gather_from_sequence_parallel_region:是对_GatherFromSequenceParallelRegion类使用的封装,_GatherFromSequenceParallelRegion继承自torch.autograd.Function的自定义Function,在parallel并行前向进行all_gather操作,反向是梯度reduce_scatter输出。对应Pipeline Parallel Linear Layer中的g函数。 class _GatherFromSequenceParallelRe...
这也是上面的"4 pipeline model-parallel groups"的结果。 那么,如果要搞”数据并行“的话,是”含有相同参数的模型的子块“之间进行数据并行。 简单理解就是,model parallel (包括tensor parallel和pipeline parallel)已经把一个模型大卸八块了,那么每一个小块,就是一个”独立王国“。 现在是两个大模型,分别被切...
Tensor parallelism is also useful for extremely large models in which a pure pipelining is simply not enough. For example, with GPT-3-scale models that require partitioning over tens of instances, a pure microbatch pipelining is inefficient because the pipeline depth becomes too high and the over...
Create a Pipeline Model Real-time Inference Batch transforms Logs and Metrics Troubleshooting Delete Endpoints and Resources Automatic scaling Auto scaling policy overview Prerequisites Configure model auto scaling with the console Register a model Define a scaling policy Apply a scaling policy Instructions ...
nlpbloomdistributed-systemsmachine-learningdeep-learningchatbotpytorchfalcontransformerneural-networksllamagptpretrained-modelslanguage-modelsvolunteer-computingpipeline-parallelismguanacotensor-parallelismlarge-language-modelsllama2 UpdatedApr 29, 2024 Python Slicing a PyTorch Tensor Into Parallel Shards ...
core.tensor_parallel.split_tensor_into_1d_equal_chunks(tensor,new_buffer=False) Break a tensor into equal 1D chunks across tensor parallel ranks. Returns a Tensor or View with this rank’s portion of the data. Parameters tensor– The tensor to split ...
TePDist's distributed strategy exploration is fully automated.TePDist's automatic planned strategies can cover all kinds of current known parallel schemes, such as Data parallel (including token parallel), Model parallel (e.g, sharding or Zero) and Pipeline parallel. Of course, TePDist also allow...
In several studies, tensor decomposition [26], in particular Parallel Factor Analysis (PARAFAC) [27], also known as canonical decomposition [28], was employed to achieve dimensionality reduction, clustering and classification of hyperspectral images in a trilinear fashion without resorting to flattening...
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For example, CGRAs have been proposed that can enable implementation of energy-efficient accelerators for machine learning and artificial intelligence workloads. See, Prabhakar, et al., “Plasticine: A Reconfigurable Architecture for Parallel Patterns,” ISCA '17, Jun. 24-28, 2017, Toronto, ON, ...