简易实现里面没有考虑模型参数备份、显存优化、异步通信等等问题,只为了解如何通过torch.distributed接口来实现PipeDream的并行。 pp_group=get_pipeline_parallel_group()pp_size=pp_group.size()# Run warmup forward processoutput_chunks=[]num_warmp=min(pp_
三、Pipeline Parallel的性能优化策略 为了克服Pipeline Parallel的性能瓶颈,提高大模型训练的效率,我们可以采取以下优化策略,同时利用百度智能云文心快码(Comate)快速生成和优化相关代码: 优化Bubble Time 增加Micro-Batch Size:通过增加每个Mini-Batch中的Micro-Batch数量,可以减少Bubble Time的占比。这是因为更多的Micro-B...
(#4412,#5408,#6115,#6120). You can now run the API server with--pipeline-parallel-size. This feature is in early stage, please let us know your feedback. 2. 配置 ParallelConfig: pipeline_parallel_size: Number of pipeline parallel groups. 参数验证: EngineConfig self.model_config.verify_w...
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...
tensor_parallel_size=1, pipeline_parallel_size=16, disable_custom_all_reduce=False, quantization=gptq_marlin, enforce_eager=False, kv_cache_dtype=auto, device_config=cuda, decoding_config=DecodingCo nfig(guided_decoding_backend='xgrammar'), observability_config=ObservabilityConfig(otlp_traces_endpoin...
Tensor 并行在层内进行通信,通常涉及大量的数据交换,特别是在层与层之间需要合并结果时。 Pipeline 并行主要在层的边界进行通信,通信次数和数据量相对较少。 负载均衡: Tensor 并行可以更好地实现 GPU 之间的负载均衡,因为它将单个层的计算任务分割到多个 GPU 上。 Pipeline 并行可能会引入流水线气泡(即 GPU 空闲...
pipeline_parallel_size): self.pp_tp_workers.append([]) for tp_rank in range( self.parallel_config.tensor_parallel_size): # PP=2, TP=4 # pp_tp_workers = [[0, 1, 2, 3], [4, 5, 6, 7]] rank = (pp_rank * self.parallel_config.tensor_parallel_size ) + tp_rank assert len...
batches and then aggregating the gradient update simultaneously at the end. Megatron-LM[109]is an intra-layer model parallel approach for transformer networks, which adds a few synchronization primitives on the self-attention and multi-layerperceptronblocks. PTD-P[110]combines pipeline, tensor, and ...
pipe=my_pipe(True,False,batch_size=32,num_threads=1,device_id=0) The pipeline is properly configured, we can run it now. The outputs from the original function became the outputs of the Pipeline: flipped,img=pipe.run() When some of the pipeline parameters are fixed, they can be specifi...
dataset=dataset.apply(map_and_batch(parse_example,batch_size=batch_size,drop_remainder=True,num_parallel_calls=8))dataset=dataset.prefetch(1)returndataset 在SIGAI提供的实验过程中,验证读取数据的内容如下图所示: 本文主要介绍了TFRecord文件,然后以MNIST数据集为例讲解了如何制作MNIST数据集的TFRecord文件,接...