From a51716b9f74466e2f6c0eedb934ef1c26825da1b Mon Sep 17 00:00:00 2001 From: Charlie Cheng-Jie Ji Date: Mon, 18 Nov 2024 08:04:32 +0000 Subject: [PATCH 01/65] litellm online request processer init commit --- examples/litellm_example.py | 33 ++++ src/bespokelabs/curator/prompter/prompter.py | 54 +++-- .../litellm_online_request_processor.py | 186 ++++++++++++++++++ 3 files changed, 255 insertions(+), 18 deletions(-) create mode 100644 examples/litellm_example.py create mode 100644 src/bespokelabs/curator/request_processor/litellm_online_request_processor.py diff --git a/examples/litellm_example.py b/examples/litellm_example.py new file mode 100644 index 00000000..adb569f3 --- /dev/null +++ b/examples/litellm_example.py @@ -0,0 +1,33 @@ +from typing import List +from pydantic import BaseModel, Field +from bespokelabs import curator + +# Define response format using Pydantic +class Recipe(BaseModel): + title: str = Field(description="Title of the recipe") + ingredients: List[str] = Field(description="List of ingredients needed") + instructions: List[str] = Field(description="Step by step cooking instructions") + +def prompt_func(row=None): + return "Generate a random recipe. Be creative but keep it realistic." + +def parse_func(row, response): + # Response is already a Recipe object thanks to response_format + return { + "title": response.title, + "ingredients": response.ingredients, + "instructions": response.instructions + } + +# Create prompter using LiteLLM backend +recipe_prompter = curator.Prompter( + model_name="gpt-3.5-turbo", # Could also use "anthropic/claude-2", "bedrock/claude", etc + prompt_func=prompt_func, + parse_func=parse_func, + response_format=Recipe, + backend="litellm", # Specify LiteLLM backend +) + +# Generate recipes +recipes = recipe_prompter() +print(recipes.to_pandas()) \ No newline at end of file diff --git a/src/bespokelabs/curator/prompter/prompter.py b/src/bespokelabs/curator/prompter/prompter.py index a8afa2da..565361eb 100644 --- a/src/bespokelabs/curator/prompter/prompter.py +++ b/src/bespokelabs/curator/prompter/prompter.py @@ -21,6 +21,7 @@ from bespokelabs.curator.request_processor.openai_online_request_processor import ( OpenAIOnlineRequestProcessor, ) +from bespokelabs.curator.request_processor.litellm_online_request_processor import LiteLLMOnlineRequestProcessor _CURATOR_DEFAULT_CACHE_DIR = "~/.cache/curator" T = TypeVar("T") @@ -47,6 +48,7 @@ def __init__( ] ] = None, response_format: Optional[Type[BaseModel]] = None, + backend: str = "openai", batch: bool = False, batch_size: Optional[int] = None, temperature: Optional[float] = None, @@ -64,6 +66,7 @@ def __init__( response object and returns the parsed output response_format (Optional[Type[BaseModel]]): A Pydantic model specifying the response format from the LLM. + backend (str): The backend to use ("openai" or "litellm") batch (bool): Whether to use batch processing batch_size (Optional[int]): The size of the batch to use, only used if batch is True temperature (Optional[float]): The temperature to use for the LLM, only used if batch is False @@ -88,32 +91,47 @@ def __init__( model_name, prompt_func, parse_func, response_format ) self.batch_mode = batch - if batch: - if batch_size is None: - batch_size = 1_000 - logger.info( - f"batch=True but no batch_size provided, using default batch_size of {batch_size:,}" + + # Select request processor based on backend + if backend == "openai": + if batch: + if batch_size is None: + batch_size = 1_000 + logger.info( + f"batch=True but no batch_size provided, using default batch_size of {batch_size:,}" + ) + self._request_processor = OpenAIBatchRequestProcessor( + model=model_name, + batch_size=batch_size, + temperature=temperature, + top_p=top_p, + presence_penalty=presence_penalty, + frequency_penalty=frequency_penalty, ) - self._request_processor = OpenAIBatchRequestProcessor( - model=model_name, - batch_size=batch_size, - temperature=temperature, - top_p=top_p, - presence_penalty=presence_penalty, - frequency_penalty=frequency_penalty, - ) - else: - if batch_size is not None: - logger.warning( - f"Prompter argument `batch_size` {batch_size} is ignored because `batch` is False" + else: + if batch_size is not None: + logger.warning( + f"Prompter argument `batch_size` {batch_size} is ignored because `batch` is False" + ) + self._request_processor = OpenAIOnlineRequestProcessor( + model=model_name, + temperature=temperature, + top_p=top_p, + presence_penalty=presence_penalty, + frequency_penalty=frequency_penalty, ) - self._request_processor = OpenAIOnlineRequestProcessor( + elif backend == "litellm": + if batch: + logger.warning("Batch mode is not supported with LiteLLM backend, ignoring batch=True") + self._request_processor = LiteLLMOnlineRequestProcessor( model=model_name, temperature=temperature, top_p=top_p, presence_penalty=presence_penalty, frequency_penalty=frequency_penalty, ) + else: + raise ValueError(f"Unknown backend: {backend}") def __call__( self, dataset: Optional[Iterable] = None, working_dir: str = None diff --git a/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py b/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py new file mode 100644 index 00000000..4ff966f3 --- /dev/null +++ b/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py @@ -0,0 +1,186 @@ +import json +import logging +import os +from typing import Optional + +import instructor +from litellm import completion, get_supported_openai_params, model_cost + +from bespokelabs.curator.dataset import Dataset +from bespokelabs.curator.prompter.prompter import PromptFormatter +from bespokelabs.curator.request_processor.base_request_processor import ( + BaseRequestProcessor, + GenericRequest, + GenericResponse, +) + +logger = logging.getLogger(__name__) + +class LiteLLMOnlineRequestProcessor(BaseRequestProcessor): + """ + Request processor for LiteLLM that supports structured outputs via instructor. + """ + + def __init__( + self, + model: str, + temperature: Optional[float] = None, + top_p: Optional[float] = None, + presence_penalty: Optional[float] = None, + frequency_penalty: Optional[float] = None, + ): + """Initialize LiteLLM request processor. + + Args: + model (str): Name of the model to use + temperature (Optional[float]): Sampling temperature + top_p (Optional[float]): Nucleus sampling parameter + presence_penalty (Optional[float]): Presence penalty parameter + frequency_penalty (Optional[float]): Frequency penalty parameter + """ + super().__init__(batch_size=None) # LiteLLM doesn't support batching yet + self.model = model + self.temperature = temperature + self.top_p = top_p + self.presence_penalty = presence_penalty + self.frequency_penalty = frequency_penalty + + # Initialize instructor client + self.client = instructor.from_litellm(completion) + + def get_rate_limits(self) -> dict: + """Get rate limits for the model from LiteLLM.""" + # Get model costs/limits from LiteLLM + costs = model_cost.get(self.model, {}) + + # Default conservative limits if not found + return { + "max_tokens_per_minute": costs.get("max_tokens", 150_000), + "max_requests_per_minute": 3000 + } + + def create_api_specific_request(self, generic_request: GenericRequest) -> dict: + """Create a LiteLLM-specific request from a generic request. + + Args: + generic_request (GenericRequest): The generic request to convert + + Returns: + dict: LiteLLM-specific request parameters + """ + # Get supported parameters for this model + supported_params = get_supported_openai_params(model=self.model) + + request = { + "model": generic_request.model, + "messages": generic_request.messages, + } + + # Only add parameters that are supported by this model + if "temperature" in supported_params and self.temperature is not None: + request["temperature"] = self.temperature + + if "top_p" in supported_params and self.top_p is not None: + request["top_p"] = self.top_p + + if "presence_penalty" in supported_params and self.presence_penalty is not None: + request["presence_penalty"] = self.presence_penalty + + if "frequency_penalty" in supported_params and self.frequency_penalty is not None: + request["frequency_penalty"] = self.frequency_penalty + + return request + + def run( + self, + dataset: Optional[Dataset], + working_dir: str, + parse_func_hash: str, + prompt_formatter: PromptFormatter, + ) -> Dataset: + """Run completions using LiteLLM. + + Args: + dataset (Optional[Dataset]): Input dataset + working_dir (str): Directory for saving request/response files + parse_func_hash (str): Hash of the parse function + prompt_formatter (PromptFormatter): Prompt formatting logic + + Returns: + Dataset: Dataset with completion results + """ + # Create request files + generic_requests_files = self.create_request_files( + dataset, working_dir, prompt_formatter + ) + + # Create response files + generic_responses_files = [ + f"{working_dir}/responses_{i}.jsonl" + for i in range(len(generic_requests_files)) + ] + + # Process each request file + for request_file, response_file in zip( + generic_requests_files, generic_responses_files + ): + self._process_requests( + request_file, + response_file, + prompt_formatter.response_format + ) + + # Create final dataset + return self.create_dataset_files( + working_dir, parse_func_hash, prompt_formatter + ) + + def _process_requests( + self, + request_file: str, + response_file: str, + response_format: Optional[type] = None + ): + """Process requests from file using LiteLLM.""" + with open(request_file, "r") as f: + for line in f: + request = GenericRequest.model_validate_json(line) + + try: + # Make request with structured output if response_format provided + if response_format: + response = self.client.chat.completions.create( + **self.create_api_specific_request(request), + response_model=response_format + ) + # Convert Pydantic model to dict for storage + response_message = response.model_dump() + else: + response = self.client.chat.completions.create( + **self.create_api_specific_request(request) + ) + response_message = response.content if hasattr(response, 'content') else str(response) + + # Create generic response + generic_response = GenericResponse( + response_message=response_message, # Now using the properly formatted response + response_errors=None, + raw_request=self.create_api_specific_request(request), + raw_response=None, # LiteLLM response object isn't JSON serializable + generic_request=request, + ) + + except Exception as e: + logger.error(f"Error processing request: {e}") + # Create error response + generic_response = GenericResponse( + response_message=None, + response_errors=[str(e)], + raw_request=self.create_api_specific_request(request), + raw_response=None, + generic_request=request, + ) + + # Save response to file + with open(response_file, "a") as f_out: + f_out.write(json.dumps(generic_response.model_dump(), default=str) + "\n") From cbc8797f990eadfe406caafba037954e92d62bd4 Mon Sep 17 00:00:00 2001 From: Charlie Cheng-Jie Ji Date: Mon, 18 Nov 2024 08:04:41 +0000 Subject: [PATCH 02/65] poetry add litellm package --- poetry.lock | 332 ++++++++++++++++++++++++++++++++++++++++++++++++- pyproject.toml | 1 + 2 files changed, 327 insertions(+), 6 deletions(-) diff --git a/poetry.lock b/poetry.lock index 805672b6..06c1d4df 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.4 and should not be changed by hand. 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17 00:00:00 2001 From: Charlie Cheng-Jie Ji Date: Mon, 18 Nov 2024 21:39:08 +0000 Subject: [PATCH 03/65] claude example --- examples/litellm_example.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/litellm_example.py b/examples/litellm_example.py index adb569f3..c43430cd 100644 --- a/examples/litellm_example.py +++ b/examples/litellm_example.py @@ -21,7 +21,7 @@ def parse_func(row, response): # Create prompter using LiteLLM backend recipe_prompter = curator.Prompter( - model_name="gpt-3.5-turbo", # Could also use "anthropic/claude-2", "bedrock/claude", etc + model_name="claude-3-opus-20240229", # Could also use "anthropic/claude-2", "bedrock/claude", etc prompt_func=prompt_func, parse_func=parse_func, response_format=Recipe, @@ -30,4 +30,4 @@ def parse_func(row, response): # Generate recipes recipes = recipe_prompter() -print(recipes.to_pandas()) \ No newline at end of file +print(recipes.to_pandas()) \ No newline at end of file From c8623cf7d8c27efd975c52af89fc9b246032fff2 Mon Sep 17 00:00:00 2001 From: Charlie Cheng-Jie Ji Date: Mon, 18 Nov 2024 22:50:44 +0000 Subject: [PATCH 04/65] use client.chat.completions.create_with_completion in litellm such that I get the response and completion objects. --- .../request_processor/litellm_online_request_processor.py | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py b/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py index 4ff966f3..c8a7451f 100644 --- a/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py +++ b/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py @@ -149,24 +149,23 @@ def _process_requests( try: # Make request with structured output if response_format provided if response_format: - response = self.client.chat.completions.create( + response, completion = self.client.chat.completions.create_with_completion( **self.create_api_specific_request(request), response_model=response_format ) # Convert Pydantic model to dict for storage response_message = response.model_dump() else: - response = self.client.chat.completions.create( + response, completion = self.client.chat.completions.create_with_completion( **self.create_api_specific_request(request) ) response_message = response.content if hasattr(response, 'content') else str(response) - # Create generic response generic_response = GenericResponse( response_message=response_message, # Now using the properly formatted response response_errors=None, raw_request=self.create_api_specific_request(request), - raw_response=None, # LiteLLM response object isn't JSON serializable + raw_response=completion.json(), generic_request=request, ) From d72d9173eb43f07566b3bd671afb0de9a341c06d Mon Sep 17 00:00:00 2001 From: Charlie Cheng-Jie Ji Date: Tue, 19 Nov 2024 03:23:43 +0000 Subject: [PATCH 05/65] ckpt current progress --- examples/litellm_example.py | 58 ++- .../litellm_online_request_processor.py | 329 +++++++++++++----- 2 files changed, 281 insertions(+), 106 deletions(-) diff --git a/examples/litellm_example.py b/examples/litellm_example.py index c43430cd..a4cb522e 100644 --- a/examples/litellm_example.py +++ b/examples/litellm_example.py @@ -1,6 +1,7 @@ from typing import List from pydantic import BaseModel, Field from bespokelabs import curator +from datasets import Dataset # Define response format using Pydantic class Recipe(BaseModel): @@ -8,26 +9,53 @@ class Recipe(BaseModel): ingredients: List[str] = Field(description="List of ingredients needed") instructions: List[str] = Field(description="Step by step cooking instructions") -def prompt_func(row=None): - return "Generate a random recipe. Be creative but keep it realistic." +def prompt_func(cuisine_type): + return f"Generate a random {cuisine_type} recipe. Be creative but keep it realistic." def parse_func(row, response): - # Response is already a Recipe object thanks to response_format return { "title": response.title, "ingredients": response.ingredients, - "instructions": response.instructions + "instructions": response.instructions, + "cuisine": row["cuisine"] # Keep track of cuisine type } -# Create prompter using LiteLLM backend -recipe_prompter = curator.Prompter( - model_name="claude-3-opus-20240229", # Could also use "anthropic/claude-2", "bedrock/claude", etc - prompt_func=prompt_func, - parse_func=parse_func, - response_format=Recipe, - backend="litellm", # Specify LiteLLM backend -) +def main(): + # List of cuisines to generate recipes for + cuisines = ["Chinese"] * 3000 + + # Create input dataset with cuisine types + input_data = [{"cuisine": cuisine} for cuisine in cuisines] + input_dataset = Dataset.from_list(input_data) -# Generate recipes -recipes = recipe_prompter() -print(recipes.to_pandas()) \ No newline at end of file + # Create prompter using LiteLLM backend + recipe_prompter = curator.Prompter( + # model_name="claude-3-haiku-20240307", + model_name="gpt-4o-mini", + prompt_func=prompt_func, + parse_func=parse_func, + response_format=Recipe, + backend="litellm", + # backend="openai", + ) + + # Generate recipes for all cuisines + recipes = recipe_prompter(input_dataset) + + # Print results + df = recipes.to_pandas() + print(df.head()) + # print(f"Generated {len(df)} recipes:") + # for _, row in df.iterrows(): + # print(f"\n{'-'*50}") + # print(f"Cuisine: {row['cuisine']}") + # print(f"Title: {row['title']}") + # print("\nIngredients:") + # for ingredient in row['ingredients']: + # print(f"- {ingredient}") + # print("\nInstructions:") + # for i, step in enumerate(row['instructions'], 1): + # print(f"{i}. {step}") + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py b/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py index c8a7451f..b5fad66b 100644 --- a/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py +++ b/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py @@ -1,11 +1,19 @@ import json import logging import os +import argparse from typing import Optional - +import asyncio +import time +import aiohttp +import resource +from tqdm import tqdm +from dataclasses import dataclass, field +from pydantic import BaseModel import instructor -from litellm import completion, get_supported_openai_params, model_cost - +import litellm +from litellm import completion, get_supported_openai_params +import aiofiles from bespokelabs.curator.dataset import Dataset from bespokelabs.curator.prompter.prompter import PromptFormatter from bespokelabs.curator.request_processor.base_request_processor import ( @@ -13,13 +21,95 @@ GenericRequest, GenericResponse, ) +from bespokelabs.curator.request_processor.event_loop import run_in_event_loop logger = logging.getLogger(__name__) +@dataclass +class StatusTracker: + """Tracks the status of all requests.""" + num_tasks_started: int = 0 + num_tasks_in_progress: int = 0 + num_tasks_succeeded: int = 0 + num_tasks_failed: int = 0 + num_tasks_already_completed: int = 0 + num_other_errors: int = 0 + + def __str__(self): + return ( + f"Tasks - Started: {self.num_tasks_started}, " + f"In Progress: {self.num_tasks_in_progress}, " + f"Succeeded: {self.num_tasks_succeeded}, " + f"Failed: {self.num_tasks_failed}, " + f"Already Completed: {self.num_tasks_already_completed}\n" + f"Errors: {self.num_other_errors}" + ) + +@dataclass +class APIRequest: + """Stores an API request's inputs, outputs, and other metadata.""" + task_id: int + generic_request: GenericRequest + api_specific_request: dict + attempts_left: int + result: list = field(default_factory=list) + prompt_formatter: PromptFormatter = field(default=None) + + async def call_api( + self, + session: aiohttp.ClientSession, + client, + retry_queue: asyncio.Queue, + save_filepath: str, + status_tracker: StatusTracker, + ) -> None: + """Calls the LiteLLM API and saves results.""" + try: + # Get response directly without extra logging + if self.generic_request.response_format: + response, completion_obj = await client.chat.completions.create_with_completion( + **self.api_specific_request, + response_model=self.prompt_formatter.response_format + ) + response_message = response.model_dump() if hasattr(response, 'model_dump') else response + else: + completion_obj = await completion(**self.api_specific_request) + response_message = completion_obj.content if hasattr(completion_obj, 'content') else str(completion_obj) + + # Create and save response immediately + generic_response = GenericResponse( + response_message=response_message, + response_errors=None, + raw_request=self.api_specific_request, + raw_response=completion_obj.model_dump(), + generic_request=self.generic_request, + ) + + await append_generic_response(generic_response, save_filepath) + status_tracker.num_tasks_in_progress -= 1 + status_tracker.num_tasks_succeeded += 1 + + except Exception as e: + status_tracker.num_other_errors += 1 + self.result.append(e) + + if self.attempts_left > 0: + self.attempts_left -= 1 + retry_queue.put_nowait(self) + else: + generic_response = GenericResponse( + response_message=None, + response_errors=[str(e) for e in self.result], + raw_request=self.api_specific_request, + raw_response=None, + generic_request=self.generic_request, + ) + await append_generic_response(generic_response, save_filepath) + status_tracker.num_tasks_in_progress -= 1 + status_tracker.num_tasks_failed += 1 + class LiteLLMOnlineRequestProcessor(BaseRequestProcessor): - """ - Request processor for LiteLLM that supports structured outputs via instructor. - """ + """Request processor for LiteLLM that supports structured outputs via instructor.""" def __init__( self, @@ -29,34 +119,40 @@ def __init__( presence_penalty: Optional[float] = None, frequency_penalty: Optional[float] = None, ): - """Initialize LiteLLM request processor. - - Args: - model (str): Name of the model to use - temperature (Optional[float]): Sampling temperature - top_p (Optional[float]): Nucleus sampling parameter - presence_penalty (Optional[float]): Presence penalty parameter - frequency_penalty (Optional[float]): Frequency penalty parameter - """ - super().__init__(batch_size=None) # LiteLLM doesn't support batching yet + super().__init__(batch_size=None) self.model = model self.temperature = temperature self.top_p = top_p self.presence_penalty = presence_penalty self.frequency_penalty = frequency_penalty - # Initialize instructor client - self.client = instructor.from_litellm(completion) + logger.info(f"Using model: {self.model}") + logger.debug(f"Parameters - Temperature: {self.temperature}, Top P: {self.top_p}, " + f"Presence Penalty: {self.presence_penalty}, Frequency Penalty: {self.frequency_penalty}") + + self.client = instructor.from_litellm(litellm.acompletion) + logger.info("Instructor client initialized with LiteLLM backend") def get_rate_limits(self) -> dict: - """Get rate limits for the model from LiteLLM.""" - # Get model costs/limits from LiteLLM - costs = model_cost.get(self.model, {}) + """Get rate limits from LiteLLM response headers.""" + logger.info(f"Getting rate limits for model: {self.model}") + + completion = litellm.completion( + model=self.model, + messages=[{"role": "user", "content": "hi"}], + ) + + headers = completion._hidden_params.get('additional_headers', {}) + logger.debug(f"Rate limit headers: {headers}") + + rpm = int(headers.get('x-ratelimit-limit-requests', 3000)) + tpm = int(headers.get('x-ratelimit-limit-tokens', 150_000)) + + logger.info(f"Rate limits - Requests/min: {rpm}, Tokens/min: {tpm}") - # Default conservative limits if not found return { - "max_tokens_per_minute": costs.get("max_tokens", 150_000), - "max_requests_per_minute": 3000 + "max_requests_per_minute": rpm, + "max_tokens_per_minute": tpm } def create_api_specific_request(self, generic_request: GenericRequest) -> dict: @@ -91,6 +187,109 @@ def create_api_specific_request(self, generic_request: GenericRequest) -> dict: return request + async def process_requests_from_file( + self, + generic_requests_filepath: str, + save_filepath: str, + max_attempts: int, + resume: bool, + ) -> None: + """Processes API requests in parallel, throttling to stay under rate limits.""" + # Increase file descriptor limit for higher throughput + soft, hard = resource.getrlimit(resource.RLIMIT_NOFILE) + resource.setrlimit(resource.RLIMIT_NOFILE, (min(hard, 10000), hard)) + + # Initialize trackers + queue_of_requests_to_retry = asyncio.Queue() + status_tracker = StatusTracker() + next_request = None + file_not_finished = True + + # Get rate limits + rate_limits = self.get_rate_limits() + rpm = rate_limits["max_requests_per_minute"] + + # Track completed requests for resume functionality + completed_request_ids = set() + if os.path.exists(save_filepath) and resume: + with open(save_filepath, "r") as f: + for line in f: + response = GenericResponse.model_validate_json(line) + if not response.response_errors: + completed_request_ids.add(response.generic_request.original_row_idx) + + # Count total requests + total_requests = sum(1 for _ in open(generic_requests_filepath)) + + # Create progress bar + pbar = tqdm(total=total_requests, desc="Processing LiteLLM requests") + + # Use higher connector limit for better throughput + connector = aiohttp.TCPConnector(limit=10 * rpm) + async with aiohttp.ClientSession(connector=connector) as session: + async with aiofiles.open(generic_requests_filepath) as file: + async for line in file: + if next_request is None: + if not queue_of_requests_to_retry.empty(): + next_request = queue_of_requests_to_retry.get_nowait() + elif file_not_finished: + try: + generic_request = GenericRequest.model_validate_json(line) + + if resume and generic_request.original_row_idx in completed_request_ids: + status_tracker.num_tasks_already_completed += 1 + continue + + next_request = APIRequest( + task_id=status_tracker.num_tasks_started, + generic_request=generic_request, + api_specific_request=self.create_api_specific_request(generic_request), + attempts_left=max_attempts, + prompt_formatter=self.prompt_formatter + ) + status_tracker.num_tasks_started += 1 + status_tracker.num_tasks_in_progress += 1 + except StopIteration: + file_not_finished = False + + if next_request: + asyncio.create_task( + next_request.call_api( + session=session, + client=self.client, + retry_queue=queue_of_requests_to_retry, + save_filepath=save_filepath, + status_tracker=status_tracker, + ) + ) + next_request = None + + # Update progress + total_completed = ( + status_tracker.num_tasks_succeeded + + status_tracker.num_tasks_failed + + status_tracker.num_tasks_already_completed + ) + if total_completed > pbar.n: + pbar.update(total_completed - pbar.n) + + if status_tracker.num_tasks_in_progress == 0: + break + + await asyncio.sleep(0.001) # 1ms sleep to prevent CPU spinning while allowing high throughput + + pbar.close() + + # Log final status + logger.info(f"Processing complete. Results saved to {save_filepath}") + logger.info(f"Status tracker: {status_tracker}") + + if status_tracker.num_tasks_failed > 0: + logger.warning( + f"{status_tracker.num_tasks_failed} / {status_tracker.num_tasks_started} " + f"requests failed. Errors logged to {save_filepath}." + ) + def run( self, dataset: Optional[Dataset], @@ -98,88 +297,36 @@ def run( parse_func_hash: str, prompt_formatter: PromptFormatter, ) -> Dataset: - """Run completions using LiteLLM. - - Args: - dataset (Optional[Dataset]): Input dataset - working_dir (str): Directory for saving request/response files - parse_func_hash (str): Hash of the parse function - prompt_formatter (PromptFormatter): Prompt formatting logic - - Returns: - Dataset: Dataset with completion results - """ - # Create request files + """Run completions using LiteLLM with async processing.""" + self.prompt_formatter = prompt_formatter generic_requests_files = self.create_request_files( dataset, working_dir, prompt_formatter ) - # Create response files generic_responses_files = [ f"{working_dir}/responses_{i}.jsonl" for i in range(len(generic_requests_files)) ] - # Process each request file for request_file, response_file in zip( generic_requests_files, generic_responses_files ): - self._process_requests( - request_file, - response_file, - prompt_formatter.response_format + run_in_event_loop( + self.process_requests_from_file( + generic_requests_filepath=request_file, + save_filepath=response_file, + max_attempts=5, + resume=True, + ) ) - # Create final dataset return self.create_dataset_files( working_dir, parse_func_hash, prompt_formatter ) - def _process_requests( - self, - request_file: str, - response_file: str, - response_format: Optional[type] = None - ): - """Process requests from file using LiteLLM.""" - with open(request_file, "r") as f: - for line in f: - request = GenericRequest.model_validate_json(line) - - try: - # Make request with structured output if response_format provided - if response_format: - response, completion = self.client.chat.completions.create_with_completion( - **self.create_api_specific_request(request), - response_model=response_format - ) - # Convert Pydantic model to dict for storage - response_message = response.model_dump() - else: - response, completion = self.client.chat.completions.create_with_completion( - **self.create_api_specific_request(request) - ) - response_message = response.content if hasattr(response, 'content') else str(response) - # Create generic response - generic_response = GenericResponse( - response_message=response_message, # Now using the properly formatted response - response_errors=None, - raw_request=self.create_api_specific_request(request), - raw_response=completion.json(), - generic_request=request, - ) - - except Exception as e: - logger.error(f"Error processing request: {e}") - # Create error response - generic_response = GenericResponse( - response_message=None, - response_errors=[str(e)], - raw_request=self.create_api_specific_request(request), - raw_response=None, - generic_request=request, - ) - - # Save response to file - with open(response_file, "a") as f_out: - f_out.write(json.dumps(generic_response.model_dump(), default=str) + "\n") +async def append_generic_response(data: GenericResponse, filename: str) -> None: + """Append a response to a jsonl file with async file operations.""" + json_string = json.dumps(data.model_dump(), default=str) + async with aiofiles.open(filename, "a") as f: + await f.write(json_string + "\n") + logger.debug(f"Successfully appended response to {filename}") From db6ebef005b8dc409cdf6a9ac18e5e3cec0322a0 Mon Sep 17 00:00:00 2001 From: Charlie Cheng-Jie Ji Date: Wed, 20 Nov 2024 02:23:44 +0000 Subject: [PATCH 06/65] add time related logging for litellm --- .../request_processor/litellm_online_request_processor.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py b/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py index b5fad66b..f51e1853 100644 --- a/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py +++ b/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py @@ -22,6 +22,7 @@ GenericResponse, ) from bespokelabs.curator.request_processor.event_loop import run_in_event_loop +import datetime logger = logging.getLogger(__name__) @@ -54,6 +55,7 @@ class APIRequest: attempts_left: int result: list = field(default_factory=list) prompt_formatter: PromptFormatter = field(default=None) + created_at: datetime.datetime = field(default_factory=datetime.datetime.now) async def call_api( self, @@ -83,6 +85,8 @@ async def call_api( raw_request=self.api_specific_request, raw_response=completion_obj.model_dump(), generic_request=self.generic_request, + created_at=self.created_at, + finished_at=datetime.datetime.now() ) await append_generic_response(generic_response, save_filepath) @@ -103,6 +107,8 @@ async def call_api( raw_request=self.api_specific_request, raw_response=None, generic_request=self.generic_request, + created_at=self.created_at, + finished_at=datetime.datetime.now() ) await append_generic_response(generic_response, save_filepath) status_tracker.num_tasks_in_progress -= 1 From feb042d19529ead92d9efad14b87eb8b591d39df Mon Sep 17 00:00:00 2001 From: Charlie Cheng-Jie Ji Date: Wed, 20 Nov 2024 04:59:05 +0000 Subject: [PATCH 07/65] add backend as part of hash --- src/bespokelabs/curator/prompter/prompter.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/src/bespokelabs/curator/prompter/prompter.py b/src/bespokelabs/curator/prompter/prompter.py index 565361eb..4f3d7e46 100644 --- a/src/bespokelabs/curator/prompter/prompter.py +++ b/src/bespokelabs/curator/prompter/prompter.py @@ -91,7 +91,7 @@ def __init__( model_name, prompt_func, parse_func, response_format ) self.batch_mode = batch - + self.backend = backend # Select request processor based on backend if backend == "openai": if batch: @@ -202,6 +202,7 @@ def _completions( else "text" ), str(self.batch_mode), + str(self.backend), ] ) From a9c65a492c85d6157c9cfd1694a032eb69c5b06e Mon Sep 17 00:00:00 2001 From: Charlie Cheng-Jie Ji Date: Wed, 20 Nov 2024 19:51:38 +0000 Subject: [PATCH 08/65] update poetry lock --- poetry.lock | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/poetry.lock b/poetry.lock index 9923eda1..35816885 100644 --- a/poetry.lock +++ b/poetry.lock @@ -3575,4 +3575,4 @@ type = ["pytest-mypy"] [metadata] lock-version = "2.0" python-versions = "^3.10" -content-hash = "11130b965eca4d2384a748ca7556cf1f3e0c60870503722526c277e522601fab" +content-hash = "fed39aa34f2197bec686490d874234882f77105d82125436b22b32af003b9a14" From f755b73676a355e2532d433b50b5fdba87408508 Mon Sep 17 00:00:00 2001 From: Charlie Cheng-Jie Ji Date: Wed, 20 Nov 2024 21:22:47 +0000 Subject: [PATCH 09/65] lock revamp --- poetry.lock | 3586 ++++++++++++++++++++++++++++++++++++++++++++++++ pyproject.toml | 1 - 2 files changed, 3586 insertions(+), 1 deletion(-) create mode 100644 poetry.lock diff --git a/poetry.lock 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nest-asyncio = "^1.6.0" -litellm = "^1.52.9" rich = "^13.7.0" litellm = "^1.52.11" From f7be7e979dfac1db5f8572e7f95d54efed16f551 Mon Sep 17 00:00:00 2001 From: Charlie Cheng-Jie Ji Date: Thu, 21 Nov 2024 00:09:02 +0000 Subject: [PATCH 10/65] add cost and token calculations --- .../litellm_online_request_processor.py | 20 +++++++++++++++++-- 1 file changed, 18 insertions(+), 2 deletions(-) diff --git a/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py b/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py index f51e1853..377c76ba 100644 --- a/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py +++ b/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py @@ -23,6 +23,7 @@ ) from bespokelabs.curator.request_processor.event_loop import run_in_event_loop import datetime +from bespokelabs.curator.request_processor.generic_response import TokenUsage logger = logging.getLogger(__name__) @@ -78,6 +79,19 @@ async def call_api( completion_obj = await completion(**self.api_specific_request) response_message = completion_obj.content if hasattr(completion_obj, 'content') else str(completion_obj) + # Extract token usage + usage = completion_obj.usage if hasattr(completion_obj, 'usage') else {} + token_usage = TokenUsage( + prompt_tokens=usage.get('prompt_tokens', 0), + completion_tokens=usage.get('completion_tokens', 0), + total_tokens=usage.get('total_tokens', 0) + ) + + # Calculate cost using litellm + cost = litellm.completion_cost( + completion_response=completion_obj.model_dump() + ) + # Create and save response immediately generic_response = GenericResponse( response_message=response_message, @@ -86,7 +100,9 @@ async def call_api( raw_response=completion_obj.model_dump(), generic_request=self.generic_request, created_at=self.created_at, - finished_at=datetime.datetime.now() + finished_at=datetime.datetime.now(), + token_usage=token_usage, + response_cost=cost ) await append_generic_response(generic_response, save_filepath) @@ -108,7 +124,7 @@ async def call_api( raw_response=None, generic_request=self.generic_request, created_at=self.created_at, - finished_at=datetime.datetime.now() + finished_at=datetime.datetime.now(), ) await append_generic_response(generic_response, save_filepath) status_tracker.num_tasks_in_progress -= 1 From 973fca43fd139b2985c65469b0df8800de7b758f Mon Sep 17 00:00:00 2001 From: Charlie Cheng-Jie Ji Date: Fri, 22 Nov 2024 00:30:35 +0000 Subject: [PATCH 11/65] ckpt example with many models and model providers --- examples/litellm_example.py | 71 +++++++++++++++++++++---------------- 1 file changed, 40 insertions(+), 31 deletions(-) diff --git a/examples/litellm_example.py b/examples/litellm_example.py index a4cb522e..44321210 100644 --- a/examples/litellm_example.py +++ b/examples/litellm_example.py @@ -9,8 +9,8 @@ class Recipe(BaseModel): ingredients: List[str] = Field(description="List of ingredients needed") instructions: List[str] = Field(description="Step by step cooking instructions") -def prompt_func(cuisine_type): - return f"Generate a random {cuisine_type} recipe. Be creative but keep it realistic." +def prompt_func(cuisine): + return f"Generate a random {cuisine['cuisine_type']} recipe. Be creative but keep it realistic." def parse_func(row, response): return { @@ -22,40 +22,49 @@ def parse_func(row, response): def main(): # List of cuisines to generate recipes for - cuisines = ["Chinese"] * 3000 + cuisines = ["Chinese"] * 100 # Create input dataset with cuisine types - input_data = [{"cuisine": cuisine} for cuisine in cuisines] + input_data = [{"cuisine_type": cuisine} for cuisine in cuisines] input_dataset = Dataset.from_list(input_data) - # Create prompter using LiteLLM backend - recipe_prompter = curator.Prompter( - # model_name="claude-3-haiku-20240307", - model_name="gpt-4o-mini", - prompt_func=prompt_func, - parse_func=parse_func, - response_format=Recipe, - backend="litellm", - # backend="openai", - ) - - # Generate recipes for all cuisines - recipes = recipe_prompter(input_dataset) + # List of models for testing + model_names = [ + # "claude-3-5-sonnet-20240620", # https://docs.litellm.ai/docs/providers/anthropic + # "claude-3-haiku-20240307", + # "claude-3-opus-20240229", + # "claude-3-sonnet-20240229", + "gpt-4o-mini", # https://docs.litellm.ai/docs/providers/openai + # "gpt-4o-mini-2024-07-18 ", # https://docs.litellm.ai/docs/providers/openai + # "gpt-4o-2024-08-06", + # "gpt-4-0125-preview", + # "gpt-3.5-turbo-1106", + # "o1-mini", + # "gemini/gemini-1.5-flash", # https://docs.litellm.ai/docs/providers/gemini; https://ai.google.dev/gemini-api/docs/models + # "gemini/gemini-1.5-pro", + # "sambanova/Meta-Llama-3.1-8B-Instruct", # https://docs.litellm.ai/docs/providers/sambanova; https://community.sambanova.ai/t/supported-models + # "sambanova/Meta-Llama-3.1-70B-Instruct", + # "together/meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo", # https://docs.together.ai/docs/serverless-models + # "together/meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo", + # "together/nvidia/Llama-3.1-Nemotron-70B-Instruct-HF", + ] + + for model_name in model_names: + # Create prompter using LiteLLM backend + recipe_prompter = curator.Prompter( + model_name=model_name, + prompt_func=prompt_func, + parse_func=parse_func, + response_format=Recipe, + backend="litellm", + ) + + # Generate recipes for all cuisines + recipes = recipe_prompter(input_dataset) - # Print results - df = recipes.to_pandas() - print(df.head()) - # print(f"Generated {len(df)} recipes:") - # for _, row in df.iterrows(): - # print(f"\n{'-'*50}") - # print(f"Cuisine: {row['cuisine']}") - # print(f"Title: {row['title']}") - # print("\nIngredients:") - # for ingredient in row['ingredients']: - # print(f"- {ingredient}") - # print("\nInstructions:") - # for i, step in enumerate(row['instructions'], 1): - # print(f"{i}. {step}") + # Print results + df = recipes.to_pandas() + print(df.head()) if __name__ == "__main__": main() \ No newline at end of file From e94ad4a528e0b689651f5a4db030355dec407e27 Mon Sep 17 00:00:00 2001 From: Charlie Cheng-Jie Ji Date: Fri, 22 Nov 2024 00:41:12 +0000 Subject: [PATCH 12/65] fix example --- examples/litellm_example.