world_size =int(os.environ['WORLD_SIZE'])# Set dataset and dataloader heret5_auto_wrap_policy = functools.partial( transformer_auto_wrap_policy, transformer_layer_cls={ T5Block, }, ) sharding_strategy: ShardingStrategy = ShardingStrategy.SHARD_GRAD_OP#for Zero2 and FULL_SHARD for Zero3torch...
world_size =int(os.environ['WORLD_SIZE'])# Set dataset and dataloader heret5_auto_wrap_policy = functools.partial( transformer_auto_wrap_policy, transformer_layer_cls={ T5Block, }, ) sharding_strategy: ShardingStrategy = ShardingStrategy.SHARD_GRAD_OP#for Zero2 and FULL_SHARD for Zero3torch...
在数据集和数据加载器设置之后,通过functools.partial函数部分应用了transformer_auto_wrap_policy,并指定T5Block为要自动包装的变压器层。这一步的目的是定义一个自动包装策略,用于后续的模型分片和并行处理。 接下来,定义了sharding_strategy变量,并将其设为ShardingStrategy.SHARD_GRAD_OP。这表示使用 Zero2 分片策略,...
--logging_steps 1 \ --fsdp "full_shard auto_wrap offload" \ --fsdp_transformer_layer_cls_to_wrap 'LlamaDecoderLayer' \ --tf32 True slow: + offload 显存在OOM边缘横跳:40426MiB / 40537MiB --num_train_epochs 3 \ --bf16 True \ --per_device_train_batch_size 1 \ --per_device_eval...
(FullyShardedDataParallelasFSDP,CPUOffload,BackwardPrefetch,)fromtorch.distributed.fsdp.wrapimport(default_auto_wrap_policy,enable_wrap,wrap,)defsetup(rank,world_size):os.environ['MASTER_ADDR']='localhost'os.environ['MASTER_PORT']='12355'# initialize the process groupdist.init_process_group("nccl"...
分片策略: [1] FULL_SHARD, [2] SHARD_GRAD_OPMin Num Params: FSDP 默认自动包装的最小参数量。Offload Params: 是否将参数和梯度卸载到 CPU。如果想要对更多的控制参数进行配置，用户可以利用 FullyShardedDataParallelPlugin ，其可以指定 auto_wrap_policy 、 backward_prefetch 以及 ignored_modules 。创建该类...
The bug occurred when I was usingtransformers.Trainerto train aLlamaForSequenceClassificationmodel with the FSDP arguments--fsdp "full_shard auto_wrap --fsdp_transformer_layer_cls_to_wrap "LlamaDecoderLayer". Specifically, when I used theTrainer.save_model()function to save the training results to...
Wrapping this T5 model with FSDP and inspecting the details of embedding layer in a transformer (T5 model) model wrapped in FSDP withShardingStrategy.FULL_SHARDandauto_wrap_policy=transformer_auto_wrap_policy, I'm observing that withStateDictType.SHARDED_STATE_DICT: ...
计算环境：LOCAL_MACHINE调试：false分布式类型：FSDP downcast_bf16：'否' fsdp_config：fsdp_auto_wrap_policy：TRANSFORMER_BASED_WRAP fsdp_backward_prefetch：BACKWARD_PRE fsdp_cpu_ram_efficient_loading：true fsdp_forward_prefetch：false fsdp_offload_params：true fsdp_sharding_strategy：FULL_SHARD fsdp_state...
auto_wrap_policy=t5_auto_wrap_policy, mixed_precision=bfSixteen) 损失缩放 解决FP16 下溢问题的另一个方法是损失缩放(Loss Scale)。刚才提到,训练到了后期,梯度(特别是激活函数平滑段的梯度)会特别小,FP16 表示容易产生下溢现象。为了解决梯度过小的问题,需要对损失进行缩放,由于链式法则的存在,损失的缩放也会...