这个参数表示在执行参数更新之前,将多少个微批量(micro-batch)的梯度累积起来。例如,如果gradient_accumulation_steps设置为4,那么系统将累积4个微批量的梯度,然后才进行一次参数更新。 例如,假设你有4个GPU,train_micro_batch_size_per_gpu为32,gradient_accumulation_steps为4。那么,train_batch_size将是 32 * 4 ...
"gradient_accumulation_steps": "auto", "train_micro_batch_size_per_gpu": "auto", train_batch_size = train_micro_batch_size_per_gpu * gradient_accumulation * number of GPUs.(即训练批次的大小 = 每个GPU上的微批次大小 * 几个微批次 * 几个GPU) 优化器 "optimizer": { "type": "Adam", ...
train_micro_batch_size_per_gpu:每个GPU上处理的单个微批量的大小。 gradient_accumulation_steps:在执行参数更新之前,累积的微批量梯度数量。 train_batch_size:整个训练批量的大小,即所有GPU上并行处理的总样本数。 optimizer:优化器配置,包括学习率、动量等参数。 此外,配置文件还可以包括其他高级选项,如学习率调度...
{"train_batch_size": 128,"gradient_accumulation_steps": 1,"optimizer": {"type":"Adam","params": {"lr": 0.00015} },"zero_optimization": {"stage": 2} } deepseed安装好后,直接一行命令就开始运行:deepspeed ds_train.py --epoch 2 --deepspeed --deepspeed_config ds_config.json ;从日志可...
"gradient_accumulation_steps":"auto", "gradient_clipping":"auto", "steps_per_print":2000, "train_batch_size":"auto", "train_micro_batch_size_per_gpu":"auto", "wall_clock_breakdown":false } 启动deepspeed 我们在LLaMA-Factory的目录下,运行该命令即可启动 ...
{"train_batch_size":"auto","train_micro_batch_size_per_gpu":"auto","gradient_accumulation_steps":"auto","gradient_clipping":"auto","zero_allow_untested_optimizer":true,"fp16":{"enabled":"auto","loss_scale":0,"initial_scale_power":16,"loss_scale_window":1000,"hysteresis":2,"min_...
gradient_accumulation_steps＝2, per_device_train_batch_size=2,per_device_mini_train_batch_size=2...
梯度累积步数 (gradient_accumulation_steps):通过设置这个参数，可以定义梯度累积的步数。这意味着在执行...
--gradient_accumulation_steps 1 \ --save_strategy epoch \ --learning_rate 2e-4 \ --lr_scheduler_type constant \ --adam_beta1 0.9 \ --adam_beta2 0.98 \ --adam_epsilon 1e-8 \ --max_grad_norm 1.0 \ --weight_decay 1e-4 \ ...
'--gradient_accumulation_steps', '1', '--lr_scheduler_type', 'cosine', '--num_warmup_steps', '0', '--seed', '1234', '--gradient_checkpointing', '--zero_stage', '3', '--deepspeed', '--lora_dim', '128', '--lora_module_name', 'layers.', '--output_dir', './output...