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/litellm_example.py b/examples/litellm_example.py index 44321210..46a7c162 100644 --- a/examples/litellm_example.py +++ b/examples/litellm_example.py @@ -17,7 +17,7 @@ def parse_func(row, response): "title": response.title, "ingredients": response.ingredients, "instructions": response.instructions, - "cuisine": row["cuisine"] # Keep track of cuisine type + "cuisine_type": row["cuisine_type"] # Keep track of cuisine type } def main(): From 74e63755971daa2b9bf5928526421ed00cf72ce5 Mon Sep 17 00:00:00 2001 From: Charlie Cheng-Jie Ji Date: Sat, 23 Nov 2024 01:23:00 +0000 Subject: [PATCH 13/65] revamped async litellm from yesterday checkpoint --- .../litellm_online_request_processor.py | 238 +++++++++--------- 1 file changed, 113 insertions(+), 125 deletions(-) diff --git a/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py b/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py index 377c76ba..d17ab8bf 100644 --- a/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py +++ b/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py @@ -1,19 +1,16 @@ import json import logging import os -import argparse from typing import Optional import asyncio import time +from dataclasses import dataclass, field +import datetime import aiohttp -import resource from tqdm import tqdm -from dataclasses import dataclass, field -from pydantic import BaseModel -import instructor import litellm -from litellm import completion, get_supported_openai_params -import aiofiles + +import instructor from bespokelabs.curator.dataset import Dataset from bespokelabs.curator.prompter.prompter import PromptFormatter from bespokelabs.curator.request_processor.base_request_processor import ( @@ -22,7 +19,6 @@ GenericResponse, ) from bespokelabs.curator.request_processor.event_loop import run_in_event_loop -import datetime from bespokelabs.curator.request_processor.generic_response import TokenUsage logger = logging.getLogger(__name__) @@ -35,7 +31,8 @@ class StatusTracker: num_tasks_succeeded: int = 0 num_tasks_failed: int = 0 num_tasks_already_completed: int = 0 - num_other_errors: int = 0 + num_rate_limit_errors: int = 0 + time_of_last_rate_limit_error: float = 0 def __str__(self): return ( @@ -43,8 +40,8 @@ def __str__(self): f"In Progress: {self.num_tasks_in_progress}, " f"Succeeded: {self.num_tasks_succeeded}, " f"Failed: {self.num_tasks_failed}, " - f"Already Completed: {self.num_tasks_already_completed}\n" - f"Errors: {self.num_other_errors}" + f"Already Completed: {self.num_tasks_already_completed}, " + f"Rate Limit Errors: {self.num_rate_limit_errors}" ) @dataclass @@ -60,7 +57,6 @@ class APIRequest: async def call_api( self, - session: aiohttp.ClientSession, client, retry_queue: asyncio.Queue, save_filepath: str, @@ -68,31 +64,30 @@ async def call_api( ) -> None: """Calls the LiteLLM API and saves results.""" try: - # Get response directly without extra logging + # Make API call with instructor if self.generic_request.response_format: response, completion_obj = await client.chat.completions.create_with_completion( **self.api_specific_request, - response_model=self.prompt_formatter.response_format + response_model=self.prompt_formatter.response_format, + timeout=60.0 ) response_message = response.model_dump() if hasattr(response, 'model_dump') else response else: - completion_obj = await completion(**self.api_specific_request) - response_message = completion_obj.content if hasattr(completion_obj, 'content') else str(completion_obj) + completion_obj = await litellm.acompletion(**self.api_specific_request, timeout=60.0) + response_message = completion_obj.choices[0].message.content # Extract token usage - usage = completion_obj.usage if hasattr(completion_obj, 'usage') else {} + usage = completion_obj.usage token_usage = TokenUsage( - prompt_tokens=usage.get('prompt_tokens', 0), - completion_tokens=usage.get('completion_tokens', 0), - total_tokens=usage.get('total_tokens', 0) + prompt_tokens=usage.prompt_tokens, + completion_tokens=usage.completion_tokens, + total_tokens=usage.total_tokens ) - # Calculate cost using litellm - cost = litellm.completion_cost( - completion_response=completion_obj.model_dump() - ) + # Calculate cost + cost = litellm.completion_cost(completion_response=completion_obj.model_dump()) - # Create and save response immediately + # Create and save response generic_response = GenericResponse( response_message=response_message, response_errors=None, @@ -110,9 +105,13 @@ async def call_api( status_tracker.num_tasks_succeeded += 1 except Exception as e: - status_tracker.num_other_errors += 1 + logger.error(f"Error in request {self.task_id}: {str(e)}") self.result.append(e) + if "rate limit" in str(e).lower(): + status_tracker.num_rate_limit_errors += 1 + status_tracker.time_of_last_rate_limit_error = time.time() + if self.attempts_left > 0: self.attempts_left -= 1 retry_queue.put_nowait(self) @@ -147,34 +146,34 @@ def __init__( self.top_p = top_p self.presence_penalty = presence_penalty self.frequency_penalty = frequency_penalty - - logger.info(f"Using model: {self.model}") - logger.debug(f"Parameters - Temperature: {self.temperature}, Top P: {self.top_p}, " - f"Presence Penalty: {self.presence_penalty}, Frequency Penalty: {self.frequency_penalty}") - self.client = instructor.from_litellm(litellm.acompletion) - logger.info("Instructor client initialized with LiteLLM backend") + logger.info(f"Initialized LiteLLM processor with model: {self.model}") def get_rate_limits(self) -> dict: - """Get rate limits from LiteLLM response headers.""" - logger.info(f"Getting rate limits for model: {self.model}") - - completion = litellm.completion( - model=self.model, - messages=[{"role": "user", "content": "hi"}], - ) - - headers = completion._hidden_params.get('additional_headers', {}) - logger.debug(f"Rate limit headers: {headers}") - - rpm = int(headers.get('x-ratelimit-limit-requests', 3000)) - tpm = int(headers.get('x-ratelimit-limit-tokens', 150_000)) - - logger.info(f"Rate limits - Requests/min: {rpm}, Tokens/min: {tpm}") + """Get rate limits for the model. Uses default conservative values.""" + try: + # Try to get a test completion to extract rate limits + completion = litellm.completion( + model=self.model, + messages=[{"role": "user", "content": "test"}], + ) + + # Try to get rate limits from response headers + headers = completion._hidden_params.get('additional_headers', {}) + rpm = int(headers.get('x-ratelimit-limit-requests', 3000)) + tpm = int(headers.get('x-ratelimit-limit-tokens', 150_000)) + + except Exception as e: + logger.warning(f"Failed to get rate limits from API, using default values: {str(e)}") + # Use conservative default values + rpm = 3000 # requests per minute + tpm = 150_000 # tokens per minute + + logger.info(f"Rate limits for {self.model} - RPM: {rpm}, TPM: {tpm}") return { "max_requests_per_minute": rpm, - "max_tokens_per_minute": tpm + "max_tokens_per_minute": tpm, } def create_api_specific_request(self, generic_request: GenericRequest) -> dict: @@ -187,7 +186,7 @@ def create_api_specific_request(self, generic_request: GenericRequest) -> dict: dict: LiteLLM-specific request parameters """ # Get supported parameters for this model - supported_params = get_supported_openai_params(model=self.model) + supported_params = litellm.get_supported_openai_params(model=self.model) request = { "model": generic_request.model, @@ -216,20 +215,17 @@ async def process_requests_from_file( max_attempts: int, resume: bool, ) -> None: - """Processes API requests in parallel, throttling to stay under rate limits.""" - # Increase file descriptor limit for higher throughput - soft, hard = resource.getrlimit(resource.RLIMIT_NOFILE) - resource.setrlimit(resource.RLIMIT_NOFILE, (min(hard, 10000), hard)) + """Process API requests with simple rate limiting.""" + # Get rate limits + rate_limits = self.get_rate_limits() + requests_per_minute = rate_limits["max_requests_per_minute"] + seconds_per_request = 60.0 / requests_per_minute # Initialize trackers - queue_of_requests_to_retry = asyncio.Queue() status_tracker = StatusTracker() + retry_queue = asyncio.Queue() next_request = None - file_not_finished = True - - # Get rate limits - rate_limits = self.get_rate_limits() - rpm = rate_limits["max_requests_per_minute"] + last_request_time = 0 # Track completed requests for resume functionality completed_request_ids = set() @@ -242,75 +238,68 @@ async def process_requests_from_file( # Count total requests total_requests = sum(1 for _ in open(generic_requests_filepath)) - - # Create progress bar pbar = tqdm(total=total_requests, desc="Processing LiteLLM requests") - # Use higher connector limit for better throughput - connector = aiohttp.TCPConnector(limit=10 * rpm) - async with aiohttp.ClientSession(connector=connector) as session: - async with aiofiles.open(generic_requests_filepath) as file: - async for line in file: - if next_request is None: - if not queue_of_requests_to_retry.empty(): - next_request = queue_of_requests_to_retry.get_nowait() - elif file_not_finished: - try: - generic_request = GenericRequest.model_validate_json(line) - - if resume and generic_request.original_row_idx in completed_request_ids: - status_tracker.num_tasks_already_completed += 1 - continue - - next_request = APIRequest( - task_id=status_tracker.num_tasks_started, - generic_request=generic_request, - api_specific_request=self.create_api_specific_request(generic_request), - attempts_left=max_attempts, - prompt_formatter=self.prompt_formatter - ) - status_tracker.num_tasks_started += 1 - status_tracker.num_tasks_in_progress += 1 - except StopIteration: - file_not_finished = False - - if next_request: - asyncio.create_task( - next_request.call_api( - session=session, - client=self.client, - retry_queue=queue_of_requests_to_retry, - save_filepath=save_filepath, - status_tracker=status_tracker, + with open(generic_requests_filepath) as file: + while True: + # Get next request + if next_request is None: + if not retry_queue.empty(): + next_request = retry_queue.get_nowait() + else: + try: + line = next(file) + generic_request = GenericRequest.model_validate_json(line) + + if resume and generic_request.original_row_idx in completed_request_ids: + status_tracker.num_tasks_already_completed += 1 + continue + + next_request = APIRequest( + task_id=status_tracker.num_tasks_started, + generic_request=generic_request, + api_specific_request=self.create_api_specific_request(generic_request), + attempts_left=max_attempts, + prompt_formatter=self.prompt_formatter ) - ) - next_request = None + status_tracker.num_tasks_started += 1 + status_tracker.num_tasks_in_progress += 1 + except StopIteration: + if status_tracker.num_tasks_in_progress == 0: + break - # Update progress - total_completed = ( - status_tracker.num_tasks_succeeded - + status_tracker.num_tasks_failed - + status_tracker.num_tasks_already_completed + # Process request with rate limiting + if next_request: + current_time = time.time() + time_since_last_request = current_time - last_request_time + + if time_since_last_request < seconds_per_request: + await asyncio.sleep(seconds_per_request - time_since_last_request) + + asyncio.create_task( + next_request.call_api( + client=self.client, + retry_queue=retry_queue, + save_filepath=save_filepath, + status_tracker=status_tracker, + ) ) - if total_completed > pbar.n: - pbar.update(total_completed - pbar.n) + last_request_time = time.time() + next_request = None - if status_tracker.num_tasks_in_progress == 0: - break + # Update progress + total_completed = ( + status_tracker.num_tasks_succeeded + + status_tracker.num_tasks_failed + + status_tracker.num_tasks_already_completed + ) + if total_completed > pbar.n: + pbar.update(total_completed - pbar.n) - await asyncio.sleep(0.001) # 1ms sleep to prevent CPU spinning while allowing high throughput + await asyncio.sleep(0.1) - pbar.close() - - # Log final status - logger.info(f"Processing complete. Results saved to {save_filepath}") - logger.info(f"Status tracker: {status_tracker}") - - if status_tracker.num_tasks_failed > 0: - logger.warning( - f"{status_tracker.num_tasks_failed} / {status_tracker.num_tasks_started} " - f"requests failed. Errors logged to {save_filepath}." - ) + pbar.close() + logger.info(f"Processing complete. Final status: {status_tracker}") def run( self, @@ -319,7 +308,7 @@ def run( parse_func_hash: str, prompt_formatter: PromptFormatter, ) -> Dataset: - """Run completions using LiteLLM with async processing.""" + """Run the LiteLLM processor on the dataset.""" self.prompt_formatter = prompt_formatter generic_requests_files = self.create_request_files( dataset, working_dir, prompt_formatter @@ -347,8 +336,7 @@ def run( ) async def append_generic_response(data: GenericResponse, filename: str) -> None: - """Append a response to a jsonl file with async file operations.""" + """Append a response to a jsonl file.""" json_string = json.dumps(data.model_dump(), default=str) - async with aiofiles.open(filename, "a") as f: - await f.write(json_string + "\n") - logger.debug(f"Successfully appended response to {filename}") + with open(filename, "a") as f: + f.write(json_string + "\n") From 4f0e1cec424b2e9e9cacf4b067b6841bd2322cc3 Mon Sep 17 00:00:00 2001 From: Charlie Cheng-Jie Ji Date: Sat, 23 Nov 2024 23:06:10 +0000 Subject: [PATCH 14/65] unused import --- .../request_processor/litellm_online_request_processor.py | 1 - 1 file changed, 1 deletion(-) diff --git a/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py b/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py index d17ab8bf..eb52ca24 100644 --- a/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py +++ b/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py @@ -6,7 +6,6 @@ import time from dataclasses import dataclass, field import datetime -import aiohttp from tqdm import tqdm import litellm From e045cda46d0f17f0313ffe112e09d6a768cb6cd0 Mon Sep 17 00:00:00 2001 From: Charlie Cheng-Jie Ji Date: Sat, 23 Nov 2024 23:06:29 +0000 Subject: [PATCH 15/65] ckpt example --- examples/litellm_example.py | 21 +++++++++++++-------- 1 file changed, 13 insertions(+), 8 deletions(-) diff --git a/examples/litellm_example.py b/examples/litellm_example.py index 46a7c162..642e4071 100644 --- a/examples/litellm_example.py +++ b/examples/litellm_example.py @@ -2,7 +2,11 @@ from pydantic import BaseModel, Field from bespokelabs import curator from datasets import Dataset - +import logging +import time +# Set up logging +logger = logging.getLogger(__name__) +logger.setLevel(logging.DEBUG) # Define response format using Pydantic class Recipe(BaseModel): title: str = Field(description="Title of the recipe") @@ -33,9 +37,8 @@ def main(): # "claude-3-5-sonnet-20240620", # https://docs.litellm.ai/docs/providers/anthropic # "claude-3-haiku-20240307", # "claude-3-opus-20240229", - # "claude-3-sonnet-20240229", - "gpt-4o-mini", # https://docs.litellm.ai/docs/providers/openai - # "gpt-4o-mini-2024-07-18 ", # https://docs.litellm.ai/docs/providers/openai + "claude-3-sonnet-20240229", + # "gpt-4o-mini", # https://docs.litellm.ai/docs/providers/openai # "gpt-4o-2024-08-06", # "gpt-4-0125-preview", # "gpt-3.5-turbo-1106", @@ -44,13 +47,14 @@ def main(): # "gemini/gemini-1.5-pro", # "sambanova/Meta-Llama-3.1-8B-Instruct", # https://docs.litellm.ai/docs/providers/sambanova; https://community.sambanova.ai/t/supported-models # "sambanova/Meta-Llama-3.1-70B-Instruct", - # "together/meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo", # https://docs.together.ai/docs/serverless-models - # "together/meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo", - # "together/nvidia/Llama-3.1-Nemotron-70B-Instruct-HF", + # "together_ai/meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo", # https://docs.together.ai/docs/serverless-models + # "together_ai/meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo", + # "together_ai/nvidia/Llama-3.1-Nemotron-70B-Instruct-HF", ] for model_name in model_names: # Create prompter using LiteLLM backend + logger.info(f"Using model: {model_name}") recipe_prompter = curator.Prompter( model_name=model_name, prompt_func=prompt_func, @@ -64,7 +68,8 @@ def main(): # Print results df = recipes.to_pandas() - print(df.head()) + print(df) + time.sleep(2) if __name__ == "__main__": main() \ No newline at end of file From cddf82054db7c91486145d93a654112baf6e4fd8 Mon Sep 17 00:00:00 2001 From: Charlie Cheng-Jie Ji Date: Sat, 23 Nov 2024 23:40:49 +0000 Subject: [PATCH 16/65] revert back to the old litellm backend, with fixes. This is more stable --- .../litellm_online_request_processor.py | 231 +++++++++--------- 1 file changed, 122 insertions(+), 109 deletions(-) diff --git a/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py b/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py index eb52ca24..6ce894c9 100644 --- a/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py +++ b/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py @@ -1,15 +1,19 @@ import json import logging import os -from typing import Optional +import argparse +from typing import Optional, Any import asyncio import time -from dataclasses import dataclass, field -import datetime +import aiohttp +import resource from tqdm import tqdm -import litellm - +from dataclasses import dataclass, field +from pydantic import BaseModel import instructor +import litellm +from litellm import completion, get_supported_openai_params +import aiofiles from bespokelabs.curator.dataset import Dataset from bespokelabs.curator.prompter.prompter import PromptFormatter from bespokelabs.curator.request_processor.base_request_processor import ( @@ -18,6 +22,7 @@ GenericResponse, ) from bespokelabs.curator.request_processor.event_loop import run_in_event_loop +import datetime from bespokelabs.curator.request_processor.generic_response import TokenUsage logger = logging.getLogger(__name__) @@ -30,8 +35,8 @@ class StatusTracker: num_tasks_succeeded: int = 0 num_tasks_failed: int = 0 num_tasks_already_completed: int = 0 - num_rate_limit_errors: int = 0 - time_of_last_rate_limit_error: float = 0 + num_other_errors: int = 0 + pbar: tqdm = field(default=None) def __str__(self): return ( @@ -39,8 +44,8 @@ def __str__(self): f"In Progress: {self.num_tasks_in_progress}, " f"Succeeded: {self.num_tasks_succeeded}, " f"Failed: {self.num_tasks_failed}, " - f"Already Completed: {self.num_tasks_already_completed}, " - f"Rate Limit Errors: {self.num_rate_limit_errors}" + f"Already Completed: {self.num_tasks_already_completed}\n" + f"Errors: {self.num_other_errors}" ) @dataclass @@ -56,37 +61,39 @@ class APIRequest: async def call_api( self, - client, + session: aiohttp.ClientSession, + client: Any, retry_queue: asyncio.Queue, save_filepath: str, status_tracker: StatusTracker, ) -> None: """Calls the LiteLLM API and saves results.""" try: - # Make API call with instructor + # Get response directly without extra logging if self.generic_request.response_format: response, completion_obj = await client.chat.completions.create_with_completion( **self.api_specific_request, - response_model=self.prompt_formatter.response_format, - timeout=60.0 + response_model=self.prompt_formatter.response_format ) response_message = response.model_dump() if hasattr(response, 'model_dump') else response else: - completion_obj = await litellm.acompletion(**self.api_specific_request, timeout=60.0) - response_message = completion_obj.choices[0].message.content + completion_obj = await completion(**self.api_specific_request) + response_message = completion_obj.content if hasattr(completion_obj, 'content') else str(completion_obj) # Extract token usage - usage = completion_obj.usage + usage = completion_obj.usage if hasattr(completion_obj, 'usage') else {} token_usage = TokenUsage( prompt_tokens=usage.prompt_tokens, completion_tokens=usage.completion_tokens, total_tokens=usage.total_tokens ) - # Calculate cost - cost = litellm.completion_cost(completion_response=completion_obj.model_dump()) + # Calculate cost using litellm + cost = litellm.completion_cost( + completion_response=completion_obj.model_dump() + ) - # Create and save response + # Create and save response immediately generic_response = GenericResponse( response_message=response_message, response_errors=None, @@ -102,15 +109,12 @@ async def call_api( await append_generic_response(generic_response, save_filepath) status_tracker.num_tasks_in_progress -= 1 status_tracker.num_tasks_succeeded += 1 + status_tracker.pbar.update(1) except Exception as e: - logger.error(f"Error in request {self.task_id}: {str(e)}") + status_tracker.num_other_errors += 1 self.result.append(e) - if "rate limit" in str(e).lower(): - status_tracker.num_rate_limit_errors += 1 - status_tracker.time_of_last_rate_limit_error = time.time() - if self.attempts_left > 0: self.attempts_left -= 1 retry_queue.put_nowait(self) @@ -145,34 +149,34 @@ def __init__( self.top_p = top_p self.presence_penalty = presence_penalty self.frequency_penalty = frequency_penalty + + logger.info(f"Using model: {self.model}") + logger.debug(f"Parameters - Temperature: {self.temperature}, Top P: {self.top_p}, " + f"Presence Penalty: {self.presence_penalty}, Frequency Penalty: {self.frequency_penalty}") + self.client = instructor.from_litellm(litellm.acompletion) - logger.info(f"Initialized LiteLLM processor with model: {self.model}") + logger.info("Instructor client initialized with LiteLLM backend") def get_rate_limits(self) -> dict: - """Get rate limits for the model. Uses default conservative values.""" - try: - # Try to get a test completion to extract rate limits - completion = litellm.completion( - model=self.model, - messages=[{"role": "user", "content": "test"}], - ) - - # Try to get rate limits from response headers - headers = completion._hidden_params.get('additional_headers', {}) - rpm = int(headers.get('x-ratelimit-limit-requests', 3000)) - tpm = int(headers.get('x-ratelimit-limit-tokens', 150_000)) - - except Exception as e: - logger.warning(f"Failed to get rate limits from API, using default values: {str(e)}") - # Use conservative default values - rpm = 3000 # requests per minute - tpm = 150_000 # tokens per minute - - logger.info(f"Rate limits for {self.model} - RPM: {rpm}, TPM: {tpm}") + """Get rate limits from LiteLLM response headers.""" + logger.info(f"Getting rate limits for model: {self.model}") + + completion = litellm.completion( + model=self.model, + messages=[{"role": "user", "content": "hi"}], + ) + + headers = completion._hidden_params.get('additional_headers', {}) + logger.debug(f"Rate limit headers: {headers}") + + rpm = int(headers.get('x-ratelimit-limit-requests', 3000)) + tpm = int(headers.get('x-ratelimit-limit-tokens', 150_000)) + + logger.info(f"Rate limits - Requests/min: {rpm}, Tokens/min: {tpm}") return { "max_requests_per_minute": rpm, - "max_tokens_per_minute": tpm, + "max_tokens_per_minute": tpm } def create_api_specific_request(self, generic_request: GenericRequest) -> dict: @@ -185,7 +189,7 @@ def create_api_specific_request(self, generic_request: GenericRequest) -> dict: dict: LiteLLM-specific request parameters """ # Get supported parameters for this model - supported_params = litellm.get_supported_openai_params(model=self.model) + supported_params = get_supported_openai_params(model=self.model) request = { "model": generic_request.model, @@ -214,17 +218,20 @@ async def process_requests_from_file( max_attempts: int, resume: bool, ) -> None: - """Process API requests with simple rate limiting.""" - # Get rate limits - rate_limits = self.get_rate_limits() - requests_per_minute = rate_limits["max_requests_per_minute"] - seconds_per_request = 60.0 / requests_per_minute + """Processes API requests in parallel, throttling to stay under rate limits.""" + # Increase file descriptor limit for higher throughput + soft, hard = resource.getrlimit(resource.RLIMIT_NOFILE) + resource.setrlimit(resource.RLIMIT_NOFILE, (min(hard, 10000), hard)) # Initialize trackers + queue_of_requests_to_retry = asyncio.Queue() status_tracker = StatusTracker() - retry_queue = asyncio.Queue() next_request = None - last_request_time = 0 + file_not_finished = True + + # Get rate limits + rate_limits = self.get_rate_limits() + rpm = rate_limits["max_requests_per_minute"] # Track completed requests for resume functionality completed_request_ids = set() @@ -237,68 +244,73 @@ async def process_requests_from_file( # Count total requests total_requests = sum(1 for _ in open(generic_requests_filepath)) - pbar = tqdm(total=total_requests, desc="Processing LiteLLM requests") - - with open(generic_requests_filepath) as file: - while True: - # Get next request - if next_request is None: - if not retry_queue.empty(): - next_request = retry_queue.get_nowait() - else: - try: - line = next(file) - generic_request = GenericRequest.model_validate_json(line) - - if resume and generic_request.original_row_idx in completed_request_ids: - status_tracker.num_tasks_already_completed += 1 - continue - - next_request = APIRequest( - task_id=status_tracker.num_tasks_started, - generic_request=generic_request, - api_specific_request=self.create_api_specific_request(generic_request), - attempts_left=max_attempts, - prompt_formatter=self.prompt_formatter - ) - status_tracker.num_tasks_started += 1 - status_tracker.num_tasks_in_progress += 1 - except StopIteration: - if status_tracker.num_tasks_in_progress == 0: - break + + # Create progress bar + status_tracker.pbar = tqdm(total=total_requests, desc="Processing LiteLLM requests") - # Process request with rate limiting - if next_request: - current_time = time.time() - time_since_last_request = current_time - last_request_time + # Use higher connector limit for better throughput + connector = aiohttp.TCPConnector(limit=10 * rpm) + async with aiohttp.ClientSession(connector=connector) as session: + # Create a list to track all tasks + tasks = [] + + async with aiofiles.open(generic_requests_filepath) as file: + async for line in file: + generic_request = GenericRequest.model_validate_json(line) - if time_since_last_request < seconds_per_request: - await asyncio.sleep(seconds_per_request - time_since_last_request) + if resume and generic_request.original_row_idx in completed_request_ids: + status_tracker.num_tasks_already_completed += 1 + continue + + request = APIRequest( + task_id=status_tracker.num_tasks_started, + generic_request=generic_request, + api_specific_request=self.create_api_specific_request(generic_request), + attempts_left=max_attempts, + prompt_formatter=self.prompt_formatter + ) - asyncio.create_task( - next_request.call_api( + # Create and track the task + task = asyncio.create_task( + request.call_api( + session=session, client=self.client, - retry_queue=retry_queue, + retry_queue=queue_of_requests_to_retry, save_filepath=save_filepath, status_tracker=status_tracker, ) ) - last_request_time = time.time() - next_request = None + tasks.append(task) + + status_tracker.num_tasks_started += 1 + status_tracker.num_tasks_in_progress += 1 - # Update progress - total_completed = ( - status_tracker.num_tasks_succeeded - + status_tracker.num_tasks_failed - + status_tracker.num_tasks_already_completed + # Wait for all tasks to complete + if tasks: + await asyncio.gather(*tasks) + + # Process any retries if needed + while not queue_of_requests_to_retry.empty(): + request = await queue_of_requests_to_retry.get() + await request.call_api( + session=session, + client=self.client, + retry_queue=queue_of_requests_to_retry, + save_filepath=save_filepath, + status_tracker=status_tracker, ) - if total_completed > pbar.n: - pbar.update(total_completed - pbar.n) - await asyncio.sleep(0.1) - - pbar.close() - logger.info(f"Processing complete. Final status: {status_tracker}") + status_tracker.pbar.close() + + # Log final status + logger.info(f"Processing complete. Results saved to {save_filepath}") + logger.info(f"Status tracker: {status_tracker}") + + if status_tracker.num_tasks_failed > 0: + logger.warning( + f"{status_tracker.num_tasks_failed} / {status_tracker.num_tasks_started} " + f"requests failed. Errors logged to {save_filepath}." + ) def run( self, @@ -307,7 +319,7 @@ def run( parse_func_hash: str, prompt_formatter: PromptFormatter, ) -> Dataset: - """Run the LiteLLM processor on the dataset.""" + """Run completions using LiteLLM with async processing.""" self.prompt_formatter = prompt_formatter generic_requests_files = self.create_request_files( dataset, working_dir, prompt_formatter @@ -335,7 +347,8 @@ def run( ) async def append_generic_response(data: GenericResponse, filename: str) -> None: - """Append a response to a jsonl file.""" + """Append a response to a jsonl file with async file operations.""" json_string = json.dumps(data.model_dump(), default=str) - with open(filename, "a") as f: - f.write(json_string + "\n") + async with aiofiles.open(filename, "a") as f: + await f.write(json_string + "\n") + logger.debug(f"Successfully appended response to {filename}") \ No newline at end of file From 56e233877cb07a386fb21dbc17401bfaef24d7c4 Mon Sep 17 00:00:00 2001 From: Charlie Cheng-Jie Ji Date: Sun, 24 Nov 2024 01:40:27 +0000 Subject: [PATCH 17/65] add default timeout --- .../request_processor/litellm_online_request_processor.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py b/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py index 6ce894c9..3c1e7de7 100644 --- a/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py +++ b/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py @@ -73,11 +73,12 @@ async def call_api( if self.generic_request.response_format: response, completion_obj = await client.chat.completions.create_with_completion( **self.api_specific_request, - response_model=self.prompt_formatter.response_format + response_model=self.prompt_formatter.response_format, + timeout=60.0 ) response_message = response.model_dump() if hasattr(response, 'model_dump') else response else: - completion_obj = await completion(**self.api_specific_request) + completion_obj = await completion(**self.api_specific_request, timeout=60.0) response_message = completion_obj.content if hasattr(completion_obj, 'content') else str(completion_obj) # Extract token usage @@ -153,7 +154,6 @@ def __init__( logger.info(f"Using model: {self.model}") logger.debug(f"Parameters - Temperature: {self.temperature}, Top P: {self.top_p}, " f"Presence Penalty: {self.presence_penalty}, Frequency Penalty: {self.frequency_penalty}") - self.client = instructor.from_litellm(litellm.acompletion) logger.info("Instructor client initialized with LiteLLM backend") From e5713439e0bc7de80c21820937c2520d9e5baeb5 Mon Sep 17 00:00:00 2001 From: Charlie Cheng-Jie Ji Date: Sun, 24 Nov 2024 01:40:37 +0000 Subject: [PATCH 18/65] add simple litellm example --- examples/single_litellm_example.py | 48 ++++++++++++++++++++++++++++++ 1 file changed, 48 insertions(+) create mode 100644 examples/single_litellm_example.py diff --git a/examples/single_litellm_example.py b/examples/single_litellm_example.py new file mode 100644 index 00000000..3c8d40df --- /dev/null +++ b/examples/single_litellm_example.py @@ -0,0 +1,48 @@ +from typing import List +from pydantic import BaseModel, Field +from bespokelabs import curator +from datasets import Dataset + +# Define response format using Pydantic +class Recipe(BaseModel): + title: str = Field(description="Title of the recipe") + ingredients: List[str] = Field(description="List of ingredients needed") + instructions: List[str] = Field(description="Step by step cooking instructions") + +def prompt_func(cuisine_type): + return f"Generate a random {cuisine_type} recipe. Be creative but keep it realistic." + +def parse_func(row, response): + return { + "title": response.title, + "ingredients": response.ingredients, + "instructions": response.instructions, + "cuisine": row["cuisine"] # Keep track of cuisine type + } + +def main(): + # List of cuisines to generate recipes for + cuisines = ["Chinese"] * 1001 + + # Create input dataset with cuisine types + input_data = [{"cuisine": cuisine} for cuisine in cuisines] + input_dataset = Dataset.from_list(input_data) + + # Create prompter using LiteLLM backend + recipe_prompter = curator.Prompter( + model_name="gpt-4o-mini", + prompt_func=prompt_func, + parse_func=parse_func, + response_format=Recipe, + backend="litellm", + ) + + # Generate recipes for all cuisines + recipes = recipe_prompter(input_dataset) + + # Print results + df = recipes.to_pandas() + print(df) + +if __name__ == "__main__": + main() \ No newline at end of file From ad0fd1f75c72c88f63d6cb30fcb294bcc590f8a7 Mon Sep 17 00:00:00 2001 From: Charlie Cheng-Jie Ji Date: Mon, 25 Nov 2024 22:44:29 +0000 Subject: [PATCH 19/65] resume pbar display --- .../request_processor/litellm_online_request_processor.