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gpt-2

README.md

gpt-2

This is a C++ example running GPT-2 inference using the ggml library.

The program runs on the CPU - no video card is required.

The Cerebras-GPT models are also supported.

The example supports the following GPT-2 models:

Model Description Disk Size
117M Small model 240 MB
345M Medium model 680 MB
774M Large model 1.5 GB
1558M XL model 3.0 GB

Sample performance on MacBook M1 Pro:

Model Size Time / Token
GPT-2 117M 5 ms
GPT-2 345M 12 ms
GPT-2 774M 23 ms
GPT-2 1558M 42 ms

TODO: add tables for Cerebras-GPT models

Sample output:

$ ./bin/gpt-2 -h
usage: ./bin/gpt-2 [options]

options:
  -h, --help            show this help message and exit
  -s SEED, --seed SEED  RNG seed (default: -1)
  -t N, --threads N     number of threads to use during computation (default: 8)
  -p PROMPT, --prompt PROMPT
                        prompt to start generation with (default: random)
  -n N, --n_predict N   number of tokens to predict (default: 200)
  --top_k N             top-k sampling (default: 40)
  --top_p N             top-p sampling (default: 0.9)
  --temp N              temperature (default: 1.0)
  -b N, --batch_size N  batch size for prompt processing (default: 8)
  -m FNAME, --model FNAME
                        model path (default: models/gpt-2-117M/ggml-model.bin)

$ ./bin/gpt-2
gpt2_model_load: loading model from 'models/gpt-2-117M/ggml-model.bin'
gpt2_model_load: n_vocab = 50257
gpt2_model_load: n_ctx   = 1024
gpt2_model_load: n_embd  = 768
gpt2_model_load: n_head  = 12
gpt2_model_load: n_layer = 12
gpt2_model_load: f16     = 1
gpt2_model_load: ggml ctx size = 311.12 MB
gpt2_model_load: memory size =    72.00 MB, n_mem = 12288
gpt2_model_load: model size  =   239.08 MB
main: number of tokens in prompt = 1

So this is going to be the end of the line for us.

If the Dolphins continue to do their business, it's possible that the team could make a bid to bring in new defensive coordinator Scott Linehan.

Linehan's job is a little daunting, but he's a great coach and an excellent coach. I don't believe we're going to make the playoffs.

We're going to have to work hard to keep our heads down and get ready to go.<|endoftext|>

main: mem per token =  2048612 bytes
main:     load time =   106.32 ms
main:   sample time =     7.10 ms
main:  predict time =   506.40 ms / 5.06 ms per token
main:    total time =   629.84 ms

Downloading and converting the original models (GPT-2)

You can download the original model files using the download-model.sh Bash script. The models are in Tensorflow format, so in order to use them with ggml, you need to convert them to appropriate format. This is done via the convert-ckpt-to-ggml.py python script.

Here is the entire process for the GPT-2 117M model (download from official site + conversion):

cd ggml/build
../examples/gpt-2/download-model.sh 117M

Downloading model 117M ...
models/gpt-2-117M/checkpoint                      100%[=============================>]      77  --.-KB/s    in 0s
models/gpt-2-117M/encoder.json                    100%[=============================>]   1018K  1.20MB/s    in 0.8s
models/gpt-2-117M/hparams.json                    100%[=============================>]      90  --.-KB/s    in 0s
models/gpt-2-117M/model.ckpt.data-00000-of-00001  100%[=============================>] 474.70M  1.21MB/s    in 8m 39s
models/gpt-2-117M/model.ckpt.index                100%[=============================>]   5.09K  --.-KB/s    in 0s
models/gpt-2-117M/model.ckpt.meta                 100%[=============================>] 460.11K   806KB/s    in 0.6s
models/gpt-2-117M/vocab.bpe                       100%[=============================>] 445.62K   799KB/s    in 0.6s
Done! Model '117M' saved in 'models/gpt-2-117M/'

Run the convert-ckpt-to-ggml.py script to convert the model to ggml format.

  python /Users/john/ggml/examples/gpt-2/convert-ckpt-to-ggml.py models/gpt-2-117M/ 1

This conversion requires that you have python and Tensorflow installed on your computer. Still, if you want to avoid this, you can download the already converted ggml models as described below.

Downloading and converting the original models (Cerebras-GPT)

Clone the respective repository from here: https://huggingface.co/cerebras

Use the convert-cerebras-to-ggml.py script to convert the model to ggml format:

cd ggml/build
git clone https://huggingface.co/cerebras/Cerebras-GPT-111M models/
python ../examples/gpt-2/convert-cerebras-to-ggml.py models/Cerebras-GPT-111M/

Downloading the ggml model directly (GPT-2)

For convenience, I will be hosting the converted ggml model files in order to make it easier to run the examples. This way, you can directly download a single binary file and start using it. No python or Tensorflow is required.

Here is how to get the 117M ggml model:

cd ggml/build
../examples/gpt-2/download-ggml-model.sh 117M

Downloading ggml model 117M ...
models/gpt-2-117M/ggml-model.bin         100%[===============================>] 239.58M  8.52MB/s    in 28s
Done! Model '117M' saved in 'models/gpt-2-117M/ggml-model.bin'
You can now use it like this:

  $ ./bin/gpt-2 -m models/gpt-2-117M/ggml-model.bin -p "This is an example"

At some point, I might decide to stop hosting these models. So in that case, simply revert to the manual process above.

Quantizing the models

You can also try to quantize the ggml models via 4-bit integer quantization. Keep in mind that for smaller models, this will render them completely useless. You generally want to quantize larger models.

# quantize GPT-2 F16 to Q4_0 (faster but less precise)
./bin/gpt-2-quantize models/gpt-2-1558M/ggml-model-f16.bin models/gpt-2-1558M/ggml-model-q4_0.bin 2
./bin/gpt-2 -m models/gpt-2-1558M/ggml-model-q4_0.bin -p "This is an example"

# quantize Cerebras F16 to Q4_1 (slower but more precise)
./bin/gpt-2-quantize models/Cerebras-GPT-6.7B/ggml-model-f16.bin models/Cerebras-GPT-6.7B/ggml-model-q4_1.bin 3
./bin/gpt-2 -m models/Cerebras-GPT-6.7B/ggml-model-q4_1.bin -p "This is an example"