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llm.c

LLMs in simple, pure C/CUDA with no need for 245MB of PyTorch or 107MB of cPython. Current focus is on pretraining, in particular reproducing the GPT-2 and GPT-3 miniseries, along with a parallel PyTorch reference implementation in train_gpt2.py. You'll recognize this file as a slightly tweaked nanoGPT, an earlier project of mine. Currently, llm.c is a bit faster than PyTorch Nightly (by about 7%). In addition to the bleeding edge mainline code in train_gpt2.cu, we have a simple reference CPU fp32 implementation in ~1,000 lines of clean code in one file train_gpt2.c. I'd like this repo to only maintain C and CUDA code. Ports to other languages or repos are very welcome, but should be done in separate repos, and I am happy to link to them below in the "notable forks" section. Developer coordination happens in the Discussions and on Discord, either the #llmc channel on the Zero to Hero channel, or on #llmdotc on CUDA MODE Discord.

quick start

The best introduction to the llm.c repo today is reproducing the GPT-2 (124M) model. Discussion #481 steps through this in detail. We can reproduce other models from the GPT-2 and GPT-3 series in both llm.c and in the parallel implementation of PyTorch. Have a look at the scripts README.

debugging tip: when you run the make command to build the binary, modify it by replacing -O3 with -g so you can step through the code in your favorite IDE (e.g. vscode).

quick start (1 GPU, fp32 only)

If you won't be training on multiple nodes, aren't interested in mixed precision, and are interested in learning CUDA, the fp32 (legacy) files might be of interest to you. These are files that were "checkpointed" early in the history of llm.c and frozen in time. They are simpler, more portable, and possibly easier to understand. Run the 1 GPU, fp32 code like this:

chmod u+x ./dev/download_starter_pack.sh
./dev/download_starter_pack.sh
make train_gpt2fp32cu
./train_gpt2fp32cu

The download_starter_pack.sh script is a quick & easy way to get started and it downloads a bunch of .bin files that help get you off the ground. These contain: 1) the GPT-2 124M model saved in fp32, in bfloat16, 2) a "debug state" used in unit testing (a small batch of data, and target activations and gradients), 3) the GPT-2 tokenizer, and 3) the tokenized tinyshakespeare dataset. Alternatively, instead of running the .sh script, you can re-create these artifacts manually as follows:

pip install -r requirements.txt
python dev/data/tinyshakespeare.py
python train_gpt2.py

quick start (CPU)

The "I am so GPU poor that I don't even have one GPU" section. You can still enjoy seeing llm.c train! But you won't go too far. Just like the fp32 version above, the CPU version is an even earlier checkpoint in the history of llm.c, back when it was just a simple reference implementation in C. For example, instead of training from scratch, you can finetune a GPT-2 small (124M) to output Shakespeare-like text, as an example:

chmod u+x ./dev/download_starter_pack.sh
./dev/download_starter_pack.sh
make train_gpt2
OMP_NUM_THREADS=8 ./train_gpt2

If you'd prefer to avoid running the starter pack script, then as mentioned in the previous section you can reproduce the exact same .bin files and artifacts by running python dev/data/tinyshakespeare.py and then python train_gpt2.py.

The above lines (1) download an already tokenized tinyshakespeare dataset and download the GPT-2 (124M) weights, (3) init from them in C and train for 40 steps on tineshakespeare with AdamW (using batch size 4, context length only 64), evaluate validation loss, and sample some text. Honestly, unless you have a beefy CPU (and can crank up the number of OMP threads in the launch command), you're not going to get that far on CPU training LLMs, but it might be a good demo/reference. The output looks like this on my MacBook Pro (Apple Silicon M3 Max):

[GPT-2]
max_seq_len: 1024
vocab_size: 50257
num_layers: 12
num_heads: 12
channels: 768
num_parameters: 124439808
train dataset num_batches: 1192
val dataset num_batches: 128
num_activations: 73323776
val loss 5.252026
step 0: train loss 5.356189 (took 1452.121000 ms)
step 1: train loss 4.301069 (took 1288.673000 ms)
step 2: train loss 4.623322 (took 1369.394000 ms)
step 3: train loss 4.600470 (took 1290.761000 ms)
... (trunctated) ...
step 39: train loss 3.970751 (took 1323.779000 ms)
val loss 4.107781
generating:
---
Come Running Away,
Greater conquer
With the Imperial blood
the heaviest host of the gods
into this wondrous world beyond.
I will not back thee, for how sweet after birth
Netflix against repounder,
will not
flourish against the earlocks of
Allay
---

datasets

The data files inside /dev/data/(dataset).py are responsible for downloading, tokenizing and saving the tokens to .bin files, readable easily from C. So for example when you run:

python dev/data/tinyshakespeare.py

We download and tokenize the tinyshakespeare dataset. The output of this looks like this:

writing 32,768 tokens to ./dev/data/tinyshakespeare/tiny_shakespeare_val.bin
writing 305,260 tokens to ./dev/data/tinyshakespeare/tiny_shakespeare_train.bin

The .bin files contain a short header (1024 bytes) and then a stream of tokens in uint16, indicating the token ids with the GPT-2 tokenizer. More datasets are available in /dev/data.

test

I am also attaching a simple unit test for making sure our C code agrees with the PyTorch code. On the CPU as an example, compile and run with:

make test_gpt2
./test_gpt2

This now loads the gpt2_124M_debug_state.bin file that gets written by train_gpt2.py, runs a forward pass, compares the logits and loss with the PyTorch reference implementation, then it does 10 iterations of training with Adam and makes sure the losses match PyTorch. To test the GPU version we run:

# fp32 test (cudnn not supported)
make test_gpt2cu PRECISION=FP32 && ./test_gpt2cu
# mixed precision cudnn test
make test_gpt2cu USE_CUDNN=1 && ./test_gpt2cu