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py b/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py index 3c1e7de7..6d2dd03d 100644 --- a/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py +++ b/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py @@ -26,6 +26,7 @@ from bespokelabs.curator.request_processor.generic_response import TokenUsage logger = logging.getLogger(__name__) +litellm.set_verbose=True @dataclass class StatusTracker: @@ -163,7 +164,7 @@ def get_rate_limits(self) -> dict: completion = litellm.completion( model=self.model, - messages=[{"role": "user", "content": "hi"}], + messages=[], ) headers = completion._hidden_params.get('additional_headers', {}) @@ -246,7 +247,7 @@ async def process_requests_from_file( total_requests = sum(1 for _ in open(generic_requests_filepath)) # Create progress bar - status_tracker.pbar = tqdm(total=total_requests, desc="Processing LiteLLM requests") + status_tracker.pbar = tqdm(initial=len(completed_request_ids), total=total_requests, desc="Processing LiteLLM requests") # Use higher connector limit for better throughput connector = aiohttp.TCPConnector(limit=10 * rpm) From 50053c93ef0f5adcaac0a571cc19dab66ae56cef Mon Sep 17 00:00:00 2001 From: Charlie Cheng-Jie Ji Date: Tue, 26 Nov 2024 02:22:33 +0000 Subject: [PATCH 20/65] add token based rate limiting (openai api like token usage, i.e. prompt+completion token limits), in status tracker --- .../litellm_online_request_processor.py | 78 ++++++++++++++++--- 1 file changed, 67 insertions(+), 11 deletions(-) diff --git a/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py b/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py index 6d2dd03d..cc402db2 100644 --- a/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py +++ b/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py @@ -24,9 +24,10 @@ from bespokelabs.curator.request_processor.event_loop import run_in_event_loop import datetime from bespokelabs.curator.request_processor.generic_response import TokenUsage +from litellm import token_counter, get_max_tokens logger = logging.getLogger(__name__) -litellm.set_verbose=True +# litellm.set_verbose=True @dataclass class StatusTracker: @@ -37,6 +38,11 @@ class StatusTracker: num_tasks_failed: int = 0 num_tasks_already_completed: int = 0 num_other_errors: int = 0 + available_request_capacity: float = 0 + available_token_capacity: float = 0 + last_update_time: float = field(default_factory=time.time) + max_requests_per_minute: int = 0 + max_tokens_per_minute: int = 0 pbar: tqdm = field(default=None) def __str__(self): @@ -49,6 +55,36 @@ def __str__(self): f"Errors: {self.num_other_errors}" ) + def update_capacity(self): + """Update available capacity based on time elapsed""" + current_time = time.time() + seconds_since_update = current_time - self.last_update_time + + self.available_request_capacity = min( + self.available_request_capacity + + self.max_requests_per_minute * seconds_since_update / 60.0, + self.max_requests_per_minute + ) + + self.available_token_capacity = min( + self.available_token_capacity + + self.max_tokens_per_minute * seconds_since_update / 60.0, + self.max_tokens_per_minute + ) + + self.last_update_time = current_time + + def has_capacity(self, token_estimate: int) -> bool: + """Check if there's enough capacity for a request""" + self.update_capacity() + return (self.available_request_capacity >= 1 and + self.available_token_capacity >= token_estimate) + + def consume_capacity(self, token_estimate: int): + """Consume capacity for a request""" + self.available_request_capacity -= 1 + self.available_token_capacity -= token_estimate + @dataclass class APIRequest: """Stores an API request's inputs, outputs, and other metadata.""" @@ -151,6 +187,7 @@ def __init__( self.top_p = top_p self.presence_penalty = presence_penalty self.frequency_penalty = frequency_penalty + self.token_estimate_cache = {} logger.info(f"Using model: {self.model}") logger.debug(f"Parameters - Temperature: {self.temperature}, Top P: {self.top_p}, " @@ -158,13 +195,23 @@ def __init__( self.client = instructor.from_litellm(litellm.acompletion) logger.info("Instructor client initialized with LiteLLM backend") + def estimate_output_tokens(self, messages: list) -> int: + """Estimate output tokens for a request""" + return get_max_tokens(model=self.model) // 5 + + def estimate_total_tokens(self, messages: list) -> int: + """Estimate total tokens for a request""" + input_tokens = token_counter(model=self.model, messages=messages) + output_tokens = self.estimate_output_tokens(messages) + return input_tokens + output_tokens + def get_rate_limits(self) -> dict: """Get rate limits from LiteLLM response headers.""" logger.info(f"Getting rate limits for model: {self.model}") completion = litellm.completion( model=self.model, - messages=[], + messages=[{"role": "user", "content": "hi"}], ) headers = completion._hidden_params.get('additional_headers', {}) @@ -232,6 +279,8 @@ async def process_requests_from_file( # Get rate limits rate_limits = self.get_rate_limits() + status_tracker.max_requests_per_minute = rate_limits["max_requests_per_minute"] + status_tracker.max_tokens_per_minute = rate_limits["max_tokens_per_minute"] rpm = rate_limits["max_requests_per_minute"] # Track completed requests for resume functionality @@ -250,19 +299,18 @@ async def process_requests_from_file( status_tracker.pbar = tqdm(initial=len(completed_request_ids), total=total_requests, desc="Processing LiteLLM requests") # Use higher connector limit for better throughput - connector = aiohttp.TCPConnector(limit=10 * rpm) + connector = aiohttp.TCPConnector(limit=rpm) async with aiohttp.ClientSession(connector=connector) as session: - # Create a list to track all tasks - tasks = [] - async with aiofiles.open(generic_requests_filepath) as file: + pending_requests = [] + async for line in file: generic_request = GenericRequest.model_validate_json(line) if resume and generic_request.original_row_idx in completed_request_ids: status_tracker.num_tasks_already_completed += 1 continue - + request = APIRequest( task_id=status_tracker.num_tasks_started, generic_request=generic_request, @@ -271,7 +319,15 @@ async def process_requests_from_file( prompt_formatter=self.prompt_formatter ) - # Create and track the task + token_estimate = self.estimate_total_tokens(request.generic_request.messages) + + # Wait for capacity if needed + while not status_tracker.has_capacity(token_estimate): + await asyncio.sleep(0.1) # Short sleep to prevent CPU spinning + + # Consume capacity before making request + status_tracker.consume_capacity(token_estimate) + task = asyncio.create_task( request.call_api( session=session, @@ -281,14 +337,14 @@ async def process_requests_from_file( status_tracker=status_tracker, ) ) - tasks.append(task) + pending_requests.append(task) status_tracker.num_tasks_started += 1 status_tracker.num_tasks_in_progress += 1 # Wait for all tasks to complete - if tasks: - await asyncio.gather(*tasks) + if pending_requests: + await asyncio.gather(*pending_requests) # Process any retries if needed while not queue_of_requests_to_retry.empty(): From 3e843f8890cf36f3b2d77c031017acd7e4989848 Mon Sep 17 00:00:00 2001 From: Charlie Cheng-Jie Ji Date: Tue, 26 Nov 2024 23:01:47 +0000 Subject: [PATCH 21/65] parallel retry on litellm --- .../litellm_online_request_processor.py | 46 +++++++++++++------ 1 file changed, 31 insertions(+), 15 deletions(-) diff --git a/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py b/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py index cc402db2..7b1cf689 100644 --- a/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py +++ b/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py @@ -1,7 +1,6 @@ import json import logging import os -import argparse from typing import Optional, Any import asyncio import time @@ -28,6 +27,7 @@ logger = logging.getLogger(__name__) # litellm.set_verbose=True +litellm.suppress_debug_info = True @dataclass class StatusTracker: @@ -197,7 +197,10 @@ def __init__( def estimate_output_tokens(self, messages: list) -> int: """Estimate output tokens for a request""" - return get_max_tokens(model=self.model) // 5 + try: + return get_max_tokens(model=self.model) // 4 + except Exception: + return 0 def estimate_total_tokens(self, messages: list) -> int: """Estimate total tokens for a request""" @@ -211,11 +214,12 @@ def get_rate_limits(self) -> dict: completion = litellm.completion( model=self.model, - messages=[{"role": "user", "content": "hi"}], + messages=[{"role": "user", "content": "hi"}], # Some models (e.g. Claude) require an non-empty message to get rate limits. ) headers = completion._hidden_params.get('additional_headers', {}) logger.debug(f"Rate limit headers: {headers}") + print(headers) rpm = int(headers.get('x-ratelimit-limit-requests', 3000)) tpm = int(headers.get('x-ratelimit-limit-tokens', 150_000)) @@ -323,7 +327,7 @@ async def process_requests_from_file( # Wait for capacity if needed while not status_tracker.has_capacity(token_estimate): - await asyncio.sleep(0.1) # Short sleep to prevent CPU spinning + await asyncio.sleep(0.1) # Consume capacity before making request status_tracker.consume_capacity(token_estimate) @@ -342,20 +346,32 @@ async def process_requests_from_file( status_tracker.num_tasks_started += 1 status_tracker.num_tasks_in_progress += 1 + # Process any retries that are in the queue + while not queue_of_requests_to_retry.empty(): + retry_request = await queue_of_requests_to_retry.get() + token_estimate = self.estimate_total_tokens(retry_request.generic_request.messages) + + # Wait for capacity if needed + while not status_tracker.has_capacity(token_estimate): + await asyncio.sleep(0.1) + + # Consume capacity before making request + status_tracker.consume_capacity(token_estimate) + + task = asyncio.create_task( + retry_request.call_api( + session=session, + client=self.client, + retry_queue=queue_of_requests_to_retry, + save_filepath=save_filepath, + status_tracker=status_tracker, + ) + ) + pending_requests.append(task) + # Wait for all tasks to complete if pending_requests: await asyncio.gather(*pending_requests) - - # Process any retries if needed - while not queue_of_requests_to_retry.empty(): - request = await queue_of_requests_to_retry.get() - await request.call_api( - session=session, - client=self.client, - retry_queue=queue_of_requests_to_retry, - save_filepath=save_filepath, - status_tracker=status_tracker, - ) status_tracker.pbar.close() From 8f25ed914a3a911b33d8365b23b356f7cdf7990c Mon Sep 17 00:00:00 2001 From: Charlie Cheng-Jie Ji Date: Tue, 26 Nov 2024 23:02:28 +0000 Subject: [PATCH 22/65] unused input param --- .../request_processor/litellm_online_request_processor.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py b/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py index 7b1cf689..13b5ac96 100644 --- a/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py +++ b/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py @@ -195,7 +195,7 @@ def __init__( self.client = instructor.from_litellm(litellm.acompletion) logger.info("Instructor client initialized with LiteLLM backend") - def estimate_output_tokens(self, messages: list) -> int: + def estimate_output_tokens(self) -> int: """Estimate output tokens for a request""" try: return get_max_tokens(model=self.model) // 4 @@ -205,7 +205,7 @@ def estimate_output_tokens(self, messages: list) -> int: def estimate_total_tokens(self, messages: list) -> int: """Estimate total tokens for a request""" input_tokens = token_counter(model=self.model, messages=messages) - output_tokens = self.estimate_output_tokens(messages) + output_tokens = self.estimate_output_tokens() return input_tokens + output_tokens def get_rate_limits(self) -> dict: From 9065f55ec988cbb1c8967480829c98a328336368 Mon Sep 17 00:00:00 2001 From: Charlie Cheng-Jie Ji Date: Wed, 27 Nov 2024 19:07:11 +0000 Subject: [PATCH 23/65] renamed examples --- ...e.py => litellm_model_coverage_example.py} | 26 ++++++++--- examples/litellm_prompting_example.py | 46 +++++++++++++++++++ ...y => litellm_structured_output_example.py} | 2 +- 3 files changed, 66 insertions(+), 8 deletions(-) rename examples/{litellm_example.py => litellm_model_coverage_example.py} (76%) create mode 100644 examples/litellm_prompting_example.py rename examples/{single_litellm_example.py => litellm_structured_output_example.py} (97%) diff --git a/examples/litellm_example.py b/examples/litellm_model_coverage_example.py similarity index 76% rename from examples/litellm_example.py rename to examples/litellm_model_coverage_example.py index 642e4071..c8a5dcdc 100644 --- a/examples/litellm_example.py +++ b/examples/litellm_model_coverage_example.py @@ -7,6 +7,18 @@ # Set up logging logger = logging.getLogger(__name__) logger.setLevel(logging.DEBUG) + +# Add StreamHandler to output logs to console +console_handler = logging.StreamHandler() +console_handler.setLevel(logging.DEBUG) + +# Optional: Create a formatter and set it for the handler +formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s') +console_handler.setFormatter(formatter) + +# Add the handler to the logger +logger.addHandler(console_handler) + # Define response format using Pydantic class Recipe(BaseModel): title: str = Field(description="Title of the recipe") @@ -34,22 +46,22 @@ def main(): # List of models for testing model_names = [ - # "claude-3-5-sonnet-20240620", # https://docs.litellm.ai/docs/providers/anthropic + # "claude-3-5-sonnet-20240620", # https://docs.litellm.ai/docs/providers/anthropic # anthropic has a different hidden param tokens structure. + # "claude-3-5-haiku-20241022", # "claude-3-haiku-20240307", # "claude-3-opus-20240229", - "claude-3-sonnet-20240229", + # "claude-3-sonnet-20240229", # "gpt-4o-mini", # https://docs.litellm.ai/docs/providers/openai # "gpt-4o-2024-08-06", # "gpt-4-0125-preview", # "gpt-3.5-turbo-1106", - # "o1-mini", - # "gemini/gemini-1.5-flash", # https://docs.litellm.ai/docs/providers/gemini; https://ai.google.dev/gemini-api/docs/models - # "gemini/gemini-1.5-pro", - # "sambanova/Meta-Llama-3.1-8B-Instruct", # https://docs.litellm.ai/docs/providers/sambanova; https://community.sambanova.ai/t/supported-models - # "sambanova/Meta-Llama-3.1-70B-Instruct", + # "gemini/gemini-1.5-flash", # https://docs.litellm.ai/docs/providers/gemini; https://ai.google.dev/gemini-api/docs/models # 20-30 iter/s + # "gemini/gemini-1.5-pro", # 20-30 iter/s # "together_ai/meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo", # https://docs.together.ai/docs/serverless-models # "together_ai/meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo", # "together_ai/nvidia/Llama-3.1-Nemotron-70B-Instruct-HF", + "together_ai/mistralai/Mixtral-8x7B-Instruct-v0.1", + # "deepinfra/nvidia/Llama-3.1-Nemotron-70B-Instruct" ] for model_name in model_names: diff --git a/examples/litellm_prompting_example.py b/examples/litellm_prompting_example.py new file mode 100644 index 00000000..fa8c7cc2 --- /dev/null +++ b/examples/litellm_prompting_example.py @@ -0,0 +1,46 @@ +from typing import List +from pydantic import BaseModel, Field +from bespokelabs import curator +from datasets import Dataset + +# Define response format using Pydantic +class Recipe(BaseModel): + title: str = Field(description="Title of the recipe") + ingredients: List[str] = Field(description="List of ingredients needed") + instructions: List[str] = Field(description="Step by step cooking instructions") + +def prompt_func(cuisine_type): + return f"Generate a random {cuisine_type} recipe. Be creative but keep it realistic." + +def parse_func(row, response): + return { + "response": response, + "cuisine": row["cuisine"] # Keep track of cuisine type + } + +def main(): + # List of cuisines to generate recipes for + cuisines = ["Chinese"] * 100 + + # Create input dataset with cuisine types + input_data = [{"cuisine": cuisine} for cuisine in cuisines] + input_dataset = Dataset.from_list(input_data) + + # Create prompter using LiteLLM backend + recipe_prompter = curator.Prompter( + # model_name="gpt-4o-mini", + model_name="together_ai/mistralai/Mistral-7B-Instruct-v0.1", + prompt_func=prompt_func, + parse_func=parse_func, + backend="litellm", + ) + + # Generate recipes for all cuisines + recipes = recipe_prompter(input_dataset) + + # Print results + df = recipes.to_pandas() + print(df) + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/examples/single_litellm_example.py b/examples/litellm_structured_output_example.py similarity index 97% rename from examples/single_litellm_example.py rename to examples/litellm_structured_output_example.py index 3c8d40df..3658a77d 100644 --- a/examples/single_litellm_example.py +++ b/examples/litellm_structured_output_example.py @@ -22,7 +22,7 @@ def parse_func(row, response): def main(): # List of cuisines to generate recipes for - cuisines = ["Chinese"] * 1001 + cuisines = ["Chinese"] * 1000 # Create input dataset with cuisine types input_data = [{"cuisine": cuisine} for cuisine in cuisines] From 2c8019562f81927d1c74361f5326f04efbf9950d Mon Sep 17 00:00:00 2001 From: Charlie Cheng-Jie Ji Date: Wed, 27 Nov 2024 21:07:02 +0000 Subject: [PATCH 24/65] added check instructor + litellm coverage before using instructor's structured output --- .../litellm_online_request_processor.py | 55 ++++++++++++++----- 1 file changed, 41 insertions(+), 14 deletions(-) diff --git a/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py b/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py index 13b5ac96..6ef1ab9d 100644 --- a/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py +++ b/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py @@ -11,7 +11,7 @@ from pydantic import BaseModel import instructor import litellm -from litellm import completion, get_supported_openai_params +from litellm import get_supported_openai_params import aiofiles from bespokelabs.curator.dataset import Dataset from bespokelabs.curator.prompter.prompter import PromptFormatter @@ -26,9 +26,9 @@ from litellm import token_counter, get_max_tokens logger = logging.getLogger(__name__) -# litellm.set_verbose=True +logger.setLevel(logging.INFO) litellm.suppress_debug_info = True - +# litellm.set_verbose = True @dataclass class StatusTracker: """Tracks the status of all requests.""" @@ -112,11 +112,11 @@ async def call_api( **self.api_specific_request, response_model=self.prompt_formatter.response_format, timeout=60.0 - ) + ) response_message = response.model_dump() if hasattr(response, 'model_dump') else response else: - completion_obj = await completion(**self.api_specific_request, timeout=60.0) - response_message = completion_obj.content if hasattr(completion_obj, 'content') else str(completion_obj) + completion_obj = await litellm.acompletion(**self.api_specific_request, timeout=60.0) + response_message = completion_obj['choices'][0]['message']['content'] # Extract token usage usage = completion_obj.usage if hasattr(completion_obj, 'usage') else {} @@ -150,6 +150,7 @@ async def call_api( status_tracker.pbar.update(1) except Exception as e: + logger.error(f"Error in API request: {e}") status_tracker.num_other_errors += 1 self.result.append(e) @@ -193,7 +194,6 @@ def __init__( logger.debug(f"Parameters - Temperature: {self.temperature}, Top P: {self.top_p}, " f"Presence Penalty: {self.presence_penalty}, Frequency Penalty: {self.frequency_penalty}") self.client = instructor.from_litellm(litellm.acompletion) - logger.info("Instructor client initialized with LiteLLM backend") def estimate_output_tokens(self) -> int: """Estimate output tokens for a request""" @@ -218,8 +218,7 @@ def get_rate_limits(self) -> dict: ) headers = completion._hidden_params.get('additional_headers', {}) - logger.debug(f"Rate limit headers: {headers}") - print(headers) + logger.info(f"Rate limit headers: {headers}") rpm = int(headers.get('x-ratelimit-limit-requests', 3000)) tpm = int(headers.get('x-ratelimit-limit-tokens', 150_000)) @@ -242,7 +241,6 @@ def create_api_specific_request(self, generic_request: GenericRequest) -> dict: """ # Get supported parameters for this model supported_params = get_supported_openai_params(model=self.model) - request = { "model": generic_request.model, "messages": generic_request.messages, @@ -262,7 +260,30 @@ def create_api_specific_request(self, generic_request: GenericRequest) -> dict: request["frequency_penalty"] = self.frequency_penalty return request - + + def check_instructor_structured_output_support(self, prompt_formatter: PromptFormatter): + """Check if the model supports structured output via instructor.""" + class User(BaseModel): + name: str + age: int + try: + if prompt_formatter.response_format: + client = instructor.from_litellm(litellm.completion) + response = client.chat.completions.create( + model=self.model, + messages=[{"role": "user", "content": "Extract Jason is 25 years old."}], + response_model=User, + ) + logger.info(f"Check instructor structure output response: {response}") + assert isinstance(response, User) + logger.info(f"Model {self.model} supports structured output via instructor, response: {response}") + return True + else: + return True + except Exception as e: + logger.warning(f"Model {self.model} does not support structured output via instructor: {e}") + return False + async def process_requests_from_file( self, generic_requests_filepath: str, @@ -320,7 +341,7 @@ async def process_requests_from_file( generic_request=generic_request, api_specific_request=self.create_api_specific_request(generic_request), attempts_left=max_attempts, - prompt_formatter=self.prompt_formatter + prompt_formatter=self.prompt_formatter, ) token_estimate = self.estimate_total_tokens(request.generic_request.messages) @@ -393,16 +414,22 @@ def run( prompt_formatter: PromptFormatter, ) -> Dataset: """Run completions using LiteLLM with async processing.""" + logger.info(f"Running LiteLLM completions with model: {self.model}," + f" Supported params: {get_supported_openai_params(model=self.model)}") + self.prompt_formatter = prompt_formatter + + if not self.check_instructor_structured_output_support(prompt_formatter): + raise ValueError(f"Model {self.model} does not support structured output via instructor.") + generic_requests_files = self.create_request_files( dataset, working_dir, prompt_formatter ) - generic_responses_files = [ f"{working_dir}/responses_{i}.jsonl" for i in range(len(generic_requests_files)) ] - + for request_file, response_file in zip( generic_requests_files, generic_responses_files ): From a8c547b7463fcd6923290d4b4a0f2368e4bb5193 Mon Sep 17 00:00:00 2001 From: Charlie Cheng-Jie Ji Date: Wed, 27 Nov 2024 22:52:48 +0000 Subject: [PATCH 25/65] rename examples --- examples/litellm_model_coverage_example.py | 87 ------------------- examples/litellm_prompting_example.py | 46 ---------- ...py => litellm_recipe_prompting_example.py} | 20 +---- ...itellm_recipe_structured_output_example.py | 61 +++++++++++++ 4 files changed, 65 insertions(+), 149 deletions(-) delete mode 100644 examples/litellm_model_coverage_example.py delete mode 100644 examples/litellm_prompting_example.py rename examples/{litellm_structured_output_example.py => litellm_recipe_prompting_example.py} (53%) create mode 100644 examples/litellm_recipe_structured_output_example.py diff --git a/examples/litellm_model_coverage_example.py b/examples/litellm_model_coverage_example.py deleted file mode 100644 index c8a5dcdc..00000000 --- a/examples/litellm_model_coverage_example.py +++ /dev/null @@ -1,87 +0,0 @@ -from typing import List -from pydantic import BaseModel, Field -from bespokelabs import curator -from datasets import Dataset -import logging -import time -# Set up logging -logger = logging.getLogger(__name__) -logger.setLevel(logging.DEBUG) - -# Add StreamHandler to output logs to console -console_handler = logging.StreamHandler() -console_handler.setLevel(logging.DEBUG) - -# Optional: Create a formatter and set it for the handler -formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s') -console_handler.setFormatter(formatter) - -# Add the handler to the logger -logger.addHandler(console_handler) - -# Define response format using Pydantic -class Recipe(BaseModel): - title: str = Field(description="Title of the recipe") - ingredients: List[str] = Field(description="List of ingredients needed") - instructions: List[str] = Field(description="Step by step cooking instructions") - -def prompt_func(cuisine): - return f"Generate a random {cuisine['cuisine_type']} recipe. Be creative but keep it realistic." - -def parse_func(row, response): - return { - "title": response.title, - "ingredients": response.ingredients, - "instructions": response.instructions, - "cuisine_type": row["cuisine_type"] # Keep track of cuisine type - } - -def main(): - # List of cuisines to generate recipes for - cuisines = ["Chinese"] * 100 - - # Create input dataset with cuisine types - input_data = [{"cuisine_type": cuisine} for cuisine in cuisines] - input_dataset = Dataset.from_list(input_data) - - # List of models for testing - model_names = [ - # "claude-3-5-sonnet-20240620", # https://docs.litellm.ai/docs/providers/anthropic # anthropic has a different hidden param tokens structure. - # "claude-3-5-haiku-20241022", - # "claude-3-haiku-20240307", - # "claude-3-opus-20240229", - # "claude-3-sonnet-20240229", - # "gpt-4o-mini", # https://docs.litellm.ai/docs/providers/openai - # "gpt-4o-2024-08-06", - # "gpt-4-0125-preview", - # "gpt-3.5-turbo-1106", - # "gemini/gemini-1.5-flash", # https://docs.litellm.ai/docs/providers/gemini; https://ai.google.dev/gemini-api/docs/models # 20-30 iter/s - # "gemini/gemini-1.5-pro", # 20-30 iter/s - # "together_ai/meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo", # https://docs.together.ai/docs/serverless-models - # "together_ai/meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo", - # "together_ai/nvidia/Llama-3.1-Nemotron-70B-Instruct-HF", - "together_ai/mistralai/Mixtral-8x7B-Instruct-v0.1", - # "deepinfra/nvidia/Llama-3.1-Nemotron-70B-Instruct" - ] - - for model_name in model_names: - # Create prompter using LiteLLM backend - logger.info(f"Using model: {model_name}") - recipe_prompter = curator.Prompter( - model_name=model_name, - prompt_func=prompt_func, - parse_func=parse_func, - response_format=Recipe, - backend="litellm", - ) - - # Generate recipes for all cuisines - recipes = recipe_prompter(input_dataset) - - # Print results - df = recipes.to_pandas() - print(df) - time.sleep(2) - -if __name__ == "__main__": - main() \ No newline at end of file diff --git a/examples/litellm_prompting_example.py b/examples/litellm_prompting_example.py deleted file mode 100644 index fa8c7cc2..00000000 --- a/examples/litellm_prompting_example.py +++ /dev/null @@ -1,46 +0,0 @@ -from typing import List -from pydantic import BaseModel, Field -from bespokelabs import curator -from datasets import Dataset - -# Define response format using Pydantic -class Recipe(BaseModel): - title: str = Field(description="Title of the recipe") - ingredients: List[str] = Field(description="List of ingredients needed") - instructions: List[str] = Field(description="Step by step cooking instructions") - -def prompt_func(cuisine_type): - return f"Generate a random {cuisine_type} recipe. Be creative but keep it realistic." - -def parse_func(row, response): - return { - "response": response, - "cuisine": row["cuisine"] # Keep track of cuisine type - } - -def main(): - # List of cuisines to generate recipes for - cuisines = ["Chinese"] * 100 - - # Create input dataset with cuisine types - input_data = [{"cuisine": cuisine} for cuisine in cuisines] - input_dataset = Dataset.from_list(input_data) - - # Create prompter using LiteLLM backend - recipe_prompter = curator.Prompter( - # model_name="gpt-4o-mini", - model_name="together_ai/mistralai/Mistral-7B-Instruct-v0.1", - prompt_func=prompt_func, - parse_func=parse_func, - backend="litellm", - ) - - # Generate recipes for all cuisines - recipes = recipe_prompter(input_dataset) - - # Print results - df = recipes.to_pandas() - print(df) - -if __name__ == "__main__": - main() \ No newline at end of file diff --git a/examples/litellm_structured_output_example.py b/examples/litellm_recipe_prompting_example.py similarity index 53% rename from examples/litellm_structured_output_example.py rename to examples/litellm_recipe_prompting_example.py index 3658a77d..78c443cc 100644 --- a/examples/litellm_structured_output_example.py +++ b/examples/litellm_recipe_prompting_example.py @@ -3,42 +3,30 @@ from bespokelabs import curator from datasets import Dataset -# Define response format using Pydantic -class Recipe(BaseModel): - title: str = Field(description="Title of the recipe") - ingredients: List[str] = Field(description="List of ingredients needed") - instructions: List[str] = Field(description="Step by step cooking instructions") - def prompt_func(cuisine_type): return f"Generate a random {cuisine_type} recipe. Be creative but keep it realistic." def parse_func(row, response): return { - "title": response.title, - "ingredients": response.ingredients, - "instructions": response.instructions, + "recipe": response, "cuisine": row["cuisine"] # Keep track of cuisine type } def main(): # List of cuisines to generate recipes for - cuisines = ["Chinese"] * 1000 - - # Create input dataset with cuisine types - input_data = [{"cuisine": cuisine} for cuisine in cuisines] - input_dataset = Dataset.from_list(input_data) + cuisines = [{"cuisine": cuisine} for cuisine in ["Chinese", "Italian", "Mexican", "French", "Japanese", "Indian", "Thai", "Korean", "Vietnamese", "Brazilian"]] + cuisines = Dataset.from_list(cuisines) # Create prompter using LiteLLM backend recipe_prompter = curator.Prompter( model_name="gpt-4o-mini", prompt_func=prompt_func, parse_func=parse_func, - response_format=Recipe, backend="litellm", ) # Generate recipes for all cuisines - recipes = recipe_prompter(input_dataset) + recipes = recipe_prompter(cuisines) # Print results df = recipes.to_pandas() diff --git a/examples/litellm_recipe_structured_output_example.py b/examples/litellm_recipe_structured_output_example.py new file mode 100644 index 00000000..67a459bc --- /dev/null +++ b/examples/litellm_recipe_structured_output_example.py @@ -0,0 +1,61 @@ +from typing import List +from pydantic import BaseModel, Field +from bespokelabs import curator +from datasets import Dataset +import logging + +logger = logging.getLogger(__name__) + +# Define response format using Pydantic +class Recipe(BaseModel): + title: str = Field(description="Title of the recipe") + ingredients: List[str] = Field(description="List of ingredients needed") + instructions: List[str] = Field(description="Step by step cooking instructions") + prep_time: int = Field(description="Preparation time in minutes") + cook_time: int = Field(description="Cooking time in minutes") + servings: int = Field(description="Number of servings") + +class Cuisines(BaseModel): + cuisines_list: List[str] = Field(description="A list of cuisines.") + +def prompt_func(row): + return f"Generate a random {row['cuisine']} recipe. Be creative but keep it realistic." + +def parse_func(row, response): + return { + "title": response.title, + "ingredients": response.ingredients, + "instructions": response.instructions, + "cuisine": row["cuisine"] # Keep track of cuisine type + } + +def main(): + # List of cuisines to generate recipes for + # We define a prompter that generates topics. + cuisines_generator = curator.Prompter( + prompt_func=lambda: f"Generate 10 diverse cuisines.", + model_name="gpt-4o-mini", + response_format=Cuisines, + parse_func=lambda _, cuisines: [{"cuisine": t} for t in cuisines.cuisines_list], + backend="litellm", + ) + cuisines = cuisines_generator() + print(cuisines) + + recipe_prompter = curator.Prompter( + model_name="together_ai/mistralai/Mixtral-8x7B-Instruct-v0.1", + prompt_func=prompt_func, + parse_func=parse_func, + response_format=Recipe, + backend="litellm", + ) + + # Generate recipes for all cuisines + recipes = recipe_prompter(cuisines) + + # Print results + df = recipes.to_pandas() + print(df) + +if __name__ == "__main__": + main() \ No newline at end of file From 5c88363343745e37300dd587372f8bfaf1dabcd1 Mon Sep 17 00:00:00 2001 From: Charlie Cheng-Jie Ji Date: Wed, 27 Nov 2024 22:53:55 +0000 Subject: [PATCH 26/65] rm commented litellm debug message --- .../request_processor/litellm_online_request_processor.