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add batch_completion with rate limits to utils

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ishaan-jaff 2023-10-04 14:45:03 -07:00
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litellm/utils.py
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@ -3713,3 +3713,407 @@ def get_valid_models():
return valid_models
except:
return [] # NON-Blocking
############################# BATCH COMPLETION with Rate Limit Throttling #######################
"""
API REQUEST PARALLEL PROCESSOR
Using the OpenAI API to process lots of text quickly takes some care.
If you trickle in a million API requests one by one, they'll take days to complete.
If you flood a million API requests in parallel, they'll exceed the rate limits and fail with errors.
To maximize throughput, parallel requests need to be throttled to stay under rate limits.
This script parallelizes requests to the OpenAI API while throttling to stay under rate limits.
Features:
- Streams requests from file, to avoid running out of memory for giant jobs
- Makes requests concurrently, to maximize throughput
- Throttles request and token usage, to stay under rate limits
- Retries failed requests up to {max_attempts} times, to avoid missing data
- Logs errors, to diagnose problems with requests
```
Inputs:
- requests_filepath : str
- path to the file containing the requests to be processed
- file should be a jsonl file, where each line is a json object with API parameters and an optional metadata field
- e.g., {"model": "text-embedding-ada-002", "input": "embed me", "metadata": {"row_id": 1}}
- as with all jsonl files, take care that newlines in the content are properly escaped (json.dumps does this automatically)
- an example file is provided at examples/data/example_requests_to_parallel_process.jsonl
- the code to generate the example file is appended to the bottom of this script
- save_filepath : str, optional
- path to the file where the results will be saved
- file will be a jsonl file, where each line is an array with the original request plus the API response
- e.g., [{"model": "text-embedding-ada-002", "input": "embed me"}, {...}]
- if omitted, results will be saved to {requests_filename}_results.jsonl
- api_key : str, optional
- API key to use
- if omitted, the script will attempt to read it from an environment variable {os.getenv("OPENAI_API_KEY")}
- max_requests_per_minute : float, optional
- target number of requests to make per minute (will make less if limited by tokens)
- leave headroom by setting this to 50% or 75% of your limit
- if requests are limiting you, try batching multiple embeddings or completions into one request
- if omitted, will default to 1,500
- max_tokens_per_minute : float, optional
- target number of tokens to use per minute (will use less if limited by requests)
- leave headroom by setting this to 50% or 75% of your limit
- if omitted, will default to 125,000
- token_encoding_name : str, optional
- name of the token encoding used, as defined in the `tiktoken` package
- if omitted, will default to "cl100k_base" (used by `text-embedding-ada-002`)
- max_attempts : int, optional
- number of times to retry a failed request before giving up
- if omitted, will default to 5
- logging_level : int, optional
- level of logging to use; higher numbers will log fewer messages
- 40 = ERROR; will log only when requests fail after all retries
- 30 = WARNING; will log when requests his rate limits or other errors
- 20 = INFO; will log when requests start and the status at finish
- 10 = DEBUG; will log various things as the loop runs to see when they occur
- if omitted, will default to 20 (INFO).
The script is structured as follows:
- Imports
- Define main()
- Initialize things
- In main loop:
- Get next request if one is not already waiting for capacity
- Update available token & request capacity
- If enough capacity available, call API
- The loop pauses if a rate limit error is hit
- The loop breaks when no tasks remain
- Define dataclasses
- StatusTracker (stores script metadata counters; only one instance is created)
- APIRequest (stores API inputs, outputs, metadata; one method to call API)
- Define functions
- append_to_jsonl (writes to results file)
- num_tokens_consumed_from_request (bigger function to infer token usage from request)
- task_id_generator_function (yields 1, 2, 3, ...)
- Run main()
"""
# imports
import asyncio # for running API calls concurrently
import json # for saving results to a jsonl file
import logging # for logging rate limit warnings and other messages
import os # for reading API key
import re # for matching endpoint from request URL
import tiktoken # for counting tokens
import time # for sleeping after rate limit is hit
from dataclasses import (
dataclass,
field,
) # for storing API inputs, outputs, and metadata
async def batch_completion_rate_limits(
requests_filepath: str = "",
jobs: list = [],
save_filepath: str = None,
api_key: str = os.getenv("OPENAI_API_KEY"),
max_requests_per_minute: float = 3_000 * 0.5,
max_tokens_per_minute: float = 250_000 * 0.5,
token_encoding_name: str = "cl100k_base",
max_attempts: int = 5,
logging_level: int = logging.INFO,
):
if save_filepath == None:
save_filepath = "litellm_results.jsonl"
# constants
seconds_to_pause_after_rate_limit_error = 15
seconds_to_sleep_each_loop = (
0.001 # 1 ms limits max throughput to 1,000 requests per second
)
# initialize logging
logging.basicConfig(level=logging_level)
logging.debug(f"Logging initialized at level {logging_level}")
# infer API endpoint and construct request header
request_header = {"Authorization": f"Bearer {api_key}"}
# initialize trackers
queue_of_requests_to_retry = asyncio.Queue()
task_id_generator = (
task_id_generator_function()
) # generates integer IDs of 1, 2, 3, ...
