Created configureble batch sizes

inference.py

@@ -165,7 +165,7 @@ def get_dataset(dataset_name: str, split_name: str, sample: int = None) -> pd.Da
  return dataset if sample is None else dataset.sample(sample).reset_index(drop=True)
 
 
-def run_model_inferences(split_name: str, run_id: str, dataset: str, features: list, sample_size: int = None):
+def run_model_inferences(split_name: str, run_id: str, dataset: str, features: list, batch_size: int, sample_size: int = None):
  """
  Run inference for the given model and dataset. Save the results to a CSV file.
 
@@ -180,7 +180,6 @@ def run_model_inferences(split_name: str, run_id: str, dataset: str, features: l
  pythia_model = load_model(split_name)
  pile_sequences = get_dataset(dataset, split_name, sample=sample_size)
  pile_dataset = PileDataset(pile_sequences, tokenizer)
- batch_size = get_batch_size(split_name)
  data_loader = DataLoader(pile_dataset, batch_size=batch_size)
 
  with torch.no_grad():
@@ -345,6 +344,8 @@ def parse_cli_args():
  default=None,
  )
 
+ parser.add_argument("--batch_size", type=int, default=None, help="Batch size for inference")
+
  return parser.parse_args()
 
 
@@ -369,7 +370,8 @@ def main():
  for dataset in args.datasets if isinstance(args.datasets, list) else args.datasets.split(","):
  split_name = f"{data_scheme}.{model_size}"
  print(f"Collecting inferences for {split_name} on {dataset} dataset")
- run_model_inferences(split_name, experiment_timestamp, dataset, args.features, args.sample_size)
+ batch_size = args.batch_size if args.batch_size is not None else get_batch_size(model_size)
+ run_model_inferences(split_name, experiment_timestamp, dataset, args.features, batch_size, args.sample_size)
 
 
 if __name__ == "__main__":

working_dirs/kyle/upload-ppl/upload.ipynb

@@ -0,0 +1,108 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "import pandas as pd\n",
+ "from tqdm import tqdm\n",
+ "from huggingface_hub import HfApi\n",
+ "from datasets import load_dataset, Dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "RepoUrl('https://huggingface.co/datasets/Kyle1668/pythia-semantic-memorization-perplexities', endpoint='https://huggingface.co', repo_type='dataset', repo_id='Kyle1668/pythia-semantic-memorization-perplexities')"
+ ]
+ },
+ "execution_count": 17,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "api = HfApi()\n",
+ "api.create_repo(\"pythia-semantic-memorization-perplexities\", repo_type=\"dataset\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ " 0%| | 0/1 [00:00<?, ?it/s]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Uploading pile_duped_12b.csv...\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "pile_duped_12b.csv: 100%|██████████| 207M/207M [00:03<00:00, 52.6MB/s]\n",
+ "100%|██████████| 1/1 [00:04<00:00, 4.88s/it]\n"
+ ]
+ }
+ ],
+ "source": [
+ "deduped_path = \"/home/kyle/repos/semantic-memorization/datasets/2023-08-07_22-32-14\"\n",
+ "for file_name in tqdm(os.listdir(deduped_path)):\n",
+ " print(f\"Uploading {file_name}...\")\n",
+ " \n",
+ " api.upload_file(\n",
+ " path_or_fileobj=f\"{deduped_path}/{file_name}\",\n",
+ " path_in_repo=f\"{file_name}\",\n",
+ " repo_id=\"Kyle1668/pythia-semantic-memorization-perplexities\",\n",
+ " repo_type=\"dataset\",\n",
+ " )"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "memorization",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.11"
+ },
+ "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}