From c714f224f93612374f5fb2271eb6c9429647f1c4 Mon Sep 17 00:00:00 2001 From: Mahesh Sathiamoorthy Date: Thu, 12 Dec 2024 18:11:18 +0000 Subject: [PATCH 01/34] Remove code accidentally added, and add a type shortcut. --- src/bespokelabs/curator/llm/llm.py | 23 ++++------------------- 1 file changed, 4 insertions(+), 19 deletions(-) diff --git a/src/bespokelabs/curator/llm/llm.py b/src/bespokelabs/curator/llm/llm.py index e5eadaa5..69dc7b6e 100644 --- a/src/bespokelabs/curator/llm/llm.py +++ b/src/bespokelabs/curator/llm/llm.py @@ -29,6 +29,7 @@ _CURATOR_DEFAULT_CACHE_DIR = "~/.cache/curator" T = TypeVar("T") +_DictOrBaseModel = Union[Dict[str, Any], BaseModel] logger = logger = logging.getLogger(__name__) @@ -70,28 +71,12 @@ def _determine_backend( ) return "litellm" - @staticmethod - def _convert_response_to_dict(response): - if hasattr(response, "model_dump"): - return response.model_dump() - elif isinstance(response, dict): - return response - elif hasattr(response, "__dict__"): - return response.__dict__ - return response - def __init__( self, model_name: str, - prompt_func: Callable[[Union[Dict[str, Any], BaseModel]], Dict[str, str]], + prompt_func: Callable[[_DictOrBaseModel], _DictOrBaseModel], parse_func: Optional[ - Callable[ - [ - Union[Dict[str, Any], BaseModel], - Union[Dict[str, Any], BaseModel], - ], - T, - ] + Callable[[_DictOrBaseModel, _DictOrBaseModel], _DictOrBaseModel] ] = None, response_format: Optional[Type[BaseModel]] = None, backend: Optional[str] = None, @@ -101,7 +86,7 @@ def __init__( batch_size: Optional[int] = None, batch_check_interval: Optional[int] = 60, delete_successful_batch_files: bool = True, - delete_failed_batch_files: bool = False, # To allow users to debug failed batches + delete_failed_batch_files: bool = False, temperature: Optional[float] = None, top_p: Optional[float] = None, presence_penalty: Optional[float] = None, From 110aa149325141afb16b9a1128faf777d060bef4 Mon Sep 17 00:00:00 2001 From: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> Date: Fri, 13 Dec 2024 06:24:15 +0000 Subject: [PATCH 02/34] Refactor LLM class to use context manager for batch parameters - Add BatchConfig dataclass for batch configuration - Implement context manager for batch processing - Update batch processing initialization to maintain state - Update simple_batch.py to use new context manager API - Maintain backward compatibility with existing batch parameters Co-Authored-By: mahesh@bespokelabs.ai --- src/bespokelabs/curator/llm/llm.py | 251 ++++++++++++++++++++++------- tests/batch/simple_batch.py | 11 +- 2 files changed, 196 insertions(+), 66 deletions(-) diff --git a/src/bespokelabs/curator/llm/llm.py b/src/bespokelabs/curator/llm/llm.py index e5eadaa5..7fd16ec2 100644 --- a/src/bespokelabs/curator/llm/llm.py +++ b/src/bespokelabs/curator/llm/llm.py @@ -3,6 +3,7 @@ import inspect import logging import os +from dataclasses import dataclass from datetime import datetime from io import BytesIO from typing import Any, Callable, Dict, Iterable, Optional, Type, TypeVar, Union @@ -33,6 +34,25 @@ logger = logger = logging.getLogger(__name__) +@dataclass +class BatchConfig: + """Configuration for batch processing in LLM. + + This class holds all configuration parameters related to batch processing, + used by the LLM context manager for batch operations. + + Args: + batch_size: Maximum number of requests per batch. If None, defaults to 1,000. + batch_check_interval: How often to check batch status, in seconds. + delete_successful_batch_files: Whether to delete batch files after successful processing. + delete_failed_batch_files: Whether to delete batch files after failed processing. + """ + batch_size: Optional[int] = None + batch_check_interval: int = 60 + delete_successful_batch_files: bool = True + delete_failed_batch_files: bool = False + + class LLM: """Interface for prompting LLMs.""" @@ -97,15 +117,16 @@ def __init__( backend: Optional[str] = None, max_requests_per_minute: Optional[int] = None, max_tokens_per_minute: Optional[int] = None, - batch: bool = False, - batch_size: Optional[int] = None, - batch_check_interval: Optional[int] = 60, - delete_successful_batch_files: bool = True, - delete_failed_batch_files: bool = False, # To allow users to debug failed batches temperature: Optional[float] = None, top_p: Optional[float] = None, presence_penalty: Optional[float] = None, frequency_penalty: Optional[float] = None, + # Deprecated parameters + batch: bool = False, + batch_size: Optional[int] = None, + batch_check_interval: Optional[int] = 60, + delete_successful_batch_files: bool = True, + delete_failed_batch_files: bool = False, ): """Initialize a LLM. @@ -118,82 +139,190 @@ def __init__( response_format: A Pydantic model specifying the response format from the LLM. backend: The backend to use ("openai" or "litellm"). If None, will be auto-determined - batch: Whether to use batch processing - batch_size: The size of the batch to use, only used if batch is True - batch_check_interval: The interval to check for batch completions, only used if batch is True - delete_successful_batch_files: Whether to delete successful batch files, only used if batch is True - delete_failed_batch_files: Whether to delete failed batch files, only used if batch is True - temperature: The temperature to use for the LLM, only used if batch is False - top_p: The top_p to use for the LLM, only used if batch is False - presence_penalty: The presence_penalty to use for the LLM, only used if batch is False - frequency_penalty: The frequency_penalty to use for the LLM, only used if batch is False + max_requests_per_minute: Maximum requests per minute (not supported in batch mode) + max_tokens_per_minute: Maximum tokens per minute (not supported in batch mode) + temperature: The temperature to use for the LLM + top_p: The top_p to use for the LLM + presence_penalty: The presence_penalty to use for the LLM + frequency_penalty: The frequency_penalty to use for the LLM + + Deprecated Args: + batch: Whether to use batch processing (deprecated, use context manager instead) + batch_size: The size of the batch to use (deprecated, use context manager instead) + batch_check_interval: The interval to check for batch completions (deprecated) + delete_successful_batch_files: Whether to delete successful batch files (deprecated) + delete_failed_batch_files: Whether to delete failed batch files (deprecated) """ + import warnings + + if batch or batch_size is not None: + warnings.warn( + "The 'batch' and batch-related parameters are deprecated. " + "Please use the context manager instead:\n" + "with llm.batch(batch_size=N, batch_check_interval=X): ...", + DeprecationWarning, + stacklevel=2, + ) + self.prompt_formatter = PromptFormatter( model_name, prompt_func, parse_func, response_format ) - self.batch_mode = batch + + # Initialize context manager state + self._batch_config = None + self._original_request_processor = None + + # Store model parameters + self.temperature = temperature + self.top_p = top_p + self.presence_penalty = presence_penalty + self.frequency_penalty = frequency_penalty + self.model_name = model_name # 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: - 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:,}" - ) - if max_requests_per_minute is not None or max_tokens_per_minute is not None: - logger.warning( - "max_requests_per_minute and max_tokens_per_minute not supported with batch mode" - ) - self._request_processor = OpenAIBatchRequestProcessor( - model=model_name, - batch_size=batch_size, - temperature=temperature, - top_p=top_p, - batch_check_interval=batch_check_interval, - presence_penalty=presence_penalty, - frequency_penalty=frequency_penalty, - delete_successful_batch_files=delete_successful_batch_files, - delete_failed_batch_files=delete_failed_batch_files, - ) - else: - if batch_size is not None: - logger.warning( - f"LLM 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, - max_requests_per_minute=max_requests_per_minute, - max_tokens_per_minute=max_tokens_per_minute, + # If using deprecated batch parameters, set up batch config + if batch or batch_size is not None: + self._batch_config = BatchConfig( + batch_size=batch_size, + batch_check_interval=batch_check_interval, + delete_successful_batch_files=delete_successful_batch_files, + delete_failed_batch_files=delete_failed_batch_files, + ) + + # Initialize request processor + self._setup_request_processor( + max_requests_per_minute=max_requests_per_minute, + max_tokens_per_minute=max_tokens_per_minute, + ) + + def _setup_request_processor( + self, + max_requests_per_minute: Optional[int] = None, + max_tokens_per_minute: Optional[int] = None, + ): + """Set up the appropriate request processor based on current config. + + This method initializes the request processor based on the current configuration, + including batch mode settings if a batch context is active. It handles both + OpenAI and LiteLLM backends, with appropriate warnings for unsupported configurations. + + Args: + max_requests_per_minute: Maximum requests per minute (not supported in batch mode) + max_tokens_per_minute: Maximum tokens per minute (not supported in batch mode) + """ + # Store current processor before potentially switching to batch mode + if hasattr(self, '_request_processor'): + self._original_request_processor = self._request_processor + + # Check if we're in batch mode (either via context manager or deprecated params) + is_batch_mode = self._batch_config is not None + + # If we already have a batch processor of the same type, keep it to maintain state + if (is_batch_mode and + hasattr(self, '_request_processor') and + isinstance(self._request_processor, OpenAIBatchRequestProcessor)): + return + + if is_batch_mode and self.backend == "openai": + if max_requests_per_minute is not None or max_tokens_per_minute is not None: + logger.warning( + "max_requests_per_minute and max_tokens_per_minute not supported with batch mode" ) + self._request_processor = OpenAIBatchRequestProcessor( + model=self.model_name, + batch_size=self._batch_config.batch_size or 1_000, + batch_check_interval=self._batch_config.batch_check_interval, + temperature=self.temperature, + top_p=self.top_p, + presence_penalty=self.presence_penalty, + frequency_penalty=self.frequency_penalty, + delete_successful_batch_files=self._batch_config.delete_successful_batch_files, + delete_failed_batch_files=self._batch_config.delete_failed_batch_files, + ) + elif self.backend == "openai": + self._request_processor = OpenAIOnlineRequestProcessor( + model=self.model_name, + temperature=self.temperature, + top_p=self.top_p, + presence_penalty=self.presence_penalty, + frequency_penalty=self.frequency_penalty, + max_requests_per_minute=max_requests_per_minute, + max_tokens_per_minute=max_tokens_per_minute, + ) elif self.backend == "litellm": - if batch: + if is_batch_mode: logger.warning( - "Batch mode is not supported with LiteLLM backend, ignoring batch=True" + "Batch mode is not supported with LiteLLM backend, ignoring batch context" ) self._request_processor = LiteLLMOnlineRequestProcessor( - model=model_name, - temperature=temperature, - top_p=top_p, - presence_penalty=presence_penalty, - frequency_penalty=frequency_penalty, + model=self.model_name, + temperature=self.temperature, + top_p=self.top_p, + presence_penalty=self.presence_penalty, + frequency_penalty=self.frequency_penalty, max_requests_per_minute=max_requests_per_minute, max_tokens_per_minute=max_tokens_per_minute, ) else: raise ValueError(f"Unknown backend: {self.backend}") + def batch( + self, + batch_size: Optional[int] = None, + batch_check_interval: int = 60, + delete_successful_batch_files: bool = True, + delete_failed_batch_files: bool = False, + ) -> "LLM": + """Create a context manager for batch processing. + + Args: + batch_size: Maximum number of requests per batch. If None, defaults to 1,000. + batch_check_interval: How often to check batch status, in seconds. + delete_successful_batch_files: Whether to delete batch files after successful processing. + delete_failed_batch_files: Whether to delete batch files after failed processing. + + Returns: + self: The LLM instance configured for batch processing. + + Example: + ```python + llm = LLM(...) + with llm.batch(batch_size=100): + results = llm(dataset) + ``` + """ + self._batch_config = BatchConfig( + batch_size=batch_size, + batch_check_interval=batch_check_interval, + delete_successful_batch_files=delete_successful_batch_files, + delete_failed_batch_files=delete_failed_batch_files, + ) + return self + + def __enter__(self): + """Enter batch context. + + Raises: + RuntimeError: If already in a batch context or if batch() wasn't called. + """ + if self._batch_config is None: + raise RuntimeError("Must use 'with llm.batch()' to enter batch context") + if self._original_request_processor is not None: + raise RuntimeError("Already in batch context") + self._setup_request_processor() + return self + + def __exit__(self, exc_type, exc_val, exc_tb): + """Exit batch context and restore original request processor.""" + self._batch_config = None + if self._original_request_processor is not None: + self._request_processor = self._original_request_processor + def __call__( self, dataset: Optional[Iterable] = None, @@ -263,7 +392,7 @@ def _completions( if self.prompt_formatter.response_format else "text" ), - str(self.batch_mode), + str(self._batch_config is not None), str(self.backend), ] ) @@ -293,7 +422,7 @@ def _completions( else "text" ), "run_hash": fingerprint, - "batch_mode": self.batch_mode, + "batch_mode": self._batch_config is not None, } metadata_db.store_metadata(metadata_dict) diff --git a/tests/batch/simple_batch.py b/tests/batch/simple_batch.py index 251296ae..0a166658 100644 --- a/tests/batch/simple_batch.py +++ b/tests/batch/simple_batch.py @@ -15,14 +15,15 @@ def main(args): prompter = LLM( prompt_func=lambda row: row["prompt"], - model_name="gpt-4o-mini", + model_name="gpt-3.5-turbo", response_format=None, - batch=True, - batch_size=args.batch_size, - batch_check_interval=args.batch_check_interval, ) - dataset = prompter(dataset, batch_cancel=args.cancel) + with prompter.batch( + batch_size=args.batch_size, + batch_check_interval=args.batch_check_interval, + ): + dataset = prompter(dataset, batch_cancel=args.cancel) print(dataset.to_pandas()) From 843a2e63e2a7886b1ed3ea5e634d72076e1be7e4 Mon Sep 17 00:00:00 2001 From: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> Date: Fri, 13 Dec 2024 06:33:41 +0000 Subject: [PATCH 03/34] Apply black formatting to llm.py Co-Authored-By: mahesh@bespokelabs.ai --- src/bespokelabs/curator/llm/llm.py | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/src/bespokelabs/curator/llm/llm.py b/src/bespokelabs/curator/llm/llm.py index 7fd16ec2..ca8e24fd 100644 --- a/src/bespokelabs/curator/llm/llm.py +++ b/src/bespokelabs/curator/llm/llm.py @@ -47,6 +47,7 @@ class BatchConfig: delete_successful_batch_files: Whether to delete batch files after successful processing. delete_failed_batch_files: Whether to delete batch files after failed processing. """ + batch_size: Optional[int] = None batch_check_interval: int = 60 delete_successful_batch_files: bool = True @@ -216,16 +217,18 @@ def _setup_request_processor( max_tokens_per_minute: Maximum tokens per minute (not supported in batch mode) """ # Store current processor before potentially switching to batch mode - if hasattr(self, '_request_processor'): + if hasattr(self, "_request_processor"): self._original_request_processor = self._request_processor # Check if we're in batch mode (either via context manager or deprecated params) is_batch_mode = self._batch_config is not None # If we already have a batch processor of the same type, keep it to maintain state - if (is_batch_mode