This tests both the fp32 path and the mixed precision path. The test should pass and print overall okay: 1.

tutorial

I attached a very small tutorial here, in doc/layernorm/layernorm.md. It's a simple, step-by-step guide to implementing a single layer of the GPT-2 model, the layernorm layer. This is a good starting point to understand how the layers are implemented in C.

flash attention. As of May 1, 2024 we use the Flash Attention from cuDNN. Because cuDNN bloats the compile time from a few seconds to ~minute and this code path is right now very new, this is disabled by default. You can enable it by compiling like this:

make train_gpt2cu USE_CUDNN=1

This will try to compile with cudnn and run it. You have to have cuDNN installed on your system. The cuDNN installation instructions with apt-get will grab the default set of cuDNN packages. For a minimal setup, the cuDNN dev package is sufficient, e.g. on Ubuntu 22.04 for CUDA 12.x:

wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-keyring_1.1-1_all.deb
sudo dpkg -i cuda-keyring_1.1-1_all.deb
sudo apt-get update
sudo apt-get -y install libcudnn9-dev-cuda-12

On top of this you need the cuDNN frontend, but this is just header files. Simply clone the repo to your disk. The Makefile currently looks for it in either your home directory or the current directory. If you have put it elsewhere, add CUDNN_FRONTEND_PATH=/path/to/your/cudnn-frontend/include to the make command-line.

multi-GPU training

Make sure you install MPI and NCCL, e.g. on Linux:

sudo apt install openmpi-bin openmpi-doc libopenmpi-dev

For NCCL follow the instructions from the official website (e.g. network installer)

and then:

make train_gpt2cu
mpirun -np <number of GPUs> ./train_gpt2cu

or simply run one of our scripts under ./scripts/.

multi-node training

Make sure you've installed NCCL following instructions from multi-GPU section.

There are 3 ways we currently support that allow you to run multi-node training:

  1. Use OpenMPI to exchange nccl id and initialize NCCL. See e.g. ./scripts/multi_node/run_gpt2_124M_mpi.sh script for details.
  2. Use shared file system to init NCCL. See ./scripts/multi_node/run_gpt2_124M_fs.sbatch script for details.
  3. Use TCP sockets to init NCCL. See ./scripts/multi_node/run_gpt2_124M_tcp.sbatch script for details.

Note:

  • If you're running in a slurm environment and your slurm doesn't support PMIx (which we assume will be a common situation given that slurm-wlm dropped PMIx support) you will have to use FS (2) or TCP (3) approach. To test whether your slurm supports PMIx run: srun --mpi=list and see whether you get pmix in the output.
  • If you don't have slurm set up, you can kick off a multi-node run using mpirun - MPI (1).

None of these 3 methods is superior, we just offer you options so that you can run in your specific environment.

experiments / sweeps

Just as an example process to sweep learning rates on a machine with 4 GPUs on TinyStories. Run a shell script sweep.sh (after you of course chmod u+x sweep.sh):

#!/bin/bash

learning_rates=(3e-5 1e-4 3e-4 1e-3)

for i in {0..3}; do
    export CUDA_VISIBLE_DEVICES=$i
    screen -dmS "tr$i" bash -c "./train_gpt2cu -i data/TinyStories -v 250 -s 250 -g 144 -l ${learning_rates[$i]} -o stories$i.log"
done

# you can bring these down with
# screen -ls | grep -E "tr[0-3]" | cut -d. -f1 | xargs -I {} screen -X -S {} quit

This example opens up 4 screen sessions and runs the four commands with different LRs. This writes the log files stories$i.log with all the losses, which you can plot as you wish in Python. A quick example of how to parse and plot these logfiles is in dev/vislog.ipynb.

repo

A few more words on what I want this repo to be:

First, I want llm.c to be a place for education. E.g. our dev/cuda folder is a place for a library of kernels for all the layers that are manually hand-written and very well documented, starting from very simple kernels all the way to more complex / faster kernels. If you have a new kernel with various different tradeoffs, please feel free to contribute it here.

That said, I also want llm.c to be very fast too, even practically useful to train networks. E.g. to start, we should be able to reproduce the big GPT-2 (1.6B) training run. This requires that we incorporate whatever fastest kernels there are, including the use of libraries such as cuBLAS, cuBLASLt, CUTLASS, cuDNN, etc. I also think doing so serves an educational purpose to establish an expert upper bound, and a unit of measurement, e.g. you could say that your manually written kernels are 80% of cuBLAS speed, etc. Then you can choose to do a super fast run, or you can choose to "drag and drop" whatever manual kernels you wish to use, and run with those.

However, as a constraint, I want to keep the mainline llm.c in the root folder simple and readable. If there is a PR that e.g. improves performance by 2% but it "costs" 500 lines of complex C code, and maybe an exotic 3rd party dependency, I may reject the PR because the complexity is not worth it. As a concrete example - making cuBLAS for matmuls the default in the root training loop is a no-brainer: it makes the mainline code much faster, it is a single line of interpretable code, and it is a very common dependency. On the side of this, we can have manual implementations that can compete with cuBLAS in dev/cuda.

Lastly, I will be a lot more sensitive to complexity in the root folder of the project, which contains the main / default files of the project. In comparison, the dev/ folder is a bit more of a scratch space for us to develop a library of kernels or classes and share useful or related or educational code, and some of this code could be ok to be (locally) complex.

notable forks

discussions

Ways of organizing development:

  • Experiencing a concrete issue with the repo? Use Issues.
  • Have some code to contribute? Open a PR
  • Chat about the repo, ask questions, etc.? Look at Discussions.
  • Something faster? I created a new #llmc channel on my Zero to Hero Discord channel.

license

MIT