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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
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_GET(self):
query = unquote(urlparse(self.path).query)
if not query:
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')
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()
self.wfile.write(json.dumps(dict(
all_tokens=all_tokens,
used_tokens=used_tokens,
real_probability=real,
fake_probability=fake
)).encode())
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('Ready to serve')
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(['python', '-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)
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