utils.py

import dataclasses
import logging
import math
import os
import io
import sys
import time
import json
from typing import Optional, Sequence, Union
from datasets import load_dataset, Dataset, concatenate_datasets

import openai
import tqdm
from openai import openai_object
import copy

StrOrOpenAIObject = Union[str, openai_object.OpenAIObject]

openai_org = os.getenv("OPENAI_ORG")
if openai_org is not None:
    openai.organization = openai_org
    logging.warning(f"Switching to organization: {openai_org} for OAI API key.")


@dataclasses.dataclass
class OpenAIDecodingArguments(object):
    max_tokens: int = 1800
    temperature: float = 0.2
    top_p: float = 1.0
    n: int = 1
    stream: bool = False
    stop: Optional[Sequence[str]] = None
    presence_penalty: float = 0.0
    frequency_penalty: float = 0.0
    suffix: Optional[str] = None
    logprobs: Optional[int] = None
    echo: bool = False


def openai_completion(
    prompts: Union[str, Sequence[str], Sequence[dict[str, str]], dict[str, str]],
    decoding_args: OpenAIDecodingArguments,
    model_name="text-davinci-003",
    sleep_time=2,
    batch_size=1,
    max_instances=sys.maxsize,
    max_batches=sys.maxsize,
    return_text=False,
    **decoding_kwargs,
) -> Union[StrOrOpenAIObject, Sequence[StrOrOpenAIObject], Sequence[Sequence[StrOrOpenAIObject]]]:
    """Decode with OpenAI API.

    Args:
        prompts: A string or a list of strings to complete. If it is a chat model the strings should be formatted
            as explained here: https://github.com/openai/openai-python/blob/main/chatml.md. If it is a chat model
            it can also be a dictionary (or list thereof) as explained here:
            https://github.com/openai/openai-cookbook/blob/main/examples/How_to_format_inputs_to_ChatGPT_models.ipynb
        decoding_args: Decoding arguments.
        model_name: Model name. Can be either in the format of "org/model" or just "model".
        sleep_time: Time to sleep once the rate-limit is hit.
        batch_size: Number of prompts to send in a single request. Only for non chat model.
        max_instances: Maximum number of prompts to decode.
        max_batches: Maximum number of batches to decode. This argument will be deprecated in the future.
        return_text: If True, return text instead of full completion object (which contains things like logprob).
        decoding_kwargs: Additional decoding arguments. Pass in `best_of` and `logit_bias` if you need them.

    Returns:
        A completion or a list of completions.
        Depending on return_text, return_openai_object, and decoding_args.n, the completion type can be one of
            - a string (if return_text is True)
            - an openai_object.OpenAIObject object (if return_text is False)
            - a list of objects of the above types (if decoding_args.n > 1)
    """
    is_single_prompt = isinstance(prompts, (str, dict))
    if is_single_prompt:
        prompts = [prompts]

    if max_batches < sys.maxsize:
        logging.warning(
            "`max_batches` will be deprecated in the future, please use `max_instances` instead."
            "Setting `max_instances` to `max_batches * batch_size` for now."
        )
        max_instances = max_batches * batch_size

    prompts = prompts[:max_instances]
    num_prompts = len(prompts)
    prompt_batches = [
        prompts[batch_id * batch_size : (batch_id + 1) * batch_size]
        for batch_id in range(int(math.ceil(num_prompts / batch_size)))
    ]

    completions = []
    for batch_id, prompt_batch in tqdm.tqdm(
        enumerate(prompt_batches),
        desc="prompt_batches",
        total=len(prompt_batches),
    ):
        batch_decoding_args = copy.deepcopy(decoding_args)  # cloning the decoding_args

        while True:
            try:
                shared_kwargs = dict(
                    model=model_name,
                    **batch_decoding_args.__dict__,
                    **decoding_kwargs,
                )
                completion_batch = openai.Completion.create(prompt=prompt_batch, **shared_kwargs)
                choices = completion_batch.choices

                for choice in choices:
                    choice["total_tokens"] = completion_batch.usage.total_tokens
                completions.extend(choices)
                break
            except openai.error.OpenAIError as e:
                logging.warning(f"OpenAIError: {e}.")
                if "Please reduce your prompt" in str(e):
                    batch_decoding_args.max_tokens = int(batch_decoding_args.max_tokens * 0.8)
                    logging.warning(f"Reducing target length to {batch_decoding_args.max_tokens}, Retrying...")
                else:
                    logging.warning("Hit request rate limit; retrying...")
                    time.sleep(sleep_time)  # Annoying rate limit on requests.

    if return_text:
        completions = [completion.text for completion in completions]
    if decoding_args.n > 1:
        # make completions a nested list, where each entry is a consecutive decoding_args.n of original entries.
        completions = [completions[i : i + decoding_args.n] for i in range(0, len(completions), decoding_args.n)]
    if is_single_prompt:
        # Return non-tuple if only 1 input and 1 generation.
        (completions,) = completions
    return completions


def _make_w_io_base(f, mode: str):
    if not isinstance(f, io.IOBase):
        f_dirname = os.path.dirname(f)
        if f_dirname != "":
            os.makedirs(f_dirname, exist_ok=True)
        f = open(f, mode=mode)
    return f


def _make_r_io_base(f, mode: str):
    if not isinstance(f, io.IOBase):
        f = open(f, mode=mode)
    return f


def jdump(obj, f, mode="w", indent=4, default=str):
    """Dump a str or dictionary to a file in json format.

    Args:
        obj: An object to be written.
        f: A string path to the location on disk.
        mode: Mode for opening the file.
        indent: Indent for storing json dictionaries.
        default: A function to handle non-serializable entries; defaults to `str`.
    """
    f = _make_w_io_base(f, mode)
    if isinstance(obj, (dict, list)):
        json.dump(obj, f, indent=indent, default=default)
    elif isinstance(obj, str):
        f.write(obj)
    else:
        raise ValueError(f"Unexpected type: {type(obj)}")
    f.close()


def jload(f, mode="r"):
    """Load a .json file into a dictionary."""
    f = _make_r_io_base(f, mode)
    jdict = json.load(f)
    f.close()
    return jdict

def load_datasets(data_path, split:str="train", streaming_name_whitelist=["translation", "mc4"]):
    data_path_base, data_path_names = data_path.rsplit(os.path.sep, maxsplit=1)
    datasets = []
    for data_path_name in data_path_names.split("+"):
        dataset_name, dataset_config = data_path_name.split("_", maxsplit=1)
        if "[" in dataset_config:
            dataset_config, dataset_range = dataset_config[:dataset_config.index("[")], dataset_config[dataset_config.index("["):]
        else:
            dataset_range = "[:]"
        if dataset_name in streaming_name_whitelist:
            dataset = load_dataset(os.path.join(data_path_base, dataset_name), config=dataset_config, split=split, streaming=True)
            start, end = dataset_range[1:-1].split(":")
            start = int(start) if start else 0
            end = int(end) if end else int(1e9)
            dataset = Dataset.from_list(list(dataset.skip(start).take(end - start)))
        else:
            dataset = load_dataset(os.path.join(data_path_base, dataset_name), config=dataset_config, split=f"{split}{dataset_range}")
        try:
            datasets.append(dataset.add_column("from", [f"{dataset_name}_{dataset_config}"] * len(dataset)))
        except ValueError:
            datasets.append(dataset)
    dataset = concatenate_datasets(datasets)
    return dataset