262 lines
8.7 KiB
Python
262 lines
8.7 KiB
Python
import asyncio
|
|
import os
|
|
import torch
|
|
import time
|
|
import signal
|
|
|
|
from grpc import aio
|
|
from loguru import logger
|
|
|
|
from grpc_reflection.v1alpha import reflection
|
|
from pathlib import Path
|
|
from typing import List, Optional
|
|
|
|
from text_generation_server.cache import Cache
|
|
from text_generation_server.interceptor import ExceptionInterceptor
|
|
from text_generation_server.models import Model, get_model
|
|
from text_generation_server.models.pali_gemma import PaliGemmaBatch
|
|
from text_generation_server.models.vlm_causal_lm import (
|
|
VlmCausalLMBatch,
|
|
)
|
|
from text_generation_server.pb import generate_pb2_grpc, generate_pb2
|
|
from text_generation_server.tracing import UDSOpenTelemetryAioServerInterceptor
|
|
from text_generation_server.models.idefics_causal_lm import IdeficsCausalLMBatch
|
|
from text_generation_server.models.globals import set_model_id
|
|
|
|
|
|
class SignalHandler:
|
|
KEEP_PROCESSING = True
|
|
|
|
def __init__(self):
|
|
signal.signal(signal.SIGINT, self.exit_gracefully)
|
|
signal.signal(signal.SIGTERM, self.exit_gracefully)
|
|
|
|
def exit_gracefully(self, signum, frame):
|
|
print(f"Exiting gracefully: Signal {signum}")
|
|
self.KEEP_PROCESSING = False
|
|
|
|
|
|
class TextGenerationService(generate_pb2_grpc.TextGenerationServiceServicer):
|
|
def __init__(
|
|
self,
|
|
model: Model,
|
|
cache: Cache,
|
|
quantize: Optional[str],
|
|
server_urls: List[str],
|
|
):
|
|
self.cache = cache
|
|
self.model = model
|
|
self.quantize = quantize
|
|
self.server_urls = server_urls
|
|
# For some reason, inference_mode does not work well with GLOO which we use on CPU
|
|
if model.device.type == "cuda":
|
|
# Force inference mode for the lifetime of TextGenerationService
|
|
self._inference_mode_raii_guard = torch._C._InferenceMode(True)
|
|
|
|
async def Info(self, request, context):
|
|
return self.model.info
|
|
|
|
async def Health(self, request, context):
|
|
if self.model.device.type == "cuda":
|
|
torch.zeros((2, 2)).cuda()
|
|
return generate_pb2.HealthResponse()
|
|
|
|
async def ServiceDiscovery(self, request, context):
|
|
return generate_pb2.ServiceDiscoveryResponse(urls=self.server_urls)
|
|
|
|
async def ClearCache(self, request, context):
|
|
if request.HasField("id"):
|
|
self.cache.delete(request.id)
|
|
else:
|
|
self.cache.clear()
|
|
return generate_pb2.ClearCacheResponse()
|
|
|
|
async def FilterBatch(self, request, context):
|
|
batch = self.cache.pop(request.batch_id)
|
|
if batch is None:
|
|
raise ValueError(f"Batch ID {request.batch_id} not found in cache.")
|
|
filtered_batch = batch.filter(request.request_ids)
|
|
self.cache.set(filtered_batch)
|
|
|
|
return generate_pb2.FilterBatchResponse(batch=filtered_batch.to_pb())
|
|
|
|
async def Warmup(self, request, context):
|
|
if self.quantize == "gptq":
|
|
try:
