AttributeErrorAttributeError: : 'DeepSpeedEngineWrapper' object has no attribute '_zero3_consolidated_fp16_state_dict'

[KOVVURISATYANARAYANAREDDY]

Accelerator with Deepspeed stage 3 when saving the model giving following error
Code I used:

  import torch
  import torch.nn.functional as F
  from datasets import load_dataset
  from accelerate import Accelerator, DeepSpeedPlugin
  from transformers import AutoModelForCausalLM, AutoTokenizer, set_seed
  from data import CustomDataset
  import pandas as pd
  from tqdm import tqdm
  from transformers import AdamW
  set_seed(42)
  
  
  BATCH_SIZE = 4
  
  deepspeed_plugin = DeepSpeedPlugin(zero_stage=3, gradient_accumulation_steps=4, offload_optimizer_device='cpu')
  accelerator = Accelerator(fp16=True, deepspeed_plugin=deepspeed_plugin)
  #accelerator = Accelerator()
  
  model = AutoModelForCausalLM.from_pretrained("facebook/opt-125m") #, torch_dtype=torch.float16)
  tokenizer = AutoTokenizer.from_pretrained("facebook/opt-125m", use_fast=False)
  
  
  df = pd.read_excel("dataset.xlsx")
  dataset = CustomDataset(df, tokenizer, max_len=512)
  data = torch.utils.data.DataLoader(dataset, batch_size=BATCH_SIZE, shuffle=True,  num_workers=5)
  
  optimizer = torch.optim.AdamW(model.parameters(), lr=0.0001) #, correct_bias=True)
  
  model, optimizer, data = accelerator.prepare(model, optimizer, data)
  device = accelerator.device
  accelerator.print("Device: ", device)
  
  gradient_accum_steps = 4
  
  model.train()
  for epoch in range(10):
      
      running_loss = 0
      for batch_idx, batch in enumerate(data):
          
          input_ids = batch["Input_ids"].to(device)
          attention_mask = batch["attentions"].to(device)
          labels = batch["Input_ids"].to(device)
          
          #print("Input_ids shape", input_ids.shape)
          
          optimizer.zero_grad()
  
          output = model(input_ids,
                          attention_mask=attention_mask,
                          labels=labels
                        )
          
          #wandb.log({"Loss", output['loss'].item()})
          
          accelerator.print(f"Loss - {round(output['loss'].item(), 3)}, Batch_id - {batch_idx}, Epoch - {epoch}")
          
          
          accelerator.backward(output['loss']/gradient_accum_steps)
          
          if (batch_idx+1) % gradient_accum_steps == 0:
              optimizer.step()
          
          torch.cuda.empty_cache()
          
          running_loss += output['loss'].item()
          
      avg_loss = running_loss / ((batch_idx+1) * BATCH_SIZE)
          
      accelerator.print("Avg_loss:", avg_loss)
      if avg_loss < 100 :
          accelerator.wait_for_everyone()
          unwrapped_model = accelerator.unwrap_model(model)
          save_dir = f"MODEL/{epoch}/"
          print(f"Saving Model to {save_dir}")
          unwrapped_model.save_pretrained(save_dir, save_function=accelerator.save, state_dict=accelerator.get_state_dict(model))

The above code is giving following error:

         Traceback (most recent call last):
          File "untitled.py", line 79, in <module>
        Traceback (most recent call last):
        Traceback (most recent call last):
          File "untitled.py", line 79, in <module>
          File "untitled.py", line 79, in <module>
            unwrapped_model.save_pretrained(save_dir, save_function=accelerator.save, state_dict=accelerator.get_state_dict(model))
          File "/opt/conda/envs/t5/lib/python3.8/site-packages/accelerate/accelerator.py", line 884, in get_state_dict
                unwrapped_model.save_pretrained(save_dir, save_function=accelerator.save, state_dict=accelerator.get_state_dict(model))unwrapped_model.save_pretrained(save_dir, save_function=accelerator.save, state_dict=accelerator.get_state_dict(model))
      
        File "/opt/conda/envs/t5/lib/python3.8/site-packages/accelerate/accelerator.py", line 884, in get_state_dict
        File "/opt/conda/envs/t5/lib/python3.8/site-packages/accelerate/accelerator.py", line 884, in get_state_dict
          state_dict = model._zero3_consolidated_fp16_state_dict()
        File "/opt/conda/envs/t5/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1177, in __getattr__
          state_dict = model._zero3_consolidated_fp16_state_dict()    
      state_dict = model._zero3_consolidated_fp16_state_dict()  File "/opt/conda/envs/t5/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1177, in __getattr__
      
        File "/opt/conda/envs/t5/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1177, in __getattr__
          raise AttributeError("'{}' object has no attribute '{}'".format(
      AttributeError: 'DeepSpeedEngineWrapper' object has no attribute '_zero3_consolidated_fp16_state_dict'
              raise AttributeError("'{}' object has no attribute '{}'".format(raise AttributeError("'{}' object has no attribute '{}'".format(
      
      AttributeErrorAttributeError: : 'DeepSpeedEngineWrapper' object has no attribute '_zero3_consolidated_fp16_state_dict'

Can someone help?

[pacman100]

please update your code to following after the PR #370 is merged.