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py b/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py index 6ef1ab9d..389f2204 100644 --- a/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py +++ b/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py @@ -27,8 +27,9 @@ logger = logging.getLogger(__name__) logger.setLevel(logging.INFO) + litellm.suppress_debug_info = True -# litellm.set_verbose = True + @dataclass class StatusTracker: """Tracks the status of all requests.""" From 4115176a883464c795edab8d762b0826b5155f7d Mon Sep 17 00:00:00 2001 From: Charlie Cheng-Jie Ji Date: Wed, 27 Nov 2024 22:59:21 +0000 Subject: [PATCH 27/65] run black --- examples/litellm_recipe_prompting_example.py | 29 ++- ...itellm_recipe_structured_output_example.py | 24 ++- src/bespokelabs/curator/prompter/prompter.py | 8 +- .../litellm_online_request_processor.py | 185 ++++++++++-------- 4 files changed, 145 insertions(+), 101 deletions(-) diff --git a/examples/litellm_recipe_prompting_example.py b/examples/litellm_recipe_prompting_example.py index 78c443cc..31d6aba9 100644 --- a/examples/litellm_recipe_prompting_example.py +++ b/examples/litellm_recipe_prompting_example.py @@ -3,18 +3,32 @@ from bespokelabs import curator from datasets import Dataset + def prompt_func(cuisine_type): return f"Generate a random {cuisine_type} recipe. Be creative but keep it realistic." + def parse_func(row, response): - return { - "recipe": response, - "cuisine": row["cuisine"] # Keep track of cuisine type - } + return {"recipe": response, "cuisine": row["cuisine"]} # Keep track of cuisine type + def main(): # List of cuisines to generate recipes for - cuisines = [{"cuisine": cuisine} for cuisine in ["Chinese", "Italian", "Mexican", "French", "Japanese", "Indian", "Thai", "Korean", "Vietnamese", "Brazilian"]] + cuisines = [ + {"cuisine": cuisine} + for cuisine in [ + "Chinese", + "Italian", + "Mexican", + "French", + "Japanese", + "Indian", + "Thai", + "Korean", + "Vietnamese", + "Brazilian", + ] + ] cuisines = Dataset.from_list(cuisines) # Create prompter using LiteLLM backend @@ -27,10 +41,11 @@ def main(): # Generate recipes for all cuisines recipes = recipe_prompter(cuisines) - + # Print results df = recipes.to_pandas() print(df) + if __name__ == "__main__": - main() \ No newline at end of file + main() diff --git a/examples/litellm_recipe_structured_output_example.py b/examples/litellm_recipe_structured_output_example.py index 67a459bc..5e056199 100644 --- a/examples/litellm_recipe_structured_output_example.py +++ b/examples/litellm_recipe_structured_output_example.py @@ -6,6 +6,7 @@ logger = logging.getLogger(__name__) + # Define response format using Pydantic class Recipe(BaseModel): title: str = Field(description="Title of the recipe") @@ -13,22 +14,26 @@ class Recipe(BaseModel): instructions: List[str] = Field(description="Step by step cooking instructions") prep_time: int = Field(description="Preparation time in minutes") cook_time: int = Field(description="Cooking time in minutes") - servings: int = Field(description="Number of servings") + servings: int = Field(description="Number of servings") + class Cuisines(BaseModel): cuisines_list: List[str] = Field(description="A list of cuisines.") + def prompt_func(row): return f"Generate a random {row['cuisine']} recipe. Be creative but keep it realistic." + def parse_func(row, response): return { "title": response.title, "ingredients": response.ingredients, "instructions": response.instructions, - "cuisine": row["cuisine"] # Keep track of cuisine type + "cuisine": row["cuisine"], # Keep track of cuisine type } + def main(): # List of cuisines to generate recipes for # We define a prompter that generates topics. @@ -41,21 +46,22 @@ def main(): ) cuisines = cuisines_generator() print(cuisines) - + recipe_prompter = curator.Prompter( - model_name="together_ai/mistralai/Mixtral-8x7B-Instruct-v0.1", - prompt_func=prompt_func, - parse_func=parse_func, - response_format=Recipe, + model_name="together_ai/mistralai/Mixtral-8x7B-Instruct-v0.1", + prompt_func=prompt_func, + parse_func=parse_func, + response_format=Recipe, backend="litellm", ) # Generate recipes for all cuisines recipes = recipe_prompter(cuisines) - + # Print results df = recipes.to_pandas() print(df) + if __name__ == "__main__": - main() \ No newline at end of file + main() diff --git a/src/bespokelabs/curator/prompter/prompter.py b/src/bespokelabs/curator/prompter/prompter.py index 9a3c705e..6025b957 100644 --- a/src/bespokelabs/curator/prompter/prompter.py +++ b/src/bespokelabs/curator/prompter/prompter.py @@ -21,7 +21,9 @@ from bespokelabs.curator.request_processor.openai_online_request_processor import ( OpenAIOnlineRequestProcessor, ) -from bespokelabs.curator.request_processor.litellm_online_request_processor import LiteLLMOnlineRequestProcessor +from bespokelabs.curator.request_processor.litellm_online_request_processor import ( + LiteLLMOnlineRequestProcessor, +) _CURATOR_DEFAULT_CACHE_DIR = "~/.cache/curator" T = TypeVar("T") @@ -120,7 +122,9 @@ def __init__( ) elif backend == "litellm": if batch: - logger.warning("Batch mode is not supported with LiteLLM backend, ignoring batch=True") + logger.warning( + "Batch mode is not supported with LiteLLM backend, ignoring batch=True" + ) self._request_processor = LiteLLMOnlineRequestProcessor( model=model_name, temperature=temperature, diff --git a/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py b/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py index 389f2204..73b2fe99 100644 --- a/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py +++ b/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py @@ -30,9 +30,11 @@ litellm.suppress_debug_info = True + @dataclass class StatusTracker: """Tracks the status of all requests.""" + num_tasks_started: int = 0 num_tasks_in_progress: int = 0 num_tasks_succeeded: int = 0 @@ -60,35 +62,38 @@ def update_capacity(self): """Update available capacity based on time elapsed""" current_time = time.time() seconds_since_update = current_time - self.last_update_time - + self.available_request_capacity = min( - self.available_request_capacity + - self.max_requests_per_minute * seconds_since_update / 60.0, - self.max_requests_per_minute + self.available_request_capacity + + self.max_requests_per_minute * seconds_since_update / 60.0, + self.max_requests_per_minute, ) - + self.available_token_capacity = min( - self.available_token_capacity + - self.max_tokens_per_minute * seconds_since_update / 60.0, - self.max_tokens_per_minute + self.available_token_capacity + + self.max_tokens_per_minute * seconds_since_update / 60.0, + self.max_tokens_per_minute, ) - + self.last_update_time = current_time def has_capacity(self, token_estimate: int) -> bool: """Check if there's enough capacity for a request""" self.update_capacity() - return (self.available_request_capacity >= 1 and - self.available_token_capacity >= token_estimate) + return ( + self.available_request_capacity >= 1 and self.available_token_capacity >= token_estimate + ) def consume_capacity(self, token_estimate: int): """Consume capacity for a request""" self.available_request_capacity -= 1 self.available_token_capacity -= token_estimate + @dataclass class APIRequest: """Stores an API request's inputs, outputs, and other metadata.""" + task_id: int generic_request: GenericRequest api_specific_request: dict @@ -112,25 +117,27 @@ async def call_api( response, completion_obj = await client.chat.completions.create_with_completion( **self.api_specific_request, response_model=self.prompt_formatter.response_format, - timeout=60.0 - ) - response_message = response.model_dump() if hasattr(response, 'model_dump') else response + timeout=60.0, + ) + response_message = ( + response.model_dump() if hasattr(response, "model_dump") else response + ) else: - completion_obj = await litellm.acompletion(**self.api_specific_request, timeout=60.0) - response_message = completion_obj['choices'][0]['message']['content'] + completion_obj = await litellm.acompletion( + **self.api_specific_request, timeout=60.0 + ) + response_message = completion_obj["choices"][0]["message"]["content"] # Extract token usage - usage = completion_obj.usage if hasattr(completion_obj, 'usage') else {} + usage = completion_obj.usage if hasattr(completion_obj, "usage") else {} token_usage = TokenUsage( prompt_tokens=usage.prompt_tokens, completion_tokens=usage.completion_tokens, - total_tokens=usage.total_tokens + total_tokens=usage.total_tokens, ) # Calculate cost using litellm - cost = litellm.completion_cost( - completion_response=completion_obj.model_dump() - ) + cost = litellm.completion_cost(completion_response=completion_obj.model_dump()) # Create and save response immediately generic_response = GenericResponse( @@ -142,9 +149,9 @@ async def call_api( created_at=self.created_at, finished_at=datetime.datetime.now(), token_usage=token_usage, - response_cost=cost + response_cost=cost, ) - + await append_generic_response(generic_response, save_filepath) status_tracker.num_tasks_in_progress -= 1 status_tracker.num_tasks_succeeded += 1 @@ -154,7 +161,7 @@ async def call_api( logger.error(f"Error in API request: {e}") status_tracker.num_other_errors += 1 self.result.append(e) - + if self.attempts_left > 0: self.attempts_left -= 1 retry_queue.put_nowait(self) @@ -172,6 +179,7 @@ async def call_api( status_tracker.num_tasks_in_progress -= 1 status_tracker.num_tasks_failed += 1 + class LiteLLMOnlineRequestProcessor(BaseRequestProcessor): """Request processor for LiteLLM that supports structured outputs via instructor.""" @@ -190,10 +198,12 @@ def __init__( self.presence_penalty = presence_penalty self.frequency_penalty = frequency_penalty self.token_estimate_cache = {} - + logger.info(f"Using model: {self.model}") - logger.debug(f"Parameters - Temperature: {self.temperature}, Top P: {self.top_p}, " - f"Presence Penalty: {self.presence_penalty}, Frequency Penalty: {self.frequency_penalty}") + logger.debug( + f"Parameters - Temperature: {self.temperature}, Top P: {self.top_p}, " + f"Presence Penalty: {self.presence_penalty}, Frequency Penalty: {self.frequency_penalty}" + ) self.client = instructor.from_litellm(litellm.acompletion) def estimate_output_tokens(self) -> int: @@ -212,31 +222,30 @@ def estimate_total_tokens(self, messages: list) -> int: def get_rate_limits(self) -> dict: """Get rate limits from LiteLLM response headers.""" logger.info(f"Getting rate limits for model: {self.model}") - + completion = litellm.completion( model=self.model, - messages=[{"role": "user", "content": "hi"}], # Some models (e.g. Claude) require an non-empty message to get rate limits. + messages=[ + {"role": "user", "content": "hi"} + ], # Some models (e.g. Claude) require an non-empty message to get rate limits. ) - - headers = completion._hidden_params.get('additional_headers', {}) + + headers = completion._hidden_params.get("additional_headers", {}) logger.info(f"Rate limit headers: {headers}") - - rpm = int(headers.get('x-ratelimit-limit-requests', 3000)) - tpm = int(headers.get('x-ratelimit-limit-tokens', 150_000)) - + + rpm = int(headers.get("x-ratelimit-limit-requests", 3000)) + tpm = int(headers.get("x-ratelimit-limit-tokens", 150_000)) + logger.info(f"Rate limits - Requests/min: {rpm}, Tokens/min: {tpm}") - - return { - "max_requests_per_minute": rpm, - "max_tokens_per_minute": tpm - } + + return {"max_requests_per_minute": rpm, "max_tokens_per_minute": tpm} def create_api_specific_request(self, generic_request: GenericRequest) -> dict: """Create a LiteLLM-specific request from a generic request. - + Args: generic_request (GenericRequest): The generic request to convert - + Returns: dict: LiteLLM-specific request parameters """ @@ -250,23 +259,25 @@ def create_api_specific_request(self, generic_request: GenericRequest) -> dict: # Only add parameters that are supported by this model if "temperature" in supported_params and self.temperature is not None: request["temperature"] = self.temperature - + if "top_p" in supported_params and self.top_p is not None: request["top_p"] = self.top_p - + if "presence_penalty" in supported_params and self.presence_penalty is not None: request["presence_penalty"] = self.presence_penalty - + if "frequency_penalty" in supported_params and self.frequency_penalty is not None: request["frequency_penalty"] = self.frequency_penalty return request - + def check_instructor_structured_output_support(self, prompt_formatter: PromptFormatter): """Check if the model supports structured output via instructor.""" + class User(BaseModel): name: str age: int + try: if prompt_formatter.response_format: client = instructor.from_litellm(litellm.completion) @@ -277,14 +288,18 @@ class User(BaseModel): ) logger.info(f"Check instructor structure output response: {response}") assert isinstance(response, User) - logger.info(f"Model {self.model} supports structured output via instructor, response: {response}") + logger.info( + f"Model {self.model} supports structured output via instructor, response: {response}" + ) return True else: return True except Exception as e: - logger.warning(f"Model {self.model} does not support structured output via instructor: {e}") + logger.warning( + f"Model {self.model} does not support structured output via instructor: {e}" + ) return False - + async def process_requests_from_file( self, generic_requests_filepath: str, @@ -320,23 +335,27 @@ async def process_requests_from_file( # Count total requests total_requests = sum(1 for _ in open(generic_requests_filepath)) - + # Create progress bar - status_tracker.pbar = tqdm(initial=len(completed_request_ids), total=total_requests, desc="Processing LiteLLM requests") + status_tracker.pbar = tqdm( + initial=len(completed_request_ids), + total=total_requests, + desc="Processing LiteLLM requests", + ) # Use higher connector limit for better throughput connector = aiohttp.TCPConnector(limit=rpm) async with aiohttp.ClientSession(connector=connector) as session: async with aiofiles.open(generic_requests_filepath) as file: pending_requests = [] - + async for line in file: generic_request = GenericRequest.model_validate_json(line) - + if resume and generic_request.original_row_idx in completed_request_ids: status_tracker.num_tasks_already_completed += 1 continue - + request = APIRequest( task_id=status_tracker.num_tasks_started, generic_request=generic_request, @@ -344,16 +363,16 @@ async def process_requests_from_file( attempts_left=max_attempts, prompt_formatter=self.prompt_formatter, ) - + token_estimate = self.estimate_total_tokens(request.generic_request.messages) - + # Wait for capacity if needed while not status_tracker.has_capacity(token_estimate): await asyncio.sleep(0.1) - + # Consume capacity before making request status_tracker.consume_capacity(token_estimate) - + task = asyncio.create_task( request.call_api( session=session, @@ -364,22 +383,24 @@ async def process_requests_from_file( ) ) pending_requests.append(task) - + status_tracker.num_tasks_started += 1 status_tracker.num_tasks_in_progress += 1 # Process any retries that are in the queue while not queue_of_requests_to_retry.empty(): retry_request = await queue_of_requests_to_retry.get() - token_estimate = self.estimate_total_tokens(retry_request.generic_request.messages) - + token_estimate = self.estimate_total_tokens( + retry_request.generic_request.messages + ) + # Wait for capacity if needed while not status_tracker.has_capacity(token_estimate): await asyncio.sleep(0.1) - + # Consume capacity before making request status_tracker.consume_capacity(token_estimate) - + task = asyncio.create_task( retry_request.call_api( session=session, @@ -396,11 +417,11 @@ async def process_requests_from_file( await asyncio.gather(*pending_requests) status_tracker.pbar.close() - + # Log final status logger.info(f"Processing complete. Results saved to {save_filepath}") logger.info(f"Status tracker: {status_tracker}") - + if status_tracker.num_tasks_failed > 0: logger.warning( f"{status_tracker.num_tasks_failed} / {status_tracker.num_tasks_started} " @@ -415,25 +436,24 @@ def run( prompt_formatter: PromptFormatter, ) -> Dataset: """Run completions using LiteLLM with async processing.""" - logger.info(f"Running LiteLLM completions with model: {self.model}," - f" Supported params: {get_supported_openai_params(model=self.model)}") + logger.info( + f"Running LiteLLM completions with model: {self.model}," + f" Supported params: {get_supported_openai_params(model=self.model)}" + ) self.prompt_formatter = prompt_formatter - + if not self.check_instructor_structured_output_support(prompt_formatter): - raise ValueError(f"Model {self.model} does not support structured output via instructor.") - - generic_requests_files = self.create_request_files( - dataset, working_dir, prompt_formatter - ) + raise ValueError( + f"Model {self.model} does not support structured output via instructor." + ) + + generic_requests_files = self.create_request_files(dataset, working_dir, prompt_formatter) generic_responses_files = [ - f"{working_dir}/responses_{i}.jsonl" - for i in range(len(generic_requests_files)) + f"{working_dir}/responses_{i}.jsonl" for i in range(len(generic_requests_files)) ] - - for request_file, response_file in zip( - generic_requests_files, generic_responses_files - ): + + for request_file, response_file in zip(generic_requests_files, generic_responses_files): run_in_event_loop( self.process_requests_from_file( generic_requests_filepath=request_file, @@ -443,13 +463,12 @@ def run( ) ) - return self.create_dataset_files( - working_dir, parse_func_hash, prompt_formatter - ) + return self.create_dataset_files(working_dir, parse_func_hash, prompt_formatter) + async def append_generic_response(data: GenericResponse, filename: str) -> None: """Append a response to a jsonl file with async file operations.""" json_string = json.dumps(data.model_dump(), default=str) async with aiofiles.open(filename, "a") as f: await f.write(json_string + "\n") - logger.debug(f"Successfully appended response to {filename}") \ No newline at end of file + logger.debug(f"Successfully appended response to {filename}") From 80229e881613902eded4c58cc5507ced6b3561e2 Mon Sep 17 00:00:00 2001 From: Charlie Cheng-Jie Ji Date: Wed, 27 Nov 2024 23:30:54 +0000 Subject: [PATCH 28/65] add model init logging info --- .../litellm_online_request_processor.py | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py b/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py index 73b2fe99..a13eafc3 100644 --- a/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py +++ b/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py @@ -199,10 +199,13 @@ def __init__( self.frequency_penalty = frequency_penalty self.token_estimate_cache = {} - logger.info(f"Using model: {self.model}") - logger.debug( - f"Parameters - Temperature: {self.temperature}, Top P: {self.top_p}, " - f"Presence Penalty: {self.presence_penalty}, Frequency Penalty: {self.frequency_penalty}" + logger.info( + "Initializing LiteLLM processor with:\n" + f"- Model: {self.model}\n" + f"- Temperature: {self.temperature}\n" + f"- Top P: {self.top_p}\n" + f"- Presence Penalty: {self.presence_penalty}\n" + f"- Frequency Penalty: {self.frequency_penalty}" ) self.client = instructor.from_litellm(litellm.acompletion) From b0cae1f1d5ab7287a4e75fe7db9297c25b9fc318 Mon Sep 17 00:00:00 2001 From: Charlie Cheng-Jie Ji Date: Thu, 28 Nov 2024 00:03:17 +0000 Subject: [PATCH 29/65] cleanup examples --- examples/litellm_recipe_prompting_example.py | 18 ++++------- ...itellm_recipe_structured_output_example.py | 32 +++++++------------ 2 files changed, 17 insertions(+), 33 deletions(-) diff --git a/examples/litellm_recipe_prompting_example.py b/examples/litellm_recipe_prompting_example.py index 31d6aba9..c4587c48 100644 --- a/examples/litellm_recipe_prompting_example.py +++ b/examples/litellm_recipe_prompting_example.py @@ -4,14 +4,6 @@ from datasets import Dataset -def prompt_func(cuisine_type): - return f"Generate a random {cuisine_type} recipe. Be creative but keep it realistic." - - -def parse_func(row, response): - return {"recipe": response, "cuisine": row["cuisine"]} # Keep track of cuisine type - - def main(): # List of cuisines to generate recipes for cuisines = [ @@ -34,8 +26,11 @@ def main(): # Create prompter using LiteLLM backend recipe_prompter = curator.Prompter( model_name="gpt-4o-mini", - prompt_func=prompt_func, - parse_func=parse_func, + prompt_func=lambda row: f"Generate a random {row['cuisine']} recipe. Be creative but keep it realistic.", + parse_func=lambda row, response: { + "recipe": response, + "cuisine": row["cuisine"], + }, backend="litellm", ) @@ -43,8 +38,7 @@ def main(): recipes = recipe_prompter(cuisines) # Print results - df = recipes.to_pandas() - print(df) + print(recipes.to_pandas()) if __name__ == "__main__": diff --git a/examples/litellm_recipe_structured_output_example.py b/examples/litellm_recipe_structured_output_example.py index 5e056199..f429239a 100644 --- a/examples/litellm_recipe_structured_output_example.py +++ b/examples/litellm_recipe_structured_output_example.py @@ -21,22 +21,8 @@ class Cuisines(BaseModel): cuisines_list: List[str] = Field(description="A list of cuisines.") -def prompt_func(row): - return f"Generate a random {row['cuisine']} recipe. Be creative but keep it realistic." - - -def parse_func(row, response): - return { - "title": response.title, - "ingredients": response.ingredients, - "instructions": response.instructions, - "cuisine": row["cuisine"], # Keep track of cuisine type - } - - def main(): - # List of cuisines to generate recipes for - # We define a prompter that generates topics. + # We define a prompter that generates cuisines cuisines_generator = curator.Prompter( prompt_func=lambda: f"Generate 10 diverse cuisines.", model_name="gpt-4o-mini", @@ -45,12 +31,17 @@ def main(): backend="litellm", ) cuisines = cuisines_generator() - print(cuisines) + print(cuisines.to_pandas()) recipe_prompter = curator.Prompter( - model_name="together_ai/mistralai/Mixtral-8x7B-Instruct-v0.1", - prompt_func=prompt_func, - parse_func=parse_func, + model_name="claude-3-5-haiku-20241022", + prompt_func=lambda row: f"Generate a random {row['cuisine']} recipe. Be creative but keep it realistic.", + parse_func=lambda row, response: { + "title": response.title, + "ingredients": response.ingredients, + "instructions": response.instructions, + "cuisine": row["cuisine"], + }, response_format=Recipe, backend="litellm", ) @@ -59,8 +50,7 @@ def main(): recipes = recipe_prompter(cuisines) # Print results - df = recipes.to_pandas() - print(df) + print(recipes.to_pandas()) if __name__ == "__main__": From b438e2ce7bfc7def0169f86bedd73677b2850a95 Mon Sep 17 00:00:00 2001 From: Charlie Cheng-Jie Ji Date: Thu, 28 Nov 2024 00:10:02 +0000 Subject: [PATCH 30/65] rename example files --- ...lm_recipe_prompting_example.py => litellm_recipe_prompting.py} | 0 ...ured_output_example.py => litellm_recipe_structured_output.py} | 0 2 files changed, 0 insertions(+), 0 deletions(-) rename examples/{litellm_recipe_prompting_example.py => litellm_recipe_prompting.py} (100%) rename examples/{litellm_recipe_structured_output_example.py => litellm_recipe_structured_output.py} (100%) diff --git a/examples/litellm_recipe_prompting_example.py b/examples/litellm_recipe_prompting.py similarity index 100% rename from examples/litellm_recipe_prompting_example.py rename to examples/litellm_recipe_prompting.py diff --git a/examples/litellm_recipe_structured_output_example.py b/examples/litellm_recipe_structured_output.py similarity index 100% rename from examples/litellm_recipe_structured_output_example.py rename to examples/litellm_recipe_structured_output.py From 50f631bcd74617d1fbcdd416f1dadf7f1597bcfd Mon Sep 17 00:00:00 2001 From: Charlie Cheng-Jie Ji Date: Thu, 28 Nov 2024 01:14:31 +0000 Subject: [PATCH 31/65] add parse func fields in example --- examples/litellm_recipe_structured_output.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/examples/litellm_recipe_structured_output.py b/examples/litellm_recipe_structured_output.py index f429239a..a24b2ffd 100644 --- a/examples/litellm_recipe_structured_output.py +++ b/examples/litellm_recipe_structured_output.py @@ -40,6 +40,9 @@ def main(): "title": response.title, "ingredients": response.ingredients, "instructions": response.instructions, + "prep_time": response.prep_time, + "cook_time": response.cook_time, + "servings": response.servings, "cuisine": row["cuisine"], }, response_format=Recipe, From 8a89204772a2a5f60c517eb7109b0836434f06f6 Mon Sep 17 00:00:00 2001 From: Charlie Cheng-Jie Ji Date: Sun, 1 Dec 2024 21:53:03 +0000 Subject: [PATCH 32/65] litellm refactoring base online request processor --- .../litellm_online_request_processor.py | 589 +++++------- .../online_request_processor.py | 306 ++++++ .../openai_online_request_processor.py | 878 +++++------------- 3 files changed, 770 insertions(+), 1003 deletions(-) create mode 100644 src/bespokelabs/curator/request_processor/online_request_processor.py diff --git a/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py b/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py index a13eafc3..3c36ca0d 100644 --- a/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py +++ b/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py @@ -1,29 +1,23 @@ import json import logging import os -from typing import Optional, Any +from typing import Optional import asyncio -import time import aiohttp -import resource -from tqdm import tqdm -from dataclasses import dataclass, field -from pydantic import BaseModel -import instructor import litellm from litellm import get_supported_openai_params import aiofiles -from bespokelabs.curator.dataset import Dataset -from bespokelabs.curator.prompter.prompter import PromptFormatter -from bespokelabs.curator.request_processor.base_request_processor import ( - BaseRequestProcessor, - GenericRequest, - GenericResponse, -) -from bespokelabs.curator.request_processor.event_loop import run_in_event_loop import datetime -from bespokelabs.curator.request_processor.generic_response import TokenUsage -from litellm import token_counter, get_max_tokens +import instructor +from bespokelabs.curator.request_processor.online_request_processor import ( + OnlineRequestProcessor, + APIRequest, + StatusTracker, +) +from bespokelabs.curator.request_processor.generic_request import GenericRequest +from bespokelabs.curator.request_processor.generic_response import TokenUsage, GenericResponse +from pydantic import BaseModel +from bespokelabs.curator.prompter.prompt_formatter import PromptFormatter logger = logging.getLogger(__name__) logger.setLevel(logging.INFO) @@ -31,157 +25,27 @@ litellm.suppress_debug_info = True -@dataclass -class StatusTracker: - """Tracks the status of all requests.""" - - num_tasks_started: int = 0 - num_tasks_in_progress: int = 0 - num_tasks_succeeded: int = 0 - num_tasks_failed: int = 0 - num_tasks_already_completed: int = 0 - num_other_errors: int = 0 - available_request_capacity: float = 0 - available_token_capacity: float = 0 - last_update_time: float = field(default_factory=time.time) - max_requests_per_minute: int = 0 - max_tokens_per_minute: int = 0 - pbar: tqdm = field(default=None) - - def __str__(self): - return ( - f"Tasks - Started: {self.num_tasks_started}, " - f"In Progress: {self.num_tasks_in_progress}, " - f"Succeeded: {self.num_tasks_succeeded}, " - f"Failed: {self.num_tasks_failed}, " - f"Already Completed: {self.num_tasks_already_completed}\n" - f"Errors: {self.num_other_errors}" - ) - - def update_capacity(self): - """Update available capacity based on time elapsed""" - current_time = time.time() - seconds_since_update = current_time - self.last_update_time - - self.available_request_capacity = min( - self.available_request_capacity - + self.max_requests_per_minute * seconds_since_update / 60.0, - self.max_requests_per_minute, - ) - - self.available_token_capacity = min( - self.available_token_capacity - + self.max_tokens_per_minute * seconds_since_update / 60.0, - self.max_tokens_per_minute, - ) - - self.last_update_time = current_time - - def has_capacity(self, token_estimate: int) -> bool: - """Check if there's enough capacity for a request""" - self.update_capacity() - return ( - self.available_request_capacity >= 1 and self.available_token_capacity >= token_estimate - ) - - def consume_capacity(self, token_estimate: int): - """Consume capacity for a request""" - self.available_request_capacity -= 1 - self.available_token_capacity -= token_estimate - - -@dataclass -class APIRequest: - """Stores an API request's inputs, outputs, and other metadata.""" - - task_id: int - generic_request: GenericRequest - api_specific_request: dict - attempts_left: int - result: list = field(default_factory=list) - prompt_formatter: PromptFormatter = field(default=None) - created_at: datetime.datetime = field(default_factory=datetime.datetime.now) - - async def call_api( - self, - session: aiohttp.ClientSession, - client: Any, - retry_queue: asyncio.Queue, - save_filepath: str, - status_tracker: StatusTracker, - ) -> None: - """Calls the LiteLLM API and saves results.""" - try: - # Get response directly without extra logging - if self.generic_request.response_format: - response, completion_obj = await client.chat.completions.create_with_completion( - **self.api_specific_request, - response_model=self.prompt_formatter.response_format, - timeout=60.0, - ) - response_message = ( - response.model_dump() if hasattr(response, "model_dump") else response - ) - else: - completion_obj = await litellm.acompletion( - **self.api_specific_request, timeout=60.0 - ) - response_message = completion_obj["choices"][0]["message"]["content"] - - # Extract token usage - usage = completion_obj.usage if hasattr(completion_obj, "usage") else {} - token_usage = TokenUsage( - prompt_tokens=usage.prompt_tokens, - completion_tokens=usage.completion_tokens, - total_tokens=usage.total_tokens, - ) - - # Calculate cost using litellm - cost = litellm.completion_cost(completion_response=completion_obj.model_dump()) - - # Create and save response immediately - generic_response = GenericResponse( - response_message=response_message, - response_errors=None, - raw_request=self.api_specific_request, - raw_response=completion_obj.model_dump(), - generic_request=self.generic_request, - created_at=self.created_at, - finished_at=datetime.datetime.now(), - token_usage=token_usage, - response_cost=cost, - ) - - await append_generic_response(generic_response, save_filepath) - status_tracker.num_tasks_in_progress -= 1 - status_tracker.num_tasks_succeeded += 1 - status_tracker.pbar.update(1) - - except Exception as e: - logger.error(f"Error in API request: {e}") - status_tracker.num_other_errors += 1 - self.result.append(e) - - if self.attempts_left > 0: - self.attempts_left -= 1 - retry_queue.put_nowait(self) - else: - generic_response = GenericResponse( - response_message=None, - response_errors=[str(e) for e in self.result], - raw_request=self.api_specific_request, - raw_response=None, - generic_request=self.generic_request, - created_at=self.created_at, - finished_at=datetime.datetime.now(), - ) - await append_generic_response(generic_response, save_filepath) - status_tracker.num_tasks_in_progress -= 1 - status_tracker.num_tasks_failed += 1 - - -class LiteLLMOnlineRequestProcessor(BaseRequestProcessor): - """Request processor for LiteLLM that supports structured outputs via instructor.""" +class LiteLLMOnlineRequestProcessor(OnlineRequestProcessor): + """LiteLLM implementation of the OnlineRequestProcessor for multi-provider LLM support. + + This processor uses LiteLLM to handle requests across different LLM providers (OpenAI, Anthropic, etc.) + with unified interface and structured output support via instructor. + + Features: + - Multi-provider support through LiteLLM + - Structured output via instructor + - Automatic token counting and rate limiting + - Provider-specific parameter handling + - Cost tracking per request + + Attributes: + model (str): The model identifier (e.g., "gpt-4", "claude-2") + temperature (Optional[float]): Temperature for response randomness + top_p (Optional[float]): Top-p sampling parameter + presence_penalty (Optional[float]): Presence penalty for response diversity + frequency_penalty (Optional[float]): Frequency penalty for response diversity + client: Instructor-wrapped LiteLLM client for structured outputs + """ def __init__( self, @@ -191,39 +55,102 @@ def __init__( presence_penalty: Optional[float] = None, frequency_penalty: Optional[float] = None, ): - super().__init__(batch_size=None) - self.model = model - self.temperature = temperature - self.top_p = top_p - self.presence_penalty = presence_penalty - self.frequency_penalty = frequency_penalty - self.token_estimate_cache = {} - - logger.info( - "Initializing LiteLLM processor with:\n" - f"- Model: {self.model}\n" - f"- Temperature: {self.temperature}\n" - f"- Top P: {self.top_p}\n" - f"- Presence Penalty: {self.presence_penalty}\n" - f"- Frequency Penalty: {self.frequency_penalty}" + super().__init__( + model=model, + temperature=temperature, + top_p=top_p, + presence_penalty=presence_penalty, + frequency_penalty=frequency_penalty, ) self.client = instructor.from_litellm(litellm.acompletion) + def check_structured_output_support(self, prompt_formatter: PromptFormatter): + """Verify if the model supports structured output via instructor. + + Tests the model's capability to handle structured output by making a test request + with a simple schema. + + Args: + prompt_formatter (PromptFormatter): Contains response format requirements + + Returns: + bool: True if structured output is supported, False otherwise + + Note: + - Uses a simple User schema as test case + - Logs detailed information about support status + - Required for models that will use JSON schema responses + """ + assert prompt_formatter.response_format + + class User(BaseModel): + name: str + age: int + + try: + client = instructor.from_litellm(litellm.completion) + response = client.chat.completions.create( + model=self.model, + messages=[{"role": "user", "content": "Jason is 25 years old."}], + response_model=User, + ) + logger.info(f"Check instructor structure output response: {response}") + assert isinstance(response, User) + logger.info( + f"Model {self.model} supports structured output via instructor, response: {response}" + ) + return True + except Exception as e: + logger.warning( + f"Model {self.model} does not support structured output via instructor: {e}" + ) + return False + def estimate_output_tokens(self) -> int: - """Estimate output tokens for a request""" + """Estimate the number of tokens in the model's response. + + Uses LiteLLM's get_max_tokens and applies a conservative estimate + by dividing by 4 to avoid hitting context limits. + + Returns: + int: Estimated number of output tokens + + Note: + Falls back to 0 if token estimation fails + """ try: - return get_max_tokens(model=self.model) // 4 + return litellm.get_max_tokens(model=self.model) // 4 except Exception: return 0 def estimate_total_tokens(self, messages: list) -> int: - """Estimate total tokens for a request""" - input_tokens = token_counter(model=self.model, messages=messages) + """Calculate the total token usage for a request. + + Uses LiteLLM's token_counter for accurate input token counting + and adds estimated output tokens. + + Args: + messages (list): List of message dictionaries + + Returns: + int: Total estimated tokens (input + output) + """ + input_tokens = litellm.token_counter(model=self.model, messages=messages) output_tokens = self.estimate_output_tokens() return input_tokens + output_tokens def get_rate_limits(self) -> dict: - """Get rate limits from LiteLLM response headers.""" + """Retrieve rate limits from the LLM provider via LiteLLM. + + Makes a test request to get rate limit information from response headers. + + Returns: + dict: Contains 'max_requests_per_minute' and 'max_tokens_per_minute' + + Note: + - Falls back to default values if headers are missing + - Some providers (e.g., Claude) require non-empty messages + """ logger.info(f"Getting rate limits for model: {self.model}") completion = litellm.completion( @@ -244,13 +171,19 @@ def get_rate_limits(self) -> dict: return {"max_requests_per_minute": rpm, "max_tokens_per_minute": tpm} def create_api_specific_request(self, generic_request: GenericRequest) -> dict: - """Create a LiteLLM-specific request from a generic request. - + """Convert a generic request into a LiteLLM-compatible format. + + Checks supported parameters for the specific model and only includes + applicable parameters. + Args: generic_request (GenericRequest): The generic request to convert - + Returns: - dict: LiteLLM-specific request parameters + dict: LiteLLM-compatible request parameters + + Note: + Uses LiteLLM's get_supported_openai_params to check parameter support """ # Get supported parameters for this model supported_params = get_supported_openai_params(model=self.model) @@ -274,204 +207,98 @@ def create_api_specific_request(self, generic_request: GenericRequest) -> dict: return request - def check_instructor_structured_output_support(self, prompt_formatter: PromptFormatter): - """Check if the model supports structured output via instructor.""" - - class User(BaseModel): - name: str - age: int - - try: - if prompt_formatter.response_format: - client = instructor.from_litellm(litellm.completion) - response = client.chat.completions.create( - model=self.model, - messages=[{"role": "user", "content": "Extract Jason is 25 years old."