[Enhancement] One script to export bert for classification/regression…

…/QA (#705)

* rename folder

* rename folder

* draft for export

* suppport qa,regression, classification

* add readme and tasks

* fix lint

* fix unit test

* fix test

* remove test_bert_static_base_export

* address comments

* update default seq len

scripts/bert/staticbert/__init__.py → scripts/bert/export/__init__.py

@@ -18,5 +18,5 @@
 # under the License.
 
 # pylint: disable=wildcard-import
-"""static BERT example."""
-from . import static_bert, static_bert_qa_model
+"""Hybrid BERT for deployment."""
+from . import hybrid_bert

scripts/bert/export/export.py

@@ -0,0 +1,217 @@
+"""
+Export the BERT Model for Deployment
+
+====================================
+
+This script exports the BERT model to a hybrid model serialized as a symbol.json file,
+which is suitable for deployment, or use with MXNet Module API.
+
+@article{devlin2018bert,
+  title={BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding},
+  author={Devlin, Jacob and Chang, Ming- \
+      Wei and Lee, Kenton and Toutanova, Kristina},
+  journal={arXiv preprint arXiv:1810.04805},
+  year={2018}
+}
+"""
+
+# coding=utf-8
+
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you 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.
+# pylint:disable=redefined-outer-name,logging-format-interpolation
+
+import argparse
+import logging
+import warnings
+import os
+import time
+
+import mxnet as mx
+import gluonnlp as nlp
+from hybrid_bert import get_hybrid_model
+from hybrid_bert import HybridBERTClassifier, HybridBERTRegression, HybridBERTForQA
+
+parser = argparse.ArgumentParser(description='Export hybrid BERT base model.')
+
+parser.add_argument('--model_parameters',
+                    type=str,
+                    default=None,
+                    help='The model parameter file saved from training.')
+
+parser.add_argument('--model_name',
+                    type=str,
+                    default='bert_12_768_12',
+                    choices=['bert_12_768_12', 'bert_24_1024_16'],
+                    help='BERT model name. Options are "bert_12_768_12" and "bert_24_1024_16"')
+
+parser.add_argument('--task',
+                    type=str,
+                    choices=['classification', 'regression', 'question_answering'],
+                    required=True,
+                    help='Task to export. Options are "classification", "regression", '
+                         '"question_answering"')
+
+parser.add_argument('--dataset_name',
+                    type=str,
+                    default='book_corpus_wiki_en_uncased',
+                    choices=['book_corpus_wiki_en_uncased', 'book_corpus_wiki_en_cased',
+                             'wiki_multilingual_uncased', 'wiki_multilingual_cased',
+                             'wiki_cn_cased'],
+                    help='BERT dataset name. Options include '
+                         '"book_corpus_wiki_en_uncased", "book_corpus_wiki_en_cased", '
+                         '"wiki_multilingual_uncased", "wiki_multilingual_cased", '
+                         '"wiki_cn_cased"')
+
+parser.add_argument('--output_dir',
+                    type=str,
+                    default='./output_dir',
+                    help='The directory where the exported model symbol will be created. '
+                         'The default is ./output_dir')
+
+parser.add_argument('--seq_length',
+                    type=int,
+                    default=384,
+                    help='The maximum total input sequence length after WordPiece tokenization.'
+                         'Sequences longer than this needs to be truncated, and sequences shorter '
+                         'than this needs to be padded. Default is 384')
+
+parser.add_argument('--dropout',
+                    type=float,
+                    default=0.1,
+                    help='The dropout probability for the classification/regression head.')
+
+args = parser.parse_args()
+
+# create output dir
+output_dir = args.output_dir
+nlp.utils.mkdir(output_dir)
+
+###############################################################################
+#                                Logging                                      #
+###############################################################################
