olmocr/scripts/data/runopenaibatch.py

# Sends list of batch files to OpenAI for processing
# However, it also waits and gets the files when they are done, saves its state, and
# allows you to submit more than the 100GB of file request limits that the openaiAPI has
import argparse
import datetime
import json
import os
import time

from openai import OpenAI
from tqdm import tqdm

# Set up OpenAI client (API key should be set in the environment)
client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))

MAX_OPENAI_DISK_SPACE = 100 * 1024 * 1024 * 1024  # Max is 100GB on openAI
UPLOAD_STATE_FILENAME = "SENDSILVER_DATA"


# Function to upload a file to OpenAI and start batch processing
def upload_and_start_batch(file_path):
    # Upload the file to OpenAI
    with open(file_path, "rb") as file:
        print(f"Uploading {file_path} to OpenAI Batch API...")
        upload_response = client.files.create(file=file, purpose="batch")
        file_id = upload_response.id
        print(f"File uploaded successfully: {file_id}")

    # Create a batch job
    print(f"Creating batch job for {file_path}...")
    batch_response = client.batches.create(
        input_file_id=file_id, endpoint="/v1/chat/completions", completion_window="24h", metadata={"description": "pdf gold/silver data"}
    )

    batch_id = batch_response.id
    print(f"Batch created successfully: {batch_id}")
    return batch_id


def download_batch_result(batch_id, output_folder):
    # Retrieve the batch result from OpenAI API
    batch_data = client.batches.retrieve(batch_id)

    if batch_data.status != "completed":
        print(f"WARNING: {batch_id} is not completed, status: {batch_data.status}")
        return batch_id, False

    if batch_data.output_file_id is None:
        print(f"WARNING: {batch_id} is completed, but no output file was generated")
        return batch_id, False

    print(f"Downloading batch data for {batch_id}")

    file_response = client.files.content(batch_data.output_file_id)

    # Define output file path
    output_file = os.path.join(output_folder, f"{batch_id}.json")

    # Save the result to a file
    with open(output_file, "w") as f:
        f.write(str(file_response.text))

    return batch_id, True


ALL_STATES = ["init", "processing", "completed", "errored_out", "could_not_upload"]
FINISHED_STATES = ["completed", "errored_out"]


def _json_datetime_decoder(obj):
    if "last_checked" in obj:
        try:
            obj["last_checked"] = datetime.datetime.fromisoformat(obj["last_checked"])
        except (TypeError, ValueError):
            pass  # If it's not a valid ISO format, leave it as is
    return obj


def _json_datetime_encoder(obj):
    if isinstance(obj, datetime.datetime):
        return obj.isoformat()  # Convert datetime to ISO format string
    raise TypeError(f"Object of type {obj.__class__.__name__} is not JSON serializable")


def get_state(folder_path: str) -> dict:
    state_file = os.path.join(folder_path, UPLOAD_STATE_FILENAME)

    try:
        with open(state_file, "r") as f:
            return json.load(f, object_hook=_json_datetime_decoder)
    except (json.decoder.JSONDecodeError, FileNotFoundError):
        # List all .jsonl files in the specified folder
        jsonl_files = [f for f in os.listdir(folder_path) if f.endswith(".jsonl")]

        if not jsonl_files:
            raise Exception("No JSONL files found to process")

        state = {
            f: {
                "filename": f,
                "batch_id": None,
                "state": "init",
                "size": os.path.getsize(os.path.join(folder_path, f)),
                "last_checked": datetime.datetime.now(),
            }
            for f in jsonl_files
        }

        with open(state_file, "w") as f:
            json.dump(state, f, default=_json_datetime_encoder)

        return state


def update_state(folder_path: str, filename: str, **kwargs):
    all_state = get_state(folder_path)
    for kwarg_name, kwarg_value in kwargs.items():
        all_state[filename][kwarg_name] = kwarg_value

    all_state[filename]["last_checked"] = datetime.datetime.now()

    state_file = os.path.join(folder_path, UPLOAD_STATE_FILENAME)
    temp_file = state_file + ".tmp"

    # Write to temporary file first
    with open(temp_file, "w") as f:
        json.dump(all_state, f, default=_json_datetime_encoder)
        f.flush()
        os.fsync(f.fileno())

