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Copy pathllama_cpp_worker.py
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267 lines (240 loc) · 10.8 KB
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import json
import re
import sys
import uuid
from typing import Any
def sanitize_unicode(value: Any) -> Any:
if isinstance(value, str):
# JSON permits lone UTF-16 surrogates, while the llama.cpp Python binding
# encodes prompts as strict UTF-8. Replace only those invalid code units
# so one malformed file/prompt character cannot abort an entire turn.
return value.encode("utf-8", "surrogatepass").decode("utf-8", "replace")
if isinstance(value, list):
return [sanitize_unicode(item) for item in value]
if isinstance(value, dict):
return {sanitize_unicode(key): sanitize_unicode(item) for key, item in value.items()}
return value
def hide_thinking_content(value: Any) -> Any:
if not isinstance(value, str):
return value
visible = re.sub(r"<think\b[^>]*>.*?</think\s*>\s*", "", value, flags=re.IGNORECASE | re.DOTALL)
closing_tag = re.search(r"</think\s*>", visible, flags=re.IGNORECASE)
if closing_tag:
visible = visible[closing_tag.end():]
return visible.lstrip()
def parse_tool_argument(value: str) -> Any:
candidate = value.strip()
try:
return json.loads(candidate)
except json.JSONDecodeError:
return candidate
def extract_text_tool_calls(content: Any, tools: list[dict[str, Any]]) -> tuple[Any, list[dict[str, Any]]]:
if not isinstance(content, str):
return content, []
allowed_names = {
function.get("name")
for tool in tools
if isinstance(tool, dict)
and isinstance((function := tool.get("function")), dict)
and isinstance(function.get("name"), str)
}
tool_calls: list[dict[str, Any]] = []
def replace(match: re.Match[str]) -> str:
name = match.group("name")
if name not in allowed_names:
return match.group(0)
arguments: dict[str, Any] = {}
for parameter in re.finditer(
r"<parameter=(?P<name>[A-Za-z_][A-Za-z0-9_]*)>\s*(?P<value>.*?)\s*</parameter\s*>",
match.group("body"),
flags=re.IGNORECASE | re.DOTALL,
):
arguments[parameter.group("name")] = parse_tool_argument(parameter.group("value"))
tool_calls.append({
"id": f"call_{uuid.uuid4().hex}",
"type": "function",
"function": {"name": name, "arguments": json.dumps(arguments)},
})
return ""
visible = re.sub(
r"<tool_call\s*>\s*<function=(?P<name>[A-Za-z_][A-Za-z0-9_]*)>\s*(?P<body>.*?)\s*</function\s*>\s*</tool_call\s*>",
replace,
content,
flags=re.IGNORECASE | re.DOTALL,
)
return visible.strip(), tool_calls
def resolve_kv_cache_type(value: Any, llama_cpp: Any, setting_name: str) -> Any:
if value is None:
return None
type_name = str(value).strip().lower()
constant_name = {"f16": "GGML_TYPE_F16", "q8_0": "GGML_TYPE_Q8_0"}.get(type_name)
if constant_name is None:
raise RuntimeError(f"nativeLlamaCpp.{setting_name} must be f16 or q8_0")
cache_type = getattr(llama_cpp, constant_name, None)
if cache_type is None:
raise RuntimeError(f"llama-cpp-python does not expose {constant_name} for native KV cache")
return cache_type
class Worker:
def __init__(self) -> None:
self.llm: Any = None
self.session_id: str | None = None
self.history: list[dict[str, Any]] = []
self.tools: list[dict[str, Any]] = []
self.generation: dict[str, Any] = {}
self.model_key: str | None = None
self.max_tokens: int | None = None
def emit_progress(self, request_id: str, event: str, **payload: Any) -> None:
sys.stdout.write(json.dumps({"id": request_id, "event": event, **payload}, default=str) + "\n")
sys.stdout.flush()
def handle(self, request: dict[str, Any]) -> dict[str, Any]:
request_id = request["id"]
try:
request_type = request["type"]
if request_type == "init":
self.emit_progress(request_id, "model_load_started", config=request.get("config", {}))
self.initialize(request)
self.emit_progress(
request_id,
"model_load_complete",
context_tokens=self.llm.n_ctx() if self.llm is not None else None,
initial_messages=len(self.history),
initial_payload_chars=len(json.dumps(self.history, default=str)),
)
return {"id": request_id, "ok": True}
if request_type == "append":
self.require_session(request)
messages = request.get("messages", [])
self.history.extend(messages)
self.emit_progress(
request_id,
"context_appended",
messages=len(messages),
payload_chars=len(json.dumps(messages, default=str)),
history_messages=len(self.history),
)
return {"id": request_id, "ok": True}