}], - response_model=User, - ) - logger.info(f"Check instructor structure output response: {response}") - assert isinstance(response, User) - logger.info( - f"Model {self.model} supports structured output via instructor, response: {response}" - ) - return True - else: - return True - except Exception as e: - logger.warning( - f"Model {self.model} does not support structured output via instructor: {e}" - ) - return False - - async def process_requests_from_file( + async def process_single_request( self, - generic_requests_filepath: str, + request: APIRequest, + session: aiohttp.ClientSession, + retry_queue: asyncio.Queue, save_filepath: str, - max_attempts: int, - resume: bool, + status_tracker: StatusTracker, ) -> None: - """Processes API requests in parallel, throttling to stay under rate limits.""" - # Increase file descriptor limit for higher throughput - soft, hard = resource.getrlimit(resource.RLIMIT_NOFILE) - resource.setrlimit(resource.RLIMIT_NOFILE, (min(hard, 10000), hard)) - - # Initialize trackers - queue_of_requests_to_retry = asyncio.Queue() - status_tracker = StatusTracker() - next_request = None - file_not_finished = True - - # Get rate limits - rate_limits = self.get_rate_limits() - status_tracker.max_requests_per_minute = rate_limits["max_requests_per_minute"] - status_tracker.max_tokens_per_minute = rate_limits["max_tokens_per_minute"] - rpm = rate_limits["max_requests_per_minute"] - - # Track completed requests for resume functionality - completed_request_ids = set() - if os.path.exists(save_filepath) and resume: - with open(save_filepath, "r") as f: - for line in f: - response = GenericResponse.model_validate_json(line) - if not response.response_errors: - completed_request_ids.add(response.generic_request.original_row_idx) - - # Count total requests - total_requests = sum(1 for _ in open(generic_requests_filepath)) - - # Create progress bar - status_tracker.pbar = tqdm( - initial=len(completed_request_ids), - total=total_requests, - desc="Processing LiteLLM requests", - ) - - # Use higher connector limit for better throughput - connector = aiohttp.TCPConnector(limit=rpm) - async with aiohttp.ClientSession(connector=connector) as session: - async with aiofiles.open(generic_requests_filepath) as file: - pending_requests = [] - - async for line in file: - generic_request = GenericRequest.model_validate_json(line) - - if resume and generic_request.original_row_idx in completed_request_ids: - status_tracker.num_tasks_already_completed += 1 - continue - - request = APIRequest( - task_id=status_tracker.num_tasks_started, - generic_request=generic_request, - api_specific_request=self.create_api_specific_request(generic_request), - attempts_left=max_attempts, - prompt_formatter=self.prompt_formatter, - ) - - token_estimate = self.estimate_total_tokens(request.generic_request.messages) - - # Wait for capacity if needed - while not status_tracker.has_capacity(token_estimate): - await asyncio.sleep(0.1) - - # Consume capacity before making request - status_tracker.consume_capacity(token_estimate) - - task = asyncio.create_task( - request.call_api( - session=session, - client=self.client, - retry_queue=queue_of_requests_to_retry, - save_filepath=save_filepath, - status_tracker=status_tracker, - ) + """Process a single request through LiteLLM with error handling. + + Handles both structured and unstructured outputs, tracks token usage + and costs, and manages retries. + + Args: + request (APIRequest): Request to process + session (aiohttp.ClientSession): Async HTTP session + retry_queue (asyncio.Queue): Queue for failed requests + save_filepath (str): Path to save responses + status_tracker (StatusTracker): Tracks request status and limits + + Note: + - Supports both instructor-based structured output and raw completions + - Calculates and tracks costs using LiteLLM's completion_cost + - Implements retry logic for failed requests + - Saves responses immediately to prevent data loss + """ + try: + # Get response directly without extra logging + if request.generic_request.response_format: + response, completion_obj = ( + await self.client.chat.completions.create_with_completion( + **request.api_specific_request, + response_model=request.prompt_formatter.response_format, + timeout=60.0, ) - pending_requests.append(task) - - status_tracker.num_tasks_started += 1 - status_tracker.num_tasks_in_progress += 1 - - # Process any retries that are in the queue - while not queue_of_requests_to_retry.empty(): - retry_request = await queue_of_requests_to_retry.get() - token_estimate = self.estimate_total_tokens( - retry_request.generic_request.messages - ) - - # Wait for capacity if needed - while not status_tracker.has_capacity(token_estimate): - await asyncio.sleep(0.1) - - # Consume capacity before making request - status_tracker.consume_capacity(token_estimate) - - task = asyncio.create_task( - retry_request.call_api( - session=session, - client=self.client, - retry_queue=queue_of_requests_to_retry, - save_filepath=save_filepath, - status_tracker=status_tracker, - ) - ) - pending_requests.append(task) - - # Wait for all tasks to complete - if pending_requests: - await asyncio.gather(*pending_requests) - - status_tracker.pbar.close() - - # Log final status - logger.info(f"Processing complete. Results saved to {save_filepath}") - logger.info(f"Status tracker: {status_tracker}") - - if status_tracker.num_tasks_failed > 0: - logger.warning( - f"{status_tracker.num_tasks_failed} / {status_tracker.num_tasks_started} " - f"requests failed. Errors logged to {save_filepath}." - ) - - def run( - self, - dataset: Optional[Dataset], - working_dir: str, - parse_func_hash: str, - prompt_formatter: PromptFormatter, - ) -> Dataset: - """Run completions using LiteLLM with async processing.""" - logger.info( - f"Running LiteLLM completions with model: {self.model}," - f" Supported params: {get_supported_openai_params(model=self.model)}" - ) - - self.prompt_formatter = prompt_formatter + ) + response_message = ( + response.model_dump() if hasattr(response, "model_dump") else response + ) + else: + completion_obj = await litellm.acompletion( + **request.api_specific_request, timeout=60.0 + ) + response_message = completion_obj["choices"][0]["message"]["content"] - if not self.check_instructor_structured_output_support(prompt_formatter): - raise ValueError( - f"Model {self.model} does not support structured output via instructor." + # Extract token usage + usage = completion_obj.usage if hasattr(completion_obj, "usage") else {} + token_usage = TokenUsage( + prompt_tokens=usage.prompt_tokens, + completion_tokens=usage.completion_tokens, + total_tokens=usage.total_tokens, ) - generic_requests_files = self.create_request_files(dataset, working_dir, prompt_formatter) - generic_responses_files = [ - f"{working_dir}/responses_{i}.jsonl" for i in range(len(generic_requests_files)) - ] + # Calculate cost using litellm + cost = litellm.completion_cost(completion_response=completion_obj.model_dump()) - for request_file, response_file in zip(generic_requests_files, generic_responses_files): - run_in_event_loop( - self.process_requests_from_file( - generic_requests_filepath=request_file, - save_filepath=response_file, - max_attempts=5, - resume=True, - ) + # Create and save response immediately + generic_response = GenericResponse( + response_message=response_message, + response_errors=None, + raw_request=request.api_specific_request, + raw_response=completion_obj.model_dump(), + generic_request=request.generic_request, + created_at=request.created_at, + finished_at=datetime.datetime.now(), + token_usage=token_usage, + response_cost=cost, ) - return self.create_dataset_files(working_dir, parse_func_hash, prompt_formatter) + await self.append_generic_response(generic_response, save_filepath) + status_tracker.num_tasks_in_progress -= 1 + status_tracker.num_tasks_succeeded += 1 + status_tracker.pbar.update(1) + except Exception as e: + logger.error(f"Error in API request: {e}") + status_tracker.num_other_errors += 1 + request.result.append(e) -async def append_generic_response(data: GenericResponse, filename: str) -> None: - """Append a response to a jsonl file with async file operations.""" - json_string = json.dumps(data.model_dump(), default=str) - async with aiofiles.open(filename, "a") as f: - await f.write(json_string + "\n") - logger.debug(f"Successfully appended response to {filename}") + if request.attempts_left > 0: + request.attempts_left -= 1 + retry_queue.put_nowait(request) + else: + generic_response = GenericResponse( + response_message=None, + response_errors=[str(e) for e in request.result], + raw_request=request.api_specific_request, + raw_response=None, + generic_request=request.generic_request, + created_at=request.created_at, + finished_at=datetime.datetime.now(), + ) + await self.append_generic_response(generic_response, save_filepath) + status_tracker.num_tasks_in_progress -= 1 + status_tracker.num_tasks_failed += 1 diff --git a/src/bespokelabs/curator/request_processor/online_request_processor.py b/src/bespokelabs/curator/request_processor/online_request_processor.py new file mode 100644 index 00000000..c4e2babb --- /dev/null +++ b/src/bespokelabs/curator/request_processor/online_request_processor.py @@ -0,0 +1,306 @@ +from abc import ABC, abstractmethod +from dataclasses import dataclass, field +import datetime +import time +from typing import Optional +from tqdm import tqdm +import logging +import asyncio +import aiohttp +import os +import json +import resource + +from bespokelabs.curator.dataset import Dataset +from bespokelabs.curator.request_processor.base_request_processor import BaseRequestProcessor +from bespokelabs.curator.prompter.prompter import PromptFormatter +from bespokelabs.curator.request_processor.generic_request import GenericRequest +from bespokelabs.curator.request_processor.event_loop import run_in_event_loop +from bespokelabs.curator.request_processor.generic_response import GenericResponse +import aiofiles + +logger = logging.getLogger(__name__) + + +@dataclass +class StatusTracker: + """Tracks the status of all requests.""" + + num_tasks_started: int = 0 + num_tasks_in_progress: int = 0 + num_tasks_succeeded: int = 0 + num_tasks_failed: int = 0 + num_tasks_already_completed: int = 0 + num_api_errors: int = 0 + num_other_errors: int = 0 + available_request_capacity: float = 0 + available_token_capacity: float = 0 + last_update_time: float = field(default_factory=time.time) + max_requests_per_minute: int = 0 + max_tokens_per_minute: int = 0 + pbar: tqdm = field(default=None) + response_cost: float = 0 + + def __str__(self): + return ( + f"Tasks - Started: {self.num_tasks_started}, " + f"In Progress: {self.num_tasks_in_progress}, " + f"Succeeded: {self.num_tasks_succeeded}, " + f"Failed: {self.num_tasks_failed}, " + f"Already Completed: {self.num_tasks_already_completed}\n" + f"Errors: {self.num_other_errors}" + ) + + def update_capacity(self): + """Update available capacity based on time elapsed""" + current_time = time.time() + seconds_since_update = current_time - self.last_update_time + + self.available_request_capacity = min( + self.available_request_capacity + + self.max_requests_per_minute * seconds_since_update / 60.0, + self.max_requests_per_minute, + ) + + self.available_token_capacity = min( + self.available_token_capacity + + self.max_tokens_per_minute * seconds_since_update / 60.0, + self.max_tokens_per_minute, + ) + + self.last_update_time = current_time + + def has_capacity(self, token_estimate: int) -> bool: + """Check if there's enough capacity for a request""" + self.update_capacity() + return ( + self.available_request_capacity >= 1 and self.available_token_capacity >= token_estimate + ) + + def consume_capacity(self, token_estimate: int): + """Consume capacity for a request""" + self.available_request_capacity -= 1 + self.available_token_capacity -= token_estimate + + +@dataclass +class APIRequest: + """Stores an API request's inputs, outputs, and other metadata.""" + + task_id: int + generic_request: GenericRequest + api_specific_request: dict + attempts_left: int + result: list = field(default_factory=list) + prompt_formatter: PromptFormatter = field(default=None) + created_at: datetime.datetime = field(default_factory=datetime.datetime.now) + + +class OnlineRequestProcessor(BaseRequestProcessor, ABC): + """Abstract base class for online request processors that make real-time API calls.""" + + def __init__( + self, + model: str, + temperature: Optional[float] = None, + top_p: Optional[float] = None, + presence_penalty: Optional[float] = None, + frequency_penalty: Optional[float] = None, + ): + super().__init__(batch_size=None) + self.model = model + self.temperature = temperature + self.top_p = top_p + self.presence_penalty = presence_penalty + self.frequency_penalty = frequency_penalty + self.prompt_formatter: Optional[PromptFormatter] = None + + @abstractmethod + def estimate_total_tokens(self, messages: list) -> int: + """Estimate total tokens for a request""" + pass + + @abstractmethod + def estimate_output_tokens(self) -> int: + """Estimate output tokens for a request""" + pass + + @abstractmethod + def process_single_request( + self, + request: APIRequest, + session: aiohttp.ClientSession, + retry_queue: asyncio.Queue, + save_filepath: str, + status_tracker: StatusTracker, + ) -> None: + """Process a single API request""" + pass + + def check_structured_output_support(self, prompt_formatter: PromptFormatter) -> bool: + """Check if the model supports structured output""" + return True + + def run( + self, + dataset: Optional[Dataset], + working_dir: str, + parse_func_hash: str, + prompt_formatter: PromptFormatter, + ) -> Dataset: + """Run completions using the online API with async processing.""" + logger.info(f"Running {self.__class__.__name__} completions with model: {self.model}") + + self.prompt_formatter = prompt_formatter + if self.prompt_formatter.response_format: + assert self.check_structured_output_support( + prompt_formatter + ), f"Model {self.model} does not support structured output, response_format: {self.prompt_formatter.response_format}" + generic_requests_files = self.create_request_files(dataset, working_dir, prompt_formatter) + generic_responses_files = [ + f"{working_dir}/responses_{i}.jsonl" for i in range(len(generic_requests_files)) + ] + + for request_file, response_file in zip(generic_requests_files, generic_responses_files): + run_in_event_loop( + self.process_requests_from_file( + generic_requests_filepath=request_file, + save_filepath=response_file, + max_attempts=5, + resume=True, + ) + ) + + return self.create_dataset_files(working_dir, parse_func_hash, prompt_formatter) + + async def process_requests_from_file( + self, + generic_requests_filepath: str, + save_filepath: str, + max_attempts: int, + resume: bool, + ) -> None: + """Processes API requests in parallel, throttling to stay under rate limits.""" + soft, hard = resource.getrlimit(resource.RLIMIT_NOFILE) + resource.setrlimit(resource.RLIMIT_NOFILE, (min(hard, 10000), hard)) + + # Initialize trackers + queue_of_requests_to_retry = asyncio.Queue() + status_tracker = StatusTracker() + + # Get rate limits + rate_limits = self.get_rate_limits() + status_tracker.max_requests_per_minute = rate_limits["max_requests_per_minute"] + status_tracker.max_tokens_per_minute = rate_limits["max_tokens_per_minute"] + rpm = rate_limits["max_requests_per_minute"] + + # Track completed requests for resume functionality + completed_request_ids = set() + if os.path.exists(save_filepath) and resume: + with open(save_filepath, "r") as f: + for line in f: + response = GenericResponse.model_validate_json(line) + if not response.response_errors: + completed_request_ids.add(response.generic_request.original_row_idx) + + # Count total requests + total_requests = sum(1 for _ in open(generic_requests_filepath)) + + # Create progress bar + status_tracker.pbar = tqdm( + initial=len(completed_request_ids), + total=total_requests, + desc=f"Processing {self.__class__.__name__} requests", + ) + + # Use higher connector limit for better throughput + connector = aiohttp.TCPConnector(limit=rpm) + async with aiohttp.ClientSession(connector=connector) as session: + async with aiofiles.open(generic_requests_filepath) as file: + pending_requests = [] + + async for line in file: + generic_request = GenericRequest.model_validate_json(line) + + if resume and generic_request.original_row_idx in completed_request_ids: + status_tracker.num_tasks_already_completed += 1 + continue + + request = APIRequest( + task_id=status_tracker.num_tasks_started, + generic_request=generic_request, + api_specific_request=self.create_api_specific_request(generic_request), + attempts_left=max_attempts, + prompt_formatter=self.prompt_formatter, + ) + + token_estimate = self.estimate_total_tokens(request.generic_request.messages) + + # Wait for capacity if needed + while not status_tracker.has_capacity(token_estimate): + await asyncio.sleep(0.1) + + # Consume capacity before making request + status_tracker.consume_capacity(token_estimate) + + task = asyncio.create_task( + self.process_single_request( + request=request, + session=session, + retry_queue=queue_of_requests_to_retry, + save_filepath=save_filepath, + status_tracker=status_tracker, + ) + ) + pending_requests.append(task) + + status_tracker.num_tasks_started += 1 + status_tracker.num_tasks_in_progress += 1 + + # Process any retries that are in the queue + while not queue_of_requests_to_retry.empty(): + retry_request = await queue_of_requests_to_retry.get() + token_estimate = self.estimate_total_tokens( + retry_request.generic_request.messages + ) + + # Wait for capacity if needed + while not status_tracker.has_capacity(token_estimate): + await asyncio.sleep(0.1) + + # Consume capacity before making request + status_tracker.consume_capacity(token_estimate) + + task = asyncio.create_task( + self.process_single_request( + request=retry_request, + session=session, + retry_queue=queue_of_requests_to_retry, + save_filepath=save_filepath, + status_tracker=status_tracker, + ) + ) + pending_requests.append(task) + + # Wait for all tasks to complete + if pending_requests: + await asyncio.gather(*pending_requests) + + status_tracker.pbar.close() + + # Log final status + logger.info(f"Processing complete. Results saved to {save_filepath}") + logger.info(f"Status tracker: {status_tracker}") + + if status_tracker.num_tasks_failed > 0: + logger.warning( + f"{status_tracker.num_tasks_failed} / {status_tracker.num_tasks_started} " + f"requests failed. Errors logged to {save_filepath}." + ) + + async def append_generic_response(self, data: GenericResponse, filename: str) -> None: + """Append a response to a jsonl file with async file operations.""" + json_string = json.dumps(data.model_dump(), default=str) + async with aiofiles.open(filename, "a") as f: + await f.write(json_string + "\n") + logger.debug(f"Successfully appended response to {filename}") diff --git a/src/bespokelabs/curator/request_processor/openai_online_request_processor.py b/src/bespokelabs/curator/request_processor/openai_online_request_processor.py index 132ae01a..5be474be 100644 --- a/src/bespokelabs/curator/request_processor/openai_online_request_processor.py +++ b/src/bespokelabs/curator/request_processor/openai_online_request_processor.py @@ -5,36 +5,110 @@ import os import re import resource -import time -from dataclasses import dataclass, field -from functools import partial -from typing import Any, Callable, Dict, Optional, Set, Tuple, TypeVar +from typing import Optional import aiohttp -import litellm import requests import tiktoken -from tqdm import tqdm - -from bespokelabs.curator.dataset import Dataset -from bespokelabs.curator.prompter.prompter import PromptFormatter -from bespokelabs.curator.request_processor.base_request_processor import ( - BaseRequestProcessor, - GenericRequest, - GenericResponse, - parse_response_message, +import litellm +import time + +from bespokelabs.curator.request_processor.online_request_processor import ( + OnlineRequestProcessor, + APIRequest, + StatusTracker, ) -from bespokelabs.curator.request_processor.event_loop import run_in_event_loop -from bespokelabs.curator.request_processor.generic_response import TokenUsage +from bespokelabs.curator.request_processor.generic_request import GenericRequest +from bespokelabs.curator.request_processor.generic_response import TokenUsage, GenericResponse -T = TypeVar("T") logger = logging.getLogger(__name__) -class OpenAIOnlineRequestProcessor(BaseRequestProcessor): +def get_token_encoding_name(model_name: str) -> str: + """Get the token encoding name for a given model.""" + if model_name.startswith("gpt-4"): + return "cl100k_base" + elif model_name.startswith("gpt-3.5"): + return "cl100k_base" + else: + return "cl100k_base" # Default to cl100k_base + + +def api_endpoint_from_url(url: str) -> str: + """Extract the API endpoint from a URL.""" + match = re.match(r"^https://[^/]+/v\d+/(.+)$", url) + if match: + return match.group(1) + return url + + +def num_tokens_consumed_from_request( + api_specific_request_json: dict, + api_endpoint: str, + token_encoding_name: str, +) -> int: + """Count the number of tokens in the request. Only supports completion and embedding requests.""" + encoding = tiktoken.get_encoding(token_encoding_name) + + if api_endpoint.endswith("completions"): + max_tokens = api_specific_request_json.get("max_tokens", 15) + n = api_specific_request_json.get("n", 1) + completion_tokens = n * max_tokens + + if api_endpoint.startswith("chat/"): + num_tokens = 0 + for message in api_specific_request_json["messages"]: + num_tokens += 4 # every message follows {role/name}\n{content}\n + for key, value in message.items(): + try: + num_tokens += len(encoding.encode(str(value))) + except TypeError: + logger.warning( + f"Failed to encode value {value} with tiktoken to count tokens. Instead assuming a token for every 4 characters." + ) + num_tokens += len(str(value)) // 4 + 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 assistant + return num_tokens + completion_tokens + else: + prompt = api_specific_request_json["prompt"] + if isinstance(prompt, str): + prompt_tokens = len(encoding.encode(prompt)) + return prompt_tokens + completion_tokens + elif isinstance(prompt, list): + prompt_tokens = sum([len(encoding.encode(p)) for p in prompt]) + return prompt_tokens + completion_tokens * len(prompt) + else: + raise TypeError('Expecting string or list of strings for "prompt" field') + elif api_endpoint == "embeddings": + input = api_specific_request_json["input"] + if isinstance(input, str): + return len(encoding.encode(input)) + elif isinstance(input, list): + return sum([len(encoding.encode(i)) for i in input]) + else: + raise TypeError('Expecting string or list of strings for "inputs" field') + else: + raise NotImplementedError(f'API endpoint "{api_endpoint}" not implemented') + + +class OpenAIOnlineRequestProcessor(OnlineRequestProcessor): + """OpenAI-specific implementation of the OnlineRequestProcessor. + + Handles API requests to OpenAI's chat completion endpoints with rate limiting, + token counting, and error handling specific to OpenAI's API. + + Note: + - Supports both OpenAI and Azure OpenAI endpoints + - Automatically detects and respects API rate limits + - Handles token counting using tiktoken + - Supports structured output via JSON schema + """ + def __init__( self, - model: str = "gpt-4o-mini", + model: str = "gpt-4", api_key: str = os.getenv("OPENAI_API_KEY"), url: str = "https://api.openai.com/v1/chat/completions", temperature: Optional[float] = None, @@ -42,31 +116,34 @@ def __init__( presence_penalty: Optional[float] = None, frequency_penalty: Optional[float] = None, ): - super().__init__(batch_size=None) - self.model: str = model - self.url: str = url - self.api_key: str = api_key - self.temperature: float = temperature - self.top_p: float = top_p - self.presence_penalty: float = presence_penalty - self.frequency_penalty: float = frequency_penalty - - def get_rate_limits(self) -> dict: - """ - Function to get rate limits for a given annotator. Makes a single request to openAI API - and gets the rate limits from the response headers. These rate limits vary per model - and are determined by your organization's usage tier. View the following: - https://platform.openai.com/docs/guides/rate-limits/usage-tiers - https://platform.openai.com/settings/organization/limits + super().__init__( + model=model, + temperature=temperature, + top_p=top_p, + presence_penalty=presence_penalty, + frequency_penalty=frequency_penalty, + ) + self.url = url + self.api_key = api_key + self.token_encoding = tiktoken.get_encoding(get_token_encoding_name(model)) - Args: - model (str): The model for which to get the rate limits. - request_url (str): The request URL for which to get the rate limits. + # Set resource limits for file descriptors + soft, hard = resource.getrlimit(resource.RLIMIT_NOFILE) + resource.setrlimit( + resource.RLIMIT_NOFILE, (min(hard, 10 * 3000), hard) + ) # default to 3000 rpm + def get_rate_limits(self) -> dict: + """Get rate limits from OpenAI API headers. + Returns: - tuple[int, int]: A tuple containing the maximum number of requests and tokens per minute. + dict: Contains 'max_requests_per_minute' and 'max_tokens_per_minute' + + Note: + - Makes a dummy request to get actual rate limits + - Falls back to default values if headers are missing + - Supports both OpenAI and Azure endpoints """ - # Send a dummy request to get rate limit information response = requests.post( self.url, headers={"Authorization": f"Bearer {self.api_key}"}, @@ -84,22 +161,73 @@ def get_rate_limits(self) -> dict: logger.info(f"Automatically set max_requests_per_minute to {rpm}") logger.info(f"Automatically set max_tokens_per_minute to {tpm}") - rate_limits = { + return { "max_requests_per_minute": rpm, "max_tokens_per_minute": tpm, } - return rate_limits + def estimate_output_tokens(self) -> int: + """Estimate number of tokens in the response. + + Returns: + int: Estimated number of output tokens + + Note: + Default implementation returns a conservative estimate. + Override this method for more accurate model-specific estimates. + """ + try: + return litellm.get_max_tokens(model=self.model) // 4 + except Exception: + return 0 + + def estimate_total_tokens(self, messages: list) -> int: + """Estimate total tokens for a request using OpenAI's token counting rules. + + Args: + messages (list): List of message dictionaries with role and content - def create_api_specific_request(self, generic_request: GenericRequest) -> dict: + Returns: + int: Estimated total tokens including message formatting tokens + + Note: + Includes: + - 4 tokens per message for formatting + - Role/name tokens + - Content tokens + - 2 tokens for assistant reply priming """ - Creates a API-specific request body from a generic request body. + num_tokens = 0 + for message in messages: + num_tokens += 4 # every message follows {role/name}\n{content}\n + for key, value in message.items(): + try: + num_tokens += len(self.token_encoding.encode(str(value))) + except TypeError: + logger.warning( + f"Failed to encode value {value} with tiktoken. Assuming 1 token per 4 chars." + ) + num_tokens += len(str(value)) // 4 + if key == "name": # if there's a name, the role is omitted + num_tokens -= 1 # role is always required and always 1 token - Using the api_parallel_processor, we can store whatever we want in the metadata. We will store both the row and the index. - This is so we can later construct the new dataset row. + num_tokens += 2 # every reply is primed with assistant + output_tokens = self.estimate_output_tokens() + return num_tokens + output_tokens + + def create_api_specific_request(self, generic_request: GenericRequest) -> dict: + """Create an OpenAI-specific request from a generic request. + + Args: + generic_request (GenericRequest): Generic request object Returns: - dict: API specific request body + dict: OpenAI API-compatible request dictionary + + Note: + - Handles JSON schema response format if specified + - Applies optional parameters (temperature, top_p, etc.) + - Maintains compatibility with both chat and completion endpoints """ request = { "model": generic_request.model, @@ -128,598 +256,104 @@ def create_api_specific_request(self, generic_request: GenericRequest) -> dict: return request - def run( - self, - dataset: Optional[Dataset], - working_dir: str, - parse_func_hash: str, - prompt_formatter: PromptFormatter, - ) -> Dataset: - """ - Uses the API to completing the specific map by calling the LLM. - - Args: - dataset (Dataset): Dataset that is being mapped over - working_dir (str): Working directory to save files (requests.jsonl, responses.jsonl, dataset.arrow) - parse_func_hash (str): Hash of the parse_func to be used as the dataset file name - prompt_formatter (PromptFormatter): Prompt formatter to be used to format the prompt - - Returns: - Dataset: Completed dataset - """ - generic_requests_files = self.create_request_files(dataset, working_dir, prompt_formatter) - generic_responses_files = [ - f"{working_dir}/responses_{i}.jsonl" for i in range(len(generic_requests_files)) - ] - - rate_limits = self.get_rate_limits() - rpm = rate_limits["max_requests_per_minute"] - tpm = rate_limits["max_tokens_per_minute"] - - token_encoding_name = get_token_encoding_name(prompt_formatter.model_name) - - # NOTE(Ryan): If you wanted to do this on batches, you could run a for loop here about request_files. Although I don't recommend it because you are waiting for straggler requests to finish for each batch. - # NOTE(Ryan): And if you wanted to do batches in parallel, you would have to divide rpm and tpm by the number of parallel batches. - # TODO(Ryan): Can we abstract retries from process_api_requests_from_file so you can use it even if you use liteLLM. - for generic_requests_file, generic_responses_file in zip( - generic_requests_files, generic_responses_files - ): - run_in_event_loop( - self.process_generic_requests_from_file( - generic_requests_filepath=generic_requests_file, - save_filepath=generic_responses_file, - request_url=self.url, - max_requests_per_minute=rpm, - max_tokens_per_minute=tpm, - token_encoding_name=token_encoding_name, - max_attempts=5, - resume=True, # detects existing jobs and resume from there - ) - ) - - dataset = self.create_dataset_files(working_dir, parse_func_hash, prompt_formatter) - return dataset - - async def process_generic_requests_from_file( + async def process_single_request( self, - generic_requests_filepath: str, + request: APIRequest, + session: aiohttp.ClientSession, + retry_queue: asyncio.Queue, save_filepath: str, - request_url: str, - max_requests_per_minute: float, - max_tokens_per_minute: float, - token_encoding_name: str, - max_attempts: int, - resume: bool, - resume_no_retry: bool = False, + status_tracker: StatusTracker, ) -> None: - """Processes API requests in parallel, throttling to stay under rate limits.""" - - # Increase the number of open file descriptors to avoid "Too many open files" errors - soft, hard = resource.getrlimit(resource.RLIMIT_NOFILE) - resource.setrlimit( - resource.RLIMIT_NOFILE, - (min(hard, 10 * max_requests_per_minute), hard), - ) - - # 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 - ) - - # infer API endpoint and construct request header - api_endpoint = api_endpoint_from_url(self.url) - request_header = {"Authorization": f"Bearer {self.api_key}"} - # use api-key header for Azure deployments - if "/deployments" in self.url: - request_header = {"api-key": f"{self.api_key}"} - - # initialize trackers - queue_of_requests_to_retry = asyncio.Queue() - task_id_generator = task_id_generator_function() # generates integer IDs of 0, 1, 2, ... - 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 - logger.debug(f"Initialization complete.") - - completed_request_ids: Set[int] = set() - if os.path.exists(save_filepath): - if resume: - # save all successfully completed requests to a temporary file, then overwrite the original file with the temporary file - logger.debug(f"Resuming progress from existing file: {save_filepath}") - logger.debug( - f"Removing all failed requests from {save_filepath} so they can be retried" - ) - temp_filepath = f"{save_filepath}.temp" - num_previously_failed_requests = 0 - with open(save_filepath, "r") as input_file, open( - temp_filepath, "w" - ) as output_file: - for line in input_file: - response = GenericResponse.model_validate_json(line) - if response.response_errors: - # this means that the request failed and we have a list of errors - logger.debug( - f"Request {response.generic_request.original_row_idx} previously failed due to errors: {response.response_errors}, removing from output and will retry" - ) - num_previously_failed_requests += 1 - else: - completed_request_ids.add(response.generic_request.original_row_idx) - output_file.write(line) - logger.info( - f"Found {len(completed_request_ids)} completed requests and {num_previously_failed_requests} previously failed requests" - ) - logger.info("Failed requests and remaining requests will now be processed.") - os.replace(temp_filepath, save_filepath) - elif resume_no_retry: - logger.warning( - f"Resuming progress from existing file: {save_filepath}, without retrying failed requests" - ) - num_previously_failed_requests = 0 - with open(save_filepath, "r") as input_file, open( - temp_filepath, "w" - ) as output_file: - for line in tqdm(input_file, desc="Processing existing requests"): - data = json.loads(line) - if isinstance(data[1], list): - # this means that the request failed and we have a list of errors - logger.debug( - f"Request {data[2].get('request_idx')} previously failed due to errors: {data[1]}, will NOT retry" - ) - num_previously_failed_requests += 1 - completed_request_ids.add(data[2].get("request_idx")) - logger.info( - f"Found {len(completed_request_ids)} total requests and {num_previously_failed_requests} previously failed requests" - ) - logger.info("Remaining requests will now be processed.") - else: - user_input = input( - f"File {save_filepath} already exists.\nTo resume if there are remaining requests without responses, run with --resume flag.\nOverwrite? (Y/n): " - ) - if user_input.lower() != "y" and user_input.lower() != "": - logger.info("Aborting operation.") - return - - # initialize file reading - with open(generic_requests_filepath) as file: - # `requests` will provide requests one at a time - generic_requests = file.__iter__() - logger.debug(f"File opened. Entering main loop") - - # Count total number of requests - total_requests = sum(1 for _ in open(generic_requests_filepath)) - if total_requests == len(completed_request_ids): - logger.debug("All requests have already been completed so will just reuse cache.") - return - - # Create progress bar - pbar = tqdm( - total=total_requests, - desc="Processing parallel requests to OpenAI", - ) - - connector = aiohttp.TCPConnector(limit=10 * max_requests_per_minute) - async with aiohttp.ClientSession( - connector=connector - ) as session: # Initialize ClientSession here - 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() - logger.debug(f"Retrying request {next_request.task_id}: {next_request}") - elif file_not_finished: - try: - # get new generic request - generic_request_json = json.loads(next(generic_requests)) - generic_request = GenericRequest.model_validate( - generic_request_json - ) - request_idx = generic_request.original_row_idx - - # Skip requests we already have responses for - if resume and request_idx in completed_request_ids: - logger.debug( - f"Skipping already completed request {request_idx}" - ) - status_tracker.num_tasks_already_completed += 1 - continue - - # Create API-specific request - api_specific_request_json = self.create_api_specific_request( - generic_request - ) - next_request = APIRequest( - task_id=next(task_id_generator), - api_specific_request_json=api_specific_request_json, - generic_request=generic_request, - token_consumption=num_tokens_consumed_from_request( - api_specific_request_json, - api_endpoint, - token_encoding_name, - ), - attempts_left=max_attempts, - ) - status_tracker.num_tasks_started += 1 - status_tracker.num_tasks_in_progress += 1 - logger.debug( - f"Reading request {next_request.task_id}: {next_request}" - ) - except StopIteration: - # if file runs out, set flag to stop reading it - logger.debug("Read file exhausted") - 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 - asyncio.create_task( - next_request.call_api( - session=session, - request_url=request_url, - 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 - else: - logger.debug( - f"Not Enough Capacity: Request tokens: {next_request_tokens}, Available request capacity: {available_request_capacity}, Available token capacity: {available_token_capacity}" - ) - - # Update progress bar when a task is completed - total_completed = ( - status_tracker.num_tasks_succeeded - + status_tracker.num_tasks_failed - + status_tracker.num_tasks_already_completed - ) - if total_completed > pbar.n: - pbar.update(total_completed - pbar.n) - - # 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) + """Process a single OpenAI API request with error handling and retry logic. + + Args: + request (APIRequest): The request to process + session (aiohttp.ClientSession): Async HTTP session + retry_queue (asyncio.Queue): Queue for failed requests to retry + save_filepath (str): Path to save response + status_tracker (StatusTracker): Tracks request status and rate limits + + Note: + - Handles rate limit errors with exponential backoff + - Tracks token usage and costs + - Saves responses immediately to avoid data loss + - Updates status tracker for monitoring + """ + try: + api_endpoint = api_endpoint_from_url(self.url) + request_header = {"Authorization": f"Bearer {self.api_key}"} + if "/deployments" in self.url: # Azure deployment + request_header = {"api-key": f"{self.api_key}"} - # 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 + async with session.post( + self.url, + headers=request_header, + json=request.api_specific_request, + timeout=60.0, + ) as response: + response_json = await response.json() + + if "error" in response_json: + status_tracker.num_api_errors += 1 + error = response_json["error"] + if "rate limit" in error.get("message", "").lower(): + status_tracker.time_of_last_rate_limit_error = time.time() + status_tracker.num_rate_limit_errors += 1 + status_tracker.num_api_errors -= 1 + raise Exception(f"API error: {error}") + + if response.status != 200: + raise Exception( + f"API request failed with status {response.status}: {response_json}" ) - 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 - logger.warn( - f"Pausing to cool down until {time.ctime(status_tracker.time_of_last_rate_limit_error + seconds_to_pause_after_rate_limit_error)}" - ) - - # Close the progress bar - pbar.close() - - # after finishing, log final status - logger.info(f"""Parallel processing complete. Results saved to {save_filepath}""") - logger.info(f"Status tracker: {status_tracker}") - - if status_tracker.num_tasks_failed > 0: - logger.