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e12a049 d0718ca e12a049 d0718ca e12a049 13fe947 e12a049 e493b7e e12a049 ca766b5 e12a049 ca766b5 e12a049 ca766b5 e12a049 d0718ca e12a049 13fe947 e12a049 d0718ca 13fe947 d0718ca | 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 | from __future__ import annotations
from collections.abc import Sequence
import atexit
import json
import os
from pathlib import Path
import platform
import subprocess
import sys
import threading
from typing import Any
from hackathon_advisor.config import bool_env, int_env, optional_int_env, tri_state_env
from hackathon_advisor.data import (
DEFAULT_EMBEDDING_MODEL_FILE,
DEFAULT_EMBEDDING_MODEL_REPO,
)
DEFAULT_N_CTX = 2048
class LlamaCppEmbedder:
def __init__(
self,
*,
model_repo: str = DEFAULT_EMBEDDING_MODEL_REPO,
model_file: str = DEFAULT_EMBEDDING_MODEL_FILE,
model_path: str = "",
n_ctx: int = DEFAULT_N_CTX,
n_batch: int | None = None,
n_threads: int | None = None,
n_gpu_layers: int = 0,
verbose: bool = False,
) -> None:
self.model_repo = model_repo.strip() or DEFAULT_EMBEDDING_MODEL_REPO
self.model_file = model_file.strip() or DEFAULT_EMBEDDING_MODEL_FILE
self.model_path = model_path.strip()
self.n_ctx = n_ctx
self.n_batch = n_batch or n_ctx
self.n_threads = n_threads
self.n_gpu_layers = n_gpu_layers
self.verbose = verbose
self._model = None
def __call__(self, text: str) -> Sequence[float]:
return self.embed(text)
def embed(self, text: str) -> Sequence[float]:
model = self._ensure_model()
return model.embed(text, normalize=True)
def _ensure_model(self):
if self._model is not None:
return self._model
from huggingface_hub import hf_hub_download
from llama_cpp import LLAMA_POOLING_TYPE_MEAN, Llama
model_path = self.model_path
if not model_path:
model_path = hf_hub_download(
repo_id=self.model_repo,
filename=self.model_file,
repo_type="model",
)
if not Path(model_path).is_file():
raise RuntimeError(f"llama.cpp embedding model was not found: {model_path}")
self._model = Llama(
model_path=model_path,
embedding=True,
pooling_type=LLAMA_POOLING_TYPE_MEAN,
n_ctx=self.n_ctx,
n_batch=self.n_batch,
n_ubatch=self.n_batch,
n_threads=self.n_threads,
n_gpu_layers=self.n_gpu_layers,
verbose=self.verbose,
)
return self._model
class SubprocessLlamaCppEmbedder:
def __init__(
self,
*,
model_repo: str = DEFAULT_EMBEDDING_MODEL_REPO,
model_file: str = DEFAULT_EMBEDDING_MODEL_FILE,
model_path: str = "",
n_ctx: int = DEFAULT_N_CTX,
n_batch: int | None = None,
n_threads: int | None = None,
n_gpu_layers: int = 0,
verbose: bool = False,
) -> None:
self.model_repo = model_repo.strip() or DEFAULT_EMBEDDING_MODEL_REPO
self.model_file = model_file.strip() or DEFAULT_EMBEDDING_MODEL_FILE
self.model_path = model_path.strip()
self.n_ctx = n_ctx
self.n_batch = n_batch or n_ctx
self.n_threads = n_threads
self.n_gpu_layers = n_gpu_layers
self.verbose = verbose
self._process: subprocess.Popen[str] | None = None
self._request_id = 0
self._lock = threading.Lock()
atexit.register(self.close)
def __call__(self, text: str) -> Sequence[float]:
return self.embed(text)
def embed(self, text: str) -> Sequence[float]:
with self._lock:
process = self._ensure_process()
self._request_id += 1
request_id = self._request_id
request = json.dumps({"id": request_id, "text": text}, ensure_ascii=False)
try:
assert process.stdin is not None
assert process.stdout is not None
process.stdin.write(f"{request}\n")
process.stdin.flush()
line = process.stdout.readline()
except (BrokenPipeError, OSError) as error:
self.close()
raise RuntimeError("llama.cpp embedding worker stopped before returning a vector.") from error
if not line:
returncode = process.poll()
self.close()
detail = f" with exit code {returncode}" if returncode is not None else ""
raise RuntimeError(f"llama.cpp embedding worker exited{detail}.")
