TensorRT-LLMs/tensorrt_llm/commands/build.py

# 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.
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#
# 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,
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import argparse
import copy
import os
import time
import traceback
from concurrent.futures import ProcessPoolExecutor, as_completed
from importlib.machinery import SourceFileLoader
from multiprocessing import get_context
from typing import Optional, Union

import torch

from tensorrt_llm.auto_parallel import infer_cluster_config
from tensorrt_llm.auto_parallel.cluster_info import cluster_infos
from tensorrt_llm.bindings import KVCacheType
from tensorrt_llm.builder import BuildConfig, Engine, build
from tensorrt_llm.logger import logger, severity_map
from tensorrt_llm.lora_manager import LoraConfig, LoraManager
from tensorrt_llm.models import MODEL_MAP, PretrainedConfig
from tensorrt_llm.models.modeling_utils import SpeculativeDecodingMode
from tensorrt_llm.plugin import PluginConfig, add_plugin_argument
from tensorrt_llm.quantization.mode import QuantAlgo


def parse_arguments():
    parser = argparse.ArgumentParser(
        formatter_class=argparse.ArgumentDefaultsHelpFormatter)
    parser.add_argument(
        '--checkpoint_dir',
        type=str,
        default=None,
        help="The directory path that contains TensorRT-LLM checkpoint.")
    parser.add_argument(
        '--model_config',
        type=str,
        default=None,
        help="The file path that saves TensorRT-LLM checkpoint config.")
    parser.add_argument(
        '--build_config',
        type=str,
        default=None,
        help="The file path that saves TensorRT-LLM build config.")
    parser.add_argument(
        '--model_cls_file',
        type=str,
        default=None,
        help="The file path that defines customized TensorRT-LLM model.")
    parser.add_argument('--model_cls_name',
                        type=str,
                        default=None,
                        help="The customized TensorRT-LLM model class name.")
    parser.add_argument(
        '--output_dir',
        type=str,
        default='engine_outputs',
        help=
        "The directory path to save the serialized engine files and engine config file."
    )

    parser.add_argument(
        '--max_batch_size',
        type=int,
        default=2048,
        help="Maximum number of requests that the engine can schedule.")
    parser.add_argument('--max_input_len',
                        type=int,
                        default=1024,
                        help="Maximum input length of one request.")
    parser.add_argument(
        '--max_seq_len',
        '--max_decoder_seq_len',
        dest='max_seq_len',
        type=int,
        default=None,
        help="Maximum total length of one request, including prompt and outputs. "
        "If unspecified, the value is deduced from the model config.")
    parser.add_argument(
        '--max_beam_width',
        type=int,
        default=1,
        help="Maximum number of beams for beam search decoding.")
    parser.add_argument(
        '--max_num_tokens',
        type=int,
        default=8192,
        help=
        "Maximum number of batched input tokens after padding is removed in each batch. "
        "Currently, the input padding is removed by default; "
        "you may explicitly disable it by specifying ``--remove_input_padding disable``."
    )
    parser.add_argument(
        '--opt_num_tokens',
        type=int,
        default=None,
        help=
        "Optimal number of batched input tokens after padding is removed in each batch "
        "It equals to ``max_batch_size * max_beam_width`` by default, set this "
        "value as close as possible to the actual number of tokens on your workload. "
        "Note that this argument might be removed in the future.")
    parser.add_argument(
        '--max_encoder_input_len',
        type=int,
        default=1024,
        help="Maximum encoder input length for enc-dec models. "
        "Set ``max_input_len`` to 1 to start generation from decoder_start_token_id of length 1."
    )
    parser.add_argument(
        '--max_prompt_embedding_table_size',
        '--max_multimodal_len',
        type=int,
        default=0,
        help=
        "Maximum prompt embedding table size for prompt tuning, or maximum multimodal input size for multimodal models. "
        "Setting a value > 0 enables prompt tuning or multimodal input.")
    parser.add_argument(
        '--kv_cache_type',
        default=argparse.SUPPRESS,
        type=KVCacheType,
        help=
        "Set KV cache type (continuous, paged, or disabled). For disabled case, KV cache is disabled and only context phase is allowed."
    )
    parser.add_argument(
        '--paged_kv_cache',
        type=str,
        default=argparse.SUPPRESS,
        help=
        "Deprecated. Enabling this option is equvilient to ``--kv_cache_type paged`` for transformer based models."
    )

