136 lines
4.5 KiB
Python
136 lines
4.5 KiB
Python
import asyncio
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import os
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import torch
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from grpc import aio
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from loguru import logger
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from grpc_reflection.v1alpha import reflection
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from pathlib import Path
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from typing import List, Optional
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from text_generation_server.cache import Cache
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from text_generation_server.interceptor import ExceptionInterceptor
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from text_generation_server.models import Model, get_model
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from text_generation_server.pb import generate_pb2_grpc, generate_pb2
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from text_generation_server.tracing import UDSOpenTelemetryAioServerInterceptor
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class TextGenerationService(generate_pb2_grpc.TextGenerationServiceServicer):
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def __init__(self, model: Model, cache: Cache, server_urls: List[str]):
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self.cache = cache
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self.model = model
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self.server_urls = server_urls
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# For some reason, inference_mode does not work well with GLOO which we use on CPU
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if model.device.type == "cuda":
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# Force inference mode for the lifetime of TextGenerationService
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self._inference_mode_raii_guard = torch._C._InferenceMode(True)
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async def ServiceDiscovery(self, request, context):
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return generate_pb2.ServiceDiscoveryResponse(urls=self.server_urls)
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async def ClearCache(self, request, context):
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if request.HasField("id"):
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self.cache.delete(request.id)
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else:
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self.cache.clear()
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if torch.cuda.is_available():
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torch.cuda.empty_cache()
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return generate_pb2.ClearCacheResponse()
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async def Prefill(self, request, context):
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batch = self.model.batch_type.from_pb(
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request.batch, self.model.tokenizer, self.model.device
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)
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generations, next_batch = self.model.generate_token(batch)
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self.cache.set(next_batch)
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return generate_pb2.PrefillResponse(
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generations=[generation.to_pb() for generation in generations],
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batch=next_batch.to_pb() if next_batch else None,
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)
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async def Decode(self, request, context):
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if len(request.batches) == 0:
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raise ValueError("Must provide at least one batch")
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batches = []
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for batch_pb in request.batches:
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batch = self.cache.pop(batch_pb.id)
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if batch is None:
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raise ValueError(f"Batch ID {batch_pb.id} not found in cache.")
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batches.append(batch)
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if len(batches) > 1:
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batch = self.model.batch_type.concatenate(batches)
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else:
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batch = batches[0]
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generations, next_batch = self.model.generate_token(batch)
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self.cache.set(next_batch)
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return generate_pb2.DecodeResponse(
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generations=[generation.to_pb() for generation in generations],
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batch=next_batch.to_pb() if next_batch else None,
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)
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def serve(
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model_id: str,
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revision: Optional[str],
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sharded: bool,
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quantize: bool,
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uds_path: Path,
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):
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async def serve_inner(
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model_id: str,
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revision: Optional[str],
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sharded: bool = False,
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quantize: bool = False,
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):
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unix_socket_template = "unix://{}-{}"
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if sharded:
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server_urls = [
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unix_socket_template.format(uds_path, rank)
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for rank in range(int(os.environ["WORLD_SIZE"]))
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]
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local_url = server_urls[int(os.environ["RANK"])]
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else:
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local_url = unix_socket_template.format(uds_path, 0)
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server_urls = [local_url]
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try:
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model = get_model(model_id, revision, sharded, quantize)
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except Exception:
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logger.exception("Error when initializing model")
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raise
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server = aio.server(
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interceptors=[
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ExceptionInterceptor(),
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UDSOpenTelemetryAioServerInterceptor(),
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]
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)
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generate_pb2_grpc.add_TextGenerationServiceServicer_to_server(
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TextGenerationService(model, Cache(), server_urls), server
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)
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SERVICE_NAMES = (
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generate_pb2.DESCRIPTOR.services_by_name["TextGenerationService"].full_name,
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reflection.SERVICE_NAME,
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)
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reflection.enable_server_reflection(SERVICE_NAMES, server)
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server.add_insecure_port(local_url)
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await server.start()
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logger.info("Server started at {}".format(local_url))
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try:
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await server.wait_for_termination()
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except KeyboardInterrupt:
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logger.info("Signal received. Shutting down")
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await server.stop(0)
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asyncio.run(serve_inner(model_id, revision, sharded, quantize))
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