# 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 time from pathlib import Path from typing import List import torch import torch.multiprocessing as mp import tensorrt_llm from tensorrt_llm._utils import str_dtype_to_trt from tensorrt_llm.builder import Builder from tensorrt_llm.layers import PositionEmbeddingType from tensorrt_llm.logger import logger from tensorrt_llm.mapping import Mapping from tensorrt_llm.models import smooth_quantize, weight_only_quantize from tensorrt_llm.network import net_guard from tensorrt_llm.plugin.plugin import ContextFMHAType from tensorrt_llm.quantization import QuantMode from weight import load_from_ft, parse_ft_config, check_embedding_share # isort:skip MODEL_NAME = "gpt" def get_engine_name(model, dtype, tp_size, rank): return '{}_{}_tp{}_rank{}.engine'.format(model, dtype, tp_size, rank) def find_engines(dir: Path, model_name: str = "*", dtype: str = "*", tp_size: str = "*", rank: str = "*") -> List[Path]: template = f"{model_name}_{dtype}_tp{tp_size}_rank{rank}.engine" return list(dir.glob(template)) def serialize_engine(engine, path): logger.info(f'Serializing engine to {path}...') tik = time.time() with open(path, 'wb') as f: f.write(bytearray(engine)) tok = time.time() t = time.strftime('%H:%M:%S', time.gmtime(tok - tik)) logger.info(f'Engine serialized. Total time: {t}') def parse_arguments(args): parser = argparse.ArgumentParser() parser.add_argument('--world_size', type=int, default=1, help='world size, only support tensor parallelism now') parser.add_argument('--model_dir', type=str, default=None) parser.add_argument('--dtype', type=str, default='float16', choices=['float16', 'float32', 'bfloat16']) parser.add_argument('--logits_dtype', type=str, default='float32', choices=['float16', 'float32']) parser.add_argument( '--timing_cache', type=str, default='model.cache', help= 'The path of to read timing cache from, will be ignored if the file does not exist' ) parser.add_argument('--log_level', type=str, default='info') parser.add_argument('--vocab_size', type=int, default=51200) 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( '--rotary_pct', type=float, default=0.0, help="Setting this to a value > 0.0 (and <= 1.0) activates RoPE.") parser.add_argument('--inter_size', type=int, default=None) parser.add_argument('--no_bias', action="store_false") parser.add_argument('--max_batch_size', type=int, default=256) parser.add_argument('--max_input_len', type=int, default=200) parser.add_argument('--max_output_len', type=int, default=200) parser.add_argument('--max_beam_width', type=int, default=1) parser.add_argument( '--use_gpt_attention_plugin', nargs='?', const=None, type=str, default=False, choices=['float16', 'float32', 'bfloat16'], help= "Activates attention plugin. You can specify the plugin dtype or leave blank to use the model dtype." ) parser.add_argument( '--use_gemm_plugin', nargs='?', const=None, type=str, default=False, choices=['float16', 'float32', 'bfloat16'], help= "Activates GEMM plugin. You can specify the plugin dtype or leave blank to use the model dtype." ) parser.add_argument( '--use_layernorm_plugin', nargs='?', const=None, type=str, default=False, choices=['float16', 'float32', 'bfloat16'], help= "Activates layernorm plugin. You can specify the plugin dtype or leave blank to use the model dtype." ) parser.add_argument('--parallel_build', 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('--gpus_per_node', type=int, default=8) parser.add_argument('--builder_opt', type=int, default=None) parser.add_argument( '--output_dir', type=Path, default='gpt_outputs', help= 'The path to save the serialized engine files, timing cache file and model configs' ) parser.add_argument( "--multi_query_mode", "-mq", default=False, action='store_true', help= "Whether this model uses multi-query attention mechanism (default: False)" ) parser.add_argument('--remove_input_padding', default=False, action='store_true') # Arguments related to the quantization of the model. parser.add_argument( '--use_smooth_quant', default=False, action="store_true", help= 'Use the SmoothQuant method to quantize activations and weights for the various GEMMs.' 'See --per_channel and --per_token for finer-grained quantization options.' ) parser.add_argument( '--use_weight_only', default=False, action="store_true", help='Quantize weights for the various GEMMs to INT4/INT8.' 