TensorRT-LLMs/examples/baichuan/run.py

# 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 csv
import json
import os
from pathlib import Path

import numpy as np
import torch
from transformers import AutoTokenizer

import tensorrt_llm
from tensorrt_llm.runtime import ModelConfig, SamplingConfig

from build import get_engine_name  # isort:skip

EOS_TOKEN = 2
PAD_TOKEN = 0


def parse_arguments():
    parser = argparse.ArgumentParser()
    parser.add_argument('--max_output_len', type=int, required=True)
    parser.add_argument('--log_level', type=str, default='error')
    parser.add_argument('--model_version',
                        type=str,
                        default='v1_13b',
                        choices=['v1_7b', 'v1_13b', 'v2_7b', 'v2_13b'])
    parser.add_argument('--engine_dir', type=str, default='baichuan_outputs')
    parser.add_argument('--tokenizer_dir',
                        type=str,
                        default="baichuan-inc/Baichuan-13B-Chat",
                        help="Directory containing the tokenizer.model.")
    parser.add_argument('--input_text', type=str, default='世界上第二高的山峰是哪座？')
    parser.add_argument(
        '--input_tokens',
        dest='input_file',
        type=str,
        help=
        'CSV or Numpy file containing tokenized input. Alternative to text input.',
        default=None)
    parser.add_argument('--output_csv',
                        type=str,
                        help='CSV file where the tokenized output is stored.',
                        default=None)
    parser.add_argument('--output_npy',
                        type=str,
                        help='Numpy file where the tokenized output is stored.',
                        default=None)
    parser.add_argument('--num_beams',
                        type=int,
                        help="Use beam search if num_beams >1",
                        default=1)
    return parser.parse_args()


def generate(
    max_output_len: int,
    log_level: str = 'error',
    model_version: str = 'v1_13b',
    engine_dir: str = 'baichuan_outputs',
    input_text: str = '世界上第二高的山峰是哪座？',
    input_file: str = None,
    output_csv: str = None,
    output_npy: str = None,
    tokenizer_dir: str = None,
    num_beams: int = 1,
):
    tensorrt_llm.logger.set_level(log_level)

    config_path = os.path.join(engine_dir, 'config.json')
    with open(config_path, 'r') as f:
        config = json.load(f)
    use_gpt_attention_plugin = config['plugin_config']['gpt_attention_plugin']
    remove_input_padding = config['plugin_config']['remove_input_padding']
    paged_kv_cache = config['plugin_config']['paged_kv_cache']
    tokens_per_block = config['plugin_config']['tokens_per_block']
    dtype = config['builder_config']['precision']
    world_size = config['builder_config']['tensor_parallel']
    assert world_size == tensorrt_llm.mpi_world_size(), \
        f'Engine world size ({world_size}) != Runtime world size ({tensorrt_llm.mpi_world_size()})'
    num_heads = config['builder_config']['num_heads'] // world_size
    hidden_size = config['builder_config']['hidden_size'] // world_size
    vocab_size = config['builder_config']['vocab_size']
    num_layers = config['builder_config']['num_layers']

    runtime_rank = tensorrt_llm.mpi_rank()
    runtime_mapping = tensorrt_llm.Mapping(world_size,
                                           runtime_rank,
                                           tp_size=world_size)
    torch.cuda.set_device(runtime_rank % runtime_mapping.gpus_per_node)

    tokenizer = AutoTokenizer.from_pretrained(tokenizer_dir,
                                              use_fast=False,
                                              trust_remote_code=True)

    model_config = ModelConfig(num_heads=num_heads,
                               num_kv_heads=num_heads,
                               hidden_size=hidden_size,
                               vocab_size=vocab_size,
                               num_layers=num_layers,
                               gpt_attention_plugin=use_gpt_attention_plugin,
                               paged_kv_cache=paged_kv_cache,
                               tokens_per_block=tokens_per_block,
                               remove_input_padding=remove_input_padding,
                               dtype=dtype)

