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Serves ``/v1/chat/completions`` and ``/v1/models`` so any OpenAI-API chat client can talk to a
hybrid checkpoint with its own sampler — the model cannot run under llama.cpp-family runtimes,
whose autoregressive decoding never matches the block-denoising objective.
Clients resend the full message history on every request and carry no thinking notes, while the
training layout replaces messages outside the visible window with the ledger of notes taken on
them. The server therefore caches each turn's notes keyed by a hash of the exact history that
produced it: a client that resends history verbatim reconstructs the same ledger the playground
would hold. On a cache miss (server restart, edited history) the older turns simply contribute
nothing, which is the playground's restart behaviour as well.
"""
from __future__ import annotations
import argparse
import hashlib
import json
import threading
import time
import uuid
from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer
from pathlib import Path
from diffusion_lm.claims import (
KEEP_MESSAGES,
SYSTEM,
chat_prefix,
ledger_line,
ledger_notes,
merge_notes,
)
from diffusion_lm.reasoning_playground import ReasoningEngine
from diffusion_lm.train import resolve_device
MAX_ANSWER_TOKENS = 384
NOTE_CACHE_LIMIT = 4096
def _turn_key(system: str, messages: list[dict[str, str]]) -> str:
payload = json.dumps(
[system] + [[m['role'], m['content']] for m in messages],
ensure_ascii=False, sort_keys=False,
)
return hashlib.sha256(payload.encode('utf-8')).hexdigest()
class ChatService:
"""One engine plus the note cache; generation is serialized on the GPU."""
def __init__(self, engine: ReasoningEngine, model_id: str, keep_messages: int) -> None:
self.engine = engine
self.model_id = model_id
self.keep_messages = keep_messages
self.notes: dict[str, str] = {}
self.lock = threading.Lock()
def _attach_notes(self, system: str, messages: list[dict[str, str]]) -> list[dict[str, str]]:
attached = []
for index, message in enumerate(messages):
entry = {'role': message['role'], 'content': message['content']}
if message['role'] == 'assistant':
note = self.notes.get(_turn_key(system, messages[: index + 1]))
if note:
entry['note'] = note
attached.append(entry)
return attached
def build_prefix(self, system: str, messages: list[dict[str, str]]) -> str:
attached = self._attach_notes(system, messages)
older = merge_notes(ledger_notes(attached, self.keep_messages))
window = attached[max(0, len(attached) - self.keep_messages):]
return chat_prefix(
[{'role': m['role'], 'content': m['content']} for m in window],
system=system, extra=ledger_line(older),
)
def remember(self, system: str, messages: list[dict[str, str]],
answer: str, note: str) -> None:
if not note:
return
if len(self.notes) >= NOTE_CACHE_LIMIT:
self.notes.clear()
turn = messages + [{'role': 'assistant', 'content': answer}]
self.notes[_turn_key(system, turn)] = note
def generate(self, system: str, messages: list[dict[str, str]], *, temperature: float,
top_p: float, max_tokens: int, seed: int):
"""Yield ``(answer_so_far, note, done)``; the note arrives with the final snapshot."""
prefix = self.build_prefix(system, messages)
blocks: list[tuple[int, str]] = []
answer = ''
with self.lock:
