llama.cpp/examples/server/tests/utils.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-

# type: ignore[reportUnusedImport]

import subprocess
import os
import re
import json
import sys
import requests
import time
from concurrent.futures import ThreadPoolExecutor, as_completed
from typing import (
    Any,
    Callable,
    ContextManager,
    Iterable,
    Iterator,
    List,
    Literal,
    Tuple,
    Set,
)
from re import RegexFlag


class ServerResponse:
    headers: dict
    status_code: int
    body: dict | Any


class ServerProcess:
    # default options
    debug: bool = False
    server_port: int = 8080
    server_host: str = "127.0.0.1"
    model_hf_repo: str = "ggml-org/models"
    model_hf_file: str = "tinyllamas/stories260K.gguf"
    model_alias: str = "tinyllama-2"
    temperature: float = 0.8
    seed: int = 42

    # custom options
    model_alias: str | None = None
    model_url: str | None = None
    model_file: str | None = None
    model_draft: str | None = None
    n_threads: int | None = None
    n_gpu_layer: int | None = None
    n_batch: int | None = None
    n_ubatch: int | None = None
    n_ctx: int | None = None
    n_ga: int | None = None
    n_ga_w: int | None = None
    n_predict: int | None = None
    n_prompts: int | None = 0
    slot_save_path: str | None = None
    id_slot: int | None = None
    cache_prompt: bool | None = None
    n_slots: int | None = None
    server_continuous_batching: bool | None = False
    server_embeddings: bool | None = False
    server_reranking: bool | None = False
    server_metrics: bool | None = False
    draft: int | None = None
    api_key: str | None = None
    response_format: str | None = None
    lora_files: List[str] | None = None
    disable_ctx_shift: int | None = False
    draft_min: int | None = None
    draft_max: int | None = None

    # session variables
    process: subprocess.Popen | None = None

    def __init__(self):
        if "N_GPU_LAYERS" in os.environ:
            self.n_gpu_layer = int(os.environ["N_GPU_LAYERS"])
        if "DEBUG" in os.environ:
            self.debug = True
        if "PORT" in os.environ:
            self.server_port = int(os.environ["PORT"])

    def start(self, timeout_seconds: int = 10) -> None:
        if "LLAMA_SERVER_BIN_PATH" in os.environ:
            server_path = os.environ["LLAMA_SERVER_BIN_PATH"]
        elif os.name == "nt":
            server_path = "../../../build/bin/Release/llama-server.exe"
        else:
            server_path = "../../../build/bin/llama-server"
        server_args = [
            "--slots",  # requires to get slot status via /slots endpoint
            "--host",
            self.server_host,
            "--port",
            self.server_port,
            "--temp",
            self.temperature,
            "--seed",
            self.seed,
        ]
        if self.model_file:
            server_args.extend(["--model", self.model_file])
        if self.model_url:
            server_args.extend(["--model-url", self.model_url])
        if self.model_draft:
            server_args.extend(["--model-draft", self.model_draft])
        if self.model_hf_repo:
            server_args.extend(["--hf-repo", self.model_hf_repo])
        if self.model_hf_file:
            server_args.extend(["--hf-file", self.model_hf_file])
        if self.n_batch:
            server_args.extend(["--batch-size", self.n_batch])
        if self.n_ubatch:
            server_args.extend(["--ubatch-size", self.n_ubatch])
        if self.n_threads:
            server_args.extend(["--threads", self.n_threads])
        if self.n_gpu_layer:
            server_args.extend(["--n-gpu-layers", self.n_gpu_layer])
        if self.draft is not None:
            server_args.extend(["--draft", self.draft])
        if self.server_continuous_batching:
            server_args.append("--cont-batching")
        if self.server_embeddings:
            server_args.append("--embedding")
        if self.server_reranking:
            server_args.append("--reranking")
        if self.server_metrics:
            server_args.append("--metrics")
        if self.model_alias:
            server_args.extend(["--alias", self.model_alias])
        if self.n_ctx:
            server_args.extend(["--ctx-size", self.n_ctx])
        if self.n_slots:
            server_args.extend(["--parallel", self.n_slots])
        if self.n_predict:
            server_args.extend(["--n-predict", self.n_predict])
        if self.slot_save_path:
            server_args.extend(["--slot-save-path", self.slot_save_path])
        if self.n_ga:
            server_args.extend(["--grp-attn-n", self.n_ga])
        if self.n_ga_w:
            server_args.extend(["--grp-attn-w", self.n_ga_w])
        if self.debug:
            server_args.append("--verbose")
        if self.lora_files:
            for lora_file in self.lora_files:
                server_args.extend(["--lora", lora_file])
        if self.disable_ctx_shift:
            server_args.extend(["--no-context-shift"])
        if self.api_key:
            server_args.extend(["--api-key", self.api_key])
        if self.draft_max:
            server_args.extend(["--draft-max", self.draft_max])
        if self.draft_min:
            server_args.extend(["--draft-min", self.draft_min])

        args = [str(arg) for arg in [server_path, *server_args]]
        print(f"bench: starting server with: {' '.join(args)}")

        flags = 0
        if "nt" == os.name:
            flags |= subprocess.DETACHED_PROCESS
            flags |= subprocess.CREATE_NEW_PROCESS_GROUP
            flags |= subprocess.CREATE_NO_WINDOW

        self.process = subprocess.Popen(
            [str(arg) for arg in [server_path, *server_args]],
            creationflags=flags,
            stdout=sys.stdout,
            stderr=sys.stdout,
            env={**os.environ, "LLAMA_CACHE": "tmp"},
        )
        server_instances.add(self)

        print(f"server pid={self.process.pid}, pytest pid={os.getpid()}")

