File size: 15,680 Bytes
834cedc | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 | """
OpenAI-compatible HTTP server for DFlash speculative decoding on MLX.
Supports:
- POST /v1/chat/completions (with streaming via SSE)
- POST /v1/completions
- GET /v1/models
- GET /health
- GET /metrics (DFlash-specific diagnostics)
Inspired by bstnxbt/dflash-mlx server architecture and Aryagm's OpenAI server.
"""
import json
import time
from typing import Any, Dict, List, Optional
from .speculative_decode import DFlashSpeculativeDecoder
from .adapters import load_target_model, LoadedTargetModel
from .convert import load_mlx_dflash
class DFlashServer:
"""OpenAI-compatible server wrapping a DFlashSpeculativeDecoder."""
def __init__(
self,
target_model_path: str,
draft_model_path: Optional[str] = None,
block_size: int = 16,
device: str = "metal",
):
"""Initialize server with target and optional draft model.
Args:
target_model_path: Path or HF ID of MLX target model
draft_model_path: Path or HF ID of converted DFlash drafter
block_size: Draft block size
device: MLX device
"""
print(f"[Server] Loading target model: {target_model_path}...")
self.loaded_target = load_target_model(target_model_path)
if draft_model_path:
print(f"[Server] Loading DFlash drafter: {draft_model_path}...")
self.draft_model, self.draft_config = load_mlx_dflash(draft_model_path)
else:
# Try to auto-resolve draft model
from .convert import _infer_target_model
inferred = _infer_target_model(target_model_path)
if inferred and inferred != "unknown":
print(f"[Server] Auto-resolved drafter: {inferred}")
# Look up in registry...
self.draft_model, self.draft_config = None, None
else:
print("[Server] No draft model — will use baseline generation")
self.draft_model, self.draft_config = None, None
if self.draft_model is not None:
self.decoder = DFlashSpeculativeDecoder(
target_model=self.loaded_target,
draft_model=self.draft_model,
tokenizer=self.loaded_target.tokenizer,
block_size=block_size,
device=device,
)
self.mode = "dflash"
else:
self.decoder = None
self.mode = "baseline"
# Metrics
self.request_count = 0
self.total_tokens = 0
self.total_time = 0.0
self.recent_requests: List[Dict] = []
def health(self) -> Dict[str, Any]:
return {"status": "ok", "mode": self.mode, "model": self.loaded_target.requested_model}
def models(self) -> Dict[str, Any]:
return {
"object": "list",
"data": [{
"id": self.loaded_target.requested_model,
"object": "model",
"owned_by": "dflash-mlx-universal",
}]
}
def metrics(self) -> Dict[str, Any]:
avg_tok_s = self.total_tokens / self.total_time if self.total_time > 0 else 0
return {
"request_count": self.request_count,
"total_tokens": self.total_tokens,
"avg_tokens_per_sec": avg_tok_s,
"recent_requests": self.recent_requests[-32:],
"mode": self.mode,
}
def _update_metrics(self, num_tokens: int, elapsed: float):
self.request_count += 1
self.total_tokens += num_tokens
self.total_time += elapsed
self.recent_requests.append({
"timestamp": time.time(),
"tokens": num_tokens,
"time_sec": elapsed,
"tok_s": num_tokens / elapsed if elapsed > 0 else 0,
})
if len(self.recent_requests) > 32:
self.recent_requests = self.recent_requests[-32:]
def chat_completions(
self,
messages: List[Dict[str, str]],
max_tokens: int = 1024,
temperature: float = 0.0,
stream: bool = False,
stop: Optional[List[str]] = None,
) -> Dict[str, Any] | Any:
"""Handle chat completion request.
Returns dict for non-streaming, generator for streaming.