for _, answer, _ in self.engine.stream_chat(
prefix,
temperature=temperature,
# 16 measured optimal on qwen06b-genchat-sft (2026-08-12 five-point sweep):
# best numeric fidelity, clean doubling, half the latency of 32. Below 8 both
# fidelity and fluency degrade while latency barely moves.
steps_per_block=16,
max_answer_tokens=min(MAX_ANSWER_TOKENS, max_tokens),
top_p=top_p,
seed=seed,
blocks_out=blocks,
):
yield answer, '', False
note = '; '.join(text for _, text in blocks if text)
self.remember(system, messages, answer.strip(), note)
yield answer, note, True
def _split_messages(raw: list[dict]) -> tuple[str, list[dict[str, str]]]:
system = SYSTEM
messages = []
for message in raw:
role, content = message.get('role'), str(message.get('content') or '')
if role == 'system':
system = content or system
elif role in ('user', 'assistant'):
messages.append({'role': role, 'content': content})
return system, messages
class Handler(BaseHTTPRequestHandler):
service: ChatService
def log_message(self, format: str, *args) -> None: # noqa: A002
pass
def _json(self, status: int, body: dict) -> None:
data = json.dumps(body, ensure_ascii=False).encode('utf-8')
self.send_response(status)
self.send_header('Content-Type', 'application/json')
self.send_header('Content-Length', str(len(data)))
self.send_header('Access-Control-Allow-Origin', '*')
self.end_headers()
self.wfile.write(data)
def do_OPTIONS(self) -> None: # noqa: N802
self.send_response(204)
self.send_header('Access-Control-Allow-Origin', '*')
self.send_header('Access-Control-Allow-Methods', 'GET, POST, OPTIONS')
self.send_header('Access-Control-Allow-Headers', 'Content-Type, Authorization')
self.end_headers()
def do_GET(self) -> None: # noqa: N802
if self.path.rstrip('/') in ('/v1/models', '/models'):
self._json(200, {'object': 'list', 'data': [
{'id': self.service.model_id, 'object': 'model', 'owned_by': 'mini-mdlm'},
]})
else:
self._json(404, {'error': 'not found'})
def do_POST(self) -> None: # noqa: N802
if self.path.rstrip('/') not in ('/v1/chat/completions', '/chat/completions'):
self._json(404, {'error': 'not found'})
return
try:
length = int(self.headers.get('Content-Length', 0))
request = json.loads(self.rfile.read(length))
system, messages = _split_messages(request.get('messages') or [])
if not messages or messages[-1]['role'] != 'user':
raise ValueError('last message must be from the user')
except (ValueError, json.JSONDecodeError) as error:
self._json(400, {'error': {'message': str(error), 'type': 'invalid_request_error'}})
return
temperature = float(request.get('temperature') or 0.8)
top_p = float(request.get('top_p') or 0.95)
max_tokens = int(request.get('max_tokens') or MAX_ANSWER_TOKENS)
seed = int(request.get('seed') or 0)
stream = bool(request.get('stream'))
completion_id = f'chatcmpl-{uuid.uuid4().hex[:24]}'
created = int(time.time())
snapshots = self.service.generate(
system, messages, temperature=temperature, top_p=top_p,
max_tokens=max_tokens, seed=seed,
)
if not stream:
answer = note = ''
for answer, note, _ in snapshots:
pass
message = {'role': 'assistant', 'content': answer.strip()}
if note:
message['reasoning_content'] = note
self._json(200, {
'id': completion_id, 'object': 'chat.completion', 'created': created,
'model': self.service.model_id,
'choices': [{'index': 0, 'message': message, 'finish_reason': 'stop'}],
})
return
self.send_response(200)
self.send_header('Content-Type', 'text/event-stream')
self.send_header('Cache-Control', 'no-cache')
self.send_header('Access-Control-Allow-Origin', '*')
self.end_headers()
def chunk(delta: dict, finish: str | None = None) -> bytes:
body = {
'id': completion_id, 'object': 'chat.completion.chunk', 'created': created,
'model': self.service.model_id,
'choices': [{'index': 0, 'delta': delta, 'finish_reason': finish}],
}
return f'data: {json.dumps(body, ensure_ascii=False)}\n\n'.encode('utf-8')
try:
self.wfile.write(chunk({'role': 'assistant'}))
sent = ''
for answer, _, done in snapshots:
if len(answer) > len(sent):
self.wfile.write(chunk({'content': answer[len(sent):]}))
self.wfile.flush()
sent = answer
self.wfile.write(chunk({}, finish='stop'))
self.wfile.write(b'data: [DONE]\n\n')
except (BrokenPipeError, ConnectionResetError):
pass
def main() -> None:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument('--checkpoint', type=Path,
default=Path('outputs/qwen06b-genchat-sft/inference-latest.pt'))
parser.add_argument('--tokenizer', type=Path,
default=Path('artifacts/tokenizer-qwen3-adaptive.json'))
parser.add_argument('--model-id', default=None,
help='name reported to clients; defaults to the checkpoint directory')
parser.add_argument('--keep-messages', type=int, default=KEEP_MESSAGES)
parser.add_argument('--host', default='127.0.0.1')
parser.add_argument('--port', type=int, default=7998)
parser.add_argument('--device', default='auto')
args = parser.parse_args()
device = resolve_device(args.device)
engine = ReasoningEngine(args.checkpoint, args.tokenizer, device)
if not engine.chat_ready:
raise SystemExit(f'{args.checkpoint} is not an adaptive hybrid over a ChatML tokenizer')
Handler.service = ChatService(
engine, args.model_id or args.checkpoint.parent.name, args.keep_messages,
)
server = ThreadingHTTPServer((args.host, args.port), Handler)
print(f'serving {Handler.service.model_id} on http://{args.host}:{args.port}/v1')
server.serve_forever()
if __name__ == '__main__':
main()
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