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https://github.com/zphang/minimal-gpt-neox-20b
https://github.com/zphang/minimal-gpt-neox-20b
Last synced: 3 days ago
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- Host: GitHub
- URL: https://github.com/zphang/minimal-gpt-neox-20b
- Owner: zphang
- Created: 2022-03-09T21:35:30.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2022-06-09T22:52:57.000Z (over 2 years ago)
- Last Synced: 2024-10-31T19:24:32.578Z (10 days ago)
- Language: Python
- Size: 79.1 KB
- Stars: 128
- Watchers: 5
- Forks: 18
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
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README
# Minimal GPT-NeoX-20B
This is a fairly minimal implementation of GPT-NeoX-20B in PyTorch. It is meant primarily as an educational/reference implementation, rather than an optimized or feature-full implementation.
GPT-NeoX-20B is a 20B-parameter autoregressive Transformer model developed by [EleutherAI](https://www.eleuther.ai/) with the support of [CoreWeave](https://www.coreweave.com/), trained using the [GPT-NeoX](https://github.com/EleutherAI/gpt-neox) library.
Some notes about the model:
* The model weights and activations come in half-precision (fp16).
* In fp16, loading the model weights requires about 40GB of GPU memory. Running inference on a single batch requires some more.
* The model supports up to a maximum sequence length of 2048 tokens.
## Setup
### Installation
Install PyTorch with your appropriate CUDA version, and then install from the `requirements.txt` (basically just `tokenizers`).
```bash
pip install -r requirements.txt
```
### Download weights
Following the [NeoX guide](https://github.com/EleutherAI/gpt-neox#download-links), download the model weights and tokenizer JSON file with the following command:
```bash
wget --cut-dirs=5 -nH -r --no-parent --reject "index.html*" https://mystic.the-eye.eu/public/AI/models/GPT-NeoX-20B/slim_weights/ -P 20B_checkpoints
```
You can also manually down them from [here](https://mystic.the-eye.eu/public/AI/models/GPT-NeoX-20B/slim_weights/). Because of the size of the model, the model weights are broken into multiple files, based on the DeepSpeed save format.
#### Generate text
Here is some sample code to generate text. Note that since we are greedily decoding with no fancy tricks, there tends to be quite some repetitiion in generations.
```python
import minimal20b
import torch
model = minimal20b.create_model(
"/path/to/20B_checkpoints/global_step150000",
use_cache=True,
device="cuda:0",
)
tokenizer = minimal20b.create_tokenizer(
"/path/to/20B_checkpoints/20B_tokenizer.json",
)
with torch.inference_mode():
minimal20b.greedy_generate_text(
model, tokenizer,
"GPTNeoX20B is a 20B-parameter autoregressive Transformer model developed by EleutherAI.",
max_seq_len=100,
)
```
#### Evaluation
To run evaluation with the LM-eval-harness, you will need to install some additional dependencies (mostly just the eval harness library):
```bash
pip install -r scripts/eval/requirements.txt
```
Most datasets are automatically downloaded via Hugging Face `datasets`, but if you are evaluating on lambada, you will need to separately download the data.
```bash
mkdir -p data/lambada
wget http://eaidata.bmk.sh/data/lambada_test.jsonl -O data/lambada/lambada_test.jsonl
```
Then, you can run the following command.
```bash
python scripts/eval/eval_harness.py \
--model_path /path/to/20B_checkpoints/global_step150000 \
--tokenizer_path /path/to/20B_checkpoints/20B_tokenizer.json \
--tasks lambada,anli_r1,anli_r2,anli_r3,wsc,winogrande,hellaswag,piqa
```
| Task | Metric | NeoX Impl (2 GPU) | This Repo (1 GPU) |
|------------|-----------------|-------------------|-------------------|
| anli_r1 | acc | 0.3270 | 0.3300 |
| | acc_stderr | 0.0148 | 0.0149 |
| anli_r2 | acc | 0.3410 | 0.3420 |
| | acc_stderr | 0.0150 | 0.0150 |
| anli_r3 | acc | 0.3567 | 0.3617 |
| | acc_stderr | 0.0138 | 0.0139 |
| hellaswag | acc | 0.5351 | 0.5335 |
| | acc_stderr | 0.0050 | 0.0050 |
| | acc_norm | 0.7140 | 0.7126 |
| | acc_norm_stderr | 0.0045 | 0.0045 |
| lambada | acc | 0.7211 | 0.7223 |
| | acc_stderr | 0.0062 | 0.0062 |
| | ppl | 3.6760 | 3.6559 |
| | ppl_stderr | 0.0760 | 0.0757 |
| piqa | acc | 0.7748 | 0.7758 |
| | acc_stderr | 0.0097 | 0.0097 |
| | acc_norm | 0.7786 | 0.7856 |
| | acc_norm_stderr | 0.0097 | 0.0096 |
| winogrande | acc | 0.6598 | 0.6598 |
| | acc_stderr | 0.0133 | 0.0133 |
| wsc | acc | 0.5096 | 0.4808 |
| | acc_stderr | 0.0493 | 0.0492 |