diff --git a/benchmarks/_plotter_combined.py b/benchmarks/_plotter_combined.py index 0f8a03b..9e55244 100644 --- a/benchmarks/_plotter_combined.py +++ b/benchmarks/_plotter_combined.py @@ -381,13 +381,15 @@ def __aggregate_stats_for_cache_hit_error_rate(run_dirs: List[str], z: float = 1 def __get_result_files(results_dir: str): if not os.path.exists(results_dir): print(f"No results found in {results_dir}") - return [], [], [], [], [] + return [], [], [], [], [], [], [] gptcache_files: List[str] = [] vcache_local_files: List[str] = [] vcache_global_files: List[str] = [] berkeley_embedding_files: List[str] = [] vcache_berkeley_embedding_files: List[str] = [] + sigmoid_probability_files: List[str] = [] + sigmoid_only_files: List[str] = [] for d in os.listdir(results_dir): # Process GPTCache (static threshold) directories @@ -435,12 +437,32 @@ def __get_result_files(results_dir: str): if file.startswith("results_") and file.endswith(".json"): vcache_berkeley_embedding_files.append(os.path.join(dir_path, file)) + # Process Sigmoid Probability directories + elif d.startswith("sigmoid_probability_") and os.path.isdir( + os.path.join(results_dir, d) + ): + dir_path: str = os.path.join(results_dir, d) + for file in os.listdir(dir_path): + if file.startswith("results_") and file.endswith(".json"): + sigmoid_probability_files.append(os.path.join(dir_path, file)) + + # Process Sigmoid Only directories + elif d.startswith("sigmoid_only_") and os.path.isdir( + os.path.join(results_dir, d) + ): + dir_path: str = os.path.join(results_dir, d) + for file in os.listdir(dir_path): + if file.startswith("results_") and file.endswith(".json"): + sigmoid_only_files.append(os.path.join(dir_path, file)) + return ( gptcache_files, vcache_local_files, vcache_global_files, berkeley_embedding_files, vcache_berkeley_embedding_files, + sigmoid_probability_files, + sigmoid_only_files, ) @@ -463,6 +485,8 @@ def generate_combined_plots( vcache_global_files, berkeley_embedding_files, vcache_berkeley_embedding_files, + sigmoid_probability_files, + sigmoid_only_files, ) = __get_result_files(results_dir) if ( @@ -471,6 +495,8 @@ def generate_combined_plots( and not vcache_global_files and not berkeley_embedding_files and not vcache_berkeley_embedding_files + and not sigmoid_probability_files + and not sigmoid_only_files ): print( f"No folders found for {dataset}, {embedding_model_name}, {llm_model_name}\n" @@ -565,6 +591,40 @@ def generate_combined_plots( print(f"Error loading {vcache_berkeley_embedding_file_path}: {e}") continue + ############################################################ + ### Sigmoid Probability + sigmoid_probability_data_frames: Dict[float, pd.DataFrame] = {} + for sigmoid_probability_file_path in sigmoid_probability_files: + with open(sigmoid_probability_file_path, "r") as f: + try: + data: Any = json.load(f) + dataframe, _, chopped_index = convert_to_dataframe_from_json_file( + json_data=data, keep_split=keep_split + ) + delta: float = data["config"]["delta"] + sigmoid_probability_data_frames[delta] = dataframe + chopped_index = chopped_index + except Exception as e: + print(f"Error loading {sigmoid_probability_file_path}: {e}") + continue + + ############################################################ + ### Sigmoid Only + sigmoid_only_data_frames: Dict[float, pd.DataFrame] = {} + for sigmoid_only_file_path in sigmoid_only_files: + with open(sigmoid_only_file_path, "r") as f: + try: + data: Any = json.load(f) + dataframe, _, chopped_index = convert_to_dataframe_from_json_file( + json_data=data, keep_split=keep_split + ) + delta: float = data["config"]["delta"] + sigmoid_only_data_frames[delta] = dataframe + chopped_index = chopped_index + except Exception as e: + print(f"Error loading {sigmoid_only_file_path}: {e}") + continue + if chopped_index is None: print( f"No data found for {dataset}, {embedding_model_name}, {llm_model_name} in {results_dir}" @@ -577,6 +637,8 @@ def generate_combined_plots( vcache_global_data_frames=vcache_global_data_frames, berkeley_embedding_data_frames=berkeley_embedding_data_frames, vcache_berkeley_embedding_data_frames=vcache_berkeley_embedding_data_frames, + sigmoid_probability_data_frames=sigmoid_probability_data_frames, + sigmoid_only_data_frames=sigmoid_only_data_frames, results_dir=results_dir, timestamp=timestamp, font_size=font_size, @@ -620,6 +682,8 @@ def generate_combined_plots( vcache_global_data_frames=vcache_global_data_frames, berkeley_embedding_data_frames=berkeley_embedding_data_frames, vcache_berkeley_embedding_data_frames=vcache_berkeley_embedding_data_frames, + sigmoid_probability_data_frames=sigmoid_probability_data_frames, + sigmoid_only_data_frames=sigmoid_only_data_frames, results_dir=results_dir, timestamp=timestamp, font_size=font_size, @@ -635,6 +699,8 @@ def generate_combined_plots( vcache_global_data_frames=vcache_global_data_frames, berkeley_embedding_data_frames=berkeley_embedding_data_frames, vcache_berkeley_embedding_data_frames=vcache_berkeley_embedding_data_frames, + sigmoid_probability_data_frames=sigmoid_probability_data_frames, + sigmoid_only_data_frames=sigmoid_only_data_frames, results_dir=results_dir, timestamp=timestamp, font_size=font_size, @@ -679,6 +745,8 @@ def __plot_legend( vcache_global_data_frames: Dict[float, pd.DataFrame], berkeley_embedding_data_frames: Dict[float, pd.DataFrame], vcache_berkeley_embedding_data_frames: Dict[float, pd.DataFrame], + sigmoid_probability_data_frames: Dict[float, pd.DataFrame], + sigmoid_only_data_frames: Dict[float, pd.DataFrame], results_dir: str, timestamp: str, font_size: int, @@ -760,6 +828,34 @@ def __plot_legend( ) labels.append("Fine-tuned Embedding") + if sigmoid_probability_data_frames: + lines.append( + Line2D( + [0], + [0], + color="#89D572", + linewidth=3, + linestyle="-", + marker="o", + markersize=8, + ) + ) + labels.append("Sigmoid Probability") + + if sigmoid_only_data_frames: + lines.append( + Line2D( + [0], + [0], + color="#E2A043", + linewidth=3, + linestyle="-", + marker="o", + markersize=8, + ) + ) + labels.append("Sigmoid Only") + ax.legend(lines, labels, loc="center", ncol=2, fontsize=font_size, frameon=False) legend_filename = results_dir + "/legend.pdf" @@ -1245,6 +1341,8 @@ def __plot_avg_latency_vs_error_rate( vcache_global_data_frames: Dict[float, pd.DataFrame], berkeley_embedding_data_frames: Dict[float, pd.DataFrame], vcache_berkeley_embedding_data_frames: Dict[float, pd.DataFrame], + sigmoid_probability_data_frames: Dict[float, pd.DataFrame], + sigmoid_only_data_frames: Dict[float, pd.DataFrame], results_dir: str, timestamp: str, font_size: int, @@ -1269,6 +1367,12 @@ def __plot_avg_latency_vs_error_rate( vcache_berkeley_embedding_run_dirs_map = __collect_run_dirs_by_prefix_and_key( results_dir, "vcache_berkeley_embedding_" ) + sigmoid_probability_run_dirs_map = __collect_run_dirs_by_prefix_and_key( + results_dir, "sigmoid_probability_" + ) + sigmoid_only_run_dirs_map = __collect_run_dirs_by_prefix_and_key( + results_dir, "sigmoid_only_" + ) avg_latency_no_cache = -1 # Initialize @@ -1470,6 +1574,52 @@ def prepare_latency_error_series_data( 8, ) + ############################################################ + ### Sigmoid Probability + if sigmoid_probability_data_frames: + sp_lat, sp_err, sp_lat_le, sp_lat_ue, sp_err_le, sp_err_ue, sp_multi = ( + prepare_latency_error_series_data( + sigmoid_probability_data_frames, + sigmoid_probability_run_dirs_map, + ERROR_RATE_UPPER_BOUND, + ) + ) + __draw_confidence_series( + sp_lat, + sp_err, + sp_lat_le, + sp_lat_ue, + sp_err_le, + sp_err_ue, + sp_multi, + "#89D572", + "Sigmoid Probability", + 8, + ) + + ############################################################ + ### Sigmoid Only + if sigmoid_only_data_frames: + so_lat, so_err, so_lat_le, so_lat_ue, so_err_le, so_err_ue, so_multi = ( + prepare_latency_error_series_data( + sigmoid_only_data_frames, + sigmoid_only_run_dirs_map, + ERROR_RATE_UPPER_BOUND, + ) + ) + __draw_confidence_series( + so_lat, + so_err, + so_lat_le, + so_lat_ue, + so_err_le, + so_err_ue, + so_multi, + "#E2A043", + "Sigmoid Only", + 8, + ) + ############################################################ ### Baseline: No Cache Plotting # Ensure avg_latency_no_cache has been set by one