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https://github.com/mudler/LocalAI.git
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112 lines
3.7 KiB
Python
Executable File
112 lines
3.7 KiB
Python
Executable File
#!/usr/bin/env python3
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from concurrent import futures
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import argparse
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import signal
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import sys
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import os
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import time
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import grpc
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import backend_pb2
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import backend_pb2_grpc
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from auto_gptq import AutoGPTQForCausalLM
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from transformers import AutoTokenizer
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from transformers import TextGenerationPipeline
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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 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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device = "cuda:0"
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if request.Device != "":
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device = request.Device
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tokenizer = AutoTokenizer.from_pretrained(request.Model, use_fast=request.UseFastTokenizer)
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model = AutoGPTQForCausalLM.from_quantized(request.Model,
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model_basename=request.ModelBaseName,
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use_safetensors=True,
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trust_remote_code=request.TrustRemoteCode,
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device=device,
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use_triton=request.UseTriton,
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quantize_config=None)
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self.model = model
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self.tokenizer = tokenizer
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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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penalty = 1.0
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if request.Penalty != 0.0:
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penalty = request.Penalty
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tokens = 512
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if request.Tokens != 0:
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tokens = request.Tokens
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top_p = 0.95
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if request.TopP != 0.0:
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top_p = request.TopP
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# Implement Predict RPC
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pipeline = TextGenerationPipeline(
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model=self.model,
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tokenizer=self.tokenizer,
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max_new_tokens=tokens,
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temperature=request.Temperature,
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top_p=top_p,
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repetition_penalty=penalty,
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)
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t = pipeline(request.Prompt)[0]["generated_text"]
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# Remove prompt from response if present
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if request.Prompt in t:
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t = t.replace(request.Prompt, "")
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return backend_pb2.Result(message=bytes(t, 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) |