if request_type == "complete":
self.require_session(request)
self.emit_progress(
request_id,
"inference_started",
cached_tokens=self.llm.n_tokens if self.llm is not None else 0,
history_messages=len(self.history),
)
response = self.complete()
self.emit_progress(
request_id,
"inference_complete",
cached_tokens=self.llm.n_tokens if self.llm is not None else 0,
usage=response.get("usage"),
)
return {
"id": request_id,
"ok": True,
"message": response["message"],
"usage": response.get("usage"),
}
if request_type == "reset":
self.require_session(request)
self.history = []
if self.llm is not None:
self.llm.reset()
return {"id": request_id, "ok": True}
raise RuntimeError(f"Unknown request type: {request_type}")
except Exception as error:
return {"id": request_id, "ok": False, "error": str(error)}
def initialize(self, request: dict[str, Any]) -> None:
config = request["config"]
model_path = str(config.get("modelPath") or "").strip()
if not model_path:
raise RuntimeError("nativeLlamaCpp.modelPath is required")
model_key = json.dumps({
"modelPath": model_path,
"chatFormat": config.get("chatFormat"),
"nCtx": config.get("nCtx"),
"nGpuLayers": config.get("nGpuLayers"),
"flashAttn": config.get("flashAttn"),
"nBatch": config.get("nBatch"),
"kvTypeK": config.get("kvTypeK"),
"kvTypeV": config.get("kvTypeV"),
"useMmap": config.get("useMmap"),
"maxTokens": config.get("maxTokens"),
}, sort_keys=True)
if self.llm is None or self.model_key != model_key:
try:
from llama_cpp import Llama
import llama_cpp.llama_cpp as llama_cpp
except Exception as error:
raise RuntimeError(
"Cannot import llama_cpp. Install llama-cpp-python in the configured Python environment."
) from error
options: dict[str, Any] = {
"model_path": model_path,
"verbose": False,
"flash_attn": config.get("flashAttn") is not False,
"use_mmap": config.get("useMmap") is not False,
}
if isinstance(config.get("nCtx"), int) and config["nCtx"] > 0:
options["n_ctx"] = config["nCtx"]
if isinstance(config.get("nGpuLayers"), int):
options["n_gpu_layers"] = config["nGpuLayers"]
if isinstance(config.get("nBatch"), int) and config["nBatch"] > 0:
options["n_batch"] = config["nBatch"]
kv_type_k = resolve_kv_cache_type(config.get("kvTypeK"), llama_cpp, "kvTypeK")
if kv_type_k is not None:
options["type_k"] = kv_type_k
kv_type_v = resolve_kv_cache_type(config.get("kvTypeV"), llama_cpp, "kvTypeV")
if kv_type_v is not None:
options["type_v"] = kv_type_v
if isinstance(config.get("chatFormat"), str) and config["chatFormat"].strip():
options["chat_format"] = config["chatFormat"].strip()
self.llm = Llama(**options)
self.model_key = model_key
else:
self.llm.reset()
self.session_id = request["sessionId"]
self.history = list(request.get("messages", []))
self.tools = list(request.get("tools", []))
self.generation = dict(request.get("generation", {}))
self.max_tokens = config.get("maxTokens") if isinstance(config.get("maxTokens"), int) else None
def require_session(self, request: dict[str, Any]) -> None:
if self.llm is None or self.session_id != request.get("sessionId"):
raise RuntimeError("Native llama.cpp session is not initialized")
def complete(self) -> dict[str, Any]:
options: dict[str, Any] = {
"messages": self.history,
"stream": False,
}
if self.tools:
options["tools"] = self.tools
options["tool_choice"] = "auto"
if self.max_tokens is not None:
options["max_tokens"] = self.max_tokens
for source, target in (("temperature", "temperature"), ("topP", "top_p"), ("repetitionPenalty", "repeat_penalty")):
value = self.generation.get(source)
if isinstance(value, (int, float)):
options[target] = value
raw = self.llm.create_chat_completion(**options)
choice = raw["choices"][0]["message"]
history_message = dict(choice)
self.history.append(history_message)
message = dict(history_message)
content, tool_calls = extract_text_tool_calls(hide_thinking_content(message.get("content")), self.tools)
message["content"] = content
if tool_calls:
message["tool_calls"] = tool_calls
return {"message": message, "usage": raw.get("usage")}
def main() -> None:
worker = Worker()
for line in sys.stdin:
if not line.strip():
continue
try:
request = sanitize_unicode(json.loads(line))
response = worker.handle(request)
except Exception as error:
response = {"id": None, "ok": False, "error": str(error)}
sys.stdout.write(json.dumps(response, default=str) + "\n")
sys.stdout.flush()
if __name__ == "__main__":
main()