of the prepare_latency_error_series_data calls @@ -1508,6 +1658,8 @@ def __plot_cache_hit_vs_error_rate( vcache_global_data_frames: Dict[float, pd.DataFrame], berkeley_embedding_data_frames: Dict[float, pd.DataFrame], vcache_berkeley_embedding_data_frames: Dict[float, pd.DataFrame], + sigmoid_probability_data_frames: Dict[float, pd.DataFrame], + sigmoid_only_data_frames: Dict[float, pd.DataFrame], results_dir: str, timestamp: str, font_size: int, @@ -1532,6 +1684,12 @@ def __plot_cache_hit_vs_error_rate( vcache_berkeley_embedding_run_dirs_map = __collect_run_dirs_by_prefix_and_key( results_dir, "vcache_berkeley_embedding_" ) + sigmoid_probability_run_dirs_map = __collect_run_dirs_by_prefix_and_key( + results_dir, "sigmoid_probability_" + ) + sigmoid_only_run_dirs_map = __collect_run_dirs_by_prefix_and_key( + results_dir, "sigmoid_only_" + ) # Helper to prepare data for a series def prepare_cache_hit_error_series_data( @@ -1718,6 +1876,50 @@ def prepare_cache_hit_error_series_data( 8, ) + # Sigmoid Probability + if sigmoid_probability_data_frames: + sp_err, sp_ch, sp_err_le, sp_err_ue, sp_ch_le, sp_ch_ue, sp_multi = ( + prepare_cache_hit_error_series_data( + sigmoid_probability_data_frames, + sigmoid_probability_run_dirs_map, + ERROR_RATE_UPPER_BOUND, + ) + ) + __draw_confidence_series( + sp_err, + sp_ch, + sp_err_le, + sp_err_ue, + sp_ch_le, + sp_ch_ue, + sp_multi, + "#89D572", + "Sigmoid Probability", + 8, + ) + + # Sigmoid Only + if sigmoid_only_data_frames: + so_err, so_ch, so_err_le, so_err_ue, so_ch_le, so_ch_ue, so_multi = ( + prepare_cache_hit_error_series_data( + sigmoid_only_data_frames, + sigmoid_only_run_dirs_map, + ERROR_RATE_UPPER_BOUND, + ) + ) + __draw_confidence_series( + so_err, + so_ch, + so_err_le, + so_err_ue, + so_ch_le, + so_ch_ue, + so_multi, + "#E2A043", + "Sigmoid Only", + 8, + ) + plt.xlabel("Error Rate (%)", fontsize=font_size) plt.ylabel("Cache Hit Rate (%)", fontsize=font_size) plt.tick_params(axis="both", labelsize=font_size - 2) diff --git a/benchmarks/benchmark.py b/benchmarks/benchmark.py index a816145..7946c32 100644 --- a/benchmarks/benchmark.py +++ b/benchmarks/benchmark.py @@ -103,6 +103,12 @@ from vcache.vcache_policy.strategies.benchmark_iid_verified import ( BenchmarkVerifiedIIDDecisionPolicy, ) +from vcache.vcache_policy.strategies.benchmark_sigmoid_only import ( + SigmoidOnlyDecisionPolicy, +) +from vcache.vcache_policy.strategies.benchmark_sigmoid_probability import ( + SigmoidProbabilityDecisionPolicy, +) from vcache.vcache_policy.strategies.benchmark_static import ( BenchmarkStaticDecisionPolicy, ) @@ -164,6 +170,12 @@ class LargeLanguageModel(Enum): LLAMA_3_8B = ("response_llama_3_8b", "Llama_3_8B_Instruct", "float16", None) LLAMA_3_70B = ("response_llama_3_70b", "Llama_3_70B_Instruct", "float16", None) + LLAMA_3_70B_VLLM = ( + "response_llama_3_70b_vllm", + "Llama_3_70B_Instruct_VLLM", + "float16", + None, + ) GPT_4O_MINI = ("response_gpt-4o-mini", "GPT-4o-mini", "float16", None) GPT_4O_NANO = ("response_gpt-4.1-nano", "GPT-4.1-nano", "float16", None) GPT_4_1 = ("response_gpt-4.1", "gpt-4.1-2025-04-14", "float16", None) @@ -179,6 +191,8 @@ class Baseline(Enum): - BerkeleyEmbedding: Fine-tuned embeddings with static threshold - VCacheBerkeleyEmbedding: vCache with fine-tuned embeddings - IID: Independent and Identically Distributed threshold policy + - SigmoidProbability: Sigmoid probability-based threshold policy + - SigmoidOnly: Sigmoid only-based threshold policy """ GPTCache = "GPTCache" @@ -187,6 +201,9 @@ class Baseline(Enum): BerkeleyEmbedding = "BerkeleyEmbedding" VCacheBerkeleyEmbedding = "VCacheBerkeleyEmbedding" IID = "iid" + SigmoidProbability = "SigmoidProbability" + SigmoidOnly = "SigmoidOnly" + NoCache = "NoCache" class Dataset(Enum): @@ -236,31 +253,31 @@ class GeneratePlotsOnly(Enum): ] ] = [ ( - EmbeddingModel.GTE, + EmbeddingModel.E5_LARGE_V2, LargeLanguageModel.GPT_4O_MINI, Dataset.SEM_BENCHMARK_ARENA, GeneratePlotsOnly.NO, BenchmarkComparisonSimilarityEvaluator(), - SCUEvictionPolicy(max_size=6000, watermark=0.99, eviction_percentage=0.1), + SCUEvictionPolicy(max_size=100000, watermark=0.99, eviction_percentage=0.1), 60000, ), ( - EmbeddingModel.E5_LARGE_V2, - LargeLanguageModel.GPT_4O_MINI, - Dataset.SEM_BENCHMARK_SEARCH_QUERIES, + EmbeddingModel.GTE, + LargeLanguageModel.LLAMA_3_8B, + Dataset.SEM_BENCHMARK_ARENA, GeneratePlotsOnly.NO, - BenchmarkComparisonSimilarityEvaluator(), - SCUEvictionPolicy(max_size=15000, watermark=0.99, eviction_percentage=0.1), - 150000, + StringComparisonSimilarityEvaluator(), + SCUEvictionPolicy(max_size=100000, watermark=0.99, eviction_percentage=0.1), + 45000, ), ( EmbeddingModel.GTE, LargeLanguageModel.LLAMA_3_8B, - Dataset.SEM_BENCHMARK_CLASSIFICATION, + Dataset.SEM_BENCHMARK_SEARCH_QUERIES, GeneratePlotsOnly.NO, - StringComparisonSimilarityEvaluator(), - SCUEvictionPolicy(max_size=4500, watermark=0.99, eviction_percentage=0.1), - 45000, + BenchmarkComparisonSimilarityEvaluator(), + SCUEvictionPolicy(max_size=160000, watermark=0.99, eviction_percentage=0.1), + 150000, ), ( EmbeddingModel.OPENAI_TEXT_EMBEDDING_SMALL, @@ -282,6 +299,8 @@ class GeneratePlotsOnly(Enum): Baseline.IID, Baseline.GPTCache, Baseline.BerkeleyEmbedding, + Baseline.SigmoidProbability, + Baseline.SigmoidOnly, Baseline.VCacheBerkeleyEmbedding, ] @@ -1072,7 +1091,7 @@ def main(): continue logging.info( - f"Using dynamic threshold with delta: {delta}. Run {i + 1} of {CONFIDENCE_INTERVALS_ITERATIONS}" + f"vCache Local: Using dynamic threshold with delta: {delta}. Run {i + 1} of {CONFIDENCE_INTERVALS_ITERATIONS}" ) __run_baseline( @@ -1108,7 +1127,7 @@ def main(): continue logging.info( - f"Using dynamic threshold with delta: {delta}. Run {i + 1} of {CONFIDENCE_INTERVALS_ITERATIONS}" + f"vCache Global: Using dynamic threshold with delta: {delta}. Run {i + 1} of {CONFIDENCE_INTERVALS_ITERATIONS}" ) __run_baseline( @@ -1161,7 +1180,9 @@ def main(): if os.path.exists(path) and os.listdir(path): continue - logging.info(f"Using static threshold: {threshold}") + logging.info( + f"Berkeley Embedding: Using static threshold: {threshold}" + ) __run_baseline( vcache_policy=BenchmarkStaticDecisionPolicy( @@ -1215,7 +1236,7 @@ def main(): continue logging.info( - f"Using dynamic threshold with delta: {delta}. Run {i + 1} of {CONFIDENCE_INTERVALS_ITERATIONS}" + f"vCache + Berkeley Embedding: Using dynamic threshold with delta: {delta}. Run {i + 1} of {CONFIDENCE_INTERVALS_ITERATIONS}" ) __run_baseline( @@ -1249,7 +1270,7 @@ def main(): continue logging.info( - f"Using IID local threshold with delta: {delta}. Run {i + 1} of {CONFIDENCE_INTERVALS_ITERATIONS}" + f"IID: Using delta {delta}. Run {i + 1} of {CONFIDENCE_INTERVALS_ITERATIONS}" ) __run_baseline( @@ -1283,7 +1304,7 @@ def main(): if os.path.exists(path) and os.listdir(path): continue - logging.info(f"Using static threshold: {threshold}") + logging.info(f"GPTCache: Using static threshold: {threshold}") __run_baseline( vcache_policy=BenchmarkStaticDecisionPolicy( @@ -1302,6 +1323,80 @@ def main(): is_custom_dataset=is_custom_dataset, ) + ##################################################### + ### Baseline: Sigmoid Probability + if ( + Baseline.SigmoidProbability in BASELINES_TO_RUN + and not generate_plots_only.value + ): + for delta in DELTAS: + for i in range(0, CONFIDENCE_INTERVALS_ITERATIONS): + path = os.path.join( + results_dir, + dataset_name, + embedding_model.value[1], + llm_model.value[1], + f"sigmoid_probability_{delta}_run_{i + 1}", + ) + if os.path.exists(path) and os.listdir(path): + continue + + logging.info( + f"Sigmoid Probability: Using dynamic threshold with delta: {delta}. Run {i + 1} of {CONFIDENCE_INTERVALS_ITERATIONS}" + ) + + __run_baseline( + vcache_policy=SigmoidProbabilityDecisionPolicy(delta=delta), + path=path, + dataset_file=dataset_path, + embedding_model=embedding_model.value, + llm_model=llm_model.value, + timestamp=timestamp, + delta=delta, + threshold=-1, + similarity_evaluator=similarity_evaluator, + eviction_policy=eviction_policy, + max_samples=max_samples, + is_custom_dataset=is_custom_dataset, + ) + + ##################################################### + ### Baseline: Sigmoid Only + if ( + Baseline.SigmoidOnly in BASELINES_TO_RUN + and not generate_plots_only.value + ): + for delta in DELTAS: + for i in range(0, CONFIDENCE_INTERVALS_ITERATIONS): + path = os.path.join( + results_dir, + dataset_name, + embedding_model.value[1], + llm_model.value[1], + f"sigmoid_only_{delta}_run_{i + 1}", + ) + if os.path.exists(path) and os.listdir(path): + continue + + logging.info( + f"Sigmoid Only: Using dynamic threshold with delta: {delta}. Run {i + 1} of {CONFIDENCE_INTERVALS_ITERATIONS}" + ) + + __run_baseline( + vcache_policy=SigmoidOnlyDecisionPolicy(delta=delta), + path=path, + dataset_file=dataset_path, + embedding_model=embedding_model.value, + llm_model=llm_model.value, + timestamp=timestamp, + delta=delta, + threshold=-1, + similarity_evaluator=similarity_evaluator, + eviction_policy=eviction_policy, + max_samples=max_samples, + is_custom_dataset=is_custom_dataset, + ) + ##################################################### generate_combined_plots( dataset=dataset_name, diff --git a/poetry.lock b/poetry.lock index d05ea72..d2d1ba1 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 2.1.2 and should not be changed by hand. +# This file is automatically @generated by Poetry 2.1.3 and should not be changed by hand. 