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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 multiprocessing as mp
import os
import time
from collections import OrderedDict
# isort: off
import torch
import tensorrt as trt
# isort: on
from allowed_configs import (get_allowed_models, get_build_config,
get_model_family)
from base_benchmark import get_engine_name, serialize_engine
import tensorrt_llm
from tensorrt_llm._utils import str_dtype_to_trt
from tensorrt_llm.builder import Builder
from tensorrt_llm.functional import LayerNormPositionType, LayerNormType
from tensorrt_llm.layers import MoeConfig, PositionEmbeddingType
from tensorrt_llm.logger import logger
from tensorrt_llm.mapping import Mapping
from tensorrt_llm.models import PretrainedConfig, quantize_model
from tensorrt_llm.models.modeling_utils import optimize_model
from tensorrt_llm.network import net_guard
from tensorrt_llm.plugin.plugin import ContextFMHAType
from tensorrt_llm.quantization import QuantMode
def parse_arguments():
parser = argparse.ArgumentParser(description='Build TensorRT-LLM models.')
parser.add_argument('-m',
'--model',
type=str,
required=True,
choices=get_allowed_models(),
help='Specify model you want to build.')
parser.add_argument(
'--mode',
type=str,
default="plugin",
choices=['ootb', 'plugin', 'ootb-except-mha'],
help=
('Choose mode between ootb/plugin/ootb-except-mha. '
'\"ootb\" means the engines will be built without any plugins, '
'\"plugin\" means the engines will be built with tuned recipe of using plugins.'
'\"ootb-except-mha\" means the engines will be built with only attention plugins.'
))
parser.add_argument(
'--dtype',
type=str,
default='float16',
choices=['float16', 'bfloat16', 'float32'],
help='Choose data type between float16/bfloat16/float32.')
parser.add_argument(
'--quantization',
type=str,
default=None,
choices=[
'fp8', 'fp8_gemm', 'fp8_kv_cache', 'int8_sq_per_tensor',
'int8_sq_per_token_channel', 'int8_weight_only', 'int4_weight_only',
'int4_weight_only_awq', 'int4_weight_only_gptq'
],
help="Optimize the model with specified quantization recipe")
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(
'--log_level',
type=str,
default="error",
choices=['verbose', 'info', 'warning', 'error', 'internal_error'],
help=
'Choose log level between verbose/info/warning/error/internal_error.')
parser.add_argument(
'--output_dir',
type=str,
required=True,
help='TensorRT engines will be saved to the specified path.')
parser.add_argument(
'--max_beam_width',
type=int,
default=None,
help=
('If this option is specified, it will override the max beam width of '
'TRT engines to the specified value instead of using pre-defined one'))
parser.add_argument(
'--max_input_len',
type=int,
default=None,
help=
('If this option is specified, it will override the max input len of '
'TRT engines to the specified value instead of using pre-defined one'))
parser.add_argument(
'--max_output_len',
type=int,
default=None,
help=
('If this option is specified, it will override the max output len of '
'TRT engines to the specified value instead of using pre-defined one'))
parser.add_argument(
'--max_batch_size',
type=int,
default=None,
help=
('If this option is specified, it will override the max batch size of '
'TRT engines to the specified value instead of using pre-defined one'))
parser.add_argument('--force_num_layer_1',
default=False,
action='store_true',
help='Quick sanity check with num_layer=1.')
parser.add_argument('--serial_build',
default=False,
action='store_true',
help="Build engines serially")
parser.add_argument('--strongly_typed',
default=False,
action='store_true',
help='This option will reduce the building time.')
