sub_group_size: 子组大小。 allgather_partitions: 是否聚集所有分区。 allgather_bucket_size: allgather的桶大小。 overlap_comm: 是否重叠通信。 reduce_scatter: 是否使用reduce scatter。 reduce_bucket_size: reduce的桶大小。 contiguous_gradients: 是否使梯度连续。 速度方面(左边比右边快) 阶段0 (DDP) >...
reduce_bucket_size:默认值500000000; use_multi_rank_bucket_allreduce:true; elements_in_ipg_bucket:默认值0; dtype:模型参数类型 gradient_accumulation_dtype:float32类型, use_separate_grad_accum:如果dtype不等于gradient_accumulation_dtype,则取值true,否则为false; use_grad_accum_attribute:是否做梯度累加(由...
"contiguous_gradients": true, "sub_group_size": 1e9, "reduce_bucket_size": 1e6, "stage3_prefetch_bucket_size": 0.94e6, "stage3_param_persistence_threshold": 1e4, "stage3_max_live_parameters": 1e9, "stage3_max_reuse_distance": 1e9, "stage3_gather_16bit_weights_on_model_save": ...
"contiguous_gradients":true, "sub_group_size":1e9, "reduce_bucket_size":"auto", "stage3_prefetch_bucket_size":"auto", "stage3_param_persistence_threshold":"auto", "stage3_max_live_parameters":1e9, "stage3_max_reuse_distance":1e9, "stage3_gather_16bit_weights_on_model_save":true ...
"reduce_scatter": true, "reduce_bucket_size": 2e8, "overlap_comm": true, "contiguous_gradients": true, "cpu_offload": true, "cpu_offload_params": false, "cpu_offload_use_pin_memory": false, "sub_group_size": 1e9, "stage3_prefetch_bucket_size": 5e7, ...
{ "zero_optimization": { "stage": 1, "reduce_bucket_size": 5e8 } } 如上所示,我们在zero_optimization键中设置了两个字段。具体来说,我们将stage字段设置为1,并将可选的reduce_bucket_size设置为500M。启用ZeRO Stage1后,模型现在可以在8个GPU上平稳地训练,而不会耗尽内存。以下是模型训练的一些屏幕...
reduce_bucket_size：用于指定每次进行 reduce 或 allreduce 操作时处理的元素数量，以便在分布式训练中...
"stage": 2, "allgather_partitions": true, "allgather_bucket_size": 5e8, "reduce_scatter": true, "reduce_bucket_size": 5e8, "overlap_comm": false, "contiguous_gradients": true } }JustinWang0121 changed the title deepspeed多卡训练Mixtral,八张H800爆显卡,求大神帮忙看看 deepspeed多卡训练Mix...
"reduce_bucket_size": "auto", "stage3_prefetch_bucket_size": "auto", "stage3_param_persistence_threshold": "auto", "gather_16bit_weights_on_model_save": True, "round_robin_gradients": True, }, "gradient_accumulation_steps": "auto", ...
"reduce_bucket_size": "auto", "stage3_prefetch_bucket_size": "auto", "stage3_param_persistence_threshold": "auto", "stage3_max_live_parameters": 1e9, "stage3_max_reuse_distance": 1e9, "stage3_gather_16bit_weights_on_model_save": true ...