local_rank = int(os.getenv('LOCAL_RANK', '0')) world_size = int(os.getenv('WORLD_SIZE', '1')) generator = pipeline('text-generation', model='EleutherAI/gpt-neo-2.7B', device=local_rank) generator.model = deepspeed.init_inference(generator.model, mp_size=world_size, dtype=torch.fl...
world size: 1 data parallel size: 1 model parallel size: 1 batch size per GPU: 80 params per gpu: 336.23 M params of model = params per GPU * mp_size: 336.23 M fwd MACs per GPU: 3139.93 G fwd flops per GPU: 6279.86 G fwd flops of model = fwd flops per GPU * mp_size: 6279....
model parallelsize(mp_size),numberofparameters(params),numberofmultiply-accumulateoperations(MACs),numberoffloating-pointoperations(flops),floating-point operations persecond(FLOPS),fwdlatency(forward propagation latency),bwdlatency
这是因为MP是针对Expert Variable说的。再具体一点，就是在Expert variable切分为MP 0和MP 1的两个GPU...
理解下图关键在于搞清楚我们对谁做All-to-All通信,显然是对input data而非Expert variable。所以为什么下图中MP(Model Parallel)rank不同的Device上会有相同的Data?这是因为MP是针对Expert Variable说的。再具体一点,就是在Expert variable切分为MP 0和MP 1的两个GPU上,其input data是replicate,千万不要搞混。
资源情况做的优化。[译] DeepSpeed：所有人都能用的超大规模模型训练工具346 赞同 · 11 评论文章 ...
BATCH_SIZE=1 ## Model parallelism, 1 is no MP ## Currently MoE models have divergence issue when MP > 1. MP_SIZE=1 ## Pipeline parallelism ## Currently we don't support PP for MoE. To disable PP, set PP_SIZE ## to 1 and use the "--no-pipeline-parallel" arg. ...
mp_size field is deprecated in flavor of tensor_parallel/tp https://github.com/microsoft/DeepSpeed/blob/master/deepspeed/inference/engine.py so update related docs that are still sticking to mp_size update doc to migrate init_inference from mp_size to `tensor_para… … Verified 5f7f6fa yu...
> number of parameters on model parallel rank 0: 178100224 > number of parameters on model parallel rank 1: 178100224 Optimizer = FusedAdam learning rate decaying cosine WARNING: could not find the metadata file checkpoints/gpt2_345m_mp2/latest_checkpointed_iteration.txt will not load any ...
init_inference(model, mp_size=world_size, dtype=torch.float16, replace_method="auto") # Apply BigDL-LLM INT4 optimization to enable BenchmarkWrapper # Note: only tested sym_int4 model = optimize_model(model.module.to(f'cpu'), low_bit=low_bit) model = model.to(f'cpu:{local_rank}'...