decode_seq2seq.py

# coding=utf-8
# The MIT License (MIT)

# Copyright (c) Microsoft Corporation

# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
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# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os
import logging
import glob
import argparse
import math
import random
from tqdm import tqdm, trange
import pickle
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler
from torch.utils.data.distributed import DistributedSampler

from tokenization_unilm import UnilmTokenizer, WhitespaceTokenizer
from transformers import AdamW, get_linear_schedule_with_warmup
from modeling_unilm import UnilmForSeq2SeqDecode, UnilmConfig
# from transformers import (UnilmTokenizer, WhitespaceTokenizer,
#                           UnilmForSeq2SeqDecode, AdamW, UnilmConfig)


import utils_seq2seq

ALL_MODELS = sum((tuple(conf.pretrained_config_archive_map.keys())
                  for conf in (UnilmConfig,)), ())
MODEL_CLASSES = {
    'unilm': (UnilmConfig, UnilmForSeq2SeqDecode, UnilmTokenizer)
}

logging.basicConfig(format='%(asctime)s - %(levelname)s - %(name)s -   %(message)s',
                    datefmt='%m/%d/%Y %H:%M:%S',
                    level=logging.INFO)
logger = logging.getLogger(__name__)


def detokenize(tk_list):
    r_list = []
    for tk in tk_list:
        if tk.startswith('##') and len(r_list) > 0:
            r_list[-1] = r_list[-1] + tk[2:]
        else:
            r_list.append(tk)
    return r_list


def main():
    parser = argparse.ArgumentParser()

    # Required parameters
    parser.add_argument("--model_type", default=None, type=str, required=True,
                        help="Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys()))
    parser.add_argument("--model_name_or_path", default=None, type=str, required=True,
                        help="Path to pre-trained model or shortcut name selected in the list: " + ", ".join(ALL_MODELS))
    parser.add_argument("--model_recover_path", default=None, type=str,
                        help="The file of fine-tuned pretraining model.")
    parser.add_argument("--config_name", default="", type=str,
                        help="Pretrained config name or path if not the same as model_name")
    parser.add_argument("--tokenizer_name", default="", type=str,
                        help="Pretrained tokenizer name or path if not the same as model_name")
    parser.add_argument("--max_seq_length", default=512, type=int,
                        help="The maximum total input sequence length after WordPiece tokenization. \n"
                             "Sequences longer than this will be truncated, and sequences shorter \n"
                             "than this will be padded.")

    # decoding parameters
    parser.add_argument('--fp16', action='store_true',
                        help="Whether to use 16-bit float precision instead of 32-bit")
    parser.add_argument('--fp16_opt_level', type=str, default='O1',
                        help="For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
                        "See details at https://nvidia.github.io/apex/amp.html")
    parser.add_argument("--input_file", type=str, help="Input file")
    parser.add_argument("--input_aspect", type=str, help="Input file")
    parser.add_argument('--subset', type=int, default=0,
                        help="Decode a subset of the input dataset.")
    parser.add_argument("--output_file", type=str, help="output file")
    parser.add_argument("--split", type=str, default="",
                        help="Data split (train/val/test).")
    parser.add_argument('--tokenized_input', action='store_true',
                        help="Whether the input is tokenized.")
    parser.add_argument('--seed', type=int, default=123,
                        help="random seed for initialization")
    parser.add_argument("--do_lower_case", action='store_true',
                        help="Set this flag if you are using an uncased model.")
    parser.add_argument('--batch_size', type=int, default=4,
                        help="Batch size for decoding.")
    parser.add_argument('--beam_size', type=int, default=1,
                        help="Beam size for searching")
    parser.add_argument('--length_penalty', type=float, default=0,
                        help="Length penalty for beam search")
    parser.add_argument('--forbid_duplicate_ngrams', action='store_true')
    parser.add_argument('--forbid_ignore_word', type=str, default=None,
                        help="Forbid the word during forbid_duplicate_ngrams")
    parser.add_argument("--min_len", default=None, type=int)
    parser.add_argument('--need_score_traces', action='store_true')
    parser.add_argument('--ngram_size', type=int, default=3)
    parser.add_argument('--max_tgt_length', type=int, default=128,
                        help="maximum length of target sequence")

    args = parser.parse_args()

    if args.need_score_traces and args.beam_size <= 1:
        raise ValueError(
            "Score trace is only available for beam search with beam size > 1.")
    if args.max_tgt_length >= args.max_seq_length - 2:
        raise ValueError("Maximum tgt length exceeds max seq length - 2.")

    device = torch.device(
        "cuda" if torch.cuda.is_available() else "cpu")
    n_gpu = torch.cuda.device_count()

    random.seed(args.seed)
    np.random.seed(args.seed)
    torch.manual_seed(args.seed)
    if n_gpu > 0:
        torch.cuda.manual_seed_all(args.seed)

    args.model_type = args.model_type.lower()
    config_class, model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
    config = config_class.from_pretrained(
        args.config_name if args.config_name else args.model_name_or_path, max_position_embeddings=args.max_seq_length)
    tokenizer = tokenizer_class.from_pretrained(
        args.tokenizer_name if args.tokenizer_name else args.model_name_or_path, do_lower_case=args.do_lower_case)

    bi_uni_pipeline = []
    bi_uni_pipeline.append(utils_seq2seq.Preprocess4Seq2seqDecode(list(tokenizer.vocab.keys()), tokenizer.convert_tokens_to_ids,
                                                                  args.max_seq_length, max_tgt_length=args.max_tgt_length))

