# 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.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(False) elif quantization == "int4_weight_only": weight_only_precision = 'int4' quant_mode = QuantMode.use_weight_only(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 == None: pass else: raise Exception(f'Unexpected quantization: {quantization}') return quant_mode, use_smooth_quant, weight_only_precision 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': { 'use_smooth_quant': quant_mode.has_act_and_weight_quant(), 'per_channel': quant_mode.has_per_channel_scaling(), 'per_token': quant_mode.has_per_token_dynamic_scaling(), 'per_group': quant_mode.has_per_group_scaling(), 'group_size': 128, 'int8_kv_cache': quant_mode.has_int8_kv_cache(), 'enable_fp8': quant_mode.has_fp8_qdq(), 'fp8_kv_cache': quant_mode.has_fp8_kv_cache(), 'use_weight_only': quant_mode.is_weight_only(), 'weight_only_precision': 'int8' if quant_mode.is_int8_weight_only() else 'int4', } } config = PretrainedConfig.from_dict(config) tensorrt_llm_model = tensorrt_llm.models.OPTForCausalLM(config) elif family == "llama": tensorrt_llm_model = tensorrt_llm.models.LLaMAForCausalLM( 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'], dtype=kv_dtype, mlp_hidden_size=build_config['inter_size'], mapping=tensorrt_llm.Mapping(world_size=world_size, tp_size=world_size), # TP only quant_mode=quant_mode, use_fused_mlp=True, moe_config=MoeConfig(build_config["moe_num_experts"], build_config["moe_top_k"])) elif family == "gptj": tensorrt_llm_model = tensorrt_llm.models.GPTJForCausalLM( 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'], rotary_dim=build_config['rotary_dim'], dtype=kv_dtype, mapping=tensorrt_llm.Mapping(world_size=world_size, tp_size=world_size), # TP only quant_mode=quant_mode) elif family == "gptneox": tensorrt_llm_model = tensorrt_llm.models.GPTNeoXForCausalLM( 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'], rotary_dim=build_config['rotary_dim'], 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) elif family == "chatglm": tensorrt_llm_model = tensorrt_llm.models.ChatGLMHeadModel( 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, quant_mode=quant_mode, model_name="chatglm_6b") elif family == "chatglm2": tensorrt_llm_model = tensorrt_llm.models.ChatGLMHeadModel( 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, quant_mode=quant_mode, model_name="chatglm2_6b") elif family == "chatglm3": tensorrt_llm_model = tensorrt_llm.models.ChatGLMHeadModel( 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, quant_mode=quant_mode, model_name="chatglm3_6b") 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': { 'use_smooth_quant': quant_mode.has_act_and_weight_quant(), 'per_channel': quant_mode.has_per_channel_scaling(), 'per_token': quant_mode.has_per_token_dynamic_scaling(), 'per_group': quant_mode.has_per_group_scaling(), 'group_size': 128, 'int8_kv_cache': quant_mode.has_int8_kv_cache(), 'enable_fp8': quant_mode.has_fp8_qdq(), 'fp8_kv_cache': quant_mode.has_fp8_kv_cache(), 'use_weight_only': quant_mode.is_weight_only(), 'weight_only_precision': 'int8' if quant_mode.is_int8_weight_only() else 'int4', } } 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': { 'use_smooth_quant': quant_mode.has_act_and_weight_quant(), 'per_channel': quant_mode.has_per_channel_scaling(), 'per_token': quant_mode.has_per_token_dynamic_scaling(), 'per_group': quant_mode.has_per_group_scaling(), 'group_size': 128, 'int8_kv_cache': quant_mode.has_int8_kv_cache(), 'enable_fp8': quant_mode.has_fp8_qdq(), 'fp8_kv_cache': quant_mode.has_fp8_kv_cache(), 'use_weight_only': quant_mode.is_weight_only(), 'weight_only_precision': 'int8' if quant_mode.is_int8_weight_only() else 'int4', }, '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'], } if quant_mode.is_weight_only() and quant_mode.has_per_group_scaling(): config['quantization'].update({ 'zero': 