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bdf3f95346
**Description** this allows to customize the maximum number of grpc workers for python backends Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
105 lines
3.7 KiB
Python
105 lines
3.7 KiB
Python
#!/usr/bin/env python3
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import grpc
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from concurrent import futures
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import time
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import backend_pb2
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import backend_pb2_grpc
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import argparse
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import signal
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import sys
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import os, glob
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from pathlib import Path
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from vllm import LLM, SamplingParams
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_ONE_DAY_IN_SECONDS = 60 * 60 * 24
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# If MAX_WORKERS are specified in the environment use it, otherwise default to 1
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MAX_WORKERS = int(os.environ.get('PYTHON_GRPC_MAX_WORKERS', '1'))
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# Implement the BackendServicer class with the service methods
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class BackendServicer(backend_pb2_grpc.BackendServicer):
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def generate(self,prompt, max_new_tokens):
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self.generator.end_beam_search()
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# Tokenizing the input
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ids = self.generator.tokenizer.encode(prompt)
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self.generator.gen_begin_reuse(ids)
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initial_len = self.generator.sequence[0].shape[0]
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has_leading_space = False
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decoded_text = ''
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for i in range(max_new_tokens):
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token = self.generator.gen_single_token()
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if i == 0 and self.generator.tokenizer.tokenizer.IdToPiece(int(token)).startswith('▁'):
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has_leading_space = True
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decoded_text = self.generator.tokenizer.decode(self.generator.sequence[0][initial_len:])
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if has_leading_space:
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decoded_text = ' ' + decoded_text
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if token.item() == self.generator.tokenizer.eos_token_id:
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break
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return decoded_text
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def Health(self, request, context):
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return backend_pb2.Reply(message=bytes("OK", 'utf-8'))
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def LoadModel(self, request, context):
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try:
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# https://github.com/vllm-project/vllm/blob/main/examples/offline_inference.py
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self.llm = LLM(model=request.Model)
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except Exception as err:
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return backend_pb2.Result(success=False, message=f"Unexpected {err=}, {type(err)=}")
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return backend_pb2.Result(message="Model loaded successfully", success=True)
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def Predict(self, request, context):
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if request.TopP == 0:
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request.TopP = 0.9
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sampling_params = SamplingParams(temperature=request.Temperature, top_p=request.TopP)
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outputs = self.llm.generate([request.Prompt], sampling_params)
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generated_text = outputs[0].outputs[0].text
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# Remove prompt from response if present
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if request.Prompt in generated_text:
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generated_text = generated_text.replace(request.Prompt, "")
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return backend_pb2.Result(message=bytes(generated_text, encoding='utf-8'))
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def PredictStream(self, request, context):
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# Implement PredictStream RPC
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#for reply in some_data_generator():
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# yield reply
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# Not implemented yet
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return self.Predict(request, context)
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def serve(address):
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server = grpc.server(futures.ThreadPoolExecutor(max_workers=MAX_WORKERS))
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backend_pb2_grpc.add_BackendServicer_to_server(BackendServicer(), server)
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server.add_insecure_port(address)
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server.start()
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print("Server started. Listening on: " + address, file=sys.stderr)
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# Define the signal handler function
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def signal_handler(sig, frame):
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print("Received termination signal. Shutting down...")
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server.stop(0)
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sys.exit(0)
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# Set the signal handlers for SIGINT and SIGTERM
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signal.signal(signal.SIGINT, signal_handler)
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signal.signal(signal.SIGTERM, signal_handler)
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try:
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while True:
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time.sleep(_ONE_DAY_IN_SECONDS)
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except KeyboardInterrupt:
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server.stop(0)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="Run the gRPC server.")
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parser.add_argument(
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"--addr", default="localhost:50051", help="The address to bind the server to."
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)
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args = parser.parse_args()
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serve(args.addr) |