1/4/2023 0 Comments Bert finetune![]() ![]() I also tried that, but have the same above issues that I mentioned: 1) the performance does not yield to that of setting without gradient-checkpointing. Initialize a new run for the evaluation-job. Here we will pass the evaluation dictionary as it is and log it. That’s an argument that is specified in BertConfig and then the object is passed to om_pretrained. wandb.log (): Log a dictionary of scalars (metrics like accuracy and loss) and any other type of wandb object. I also noticed that there’s a recently implemented option in Huggingface’s BERT which allows us to apply gradient checkpointing easily. Bert finetune update#Isn’t that true argument, wherever we have learning (i.e., the update of model parameters), we can use gradient checkpointing? Upon my investigations, I noticed that this part of the model consumes much of a memory, so that I thought it’d be better to checkpoint it. I a sense, the weights associated with this class should be updated (i.e., learned) during training. I see top_vec as a vector that has the encoded version of vector x (i…e, src) by the BERT. Yes, this model is just part of a larger network, i.e., top_vec which is the output of this model is being used by another model. Is there any problem with the implementation? or have I done any part thanks for your response! Another observation: while checkpointing, the model’s training speed also increases considerably which is totally odd to what I have learned from gradient checkpointing. Top_vec, _ = self.model(x, attention_mask=mask, token_type_ids=segs)Īs I’m checkpointing the BERT’s forward function, the memory usage drops significantly (~1/5), but I’m getting relatively inferior performance compared to non-checkpointing, in terms of the metrics (for my task, which is summarization) that I’m calculating on the validation set. Top_vec, _ = self.model(x.long(), attention_mask=mask.long(), token_type_ids=segs.long()) Output = module(inputs, attention_mask=inputs, token_type_ids=inputs) Self.finetune = finetune # either the bert should be finetuned or not. Self.model = om_pretrained('allenai/scibert_scivocab_uncased', cache_dir=temp_dir) Bert finetune code#I’m skeptical if I’m doing it right, though! Here is my code snippet wrapped around the BERT class: class Bert(nn.Module):ĭef _init_(self, large, temp_dir, finetune=False): I’m trying to apply gradient checkpointing to the huggingface’s Transformers BERT model. ![]()
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