parser.add_argument(
'--rank',
type=int,
default=None,
help=
"The rank of the model to be built, only used when --serial_build is specified"
)
parser.add_argument(
'--world_size',
type=int,
default=None,
help=
"The number of gpus to be used for inference, only used when --serial_build is specified"
)
return parser.parse_args()
def get_quant_mode(quantization):
quant_mode = QuantMode(0)
use_smooth_quant = False
per_token = False
per_channel = False
weight_only_precision = 'int8'
if quantization == "fp8":
quant_mode = quant_mode.set_fp8_qdq()
quant_mode = quant_mode.set_fp8_kv_cache()
elif quantization == "fp8_gemm":
quant_mode = quant_mode.set_fp8_qdq()
elif quantization == "fp8_kv_cache":
quant_mode = quant_mode.set_fp8_kv_cache()
elif quantization == "int8_sq_per_tensor":
use_smooth_quant = True
quant_mode = QuantMode.use_smooth_quant(per_token, per_channel)
elif quantization == "int8_sq_per_token_channel":
use_smooth_quant = True
per_token = True
per_channel = True
quant_mode = QuantMode.use_smooth_quant(per_token, per_channel)
elif quantization == "int8_weight_only":
use_smooth_quant = False
weight_only_precision = 'int8'
quant_mode = QuantMode.use_weight_only(use_int4_weights=False)
elif quantization == "int4_weight_only":
weight_only_precision = 'int4'
quant_mode = QuantMode.use_weight_only(use_int4_weights=True)
elif quantization == "int4_weight_only_awq":
weight_only_precision = 'int4_awq'
quant_mode = QuantMode.from_description(quantize_weights=True,
quantize_activations=False,
per_token=False,
per_channel=False,
per_group=True,
use_int4_weights=True)
elif quantization == "int4_weight_only_gptq":
weight_only_precision = 'int4_gptq'
quant_mode = QuantMode.from_description(quantize_weights=True,
quantize_activations=False,
per_token=False,
per_channel=False,
per_group=True,
use_int4_weights=True)
elif quantization is None:
pass
else:
raise Exception(f'Unexpected quantization: {quantization}')
return quant_mode, use_smooth_quant, weight_only_precision
def get_quant_algo(quantization):
if quantization == "fp8":
return "FP8", "FP8"
elif quantization == "fp8_gemm":
return "FP8", None
elif quantization == "fp8_kv_cache":
return None, "FP8"
elif quantization == "int8_sq_per_tensor":
return "W8A8_SQ_PER_TENSOR_PLUGIN", None
elif quantization == "int8_sq_per_token_channel":
return "W8A8_SQ_PER_CHANNEL_PER_TOKEN_PLUGIN", None
elif quantization == "int8_weight_only":
return "W8A16", None
elif quantization == "int4_weight_only":
return "W4A16", None
elif quantization == "int4_weight_only_awq":
return "W4A16_AWQ", None
elif quantization == "int4_weight_only_gptq":
return "W4A16_GPTQ", None
elif quantization is None:
return None, None
def build_gpt(args):
build_config = get_build_config(args.model)
if args.force_num_layer_1:
build_config['num_layers'] = 1
# More parameters
if args.serial_build and args.rank is not None and args.world_size is not None:
runtime_rank = args.rank
world_size = args.world_size
else:
runtime_rank = tensorrt_llm.mpi_rank()
world_size = tensorrt_llm.mpi_world_size()
if not args.serial_build:
torch.cuda.set_device(runtime_rank)
strongly_typed = args.strongly_typed
if args.quantization is not None and "fp8" in args.quantization:
strongly_typed = True
num_kv_heads = build_config['num_heads'] \
if build_config['num_kv_heads'] is None else build_config['num_kv_heads']
apply_query_key_layer_scaling = False
max_batch_size = build_config['max_batch_size'] \
if args.max_batch_size is None else args.max_batch_size
max_input_len = build_config['max_input_len'] \
if args.max_input_len is None else args.max_input_len
max_output_len = build_config['max_output_len'] \
if args.max_output_len is None else args.max_output_len
max_beam_width = build_config['max_beam_width'] \
if args.max_beam_width is None else args.max_beam_width
quant_mode, use_smooth_quant, weight_only_precision = get_quant_mode(
args.quantization)
use_weight_only = quant_mode.is_weight_only()
builder = Builder()
builder_config = builder.create_builder_config(
name=args.model,
precision=args.dtype,
timing_cache=None,
profiling_verbosity=args.profiling_verbosity,
tensor_parallel=world_size, # TP only
parallel_build=True,
num_layers=build_config['num_layers'],
num_heads=build_config['num_heads'],
num_kv_heads=num_kv_heads,
hidden_size=build_config['hidden_size'],
vocab_size=build_config['vocab_size'],
hidden_act=build_config['hidden_act'],
max_position_embeddings=build_config['n_positions'],
apply_query_key_layer_scaling=apply_query_key_layer_scaling,
max_batch_size=max_batch_size,
max_input_len=max_input_len,
max_output_len=max_output_len,
int8=(quant_mode.has_act_and_weight_quant()
or quant_mode.is_int8_weight_only()),
quant_mode=quant_mode,
use_refit=False,
opt_level=build_config['builder_opt'],
strongly_typed=strongly_typed)
engine_name = get_engine_name(args.model, args.dtype, world_size,
runtime_rank)
kv_dtype = str_dtype_to_trt(args.dtype)
# Initialize Module
family = get_model_family(args.model)
if family == "gpt":
tensorrt_llm_model = tensorrt_llm.models.GPTLMHeadModel(
num_layers=build_config['num_layers'],
num_heads=build_config['num_heads'],
hidden_size=build_config['hidden_size'],
vocab_size=build_config['vocab_size'],
hidden_act=build_config['hidden_act'],
max_position_embeddings=build_config['n_positions'],
dtype=kv_dtype,
mapping=tensorrt_llm.Mapping(world_size=world_size,
tp_size=world_size), # TP only
apply_query_key_layer_scaling=builder_config.