'See --weight_only_precision to set the precision') parser.add_argument( '--weight_only_precision', const='int8', type=str, nargs='?', default='int8', choices=['int8', 'int4'], help= 'Define the precision for the weights when using weight-only quantization.' 'You must also use --use_weight_only for that argument to have an impact.' ) parser.add_argument( '--per_channel', default=False, action="store_true", help= 'By default, we use a single static scaling factor for the GEMM\'s result. ' 'per_channel instead uses a different static scaling factor for each channel. ' 'The latter is usually more accurate, but a little slower.') parser.add_argument( '--per_token', default=False, action="store_true", help= 'By default, we use a single static scaling factor to scale activations in the int8 range. ' 'per_token chooses at run time, and for each token, a custom scaling factor. ' 'The latter is usually more accurate, but a little slower.') parser.add_argument( '--int8_kv_cache', default=False, action="store_true", help= 'By default, we use dtype for KV cache. int8_kv_cache chooses int8 quantization for KV' ) parser.add_argument( '--random_seed', type=int, default=None, help= 'Seed to use when initializing the random number generator for torch.') parser.add_argument( '--paged_kv_cache', action="store_true", default=False, help= 'By default we use contiguous KV cache. By setting this flag you enable paged KV cache' ) parser.add_argument('--tokens_per_block', type=int, default=64, help='Number of tokens per block in paged KV cache') parser.add_argument( '--max_num_tokens', type=int, default=None, help='Define the max number of tokens supported by the engine') parser.add_argument( '--max_prompt_embedding_table_size', type=int, default=0, help='Setting to a value > 0 enables support for prompt tuning.') parser.add_argument( '--use_inflight_batching', action="store_true", default=False, help="Activates inflight batching mode of gptAttentionPlugin.") parser.add_argument( '--use_parallel_embedding', action="store_true", default=False, help= 'By default embedding parallelism is disabled. By setting this flag, embedding parallelism is enabled' ) parser.add_argument( '--embedding_sharding_dim', type=int, default=0, choices=[0, 1], help= 'By default the embedding lookup table is sharded along vocab dimension (embedding_sharding_dim=0). ' 'To shard it along hidden dimension, set embedding_sharding_dim=1' 'Note: embedding sharing is only enabled when embedding_sharding_dim = 0' ) parser.add_argument( '--use_embedding_sharing', action="store_true", default=False, help= 'Try to reduce the engine size by sharing the embedding lookup table between two layers.' 'Note: the flag might not take effect when the criteria are not met.') parser.add_argument( '--use_lookup_plugin', nargs='?', const=None, default=False, choices=['float16', 'float32', 'bfloat16'], help="Activates the lookup plugin which enables embedding sharing.") parser.add_argument('--enable_fp8', default=False, action='store_true') parser.add_argument( '--fp8_kv_cache', default=False, action="store_true", help= 'By default, we use dtype for KV cache. fp8_kv_cache chooses fp8 quantization for KV' ) parser.add_argument( '--strongly_typed', default=False, action="store_true", help= 'This option is introduced with trt 9.1.0.1+ and will reduce the building time significantly for fp8.' ) parser.add_argument( '--position_embedding_type', default='alibi', choices=PositionEmbeddingType.choices(), help='Set the postion embedding type.', ) args = parser.parse_args(args) logger.set_level(args.log_level) args.bias = not args.no_bias if args.inter_size is None: args.inter_size = 4 * args.n_embd if args.model_dir is not None: logger.info(f"Setting model configuration from {args.model_dir}.") n_embd, n_head, n_layer, n_positions, vocab_size, _, hidden_act, rotary_pct, bias, inter_size, multi_query_mode, dtype, prompt_num_tasks, prompt_max_vocab_size, position_embedding_type = parse_ft_config( Path(args.model_dir) / "config.ini") args.n_embd = n_embd args.n_head = n_head args.n_layer = n_layer args.n_positions = n_positions args.vocab_size = vocab_size args.hidden_act = hidden_act args.rotary_pct = rotary_pct args.bias = bias args.dtype = dtype args.inter_size = inter_size args.multi_query_mode = multi_query_mode args.position_embedding_type = position_embedding_type plugins_args = [ 'use_gpt_attention_plugin', 'use_gemm_plugin', 'use_layernorm_plugin', 'use_lookup_plugin' ] for plugin_arg in plugins_args: if getattr(args, plugin_arg) is None: