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https://github.com/certik/fastgpt

Fast GPT-2 inference written in Fortran
https://github.com/certik/fastgpt

fortran gpt-2 high-performance

Last synced: 24 days ago
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Fast GPT-2 inference written in Fortran

Host: GitHub
URL: https://github.com/certik/fastgpt
Owner: certik
License: mit
Created: 2023-02-17T05:19:27.000Z (over 1 year ago)
Default Branch: main
Last Pushed: 2024-03-18T10:51:18.000Z (8 months ago)
Last Synced: 2024-08-02T01:25:48.121Z (3 months ago)
Topics: fortran, gpt-2, high-performance
Language: Fortran
Homepage:
Size: 196 KB
Stars: 180
Watchers: 7
Forks: 16
Open Issues: 18
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

awesome-ChatGPT-repositories - fastGPT - Fast GPT-2 inference written in Fortran (Others)

README

# fastGPT

The progression of GPT-2 codes from the original to "minimal", "nano" and
"pico":

* [openai/gpt-2](https://github.com/openai/gpt-2)
* [karpathy/minGPT](https://github.com/karpathy/mingpt)
* [karpathy/nanoGPT](https://github.com/karpathy/nanogpt)
* [jaymody/picoGPT](https://github.com/jaymody/picoGPT)

`fastGPT` is very similar to `picoGPT` (very small and readable), but it is
also fast (see the Benchmarks section below). The speed and readability is
achieved by using Fortran. I wrote a
[blog post](https://ondrejcertik.com/blog/2023/03/fastgpt-faster-than-pytorch-in-300-lines-of-fortran/)
introducing fastGPT.

`fastGPT` features:
* Fast? ✅
* Training code? ❌
* Batch inference? ❌
* top-p sampling? ❌ top-k? ❌ temperature? ❌ categorical sampling?! ❌ greedy? ✅
* Readable? ✅
* Small? ✅

A quick breakdown of each of the files:

* `gpt2.f90`: the actual GPT-2 model and a decoder
* `main.f90`: the main driver
* `create_model.py`: downloads the TensorFlow model and converts to the GGUF
format (`model.gguf`)
* `encode_input.py`: encodes the text input into tokens (input file for `gpt2`)
* Matmul implementations
* `linalg_f.f90` native Fortran
* `linalg_c.f90`, `linalg_accelerate.c` macOS Accelerate Framework
* `pt.py`: a reference script to run PyTorch (returns the same answer)

## Build and Run

Install prerequisites:

mamba env create -f environment.yml
conda activate fastgpt

Configure and build:

FC=gfortran cmake .
make

Download the GPT2 model weights:

curl -o model.gguf -L https://huggingface.co/certik/fastGPT/resolve/main/model_fastgpt_124M_v2.gguf

You can also download 355M for the `gpt-medium` model.

Now you can modify the `input` file to change the input string and set other
parameters.

Run (requires `model.gguf` and `input` in the current directory):

./gpt2

## Creating the GGUF file

Create the `model.gguf` file from a given GPT-2 model. Supported sizes (and the
corresponding names to be used in `pt.py`, and the approximate download size):
"124M" (`gpt2`, 0.5GB), "355M" (`gpt-medium`, 1.5GB), "774M" (`gpt-large`,
3GB), "1558M" (`gpt-xl`, 6GB). This will download the model and cache it for
subsequent runs:

python create_model.py --models_dir "models" --model_size "124M"

This script depends on the `gguf` Python library, that you can install using:

git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp
git checkout 4e9a7f7f7fb6acbddd1462909c8d696e38edbfcc
cd gguf-py
pip install .

The `gguf` library is available in pip and conda, but we currently require the
latest version that is not available there yet.

We used this script to create several GGUF files and uploaded them to:
https://huggingface.co/certik/fastGPT, so that you can just download the
pre-generated files.

