get_next_logprobs revamp

scripts/get_next_logprobs.py

@@ -0,0 +1,115 @@
+#!/usr/bin/env python3
+import argparse
+
+import numpy as np
+import torch
+from datasets import Dataset
+from tqdm.auto import trange
+from transformers import AutoModelForCausalLM
+
+from delphi import utils
+from delphi.eval.utils import get_all_and_next_logprobs
+
+torch.set_grad_enabled(False)
+
+
+def main(
+    in_model_repo_id: str,
+    in_dataset_repo_id: str,
+    split: str,
+    feature: str,
+    batch_size: int,
+    out_repo_id: str,
+):
+    """
+    Outputs the log probabilities of the next token for each token in the dataset.
+    And uploads the resulting dataset to huggingface.
+    """
+    model = AutoModelForCausalLM.from_pretrained(in_model_repo_id)
+    in_dataset_split = utils.load_dataset_split_sequence_int32_feature(
+        in_dataset_repo_id, split, feature
+    )
+    in_dataset_split.set_format("torch")
+    n_seq = len(in_dataset_split)
+    seq_len = len(in_dataset_split[0][feature])
+    logprobs = np.empty((n_seq, seq_len))
+    logprobs[:, 0] = float("nan")
+    print("Running inference...")
+    for i in trange(0, n_seq, batch_size):
+        batch_tokens = in_dataset_split[i : i + batch_size][feature]
+        logprobs[i : i + batch_size, 1:] = (
+            get_all_and_next_logprobs(model, batch_tokens)[1].cpu().numpy()
+        )
+
+    hf_dataset = Dataset.from_dict({"logprobs": [row for row in logprobs]})
+
+    hf_dataset.push_to_hub(
+        repo_id=out_repo_id,
+        split=utils.hf_split_to_split_name(split),
+    )
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="Run inference and generate log probabilities."
+    )
+    parser.add_argument(
+        "--in-model-repo-id",
+        "--im",
+        type=str,
+        required=True,
+        help="The model",
+    )
+    parser.add_argument(
+        "--in-dataset-repo-id",
+        "--id",
+        type=str,
+        required=True,
+        help="The tokenized dataset",
+    )
+    parser.add_argument(
+        "--feature",
+        "-f",
+        type=str,
+        required=True,
+        help="Name of the column containing token sequences in the input dataset",
+    )
+    parser.add_argument(
+        "--split",
+        "-s",
+        type=str,
+        required=True,
+        help="Split of the tokenized dataset, supports slicing like 'train[:10%%]'",
+    )
+    parser.add_argument(
+        "--out-repo-id",
+        "-o",
+        type=str,
+        required=True,
+        help="Where to upload the next logprobs",
+    )
+    parser.add_argument(
+        "--batch-size",
+        "-b",
+        type=int,
+        default=80,
+        help="How many sequences to evaluate at once",
+    )
+    # TODO
+    # parser.add_argument(
+    #     "--chunk-size",
+    #     "-c",
+    #     type=int,
+    #     default=200_000,
+    #     help="Size of the parquet chunks uploaded to HuggingFace",
+    # )
+    args = parser.parse_args()
+
+    main(
+        in_model_repo_id=args.in_model_repo_id,
+        in_dataset_repo_id=args.in_dataset_repo_id,
+        split=args.split,
+        feature=args.feature,
+        batch_size=args.batch_size,
+        out_repo_id=args.out_repo_id,
+    )

src/delphi/utils.py

@@ -0,0 +1,50 @@
+from typing import cast
+
+from datasets import Dataset, Features, Sequence, Value, load_dataset
+
+
+def hf_split_to_split_name(split: str) -> str:
+    return split.split("[")[0]
+
+
+# TODO: test load_dataset functions
+def load_dataset_split_features(
+    repo_id: str,
+    split: str,
+    features: Features,
+) -> Dataset:
+    dataset = load_dataset(
+        repo_id,
+        split=split,
+        features=features,
+    )
+    dataset = cast(Dataset, dataset)
+    return dataset
+
+
+def load_dataset_split_string_feature(
+    repo_id: str,
+    split: str,
+    feature_name: str,
+) -> Dataset:
+    print("Loading string dataset")
+    print(f"{repo_id=}, {split=}, {feature_name=}")
+    return load_dataset_split_features(
+        repo_id,
+        split,
+        Features({feature_name: Value("string")}),
+    )
+
+
+def load_dataset_split_sequence_int32_feature(
+    repo_id: str,
+    split: str,
+    feature_name: str,
+) -> Dataset:
+    print("Loading sequence int32 dataset")
+    print(f"{repo_id=}, {split=}, {feature_name=}")
+    return load_dataset_split_features(
+        repo_id,
+        split,
+        Features({feature_name: Sequence(Value("int32"))}),
+    )

tests/test_utils.py

@@ -0,0 +1,14 @@
+from delphi.utils import hf_split_to_split_name
+
+from .utils import random_string
+
+
+def test_hf_split_to_split_name():
+    random_split_name = random_string(5)
+    assert hf_split_to_split_name(random_split_name) == random_split_name
+    assert hf_split_to_split_name(f"{random_split_name}[:10%]") == random_split_name
+    assert hf_split_to_split_name(f"{random_split_name}[10%:]") == random_split_name
+    assert hf_split_to_split_name(f"{random_split_name}[10%:20%]") == random_split_name
+    assert hf_split_to_split_name(f"{random_split_name}[:200]") == random_split_name
+    assert hf_split_to_split_name(f"{random_split_name}[200:]") == random_split_name
+    assert hf_split_to_split_name(f"{random_split_name}[200:400]") == random_split_name

tests/utils.py

@@ -0,0 +1,6 @@
+import random
+import string
+
+
+def random_string(length: int) -> str:
+    return "".join(random.choices(string.ascii_lowercase, k=length))