runtime/triton_trtllm/scripts/convert_checkpoint.py

import argparse
import os
import time
import traceback
from concurrent.futures import ThreadPoolExecutor, as_completed

from transformers import AutoConfig

import tensorrt_llm
from tensorrt_llm._utils import release_gc
from tensorrt_llm.logger import logger
from tensorrt_llm.mapping import Mapping
from tensorrt_llm.models import QWenForCausalLM
from tensorrt_llm.models.modeling_utils import QuantConfig
from tensorrt_llm.quantization import QuantAlgo


def parse_arguments():
    parser = argparse.ArgumentParser()
    parser.add_argument('--model_dir', type=str, default=None, required=True)
    parser.add_argument('--tp_size',
                        type=int,
                        default=1,
                        help='N-way tensor parallelism size')
    parser.add_argument('--pp_size',
                        type=int,
                        default=1,
                        help='N-way pipeline parallelism size')
    parser.add_argument(
        '--dtype',
        type=str,
        default='auto',
        choices=['auto', 'float16', 'bfloat16', 'float32'],
        help=
        "The data type for the model weights and activations if not quantized. "
        "If 'auto', the data type is automatically inferred from the source model; "
        "however, if the source dtype is float32, it is converted to float16.")
    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(
        '--disable_weight_only_quant_plugin',
        default=False,
        action="store_true",
        help=
        'By default, using plugin implementation for weight quantization. Enabling disable_weight_only_quant_plugin flag will use ootb implementation instead of plugin.'
        'You must also use --use_weight_only for that argument to have an impact.'
    )
    parser.add_argument(
        '--weight_only_precision',
        const='int8',
        type=str,
        nargs='?',
        default='int8',
        choices=['int8', 'int4', 'int4_gptq'],
        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(
        '--calib_dataset',
        type=str,
        default='ccdv/cnn_dailymail',
        help=
        "The huggingface dataset name or the local directory of the dataset for calibration."
    )
    parser.add_argument(
        "--smoothquant",
        "-sq",
        type=float,
        default=None,
        help="Set the α parameter (see https://arxiv.org/pdf/2211.10438.pdf)"
        " to Smoothquant the model, and output int8 weights."
        " A good first try is 0.5. Must be in [0, 1]")
    parser.add_argument(
        '--per_channel',
        action="store_true",
        default=False,
        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',
        action="store_true",
        default=False,
        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(
        '--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 flag is built for GPTQ/AWQ quantization.')

    parser.add_argument('--group_size',
                        type=int,
                        default=128,
                        help='Group size used in GPTQ quantization.')

    parser.add_argument("--load_model_on_cpu", action="store_true")
    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('--output_dir',
                        type=str,
                        default='tllm_checkpoint',
                        help='The path to save the TensorRT-LLM checkpoint')
    parser.add_argument(
        '--workers',
        type=int,
        default=1,
        help='The number of workers for converting checkpoint in parallel')
    parser.add_argument(
        '--moe_tp_size',
        type=int,
        default=-1,
        help=
        'N-way tensor parallelism size for MOE, default is tp_size, which will do tp-only for MoE'
    )
    parser.add_argument(
        '--moe_ep_size',
        type=int,
        default=-1,
        help=
        'N-way expert parallelism size for MOE, default is 1, which will do tp-only for MoE'
    )
    args = parser.parse_args()
    return args


def args_to_quant_config(args: argparse.Namespace) -> QuantConfig:
    '''return config dict with quantization info based on the command line args
    '''
    quant_config = QuantConfig()
    if args.use_weight_only:
        if args.weight_only_precision == 'int8':
            quant_config.quant_algo = QuantAlgo.W8A16
        elif args.weight_only_precision == 'int4':
            quant_config.quant_algo = QuantAlgo.W4A16
    elif args.smoothquant:
        quant_config.smoothquant_val = args.smoothquant
        if args.per_channel:
            if args.per_token:
                quant_config.quant_algo = QuantAlgo.W8A8_SQ_PER_CHANNEL_PER_TOKEN_PLUGIN
            else:
                quant_config.quant_algo = QuantAlgo.W8A8_SQ_PER_CHANNEL_PER_TENSOR_PLUGIN
        else:
            if args.per_token:
                quant_config.quant_algo = QuantAlgo.W8A8_SQ_PER_TENSOR_PER_TOKEN_PLUGIN
            else:
                quant_config.quant_algo = QuantAlgo.W8A8_SQ_PER_TENSOR_PLUGIN

    if args.int8_kv_cache:
        quant_config.kv_cache_quant_algo = QuantAlgo.INT8

    if args.weight_only_precision == 'int4_gptq':
        quant_config.group_size = args.group_size
        quant_config.has_zero_point = True
        quant_config.pre_quant_scale = False
        quant_config.quant_algo = QuantAlgo.W4A16_GPTQ