### Example Output

The above `./gpt2` command prints on Apple M1 Max:
```
$ ./gpt2
Loading the model...
done. Time: 0.111s

Model parameters:
n_vocab = 50257
n_ctx = 1024
n_embd = 768
n_layer = 12
n_head = 12

Input text
Alan Turing theorized that computers would one day become very powerful, but even he could not imagine

Encoding: tokenizing input text into tokens (currently slow)...
done. Time: 0.074s

Input parameters:
n_seq = 19
n_tokens_to_generate = 20

Input tokens:
36235 39141 18765 1143 326 9061 561 530 1110 1716 845 3665 11 475 772 339 714 407 5967

Decoded input as text:
Alan Turing theorized that computers would one day become very powerful, but even he could not imagine

Running model...
how they would be able to do so.

"I think that the most important thing is
done. Time: 0.304s (1.01x)

Output tokens:
703 484 561 307 1498 284 466 523 13 198 198 1 40 892 326 262 749 1593 1517 318

Decoded output as text:
how they would be able to do so.

"I think that the most important thing is
```

### Chat interface

Here is an example chat using the largest 1558M model:

```
$ ./chat
Your name is fastGPT and you are an AI bot. The user will ask you questions and you answer in a nice, truthful, short way.
User: What is the capital of Czechia?
fastGPT: Prague.
User: How many legs does a dog have?
fastGPT: Four.
User: What color does the sky have?
fastGPT: Blue.
User: What can you type a document on?
fastGPT: A typewriter.
User: What can you drive in?
fastGPT: A car.
User: What can you fly in?
fastGPT: A plane.
User: What continent is Germany in?
fastGPT: Europe.
User: When did Second World War start?
fastGPT: 1939.
User: When did it end?
fastGPT: 1945.
User: When did the U.S. enter the Second World War?
fastGPT: 1941.
User: When did the First World War start?
fastGPT: 1914.
User: When did it end?
fastGPT: 1918.
User: When did the Mexican-American war start?
fastGPT: 1846.
User: When did it end?
fastGPT: 1848.
User: What color is snow?
fastGPT: White.
User: What color do plants usually have?
fastGPT: Green.
User: What is your name?
fastGPT: fastGPT.
```

### BLAS Implementation

You can choose which BLAS implementation to use for `matmul` using:
* `-DFASTGPT_BLAS=OpenBLAS`: Use OpenBLAS
* `-DFASTGPT_BLAS=Accelerate`: Use the macOS Accelerate Framework
* `-DFASTGPT_BLAS=Fortran`: Use the default Fortran's intrinsic `matmul`

## Benchmarks

On Apple M1 Max, inference of the above input file (20 tokens):

1 core 2 cores 4 cores 8 cores

fastGPT (Accelerate, fast_tanh) 0.288s

fastGPT (Accelerate) 0.299s
PyTorch (Accelerate) 0.346s

fastGPT (OpenBLAS) 0.837s 0.514s 0.341s 0.339s
PyTorch (OpenBLAS) 0.873s 0.539s 0.386s 0.392s

fastGPT (Accelerate, no cache) 0.717s
picoGPT (Accelerate, no cache) 0.765s
PyTorch (Accelerate, no cache) 0.787s

fastGPT (OpenBLAS, no cache) 2.343s 1.603s 1.209s 1.018s
PyTorch (OpenBLAS, no cache) 2.356s 1.520s 1.104s 0.997s
picoGPT (OpenBLAS, no cache) 2.427s 1.645s 1.272s 1.081s

Total run (includes loading the model and Python imports):

fastGPT (Accelerate, fast_tanh): 0.401s
picoGPT (8 cores): 3.445s
PyTorch (OpenBLAS, 4 cores): 4.867s

## TODO

* [ ] Parallelization:
* [ ] Over heads: https://github.com/certik/fastGPT/issues/2
* [ ] MPI: https://github.com/certik/fastGPT/issues/5
* [ ] Other sampling methods: https://github.com/certik/fastGPT/issues/8
* [ ] Batching: https://github.com/certik/fastGPT/issues/7
* [x] Improve the UI:
* [x] Implement the input tokenizer in Fortran: https://github.com/certik/fastGPT/issues/1
* [x] Show the words as they are generated: https://github.com/certik/fastGPT/issues/6