fsdp_sync_module_states: true fsdp_transformer_layer_cls_to_wrap: BertLayer fsdp_use_orig_params: true machine_rank: 0 main_training_function: main mixed_precision: bf16 num_machines: 1 num_processes: 2 rdzv_backend: static same_network: true tpu_env: [] tpu_use_cluster: false tpu_use...
Hey there, Trying to fine-tune your model. What is the FSDP value for fsdp_transformer_layer_cls_to_wrap? Thanks!Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment Assignees No one assigned Labels None yet Projects None yet Milestone No...
--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...
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...
激活工作内存:激活工作内存是反向传播过程中所需的内存,用于在执行实际反向传播之前重新计算激活。是两个连续激活检查点之间的激活量。例如,如果为每个 Transformer 块创建一个激活检查点,那么内存就是每个 Transformer 块的总激活量。其字节数约为: bsz×seq×ci×(16×hd+2×attn_heads×seq) ...
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...
FULL_STATE_DICTfsdp_transformer_layer_cls_to_wrap:Qwen2DecoderLayerfsdp_sync_module_states:truefsdp_use_orig_params:falsemachine_rank:0num_machines:1num_processes:2main_training_function:mainmixed_precision:bf16rdzv_backend:staticsame_network:truetpu_env:[]tpu_use_cluster:falsetpu_use_sudo:false...
true fsdp_transformer_layer_cls_to_wrap: BertLayer fsdp_use_orig_params: true machine_rank: 0 main_training_function: main mixed_precision: bf16 num_machines: 1 num_processes: 2 rdzv_backend: static same_network: true tpu_env: [] tpu_use_cluster: false tpu_use_sudo: fals...
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...
environ['WORLD_SIZE']) # Set dataset and dataloader here t5_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 Zero3 torch.cuda.set_...