status_tracker = (
StatusTracker()
) # single instance to track a collection of variables
next_request = None # variable to hold the next request to call
# initialize available capacity counts
available_request_capacity = max_requests_per_minute
available_token_capacity = max_tokens_per_minute
last_update_time = time.time()
# initialize flags
file_not_finished = True # after file is empty, we'll skip reading it
logging.debug(f"Initialization complete.")
requests = iter(jobs)
while True:
# get next request (if one is not already waiting for capacity)
if next_request is None:
if not queue_of_requests_to_retry.empty():
next_request = queue_of_requests_to_retry.get_nowait()
logging.debug(
f"Retrying request {next_request.task_id}: {next_request}"
)
elif file_not_finished:
try:
# get new request
request_json = next(requests)
if "api_key" not in request_json:
request_json["api_key"] = api_key
# print("CREATING API REQUEST")
next_request = APIRequest(
task_id=next(task_id_generator),
request_json=request_json,
token_consumption=num_tokens_consumed_from_request(
request_json, token_encoding_name
),
attempts_left=max_attempts,
metadata=request_json.pop("metadata", None),
)
# print("AFTER INIT API REQUEST")
status_tracker.num_tasks_started += 1
status_tracker.num_tasks_in_progress += 1
logging.debug(
f"Reading request {next_request.task_id}: {next_request}"
)
except:
logging.debug("Jobs finished")
file_not_finished = False
# update available capacity
current_time = time.time()
seconds_since_update = current_time - last_update_time
available_request_capacity = min(
available_request_capacity
+ max_requests_per_minute * seconds_since_update / 60.0,
max_requests_per_minute,
)
available_token_capacity = min(
available_token_capacity
+ max_tokens_per_minute * seconds_since_update / 60.0,
max_tokens_per_minute,
)
last_update_time = current_time
# if enough capacity available, call API
if next_request:
next_request_tokens = next_request.token_consumption
if (
available_request_capacity >= 1
and available_token_capacity >= next_request_tokens
):
# update counters
available_request_capacity -= 1
available_token_capacity -= next_request_tokens
next_request.attempts_left -= 1
# call API
# after finishing, log final status
logging.info(
f"""Running Request {next_request.task_id}, using tokens: {next_request.token_consumption} remaining available tokens: {available_token_capacity}"""
)
next_request.task_id
asyncio.create_task(
next_request.call_api(
request_header=request_header,
retry_queue=queue_of_requests_to_retry,
save_filepath=save_filepath,
status_tracker=status_tracker,
)
)
next_request = None # reset next_request to empty
# if all tasks are finished, break
if status_tracker.num_tasks_in_progress == 0:
break
# main loop sleeps briefly so concurrent tasks can run
await asyncio.sleep(seconds_to_sleep_each_loop)
# if a rate limit error was hit recently, pause to cool down
seconds_since_rate_limit_error = (
time.time() - status_tracker.time_of_last_rate_limit_error
)
if (
seconds_since_rate_limit_error
< seconds_to_pause_after_rate_limit_error
):
remaining_seconds_to_pause = (
seconds_to_pause_after_rate_limit_error
- seconds_since_rate_limit_error
)
await asyncio.sleep(remaining_seconds_to_pause)
# ^e.g., if pause is 15 seconds and final limit was hit 5 seconds ago
logging.warn(
f"Pausing to cool down until {time.ctime(status_tracker.time_of_last_rate_limit_error + seconds_to_pause_after_rate_limit_error)}"
)
# after finishing, log final status
logging.info(
f"""Parallel processing complete. Results saved to {save_filepath}"""
)
if status_tracker.num_tasks_failed > 0:
logging.warning(
f"{status_tracker.num_tasks_failed} / {status_tracker.num_tasks_started} requests failed. Errors logged to {save_filepath}."
)
if status_tracker.num_rate_limit_errors > 0:
logging.warning(
f"{status_tracker.num_rate_limit_errors} rate limit errors received. Consider running at a lower rate."