and - hasattr(self, '_request_processor') and - isinstance(self._request_processor, OpenAIBatchRequestProcessor)): + if ( + is_batch_mode + and hasattr(self, "_request_processor") + and isinstance(self._request_processor, OpenAIBatchRequestProcessor) + ): return if is_batch_mode and self.backend == "openai": From 6d801d8ae6464e76637601e7dc75b5e8d5422287 Mon Sep 17 00:00:00 2001 From: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> Date: Fri, 13 Dec 2024 06:58:20 +0000 Subject: [PATCH 04/34] Move batch processing to top-level context manager Co-Authored-By: mahesh@bespokelabs.ai --- src/bespokelabs/curator/llm/batch.py | 104 ++++++++++++++++++++++++ src/bespokelabs/curator/llm/llm.py | 114 ++------------------------- tests/batch/simple_batch.py | 4 +- 3 files changed, 113 insertions(+), 109 deletions(-) create mode 100644 src/bespokelabs/curator/llm/batch.py diff --git a/src/bespokelabs/curator/llm/batch.py b/src/bespokelabs/curator/llm/batch.py new file mode 100644 index 00000000..a01cc79c --- /dev/null +++ b/src/bespokelabs/curator/llm/batch.py @@ -0,0 +1,104 @@ +"""Batch processing functionality for LLM.""" + +from dataclasses import dataclass +from typing import Optional, TYPE_CHECKING + +if TYPE_CHECKING: + from .llm import LLM + + +@dataclass +class BatchConfig: + """Configuration for batch processing in LLM. + + This class holds all configuration parameters related to batch processing, + used by the LLM context manager for batch operations. + + Args: + batch_size: Maximum number of requests per batch. If None, defaults to 1,000. + batch_check_interval: How often to check batch status, in seconds. + delete_successful_batch_files: Whether to delete batch files after successful processing. + delete_failed_batch_files: Whether to delete batch files after failed processing. + """ + + batch_size: Optional[int] = None + batch_check_interval: int = 60 + delete_successful_batch_files: bool = True + delete_failed_batch_files: bool = False + + +class BatchContext: + """Context manager for batch processing. + + This class provides a context manager interface for batch processing with LLM instances. + It handles the setup and teardown of batch processing configuration, ensuring proper + state management of the LLM instance. + + Example: + ```python + from bespokelabs.curator.llm import LLM, batch + + llm = LLM(...) + with batch(llm, batch_size=100): + results = llm(dataset) + ``` + """ + + def __init__(self, llm: "LLM", **kwargs): + """Initialize batch context. + + Args: + llm: The LLM instance to use for batch processing. + **kwargs: Batch configuration parameters passed to BatchConfig. + """ + self.llm = llm + self.config = BatchConfig(**kwargs) + self._original_processor = None + + def __enter__(self): + """Enter batch context. + + Returns: + The LLM instance configured for batch processing. + + Raises: + RuntimeError: If already in a batch context. + """ + if hasattr(self.llm, "_batch_config") and self.llm._batch_config is not None: + raise RuntimeError("Already in batch context") + self.llm._batch_config = self.config + self.llm._setup_request_processor() + return self.llm + + def __exit__(self, exc_type, exc_val, exc_tb): + """Exit batch context and restore original request processor.""" + self.llm._batch_config = None + if self.llm._original_request_processor is not None: + self.llm._request_processor = self.llm._original_request_processor + + +def batch(llm: "LLM", **kwargs) -> BatchContext: + """Create a batch processing context for an LLM instance. + + This function creates a context manager that configures an LLM instance for + batch processing. The batch processing configuration is active only within + the context manager's scope. + + Args: + llm: The LLM instance to configure for batch processing. + **kwargs: Configuration parameters for batch processing. + See BatchConfig for available parameters. + + Returns: + A BatchContext instance that can be used as a context manager. + + Example: + ```python + from bespokelabs.curator.llm import LLM, batch + + llm = LLM(...) + with batch(llm, batch_size=100): + results = llm(dataset) + ``` + """ + return BatchContext(llm, **kwargs) diff --git a/src/bespokelabs/curator/llm/llm.py b/src/bespokelabs/curator/llm/llm.py index ca8e24fd..5df35b5e 100644 --- a/src/bespokelabs/curator/llm/llm.py +++ b/src/bespokelabs/curator/llm/llm.py @@ -3,7 +3,6 @@ import inspect import logging import os -from dataclasses import dataclass from datetime import datetime from io import BytesIO from typing import Any, Callable, Dict, Iterable, Optional, Type, TypeVar, Union @@ -14,6 +13,7 @@ from xxhash import xxh64 from bespokelabs.curator.db import MetadataDB +from bespokelabs.curator.llm.batch import BatchConfig from bespokelabs.curator.llm.prompt_formatter import PromptFormatter from bespokelabs.curator.request_processor.base_request_processor import ( BaseRequestProcessor, @@ -34,26 +34,6 @@ logger = logger = logging.getLogger(__name__) -@dataclass -class BatchConfig: - """Configuration for batch processing in LLM. - - This class holds all configuration parameters related to batch processing, - used by the LLM context manager for batch operations. - - Args: - batch_size: Maximum number of requests per batch. If None, defaults to 1,000. - batch_check_interval: How often to check batch status, in seconds. - delete_successful_batch_files: Whether to delete batch files after successful processing. - delete_failed_batch_files: Whether to delete batch files after failed processing. - """ - - batch_size: Optional[int] = None - batch_check_interval: int = 60 - delete_successful_batch_files: bool = True - delete_failed_batch_files: bool = False - - class LLM: """Interface for prompting LLMs.""" @@ -122,12 +102,6 @@ def __init__( top_p: Optional[float] = None, presence_penalty: Optional[float] = None, frequency_penalty: Optional[float] = None, - # Deprecated parameters - batch: bool = False, - batch_size: Optional[int] = None, - batch_check_interval: Optional[int] = 60, - delete_successful_batch_files: bool = True, - delete_failed_batch_files: bool = False, ): """Initialize a LLM. @@ -146,25 +120,7 @@ def __init__( top_p: The top_p to use for the LLM presence_penalty: The presence_penalty to use for the LLM frequency_penalty: The frequency_penalty to use for the LLM - - Deprecated Args: - batch: Whether to use batch processing (deprecated, use context manager instead) - batch_size: The size of the batch to use (deprecated, use context manager instead) - batch_check_interval: The interval to check for batch completions (deprecated) - delete_successful_batch_files: Whether to delete successful batch files (deprecated) - delete_failed_batch_files: Whether to delete failed batch files (deprecated) """ - import warnings - - if batch or batch_size is not None: - warnings.warn( - "The 'batch' and batch-related parameters are deprecated. " - "Please use the context manager instead:\n" - "with llm.batch(batch_size=N, batch_check_interval=X): ...", - DeprecationWarning, - stacklevel=2, - ) - self.prompt_formatter = PromptFormatter( model_name, prompt_func, parse_func, response_format ) @@ -186,15 +142,6 @@ def __init__( else: self.backend = self._determine_backend(model_name, response_format) - # If using deprecated batch parameters, set up batch config - if batch or batch_size is not None: - self._batch_config = BatchConfig( - batch_size=batch_size, - batch_check_interval=batch_check_interval, - delete_successful_batch_files=delete_successful_batch_files, - delete_failed_batch_files=delete_failed_batch_files, - ) - # Initialize request processor self._setup_request_processor( max_requests_per_minute=max_requests_per_minute, @@ -210,7 +157,10 @@ def _setup_request_processor( This method initializes the request processor based on the current configuration, including batch mode settings if a batch context is active. It handles both - OpenAI and LiteLLM backends, with appropriate warnings for unsupported configurations. + OpenAI and LiteLLM backends, with appropriate processor initialization. + + The batch configuration is managed by the external BatchContext class, which + sets self._batch_config when entering the context and clears it when exiting. Args: max_requests_per_minute: Maximum requests per minute (not supported in batch mode) @@ -220,7 +170,7 @@ def _setup_request_processor( if hasattr(self, "_request_processor"): self._original_request_processor = self._request_processor - # Check if we're in batch mode (either via context manager or deprecated params) + # Check if we're in batch mode via external BatchContext is_batch_mode = self._batch_config is not None # If we already have a batch processor of the same type, keep it to maintain state @@ -274,58 +224,6 @@ def _setup_request_processor( else: raise ValueError(f"Unknown backend: {self.backend}") - def batch( - self, - batch_size: Optional[int] = None, - batch_check_interval: int = 60, - delete_successful_batch_files: bool = True, - delete_failed_batch_files: bool = False, - ) -> "LLM": - """Create a context manager for batch processing. - - Args: - batch_size: Maximum number of requests per batch. If None, defaults to 1,000. - batch_check_interval: How often to check batch status, in seconds. - delete_successful_batch_files: Whether to delete batch files after successful processing. - delete_failed_batch_files: Whether to delete batch files after failed processing. - - Returns: - self: The LLM instance configured for batch processing. - - Example: - ```python - llm = LLM(...) - with llm.batch(batch_size=100): - results = llm(dataset) - ``` - """ - self._batch_config = BatchConfig( - batch_size=batch_size, - batch_check_interval=batch_check_interval, - delete_successful_batch_files=delete_successful_batch_files, - delete_failed_batch_files=delete_failed_batch_files, - ) - return self - - def __enter__(self): - """Enter batch context. - - Raises: - RuntimeError: If already in a batch context or if batch() wasn't called. - """ - if self._batch_config is None: - raise RuntimeError("Must use 'with llm.batch()' to enter batch context") - if self._original_request_processor is not None: - raise RuntimeError("Already in batch context") - self._setup_request_processor() - return self - - def __exit__(self, exc_type, exc_val, exc_tb): - """Exit batch context and restore original request processor.""" - self._batch_config = None - if self._original_request_processor is not None: - self._request_processor = self._original_request_processor - def __call__( self, dataset: Optional[Iterable] = None, diff --git a/tests/batch/simple_batch.py b/tests/batch/simple_batch.py index 0a166658..9ad767e7 100644 --- a/tests/batch/simple_batch.py +++ b/tests/batch/simple_batch.py @@ -1,4 +1,5 @@ from bespokelabs.curator import LLM +from bespokelabs.curator.llm.batch import batch from datasets import Dataset import logging import argparse @@ -19,7 +20,8 @@ def main(args): response_format=None, ) - with prompter.batch( + with batch( + prompter, batch_size=args.batch_size, batch_check_interval=args.batch_check_interval, ): From 1042e23d81560d5a1123486fe0579af1f41eb113 Mon Sep 17 00:00:00 2001 From: Ryan Marten Date: Fri, 13 Dec 2024 09:52:52 -0800 Subject: [PATCH 05/34] set default max attempts to 10 --- .../base_online_request_processor.py | 26 ++++++++++++------- 1 file changed, 16 insertions(+), 10 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 19032f75..e6650d9b 100644 --- a/src/bespokelabs/curator/request_processor/base_online_request_processor.py +++ b/src/bespokelabs/curator/request_processor/base_online_request_processor.py @@ -22,8 +22,9 @@ logger = logging.getLogger(__name__) logger.setLevel(logging.INFO) -DEFAULT_REQUESTS_PER_MINUTE = 100 -DEFAULT_TOKENS_PER_MINUTE = 100_000 +DEFAULT_MAX_REQUESTS_PER_MINUTE = 100 +DEFAULT_MAX_TOKENS_PER_MINUTE = 100_000 +DEFAULT_MAX_RETRIES = 10 @dataclass @@ -134,13 +135,18 @@ def __init__( self.prompt_formatter: Optional[PromptFormatter] = None self.max_requests_per_minute: Optional[int] = max_requests_per_minute self.max_tokens_per_minute: Optional[int] = max_tokens_per_minute - self.DEFAULT_MAX_REQUESTS_PER_MINUTE = DEFAULT_REQUESTS_PER_MINUTE - self.DEFAULT_MAX_TOKENS_PER_MINUTE = DEFAULT_TOKENS_PER_MINUTE def get_rate_limit(self, name, header_value): """Uses manual values if set, otherwise uses headers if available, and if not available uses defaults.""" - manual_value = getattr(self, name) - default_value = getattr(self, f"DEFAULT_{name.upper()}") + if name == "max_requests_per_minute": + manual_value = self.max_requests_per_minute + default_value = DEFAULT_MAX_REQUESTS_PER_MINUTE + elif name == "max_tokens_per_minute": + manual_value = self.max_tokens_per_minute + default_value = DEFAULT_MAX_TOKENS_PER_MINUTE + else: + raise ValueError(f"Invalid rate limit name: {name}") + if manual_value is not None: logger.info(f"Manually set {name} to {manual_value}") return manual_value @@ -213,7 +219,7 @@ def run( self.process_requests_from_file( generic_request_filepath=request_file, save_filepath=response_file, - max_attempts=5, + max_attempts=DEFAULT_MAX_RETRIES, resume=True, ) ) @@ -380,10 +386,10 @@ async def process_requests_from_file( token_estimate = self.estimate_total_tokens( retry_request.generic_request.messages ) - attempt_number = 6 - retry_request.attempts_left + attempt_number = DEFAULT_RETRIES + 1 - retry_request.attempts_left logger.info( f"Processing retry for request {retry_request.task_id} " - f"(attempt #{attempt_number} of 5). " + f"(attempt #{attempt_number} of {DEFAULT_RETRIES}). " f"Previous errors: {retry_request.result}" ) @@ -472,7 +478,7 @@ async def handle_single_request_with_retries( retry_queue.put_nowait(request) else: logger.error( - f"Request {request.task_id} failed permanently after exhausting all 5 retry attempts. " + f"Request {request.task_id} failed permanently after exhausting all {DEFAULT_MAX_RETRIES} retry attempts. " f"Errors: {[str(e) for e in request.result]}" ) generic_response = GenericResponse( From d642227577e6ac26a2a63892913e3068f8b5ead4 Mon Sep 17 00:00:00 2001 From: Ryan Marten Date: Fri, 13 Dec 2024 09:53:32 -0800 Subject: [PATCH 06/34] update logging --- .../request_processor/base_online_request_processor.py | 4 ++-- 1 file changed, 2 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 e6650d9b..895d59e5 100644 --- a/src/bespokelabs/curator/request_processor/base_online_request_processor.py +++ b/src/bespokelabs/curator/request_processor/base_online_request_processor.py @@ -386,10 +386,10 @@ async def process_requests_from_file( token_estimate = self.estimate_total_tokens( retry_request.generic_request.messages ) - attempt_number = DEFAULT_RETRIES + 1 - retry_request.attempts_left + attempt_number = DEFAULT_MAX_RETRIES + 1 - retry_request.attempts_left logger.info( f"Processing retry for request {retry_request.task_id} " - f"(attempt #{attempt_number} of {DEFAULT_RETRIES}). " + f"(attempt #{attempt_number} of {DEFAULT_MAX_RETRIES}). " f"Previous errors: {retry_request.result}" ) From fe5c123b849d719b6bf0d73e98c786942e660e98 Mon Sep 17 00:00:00 2001 From: Ryan Marten Date: Fri, 13 Dec 2024 10:06:37 -0800 Subject: [PATCH 07/34] add a strict check on requiring all responses --- .../base_online_request_processor.py | 5 +-- .../base_request_processor.py | 36 ++++++++++++++----- .../litellm_online_request_processor.py | 2 ++ .../openai_batch_request_processor.py | 3 +- .../openai_online_request_processor.py | 2 ++ 5 files changed, 37 insertions(+), 11 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 895d59e5..f4d9691b 100644 --- a/src/bespokelabs/curator/request_processor/base_online_request_processor.py +++ b/src/bespokelabs/curator/request_processor/base_online_request_processor.py @@ -125,8 +125,9 @@ def __init__( frequency_penalty: Optional[float] = None, max_requests_per_minute: Optional[int] = None, max_tokens_per_minute: Optional[int] = None, + require_all_responses: bool = False, ): - super().