    # Prepare model
    mask_word_id, eos_word_ids, sos_word_id = tokenizer.convert_tokens_to_ids(
        ["[MASK]", "[SEP]", "[S2S_SOS]"])
    forbid_ignore_set = None
    if args.forbid_ignore_word:
        w_list = []
        for w in args.forbid_ignore_word.split('|'):
            if w.startswith('[') and w.endswith(']'):
                w_list.append(w.upper())
            else:
                w_list.append(w)
        forbid_ignore_set = set(tokenizer.convert_tokens_to_ids(w_list))
    print(args.model_recover_path)
    for model_recover_path in glob.glob(args.model_recover_path.strip()):
        logger.info("***** Recover model: %s *****", model_recover_path)
        model_recover = torch.load(model_recover_path)
        model = model_class.from_pretrained(args.model_name_or_path, state_dict=model_recover, config=config, mask_word_id=mask_word_id, search_beam_size=args.beam_size, length_penalty=args.length_penalty,
                                            eos_id=eos_word_ids, sos_id=sos_word_id, forbid_duplicate_ngrams=args.forbid_duplicate_ngrams, forbid_ignore_set=forbid_ignore_set, ngram_size=args.ngram_size, min_len=args.min_len)
        del model_recover

        model.to(device)

        if args.fp16:
            try:
                from apex import amp
            except ImportError:
                raise ImportError(
                    "Please install apex from https://www.github.com/nvidia/apex to use fp16 training.")
            model = amp.initialize(model, opt_level=args.fp16_opt_level)

        if n_gpu > 1:
            model = torch.nn.DataParallel(model)

        torch.cuda.empty_cache()
        model.eval()
        next_i = 0
        max_src_length = args.max_seq_length - 2 - args.max_tgt_length

        with open(args.input_file, encoding="utf-8") as fin:
            input_lines = [x.split('##')[0].split('$$')[1].strip()+'。' for x in fin.readlines()]
            if args.subset > 0:
                logger.info("Decoding subset: %d", args.subset)
                input_lines = input_lines[:args.subset]
        with open(args.input_aspect, encoding="utf-8") as fin:
            input_aspects = [int(x.strip()) for x in fin.readlines()]
#             if args.subset > 0:
#                 logger.info("Decoding subset: %d", args.subset)
#                 input_lines = input_lines[:args.subset]

        data_tokenizer = WhitespaceTokenizer() if args.tokenized_input else tokenizer
        input_lines = [data_tokenizer.tokenize(
            x)[:max_src_length] for x in input_lines]
        
        input_lines = sorted(list(enumerate(input_lines)),
                             key=lambda x: -len(x[1]))
        new_input_aspects = [input_aspects[i[0]] for i in input_lines]
        output_lines = [""] * len(input_lines)
        score_trace_list = [None] * len(input_lines)
        total_batch = math.ceil(len(input_lines) / args.batch_size)

        with tqdm(total=total_batch) as pbar:
            while next_i < len(input_lines):
                _chunk = input_lines[next_i:next_i + args.batch_size]
                _chunk_asp = new_input_aspects[next_i:next_i + args.batch_size]
                buf_id = [x[0] for x in _chunk]
                buf = [x[1] for x in _chunk]
                next_i += args.batch_size
                max_a_len = max([len(x) for x in buf])
                instances = []
                for instance in [(x, max_a_len, y) for x, y in zip(buf, _chunk_asp)]:
                    for proc in bi_uni_pipeline:
                        instances.append(proc(instance))
                with torch.no_grad():
                    batch = utils_seq2seq.batch_list_to_batch_tensors(
                        instances)
                    batch = [
                        t.to(device) if t is not None else None for t in batch]
                    input_ids, input_ids_pad, token_type_ids, position_ids, input_mask, asps, clss, mask_cls, mask_word_sent_id = batch
                    traces = model(input_ids, input_ids_pad, token_type_ids,
                                   position_ids, input_mask, aspects=asps, clss=clss, mask_cls=mask_cls, mask_word_sent_id=mask_word_sent_id)
                    if args.beam_size > 1:
                        traces = {k: v.tolist() for k, v in traces.items()}
                        output_ids = traces['pred_seq']
                    else:
                        output_ids = traces.tolist()
                    for i in range(len(buf)):
                        w_ids = output_ids[i]
                        output_buf = tokenizer.convert_ids_to_tokens(w_ids)
                        output_tokens = []
                        for t in output_buf:
                            if t in ("[SEP]", "[PAD]"):
                                break
                            output_tokens.append(t)
                        output_sequence = ' '.join(detokenize(output_tokens))
                        output_lines[buf_id[i]] = output_sequence
                        if args.need_score_traces:
                            score_trace_list[buf_id[i]] = {
                                'scores': traces['scores'][i], 'wids': traces['wids'][i], 'ptrs': traces['ptrs'][i]}
                pbar.update(1)
        if args.output_file:
            fn_out = args.output_file
        else:
            fn_out = model_recover_path+'.'+args.split
        with open(fn_out, "w", encoding="utf-8") as fout:
            for l in output_lines:
                fout.write(l)
                fout.write("\n")

        if args.need_score_traces:
            with open(fn_out + ".trace.pickle", "wb") as fout_trace:
                pickle.dump(
                    {"version": 0.0, "num_samples": len(input_lines)}, fout_trace)
                for x in score_trace_list:
                    pickle.dump(x, fout_trace)


if __name__ == "__main__":
    main()