import dataclasses
import logging
import math
import os
import io
import sys
import time
import json
from typing import Optional, Sequence, Union
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[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