Problem: Trying to convert DeepSpeed zero checkpoints to PyTorch state_dicts leads to one layer not being present in the generated state dict. I am using the zero_to_fp32.py script. I'm trying to train a GPT2 like model, and it looks lik...
The problem is the module is load with dataparallel activated and you are trying to load it without data parallel. That's why there's an extra module at the beginning of each key! 错误原因就是net.load_state_dict的时候,net的状态不是处在gpu并行状态,而存储的net模型checkpoint是在gpu并行状态下...
state_dict作为python的字典对象将每一层的参数映射成tensor张量,需要注意的是torch.nn.Module模块中的state_dict只包含卷积层和全连接层的参数,当网络中存在batchnorm时,例如vgg网络结构,torch.nn.Module模块中的state_dict也会存放batchnorm's running_mean。
Trainerfrompytorch_lightning.callbacksimportModelCheckpointfrompytorch_lightning.pluginsimportDeepSpeedPluginfrompytorch_lightning.utilities.deepspeedimportconvert_zero_checkpoint_to_fp32_state_dictdefset_environment_variables_for_nccl_backend(single_node=False,master_port=6105):ifnotsingle_node:master_node...
(representing the sharding of the data employed by the application) and using the dist_checkpointing.save and dist_checkpointing.load entrypoints as replacements for torch.save and torch.load. In Megatron Core, the sharded state dictionary preparation is already implemented in a sharded_state_dict ...
错误信息表明在加载state_dict时遇到了尺寸不匹配的问题。具体来说,img_in.weight这一层在检查点(checkpoint)中的形状是torch.size([3072, 384]),但在当前模型中的形状是torch.size([3072, 64])。这意味着这两个形状不一致,导致无法正确加载参数。 检查模型定义中的img_in.weight层: 你需要查看你的模型定义中...
classBasicServer(BasicParty):defsave_checkpoint(self):cpt={'round':self.current_round,# 当前训练轮数'learning_rate':self.learning_rate,# 当前学习率'model_state_dict':self.model.state_dict(),# 当前模型参数'early_stop_option':{# 当前早停选项'_es_best_score':self.gv.logger._es_best_score...
step, state_dict, ckpt_path, storage_type=StorageType.MEMORY ) #将 checkpoint 异步存入到存储中,可以低频导出。也可以高频导出,但是高频导出会 # 占据很多存储空间,用户需要自行清理老的Checkpoint。 if iter_num % save_storage_interval == 0:
model_dict = model.local_state_dict() # save a partial model opt_dict = optimizer.local_state_dict() # save a partial optimizer state # Save the dictionaries at rdp_rank 0 as a checkpoint if smp.rdp_rank() == 0: smp.save( {"model_state_dict": model_dict, "optimizer_...
🐛 Describe the bug pytorch/torch/distributed/checkpoint/state_dict.py Lines 611 to 614 in 585dbfa for param_group in optim.param_groups: if "lr" in param_group: lrs.append(param_group["lr"]) param_group["lr"] = 0.0 When the original LR i...