mirror of
https://github.com/NousResearch/atropos.git
synced 2026-04-22 16:48:57 +00:00
first commit
This commit is contained in:
Dakota Nous
commit
621d00dd80
89 changed files with 15315 additions and 0 deletions
296
atroposlib/envs/server_handling/openai_server.py
Normal file
296
atroposlib/envs/server_handling/openai_server.py
Normal file
|
|
@ -0,0 +1,296 @@
|
|||
import asyncio
|
||||
import collections
|
||||
import time
|
||||
from asyncio import exceptions
|
||||
from typing import Optional
|
||||
|
||||
import aiohttp
|
||||
import numpy as np
|
||||
import openai
|
||||
from openai.types.chat.chat_completion import ChatCompletion
|
||||
from openai.types.completion import Completion
|
||||
from pydantic import BaseModel, Field
|
||||
from tenacity import retry, stop_after_attempt, wait_random_exponential
|
||||
|
||||
|
||||
class OpenaiConfig(BaseModel):
|
||||
"""
|
||||
Configuration for the server manager.
|
||||
"""
|
||||
|
||||
api_key: Optional[str] = Field(
|
||||
default=None, description="API key for OpenAI API. Use 'x' for local servers."
|
||||
)
|
||||
base_url: Optional[str] = Field(
|
||||
default=None,
|
||||
description="URL of the API endpoint. None if using official OpenAI API, otherwise local server URL.",
|
||||
)
|
||||
timeout: int = Field(
|
||||
default=1200, description="Timeout for the request in seconds."
|
||||
)
|
||||
num_max_requests_at_once: int = Field(
|
||||
default=512,
|
||||
description="Maximum number of concurrent requests. Note: You should divide this by the n kwarg.",
|
||||
)
|
||||
num_requests_for_eval: int = Field(
|
||||
default=64, description="Maximum number of concurrent requests for evaluation."
|
||||
)
|
||||
model_name: str = Field(
|
||||
default="default",
|
||||
description="The model name to use. Required for both OpenAI and local models.",
|
||||
)
|
||||
rolling_buffer_length: int = Field(
|
||||
default=1000, description="Length of the rolling buffer to store metrics."
|
||||
)
|
||||
|
||||
|
||||
class AsyncSemWithAdaptiveWeight(asyncio.Semaphore):
|
||||
def __init__(self, value: int):
|
||||
super().__init__(value=value)
|
||||
self.max_val = value
|
||||
self.weight = 1.0
|
||||
|
||||
def update_weight(self, weight: float) -> None:
|
||||
self.weight = weight
|
||||
|
||||
def min_val(self):
|
||||
return self.max_val * (1.0 - self.weight)
|
||||
|
||||
def release(self):
|
||||
"""Release a semaphore, incrementing the internal counter by one.
|
||||
|
||||
When it was zero on entry and another coroutine is waiting for it to
|
||||
become larger than zero again, wake up that coroutine.
|
||||
|
||||
If weight is set, it'll only wake up next if the value is greater than the max_val * weight
|
||||
"""
|
||||
self._value += 1
|
||||
if self._value > self.min_val():
|
||||
self._wake_up_next()
|
||||
|
||||
def locked(self):
|
||||
"""Returns True if semaphore cannot be acquired immediately."""
|
||||
return self._value <= self.min_val() or (
|
||||
any(not w.cancelled() for w in (self._waiters or ()))
|
||||
)
|
||||
|
||||
async def acquire(self):
|
||||
"""Acquire a semaphore.
|
||||
|
||||
If the internal counter is larger than zero on entry,
|
||||
decrement it by one and return True immediately. If it is
|
||||
zero on entry, block, waiting until some other coroutine has
|
||||
called release() to make it larger than 0, and then return
|
||||
True.
|
||||
"""
|
||||
if not self.locked():
|
||||
self._value -= 1
|
||||
return True
|
||||
|
||||
if self._waiters is None:
|
||||
self._waiters = collections.deque()
|
||||
fut = self._get_loop().create_future()
|
||||
self._waiters.append(fut)