+
+log = logging.getLogger('gluonnlp')
+log.setLevel(logging.DEBUG)
+formatter = logging.Formatter(fmt='%(levelname)s:%(name)s:%(asctime)s %(message)s',
+                              datefmt='%H:%M:%S')
+fh = logging.FileHandler(os.path.join(args.output_dir, 'hybrid_export_bert.log'), mode='w')
+fh.setLevel(logging.INFO)
+fh.setFormatter(formatter)
+console = logging.StreamHandler()
+console.setLevel(logging.INFO)
+console.setFormatter(formatter)
+log.addHandler(console)
+log.addHandler(fh)
+log.info(args)
+
+###############################################################################
+#                              Hybridize the model                            #
+###############################################################################
+
+seq_length = args.seq_length
+
+if args.task == 'classification':
+    bert, _ = get_hybrid_model(
+        name=args.model_name,
+        dataset_name=args.dataset_name,
+        pretrained=False,
+        use_pooler=True,
+        use_decoder=False,
+        use_classifier=False,
+        seq_length=args.seq_length)
+    net = HybridBERTClassifier(bert, num_classes=2, dropout=args.dropout)
+elif args.task == 'regression':
+    bert, _ = get_hybrid_model(
+        name=args.model_name,
+        dataset_name=args.dataset_name,
+        pretrained=False,
+        use_pooler=True,
+        use_decoder=False,
+        use_classifier=False,
+        seq_length=args.seq_length)
+    net = HybridBERTRegression(bert, dropout=args.dropout)
+elif args.task == 'question_answering':
+    bert, _ = get_hybrid_model(
+        name=args.model_name,
+        dataset_name=args.dataset_name,
+        pretrained=False,
+        use_pooler=False,
+        use_decoder=False,
+        use_classifier=False,
+        seq_length=args.seq_length)
+    net = HybridBERTForQA(bert)
+else:
+    raise ValueError('unknown task: %s'%args.task)
+
+if args.model_parameters:
+    net.load_parameters(args.model_parameters)
+else:
+    net.initialize()
+    warnings.warn('--model_parameters is not provided. The parameter checkpoint (.params) '
+                  'file will be created based on default parameter intialization.')
+
+net.hybridize(static_alloc=True, static_shape=True)
+
+###############################################################################
+#                            Prepare dummy input data                         #
+###############################################################################
+
+test_batch_size = 1
+
+inputs = mx.nd.arange(test_batch_size * seq_length)
+inputs = inputs.reshape(shape=(test_batch_size, seq_length))
+token_types = mx.nd.zeros_like(inputs)
+valid_length = mx.nd.arange(test_batch_size)
+batch = inputs, token_types, valid_length
+
+def export(batch, prefix):
+    """Export the model."""
+    log.info('Exporting the model ... ')
+    inputs, token_types, valid_length = batch
+    net(inputs, token_types, valid_length)
+    net.export(prefix, epoch=0)
+    assert os.path.isfile(prefix + '-symbol.json')
+    assert os.path.isfile(prefix + '-0000.params')
+
+def infer(batch, prefix):
+    """Evaluate the model on a mini-batch."""
+    log.info('Start inference ... ')
+
+    # import with SymbolBlock. Alternatively, you can use Module.load APIs.
+    imported_net = mx.gluon.nn.SymbolBlock.imports(prefix + '-symbol.json',
+                                                   ['data0', 'data1', 'data2'],
+                                                   prefix + '-0000.params')
+    tic = time.time()
+    # run forward inference
+    inputs, token_types, valid_length = batch
+    num_trials = 10
+    for _ in range(num_trials):
+        imported_net(inputs, token_types, valid_length)
+    mx.nd.waitall()
+    toc = time.time()
+    log.info('Inference time cost={:.2f} s, Thoughput={:.2f} samples/s'
+             .format(toc - tic, num_trials / (toc - tic)))
+
+
+###############################################################################
+#                              Export the model                               #
+###############################################################################
+if __name__ == '__main__':
+    prefix = os.path.join(args.output_dir, args.task)
+    export(batch, prefix)
+    infer(batch, prefix)