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(>=0.3.23,<0.4.0)", @@ -24,6 +24,7 @@ dependencies = [ "scipy (>=1.10.0,<1.16.0)", "pytest (>=8.3.5,<9.0.0)", "pandas (>=2.3.0,<3.0.0)", + "vllm (>=0.10.0,<0.11.0)", ] diff --git a/vcache/__init__.py b/vcache/__init__.py index 9bd21ea..10e2261 100644 --- a/vcache/__init__.py +++ b/vcache/__init__.py @@ -12,6 +12,7 @@ InferenceEngine, LangChainInferenceEngine, OpenAIInferenceEngine, + VLLMInferenceEngine, ) from .main import VCache from .vcache_core import ( @@ -70,6 +71,8 @@ BenchmarkVerifiedGlobalDecisionPolicy, BenchmarkVerifiedIIDDecisionPolicy, NoCachePolicy, + SigmoidOnlyDecisionPolicy, + SigmoidProbabilityDecisionPolicy, VCachePolicy, VerifiedDecisionPolicy, ) @@ -92,12 +95,15 @@ # Concrete vCache Policies "VerifiedDecisionPolicy", "NoCachePolicy", + "SigmoidProbabilityDecisionPolicy", + "SigmoidOnlyDecisionPolicy", "BenchmarkStaticDecisionPolicy", "BenchmarkVerifiedGlobalDecisionPolicy", "BenchmarkVerifiedIIDDecisionPolicy", # Concrete Inference engines "OpenAIInferenceEngine", "LangChainInferenceEngine", + "VLLMInferenceEngine", "BenchmarkInferenceEngine", # Concrete Embedding engines "OpenAIEmbeddingEngine", diff --git a/vcache/inference_engine/__init__.py b/vcache/inference_engine/__init__.py index 1845644..07328d1 100644 --- a/vcache/inference_engine/__init__.py +++ b/vcache/inference_engine/__init__.py @@ -2,10 +2,12 @@ from vcache.inference_engine.strategies.benchmark import BenchmarkInferenceEngine from vcache.inference_engine.strategies.lang_chain import LangChainInferenceEngine from vcache.inference_engine.strategies.open_ai import OpenAIInferenceEngine +from vcache.inference_engine.strategies.vllm import VLLMInferenceEngine __all__ = [ "InferenceEngine", "LangChainInferenceEngine", "OpenAIInferenceEngine", "BenchmarkInferenceEngine", + "VLLMInferenceEngine", ] diff --git a/vcache/inference_engine/strategies/__init__.py b/vcache/inference_engine/strategies/__init__.py index fa5db4e..61c8bf6 100644 --- a/vcache/inference_engine/strategies/__init__.py +++ b/vcache/inference_engine/strategies/__init__.py @@ -1,9 +1,11 @@ from .benchmark import BenchmarkInferenceEngine from .lang_chain import LangChainInferenceEngine from .open_ai import OpenAIInferenceEngine +from .vllm import VLLMInferenceEngine __all__ = [ "BenchmarkInferenceEngine", "LangChainInferenceEngine", "OpenAIInferenceEngine", + "VLLMInferenceEngine", ] diff --git a/vcache/inference_engine/strategies/vllm.py b/vcache/inference_engine/strategies/vllm.py new file mode 100644 index 0000000..80f5bc7 --- /dev/null +++ b/vcache/inference_engine/strategies/vllm.py @@ -0,0 +1,330 @@ +import logging +import os +from typing import Any, Dict, List, Optional + +import torch +from vllm import LLM, RequestOutput, SamplingParams + +from vcache.inference_engine.inference_engine import InferenceEngine + +logger = logging.getLogger(__name__) + + +class VLLMInferenceEngine(InferenceEngine): + """ + vLLM-based inference engine for generating responses using vLLM's optimized inference. + + This engine provides efficient GPU-accelerated inference with support for: + - Multi-GPU tensor parallelism + - Memory optimization + - Quantization + - Custom model configurations + """ + + def __init__( + self, + model_name: str, + temperature: float = 0.8, + gpu_ids: Optional[List[int]] = None, + gpu_memory_utilization: float = 0.9, + max_model_len: Optional[int] = None, + dtype: str = "auto", + trust_remote_code: bool = False, + quantization: Optional[str] = None, + max_tokens: int = 512, + top_p: float = 0.9, + top_k: int = -1, + enforce_eager: bool = False, + ): + """ + Initialize vLLM inference engine. + + Args: + model_name: Hugging Face model ID or local path to model + temperature: Sampling temperature (0.0 to 2.0). Higher = more random + gpu_ids: List of GPU IDs to use (e.g., [0, 1]). None = use all available + gpu_memory_utilization: Fraction of GPU memory to use (0.0 to 1.0) + max_model_len: Maximum context length. None = use model default + dtype: Model data type ('auto', 'float16', 'bfloat16', 'float32') + trust_remote_code: Allow execution of custom model code + quantization: Quantization method ('awq', 'fp8', 'gptq', etc.) + max_tokens: Maximum number of tokens to generate per request + top_p: Nucleus sampling parameter (0.0 to 1.0) + top_k: Top-k sampling parameter (-1 = disabled) + enforce_eager: Disable CUDA graphs for debugging (uses more memory) + """ + super().__init__() + + # Store configuration with proper typing + self.model_name: str = model_name + self.temperature: float = temperature + self.gpu_memory_utilization: float = gpu_memory_utilization + self.max_model_len: Optional[int] = max_model_len + self.dtype: str = dtype + self.trust_remote_code: bool = trust_remote_code + self.quantization: Optional[str] = quantization + self.max_tokens: int = max_tokens + self.top_p: float = top_p + self.top_k: int = top_k + self.enforce_eager: bool = enforce_eager + self.tensor_parallel_size: int = self._configure_gpus(gpu_ids) + + self.llm: LLM = None + self.sampling_params: SamplingParams = None + self._is_warmed_up: bool = False + + self._initialize_engine() + + def _configure_gpus(self, gpu_ids: Optional[List[int]]) -> int: + """ + Configure GPU settings and return tensor parallel size. + + Args: + gpu_ids: List of GPU IDs to use + + Returns: + Number of GPUs to use for tensor parallelism + """ + if not torch.cuda.is_available(): + raise RuntimeError("CUDA is not available. vLLM requires GPU support.") + + available_gpus: int = torch.cuda.device_count() + + if gpu_ids is not None: + for gpu_id in gpu_ids: + if gpu_id >= available_gpus: + raise ValueError( + f"GPU ID {gpu_id} not available. Only {available_gpus} GPUs detected." + ) + + os.environ["CUDA_VISIBLE_DEVICES"] = ",".join(map(str, gpu_ids)) + tensor_parallel_size: int = len(gpu_ids) + else: + # Use all available GPUs + tensor_parallel_size = available_gpus + + logger.info(f"Configured vLLM to use {tensor_parallel_size} GPU(s)") + return tensor_parallel_size + + def _initialize_engine(self) -> None: + """Initialize the vLLM LLM engine with configured parameters.""" + logger.info(f"Initializing vLLM engine with model: {self.model_name}") + + llm_kwargs: Dict[str, Any] = { + "model": self.model_name, + "tensor_parallel_size": self.tensor_parallel_size, + "gpu_memory_utilization": self.gpu_memory_utilization, + "dtype": self.dtype, + "trust_remote_code": self.trust_remote_code, + "enforce_eager": self.enforce_eager, + } + + if self.max_model_len is not None: + llm_kwargs["max_model_len"] = self.max_model_len + + if self.quantization is not None: + llm_kwargs["quantization"] = self.quantization + + try: + self.llm = LLM(**llm_kwargs) + + sampling_kwargs: Dict[str, Any] = { + "temperature": self.temperature, + "top_p": self.top_p, + "max_tokens": self.max_tokens, + } + + if self.top_k > 0: + sampling_kwargs["top_k"] = self.top_k + + self.sampling_params = SamplingParams(**sampling_kwargs) + + logger.info("vLLM engine