    parser.add_argument(
        '--input_timing_cache',
        type=str,
        default=None,
        help=
        "The file path to read the timing cache. This option is ignored if the file does not exist."
    )
    parser.add_argument('--output_timing_cache',
                        type=str,
                        default='model.cache',
                        help="The file path to write the timing cache.")
    parser.add_argument(
        '--profiling_verbosity',
        type=str,
        default='layer_names_only',
        choices=['layer_names_only', 'detailed', 'none'],
        help=
        "The profiling verbosity for the generated TensorRT engine. Setting to detailed allows inspecting tactic choices and kernel parameters."
    )
    parser.add_argument(
        '--builder_force_num_profiles',
        type=int,
        default=None,
        help="If specified, force to use the number of profiles.")
    parser.add_argument(
        '--strip_plan',
        default=False,
        action='store_true',
        help=
        "Enable stripping weights from the final TensorRT engine under the assumption that the refit weights are identical to those provided at build time."
    )
    parser.add_argument('--weight_sparsity',
                        default=False,
                        action='store_true',
                        help="Enable weight sparsity.")
    parser.add_argument(
        '--weight_streaming',
        default=False,
        action='store_true',
        help=
        "Enable offloading weights to CPU and streaming loading at runtime.",
    )
    parser.add_argument(
        '--fast_build',
        default=False,
        action='store_true',
        help=
        "Enable features for faster engine building. This may cause some performance degradation and is currently incompatible with int8/int4 quantization.",
    )

    parser.add_argument('--workers',
                        type=int,
                        default=1,
                        help="The number of workers for building in parallel.")
    parser.add_argument('--log_level',
                        type=str,
                        default='info',
                        choices=severity_map.keys(),
                        help="The logging level.")
    parser.add_argument('--enable_debug_output',
                        default=False,
                        action='store_true',
                        help="Enable debug output.")
    parser.add_argument(
        '--visualize_network',
        default=False,
        action='store_true',
        help=
        "Export TensorRT Networks to ONNX prior to Engine build for debugging.")
    parser.add_argument(
        '--dry_run',
        default=False,
        action='store_true',
        help=
        "Run through the build process except the actual Engine build for debugging."
    )

    logits_parser = parser.add_argument_group("Logits arguments")
    logits_parser.add_argument('--logits_dtype',
                               type=str,
                               default=None,
                               choices=['float16', 'float32'],
                               help="The data type of logits.")
    logits_parser.add_argument('--gather_context_logits',
                               action='store_true',
                               default=False,
                               help="Enable gathering context logits.")
    logits_parser.add_argument('--gather_generation_logits',
                               action='store_true',
                               default=False,
                               help="Enable gathering generation logits.")
    logits_parser.add_argument(
        '--gather_all_token_logits',
        action='store_true',
        default=False,
        help=
        "Enable both ``gather_context_logits`` and ``gather_generation_logits``."
    )

    lora_parser = parser.add_argument_group("LoRA arguments")
    lora_parser.add_argument(
        '--lora_dir',
        type=str,
        default=None,
        nargs="+",
        help="The directory of LoRA weights. "
        "If multiple directories are provided, the first one is used for configuration."
    )
    lora_parser.add_argument('--lora_ckpt_source',
                             type=str,
                             default="hf",
                             choices=["hf", "nemo"],
                             help="The source type of LoRA checkpoint.")
    lora_parser.add_argument(
        '--lora_target_modules',
        nargs='+',
        default=None,
        choices=LoraManager.LORA_MODULE_IDS.keys(),
        help=
        "The target module names that LoRA is applied. Only effective when ``lora_plugin`` is enabled."
    )
    lora_parser.add_argument(
        '--max_lora_rank',
        type=int,
        default=64,
        help="Maximum LoRA rank for different LoRA modules. "
        "It is used to compute the workspace size of LoRA plugin.")

