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build.py
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# SPDX-FileCopyrightText: Copyright (c) 2022-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# 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 copy
import os
import time
import traceback
from concurrent.futures import ProcessPoolExecutor, as_completed
from importlib.machinery import SourceFileLoader
from multiprocessing import get_context
from typing import Optional, Union
import torch
from tensorrt_llm.auto_parallel import infer_cluster_config
from tensorrt_llm.auto_parallel.cluster_info import cluster_infos
from tensorrt_llm.bindings import KVCacheType
from tensorrt_llm.builder import BuildConfig, Engine, build
from tensorrt_llm.logger import logger
from tensorrt_llm.lora_manager import LoraConfig, LoraManager
from tensorrt_llm.models import MODEL_MAP, PretrainedConfig
from tensorrt_llm.models.modeling_utils import SpeculativeDecodingMode
from tensorrt_llm.plugin import PluginConfig, add_plugin_argument
from tensorrt_llm.quantization.mode import QuantAlgo
def parse_arguments():
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--checkpoint_dir', type=str, default=None)
parser.add_argument('--model_config', type=str, default=None)
parser.add_argument('--build_config', type=str, default=None)
parser.add_argument('--model_cls_file', type=str, default=None)
parser.add_argument('--model_cls_name', type=str, default=None)
parser.add_argument(
'--input_timing_cache',
type=str,
default=None,
help=
'The path to read timing cache, will be ignored if the file does not exist'
)
parser.add_argument('--output_timing_cache',
type=str,
default='model.cache',
help='The path to write timing cache')
parser.add_argument('--log_level', type=str, default='info')
parser.add_argument(
'--profiling_verbosity',
type=str,
default='layer_names_only',
choices=['layer_names_only', 'detailed', 'none'],
help=
'The profiling verbosity for the generated TRT engine. Set to detailed can inspect tactic choices and kernel parameters.'
)
parser.add_argument('--enable_debug_output',
default=False,
action='store_true')
parser.add_argument(
'--output_dir',
type=str,
default='engine_outputs',
help='The path to save the serialized engine files and model configs')
parser.add_argument('--workers',
type=int,
default='1',
help='The number of workers for building in parallel')
parser.add_argument(
'--max_batch_size',
type=int,
default=256,
help="Max number of requests that the engine can handle.")
parser.add_argument('--max_input_len',
type=int,
default=1024,
help="Max input length of one request.")
parser.add_argument(
'--max_seq_len',
'--max_decoder_seq_len',
dest='max_seq_len',
type=int,
default=None,
help="Max total length of one request, including prompt and outputs. "
"If unspecified, will try to deduce from the model config.")
parser.add_argument('--max_beam_width', type=int, default=1)
parser.add_argument(
'--max_num_tokens',
type=int,
default=8192,
help="Max number of batched input tokens after padding is removed "
"(triggered by `--remove_input_padding`) in each batch.")
parser.add_argument(
'--opt_num_tokens',
type=int,
default=None,
help='It equals to max_batch_size*max_beam_width by default, set this '
'value as close as possible to the actual number of tokens on your workload. '
'Note that this argument might be removed in the future.')
parser.add_argument(
'--max_prompt_embedding_table_size',
'--max_multimodal_len',
type=int,
default=0,
help=
'Setting to a value > 0 enables support for prompt tuning or multimodal input.'
)
parser.add_argument(
'--kv_cache_type',
default=argparse.SUPPRESS,
type=KVCacheType,
help=
'Set KV cache type (continuous, paged, or disabled). For disabled case, KV cache is disabled and only context phase is allowed.'
)
parser.add_argument(
'--paged_kv_cache',
type=str,
default=argparse.SUPPRESS,
help=
'Deprecated. Set this option to enable is equvilient to `--kv_cache_type paged` for transformer based models.'