apply_query_key_layer_scaling,
position_embedding_type=PositionEmbeddingType.learned_absolute
if build_config['position_embedding_type'] is None else
PositionEmbeddingType[build_config['position_embedding_type']],
rotary_embedding_percentage=build_config['rotary_pct'],
quant_mode=quant_mode,
bias=build_config['bias'],
moe_config=MoeConfig(build_config["moe_num_experts"],
build_config["moe_top_k"]))
elif family == "opt":
config = {
'architecture': 'OPTForCausalLM',
'dtype': args.dtype,
'vocab_size': build_config['vocab_size'],
'hidden_size': build_config['hidden_size'],
'num_hidden_layers': build_config['num_layers'],
'num_attention_heads': build_config['num_heads'],
'hidden_act': build_config['hidden_act'],
'max_position_embeddings': build_config['n_positions'],
'mapping': {
'world_size': world_size,
'tp_size': world_size
},
'use_parallel_embedding': False,
'share_embedding_table': False,
'embedding_sharding_dim': 0,
'do_layer_norm_before': build_config['do_layer_norm_before'],
'quantization': {
'group_size': 128
}
}
quant_algo, kv_cache_quant_algo = get_quant_algo(args.quantization)
config['quantization']['quant_algo'] = quant_algo
config['quantization']['kv_cache_quant_algo'] = kv_cache_quant_algo
config = PretrainedConfig.from_dict(config)
tensorrt_llm_model = tensorrt_llm.models.OPTForCausalLM(config)
elif family == "llama":
config = {
'architecture':
'LLaMAForCausalLM',
'dtype':
args.dtype,
'num_hidden_layers':
build_config['num_layers'],
'num_attention_heads':
build_config['num_heads'],
'num_key_value_heads':
build_config['num_heads'] if build_config['num_kv_heads'] is None
else build_config['num_kv_heads'],
'hidden_size':
build_config['hidden_size'],
'vocab_size':
build_config['vocab_size'],
'position_embedding_type':
'rope_gpt_neox',
'max_position_embeddings':
build_config['n_positions'],
'hidden_act':
build_config['hidden_act'],
'quantization': {
'group_size': 128
},
'mapping': {
'world_size': world_size,
'tp_size': world_size
},
'moe_num_experts':
build_config["moe_num_experts"],
'moe_top_k':
build_config["moe_top_k"],
}
quant_algo, kv_cache_quant_algo = get_quant_algo(args.quantization)
config['quantization']['quant_algo'] = quant_algo
config['quantization']['kv_cache_quant_algo'] = kv_cache_quant_algo
config = PretrainedConfig.from_dict(config)
tensorrt_llm_model = tensorrt_llm.models.LLaMAForCausalLM(config)
elif family == "gptj":
config = {
'architecture': 'GPTJForCausalLM',
'dtype': args.dtype,
'vocab_size': build_config['vocab_size'],
'hidden_size': build_config['hidden_size'],
'num_hidden_layers': build_config['num_layers'],
'num_attention_heads': build_config['num_heads'],
'hidden_act': build_config['hidden_act'],
'max_position_embeddings': build_config['n_positions'],
'rotary_dim': build_config['rotary_dim'],
'mapping': {
'world_size': world_size,
'tp_size': world_size
},
'use_parallel_embedding': False,
'share_embedding_table': False,
'embedding_sharding_dim': 0,
'do_layer_norm_before': build_config['do_layer_norm_before'],
'quantization': {
'group_size': 128
}
}
quant_algo, kv_cache_quant_algo = get_quant_algo(args.quantization)
config['quantization']['quant_algo'] = quant_algo
config['quantization']['kv_cache_quant_algo'] = kv_cache_quant_algo
config = PretrainedConfig.from_dict(config)