    spec_parser = parser.add_argument_group("Speculative decoding arguments")
    spec_parser.add_argument('--speculative_decoding_mode',
                             default=None,
                             choices=[
                                 "draft_tokens_external",
                                 "lookahead_decoding",
                                 "medusa",
                                 "explicit_draft_tokens",
                             ],
                             help="Mode of speculative decoding.")
    spec_parser.add_argument(
        '--max_draft_len',
        type=int,
        default=0,
        help=
        "Maximum lengths of draft tokens for speculative decoding target model."
    )

    autopp_parser = parser.add_argument_group("Auto parallel arguments")
    autopp_parser.add_argument('--auto_parallel',
                               type=int,
                               default=1,
                               help="MPI world size for auto parallel.")
    autopp_parser.add_argument(
        '--gpus_per_node',
        type=int,
        default=8,
        help=
        "Number of GPUs each node has in a multi-node setup. This is a cluster spec and can be greater/smaller than world size. "
        "This option is only used for auto parallel specified with ``--auto_parallel``."
    )
    autopp_parser.add_argument(
        '--cluster_key',
        type=str,
        default=None,
        choices=cluster_infos.keys(),
        help=
        "Unique name for target GPU type. Inferred from current GPU type if not specified. "
        "This option is only used for auto parallel specified with ``--auto_parallel``."
    )

    plugin_config_parser = parser.add_argument_group("Plugin config arguments")
    add_plugin_argument(plugin_config_parser)
    return parser


def build_model(
    build_config: BuildConfig,
    rank: int = 0,
    ckpt_dir: str = None,
    model_config: Union[str, PretrainedConfig] = None,
    model_cls=None,
    dry_run:
    bool = False,  # return the modified BuildConfig without actually building the engine
    **kwargs
) -> Union[Engine, BuildConfig]:
    model_config = copy.deepcopy(model_config)

    logits_dtype = kwargs.get('logits_dtype')
    if logits_dtype is not None:
        model_config.logits_dtype = logits_dtype

    architecture = model_config.architecture
    assert not build_config.plugin_config.streamingllm or architecture == "LlamaForCausalLM", \
        "StreamingLLM is only supported in the llama model."
    real_rank = rank

    model_config.mapping.gpus_per_node = build_config.auto_parallel_config.gpus_per_node
    if build_config.auto_parallel_config.enabled:
        assert rank < build_config.auto_parallel_config.world_size
        assert model_config.mapping.pp_size == 1 and model_config.mapping.tp_size == 1, \
            "You must convert to full model with TP=1&&PP=1 to use auto parallel planner"
        #TODO: TRTLLM-193 remove this after the new build API for autopp is done
        rank = 0  # This is a WAR to construct a whole model and load all the weights before auto parallel
    else:
        assert rank < model_config.mapping.world_size

    rank_config = copy.deepcopy(model_config)
    rank_config.set_rank(rank)

    if model_cls is None:
        assert architecture in MODEL_MAP, \
            f"Unsupported model architecture: {architecture}"
        model_cls = MODEL_MAP[architecture]
    if ckpt_dir is None:
        model = model_cls(rank_config)
    else:
        model = model_cls.from_checkpoint(ckpt_dir, config=rank_config)
    is_checkpoint_pruned = getattr(rank_config, 'is_pruned', False)

