mirror of
https://github.com/NousResearch/atropos.git
synced 2026-04-19 12:57:58 +00:00
340 lines
12 KiB
Python
340 lines
12 KiB
Python
import asyncio
|
|
import collections
|
|
import time
|
|
from abc import ABC, abstractmethod
|
|
from asyncio import exceptions
|
|
from typing import Literal, Optional
|
|
|
|
import numpy as np
|
|
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 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:
|
|
"""
|
|
Update the weight of the semaphore.
|
|
"""
|
|
self.weight = weight
|
|
|
|
def min_val(self):
|
|
"""
|
|
Returns the minimum value of the semaphore.
|
|
"""
|
|
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 ServerBaseline(BaseModel):
|
|
"""
|
|
Baseline configuration for server information. If local, uses ports 9004-9007 for the servers,
|
|
assuming a 1:1 split of GPUs.
|
|
"""
|
|
|
|
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. 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. Only works with sglang, please provide the model name.",
|
|
)
|
|
rolling_buffer_length: int = Field(
|
|
default=1000, description="Length of the rolling buffer to store metrics."
|
|
)
|
|
|
|
|
|
class APIServerConfig(ServerBaseline):
|
|
"""
|
|
API server configuration.
|
|
"""
|
|
|
|
api_key: Optional[str] = Field(default="", description="API key for the server.")
|
|
base_url: Optional[str] = Field(default="", description="Base URL for the server.")
|
|
server_type: Literal["openai", "trl"] = Field(
|
|
default="openai", description="Type of server to use, openai or trl"
|
|
)
|
|
n_kwarg_is_ignored: bool = Field(
|
|
default=False, description="Whether the n kwarg is ignored by this API server."
|
|
)
|
|
|
|
|
|
class APIServer(ABC):
|
|
"""
|
|
Abstract class for API servers.
|
|
"""
|
|
|
|
def __init__(self, config: APIServerConfig):
|
|
self.config = config
|
|
self.sem = AsyncSemWithAdaptiveWeight(config.num_max_requests_at_once)
|
|
self.eval_sem = AsyncSemWithAdaptiveWeight(config.num_requests_for_eval)
|
|
self.server_healthy = False
|
|
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:
|
|
"""
|
|
Update the weight of the semaphores
|
|
"""
|
|
# need to update sems
|
|
self.sem.update_weight(weight)
|
|
self.eval_sem.update_weight(weight)
|
|
|
|
@abstractmethod
|
|
async def check_server_status_task(self):
|
|
"""
|
|
Check the status of the server. Should be overridden by the child class.
|
|
Set self.server_healthy to True if the server is healthy.
|
|
"""
|
|
self.server_healthy = False
|
|
|
|
async def wandb_metrics(
|
|
self, metrics_dict: Optional[dict], server_name: Optional[str]
|
|
):
|
|
"""
|
|
Add metrics to the metrics dictionary.
|
|
|
|
If you want to add more metrics, you can do so by overriding this method, but make sure to call
|
|
super().wandb_metrics(metrics_dict, server_name) first to get the default metrics, if you still want them.
|
|
"""
|
|
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
|
|
|
|
@abstractmethod
|
|
async def _chat_completion_wrapper(self, **kwargs) -> ChatCompletion:
|
|
"""
|
|
Wrapper for the chat completion. Should be overridden by the child class and return a ChatCompletion object.
|
|
"""
|
|
pass
|
|
|
|
@abstractmethod
|
|
async def _completion_wrapper(self, **kwargs) -> Completion:
|
|
"""
|
|
Wrapper for the completion. Should be overridden by the child class and return a Completion object.
|
|
"""
|
|
pass
|
|
|
|
@retry(
|
|
stop=stop_after_attempt(3), wait=wait_random_exponential(multiplier=1, max=10)
|
|
)
|
|
async def _chat_comp(self, stat_dict, **kwargs) -> ChatCompletion:
|
|
"""
|
|
Simple retry and stat collection wrapper for the chat 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._chat_completion_wrapper(**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:
|
|
"""
|
|
Simple retry and stat collection wrapper for the chat 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._chat_completion_wrapper(**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:
|
|
"""
|
|
Chat completion handler, waits for the server to be healthy and then calls the chat completion wrapper.
|
|
"""
|
|
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:
|
|
"""
|
|
Simple retry and stat collection wrapper for the 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._completion_wrapper(**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:
|
|
"""
|
|
Simple retry and stat collection wrapper for the 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._completion_wrapper(**kwargs)
|
|
stat_dict["end"] = time.time()
|
|
return completions
|
|
|
|
async def completion(self, **kwargs) -> Completion:
|
|
"""
|
|
Completion handler, waits for the server to be healthy and then calls the completion wrapper.
|
|
"""
|
|
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
|