try:
response = json.loads(line)
except json.JSONDecodeError as error:
raise RuntimeError("llama.cpp embedding worker returned invalid JSON.") from error
if response.get("id") != request_id:
raise RuntimeError("llama.cpp embedding worker returned an out-of-order response.")
if response.get("error"):
raise RuntimeError(str(response["error"]))
vector = response.get("vector")
if not isinstance(vector, list):
raise RuntimeError("llama.cpp embedding worker did not return a vector.")
return vector
def close(self) -> None:
process = self._process
self._process = None
if process is None:
return
if process.poll() is None:
process.terminate()
try:
process.wait(timeout=2)
except subprocess.TimeoutExpired:
process.kill()
process.wait(timeout=2)
def _ensure_process(self) -> subprocess.Popen[str]:
if self._process is not None and self._process.poll() is None:
return self._process
self._process = subprocess.Popen(
[sys.executable, "-u", "-m", "hackathon_advisor.llama_embedding", "--worker"],
stdin=subprocess.PIPE,
stdout=subprocess.PIPE,
stderr=None if self.verbose else subprocess.DEVNULL,
text=True,
cwd=Path(__file__).resolve().parents[1],
)
config = json.dumps(
{
"model_repo": self.model_repo,
"model_file": self.model_file,
"model_path": self.model_path,
"n_ctx": self.n_ctx,
"n_batch": self.n_batch,
"n_threads": self.n_threads,
"n_gpu_layers": self.n_gpu_layers,
"verbose": self.verbose,
},
ensure_ascii=False,
)
assert self._process.stdin is not None
self._process.stdin.write(f"{config}\n")
self._process.stdin.flush()
return self._process
def create_llama_cpp_embedder(metadata: dict[str, Any]) -> LlamaCppEmbedder | SubprocessLlamaCppEmbedder:
embedder_cls = SubprocessLlamaCppEmbedder if _use_subprocess_embedder() else LlamaCppEmbedder
return embedder_cls(
model_repo=os.environ.get(
"ADVISOR_EMBEDDING_MODEL_REPO",
str(metadata.get("model_repo") or DEFAULT_EMBEDDING_MODEL_REPO),
),
model_file=os.environ.get(
"ADVISOR_EMBEDDING_MODEL_FILE",
str(metadata.get("model_file") or DEFAULT_EMBEDDING_MODEL_FILE),
),
model_path=os.environ.get("ADVISOR_EMBEDDING_MODEL_PATH", ""),
n_ctx=int_env("ADVISOR_EMBEDDING_N_CTX", DEFAULT_N_CTX, minimum=0),
n_batch=optional_int_env("ADVISOR_EMBEDDING_BATCH"),
n_threads=optional_int_env("ADVISOR_EMBEDDING_THREADS"),
n_gpu_layers=int_env("ADVISOR_EMBEDDING_GPU_LAYERS", 0, minimum=0),
verbose=bool_env("ADVISOR_EMBEDDING_VERBOSE"),
)
def _use_subprocess_embedder() -> bool:
forced = tri_state_env("ADVISOR_EMBEDDING_SUBPROCESS")
if forced is not None:
return forced
backend = os.environ.get("ADVISOR_MODEL_BACKEND", "").strip().lower()
return platform.system() == "Darwin" and backend in {"minicpm", "minicpm-transformers"}
def _worker_loop() -> None:
config_line = sys.stdin.readline()
if not config_line:
return
embedder = LlamaCppEmbedder(**json.loads(config_line))
for line in sys.stdin:
if not line.strip():
continue
request = json.loads(line)
request_id = request.get("id")
try:
vector = list(embedder.embed(str(request.get("text") or "")))
response = {"id": request_id, "vector": vector}
except Exception as error:
response = {"id": request_id, "error": str(error)}
print(json.dumps(response), flush=True)
if __name__ == "__main__":
if len(sys.argv) == 2 and sys.argv[1] == "--worker":
_worker_loop()
else:
raise SystemExit("usage: python -m hackathon_advisor.llama_embedding --worker")
|