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OpenAI GPT2

Overview

OpenAI GPT-2 model was proposed in Language Models are Unsupervised Multitask Learners by Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei and Ilya Sutskever from OpenAI. It's a causal (unidirectional) transformer pretrained using language modeling on a very large corpus of ~40 GB of text data.

The abstract from the paper is the following:

GPT-2 is a large transformer-based language model with 1.5 billion parameters, trained on a dataset[1] of 8 million web pages. GPT-2 is trained with a simple objective: predict the next word, given all of the previous words within some text. The diversity of the dataset causes this simple goal to contain naturally occurring demonstrations of many tasks across diverse domains. GPT-2 is a direct scale-up of GPT, with more than 10X the parameters and trained on more than 10X the amount of data.

Write With Transformer is a webapp created and hosted by Hugging Face showcasing the generative capabilities of several models. GPT-2 is one of them and is available in five different sizes: small, medium, large, xl and a distilled version of the small checkpoint: distilgpt-2.

This model was contributed by thomwolf. The original code can be found here.

Usage tips

  • GPT-2 is a model with absolute position embeddings so it's usually advised to pad the inputs on the right rather than the left.
  • GPT-2 was trained with a causal language modeling (CLM) objective and is therefore powerful at predicting the next token in a sequence. Leveraging this feature allows GPT-2 to generate syntactically coherent text as it can be observed in the run_generation.py example script.
  • The model can take the past_key_values (for PyTorch) or past (for TF) as input, which is the previously computed key/value attention pairs. Using this (past_key_values or past) value prevents the model from re-computing pre-computed values in the context of text generation. For PyTorch, see past_key_values argument of the [GPT2Model.forward] method, or for TF the past argument of the [TFGPT2Model.call] method for more information on its usage.
  • Enabling the scale_attn_by_inverse_layer_idx and reorder_and_upcast_attn flags will apply the training stability improvements from Mistral (for PyTorch only).

Usage example

The generate() method can be used to generate text using GPT2 model.

>>> from transformers import AutoModelForCausalLM, AutoTokenizer

>>> model = AutoModelForCausalLM.from_pretrained("gpt2")
>>> tokenizer = AutoTokenizer.from_pretrained("gpt2")

>>> prompt = "GPT2 is a model developed by OpenAI."

>>> input_ids = tokenizer(prompt, return_tensors="pt").input_ids

>>> gen_tokens = model.generate(
...     input_ids,
...     do_sample=True,
...     temperature=0.9,
...     max_length=100,
... )
>>> gen_text = tokenizer.batch_decode(gen_tokens)[0]

Using Flash Attention 2

Flash Attention 2 is a faster, optimized version of the attention scores computation which relies on cuda kernels.

Installation

First, check whether your hardware is compatible with Flash Attention 2. The latest list of compatible hardware can be found in the official documentation. If your hardware is not compatible with Flash Attention 2, you can still benefit from attention kernel optimisations through Better Transformer support covered above.

Next, install the latest version of Flash Attention 2:

pip install -U flash-attn --no-build-isolation

Usage

To load a model using Flash Attention 2, we can pass the argument attn_implementation="flash_attention_2" to .from_pretrained. We'll also load the model in half-precision (e.g. torch.float16), since it results in almost no degradation to audio quality but significantly lower memory usage and faster inference:

>>> import torch
>>> from transformers import AutoModelForCausalLM, AutoTokenizer
>>> device = "cuda" # the device to load the model onto

>>> model = AutoModelForCausalLM.from_pretrained("gpt2", torch_dtype=torch.float16, attn_implementation="flash_attention_2")
>>> tokenizer = AutoTokenizer.from_pretrained("gpt2")

>>> prompt = "def hello_world():"

>>> model_inputs = tokenizer([prompt], return_tensors="pt").to(device)
>>> model.to(device)

>>> generated_ids = model.generate(**model_inputs, max_new_tokens=100, do_sample=True)
>>> tokenizer.batch_decode(generated_ids)[0]

Expected speedups

Below is an expected speedup diagram that compares pure inference time between the native implementation in transformers using gpt2 checkpoint and the Flash Attention 2 version of the model using a sequence length of 512.

Resources

A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with GPT2. If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.

GPT2Config

autodoc GPT2Config

GPT2Tokenizer

autodoc GPT2Tokenizer - save_vocabulary

GPT2TokenizerFast

autodoc GPT2TokenizerFast

GPT2 specific outputs

autodoc models.gpt2.modeling_gpt2.GPT2DoubleHeadsModelOutput

autodoc models.gpt2.modeling_tf_gpt2.TFGPT2DoubleHeadsModelOutput

GPT2Model

autodoc GPT2Model - forward

GPT2LMHeadModel

autodoc GPT2LMHeadModel - forward

GPT2DoubleHeadsModel

autodoc GPT2DoubleHeadsModel - forward

GPT2ForQuestionAnswering

autodoc GPT2ForQuestionAnswering - forward

GPT2ForSequenceClassification

autodoc GPT2ForSequenceClassification - forward

GPT2ForTokenClassification

autodoc GPT2ForTokenClassification - forward

TFGPT2Model

autodoc TFGPT2Model - call

TFGPT2LMHeadModel

autodoc TFGPT2LMHeadModel - call

TFGPT2DoubleHeadsModel

autodoc TFGPT2DoubleHeadsModel - call

TFGPT2ForSequenceClassification

autodoc TFGPT2ForSequenceClassification - call

TFSequenceClassifierOutputWithPast

autodoc modeling_tf_outputs.TFSequenceClassifierOutputWithPast

TFGPT2Tokenizer

autodoc TFGPT2Tokenizer

FlaxGPT2Model

autodoc FlaxGPT2Model - call

FlaxGPT2LMHeadModel

autodoc FlaxGPT2LMHeadModel - call