__init__(batch_size=None) + super().__init__(batch_size=None, require_all_responses=require_all_responses) self.model: str = model self.temperature: float | None = temperature self.top_p: float | None = top_p @@ -386,7 +387,7 @@ async def process_requests_from_file( token_estimate = self.estimate_total_tokens( retry_request.generic_request.messages ) - attempt_number = DEFAULT_MAX_RETRIES + 1 - retry_request.attempts_left + attempt_number = 1 + DEFAULT_MAX_RETRIES - retry_request.attempts_left logger.info( f"Processing retry for request {retry_request.task_id} " f"(attempt #{attempt_number} of {DEFAULT_MAX_RETRIES}). " diff --git a/src/bespokelabs/curator/request_processor/base_request_processor.py b/src/bespokelabs/curator/request_processor/base_request_processor.py index b5163e34..ca79b9e1 100644 --- a/src/bespokelabs/curator/request_processor/base_request_processor.py +++ b/src/bespokelabs/curator/request_processor/base_request_processor.py @@ -29,8 +29,9 @@ class BaseRequestProcessor(ABC): Base class for all request processors. """ - def __init__(self, batch_size: Optional[int] = None): + def __init__(self, batch_size: Optional[int] = None, require_all_responses: bool = False): self.batch_size = batch_size + self.require_all_responses = require_all_responses # Increase the number of open file descriptors to avoid "Too many open files" errors soft, hard = resource.getrlimit(resource.RLIMIT_NOFILE) desired_limit = min(10_000_000, hard) @@ -216,9 +217,6 @@ def create_dataset_files( Returns: Dataset: Completed dataset """ - total_responses_count = 0 - failed_responses_count = 0 - responses_files = glob.glob(f"{working_dir}/responses_*.jsonl") if len(responses_files) == 0: raise ValueError(f"No responses files found in {working_dir}") @@ -230,6 +228,8 @@ def create_dataset_files( ) # Process all response files + total_responses_count = 0 + failed_responses_count = 0 dataset_file = f"{working_dir}/{parse_func_hash}.arrow" with ArrowWriter(path=dataset_file) as writer: for responses_file in responses_files: @@ -319,14 +319,34 @@ def create_dataset_files( writer.write(row) - logger.info(f"Read {total_responses_count} responses, {failed_responses_count} failed") + logger.info("Finalizing writer") + writer.finalize() + + logger.info(f"Read {total_responses_count} responses.") if failed_responses_count == total_responses_count: os.remove(dataset_file) raise ValueError("All requests failed") + if failed_responses_count > 0: + logger.warning(f"{failed_responses_count} requests failed.") + + if self.require_all_responses: + # all responses succeeded + if failed_responses_count > 0: + os.remove(dataset_file) + raise ValueError( + f"{failed_responses_count} requests failed and require_all_responses is True" + ) - logger.info("Finalizing writer") - - writer.finalize() + # number of responses matches number of requests + request_files = glob.glob(f"{working_dir}/requests_*.jsonl") + n_requests = 0 + for request_file in request_files: + n_requests += len(open(request_file, "r").readlines()) + if n_requests != total_responses_count: + os.remove(dataset_file) + raise ValueError( + f"Some requests do not have responses and require_all_responses is True. n_requests is {n_requests} and n_responses is {total_responses_count}" + ) return Dataset.from_file(dataset_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 9c4131c3..6a8f8c81 100644 --- a/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py +++ b/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py @@ -49,6 +49,7 @@ def __init__( frequency_penalty: Optional[float] = None, max_requests_per_minute: Optional[int] = None, max_tokens_per_minute: Optional[int] = None, + require_all_responses: bool = False, ): super().__init__( model=model, @@ -58,6 +59,7 @@ def __init__( frequency_penalty=frequency_penalty, max_requests_per_minute=max_requests_per_minute, max_tokens_per_minute=max_tokens_per_minute, + require_all_responses=require_all_responses, ) self.client = instructor.from_litellm(litellm.acompletion) diff --git a/src/bespokelabs/curator/request_processor/openai_batch_request_processor.py b/src/bespokelabs/curator/request_processor/openai_batch_request_processor.py index 202c214c..37be0c95 100644 --- a/src/bespokelabs/curator/request_processor/openai_batch_request_processor.py +++ b/src/bespokelabs/curator/request_processor/openai_batch_request_processor.py @@ -47,6 +47,7 @@ def __init__( url: str = "https://api.openai.com/v1/chat/completions", presence_penalty: float | None = None, frequency_penalty: float | None = None, + require_all_responses: bool = False, ): if batch_size > MAX_REQUESTS_PER_BATCH: raise ValueError( @@ -54,7 +55,7 @@ def __init__( f"{MAX_REQUESTS_PER_BATCH:,} requests per batch that OpenAI supports. " f"Please set your batch_size to be less than or equal to {MAX_REQUESTS_PER_BATCH:,}." ) - super().__init__(batch_size) + super().__init__(batch_size, require_all_responses=require_all_responses) self.model = model self.url: str = url self.check_interval: int = batch_check_interval 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 7501d0a1..c53454b5 100644 --- a/src/bespokelabs/curator/request_processor/openai_online_request_processor.py +++ b/src/bespokelabs/curator/request_processor/openai_online_request_processor.py @@ -81,6 +81,7 @@ def __init__( frequency_penalty: Optional[float] = None, max_requests_per_minute: Optional[int] = None, max_tokens_per_minute: Optional[int] = None, + require_all_responses: bool = False, ): super().__init__( model=model, @@ -90,6 +91,7 @@ def __init__( frequency_penalty=frequency_penalty, max_requests_per_minute=max_requests_per_minute, max_tokens_per_minute=max_tokens_per_minute, + require_all_responses=require_all_responses, ) self.url = url self.api_key = api_key From 1a60a89a29344586507a125419bf6856ebc4834b Mon Sep 17 00:00:00 2001 From: Ryan Marten Date: Fri, 13 Dec 2024 10:09:40 -0800 Subject: [PATCH 08/34] update logging --- .../base_request_processor.py | 27 +++++++++---------- 1 file changed, 13 insertions(+), 14 deletions(-) diff --git a/src/bespokelabs/curator/request_processor/base_request_processor.py b/src/bespokelabs/curator/request_processor/base_request_processor.py index ca79b9e1..aef8ac59 100644 --- a/src/bespokelabs/curator/request_processor/base_request_processor.py +++ b/src/bespokelabs/curator/request_processor/base_request_processor.py @@ -326,26 +326,25 @@ def create_dataset_files( if failed_responses_count == total_responses_count: os.remove(dataset_file) raise ValueError("All requests failed") + if failed_responses_count > 0: logger.warning(f"{failed_responses_count} requests failed.") - - if self.require_all_responses: - # all responses succeeded - if failed_responses_count > 0: + if self.require_all_responses: os.remove(dataset_file) - raise ValueError( - f"{failed_responses_count} requests failed and require_all_responses is True" - ) + raise ValueError(f"Some requests failed and require_all_responses is True") + + # number of responses matches number of requests + request_files = glob.glob(f"{working_dir}/requests_*.jsonl") + n_requests = 0 + for request_file in request_files: + n_requests += len(open(request_file, "r").readlines()) - # number of responses matches number of requests - request_files = glob.glob(f"{working_dir}/requests_*.jsonl") - n_requests = 0 - for request_file in request_files: - n_requests += len(open(request_file, "r").readlines()) - if n_requests != total_responses_count: + if n_requests != total_responses_count: + logger.warning(f"{n_requests - total_responses_count} requests do not have responses. n_requests is {n_requests} and n_responses is {total_responses_count}") + if self.require_all_responses: os.remove(dataset_file) raise ValueError( - f"Some requests do not have responses and require_all_responses is True. n_requests is {n_requests} and n_responses is {total_responses_count}" + f"Some requests do not have responses and require_all_responses is True." ) return Dataset.from_file(dataset_file) From 44bb6c06440955e12a8dae45c99dbe339a3d244f Mon Sep 17 00:00:00 2001 From: Ryan Marten Date: Fri, 13 Dec 2024 10:21:01 -0800 Subject: [PATCH 09/34] fast line counting in python --- .../request_processor/base_request_processor.py | 9 ++++++--- .../openai_batch_request_processor.py | 4 ++-- src/bespokelabs/curator/utils.py | 12 ++++++++++++ 3 files changed, 20 insertions(+), 5 deletions(-) create mode 100644 src/bespokelabs/curator/utils.py diff --git a/src/bespokelabs/curator/request_processor/base_request_processor.py b/src/bespokelabs/curator/request_processor/base_request_processor.py index aef8ac59..e278ab7b 100644 --- a/src/bespokelabs/curator/request_processor/base_request_processor.py +++ b/src/bespokelabs/curator/request_processor/base_request_processor.py @@ -14,6 +14,7 @@ from datasets.arrow_writer import ArrowWriter from pydantic import BaseModel, ValidationError +from bespokelabs.curator.utils import count_lines from bespokelabs.curator.llm.prompt_formatter import PromptFormatter from bespokelabs.curator.request_processor.event_loop import run_in_event_loop from bespokelabs.curator.request_processor.generic_request import GenericRequest @@ -326,7 +327,7 @@ def create_dataset_files( if failed_responses_count == total_responses_count: os.remove(dataset_file) raise ValueError("All requests failed") - + if failed_responses_count > 0: logger.warning(f"{failed_responses_count} requests failed.") if self.require_all_responses: @@ -337,10 +338,12 @@ def create_dataset_files( request_files = glob.glob(f"{working_dir}/requests_*.jsonl") n_requests = 0 for request_file in request_files: - n_requests += len(open(request_file, "r").readlines()) + n_requests += count_lines(request_file) if n_requests != total_responses_count: - logger.warning(f"{n_requests - total_responses_count} requests do not have responses. n_requests is {n_requests} and n_responses is {total_responses_count}") + logger.warning( + f"{n_requests - total_responses_count} requests do not have responses. n_requests is {n_requests} and n_responses is {total_responses_count}" + ) if self.require_all_responses: os.remove(dataset_file) raise ValueError( diff --git a/src/bespokelabs/curator/request_processor/openai_batch_request_processor.py b/src/bespokelabs/curator/request_processor/openai_batch_request_processor.py index 37be0c95..fc679501 100644 --- a/src/bespokelabs/curator/request_processor/openai_batch_request_processor.py +++ b/src/bespokelabs/curator/request_processor/openai_batch_request_processor.py @@ -12,6 +12,7 @@ from openai.types import Batch from tqdm import tqdm +from bespokelabs.curator.utils import count_lines from bespokelabs.curator.dataset import Dataset from bespokelabs.curator.llm.prompt_formatter import PromptFormatter from bespokelabs.curator.request_processor.base_request_processor import ( @@ -776,8 +777,7 @@ async def track_already_submitted_batches(self): # Edge case where the batch is still validating, and we need to know the total number of requests if batch_object.status == "validating": - n_requests = len(open(request_file_name, "r").readlines()) - batch_object.request_counts.total = n_requests + batch_object.request_counts.total = count_lines(request_file_name) else: n_requests = batch_object.request_counts.total diff --git a/src/bespokelabs/curator/utils.py b/src/bespokelabs/curator/utils.py new file mode 100644 index 00000000..24cfa204 --- /dev/null +++ b/src/bespokelabs/curator/utils.py @@ -0,0 +1,12 @@ +# https://stackoverflow.com/questions/845058/how-to-get-the-line-count-of-a-large-file-cheaply-in-python +def _file_gen(reader): + b = reader(1024 * 1024) + while b: + yield b + b = reader(1024 * 1024) + + +def count_lines(filename): + f = open(filename, "rb") + f_gen = _file_gen(f.raw.read) + return sum(buf.count(b"\n") for buf in f_gen) From 12da38eaf445e8e6dcc066b7b0e6c4af3ef1ebc5 Mon Sep 17 00:00:00 2001 From: Ryan Marten Date: Fri, 13 Dec 2024 10:27:32 -0800 Subject: [PATCH 10/34] move line count to another file --- src/bespokelabs/curator/utils.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/src/bespokelabs/curator/utils.py b/src/bespokelabs/curator/utils.py index 24cfa204..444c324d 100644 --- a/src/bespokelabs/curator/utils.py +++ b/src/bespokelabs/curator/utils.py @@ -1,4 +1,5 @@ # https://stackoverflow.com/questions/845058/how-to-get-the-line-count-of-a-large-file-cheaply-in-python +# https://stackoverflow.com/a/68385697 def _file_gen(reader): b = reader(1024 * 1024) while b: @@ -6,6 +7,7 @@ def _file_gen(reader): b = reader(1024 * 1024) +# instead of requiring counting lines, we can store metadata file that has the number of requests in each file def count_lines(filename): f = open(filename, "rb") f_gen = _file_gen(f.raw.read) From e7ab3510f51a88359af3067c4fdf6395adf62823 Mon Sep 17 00:00:00 2001 From: Ryan Marten Date: Fri, 13 Dec 2024 13:03:29 -0800 Subject: [PATCH 11/34] add max retries arg --- src/bespokelabs/curator/llm/llm.py | 6 ++++++ .../base_online_request_processor.py | 16 ++++++++++------ .../openai_batch_request_processor.py | 12 ++++++++++-- tests/simple_online.py | 2 ++ 4 files changed, 28 insertions(+), 8 deletions(-) diff --git a/src/bespokelabs/curator/llm/llm.py b/src/bespokelabs/curator/llm/llm.py index 5df35b5e..9cab6c89 100644 --- a/src/bespokelabs/curator/llm/llm.py +++ b/src/bespokelabs/curator/llm/llm.py @@ -102,6 +102,7 @@ def __init__( top_p: Optional[float] = None, presence_penalty: Optional[float] = None, frequency_penalty: Optional[float] = None, + max_retries: Optional[int] = None, ): """Initialize a LLM. @@ -146,12 +147,14 @@ def __init__( self._setup_request_processor( max_requests_per_minute=max_requests_per_minute, max_tokens_per_minute=max_tokens_per_minute, + max_retries=max_retries, ) def _setup_request_processor( self, max_requests_per_minute: Optional[int] = None, max_tokens_per_minute: Optional[int] = None, + max_retries: Optional[int] = None, ): """Set up the appropriate request processor based on current config. @@ -196,6 +199,7 @@ def _setup_request_processor( frequency_penalty=self.frequency_penalty, delete_successful_batch_files=self._batch_config.delete_successful_batch_files, delete_failed_batch_files=self._batch_config.delete_failed_batch_files, + max_retries=max_retries, ) elif self.backend == "openai": self._request_processor = OpenAIOnlineRequestProcessor( @@ -206,6 +210,7 @@ def _setup_request_processor( frequency_penalty=self.frequency_penalty, max_requests_per_minute=max_requests_per_minute, max_tokens_per_minute=max_tokens_per_minute, + max_retries=max_retries, ) elif self.backend == "litellm": if is_batch_mode: @@ -220,6 +225,7 @@ def _setup_request_processor( frequency_penalty=self.frequency_penalty, max_requests_per_minute=max_requests_per_minute, max_tokens_per_minute=max_tokens_per_minute, + max_retries=max_retries, ) else: raise ValueError(f"Unknown backend: {self.backend}") 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 f4d9691b..a46df943 100644 --- a/src/bespokelabs/curator/request_processor/base_online_request_processor.py +++ b/src/bespokelabs/curator/request_processor/base_online_request_processor.py @@ -125,9 +125,9 @@ def __init__( frequency_penalty: Optional[float] = None, max_requests_per_minute: Optional[int] = None, max_tokens_per_minute: Optional[int] = None, - require_all_responses: bool = False, + max_retries: Optional[int] = None, ): - super().__init__(batch_size=None, require_all_responses=require_all_responses) + super().