detector/server.py

import os
import sys
from http.server import HTTPServer, SimpleHTTPRequestHandler
from multiprocessing import Process
import subprocess
from transformers import RobertaForSequenceClassification, RobertaTokenizer
import json
import fire
import torch
from urllib.parse import urlparse, unquote, parse_qs


model: RobertaForSequenceClassification = None
tokenizer: RobertaTokenizer = None
device: str = None

def log(*args):
    print(f"[{os.environ.get('RANK', '')}]", *args, file=sys.stderr)


class RequestHandler(SimpleHTTPRequestHandler):

    def do_POST(self):
        self.begin_content('application/json,charset=UTF-8')

        content_length = int(self.headers['Content-Length'])
        if content_length > 0:
            post_data = self.rfile.read(content_length).decode('utf-8')
            try:
                post_data = json.loads(post_data)

                if 'text' not in post_data:
                    self.wfile.write(json.dumps({"error": "missing key 'text'"}).encode('utf-8'))
                else:
                    all_tokens, used_tokens, fake, real = self.infer(post_data['text'])

                    self.wfile.write(json.dumps(dict(
                        all_tokens=all_tokens,
                        used_tokens=used_tokens,
                        real_probability=real,
                        fake_probability=fake
                    )).encode('utf-8'))

            except Exception as e:
                self.wfile.write(json.dumps({"error": str(e)}).encode('utf-8'))

    def do_GET(self):
        parsed = urlparse(self.path)
        query_params = parse_qs(parsed.query)

        if 'text' not in query_params:
            self.begin_content('text/html')

            html = os.path.join(os.path.dirname(__file__), 'index.html')
            self.wfile.write(open(html).read().encode())
            return

        self.begin_content('application/json;charset=UTF-8')

        all_tokens, used_tokens, fake, real = self.infer(unquote(query_params['text'][0]))

        self.wfile.write(json.dumps(dict(
            all_tokens=all_tokens,
            used_tokens=used_tokens,
            real_probability=real,
            fake_probability=fake
        )).encode())

    def infer(self, query):
        tokens = tokenizer.encode(query)
        all_tokens = len(tokens)
        tokens = tokens[:tokenizer.max_len - 2]
        used_tokens = len(tokens)
        tokens = torch.tensor([tokenizer.bos_token_id] + tokens + [tokenizer.eos_token_id]).unsqueeze(0)
        mask = torch.ones_like(tokens)

        with torch.no_grad():
            logits = model(tokens.to(device), attention_mask=mask.to(device))[0]
            probs = logits.softmax(dim=-1)

        fake, real = probs.detach().cpu().flatten().numpy().tolist()

        return all_tokens, used_tokens, fake, real

    def begin_content(self, content_type):
        self.send_response(200)
        self.send_header('Content-Type', content_type)
        self.send_header('Access-Control-Allow-Origin', '*')
        self.end_headers()

    def log_message(self, format, *args):
        log(format % args)


def serve_forever(server, model, tokenizer, device):
    log('Process has started; loading the model ...')
    globals()['model'] = model.to(device)
    globals()['tokenizer'] = tokenizer
    globals()['device'] = device

    log(f'Ready to serve at http://localhost:{server.server_address[1]}')
    server.serve_forever()


def main(checkpoint, port=8080, device='cuda' if torch.cuda.is_available() else 'cpu'):
    if checkpoint.startswith('gs://'):
        print(f'Downloading {checkpoint}', file=sys.stderr)
        subprocess.check_output(['gsutil', 'cp', checkpoint, '.'])
        checkpoint = os.path.basename(checkpoint)
        assert os.path.isfile(checkpoint)

    print(f'Loading checkpoint from {checkpoint}')
    data = torch.load(checkpoint, map_location='cpu')

    model_name = 'roberta-large' if data['args']['large'] else 'roberta-base'
    model = RobertaForSequenceClassification.from_pretrained(model_name)
    tokenizer = RobertaTokenizer.from_pretrained(model_name)

    model.load_state_dict(data['model_state_dict'])
    model.eval()

    print(f'Starting HTTP server on port {port}', file=sys.stderr)
    server = HTTPServer(('0.0.0.0', port), RequestHandler)

    # avoid calling CUDA API before forking; doing so in a subprocess is fine.
    num_workers = int(subprocess.check_output([sys.executable, '-c', 'import torch; print(torch.cuda.device_count())']))

    if num_workers <= 1:
        serve_forever(server, model, tokenizer, device)
    else:
        print(f'Launching {num_workers} worker processes...')

        subprocesses = []

        for i in range(num_workers):
            os.environ['RANK'] = f'{i}'
            os.environ['CUDA_VISIBLE_DEVICES'] = f'{i}'
            process = Process(target=serve_forever, args=(server, model, tokenizer, device))
            process.start()
            subprocesses.append(process)

        del os.environ['RANK']
        del os.environ['CUDA_VISIBLE_DEVICES']

        for process in subprocesses:
            process.join()


if __name__ == '__main__':
    fire.Fire(main)