logger.info( f"{plugin_arg} set, without specifying a value. Using {args.dtype} automatically." ) setattr(args, plugin_arg, args.dtype) assert not ( args.use_smooth_quant and args.use_weight_only ), "You cannot enable both SmoothQuant and INT8 weight-only together." if args.use_inflight_batching: if not args.use_gpt_attention_plugin: args.use_gpt_attention_plugin = 'float16' logger.info( f"Using GPT attention plugin for inflight batching mode. Setting to default '{args.use_gpt_attention_plugin}'" ) if not args.remove_input_padding: args.remove_input_padding = True logger.info( "Using remove input padding for inflight batching mode.") if not args.paged_kv_cache: args.paged_kv_cache = True logger.info("Using paged KV cache for inflight batching mode.") if args.use_smooth_quant: args.quant_mode = QuantMode.use_smooth_quant(args.per_token, args.per_channel) elif args.use_weight_only: args.quant_mode = QuantMode.use_weight_only( args.weight_only_precision == 'int4') else: args.quant_mode = QuantMode(0) if args.int8_kv_cache: args.quant_mode = args.quant_mode.set_int8_kv_cache() if args.fp8_kv_cache: assert ( args.use_gpt_attention_plugin or args.use_inflight_batching ), "You have to use GPT attention plugin when fp8 KV cache is set" args.quant_mode = args.quant_mode.set_fp8_kv_cache() if args.enable_fp8: args.quant_mode = args.quant_mode.set_fp8_qdq() args.position_embedding_type = PositionEmbeddingType[ args.position_embedding_type] if args.max_num_tokens is not None: assert args.enable_context_fmha return args def build_rank_engine(builder: Builder, builder_config: tensorrt_llm.builder.BuilderConfig, engine_name, rank, args): ''' @brief: Build the engine on the given rank. @param rank: The rank to build the engine. @param args: The cmd line arguments. @return: The built engine. ''' kv_dtype = str_dtype_to_trt(args.dtype) # Share_embedding_table can be set True only when: # 1) the weight for lm_head() does not exist while other weights exist # 2) For multiple-processes, use_parallel_embedding=True and embedding_sharding_dim == 0. # Besides, for TensorRT 9.0, we can observe the engine size reduction when the lookup and gemm plugin are enabled. share_embedding_table = False if args.use_embedding_sharing: if args.world_size > 1: if args.model_dir is not None and args.embedding_sharding_dim == 0 and args.use_parallel_embedding: share_embedding_table = check_embedding_share(args.model_dir) else: if args.model_dir is not None: share_embedding_table = check_embedding_share(args.model_dir) if not share_embedding_table: logger.warning(f'Cannot share the embedding lookup table.') if share_embedding_table: logger.info( 'Engine will share embedding and language modeling weights.') # Initialize Module tensorrt_llm_gpt = tensorrt_llm.models.GPTLMHeadModel( num_layers=args.n_layer, num_heads=args.n_head, hidden_size=args.n_embd, inter_size=args.inter_size, vocab_size=args.vocab_size, hidden_act=args.hidden_act, max_position_embeddings=args.n_positions, position_embedding_type=args.position_embedding_type, rotary_embedding_percentage=args.rotary_pct, dtype=kv_dtype, logits_dtype=args.logits_dtype, mapping=Mapping(world_size=args.world_size, rank=rank, tp_size=args.world_size), # TP only apply_query_key_layer_scaling=builder_config. apply_query_key_layer_scaling, quant_mode=args.quant_mode, bias=args.bias, multi_query_mode=args.multi_query_mode, use_prompt_tuning=args.max_prompt_embedding_table_size > 0, use_parallel_embedding=args.use_parallel_embedding, embedding_sharding_dim=args.embedding_sharding_dim, share_embedding_table=share_embedding_table) if args.use_smooth_quant: tensorrt_llm_gpt = smooth_quantize(tensorrt_llm_gpt, args.quant_mode) elif args.use_weight_only: tensorrt_llm_gpt = weight_only_quantize(tensorrt_llm_gpt, args.quant_mode) if args.model_dir is not None: gpt_dummy_fp8_scaling_factors = { 'fc_act': [0.5 for _ in range(args.n_layer)], 'fc_weights': [0.5 for _ in range(args.n_layer)], 'proj_act': [0.5 for _ in range(args.n_layer)], 'proj_weights': [0.5 for _ in range(args.n_layer)], 'qkv_act': [0.5 for _ in range(args.n_layer)], 'qkv_weights': [0.5 for _ in range(args.n_layer)], 'qkv_output': [0.5 for _ in range(args.n_layer)], 'dense_act': [0.5 for _ in range(args.n_layer)], 'dense_weights': [0.5 for _ in range(args.n_layer)], } load_from_ft(tensorrt_llm_gpt, args.model_dir, rank, args.world_size, args.dtype, args.use_parallel_embedding, args.embedding_sharding_dim, share_embedding_table, scaling_factors=gpt_dummy_fp8_scaling_factors if args.enable_fp8 else None) # Module -> Network network = builder.create_network() network.trt_network.name = engine_name if args.use_gpt_attention_plugin: network.plugin_config.set_gpt_attention_plugin( dtype=args.use_gpt_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) 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.remove_input_padding: network.plugin_config.enable_remove_input_padding() if args.paged_kv_cache: network.plugin_config.enable_paged_kv_cache(args.tokens_per_block) # Quantization plugins. if args.