__init__(batch_size=None) self.model: str = model self.temperature: float | None = temperature self.top_p: float | None = top_p @@ -136,6 +136,10 @@ def __init__( self.prompt_formatter: Optional[PromptFormatter] = None self.max_requests_per_minute: Optional[int] = max_requests_per_minute self.max_tokens_per_minute: Optional[int] = max_tokens_per_minute + if max_retries is None: + self.max_retries = DEFAULT_MAX_RETRIES + else: + self.max_retries = max_retries def get_rate_limit(self, name, header_value): """Uses manual values if set, otherwise uses headers if available, and if not available uses defaults.""" @@ -220,7 +224,7 @@ def run( self.process_requests_from_file( generic_request_filepath=request_file, save_filepath=response_file, - max_attempts=DEFAULT_MAX_RETRIES, + max_attempts=self.max_retries, resume=True, ) ) @@ -387,10 +391,10 @@ async def process_requests_from_file( token_estimate = self.estimate_total_tokens( retry_request.generic_request.messages ) - attempt_number = 1 + DEFAULT_MAX_RETRIES - retry_request.attempts_left + attempt_number = 1 + self.max_retries - retry_request.attempts_left logger.info( f"Processing retry for request {retry_request.task_id} " - f"(attempt #{attempt_number} of {DEFAULT_MAX_RETRIES}). " + f"(attempt #{attempt_number} of {self.max_retries}). " f"Previous errors: {retry_request.result}" ) @@ -479,7 +483,7 @@ async def handle_single_request_with_retries( retry_queue.put_nowait(request) else: logger.error( - f"Request {request.task_id} failed permanently after exhausting all {DEFAULT_MAX_RETRIES} retry attempts. " + f"Request {request.task_id} failed permanently after exhausting all {self.max_retries} retry attempts. " f"Errors: {[str(e) for e in request.result]}" ) generic_response = GenericResponse( diff --git a/src/bespokelabs/curator/request_processor/openai_batch_request_processor.py b/src/bespokelabs/curator/request_processor/openai_batch_request_processor.py index fc679501..7588a852 100644 --- a/src/bespokelabs/curator/request_processor/openai_batch_request_processor.py +++ b/src/bespokelabs/curator/request_processor/openai_batch_request_processor.py @@ -5,7 +5,7 @@ import logging import os from dataclasses import dataclass, field -from typing import Callable +from typing import Callable, Optional import litellm from openai import AsyncOpenAI, NotFoundError @@ -49,6 +49,7 @@ def __init__( presence_penalty: float | None = None, frequency_penalty: float | None = None, require_all_responses: bool = False, + max_retries: Optional[int] = None, ): if batch_size > MAX_REQUESTS_PER_BATCH: raise ValueError( @@ -66,6 +67,10 @@ def __init__( self.frequency_penalty: float | None = frequency_penalty self.delete_successful_batch_files: bool = delete_successful_batch_files self.delete_failed_batch_files: bool = delete_failed_batch_files + if max_retries is None: + self.max_retries = MAX_RETRIES_PER_OPERATION + else: + self.max_retries = max_retries def get_rate_limits(self) -> dict: """ @@ -337,6 +342,7 @@ def run( prompt_formatter, delete_successful_batch_files=self.delete_successful_batch_files, delete_failed_batch_files=self.delete_failed_batch_files, + max_retries=self.max_retries, ) run_in_event_loop(self.run_batch_operations(batch_manager, request_files)) @@ -355,6 +361,7 @@ def cancel_batches(self, working_dir: str) -> Dataset: self.check_interval, delete_successful_batch_files=self.delete_successful_batch_files, delete_failed_batch_files=self.delete_failed_batch_files, + max_retries=self.max_retries, ) run_in_event_loop(batch_manager.cancel_batches()) @@ -512,6 +519,7 @@ def __init__( prompt_formatter: PromptFormatter | None = None, delete_successful_batch_files: bool = False, delete_failed_batch_files: bool = False, + max_retries: Optional[int] = None, ) -> None: """Initialize BatchManager to handle OpenAI batch processing operations. @@ -525,7 +533,7 @@ def __init__( delete_failed_batch_files (bool): Whether to delete input/error files from OpenAI after batch failure. """ - self.client = AsyncOpenAI(max_retries=MAX_RETRIES_PER_OPERATION) + self.client = AsyncOpenAI(max_retries=max_retries) self.check_interval = check_interval self.working_dir = working_dir self.tracker = BatchStatusTracker() diff --git a/tests/simple_online.py b/tests/simple_online.py index fd850592..601c280f 100644 --- a/tests/simple_online.py +++ b/tests/simple_online.py @@ -18,6 +18,7 @@ def main(args): model_name=args.model, max_requests_per_minute=args.max_requests_per_minute, max_tokens_per_minute=args.max_tokens_per_minute, + max_retries=args.max_retries, ) dataset = prompter(dataset, batch_cancel=args.cancel) @@ -41,5 +42,6 @@ def main(args): parser.add_argument( "--max-tokens-per-minute", type=int, help="Max tokens per minute", default=None ) + parser.add_argument("--max-retries", type=int, help="Max retries", default=None) args = parser.parse_args() main(args) From 3a2bc17f9fdd5e9a9b00817bd0f0d90492a2039c Mon Sep 17 00:00:00 2001 From: Mahesh Sathiamoorthy Date: Fri, 13 Dec 2024 16:11:59 -0500 Subject: [PATCH 12/34] Revert "Refactor LLM class to use context manager for batch parameters" --- src/bespokelabs/curator/llm/batch.py | 104 ------------------ src/bespokelabs/curator/llm/llm.py | 152 +++++++++++---------------- tests/batch/simple_batch.py | 13 +-- 3 files changed, 66 insertions(+), 203 deletions(-) delete mode 100644 src/bespokelabs/curator/llm/batch.py diff --git a/src/bespokelabs/curator/llm/batch.py b/src/bespokelabs/curator/llm/batch.py deleted file mode 100644 index a01cc79c..00000000 --- a/src/bespokelabs/curator/llm/batch.py +++ /dev/null @@ -1,104 +0,0 @@ -"""Batch processing functionality for LLM.""" - -from dataclasses import dataclass -from typing import Optional, TYPE_CHECKING - -if TYPE_CHECKING: - from .llm import LLM - - -@dataclass -class BatchConfig: - """Configuration for batch processing in LLM. - - This class holds all configuration parameters related to batch processing, - used by the LLM context manager for batch operations. - - Args: - batch_size: Maximum number of requests per batch. If None, defaults to 1,000. - batch_check_interval: How often to check batch status, in seconds. - delete_successful_batch_files: Whether to delete batch files after successful processing. - delete_failed_batch_files: Whether to delete batch files after failed processing. - """ - - batch_size: Optional[int] = None - batch_check_interval: int = 60 - delete_successful_batch_files: bool = True - delete_failed_batch_files: bool = False - - -class BatchContext: - """Context manager for batch processing. - - This class provides a context manager interface for batch processing with LLM instances. - It handles the setup and teardown of batch processing configuration, ensuring proper - state management of the LLM instance. - - Example: - ```python - from bespokelabs.curator.llm import LLM, batch - - llm = LLM(...) - with batch(llm, batch_size=100): - results = llm(dataset) - ``` - """ - - def __init__(self, llm: "LLM", **kwargs): - """Initialize batch context. - - Args: - llm: The LLM instance to use for batch processing. - **kwargs: Batch configuration parameters passed to BatchConfig. - """ - self.llm = llm - self.config = BatchConfig(**kwargs) - self._original_processor = None - - def __enter__(self): - """Enter batch context. - - Returns: - The LLM instance configured for batch processing. - - Raises: - RuntimeError: If already in a batch context. - """ - if hasattr(self.llm, "_batch_config") and self.llm._batch_config is not None: - raise RuntimeError("Already in batch context") - self.llm._batch_config = self.config - self.llm._setup_request_processor() - return self.llm - - def __exit__(self, exc_type, exc_val, exc_tb): - """Exit batch context and restore original request processor.""" - self.llm._batch_config = None - if self.llm._original_request_processor is not None: - self.llm._request_processor = self.llm._original_request_processor - - -def batch(llm: "LLM", **kwargs) -> BatchContext: - """Create a batch processing context for an LLM instance. - - This function creates a context manager that configures an LLM instance for - batch processing. The batch processing configuration is active only within - the context manager's scope. - - Args: - llm: The LLM instance to configure for batch processing. - **kwargs: Configuration parameters for batch processing. - See BatchConfig for available parameters. - - Returns: - A BatchContext instance that can be used as a context manager. - - Example: - ```python - from bespokelabs.curator.llm import LLM, batch - - llm = LLM(...) - with batch(llm, batch_size=100): - results = llm(dataset) - ``` - """ - return BatchContext(llm, **kwargs) diff --git a/src/bespokelabs/curator/llm/llm.py b/src/bespokelabs/curator/llm/llm.py index eec61f2c..59ceb4cc 100644 --- a/src/bespokelabs/curator/llm/llm.py +++ b/src/bespokelabs/curator/llm/llm.py @@ -13,7 +13,6 @@ from xxhash import xxh64 from bespokelabs.curator.db import MetadataDB -from bespokelabs.curator.llm.batch import BatchConfig from bespokelabs.curator.llm.prompt_formatter import PromptFormatter from bespokelabs.curator.request_processor.base_request_processor import ( BaseRequestProcessor, @@ -83,6 +82,11 @@ def __init__( backend: Optional[str] = None, max_requests_per_minute: Optional[int] = None, max_tokens_per_minute: Optional[int] = None, + batch: bool = False, + batch_size: Optional[int] = None, + batch_check_interval: Optional[int] = 60, + delete_successful_batch_files: bool = True, + delete_failed_batch_files: bool = False, # To allow users to debug failed batches temperature: Optional[float] = None, top_p: Optional[float] = None, presence_penalty: Optional[float] = None, @@ -99,110 +103,76 @@ def __init__( response_format: A Pydantic model specifying the response format from the LLM. backend: The backend to use ("openai" or "litellm"). If None, will be auto-determined - max_requests_per_minute: Maximum requests per minute (not supported in batch mode) - max_tokens_per_minute: Maximum tokens per minute (not supported in batch mode) - temperature: The temperature to use for the LLM - top_p: The top_p to use for the LLM - presence_penalty: The presence_penalty to use for the LLM - frequency_penalty: The frequency_penalty to use for the LLM + batch: Whether to use batch processing + batch_size: The size of the batch to use, only used if batch is True + batch_check_interval: The interval to check for batch completions, only used if batch is True + delete_successful_batch_files: Whether to delete successful batch files, only used if batch is True + delete_failed_batch_files: Whether to delete failed batch files, only used if batch is True + temperature: The temperature to use for the LLM, only used if batch is False + top_p: The top_p to use for the LLM, only used if batch is False + presence_penalty: The presence_penalty to use for the LLM, only used if batch is False + frequency_penalty: The frequency_penalty to use for the LLM, only used if batch is False """ self.prompt_formatter = PromptFormatter( model_name, prompt_func, parse_func, response_format ) - - # Initialize context manager state - self._batch_config = None - self._original_request_processor = None - - # Store model parameters - self.temperature = temperature - self.top_p = top_p - self.presence_penalty = presence_penalty - self.frequency_penalty = frequency_penalty - self.model_name = model_name + 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) - # Initialize request processor - self._setup_request_processor( - max_requests_per_minute=max_requests_per_minute, - max_tokens_per_minute=max_tokens_per_minute, - ) - - def _setup_request_processor( - self, - max_requests_per_minute: Optional[int] = None, - max_tokens_per_minute: Optional[int] = None, - ): - """Set up the appropriate request processor based on current config. - - This method initializes the request processor based on the current configuration, - including batch mode settings if a batch context is active. It handles both - OpenAI and LiteLLM backends, with appropriate processor initialization. - - The batch configuration is managed by the external BatchContext class, which - sets self._batch_config when entering the context and clears it when exiting. - - Args: - max_requests_per_minute: Maximum requests per minute (not supported in batch mode) - max_tokens_per_minute: Maximum tokens per minute (not supported in batch mode) - """ - # Store current processor before potentially switching to batch mode - if hasattr(self, "_request_processor"): - self._original_request_processor = self._request_processor - - # Check if we're in batch mode via external BatchContext - is_batch_mode = self._batch_config is not None - - # If we already have a batch processor of the same type, keep it to maintain state - if ( - is_batch_mode - and hasattr(self, "_request_processor") - and isinstance(self._request_processor, OpenAIBatchRequestProcessor) - ): - return - - if is_batch_mode and self.backend == "openai": - if max_requests_per_minute is not None or max_tokens_per_minute is not None: - logger.warning( - "max_requests_per_minute and max_tokens_per_minute not supported with batch mode" + # Select request processor based on backend + if self.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:,}" + ) + if max_requests_per_minute is not None or max_tokens_per_minute is not None: + logger.warning( + "max_requests_per_minute and max_tokens_per_minute not supported with batch mode" + ) + self._request_processor = OpenAIBatchRequestProcessor( + model=model_name, + batch_size=batch_size, + temperature=temperature, + top_p=top_p, + batch_check_interval=batch_check_interval, + presence_penalty=presence_penalty, + frequency_penalty=frequency_penalty, + delete_successful_batch_files=delete_successful_batch_files, + delete_failed_batch_files=delete_failed_batch_files, + ) + else: + if batch_size is not None: + logger.warning( + f"LLM 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, + max_requests_per_minute=max_requests_per_minute, + max_tokens_per_minute=max_tokens_per_minute, ) - self._request_processor = OpenAIBatchRequestProcessor( - model=self.model_name, - batch_size=self._batch_config.batch_size or 1_000, - batch_check_interval=self._batch_config.batch_check_interval, - temperature=self.temperature, - top_p=self.top_p, - presence_penalty=self.presence_penalty, - frequency_penalty=self.frequency_penalty, - delete_successful_batch_files=self._batch_config.delete_successful_batch_files, - delete_failed_batch_files=self._batch_config.delete_failed_batch_files, - ) - elif self.backend == "openai": - self._request_processor = OpenAIOnlineRequestProcessor( - model=self.model_name, - temperature=self.temperature, - top_p=self.top_p, - presence_penalty=self.presence_penalty, - frequency_penalty=self.frequency_penalty, - max_requests_per_minute=max_requests_per_minute, - max_tokens_per_minute=max_tokens_per_minute, - ) elif self.backend == "litellm": - if is_batch_mode: + if batch: logger.warning( - "Batch mode is not supported with LiteLLM backend, ignoring batch context" + "Batch mode is not supported with LiteLLM backend, ignoring batch=True" ) self._request_processor = LiteLLMOnlineRequestProcessor( - model=self.model_name, - temperature=self.temperature, - top_p=self.top_p, - presence_penalty=self.presence_penalty, - frequency_penalty=self.frequency_penalty, + model=model_name, + temperature=temperature, + top_p=top_p, + presence_penalty=presence_penalty, + frequency_penalty=frequency_penalty, max_requests_per_minute=max_requests_per_minute, max_tokens_per_minute=max_tokens_per_minute, ) @@ -278,7 +248,7 @@ def _completions( if self.prompt_formatter.response_format else "text" ), - str(self._batch_config is not None), + str(self.batch_mode), str(self.backend), ] ) @@ -308,7 +278,7 @@ def _completions( else "text" ), "run_hash": fingerprint, - "batch_mode": self._batch_config is not None, + "batch_mode": self.batch_mode, } metadata_db.store_metadata(metadata_dict) diff --git a/tests/batch/simple_batch.py b/tests/batch/simple_batch.py index 9ad767e7..251296ae 100644 --- a/tests/batch/simple_batch.py +++ b/tests/batch/simple_batch.py @@ -1,5 +1,4 @@ from bespokelabs.curator import LLM -from bespokelabs.curator.llm.batch import batch from datasets import Dataset import logging import argparse @@ -16,16 +15,14 @@ def main(args): prompter = LLM( prompt_func=lambda row: row["prompt"], - model_name="gpt-3.5-turbo", + model_name="gpt-4o-mini", response_format=None, - ) - - with batch( - prompter, + batch=True, batch_size=args.batch_size, batch_check_interval=args.batch_check_interval, - ): - dataset = prompter(dataset, batch_cancel=args.cancel) + ) + + dataset = prompter(dataset, batch_cancel=args.cancel) print(dataset.to_pandas()) From f2b99c6c8305767245b15e6f9b9d159c8c3bacc7 Mon Sep 17 00:00:00 2001 From: Ryan Marten Date: Fri, 13 Dec 2024 13:15:10 -0800 Subject: [PATCH 13/34] tests --- src/bespokelabs/curator/llm/llm.py | 98 ++++++++++--------- .../base_online_request_processor.py | 3 +- .../litellm_online_request_processor.py | 2 + .../openai_online_request_processor.py | 2 + tests/simple_online.py | 7 ++ 5 files changed, 67 insertions(+), 45 deletions(-) diff --git a/src/bespokelabs/curator/llm/llm.py b/src/bespokelabs/curator/llm/llm.py index 9cab6c89..c6df56bd 100644 --- a/src/bespokelabs/curator/llm/llm.py +++ b/src/bespokelabs/curator/llm/llm.py @@ -37,50 +37,6 @@ class LLM: """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" - - @staticmethod - def _convert_response_to_dict(response): - if hasattr(response, "model_dump"): - return response.model_dump() - elif isinstance(response, dict): - return response - elif hasattr(response, "__dict__"): - return response.