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": tensorrt_llm_model = tensorrt_llm.models.LLaMAForCausalLM( 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'], dtype=kv_dtype, mlp_hidden_size=build_config['inter_size'], mapping=tensorrt_llm.Mapping(world_size=world_size, tp_size=world_size), # TP only quant_mode=quant_mode, embedding_sharding_dim=1, use_fused_mlp=False, attn_bias=build_config['bias']) 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) else: raise Exception(f'Unexpected model: {args.model}') quant_kwargs = {} if family == "llama" and use_weight_only: if weight_only_precision == 'int4_awq': quant_kwargs = { "group_size": 128, "zero": False, "pre_quant_scale": True, "exclude_modules": [], } elif weight_only_precision == 'int4_gptq': quant_kwargs = { "group_size": 128, "zero": True, "pre_quant_scale": False, } if family not in ['opt', 'bloom', 'falcon']: tensorrt_llm_model = quantize_model(tensorrt_llm_model, quant_mode, **quant_kwargs) # Module -> Network network = builder.create_network() network.trt_network.name = engine_name # 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']: 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): 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'], mapping=tensorrt_llm.Mapping(world_size=world_size, tp_size=world_size)) # Module -> Network network = builder.create_network() network.trt_network.name = engine_name # 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'], cross_attention=(component == 'decoder'), has_position_embedding=has_position_embedding, has_token_type_embedding=False, # by default strongly_typed=False, # by default gather_all_token_logits=False, # by default ) # build engine dtype = str_dtype_to_trt(args.dtype) mapping = Mapping(world_size=world_size, rank=runtime_rank, tp_size=world_size, pp_size=1) # TP only if component == 'encoder': tllm_model = tensorrt_llm.models.EncoderModel( num_layers=config['num_layers'], num_heads=config['num_heads'], num_kv_heads=config['num_heads'], head_size=config['head_size'], hidden_size=config['hidden_size'], ffn_hidden_size=config['ffn_hidden_size'], vocab_size=config['vocab_size'], max_position_embeddings=config.get('n_positions', 0), has_position_embedding=has_position_embedding, relative_attention=relative_attention, max_distance=config.get('max_distance', 0), num_buckets=config.get('num_buckets', 0), has_embedding_layernorm=has_embedding_layernorm, has_embedding_scale=config.get('has_embedding_scale', False), q_scaling=q_scaling, has_attention_qkvo_bias=has_attention_qkvo_bias, has_mlp_bias=has_mlp_bias, has_model_final_layernorm=has_model_final_layernorm, layernorm_eps=1e-6, layernorm_position=layernorm_position, layernorm_type=layernorm_type, hidden_act=config['hidden_act'], dtype=dtype, use_parallel_embedding=False, # by default embedding_sharding_dim=0, # by default mapping=mapping) elif component == 'decoder': tllm_model = tensorrt_llm.models.DecoderModel( num_layers=config['num_layers'], num_heads=config['num_heads'], num_kv_heads=config['num_heads'], head_size=config['head_size'], hidden_size=config['hidden_size'], ffn_hidden_size=config['ffn_hidden_size'], encoder_hidden_size=config['hidden_size'], encoder_num_heads=config['num_heads'], encoder_head_size=config['head_size'], vocab_size=config['vocab_size'], max_position_embeddings=config.get('n_positions', 0), has_position_embedding=has_position_embedding, relative_attention=relative_attention, max_distance=config.get('max_distance', 0), num_buckets=config.get('num_buckets', 0), has_embedding_layernorm=has_embedding_layernorm, has_embedding_scale=config.get('has_embedding_scale', False), q_scaling=q_scaling, has_attention_qkvo_bias=has_attention_qkvo_bias, has_mlp_bias=has_mlp_bias, has_model_final_layernorm=has_model_final_layernorm, layernorm_eps=1e-6, layernorm_position=layernorm_position, layernorm_type=layernorm_type, hidden_act=config['hidden_act'], dtype=dtype, use_parallel_embedding=False, # by