Add BERT QA Scala/Java example

scala-package/core/src/main/scala/org/apache/mxnet/javaapi/Layout.scala

@@ -0,0 +1,34 @@
+/*
+ * 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.
+ */
+package org.apache.mxnet.javaapi
+
+/**
+  * Layout definition of DataDesc
+  * N Batch size
+  * C channels
+  * H Height
+  * W Weight
+  * T sequence length
+  * __undefined__ default value of Layout
+  */
+object Layout {
+  val UNDEFINED: String = org.apache.mxnet.Layout.UNDEFINED
+  val NCHW: String = org.apache.mxnet.Layout.NCHW
+  val NTC: String = org.apache.mxnet.Layout.NTC
+  val NT: String = org.apache.mxnet.Layout.NT
+  val N: String = org.apache.mxnet.Layout.N
+}

scala-package/examples/pom.xml

@@ -145,5 +145,10 @@
       <artifactId>slf4j-simple</artifactId>
       <version>1.7.5</version>
     </dependency>
+    <dependency>
+      <groupId>com.google.code.gson</groupId>
+      <artifactId>gson</artifactId>
+      <version>2.8.5</version>
+    </dependency>
   </dependencies>
 </project>

scala-package/examples/scripts/infer/bert/get_bert_data.sh

@@ -0,0 +1,31 @@
+#!/bin/bash
+
+# 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.
+
+set -e
+
+MXNET_ROOT=$(cd "$(dirname $0)/../../.."; pwd)
+
+data_path=$MXNET_ROOT/scripts/infer/models/static-bert-qa/
+
+if [ ! -d "$data_path" ]; then
+  mkdir -p "$data_path"
+  curl https://s3.us-east-2.amazonaws.com/mxnet-scala/scala-example-ci/BertQA/vocab.json -o $data_path/vocab.json
+  curl https://s3.us-east-2.amazonaws.com/mxnet-scala/scala-example-ci/BertQA/static_bert_qa-0002.params -o $data_path/static_bert_qa-0002.params
+  curl https://s3.us-east-2.amazonaws.com/mxnet-scala/scala-example-ci/BertQA/static_bert_qa-symbol.json -o $data_path/static_bert_qa-symbol.json
+fi

scala-package/examples/scripts/infer/bert/run_bert_qa_example.sh

@@ -0,0 +1,27 @@
+#!/bin/bash
+
+# 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.
+
+set -e
+
+MXNET_ROOT=$(cd "$(dirname $0)/../../../../.."; pwd)
+
+CLASS_PATH=$MXNET_ROOT/scala-package/assembly/target/*:$MXNET_ROOT/scala-package/examples/target/*:$MXNET_ROOT/scala-package/examples/target/classes/lib/*
+
+java -Xmx8G -Dmxnet.traceLeakedObjects=true -cp $CLASS_PATH \
+	org.apache.mxnetexamples.javaapi.infer.bert.BertQA $@

scala-package/examples/src/main/java/org/apache/mxnetexamples/javaapi/infer/bert/BertDataParser.java