|
|
# When using GPTQ, Exllama kernels need some global kernels
|
|
# For which we have the finale shapes only after the model has loaded
|
|
# This will allocate those buffers.
|
|
from text_generation_server.layers.gptq import (
|
|
create_exllama_buffers,
|
|
set_device,
|
|
)
|
|
|
|
set_device(self.model.device)
|
|
create_exllama_buffers(request.max_prefill_tokens)
|
|
except ImportError:
|
|
pass
|
|
|
|
if self.model.batch_type in {
|
|
IdeficsCausalLMBatch,
|
|
VlmCausalLMBatch,
|
|
PaliGemmaBatch,
|
|
}: # Hack, i would rather use kwargs in the `from_pb` call
|
|
batch = self.model.batch_type.from_pb_processor(
|
|
request.batch,
|
|
self.model.tokenizer,
|
|
self.model.processor,
|
|
self.model.model.config,
|
|
self.model.dtype,
|
|
self.model.device,
|
|
)
|
|
else:
|
|
batch = self.model.batch_type.from_pb(
|
|
request.batch, self.model.tokenizer, self.model.dtype, self.model.device
|
|
)
|
|
max_supported_total_tokens = self.model.warmup(batch)
|
|
|
|
return generate_pb2.WarmupResponse(
|
|
max_supported_total_tokens=max_supported_total_tokens
|
|
)
|
|
|
|
async def Prefill(self, request, context):
|
|
start = time.time_ns()
|
|
if self.model.batch_type in {
|
|
IdeficsCausalLMBatch,
|
|
VlmCausalLMBatch,
|
|
PaliGemmaBatch,
|
|
}: # Hack, i would rather use kwargs in the `from_pb` call
|
|
batch = self.model.batch_type.from_pb_processor(
|
|
request.batch,
|
|
self.model.tokenizer,
|
|
self.model.processor,
|
|
self.model.model.config,
|
|
self.model.dtype,
|
|
self.model.device,
|
|
)
|
|
else:
|
|
batch = self.model.batch_type.from_pb(
|
|
request.batch, self.model.tokenizer, self.model.dtype, self.model.device
|
|
)
|
|
|
|
generations, next_batch, timings = self.model.generate_token(batch)
|
|
self.cache.set(next_batch)
|
|
|
|
return generate_pb2.PrefillResponse(
|
|
generations=[generation.to_pb() for generation in generations],
|
|
batch=next_batch.to_pb() if next_batch else None,
|
|
forward_ns=timings[0],
|
|
decode_ns=timings[1],
|
|
total_ns=time.time_ns() - start,
|
|
)
|
|
|
|
async def Decode(self, request, context):
|
|
start = time.time_ns()
|
|
if len(request.batches) == 0:
|
|
raise ValueError("Must provide at least one batch")
|
|
|
|
batches = []
|
|
for batch_pb in request.batches:
|
|
batch = self.cache.pop(batch_pb.id)
|
|
if batch is None:
|
|
raise ValueError(f"Batch ID {batch_pb.id} not found in cache.")
|
|
batches.append(batch)
|
|
|
|
if len(batches) == 0:
|
|
raise ValueError("All batches are empty")
|
|
|
|
if len(batches) > 1:
|
|
start_concat = time.time_ns()
|
|
batch = self.model.batch_type.concatenate(batches)
|
|
concat_ns = time.time_ns() - start_concat
|
|
else:
|
|
batch = batches[0]
|
|
concat_ns = None
|
|
|
|
generations, next_batch, timings = self.model.generate_token(batch)
|
|
self.cache.set(next_batch)
|
|
|
|
return generate_pb2.DecodeResponse(
|
|
generations=[generation.to_pb() for generation in generations],
|
|
batch=next_batch.to_pb() if next_batch else None,
|
|
concat_ns=concat_ns,
|
|
forward_ns=timings[0],
|
|
decode_ns=timings[1],
|
|
total_ns=time.time_ns() - start,
|
|
)
|
|
|
|
|
|
def serve(
|
|
model_id: str,
|
|
revision: Optional[str],
|
|
sharded: bool,
|
|
quantize: Optional[str],
|
|
speculate: Optional[int],
|
|
dtype: Optional[str],
|
|
trust_remote_code: bool,
|
|
uds_path: Path,
|
|
):
|
|
async def serve_inner(
|
|
model_id: str,
|
|
revision: Optional[str],
|
|
sharded: bool = False,
|
|
quantize: Optional[str] = None,
|
|
speculate: Optional[int] = None,
|
|
dtype: Optional[str] = None,
|
|
trust_remote_code: bool = False,
|
|
):
|
|
unix_socket_template = "unix://{}-{}"
|
|
if sharded:
|
|
server_urls = [
|
|
unix_socket_template.format(uds_path, rank)
|
|
for rank in range(int(os.environ["WORLD_SIZE"]))
|
|
]
|
|
local_url = server_urls[int(os.environ["RANK"])]
|
|
else:
|
|
local_url = unix_socket_template.format(uds_path, 0)
|
|
server_urls = [local_url]
|
|
|
|
try:
|
|
model = get_model(
|
|
model_id,
|
|
revision,
|
|
sharded,
|
|
quantize,
|
|
speculate,
|
|
dtype,
|
|
trust_remote_code,
|
|
)
|
|
except Exception:
|
|
logger.exception("Error when initializing model")
|
|
raise
|
|
|
|
server = aio.server(
|
|
interceptors=[
|
|
ExceptionInterceptor(),
|
|
UDSOpenTelemetryAioServerInterceptor(),
|
|
]
|
|
)
|
|
generate_pb2_grpc.add_TextGenerationServiceServicer_to_server(
|
|
TextGenerationService(model, Cache(), quantize, server_urls), server
|
|
)
|
|
SERVICE_NAMES = (
|
|
generate_pb2.DESCRIPTOR.services_by_name["TextGenerationService"].full_name,
|
|
reflection.SERVICE_NAME,
|
|
)
|
|
reflection.enable_server_reflection(SERVICE_NAMES, server)
|
|
server.add_insecure_port(local_url)
|
|
|
|
await server.start()
|
|
|
|
logger.info("Server started at {}".format(local_url))
|
|
signal_handler = SignalHandler()
|
|
while signal_handler.KEEP_PROCESSING:
|
|
await asyncio.sleep(0.5)
|
|
|
|
set_model_id(model_id)
|
|
asyncio.run(
|
|
serve_inner(
|
|
model_id, revision, sharded, quantize, speculate, dtype, trust_remote_code
|
|
)
|
|
)
|