"""
# Build prompt from messages
prompt = self._messages_to_prompt(messages)
if stream:
return self._stream_chat(prompt, max_tokens, temperature, stop)
# Non-streaming
start = time.time()
if self.mode == "dflash" and self.decoder is not None:
output = self.decoder.generate(
prompt=prompt,
max_tokens=max_tokens,
temperature=temperature,
stop_strings=stop,
)
else:
# Baseline mlx_lm generation
from mlx_lm.utils import generate as mlx_generate
output = mlx_generate(
model=self.loaded_target.model,
tokenizer=self.loaded_target.tokenizer,
prompt=prompt,
max_tokens=max_tokens,
temp=temperature,
)
elapsed = time.time() - start
num_tokens = len(self.loaded_target.tokenizer.encode(output))
self._update_metrics(num_tokens, elapsed)
return {
"id": f"chatcmpl-{int(time.time()*1000)}",
"object": "chat.completion",
"created": int(time.time()),
"model": self.loaded_target.requested_model,
"choices": [{
"index": 0,
"message": {
"role": "assistant",
"content": output,
},
"finish_reason": "stop",
}],
"usage": {
"prompt_tokens": len(self.loaded_target.tokenizer.encode(prompt)),
"completion_tokens": num_tokens,
"total_tokens": len(self.loaded_target.tokenizer.encode(prompt)) + num_tokens,
}
}
def _stream_chat(self, prompt: str, max_tokens: int, temperature: float, stop):
"""Generator for streaming SSE chunks."""
def event(data: Dict) -> str:
return f"data: {json.dumps(data)}\n\n"
# Yield initial role
yield event({
"id": f"chatcmpl-{int(time.time()*1000)}",
"object": "chat.completion.chunk",
"created": int(time.time()),
"model": self.loaded_target.requested_model,
"choices": [{"index": 0, "delta": {"role": "assistant"}, "finish_reason": None}],
})
accumulated = ""
if self.mode == "dflash" and self.decoder is not None:
# Use streaming generate
for chunk in self.decoder.generate(
prompt=prompt,
max_tokens=max_tokens,
temperature=temperature,
stop_strings=stop,
stream=True,
):
accumulated += chunk
yield event({
"id": f"chatcmpl-{int(time.time()*1000)}",
"object": "chat.completion.chunk",
"created": int(time.time()),
"model": self.loaded_target.requested_model,
"choices": [{"index": 0, "delta": {"content": chunk}, "finish_reason": None}],
})
else:
# Baseline: generate then stream word-by-word (not true streaming)
from mlx_lm.utils import generate as mlx_generate
output = mlx_generate(
model=self.loaded_target.model,
tokenizer=self.loaded_target.tokenizer,
prompt=prompt,
max_tokens=max_tokens,
temp=temperature,
)
for word in output.split(" "):
chunk = word + " "
accumulated += chunk
yield event({
"id": f"chatcmpl-{int(time.time()*1000)}",
"object": "chat.completion.chunk",
"created": int(time.time()),
"model": self.loaded_target.requested_model,
"choices": [{"index": 0, "delta": {"content": chunk}, "finish_reason": None}],
})
# Final chunk
yield event({
"id": f"chatcmpl-{int(time.time()*1000)}",
"object": "chat.completion.chunk",
"created": int(time.time()),
"model": self.loaded_target.requested_model,
"choices": [{"index": 0, "delta": {}, "finish_reason": "stop"}],
})
yield "data: [DONE]\n\n"
def _messages_to_prompt(self, messages: List[Dict[str, str]]) -> str:
"""Convert OpenAI messages format to prompt string."""
# Try chat template
tokenizer = self.loaded_target.tokenizer
if hasattr(tokenizer, "apply_chat_template"):
try:
return tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
)
except Exception:
pass
# Fallback: simple concatenation
prompt = ""
for msg in messages:
role = msg.get("role", "user")
content = msg.get("content", "")
if role == "system":
prompt += f"System: {content}\n"
elif role == "user":
prompt += f"User: {content}\n"
elif role == "assistant":
prompt += f"Assistant: {content}\n"
prompt += "Assistant: "
return prompt
def create_app(target_model: str, draft_model: Optional[str] = None, block_size: int = 16):
"""Create a Flask/FastAPI-style app for serving."""
