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...
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: 6279.86 G fwd latency: 76.67 ms bwd latency: 108.02 ms fwd FLOPS...
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....
mp_size = world_size, dtype=torch.float16, replace_method="auto" ) # Apply BigDL-LLM INT4 optimizations on transformers model = optimize_model(model.module.to(f'cpu'), low_bit='sym_int4') model = model.to(f'cpu:{local_rank}') print(model) model = BenchmarkWrapper(model, do_prin...
其中 mp.spawn 的参数分别是: fn: example,也就是每个进程中需要运行的函数。函数的调用方式为 fn(i,*args),其中 i 是进程索引,args 是传递的参数元组。 args:如上所述,是我们传递给 fn 的参数,在代码中,我们传递的是 world_size 也就是参与计算的进程总数。 nprocs: world_size 要启动的进程数。 join...
input.size()[1], input.size()[0], DeepSpeedTransformerInference.layer_id, self.config.mp_size, self.config.bigscience_bloom, dist.get_rank() if dist.is_initialized() else 0, self.config.max_out_tokens) dist.get_rank() if dist.is_initialized() else 0, self.config.max_out_tokens, ...
FairSeq, EluetherAI, etc. It supports dense models based on BERT, RoBERTa, GPT, OPT, and BLOOM architectures ranging from a few hundred million parameters in size to hundreds of billions of parameters in size. At the same time, it supports recent image generation models such as Stable Diffus...
> 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 ...
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。
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...