File size: 8,899 Bytes
c5cfd97 | 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 | """Loopback OpenAI chat adapter. Native Spark tool calls are parsed, not repaired."""
import argparse
import copy
import json
import re
import threading
import time
import uuid
from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer
from pathlib import Path
ROOT = Path(r'D:\webbrain-training\compass-tiny-v3-spark-x25-20260926')
MODEL_ID = 'webbrain-compass-tiny-v3-bf16'
PACKAGE = ROOT / 'release-compat-v3'
def parse_tools(text, tools):
"""Only lossless native-format/schema decoding; errors stay visible."""
definitions = {t['function']['name']: t['function'] for t in tools or []}
calls = []
pattern = re.compile(r'<tool_call>(.*?)</tool_call>', re.S)
spans = list(pattern.finditer(text))
if text.count('<tool_call>') != len(spans) or text.count('</tool_call>') != len(spans):
raise ValueError('Incomplete native tool call')
for span in spans:
body = span.group(1)
name = body.split('<arg_key>', 1)[0].strip()
if name not in definitions:
raise ValueError(f'Unknown model-emitted function: {name}')
tail = body[len(body.split('<arg_key>', 1)[0]):]
arg_pattern = re.compile(r'<arg_key>(.*?)</arg_key><arg_value>(.*?)</arg_value>', re.S)
args = {}
cursor = 0
schema = definitions[name].get('parameters', {})
for arg in arg_pattern.finditer(tail):
if tail[cursor:arg.start()].strip():
raise ValueError('Malformed native argument sequence')
key, raw = arg.group(1), arg.group(2)
if key in args:
raise ValueError('Duplicate native argument key')
prop = schema.get('properties', {}).get(key, {})
args[key] = raw if prop.get('type') == 'string' else json.loads(raw)
cursor = arg.end()
if tail[cursor:].strip() or any(k not in args for k in schema.get('required', [])):
raise ValueError('Malformed or incomplete native arguments')
calls.append({'id': 'call_' + uuid.uuid4().hex[:16], 'type': 'function',
'function': {'name': name, 'arguments': json.dumps(args, ensure_ascii=False)}})
return calls, pattern.sub('', text).strip()
def normalize_messages(messages):
messages = copy.deepcopy(messages)
for message in messages:
for tool in message.get('tool_calls', []):
arguments = tool['function']['arguments']
if isinstance(arguments, str):
tool['function']['arguments'] = json.loads(arguments)
if not isinstance(tool['function']['arguments'], dict):
raise ValueError('Tool arguments must decode to an object')
return messages
class Bf16Backend:
def __init__(self):
import os
import sys
source = Path(__file__).parents[1] / 'spark_x25_17b_webbrain_compass_v3'
sys.path.insert(0, str(source))
from train import gpu_guard
cfg = json.loads((source / 'train-config.json').read_text())
gpu_guard(cfg)
import torch
import runtime
from transformers import AutoTokenizer
torch.set_num_threads(8)
self.torch = torch
self.tokenizer = AutoTokenizer.from_pretrained(PACKAGE, local_files_only=True)
self.model = runtime.load_native(str(PACKAGE), dtype=torch.bfloat16, device_map={'': 0}).eval()
self.lock = threading.Lock()
uploaded = json.loads((ROOT / 'upload-verified.json').read_text())
self.info = {'id': MODEL_ID, 'precision': 'BF16', 'runtime': 'Transformers/PyTorch CUDA',
'hardware': 'NVIDIA GeForce RTX 5090', 'revision': uploaded['revision'],
'max_context_tokens': 16384, 'thinking_default': False,
'onnx': False, 'webgpu': False, 'cloud_fallback': False}
def generate(self, request):
if request.get('model') != MODEL_ID:
raise ValueError(f'Use exact model id {MODEL_ID}')
tools = request.get('tools')
messages = normalize_messages(request['messages'])
options = request.get('chat_template_kwargs', {})
if options.get('enable_thinking', False):
raise ValueError('This benchmark endpoint serves thinking-disabled Compass')
