RuntimeError: params[0] in this process with sizes [253991, 1024] appears not to match sizes of the same param in process 0
I am fine tuning masked language model from XLM Roberta large on google machine specs.I have extended vocabulary by adding extra tokens.I am using pre-trained Hugging face model.I launch it as train.py file which I copy inside docker image and use vertex-ai ( GCP) to launch it using ContainerspecmachineSpec = MachineSpec(machine_type=“a2-highgpu-4g”,accelerator_count=4,accelerator_type=“NVIDIA_TESLA_A100”)python -m torch.distributed.launch --nproc_per_node 4 train.py --gradient_accumulation_steps 16 --per_device_train_batch_size 4 --optim adamw_hf --tf32 --bf16"])device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')Traceback (most recent call last):\n', ' File "train.py", line 215, in <module>\n trainer.train()\n', ' File "/opt/conda/lib/python3.7/site-packages/transformers/trainer.py", line 1258, in train\n model = self._wrap_model(self.model_wrapped)\n', ' File "/opt/conda/lib/python3.7/site-packages/transformers/trainer.py", line 1088, in _wrap_model\n **kwargs,\n', ' File "/opt/conda/lib/python3.7/site-packages/torch/nn/parallel/distributed.py", line 641, in __init__\n dist._verify_params_across_processes(self.process_group, parameters)\n', 'RuntimeError: params[0] in this process with sizes [253991, 1024] appears not to match sizes of the same param in process 0.\n'torch==1.11.0+cu113

torchvision==0.12.0+cu113

torchaudio==0.11.0+cu113

transformers==4.17.0

Using GPU in script?: Yes

Using distributed or parallel set-up in script?: YesWho can help@sguggerModels:Roberta-xlm-largeLibrary:Tokenizers:@SaulLuTrainer:@sguggerInformationModel I am using (Bert, XLNet …):The problem arises when using:[ ] my own modified scripts: (give details below)The tasks I am working on is:[ ] my own task or dataset: (give details below)To reproduceSteps to reproduce the behavior:tokenizer = tr.XLMRobertaTokenizer.from_pretrained("xlm-roberta-large",local_files_only=True)

tokenizer.add_tokens(joined_keywords)

tokenizer_org = tr.XLMRobertaTokenizer.from_pretrained("xlm-roberta-large",local_files_only=True)

model = tr.XLMRobertaForMaskedLM.from_pretrained("xlm-roberta-large", return_dict=True,local_files_only=True)


# add embedding params for new vocab words
model.resize_token_embeddings(len(tokenizer))
weights = model.roberta.embeddings.word_embeddings.weight


    
# initialize new embedding weights as mean of original tokens
with torch.no_grad():
    emb = []
    for i in range(len(joined_keywords)):
        word = joined_keywords[i]
        # first & last tokens are just string start/end; don't keep
        tok_ids = tokenizer_org(word)["input_ids"][1:-1]
        tok_weights = weights[tok_ids]

        # average over tokens in original tokenization
        weight_mean = torch.mean(tok_weights, axis=0)
        emb.append(weight_mean)
    weights[-len(joined_keywords):,:] = torch.vstack(emb).requires_grad_()

# tokenizer.convert_ids_to_tokens(encoded_input["input_ids"][0])
tokenizer_out_files = tokenizer.save_pretrained("tokenizer_xlm")model.to(device)train_encodings = tokenizer(train_df, truncation=True, padding=True, max_length=512, return_tensors="pt")class SEDataset(torch.utils.data.Dataset):
    def __init__(self, encodings):
        self.encodings = encodings
        
    def __getitem__(self, idx):
        item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
        return item

    def __len__(self):
        return len(self.encodings["attention_mask"])

train_data = SEDataset(train_encodings)

# print("train data created")

training_args = tr.TrainingArguments(

     output_dir='results_mlm_vocab_exp'
    ,logging_dir='logs_mlm_vocab_exp'        # directory for storing logs
    ,save_strategy="epoch"
#     ,run_name="MLM_Exp1"
    ,learning_rate=2e-5
    ,logging_steps=2000
    ,overwrite_output_dir=True
    ,num_train_epochs=20
    ,per_device_train_batch_size=4
    ,prediction_loss_only=True
    ,gradient_accumulation_steps=16
#     ,sharded_ddp='zero_dp_3'
#     ,gradient_checkpointing=True
    ,bf16=True #Ampere GPU
#     ,fp16=True
    ,optim="adamw_hf"
#     ,dataloader_num_workers=20
#     ,logging_strategy='no'
    #     per_device_train_batch_size
#     per_gpu_train_batch_size
    #     disable_tqdm=True
)

# print("training sample is 200001")
# print("Included ,gradient_accumulation_steps=8 ,bf16=True and per_device_train_batch_size=16 " )
print("start time",start)
trainer = tr.Trainer(
    model=model,
    args=training_args,
    data_collator=data_collator,
    train_dataset=train_data
)
# print("training to start without bf16")
trainer.train()