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import math
import os
import time
from concurrent.futures import ThreadPoolExecutor
from dataclasses import dataclass
from statistics import mean
from typing import Any, Protocol
from openai import OpenAI
from llmserve_env.models import MetricsSnapshot, QuantizationTier, ServeAction, WorkloadSnapshot
from server.trace_simulator import TraceSimulator
class ServingBackend(Protocol):
mode: str
def reset(self, seed: int | None = None) -> None: ...
def run_step(self, task_id: str, action: ServeAction, workload: WorkloadSnapshot) -> MetricsSnapshot: ...
def describe(self) -> dict[str, Any]: ...
class SimulatedServingBackend:
mode = "sim"
def __init__(self, seed: int = 42) -> None:
try:
self.simulator = TraceSimulator(seed=seed)
except Exception as e:
raise RuntimeError(f"Failed to initialize TraceSimulator in SimulatedServingBackend: {e}") from e
def reset(self, seed: int | None = None) -> None:
self.simulator.reset(seed=seed)
def run_step(self, task_id: str, action: ServeAction, workload: WorkloadSnapshot) -> MetricsSnapshot:
return self.simulator.simulate_step(task_id, action, workload)
def describe(self) -> dict[str, Any]:
return {"mode": self.mode, "provider": "simulator"}
@dataclass
class _RequestResult:
latency_s: float
ttft_ms: float
itl_ms: float
prompt_tokens: int
completion_tokens: int
total_tokens: int
cost_usd: float
truncated: bool
class RealOpenAIBackend:
mode = "real"
def __init__(
self,
seed: int = 42,
api_key: str | None = None,
model: str | None = None,
base_url: str | None = None,
max_requests_per_step: int | None = None,
max_prompt_tokens: int | None = None,
max_completion_tokens: int | None = None,
client: OpenAI | None = None,
) -> None:
resolved_key = api_key or os.getenv("OPENAI_API_KEY")
if not resolved_key and client is None:
raise RuntimeError("OPENAI_API_KEY is required when LLMSERVE_MODE=real.")
env_base_url = os.getenv("OPENAI_BASE_URL")
resolved_base_url = (base_url or env_base_url or "").strip() or None
self.seed = seed
self.model = (model or os.getenv("LLMSERVE_REAL_MODEL", "gpt-4.1-mini")).strip()
self.base_url = resolved_base_url
self.max_requests_per_step = max_requests_per_step or int(os.getenv("LLMSERVE_REAL_MAX_REQUESTS_PER_STEP", "4"))
self.max_prompt_tokens = max_prompt_tokens or int(os.getenv("LLMSERVE_REAL_MAX_PROMPT_TOKENS", "512"))
self.max_completion_tokens = max_completion_tokens or int(os.getenv("LLMSERVE_REAL_MAX_COMPLETION_TOKENS", "64"))
self.client = client or OpenAI(
api_key=resolved_key,
base_url=self.base_url,
timeout=60.0,
max_retries=2,
)
self.pricing = {
"gpt-4.1-mini": {"input_per_million": 0.40, "output_per_million": 1.60},
"gpt-4.1": {"input_per_million": 2.00, "output_per_million": 8.00},
"gpt-4o-mini": {"input_per_million": 0.15, "output_per_million": 0.60},
"gpt-4o": {"input_per_million": 2.50, "output_per_million": 10.00},
}
def reset(self, seed: int | None = None) -> None:
if seed is not None:
self.seed = seed
def run_step(self, task_id: str, action: ServeAction, workload: WorkloadSnapshot) -> MetricsSnapshot:
request_count = max(
1,
min(
self.max_requests_per_step,
action.batch_cap,
max(1, workload.queue_depth + int(math.ceil(workload.arrival_rate))),
),
)
prompts = [
self._build_request_payload(task_id, workload, action, request_index=index, request_count=request_count)
for index in range(request_count)
]
batch_start = time.perf_counter()
with ThreadPoolExecutor(max_workers=request_count) as executor:
results = list(executor.map(self._execute_request, prompts))
batch_latency_s = max(time.perf_counter() - batch_start, 1e-6)
total_completion_tokens = sum(result.completion_tokens for result in results)
total_prompt_tokens = sum(result.prompt_tokens for result in results)
total_cost = sum(result.cost_usd for result in results)
mean_total_tokens = sum(result.total_tokens for result in results) / len(results)
throughput_tps = total_completion_tokens / batch_latency_s
mean_prompt = workload.mean_prompt_length
memory_factor = {
"gpt-4.1-mini": 0.010,
"gpt-4.1": 0.018,
"gpt-4o-mini": 0.009,
"gpt-4o": 0.016,
}.get(self.model, 0.012)
quant_factor = {
QuantizationTier.FP16.value: 1.00,
QuantizationTier.INT8.value: 0.84,
QuantizationTier.INT4.value: 0.72,
}[action.quantization_tier]
gpu_memory_used_gb = max(
2.0,
(total_prompt_tokens * memory_factor * quant_factor * max(action.kv_budget_fraction, 0.1)) / 10.0
+ request_count * 0.35,
)