|
||||
|
||||
# Finally block should be called before the CancelledError
|
||||
# handling as we don't want CancelledError to call
|
||||
# _wake_up_first() and attempt to wake up itself.
|
||||
try:
|
||||
try:
|
||||
await fut
|
||||
finally:
|
||||
self._waiters.remove(fut)
|
||||
except exceptions.CancelledError:
|
||||
if not fut.cancelled():
|
||||
self._value += 1
|
||||
self._wake_up_next()
|
||||
raise
|
||||
|
||||
if self._value > self.min_val():
|
||||
self._wake_up_next()
|
||||
return True
|
||||
|
||||
|
||||
class OpenAIServer:
|
||||
def __init__(self, config: OpenaiConfig):
|
||||
self.config = config
|
||||
self.openai = openai.AsyncClient(
|
||||
api_key=config.api_key,
|
||||
base_url=config.base_url,
|
||||
timeout=config.timeout,
|
||||
)
|
||||
self.sem = AsyncSemWithAdaptiveWeight(config.num_max_requests_at_once)
|
||||
self.eval_sem = AsyncSemWithAdaptiveWeight(config.num_requests_for_eval)
|
||||
self.server_healthy = True
|
||||
self.attempts_list = []
|
||||
self.request_timings = []
|
||||
# in case eval is much different, we should keep different buffers
|
||||
self.eval_attempts_list = []
|
||||
self.eval_request_timings = []
|
||||
self.check_task = None
|
||||
self.initialized = False
|
||||
|
||||
async def update_weight(self, weight: float) -> None:
|
||||
# need to update sems
|
||||
self.sem.update_weight(weight)
|
||||
self.eval_sem.update_weight(weight)
|
||||
|
||||
async def check_server_status_task(self):
|
||||
while True:
|
||||
try:
|
||||
await self.openai.completions.create(
|
||||
model=self.config.model_name,
|
||||
prompt="hi",
|
||||
max_tokens=1,
|
||||
)
|
||||
self.server_healthy = True
|
||||
except (
|
||||
aiohttp.ClientError,
|
||||
openai.OpenAIError,
|
||||
openai.APITimeoutError,
|
||||
Exception,
|
||||
):
|
||||
self.server_healthy = False
|
||||
await asyncio.sleep(1)
|
||||
|
||||
async def wandb_metrics(
|
||||
self, metrics_dict: Optional[dict], server_name: Optional[str]
|
||||
):
|
||||
if server_name is None:
|
||||
server_name = "server"
|
||||
if len(self.request_timings) > 0:
|
||||
metrics_dict[f"server/{server_name}_request_time_avg"] = np.mean(
|
||||
self.request_timings
|
||||
)
|
||||
metrics_dict[f"server/{server_name}_request_time_std"] = np.std(
|
||||
self.request_timings
|
||||
)
|
||||
metrics_dict[f"server/{server_name}_request_time_99p"] = np.percentile(
|
||||
self.request_timings, 99
|
||||
)
|
||||
if len(self.eval_request_timings) > 0:
|
||||
metrics_dict[f"server/{server_name}_eval_request_time_avg"] = np.mean(
|
||||
self.eval_request_timings
|
||||
)
|
||||
metrics_dict[f"server/{server_name}_eval_request_time_std"] = np.std(
|
||||
self.eval_request_timings
|
||||
)
|
||||
metrics_dict[f"server/{server_name}_eval_request_time_99p"] = np.percentile(
|
||||
self.eval_request_timings, 99
|
||||
)
|
||||
if len(self.attempts_list) > 0:
|
||||
metrics_dict[f"server/{server_name}_average_num_attempts"] = np.mean(
|
||||
self.attempts_list
|
||||
)
|
||||
if len(self.eval_attempts_list) > 0:
|
||||
metrics_dict[f"server/{server_name}_eval_retry_rate"] = np.mean(
|
||||
self.eval_attempts_list
|
||||
)
|
||||
return metrics_dict
|
||||
|
||||
@retry(
|
||||
stop=stop_after_attempt(3), wait=wait_random_exponential(multiplier=1, max=10)
|
||||
)
|
||||
async def _chat_comp(self, stat_dict, **kwargs) -> ChatCompletion:
|
||||
while not self.server_healthy:
|
||||
await asyncio.sleep(1)
|
||||
async with self.sem:
|
||||
if stat_dict.get("start", None) is None:
|
||||
stat_dict["start"] = time.time()
|
||||
stat_dict["attempts"] += 1
|
||||
completions = await self.openai.chat.completions.create(**kwargs)
|
||||
stat_dict["end"] = time.time()
|
||||
return completions
|
||||
|
||||
@retry(
|
||||
stop=stop_after_attempt(3), wait=wait_random_exponential(multiplier=1, max=10)