)
parser.add_argument(
'--gather_all_token_logits',
action='store_true',
default=False,
help='Enable both gather_context_logits and gather_generation_logits')
parser.add_argument('--gather_context_logits',
action='store_true',
default=False,
help='Gather context logits')
parser.add_argument('--gather_generation_logits',
action='store_true',
default=False,
help='Gather generation logits')
parser.add_argument('--builder_opt', type=int, default=None)
parser.add_argument('--builder_force_num_profiles', type=int, default=None)
parser.add_argument('--logits_dtype',
type=str,
default=None,
choices=['float16', 'float32'])
parser.add_argument('--weight_sparsity', default=False, action='store_true')
parser.add_argument(
'--max_draft_len',
type=int,
default=0,
help=
'Maximum lengths of draft tokens for speculative decoding target model.'
)
parser.add_argument(
'--lora_dir',
type=str,
default=None,
nargs="+",
help="The directory of LoRA weights. "
"Use config from the first directory if multiple directories are provided."
)
parser.add_argument('--lora_ckpt_source',
type=str,
default="hf",
choices=["hf", "nemo"],
help="The source of lora checkpoint.")
parser.add_argument(
'--lora_target_modules',
nargs='+',
default=None,
choices=LoraManager.LORA_MODULE_IDS.keys(),
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('--auto_parallel',
type=int,
default=1,
help='MPI world size for auto parallel.')
parser.add_argument(
'--gpus_per_node',
type=int,
default=8,
help=
'Number of GPUs each node has in a multi-node setup. This is a cluster spec and can be greater/smaller than world size'
)
parser.add_argument(
'--cluster_key',
type=str,
default=None,
choices=cluster_infos.keys(),
help=
'Unique name for target GPU type. Inferred from current GPU type if not specified.'
)
parser.add_argument(
'--strip_plan',
default=False,
action='store_true',
help=
'Whether to strip weights from the final TRT engine under the assumption that the refit weights will be identical to those provided at build time.'
)
parser.add_argument(
'--max_encoder_input_len',
type=int,
default=1024,
help=
'Specify max encoder input length when using enc-dec models. Set max_input_len to 1 to start generation from decoder_start_token_id of length 1.'
)
parser.add_argument(
'--visualize_network',
default=False,
action='store_true',
help=
'TRT Networks will be exported to ONNX prior to Engine build for debugging. '
)
parser.add_argument(
'--dry_run',
default=False,
action='store_true',
help=
'Run through the build process except the actual Engine build for debugging. '
)
parser.add_argument('--speculative_decoding_mode',
default=None,
choices=[
"draft_tokens_external",
"lookahead_decoding",
"medusa",
"explicit_draft_tokens",
],
help='Mode of speculative decoding.')
parser.add_argument(
'--weight_streaming',
default=False,
action='store_true',
help=
'Specify whether offloading weights to CPU and streaming loading at runtime.',
)
parser.add_argument(
'--fast_build',
default=False,
action='store_true',
help=
'Enable features for faster engine building. This may cause some performance degradation and is currently incompatible with int8/int4 quantization.',
)
plugin_config_parser = parser.add_argument_group("plugin_config")
add_plugin_argument(plugin_config_parser)
args = parser.parse_args()
if args.gather_all_token_logits:
args.gather_context_logits = True
args.gather_generation_logits = True
if args.gather_context_logits and args.max_draft_len > 0:
raise RuntimeError(
"Gather context logits is not support with draft len > 0. "
"If want to get the accepted tokens' logits from target model, please just enable gather_generation_logits"
)
return args
def build_model(
build_config: BuildConfig,
rank: int = 0,
ckpt_dir: str = None,
model_config: Union[str, PretrainedConfig] = None,
model_cls=None,
dry_run:
bool = False, # return the modified BuildConfig without actually building the engine
**kwargs
) -> Union[Engine, BuildConfig]:
model_config = copy.deepcopy(model_config)
logits_dtype = kwargs.get('logits_dtype')
if logits_dtype is not None:
model_config.logits_dtype = logits_dtype
architecture = model_config.architecture
assert not build_config.plugin_config.streamingllm or architecture == "LlamaForCausalLM", \
"StreamingLLM is only supported in the llama model."