__dict__ - return response - def __init__( self, model_name: str, @@ -103,6 +59,7 @@ def __init__( presence_penalty: Optional[float] = None, frequency_penalty: Optional[float] = None, max_retries: Optional[int] = None, + require_all_responses: Optional[bool] = None, ): """Initialize a LLM. @@ -121,6 +78,8 @@ def __init__( top_p: The top_p to use for the LLM presence_penalty: The presence_penalty to use for the LLM frequency_penalty: The frequency_penalty to use for the LLM + max_retries: The maximum number of retries to use for the LLM + require_all_responses: Whether to require all responses """ self.prompt_formatter = PromptFormatter( model_name, prompt_func, parse_func, response_format @@ -148,13 +107,59 @@ def __init__( max_requests_per_minute=max_requests_per_minute, max_tokens_per_minute=max_tokens_per_minute, max_retries=max_retries, + require_all_responses=require_all_responses, ) + @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" + + @staticmethod + def _convert_response_to_dict(response): + if hasattr(response, "model_dump"): + return response.model_dump() + elif isinstance(response, dict): + return response + elif hasattr(response, "__dict__"): + return response.__dict__ + return response + def _setup_request_processor( self, max_requests_per_minute: Optional[int] = None, max_tokens_per_minute: Optional[int] = None, max_retries: Optional[int] = None, + require_all_responses: Optional[bool] = None, ): """Set up the appropriate request processor based on current config. @@ -168,6 +173,8 @@ def _setup_request_processor( Args: max_requests_per_minute: Maximum requests per minute (not supported in batch mode) max_tokens_per_minute: Maximum tokens per minute (not supported in batch mode) + max_retries: The maximum number of retries to use for the LLM + require_all_responses: Whether to require all responses """ # Store current processor before potentially switching to batch mode if hasattr(self, "_request_processor"): @@ -200,6 +207,7 @@ def _setup_request_processor( delete_successful_batch_files=self._batch_config.delete_successful_batch_files, delete_failed_batch_files=self._batch_config.delete_failed_batch_files, max_retries=max_retries, + require_all_responses=require_all_responses, ) elif self.backend == "openai": self._request_processor = OpenAIOnlineRequestProcessor( @@ -211,6 +219,7 @@ def _setup_request_processor( max_requests_per_minute=max_requests_per_minute, max_tokens_per_minute=max_tokens_per_minute, max_retries=max_retries, + require_all_responses=require_all_responses, ) elif self.backend == "litellm": if is_batch_mode: @@ -226,6 +235,7 @@ def _setup_request_processor( max_requests_per_minute=max_requests_per_minute, max_tokens_per_minute=max_tokens_per_minute, max_retries=max_retries, + require_all_responses=require_all_responses, ) else: raise ValueError(f"Unknown backend: {self.backend}") 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 a46df943..44eb32c1 100644 --- a/src/bespokelabs/curator/request_processor/base_online_request_processor.py +++ b/src/bespokelabs/curator/request_processor/base_online_request_processor.py @@ -125,9 +125,10 @@ def __init__( frequency_penalty: Optional[float] = None, max_requests_per_minute: Optional[int] = None, max_tokens_per_minute: Optional[int] = None, + require_all_responses: bool = False, max_retries: Optional[int] = None, ): - super().__init__(batch_size=None) + super().__init__(batch_size=None, require_all_responses=require_all_responses) self.model: str = model self.temperature: float | None = temperature self.top_p: float | None = top_p 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 6a8f8c81..7e18f6bc 100644 --- a/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py +++ b/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py @@ -50,6 +50,7 @@ def __init__( max_requests_per_minute: Optional[int] = None, max_tokens_per_minute: Optional[int] = None, require_all_responses: bool = False, + max_retries: Optional[int] = None, ): super().__init__( model=model, @@ -60,6 +61,7 @@ def __init__( max_requests_per_minute=max_requests_per_minute, max_tokens_per_minute=max_tokens_per_minute, require_all_responses=require_all_responses, + max_retries=max_retries, ) self.client = instructor.from_litellm(litellm.acompletion) 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 c53454b5..d5cec379 100644 --- a/src/bespokelabs/curator/request_processor/openai_online_request_processor.py +++ b/src/bespokelabs/curator/request_processor/openai_online_request_processor.py @@ -82,6 +82,7 @@ def __init__( max_requests_per_minute: Optional[int] = None, max_tokens_per_minute: Optional[int] = None, require_all_responses: bool = False, + max_retries: Optional[int] = None, ): super().__init__( model=model, @@ -92,6 +93,7 @@ def __init__( max_requests_per_minute=max_requests_per_minute, max_tokens_per_minute=max_tokens_per_minute, require_all_responses=require_all_responses, + max_retries=max_retries, ) self.url = url self.api_key = api_key diff --git a/tests/simple_online.py b/tests/simple_online.py index 601c280f..12731eb3 100644 --- a/tests/simple_online.py +++ b/tests/simple_online.py @@ -19,6 +19,7 @@ def main(args): max_requests_per_minute=args.max_requests_per_minute, max_tokens_per_minute=args.max_tokens_per_minute, max_retries=args.max_retries, + require_all_responses=args.require_all_responses, ) dataset = prompter(dataset, batch_cancel=args.cancel) @@ -43,5 +44,11 @@ def main(args): "--max-tokens-per-minute", type=int, help="Max tokens per minute", default=None ) parser.add_argument("--max-retries", type=int, help="Max retries", default=None) + parser.add_argument( + "--require-all-responses", + action="store_true", + default=None, + help="Require all responses", + ) args = parser.parse_args() main(args) From a2adafbce620122a2b9d711b91210136d67acd75 Mon Sep 17 00:00:00 2001 From: Ryan Marten Date: Fri, 13 Dec 2024 14:12:01 -0800 Subject: [PATCH 14/34] addressing comments --- src/bespokelabs/curator/{utils.py => file_utilities.py} | 2 +- src/bespokelabs/curator/llm/llm.py | 2 +- .../request_processor/base_online_request_processor.py | 2 +- .../curator/request_processor/base_request_processor.py | 4 ++-- .../request_processor/openai_batch_request_processor.py | 4 ++-- .../request_processor/openai_online_request_processor.py | 2 +- 6 files changed, 8 insertions(+), 8 deletions(-) rename src/bespokelabs/curator/{utils.py => file_utilities.py} (87%) diff --git a/src/bespokelabs/curator/utils.py b/src/bespokelabs/curator/file_utilities.py similarity index 87% rename from src/bespokelabs/curator/utils.py rename to src/bespokelabs/curator/file_utilities.py index 444c324d..6ee606e7 100644 --- a/src/bespokelabs/curator/utils.py +++ b/src/bespokelabs/curator/file_utilities.py @@ -7,7 +7,7 @@ def _file_gen(reader): b = reader(1024 * 1024) -# instead of requiring counting lines, we can store metadata file that has the number of requests in each file +# Instead of requiring counting lines, we can store metadata file that has the number of requests in each file def count_lines(filename): f = open(filename, "rb") f_gen = _file_gen(f.raw.read) diff --git a/src/bespokelabs/curator/llm/llm.py b/src/bespokelabs/curator/llm/llm.py index f5589120..08bad431 100644 --- a/src/bespokelabs/curator/llm/llm.py +++ b/src/bespokelabs/curator/llm/llm.py @@ -78,7 +78,7 @@ def __init__( top_p: The top_p to use for the LLM presence_penalty: The presence_penalty to use for the LLM frequency_penalty: The frequency_penalty to use for the LLM - max_retries: The maximum number of retries to use for the LLM + max_retries: The maximum number of retries to use for the LLM. If 0, will only try a request once. require_all_responses: Whether to require all responses """ self.prompt_formatter = PromptFormatter( 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 44eb32c1..09c3c432 100644 --- a/src/bespokelabs/curator/request_processor/base_online_request_processor.py +++ b/src/bespokelabs/curator/request_processor/base_online_request_processor.py @@ -125,7 +125,7 @@ def __init__( frequency_penalty: Optional[float] = None, max_requests_per_minute: Optional[int] = None, max_tokens_per_minute: Optional[int] = None, - require_all_responses: bool = False, + require_all_responses: bool = None, max_retries: Optional[int] = None, ): super().__init__(batch_size=None, require_all_responses=require_all_responses) diff --git a/src/bespokelabs/curator/request_processor/base_request_processor.py b/src/bespokelabs/curator/request_processor/base_request_processor.py index e278ab7b..ddc7d268 100644 --- a/src/bespokelabs/curator/request_processor/base_request_processor.py +++ b/src/bespokelabs/curator/request_processor/base_request_processor.py @@ -14,7 +14,7 @@ from datasets.arrow_writer import ArrowWriter from pydantic import BaseModel, ValidationError -from bespokelabs.curator.utils import count_lines +from bespokelabs.curator.file_utilities import count_lines from bespokelabs.curator.llm.prompt_formatter import PromptFormatter from bespokelabs.curator.request_processor.event_loop import run_in_event_loop from bespokelabs.curator.request_processor.generic_request import GenericRequest @@ -30,7 +30,7 @@ class BaseRequestProcessor(ABC): Base class for all request processors. """ - def __init__(self, batch_size: Optional[int] = None, require_all_responses: bool = False): + def __init__(self, batch_size: Optional[int] = None, require_all_responses: bool = True): self.batch_size = batch_size self.require_all_responses = require_all_responses # Increase the number of open file descriptors to avoid "Too many open files" errors diff --git a/src/bespokelabs/curator/request_processor/openai_batch_request_processor.py b/src/bespokelabs/curator/request_processor/openai_batch_request_processor.py index 7588a852..b5b64994 100644 --- a/src/bespokelabs/curator/request_processor/openai_batch_request_processor.py +++ b/src/bespokelabs/curator/request_processor/openai_batch_request_processor.py @@ -12,7 +12,7 @@ from openai.types import Batch from tqdm import tqdm -from bespokelabs.curator.utils import count_lines +from bespokelabs.curator.file_utilities import count_lines from bespokelabs.curator.dataset import Dataset from bespokelabs.curator.llm.prompt_formatter import PromptFormatter from bespokelabs.curator.request_processor.base_request_processor import ( @@ -48,7 +48,7 @@ def __init__( url: str = "https://api.openai.com/v1/chat/completions", presence_penalty: float | None = None, frequency_penalty: float | None = None, - require_all_responses: bool = False, + require_all_responses: bool = None, max_retries: Optional[int] = None, ): if batch_size > MAX_REQUESTS_PER_BATCH: 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 d5cec379..2d21e06d 100644 --- a/src/bespokelabs/curator/request_processor/openai_online_request_processor.py +++ b/src/bespokelabs/curator/request_processor/openai_online_request_processor.py @@ -81,7 +81,7 @@ def __init__( frequency_penalty: Optional[float] = None, max_requests_per_minute: Optional[int] = None, max_tokens_per_minute: Optional[int] = None, - require_all_responses: bool = False, + require_all_responses: bool = None, max_retries: Optional[int] = None, ): super().__init__( From 8c9ae102f165f8c24df3f8c037e8a3b1f71f7e7d Mon Sep 17 00:00:00 2001 From: Mahesh Sathiamoorthy Date: Fri, 13 Dec 2024 22:14:47 +0000 Subject: [PATCH 15/34] Add a simple llm interface. --- README.md | 21 ++++++++++++--- examples/simple_poem.py | 13 +++++++++ src/bespokelabs/curator/__init__.py | 1 + src/bespokelabs/curator/llm/simple_llm.py | 32 +++++++++++++++++++++++ 4 files changed, 63 insertions(+), 4 deletions(-) create mode 100644 examples/simple_poem.py create mode 100644 src/bespokelabs/curator/llm/simple_llm.py diff --git a/README.md b/README.md index b7bf7940..2b4da899 100644 --- a/README.md +++ b/README.md @@ -29,7 +29,7 @@

