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# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import importlib
import logging
from abc import ABC, abstractmethod
use_pytorch_lightning = True
try:
from lightning.pytorch import Trainer
except Exception:
use_pytorch_lightning = False
from nemo.deploy.triton_deployable import ITritonDeployable
use_nemo = True
try:
from nemo.core.classes.modelPT import ModelPT
except Exception:
use_nemo = False
LOGGER = logging.getLogger("NeMo")
class DeployBase(ABC):
def __init__(
self,
triton_model_name: str,
triton_model_version: int = 1,
checkpoint_path: str = None,
model=None,
max_batch_size: int = 128,
http_port: int = 8000,
grpc_port: int = 8001,
address="0.0.0.0",
allow_grpc=True,
allow_http=True,
streaming=False,
pytriton_log_verbose=0,
):
self.checkpoint_path = checkpoint_path
self.triton_model_name = triton_model_name
self.triton_model_version = triton_model_version
self.max_batch_size = max_batch_size
self.model = model
self.http_port = http_port
self.grpc_port = grpc_port
self.address = address
self.triton = None
self.allow_grpc = allow_grpc
self.allow_http = allow_http
self.streaming = streaming
self.pytriton_log_verbose = pytriton_log_verbose
if checkpoint_path is None and model is None:
raise Exception("Either checkpoint_path or model should be provided.")
@abstractmethod
def deploy(self):
pass
@abstractmethod
def serve(self):
pass
@abstractmethod
def run(self):
pass
@abstractmethod
def stop(self):
pass
def _init_nemo_model(self):
if self.checkpoint_path is not None:
model_config = ModelPT.restore_from(self.checkpoint_path, return_config=True)
module_path, class_name = DeployBase.get_module_and_class(model_config.target)
cls = getattr(importlib.import_module(module_path), class_name)
self.model = cls.restore_from(restore_path=self.checkpoint_path, trainer=Trainer())
self.model.freeze()
# has to turn off activations_checkpoint_method for inference
try:
self.model.model.language_model.encoder.activations_checkpoint_method = None
except AttributeError as e:
LOGGER.warning(e)
if self.model is None:
raise Exception("There is no model to deploy.")
self._is_model_deployable()
def _is_model_deployable(self):
if not issubclass(type(self.model), ITritonDeployable):
raise Exception(
"This model is not deployable to Triton." "nemo.deploy.ITritonDeployable class should be inherited"
)
else:
return True
@staticmethod
def get_module_and_class(target: str):
ln = target.rindex(".")
return target[0:ln], target[ln + 1 : len(target)]
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