U JºcÄã @sdZddlmZmZmZmZddlZddlmZddlm Z ddl m Z ddl m Z mZdd gZeejeefZeeZeeefZeeeeed œd d „Zd e d¡dœee jeeeefeeeedœdd„Zdde d¡dœee jeeeefeeeeedœdd „ZdS)aiA helper to roughly balance a sequential module. Usage:: import torch from torch.distributed.pipeline.sync import Pipe from torch.distributed.pipeline.sync.balance import balance_by_time sample = torch.empty(128, 3, 224, 224) balance = balance_by_time(torch.cuda.device_count(), model, sample) pipe = Pipe(model, balance, chunks=8) é)ÚAnyÚListÚUnionÚSequenceN)ÚTensoré)Úblockpartition)Ú profile_sizesÚ profile_timesÚbalance_by_timeÚbalance_by_size)ÚcostÚ partitionsÚreturncCst ||¡}dd„|DƒS)NcSsg|] }t|ƒ‘qS©)Úlen)Ú.0ÚprrúU/tmp/pip-unpacked-wheel-gikjz4vx/torch/distributed/pipeline/sync/_balance/__init__.pyÚ )sz balance_cost..)rZsolve)r rZ partitionedrrrÚ balance_cost's rgð?Zcuda)ÚtimeoutÚdevice)rÚmoduleÚsamplerrrcCst|||t |¡ƒ}t||ƒS)aîNaive automatic balancing by elapsed time per layer. :: sample = torch.empty(128, 3, 224, 224) balance = balance_by_time(torch.cuda.device_count(), model, sample) pipe = Pipe(model, balance, chunks=8) Args: partitions (int): intended number of partitions module (torch.nn.Sequential): sequential module to be partitioned sample (torch.Tensor): example input with arbitrary batch size Keyword Args: timeout (float): profiling iterates again if the timeout (in second) is not exceeded (default: ``1.0``) device ('cpu' or 'cuda' device): CPU or CUDA device where each layer is profiled (default: the current CUDA device) Returns: A list of number of layers in each partition. Use it for the `balance` parameter of :class:`~torchpipe.Pipe`. .. note:: `module` and `sample` must be placed on the same device. )r Útorchrr)rrrrrÚtimesrrrr ,s'g@)ÚchunksÚ param_scaler)rrÚinputrrrrcCs t||||t |¡ƒ}t||ƒS)aÎ Naive automatic balancing by CUDA memory usage per layer. During training, required memory for parameters depends on which optimizer is used. Optimizers may use buffers for each parameter to track optimization statistics internally, such as momentum buffer in SGD. To get more reliable size based balance, you should specify `param_scale` with regard to your optimizer. The default `param_scale` is 2 instead of 1 due to gradient accumulation which is necessary for every optimizer. Follow this guide to choose correct `param_scale` for typical optimizers: ========= ============= ========================================= Optimizer `param_scale` Internal State ========= ============= ========================================= SGD 2--3 (momentum_buffer) Adam 4--5 exp_avg, exp_avg_sq, (max_exp_avg_sq) Adadelta 4 square_avg, acc_delta Adagrad 3 sum RMSprop 3--5 square_avg, (momentum_buffer), (grad_avg) ========= ============= ========================================= Here's a simple example with the Adam optimizer:: balance = balance_by_size( torch.cuda.device_count(), model, # Same size with mini-batch to train torch.empty(1024, 3, 224, 224), # Number of micro-batches to train with Pipe chunks=8, # 4 for Adam param_scale=4.0, ) pipe = Pipe(model, balance, chunks=8) adam = Adam(pipe.parameters()) Args: partitions (int): intended number of partitions module (torch.nn.Sequential): sequential module to be partitioned input (torch.Tensor): example mini-batch with the same size to train Keyword Args: chunks (int): number of micro-batches will be used to train (default: ``1``) param_scale (float): how many copies of parameters would be allocated for training. It depends on optimizer. See the above guide. (default: ``2.0``) device ('cuda' device): CUDA device where each layer is profiled (default: the current CUDA device) Returns: A list of number of layers in each partition. Use it for the `balance` parameter of :class:`~torchpipe.Pipe`. .. note:: `module` and `input` must be placed on the same CUDA device. )r rrr)rrrrrrZsizesrrrr WsL)Ú__doc__ÚtypingrrrrrrZtorch.nnÚnnÚrZprofiler r Ú__all__rÚintÚstrZDeviceZTensorsZTensorOrTensorsrZ SequentialÚfloatr r rrrrÚsB     úù 0ùø