    repetition_penalty = 1.1
    temperature = 0.3
    top_k = 5
    top_p = 0.85
    if args.model_version == 'v1_7b':
        temperature = 1
        top_k = 1
        top_p = 0
    elif args.model_version == 'v2_7b' or args.model_version == 'v2_13b':
        repetition_penalty = 1.05
    sampling_config = SamplingConfig(end_id=EOS_TOKEN,
                                     pad_id=PAD_TOKEN,
                                     num_beams=num_beams,
                                     repetition_penalty=repetition_penalty,
                                     temperature=temperature,
                                     top_k=top_k,
                                     top_p=top_p)

    engine_name = get_engine_name('baichuan', dtype, world_size, runtime_rank)
    serialize_path = os.path.join(engine_dir, engine_name)
    with open(serialize_path, 'rb') as f:
        engine_buffer = f.read()
    decoder = tensorrt_llm.runtime.GenerationSession(model_config,
                                                     engine_buffer,
                                                     runtime_mapping)

    input_tokens = []
    if input_file is None:
        input_tokens.append(
            tokenizer.encode(input_text, add_special_tokens=False))
    else:
        if input_file.endswith('.csv'):
            with open(input_file, 'r') as csv_file:
                csv_reader = csv.reader(csv_file, delimiter=',')
                for line in csv_reader:
                    input_tokens.append(np.array(line, dtype='int32'))
        elif input_file.endswith('.npy'):
            inputs = np.load(input_file)
            for row in inputs:
                row = row[row != EOS_TOKEN]
                input_tokens.append(row)
        else:
            print('Input file format not supported.')
            raise SystemExit

    input_ids = None
    input_lengths = None
    if input_file is None:
        input_ids = torch.tensor(input_tokens, dtype=torch.int32, device='cuda')
        input_lengths = torch.tensor([input_ids.size(1)],
                                     dtype=torch.int32,
                                     device='cuda')
    else:
        input_lengths = torch.tensor([len(x) for x in input_tokens],
                                     dtype=torch.int32,
                                     device='cuda')
        if remove_input_padding:
            input_ids = np.concatenate(input_tokens)
            input_ids = torch.tensor(input_ids,
                                     dtype=torch.int32,
                                     device='cuda').unsqueeze(0)
        else:
            input_ids = torch.nested.to_padded_tensor(
                torch.nested.nested_tensor(input_tokens, dtype=torch.int32),
                EOS_TOKEN).cuda()

    max_input_length = torch.max(input_lengths).item()
    decoder.setup(input_lengths.size(0),
                  max_input_length,
                  max_output_len,
                  beam_width=num_beams)

    output_ids = decoder.decode(input_ids, input_lengths, sampling_config)
    torch.cuda.synchronize()

    if runtime_rank == 0:
        if output_csv is None and output_npy is None:
            for b in range(input_lengths.size(0)):
                inputs = input_tokens[b]
                input_text = tokenizer.decode(inputs)
                print(f'Input: \"{input_text}\"')
                if num_beams <= 1:
                    output_begin = max_input_length
                    outputs = output_ids[b][0][output_begin:].tolist()
                    output_text = tokenizer.decode(outputs)
                    print(f'Output: \"{output_text}\"')
                else:
                    for beam in range(num_beams):
                        output_begin = input_lengths[b]
                        output_end = input_lengths[b] + max_output_len
                        outputs = output_ids[b][beam][
                            output_begin:output_end].tolist()
                        output_text = tokenizer.decode(outputs)
                        print(f'Output: \"{output_text}\"')

        output_ids = output_ids.reshape((-1, output_ids.size(2)))

        if output_csv is not None:
            output_file = Path(output_csv)
            output_file.parent.mkdir(exist_ok=True, parents=True)
            outputs = output_ids.tolist()
            with open(output_file, 'w') as csv_file:
                writer = csv.writer(csv_file, delimiter=',')
                writer.writerows(outputs)

        if output_npy is not None:
            output_file = Path(output_npy)
            output_file.parent.mkdir(exist_ok=True, parents=True)
            outputs = np.array(output_ids.cpu().contiguous(), dtype='int32')
            np.save(output_file, outputs)
    return


if __name__ == '__main__':
    args = parse_arguments()
    generate(**vars(args))