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*This model was released on 2022-04-14 and added to Hugging Face Transformers on 2022-05-24.*
# GPT-NeoX
<div class="flex flex-wrap space-x-1">
<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
<img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
</div>
## Overview
We introduce [GPT-NeoX-20B](https://huggingface.co/papers/2204.06745), a 20 billion parameter autoregressive language model trained on the Pile, whose weights will
be made freely and openly available to the public through a permissive license. It is, to the best of our knowledge,
the largest dense autoregressive model that has publicly available weights at the time of submission. In this work,
we describe GPT-NeoX-20B's architecture and training and evaluate its performance on a range of language-understanding,
mathematics, and knowledge-based tasks. We find that GPT-NeoX-20B is a particularly powerful few-shot reasoner and
gains far more in performance when evaluated five-shot than similarly sized GPT-3 and FairSeq models. We open-source
the training and evaluation code, as well as the model weights, at [https://github.com/EleutherAI/gpt-neox](https://github.com/EleutherAI/gpt-neox).
Development of the model was led by Sid Black, Stella Biderman and Eric Hallahan, and the model was trained with
generous the support of [CoreWeave](https://www.coreweave.com/).
GPT-NeoX-20B was trained with fp16, thus it is recommended to initialize the model as follows:
```python
model = GPTNeoXForCausalLM.from_pretrained("EleutherAI/gpt-neox-20b", device_map="auto", dtype=torch.float16)
```
GPT-NeoX-20B also has a different tokenizer from the one used in GPT-J-6B and GPT-Neo. The new tokenizer allocates
additional tokens to whitespace characters, making the model more suitable for certain tasks like code generation.
## Usage example
The `generate()` method can be used to generate text using GPT Neo model.
```python
>>> from transformers import GPTNeoXForCausalLM, GPTNeoXTokenizerFast
>>> model = GPTNeoXForCausalLM.from_pretrained("EleutherAI/gpt-neox-20b")
>>> tokenizer = GPTNeoXTokenizerFast.from_pretrained("EleutherAI/gpt-neox-20b")
>>> prompt = "GPTNeoX20B is a 20B-parameter autoregressive Transformer model developed by EleutherAI."
>>> 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 an faster, optimized version of the model.
### 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](https://github.com/Dao-AILab/flash-attention#installation-and-features). If your hardware is not compatible with Flash Attention 2, you can still benefit from attention kernel optimisations through Better Transformer support covered [above](https://huggingface.co/docs/transformers/main/en/model_doc/bark#using-better-transformer).
Next, [install](https://github.com/Dao-AILab/flash-attention#installation-and-features) the latest version of Flash Attention 2:
```bash
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`](https://huggingface.co/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.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:
```python
>>> from transformers import GPTNeoXForCausalLM, GPTNeoXTokenizerFast
model = GPTNeoXForCausalLM.from_pretrained("EleutherAI/gpt-neox-20b", dtype=torch.float16, attn_implementation="flash_attention_2").to(device)
...
```
### Expected speedups
Below is an expected speedup diagram that compares pure inference time between the native implementation in transformers using `stockmark/gpt-neox-japanese-1.4b` checkpoint and the Flash Attention 2 version of the model using a sequence length of 2048.
<div style="text-align: center">
<img src="https://huggingface.co/datasets/ybelkada/documentation-images/resolve/main/gpt-neox-1.8b-speedup.jpg">
</div>
## Using Scaled Dot Product Attention (SDPA)
PyTorch includes a native scaled dot-product attention (SDPA) operator as part of `torch.nn.functional`. This function
encompasses several implementations that can be applied depending on the inputs and the hardware in use. See the
[official documentation](https://pytorch.org/docs/stable/generated/torch.nn.functional.scaled_dot_product_attention.html)
or the [GPU Inference](https://huggingface.co/docs/transformers/main/en/perf_infer_gpu_one#pytorch-scaled-dot-product-attention)
page for more information.
SDPA is used by default for `torch>=2.1.1` when an implementation is available, but you may also set
`attn_implementation="sdpa"` in `from_pretrained()` to explicitly request SDPA to be used.
```python
from transformers import GPTNeoXForCausalLM
model = GPTNeoXForCausalLM.from_pretrained("EleutherAI/gpt-neox-20b", dtype=torch.float16, attn_implementation="sdpa")
...
```
For the best speedups, we recommend loading the model in half-precision (e.g. `torch.float16` or `torch.bfloat16`).
On a local benchmark (rtx3080ti-16GB, PyTorch 2.2.1, OS Ubuntu 22.04) using `float16` with
[pythia-410m-deduped](https://huggingface.co/EleutherAI/pythia-410m-deduped), we saw the
following speedups during training and inference.
