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* First pass * Apply suggestions from code review * Apply suggestions from code review Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
132 lines
4.4 KiB
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132 lines
4.4 KiB
Plaintext
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# OpenAI GPT2
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## Overview
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OpenAI GPT-2 model was proposed in [Language Models are Unsupervised Multitask Learners](https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) by Alec
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Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei and Ilya Sutskever. It's a causal (unidirectional)
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transformer pretrained using language modeling on a very large corpus of ~40 GB of text data.
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The abstract from the paper is the following:
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*GPT-2 is a large transformer-based language model with 1.5 billion parameters, trained on a dataset[1] of 8 million
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web pages. GPT-2 is trained with a simple objective: predict the next word, given all of the previous words within some
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text. The diversity of the dataset causes this simple goal to contain naturally occurring demonstrations of many tasks
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across diverse domains. GPT-2 is a direct scale-up of GPT, with more than 10X the parameters and trained on more than
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10X the amount of data.*
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Tips:
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- GPT-2 is a model with absolute position embeddings so it's usually advised to pad the inputs on the right rather than
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the left.
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- GPT-2 was trained with a causal language modeling (CLM) objective and is therefore powerful at predicting the next
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token in a sequence. Leveraging this feature allows GPT-2 to generate syntactically coherent text as it can be
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observed in the *run_generation.py* example script.
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- The model can take the *past_key_values* (for PyTorch) or *past* (for TF) as input, which is the previously computed
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key/value attention pairs. Using this (*past_key_values* or *past*) value prevents the model from re-computing
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pre-computed values in the context of text generation. For PyTorch, see *past_key_values* argument of the
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[`GPT2Model.forward`] method, or for TF the *past* argument of the
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[`TFGPT2Model.call`] method for more information on its usage.
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- Enabling the *scale_attn_by_inverse_layer_idx* and *reorder_and_upcast_attn* flags will apply the training stability
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improvements from [Mistral](https://github.com/stanford-crfm/mistral/) (for PyTorch only).
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[Write With Transformer](https://transformer.huggingface.co/doc/gpt2-large) is a webapp created and hosted by
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Hugging Face showcasing the generative capabilities of several models. GPT-2 is one of them and is available in five
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different sizes: small, medium, large, xl and a distilled version of the small checkpoint: *distilgpt-2*.
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This model was contributed by [thomwolf](https://huggingface.co/thomwolf). The original code can be found [here](https://openai.com/blog/better-language-models/).
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## GPT2Config
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[[autodoc]] GPT2Config
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## GPT2Tokenizer
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[[autodoc]] GPT2Tokenizer
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- save_vocabulary
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## GPT2TokenizerFast
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[[autodoc]] GPT2TokenizerFast
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## GPT2 specific outputs
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[[autodoc]] models.gpt2.modeling_gpt2.GPT2DoubleHeadsModelOutput
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[[autodoc]] models.gpt2.modeling_tf_gpt2.TFGPT2DoubleHeadsModelOutput
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## GPT2Model
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[[autodoc]] GPT2Model
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- forward
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- parallelize
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- deparallelize
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## GPT2LMHeadModel
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[[autodoc]] GPT2LMHeadModel
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- forward
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- parallelize
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- deparallelize
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## GPT2DoubleHeadsModel
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[[autodoc]] GPT2DoubleHeadsModel
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- forward
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## GPT2ForSequenceClassification
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[[autodoc]] GPT2ForSequenceClassification
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- forward
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## GPT2ForTokenClassification
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[[autodoc]] GPT2ForTokenClassification
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- forward
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## TFGPT2Model
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[[autodoc]] TFGPT2Model
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- call
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## TFGPT2LMHeadModel
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[[autodoc]] TFGPT2LMHeadModel
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- call
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## TFGPT2DoubleHeadsModel
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[[autodoc]] TFGPT2DoubleHeadsModel
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- call
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## TFGPT2ForSequenceClassification
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[[autodoc]] TFGPT2ForSequenceClassification
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- call
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## TFSequenceClassifierOutputWithPast
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[[autodoc]] modeling_tf_outputs.TFSequenceClassifierOutputWithPast
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## FlaxGPT2Model
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[[autodoc]] FlaxGPT2Model
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- __call__
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## FlaxGPT2LMHeadModel
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[[autodoc]] FlaxGPT2LMHeadModel
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- __call__
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