cost_per_1k = max(0.0001, (total_cost / max(total_prompt_tokens + total_completion_tokens, 1)) * 1000.0)
evictions = sum(1 for result in results if result.truncated)
return MetricsSnapshot(
p50_ttft_ms=_percentile([result.ttft_ms for result in results], 0.50),
p99_ttft_ms=_percentile([result.ttft_ms for result in results], 0.99),
p50_itl_ms=_percentile([result.itl_ms for result in results], 0.50),
throughput_tps=max(1.0, throughput_tps),
gpu_memory_used_gb=gpu_memory_used_gb,
estimated_cost_per_1k=cost_per_1k,
spec_acceptance_rate=min(0.6, action.speculation_depth / 8.0 * 0.35),
eviction_events=evictions,
slo_violations=0,
requests_served=request_count,
)
def describe(self) -> dict[str, Any]:
return {
"mode": self.mode,
"provider": "openai",
"model": self.model,
"max_requests_per_step": self.max_requests_per_step,
"max_prompt_tokens": self.max_prompt_tokens,
"max_completion_tokens": self.max_completion_tokens,
}
def _build_request_payload(
self,
task_id: str,
workload: WorkloadSnapshot,
action: ServeAction,
request_index: int,
request_count: int,
) -> dict[str, Any]:
priority_cutoff = max(1, int(round(request_count * workload.priority_fraction)))
is_priority = request_index < priority_cutoff if action.priority_routing else False
spread = (request_index - (request_count / 2.0)) / max(request_count, 1)
target_prompt_tokens = max(32, int(workload.mean_prompt_length * (1.0 + spread * 0.35)))
effective_prompt_tokens = max(16, int(target_prompt_tokens * action.kv_budget_fraction))
truncated = effective_prompt_tokens < target_prompt_tokens
prompt = self._build_prompt(task_id, workload.phase, effective_prompt_tokens, is_priority=is_priority)
return {
"prompt": prompt,
"target_prompt_tokens": target_prompt_tokens,
"effective_prompt_tokens": effective_prompt_tokens,
"truncated": truncated,
"priority": is_priority,
}
def _execute_request(self, payload: dict[str, Any]) -> _RequestResult:
start = time.perf_counter()
response = self.client.chat.completions.create(
model=self.model,
temperature=0,
max_completion_tokens=self.max_completion_tokens,
messages=[
{
"role": "system",
"content": "You are a concise assistant. Answer the request directly in plain text.",
},
{"role": "user", "content": payload["prompt"]},
],
)
latency_s = max(time.perf_counter() - start, 1e-6)
usage = response.usage
prompt_tokens = int(getattr(usage, "prompt_tokens", payload["effective_prompt_tokens"]))
completion_tokens = int(getattr(usage, "completion_tokens", self.max_completion_tokens // 2))
total_tokens = int(getattr(usage, "total_tokens", prompt_tokens + completion_tokens))
ttft_ms = latency_s * 1000.0 * 0.35
itl_ms = max(1.0, ((latency_s * 1000.0) - ttft_ms) / max(completion_tokens, 1))
pricing = self.pricing.get(self.model, {"input_per_million": 0.40, "output_per_million": 1.60})
cost_usd = (
(prompt_tokens / 1_000_000.0) * pricing["input_per_million"]
+ (completion_tokens / 1_000_000.0) * pricing["output_per_million"]
)
return _RequestResult(
latency_s=latency_s,
ttft_ms=ttft_ms,
itl_ms=itl_ms,
prompt_tokens=prompt_tokens,
completion_tokens=completion_tokens,
total_tokens=total_tokens,
cost_usd=cost_usd,
truncated=bool(payload["truncated"]),
)
def _build_prompt(self, task_id: str, phase: str, target_tokens: int, is_priority: bool) -> str:
header = (
f"Task: {task_id}\n"
f"Phase: {phase}\n"
f"Priority: {is_priority}\n"
"Summarize the impact of serving-policy changes on latency, throughput, and user experience.\n"
)
filler_unit = "latency throughput queue kv cache scheduling token generation "
filler = (filler_unit * ((target_tokens // 8) + 8)).strip()
words = filler.split()
return header + " ".join(words[:target_tokens])
def create_serving_backend(mode: str | None = None, seed: int = 42) -> ServingBackend:
resolved_mode = (mode or os.getenv("LLMSERVE_MODE", "sim")).strip().lower()
if resolved_mode == "sim":
return SimulatedServingBackend(seed=seed)
if resolved_mode == "real":
provider = os.getenv("LLMSERVE_REAL_PROVIDER", "openai").strip().lower()
if provider != "openai":
raise RuntimeError(f"Unsupported LLMSERVE_REAL_PROVIDER: {provider}")
return RealOpenAIBackend(seed=seed)
raise RuntimeError(f"Unsupported LLMSERVE_MODE: {resolved_mode}")
def _percentile(values: list[float], pct: float) -> float:
ordered = sorted(values)
if not ordered:
return 0.0
index = min(len(ordered) - 1, max(0, int(round((len(ordered) - 1) * pct))))
return ordered[index]
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