inputs = self.tokenizer.apply_chat_template(messages, tools=tools, enable_thinking=False,
add_generation_prompt=True, return_tensors='pt', return_dict=True).to('cuda')
tokens = int(inputs.input_ids.shape[1])
maximum = int(request.get('max_completion_tokens', request.get('max_tokens', 512)))
if maximum < 1 or maximum > 4096 or tokens + maximum > self.info['max_context_tokens']:
raise ValueError('Requested input/output exceeds the deployed 16K context limit')
temperature = float(request.get('temperature', 0))
if temperature < 0:
raise ValueError('Negative temperature')
kwargs = {'max_new_tokens': maximum, 'do_sample': temperature > 0,
'pad_token_id': self.tokenizer.pad_token_id, 'use_cache': True}
if temperature > 0:
kwargs.update(temperature=temperature, top_p=float(request.get('top_p', 1.0)))
with self.lock, self.torch.no_grad():
output = self.model.generate(**inputs, **kwargs)
tail = output[0, tokens:]
raw = self.tokenizer.decode(tail, skip_special_tokens=True)
finish = 'length' if len(tail) == maximum else 'stop'
stop = request.get('stop') or []
if isinstance(stop, str):
stop = [stop]
for marker in stop:
if marker in raw:
raw = raw.split(marker, 1)[0]
finish = 'stop'
parser_error = None
try:
calls, content = parse_tools(raw, tools)
except (ValueError, KeyError, TypeError, json.JSONDecodeError) as exc:
calls, content, parser_error = [], raw, str(exc)
message = {'role': 'assistant', 'content': content or None}
if calls:
message['tool_calls'] = calls
finish = 'tool_calls'
result = {'id': 'chatcmpl-' + uuid.uuid4().hex, 'object': 'chat.completion', 'created': int(time.time()),
'model': MODEL_ID, 'choices': [{'index': 0, 'message': message, 'finish_reason': finish}],
'usage': {'prompt_tokens': tokens, 'completion_tokens': len(tail), 'total_tokens': tokens + len(tail)},
'x_runtime': self.info}
if parser_error:
result['x_parser_error'] = parser_error
return result
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--port', type=int, default=8560)
args = parser.parse_args()
backend = Bf16Backend()
class Handler(BaseHTTPRequestHandler):
def log_message(self, fmt, *args):
# No prompts, cookies or credentials are logged.
print(json.dumps({'http': fmt % args, 'time': time.time()}), flush=True)
def send_json(self, status, data):
body = json.dumps(data, ensure_ascii=False).encode()
self.send_response(status)
self.send_header('Content-Type', 'application/json')
self.send_header('Content-Length', str(len(body)))
self.end_headers()
self.wfile.write(body)
def do_GET(self):
if self.path == '/v1/models':
self.send_json(200, {'object': 'list', 'data': [{**backend.info, 'object': 'model', 'owned_by': 'webbrain-one'}]})
elif self.path == '/health':
self.send_json(200, {'ready': True, **backend.info})
else:
self.send_json(404, {'error': 'Unknown route'})
def do_POST(self):
if self.path != '/v1/chat/completions':
return self.send_json(404, {'error': 'Unknown route'})
try:
length = int(self.headers.get('Content-Length', '0'))
if not 0 < length <= 4 * 1024 * 1024:
raise ValueError('Invalid body length')
request = json.loads(self.rfile.read(length))
if request.get('stream', False):
raise ValueError('Use stream=false on this benchmark endpoint')
result = backend.generate(request)
self.send_json(200, result)
except (ValueError, KeyError, TypeError) as exc:
self.send_json(400, {'error': {'message': str(exc), 'type': 'invalid_request_error'}})
except Exception as exc:
self.send_json(500, {'error': {'message': str(exc), 'type': 'runtime_error'}})
print(json.dumps({'ready': True, 'baseUrl': f'http://127.0.0.1:{args.port}/v1', **backend.info}), flush=True)
ThreadingHTTPServer(('127.0.0.1', args.port), Handler).serve_forever()
if __name__ == '__main__':
main()
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