loss = model_engine(batch) #runs backpropagation model_engine.backward(loss) #weight update model_engine.step() #save checkpoint if step % args.save_interval: client_sd['step'] = step ckpt_id = loss.item() model_engine.save_checkpoint(args.save_dir, ckpt_id, client_sd ...
loss = model_engine(batch) #runs backpropagation model_engine.backward(loss) #weight update model_engine.step() #save checkpoint if step % args.save_interval: client_sd['step'] = step ckpt_id = loss.item() model_engine.save_checkpoint(args.save_dir, ckpt_id, client_sd = client_sd) ...
Loss Scaling: 在FP16/混合精度训练中, DeepSpeed 引擎会自动处理缩放损失,以避免梯度中的精度损失。 Learning Rate Scheduler: 当使用 DeepSpeed 的学习率调度器(在ds_config.json文件中指定)时, DeepSpeed 会在每次训练步骤(执行model_engine.step()时)调用调度器的step()方法。当不使用DeepSpeed的学习率调度器时:...
for i, data in enumerate(trainloader): inputs, labels = data[0].to(model_engine.local_rank), data[1].to( model_engine.local_rank) outputs = model_engine(inputs) loss = criterion(outputs, labels) model_engine.backward(loss) model_engine.step() # print statistics running_loss += loss...
optimizer = optim.SGD(ddp_model.parameters(), lr=0.001)# forward passoutputs = ddp_model(torch.randn(20,10).to(rank)) labels = torch.randn(20,10).to(rank)# backward passloss_fn(outputs, labels).backward()# update parametersoptimizer.step()print(f"Rank{rank}: Successfully completed train...
model_engine,optimizer,_,_=deepspeed.initialize(args=...,model=model)forepochinrange(num_epochs):fordata,targetindataloader:model_engine.zero_grad()# 清零梯度output=model_engine(data)# 前向传播loss=loss_fn(output,target)# 计算损失model_engine.backward(loss)# 反向传播model_engine.step()# 更新...
由deepspeed.initialize 返回的模型是 DeepSpeed 模型引擎,我们将基于该引擎使用 forward、backward 和 step API 训练模型。 前向传播 前向传播API与PyTorch兼容,不需要进行任何更改。 反向传播 通过在模型引擎上直接调用 backward(loss) 来进行反向传播。 代码语言:javascript 复制 def backward_step(optimizer, model, ...
actor_model.step() # 获得seq的critic得分 value = self.critic_model.forward_value(**batch, return_value_only=True, use_cache=False)[:, :-1] # 计算Critic loss critic_loss = self.critic_loss_fn(value[:, start:], old_values[:, start:], returns, action_mask[:, start:]) """ def...
DeepSpeed has been used to train many different large-scale models, below is a list of several examples that we are aware of (if you'd like to include your model please submit a PR): Megatron-Turing NLG (530B) Jurassic-1 (178B) ...
来实现类似的功能，FSDP可以看成是ZERO-3的实现，传统的数据并行(DDP)是在每一个GPU卡上保存整个model...