-### Overview +## Overview Bespoke Curator makes it very easy to create high-quality synthetic data at scale, which you can use to finetune models or use for structured data extraction at scale. @@ -38,7 +38,7 @@ Bespoke Curator is an open-source project: * A Curator Viewer which makes it easy to view the datasets, thus aiding in the dataset creation. * We will also be releasing high-quality datasets that should move the needle on post-training. -### Key Features +## Key Features 1. **Programmability and Structured Outputs**: Synthetic data generation is lot more than just using a single prompt -- it involves calling LLMs multiple times and orchestrating control-flow. Curator treats structured outputs as first class citizens and helps you design complex pipelines. 2. **Built-in Performance Optimization**: We often see calling LLMs in loops, or inefficient implementation of multi-threading. We have baked in performance optimizations so that you don't need to worry about those! @@ -46,20 +46,33 @@ Bespoke Curator is an open-source project: 4. **Native HuggingFace Dataset Integration**: Work directly on HuggingFace Dataset objects throughout your pipeline. Your synthetic data is immediately ready for fine-tuning! 5. **Interactive Curator Viewer**: Improve and iterate on your prompts using our built-in viewer. Inspect LLM requests and responses in real-time, allowing you to iterate and refine your data generation strategy with immediate feedback. -### Installation +## Installation ```bash pip install bespokelabs-curator ``` -### Usage +## Usage +### Imports ```python from bespokelabs import curator from datasets import Dataset from pydantic import BaseModel, Field from typing import List +``` +### SimpleLLM: A simple interface for calling LLMs + +```python +llm = curator.SimpleLLM(model_name="gpt-4o-mini") +poem = llm("Write a poem about the bitter lesson in AI and keep it 100 words or less.") +print(poem) +``` + +### LLM: A more complex interface for calling LLMs + +```python # Create a dataset object for the topics you want to create the poems. topics = Dataset.from_dict({"topic": [ "Urban loneliness in a bustling city", diff --git a/examples/simple_poem.py b/examples/simple_poem.py new file mode 100644 index 00000000..474840bf --- /dev/null +++ b/examples/simple_poem.py @@ -0,0 +1,13 @@ +from bespokelabs import curator + +# Use GPT-4o-mini for this example. +llm = curator.SimpleLLM(model_name="gpt-4o-mini") + +poem = llm("Write a poem about the bitter lesson in AI and keep it 100 words or less.") +print(poem) + +# Use Claude 3.5 Sonnet for this example. +llm = curator.SimpleLLM(model_name="claude-3-5-sonnet-20240620", backend="litellm") + +poem = llm("Write a sonnet about the bitter lesson in AI and make it visual.") +print(poem) diff --git a/src/bespokelabs/curator/__init__.py b/src/bespokelabs/curator/__init__.py index a9d4cc6e..b02aab46 100644 --- a/src/bespokelabs/curator/__init__.py +++ b/src/bespokelabs/curator/__init__.py @@ -1,2 +1,3 @@ from .dataset import Dataset from .llm.llm import LLM +from .llm.simple_llm import SimpleLLM \ No newline at end of file diff --git a/src/bespokelabs/curator/llm/simple_llm.py b/src/bespokelabs/curator/llm/simple_llm.py new file mode 100644 index 00000000..9181a614 --- /dev/null +++ b/src/bespokelabs/curator/llm/simple_llm.py @@ -0,0 +1,32 @@ +from bespokelabs.curator.llm.llm import LLM +from datasets import Dataset +from typing import Union, List + + +class SimpleLLM: + """A simpler interface for the LLM class. + + Usage: + llm = SimpleLLM(model_name="gpt-4o-mini") + llm("Do you know about the bitter lesson?") + llm(["What is the capital of France?", "What is the capital of Germany?"]) + For more complex use cases (e.g. structured outputs and custom prompt functions), see the LLM class. + """ + def __init__(self, model_name: str, backend: str = "openai"): + self._model_name = model_name + self._backend = backend + + def __call__(self, prompt: Union[str, List[str]]) -> Union[str, List[str]]: + prompt_list = [prompt] if isinstance(prompt, str) else prompt + dataset: Dataset = Dataset.from_dict({"prompt": prompt_list}) + + llm = LLM( + prompt_func=lambda row: row['prompt'], + model_name=self._model_name, + response_format=None, + backend=self._backend + ) + response = llm(dataset) + if isinstance(prompt, str): + return response["response"][0] + return response["response"] From 7674a6d2c9ff4c81e8d4899c6609d696fe6dc1b8 Mon Sep 17 00:00:00 2001 From: Mahesh Sathiamoorthy Date: Fri, 13 Dec 2024 22:21:12 +0000 Subject: [PATCH 16/34] Update README. --- README.md | 30 +++++++++++++++++++++++------- 1 file changed, 23 insertions(+), 7 deletions(-) diff --git a/README.md b/README.md index 2b4da899..fe3327d2 100644 --- a/README.md +++ b/README.md @@ -61,33 +61,49 @@ from datasets import Dataset from pydantic import BaseModel, Field from typing import List ``` -### SimpleLLM: A simple interface for calling LLMs +### `SimpleLLM`: A simple interface for calling LLMs ```python llm = curator.SimpleLLM(model_name="gpt-4o-mini") - poem = llm("Write a poem about the bitter lesson in AI and keep it 100 words or less.") print(poem) ``` -### LLM: A more complex interface for calling LLMs +#### Use LiteLLM backend for calling other models +You can use the [LiteLLM](https://docs.litellm.ai/docs/providers) backend for calling other models. + +```python +llm = curator.SimpleLLM(model_name="claude-3-5-sonnet-20240620", backend="litellm") + +poem = llm("Write a sonnet about the bitter lesson in AI and make it visual.") +print(poem) +``` + +### Visualize in Curator Viewer +Run `curator-viewer` to see the dataset in the viewer. + +### `LLM`: A more powerful interface for calling LLMs + +Create a dataset object for the topics you want to create the poems. ```python -# Create a dataset object for the topics you want to create the poems. topics = Dataset.from_dict({"topic": [ "Urban loneliness in a bustling city", "Beauty of Bespoke Labs's Curator library" ]}) +``` -# Define a class to encapsulate a list of poems. +Define a class to encapsulate a list of poems. +```python class Poem(BaseModel): poem: str = Field(description="A poem.") class Poems(BaseModel): poems_list: List[Poem] = Field(description="A list of poems.") +``` - -# We define an `LLM` object that generates poems which gets applied to the topics dataset. +We define an `LLM` object that generates poems which gets applied to the topics dataset. +```python poet = curator.LLM( # `prompt_func` takes a row of the dataset as input. # `row` is a dictionary with a single key 'topic' in this case. From 286be1e2c3f8be54cf11ebdd2155f49b59654d76 Mon Sep 17 00:00:00 2001 From: Ryan Marten Date: Fri, 13 Dec 2024 14:33:09 -0800 Subject: [PATCH 17/34] rate limit properties --- .../base_online_request_processor.py | 72 +++++++++---------- .../base_request_processor.py | 10 --- .../litellm_online_request_processor.py | 9 ++- .../openai_batch_request_processor.py | 42 ----------- .../openai_online_request_processor.py | 9 ++- 5 files changed, 45 insertions(+), 97 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 09c3c432..02f4a79f 100644 --- a/src/bespokelabs/curator/request_processor/base_online_request_processor.py +++ b/src/bespokelabs/curator/request_processor/base_online_request_processor.py @@ -135,54 +135,48 @@ def __init__( self.presence_penalty: float | None = presence_penalty self.frequency_penalty: float | None = frequency_penalty self.prompt_formatter: Optional[PromptFormatter] = None - self.max_requests_per_minute: Optional[int] = max_requests_per_minute - self.max_tokens_per_minute: Optional[int] = max_tokens_per_minute + self.manual_max_requests_per_minute: Optional[int] = max_requests_per_minute + self.manual_max_tokens_per_minute: Optional[int] = max_tokens_per_minute if max_retries is None: self.max_retries = DEFAULT_MAX_RETRIES else: self.max_retries = max_retries - def get_rate_limit(self, name, header_value): - """Uses manual values if set, otherwise uses headers if available, and if not available uses defaults.""" - if name == "max_requests_per_minute": - manual_value = self.max_requests_per_minute - default_value = DEFAULT_MAX_REQUESTS_PER_MINUTE - elif name == "max_tokens_per_minute": - manual_value = self.max_tokens_per_minute - default_value = DEFAULT_MAX_TOKENS_PER_MINUTE - else: - raise ValueError(f"Invalid rate limit name: {name}") - - if manual_value is not None: - logger.info(f"Manually set {name} to {manual_value}") - return manual_value - elif header_value != 0: - logger.info(f"Automatically set {name} to {header_value}") - return header_value + @property + def max_requests_per_minute(self) -> int: + if self.manual_max_requests_per_minute: + logger.info( + f"Manually set max_requests_per_minute to {self.manual_max_requests_per_minute}" + ) + return self.manual_max_requests_per_minute + elif self.header_based_max_requests_per_minute: + logger.info( + f"Automatically set max_requests_per_minute to {self.header_based_max_requests_per_minute}" + ) + return self.header_based_max_requests_per_minute else: logger.warning( - f"No manual {name} set, and headers based detection failed, using default value of {default_value}" + f"No manual max_requests_per_minute set, and headers based detection failed, using default value of {DEFAULT_MAX_REQUESTS_PER_MINUTE}" ) - return default_value - - def get_rate_limits(self) -> dict: - """Get rate limits for the API. Returns a dictionary with max_requests_per_minute and max_tokens_per_minute""" - - # Get values from headers - header_based_rate_limits = self.get_header_based_rate_limits() - header_tpm = header_based_rate_limits["max_tokens_per_minute"] - header_rpm = header_based_rate_limits["max_requests_per_minute"] - - # Determine final rate limit - tpm = self.get_rate_limit("max_tokens_per_minute", header_tpm) - rpm = self.get_rate_limit("max_requests_per_minute", header_rpm) - - return {"max_requests_per_minute": rpm, "max_tokens_per_minute": tpm} + return DEFAULT_MAX_REQUESTS_PER_MINUTE - @abstractmethod - def get_header_based_rate_limits(self) -> dict: - """Get rate limits for the API from headers. Returns a dictionary with max_requests_per_minute and max_tokens_per_minute""" - pass + @property + def max_tokens_per_minute(self) -> int: + if self.manual_max_tokens_per_minute: + logger.info( + f"Manually set max_tokens_per_minute to {self.manual_max_tokens_per_minute}" + ) + return self.manual_max_tokens_per_minute + elif self.header_based_max_tokens_per_minute: + logger.info( + f"Automatically set max_tokens_per_minute to {self.header_based_max_tokens_per_minute}" + ) + return self.header_based_max_tokens_per_minute + else: + logger.warning( + f"No manual max_tokens_per_minute set, and headers based detection failed, using default value of {DEFAULT_MAX_TOKENS_PER_MINUTE}" + ) + return DEFAULT_MAX_TOKENS_PER_MINUTE @abstractmethod def estimate_total_tokens(self, messages: list) -> int: diff --git a/src/bespokelabs/curator/request_processor/base_request_processor.py b/src/bespokelabs/curator/request_processor/base_request_processor.py index ddc7d268..b9fb09f4 100644 --- a/src/bespokelabs/curator/request_processor/base_request_processor.py +++ b/src/bespokelabs/curator/request_processor/base_request_processor.py @@ -41,16 +41,6 @@ def __init__(self, batch_size: Optional[int] = None, require_all_responses: bool ) resource.setrlimit(resource.RLIMIT_NOFILE, (desired_limit, hard)) - @abstractmethod - def get_rate_limits(self) -> dict: - """ - Returns the rate limits for the API. - - Returns: - dict: A dictionary containing the rate limit information. - """ - pass - @abstractmethod def create_api_specific_request(self, generic_request: GenericRequest) -> dict: """ 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 7e18f6bc..4088a91c 100644 --- a/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py +++ b/src/bespokelabs/curator/request_processor/litellm_online_request_processor.py @@ -64,6 +64,9 @@ def __init__( max_retries=max_retries, ) self.client = instructor.from_litellm(litellm.acompletion) + self.header_based_max_requests_per_minute, self.header_based_max_tokens_per_minute = ( + self.get_header_based_rate_limits() + ) def check_structured_output_support(self): """Verify if the model supports structured output via instructor. @@ -158,11 +161,11 @@ def test_call(self): logger.info(f"Test call headers: {headers}") return headers - def get_header_based_rate_limits(self) -> dict: + def get_header_based_rate_limits(self) -> tuple[int, int]: """Retrieve rate limits from the LLM provider via LiteLLM. Returns: - dict: Contains 'max_requests_per_minute' and 'max_tokens_per_minute' + tuple[int, int]: Contains 'max_requests_per_minute' and 'max_tokens_per_minute' Note: - Makes a test request to get rate limit information from response headers. @@ -174,7 +177,7 @@ def get_header_based_rate_limits(self) -> dict: rpm = int(headers.get("x-ratelimit-limit-requests", 0)) tpm = int(headers.get("x-ratelimit-limit-tokens", 0)) - return {"max_requests_per_minute": rpm, "max_tokens_per_minute": tpm} + return rpm, tpm def create_api_specific_request(self, generic_request: GenericRequest) -> dict: """Convert a generic request into a LiteLLM-compatible format. diff --git a/src/bespokelabs/curator/request_processor/openai_batch_request_processor.py b/src/bespokelabs/curator/request_processor/openai_batch_request_processor.py index b5b64994..9dcf97d4 100644 --- a/src/bespokelabs/curator/request_processor/openai_batch_request_processor.py +++ b/src/bespokelabs/curator/request_processor/openai_batch_request_processor.py @@ -72,48 +72,6 @@ def __init__( else: self.max_retries = max_retries - def get_rate_limits(self) -> dict: - """ - Function to get rate limits for a given annotator. Not available via response headers, so - the following is based on tier 5 limits on Nov 6th, 2024. - - 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: - dict: A dictionary containing max_tokens_per_day - """ - model_tpd = { - "gpt-3.5-turbo": 5_000_000_000, - "gpt-3.5-turbo-0125": 5_000_000_000, - "gpt-3.5-turbo-1106": 5_000_000_000, - "gpt-3.5-turbo-16k": 5_000_000_000, - "gpt-3.5-turbo-instruct": 200_000, - "gpt-3.5-turbo-instruct-0914": 200_000, - "gpt-4": 150_000_000, - "gpt-4-0613": 150_000_000, - "gpt-4-turbo": 300_000_000, - "gpt-4o": 10_000_000_000, - "gpt-4o-mini": 15_000_000_000, - } - - if self.model not in model_tpd: - tpd = 1_000_000_000 - else: - tpd = model_tpd[self.model] - - logger.info(f"Automatically set max_tokens_per_day to {tpd}, model: {self.model} ") - - rate_limits = {"max_tokens_per_day": tpd} - - return rate_limits - def create_api_specific_request(self, generic_request: GenericRequest) -> dict: """ Creates an API-specific request body from a generic request body. 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 2d21e06d..c9c1e34f 100644 --- a/src/bespokelabs/curator/request_processor/openai_online_request_processor.py +++ b/src/bespokelabs/curator/request_processor/openai_online_request_processor.py @@ -98,12 +98,15 @@ def __init__( self.url = url self.api_key = api_key self.token_encoding = tiktoken.get_encoding(get_token_encoding_name(model)) + self.header_based_max_requests_per_minute, self.header_based_max_tokens_per_minute = ( + self.get_header_based_rate_limits() + ) - def get_header_based_rate_limits(self) -> dict: + def get_header_based_rate_limits(self) -> tuple[int, int]: """Get rate limits from OpenAI API headers. Returns: - dict: Contains 'max_requests_per_minute' and 'max_tokens_per_minute' + tuple[int, int]: Contains 'max_requests_per_minute' and 'max_tokens_per_minute' Note: - Makes a dummy request to get actual rate limits @@ -121,7 +124,7 @@ def get_header_based_rate_limits(self) -> dict: rpm = int(response.headers.get("x-ratelimit-limit-requests", 0)) tpm = int(response.headers.get("x-ratelimit-limit-tokens", 0)) - return {"max_requests_per_minute": rpm, "max_tokens_per_minute": tpm} + return rpm, tpm def estimate_output_tokens(self) -> int: """Estimate number of tokens in the response. From 585a495a7a20b31bf96015a93ecb33ad471694fc Mon Sep 17 00:00:00 2001 From: Ryan Marten Date: Fri, 13 Dec 2024 14:34:06 -0800 Subject: [PATCH 18/34] small fix --- .../request_processor/base_online_request_processor.py | 5 ++--- 1 file changed, 2 insertions(+), 3 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 02f4a79f..5e24228f 100644 --- a/src/bespokelabs/curator/request_processor/base_online_request_processor.py +++ b/src/bespokelabs/curator/request_processor/base_online_request_processor.py @@ -241,9 +241,8 @@ async def process_requests_from_file( 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"] + status_tracker.max_requests_per_minute = self.max_requests_per_minute + status_tracker.max_tokens_per_minute = self.max_tokens_per_minute soft, hard = resource.getrlimit(resource.RLIMIT_NOFILE) resource.setrlimit( From f41aa91a711d24b7e1a2fde4ce9e4d40acd8fef3 Mon Sep 17 00:00:00 2001 From: Ryan Marten Date: Fri, 13 Dec 2024 14:39:52 -0800 Subject: [PATCH 19/34] update simple online --- tests/simple_online.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/tests/simple_online.py b/tests/simple_online.py index 12731eb3..30162241 100644 --- a/tests/simple_online.py +++ b/tests/simple_online.py @@ -19,7 +19,7 @@ def main(args): max_requests_per_minute=args.max_requests_per_minute, max_tokens_per_minute=args.max_tokens_per_minute, max_retries=args.max_retries, - require_all_responses=args.require_all_responses, + require_all_responses=not args.not_require_all_responses, ) dataset = prompter(dataset, batch_cancel=args.cancel) @@ -45,9 +45,9 @@ def main(args): ) parser.add_argument("--max-retries", type=int, help="Max retries", default=None) parser.add_argument( - "--require-all-responses", + "--not-require-all-responses", action="store_true", - default=None, + default=False, help="Require all responses", ) args = parser.parse_args() From 82817e50bc0022051fff4b649c85ea741aeda166 Mon Sep 17 00:00:00 2001 From: Ryan Marten Date: Fri, 13 Dec 2024 14:41:25 -0800 Subject: [PATCH 20/34] update simple online --- tests/simple_online.