        # wait for server to start
        start_time = time.time()
        while time.time() - start_time < timeout_seconds:
            try:
                response = self.make_request("GET", "/slots", headers={
                    "Authorization": f"Bearer {self.api_key}" if self.api_key else None
                })
                if response.status_code == 200:
                    self.ready = True
                    return  # server is ready
            except Exception as e:
                pass
            print(f"Waiting for server to start...")
            time.sleep(0.5)
        raise TimeoutError(f"Server did not start within {timeout_seconds} seconds")

    def stop(self) -> None:
        if self in server_instances:
            server_instances.remove(self)
        if self.process:
            print(f"Stopping server with pid={self.process.pid}")
            self.process.kill()
            self.process = None

    def make_request(
        self,
        method: str,
        path: str,
        data: dict | Any | None = None,
        headers: dict | None = None,
    ) -> ServerResponse:
        url = f"http://{self.server_host}:{self.server_port}{path}"
        parse_body = False
        if method == "GET":
            response = requests.get(url, headers=headers)
            parse_body = True
        elif method == "POST":
            response = requests.post(url, headers=headers, json=data)
            parse_body = True
        elif method == "OPTIONS":
            response = requests.options(url, headers=headers)
        else:
            raise ValueError(f"Unimplemented method: {method}")
        result = ServerResponse()
        result.headers = dict(response.headers)
        result.status_code = response.status_code
        result.body = response.json() if parse_body else None
        print("Response from server", result.body)
        return result

    def make_stream_request(
        self,
        method: str,
        path: str,
        data: dict | None = None,
        headers: dict | None = None,
    ) -> Iterator[dict]:
        url = f"http://{self.server_host}:{self.server_port}{path}"
        if method == "POST":
            response = requests.post(url, headers=headers, json=data, stream=True)
        else:
            raise ValueError(f"Unimplemented method: {method}")
        for line_bytes in response.iter_lines():
            line = line_bytes.decode("utf-8")
            if '[DONE]' in line:
                break
            elif line.startswith('data: '):
                data = json.loads(line[6:])
                print("Partial response from server", data)
                yield data


server_instances: Set[ServerProcess] = set()


class ServerPreset:
    @staticmethod
    def tinyllama2() -> ServerProcess:
        server = ServerProcess()
        server.model_hf_repo = "ggml-org/models"
        server.model_hf_file = "tinyllamas/stories260K.gguf"
        server.model_alias = "tinyllama-2"
        server.n_ctx = 256
        server.n_batch = 32
        server.n_slots = 2
        server.n_predict = 64
        server.seed = 42
        return server

    @staticmethod
    def bert_bge_small() -> ServerProcess:
        server = ServerProcess()
        server.model_hf_repo = "ggml-org/models"
        server.model_hf_file = "bert-bge-small/ggml-model-f16.gguf"
        server.model_alias = "bert-bge-small"
        server.n_ctx = 512
        server.n_batch = 128
        server.n_ubatch = 128
        server.n_slots = 2
        server.seed = 42
        server.server_embeddings = True
        return server

    @staticmethod
    def tinyllama_infill() -> ServerProcess:
        server = ServerProcess()
        server.model_hf_repo = "ggml-org/models"
        server.model_hf_file = "tinyllamas/stories260K-infill.gguf"
        server.model_alias = "tinyllama-infill"
        server.n_ctx = 2048
        server.n_batch = 1024
        server.n_slots = 1
        server.n_predict = 64
        server.temperature = 0.0
        server.seed = 42
        return server

    @staticmethod
    def stories15m_moe() -> ServerProcess:
        server = ServerProcess()
        server.model_hf_repo = "ggml-org/stories15M_MOE"
        server.model_hf_file = "stories15M_MOE-F16.gguf"
        server.model_alias = "stories15m-moe"
        server.n_ctx = 2048
        server.n_batch = 1024
        server.n_slots = 1
        server.n_predict = 64
        server.temperature = 0.0
        server.seed = 42
        return server

    @staticmethod
    def jina_reranker_tiny() -> ServerProcess:
        server = ServerProcess()
        server.model_hf_repo = "ggml-org/models"
        server.model_hf_file = "jina-reranker-v1-tiny-en/ggml-model-f16.gguf"
        server.model_alias = "jina-reranker"
        server.n_ctx = 512
        server.n_batch = 512
        server.n_slots = 1
        server.seed = 42
        server.server_reranking = True
        return server


def parallel_function_calls(function_list: List[Tuple[Callable[..., Any], Tuple[Any, ...]]]) -> List[Any]:
    """
    Run multiple functions in parallel and return results in the same order as calls. Equivalent to Promise.all in JS.

    Example usage:

    results = parallel_function_calls([
        (func1, (arg1, arg2)),
        (func2, (arg3, arg4)),
    ])
    """
    results = [None] * len(function_list)
    exceptions = []

    def worker(index, func, args):
        try:
            result = func(*args)
            results[index] = result
        except Exception as e:
            exceptions.append((index, str(e)))

    with ThreadPoolExecutor() as executor:
        futures = []
        for i, (func, args) in enumerate(function_list):
            future = executor.submit(worker, i, func, args)
            futures.append(future)

        # Wait for all futures to complete
        for future in as_completed(futures):
            pass

    # Check if there were any exceptions
    if exceptions:
        print("Exceptions occurred:")
        for index, error in exceptions:
            print(f"Function at index {index}: {error}")

    return results


def match_regex(regex: str, text: str) -> bool:
    return (
        re.compile(
            regex, flags=RegexFlag.IGNORECASE | RegexFlag.MULTILINE | RegexFlag.DOTALL
        ).search(text)
        is not None
    )