tensorrt_llm_model = tensorrt_llm.models.GPTJForCausalLM(config)
elif family == "gptneox":
config = {
'architecture':
'GPTNeoXForCausalLM',
'dtype':
args.dtype,
'num_hidden_layers':
build_config['num_layers'],
'num_attention_heads':
build_config['num_heads'],
'hidden_size':
build_config['hidden_size'],
'vocab_size':
build_config['vocab_size'],
'position_embedding_type':
'learned_absolute',
'max_position_embeddings':
build_config['n_positions'],
'rotary_emb_base':
10000,
'rotary_pct':
1.0 * build_config['rotary_dim'] * build_config['num_heads'] /
build_config['hidden_size'],
'hidden_act':
build_config['hidden_act'],
'mapping': {
'world_size': world_size,
'tp_size': world_size
},
'use_parallel_embedding':
False,
'share_embedding_table':
False,
'embedding_sharding_dim':
0,
'quantization': {
'group_size': 128,
}
}
quant_algo, kv_cache_quant_algo = get_quant_algo(args.quantization)
config['quantization']['quant_algo'] = quant_algo
config['quantization']['kv_cache_quant_algo'] = kv_cache_quant_algo
config = PretrainedConfig.from_dict(config)
tensorrt_llm_model = tensorrt_llm.models.GPTNeoXForCausalLM(config)
elif family == "chatglm":
quant_algo, kv_cache_quant_algo = get_quant_algo(args.quantization)
config = {
'architecture': 'ChatGLMForCausalLM',
'dtype': args.dtype,
'num_hidden_layers': build_config['num_layers'],
'num_attention_heads': build_config['num_heads'],
'num_key_value_heads': build_config['num_kv_heads'],
'hidden_size': build_config['hidden_size'],
'intermediate_size': build_config['inter_size'],
'norm_epsilon': 1e-5,
'vocab_size': build_config['vocab_size'],
'position_embedding_type': 'chatglm',
'max_position_embeddings': build_config['n_positions'],
'hidden_act': build_config['hidden_act'],
'quantization': {
'quant_algo': quant_algo,
'kv_cache_quant_algo': kv_cache_quant_algo
},
'mapping': {
'world_size': world_size,
'tp_size': world_size
},
'chatglm_version': 'chatglm',
'add_bias_linear': True,
'add_qkv_bias': True,
'apply_query_key_layer_scaling': False,
'apply_residual_connection_post_layernorm': False,
'rmsnorm': False,
'rope_ratio': 1.0,
}
config = PretrainedConfig.from_dict(config)
tensorrt_llm_model = tensorrt_llm.models.ChatGLMForCausalLM(config)
elif family in ["chatglm2", "chatglm3"]:
quant_algo, kv_cache_quant_algo = get_quant_algo(args.quantization)
config = {
'architecture': 'ChatGLMForCausalLM',
'dtype': args.dtype,
'num_hidden_layers': build_config['num_layers'],
'num_attention_heads': build_config['num_heads'],
'num_key_value_heads': build_config['num_kv_heads'],
'hidden_size': build_config['hidden_size'],
'intermediate_size': build_config['inter_size'],
'norm_epsilon': 1e-5,
'vocab_size': build_config['vocab_size'],
'position_embedding_type': 'rope_gptj',
'max_position_embeddings': build_config['n_positions'],
'hidden_act': build_config['hidden_act'],
'quantization': {
'quant_algo': quant_algo,
'kv_cache_quant_algo': kv_cache_quant_algo
},
'mapping': {
'world_size': world_size,
'tp_size': world_size
},
'chatglm_version': family,
'add_bias_linear': False,
'add_qkv_bias': True,
'apply_query_key_layer_scaling': False,
'apply_residual_connection_post_layernorm': False,
'rmsnorm': True,
'rope_ratio': 1.0,
}
config = PretrainedConfig.from_dict(config)
tensorrt_llm_model = tensorrt_llm.models.ChatGLMForCausalLM(config)
