https://mp.weixin.qq.com/s/8F3eAHDBjQkHHBmrAEoOfw zero stage2:将优化器状态和梯度划分多份,每个GPU各自维护一份; 每块GPU上存放一份完整的参数W,将一个batch的数据划分3份,每个GPU读取各自一份,做完一轮forward和backward后,计算得到一份完整的梯度; 对梯度做一次reduce-scatter,保证每个GPU上所维持的那...
), _cleanup_gpus works as expected but when I use Deepspeed Zero 2 (accelerate launch --use_deepspeed train.py --deepspeed config.json ...) the GPU memory does not clear. { "bf16": { "enabled": true }, "optimizer": { "type": "AdamW", "params": { "lr": "auto", "betas":...
DeepSpeed config: ds_config=DeepSpeedStrategy(stage=2, offload_optimizer=False, offload_parameters=False, logging_level=logging.INFO, load_full_weights=True) How to modify the code to load the checkpoint and also resume from it ? Environment ...
My current solution to this is always usingself.deepspeed.save_16bit_model()intrainer.save_model()for zerostage2: elifself.deepspeed:# this takes care of everything as long as we aren't under zero3ifself.args.should_save:self._save(output_dir)ifis_deepspeed_zero3_enabled():# It's to...
Describe the bug When performing a training run with a model with Mixture of Experts (MoE) layers using stage 2 offload with the DeepSpeedCPUAdam optimizer, during the parameter update step the following runtime error is thrown. │ /home/...
MedicalGPT: Training Your Own Medical GPT Model with ChatGPT Training Pipeline. 训练医疗大模型,实现了包括增量预训练、有监督微调、RLHF(奖励建模、强化学习训练)和DPO(直接偏好优化)。 - MedicalGPT/deepspeed_zero_stage2_config.json at main · jiangtann/Medica
deepspeed.runtime.zero.stage_1_and_2.DeepSpeedZeroOptimizer.average_tensor only sets reduction stream waiting for default stream. This is ok in cases where the computation time is longer than the c...
DeepSpeed ZeRO INIT with Stage 3 is failing with device mismatch error To Reproduce Steps to reproduce the behavior: Run below command: accelerate launch --num_processes=2 --num_machines=1 --machine_rank=0 --mixed_precision=fp16 --use_deepspeed --gradient_accumulation_steps=1 --gradient_clip...
I am using Huggingface Seq2SeqTrainer for training Flan-T5-xl model with deepspeed stage 3. trainer = Seq2SeqTrainer( #model_init = self.model_init, model=self.model, args=training_args, train_dataset=train_ds, eval_dataset=val_ds, token...