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: - logger.warning( - f"{status_tracker.num_rate_limit_errors} rate limit errors received. Consider running at a lower rate." + response_message = response_json["choices"][0]["message"]["content"] + usage = response_json["usage"] + token_usage = TokenUsage( + prompt_tokens=usage["prompt_tokens"], + completion_tokens=usage["completion_tokens"], + total_tokens=usage["total_tokens"], ) + # Calculate cost using litellm + cost = litellm.completion_cost(completion_response=response_json) -@dataclass -class StatusTracker: - """Stores metadata about the script's progress. Only one instance is created.""" - - num_tasks_already_completed: int = 0 - 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.""" + # Create and save response immediately + generic_response = GenericResponse( + response_message=response_message, + response_errors=None, + raw_request=request.api_specific_request, + raw_response=response_json, + generic_request=request.generic_request, + created_at=request.created_at, + finished_at=datetime.datetime.now(), + token_usage=token_usage, + response_cost=cost, + ) - task_id: int - generic_request: GenericRequest - api_specific_request_json: dict - token_consumption: int - attempts_left: int - result: list = field(default_factory=list) - created_at: datetime.datetime = field(default_factory=datetime.datetime.now) + await self.append_generic_response(generic_response, save_filepath) + status_tracker.num_tasks_in_progress -= 1 + status_tracker.num_tasks_succeeded += 1 + status_tracker.pbar.update(1) - async def call_api( - self, - session: aiohttp.ClientSession, - request_url: str, - request_header: dict, - retry_queue: asyncio.Queue, - save_filepath: str, - status_tracker: StatusTracker, - ) -> None: - """Calls the OpenAI API and saves results.""" - logger.debug(f"Starting request #{self.task_id}") - error = None - try: - async with session.post( - url=request_url, - headers=request_header, - json=self.api_specific_request_json, - ) as response: - response = await response.json() - if "error" in response: - logger.warning(f"Request {self.task_id} failed with error {response['error']}") - status_tracker.num_api_errors += 1 - error = response - if "rate limit" in response["error"].get("message", "").lower(): - 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 - - except ( - Exception - ) as e: # catching naked exceptions is bad practice, but in this case we'll log & save them - logger.warning( - f"Request {self.task_id} failed with Exception {e}, attempts left {self.attempts_left}" - ) + except Exception as e: + logger.error(f"Error in API request: {e}") status_tracker.num_other_errors += 1 - error = e - if error: - self.result.append(error) - if self.attempts_left: - retry_queue.put_nowait(self) + request.result.append(e) + + if request.attempts_left > 0: + request.attempts_left -= 1 + retry_queue.put_nowait(request) else: generic_response = GenericResponse( response_message=None, - response_errors=[str(e) for e in self.result], - raw_request=self.api_specific_request_json, + response_errors=[str(e) for e in request.result], + raw_request=request.api_specific_request, raw_response=None, - generic_request=self.generic_request, - created_at=self.created_at, + generic_request=request.generic_request, + created_at=request.created_at, finished_at=datetime.datetime.now(), ) - append_generic_response(generic_response, save_filepath) + await self.append_generic_response(generic_response, save_filepath) status_tracker.num_tasks_in_progress -= 1 status_tracker.num_tasks_failed += 1 - logger.error( - f"Request {self.api_specific_request_json} failed after all attempts." - f"Saved errors {self.result} to {save_filepath}" - ) - else: - response_message = response["choices"][0]["message"]["content"] - response_message, response_errors = parse_response_message( - response_message, self.generic_request.response_format - ) - usage = response.get("usage", {}) - token_usage = TokenUsage( - prompt_tokens=usage.get("prompt_tokens", 0), - completion_tokens=usage.get("completion_tokens", 0), - total_tokens=usage.get("total_tokens", 0), - ) - - # Calculate cost using litellm - cost = litellm.completion_cost(completion_response=response) - - generic_response = GenericResponse( - response_message=response_message, - response_errors=response_errors, - raw_request=self.api_specific_request_json, - raw_response=response, - generic_request=self.generic_request, - created_at=self.created_at, - finished_at=datetime.datetime.now(), - token_usage=token_usage, - response_cost=cost, - ) - append_generic_response(generic_response, save_filepath) - status_tracker.num_tasks_in_progress -= 1 - status_tracker.num_tasks_succeeded += 1 - logger.debug(f"Request {self.task_id} saved to {save_filepath}") - - -def get_token_encoding_name(model: str) -> str: - """Get the token encoding name for a given model.""" - if "gpt" in model: - return tiktoken.encoding_for_model(model).name - else: - logger.warning( - f'Token encoding name for model "{model}" not implemented, using cl100k_base for token counting' - ) - return "cl100k_base" - - -def get_rate_limits(model: str, request_url: str, api_key: str) -> Tuple[int, int]: - """ - Function to get rate limits for a given annotator. Makes a single request to openAI API - and gets the rate limits from the response headers. These rate limits vary per model - and are determined by your organization's usage tier. View the following: - https://platform.openai.com/docs/guides/rate-limits/usage-tiers - https://platform.openai.com/settings/organization/limits - - Args: - model (str): The model for which to get the rate limits. - request_url (str): The request URL for which to get the rate limits. - - Returns: - Tuple[int, int]: The maximum number of requests and tokens per minute. - """ - if "api.openai.com" in request_url: - # Send a dummy request to get rate limit information - response = requests.post( - request_url, - headers={"Authorization": f"Bearer {api_key}"}, - json={"model": model, "messages": []}, - ) - # Extract rate limit information from headers - max_requests = int(response.headers.get("x-ratelimit-limit-requests", 30_000)) - max_tokens = int(response.headers.get("x-ratelimit-limit-tokens", 150_000_000)) - elif "api.sambanova.ai" in request_url: - # Send a dummy request to get rate limit information - max_requests = 50 - max_tokens = 100_000_000 - else: - raise NotImplementedError(f'Rate limits for API endpoint "{request_url}" not implemented') - - return max_requests, max_tokens - - -def get_api_key(request_url: str) -> str: - """Get the API key for a given request URL.""" - if "api.openai.com" in request_url: - return os.getenv("OPENAI_API_KEY") - elif "api.sambanova.ai" in request_url: - return os.getenv("SAMBANOVA_API_KEY") - else: - raise NotImplementedError( - f'Default API key environment variable for API endpoint "{request_url}" not implemented' - ) - - -def api_endpoint_from_url(request_url: str) -> str: - """Extract the API endpoint from the request URL. - This is used to determine the number of tokens consumed by the request. - """ - - # OpenAI API - match = re.search("^https://[^/]+/v\\d+/(.+)$", request_url) - if match: - return match[1] - - # for Azure OpenAI deployment urls - match = re.search(r"^https://[^/]+/openai/deployments/[^/]+/(.+?)(\?|$)", request_url) - if match: - return match[1] - - # Catch all for other API endpoints using OpenAI OpenAPI format - if "chat/completions" in request_url: - return "chat/completions" - elif "completions" in request_url: - return "completions" - else: - raise NotImplementedError(f'API endpoint "{request_url}" not implemented in this script') - - -def append_generic_response(data: GenericResponse, filename: str) -> None: - """Append a json payload to the end of a jsonl file.""" - json_string = json.dumps(data.model_dump(), default=str) - with open(filename, "a") as f: - f.write(json_string + "\n") - - -def num_tokens_consumed_from_request( - api_specific_request_json: dict, - api_endpoint: str, - 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 - if api_endpoint.endswith("completions"): - max_tokens = api_specific_request_json.get("max_tokens", 15) - n = api_specific_request_json.get("n", 1) - completion_tokens = n * max_tokens - - # chat completions - if api_endpoint.startswith("chat/"): - num_tokens = 0 - for message in api_specific_request_json["messages"]: - num_tokens += 4 # every message follows {role/name}\n{content}\n - for key, value in message.items(): - try: - num_tokens += len(encoding.encode(str(value))) - except TypeError: - logger.warning( - f"Failed to encode value {value} with tiktoken to count tokens. Instead assuming a token for every 4 characters." - ) - num_tokens += len(str(value)) // 4 - 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 assistant - return num_tokens + completion_tokens - # normal completions - else: - prompt = api_specific_request_json["prompt"] - if isinstance(prompt, str): # single prompt - prompt_tokens = len(encoding.encode(prompt)) - num_tokens = prompt_tokens + completion_tokens - return num_tokens - elif isinstance(prompt, list): # multiple prompts - prompt_tokens = sum([len(encoding.encode(p)) for p in prompt]) - num_tokens = prompt_tokens + completion_tokens * len(prompt) - return num_tokens - else: - raise TypeError( - 'Expecting either string or list of strings for "prompt" field in completion request' - ) - # if embeddings request, tokens = input tokens - elif api_endpoint == "embeddings": - input = api_specific_request_json["input"] - if isinstance(input, str): # single input - num_tokens = len(encoding.encode(input)) - return num_tokens - elif isinstance(input, list): # multiple inputs - num_tokens = sum([len(encoding.encode(i)) for i in input]) - return num_tokens - else: - raise TypeError( - 'Expecting either string or list of strings for "inputs" field in embedding request' - ) - # more logic needed to support other API calls (e.g., edits, inserts, DALL-E) - else: - raise NotImplementedError(f'API endpoint "{api_endpoint}" not implemented in this script') - - -def task_id_generator_function(): - """Generate integers 0, 1, 2, and so on.""" - task_id = 0 - while True: - yield task_id - task_id += 1 From 10ea1f4e290693ccf13910ac0942975cd255904f Mon Sep 17 00:00:00 2001 From: Charlie Cheng-Jie Ji Date: Sun, 1 Dec 2024 21:54:19 +0000 Subject: [PATCH 33/65] remove unused unused function that is been refactored --- .../openai_online_request_processor.py | 52 ------------------- 1 file changed, 52 deletions(-) diff --git a/src/bespokelabs/curator/request_processor/openai_online_request_processor.py b/src/bespokelabs/curator/request_processor/openai_online_request_processor.py index 5be474be..9182b853 100644 --- a/src/bespokelabs/curator/request_processor/openai_online_request_processor.py +++ b/src/bespokelabs/curator/request_processor/openai_online_request_processor.py @@ -41,58 +41,6 @@ def api_endpoint_from_url(url: str) -> str: return match.group(1) return url - -def num_tokens_consumed_from_request( - api_specific_request_json: dict, - api_endpoint: str, - token_encoding_name: str, -) -> int: - """Count the number of tokens in the request. Only supports completion and embedding requests.""" - encoding = tiktoken.get_encoding(token_encoding_name) - - if api_endpoint.endswith("completions"): - max_tokens = api_specific_request_json.get("max_tokens", 15) - n = api_specific_request_json.get("n", 1) - completion_tokens = n * max_tokens - - if api_endpoint.startswith("chat/"): - num_tokens = 0 - for message in api_specific_request_json["messages"]: - num_tokens += 4 # every message follows {role/name}\n{content}\n - for key, value in message.items(): - try: - num_tokens += len(encoding.encode(str(value))) - except TypeError: - logger.warning( - f"Failed to encode value {value} with tiktoken to count tokens. Instead assuming a token for every 4 characters." - ) - num_tokens += len(str(value)) // 4 - 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 assistant - return num_tokens + completion_tokens - else: - prompt = api_specific_request_json["prompt"] - if isinstance(prompt, str): - prompt_tokens = len(encoding.encode(prompt)) - return prompt_tokens + completion_tokens - elif isinstance(prompt, list): - prompt_tokens = sum([len(encoding.encode(p)) for p in prompt]) - return prompt_tokens + completion_tokens * len(prompt) - else: - raise TypeError('Expecting string or list of strings for "prompt" field') - elif api_endpoint == "embeddings": - input = api_specific_request_json["input"] - if isinstance(input, str): - return len(encoding.encode(input)) - elif isinstance(input, list): - return sum([len(encoding.encode(i)) for i in input]) - else: - raise TypeError('Expecting string or list of strings for "inputs" field') - else: - raise NotImplementedError(f'API endpoint "{api_endpoint}" not implemented') - - class OpenAIOnlineRequestProcessor(OnlineRequestProcessor): """OpenAI-specific implementation of the OnlineRequestProcessor. From f8345315a6d249e78545118c635527d88d09d7b0 Mon Sep 17 00:00:00 2001 From: Charlie Cheng-Jie Ji Date: Sun, 1 Dec 2024 21:58:02 +0000 Subject: [PATCH 34/65] black --- .../litellm_online_request_processor.py | 46 +++++++++---------- .../openai_online_request_processor.py | 21 +++++---- 2 files changed, 34 insertions(+), 33 deletions(-) diff --git a/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py b/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py index 3c36ca0d..77b217f8 100644 --- a/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py +++ b/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py @@ -27,7 +27,7 @@ class LiteLLMOnlineRequestProcessor(OnlineRequestProcessor): """LiteLLM implementation of the OnlineRequestProcessor for multi-provider LLM support. - + This processor uses LiteLLM to handle requests across different LLM providers (OpenAI, Anthropic, etc.) with unified interface and structured output support via instructor. @@ -37,7 +37,7 @@ class LiteLLMOnlineRequestProcessor(OnlineRequestProcessor): - Automatic token counting and rate limiting - Provider-specific parameter handling - Cost tracking per request - + Attributes: model (str): The model identifier (e.g., "gpt-4", "claude-2") temperature (Optional[float]): Temperature for response randomness @@ -66,18 +66,18 @@ def __init__( def check_structured_output_support(self, prompt_formatter: PromptFormatter): """Verify if the model supports structured output via instructor. - + Tests the model's capability to handle structured output by making a test request with a simple schema. - + Args: prompt_formatter (PromptFormatter): Contains response format requirements - + Returns: bool: True if structured output is supported, False otherwise - + Note: - - Uses a simple User schema as test case + - Uses a simple User schema as test case - Logs detailed information about support status - Required for models that will use JSON schema responses """ @@ -108,13 +108,13 @@ class User(BaseModel): def estimate_output_tokens(self) -> int: """Estimate the number of tokens in the model's response. - + Uses LiteLLM's get_max_tokens and applies a conservative estimate by dividing by 4 to avoid hitting context limits. - + Returns: int: Estimated number of output tokens - + Note: Falls back to 0 if token estimation fails """ @@ -125,13 +125,13 @@ def estimate_output_tokens(self) -> int: def estimate_total_tokens(self, messages: list) -> int: """Calculate the total token usage for a request. - + Uses LiteLLM's token_counter for accurate input token counting and adds estimated output tokens. - + Args: messages (list): List of message dictionaries - + Returns: int: Total estimated tokens (input + output) """ @@ -141,12 +141,12 @@ def estimate_total_tokens(self, messages: list) -> int: def get_rate_limits(self) -> dict: """Retrieve rate limits from the LLM provider via LiteLLM. - + Makes a test request to get rate limit information from response headers. - + Returns: dict: Contains 'max_requests_per_minute' and 'max_tokens_per_minute' - + Note: - Falls back to default values if headers are missing - Some providers (e.g., Claude) require non-empty messages @@ -172,16 +172,16 @@ def get_rate_limits(self) -> dict: def create_api_specific_request(self, generic_request: GenericRequest) -> dict: """Convert a generic request into a LiteLLM-compatible format. - + Checks supported parameters for the specific model and only includes applicable parameters. - + Args: generic_request (GenericRequest): The generic request to convert - + Returns: dict: LiteLLM-compatible request parameters - + Note: Uses LiteLLM's get_supported_openai_params to check parameter support """ @@ -216,17 +216,17 @@ async def process_single_request( status_tracker: StatusTracker, ) -> None: """Process a single request through LiteLLM with error handling. - + Handles both structured and unstructured outputs, tracks token usage and costs, and manages retries. - + Args: request (APIRequest): Request to process session (aiohttp.ClientSession): Async HTTP session retry_queue (asyncio.Queue): Queue for failed requests save_filepath (str): Path to save responses status_tracker (StatusTracker): Tracks request status and limits - + Note: - Supports both instructor-based structured output and raw completions - Calculates and tracks costs using LiteLLM's completion_cost diff --git a/src/bespokelabs/curator/request_processor/openai_online_request_processor.py b/src/bespokelabs/curator/request_processor/openai_online_request_processor.py index 9182b853..68f8c480 100644 --- a/src/bespokelabs/curator/request_processor/openai_online_request_processor.py +++ b/src/bespokelabs/curator/request_processor/openai_online_request_processor.py @@ -41,9 +41,10 @@ def api_endpoint_from_url(url: str) -> str: return match.group(1) return url + class OpenAIOnlineRequestProcessor(OnlineRequestProcessor): """OpenAI-specific implementation of the OnlineRequestProcessor. - + Handles API requests to OpenAI's chat completion endpoints with rate limiting, token counting, and error handling specific to OpenAI's API. @@ -83,10 +84,10 @@ def __init__( def get_rate_limits(self) -> dict: """Get rate limits from OpenAI API headers. - + Returns: dict: Contains 'max_requests_per_minute' and 'max_tokens_per_minute' - + Note: - Makes a dummy request to get actual rate limits - Falls back to default values if headers are missing @@ -116,10 +117,10 @@ def get_rate_limits(self) -> dict: def estimate_output_tokens(self) -> int: """Estimate number of tokens in the response. - + Returns: int: Estimated number of output tokens - + Note: Default implementation returns a conservative estimate. Override this method for more accurate model-specific estimates. @@ -131,13 +132,13 @@ def estimate_output_tokens(self) -> int: def estimate_total_tokens(self, messages: list) -> int: """Estimate total tokens for a request using OpenAI's token counting rules. - + Args: messages (list): List of message dictionaries with role and content Returns: int: Estimated total tokens including message formatting tokens - + Note: Includes: - 4 tokens per message for formatting @@ -171,7 +172,7 @@ def create_api_specific_request(self, generic_request: GenericRequest) -> dict: Returns: dict: OpenAI API-compatible request dictionary - + Note: - Handles JSON schema response format if specified - Applies optional parameters (temperature, top_p, etc.) @@ -213,14 +214,14 @@ async def process_single_request( status_tracker: StatusTracker, ) -> None: """Process a single OpenAI API request with error handling and retry logic. - + Args: request (APIRequest): The request to process session (aiohttp.ClientSession): Async HTTP session retry_queue (asyncio.Queue): Queue for failed requests to retry save_filepath (str): Path to save response status_tracker (StatusTracker): Tracks request status and rate limits - + Note: - Handles rate limit errors with exponential backoff - Tracks token usage and costs From e62bd9d8838ffc7c10210c4d033358a82820808b Mon Sep 17 00:00:00 2001 From: Charlie Cheng-Jie Ji Date: Sun, 1 Dec 2024 21:58:11 +0000 Subject: [PATCH 35/65] base request processor try except --- .../base_request_processor.py | 20 +++++++++++++++++-- 1 file changed, 18 insertions(+), 2 deletions(-) diff --git a/src/bespokelabs/curator/request_processor/base_request_processor.py b/src/bespokelabs/curator/request_processor/base_request_processor.py index d1f0b4e9..1b2254fb 100644 --- a/src/bespokelabs/curator/request_processor/base_request_processor.py +++ b/src/bespokelabs/curator/request_processor/base_request_processor.py @@ -239,8 +239,24 @@ def create_dataset_files( # Response message is a string, which is converted to a dict # The dict is then used to construct the response_format Pydantic model try: + # First try to parse the response message as JSON + if isinstance(response.response_message, str): + try: + response_dict = json.loads(response.response_message) + except json.JSONDecodeError as e: + warning_msg = ( + f"Failed to parse response message as JSON: {response.response_message}. " + f"The model likely returned an invalid JSON format. Will skip this response." + ) + logger.warning(warning_msg) + failed_responses_count += 1 + continue + else: + response_dict = response.response_message + + # Then construct the Pydantic model from the parsed dict response.response_message = prompt_formatter.response_format( - **response.response_message + **response_dict ) except ValidationError as e: schema_str = json.dumps( @@ -248,7 +264,7 @@ def create_dataset_files( indent=2, ) warning_msg = ( - f"Pydantic failed to parse response message {response.response_message} with `response_format` {schema_str}." + f"Pydantic failed to parse response message {response.response_message} with `response_format` {schema_str}. " f"The model likely returned a JSON that does not match the schema of the `response_format`. Will skip this response." ) logger.warning(warning_msg) From 52be21163569e4dd1a97ccc8d1cb1ea974e113e9 Mon Sep 17 00:00:00 2001 From: Charlie Cheng-Jie Ji Date: Sun, 1 Dec 2024 22:11:10 +0000 Subject: [PATCH 36/65] original api_endpoint_from_url implementation --- .../openai_online_request_processor.py | 26 +++++++++++++++---- 1 file changed, 21 insertions(+), 5 deletions(-) diff --git a/src/bespokelabs/curator/request_processor/openai_online_request_processor.py b/src/bespokelabs/curator/request_processor/openai_online_request_processor.py index 68f8c480..54368c8f 100644 --- a/src/bespokelabs/curator/request_processor/openai_online_request_processor.py +++ b/src/bespokelabs/curator/request_processor/openai_online_request_processor.py @@ -34,12 +34,28 @@ def get_token_encoding_name(model_name: str) -> str: return "cl100k_base" # Default to cl100k_base -def api_endpoint_from_url(url: str) -> str: - """Extract the API endpoint from a URL.""" - match = re.match(r"^https://[^/]+/v\d+/(.+)$", url) +def api_endpoint_from_url(request_url: str) -> str: + """Extract the API endpoint from the request URL. + This is used to determine the number of tokens consumed by the request. + """ + + # OpenAI API + match = re.search("^https://[^/]+/v\\d+/(.+)$", request_url) + if match: + return match[1] + + # for Azure OpenAI deployment urls + match = re.search(r"^https://[^/]+/openai/deployments/[^/]+/(.+?)(\?|$)", request_url) if match: - return match.group(1) - return url + return match[1] + + # Catch all for other API endpoints using OpenAI OpenAPI format + if "chat/completions" in request_url: + return "chat/completions" + elif "completions" in request_url: + return "completions" + else: + raise NotImplementedError(f'API endpoint "{request_url}" not implemented in this script') class OpenAIOnlineRequestProcessor(OnlineRequestProcessor): From f56d76655645c8fedab22d8ac8fbd0d3919e33ea Mon Sep 17 00:00:00 2001 From: Charlie Cheng-Jie Ji Date: Sun, 1 Dec 2024 23:06:00 +0000 Subject: [PATCH 37/65] cleanup after merging from dev --- .../online_request_processor.py | 18 +++++++++------ .../openai_online_request_processor.py | 22 +++++++++---------- 2 files changed, 22 insertions(+), 18 deletions(-) diff --git a/src/bespokelabs/curator/request_processor/online_request_processor.py b/src/bespokelabs/curator/request_processor/online_request_processor.py index c4e2babb..0493bf55 100644 --- a/src/bespokelabs/curator/request_processor/online_request_processor.py +++ b/src/bespokelabs/curator/request_processor/online_request_processor.py @@ -108,10 +108,10 @@ def __init__( frequency_penalty: Optional[float] = None, ): super().__init__(batch_size=None) - self.model = model - self.temperature = temperature - self.top_p = top_p - self.presence_penalty = presence_penalty + self.model: str = model + self.temperature: float | None = temperature + self.top_p: float | None = top_p + self.presence_penalty: float | None = presence_penalty self.frequency_penalty = frequency_penalty self.prompt_formatter: Optional[PromptFormatter] = None @@ -181,11 +181,9 @@ async def process_requests_from_file( resume: bool, ) -> None: """Processes API requests in parallel, throttling to stay under rate limits.""" - soft, hard = resource.getrlimit(resource.RLIMIT_NOFILE) - resource.setrlimit(resource.RLIMIT_NOFILE, (min(hard, 10000), hard)) # Initialize trackers - queue_of_requests_to_retry = asyncio.Queue() + queue_of_requests_to_retry: asyncio.Queue[APIRequest] = asyncio.Queue() status_tracker = StatusTracker() # Get rate limits @@ -194,6 +192,12 @@ async def process_requests_from_file( status_tracker.max_tokens_per_minute = rate_limits["max_tokens_per_minute"] rpm = rate_limits["max_requests_per_minute"] + soft, hard = resource.getrlimit(resource.RLIMIT_NOFILE) + resource.setrlimit( + resource.RLIMIT_NOFILE, + (min(hard, int(10 * status_tracker.max_requests_per_minute)), hard), + ) + # Track completed requests for resume functionality completed_request_ids = set() if os.path.exists(save_filepath) and resume: diff --git a/src/bespokelabs/curator/request_processor/openai_online_request_processor.py b/src/bespokelabs/curator/request_processor/openai_online_request_processor.py index f387ab2d..ef5303b8 100644 --- a/src/bespokelabs/curator/request_processor/openai_online_request_processor.py +++ b/src/bespokelabs/curator/request_processor/openai_online_request_processor.py @@ -55,7 +55,7 @@ def api_endpoint_from_url(request_url: str) -> str: elif "completions" in request_url: return "completions" else: - raise NotImplementedError(f'API endpoint "{request_url}" not implemented in this script') + raise NotImplementedError(f'API endpoint "{request_url}" not implemented in Curator yet.') class OpenAIOnlineRequestProcessor(OnlineRequestProcessor): @@ -73,7 +73,7 @@ class OpenAIOnlineRequestProcessor(OnlineRequestProcessor): def __init__( self, - model: str = "gpt-4", + model: str = "gpt-4o-mini", api_key: str = os.getenv("OPENAI_API_KEY"), url: str = "https://api.openai.com/v1/chat/completions", temperature: Optional[float] = None, @@ -255,12 +255,12 @@ async def process_single_request( headers=request_header, json=request.api_specific_request, timeout=60.0, - ) as response: - response_json = await response.json() + ) as response_obj: + response = await response_obj.json() - if "error" in response_json: + if "error" in response: status_tracker.num_api_errors += 1 - error = response_json["error"] + error = response["error"] if "rate limit" in error.get("message", "").lower(): status_tracker.time_of_last_rate_limit_error = time.time() status_tracker.num_rate_limit_errors += 1 @@ -269,11 +269,11 @@ async def process_single_request( if response.status != 200: raise Exception( - f"API request failed with status {response.status}: {response_json}" + f"API request failed with status {response.status}: {response}" ) - response_message = response_json["choices"][0]["message"]["content"] - usage = response_json["usage"] + response_message = response["choices"][0]["message"]["content"] + usage = response["usage"] token_usage = TokenUsage( prompt_tokens=usage["prompt_tokens"], completion_tokens=usage["completion_tokens"], @@ -281,14 +281,14 @@ async def process_single_request( ) # Calculate cost using litellm - cost = litellm.completion_cost(completion_response=response_json) + cost = litellm.completion_cost(completion_response=response) # Create and save response immediately generic_response = GenericResponse( response_message=response_message, response_errors=None, raw_request=request.api_specific_request, - raw_response=response_json, + raw_response=response, generic_request=request.generic_request, created_at=request.created_at, finished_at=datetime.datetime.now(), From 8dbf9e97f25c5ce591ec57c6ca17a8a6e4bb40c3 Mon Sep 17 00:00:00 2001 From: Charlie Cheng-Jie Ji Date: Sun, 1 Dec 2024 23:29:03 +0000 Subject: [PATCH 38/65] status is retrieved from the raw response_obj, not parsed --- .../request_processor/openai_online_request_processor.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/bespokelabs/curator/request_processor/openai_online_request_processor.py b/src/bespokelabs/curator/request_processor/openai_online_request_processor.py index ef5303b8..23d361d0 100644 --- a/src/bespokelabs/curator/request_processor/openai_online_request_processor.py +++ b/src/bespokelabs/curator/request_processor/openai_online_request_processor.py @@ -267,9 +267,9 @@ async def process_single_request( status_tracker.num_api_errors -= 1 raise Exception(f"API error: {error}") - if response.status != 200: + if response_obj.status != 200: raise Exception( - f"API request failed with status {response.status}: {response}" + f"API request failed with status {response_obj.status}: {response}" ) response_message = response["choices"][0]["message"]["content"] From 6fe5fd140eec955340e5dfab3d07b334f911cbab Mon Sep 17 00:00:00 2001 From: Charlie Cheng-Jie Ji Date: Sun, 1 Dec 2024 23:29:28 +0000 Subject: [PATCH 39/65] consistant typing and 10* constant introduced in openai online --- .../curator/request_processor/online_request_processor.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/src/bespokelabs/curator/request_processor/online_request_processor.py b/src/bespokelabs/curator/request_processor/online_request_processor.py index 0493bf55..ec1c4c6e 100644 --- a/src/bespokelabs/curator/request_processor/online_request_processor.py +++ b/src/bespokelabs/curator/request_processor/online_request_processor.py @@ -112,7 +112,7 @@ def __init__( self.temperature: float | None = temperature self.top_p: float | None = top_p self.presence_penalty: float | None = presence_penalty - self.frequency_penalty = frequency_penalty + self.frequency_penalty: float | None = frequency_penalty self.prompt_formatter: Optional[PromptFormatter] = None @abstractmethod @@ -218,8 +218,8 @@ async def process_requests_from_file( ) # Use higher connector limit for better throughput - connector = aiohttp.TCPConnector(limit=rpm) - async with aiohttp.ClientSession(connector=connector) as session: + connector = aiohttp.TCPConnector(limit=10 * rpm) + async with aiohttp.ClientSession(connector=connector) as session: # Initialize ClientSession here async with aiofiles.open(generic_requests_filepath) as file: pending_requests = [] From e261517d542e9dde54c5771809dce1cbb375bb17 Mon Sep 17 00:00:00 2001 From: Charlie Cheng-Jie Ji Date: Sun, 1 Dec 2024 23:29:37 +0000 Subject: [PATCH 40/65] black --- .../curator/request_processor/online_request_processor.