# 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 json import os import time import tensorrt as trt import torch import torch.multiprocessing as mp from transformers import AutoModelForCausalLM from weight import get_scaling_factors, load_from_awq_gpt_j, load_from_hf_gpt_j import tensorrt_llm from tensorrt_llm.builder import Builder from tensorrt_llm.logger import logger from tensorrt_llm.mapping import Mapping from tensorrt_llm.models import (weight_only_groupwise_quantize, weight_only_quantize) from tensorrt_llm.network import net_guard from tensorrt_llm.plugin.plugin import ContextFMHAType from tensorrt_llm.quantization import QuantMode MODEL_NAME = "gptj" hf_gpt = None awq_gptj_config = None def get_engine_name(model, dtype, tp_size, rank): return '{}_{}_tp{}_rank{}.engine'.format(model, dtype, tp_size, rank) 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, help='The path to HF GPT-J model / checkpoints to read weights from') parser.add_argument('--dtype', type=str, default='float16', choices=['float16', 'float32']) 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=50401) parser.add_argument('--n_layer', type=int, default=28) parser.add_argument('--n_positions', type=int, default=2048) parser.add_argument('--n_embd', type=int, default=4096) parser.add_argument('--n_head', type=int, default=16) parser.add_argument('--hidden_act', type=str, default='gelu') parser.add_argument('--rotary_dim', type=int, default=64) 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='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_weight_only_quant_matmul_plugin', nargs='?', const='float16', type=str, default=False, choices=['float16']) parser.add_argument('--use_layernorm_plugin', nargs='?', const='float16', type=str, default=False, choices=['float16', 'float32']) 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( '--output_dir', type=str, default='gpt_outputs', help= 'The path to save the serialized engine files, timing cache file and model configs' ) parser.add_argument('--remove_input_padding', default=False, action='store_true') parser.add_argument('--enable_fp8', default=False, action='store_true') parser.add_argument( '--quantized_fp8_model_path', type=str, default=None, help='Path of a quantized model checkpoint that in .npz format') 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( '--use_inflight_batching', action="store_true", default=False, help="Activates inflight batching mode of gptAttentionPlugin.") parser.add_argument( '--enable_two_optimization_profiles', default=False, action='store_true', help= "Enables two optimization profiles during engine build, for context and generate phases. By default (and for inflight batching too), only 1 opt profile." ) 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( '--per_group', default=False, action="store_true", help= 'By default, we use a single static scaling factor to scale weights in the int4 range. ' 'per_group chooses at run time, and for each group, a custom scaling factor. ' 'The falg is built for GPTQ/AWQ quantization.') 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( '--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.' ) args = parser.parse_args(args) logger.set_level(args.log_level) if not args.remove_input_padding: if args.use_gpt_attention_plugin: logger.warning( f"It is recommended to specify --remove_input_padding when using GPT attention plugin" ) if args.model_dir is not None: global hf_gpt if args.use_weight_only and args.weight_only_precision == 'int4' and args.per_group: logger.info(f'Loading AWQ GPTJ model from {args.model_dir}...') global awq_gptj_config with open(args.model_dir + "/config.json", encoding='utf-8') as config_file: awq_gptj_config = json.load(config_file) args.n_embd = awq_gptj_config['n_embd'] args.n_head = awq_gptj_config['n_head'] args.n_layer = awq_gptj_config['n_layer'] args.n_positions = awq_gptj_config['n_positions'] args.vocab_size = awq_gptj_config['vocab_size'] if args.vocab_size % 64 != 0: args.vocab_size = int( (awq_gptj_config['vocab_size'] + 63) / 64) * 64 print( "vocab_size is {}, to use awq we pad it to {}.".format( awq_gptj_config['vocab_size'], args.vocab_size)) hf_gpt = torch.load(args.model_dir + "/gptj_quantized.pth") else: logger.info(f'Loading HF GPTJ model from {args.model_dir}...') hf_gpt = AutoModelForCausalLM.from_pretrained(args.model_dir) args.n_embd = hf_gpt.config.n_embd args.n_head = hf_gpt.config.n_head args.n_layer = hf_gpt.config.n_layer args.n_positions = hf_gpt.config.n_positions args.vocab_size = hf_gpt.config.vocab_size assert not (args.use_weight_only and args.weight_only_precision == 'int8'), "Not support int8 weight only." assert not (args.use_weight_only and args.weight_only_precision == 'int4' and args.per_group == False), "We only support AWQ for int4 weight only." if args.use_weight_only: args.quant_mode = QuantMode.use_weight_only( args.weight_only_precision == 'int4') else: args.quant_mode = QuantMode(0) if args.fp8_kv_cache: assert ( args.use_gpt_attention_plugin ), "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() 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.max_num_tokens is not None: assert args.enable_context_fmha if args.remove_input_padding or args.use_inflight_batching or args.paged_kv_cache: assert ( not args.enable_two_optimization_profiles ), "Only 1 opt profile supported for inflight batching and paged kv cache." 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 = trt.float16 if args.dtype == 'float16' else trt.float32 # Initialize Module tensorrt_llm_gpt = tensorrt_llm.models.GPTJForCausalLM( 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, rotary_dim=args.rotary_dim, dtype=kv_dtype, logits_dtype=args.logits_dtype, mapping=Mapping(world_size=args.world_size, rank=rank, tp_size=args.world_size), # TP only quant_mode=args.quant_mode) if args.use_weight_only_quant_matmul_plugin: tensorrt_llm_gpt = weight_only_quantize(tensorrt_llm_gpt) if args.use_weight_only and args.weight_only_precision == 'int4': if args.per_group: tensorrt_llm_gpt = weight_only_groupwise_quantize( model=tensorrt_llm_gpt, quant_mode=QuantMode.from_description( quantize_weights=True, quantize_activations=False, per_token=False, per_channel=False, per_group=True, use_int4_weights=True), group_size=128, zero=False, pre_quant_scale=True, exclude_modules=[], ) if args.model_dir is not None: assert hf_gpt is not None, f'Could not load weights from hf_gpt model as it is not loaded yet.' if args.enable_fp8: gptj_scaling_factors = get_scaling_factors( args.quantized_fp8_model_path, args.n_layer, args.quant_mode) else: gptj_scaling_factors = None if args.use_weight_only and args.weight_only_precision == 'int4' and args.per_group: load_from_awq_gpt_j(tensorrt_llm_gpt, awq_gpt_j=hf_gpt, config=awq_gptj_config, fp16=(args.dtype == 'float16')) else: load_from_hf_gpt_j(tensorrt_llm_gpt, hf_gpt, fp16=(args.dtype == 'float16'), scaling_factors=gptj_scaling_factors) # 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.use_weight_only_quant_matmul_plugin: network.plugin_config.set_weight_only_quant_matmul_plugin( dtype=args.use_weight_only_quant_matmul_plugin) if args.use_weight_only: if args.per_group: network.plugin_config.set_weight_only_groupwise_quant_matmul_plugin( dtype='float16') if args.world_size > 1: network.plugin_config.set_nccl_plugin(args.dtype) 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) 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, enable_two_optimization_profiles=args. enable_two_optimization_profiles) 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 = os.path.join(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) if not os.path.exists(args.output_dir): os.makedirs(args.output_dir) # when doing serializing build, all ranks share one engine builder = Builder() cache = None 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 builder_config = builder.create_builder_config( name=MODEL_NAME, precision=args.dtype, timing_cache=args.timing_cache if cache is None else 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, 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, fp8=args.enable_fp8, quant_mode=args.quant_mode, strongly_typed=args.strongly_typed) 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: cache = builder_config.trt_builder_config.get_timing_cache() serialize_engine(engine, os.path.join(args.output_dir, engine_name)) if rank == 0: ok = builder.save_timing_cache( builder_config, os.path.join(args.output_dir, "model.cache")) assert ok, "Failed to save timing cache." def run_build(args=None): args = parse_arguments(args) 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()