elif family == "bloom":
config = {
'architecture': 'BloomForCausalLM',
'dtype': args.dtype,
'vocab_size': build_config['vocab_size'],
'hidden_size': build_config['hidden_size'],
'num_hidden_layers': build_config['num_layers'],
'num_attention_heads': build_config['num_heads'],
'hidden_act': build_config['hidden_act'],
'max_position_embeddings': build_config['n_positions'],
'mapping': {
'world_size': world_size,
'tp_size': world_size
},
'use_parallel_embedding': (args.model == 'bloom_176b'),
'share_embedding_table': False,
'embedding_sharding_dim': 0,
'quantization': {
'group_size': 128
}
}
quant_algo, kv_cache_quant_algo = get_quant_algo(args.quantization)
config['quantization']['quant_algo'] = quant_algo
config['quantization']['kv_cache_quant_algo'] = kv_cache_quant_algo
config = PretrainedConfig.from_dict(config)
tensorrt_llm_model = tensorrt_llm.models.BloomForCausalLM(config)
elif family == "falcon":
config = {
'architecture':
'FalconForCausalLM',
'dtype':
args.dtype,
'num_hidden_layers':
build_config['num_layers'],
'num_attention_heads':
build_config['num_heads'],
'num_key_value_heads':
build_config['num_heads'] if build_config['num_kv_heads'] is None
else build_config['num_kv_heads'],
'hidden_size':
build_config['hidden_size'],
'vocab_size':
build_config['vocab_size'],
'position_embedding_type':
'alibi_with_scale'
if build_config['use_alibi'] else 'rope_gpt_neox',
'max_position_embeddings':
build_config['n_positions'],
'hidden_act':
build_config['hidden_act'],
'quantization': {
'group_size': 128
},
'mapping': {
'world_size': world_size,
'tp_size': world_size
},
'bias':
build_config['bias'],
'parallel_attention':
build_config['parallel_attention'],
'new_decoder_architecture':
build_config['new_decoder_architecture'],
}
quant_algo, kv_cache_quant_algo = get_quant_algo(args.quantization)
config['quantization']['quant_algo'] = quant_algo
config['quantization']['kv_cache_quant_algo'] = kv_cache_quant_algo
if quant_mode.is_weight_only() and quant_mode.has_per_group_scaling():
config['quantization'].update({
'has_zero_point': False,
'pre_quant_scale': True,
'exclude_modules': [],
})
config = PretrainedConfig.from_dict(config)
tensorrt_llm_model = tensorrt_llm.models.FalconForCausalLM(config)
elif family == "baichuan_7b":
tensorrt_llm_model = tensorrt_llm.models.BaichuanForCausalLM(
num_layers=build_config['num_layers'],
num_heads=build_config['num_heads'],
num_kv_heads=None,
hidden_size=build_config['hidden_size'],
vocab_size=build_config['vocab_size'],
hidden_act=build_config['hidden_act'],
max_position_embeddings=build_config['n_positions'],
position_embedding_type=PositionEmbeddingType.rope_gpt_neox,
dtype=kv_dtype,
mlp_hidden_size=build_config['inter_size'],
mapping=tensorrt_llm.Mapping(world_size=world_size,
tp_size=world_size),
quant_mode=quant_mode)
elif family == "baichuan_13b":
tensorrt_llm_model = tensorrt_llm.models.BaichuanForCausalLM(
num_layers=build_config['num_layers'],
num_heads=build_config['num_heads'],
num_kv_heads=None,
hidden_size=build_config['hidden_size'],
vocab_size=build_config['vocab_size'],
hidden_act=build_config['hidden_act'],
max_position_embeddings=build_config['n_positions'],
position_embedding_type=PositionEmbeddingType.alibi,
dtype=kv_dtype,
