File size: 6,595 Bytes
b386992 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 | # Copyright (c) 2023-2024, 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 os
import sys
import tempfile
from nemo.deploy import DeployPyTriton
# Configure the NeMo logger to look the same as vLLM
logging.basicConfig(format="%(levelname)s %(asctime)s %(filename)s:%(lineno)d] %(message)s", datefmt="%m-%d %H:%M:%S")
LOGGER = logging.getLogger("NeMo")
try:
from nemo.export.vllm_exporter import vLLMExporter
except Exception as e:
LOGGER.error(f"Cannot import the vLLM exporter. {type(e).__name__}: {e}")
sys.exit(1)
def get_args(argv):
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
description=f"Export NeMo models to vLLM and deploy them on Triton",
)
parser.add_argument("-nc", "--nemo_checkpoint", type=str, help="Source .nemo file")
parser.add_argument(
"-mt",
"--model_type",
type=str,
required=True,
choices=["llama", "mistral", "mixtral", "starcoder2", "gemma"],
help="Type of the model",
)
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(
"-tmr", "--triton_model_repository", default=None, type=str, help="Folder for the vLLM conversion"
)
parser.add_argument("-tps", "--tensor_parallelism_size", default=1, type=int, help="Tensor parallelism size")
parser.add_argument(
"-dt",
"--dtype",
choices=["bfloat16", "float16", "fp8", "int8"],
default="bfloat16",
type=str,
help="dtype of the model on vLLM",
)
parser.add_argument(
"-mml", "--max_model_len", default=512, type=int, help="Max input + ouptut length of the model"
)
parser.add_argument("-mbs", "--max_batch_size", default=8, type=int, help="Max batch size of the model")
parser.add_argument(
"-lc", "--lora_ckpt", default=[], type=str, nargs="+", help="List of LoRA checkpoints in HF format"
)
parser.add_argument(
"-es", '--enable_streaming', default=False, action='store_true', help="Enables streaming sentences."
)
parser.add_argument("-dm", "--debug_mode", default=False, action='store_true', help="Enable debug mode")
parser.add_argument(
'-ws',
'--weight_storage',
default='auto',
choices=['auto', 'cache', 'file', 'memory'],
help='Strategy for storing converted weights for vLLM: "file" - always write weights into a file, '
'"memory" - always do an in-memory conversion, "cache" - reuse existing files if they are '
'newer than the nemo checkpoint, "auto" - use "cache" for multi-GPU runs and "memory" '
'for single-GPU runs.',
)
parser.add_argument(
"-gmu",
'--gpu_memory_utilization',
default=0.9,
type=float,
help="GPU memory utilization percentage for vLLM.",
)
parser.add_argument(
"-q",
"--quantization",
choices=["fp8"],
help="Quantization method for vLLM.",
)
args = parser.parse_args(argv)
return args
def get_vllm_deployable(args, model_dir):
exporter = vLLMExporter()
exporter.export(
nemo_checkpoint=args.nemo_checkpoint,
model_dir=model_dir,
model_type=args.model_type,
tensor_parallel_size=args.tensor_parallelism_size,
max_model_len=args.max_model_len,
lora_checkpoints=args.lora_ckpt,
dtype=args.dtype,
weight_storage=args.weight_storage,
gpu_memory_utilization=args.gpu_memory_utilization,
quantization=args.quantization,
)
return exporter
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 no model_dir was supplied, create a temporary directory.
# This directory should persist while the model is being served, becaue it may contain
# converted LoRA checkpoints, and those are accessed by vLLM at request time.
tempdir = None
model_dir = args.triton_model_repository
if model_dir is None:
tempdir = tempfile.TemporaryDirectory()
model_dir = tempdir.name
LOGGER.info(
f"{model_dir} will be used for the vLLM intermediate folder. "
+ "Please set the --triton_model_repository parameter if you'd like to use a path that already "
+ "includes the vLLM model files."
)
elif not os.path.exists(model_dir):
os.makedirs(model_dir)
try:
triton_deployable = get_vllm_deployable(args, model_dir=model_dir)
nm = DeployPyTriton(
model=triton_deployable,
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,
streaming=args.enable_streaming,
)
LOGGER.info("Starting the Triton server...")
nm.deploy()
nm.serve()
LOGGER.info("Stopping the Triton server...")
nm.stop()
except Exception as error:
LOGGER.error("An error has occurred while setting up or serving the model. Error message: " + str(error))
return
# Clean up the temporary directory
finally:
if tempdir is not None:
tempdir.cleanup()
if __name__ == '__main__':
nemo_deploy(sys.argv[1:])
|