# SPDX-FileCopyrightText: Copyright (c) 2022-2023 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 os from collections import OrderedDict import tensorrt as trt import torch from transformers import BertConfig, BertForQuestionAnswering, BertModel import tensorrt_llm from tensorrt_llm.builder import Builder from tensorrt_llm.mapping import Mapping from tensorrt_llm.network import net_guard from tensorrt_llm.plugin.plugin import ContextFMHAType from weight import load_from_hf_bert, load_from_hf_qa_bert # isort:skip def get_engine_name(model, dtype, tp_size, rank): return '{}_{}_tp{}_rank{}.engine'.format(model, dtype, tp_size, rank) def parse_arguments(): parser = argparse.ArgumentParser() parser.add_argument('--world_size', type=int, default=1, help='Tensor parallelism size') parser.add_argument('--rank', type=int, default=0) parser.add_argument('--dtype', type=str, default='float16', choices=['float16', 'float32']) parser.add_argument('--timing_cache', type=str, default='model.cache') parser.add_argument('--log_level', type=str, default='info') parser.add_argument('--vocab_size', type=int, default=51200) parser.add_argument('--n_labels', type=int, default=2) parser.add_argument('--n_layer', type=int, default=24) parser.add_argument('--n_positions', type=int, default=1024) parser.add_argument('--n_embd', type=int, default=1024) parser.add_argument('--n_head', type=int, default=16) parser.add_argument('--hidden_act', type=str, default='gelu') parser.add_argument('--max_batch_size', type=int, default=256) parser.add_argument('--max_input_len', type=int, default=512) parser.add_argument('--gpus_per_node', type=int, default=8) parser.add_argument('--output_dir', type=str, default='bert_outputs') parser.add_argument('--use_bert_attention_plugin', nargs='?', const='float16', type=str, default=False, choices=['float16', 'float32']) parser.add_argument('--use_gemm_plugin', nargs='?', const='float16', type=str, default=False, choices=['float16', 'float32']) parser.add_argument('--use_layernorm_plugin', nargs='?', const='float16', type=str, default=False, choices=['float16', 'float32']) parser.add_argument('--enable_qk_half_accum', default=False, action='store_true') parser.add_argument('--enable_context_fmha', default=False, action='store_true') parser.add_argument('--enable_context_fmha_fp32_acc', default=False, action='store_true') parser.add_argument( '--model', default=tensorrt_llm.models.BertModel.__name__, choices=[ tensorrt_llm.models.BertModel.__name__, tensorrt_llm.models.BertForQuestionAnswering.__name__ ]) return parser.parse_args() if __name__ == '__main__': args = parse_arguments() tensorrt_llm.logger.set_level(args.log_level) if not os.path.exists(args.output_dir): os.makedirs(args.output_dir) bs_range = [1, (args.max_batch_size + 1) // 2, args.max_batch_size] inlen_range = [1, (args.max_input_len + 1) // 2, args.max_input_len] torch_dtype = torch.float16 if args.dtype == 'float16' else torch.float32 trt_dtype = trt.float16 if args.dtype == 'float16' else trt.float32 builder = Builder() builder_config = builder.create_builder_config( name=args.model, precision=args.dtype, timing_cache=args.timing_cache, tensor_parallel=args.world_size, # TP only max_batch_size=args.max_batch_size, max_input_len=args.max_input_len, ) # Initialize model bert_config = BertConfig( vocab_size=args.vocab_size, hidden_size=args.n_embd, num_hidden_layers=args.n_layer, num_attention_heads=args.n_head, intermediate_size=4 * args.n_embd, hidden_act=args.hidden_act, max_position_embeddings=args.n_positions, torch_dtype=torch_dtype, ) output_name = 'hidden_states' if args.model == tensorrt_llm.models.BertModel.__name__: hf_bert = BertModel(bert_config, add_pooling_layer=False) tensorrt_llm_bert = tensorrt_llm.models.BertModel( num_layers=bert_config.num_hidden_layers, num_heads=bert_config.num_attention_heads, hidden_size=bert_config.hidden_size, vocab_size=bert_config.vocab_size, hidden_act=bert_config.hidden_act, max_position_embeddings=bert_config.max_position_embeddings, type_vocab_size=bert_config.type_vocab_size, mapping=Mapping(world_size=args.world_size, rank=args.rank, tp_size=args.world_size), # TP only dtype=trt_dtype) load_from_hf_bert( tensorrt_llm_bert, hf_bert, bert_config, rank=args.rank, tensor_parallel=args.world_size, fp16=(args.dtype == 'float16'), ) elif args.model == tensorrt_llm.models.BertForQuestionAnswering.__name__: hf_bert = BertForQuestionAnswering(bert_config) tensorrt_llm_bert = tensorrt_llm.models.BertForQuestionAnswering( num_layers=bert_config.num_hidden_layers, num_heads=bert_config.num_attention_heads, hidden_size=bert_config.hidden_size, vocab_size=bert_config.vocab_size, hidden_act=bert_config.hidden_act, max_position_embeddings=bert_config.max_position_embeddings, type_vocab_size=bert_config.type_vocab_size, num_labels=args. n_labels, # TODO: this might just need to be a constant mapping=Mapping(world_size=args.world_size, rank=args.rank, tp_size=args.world_size), # TP only dtype=trt_dtype) load_from_hf_qa_bert( tensorrt_llm_bert, hf_bert, bert_config, rank=args.rank, tensor_parallel=args.world_size, fp16=(args.dtype == 'float16'), ) output_name = 'logits' else: assert False, f"Unknown BERT model {args.model}" # Module -> Network network = builder.create_network() if args.use_bert_attention_plugin: network.plugin_config.set_bert_attention_plugin( dtype=args.use_bert_attention_plugin) if args.use_gemm_plugin: network.plugin_config.set_gemm_plugin(dtype=args.use_gemm_plugin) if args.use_layernorm_plugin: network.plugin_config.set_layernorm_plugin( dtype=args.use_layernorm_plugin) if args.enable_qk_half_accum: network.plugin_config.enable_qk_half_accum() assert not (args.enable_context_fmha and args.enable_context_fmha_fp32_acc) if args.enable_context_fmha: network.plugin_config.set_context_fmha(ContextFMHAType.enabled) if args.enable_context_fmha_fp32_acc: network.plugin_config.set_context_fmha( ContextFMHAType.enabled_with_fp32_acc) if args.world_size > 1: network.plugin_config.set_nccl_plugin(args.dtype) 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])]), ) # also called segment_ids token_type_ids = tensorrt_llm.Tensor( name='token_type_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]) ])) # logits for QA BERT, or hidden_state for vanila BERT output = tensorrt_llm_bert(input_ids=input_ids, input_lengths=input_lengths, token_type_ids=token_type_ids) # Mark outputs output_dtype = trt.float16 if args.dtype == 'float16' else trt.float32 output.mark_output(output_name, output_dtype) # Network -> Engine engine = builder.build_engine(network, builder_config) assert engine is not None, 'Failed to build engine.' engine_file = os.path.join( args.output_dir, get_engine_name(args.model, args.dtype, args.world_size, args.rank)) with open(engine_file, 'wb') as f: f.write(engine) builder.save_config(builder_config, os.path.join(args.output_dir, 'config.json'))