initialized successfully!") + + except Exception as e: + logger.error(f"Failed to initialize vLLM engine: {str(e)}") + raise RuntimeError(f"Failed to initialize vLLM engine: {str(e)}") + + def warm_up(self, warm_up_prompt: str = "Hello, how are you?") -> str: + """ + Warm up the model by making an initial request to prepare it for inference. + This helps reduce latency for subsequent requests by initializing CUDA contexts + and loading model weights into GPU memory. + + Args: + warm_up_prompt: The prompt to use for warming up the model + + Returns: + The response from the warm-up request + """ + if self.llm is None: + raise RuntimeError("vLLM engine not initialized") + + if self._is_warmed_up: + logger.debug("Model is already warmed up") + return "Model already warmed up" + + logger.info("Warming up vLLM model...") + + try: + response: str = self.create(warm_up_prompt) + self._is_warmed_up = True + + logger.info("Model warm-up completed successfully!") + return response + + except Exception as e: + logger.error(f"Error during model warm-up: {str(e)}") + raise RuntimeError(f"Error during model warm-up: {str(e)}") + + def create(self, prompt: str, system_prompt: Optional[str] = None) -> str: + """ + Generate a response for the given prompt using vLLM. + + Args: + prompt: The user prompt to generate a response for + system_prompt: Optional system prompt to guide model behavior + + Returns: + Generated response text + """ + if self.llm is None: + raise RuntimeError("vLLM engine not initialized") + + try: + full_prompt: str = self._format_prompt(prompt, system_prompt) + + outputs = self.llm.generate([full_prompt], self.sampling_params) + + # Extract the generated text + if outputs and len(outputs) > 0 and len(outputs[0].outputs) > 0: + generated_text: str = outputs[0].outputs[0].text.strip() + return generated_text + else: + logger.error("No output generated by vLLM") + raise RuntimeError("No output generated by vLLM") + + except Exception as e: + logger.error(f"Error during text generation: {str(e)}") + raise RuntimeError(f"Error during text generation: {str(e)}") + + def _format_prompt(self, prompt: str, system_prompt: Optional[str] = None) -> str: + """ + Format the prompt with optional system prompt. + + Args: + prompt: User prompt + system_prompt: Optional system prompt + + Returns: + Formatted prompt string + """ + if system_prompt: + formatted_prompt: str = ( + f"System: {system_prompt}\n\nUser: {prompt}\n\nAssistant:" + ) + else: + formatted_prompt = prompt + + return formatted_prompt + + def generate_batch( + self, prompts: List[str], system_prompt: Optional[str] = None + ) -> List[str]: + """ + Generate responses for multiple prompts in a batch. + + Args: + prompts: List of prompts to generate responses for + system_prompt: Optional system prompt applied to all prompts + + Returns: + List of generated responses corresponding to input prompts + """ + if self.llm is None: + raise RuntimeError("vLLM engine not initialized") + + try: + formatted_prompts: List[str] = [ + self._format_prompt(prompt, system_prompt) for prompt in prompts + ] + outputs: List[RequestOutput] = self.llm.generate( + formatted_prompts, self.sampling_params + ) + + responses: List[str] = [] + for output in outputs: + if output.outputs and len(output.outputs) > 0: + responses.append(output.outputs[0].text.strip()) + else: + responses.append("") + + logger.debug(f"Generated batch responses for {len(prompts)} prompts") + return responses + + except Exception as e: + logger.error(f"Error during batch text generation: {str(e)}") + raise RuntimeError(f"Error during batch text generation: {str(e)}") + + def update_sampling_params(self, **kwargs) -> None: + """ + Update sampling parameters for future generations. + + Args: + **kwargs: Sampling parameters to update (temperature, top_p, max_tokens, etc.) + """ + + for key, value in kwargs.items(): + if hasattr(self, key): + setattr(self, key, value) + + sampling_kwargs: Dict[str, Any] = { + "temperature": self.temperature, + "top_p": self.top_p, + "max_tokens": self.max_tokens, + } + + if self.top_k > 0: + sampling_kwargs["top_k"] = self.top_k + + sampling_kwargs.update(kwargs) + + self.sampling_params = SamplingParams(**sampling_kwargs) + + logger.debug(f"Updated sampling parameters: {kwargs}") + + def is_warmed_up(self) -> bool: + """ + Check if the model has been warmed up. + + Returns: + True if the model has been warmed up, False otherwise + """ + return self._is_warmed_up + + def get_model_info(self) -> Dict[str, Any]: + """ + Get information about the loaded model and configuration. + + Returns: + Dictionary containing model and configuration information + """ + return { + "model_name": self.model_name, + "tensor_parallel_size": self.tensor_parallel_size, + "gpu_memory_utilization": self.gpu_memory_utilization, + "max_model_len": self.max_model_len, + "dtype": self.dtype, + "temperature": self.temperature, + "top_p": self.top_p, + "top_k": self.top_k, + "max_tokens": self.max_tokens, + "quantization": self.quantization, + "trust_remote_code": self.trust_remote_code, + "enforce_eager": self.enforce_eager, + "cuda_available": torch.cuda.is_available(), + "cuda_device_count": torch.cuda.device_count() + if torch.cuda.is_available() + else 0, + "is_warmed_up": self._is_warmed_up, + } diff --git a/vcache/vcache_policy/__init__.py b/vcache/vcache_policy/__init__.py index 890489d..08432fb 100644 --- a/vcache/vcache_policy/__init__.py +++ b/vcache/vcache_policy/__init__.py @@ -1,6 +1,12 @@ from vcache.vcache_policy.strategies.benchmark_iid_verified import ( BenchmarkVerifiedIIDDecisionPolicy, ) +from vcache.vcache_policy.strategies.benchmark_sigmoid_only import ( + SigmoidOnlyDecisionPolicy, +) +from vcache.vcache_policy.strategies.benchmark_sigmoid_probability import ( + SigmoidProbabilityDecisionPolicy, +) from vcache.vcache_policy.strategies.benchmark_static import ( BenchmarkStaticDecisionPolicy, ) @@ -16,6 +22,8 @@ __all__ = [ "VCachePolicy", "BenchmarkStaticDecisionPolicy", + "SigmoidProbabilityDecisionPolicy", + "SigmoidOnlyDecisionPolicy", "VerifiedDecisionPolicy", "BenchmarkVerifiedGlobalDecisionPolicy", "BenchmarkVerifiedIIDDecisionPolicy", diff --git a/vcache/vcache_policy/strategies/__init__.py b/vcache/vcache_policy/strategies/__init__.py index 64da3c6..9fc3fce 100644 --- a/vcache/vcache_policy/strategies/__init__.py +++ b/vcache/vcache_policy/strategies/__init__.py @@ -1,4 +1,6 @@ from .benchmark_iid_verified import BenchmarkVerifiedIIDDecisionPolicy +from .benchmark_sigmoid_only import SigmoidOnlyDecisionPolicy +from .benchmark_sigmoid_probability import SigmoidProbabilityDecisionPolicy from .benchmark_static import BenchmarkStaticDecisionPolicy from .benchmark_verified_global import BenchmarkVerifiedGlobalDecisionPolicy from .no_cache import NoCachePolicy @@ -7,6 +9,8 @@ __all__ = [ "NoCachePolicy", "VerifiedDecisionPolicy", + "SigmoidProbabilityDecisionPolicy", + "SigmoidOnlyDecisionPolicy", "BenchmarkStaticDecisionPolicy", "BenchmarkVerifiedGlobalDecisionPolicy", "BenchmarkVerifiedIIDDecisionPolicy", diff --git a/vcache/vcache_policy/strategies/benchmark_iid_verified.py b/vcache/vcache_policy/strategies/benchmark_iid_verified.py index 4c52773..2bdb367 100644 --- a/vcache/vcache_policy/strategies/benchmark_iid_verified.py +++ b/vcache/vcache_policy/strategies/benchmark_iid_verified.py @@ -530,6 +530,7 @@ def select_action( metadata.t_hat = t_hat metadata.var_t = -1 # not computed + # if similarity_score <= t_hat: # This is used for ablation study if similarity_score <= t_prime: return _Action.EXPLORE else: diff --git a/vcache/vcache_policy/strategies/benchmark_sigmoid_only.py b/vcache/vcache_policy/strategies/benchmark_sigmoid_only.py new file mode 100644 index 0000000..30664fa --- /dev/null +++ b/vcache/vcache_policy/strategies/benchmark_sigmoid_only.py @@ -0,0 +1,503 @@ +import logging +import os +import queue +import threading +from concurrent.futures import ThreadPoolExecutor +from enum import Enum +from typing import Optional + +import numpy as np +import statsmodels.api as sm +from sklearn.linear_model import LogisticRegression +from typing_extensions import override + +from vcache.config import VCacheConfig +from vcache.inference_engine import InferenceEngine +from vcache.vcache_core.cache.cache import Cache +from vcache.vcache_core.cache.embedding_store.embedding_metadata_storage.embedding_metadata_obj import ( + EmbeddingMetadataObj, +) +from vcache.vcache_core.cache.embedding_store.embedding_store import EmbeddingStore +from vcache.vcache_core.similarity_evaluator import ( + SimilarityEvaluator, +) +from vcache.vcache_policy.vcache_policy import VCachePolicy + +# Disable Hugging Face tokenizer parallelism to prevent deadlocks when using +# vCache in multi-threaded applications. This is a library-level fix. +os.environ["TOKENIZERS_PARALLELISM"] = "true" + + +class CallbackQueue(queue.Queue): + """ + A queue that processes items with a callback function in a worker thread. + """ + + def __init__(self, callback_function): + """ + Initializes the CallbackQueue. + + Args: + callback_function: The function to call for each item in the queue. + It will be executed by the worker thread. + """ + super().