor_model parallel_size 为4,表示一个模型会被纵向分为2个stage进行pipeline并行...
图2:Pipeline Model Parallel过程,同一个 batch 的数据,有些提前进入后半段模型 Model Part 2,而不用等待全部数据走完前半段模型 Model Part 1之后再统一进入后半段模型 Model Part 2。这样子节约了运行时间 实验结果: 图3:Pipeline Model Parallel,Model Parallel 和 Single GPU 的运行时间比较 实验结果表明,...
class PipelineParallelResNet50(ModelParallelResNet50): def __init__(self, split_size=20, *args, **kwargs): super(PipelineParallelResNet50, self).__init__(*args, **kwargs) self.split_size = split_size def forward(self, x): splits = iter(x.split(self.split_size, dim=0)) s_ne...
[Model] Pipeline parallel support for Mixtral (vllm-project#6516) 8fe34aa gnpinkert pushed a commit to gnpinkert/vllm that referenced this pull request Aug 26, 2024 [Model] Pipeline parallel support for Mixtral (vllm-project#6516) a7b8118 DarkLight1337 mentioned this pull request Sep...
model parallel是将一个模型的不同子网络放到不同设备上,在实现forward函数时手动移动或合并不同device上的数据。由于每个设备上仅有一部分模型,所以全部设备足以撑起一个比较大的模型。本文不会构建一个大型模型然后将其压缩至有限的GPU上,而是关注弄清model parallel的原理。具体应用取决于读者的具体应用。
One of the core features of SageMaker's model parallelism library is pipeline parallelism, which determines the order in which computations are made and data is processed across devices during model training. Pipelining is a technique to achieve true parallelization in model parallelism, by having the...
Hybrid parallel pipelines are suited for many scientific algorithms, which can be sped up significantly by using CPU and GPU hardware. However, the complexity of modern applications and hardware makes it hard to optimally configure hybrid parallel pipelines. To this end, analytical modeling approaches...
mkdir weight SCRIPT_PATH=./tools/ckpt_convert/llama/convert_weights_from_huggingface.py # for ptd python $SCRIPT_PATH \ --input-model-dir ./baichuan2-7B-hf \ --output-model-dir ./weight-tp8 \ --tensor-model-parallel-size 8 \ --pipeline-model-parallel-size 1 \ --type 7B \ --merg...
fromdjl_pythonimportInput,Outputimportosimportdeepspeedimporttorchfromtransformersimportpipeline,AutoModelForCausalLM,AutoTokenizer predictor=Nonedefget_model():model_name='EleutherAI/gpt-j-6B'tensor_parallel=int(os.getenv('TENSOR_PARALLEL_DEGREE','1'))local_rank=int(os.g...
Easy Parallel Library (EPL) is a general and efficient deep learning framework for distributed model training. deep-learninggpumemory-efficientdata-parallelismmodel-parallelismdistributed-trainingpipeline-parallelism UpdatedMar 31, 2023 Python Shenggan/awesome-distributed-ml ...