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Browse files- detector-base.pt +3 -0
- detector/index.html +15 -2
- detector/server.py +36 -6
- detector/server_get.py +120 -0
detector-base.pt
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@@ -0,0 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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oid sha256:c74935bd6568940038e6bfcc9c90bf821d7ae4163ebf2327b73db2f641376376
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size 501001061
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detector/index.html
CHANGED
@@ -2,6 +2,7 @@
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<html>
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<head>
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<title>GPT-2 Output Detector</title>
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<style type="text/css">
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* {
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box-sizing: border-box;
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@@ -74,7 +75,9 @@ em {
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<p>
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This is an online demo of the
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<a href="https://github.com/openai/gpt-2-output-dataset/tree/master/detector">GPT-2 output detector</a>
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model
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<u>The results start to get reliable after around 50 tokens.</u>
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</p>
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<textarea id="textbox" placeholder="Enter text here"></textarea>
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update_graph(null);
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return;
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}
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req.open('GET', '
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req.onreadystatechange = () => {
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if (req.readyState !== 4) return;
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if (req.status !== 200) throw new Error("HTTP status: " + req.status);
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@@ -150,5 +153,15 @@ window.addEventListener('DOMContentLoaded', () => {
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textbox.focus();
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});
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</script>
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</body>
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</html>
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<html>
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<head>
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<title>GPT-2 Output Detector</title>
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<meta charset="utf-8">
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<style type="text/css">
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* {
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box-sizing: border-box;
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<p>
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This is an online demo of the
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<a href="https://github.com/openai/gpt-2-output-dataset/tree/master/detector">GPT-2 output detector</a>
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model, based on the <a href="https://github.com/huggingface/transformers/commit/1c542df7e554a2014051dd09becf60f157fed524"><code>🤗/Transformers</code></a>
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implementation of <a href="https://arxiv.org/abs/1907.11692">RoBERTa</a>.
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Enter some text in the text box; the predicted probabilities will be displayed below.
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<u>The results start to get reliable after around 50 tokens.</u>
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</p>
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<textarea id="textbox" placeholder="Enter text here"></textarea>
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update_graph(null);
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return;
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}
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req.open('GET', window.location.href + '?' + textbox.value, true);
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req.onreadystatechange = () => {
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if (req.readyState !== 4) return;
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if (req.status !== 200) throw new Error("HTTP status: " + req.status);
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textbox.focus();
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});
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</script>
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<script>
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if (! ['localhost', 'huggingface.test'].includes(window.location.hostname)) {
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(function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
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(i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o),
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m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
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})(window,document,'script','https://www.google-analytics.com/analytics.js','ga');
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ga('create', 'UA-83738774-5', 'auto');
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ga('send', 'pageview');
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}
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</script>
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</body>
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</html>
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detector/server.py
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@@ -20,6 +20,30 @@ def log(*args):
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class RequestHandler(SimpleHTTPRequestHandler):
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def do_GET(self):
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query = unquote(urlparse(self.path).query)
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@@ -32,6 +56,16 @@ class RequestHandler(SimpleHTTPRequestHandler):
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self.begin_content('application/json;charset=UTF-8')
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tokens = tokenizer.encode(query)
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all_tokens = len(tokens)
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tokens = tokens[:tokenizer.max_len - 2]
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fake, real = probs.detach().cpu().flatten().numpy().tolist()
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-
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all_tokens=all_tokens,
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used_tokens=used_tokens,
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real_probability=real,
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fake_probability=fake
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)).encode())
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def begin_content(self, content_type):
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self.send_response(200)
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if __name__ == '__main__':
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fire.Fire(main)
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class RequestHandler(SimpleHTTPRequestHandler):
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def do_POST(self):
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self.begin_content('application/json,charset=UTF-8')
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content_length = int(self.headers['Content-Length'])
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if content_length > 0:
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post_data = self.rfile.read(content_length).decode('utf-8')
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try:
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post_data = json.loads(post_data)
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if 'text' not in post_data:
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self.wfile.write(json.dumps({"error": "missing key 'text'"}).encode('utf-8'))
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else:
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all_tokens, used_tokens, fake, real = self.infer(post_data['text'])
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self.wfile.write(json.dumps(dict(
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all_tokens=all_tokens,
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used_tokens=used_tokens,
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real_probability=real,
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fake_probability=fake
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)).encode('utf-8'))
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except Exception as e:
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self.wfile.write(json.dumps({"error": str(e)}).encode('utf-8'))
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def do_GET(self):