- unwrapped_model.save_pretrained(save_dir, save_function=accelerator.save, state_dict=accelerator.get_state_dict(model))
+ unwrapped_model.save_pretrained(save_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save, state_dict=accelerator.get_state_dict(model))

[KOVVURISATYANARAYANAREDDY]

After the change has been merged. I'm getting a new error .

Code i have used.

    import torch
    import torch.nn.functional as F
    from datasets import load_dataset
    from accelerate import Accelerator, DeepSpeedPlugin
    from transformers import AutoModelForCausalLM, AutoTokenizer, set_seed
    from data import CustomDataset
    import pandas as pd
    from tqdm import tqdm
    from transformers import AdamW
    import wandb
    set_seed(42)
    import os 
    #os.system('wandb login --relogin')
    
    #wandb.login()
    #wandb.init(project="OPT-accelerate", entity="satya4093", name='exp-1')
    
    BATCH_SIZE = 4
    lr = 0.0001
    
    deepspeed_plugin = DeepSpeedPlugin(zero_stage=3, gradient_accumulation_steps=4, offload_optimizer_device='cpu')
    accelerator = Accelerator(fp16=True, deepspeed_plugin=deepspeed_plugin, log_with="wandb")
    #accelerator = Accelerator(log_with="wandb")
    hps = {"learning_rate": lr}
    accelerator.init_trackers("my_project", config=hps)
    
    model = AutoModelForCausalLM.from_pretrained("facebook/opt-125m") #6.7b, torch_dtype=torch.float16)
    tokenizer = AutoTokenizer.from_pretrained("facebook/opt-125m", use_fast=False)
    #model = accelerator.prepare(model)
    
    df = pd.read_excel("dataset.xlsx")
    dataset = CustomDataset(df, tokenizer, max_len=512)
    data = torch.utils.data.DataLoader(dataset, batch_size=BATCH_SIZE, shuffle=True,  num_workers=5)
    
    optimizer = torch.optim.AdamW(model.parameters(), lr=lr) #, correct_bias=True)
    
    model, optimizer, data = accelerator.prepare(model, optimizer, data)
    #optimizer, data = accelerator.prepare(optimizer, data)
    device = accelerator.device
    accelerator.print("Device: ", device)
    
    gradient_accum_steps = 4
    
    model.train()
    for epoch in range(10):
        
        running_loss = 0
        for batch_idx, batch in enumerate(data):
            
            input_ids = batch["Input_ids"].to(device)
            attention_mask = batch["attentions"].to(device)
            labels = batch["Input_ids"].to(device)
            
            #print("Input_ids shape", input_ids.shape)
            
            optimizer.zero_grad()
    
            output = model(input_ids,
                            attention_mask=attention_mask,
                            labels=labels
                          )
            
            #wandb.log({"Loss", output['loss'].item()})
            
            accelerator.print(f"Loss - {round(output['loss'].item(), 3)}, Batch_id - {batch_idx}, Epoch - {epoch}")
            
            accelerator.log({"train_loss": output['loss'].item()}, step=batch_idx)
            
            accelerator.backward(output['loss']/gradient_accum_steps)
            
            if (batch_idx+1) % gradient_accum_steps == 0:
                optimizer.step()
            
            torch.cuda.empty_cache()
            
            running_loss += output['loss'].item()
            
        avg_loss = running_loss / ((batch_idx+1) * BATCH_SIZE)
            
        accelerator.print("Avg_loss:", avg_loss)
        if avg_loss < 100 :
            accelerator.wait_for_everyone()
            unwrapped_model = accelerator.unwrap_model(model)
            save_dir = f"MODEL/{epoch}/"
            print(f"Saving Model to {save_dir}")
            #unwrapped_model.save_pretrained(save_dir, save_function=accelerator.save, state_dict=accelerator.get_state_dict(model))
            unwrapped_model.save_pretrained(save_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save, state_dict=accelerator.get_state_dict(model))
            break
            
            
    accelerator.end_training()

I got the following error saying 'Nonetype' object is not iterable.

    Saving Model to MODEL/0/
    Traceback (most recent call last):
    Traceback (most recent call last):
      File "untitled.py", line 87, in <module>
        unwrapped_model.save_pretrained(save_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save, state_dict=accelerator.get_state_dict(model))
      File "/opt/conda/envs/OPT/lib/python3.8/site-packages/accelerate-0.9.0.dev0-py3.8.egg/accelerate/accelerator.py", line 889, in get_state_dict
        for k in state_dict:
    TypeError: 'NoneType' object is not iterable

Installed packages version:

  deepspeed==0.6.4
  accelerate==0.9.0.dev0
  torch==1.10.1+cu111
  torchaudio==0.10.1+rocm4.1
  torchvision==0.11.2+cu111
  transformers==4.19.2

I installed accelerate by cloning latest repo and "python setup.py install"

@pacman100 Please inform if i make a mistake.

[KOVVURISATYANARAYANAREDDY]

Adding this line worked for me now.

Inside accelerator.py, "get_state_dict" method.

    if self.is_main_process:
        for k in state_dict:
            if state_dict[k].dtype == torch.float16:
                state_dict[k] = state_dict[k].float()

[pacman100]

Hello @KOVVURISATYANARAYANAREDDY , thank you for informing on the new error and also coming up with the resolution. I have checked your resolution by making the above change, using the saved model and making sure the same performance is being achieved.

[KOVVURISATYANARAYANAREDDY]

Thank you @pacman100