    # Atomic rename of temporary file to target file
    os.replace(temp_file, state_file)

    return all_state


def get_total_space_usage():
    return sum(file.bytes for file in client.files.list())


def get_estimated_space_usage(folder_path):
    all_states = get_state(folder_path)
    return sum(s["size"] for s in all_states.values() if s["state"] == "processing")


def get_next_work_item(folder_path):
    all_states = list(get_state(folder_path).values())
    all_states = [s for s in all_states if s["state"] not in FINISHED_STATES]
    all_states.sort(key=lambda s: s["last_checked"])

    return all_states[0] if len(all_states) > 0 else None


def get_done_total(folder_path):
    processing, done, total = 0, 0, 0

    for state in get_state(folder_path).values():
        if state["state"] in FINISHED_STATES:
            done += 1
        if state["state"] == "processing":
            processing += 1
        total += 1

    return processing, done, total


# Main function to process all .jsonl files in a folder
def process_folder(folder_path: str, max_gb: int):
    output_folder = f"{folder_path.rstrip('/')}_done"
    os.makedirs(output_folder, exist_ok=True)
    last_loop_time = datetime.datetime.now()

    starting_free_space = MAX_OPENAI_DISK_SPACE - get_total_space_usage()

    if starting_free_space < (max_gb * 1024**3) * 2:
        raise ValueError(
            f"Insufficient free space in OpenAI's file storage: Only {starting_free_space} GB left, but 2x{max_gb} GB are required (1x for your uploads, 1x for your results)."
        )

    while not all(state["state"] in FINISHED_STATES for state in get_state(folder_path).values()):
        processing, done, total = get_done_total(folder_path)
        print(f"Total items {total}, processing {processing}, done {done}, {done/total*100:.1f}%")

        work_item = get_next_work_item(folder_path)
        print(f"Processing {os.path.basename(work_item['filename'])}, cur status = {work_item['state']}")

        # If all work items have been checked on, then you need to sleep a bit
        if last_loop_time > datetime.datetime.now() - datetime.timedelta(seconds=1):
            time.sleep(0.2)

        if work_item["state"] == "init":
            if get_estimated_space_usage(folder_path) < (max_gb * 1024**3):
                try:
                    batch_id = upload_and_start_batch(os.path.join(folder_path, work_item["filename"]))
                    update_state(folder_path, work_item["filename"], state="processing", batch_id=batch_id)
                except Exception as ex:
                    print(ex)
                    update_state(folder_path, work_item["filename"], state="init")
            else:
                print("waiting for something to finish processing before uploading more")
                # Update the time you checked so you can move onto the next time
                update_state(folder_path, work_item["filename"])
        elif work_item["state"] == "processing":
            batch_data = client.batches.retrieve(work_item["batch_id"])

            if batch_data.status == "completed":
                batch_id, success = download_batch_result(work_item["batch_id"], output_folder)

                if success:
                    update_state(folder_path, work_item["filename"], state="completed")
                else:
                    update_state(folder_path, work_item["filename"], state="errored_out")

                try:
                    client.files.delete(batch_data.input_file_id)
                except Exception as ex:
                    print(ex)
                    print("Could not delete old input data")

                try:
                    client.files.delete(batch_data.output_file_id)
                except Exception as ex:
                    print(ex)
                    print("Could not delete old output data")
            elif batch_data.status in ["failed", "expired", "cancelled"]:
                update_state(folder_path, work_item["filename"], state="errored_out")

                try:
                    client.files.delete(batch_data.input_file_id)
                except:
                    print("Could not delete old file data")
            else:
                # Update the time you checked so you can move onto the next time
                update_state(folder_path, work_item["filename"])

        last_loop_time = datetime.datetime.now()

    print("All work has been completed")


if __name__ == "__main__":
    # Set up argument parsing for folder input
    parser = argparse.ArgumentParser(description="Upload .jsonl files and process batches in OpenAI API.")
    parser.add_argument("--max_gb", type=int, default=25, help="Max number of GB of batch processing files to upload at one time")
    parser.add_argument("--clear_all_files", action="store_true", help="Helper to delete ALL files stored in your openai account")
    parser.add_argument("folder", type=str, help="Path to the folder containing .jsonl files")

    args = parser.parse_args()

    if args.clear_all_files:
        all_files = list(client.files.list())
        if input(f"Are you sure you want to delete {len(all_files)} files from your OpenAI account? [y/N]").lower() == "y":
            for file in tqdm(all_files):
                client.files.delete(file.id)
        quit()

    # Process the folder and start batches
    process_folder(args.folder, args.max_gb)