#!/usr/bin/env python3 """ Extra gRPC server for HuggingFace AutoModel models. """ from concurrent import futures import argparse import signal import sys import os import time import backend_pb2 import backend_pb2_grpc import grpc import torch import torch.cuda from transformers import AutoTokenizer, AutoModel _ONE_DAY_IN_SECONDS = 60 * 60 * 24 # If MAX_WORKERS are specified in the environment use it, otherwise default to 1 MAX_WORKERS = int(os.environ.get('PYTHON_GRPC_MAX_WORKERS', '1')) def mean_pooling(model_output, attention_mask): """ Mean pooling to get sentence embeddings. See: https://huggingface.co/sentence-transformers/paraphrase-distilroberta-base-v1 """ token_embeddings = model_output[0] input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() sum_embeddings = torch.sum(token_embeddings * input_mask_expanded, 1) # Sum columns sum_mask = torch.clamp(input_mask_expanded.sum(1), min=1e-9) return sum_embeddings / sum_mask # Implement the BackendServicer class with the service methods class BackendServicer(backend_pb2_grpc.BackendServicer): """ A gRPC servicer for the backend service. This class implements the gRPC methods for the backend service, including Health, LoadModel, and Embedding. """ def Health(self, request, context): """ A gRPC method that returns the health status of the backend service. Args: request: A HealthRequest object that contains the request parameters. context: A grpc.ServicerContext object that provides information about the RPC. Returns: A Reply object that contains the health status of the backend service. """ return backend_pb2.Reply(message=bytes("OK", 'utf-8')) def LoadModel(self, request, context): """ A gRPC method that loads a model into memory. Args: request: A LoadModelRequest object that contains the request parameters. context: A grpc.ServicerContext object that provides information about the RPC. Returns: A Result object that contains the result of the LoadModel operation. """ model_name = request.Model try: self.model = AutoModel.from_pretrained(model_name, trust_remote_code=True) # trust_remote_code is needed to use the encode method with embeddings models like jinai-v2 self.tokenizer = AutoTokenizer.from_pretrained(model_name) if request.CUDA or torch.cuda.is_available(): try: print("Loading model", model_name, "to CUDA.", file=sys.stderr) self.model = self.model.to("cuda") except Exception as err: print("Not using CUDA:", err, file=sys.stderr) except Exception as err: return backend_pb2.Result(success=False, message=f"Unexpected {err=}, {type(err)=}") # Implement your logic here for the LoadModel service # Replace this with your desired response return backend_pb2.Result(message="Model loaded successfully", success=True) def Embedding(self, request, context): """ A gRPC method that calculates embeddings for a given sentence. Args: request: An EmbeddingRequest object that contains the request parameters. context: A grpc.ServicerContext object that provides information about the RPC. Returns: An EmbeddingResult object that contains the calculated embeddings. """ # Tokenize input max_length = 512 if request.Tokens != 0: max_length = request.Tokens encoded_input = self.tokenizer(request.Embeddings, padding=True, truncation=True, max_length=max_length, return_tensors="pt") # Create word embeddings model_output = self.model(**encoded_input) # Pool to get sentence embeddings; i.e. generate one 1024 vector for the entire sentence sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']).detach().numpy() print("Calculated embeddings for: " + request.Embeddings, file=sys.stderr) print("Embeddings:", sentence_embeddings, file=sys.stderr) return backend_pb2.EmbeddingResult(embeddings=sentence_embeddings) def Predict(self, request, context): """ Generates text based on the given prompt and sampling parameters. Args: request: The predict request. context: The gRPC context. Returns: backend_pb2.Reply: The predict result. """ if request.TopP == 0: request.TopP = 0.9 max_tokens = 200 if request.Tokens > 0: max_tokens = request.Tokens inputs = self.tokenizer.tokenizer(request.Prompt, return_tensors="pt").input_ids outputs = self.model.generate(inputs,max_tokens=max_tokens, temperature=request.Temperature, top_p=request.TopP) generated_text = self.tokenizer.tokenizer.batch_decode(outputs, skip_special_tokens=True)[0] # Remove prompt from response if present if request.Prompt in generated_text: generated_text = generated_text.replace(request.Prompt, "") return backend_pb2.Reply(message=bytes(generated_text, encoding='utf-8')) def PredictStream(self, request, context): """ Generates text based on the given prompt and sampling parameters, and streams the results. Args: request: The predict stream request. context: The gRPC context. Returns: backend_pb2.Result: The predict stream result. """ yield self.Predict(request, context) def serve(address): server = grpc.server(futures.ThreadPoolExecutor(max_workers=MAX_WORKERS)) backend_pb2_grpc.add_BackendServicer_to_server(BackendServicer(), server) server.add_insecure_port(address) server.start() print("Server started. Listening on: " + address, file=sys.stderr) # Define the signal handler function def signal_handler(sig, frame): print("Received termination signal. Shutting down...") server.stop(0) sys.exit(0) # Set the signal handlers for SIGINT and SIGTERM signal.signal(signal.SIGINT, signal_handler) signal.signal(signal.SIGTERM, signal_handler) try: while True: time.sleep(_ONE_DAY_IN_SECONDS) except KeyboardInterrupt: server.stop(0) if __name__ == "__main__": parser = argparse.ArgumentParser(description="Run the gRPC server.") parser.add_argument( "--addr", default="localhost:50051", help="The address to bind the server to." ) args = parser.parse_args() serve(args.addr)