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# Copyright (c) 2025, 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 pprint
from typing import Optional
from nemo.export.tensorrt_llm import TensorRTLLM
LOGGER = logging.getLogger("NeMo")
def get_args():
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
description="Exports NeMo checkpoint to TensorRT-LLM engine",
)
parser.add_argument("-nc", "--nemo_checkpoint", required=True, type=str, help="Source model path")
parser.add_argument("-mt", "--model_type", type=str, help="Type of the TensorRT-LLM model.")
parser.add_argument(
"-mr", "--model_repository", required=True, default=None, type=str, help="Folder for the trt-llm model files"
)
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(
"-dt",
"--dtype",
choices=["bfloat16", "float16"],
help="Data type of the model on TensorRT-LLM",
)
parser.add_argument("-mil", "--max_input_len", default=256, type=int, help="Max input length of the model")
parser.add_argument("-mol", "--max_output_len", default=256, type=int, help="Max output 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("-mnt", "--max_num_tokens", default=None, type=int, help="Max number of tokens")
parser.add_argument("-ont", "--opt_num_tokens", default=None, type=int, help="Optimum number of tokens")
parser.add_argument(
"-mpet", "--max_prompt_embedding_table_size", default=None, type=int, help="Max prompt embedding table size"
)
parser.add_argument(
"-upe",
"--use_parallel_embedding",
default=False,
action='store_true',
help="Use parallel embedding.",
)
parser.add_argument(
"-npkc", "--no_paged_kv_cache", default=False, action='store_true', help="Disable paged kv cache."
)
parser.add_argument(
"-drip",
"--disable_remove_input_padding",
default=False,
action='store_true',
help="Disables the remove input padding option.",
)
parser.add_argument(
"-mbm",
'--multi_block_mode',
default=False,
action='store_true',
help='Split long kv sequence into multiple blocks (applied to generation MHA kernels). \
It is beneifical when batchxnum_heads cannot fully utilize GPU. \
available when using c++ runtime.',
)
parser.add_argument(
'--use_lora_plugin',
nargs='?',
const=None,
choices=['float16', 'float32', 'bfloat16'],
help="Activates the lora plugin which enables embedding sharing.",
)
parser.add_argument(
'--lora_target_modules',
nargs='+',
default=None,
choices=[
"attn_qkv",
"attn_q",
"attn_k",
"attn_v",
"attn_dense",
"mlp_h_to_4h",
"mlp_gate",
"mlp_4h_to_h",
],
help="Add lora in which modules. Only be activated when use_lora_plugin is enabled.",
)
parser.add_argument(
'--max_lora_rank',
type=int,
default=64,
help='maximum lora rank for different lora modules. '
'It is used to compute the workspace size of lora plugin.',
)
parser.add_argument("-dm", "--debug_mode", default=False, action='store_true', help="Enable debug mode")
parser.add_argument(
"--use_mcore_path",
action="store_true",
help="Use Megatron-Core implementation on exporting the model. If not set, use local NeMo codebase",
)
parser.add_argument(
"-fp8",
"--export_fp8_quantized",
default="auto",
type=str,
help="Enables exporting to a FP8-quantized TRT LLM checkpoint",
)
parser.add_argument(
"-kv_fp8",
"--use_fp8_kv_cache",
default="auto",
type=str,
help="Enables exporting with FP8-quantizatized KV-cache",
)
args = parser.parse_args()
def str_to_bool(name: str, s: str, optional: bool = False) -> Optional[bool]:
s = s.lower()
true_strings = ["true", "1"]
false_strings = ["false", "0"]
if s in true_strings:
return True
if s in false_strings:
return False
if optional and s == 'auto':
return None
raise argparse.ArgumentTypeError(f"Invalid boolean value for argument --{name}: '{s}'")
args.export_fp8_quantized = str_to_bool("export_fp8_quantized", args.export_fp8_quantized, optional=True)
args.use_fp8_kv_cache = str_to_bool("use_fp8_kv_cache", args.use_fp8_kv_cache, optional=True)
return args
def nemo_export_trt_llm():
args = get_args()
loglevel = logging.DEBUG if args.debug_mode else logging.INFO
LOGGER.setLevel(loglevel)
LOGGER.info(f"Logging level set to {loglevel}")
LOGGER.info(pprint.pformat(vars(args)))
trt_llm_exporter = TensorRTLLM(
model_dir=args.model_repository, load_model=False, multi_block_mode=args.multi_block_mode
)
LOGGER.info("Export to TensorRT-LLM function is called.")
trt_llm_exporter.export(
nemo_checkpoint_path=args.nemo_checkpoint,
model_type=args.model_type,
tensor_parallelism_size=args.tensor_parallelism_size,
pipeline_parallelism_size=args.pipeline_parallelism_size,
max_input_len=args.max_input_len,
max_output_len=args.max_output_len,
max_batch_size=args.max_batch_size,
max_num_tokens=args.max_num_tokens,
opt_num_tokens=args.opt_num_tokens,
max_prompt_embedding_table_size=args.max_prompt_embedding_table_size,
use_parallel_embedding=args.use_parallel_embedding,
paged_kv_cache=not args.no_paged_kv_cache,
remove_input_padding=not args.disable_remove_input_padding,
dtype=args.dtype,
use_lora_plugin=args.use_lora_plugin,
lora_target_modules=args.lora_target_modules,
max_lora_rank=args.max_lora_rank,
fp8_quantized=args.export_fp8_quantized,
fp8_kvcache=args.use_fp8_kv_cache,
load_model=False,
use_mcore_path=args.use_mcore_path,
)
LOGGER.info("Export is successful.")
if __name__ == '__main__':
nemo_export_trt_llm()
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