__init__() + self.callback_function = callback_function + self._stop_event = threading.Event() + self.worker_thread = threading.Thread(target=self._worker, daemon=True) + + def _worker(self): + """ + The main loop for the worker thread. + + It continuously fetches items from the queue and processes them using the + callback function. The loop includes a timeout to allow for graceful + shutdown checks. + """ + while True: + should_stop = self._stop_event.is_set() + if should_stop: + break + + try: + item = self.get(timeout=1) + if item is None: # Sentinel value to stop + break + self.callback_function(item) + self.task_done() + except queue.Empty: + continue + + def start(self): + """Starts the worker thread.""" + self.worker_thread.start() + + def stop(self): + """Stops the worker thread gracefully.""" + if self.worker_thread.is_alive(): + self.put(None) + self.worker_thread.join() + + +class SigmoidOnlyDecisionPolicy(VCachePolicy): + """ + Dynamic local threshold policy that computes optimal thresholds for each embedding. + + Computes the cross-entropy-loss-based optimal threshold per embedding. Does not take + the user-defined accuracy target into account. + """ + + def __init__(self, delta: float = 0.01): + """ + Initialize dynamic local threshold policy. + + Initializes the core algorithm and sets up placeholders for the thread + pool executor and callback queue which will be created in `setup`. + + Args: + delta: The delta value to use for threshold computation. + """ + self.bayesian = _Algorithm(delta=delta) + self.similarity_evaluator: Optional[SimilarityEvaluator] = None + self.inference_engine: Optional[InferenceEngine] = None + self.cache: Optional[Cache] = None + self.logger: logging.Logger = logging.getLogger(__name__) + + self.executor: Optional[ThreadPoolExecutor] = None + self.callback_queue: Optional[CallbackQueue] = None + + def __enter__(self): + """ + Allows the policy to be used as a context manager. + + Returns: + The policy instance itself. + """ + return self + + def __exit__(self, exc_type, exc_val, exc_tb): + """ + Ensures graceful shutdown of background threads when exiting the context. + + This method is called automatically when exiting a `with` block, + triggering the shutdown of the ThreadPoolExecutor and CallbackQueue. + + Args: + exc_type: The exception type if an exception was raised in the `with` block. + exc_val: The exception value if an exception was raised. + exc_tb: The traceback if an exception was raised. + """ + self.shutdown() + + @override + def setup(self, config: VCacheConfig): + """ + Setup the policy with the given configuration. + + This method initializes the cache, similarity evaluator, and inference + engine. It also sets up and starts the background processing components: + a ThreadPoolExecutor for concurrent tasks and a CallbackQueue for + serialized cache updates. + + Args: + config: The VCache configuration to use. + """ + self.similarity_evaluator = config.similarity_evaluator + self.inference_engine = config.inference_engine + self.cache = Cache( + embedding_engine=config.embedding_engine, + embedding_store=EmbeddingStore( + embedding_metadata_storage=config.embedding_metadata_storage, + vector_db=config.vector_db, + ), + eviction_policy=config.eviction_policy, + ) + + self.callback_queue = CallbackQueue( + callback_function=self.__perform_cache_update + ) + self.callback_queue.start() + self.executor = ThreadPoolExecutor(max_workers=64) + + def shutdown(self): + """ + Shuts down the thread pool and callback queue gracefully. + """ + if self.executor: + self.executor.shutdown(wait=True) + + if self.callback_queue: + self.callback_queue.stop() + + @override + def process_request( + self, prompt: str, system_prompt: Optional[str], id_set: int + ) -> tuple[bool, str, EmbeddingMetadataObj]: + """ + Process a request using dynamic local threshold policy. + + It determines whether to serve a cached response or generate a new one. + If the policy decides to 'explore', it generates a new response and + triggers an asynchronous background task to evaluate the decision and + update the cache, without blocking the current request. The functions returns + the actual response and some metadata information—whether the response is a + cache hit and the nearest neighbor response—to enable further analysis. + + Args: + prompt: The prompt to check for cache hit. + system_prompt: The optional system prompt to use for the response. It will override the system prompt in the VCacheConfig if provided. + id_set: The set identifier for the embedding. This is used in the + benchmark to identify if the nearest neighbor is from the same set + (if the cached response is correct or incorrect). + + Returns: + Tuple containing [is_cache_hit, actual_response, nn_metadata_object]. + """ + if self.inference_engine is None or self.cache is None: + raise ValueError("Policy has not been setup") + + knn = self.cache.get_knn(prompt=prompt, k=1) + if not knn: + response = self.inference_engine.create( + prompt=prompt, system_prompt=system_prompt + ) + self.cache.add(prompt=prompt, response=response, id_set=id_set) + return False, response, EmbeddingMetadataObj(embedding_id=-1, response="") + + similarity_score, embedding_id = knn[0] + + try: + nn_metadata: EmbeddingMetadataObj = self.cache.get_metadata( + embedding_id=embedding_id + ) + except Exception: + # Cache eviction fallback + new_response: str = self.inference_engine.create( + prompt=prompt, system_prompt=system_prompt + ) + self.cache.add(prompt=prompt, response=new_response, id_set=id_set) + return ( + False, + new_response, + EmbeddingMetadataObj(embedding_id=-1, response=""), + ) + + action = self.bayesian.select_action( + similarity_score=similarity_score, metadata=nn_metadata + ) + + match action: + case _Action.EXPLOIT: + return True, nn_metadata.response, nn_metadata + case _Action.EXPLORE: + response = self.inference_engine.create( + prompt=prompt, system_prompt=system_prompt + ) + + self.__update_cache( + response=response, + nn_metadata=nn_metadata, + similarity_score=similarity_score, + embedding_id=embedding_id, + prompt=prompt, + label_id_set=id_set, + ) + + return False, response, nn_metadata + + def __update_cache( + self, + response: str, + nn_metadata: EmbeddingMetadataObj, + similarity_score: float, + embedding_id: int, + prompt: str, + label_id_set: int, + ) -> None: + """ + Asynchronously validates the correctness of the cached response and updates the cache. + + The validation whether the response is correct can involve a latency expensive LLM-judge call. + Because this evaluation does not impact the returned response, we process it in the background. + The LLM-judge call (or any other strategy like an embedding or string-based similarity check) in its own thread + and returns a label (True/False) whether the response is correct. + vCache maintains a global queue that waits for the labels. When a label gets available, + vCache updates the metadata and the vector database accordingly. + + Args: + response: The response to check for correctness. + nn_metadata: The metadata of the nearest neighbor embedding. + similarity_score: The similarity score between the query and the embedding. + embedding_id: The id of the embedding. + prompt: The prompt that was used to generate the response. + label_id_set: The set identifier for the embedding. This is used in the + benchmark to identify if the nearest neighbor is from the same set + (if the cached response is correct or incorrect). + """ + if self.executor is None: + raise ValueError("Executor not initialized. Call setup() first.") + + self.executor.submit( + self.