wait_for_everyone() accelerator.save_model(model, save_directory) 如果是加载检查点,加载时建议在展开(取消包装)的模型上加载权重。这确保了加载的权重与原始模型相匹配。 unwrapped_model = accelerator.unwrap_model(model) # 取消包装模型 path_to_checkpoint = os.path.join(save_directory, "pytorch_model...
此外,在使用wait_for_everyone()保存模型之前,暂停已完成的进程直到所有进程完成(如上一节所述)也很有用。 这是一个严格遵循上述要点的简短示例。 model=MyModel()model=accelerator.prepare(model)accelerator.wait_for_everyone()# Unwrapmodel=accelerator.unwrap_model(model)state_dict=model.state_dict()# Use...
model_path="models/llama2-7b" model = AutoModelForCausalLM.from_pretrained( model_path, device_map={"": accelerator.process_index}, torch_dtype=torch.bfloat16, ) tokenizer = AutoTokenizer.from_pretrained(model_path) # sync GPUs and start the timer accelerator.wait_for_everyone() start=time...
accelerator.wait_for_everyone()start=time.time()# divide the prompt list onto the available GPUswithaccelerator.split_between_processes(prompts_all)asprompts:results=dict(outputs=[],num_tokens=0)# have eachGPUdoinferenceinbatches prompt_batches=prepare_prompts(prompts,tokenizer,batch_size=16)forpromp...
accelerator.wait_for_everyone() start=time.time() # divide the prompt list onto the available GPUs with accelerator.split_between_processes(prompts_all) as prompts: # store output of generations in dict results=dict(outputs=[], num_tokens=0) ...
edited System Info -`Accelerate`version: 0.21.0 - Platform: Linux-5.15.0-76-generic-x86_64-with-glibc2.31 - Python version: 3.10.11 - Numpy version: 1.25.2 - PyTorch version (GPU?): 2.0.1+cu118 (True) - PyTorch XPU available: False - PyTorch NPU available: False - System RAM: 39...
model = AutoModelForCausalLM.from_pretrained( model_path, device_map={"": accelerator.process_index}, torch_dtype=torch.bfloat16, ) tokenizer = AutoTokenizer.from_pretrained(model_path) # sync GPUs and start the timer accelerator.wait_for_everyone() ...
使用accelerator.wait_for_everyone()方法; 使用accelerator.unwrap_model(model)方法: 总结来说如下: accelerator.wait_for_everyone()unwrapped_model=accelerator.unwrap_model(model)accelerator.save(unwrapped_model.state_dict(),filename) 那么这个unwrap_model到底在干嘛呢,我查看了他们的源码,其中有下面这一段: ...
accelerator.wait_for_everyone() nowtime = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S') accelerator.print(f'epoch【{epoch}】@{nowtime} --> eval_metric= {100 * eval_metric:.2f}%') net_dict = accelerator.get_state_dict(model) accelerator.save(net_dict,ckpt_path+'_'+str(...
accelerator.wait_for_everyone() nowtime = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S') accelerator.print(f"epoch【{epoch}】@{nowtime} --> eval_accuracy= {eval_metric:.2f}%") net_dict = accelerator.get_state_dict(model) ...