mlp_hidden_size=build_config['inter_size'],
mapping=tensorrt_llm.Mapping(world_size=world_size,
tp_size=world_size),
quant_mode=quant_mode)
elif family == "internlm":
config = {
'architecture':
'LLaMAForCausalLM',
'dtype':
args.dtype,
'num_hidden_layers':
build_config['num_layers'],
'num_attention_heads':
build_config['num_heads'],
'num_key_value_heads':
build_config['num_heads'] if build_config['num_kv_heads'] is None
else build_config['num_kv_heads'],
'hidden_size':
build_config['hidden_size'],
'vocab_size':
build_config['vocab_size'],
'position_embedding_type':
'rope_gpt_neox',
'max_position_embeddings':
build_config['n_positions'],
'hidden_act':
build_config['hidden_act'],
'quantization':
quant_mode.to_dict(),
'mapping': {
'world_size': world_size,
'tp_size': world_size
},
'attn_bias':
build_config['bias'],
}
if quant_mode.is_weight_only():
if weight_only_precision == 'int4_awq':
config['quantization'].update({
"group_size": 128,
"has_zero_point": False,
"pre_quant_scale": True,
"exclude_modules": [],
})
elif weight_only_precision == 'int4_gptq':
config['quantization'].update({
"group_size": 128,
"has_zero_point": True,
"pre_quant_scale": False,
})
config = PretrainedConfig.from_dict(config)
tensorrt_llm_model = tensorrt_llm.models.LLaMAForCausalLM(config)
elif family == "qwen":
tensorrt_llm_model = tensorrt_llm.models.QWenForCausalLM(
num_layers=build_config['num_layers'],
num_heads=build_config['num_heads'],
num_kv_heads=num_kv_heads,
hidden_size=build_config['hidden_size'],
seq_length=2048,
vocab_size=build_config['vocab_size'],
hidden_act=build_config['hidden_act'],
max_position_embeddings=build_config['n_positions'],
dtype=kv_dtype,
mlp_hidden_size=build_config['inter_size'],
neox_rotary_style=True,
mapping=tensorrt_llm.Mapping(world_size=world_size,
tp_size=world_size), # TP only
use_parallel_embedding=False,
embedding_sharding_dim=1,
quant_mode=quant_mode)
elif family == "mamba":
config = {
'architecture': 'MambaLMHeadModel',
'dtype': args.dtype,
'vocab_size': build_config['vocab_size'],
'hidden_size': build_config['hidden_size'],
'num_hidden_layers': build_config['num_layers'],
'num_attention_heads': build_config['num_heads'],
'hidden_act': build_config['hidden_act'],
"ssm_cfg": {},
"rms_norm": True,
"residual_in_fp32": True,
"pad_vocab_size_multiple": 8,
}
config = PretrainedConfig.from_dict(config)
tensorrt_llm_model = tensorrt_llm.models.MambaLMHeadModel(config)
else:
raise Exception(f'Unexpected model: {args.model}')
quant_kwargs = {}
if family not in ['opt', 'bloom', 'falcon', 'llama', 'gptj', 'internlm']:
tensorrt_llm_model = quantize_model(tensorrt_llm_model, quant_mode,
**quant_kwargs)
if family in ['llama']:
tensorrt_llm_model = optimize_model(tensorrt_llm_model,
use_fused_mlp=True)
# Module -> Network
network = builder.create_network()
network.trt_network.name = engine_name
network.plugin_config.to_legacy_setting()
# Plugins
if args.mode == 'plugin':
network.plugin_config.set_gpt_attention_plugin(dtype=args.dtype)
network.plugin_config.set_context_fmha(ContextFMHAType.enabled)
network.plugin_config.enable_remove_input_padding()
network.plugin_config.set_lookup_plugin(dtype=args.dtype)
if args.quantization is None or "fp8" not in args.quantization:
network.plugin_config.set_gemm_plugin(dtype=args.dtype)
