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certik / fastGPT

Fast GPT-2 inference written in Fortran
MIT License
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fortran gpt-2 high-performance

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fastGPT

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

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 introducing fastGPT.

fastGPT features:

A quick breakdown of each of the files:

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:

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