)
# dataclasses
@dataclass
class StatusTracker:
"""Stores metadata about the script's progress. Only one instance is created."""
num_tasks_started: int = 0
num_tasks_in_progress: int = 0 # script ends when this reaches 0
num_tasks_succeeded: int = 0
num_tasks_failed: int = 0
num_rate_limit_errors: int = 0
num_api_errors: int = 0 # excluding rate limit errors, counted above
num_other_errors: int = 0
time_of_last_rate_limit_error: int = 0 # used to cool off after hitting rate limits
@dataclass
class APIRequest:
"""Stores an API request's inputs, outputs, and other metadata. Contains a method to make an API call."""
task_id: int
request_json: dict
token_consumption: int
attempts_left: int
metadata: dict
result: list = field(default_factory=list)
async def call_api(
self,
request_header: dict,
retry_queue: asyncio.Queue,
save_filepath: str,
status_tracker: StatusTracker,
):
"""Calls the OpenAI API and saves results."""
logging.info(f"Starting request #{self.task_id}")
error = None
try:
response = await litellm.acompletion(
**self.request_json
)
# print("got response", response)
logging.info(f"Completed request #{self.task_id}")
except Exception as e:
logging.warning(
f"Request {self.task_id} failed with error {e}"
)
status_tracker.num_api_errors += 1
error = e
print(f"got exception {e}")
if "Rate limit" in str(e):
status_tracker.time_of_last_rate_limit_error = time.time()
status_tracker.num_rate_limit_errors += 1
status_tracker.num_api_errors -= (
1 # rate limit errors are counted separately
)
if error:
self.result.append(error)
if self.attempts_left:
retry_queue.put_nowait(self)
else:
logging.error(
f"Request {self.request_json} failed after all attempts. Saving errors: {self.result}"
)
data = (
[self.request_json, [str(e) for e in self.result], self.metadata]
if self.metadata
else [self.request_json, [str(e) for e in self.result]]
)
append_to_jsonl(data, save_filepath)
status_tracker.num_tasks_in_progress -= 1
status_tracker.num_tasks_failed += 1
else:
data = (
[self.request_json, response, self.metadata]
if self.metadata
else [self.request_json, response]
)
append_to_jsonl(data, save_filepath)
status_tracker.num_tasks_in_progress -= 1
status_tracker.num_tasks_succeeded += 1
logging.debug(f"Request {self.task_id} saved to {save_filepath}")
def append_to_jsonl(data, filename: str) -> None:
"""Append a json payload to the end of a jsonl file."""
json_string = json.dumps(data)
with open(filename, "a") as f:
f.write(json_string + "\n")
def num_tokens_consumed_from_request(
request_json: dict,
token_encoding_name: str,
):
"""Count the number of tokens in the request. Only supports completion and embedding requests."""
encoding = tiktoken.get_encoding(token_encoding_name)
# if completions request, tokens = prompt + n * max_tokens
max_tokens = request_json.get("max_tokens", 15)
n = request_json.get("n", 1)
completion_tokens = n * max_tokens
num_tokens = 0
for message in request_json["messages"]:
num_tokens += 4 # every message follows <im_start>{role/name}\n{content}<im_end>\n
for key, value in message.items():
num_tokens += len(encoding.encode(value))
if key == "name": # if there's a name, the role is omitted
num_tokens -= 1 # role is always required and always 1 token
num_tokens += 2 # every reply is primed with <im_start>assistant
return num_tokens + completion_tokens
def task_id_generator_function():
"""Generate integers 0, 1, 2, and so on."""
task_id = 0
while True:
yield task_id
task_id += 1
###### USAGE ################
# jobs = [
# {"model": "gpt-4", "messages": [{"content": "Please provide a summary of the latest scientific discoveries."*500, "role": "user"}]},
# {"model": "gpt-4", "messages": [{"content": "Please provide a summary of the latest scientific discoveries."*800, "role": "user"}]},
# {"model": "gpt-4", "messages": [{"content": "Please provide a summary of the latest scientific discoveries."*900, "role": "user"}]},
# {"model": "gpt-4", "messages": [{"content": "Please provide a summary of the latest scientific discoveries."*900, "role": "user"}]},
# {"model": "gpt-4", "messages": [{"content": "Please provide a summary of the latest scientific discoveries."*900, "role": "user"}]}
# ]
# asyncio.run(
# batch_completion_rate_limits(
# jobs = jobs,
# api_key="",
# max_requests_per_minute=60,
# max_tokens_per_minute=40000
# )
# )