real_rank = rank
if build_config.plugin_config.reduce_fusion and model_config.mapping.tp_size == 1:
build_config.plugin_config.reduce_fusion = False
model_config.mapping.gpus_per_node = build_config.auto_parallel_config.gpus_per_node
if build_config.auto_parallel_config.enabled:
assert rank < build_config.auto_parallel_config.world_size
assert model_config.mapping.pp_size == 1 and model_config.mapping.tp_size == 1, \
"You must convert to full model with TP=1&&PP=1 to use auto parallel planner"
#TODO: TRTLLM-193 remove this after the new build API for autopp is done
rank = 0 # This is a WAR to construct a whole model and load all the weights before auto parallel
else:
assert rank < model_config.mapping.world_size
rank_config = copy.deepcopy(model_config)
rank_config.set_rank(rank)
if model_cls is None:
assert architecture in MODEL_MAP, \
f"Unsupported model architecture: {architecture}"
model_cls = MODEL_MAP[architecture]
if ckpt_dir is None:
model = model_cls(rank_config)
else:
model = model_cls.from_checkpoint(ckpt_dir, config=rank_config)
is_checkpoint_pruned = getattr(rank_config, 'is_pruned', False)
if build_config.plugin_config.lora_plugin is not None:
lora_config = LoraConfig(lora_dir=kwargs['lora_dir'] or [],
lora_ckpt_source=kwargs['lora_ckpt_source'],
max_lora_rank=kwargs['max_lora_rank'])
if kwargs['lora_target_modules'] is not None:
# command line options is preferred over the modules in the lora dir
lora_config.lora_target_modules = kwargs['lora_target_modules']
build_config.lora_config = lora_config
build_config.use_fused_mlp = kwargs.get('use_fused_mlp', False)
# tells the low level build api to only build rank-th shard of the model
if build_config.auto_parallel_config.enabled:
model.config.mapping.rank = real_rank
if is_checkpoint_pruned or kwargs.pop('strip_plan', False):
build_config.use_strip_plan = True
build_config.use_refit = kwargs.get('refit', False)
if dry_run:
return build_config
return build(model, build_config)
def build_and_save(rank, gpu_id, ckpt_dir, build_config, output_dir, log_level,
model_config, model_cls, **kwargs):
torch.cuda.set_device(gpu_id)
logger.set_level(log_level)
engine = build_model(build_config,
rank,
ckpt_dir,
model_config,
model_cls=model_cls,
**kwargs)
assert engine is not None
engine.save(output_dir)
return True
def parallel_build(model_config: PretrainedConfig,
ckpt_dir: Optional[str],
build_config: BuildConfig,
output_dir: str,
workers: int = 1,
log_level: str = 'info',
model_cls=None,
**kwargs):
if build_config.auto_parallel_config.enabled:
if model_config.mapping.world_size > 1:
raise RuntimeError(
"manually TP and PP are not supported in auto parallel mode.")
if build_config.auto_parallel_config.debug_mode:
world_size = 1
else:
world_size = build_config.auto_parallel_config.world_size
else:
world_size = model_config.mapping.world_size
if workers == 1:
for rank in range(world_size):
passed = build_and_save(rank, rank % workers, ckpt_dir,
build_config, output_dir, log_level,
model_config, model_cls, **kwargs)
assert passed, "Engine building failed, please check error log."
else:
with ProcessPoolExecutor(mp_context=get_context('spawn'),
max_workers=workers) as p:
futures = [
p.submit(build_and_save, rank, rank % workers, ckpt_dir,
build_config, output_dir, log_level, model_config,
model_cls, **kwargs) for rank in range(world_size)
]
exceptions = []
for future in as_completed(futures):
try:
future.result()
except Exception as e:
traceback.print_exc()
exceptions.append(e)
assert len(exceptions
) == 0, "Engine building failed, please check error log."
def main():
args = parse_arguments()
logger.set_level(args.log_level)
tik = time.time()
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
model_cls = None
if args.model_cls_file is not None:
assert args.model_cls_name is not None
loader = SourceFileLoader('models', args.model_cls_file)
mod = loader.load_module()
model_cls = getattr(mod, args.model_cls_name)
workers = min(torch.cuda.device_count(), args.workers)
if hasattr(args, 'paged_kv_cache'):
logger.warning(
'Option --paged_kv_cache is deprecated, use --kv_cache_type=paged/disabled instead.'