    return quant_config


def update_quant_config_from_hf(quant_config, hf_config,
                                override_fields) -> tuple[QuantConfig, dict]:
    hf_config_dict = hf_config.to_dict()
    if hf_config_dict.get('quantization_config'):
        # update the quant_algo, and clamp_val.
        if hf_config_dict['quantization_config'].get('quant_method') == 'awq':
            logger.info(
                "Load quantization configs from huggingface model_config.")
            quant_config.quant_algo = QuantAlgo.W4A16_GPTQ
            quant_config.group_size = hf_config_dict['quantization_config'].get(
                'group_size', 128)
            quant_config.has_zero_point = hf_config_dict[
                'quantization_config'].get('zero_point', False)
            override_fields.update({"use_autoawq": True})
        elif hf_config_dict['quantization_config'].get(
                'quant_method') == 'gptq':
            logger.info(
                "Load quantization configs from huggingface model_config.")
            desc_act = hf_config_dict['quantization_config'].get(
                'desc_act', False)
            if desc_act:
                raise ValueError("GPTQ with desc_act=True is not implemented!")
            quant_config.quant_algo = QuantAlgo.W4A16_GPTQ
            quant_config.group_size = hf_config_dict['quantization_config'].get(
                'group_size', 128)
            quant_config.has_zero_point = hf_config_dict[
                'quantization_config'].get('sym', False)
    return quant_config, override_fields


def args_to_build_options(args):
    return {
        'use_parallel_embedding': args.use_parallel_embedding,
        'embedding_sharding_dim': args.embedding_sharding_dim,
        'disable_weight_only_quant_plugin':
        args.disable_weight_only_quant_plugin
    }


def convert_and_save_hf(args):
    model_dir = args.model_dir
    world_size = args.tp_size * args.pp_size
    # Need to convert the cli args to the kay-value pairs and override them in the generate config dict.
    # Ideally these fields will be moved out of the config and pass them into build API, keep them here for compatibility purpose for now,
    # before the refactor is done.
    override_fields = {}
    override_fields.update(args_to_build_options(args))
    quant_config = args_to_quant_config(args)

    try:
        hf_config = AutoConfig.from_pretrained(model_dir,
                                               trust_remote_code=True)
        quant_config, override_fields = update_quant_config_from_hf(
            quant_config, hf_config, override_fields)
    except:
        logger.warning("AutoConfig cannot load the huggingface config.")

    if args.smoothquant is not None or args.int8_kv_cache:
        mapping = Mapping(
            world_size=world_size,
            tp_size=args.tp_size,
            pp_size=args.pp_size,
            moe_tp_size=args.moe_tp_size,
            moe_ep_size=args.moe_ep_size,
        )
        QWenForCausalLM.quantize(args.model_dir,
                                 args.output_dir,
                                 dtype=args.dtype,
                                 mapping=mapping,
                                 quant_config=quant_config,
                                 calib_dataset=args.calib_dataset,
                                 **override_fields)
    else:

        def convert_and_save_rank(args, rank):
            mapping = Mapping(world_size=world_size,
                              rank=rank,
                              tp_size=args.tp_size,
                              pp_size=args.pp_size,
                              moe_tp_size=args.moe_tp_size,
                              moe_ep_size=args.moe_ep_size)
            qwen = QWenForCausalLM.from_hugging_face(model_dir,
                                                     args.dtype,
                                                     mapping=mapping,
                                                     quant_config=quant_config,
                                                     **override_fields)
            qwen.save_checkpoint(args.output_dir, save_config=(rank == 0))
            del qwen

        execute(args.workers, [convert_and_save_rank] * world_size, args)
        release_gc()


def execute(workers, func, args):
    if workers == 1:
        for rank, f in enumerate(func):
            f(args, rank)
    else:
        with ThreadPoolExecutor(max_workers=workers) as p:
            futures = [p.submit(f, args, rank) for rank, f in enumerate(func)]
            exceptions = []
            for future in as_completed(futures):
                try:
                    future.result()
                except Exception as e:
                    traceback.print_exc()
                    exceptions.append(e)
            assert len(
                exceptions
            ) == 0, "Checkpoint conversion failed, please check error log."


def main():
    print(tensorrt_llm.__version__)
    args = parse_arguments()

    if (args.moe_tp_size == -1 and args.moe_ep_size == -1):
        # moe default to tp-only
        args.moe_tp_size = args.tp_size
        args.moe_ep_size = 1
    elif (args.moe_tp_size == -1):
        args.moe_tp_size = args.tp_size // args.moe_ep_size
    elif (args.moe_ep_size == -1):
        args.moe_ep_size = args.tp_size // args.moe_tp_size
    assert (args.moe_tp_size * args.moe_ep_size == args.tp_size
            ), "moe_tp_size * moe_ep_size must equal to tp_size"

    tik = time.time()

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

    assert args.model_dir is not None
    convert_and_save_hf(args)

    tok = time.time()
    t = time.strftime('%H:%M:%S', time.gmtime(tok - tik))
    print(f'Total time of converting checkpoints: {t}')


if __name__ == '__main__':
    main()