__submit_for_background_update, + response, + similarity_score, + embedding_id, + prompt, + nn_metadata.response, + label_id_set, + nn_metadata.id_set, + ) + + def __submit_for_background_update( + self, + new_response: str, + similarity_score: float, + embedding_id: int, + prompt: str, + cached_response: str, + label_id_set: int, + nn_id_set: int, + ): + """ + Submits a task to check answer similarity and queue a cache update. + + This method is executed by the ThreadPoolExecutor. It performs the + potentially slow `answers_similar` check and then puts the result + and context onto the `callback_queue` for sequential processing. + + Args: + new_response: The newly generated response. + similarity_score: The similarity between the prompt and the nearest neighbor. + embedding_id: The ID of the nearest neighbor embedding. + prompt: The original user prompt. + cached_response: The response from the cached nearest neighbor. + label_id_set: The id_set of the label. + nn_id_set: The id_set of the nearest neighbor. + """ + should_have_exploited = self.similarity_evaluator.answers_similar( + a=new_response, b=cached_response, id_set_a=label_id_set, id_set_b=nn_id_set + ) + self.callback_queue.put( + ( + should_have_exploited, + new_response, + similarity_score, + embedding_id, + prompt, + label_id_set, + ) + ) + + def __perform_cache_update(self, update_args: tuple) -> None: + """ + Performs the actual cache update based on the background check. + + This method is executed sequentially by the CallbackQueue's worker + thread, ensuring thread-safe updates to the cache metadata and + vector database. It fetches the latest metadata before updating to + prevent race conditions with evictions or other updates. + + Args: + update_args: A tuple containing the context required for the update, + as passed from `__submit_for_background_update`. It + contains the following elements in order: + - should_have_exploited (bool): Whether the cache hit + should have been exploited. + - new_response (str): The newly generated response. + - similarity_score (float): The similarity score. + - embedding_id (int): The ID of the nearest neighbor. + - prompt (str): The original user prompt. + - id_set (int): The set identifier for the embedding. This is used in the + benchmark to identify if the nearest neighbor is from the same set + (if the cached response is correct or incorrect). + """ + ( + should_have_exploited, + new_response, + similarity_score, + embedding_id, + prompt, + id_set, + ) = update_args + + try: + latest_metdata_object = self.cache.get_metadata(embedding_id=embedding_id) + except (ValueError, KeyError): + logging.warning( + f"Embedding {embedding_id} was evicted between the time the request was made and the time the update was processed. We can safely ignore this update." + ) + return + + item_was_evicted = latest_metdata_object is None + if item_was_evicted: + return + + try: + self.bayesian.add_observation_to_metadata( + similarity_score=similarity_score, + is_correct=should_have_exploited, + metadata=latest_metdata_object, + ) + except (ValueError, KeyError): + self.logger.warning( + f"Embedding {embedding_id} was evicted between the time the request was made and the time the update was processed. We can safely ignore this update." + ) + return + + if not should_have_exploited: + self.cache.add(prompt=prompt, response=new_response, id_set=id_set) + + try: + self.cache.update_metadata( + embedding_id=embedding_id, embedding_metadata=latest_metdata_object + ) + except (ValueError, KeyError): + self.logger.warning( + f"Embedding {embedding_id} was evicted between the time the request was made and the time the update was processed. We can safely ignore this update." + ) + return + + +class _Action(Enum): + """ + Enumeration of possible actions for the algorithm. + """ + + EXPLORE = "explore" + EXPLOIT = "exploit" + + +class _Algorithm: + """ + Internal algorithm implementation for dynamic threshold computation. + """ + + def __init__(self, delta: float): + """ + Initialize the algorithm with the given delta value. + + Args: + delta: The delta value for confidence computation. + """ + self.delta: float = delta + self.P_c: float = 1.0 - self.delta + self.epsilon_grid: np.ndarray = np.linspace(1e-6, 1 - 1e-6, 50) + self.logistic_regression: LogisticRegression = LogisticRegression( + penalty=None, solver="lbfgs", tol=1e-8, max_iter=1000, fit_intercept=False + ) + + def add_observation_to_metadata( + self, similarity_score: float, is_correct: bool, metadata: EmbeddingMetadataObj + ) -> None: + """ + Update the metadata with the new observation + + Args + similarity_score: float - The similarity score between the query and the embedding + is_correct: bool - Whether the query was correct + metadata: EmbeddingMetadataObj - The metadata of the embedding + """ + if is_correct: + metadata.observations.append((round(similarity_score, 3), 1)) + else: + metadata.observations.append((round(similarity_score, 3), 0)) + + def select_action( + self, similarity_score: float, metadata: EmbeddingMetadataObj + ) -> _Action: + """ + Select the action to take based on the similarity score, observations, and accuracy target + + Args + similarity_score: float - The similarity score between the query and the embedding + metadata: EmbeddingMetadataObj - The metadata of the embedding + Returns + Action - Explore or Exploit + """ + similarity_score = round(similarity_score, 3) + similarities: np.ndarray = np.array([obs[0] for obs in metadata.observations]) + labels: np.ndarray = np.array([obs[1] for obs in metadata.observations]) + + if len(similarities) < 6 or len(labels) < 6: + return _Action.EXPLORE + + t_hat: float = self._estimate_parameters( + similarities=similarities, labels=labels + ) + + if similarity_score >= t_hat: + return _Action.EXPLOIT + else: + return _Action.EXPLORE + + def _estimate_parameters( + self, similarities: np.ndarray, labels: np.ndarray + ) -> _Action: + """ + Optimize parameters with logistic regression + + Args + labels: np.ndarray - The labels of the embeddings + metadata: EmbeddingMetadataObj - The metadata of the embedding + + Returns + t_hat: float - The estimated threshold + """ + + similarities = sm.add_constant(similarities) + + try: + if len(similarities) != len(labels): + print(f"len does not match: {len(similarities)} != {len(labels)}") + self.logistic_regression.fit(similarities, labels) + intercept, gamma = self.logistic_regression.coef_[0] + + gamma = max(gamma, 1e-6) + t_hat = -intercept / gamma + t_hat = float(np.clip(t_hat, 0.0, 1.0)) + + return t_hat + + except Exception as e: + print(f"Logistic regression failed: {e}") + return -1.0, -1.0, -1.0 diff --git a/vcache/vcache_policy/strategies/benchmark_sigmoid_probability.py b/vcache/vcache_policy/strategies/benchmark_sigmoid_probability.py new file mode 100644 index 0000000..0060375 --- /dev/null +++ b/vcache/vcache_policy/strategies/benchmark_sigmoid_probability.py @@ -0,0 +1,520 @@ +import logging +import os +import queue +import threading +import time +from concurrent.futures import ThreadPoolExecutor +from enum import Enum +from typing import List, Optional + +import numpy as np +import statsmodels.api as sm +from scipy.special import expit +from sklearn.linear_model import LogisticRegression +from typing_extensions import override + +from vcache.config import VCacheConfig +from vcache.inference_engine import InferenceEngine +from vcache.vcache_core.cache.cache import Cache +from vcache.vcache_core.cache.embedding_store.embedding_metadata_storage.embedding_metadata_obj import ( + EmbeddingMetadataObj, +) +from vcache.vcache_core.cache.embedding_store.embedding_store import EmbeddingStore +from vcache.vcache_core.similarity_evaluator import ( + SimilarityEvaluator, +) +from vcache.vcache_policy.vcache_policy import VCachePolicy + +# Disable Hugging Face tokenizer parallelism to prevent deadlocks when using +# vCache in multi-threaded applications. This is a library-level fix. +os.environ["TOKENIZERS_PARALLELISM"] = "true" + + +class CallbackQueue(queue.Queue): + """ + A queue that processes items with a callback function in a worker thread. + """ + + def __init__(self, callback_function): + """ + Initializes the CallbackQueue. + + Args: + callback_function: The function to call for each item in the queue. + It will be executed by the worker thread. + """ + super().