[WIP] Add UniLM model

README.md

@@ -145,7 +145,8 @@ At some point in the future, you'll be able to seamlessly move from pre-training
 10. **[CamemBERT](https://camembert-model.fr)** (from Inria/Facebook/Sorbonne) released with the paper [CamemBERT: a Tasty French Language Model](https://arxiv.org/abs/1911.03894) by Louis Martin*, Benjamin Muller*, Pedro Javier Ortiz Suárez*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot.
 11. **[ALBERT](https://github.com/google-research/ALBERT)** (from Google Research and the Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.
 12. **[T5](https://github.com/google-research/text-to-text-transfer-transformer)** (from Google AI) released with the paper [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/abs/1910.10683) by Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.
-13. Want to contribute a new model? We have added a **detailed guide and templates** to guide you in the process of adding a new model. You can find them in the [`templates`](./templates) folder of the repository. Be sure to check the [contributing guidelines](./CONTRIBUTING.md) and contact the maintainers or open an issue to collect feedbacks before starting your PR.
+13. **[UniLM](https://github.com/microsoft/unilm)** (from Microsoft Research) released with the paper [Unified Language Model Pre-training for Natural Language Understanding and Generation](https://arxiv.org/abs/1905.03197) by Li Dong* and Nan Yang* and Wenhui Wang* and Furu Wei* and Xiaodong Liu and Yu Wang and Jianfeng Gao and Ming Zhou and Hsiao-Wuen Hon.
+14. Want to contribute a new model? We have added a **detailed guide and templates** to guide you in the process of adding a new model. You can find them in the [`templates`](./templates) folder of the repository. Be sure to check the [contributing guidelines](./CONTRIBUTING.md) and contact the maintainers or open an issue to collect feedbacks before starting your PR.
 
 These implementations have been tested on several datasets (see the example scripts) and should match the performances of the original implementations (e.g. ~93 F1 on SQuAD for BERT Whole-Word-Masking, ~88 F1 on RocStories for OpenAI GPT, ~18.3 perplexity on WikiText 103 for Transformer-XL, ~0.916 Peason R coefficient on STS-B for XLNet). You can find more details on the performances in the Examples section of the [documentation](https://huggingface.co/transformers/examples.html).
 

docs/source/pretrained_models.rst

@@ -232,6 +232,11 @@ Here is the full list of the currently provided pretrained models together with
 |                   | ``t5-11B``                                                 | | ~11B parameters with 24-layers, 1024-hidden-state, 65536 feed-forward hidden-state, 128-heads,                                      |
 |                   |                                                            | | Trained on English text: the Colossal Clean Crawled Corpus (C4)                                                                     |
 +-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-
+| UniLM             | ``unilm-base-cased``                                       | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
+|                   |                                                            | | Trained on cased English text.                                                                                                      |
+|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                   | ``unilm-large-cased``                                      | | 24-layer, 1024-hidden, 16-heads, 340M parameters.                                                                                   |
+|                   |                                                            | | Trained on cased English text.                                                                                                      |
++-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 
 .. <https://huggingface.co/transformers/examples.html>`__

examples/decode_seq2seq.py

@@ -0,0 +1,257 @@
+# 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
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+
+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 transformers import (UnilmTokenizer, WhitespaceTokenizer,
+                          UnilmForSeq2SeqDecode, AdamW, WarmupLinearSchedule, 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('--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.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]))
+        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]
+                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) for x in buf]:
+                    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, token_type_ids, position_ids, input_mask = batch
+                    traces = model(input_ids, token_type_ids,
+                                   position_ids, input_mask)
+                    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()