try:
from fastapi import FastAPI, Request
from fastapi.responses import StreamingResponse
app = FastAPI(title="DFlash MLX Server")
server = DFlashServer(target_model, draft_model, block_size)
@app.get("/health")
async def health():
return server.health()
@app.get("/v1/models")
async def models():
return server.models()
@app.get("/metrics")
async def metrics():
return server.metrics()
@app.post("/v1/chat/completions")
async def chat_completions(request: Request):
body = await request.json()
messages = body.get("messages", [])
max_tokens = body.get("max_tokens", 1024)
temperature = body.get("temperature", 0.0)
stream = body.get("stream", False)
stop = body.get("stop", None)
result = server.chat_completions(
messages=messages,
max_tokens=max_tokens,
temperature=temperature,
stream=stream,
stop=stop,
)
if stream:
return StreamingResponse(result, media_type="text/event-stream")
return result
return app
except ImportError:
print("[Server] FastAPI not installed. Install with: pip install fastapi uvicorn")
# Fallback: simple HTTP server
from http.server import BaseHTTPRequestHandler, HTTPServer
import threading
class Handler(BaseHTTPRequestHandler):
server_instance = None
def do_GET(self):
if self.path == "/health":
self._json_response(200, self.server_instance.health())
elif self.path == "/v1/models":
self._json_response(200, self.server_instance.models())
elif self.path == "/metrics":
self._json_response(200, self.server_instance.metrics())
else:
self._json_response(404, {"error": "Not found"})
def do_POST(self):
if self.path == "/v1/chat/completions":
content_len = int(self.headers.get("Content-Length", 0))
body = json.loads(self.rfile.read(content_len))
result = self.server_instance.chat_completions(
messages=body.get("messages", []),
max_tokens=body.get("max_tokens", 1024),
temperature=body.get("temperature", 0.0),
stream=False,
stop=body.get("stop", None),
)
self._json_response(200, result)
else:
self._json_response(404, {"error": "Not found"})
def _json_response(self, status: int, data: Dict):
self.send_response(status)
self.send_header("Content-Type", "application/json")
self.end_headers()
self.wfile.write(json.dumps(data).encode())
Handler.server_instance = DFlashServer(target_model, draft_model, block_size)
return Handler
def main():
import argparse
parser = argparse.ArgumentParser(description="DFlash MLX OpenAI-compatible server")
parser.add_argument("--target", required=True, help="Target model path or HF ID")
parser.add_argument("--draft", default=None, help="Draft model path or HF ID")
parser.add_argument("--block-size", type=int, default=16)
parser.add_argument("--host", default="127.0.0.1")
parser.add_argument("--port", type=int, default=8000)
parser.add_argument("--device", default="metal")
args = parser.parse_args()
server = DFlashServer(args.target, args.draft, args.block_size, args.device)
try:
import uvicorn
from fastapi import FastAPI, Request
from fastapi.responses import StreamingResponse
app = FastAPI()
@app.get("/health")
async def health():
return server.health()
@app.get("/v1/models")
async def models():
return server.models()
@app.get("/metrics")
async def metrics():
return server.metrics()
@app.post("/v1/chat/completions")
async def chat_completions(request: Request):
body = await request.json()
result = server.chat_completions(
messages=body.get("messages", []),
max_tokens=body.get("max_tokens", 1024),
temperature=body.get("temperature", 0.0),
stream=body.get("stream", False),
stop=body.get("stop", None),
)
if body.get("stream", False):
return StreamingResponse(result, media_type="text/event-stream")
return result
print(f"[Server] Starting FastAPI on http://{args.host}:{args.port}")
uvicorn.run(app, host=args.host, port=args.port)
except ImportError:
print("[Server] FastAPI/uvicorn not available, using simple HTTP server")
from http.server import HTTPServer
handler = create_app(args.target, args.draft, args.block_size)
httpd = HTTPServer((args.host, args.port), handler)
print(f"[Server] Starting simple HTTP on http://{args.host}:{args.port}")
httpd.serve_forever()
if __name__ == "__main__":
main()
|