Add MMS example (#1140)

https://github.com/awslabs/multi-model-server/

Author: leezu

mms/README.rst

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+Multi-model-server example
+==========================
+
+https://github.com/awslabs/multi-model-server/
+
+Assuming you are located in the root of the GluonNLP repo, you can run this
+example via:
+
+```
+pip install --user multi-model-server
+curl https://dist-bert.s3.amazonaws.com/demo/finetune/sst.params -o mms/sst.params
+~/.local/bin/model-archiver --model-name bert_sst --model-path mms --handler bert:handle --runtime python --export-path /tmp
+~/.local/bin/multi-model-server --start --models bert_sst.mar --model-store /tmp
+curl -X POST http://127.0.0.1:8080/bert_sst/predict -F 'data=["Positive sentiment", "Negative sentiment"]'
+```
+
+

mms/bert.py

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+# 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.
+import json
+import logging
+
+import mxnet as mx
+import gluonnlp as nlp
+
+
+class BertHandler:
+    """GluonNLP based Bert Handler"""
+
+    def __init__(self):
+        self.error = None
+        self._context = None
+        self.initialized = False
+
+    def initialize(self, context):
+        """
+        Initialize model. This will be called during model loading time
+        :param context: Initial context contains model server system properties.
+        :return:
+        """
+        self._context = context
+        gpu_id = context.system_properties["gpu_id"]
+        self._mx_ctx = mx.cpu() if gpu_id is None else mx.gpu(gpu_id)
+        bert, vocab = nlp.model.get_model('bert_12_768_12',
+                                          dataset_name='book_corpus_wiki_en_uncased',
+                                          pretrained=False, ctx=self._mx_ctx, use_pooler=True,
+                                          use_decoder=False, use_classifier=False)
+        tokenizer = nlp.data.BERTTokenizer(vocab, lower=True)
+        self.sentence_transform = nlp.data.BERTSentenceTransform(tokenizer, max_seq_length=128,
+                                                                 vocab=vocab, pad=True, pair=False)
+        self.batchify = nlp.data.batchify.Tuple(
+            nlp.data.batchify.Pad(axis=0, pad_val=vocab[vocab.padding_token]),  # input
+            nlp.data.batchify.Stack(),  # length
+            nlp.data.batchify.Pad(axis=0, pad_val=0))  # segment
+        # Set dropout to non-zero, to match pretrained model parameter names
+        self.net = nlp.model.BERTClassifier(bert, dropout=0.1)
+        self.net.load_parameters('sst.params', self._mx_ctx)
+        self.net.hybridize()
+
+        self.initialized = True
+
+    def handle(self, batch, context):
+        # we're just faking batch_size==1 but allow dynamic batch size. Ie the
+        # actual batch size is the len of the first element.
+        try:
+            assert len(batch) == 1
+            batch = json.loads(batch[0]["data"].decode('utf-8'))
+        except (json.JSONDecodeError, KeyError, AssertionError) as e:
+            print('call like: curl -X POST http://127.0.0.1:8080/bert_sst/predict '
+                  '-F \'data=["sentence 1", "sentence 2"]\'')
+            raise e
+        model_input = self.batchify([self.sentence_transform(sentence) for sentence in batch])
+
+        inputs, valid_length, token_types = [arr.as_in_context(self._mx_ctx) for arr in model_input]
+        inference_output = self.net(inputs, token_types, valid_length.astype('float32'))
+        inference_output = inference_output.as_in_context(mx.cpu())
+
+        return [mx.nd.softmax(inference_output).argmax(axis=1).astype('int').asnumpy().tolist()]
+
+
+_service = BertHandler()
+
+
+def handle(data, context):
+    if not _service.initialized:
+        _service.initialize(context)
+
+    if data is None:
+        return None
+
+    return _service.handle(data, context)