### Training
| Batch size | Seq len | Time per batch (Eager - s) | Time per batch (SDPA - s) | Speedup (%) | Eager peak mem (MB) | SDPA peak mem (MB) | Mem saving (%) |
|-----------:|-----------:|---------------------------:|-----------------------------:|------------:|--------------------:|-------------------:|------------------:|
| 1 | 128 | 0.024 | 0.019 | 28.945 | 1789.95 | 1789.95 | 0 |
| 1 | 256 | 0.039 | 0.031 | 23.18 | 1845.83 | 1844.84 | 0.053 |
| 1 | 512 | 0.08 | 0.055 | 45.524 | 2278.38 | 1953.76 | 16.615 |
| 1 | 1024 | 0.19 | 0.102 | 86.777 | 4772.36 | 2408.35 | 98.159 |
| 1 | 2048 | 0.565 | 0.204 | 177.098 | 13484.1 | 3882.01 | 247.348 |
| 2 | 128 | 0.037 | 0.032 | 15.121 | 1843.86 | 1844.78 | -0.05 |
| 2 | 256 | 0.067 | 0.055 | 21.706 | 1999.72 | 1951.67 | 2.462 |
| 2 | 512 | 0.144 | 0.096 | 50.046 | 3613.16 | 2406.77 | 50.125 |
| 2 | 1024 | 0.366 | 0.193 | 89.666 | 8707.55 | 3878.86 | 124.487 |
| 2 | 2048 | OOM | 0.379 | / | OOM | 6825.13 | SDPA does not OOM |
| 4 | 128 | 0.06 | 0.054 | 11.539 | 1947.6 | 1952.06 | -0.228 |
| 4 | 256 | 0.119 | 0.093 | 28.072 | 3008.39 | 2405.99 | 25.038 |
| 4 | 512 | 0.275 | 0.187 | 47.145 | 6290.58 | 3877.29 | 62.242 |
| 4 | 1024 | OOM | 0.36 | / | OOM | 6821.98 | SDPA does not OOM |
| 4 | 2048 | OOM | 0.731 | / | OOM | 12705.1 | SDPA does not OOM |
### Inference
| Batch size | Seq len | Per token latency Eager (ms) | Per token latency SDPA (ms) | Speedup (%) | Mem Eager (MB) | Mem SDPA (MB) | Mem saved (%) |
|--------------:|-------------:|--------------------------------:|-------------------------------:|---------------:|------------------:|----------------:|-----------------:|
| 1 | 128 | 6.569 | 5.858 | 12.14 | 974.831 | 974.826 | 0 |
| 1 | 256 | 7.009 | 5.863 | 19.542 | 1029.01 | 1028.08 | 0.09 |
| 1 | 512 | 7.157 | 5.965 | 19.983 | 1137.54 | 1137.52 | 0.001 |
| 1 | 1024 | 7.523 | 6.506 | 15.637 | 1329.3 | 1329.26 | 0.003 |
| 1 | 2048 | 9.271 | 9.205 | 0.713 | 1752.47 | 1734.51 | 1.036 |
| 2 | 128 | 7.239 | 5.959 | 21.493 | 1044.8 | 1028.37 | 1.597 |
| 2 | 256 | 7.228 | 6.036 | 19.757 | 1167.32 | 1137.73 | 2.601 |
| 2 | 512 | 7.538 | 6.693 | 12.628 | 1352.93 | 1329.55 | 1.758 |
| 2 | 1024 | 8.916 | 8.632 | 3.291 | 1752.56 | 1734.62 | 1.034 |
| 2 | 2048 | 12.628 | 12.606 | 0.181 | 2558.72 | 2545.8 | 0.508 |
| 4 | 128 | 7.278 | 6.046 | 20.373 | 1168.41 | 1137.79 | 2.691 |
| 4 | 256 | 7.614 | 6.588 | 15.574 | 1353.1 | 1329.79 | 1.753 |
| 4 | 512 | 8.798 | 8.144 | 8.028 | 1752.76 | 1734.85 | 1.032 |
| 4 | 1024 | 11.765 | 11.303 | 4.09 | 2558.96 | 2546.04 | 0.508 |
| 4 | 2048 | 19.568 | 17.735 | 10.33 | 4175.5 | 4165.26 | 0.246 |
## Resources
- [Causal language modeling task guide](../tasks/language_modeling)
## GPTNeoXConfig
[[autodoc]] GPTNeoXConfig
## GPTNeoXTokenizerFast
[[autodoc]] GPTNeoXTokenizerFast
## GPTNeoXModel
[[autodoc]] GPTNeoXModel
- forward
## GPTNeoXForCausalLM
[[autodoc]] GPTNeoXForCausalLM
- forward
## GPTNeoXForQuestionAnswering
[[autodoc]] GPTNeoXForQuestionAnswering
- forward
## GPTNeoXForSequenceClassification
[[autodoc]] GPTNeoXForSequenceClassification
- forward
## GPTNeoXForTokenClassification
[[autodoc]] GPTNeoXForTokenClassification
- forward