Merge branch 'portability' of github.ugent.be:ML/neural-compression into portability

This commit is contained in:
Robin Meersman 2025-12-10 14:46:20 +01:00
commit 63119980c9
7 changed files with 128 additions and 138 deletions

21
job.pbs
View file

@ -1,35 +1,40 @@
#!/bin/bash
#PBS -N neural-compression-test-run
#PBS -N nc-cnn-enwik9-optuna
#PBS -l gpus=1
#PBS -l walltime=00:30:00
#PBS -l walltime=08:00:00
#PBS -l mem=60gb
#PBS -m abe
CACHE_DIR="${VSC_SCRATCH}/.cache" # Directory to use as cache
UV_DIR="${VSC_SCRATCH}/uv" # Directory to install packages
VENV="${UV_DIR}/venv"
DATA_DIR="${VSC_DATA}/datasets"
RESULTS_DIR="${VSC_DATA}/neural-compression/$( date +%Y%m%d-%H%M-%S%N)-results"
mkdir -p "${DATA_DIR}" "${RESULTS_DIR}" || true
module purge
module load PyTorch-bundle/2.1.2-foss-2023a-CUDA-12.1.1
module load Optuna/3.5.0-foss-2023a
module load matplotlib/2.2.5-foss-2023a-Python-2.7.18
cd "${PBS_O_WORKDIR}"
cd "${PBS_O_WORKDIR}" || exit
UV_PYTHON_INSTALL_DIR="${UV_DIR}/python" UV_PYTHON_INSTALL_DIR="${UV_DIR}/python" \
uv --cache-dir="${CACHE_DIR}/uv" \
venv "${UV_DIR}/venv" --clear
venv "${VENV}" --clear
source "${UV_DIR}/venv/bin/activate"
source "${VENV}/bin/activate"
UV_PYTHON_INSTALL_DIR="${UV_DIR}/python" UV_PYTHON_INSTALL_DIR="${UV_DIR}/python" \
uv --cache-dir="${CACHE_DIR}/uv" \
sync --active --no-dev
cd "${PBS_O_WORKDIR}/CNN-model"
cd "${PBS_O_WORKDIR}" || exit
python main_cnn.py train \
python main.py train \
--method=optuna \
--dataset=enwik9 --data-root="${DATA_DIR}" \
--model-path="${RESULTS_DIR}/model.pt"
--model=cnn --model-save-path="${RESULTS_DIR}/cnn-enwik9-optuna.pt"

37
main.py
View file

@ -1,39 +1,44 @@
import torch
from src.args import parse_arguments
from src.process import compress
from src.train import train
from src.utils import determine_device
def main():
args, print_help = parse_arguments()
if torch.accelerator.is_available():
device = torch.accelerator.current_accelerator().type
else:
device = "cpu"
device = determine_device()
print(f"Running on device: {device}...")
match args.mode:
case 'train':
size = None
if args.method == 'optuna':
size = 2 ** 12
if args.debug:
size = 2 ** 10
train(
device = device,
dataset = args.dataset,
data_root = args.data_root,
n_trials = 3 if args.debug else None,
size = 2**10 if args.debug else None,
method = args.method,
device=device,
dataset=args.dataset,
data_root=args.data_root,
n_trials=3 if args.debug else None,
size=size,
method=args.method,
model_name=args.model,
model_path = args.model_load_path,
model_out = args.model_save_path
model_path=args.model_load_path,
model_out=args.model_save_path
)
case 'compress':
compress(args.input_file)
compress(device=device,
model_path=args.model_load_path,
input_file=args.input_file,
output_file=args.output_file
)
case _:
raise NotImplementedError(f"Mode {args.mode} is not implemented yet")
print("Done")

View file

@ -5,7 +5,9 @@ description = "Add your description here"
readme = "README.md"
requires-python = ">=3.11"
dependencies = [
"datasets>=4.4.1",
"datasets>=3.2.0",
"huggingface_hub==0.27.0",
"fsspec==2024.9.0",
"lorem>=0.1.1",
]