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query = unquote(urlparse(self.path).query)
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self.begin_content('application/json;charset=UTF-8')
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all_tokens, used_tokens, fake, real = self.infer(query)
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self.wfile.write(json.dumps(dict(
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all_tokens=all_tokens,
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used_tokens=used_tokens,
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real_probability=real,
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fake_probability=fake
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)).encode())
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def infer(self, query):
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tokens = tokenizer.encode(query)
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all_tokens = len(tokens)
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tokens = tokens[:tokenizer.max_len - 2]
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fake, real = probs.detach().cpu().flatten().numpy().tolist()
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return all_tokens, used_tokens, fake, real
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def begin_content(self, content_type):
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self.send_response(200)
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if __name__ == '__main__':
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fire.Fire(main)
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detector/server_get.py
ADDED
@@ -0,0 +1,120 @@
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import os
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import sys
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from http.server import HTTPServer, SimpleHTTPRequestHandler
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from multiprocessing import Process
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import subprocess
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from transformers import RobertaForSequenceClassification, RobertaTokenizer
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import json
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import fire
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import torch
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from urllib.parse import urlparse, unquote
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model: RobertaForSequenceClassification = None
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tokenizer: RobertaTokenizer = None
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device: str = None
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def log(*args):
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print(f"[{os.environ.get('RANK', '')}]", *args, file=sys.stderr)
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class RequestHandler(SimpleHTTPRequestHandler):
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def do_GET(self):
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query = unquote(urlparse(self.path).query)
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if not query:
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self.begin_content('text/html')
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html = os.path.join(os.path.dirname(__file__), 'index.html')
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self.wfile.write(open(html).read().encode())
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return
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self.begin_content('application/json;charset=UTF-8')
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tokens = tokenizer.encode(query)
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all_tokens = len(tokens)
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tokens = tokens[:tokenizer.max_len - 2]
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used_tokens = len(tokens)
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tokens = torch.tensor([tokenizer.bos_token_id] + tokens + [tokenizer.eos_token_id]).unsqueeze(0)
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mask = torch.ones_like(tokens)
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with torch.no_grad():
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logits = model(tokens.to(device), attention_mask=mask.to(device))[0]
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probs = logits.softmax(dim=-1)
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fake, real = probs.detach().cpu().flatten().numpy().tolist()
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self.wfile.write(json.dumps(dict(
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all_tokens=all_tokens,
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used_tokens=used_tokens,
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real_probability=real,
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fake_probability=fake
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)).encode())
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def begin_content(self, content_type):
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self.send_response(200)
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self.send_header('Content-Type', content_type)
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self.send_header('Access-Control-Allow-Origin', '*')
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self.end_headers()
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def log_message(self, format, *args):
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log(format % args)
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def serve_forever(server, model, tokenizer, device):
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log('Process has started; loading the model ...')
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globals()['model'] = model.to(device)
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globals()['tokenizer'] = tokenizer
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globals()['device'] = device
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log(f'Ready to serve at http://localhost:{server.server_address[1]}')
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server.serve_forever()
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def main(checkpoint, port=8080, device='cuda' if torch.cuda.is_available() else 'cpu'):
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if checkpoint.startswith('gs://'):
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print(f'Downloading {checkpoint}', file=sys.stderr)
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subprocess.check_output(['gsutil', 'cp', checkpoint, '.'])
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checkpoint = os.path.basename(checkpoint)
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assert os.path.isfile(checkpoint)
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print(f'Loading checkpoint from {checkpoint}')
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data = torch.load(checkpoint, map_location='cpu')
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model_name = 'roberta-large' if data['args']['large'] else 'roberta-base'
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model = RobertaForSequenceClassification.from_pretrained(model_name)
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tokenizer = RobertaTokenizer.from_pretrained(model_name)
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model.load_state_dict(data['model_state_dict'])
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model.eval()
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print(f'Starting HTTP server on port {port}', file=sys.stderr)
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server = HTTPServer(('0.0.0.0', port), RequestHandler)
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# avoid calling CUDA API before forking; doing so in a subprocess is fine.
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num_workers = int(subprocess.check_output([sys.executable, '-c', 'import torch; print(torch.cuda.device_count())']))
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if num_workers <= 1:
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serve_forever(server, model, tokenizer, device)
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else:
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print(f'Launching {num_workers} worker processes...')
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subprocesses = []
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for i in range(num_workers):
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os.environ['RANK'] = f'{i}'
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os.environ['CUDA_VISIBLE_DEVICES'] = f'{i}'
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process = Process(target=serve_forever, args=(server, model, tokenizer, device))
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process.start()
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subprocesses.append(process)
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del os.environ['RANK']
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del os.environ['CUDA_VISIBLE_DEVICES']
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for process in subprocesses:
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process.join()
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if __name__ == '__main__':
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fire.Fire(main)
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