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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 argparse
import logging
import sys
import torch
from nemo.deploy import DeployPyTriton
LOGGER = logging.getLogger("NeMo")
megatron_llm_supported = True
try:
from nemo.deploy.nlp.megatronllm_deployable import MegatronLLMDeployableNemo2
except Exception as e:
LOGGER.warning(f"Cannot import MegatronLLMDeployable, it will not be available. {type(e).__name__}: {e}")
megatron_llm_supported = False
def get_args(argv):
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
description=f"Deploy nemo models to Triton",
)
parser.add_argument("-nc", "--nemo_checkpoint", type=str, help="Source .nemo file")
parser.add_argument("-tmn", "--triton_model_name", required=True, type=str, help="Name for the service")
parser.add_argument("-tmv", "--triton_model_version", default=1, type=int, help="Version for the service")
parser.add_argument(
"-trp", "--triton_port", default=8000, type=int, help="Port for the Triton server to listen for requests"
)
parser.add_argument(
"-tha", "--triton_http_address", default="0.0.0.0", type=str, help="HTTP address for the Triton server"
)
parser.add_argument("-ng", "--num_gpus", default=1, type=int, help="Number of GPUs for the deployment")
parser.add_argument("-nn", "--num_nodes", default=1, type=int, help="Number of GPUs for the deployment")
parser.add_argument("-tps", "--tensor_parallelism_size", default=1, type=int, help="Tensor parallelism size")
parser.add_argument("-pps", "--pipeline_parallelism_size", default=1, type=int, help="Pipeline parallelism size")
parser.add_argument("-cps", "--context_parallel_size", default=1, type=int, help="Pipeline parallelism size")
parser.add_argument(
"-emps",
"--expert_model_parallel_size",
default=1,
type=int,
help="Distributes MoE Experts across sub data parallel dimension.",
)
parser.add_argument("-mbs", "--max_batch_size", default=8, type=int, help="Max batch size of the model")
parser.add_argument("-dm", "--debug_mode", default=False, action='store_true', help="Enable debug mode")
parser.add_argument(
"-fd",
'--enable_flash_decode',
default=False,
action='store_true',
help='Enable flash decoding',
)
parser.add_argument("-lc", "--legacy_ckpt", action="store_true", help="Load checkpoint saved with TE < 1.14")
args = parser.parse_args(argv)
return args
def nemo_deploy(argv):
args = get_args(argv)
if args.debug_mode:
loglevel = logging.DEBUG
else:
loglevel = logging.INFO
LOGGER.setLevel(loglevel)
LOGGER.info("Logging level set to {}".format(loglevel))
LOGGER.info(args)
if not megatron_llm_supported:
raise ValueError("MegatronLLMDeployable is not supported in this environment.")
if args.nemo_checkpoint is None:
raise ValueError("In-Framework deployment requires a checkpoint folder.")
model = MegatronLLMDeployableNemo2(
nemo_checkpoint_filepath=args.nemo_checkpoint,
num_devices=args.num_gpus,
num_nodes=args.num_nodes,
tensor_model_parallel_size=args.tensor_parallelism_size,
pipeline_model_parallel_size=args.pipeline_parallelism_size,
context_parallel_size=args.context_parallel_size,
expert_model_parallel_size=args.expert_model_parallel_size,
max_batch_size=args.max_batch_size,
enable_flash_decode=args.enable_flash_decode,
legacy_ckpt=args.legacy_ckpt,
)
if torch.distributed.is_initialized():
if torch.distributed.get_rank() == 0:
try:
nm = DeployPyTriton(
model=model,
triton_model_name=args.triton_model_name,
triton_model_version=args.triton_model_version,
max_batch_size=args.max_batch_size,
http_port=args.triton_port,
address=args.triton_http_address,
)
LOGGER.info("Triton deploy function will be called.")
nm.deploy()
except Exception as error:
LOGGER.error("Error message has occurred during deploy function. Error message: " + str(error))
return
try:
LOGGER.info("Model serving on Triton will be started.")
nm.serve()
except Exception as error:
LOGGER.error("Error message has occurred during deploy function. Error message: " + str(error))
return
torch.distributed.broadcast(torch.tensor([1], dtype=torch.long, device="cuda"), src=0)
LOGGER.info("Model serving will be stopped.")
nm.stop()
elif torch.distributed.get_rank() > 0:
model.generate_other_ranks()
else:
LOGGER.info("Torch distributed wasn't initialized.")
if __name__ == '__main__':
nemo_deploy(sys.argv[1:])
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