![]() This takes in the previous state when generating successive items of text. This is the process of only changing the parameters in selected layers, made famous by the ULMFit process. ![]() ![]() Accumulated gradients - this gives larger effective batch sizes than Colab allows (GPT2 is a large model, and anything more than a batch size of 2 would be enough to get a CUDA out of memory error on Colab).Using the nlp library to load in the dataset and setting up the training workflow, which looks to streamline things rather nicely.I should mention what the script doesn’t cover: Chris’ code has practically provided the basis for this script - you should check out his tutorial series for more great content about transformers and nlp. I’ve liberally taken things from Chris McCormick’s BERT fine-tuning tutorial, Ian Porter’s GPT2 tutorial and the Hugging Face Language model fine-tuning script so full credit to them. Familiarity with the workings of GPT2 might be useful but isn’t required. If you don’t, this official PyTorch tutorial serves as a solid introduction. You should understand the basics of PyTorch and how a training loop works before getting started. There are many ways of getting PyTorch and Hugging Face to work together, but I wanted something that didn’t stray too far from the approaches shown in the PyTorch tutorials. It’s intended as an easy-to-follow introduction to using Transformers with PyTorch, and walks through the basics components and structure, specifically with GPT2 in mind. Nevertheless, it's still not working (I'm getting starting perplexities of like 10^40 - so it's clearly doing something, but not what I want).I’m sharing a Colab notebook that illustrates the basics of this fine-tuning GPT2 process with Hugging Face’s Transformers library and PyTorch. Use this code to adapt parameters (we need to skip the first four words in the vocab because they're reserved for bos, pad, eos, and unk, and we need to skip the first positional embedding due to padding)įor name, param in hf_pretrained_gpt2_model.named_parameters():įairseq_gpt2_small = paramįairseq_gpt2_small = fairseq_gpt2_smallĮlif '.'+name in fairseq_gpt2_small.keys():.Load Huggingface's pretrained gpt2 model.arch hf_gpt2 -embed-dim 768 -num-layers 12 -num-attention-heads 12 -max-target-positions 1024 \ Train fairseq's hf_gpt2 for one dummy epoch to gain access to its checkpoint1.pt file.Use a dict.txt where the words are in the same order as their indices in HF's gpt2 tokenizer (i.e.There are several things I've done to get this to work: I've been trying to adapt Huggingface's GPT2 small model. fairseq/fairseq/checkpoint_utils.py in load_checkpoint_to_cpu(path, arg_overrides)ġ71 for arg_name, arg_val in arg_ems(): > 199 state = load_checkpoint_to_cpu(filename, arg_overrides) fairseq/fairseq/checkpoint_utils.py in load_model_ensemble_and_task(filenames, arg_overrides, task, strict)ġ98 raise IOError("Model file not found: ".format(filename)) fairseq/fairseq/hub_utils.py in from_pretrained(model_name_or_path, checkpoint_file, data_name_or_path, archive_map, **kwargs)ħ1 models, args, task = checkpoint_utils.load_model_ensemble_and_task(ħ2 , in -> 1 custom_lm = om_pretrained('./checkpoints/gpt2', 'gpt2-pytorch_model.bin', tokenizer='moses', bpe='gpt2')./fairseq/fairseq/models/fairseq_model.py in from_pretrained(cls, model_name_or_path, checkpoint_file, data_name_or_path, **kwargs) KeyError Traceback (most recent call last) : ch_model.bin', tokenizer='moses', bpe='gpt2') Nonetheless, to verify here's what I got In : custom_lm = om_pretrained('./checkpoints/gpt2', 'gpt2-pytor I don't believe this will work since Fairseq has a different class structure than Pytorch transformers
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