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tests/simple_online.py b/tests/simple_online.py index 30162241..4d5f90df 100644 --- a/tests/simple_online.py +++ b/tests/simple_online.py @@ -19,7 +19,7 @@ def main(args): max_requests_per_minute=args.max_requests_per_minute, max_tokens_per_minute=args.max_tokens_per_minute, max_retries=args.max_retries, - require_all_responses=not args.not_require_all_responses, + require_all_responses=not args.partial_responses, ) dataset = prompter(dataset, batch_cancel=args.cancel) @@ -45,7 +45,7 @@ def main(args): ) parser.add_argument("--max-retries", type=int, help="Max retries", default=None) parser.add_argument( - "--not-require-all-responses", + "--partial-responses", action="store_true", default=False, help="Require all responses", From 19089cb2e9c1cbbba876ff4629884c025137057c Mon Sep 17 00:00:00 2001 From: Ryan Marten Date: Fri, 13 Dec 2024 14:43:21 -0800 Subject: [PATCH 21/34] fix typing --- src/bespokelabs/curator/llm/llm.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/bespokelabs/curator/llm/llm.py b/src/bespokelabs/curator/llm/llm.py index 08bad431..589b3e4f 100644 --- a/src/bespokelabs/curator/llm/llm.py +++ b/src/bespokelabs/curator/llm/llm.py @@ -44,8 +44,8 @@ def __init__( parse_func: Optional[ Callable[ [ - Union[Dict[str, Any], BaseModel], - Union[Dict[str, Any], BaseModel], + _DictOrBaseModel, + _DictOrBaseModel, ], T, ] From 3ef189a21d33155bb1626fac3a583180cd3f15e0 Mon Sep 17 00:00:00 2001 From: Maheswaran Sathiamoorthy Date: Fri, 13 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a/README.md b/README.md index fe3327d2..f93b1cfa 100644 --- a/README.md +++ b/README.md @@ -54,18 +54,12 @@ pip install bespokelabs-curator ## Usage -### Imports -```python -from bespokelabs import curator -from datasets import Dataset -from pydantic import BaseModel, Field -from typing import List -``` -### `SimpleLLM`: A simple interface for calling LLMs +### Hello World with `SimpleLLM`: A simple interface for calling LLMs ```python +from bespokelabs import curator llm = curator.SimpleLLM(model_name="gpt-4o-mini") -poem = llm("Write a poem about the bitter lesson in AI and keep it 100 words or less.") +poem = llm("Write a poem about the importance of data in AI.") print(poem) ``` @@ -73,20 +67,28 @@ print(poem) You can use the [LiteLLM](https://docs.litellm.ai/docs/providers) backend for calling other models. ```python +from bespokelabs import curator llm = curator.SimpleLLM(model_name="claude-3-5-sonnet-20240620", backend="litellm") - -poem = llm("Write a sonnet about the bitter lesson in AI and make it visual.") +poem = llm("Write a poem about the importance of data in AI.") print(poem) ``` ### Visualize in Curator Viewer -Run `curator-viewer` to see the dataset in the viewer. +Run `curator-viewer` on the command line to see the dataset in the viewer. +You can click on a run and then click on a specific row to see the LLM request and response. +![Curator Responses](docs/curator-responses.png) +More examples below. -### `LLM`: A more powerful interface for calling LLMs +### `LLM`: A more powerful interface for synthetic data generation Create a dataset object for the topics you want to create the poems. ```python +from bespokelabs import curator +from datasets import Dataset +from pydantic import BaseModel, Field +from typing import List + topics = Dataset.from_dict({"topic": [ "Urban loneliness in a bustling city", "Beauty of Bespoke Labs's Curator library" @@ -147,6 +149,12 @@ curator-viewer This will pop up a browser window with the viewer running on `127.0.0.1:3000` by default if you haven't specified a different host and port. +The dataset viewer shows all the different runs you have made. +![Curator Runs](docs/curator-runs.png) + +You can also see the dataset and the responses from the LLM. +![Curator Dataset](docs/curator-dataset.png) + Optional parameters to run the viewer on a different host and port: ```bash diff --git a/docs/Curator-Dataset.png b/docs/curator-dataset.png similarity index 100% rename from docs/Curator-Dataset.png rename to docs/curator-dataset.png diff --git a/docs/Curator-Responses.png b/docs/curator-responses.png similarity index 100% rename from docs/Curator-Responses.png rename to docs/curator-responses.png diff --git a/docs/Curator-Runs.png b/docs/curator-runs.png similarity index 100% rename from docs/Curator-Runs.png rename to docs/curator-runs.png From 245ccdfd32a582a068f5a19cb53daa53412fe112 Mon Sep 17 00:00:00 2001 From: Mahesh Sathiamoorthy Date: Fri, 13 Dec 2024 22:14:47 +0000 Subject: [PATCH 24/34] Add a simple llm interface. --- README.md | 21 ++++++++++++--- examples/simple_poem.py | 13 +++++++++ src/bespokelabs/curator/__init__.py | 1 + src/bespokelabs/curator/llm/simple_llm.py | 32 +++++++++++++++++++++++ 4 files changed, 63 insertions(+), 4 deletions(-) create mode 100644 examples/simple_poem.py create mode 100644 src/bespokelabs/curator/llm/simple_llm.py diff --git a/README.md b/README.md index b7bf7940..2b4da899 100644 --- a/README.md +++ b/README.md @@ -29,7 +29,7 @@

-### Overview +## Overview Bespoke Curator makes it very easy to create high-quality synthetic data at scale, which you can use to finetune models or use for structured data extraction at scale. @@ -38,7 +38,7 @@ Bespoke Curator is an open-source project: * A Curator Viewer which makes it easy to view the datasets, thus aiding in the dataset creation. * We will also be releasing high-quality datasets that should move the needle on post-training. -### Key Features +## Key Features 1. **Programmability and Structured Outputs**: Synthetic data generation is lot more than just using a single prompt -- it involves calling LLMs multiple times and orchestrating control-flow. Curator treats structured outputs as first class citizens and helps you design complex pipelines. 2. **Built-in Performance Optimization**: We often see calling LLMs in loops, or inefficient implementation of multi-threading. We have baked in performance optimizations so that you don't need to worry about those! @@ -46,20 +46,33 @@ Bespoke Curator is an open-source project: 4. **Native HuggingFace Dataset Integration**: Work directly on HuggingFace Dataset objects throughout your pipeline. Your synthetic data is immediately ready for fine-tuning! 5. **Interactive Curator Viewer**: Improve and iterate on your prompts using our built-in viewer. Inspect LLM requests and responses in real-time, allowing you to iterate and refine your data generation strategy with immediate feedback. -### Installation +## Installation ```bash pip install bespokelabs-curator ``` -### Usage +## Usage +### Imports ```python from bespokelabs import curator from datasets import Dataset from pydantic import BaseModel, Field from typing import List +``` +### SimpleLLM: A simple interface for calling LLMs + +```python +llm = curator.SimpleLLM(model_name="gpt-4o-mini") +poem = llm("Write a poem about the bitter lesson in AI and keep it 100 words or less.") +print(poem) +``` + +### LLM: A more complex interface for calling LLMs + +```python # Create a dataset object for the topics you want to create the poems. topics = Dataset.from_dict({"topic": [ "Urban loneliness in a bustling city", diff --git a/examples/simple_poem.py b/examples/simple_poem.py new file mode 100644 index 00000000..474840bf --- /dev/null +++ b/examples/simple_poem.py @@ -0,0 +1,13 @@ +from bespokelabs import curator + +# Use GPT-4o-mini for this example. +llm = curator.SimpleLLM(model_name="gpt-4o-mini") + +poem = llm("Write a poem about the bitter lesson in AI and keep it 100 words or less.") +print(poem) + +# Use Claude 3.5 Sonnet for this example. +llm = curator.SimpleLLM(model_name="claude-3-5-sonnet-20240620", backend="litellm") + +poem = llm("Write a sonnet about the bitter lesson in AI and make it visual.") +print(poem) diff --git a/src/bespokelabs/curator/__init__.py b/src/bespokelabs/curator/__init__.py index a9d4cc6e..b02aab46 100644 --- a/src/bespokelabs/curator/__init__.py +++ b/src/bespokelabs/curator/__init__.py @@ -1,2 +1,3 @@ from .dataset import Dataset from .llm.llm import LLM +from .llm.simple_llm import SimpleLLM \ No newline at end of file diff --git a/src/bespokelabs/curator/llm/simple_llm.py b/src/bespokelabs/curator/llm/simple_llm.py new file mode 100644 index 00000000..9181a614 --- /dev/null +++ b/src/bespokelabs/curator/llm/simple_llm.py @@ -0,0 +1,32 @@ +from bespokelabs.curator.llm.llm import LLM +from datasets import Dataset +from typing import Union, List + + +class SimpleLLM: + """A simpler interface for the LLM class. + + Usage: + llm = SimpleLLM(model_name="gpt-4o-mini") + llm("Do you know about the bitter lesson?") + llm(["What is the capital of France?", "What is the capital of Germany?"]) + For more complex use cases (e.g. structured outputs and custom prompt functions), see the LLM class. + """ + def __init__(self, model_name: str, backend: str = "openai"): + self._model_name = model_name + self._backend = backend + + def __call__(self, prompt: Union[str, List[str]]) -> Union[str, List[str]]: + prompt_list = [prompt] if isinstance(prompt, str) else prompt + dataset: Dataset = Dataset.from_dict({"prompt": prompt_list}) + + llm = LLM( + prompt_func=lambda row: row['prompt'], + model_name=self._model_name, + response_format=None, + backend=self._backend + ) + response = llm(dataset) + if isinstance(prompt, str): + return response["response"][0] + return response["response"] From 53c398ddd1b987efb9c58848372e9c672b2df29d Mon Sep 17 00:00:00 2001 From: Mahesh Sathiamoorthy Date: Fri, 13 Dec 2024 22:21:12 +0000 Subject: [PATCH 25/34] Update README. --- README.md | 30 +++++++++++++++++++++++------- 1 file changed, 23 insertions(+), 7 deletions(-) diff --git a/README.md b/README.md index 2b4da899..fe3327d2 100644 --- a/README.md +++ b/README.md @@ -61,33 +61,49 @@ from datasets import Dataset from pydantic import BaseModel, Field from typing import List ``` -### SimpleLLM: A simple interface for calling LLMs +### `SimpleLLM`: A simple interface for calling LLMs ```python llm = curator.SimpleLLM(model_name="gpt-4o-mini") - poem = llm("Write a poem about the bitter lesson in AI and keep it 100 words or less.") print(poem) ``` -### LLM: A more complex interface for calling LLMs +#### Use LiteLLM backend for calling other models +You can use the [LiteLLM](https://docs.litellm.ai/docs/providers) backend for calling other models. + +```python +llm = curator.SimpleLLM(model_name="claude-3-5-sonnet-20240620", backend="litellm") + +poem = llm("Write a sonnet about the bitter lesson in AI and make it visual.") +print(poem) +``` + +### Visualize in Curator Viewer +Run `curator-viewer` to see the dataset in the viewer. + +### `LLM`: A more powerful interface for calling LLMs + +Create a dataset object for the topics you want to create the poems. ```python -# Create a dataset object for the topics you want to create the poems. topics = Dataset.from_dict({"topic": [ "Urban loneliness in a bustling city", "Beauty of Bespoke Labs's Curator library" ]}) +``` -# Define a class to encapsulate a list of poems. +Define a class to encapsulate a list of poems. +```python class Poem(BaseModel): poem: str = Field(description="A poem.") class Poems(BaseModel): poems_list: List[Poem] = Field(description="A list of poems.") +``` - -# We define an `LLM` object that generates poems which gets applied to the topics dataset. +We define an `LLM` object that generates poems which gets applied to the topics dataset. +```python poet = curator.LLM( # `prompt_func` takes a row of the dataset as input. # `row` is a dictionary with a single key 'topic' in this case. 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zzW1WUdjTR{CGn>Q7*?m6H_PnZgMFxC>pak8Z)n>oh=XQ%V=mz0!O4X0(1QEg45m5t-Ia{UmyJc19|ISJ3Tknql+r5FkZ0AN}<5Icql)-K+lDx@_Xm%eOyoQ0Qz8$Q}_V7RhY}68@YV zjg5_W&^h~%j=PiEr4G_2I+1_txyn1T+$Z`Bm$*RKVh&28=yIQvY5Hc5llI5ulaI+L zEvF>iLXm>u_B_B<5jTlj93@{?mMvx&eK5GI=*lVj%CVWY0tT%Q-~|mfjcNL~Q`_-5 zH4;+Gq}S3mc=9V?@_e}^`S3@zUJ$29@^*2mf3_HOZv^+pnf#t@M+)w1DK#!Pyhe` literal 0 HcmV?d00001 From ffd8b111e4223c185087b5c7a49993a4d371562b Mon Sep 17 00:00:00 2001 From: Mahesh Sathiamoorthy Date: Fri, 13 Dec 2024 23:51:14 +0000 Subject: [PATCH 27/34] Rename images and update readme. --- README.md | 34 +++++++++++------- ...urator-Dataset.png => curator-dataset.png} | Bin ...or-Responses.png => curator-responses.png} | Bin docs/{Curator-Runs.png => curator-runs.png} | Bin 4 files changed, 21 insertions(+), 13 deletions(-) rename docs/{Curator-Dataset.png => curator-dataset.png} (100%) rename docs/{Curator-Responses.png => curator-responses.png} (100%) rename docs/{Curator-Runs.png => curator-runs.png} (100%) diff --git a/README.md b/README.md index fe3327d2..f93b1cfa 100644 --- a/README.md +++ b/README.md @@ -54,18 +54,12 @@ pip install bespokelabs-curator ## Usage -### Imports -```python -from bespokelabs import curator -from datasets import Dataset -from pydantic import BaseModel, Field -from typing import List -``` -### `SimpleLLM`: A simple interface for calling LLMs +### Hello World with `SimpleLLM`: A simple interface for calling LLMs ```python +from bespokelabs import curator llm = curator.SimpleLLM(model_name="gpt-4o-mini") -poem = llm("Write a poem about the bitter lesson in AI and keep it 100 words or less.") +poem = llm("Write a poem about the importance of data in AI.") print(poem) ``` @@ -73,20 +67,28 @@ print(poem) You can use the [LiteLLM](https://docs.litellm.ai/docs/providers) backend for calling other models. ```python +from bespokelabs import curator llm = curator.SimpleLLM(model_name="claude-3-5-sonnet-20240620", backend="litellm") - -poem = llm("Write a sonnet about the bitter lesson in AI and make it visual.") +poem = llm("Write a poem about the importance of data in AI.") print(poem) ``` ### Visualize in Curator Viewer -Run `curator-viewer` to see the dataset in the viewer. +Run `curator-viewer` on the command line to see the dataset in the viewer. +You can click on a run and then click on a specific row to see the LLM request and response. +![Curator Responses](docs/curator-responses.png) +More examples below. -### `LLM`: A more powerful interface for calling LLMs +### `LLM`: A more powerful interface for synthetic data generation Create a dataset object for the topics you want to create the poems. ```python +from bespokelabs import curator +from datasets import Dataset +from pydantic import BaseModel, Field +from typing import List + topics = Dataset.from_dict({"topic": [ "Urban loneliness in a bustling city", "Beauty of Bespoke Labs's Curator library" @@ -147,6 +149,12 @@ curator-viewer This will pop up a browser window with the viewer running on `127.0.0.1:3000` by default if you haven't specified a different host and port. +The dataset viewer shows all the different runs you have made. +![Curator Runs](docs/curator-runs.png) + +You can also see the dataset and the responses from the LLM. +![Curator Dataset](docs/curator-dataset.png) + Optional parameters to run the viewer on a different host and port: ```bash diff --git a/docs/Curator-Dataset.png b/docs/curator-dataset.png similarity index 100% rename from docs/Curator-Dataset.png rename to docs/curator-dataset.png diff --git a/docs/Curator-Responses.png b/docs/curator-responses.png similarity index 100% rename from docs/Curator-Responses.png rename to docs/curator-responses.png diff --git a/docs/Curator-Runs.png b/docs/curator-runs.png similarity index 100% rename from docs/Curator-Runs.png rename to docs/curator-runs.png From 80121c22b45cce6f78024aba40d8e9356f4bb417 Mon Sep 17 00:00:00 2001 From: Mahesh Sathiamoorthy Date: Sat, 14 Dec 2024 00:01:11 +0000 Subject: [PATCH 28/34] Update readme. --- README.md | 14 ++++++++++---- 1 file changed, 10 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index f93b1cfa..e9d91789 100644 --- a/README.md +++ b/README.md @@ -53,6 +53,8 @@ pip install bespokelabs-curator ``` ## Usage +To run the examples below, make sure to set your OpenAI API key in +the environment variable `OPENAI_API_KEY` by running `export OPENAI_API_KEY=sk-...