    if build_config.plugin_config.lora_plugin is not None:
        lora_config = LoraConfig(lora_dir=kwargs['lora_dir'] or [],
                                 lora_ckpt_source=kwargs['lora_ckpt_source'],
                                 max_lora_rank=kwargs['max_lora_rank'])
        if kwargs['lora_target_modules'] is not None:
            # command line options is preferred over the modules in the lora dir
            lora_config.lora_target_modules = kwargs['lora_target_modules']
        build_config.lora_config = lora_config

    build_config.use_fused_mlp = kwargs.get('use_fused_mlp', False)
    # tells the low level build api to only build rank-th shard of the model
    if build_config.auto_parallel_config.enabled:
        model.config.mapping.rank = real_rank

    if is_checkpoint_pruned or kwargs.pop('strip_plan', False):
        build_config.use_strip_plan = True
    build_config.use_refit = kwargs.get('refit', False)

    if dry_run:
        return build_config

    return build(model, build_config)


def build_and_save(rank, gpu_id, ckpt_dir, build_config, output_dir, log_level,
                   model_config, model_cls, **kwargs):
    torch.cuda.set_device(gpu_id)
    logger.set_level(log_level)
    engine = build_model(build_config,
                         rank,
                         ckpt_dir,
                         model_config,
                         model_cls=model_cls,
                         **kwargs)
    assert engine is not None
    engine.save(output_dir)
    return True


def parallel_build(model_config: PretrainedConfig,
                   ckpt_dir: Optional[str],
                   build_config: BuildConfig,
                   output_dir: str,
                   workers: int = 1,
                   log_level: str = 'info',
                   model_cls=None,
                   **kwargs):

    if build_config.auto_parallel_config.enabled:
        if model_config.mapping.world_size > 1:
            raise RuntimeError(
                "manually TP and PP are not supported in auto parallel mode.")
        if build_config.auto_parallel_config.debug_mode:
            world_size = 1
        else:
            world_size = build_config.auto_parallel_config.world_size
    else:
        world_size = model_config.mapping.world_size

    if workers == 1:
        for rank in range(world_size):
            passed = build_and_save(rank, rank % workers, ckpt_dir,
                                    build_config, output_dir, log_level,
                                    model_config, model_cls, **kwargs)
            assert passed, "Engine building failed, please check error log."
    else:
        with ProcessPoolExecutor(mp_context=get_context('spawn'),
                                 max_workers=workers) as p:
            futures = [
                p.submit(build_and_save, rank, rank % workers, ckpt_dir,
                         build_config, output_dir, log_level, model_config,
                         model_cls, **kwargs) for rank in range(world_size)
            ]
            exceptions = []
            for future in as_completed(futures):
                try:
                    future.result()
                except Exception as e:
                    traceback.print_exc()
                    exceptions.append(e)
            assert len(exceptions
                       ) == 0, "Engine building failed, please check error log."


def main():
    parser = parse_arguments()
    args = parser.parse_args()
    if args.gather_all_token_logits:
        args.gather_context_logits = True
        args.gather_generation_logits = True
    if args.gather_context_logits and args.max_draft_len > 0:
        raise RuntimeError(
            "Gather context logits is not support with draft len > 0. "
            "If want to get the accepted tokens' logits from target model, please just enable gather_generation_logits"
        )

    logger.set_level(args.log_level)
    tik = time.time()

    if not os.path.exists(args.output_dir):
        os.makedirs(args.output_dir)

    model_cls = None
    if args.model_cls_file is not None:
        assert args.model_cls_name is not None
        loader = SourceFileLoader('models', args.model_cls_file)
        mod = loader.load_module()
        model_cls = getattr(mod, args.model_cls_name)

    workers = min(torch.cuda.device_count(), args.workers)

    if hasattr(args, 'paged_kv_cache'):
        logger.warning(
            'Option --paged_kv_cache is deprecated, use --kv_cache_type=paged/disabled instead.'
        )

    plugin_config = PluginConfig.from_arguments(args)

