LocalAI/backend/python/mamba/backend_mamba.py

#!/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, AutoModelForCausalLM
from mamba_ssm.models.mixer_seq_simple import MambaLMHeadModel

_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'))
MAMBA_CHAT= os.environ.get('MAMBA_CHAT', '1') == '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 generate(self,prompt, max_new_tokens):
        """
        Generates text based on the given prompt and maximum number of new tokens.

        Args:
            prompt (str): The prompt to generate text from.
            max_new_tokens (int): The maximum number of new tokens to generate.

        Returns:
            str: The generated text.
        """
        self.generator.end_beam_search()

        # Tokenizing the input
        ids = self.generator.tokenizer.encode(prompt)

        self.generator.gen_begin_reuse(ids)
        initial_len = self.generator.sequence[0].shape[0]
        has_leading_space = False
        decoded_text = ''
        for i in range(max_new_tokens):
            token = self.generator.gen_single_token()
            if i == 0 and self.generator.tokenizer.tokenizer.IdToPiece(int(token)).startswith('▁'):
                has_leading_space = True

            decoded_text = self.generator.tokenizer.decode(self.generator.sequence[0][initial_len:])
            if has_leading_space:
                decoded_text = ' ' + decoded_text

            if token.item() == self.generator.tokenizer.eos_token_id:
                break
        return decoded_text

    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:
            tokenizerModel = request.Tokenizer
            if tokenizerModel == "":
                tokenizerModel = request.Model

            tokenizer = AutoTokenizer.from_pretrained(tokenizerModel)
            if MAMBA_CHAT:
                tokenizer.eos_token = "<|endoftext|>"
                tokenizer.pad_token = tokenizer.eos_token
            self.tokenizer = tokenizer
            self.model = MambaLMHeadModel.from_pretrained(request.Model, device="cuda", dtype=torch.float16)
        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.
        """
        if request.TopP == 0:
            request.TopP = 0.9

        max_tokens = request.Tokens

        if request.Tokens == 0:
            max_tokens = 2000

        encoded_input = self.tokenizer(request.Prompt)
        
        out = self.model.generate(input_ids=encoded_input["input_ids"], max_length=max_tokens, temperature=request.Temperratur,
                                     top_p=request.TopP, eos_token_id=self.tokenizer.eos_token_id)

        decoded = self.tokenizer.batch_decode(out)
       
        generated_text = decoded[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)