` in your terminal. ### Hello World with `SimpleLLM`: A simple interface for calling LLMs @@ -61,7 +63,14 @@ from bespokelabs import curator llm = curator.SimpleLLM(model_name="gpt-4o-mini") poem = llm("Write a poem about the importance of data in AI.") print(poem) +# Or you can pass a list of prompts to generate multiple responses. +poems = llm(["Write a poem about the importance of data in AI.", + "Write a haiku about the importance of data in AI."]) +print(poems) ``` +Note that retries and caching are enabled by default. +So now if you run the same prompt again, you will get the same response, pretty much instantly. +You can delete the cache at `~/.cache/curator`. #### Use LiteLLM backend for calling other models You can use the [LiteLLM](https://docs.litellm.ai/docs/providers) backend for calling other models. @@ -82,7 +91,7 @@ More examples below. ### `LLM`: A more powerful interface for synthetic data generation -Create a dataset object for the topics you want to create the poems. +Let's use structured outputs to generate poems. ```python from bespokelabs import curator from datasets import Dataset @@ -133,9 +142,6 @@ and we can scale this up to create tens of thousands of diverse poems. You can see a more detailed example in the [examples/poem.py](https://github.com/bespokelabsai/curator/blob/mahesh/update_doc/examples/poem.py) file, and other examples in the [examples](https://github.com/bespokelabsai/curator/blob/mahesh/update_doc/examples) directory. -To run the examples, make sure to set your OpenAI API key in -the environment variable `OPENAI_API_KEY` by running `export OPENAI_API_KEY=sk-...` in your terminal. - See the [docs](https://docs.bespokelabs.ai/) for more details as well as for troubleshooting information. From 574aee5ea14618756c724eab65de496cb4aaae71 Mon Sep 17 00:00:00 2001 From: Mahesh Sathiamoorthy Date: Sat, 14 Dec 2024 00:04:42 +0000 Subject: [PATCH 29/34] Update example. --- examples/simple_poem.py | 15 +++++++++++---- 1 file changed, 11 insertions(+), 4 deletions(-) diff --git a/examples/simple_poem.py b/examples/simple_poem.py index 474840bf..62cdd095 100644 --- a/examples/simple_poem.py +++ b/examples/simple_poem.py @@ -1,13 +1,20 @@ +"""Curator example that uses `SimpleLLM` to generate poems. + +Please see the poem.py for more complex use cases. +""" from bespokelabs import curator # Use GPT-4o-mini for this example. llm = curator.SimpleLLM(model_name="gpt-4o-mini") - -poem = llm("Write a poem about the bitter lesson in AI and keep it 100 words or less.") +poem = llm("Write a poem about the importance of data in AI.") print(poem) # Use Claude 3.5 Sonnet for this example. llm = curator.SimpleLLM(model_name="claude-3-5-sonnet-20240620", backend="litellm") - -poem = llm("Write a sonnet about the bitter lesson in AI and make it visual.") +poem = llm("Write a poem about the importance of data in AI.") print(poem) + +# Note that we can also pass a list of prompts to generate multiple responses. +poems = llm(["Write a sonnet about the importance of data in AI.", + "Write a haiku about the importance of data in AI."]) +print(poems) \ No newline at end of file From 419a275f7a6cebaa1c8dd0167ee747f0202510f1 Mon Sep 17 00:00:00 2001 From: Mahesh Sathiamoorthy Date: Sat, 14 Dec 2024 00:07:04 +0000 Subject: [PATCH 30/34] Run black formatter. --- examples/simple_poem.py | 11 +++-- src/bespokelabs/curator/__init__.py | 2 +- src/bespokelabs/curator/llm/simple_llm.py | 49 ++++++++++++----------- 3 files changed, 34 insertions(+), 28 deletions(-) diff --git a/examples/simple_poem.py b/examples/simple_poem.py index 62cdd095..8b1f5106 100644 --- a/examples/simple_poem.py +++ b/examples/simple_poem.py @@ -2,6 +2,7 @@ Please see the poem.py for more complex use cases. """ + from bespokelabs import curator # Use GPT-4o-mini for this example. @@ -15,6 +16,10 @@ print(poem) # Note that we can also pass a list of prompts to generate multiple responses. -poems = llm(["Write a sonnet about the importance of data in AI.", - "Write a haiku about the importance of data in AI."]) -print(poems) \ No newline at end of file +poems = llm( + [ + "Write a sonnet about the importance of data in AI.", + "Write a haiku about the importance of data in AI.", + ] +) +print(poems) diff --git a/src/bespokelabs/curator/__init__.py b/src/bespokelabs/curator/__init__.py index b02aab46..5ef73092 100644 --- a/src/bespokelabs/curator/__init__.py +++ b/src/bespokelabs/curator/__init__.py @@ -1,3 +1,3 @@ from .dataset import Dataset from .llm.llm import LLM -from .llm.simple_llm import SimpleLLM \ No newline at end of file +from .llm.simple_llm import SimpleLLM diff --git a/src/bespokelabs/curator/llm/simple_llm.py b/src/bespokelabs/curator/llm/simple_llm.py index 9181a614..7cc62dd0 100644 --- a/src/bespokelabs/curator/llm/simple_llm.py +++ b/src/bespokelabs/curator/llm/simple_llm.py @@ -4,29 +4,30 @@ class SimpleLLM: - """A simpler interface for the LLM class. - - Usage: - llm = SimpleLLM(model_name="gpt-4o-mini") - llm("Do you know about the bitter lesson?") - llm(["What is the capital of France?", "What is the capital of Germany?"]) - For more complex use cases (e.g. structured outputs and custom prompt functions), see the LLM class. - """ - def __init__(self, model_name: str, backend: str = "openai"): - self._model_name = model_name - self._backend = backend + """A simpler interface for the LLM class. - def __call__(self, prompt: Union[str, List[str]]) -> Union[str, List[str]]: - prompt_list = [prompt] if isinstance(prompt, str) else prompt - dataset: Dataset = Dataset.from_dict({"prompt": prompt_list}) + Usage: + llm = SimpleLLM(model_name="gpt-4o-mini") + llm("Do you know about the bitter lesson?") + llm(["What is the capital of France?", "What is the capital of Germany?"]) + For more complex use cases (e.g. structured outputs and custom prompt functions), see the LLM class. + """ - llm = LLM( - prompt_func=lambda row: row['prompt'], - model_name=self._model_name, - response_format=None, - backend=self._backend - ) - response = llm(dataset) - if isinstance(prompt, str): - return response["response"][0] - return response["response"] + def __init__(self, model_name: str, backend: str = "openai"): + self._model_name = model_name + self._backend = backend + + def __call__(self, prompt: Union[str, List[str]]) -> Union[str, List[str]]: + prompt_list = [prompt] if isinstance(prompt, str) else prompt + dataset: Dataset = Dataset.from_dict({"prompt": prompt_list}) + + llm = LLM( + prompt_func=lambda row: row["prompt"], + model_name=self._model_name, + response_format=None, + backend=self._backend, + ) + response = llm(dataset) + if isinstance(prompt, str): + return response["response"][0] + return response["response"] From 0b3e0143228360c24e8cb2271ca829db3b82242b Mon Sep 17 00:00:00 2001 From: Mahesh Sathiamoorthy Date: Sat, 14 Dec 2024 00:27:30 +0000 Subject: [PATCH 31/34] Remove an accidental extra __init__ that was introduced in https://github.com/bespokelabsai/curator/pull/251 --- src/bespokelabs/curator/llm/llm.py | 73 ------------------------------ 1 file changed, 73 deletions(-) diff --git a/src/bespokelabs/curator/llm/llm.py b/src/bespokelabs/curator/llm/llm.py index 589b3e4f..310c6fa7 100644 --- a/src/bespokelabs/curator/llm/llm.py +++ b/src/bespokelabs/curator/llm/llm.py @@ -37,79 +37,6 @@ class LLM: """Interface for prompting LLMs.""" - def __init__( - self, - model_name: str, - prompt_func: Callable[[Union[Dict[str, Any], BaseModel]], Dict[str, str]], - parse_func: Optional[ - Callable[ - [ - _DictOrBaseModel, - _DictOrBaseModel, - ], - T, - ] - ] = None, - response_format: Optional[Type[BaseModel]] = None, - backend: Optional[str] = None, - max_requests_per_minute: Optional[int] = None, - max_tokens_per_minute: Optional[int] = None, - temperature: Optional[float] = None, - top_p: Optional[float] = None, - presence_penalty: Optional[float] = None, - frequency_penalty: Optional[float] = None, - max_retries: Optional[int] = None, - require_all_responses: Optional[bool] = None, - ): - """Initialize a LLM. - - Args: - model_name: The name of the LLM to use - prompt_func: A function that takes a single row - and returns either a string (assumed to be a user prompt) or messages list - parse_func: A function that takes the input row and - response object and returns the parsed output - response_format: A Pydantic model specifying the - response format from the LLM. - backend: The backend to use ("openai" or "litellm"). If None, will be auto-determined - max_requests_per_minute: Maximum requests per minute (not supported in batch mode) - max_tokens_per_minute: Maximum tokens per minute (not supported in batch mode) - temperature: The temperature to use for the LLM - top_p: The top_p to use for the LLM - presence_penalty: The presence_penalty to use for the LLM - frequency_penalty: The frequency_penalty to use for the LLM - max_retries: The maximum number of retries to use for the LLM. If 0, will only try a request once. - require_all_responses: Whether to require all responses - """ - self.prompt_formatter = PromptFormatter( - model_name, prompt_func, parse_func, response_format - ) - - # Initialize context manager state - self._batch_config = None - self._original_request_processor = None - - # Store model parameters - self.temperature = temperature - self.top_p = top_p - self.presence_penalty = presence_penalty - self.frequency_penalty = frequency_penalty - self.model_name = model_name - - # Auto-determine backend if not specified - if backend is not None: - self.backend = backend - else: - self.backend = self._determine_backend(model_name, response_format) - - # Initialize request processor - self._setup_request_processor( - max_requests_per_minute=max_requests_per_minute, - max_tokens_per_minute=max_tokens_per_minute, - max_retries=max_retries, - require_all_responses=require_all_responses, - ) - @staticmethod def _determine_backend( model_name: str, response_format: Optional[Type[BaseModel]] = None From 8b9086f4a563675b414088fabfd67d3e8930cc43 Mon Sep 17 00:00:00 2001 From: Mahesh Sathiamoorthy Date: Sat, 14 Dec 2024 00:52:29 +0000 Subject: [PATCH 32/34] Address Ryan's comments. --- .DS_Store | Bin 6148 -> 0 bytes .gitignore | 1 + README.md | 11 +++-- docs/.DS_Store | Bin 6148 -> 0 bytes src/bespokelabs/curator/llm/llm.py | 71 +++++++++++++++-------------- 5 files changed, 44 insertions(+), 39 deletions(-) delete mode 100644 .DS_Store delete mode 100644 docs/.DS_Store diff --git a/.DS_Store b/.DS_Store deleted file mode 100644 index bb875642a4ca21df3c646358f901a43b482f35d4..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 6148 zcmeHK%SyvQ6rE|Kol=A>6uJz!Em-S|;wHrU14eYAQWH}&7&E0w&7u^t)*tdq{2uR} znTW-@6|wii%(>5*%z?~3A|}}#%f5bpI8Nd$?{?pLt!}NYH$+3U#Et(T3qKF?Y32o!E40p~Ou|YJ!i#uR z4DHPmndU*9jwY%gjz*AjeHo{bEIc_)qfFKMIv|>&Ikele*+IY8arTdT^Nus?_qw3( z9nR-Xv9-N(d^UVco>KXu3FW}Ik`03uyn^z%o@alOCNjMTUzK0w5fTH$05L!etS$rQ zOc1TrrGQpW3=ji9Fo64m0}atNSZY*T2XuIS#<+!u0y@4W5FLZA!BQhcK)5ai)TP`! zF}N-Vzhm-TgQZ4Y&bXQx#xXNjj~A|H2fw4z8Fw{OPYe(PiwxAYY2*2S3ct+KNB&|7 zS;PP_@Xr|F*1#Wluqbo3ep?=%wGOmfXegLhq5=Z?!X*F<+(+6fsN)XmkmnjKHR34f RSLJ|o5m1CsM-2P|10M+DN>u;= diff --git a/.gitignore b/.gitignore index 892e0790..e95f9df8 100644 --- a/.gitignore +++ b/.gitignore @@ -1,4 +1,5 @@ .venv +.DS_Store __pycache__ .vscode diff --git a/README.md b/README.md index e9d91789..81224738 100644 --- a/README.md +++ b/README.md @@ -116,18 +116,21 @@ class Poems(BaseModel): We define an `LLM` object that generates poems which gets applied to the topics dataset. ```python poet = curator.LLM( - # `prompt_func` takes a row of the dataset as input. - # `row` is a dictionary with a single key 'topic' in this case. prompt_func=lambda row: f"Write two poems about {row['topic']}.", model_name="gpt-4o-mini", response_format=Poems, - # `row` is the input row, and `poems` is the `Poems` class which - # is parsed from the structured output from the LLM. parse_func=lambda row, poems: [ {"topic": row["topic"], "poem": p.poem} for p in poems.poems_list ], ) +``` +Here: +* `prompt_func` takes a row of the dataset as input and returns the prompt for the LLM. +* `response_format` is the structured output class we defined above. +* `parse_func` takes the input (`row`) and the structured output (`poems`) and converts it to a list of dictionaries. This is so that we can easily convert the output to a HuggingFace Dataset object. +Now we can apply the `LLM` object to the dataset, which reads very pythonic. +```python poem = poet(topics) print(poem.to_pandas()) # Example output: diff --git a/docs/.DS_Store b/docs/.DS_Store deleted file mode 100644 index 432e199d3f7d83bcf2ebcce84b50fc894571d1be..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 6148 zcmeHKK}rKb5Uh?l1oRLPL2vT`1<4177(bvN&=`?GNP=tbd-Ewi#xDtDb@wQ)t5*-w z4c#@<(>24?VS5LFOm~ZOU<_bHSH!c1zUjJp&jvLkL^}6)!Um5x#TD**)jubcdxQ(@ z$h+y~wRwIN0p+G1Q z3WNfoz;^|-_tHlDjuAtFP#_feR6xHEiLRI{wvP7cV6Z0uamch8$M%*WCQT4?#nzD- znmCo{RH;u4aXRy<*5!(=qthXFGWV$`OMRh;oz8r+a!7WJ7z%^}T?O`hI@bPwN&n^j z-zCK?6bJ?Wl>#!HUC*YxQthq1%W1Do^c(tzvDV5NtQ8Zj6?3Ak`1ng*(lOU_#n#d2 S%p08;KLV;tSSaup3cLfGLnib9 diff --git a/src/bespokelabs/curator/llm/llm.py b/src/bespokelabs/curator/llm/llm.py index 310c6fa7..aa3cbbca 100644 --- a/src/bespokelabs/curator/llm/llm.py +++ b/src/bespokelabs/curator/llm/llm.py @@ -36,41 +36,6 @@ class LLM: """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, @@ -189,6 +154,42 @@ def __init__( else: raise ValueError(f"Unknown backend: {self.backend}") + @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 __call__( self, dataset: Optional[Iterable] = None, From 6c620524e27d8dbf82b57b7d2d2bfe0f5b42a82d Mon Sep 17 00:00:00 2001 From: Maheswaran Sathiamoorthy Date: Fri, 13 Dec 2024 16:54:20 -0800 Subject: [PATCH 33/34] Replace the dataset image. --- docs/curator-dataset.png | Bin 279522 -> 445480 bytes 1 file changed, 0 insertions(+), 0 deletions(-) diff --git a/docs/curator-dataset.png b/docs/curator-dataset.png index 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zMSI2^MTNg?Q(_dtG1#!;E_*1X%laxjC6IaNce2yHQqo|I&B}&CUf5i@&h>1t-)`v} z|Mt8uD?8CdEPj4r;a_AU!^1Tm3b3KAT2mTR)K2eu=j$@9BvD8d5826(mX>4`JMZ}V z@)~6KjBGT|^w(42Z#@Hc0E?(Ub{!+&A(?m`VY`D7{p7;n>0T}G%j%tG?Qf8gLbl4K zcBtm|u2=5aB%NeGNNgx*oN_NQ@D);QE15e8)YY5jxrdsq9XWdn?-NIW3NrVWcUHmKm z?Lg%jrIC>l?d1|+AP)FTX!{xD_$0@W2DY2`oiB`==(BMJH8bm;WIh;1yjg@qjA4*i z@#S%=P4!*848D193f$to+&&+LEY3Z|m6e;hy>!pIgcM8Hys_(!BO8YU2BY7N(cy7+ zQBz-BvQo#7uBh(j`dU%S_Z;MtHNqXnEiwbE`Mkv@gG}XLN7Fh~6+cQ9+>KN6-AC{d z+D=OO@5Usan3GrA*T>sRQ|xB+G2o=iw0*lcg`9$oj>bWx(kOFRUiKcGv`DYM8^>gN zsi*8@_~nB^R?KA4)4Yun%J3$6I_pWa|)jp&Tq7Mtfh!^$J_YHJu6rGjmnI4Qtrxx!l}TP(lqDY zPp4zQu*Ok7H!+%}r#~kwi cxGIgZ+i7kGBpi_yCj%cBP#R})RLlbY2hSzpumAu6 From c14233dfe2c70613f7e83c8c5be2b5aa64118cfb Mon Sep 17 00:00:00 2001 From: Mahesh Sathiamoorthy Date: Sat, 14 Dec 2024 01:51:13 +0000 Subject: [PATCH 34/34] Run black. --- src/bespokelabs/curator/llm/llm.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/src/bespokelabs/curator/llm/llm.py b/src/bespokelabs/curator/llm/llm.py index aa3cbbca..dcf3df83 100644 --- a/src/bespokelabs/curator/llm/llm.py +++ b/src/bespokelabs/curator/llm/llm.py @@ -36,6 +36,7 @@ class LLM: """Interface for prompting LLMs.""" + def __init__( self, model_name: str, @@ -188,8 +189,6 @@ def _determine_backend( ) return "litellm" - - def __call__( self, dataset: Optional[Iterable] = None,