# Quantization plugins.
if use_smooth_quant:
network.plugin_config.set_smooth_quant_gemm_plugin(dtype=args.dtype)
network.plugin_config.set_layernorm_quantization_plugin(
dtype=args.dtype)
network.plugin_config.set_quantize_tensor_plugin()
network.plugin_config.set_quantize_per_token_plugin()
elif use_weight_only:
network.plugin_config.set_weight_only_quant_matmul_plugin(
dtype=args.dtype)
elif family == "llama" and quant_mode.has_act_and_weight_quant():
# RMS norm plugin for SmoothQuant
network.plugin_config.set_rmsnorm_quantization_plugin(
dtype=args.dtype)
elif args.mode == 'ootb-except-mha':
network.plugin_config.set_gpt_attention_plugin(dtype=args.dtype)
network.plugin_config.set_context_fmha(ContextFMHAType.enabled)
if world_size > 1:
network.plugin_config.set_nccl_plugin(
dtype=args.dtype,
use_custom_all_reduce=build_config["use_custom_all_reduce"])
with net_guard(network):
# Prepare
network.set_named_parameters(tensorrt_llm_model.named_parameters())
# Forward
inputs = tensorrt_llm_model.prepare_inputs(
max_batch_size=max_batch_size,
max_input_len=max_input_len,
max_seq_len=max_input_len + max_output_len,
use_cache=True,
max_beam_width=max_beam_width)
if family in [
'opt', 'bloom', 'falcon', 'llama', 'internlm', 'gptneox',
'gptj', "mamba"
]:
tensorrt_llm_model(**inputs)
else:
tensorrt_llm_model(*inputs)
if args.mode == 'plugin':
tensorrt_llm.graph_rewriting.optimize(network)
# Network -> Engine
start = time.time()
engine = builder.build_engine(network, builder_config)
assert engine is not None, f'Failed to build engine for rank {runtime_rank}'
build_time = round(time.time() - start, 2)
if args.output_dir is not None:
os.makedirs(args.output_dir, exist_ok=True)
serialize_path = os.path.join(args.output_dir, engine_name)
serialize_engine(engine, serialize_path)
if runtime_rank == 0:
config_path = os.path.join(args.output_dir, 'config.json')
builder_config.plugin_config = network.plugin_config
builder.save_config(builder_config, config_path)
return engine, build_time
def build_bert(args):
family = get_model_family(args.model)
build_config = get_build_config(args.model)
if args.force_num_layer_1:
build_config['num_layers'] = 1
# More parameters
if args.serial_build and args.rank is not None and args.world_size is not None:
runtime_rank = args.rank
world_size = args.world_size
else:
runtime_rank = tensorrt_llm.mpi_rank()
world_size = tensorrt_llm.mpi_world_size()
if not args.serial_build:
torch.cuda.set_device(runtime_rank)
num_kv_heads = build_config['num_heads'] \
if build_config['num_kv_heads'] is None else build_config['num_kv_heads']
max_batch_size = build_config['max_batch_size'] \
if args.max_batch_size is None else args.max_batch_size
max_input_len = build_config['max_input_len'] \
if args.max_input_len is None else args.max_input_len
bs_range = [1, (max_batch_size + 1) // 2, max_batch_size]
inlen_range = [1, (max_input_len + 1) // 2, max_input_len]
builder = Builder()
builder_config = builder.create_builder_config(
name=args.model,
precision=args.dtype,
timing_cache=None,
profiling_verbosity=args.profiling_verbosity,
tensor_parallel=world_size, # TP only
parallel_build=True,
num_layers=build_config['num_layers'],
num_heads=build_config['num_heads'],
num_kv_heads=num_kv_heads,
hidden_size=build_config['hidden_size'],
vocab_size=build_config['vocab_size'],
hidden_act=build_config['hidden_act'],
max_position_embeddings=build_config['n_positions'],
max_batch_size=max_batch_size,
max_input_len=max_input_len,
opt_level=build_config['builder_opt'])
engine_name = get_engine_name(args.model, args.dtype, world_size,
runtime_rank)
# Initialize model
tensorrt_llm_bert = tensorrt_llm.models.BertModel(
num_layers=build_config['num_layers'],
num_heads=build_config['num_heads'],
hidden_size=build_config['hidden_size'],
vocab_size=build_config['vocab_size'],
hidden_act=build_config['hidden_act'],
max_position_embeddings=build_config['n_positions'],
type_vocab_size=build_config['type_vocab_size'],
pad_token_id=None
if family == 'bert' else 1, # hard code for RoBERTa here
is_roberta=(family == 'roberta'),