default embedding_sharding_dim=0, # by default mapping=mapping, rescale_before_lm_head=rescale_before_lm_head, logits_dtype='float32') # by default # Module -> Network engine_name = get_engine_name(args.model, args.dtype, world_size, runtime_rank) network = builder.create_network() network.trt_network.name = engine_name # 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.set_gpt_attention_plugin(dtype=args.dtype) if world_size > 1: network.plugin_config.set_nccl_plugin( dtype=args.dtype, use_custom_all_reduce=False) # by default with net_guard(network): # Prepare network.set_named_parameters(tllm_model.named_parameters()) # Forward if component == 'encoder': inputs = tllm_model.prepare_inputs( max_batch_size=config['max_batch_size'], max_input_len=config['max_encoder_input_len'], ) elif component == 'decoder': inputs = tllm_model.prepare_inputs( max_batch_size=config['max_batch_size'], max_beam_width=config['max_beam_width'], max_decoder_input_len=config['max_decoder_input_len'], max_new_tokens=config['max_output_len'], max_encoder_input_len=config['max_encoder_input_len'], ) tllm_model(*inputs) 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) # Get model config num_heads = config['num_heads'] assert (num_heads % world_size) == 0 num_heads = num_heads // world_size hidden_size = config['hidden_size'] // world_size model_config = tensorrt_llm.runtime.ModelConfig( num_heads=num_heads, num_kv_heads=num_heads, hidden_size=hidden_size, head_size=builder_config.head_size, vocab_size=builder_config.vocab_size, num_layers=builder_config.num_layers, gpt_attention_plugin=network.plugin_config.gpt_attention_plugin, remove_input_padding=network.plugin_config.remove_input_padding, cross_attention=builder_config.cross_attention, has_position_embedding=builder_config.has_position_embedding, has_token_type_embedding=builder_config.has_token_type_embedding, use_custom_all_reduce=False, # by default dtype=dtype, ) if args.output_dir is not None: output_dir = os.path.join(args.output_dir, component) if not os.path.exists(output_dir): os.makedirs(output_dir) serialize_path = os.path.join(output_dir, engine_name) serialize_engine(engine, serialize_path) if runtime_rank == 0: config_path = os.path.join(output_dir, 'config.json') builder_config.plugin_config = network.plugin_config builder.save_config(builder_config, config_path) return engine, model_config, build_time def build_enc_dec(args): build_config = get_build_config(args.model) if args.force_num_layer_1: build_config['num_layers'] = 1 build_config['max_batch_size'] = build_config['max_batch_size'] \ if args.max_batch_size is None else args.max_batch_size build_config['max_encoder_input_len'] = build_config['max_encoder_input_len'] \ if args.max_input_len is None else args.max_input_len build_config['max_decoder_input_len'] = 1 build_config['max_output_len'] = build_config['max_output_len'] \ if args.max_output_len is None else args.max_output_len build_config[ 'max_beam_width'] = 1 if args.max_beam_width is None else args.max_beam_width encoder_engine, encoder_model_config, encoder_build_time = enc_dec_build_helper( component='encoder', config=build_config, args=args) decoder_engine, decoder_model_config, decoder_build_time = enc_dec_build_helper( component='decoder', config=build_config, args=args) return encoder_engine, decoder_engine, encoder_model_config, decoder_model_config, encoder_build_time, decoder_build_time def main(args): logger.set_level(args.log_level) if args.model in get_allowed_models(benchmark_type="gpt"): build_gpt(args) elif args.model in get_allowed_models(benchmark_type="bert"): build_bert(args) elif args.model in get_allowed_models(benchmark_type="enc_dec"): build_enc_dec(args) else: raise Exception(f'Unexpected model: {args.model}') if __name__ == '__main__': mp.set_start_method('spawn') args = parse_arguments() main(args)