__init__() + self.callback_function = callback_function + self._stop_event = threading.Event() + self.worker_thread = threading.Thread(target=self._worker, daemon=True) + + def _worker(self): + """ + The main loop for the worker thread. + + It continuously fetches items from the queue and processes them using the + callback function. The loop includes a timeout to allow for graceful + shutdown checks. + """ + while True: + should_stop = self._stop_event.is_set() + if should_stop: + break + + try: + item = self.get(timeout=1) + if item is None: # Sentinel value to stop + break + self.callback_function(item) + self.task_done() + except queue.Empty: + continue + + def start(self): + """Starts the worker thread.""" + self.worker_thread.start() + + def stop(self): + """Stops the worker thread gracefully.""" + if self.worker_thread.is_alive(): + self.put(None) + self.worker_thread.join() + + +class SigmoidProbabilityDecisionPolicy(VCachePolicy): + """ + Dynamic local threshold policy that computes optimal thresholds for each embedding. + + Computes the cross-entropy-loss-based optimal threshold per embedding. Decides between + exploration and exploitation based on the predicted probability at a given similarity value. + """ + + def __init__(self, delta: float = 0.01): + """ + Initialize dynamic local threshold policy. + + Initializes the core algorithm and sets up placeholders for the thread + pool executor and callback queue which will be created in `setup`. + + Args: + delta: The delta value to use for threshold computation. + """ + self.bayesian = _Algorithm(delta=delta) + self.similarity_evaluator: Optional[SimilarityEvaluator] = None + self.inference_engine: Optional[InferenceEngine] = None + self.cache: Optional[Cache] = None + self.logger: logging.Logger = logging.getLogger(__name__) + + self.executor: Optional[ThreadPoolExecutor] = None + self.callback_queue: Optional[CallbackQueue] = None + + def __enter__(self): + """ + Allows the policy to be used as a context manager. + + Returns: + The policy instance itself. + """ + return self + + def __exit__(self, exc_type, exc_val, exc_tb): + """ + Ensures graceful shutdown of background threads when exiting the context. + + This method is called automatically when exiting a `with` block, + triggering the shutdown of the ThreadPoolExecutor and CallbackQueue. + + Args: + exc_type: The exception type if an exception was raised in the `with` block. + exc_val: The exception value if an exception was raised. + exc_tb: The traceback if an exception was raised. + """ + self.shutdown() + + @override + def setup(self, config: VCacheConfig): + """ + Setup the policy with the given configuration. + + This method initializes the cache, similarity evaluator, and inference + engine. It also sets up and starts the background processing components: + a ThreadPoolExecutor for concurrent tasks and a CallbackQueue for + serialized cache updates. + + Args: + config: The VCache configuration to use. + """ + self.similarity_evaluator = config.similarity_evaluator + self.inference_engine = config.inference_engine + self.cache = Cache( + embedding_engine=config.embedding_engine, + embedding_store=EmbeddingStore( + embedding_metadata_storage=config.embedding_metadata_storage, + vector_db=config.vector_db, + ), + eviction_policy=config.eviction_policy, + ) + + self.callback_queue = CallbackQueue( + callback_function=self.__perform_cache_update + ) + self.callback_queue.start() + self.executor = ThreadPoolExecutor(max_workers=64) + + def shutdown(self): + """ + Shuts down the thread pool and callback queue gracefully. + """ + if self.executor: + self.executor.shutdown(wait=True) + + if self.callback_queue: + self.callback_queue.stop() + + @override + def process_request( + self, prompt: str, system_prompt: Optional[str], id_set: int + ) -> tuple[bool, str, EmbeddingMetadataObj]: + """ + Process a request using dynamic local threshold policy. + + It determines whether to serve a cached response or generate a new one. + If the policy decides to 'explore', it generates a new response and + triggers an asynchronous background task to evaluate the decision and + update the cache, without blocking the current request. The functions returns + the actual response and some metadata information—whether the response is a + cache hit and the nearest neighbor response—to enable further analysis. + + Args: + prompt: The prompt to check for cache hit. + system_prompt: The optional system prompt to use for the response. It will override the system prompt in the VCacheConfig if provided. + id_set: The set identifier for the embedding. This is used in the + benchmark to identify if the nearest neighbor is from the same set + (if the cached response is correct or incorrect). + + Returns: + Tuple containing [is_cache_hit, actual_response, nn_metadata_object]. + """ + if self.inference_engine is None or self.cache is None: + raise ValueError("Policy has not been setup") + + knn = self.cache.get_knn(prompt=prompt, k=1) + if not knn: + response = self.inference_engine.create( + prompt=prompt, system_prompt=system_prompt + ) + self.cache.add(prompt=prompt, response=response, id_set=id_set) + return False, response, EmbeddingMetadataObj(embedding_id=-1, response="") + + similarity_score, embedding_id = knn[0] + + try: + nn_metadata: EmbeddingMetadataObj = self.cache.get_metadata( + embedding_id=embedding_id + ) + except Exception: + # Cache eviction fallback + new_response: str = self.inference_engine.create( + prompt=prompt, system_prompt=system_prompt + ) + self.cache.add(prompt=prompt, response=new_response, id_set=id_set) + return ( + False, + new_response, + EmbeddingMetadataObj(embedding_id=-1, response=""), + ) + + action = self.bayesian.select_action( + similarity_score=similarity_score, metadata=nn_metadata + ) + + match action: + case _Action.EXPLOIT: + return True, nn_metadata.response, nn_metadata + case _Action.EXPLORE: + response = self.inference_engine.create( + prompt=prompt, system_prompt=system_prompt + ) + + self.__update_cache( + response=response, + nn_metadata=nn_metadata, + similarity_score=similarity_score, + embedding_id=embedding_id, + prompt=prompt, + label_id_set=id_set, + ) + + return False, response, nn_metadata + + def __update_cache( + self, + response: str, + nn_metadata: EmbeddingMetadataObj, + similarity_score: float, + embedding_id: int, + prompt: str, + label_id_set: int, + ) -> None: + """ + Asynchronously validates the correctness of the cached response and updates the cache. + + The validation whether the response is correct can involve a latency expensive LLM-judge call. + Because this evaluation does not impact the returned response, we process it in the background. + The LLM-judge call (or any other strategy like an embedding or string-based similarity check) in its own thread + and returns a label (True/False) whether the response is correct. + vCache maintains a global queue that waits for the labels. When a label gets available, + vCache updates the metadata and the vector database accordingly. + + Args: + response: The response to check for correctness. + nn_metadata: The metadata of the nearest neighbor embedding. + similarity_score: The similarity score between the query and the embedding. + embedding_id: The id of the embedding. + prompt: The prompt that was used to generate the response. + label_id_set: The set identifier for the embedding. This is used in the + benchmark to identify if the nearest neighbor is from the same set + (if the cached response is correct or incorrect). + """ + if self.executor is None: + raise ValueError("Executor not initialized. Call setup() first.") + + self.executor.submit( + self.