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 args.use_weight_only: network.plugin_config.set_weight_only_quant_matmul_plugin( dtype=args.dtype) if args.world_size > 1: network.plugin_config.set_nccl_plugin(args.dtype) if args.use_lookup_plugin: # Use the plugin for the embedding parallelism and sharing network.plugin_config.set_lookup_plugin(dtype=args.dtype) with net_guard(network): # Prepare network.set_named_parameters(tensorrt_llm_gpt.named_parameters()) # Forward inputs = tensorrt_llm_gpt.prepare_inputs( args.max_batch_size, args.max_input_len, args.max_output_len, True, args.max_beam_width, max_num_tokens=args.max_num_tokens, prompt_embedding_table_size=args.max_prompt_embedding_table_size) tensorrt_llm_gpt(*inputs) tensorrt_llm.graph_rewriting.optimize(network) engine = None # Network -> Engine engine = builder.build_engine(network, builder_config) if rank == 0: config_path = args.output_dir / 'config.json' builder.save_config(builder_config, config_path) return engine def build(rank, args): torch.cuda.set_device(rank % args.gpus_per_node) tensorrt_llm.logger.set_level(args.log_level) args.output_dir.mkdir(parents=True, exist_ok=True) timing_cache_file = args.timing_cache if args.timing_cache else args.output_dir / "model.cache" timing_cache = timing_cache_file builder = Builder() apply_query_key_layer_scaling = False for cur_rank in range(args.world_size): # skip other ranks if parallel_build is enabled if args.parallel_build and cur_rank != rank: continue # NOTE: when only int8 kv cache is used together with paged kv cache no int8 tensors are exposed to TRT int8_trt_flag = args.quant_mode.has_act_and_weight_quant() or ( not args.paged_kv_cache and args.quant_mode.has_int8_kv_cache()) builder_config = builder.create_builder_config( name=MODEL_NAME, precision=args.dtype, timing_cache=timing_cache, tensor_parallel=args.world_size, # TP only parallel_build=args.parallel_build, num_layers=args.n_layer, num_heads=args.n_head, hidden_size=args.n_embd, vocab_size=args.vocab_size, hidden_act=args.hidden_act, max_position_embeddings=args.n_positions, apply_query_key_layer_scaling=apply_query_key_layer_scaling, max_batch_size=args.max_batch_size, max_input_len=args.max_input_len, max_output_len=args.max_output_len, max_num_tokens=args.max_num_tokens, int8=int8_trt_flag, opt_level=args.builder_opt, multi_query_mode=args.multi_query_mode, strongly_typed=args.strongly_typed, use_prompt_tuning=args.max_prompt_embedding_table_size > 0, fp8=args.enable_fp8, use_parallel_embedding=args.use_parallel_embedding) engine_name = get_engine_name(MODEL_NAME, args.dtype, args.world_size, cur_rank) engine = build_rank_engine(builder, builder_config, engine_name, cur_rank, args) assert engine is not None, f'Failed to build engine for rank {cur_rank}' if cur_rank == 0: # Use in-memory timing cache for multiple builder passes. if not args.parallel_build: timing_cache = builder_config.trt_builder_config.get_timing_cache( ) serialize_engine(engine, args.output_dir / engine_name) if rank == 0: ok = builder.save_timing_cache(builder_config, timing_cache_file) assert ok, "Failed to save timing cache." def run_build(args=None): args = parse_arguments(args) if args.random_seed is not None: torch.manual_seed(args.random_seed) logger.set_level(args.log_level) tik = time.time() if args.parallel_build and args.world_size > 1 and \ torch.cuda.device_count() >= args.world_size: logger.warning( f'Parallelly build TensorRT engines. Please make sure that all of the {args.world_size} GPUs are totally free.' ) mp.spawn(build, nprocs=args.world_size, args=(args, )) else: args.parallel_build = False logger.info('Serially build TensorRT engines.') build(0, args) tok = time.time() t = time.strftime('%H:%M:%S', time.gmtime(tok - tik)) logger.info(f'Total time of building all {args.world_size} engines: {t}') if __name__ == '__main__': run_build()