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#!/usr/bin/env python3
from concurrent import futures
import time
import argparse
import signal
import sys
import os
import backend_pb2
import backend_pb2_grpc
import grpc
import torch
from transformers import AutoTokenizer
from petals import AutoDistributedModelForCausalLM
_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'))
# Implement the BackendServicer class with the service methods
class BackendServicer(backend_pb2_grpc.BackendServicer):
"""
A gRPC servicer that implements the Backend service defined in backend.proto.
"""
def Health(self, request, context):
"""
Returns a health check message.
Args:
request: The health check request.
context: The gRPC context.
Returns:
backend_pb2.Reply: The health check reply.
"""
return backend_pb2.Reply(message=bytes("OK", 'utf-8'))
def LoadModel(self, request, context):
"""
Loads a language model.
Args:
request: The load model request.
context: The gRPC context.
Returns:
backend_pb2.Result: The load model result.
"""
try:
self.tokenizer = AutoTokenizer.from_pretrained(request.Model, use_fast=False, add_bos_token=False)
self.model = AutoDistributedModelForCausalLM.from_pretrained(request.Model)
self.cuda = False
if request.CUDA:
self.model = self.model.cuda()
self.cuda = True
except Exception as err:
return backend_pb2.Result(success=False, message=f"Unexpected {err=}, {type(err)=}")
return backend_pb2.Result(message="Model loaded successfully", success=True)
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.Result: The predict result.
"""
inputs = self.tokenizer(request.Prompt, return_tensors="pt")["input_ids"]
if self.cuda:
inputs = inputs.cuda()
if request.Tokens == 0:
# Max to max value if tokens are not specified
request.Tokens = 8192
# TODO: kwargs and map all parameters
outputs = self.model.generate(inputs, max_new_tokens=request.Tokens)
generated_text = self.tokenizer.decode(outputs[0])
# Remove prompt from response if present
if request.Prompt in generated_text:
generated_text = generated_text.replace(request.Prompt, "")
return backend_pb2.Result(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.
"""
# Implement PredictStream RPC
#for reply in some_data_generator():
# yield reply
# Not implemented yet
return 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)
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