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/src/bespokelabs/curator/request_processor/online_request_processor.py b/src/bespokelabs/curator/request_processor/online_request_processor.py index ec1c4c6e..91855775 100644 --- a/src/bespokelabs/curator/request_processor/online_request_processor.py +++ b/src/bespokelabs/curator/request_processor/online_request_processor.py @@ -219,7 +219,9 @@ async def process_requests_from_file( # Use higher connector limit for better throughput connector = aiohttp.TCPConnector(limit=10 * rpm) - async with aiohttp.ClientSession(connector=connector) as session: # Initialize ClientSession here + async with aiohttp.ClientSession( + connector=connector + ) as session: # Initialize ClientSession here async with aiofiles.open(generic_requests_filepath) as file: pending_requests = [] From 17766b5d4eb062d2b4f024e5cb29ec0ba3dda3d4 Mon Sep 17 00:00:00 2001 From: Charlie Cheng-Jie Ji Date: Mon, 2 Dec 2024 18:16:53 +0000 Subject: [PATCH 41/65] remove unused imports --- examples/litellm_recipe_structured_output.py | 1 - .../request_processor/litellm_online_request_processor.py | 3 --- .../request_processor/openai_online_request_processor.py | 1 - 3 files changed, 5 deletions(-) diff --git a/examples/litellm_recipe_structured_output.py b/examples/litellm_recipe_structured_output.py index a24b2ffd..92e118ce 100644 --- a/examples/litellm_recipe_structured_output.py +++ b/examples/litellm_recipe_structured_output.py @@ -1,7 +1,6 @@ from typing import List from pydantic import BaseModel, Field from bespokelabs import curator -from datasets import Dataset import logging logger = logging.getLogger(__name__) diff --git a/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py b/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py index 77b217f8..dc92a1cc 100644 --- a/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py +++ b/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py @@ -1,12 +1,9 @@ -import json import logging -import os from typing import Optional import asyncio import aiohttp import litellm from litellm import get_supported_openai_params -import aiofiles import datetime import instructor from bespokelabs.curator.request_processor.online_request_processor import ( diff --git a/src/bespokelabs/curator/request_processor/openai_online_request_processor.py b/src/bespokelabs/curator/request_processor/openai_online_request_processor.py index 23d361d0..7c38af31 100644 --- a/src/bespokelabs/curator/request_processor/openai_online_request_processor.py +++ b/src/bespokelabs/curator/request_processor/openai_online_request_processor.py @@ -1,6 +1,5 @@ import asyncio import datetime -import json import logging import os import re From adc04b46cf7756cfb32d679cfca89998f08a1584 Mon Sep 17 00:00:00 2001 From: Charlie Cheng-Jie Ji Date: Tue, 3 Dec 2024 19:09:55 +0000 Subject: [PATCH 42/65] rm confusing docstring --- .../request_processor/litellm_online_request_processor.py | 1 - 1 file changed, 1 deletion(-) diff --git a/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py b/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py index dc92a1cc..081f4f13 100644 --- a/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py +++ b/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py @@ -32,7 +32,6 @@ class LiteLLMOnlineRequestProcessor(OnlineRequestProcessor): - Multi-provider support through LiteLLM - Structured output via instructor - Automatic token counting and rate limiting - - Provider-specific parameter handling - Cost tracking per request Attributes: From b0e0f25574ffddf602bc15ee01442dcd5cfbb6a9 Mon Sep 17 00:00:00 2001 From: Charlie Cheng-Jie Ji Date: Tue, 3 Dec 2024 20:06:34 +0000 Subject: [PATCH 43/65] refactor, to have a `handle_single_request_with_retries` and `call_single_request` --- .../litellm_online_request_processor.py | 123 +++++---------- .../online_request_processor.py | 94 ++++++++++-- .../openai_online_request_processor.py | 143 +++++++----------- 3 files changed, 176 insertions(+), 184 deletions(-) diff --git a/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py b/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py index 081f4f13..439e6414 100644 --- a/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py +++ b/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py @@ -203,98 +203,59 @@ def create_api_specific_request(self, generic_request: GenericRequest) -> dict: return request - async def process_single_request( + async def call_single_request( self, request: APIRequest, session: aiohttp.ClientSession, - retry_queue: asyncio.Queue, - save_filepath: str, status_tracker: StatusTracker, - ) -> None: - """Process a single request through LiteLLM with error handling. + ) -> GenericResponse: + """Make a single request through LiteLLM. Handles both structured and unstructured outputs, tracks token usage - and costs, and manages retries. + and costs. Args: request (APIRequest): Request to process session (aiohttp.ClientSession): Async HTTP session - retry_queue (asyncio.Queue): Queue for failed requests - save_filepath (str): Path to save responses - status_tracker (StatusTracker): Tracks request status and limits - - Note: - - Supports both instructor-based structured output and raw completions - - Calculates and tracks costs using LiteLLM's completion_cost - - Implements retry logic for failed requests - - Saves responses immediately to prevent data loss + status_tracker (StatusTracker): Tracks request status + + Returns: + GenericResponse: The response from LiteLLM """ - try: - # Get response directly without extra logging - if request.generic_request.response_format: - response, completion_obj = ( - await self.client.chat.completions.create_with_completion( - **request.api_specific_request, - response_model=request.prompt_formatter.response_format, - timeout=60.0, - ) - ) - response_message = ( - response.model_dump() if hasattr(response, "model_dump") else response - ) - else: - completion_obj = await litellm.acompletion( - **request.api_specific_request, timeout=60.0 - ) - response_message = completion_obj["choices"][0]["message"]["content"] - - # Extract token usage - usage = completion_obj.usage if hasattr(completion_obj, "usage") else {} - token_usage = TokenUsage( - prompt_tokens=usage.prompt_tokens, - completion_tokens=usage.completion_tokens, - total_tokens=usage.total_tokens, + # Get response directly without extra logging + if request.generic_request.response_format: + response, completion_obj = await self.client.chat.completions.create_with_completion( + **request.api_specific_request, + response_model=request.prompt_formatter.response_format, + timeout=60.0, ) - - # Calculate cost using litellm - cost = litellm.completion_cost(completion_response=completion_obj.model_dump()) - - # Create and save response immediately - generic_response = GenericResponse( - response_message=response_message, - response_errors=None, - raw_request=request.api_specific_request, - raw_response=completion_obj.model_dump(), - generic_request=request.generic_request, - created_at=request.created_at, - finished_at=datetime.datetime.now(), - token_usage=token_usage, - response_cost=cost, + response_message = response.model_dump() if hasattr(response, "model_dump") else response + else: + completion_obj = await litellm.acompletion( + **request.api_specific_request, timeout=60.0 ) + response_message = completion_obj["choices"][0]["message"]["content"] + + # Extract token usage + usage = completion_obj.usage if hasattr(completion_obj, "usage") else {} + token_usage = TokenUsage( + prompt_tokens=usage.prompt_tokens, + completion_tokens=usage.completion_tokens, + total_tokens=usage.total_tokens, + ) - await self.append_generic_response(generic_response, save_filepath) - status_tracker.num_tasks_in_progress -= 1 - status_tracker.num_tasks_succeeded += 1 - status_tracker.pbar.update(1) - - except Exception as e: - logger.error(f"Error in API request: {e}") - status_tracker.num_other_errors += 1 - request.result.append(e) - - if request.attempts_left > 0: - request.attempts_left -= 1 - retry_queue.put_nowait(request) - else: - generic_response = GenericResponse( - response_message=None, - response_errors=[str(e) for e in request.result], - raw_request=request.api_specific_request, - raw_response=None, - generic_request=request.generic_request, - created_at=request.created_at, - finished_at=datetime.datetime.now(), - ) - await self.append_generic_response(generic_response, save_filepath) - status_tracker.num_tasks_in_progress -= 1 - status_tracker.num_tasks_failed += 1 + # Calculate cost using litellm + cost = litellm.completion_cost(completion_response=completion_obj.model_dump()) + + # Create and return response + return GenericResponse( + response_message=response_message, + response_errors=None, + raw_request=request.api_specific_request, + raw_response=completion_obj.model_dump(), + generic_request=request.generic_request, + created_at=request.created_at, + finished_at=datetime.datetime.now(), + token_usage=token_usage, + response_cost=cost, + ) diff --git a/src/bespokelabs/curator/request_processor/online_request_processor.py b/src/bespokelabs/curator/request_processor/online_request_processor.py index 91855775..e91fe5ae 100644 --- a/src/bespokelabs/curator/request_processor/online_request_processor.py +++ b/src/bespokelabs/curator/request_processor/online_request_processor.py @@ -125,18 +125,6 @@ def estimate_output_tokens(self) -> int: """Estimate output tokens for a request""" pass - @abstractmethod - def process_single_request( - self, - request: APIRequest, - session: aiohttp.ClientSession, - retry_queue: asyncio.Queue, - save_filepath: str, - status_tracker: StatusTracker, - ) -> None: - """Process a single API request""" - pass - def check_structured_output_support(self, prompt_formatter: PromptFormatter) -> bool: """Check if the model supports structured output""" return True @@ -250,7 +238,7 @@ async def process_requests_from_file( status_tracker.consume_capacity(token_estimate) task = asyncio.create_task( - self.process_single_request( + self.handle_single_request_with_retries( request=request, session=session, retry_queue=queue_of_requests_to_retry, @@ -278,7 +266,7 @@ async def process_requests_from_file( status_tracker.consume_capacity(token_estimate) task = asyncio.create_task( - self.process_single_request( + self.handle_single_request_with_retries( request=retry_request, session=session, retry_queue=queue_of_requests_to_retry, @@ -310,3 +298,81 @@ async def append_generic_response(self, data: GenericResponse, filename: str) -> async with aiofiles.open(filename, "a") as f: await f.write(json_string + "\n") logger.debug(f"Successfully appended response to {filename}") + + async def handle_single_request_with_retries( + self, + request: APIRequest, + session: aiohttp.ClientSession, + retry_queue: asyncio.Queue, + save_filepath: str, + status_tracker: StatusTracker, + ) -> None: + """Common wrapper for handling a single request with error handling and retries. + + This method implements the common try/except logic and retry mechanism, + while delegating the actual API call to call_single_request. + + Args: + request (APIRequest): The request to process + session (aiohttp.ClientSession): Async HTTP session + retry_queue (asyncio.Queue): Queue for failed requests + save_filepath (str): Path to save responses + status_tracker (StatusTracker): Tracks request status + """ + try: + generic_response = await self.call_single_request( + request=request, + session=session, + status_tracker=status_tracker, + ) + + # Save response in the base class + await self.append_generic_response(generic_response, save_filepath) + + status_tracker.num_tasks_in_progress -= 1 + status_tracker.num_tasks_succeeded += 1 + status_tracker.pbar.update(1) + + except Exception as e: + logger.error(f"Error in API request: {e}") + status_tracker.num_other_errors += 1 + request.result.append(e) + + if request.attempts_left > 0: + request.attempts_left -= 1 + retry_queue.put_nowait(request) + else: + generic_response = GenericResponse( + response_message=None, + response_errors=[str(e) for e in request.result], + raw_request=request.api_specific_request, + raw_response=None, + generic_request=request.generic_request, + created_at=request.created_at, + finished_at=datetime.datetime.now(), + ) + await self.append_generic_response(generic_response, save_filepath) + status_tracker.num_tasks_in_progress -= 1 + status_tracker.num_tasks_failed += 1 + + @abstractmethod + async def call_single_request( + self, + request: APIRequest, + session: aiohttp.ClientSession, + status_tracker: StatusTracker, + ) -> GenericResponse: + """Make a single API request without error handling. + + This method should implement the actual API call logic + without handling retries or errors. + + Args: + request (APIRequest): Request to process + session (aiohttp.ClientSession): Async HTTP session + status_tracker (StatusTracker): Tracks request status + + Returns: + GenericResponse: The response from the API call + """ + pass diff --git a/src/bespokelabs/curator/request_processor/openai_online_request_processor.py b/src/bespokelabs/curator/request_processor/openai_online_request_processor.py index 7c38af31..7489cb6d 100644 --- a/src/bespokelabs/curator/request_processor/openai_online_request_processor.py +++ b/src/bespokelabs/curator/request_processor/openai_online_request_processor.py @@ -220,104 +220,69 @@ def create_api_specific_request(self, generic_request: GenericRequest) -> dict: return request - async def process_single_request( + async def call_single_request( self, request: APIRequest, session: aiohttp.ClientSession, - retry_queue: asyncio.Queue, - save_filepath: str, status_tracker: StatusTracker, - ) -> None: - """Process a single OpenAI API request with error handling and retry logic. + ) -> GenericResponse: + """Make a single OpenAI API request. Args: request (APIRequest): The request to process session (aiohttp.ClientSession): Async HTTP session - retry_queue (asyncio.Queue): Queue for failed requests to retry - save_filepath (str): Path to save response - status_tracker (StatusTracker): Tracks request status and rate limits - - Note: - - Handles rate limit errors with exponential backoff - - Tracks token usage and costs - - Saves responses immediately to avoid data loss - - Updates status tracker for monitoring + status_tracker (StatusTracker): Tracks request status + + Returns: + GenericResponse: The response from OpenAI """ - try: - api_endpoint = api_endpoint_from_url(self.url) - request_header = {"Authorization": f"Bearer {self.api_key}"} - if "/deployments" in self.url: # Azure deployment - request_header = {"api-key": f"{self.api_key}"} - - async with session.post( - self.url, - headers=request_header, - json=request.api_specific_request, - timeout=60.0, - ) as response_obj: - response = await response_obj.json() - - if "error" in response: - status_tracker.num_api_errors += 1 - error = response["error"] - if "rate limit" in error.get("message", "").lower(): - status_tracker.time_of_last_rate_limit_error = time.time() - status_tracker.num_rate_limit_errors += 1 - status_tracker.num_api_errors -= 1 - raise Exception(f"API error: {error}") - - if response_obj.status != 200: - raise Exception( - f"API request failed with status {response_obj.status}: {response}" - ) + api_endpoint = api_endpoint_from_url(self.url) + request_header = {"Authorization": f"Bearer {self.api_key}"} + if "/deployments" in self.url: # Azure deployment + request_header = {"api-key": f"{self.api_key}"} - response_message = response["choices"][0]["message"]["content"] - usage = response["usage"] - token_usage = TokenUsage( - prompt_tokens=usage["prompt_tokens"], - completion_tokens=usage["completion_tokens"], - total_tokens=usage["total_tokens"], - ) - - # Calculate cost using litellm - cost = litellm.completion_cost(completion_response=response) - - # Create and save response immediately - generic_response = GenericResponse( - response_message=response_message, - response_errors=None, - raw_request=request.api_specific_request, - raw_response=response, - generic_request=request.generic_request, - created_at=request.created_at, - finished_at=datetime.datetime.now(), - token_usage=token_usage, - response_cost=cost, + async with session.post( + self.url, + headers=request_header, + json=request.api_specific_request, + timeout=60.0, + ) as response_obj: + response = await response_obj.json() + + if "error" in response: + status_tracker.num_api_errors += 1 + error = response["error"] + if "rate limit" in error.get("message", "").lower(): + status_tracker.time_of_last_rate_limit_error = time.time() + status_tracker.num_rate_limit_errors += 1 + status_tracker.num_api_errors -= 1 + raise Exception(f"API error: {error}") + + if response_obj.status != 200: + raise Exception( + f"API request failed with status {response_obj.status}: {response}" ) - await self.append_generic_response(generic_response, save_filepath) - status_tracker.num_tasks_in_progress -= 1 - status_tracker.num_tasks_succeeded += 1 - status_tracker.pbar.update(1) - - except Exception as e: - logger.error(f"Error in API request: {e}") - status_tracker.num_other_errors += 1 - request.result.append(e) - - if request.attempts_left > 0: - request.attempts_left -= 1 - retry_queue.put_nowait(request) - else: - generic_response = GenericResponse( - response_message=None, - response_errors=[str(e) for e in request.result], - raw_request=request.api_specific_request, - raw_response=None, - generic_request=request.generic_request, - created_at=request.created_at, - finished_at=datetime.datetime.now(), - ) - await self.append_generic_response(generic_response, save_filepath) - status_tracker.num_tasks_in_progress -= 1 - status_tracker.num_tasks_failed += 1 + response_message = response["choices"][0]["message"]["content"] + usage = response["usage"] + token_usage = TokenUsage( + prompt_tokens=usage["prompt_tokens"], + completion_tokens=usage["completion_tokens"], + total_tokens=usage["total_tokens"], + ) + + # Calculate cost using litellm + cost = litellm.completion_cost(completion_response=response) + + # Create and return response + return GenericResponse( + response_message=response_message, + response_errors=None, + raw_request=request.api_specific_request, + raw_response=response, + generic_request=request.generic_request, + created_at=request.created_at, + finished_at=datetime.datetime.now(), + token_usage=token_usage, + response_cost=cost, + ) From d272b59f94cfecd9eed53d3b3ae018f809ca0cf9 Mon Sep 17 00:00:00 2001 From: Charlie Cheng-Jie Ji Date: Tue, 3 Dec 2024 20:08:30 +0000 Subject: [PATCH 44/65] raise ValueError instead of assert --- .../curator/request_processor/online_request_processor.py | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/src/bespokelabs/curator/request_processor/online_request_processor.py b/src/bespokelabs/curator/request_processor/online_request_processor.py index e91fe5ae..a61cdb78 100644 --- a/src/bespokelabs/curator/request_processor/online_request_processor.py +++ b/src/bespokelabs/curator/request_processor/online_request_processor.py @@ -141,9 +141,11 @@ def run( self.prompt_formatter = prompt_formatter if self.prompt_formatter.response_format: - assert self.check_structured_output_support( - prompt_formatter - ), f"Model {self.model} does not support structured output, response_format: {self.prompt_formatter.response_format}" + if not self.check_structured_output_support(prompt_formatter): + raise ValueError( + f"Model {self.model} does not support structured output, " + f"response_format: {self.prompt_formatter.response_format}" + ) generic_requests_files = self.create_request_files(dataset, working_dir, prompt_formatter) generic_responses_files = [ f"{working_dir}/responses_{i}.jsonl" for i in range(len(generic_requests_files)) From a3a4d74c392e720f86ad626bd54203b5ba351eeb Mon Sep 17 00:00:00 2001 From: Charlie Cheng-Jie Ji Date: Tue, 3 Dec 2024 20:14:19 +0000 Subject: [PATCH 45/65] add logging when don't have capacity --- .../request_processor/online_request_processor.py | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/src/bespokelabs/curator/request_processor/online_request_processor.py b/src/bespokelabs/curator/request_processor/online_request_processor.py index a61cdb78..41572a61 100644 --- a/src/bespokelabs/curator/request_processor/online_request_processor.py +++ b/src/bespokelabs/curator/request_processor/online_request_processor.py @@ -73,9 +73,16 @@ def update_capacity(self): def has_capacity(self, token_estimate: int) -> bool: """Check if there's enough capacity for a request""" self.update_capacity() - return ( + has_capacity = ( self.available_request_capacity >= 1 and self.available_token_capacity >= token_estimate ) + if not has_capacity: + logger.debug( + f"No capacity for request with {token_estimate} tokens. " + f"Available capacity: {self.available_token_capacity} tokens, " + f"{self.available_request_capacity} requests." + ) + return has_capacity def consume_capacity(self, token_estimate: int): """Consume capacity for a request""" From f9ae234ba79813dbb00e97128ec9100238473b84 Mon Sep 17 00:00:00 2001 From: Charlie Cheng-Jie Ji Date: Tue, 3 Dec 2024 20:18:10 +0000 Subject: [PATCH 46/65] renamed online_request_processor -> base_online_request_processor --- ...ne_request_processor.py => base_online_request_processor.py} | 0 .../request_processor/litellm_online_request_processor.py | 2 +- .../request_processor/openai_online_request_processor.py | 2 +- 3 files changed, 2 insertions(+), 2 deletions(-) rename src/bespokelabs/curator/request_processor/{online_request_processor.py => base_online_request_processor.py} (100%) diff --git a/src/bespokelabs/curator/request_processor/online_request_processor.py b/src/bespokelabs/curator/request_processor/base_online_request_processor.py similarity index 100% rename from src/bespokelabs/curator/request_processor/online_request_processor.py rename to src/bespokelabs/curator/request_processor/base_online_request_processor.py diff --git a/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py b/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py index 439e6414..d545e257 100644 --- a/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py +++ b/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py @@ -6,7 +6,7 @@ from litellm import get_supported_openai_params import datetime import instructor -from bespokelabs.curator.request_processor.online_request_processor import ( +from bespokelabs.curator.request_processor.base_online_request_processor import ( OnlineRequestProcessor, APIRequest, StatusTracker, diff --git a/src/bespokelabs/curator/request_processor/openai_online_request_processor.py b/src/bespokelabs/curator/request_processor/openai_online_request_processor.py index 7489cb6d..92d73a3e 100644 --- a/src/bespokelabs/curator/request_processor/openai_online_request_processor.py +++ b/src/bespokelabs/curator/request_processor/openai_online_request_processor.py @@ -12,7 +12,7 @@ import litellm import time -from bespokelabs.curator.request_processor.online_request_processor import ( +from bespokelabs.curator.request_processor.base_online_request_processor import ( OnlineRequestProcessor, APIRequest, StatusTracker, From 4ec368d3366922f55d2e19fa020c685b8e253778 Mon Sep 17 00:00:00 2001 From: Charlie Cheng-Jie Ji Date: Tue, 3 Dec 2024 20:56:57 +0000 Subject: [PATCH 47/65] black --- .../base_online_request_processor.py | 14 +++++++------- .../litellm_online_request_processor.py | 10 +++++----- .../openai_online_request_processor.py | 6 ++---- 3 files changed, 14 insertions(+), 16 deletions(-) diff --git a/src/bespokelabs/curator/request_processor/base_online_request_processor.py b/src/bespokelabs/curator/request_processor/base_online_request_processor.py index 41572a61..1181fba2 100644 --- a/src/bespokelabs/curator/request_processor/base_online_request_processor.py +++ b/src/bespokelabs/curator/request_processor/base_online_request_processor.py @@ -317,10 +317,10 @@ async def handle_single_request_with_retries( status_tracker: StatusTracker, ) -> None: """Common wrapper for handling a single request with error handling and retries. - + This method implements the common try/except logic and retry mechanism, while delegating the actual API call to call_single_request. - + Args: request (APIRequest): The request to process session (aiohttp.ClientSession): Async HTTP session @@ -334,10 +334,10 @@ async def handle_single_request_with_retries( session=session, status_tracker=status_tracker, ) - + # Save response in the base class await self.append_generic_response(generic_response, save_filepath) - + status_tracker.num_tasks_in_progress -= 1 status_tracker.num_tasks_succeeded += 1 status_tracker.pbar.update(1) @@ -372,15 +372,15 @@ async def call_single_request( status_tracker: StatusTracker, ) -> GenericResponse: """Make a single API request without error handling. - + This method should implement the actual API call logic without handling retries or errors. - + Args: request (APIRequest): Request to process session (aiohttp.ClientSession): Async HTTP session status_tracker (StatusTracker): Tracks request status - + Returns: GenericResponse: The response from the API call """ diff --git a/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py b/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py index d545e257..59ad6a2c 100644 --- a/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py +++ b/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py @@ -218,7 +218,7 @@ async def call_single_request( request (APIRequest): Request to process session (aiohttp.ClientSession): Async HTTP session status_tracker (StatusTracker): Tracks request status - + Returns: GenericResponse: The response from LiteLLM """ @@ -229,11 +229,11 @@ async def call_single_request( response_model=request.prompt_formatter.response_format, timeout=60.0, ) - response_message = response.model_dump() if hasattr(response, "model_dump") else response - else: - completion_obj = await litellm.acompletion( - **request.api_specific_request, timeout=60.0 + response_message = ( + response.model_dump() if hasattr(response, "model_dump") else response ) + else: + completion_obj = await litellm.acompletion(**request.api_specific_request, timeout=60.0) response_message = completion_obj["choices"][0]["message"]["content"] # Extract token usage diff --git a/src/bespokelabs/curator/request_processor/openai_online_request_processor.py b/src/bespokelabs/curator/request_processor/openai_online_request_processor.py index 92d73a3e..b8b5f27f 100644 --- a/src/bespokelabs/curator/request_processor/openai_online_request_processor.py +++ b/src/bespokelabs/curator/request_processor/openai_online_request_processor.py @@ -232,7 +232,7 @@ async def call_single_request( request (APIRequest): The request to process session (aiohttp.ClientSession): Async HTTP session status_tracker (StatusTracker): Tracks request status - + Returns: GenericResponse: The response from OpenAI """ @@ -259,9 +259,7 @@ async def call_single_request( raise Exception(f"API error: {error}") if response_obj.status != 200: - raise Exception( - f"API request failed with status {response_obj.status}: {response}" - ) + raise Exception(f"API request failed with status {response_obj.status}: {response}") response_message = response["choices"][0]["message"]["content"] usage = response["usage"] From 283191470d178eadf6d48d14edbf29196760a1a2 Mon Sep 17 00:00:00 2001 From: Charlie Cheng-Jie Ji Date: Tue, 3 Dec 2024 21:54:50 +0000 Subject: [PATCH 48/65] bring back the resume logging and temp file write logic --- .../base_online_request_processor.py | 61 +++++++++++++++++-- 1 file changed, 55 insertions(+), 6 deletions(-) diff --git a/src/bespokelabs/curator/request_processor/base_online_request_processor.py b/src/bespokelabs/curator/request_processor/base_online_request_processor.py index 1181fba2..e345b9c9 100644 --- a/src/bespokelabs/curator/request_processor/base_online_request_processor.py +++ b/src/bespokelabs/curator/request_processor/base_online_request_processor.py @@ -20,7 +20,7 @@ import aiofiles logger = logging.getLogger(__name__) - +logger.setLevel(logging.INFO) @dataclass class StatusTracker: @@ -176,6 +176,7 @@ async def process_requests_from_file( save_filepath: str, max_attempts: int, resume: bool, + resume_no_retry: bool = False, ) -> None: """Processes API requests in parallel, throttling to stay under rate limits.""" @@ -197,12 +198,60 @@ async def process_requests_from_file( # Track completed requests for resume functionality completed_request_ids = set() - if os.path.exists(save_filepath) and resume: - with open(save_filepath, "r") as f: - for line in f: - response = GenericResponse.model_validate_json(line) - if not response.response_errors: + if os.path.exists(save_filepath): + if resume: + logger.debug(f"Resuming progress from existing file: {save_filepath}") + logger.debug(f"Removing all failed requests from {save_filepath} so they can be retried") + temp_filepath = f"{save_filepath}.temp" + num_previously_failed_requests = 0 + + with open(save_filepath, "r") as input_file, \ + open(temp_filepath, "w") as output_file: + for line in input_file: + response = GenericResponse.model_validate_json(line) + if response.response_errors: + logger.debug( + f"Request {response.generic_request.original_row_idx} previously failed due to errors: " + f"{response.response_errors}, removing from output and will retry" + ) + num_previously_failed_requests += 1 + else: + completed_request_ids.add(response.generic_request.original_row_idx) + output_file.write(line) + + logger.info(f"Found {len(completed_request_ids)} completed requests and " + f"{num_previously_failed_requests} previously failed requests") + logger.info("Failed requests and remaining requests will now be processed.") + os.replace(temp_filepath, save_filepath) + + elif resume_no_retry: + logger.warning(f"Resuming progress from existing file: {save_filepath}, without retrying failed requests") + num_previously_failed_requests = 0 + + with open(save_filepath, "r") as input_file: + for line in input_file: + response = GenericResponse.model_validate_json(line) + if response.response_errors: + logger.debug( + f"Request {response.generic_request.original_row_idx} previously failed due to errors: " + f"{response.response_errors}, will NOT retry" + ) + num_previously_failed_requests += 1 completed_request_ids.add(response.generic_request.original_row_idx) + + logger.info(f"Found {len(completed_request_ids)} total requests and " + f"{num_previously_failed_requests} previously failed requests") + logger.info("Remaining requests will now be processed.") + + else: + user_input = input( + f"File {save_filepath} already exists.\n" + f"To resume if there are remaining requests without responses, run with --resume flag.\n" + f"Overwrite? (Y/n): " + ) + if user_input.lower() not in ["y", ""]: + logger.info("Aborting operation.") + return # Count total requests total_requests = sum(1 for _ in open(generic_requests_filepath)) From bd99464dbacd9cd484a2ea8e03170ee42f71da93 Mon Sep 17 00:00:00 2001 From: Charlie Cheng-Jie Ji Date: Tue, 3 Dec 2024 22:15:52 +0000 Subject: [PATCH 49/65] bring better logging for try except in base online --- .../request_processor/base_online_request_processor.py | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/src/bespokelabs/curator/request_processor/base_online_request_processor.py b/src/bespokelabs/curator/request_processor/base_online_request_processor.py index e345b9c9..f43e3348 100644 --- a/src/bespokelabs/curator/request_processor/base_online_request_processor.py +++ b/src/bespokelabs/curator/request_processor/base_online_request_processor.py @@ -40,6 +40,8 @@ class StatusTracker: max_tokens_per_minute: int = 0 pbar: tqdm = field(default=None) response_cost: float = 0 + time_of_last_rate_limit_error: float = field(default=None) + num_rate_limit_errors: int = 0 def __str__(self): return ( @@ -392,7 +394,9 @@ async def handle_single_request_with_retries( status_tracker.pbar.update(1) except Exception as e: - logger.error(f"Error in API request: {e}") + logger.warning( + f"Request {request.task_id} failed with Exception {e}, attempts left {request.attempts_left}" + ) status_tracker.num_other_errors += 1 request.result.append(e) @@ -400,6 +404,7 @@ async def handle_single_request_with_retries( request.attempts_left -= 1 retry_queue.put_nowait(request) else: + logger.debug(f"Request {request.task_id} failed permanently after all retries exhausted") generic_response = GenericResponse( response_message=None, response_errors=[str(e) for e in request.result], @@ -412,6 +417,7 @@ async def handle_single_request_with_retries( await self.append_generic_response(generic_response, save_filepath) status_tracker.num_tasks_in_progress -= 1 status_tracker.num_tasks_failed += 1 + @abstractmethod async def call_single_request( From 0d5292d2b43c634ab0bb0df0b6c2bbb11cc201fb Mon Sep 17 00:00:00 2001 From: Charlie Cheng-Jie Ji Date: Tue, 3 Dec 2024 22:16:08 +0000 Subject: [PATCH 50/65] set logger to info for openai --- .../curator/request_processor/openai_online_request_processor.py | 1 + 1 file changed, 1 insertion(+) diff --git a/src/bespokelabs/curator/request_processor/openai_online_request_processor.py b/src/bespokelabs/curator/request_processor/openai_online_request_processor.py index b8b5f27f..e1c0e5c5 100644 --- a/src/bespokelabs/curator/request_processor/openai_online_request_processor.py +++ b/src/bespokelabs/curator/request_processor/openai_online_request_processor.py @@ -21,6 +21,7 @@ from bespokelabs.curator.request_processor.generic_response import TokenUsage, GenericResponse logger = logging.getLogger(__name__) +logger.setLevel(logging.INFO) def get_token_encoding_name(model_name: str) -> str: From 87b58eac5483083013a3af8af53136c19a5b9715 Mon Sep 17 00:00:00 2001 From: Charlie Cheng-Jie Ji Date: Tue, 3 Dec 2024 22:46:10 +0000 Subject: [PATCH 51/65] baseonlinerequestprocessor --- .../request_processor/base_online_request_processor.py | 2 +- .../request_processor/litellm_online_request_processor.py | 4 ++-- .../request_processor/openai_online_request_processor.py | 4 ++-- 3 files changed, 5 insertions(+), 5 deletions(-) diff --git a/src/bespokelabs/curator/request_processor/base_online_request_processor.py b/src/bespokelabs/curator/request_processor/base_online_request_processor.py index f43e3348..d026d55b 100644 --- a/src/bespokelabs/curator/request_processor/base_online_request_processor.py +++ b/src/bespokelabs/curator/request_processor/base_online_request_processor.py @@ -105,7 +105,7 @@ class APIRequest: created_at: datetime.datetime = field(default_factory=datetime.datetime.now) -class OnlineRequestProcessor(BaseRequestProcessor, ABC): +class BaseOnlineRequestProcessor(BaseRequestProcessor, ABC): """Abstract base class for online request processors that make real-time API calls.""" def __init__( diff --git a/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py b/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py index 59ad6a2c..6d6bb0ee 100644 --- a/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py +++ b/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py @@ -7,7 +7,7 @@ import datetime import instructor from bespokelabs.curator.request_processor.base_online_request_processor import ( - OnlineRequestProcessor, + BaseOnlineRequestProcessor, APIRequest, StatusTracker, ) @@ -22,7 +22,7 @@ litellm.suppress_debug_info = True -class LiteLLMOnlineRequestProcessor(OnlineRequestProcessor): +class LiteLLMOnlineRequestProcessor(BaseOnlineRequestProcessor): """LiteLLM implementation of the OnlineRequestProcessor for multi-provider LLM support. This processor uses LiteLLM to handle requests across different LLM providers (OpenAI, Anthropic, etc.) diff --git a/src/bespokelabs/curator/request_processor/openai_online_request_processor.py b/src/bespokelabs/curator/request_processor/openai_online_request_processor.py index e1c0e5c5..ab898413 100644 --- a/src/bespokelabs/curator/request_processor/openai_online_request_processor.py +++ b/src/bespokelabs/curator/request_processor/openai_online_request_processor.py @@ -13,7 +13,7 @@ import time from bespokelabs.curator.request_processor.base_online_request_processor import ( - OnlineRequestProcessor, + BaseOnlineRequestProcessor, APIRequest, StatusTracker, ) @@ -58,7 +58,7 @@ def api_endpoint_from_url(request_url: str) -> str: raise NotImplementedError(f'API endpoint "{request_url}" not implemented in Curator yet.') -class OpenAIOnlineRequestProcessor(OnlineRequestProcessor): +class OpenAIOnlineRequestProcessor(BaseOnlineRequestProcessor): """OpenAI-specific implementation of the OnlineRequestProcessor. Handles API requests to OpenAI's chat completion endpoints with rate limiting, From e24d493c72e6dee295e69d004b80050f7c0b04b7 Mon Sep 17 00:00:00 2001 From: Charlie Cheng-Jie Ji Date: Tue, 3 Dec 2024 23:18:52 +0000 Subject: [PATCH 52/65] default model backend choose if backend is None, and support openai structured output detection using model names. --- src/bespokelabs/curator/prompter/prompter.py | 48 ++++++++++++++++--- .../base_online_request_processor.py | 4 +- .../litellm_online_request_processor.py | 6 +-- .../openai_online_request_processor.py | 32 +++++++++++++ 4 files changed, 76 insertions(+), 14 deletions(-) diff --git a/src/bespokelabs/curator/prompter/prompter.py b/src/bespokelabs/curator/prompter/prompter.py index abfbdad8..086e7ba7 100644 --- a/src/bespokelabs/curator/prompter/prompter.py +++ b/src/bespokelabs/curator/prompter/prompter.py @@ -29,11 +29,38 @@ T = TypeVar("T") logger = logging.getLogger(__name__) - +logger.setLevel(logging.INFO) class Prompter: """Interface for prompting LLMs.""" + @staticmethod + def _determine_backend(model_name: str, response_format: Optional[Type[BaseModel]] = None) -> str: + """Determine which backend to use based on model name and response format. + + Args: + model_name (str): Name of the model + response_format (Optional[Type[BaseModel]]): Response format if specified + + Returns: + str: Backend to use ("openai" or "litellm") + """ + model_name = model_name.lower() + + # GPT-4o models with response format should use OpenAI + if response_format and OpenAIOnlineRequestProcessor(model_name).check_structured_output_support(): + logger.info(f"Requesting structured output from {model_name}, using OpenAI backend") + return "openai" + + # GPT models and O1 models without response format should use OpenAI + if not response_format and any(x in model_name for x in ["gpt-", "o1-preview", "o1-mini"]): + logger.info(f"Requesting text output from {model_name}, using OpenAI backend") + return "openai" + + # Default to LiteLLM for all other cases + logger.info(f"Requesting {f'structured' if response_format else 'text'} output from {model_name}, using LiteLLM backend") + return "litellm" + def __init__( self, model_name: str, @@ -48,7 +75,7 @@ def __init__( ] ] = None, response_format: Optional[Type[BaseModel]] = None, - backend: str = "openai", + backend: Optional[str] = None, batch: bool = False, batch_size: Optional[int] = None, temperature: Optional[float] = None, @@ -66,7 +93,7 @@ def __init__( response object and returns the parsed output response_format (Optional[Type[BaseModel]]): A Pydantic model specifying the response format from the LLM. - backend (str): The backend to use ("openai" or "litellm") + backend (Optional[str]): The backend to use ("openai" or "litellm"). If None, will be auto-determined batch (bool): Whether to use batch processing batch_size (Optional[int]): The size of the batch to use, only used if batch is True temperature (Optional[float]): The temperature to use for the LLM, only used if batch is False @@ -91,9 +118,16 @@ def __init__( model_name, prompt_func, parse_func, response_format ) self.batch_mode = batch - self.backend = backend + + # Auto-determine backend if not specified + # Use provided backend or auto-determine based on model and format + if backend is not None: + self.backend = backend + else: + self.backend = self._determine_backend(model_name, response_format) + # Select request processor based on backend - if backend == "openai": + if self.backend == "openai": if batch: if batch_size is None: batch_size = 1_000 @@ -120,7 +154,7 @@ def __init__( presence_penalty=presence_penalty, frequency_penalty=frequency_penalty, ) - elif backend == "litellm": + elif self.backend == "litellm": if batch: logger.warning( "Batch mode is not supported with LiteLLM backend, ignoring batch=True" @@ -133,7 +167,7 @@ def __init__( frequency_penalty=frequency_penalty, ) else: - raise ValueError(f"Unknown backend: {backend}") + raise ValueError(f"Unknown backend: {self.backend}") def __call__(self, dataset: Optional[Iterable] = None, working_dir: str = None) -> Dataset: """ diff --git a/src/bespokelabs/curator/request_processor/base_online_request_processor.py b/src/bespokelabs/curator/request_processor/base_online_request_processor.py index d026d55b..849a13e3 100644 --- a/src/bespokelabs/curator/request_processor/base_online_request_processor.py +++ b/src/bespokelabs/curator/request_processor/base_online_request_processor.py @@ -134,7 +134,7 @@ def estimate_output_tokens(self) -> int: """Estimate output tokens for a request""" pass - def check_structured_output_support(self, prompt_formatter: PromptFormatter) -> bool: + def check_structured_output_support(self) -> bool: """Check if the model supports structured output""" return True @@ -150,7 +150,7 @@ def run( self.prompt_formatter = prompt_formatter if self.prompt_formatter.response_format: - if not self.check_structured_output_support(prompt_formatter): + if not self.check_structured_output_support(): raise ValueError( f"Model {self.model} does not support structured output, " f"response_format: {self.prompt_formatter.response_format}" diff --git a/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py b/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py index 6d6bb0ee..c2d1d871 100644 --- a/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py +++ b/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py @@ -60,15 +60,12 @@ def __init__( ) self.client = instructor.from_litellm(litellm.acompletion) - def check_structured_output_support(self, prompt_formatter: PromptFormatter): + def check_structured_output_support(self): """Verify if the model supports structured output via instructor. Tests the model's capability to handle structured output by making a test request with a simple schema. - Args: - prompt_formatter (PromptFormatter): Contains response format requirements - Returns: bool: True if structured output is supported, False otherwise @@ -77,7 +74,6 @@ def check_structured_output_support(self, prompt_formatter: PromptFormatter): - Logs detailed information about support status - Required for models that will use JSON schema responses """ - assert prompt_formatter.response_format class User(BaseModel): name: str diff --git a/src/bespokelabs/curator/request_processor/openai_online_request_processor.py b/src/bespokelabs/curator/request_processor/openai_online_request_processor.py index ab898413..16760d3b 100644 --- a/src/bespokelabs/curator/request_processor/openai_online_request_processor.py +++ b/src/bespokelabs/curator/request_processor/openai_online_request_processor.py @@ -11,6 +11,7 @@ import tiktoken import litellm import time +from datetime import datetime from bespokelabs.curator.request_processor.base_online_request_processor import ( BaseOnlineRequestProcessor, @@ -180,6 +181,37 @@ def estimate_total_tokens(self, messages: list) -> int: output_tokens = self.estimate_output_tokens() return num_tokens + output_tokens + def check_structured_output_support(self) -> bool: + """Check if the model supports structured output based on model name and date. + + Returns: + bool: True if model supports structured output, False otherwise + + Note: + Supports: + - gpt-4o-mini with date >= 2024-07-18 or latest + - gpt-4o with date >= 2024-08-06 or latest + """ + model_name = self.model.lower() + + # Check gpt-4o-mini support + if model_name == "gpt-4o-mini": # Latest version + return True + if "gpt-4o-mini-" in model_name: + mini_date = datetime.strptime(model_name.split("gpt-4o-mini-")[1], "%Y-%m-%d") + if mini_date >= datetime(2024, 7, 18): + return True + + # Check gpt-4o support + if model_name == "gpt-4o": # Latest version + return True + if "gpt-4o-" in model_name: + base_date = datetime.strptime(model_name.split("gpt-4o-")[1], "%Y-%m-%d") + if base_date >= datetime(2024, 8, 6): + return True + + return False + def create_api_specific_request(self, generic_request: GenericRequest) -> dict: """Create an OpenAI-specific request from a generic request. From 6d8da4514f8920a503a95e95a0fac428a1defd23 Mon Sep 17 00:00:00 2001 From: Charlie Cheng-Jie Ji Date: Tue, 3 Dec 2024 23:19:35 +0000 Subject: [PATCH 53/65] black --- src/bespokelabs/curator/prompter/prompter.py | 28 +++++++----- .../base_online_request_processor.py | 43 ++++++++++++------- .../openai_online_request_processor.py | 10 ++--- 3 files changed, 50 insertions(+), 31 deletions(-) diff --git a/src/bespokelabs/curator/prompter/prompter.py b/src/bespokelabs/curator/prompter/prompter.py index 086e7ba7..cb8b067c 100644 --- a/src/bespokelabs/curator/prompter/prompter.py +++ b/src/bespokelabs/curator/prompter/prompter.py @@ -31,34 +31,42 @@ logger = logging.getLogger(__name__) logger.setLevel(logging.INFO) + class Prompter: """Interface for prompting LLMs.""" @staticmethod - def _determine_backend(model_name: str, response_format: Optional[Type[BaseModel]] = None) -> str: + def _determine_backend( + model_name: str, response_format: Optional[Type[BaseModel]] = None + ) -> str: """Determine which backend to use based on model name and response format. - + Args: model_name (str): Name of the model response_format (Optional[Type[BaseModel]]): Response format if specified - + Returns: str: Backend to use ("openai" or "litellm") """ model_name = model_name.lower() - + # GPT-4o models with response format should use OpenAI - if response_format and OpenAIOnlineRequestProcessor(model_name).check_structured_output_support(): + if ( + response_format + and OpenAIOnlineRequestProcessor(model_name).check_structured_output_support() + ): logger.info(f"Requesting structured output from {model_name}, using OpenAI backend") return "openai" - + # GPT models and O1 models without response format should use OpenAI if not response_format and any(x in model_name for x in ["gpt-", "o1-preview", "o1-mini"]): logger.info(f"Requesting text output from {model_name}, using OpenAI backend") return "openai" - + # Default to LiteLLM for all other cases - logger.info(f"Requesting {f'structured' if response_format else 'text'} output from {model_name}, using LiteLLM backend") + logger.info( + f"Requesting {f'structured' if response_format else 'text'} output from {model_name}, using LiteLLM backend" + ) return "litellm" def __init__( @@ -118,14 +126,14 @@ def __init__( model_name, prompt_func, parse_func, response_format ) self.batch_mode = batch - + # Auto-determine backend if not specified # Use provided backend or auto-determine based on model and format if backend is not None: self.backend = backend else: self.backend = self._determine_backend(model_name, response_format) - + # Select request processor based on backend if self.backend == "openai": if batch: diff --git a/src/bespokelabs/curator/request_processor/base_online_request_processor.py b/src/bespokelabs/curator/request_processor/base_online_request_processor.py index 849a13e3..c1685db6 100644 --- a/src/bespokelabs/curator/request_processor/base_online_request_processor.py +++ b/src/bespokelabs/curator/request_processor/base_online_request_processor.py @@ -22,6 +22,7 @@ logger = logging.getLogger(__name__) logger.setLevel(logging.INFO) + @dataclass class StatusTracker: """Tracks the status of all requests.""" @@ -203,12 +204,15 @@ async def process_requests_from_file( if os.path.exists(save_filepath): if resume: logger.debug(f"Resuming progress from existing file: {save_filepath}") - logger.debug(f"Removing all failed requests from {save_filepath} so they can be retried") + logger.debug( + f"Removing all failed requests from {save_filepath} so they can be retried" + ) temp_filepath = f"{save_filepath}.temp" num_previously_failed_requests = 0 - - with open(save_filepath, "r") as input_file, \ - open(temp_filepath, "w") as output_file: + + with open(save_filepath, "r") as input_file, open( + temp_filepath, "w" + ) as output_file: for line in input_file: response = GenericResponse.model_validate_json(line) if response.response_errors: @@ -220,16 +224,20 @@ async def process_requests_from_file( else: completed_request_ids.add(response.generic_request.original_row_idx) output_file.write(line) - - logger.info(f"Found {len(completed_request_ids)} completed requests and " - f"{num_previously_failed_requests} previously failed requests") + + logger.info( + f"Found {len(completed_request_ids)} completed requests and " + f"{num_previously_failed_requests} previously failed requests" + ) logger.info("Failed requests and remaining requests will now be processed.") os.replace(temp_filepath, save_filepath) - + elif resume_no_retry: - logger.warning(f"Resuming progress from existing file: {save_filepath}, without retrying failed requests") + logger.warning( + f"Resuming progress from existing file: {save_filepath}, without retrying failed requests" + ) num_previously_failed_requests = 0 - + with open(save_filepath, "r") as input_file: for line in input_file: response = GenericResponse.model_validate_json(line) @@ -240,11 +248,13 @@ async def process_requests_from_file( ) num_previously_failed_requests += 1 completed_request_ids.add(response.generic_request.original_row_idx) - - logger.info(f"Found {len(completed_request_ids)} total requests and " - f"{num_previously_failed_requests} previously failed requests") + + logger.info( + f"Found {len(completed_request_ids)} total requests and " + f"{num_previously_failed_requests} previously failed requests" + ) logger.info("Remaining requests will now be processed.") - + else: user_input = input( f"File {save_filepath} already exists.\n" @@ -404,7 +414,9 @@ async def handle_single_request_with_retries( request.attempts_left -= 1 retry_queue.put_nowait(request) else: - logger.debug(f"Request {request.task_id} failed permanently after all retries exhausted") + logger.debug( + f"Request {request.task_id} failed permanently after all retries exhausted" + ) generic_response = GenericResponse( response_message=None, response_errors=[str(e) for e in request.result], @@ -417,7 +429,6 @@ async def handle_single_request_with_retries( await self.append_generic_response(generic_response, save_filepath) status_tracker.num_tasks_in_progress -= 1 status_tracker.num_tasks_failed += 1 - @abstractmethod async def call_single_request( diff --git a/src/bespokelabs/curator/request_processor/openai_online_request_processor.py b/src/bespokelabs/curator/request_processor/openai_online_request_processor.py index 16760d3b..481a1481 100644 --- a/src/bespokelabs/curator/request_processor/openai_online_request_processor.py +++ b/src/bespokelabs/curator/request_processor/openai_online_request_processor.py @@ -183,17 +183,17 @@ def estimate_total_tokens(self, messages: list) -> int: def check_structured_output_support(self) -> bool: """Check if the model supports structured output based on model name and date. - + Returns: bool: True if model supports structured output, False otherwise - + Note: Supports: - gpt-4o-mini with date >= 2024-07-18 or latest - gpt-4o with date >= 2024-08-06 or latest """ model_name = self.model.lower() - + # Check gpt-4o-mini support if model_name == "gpt-4o-mini": # Latest version return True @@ -201,7 +201,7 @@ def check_structured_output_support(self) -> bool: mini_date = datetime.strptime(model_name.split("gpt-4o-mini-")[1], "%Y-%m-%d") if mini_date >= datetime(2024, 7, 18): return True - + # Check gpt-4o support if model_name == "gpt-4o": # Latest version return True @@ -209,7 +209,7 @@ def check_structured_output_support(self) -> bool: base_date = datetime.strptime(model_name.split("gpt-4o-")[1], "%Y-%m-%d") if base_date >= datetime(2024, 8, 6): return True - + return False def create_api_specific_request(self, generic_request: GenericRequest) -> dict: From 73b7ab43f1143b18bebd54a5bed97ce81644580d Mon Sep 17 00:00:00 2001 From: Charlie Cheng-Jie Ji Date: Tue, 3 Dec 2024 23:31:17 +0000 Subject: [PATCH 54/65] debug imports --- src/bespokelabs/curator/prompter/prompter.py | 1 - .../request_processor/litellm_online_request_processor.py | 1 - .../request_processor/openai_online_request_processor.py | 6 ++---- 3 files changed, 2 insertions(+), 6 deletions(-) diff --git a/src/bespokelabs/curator/prompter/prompter.py b/src/bespokelabs/curator/prompter/prompter.py index cb8b067c..b072213d 100644 --- a/src/bespokelabs/curator/prompter/prompter.py +++ b/src/bespokelabs/curator/prompter/prompter.py @@ -29,7 +29,6 @@ T = TypeVar("T") logger = logging.getLogger(__name__) -logger.setLevel(logging.INFO) class Prompter: diff --git a/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py b/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py index c2d1d871..6ca4288f 100644 --- a/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py +++ b/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py @@ -17,7 +17,6 @@ from bespokelabs.curator.prompter.prompt_formatter import PromptFormatter logger = logging.getLogger(__name__) -logger.setLevel(logging.INFO) litellm.suppress_debug_info = True diff --git a/src/bespokelabs/curator/request_processor/openai_online_request_processor.py b/src/bespokelabs/curator/request_processor/openai_online_request_processor.py index 481a1481..a26712a6 100644 --- a/src/bespokelabs/curator/request_processor/openai_online_request_processor.py +++ b/src/bespokelabs/curator/request_processor/openai_online_request_processor.py @@ -11,7 +11,6 @@ import tiktoken import litellm import time -from datetime import datetime from bespokelabs.curator.request_processor.base_online_request_processor import ( BaseOnlineRequestProcessor, @@ -22,7 +21,6 @@ from bespokelabs.curator.request_processor.generic_response import TokenUsage, GenericResponse logger = logging.getLogger(__name__) -logger.setLevel(logging.INFO) def get_token_encoding_name(model_name: str) -> str: @@ -198,7 +196,7 @@ def check_structured_output_support(self) -> bool: if model_name == "gpt-4o-mini": # Latest version return True if "gpt-4o-mini-" in model_name: - mini_date = datetime.strptime(model_name.split("gpt-4o-mini-")[1], "%Y-%m-%d") + mini_date = datetime.datetime.strptime(model_name.split("gpt-4o-mini-")[1], "%Y-%m-%d") if mini_date >= datetime(2024, 7, 18): return True @@ -206,7 +204,7 @@ def check_structured_output_support(self) -> bool: if model_name == "gpt-4o": # Latest version return True if "gpt-4o-" in model_name: - base_date = datetime.strptime(model_name.split("gpt-4o-")[1], "%Y-%m-%d") + base_date = datetime.datetime.strptime(model_name.split("gpt-4o-")[1], "%Y-%m-%d") if base_date >= datetime(2024, 8, 6): return True From 29ad907b69b4321ac56c6a643aaf5d094c5d7a9a Mon Sep 17 00:00:00 2001 From: Charlie Cheng-Jie Ji Date: Wed, 4 Dec 2024 00:12:23 +0000 Subject: [PATCH 55/65] revert retry logic to process during the end --- .../base_online_request_processor.py | 86 +++++++++++-------- 1 file changed, 50 insertions(+), 36 deletions(-) diff --git a/src/bespokelabs/curator/request_processor/base_online_request_processor.py b/src/bespokelabs/curator/request_processor/base_online_request_processor.py index c1685db6..a710d21f 100644 --- a/src/bespokelabs/curator/request_processor/base_online_request_processor.py +++ b/src/bespokelabs/curator/request_processor/base_online_request_processor.py @@ -34,6 +34,7 @@ class StatusTracker: num_tasks_already_completed: int = 0 num_api_errors: int = 0 num_other_errors: int = 0 + num_rate_limit_errors: int = 0 available_request_capacity: float = 0 available_token_capacity: float = 0 last_update_time: float = field(default_factory=time.time) @@ -42,7 +43,6 @@ class StatusTracker: pbar: tqdm = field(default=None) response_cost: float = 0 time_of_last_rate_limit_error: float = field(default=None) - num_rate_limit_errors: int = 0 def __str__(self): return ( @@ -51,7 +51,10 @@ def __str__(self): f"Succeeded: {self.num_tasks_succeeded}, " f"Failed: {self.num_tasks_failed}, " f"Already Completed: {self.num_tasks_already_completed}\n" - f"Errors: {self.num_other_errors}" + f"Errors - API: {self.num_api_errors}, " + f"Rate Limit: {self.num_rate_limit_errors}, " + f"Other: {self.num_other_errors}, " + f"Total: {self.num_other_errors + self.num_api_errors + self.num_rate_limit_errors}" ) def update_capacity(self): @@ -321,34 +324,39 @@ async def process_requests_from_file( status_tracker.num_tasks_started += 1 status_tracker.num_tasks_in_progress += 1 - # Process any retries that are in the queue - while not queue_of_requests_to_retry.empty(): - retry_request = await queue_of_requests_to_retry.get() - token_estimate = self.estimate_total_tokens( - retry_request.generic_request.messages - ) + # Wait for all tasks to complete + if pending_requests: + await asyncio.gather(*pending_requests) - # Wait for capacity if needed - while not status_tracker.has_capacity(token_estimate): - await asyncio.sleep(0.1) + # Process any remaining retries in the queue + while not queue_of_requests_to_retry.empty(): + retry_request = await queue_of_requests_to_retry.get() + token_estimate = self.estimate_total_tokens(retry_request.generic_request.messages) - # Consume capacity before making request - status_tracker.consume_capacity(token_estimate) + attempt_number = 6 - retry_request.attempts_left + logger.info( + f"Processing final retry for request {retry_request.task_id} " + f"(attempt #{attempt_number} of 5). " + f"Previous errors: {retry_request.result}" + ) - task = asyncio.create_task( - self.handle_single_request_with_retries( - request=retry_request, - session=session, - retry_queue=queue_of_requests_to_retry, - save_filepath=save_filepath, - status_tracker=status_tracker, - ) - ) - pending_requests.append(task) + # Wait for capacity if needed + while not status_tracker.has_capacity(token_estimate): + await asyncio.sleep(0.1) - # Wait for all tasks to complete - if pending_requests: - await asyncio.gather(*pending_requests) + # Consume capacity before making request + status_tracker.consume_capacity(token_estimate) + + task = asyncio.create_task( + self.handle_single_request_with_retries( + request=retry_request, + session=session, + retry_queue=queue_of_requests_to_retry, + save_filepath=save_filepath, + status_tracker=status_tracker, + ) + ) + await task status_tracker.pbar.close() @@ -362,13 +370,6 @@ async def process_requests_from_file( f"requests failed. Errors logged to {save_filepath}." ) - async def append_generic_response(self, data: GenericResponse, filename: str) -> None: - """Append a response to a jsonl file with async file operations.""" - json_string = json.dumps(data.model_dump(), default=str) - async with aiofiles.open(filename, "a") as f: - await f.write(json_string + "\n") - logger.debug(f"Successfully appended response to {filename}") - async def handle_single_request_with_retries( self, request: APIRequest, @@ -405,17 +406,23 @@ async def handle_single_request_with_retries( except Exception as e: logger.warning( - f"Request {request.task_id} failed with Exception {e}, attempts left {request.attempts_left}" + f"Request {request.task_id} failed with Exception {e}, attempts left {request.attempts_left-1}" ) status_tracker.num_other_errors += 1 request.result.append(e) if request.attempts_left > 0: request.attempts_left -= 1 + # Add retry queue logging + logger.info( + f"Adding request {request.task_id} to retry queue. Will retry in next available slot. " + f"Attempts remaining: {request.attempts_left}" + ) retry_queue.put_nowait(request) else: - logger.debug( - f"Request {request.task_id} failed permanently after all retries exhausted" + logger.error( + f"Request {request.task_id} failed permanently after exhausting all 5 retry attempts. " + f"Errors: {[str(e) for e in request.result]}" ) generic_response = GenericResponse( response_message=None, @@ -451,3 +458,10 @@ async def call_single_request( GenericResponse: The response from the API call """ pass + + async def append_generic_response(self, data: GenericResponse, filename: str) -> None: + """Append a response to a jsonl file with async file operations.""" + json_string = json.dumps(data.model_dump(), default=str) + async with aiofiles.open(filename, "a") as f: + await f.write(json_string + "\n") + logger.debug(f"Successfully appended response to {filename}") From c56a5ede8b45be5b79ff3fa2c4f2d797283fe6c1 Mon Sep 17 00:00:00 2001 From: Charlie Cheng-Jie Ji Date: Wed, 4 Dec 2024 00:15:25 +0000 Subject: [PATCH 56/65] changed the default litellm model to be gemini --- examples/litellm_recipe_prompting.py | 2 +- examples/litellm_recipe_structured_output.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/litellm_recipe_prompting.py b/examples/litellm_recipe_prompting.py index c4587c48..645712d9 100644 --- a/examples/litellm_recipe_prompting.py +++ b/examples/litellm_recipe_prompting.py @@ -25,7 +25,7 @@ def main(): # Create prompter using LiteLLM backend recipe_prompter = curator.Prompter( - model_name="gpt-4o-mini", + model_name="gemini/gemini-1.5-flash", prompt_func=lambda row: f"Generate a random {row['cuisine']} recipe. Be creative but keep it realistic.", parse_func=lambda row, response: { "recipe": response, diff --git a/examples/litellm_recipe_structured_output.py b/examples/litellm_recipe_structured_output.py index 92e118ce..a3a9ade7 100644 --- a/examples/litellm_recipe_structured_output.py +++ b/examples/litellm_recipe_structured_output.py @@ -24,7 +24,7 @@ def main(): # We define a prompter that generates cuisines cuisines_generator = curator.Prompter( prompt_func=lambda: f"Generate 10 diverse cuisines.", - model_name="gpt-4o-mini", + model_name="gemini/gemini-1.5-flash", response_format=Cuisines, parse_func=lambda _, cuisines: [{"cuisine": t} for t in cuisines.cuisines_list], backend="litellm", From f79226d1d051f7dcb9432ffe175f04f5bb93a811 Mon Sep 17 00:00:00 2001 From: Charlie Cheng-Jie Ji Date: Wed, 4 Dec 2024 00:17:18 +0000 Subject: [PATCH 57/65] reverse the order of litellm models --- examples/litellm_recipe_structured_output.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/litellm_recipe_structured_output.py b/examples/litellm_recipe_structured_output.py index a3a9ade7..d13403af 100644 --- a/examples/litellm_recipe_structured_output.py +++ b/examples/litellm_recipe_structured_output.py @@ -24,7 +24,7 @@ def main(): # We define a prompter that generates cuisines cuisines_generator = curator.Prompter( prompt_func=lambda: f"Generate 10 diverse cuisines.", - model_name="gemini/gemini-1.5-flash", + model_name="claude-3-5-haiku-20241022", response_format=Cuisines, parse_func=lambda _, cuisines: [{"cuisine": t} for t in cuisines.cuisines_list], backend="litellm", @@ -33,7 +33,7 @@ def main(): print(cuisines.to_pandas()) recipe_prompter = curator.Prompter( - model_name="claude-3-5-haiku-20241022", + model_name="gemini/gemini-1.5-flash", prompt_func=lambda row: f"Generate a random {row['cuisine']} recipe. Be creative but keep it realistic.", parse_func=lambda row, response: { "title": response.title, From d9b3cf41c077e9b48d19227df47b70753ac7bc19 Mon Sep 17 00:00:00 2001 From: Charlie Cheng-Jie Ji Date: Wed, 4 Dec 2024 01:08:25 +0000 Subject: [PATCH 58/65] typing imports --- .../request_processor/openai_online_request_processor.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/bespokelabs/curator/request_processor/openai_online_request_processor.py b/src/bespokelabs/curator/request_processor/openai_online_request_processor.py index a26712a6..6afac9a4 100644 --- a/src/bespokelabs/curator/request_processor/openai_online_request_processor.py +++ b/src/bespokelabs/curator/request_processor/openai_online_request_processor.py @@ -4,7 +4,7 @@ import os import re import resource -from typing import Optional +from typing import Optional, Any import aiohttp import requests From ec1a1e94588c070dc962e67d3917a30b67f36213 Mon Sep 17 00:00:00 2001 From: Charlie Cheng-Jie Ji Date: Wed, 4 Dec 2024 01:14:26 +0000 Subject: [PATCH 59/65] rm duplicating code --- .../request_processor/openai_online_request_processor.py | 6 ------ 1 file changed, 6 deletions(-) diff --git a/src/bespokelabs/curator/request_processor/openai_online_request_processor.py b/src/bespokelabs/curator/request_processor/openai_online_request_processor.py index 6afac9a4..c42f8b42 100644 --- a/src/bespokelabs/curator/request_processor/openai_online_request_processor.py +++ b/src/bespokelabs/curator/request_processor/openai_online_request_processor.py @@ -91,12 +91,6 @@ def __init__( self.api_key = api_key self.token_encoding = tiktoken.get_encoding(get_token_encoding_name(model)) - # Set resource limits for file descriptors - soft, hard = resource.getrlimit(resource.RLIMIT_NOFILE) - resource.setrlimit( - resource.RLIMIT_NOFILE, (min(hard, 10 * 3000), hard) - ) # default to 3000 rpm - def get_rate_limits(self) -> dict: """Get rate limits from OpenAI API headers. From 2ba4bdf6aee8ef9f23fdd56888885e8fffbb616c Mon Sep 17 00:00:00 2001 From: Charlie Cheng-Jie Ji Date: Wed, 4 Dec 2024 01:17:58 +0000 Subject: [PATCH 60/65] add key formatted instructions --- examples/litellm_recipe_prompting.py | 9 ++++++++- examples/litellm_recipe_structured_output.py | 14 +++++++++++++- 2 files changed, 21 insertions(+), 2 deletions(-) diff --git a/examples/litellm_recipe_prompting.py b/examples/litellm_recipe_prompting.py index 645712d9..8db436ae 100644 --- a/examples/litellm_recipe_prompting.py +++ b/examples/litellm_recipe_prompting.py @@ -10,7 +10,7 @@ def main(): {"cuisine": cuisine} for cuisine in [ "Chinese", - "Italian", + "Italian", "Mexican", "French", "Japanese", @@ -24,6 +24,13 @@ def main(): cuisines = Dataset.from_list(cuisines) # Create prompter using LiteLLM backend + ############################################# + # To use Gemini models: + # 1. Go to https://aistudio.google.com/app/apikey + # 2. Generate an API key + # 3. Set environment variable: GEMINI_API_KEY + ############################################# + recipe_prompter = curator.Prompter( model_name="gemini/gemini-1.5-flash", prompt_func=lambda row: f"Generate a random {row['cuisine']} recipe. Be creative but keep it realistic.", diff --git a/examples/litellm_recipe_structured_output.py b/examples/litellm_recipe_structured_output.py index d13403af..e72cd8c8 100644 --- a/examples/litellm_recipe_structured_output.py +++ b/examples/litellm_recipe_structured_output.py @@ -22,6 +22,12 @@ class Cuisines(BaseModel): def main(): # We define a prompter that generates cuisines + ############################################# + # To use Claude models: + # 1. Go to https://console.anthropic.com/settings/keys + # 2. Generate an API key or use an existing API key + # 3. Set environment variable: ANTHROPIC_API_KEY + ############################################# cuisines_generator = curator.Prompter( prompt_func=lambda: f"Generate 10 diverse cuisines.", model_name="claude-3-5-haiku-20241022", @@ -31,7 +37,13 @@ def main(): ) cuisines = cuisines_generator() print(cuisines.to_pandas()) - + + ############################################# + # To use Gemini models: + # 1. Go to https://aistudio.google.com/app/apikey + # 2. Generate an API key or use an existing API key + # 3. Set environment variable: GEMINI_API_KEY + ############################################# recipe_prompter = curator.Prompter( model_name="gemini/gemini-1.5-flash", prompt_func=lambda row: f"Generate a random {row['cuisine']} recipe. Be creative but keep it realistic.", From 232dab5e7b0ef05c3900dfd27af9dae5bbf875a5 Mon Sep 17 00:00:00 2001 From: Charlie Cheng-Jie Ji Date: Wed, 4 Dec 2024 01:50:25 +0000 Subject: [PATCH 61/65] more specific try except --- .../litellm_online_request_processor.py | 17 ++++++++++++----- 1 file changed, 12 insertions(+), 5 deletions(-) diff --git a/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py b/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py index 6ca4288f..d49b4a09 100644 --- a/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py +++ b/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py @@ -91,11 +91,18 @@ class User(BaseModel): f"Model {self.model} supports structured output via instructor, response: {response}" ) return True - except Exception as e: - logger.warning( - f"Model {self.model} does not support structured output via instructor: {e}" - ) - return False + except instructor.exceptions.InstructorRetryException as e: + if "litellm.AuthenticationError" in str(e): + logger.warning( + f"Please provide a valid API key for model {self.model}." + ) + raise e + else: + logger.warning( + f"Model {self.model} does not support structured output via instructor: {e} {type(e)} {e.__cause__}" + ) + return False + def estimate_output_tokens(self) -> int: """Estimate the number of tokens in the model's response. From 50ee9e79e5b2f691c70e1d25872065fe0e542265 Mon Sep 17 00:00:00 2001 From: Charlie Cheng-Jie Ji Date: Wed, 4 Dec 2024 02:12:28 +0000 Subject: [PATCH 62/65] avoid sequentially process the retried entries. do parallel async --- .../request_processor/base_online_request_processor.py | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/src/bespokelabs/curator/request_processor/base_online_request_processor.py b/src/bespokelabs/curator/request_processor/base_online_request_processor.py index a710d21f..7bdd129f 100644 --- a/src/bespokelabs/curator/request_processor/base_online_request_processor.py +++ b/src/bespokelabs/curator/request_processor/base_online_request_processor.py @@ -329,6 +329,7 @@ async def process_requests_from_file( await asyncio.gather(*pending_requests) # Process any remaining retries in the queue + pending_retries = [] while not queue_of_requests_to_retry.empty(): retry_request = await queue_of_requests_to_retry.get() token_estimate = self.estimate_total_tokens(retry_request.generic_request.messages) @@ -356,7 +357,12 @@ async def process_requests_from_file( status_tracker=status_tracker, ) ) - await task + pending_retries.append(task) + await asyncio.sleep(0.1) # Allow other tasks to run + + # Wait for all retry tasks to complete + if pending_retries: + await asyncio.gather(*pending_retries) status_tracker.pbar.close() @@ -406,7 +412,7 @@ async def handle_single_request_with_retries( except Exception as e: logger.warning( - f"Request {request.task_id} failed with Exception {e}, attempts left {request.attempts_left-1}" + f"Request {request.task_id} failed with Exception {e}, attempts left {request.attempts_left}" ) status_tracker.num_other_errors += 1 request.result.append(e) From cdfe4a2061f54323e3cbf439c501bf2ad334c1f6 Mon Sep 17 00:00:00 2001 From: Charlie Cheng-Jie Ji Date: Wed, 4 Dec 2024 02:13:00 +0000 Subject: [PATCH 63/65] black --- examples/litellm_recipe_prompting.py | 2 +- examples/litellm_recipe_structured_output.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/litellm_recipe_prompting.py b/examples/litellm_recipe_prompting.py index 8db436ae..87446e01 100644 --- a/examples/litellm_recipe_prompting.py +++ b/examples/litellm_recipe_prompting.py @@ -10,7 +10,7 @@ def main(): {"cuisine": cuisine} for cuisine in [ "Chinese", - "Italian", + "Italian", "Mexican", "French", "Japanese", diff --git a/examples/litellm_recipe_structured_output.py b/examples/litellm_recipe_structured_output.py index e72cd8c8..747411e9 100644 --- a/examples/litellm_recipe_structured_output.py +++ b/examples/litellm_recipe_structured_output.py @@ -37,7 +37,7 @@ def main(): ) cuisines = cuisines_generator() print(cuisines.to_pandas()) - + ############################################# # To use Gemini models: # 1. Go to https://aistudio.google.com/app/apikey From 5cf2c7fce53a50f22fbea5fa004e54f8137be8fc Mon Sep 17 00:00:00 2001 From: Charlie Cheng-Jie Ji Date: Wed, 4 Dec 2024 02:16:44 +0000 Subject: [PATCH 64/65] black --- .../request_processor/litellm_online_request_processor.py | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py b/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py index d49b4a09..fa7dc593 100644 --- a/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py +++ b/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py @@ -93,9 +93,7 @@ class User(BaseModel): return True except instructor.exceptions.InstructorRetryException as e: if "litellm.AuthenticationError" in str(e): - logger.warning( - f"Please provide a valid API key for model {self.model}." - ) + logger.warning(f"Please provide a valid API key for model {self.model}.") raise e else: logger.warning( @@ -103,7 +101,6 @@ class User(BaseModel): ) return False - def estimate_output_tokens(self) -> int: """Estimate the number of tokens in the model's response. From 2e0ac2c6c02de15e98ac0865a53bc931f279d022 Mon Sep 17 00:00:00 2001 From: Charlie Cheng-Jie Ji Date: Wed, 4 Dec 2024 05:05:07 +0000 Subject: [PATCH 65/65] remove the short async timeout --- .../curator/request_processor/base_online_request_processor.py | 1 - 1 file changed, 1 deletion(-) diff --git a/src/bespokelabs/curator/request_processor/base_online_request_processor.py b/src/bespokelabs/curator/request_processor/base_online_request_processor.py index 7bdd129f..d9338a1e 100644 --- a/src/bespokelabs/curator/request_processor/base_online_request_processor.py +++ b/src/bespokelabs/curator/request_processor/base_online_request_processor.py @@ -358,7 +358,6 @@ async def process_requests_from_file( ) ) pending_retries.append(task) - await asyncio.sleep(0.1) # Allow other tasks to run # Wait for all retry tasks to complete if pending_retries: