根据当前batch的loss调用optimizer的backward进行反向梯度传播; 执行梯度reduce操作,详见allreduce_gradients函数; f)allreduce_gradients 如果需划分梯度(zero stage>=2),调用optimizer的overlapping_partition_gradients_reduce_epilogue; 如果进划分优化器状态(zero stage=1)且micro_steps到达了gradient_accumulation_steps,则...
针对模型状态的存储优化(去除冗余),ZeRO使用的方法是分片,即每张卡只存 1/N的模型状态量,这样系统内只维护一份模型状态。 ZeRO 具有三个主要的优化阶段(ZeRO-1,ZeRO-2,ZeRO-3),它们对应于优化器状态(optimizer states)、梯度(gradients)和参数(parameters)的分片。累积启用时: 优化器状态分区 (P_{os}) – ...
#每个GPU的bs"steps_per_print":1000,#打印间隔"prescale_gradients":false,"optimizer":{#优化器相关...
worker-0: prescale_gradients ... False worker-0: scheduler_name ... WarmupLR worker-0: scheduler_params ... {'warmup_min_lr': 0, 'warmup_max_lr': 0.001, 'warmup_num_steps': 1000} worker-0: sparse_gradients_enabled ... False worker-0: steps_per_print ... 2000 worker-0: te...
[2024-01-18 10:49:19,677] [INFO] [config.py:988:print] prescale_gradients ... False [2024-01-18 10:49:19,677] [INFO] [config.py:988:print] scheduler_name ... None [2024-01-18 10:49:19,677] [INFO] [config.py:988:print] scheduler_params ... None [2024-01-18 10:49:19...
例1：人类：我觉得我很难过，能不能告诉我如何开心起来？机器：...例2：人类：我觉得很难过，能不能...
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"prescale_gradients":False,#是否在梯度累计之前就进行梯度缩放,通常用于防止梯度下溢。 "wall_clock_breakdown":False,#是否进行每步训练时间的详细分析。 "hybrid_engine":{ "enabled":enable_hybrid_engine, "max_out_tokens":max_out_tokens, "inference_tp_size":inference_tp_size, "release_inference_cache...
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{ "warmup_min_lr": 0, "warmup_max_lr": 0.001, "warmup_num_steps": 1000, }, }, "gradient_clipping": 1.0, "prescale_gradients": False, "bf16": {"enabled": args.dtype == "bf16"}, "fp16": { "enabled": args.dtype == "fp16", "fp16_master_weights_and_grads": False, ...