    if args.fast_build:
        plugin_config.manage_weights = True

    kwargs = {
        'logits_dtype': args.logits_dtype,
        'use_fused_mlp': args.use_fused_mlp,
        'lora_dir': args.lora_dir,
        'lora_ckpt_source': args.lora_ckpt_source,
        'max_lora_rank': args.max_lora_rank,
        'lora_target_modules': args.lora_target_modules,
        'strip_plan': args.strip_plan,
        'refit': False,
    }
    speculative_decoding_mode = SpeculativeDecodingMode.from_arguments(args)

    ckpt_dir_or_model_config = args.checkpoint_dir if args.checkpoint_dir is not None else args.model_config
    if ckpt_dir_or_model_config.lower().endswith('.json'):
        config_path = ckpt_dir_or_model_config
        ckpt_dir = None
    else:
        config_path = os.path.join(ckpt_dir_or_model_config, 'config.json')
        ckpt_dir = ckpt_dir_or_model_config
    model_config = PretrainedConfig.from_json_file(config_path)

    # avoid ValueError if not supported quantization is chosen with use_fused_mlp
    quant_algo = model_config.quantization.quant_algo
    if quant_algo and quant_algo not in (QuantAlgo.FP8,
                                         QuantAlgo.MIXED_PRECISION):
        kwargs['use_fused_mlp'] = False

    if args.build_config is None:
        if args.multiple_profiles == "enable" and args.opt_num_tokens is not None:
            raise RuntimeError(
                "multiple_profiles is enabled, while opt_num_tokens is set. "
                "They are not supposed to be working in the same time for now.")
        if args.cluster_key is not None:
            cluster_config = dict(cluster_key=args.cluster_key)
        else:
            cluster_config = infer_cluster_config()

        build_config = BuildConfig.from_dict(
            {
                'max_input_len': args.max_input_len,
                'max_seq_len': args.max_seq_len,
                'max_batch_size': args.max_batch_size,
                'max_beam_width': args.max_beam_width,
                'max_num_tokens': args.max_num_tokens,
                'opt_num_tokens': args.opt_num_tokens,
                'max_prompt_embedding_table_size':
                args.max_prompt_embedding_table_size,
                'gather_context_logits': args.gather_context_logits,
                'gather_generation_logits': args.gather_generation_logits,
                'strongly_typed': True,
                'force_num_profiles': args.builder_force_num_profiles,
                'weight_sparsity': args.weight_sparsity,
                'profiling_verbosity': args.profiling_verbosity,
                'enable_debug_output': args.enable_debug_output,
                'max_draft_len': args.max_draft_len,
                'speculative_decoding_mode': speculative_decoding_mode,
                'input_timing_cache': args.input_timing_cache,
                'output_timing_cache': args.output_timing_cache,
                'auto_parallel_config': {
                    'world_size':
                    args.auto_parallel,
                    'gpus_per_node':
                    args.gpus_per_node,
                    'sharded_io_allowlist': [
                        'past_key_value_\\d+',
                        'present_key_value_\\d*',
                    ],
                    'same_buffer_io': {
                        'past_key_value_(\\d+)': 'present_key_value_\\1',
                    },
                    **cluster_config,
                },
                'dry_run': args.dry_run,
                'visualize_network': args.visualize_network,
                'max_encoder_input_len': args.max_encoder_input_len,
                'weight_streaming': args.weight_streaming,
            },
            plugin_config=plugin_config)

        if hasattr(args, 'kv_cache_type'):
            build_config.update_from_dict({'kv_cache_type': args.kv_cache_type})
    else:
        build_config = BuildConfig.from_json_file(args.build_config,
                                                  plugin_config=plugin_config)

    parallel_build(model_config, ckpt_dir, build_config, args.output_dir,
                   workers, args.log_level, model_cls, **kwargs)

    tok = time.time()
    t = time.strftime('%H:%M:%S', time.gmtime(tok - tik))
    logger.info(f'Total time of building all engines: {t}')


if __name__ == '__main__':
    main()