)
plugin_config = PluginConfig.from_arguments(args)
if args.fast_build:
plugin_config.manage_weights = True
kwargs = {
'logits_dtype': args.logits_dtype,
'use_fused_mlp': args.use_fused_mlp,
'lora_dir': args.lora_dir,
'lora_ckpt_source': args.lora_ckpt_source,
'max_lora_rank': args.max_lora_rank,
'lora_target_modules': args.lora_target_modules,
'strip_plan': args.strip_plan,
'refit': False,
}
speculative_decoding_mode = SpeculativeDecodingMode.from_arguments(args)
ckpt_dir_or_model_config = args.checkpoint_dir if args.checkpoint_dir is not None else args.model_config
if ckpt_dir_or_model_config.lower().endswith('.json'):
config_path = ckpt_dir_or_model_config
ckpt_dir = None
else:
config_path = os.path.join(ckpt_dir_or_model_config, 'config.json')
ckpt_dir = ckpt_dir_or_model_config
model_config = PretrainedConfig.from_json_file(config_path)
# avoid ValueError if not supported quantization is chosen with use_fused_mlp
quant_algo = model_config.quantization.quant_algo
if quant_algo and quant_algo != QuantAlgo.FP8:
kwargs['use_fused_mlp'] = False
if args.build_config is None:
if args.multiple_profiles == "enable" and args.opt_num_tokens is not None:
raise RuntimeError(
"multiple_profiles is enabled, while opt_num_tokens is set. "
"They are not supposed to be working in the same time for now.")
if args.cluster_key is not None:
cluster_config = dict(cluster_key=args.cluster_key)
else:
cluster_config = infer_cluster_config()
build_config = BuildConfig.from_dict(
{
'max_input_len': args.max_input_len,
'max_seq_len': args.max_seq_len,
'max_batch_size': args.max_batch_size,
'max_beam_width': args.max_beam_width,
'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,
'gather_context_logits': args.gather_context_logits,
'gather_generation_logits': args.gather_generation_logits,
'strongly_typed': True,
'builder_opt': args.builder_opt,
'force_num_profiles': args.builder_force_num_profiles,
'weight_sparsity': args.weight_sparsity,
'profiling_verbosity': args.profiling_verbosity,
'enable_debug_output': args.enable_debug_output,
'max_draft_len': args.max_draft_len,
'speculative_decoding_mode': speculative_decoding_mode,
'input_timing_cache': args.input_timing_cache,
'output_timing_cache': args.output_timing_cache,
'auto_parallel_config': {
'world_size':
args.auto_parallel,
'gpus_per_node':
args.gpus_per_node,
'sharded_io_allowlist': [
'past_key_value_\\d+',
'present_key_value_\\d*',
],
'same_buffer_io': {
'past_key_value_(\\d+)': 'present_key_value_\\1',
},
**cluster_config,
},
'dry_run': args.dry_run,
'visualize_network': args.visualize_network,
'max_encoder_input_len': args.max_encoder_input_len,
'weight_streaming': args.weight_streaming,
},
plugin_config=plugin_config)
if hasattr(args, 'kv_cache_type'):
build_config.update_from_dict({'kv_cache_type': args.kv_cache_type})
else:
build_config = BuildConfig.from_json_file(args.build_config,
plugin_config=plugin_config)
parallel_build(model_config, ckpt_dir, build_config, args.output_dir,
workers, args.log_level, model_cls, **kwargs)
tok = time.time()
t = time.strftime('%H:%M:%S', time.gmtime(tok - tik))
logger.info(f'Total time of building all engines: {t}')
if __name__ == '__main__':
main()