__submit_for_background_update, + response, + similarity_score, + embedding_id, + prompt, + nn_metadata.response, + label_id_set, + nn_metadata.id_set, + ) + + def __submit_for_background_update( + self, + new_response: str, + similarity_score: float, + embedding_id: int, + prompt: str, + cached_response: str, + label_id_set: int, + nn_id_set: int, + ): + """ + Submits a task to check answer similarity and queue a cache update. + + This method is executed by the ThreadPoolExecutor. It performs the + potentially slow `answers_similar` check and then puts the result + and context onto the `callback_queue` for sequential processing. + + Args: + new_response: The newly generated response. + similarity_score: The similarity between the prompt and the nearest neighbor. + embedding_id: The ID of the nearest neighbor embedding. + prompt: The original user prompt. + cached_response: The response from the cached nearest neighbor. + label_id_set: The id_set of the label. + nn_id_set: The id_set of the nearest neighbor. + """ + should_have_exploited = self.similarity_evaluator.answers_similar( + a=new_response, b=cached_response, id_set_a=label_id_set, id_set_b=nn_id_set + ) + self.callback_queue.put( + ( + should_have_exploited, + new_response, + similarity_score, + embedding_id, + prompt, + label_id_set, + ) + ) + + def __perform_cache_update(self, update_args: tuple) -> None: + """ + Performs the actual cache update based on the background check. + + This method is executed sequentially by the CallbackQueue's worker + thread, ensuring thread-safe updates to the cache metadata and + vector database. It fetches the latest metadata before updating to + prevent race conditions with evictions or other updates. + + Args: + update_args: A tuple containing the context required for the update, + as passed from `__submit_for_background_update`. It + contains the following elements in order: + - should_have_exploited (bool): Whether the cache hit + should have been exploited. + - new_response (str): The newly generated response. + - similarity_score (float): The similarity score. + - embedding_id (int): The ID of the nearest neighbor. + - prompt (str): The original user prompt. + - id_set (int): The set identifier for the embedding. This is used in the + benchmark to identify if the nearest neighbor is from the same set + (if the cached response is correct or incorrect). + """ + ( + should_have_exploited, + new_response, + similarity_score, + embedding_id, + prompt, + id_set, + ) = update_args + + try: + latest_metdata_object = self.cache.get_metadata(embedding_id=embedding_id) + except (ValueError, KeyError): + logging.warning( + f"Embedding {embedding_id} was evicted between the time the request was made and the time the update was processed. We can safely ignore this update." + ) + return + + item_was_evicted = latest_metdata_object is None + if item_was_evicted: + return + + try: + self.bayesian.add_observation_to_metadata( + similarity_score=similarity_score, + is_correct=should_have_exploited, + metadata=latest_metdata_object, + ) + except (ValueError, KeyError): + self.logger.warning( + f"Embedding {embedding_id} was evicted between the time the request was made and the time the update was processed. We can safely ignore this update." + ) + return + + if not should_have_exploited: + self.cache.add(prompt=prompt, response=new_response, id_set=id_set) + + try: + self.cache.update_metadata( + embedding_id=embedding_id, embedding_metadata=latest_metdata_object + ) + except (ValueError, KeyError): + self.logger.warning( + f"Embedding {embedding_id} was evicted between the time the request was made and the time the update was processed. We can safely ignore this update." + ) + return + + +class _Action(Enum): + """ + Enumeration of possible actions for the algorithm. + """ + + EXPLORE = "explore" + EXPLOIT = "exploit" + + +class _Algorithm: + """ + Internal algorithm implementation for dynamic threshold computation. + """ + + def __init__(self, delta: float): + """ + Initialize the algorithm with the given delta value. + + Args: + delta: The delta value for confidence computation. + """ + self.delta: float = delta + self.P_c: float = 1.0 - self.delta + self.epsilon_grid: np.ndarray = np.linspace(1e-6, 1 - 1e-6, 50) + self.logistic_regression: LogisticRegression = LogisticRegression( + penalty=None, solver="lbfgs", tol=1e-8, max_iter=1000, fit_intercept=False + ) + + self.latency_observations: List[float] = [] + + def add_observation_to_metadata( + self, similarity_score: float, is_correct: bool, metadata: EmbeddingMetadataObj + ) -> None: + """ + Update the metadata with the new observation + + Args + similarity_score: float - The similarity score between the query and the embedding + is_correct: bool - Whether the query was correct + metadata: EmbeddingMetadataObj - The metadata of the embedding + """ + if is_correct: + metadata.observations.append((round(similarity_score, 3), 1)) + else: + metadata.observations.append((round(similarity_score, 3), 0)) + + def select_action( + self, similarity_score: float, metadata: EmbeddingMetadataObj + ) -> _Action: + """ + Select the action to take based on the similarity score, observations, and accuracy target + + Args + similarity_score: float - The similarity score between the query and the embedding + metadata: EmbeddingMetadataObj - The metadata of the embedding + Returns + Action - Explore or Exploit + """ + similarity_score = round(similarity_score, 3) + similarities: np.ndarray = np.array([obs[0] for obs in metadata.observations]) + labels: np.ndarray = np.array([obs[1] for obs in metadata.observations]) + + if len(similarities) < 6 or len(labels) < 6: + return _Action.EXPLORE + + action: _Action = self._estimate_parameters( + similarity_score=similarity_score, similarities=similarities, labels=labels + ) + + return action + + def _estimate_parameters( + self, similarity_score: float, similarities: np.ndarray, labels: np.ndarray + ) -> _Action: + """ + Optimize parameters with logistic regression + + Args + similarity_score: float - The similarity between the nearest neighbor and current request + similarities: np.ndarray - The similarities of the embeddings + labels: np.ndarray - The labels of the embeddings + metadata: EmbeddingMetadataObj - The metadata of the embedding + + Returns + action: Action - Explore or Exploit + """ + similarities_no_constant = similarities.copy() + similarities = sm.add_constant(similarities) + + try: + if len(similarities) != len(labels): + print(f"len does not match: {len(similarities)} != {len(labels)}") + start_time = time.time() + self.logistic_regression.fit(similarities, labels) + intercept, gamma = self.logistic_regression.coef_[0] + + gamma = max(gamma, 1e-6) + t_hat = -intercept / gamma + t_hat = float(np.clip(t_hat, 0.0, 1.0)) + + linear_term = intercept + gamma * similarity_score + exploration_probability_at_similarity_score = expit(linear_term) + + latency = time.time() - start_time + self.latency_observations.append(latency) + + logging.info( + f"SigmoidProbability: similarity_score: {similarity_score:.2f}, t_hat: {t_hat:.2f}, exploration_prob: {exploration_probability_at_similarity_score:.2f}, Avg. Regression latency: {np.mean(self.latency_observations):.5f} Similarities: {similarities_no_constant}, Labels: {labels}" + ) + + if exploration_probability_at_similarity_score >= self.P_c: + return _Action.EXPLOIT + else: + return _Action.EXPLORE + + except Exception as e: + print(f"Logistic regression failed: {e}") + return -1.0, -1.0, -1.0 diff --git a/vcache/vcache_policy/strategies/benchmark_verified_global.py b/vcache/vcache_policy/strategies/benchmark_verified_global.py index 8fa8842..43b0ee1 100644 --- a/vcache/vcache_policy/strategies/benchmark_verified_global.py +++ b/vcache/vcache_policy/strategies/benchmark_verified_global.py @@ -106,7 +106,10 @@ def process_request( prompt=prompt, system_prompt=system_prompt ) should_have_exploited = self.similarity_evaluator.answers_similar( - a=response, b=metadata.response + a=response, + b=metadata.response, + id_set_a=id_set, + id_set_b=metadata.id_set, ) self.bayesian.update_metadata( similarity_score=similarity_score,