load_checkpoint_in_model(unwrapped_model, save_directory, device_map={"": device}) 也可以使用load_checkpoint_and_dispatch() 函数在空模型中加载完整检查点或分片检查点,它还会自动在您可用的设备(GPU、CPU RAM)上分配这些权重。完整的模型分片推理过程见此YouTube视频。 load_checkpoint_and_dispatch函数常...
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.bin") unwrapped_model.load_...
when I useAccelerator.save(unwrapped_model.state_dict(), path), the model will be saved twice (because I used two gpus) In the PyTorch DDP example, they save the model only when the rank is 0, which avoid saving the model multiple times. How can I do that with accelerate?
Kaggle/Colab Notebook首先clone了git项目仓库,然后安装相关的Python依赖,接着运行了目录下的initialize.py,主要是下载各种需要的模型文件(预训练BERT、WavLM模型、BERT-VITS模型)。 然后需要指定相关的路径(其实真正需要指定的就是input_root,对应上传数据集的路径,其他路径如dataset_root和model_name都是输出文件的目录...
If my use-case doesn't involve pushing to hub (which save_pretrained does under the hood), then could you elaborate specific differences between save_pretrained and save_state (with regards to model saving, since save_state might be saving other entities too)? The docs for save_pretrained ar...
accelerator.save_model(model,"./weights/last") ifval_acc > best_acc: best_acc = val_acc accelerator.save_model(model,"./weights/best") accelerator.wait_for_everyone() accelerator.print( f"[Validation] Acc:{val_acc *100:.2f}%", ...
logits=model(input_ids)["logits"] logits=logits.view(-1,logits.size(-1)) labels=labels.view(-1) loss=loss_fun(logits,labels) accelerator.backward(loss) optimizer.step() lr_scheduler.step() model.save_pretrained("./lora_saver/lora_query_key_value.pth") ...
To save woman's life 12·8 11·2–17·4 39 34–53% Physical health 6·3 5·9–7·9 43 40–53% Woman's mental health 2·5 2·1–3·7 32 27–48% Socioeconomic grounds 10·3 7·5–15·6 31 22–47% On request 20·7 17·3–27·3 34 28–45% Physical or mental health,...
unwrapped_model.save_pretrained(save_dir, save_function=accelerator.save, state_dict=accelerator.get_state_dict(model)) 注意:目前DeepSpeed支持处于实验阶段。如果遇到问题,请提出问题。 5、从notebook启动训练 Accelerate还提供了一个notebook_launcher函数,您可以在笔记本中使用它来启动分布式训练。这对于具有TPU后...
"stage3_gather_16bit_weights_on_model_save":false, "offload_optimizer": { "device":"none" }, "offload_param": { "device":"none" } }, "gradient_clipping": 1.0, "train_batch_size":"auto", "train_micro_batch_size_per_gpu":"auto", ...