|
||||
)
|
||||
async def _chat_eval(self, stat_dict, **kwargs) -> ChatCompletion:
|
||||
while not self.server_healthy:
|
||||
await asyncio.sleep(1)
|
||||
async with self.eval_sem:
|
||||
if stat_dict.get("start", None) is None:
|
||||
stat_dict["start"] = time.time()
|
||||
stat_dict["attempts"] += 1
|
||||
completions = await self.openai.chat.completions.create(**kwargs)
|
||||
stat_dict["end"] = time.time()
|
||||
return completions
|
||||
|
||||
@retry(
|
||||
stop=stop_after_attempt(3), wait=wait_random_exponential(multiplier=1, max=10)
|
||||
)
|
||||
async def chat_completion(self, **kwargs) -> ChatCompletion:
|
||||
if not self.initialized:
|
||||
if (
|
||||
self.config.base_url is not None
|
||||
): # skip health check if using OpenAI API
|
||||
self.check_task = asyncio.create_task(self.check_server_status_task())
|
||||
else:
|
||||
self.server_healthy = True
|
||||
self.initialized = True
|
||||
kwargs["model"] = self.config.model_name
|
||||
split = kwargs.pop("split", "train")
|
||||
stat_dict = {}
|
||||
stat_dict["attempts"] = 0
|
||||
if split == "train":
|
||||
ret_data = await self._chat_comp(stat_dict, **kwargs)
|
||||
self.request_timings.append(stat_dict["end"] - stat_dict["start"])
|
||||
self.attempts_list.append(stat_dict["attempts"])
|
||||
else:
|
||||
# Give separate eval workers, if desired, gotta go fast for those evals
|
||||
ret_data = await self._chat_eval(stat_dict, **kwargs)
|
||||
self.eval_request_timings.append(stat_dict["end"] - stat_dict["start"])
|
||||
self.eval_attempts_list.append(stat_dict["attempts"])
|
||||
return ret_data
|
||||
|
||||
@retry(
|
||||
stop=stop_after_attempt(3), wait=wait_random_exponential(multiplier=1, max=10)
|
||||
)
|
||||
async def _comp(self, stat_dict, **kwargs) -> Completion:
|
||||
while not self.server_healthy:
|
||||
await asyncio.sleep(1)
|
||||
async with self.sem:
|
||||
if stat_dict.get("start", None) is None:
|
||||
stat_dict["start"] = time.time()
|
||||
stat_dict["attempts"] += 1
|
||||
completions = await self.openai.completions.create(**kwargs)
|
||||
stat_dict["end"] = time.time()
|
||||
return completions
|
||||
|
||||
@retry(
|
||||
stop=stop_after_attempt(3), wait=wait_random_exponential(multiplier=1, max=10)
|
||||
)
|
||||
async def _comp_eval(self, stat_dict, **kwargs) -> Completion:
|
||||
while not self.server_healthy:
|
||||
await asyncio.sleep(1)
|
||||
async with self.eval_sem:
|
||||
if stat_dict.get("start", None) is None:
|
||||
stat_dict["start"] = time.time()
|
||||
stat_dict["attempts"] += 1
|
||||
completions = await self.openai.completions.create(**kwargs)
|
||||
stat_dict["end"] = time.time()
|
||||
return completions
|
||||
|
||||
async def completion(self, **kwargs) -> Completion:
|
||||
if not self.initialized:
|
||||
if (
|
||||
self.config.base_url is not None
|
||||
): # skip health check if using OpenAI API
|
||||
self.check_task = asyncio.create_task(self.check_server_status_task())
|
||||
else:
|
||||
self.server_healthy = True
|
||||
self.initialized = True
|
||||
kwargs["model"] = self.config.model_name
|
||||
split = kwargs.pop("split", "train")
|
||||
stat_dict = {}
|
||||
stat_dict["attempts"] = 0
|
||||
if split == "train":
|
||||
ret_data = await self._comp(stat_dict, **kwargs)
|
||||
self.request_timings.append(stat_dict["end"] - stat_dict["start"])
|
||||
self.attempts_list.append(stat_dict["attempts"])
|
||||
else:
|
||||
# Give separate eval workers, if desired, gotta go fast for those evals
|
||||
ret_data = await self._comp_eval(stat_dict, **kwargs)
|
||||
self.eval_request_timings.append(stat_dict["end"] - stat_dict["start"])
|
||||
self.eval_attempts_list.append(stat_dict["attempts"])
|
||||
return ret_data
|
||||
Loading…
Add table
Add a link
Reference in a new issue