计算所有参数的梯度的L2范数,通过all_reduce的分布式操作将梯度的L2范数广播给所在进程分组,并根据梯度的L2范数对所有参数的梯度按照max_norm(取值1.0)进行正则,详见clip_fp32_gradients函数; 调用optimizer的step方法更新权重; 调用optimizer的zero_grad方法重置梯度相关数据结构; losses:置为0.0; global_steps:加1; gl...
deepspeed会在优化器参数中存储模型的主参数,存储在global_step*/*optim_states.pt文件中,数据类型为fp32。因此,想要从checkpoint中恢复训练,则保持默认即可 如果模型是在ZeRO-2模式下保存的,模型参数会以fp16的形式存储在pytorch_model.bin中 如果模型是在ZeRO-3模式下保存的,需要如下所示设置参数,否则pytorch_model...
'global_step' ] Activity LiweiPengchanged the title deepspeed_light,py bug: 'global_step' should be 'global_steps' in _load_checkpoint() deepspeed_light.py bug: 'global_step' should be 'global_steps' in _load_checkpoint() on Mar 7, 2020 ShadenSmithadded bugSomething isn't working on ...
model,epoch,last_global_step,last_global_data_samples,**kwargs):"""Utility function for checkpoi...
def checkpoint_model(PATH, ckpt_id, model, epoch, last_global_step, last_global_data_samples, **kwargs): """Utility function for checkpointing model + optimizer dictionaries The main purpose for this is to be able to resume training from that instant again """ checkpoint_state_dict = {...
一旦DeepSpeed引擎被初始化,就可以使用三个简单的API来进行前向传播(callable object)、反向传播(backward)和权重更新(step)来训练模型。 代码语言:javascript 复制 forstep,batchinenumerate(data_loader):#forward()method loss=model_engine(batch)#runs backpropagation ...
bin Processing zero checkpoint at global_step1 Detected checkpoint of type zero stage 3, world_size: 2 Saving fp32 state dict to pytorch_model.bin (total_numel=60506624) 当你保存checkpoint时,zero_to_fp32.py脚本会自动生成。注意:目前该脚本使用的内存(通用RAM)是最终checkpoint大小的两倍。 或者,...
最后,您可以查看训练模型的效果。您可以使用TensorBoard来可视化训练指标,例如损失值和准确度: # 使用TensorBoard来查看训练效果fromtorch.utils.tensorboardimportSummaryWriter writer=SummaryWriter()# 记录训练指标writer.add_scalar('loss',loss,global_step=step)writer.add_scalar('accuracy',accuracy,global_step=step...
collect_pretrain_data(GLOBAL_DATA_PATH) print('valid_medical.') process_valid_medical(tokenizer,save_all_text) if save_all_text: print('test_medical.') # 测试数据集不需要处理 process_test_medical(tokenizer, save_all_text) print('sft_process.') sft_process(save_all_text) # process_valid...
This happens specifically when a) training on a large number of GPUs relative to the global batch size, which results in small per-GPU batch size, requiring frequent communication, or b) training on low-end clusters, where cross-node network bandwidth is limited, ...