mapping=tensorrt_llm.Mapping(world_size=world_size, tp_size=world_size),
dtype=str_dtype_to_trt(args.dtype))
# Module -> Network
network = builder.create_network()
network.trt_network.name = engine_name
network.plugin_config.to_legacy_setting()
# Plugins
if args.mode == 'plugin':
network.plugin_config.set_bert_attention_plugin(dtype=args.dtype)
network.plugin_config.set_gemm_plugin(dtype=args.dtype)
network.plugin_config.enable_qk_half_accum()
network.plugin_config.set_context_fmha(ContextFMHAType.enabled)
elif args.mode == 'ootb-except-mha':
network.plugin_config.set_bert_attention_plugin(dtype=args.dtype)
network.plugin_config.set_context_fmha(ContextFMHAType.enabled)
if world_size > 1:
network.plugin_config.set_nccl_plugin(
dtype=args.dtype,
use_custom_all_reduce=build_config["use_custom_all_reduce"])
with net_guard(network):
# Prepare
network.set_named_parameters(tensorrt_llm_bert.named_parameters())
# Forward
input_ids = tensorrt_llm.Tensor(
name='input_ids',
dtype=trt.int32,
shape=[-1, -1],
dim_range=OrderedDict([('batch_size', [bs_range]),
('input_len', [inlen_range])]),
)
input_lengths = tensorrt_llm.Tensor(name='input_lengths',
dtype=trt.int32,
shape=[-1],
dim_range=OrderedDict([
('batch_size', [bs_range])
]))
hidden_states = tensorrt_llm_bert(input_ids=input_ids,
input_lengths=input_lengths)
# Mark outputs
hidden_states_dtype = str_dtype_to_trt(args.dtype)
hidden_states.mark_output('hidden_states', hidden_states_dtype)
# Network -> Engine
start = time.time()
engine = builder.build_engine(network, builder_config)
assert engine is not None, f'Failed to build engine for rank {runtime_rank}'
build_time = round(time.time() - start, 2)
if args.output_dir is not None:
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
serialize_path = os.path.join(args.output_dir, engine_name)
serialize_engine(engine, serialize_path)
if runtime_rank == 0:
config_path = os.path.join(args.output_dir, 'config.json')
builder_config.plugin_config = network.plugin_config
builder.save_config(builder_config, config_path)
return engine, build_time
def enc_dec_build_helper(component, config, args):
# More parameters
if args.serial_build and args.rank is not None and args.world_size is not None:
runtime_rank = args.rank
world_size = args.world_size
else:
runtime_rank = tensorrt_llm.mpi_rank()
world_size = tensorrt_llm.mpi_world_size()
if not args.serial_build:
torch.cuda.set_device(runtime_rank)
family = get_model_family(args.model)
if family == 'bart':
q_scaling = 1.0
has_attention_qkvo_bias = True
has_mlp_bias = True
has_model_final_layernorm = False
has_position_embedding = True
has_embedding_layernorm = True
layernorm_type = LayerNormType.LayerNorm
relative_attention = False
layernorm_position = LayerNormPositionType.pre_layernorm if config.get(
'normalize_before', True) else LayerNormPositionType.post_layernorm
rescale_before_lm_head = False
else:
q_scaling = 1 / config['head_size']**.5
has_attention_qkvo_bias = False
has_mlp_bias = False
has_model_final_layernorm = True
has_position_embedding = False
has_embedding_layernorm = False
layernorm_type = LayerNormType.RmsNorm
relative_attention = True
layernorm_position = LayerNormPositionType.pre_layernorm
if family == 't5':
rescale_before_lm_head = True
else:
rescale_before_lm_head = False
builder = Builder()
builder_config = builder.create_builder_config(
name=args.model,
precision=args.dtype,
timing_cache=None,
profiling_verbosity='layer_names_only', # by default
tensor_parallel=world_size, # TP only
parallel_build=True,
num_layers=config['num_layers'],
num_heads=config['num_heads'],
hidden_size=config['hidden_size'],
head_size=config['head_size'],
vocab_size=config['vocab_size'],
hidden_act=config['hidden_act'],
max_position_embeddings=config['n_positions'],
apply_query_key_layer_scaling=False, # by default, hardcoded
max_batch_size=config['max_batch_size'],
max_beam_width=config['max_beam_width'],
max_decoder_input_len=config['max_decoder_input_len'],
max_output_len=config['max_output_len'],
max_encoder_input_len=config['max_encoder_input_len'],
opt_level=config['builder_opt'],