View file

@ -19,7 +19,7 @@ def parse_arguments():
help="Which model to use")
modelparser.add_argument("--model-load-path", type=str, required=False,
help="Filepath to the model to load")
modelparser.add_argument("--model-save-path", type=str, required=True,
modelparser.add_argument("--model-save-path", type=str, required=False,
help="Filepath to the model to save")
fileparser = ArgumentParser(add_help=False)

View file

@ -1,7 +1,9 @@
from abc import abstractmethod, ABC
from itertools import accumulate
from os.path import join, curdir
from typing import Callable
import numpy as np
import torch
from torch import Tensor
from torch.utils.data import Dataset as TorchDataset
@ -49,25 +51,36 @@ class Dataset(TorchDataset, ABC):
return len(self.dataset)
def process_data(self):
self.chunk_offsets = self.get_offsets()
if self.size == -1:
# Just use the whole dataset
self.bytes = ''.join(tqdm(self.data, desc="Encoding data")).encode('utf-8', errors='replace')
self.bytes = ''.join(tqdm(self.data, desc="Encoding data", leave=False)).encode('utf-8', errors='replace')
else:
# Use only partition, calculate offsets
self.chunk_offsets = self.get_offsets()
self.bytes = ''.join(tqdm(self.data[:len(self.chunk_offsets)], desc="Encoding data")).encode('utf-8', errors='replace')
self.bytes = (''.join(tqdm(self.data[:len(self.chunk_offsets)], desc="Encoding data", leave=False))
.encode('utf-8', errors='replace'))
self.tensor = torch.tensor(list(self.bytes), dtype=torch.long)
bytes_array = np.frombuffer(self.bytes, dtype=np.uint8) # Zero-copy
self.tensor = torch.from_numpy(bytes_array).to(torch.long, non_blocking=True)
def get_offsets(self):
"""
Calculate for each chunk how many bytes came before it
"""
data = self.data
size = self.size
if size == -1:
return [0, *accumulate(tqdm(map(len, data), desc="Calculating offsets", leave=False, total=len(data)))]
offsets = [0]
while len(offsets) <= len(self.data) and (self.size == -1 or offsets[-1] < self.size):
idx = len(offsets) - 1
offsets.append(offsets[idx] + len(self.data[idx]))
print(offsets)
total = 0
append = offsets.append
for chunk in tqdm(data):
if total >= size:
break
total += len(chunk)
append(total)
return offsets
def get_chunked_item(self, idx: int, offsets: list[int], context_length: int):

View file

@ -1,6 +1,8 @@
from os import path
import matplotlib.pyplot as plt
import torch
from torch.utils.data import TensorDataset
import matplotlib.pyplot as plt
def make_context_pairs(data: bytes, context_length: int) -> TensorDataset:
@ -10,11 +12,13 @@ def make_context_pairs(data: bytes, context_length: int) -> TensorDataset:
y = data[context_length:]
return TensorDataset(x, y)
def print_distribution(from_to: tuple[int, int], probabilities: list[float]):
plt.hist(range(from_to[0], from_to[1]), weights=probabilities)
plt.show()
def print_losses(train_losses: list[float], validation_losses: list[float], show=False):
def print_losses(train_losses: list[float], validation_losses: list[float], filename: str | None = None, show=False):
plt.plot(train_losses, label="Training loss")
plt.plot(validation_losses, label="Validation loss")
plt.xlabel("Epoch")
@ -23,7 +27,26 @@ def print_losses(train_losses: list[float], validation_losses: list[float], show
if show:
plt.show()
plt.savefig("losses.png")
if filename is None:
filename = path.join("results", "losses.png")
print(f"Saving losses to {filename}...")
plt.savefig(filename)
def determine_device():
# NVIDIA GPUs (most HPC clusters)
if torch.cuda.is_available():
return torch.device("cuda")
# Apple Silicon (macOS)
elif getattr(torch.backends, "mps", None) and torch.backends.mps.is_available():
return torch.device("mps")
# Intel GPUs (oneAPI)
elif hasattr(torch, "xpu") and torch.xpu.is_available():
return torch.device("xpu")
else:
return torch.device("cpu")
def load_data(path: str) -> bytes:

144
uv.lock generated
View file

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