@@ -0,0 +1,126 @@
+/*
+ * 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.
+ */
+
+package org.apache.mxnetexamples.javaapi.infer.bert;
+
+import java.io.FileReader;
+import java.util.*;
+
+import com.google.gson.Gson;
+import com.google.gson.JsonArray;
+import com.google.gson.JsonElement;
+import com.google.gson.JsonObject;
+
+/**
+ * This is the Utility for pre-processing the data for Bert Model
+ * You can use this utility to parse Vocabulary JSON into Java Array and Dictionary,
+ * clean and tokenize sentences and pad the text
+ */
+public class BertDataParser {
+
+    private Map<String, Integer> token2idx;
+    private List<String> idx2token;
+
+    /**
+     * Parse the Vocabulary to JSON files
+     * [PAD], [CLS], [SEP], [MASK], [UNK] are reserved tokens
+     * @param jsonFile the filePath of the vocab.json
+     * @throws Exception
+     */
+    void parseJSON(String jsonFile) throws Exception {
+        Gson gson = new Gson();
+        token2idx = new HashMap<>();
+        idx2token = new LinkedList<>();
+        JsonObject jsonObject = gson.fromJson(new FileReader(jsonFile), JsonObject.class);
+        JsonArray arr = jsonObject.getAsJsonArray("idx_to_token");
+        for (JsonElement element : arr) {
+            idx2token.add(element.getAsString());
+        }
+        JsonObject preMap = jsonObject.getAsJsonObject("token_to_idx");
+        for (String key : preMap.keySet()) {
+            token2idx.put(key, preMap.get(key).getAsInt());
+        }
+    }
+
+    /**
+     * Tokenize the input, split all kinds of whitespace and
+     * Separate the end of sentence symbol: . , ? !
+     * @param input The input string
+     * @return List of tokens
+     */
+    List<String> tokenizer(String input) {
+        String[] step1 = input.split("\\s+");
+        List<String> finalResult = new LinkedList<>();
+        for (String item : step1) {
+            if (item.length() != 0) {
+                if ((item + "a").split("[.,?!]+").length > 1) {
+                    finalResult.add(item.substring(0, item.length() - 1));
+                    finalResult.add(item.substring(item.length() -1));
+                } else {
+                    finalResult.add(item);
+                }
+            }
+        }
+        return finalResult;
+    }
+
+    /**
+     * Pad the tokens to the required length
+     * @param tokens input tokens
+     * @param padItem things to pad at the end
+     * @param num total length after padding
+     * @return List of padded tokens
+     */
+    <E> List<E> pad(List<E> tokens, E padItem, int num) {
+        if (tokens.size() >= num) return tokens;
+        List<E> padded = new LinkedList<>(tokens);
+        for (int i = 0; i < num - tokens.size(); i++) {
+            padded.add(padItem);
+        }
+        return padded;
+    }
+
+    /**
+     * Convert tokens to indexes
+     * @param tokens input tokens
+     * @return List of indexes
+     */
+    List<Integer> token2idx(List<String> tokens) {
+        List<Integer> indexes = new ArrayList<>();
+        for (String token : tokens) {
+            if (token2idx.containsKey(token)) {
+                indexes.add(token2idx.get(token));
+            } else {
+                indexes.add(token2idx.get("[UNK]"));
+            }
+        }
+        return indexes;
+    }
+
+    /**
+     * Convert indexes to tokens
+     * @param indexes List of indexes
+     * @return List of tokens
+     */
+    List<String> idx2token(List<Integer> indexes) {
+        List<String> tokens = new ArrayList<>();
+        for (int index : indexes) {
+            tokens.add(idx2token.get(index));
+        }
+        return tokens;
+    }
+}

scala-package/examples/src/main/java/org/apache/mxnetexamples/javaapi/infer/bert/BertQA.java

@@ -0,0 +1,148 @@
+/*
+ * 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.
+ */
+
+package org.apache.mxnetexamples.javaapi.infer.bert;
+
+import org.apache.mxnet.infer.javaapi.Predictor;
+import org.apache.mxnet.javaapi.*;
+import org.kohsuke.args4j.CmdLineParser;
+import org.kohsuke.args4j.Option;
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+import java.util.*;
+
+/**
+ * This is an example of using BERT to do the general Question and Answer inference jobs
+ * Users can provide a question with a paragraph contains answer to the model and
+ * the model will be able to find the best answer from the answer paragraph
+ */
+public class BertQA {
+    @Option(name = "--model-path-prefix", usage = "input model directory and prefix of the model")
+    private String modelPathPrefix = "/model/static_bert_qa";
+    @Option(name = "--model-epoch", usage = "Epoch number of the model")
+    private int epoch = 2;
+    @Option(name = "--model-vocab", usage = "the vocabulary used in the model")
+    private String modelVocab = "/model/vocab.json";
+    @Option(name = "--input-question", usage = "the input question")
+    private String inputQ = "When did BBC Japan start broadcasting?";
+    @Option(name = "--input-answer", usage = "the input answer")
+    private String inputA =
+        "BBC Japan was a general entertainment Channel.\n" +
+                " Which operated between December 2004 and April 2006.\n" +
+            "It ceased operations after its Japanese distributor folded.";
+    @Option(name = "--seq-length", usage = "the maximum length of the sequence")
+    private int seqLength = 384;
+
+    private final static Logger logger = LoggerFactory.getLogger(BertQA.class);
+    private static NDArray$ NDArray = NDArray$.MODULE$;
+
+    private static int argmax(float[] prob) {
+        int maxIdx = 0;
+        for (int i = 0; i < prob.length; i++) {
+            if (prob[maxIdx] < prob[i]) maxIdx = i;
+        }
+        return maxIdx;
+    }
+
+    /**
+     * Do the post processing on the output, apply softmax to get the probabilities
+     * reshape and get the most probable index
+     * @param result prediction result
+     * @param tokens word tokens
+     * @return Answers clipped from the original paragraph
+     */
+    static List<String> postProcessing(NDArray result, List<String> tokens) {
+        NDArray[] output = NDArray.split(
+                NDArray.new splitParam(result, 2).setAxis(2));
+        // Get the formatted logits result
+        NDArray startLogits = output[0].reshape(new int[]{0, -3});
+        NDArray endLogits = output[1].reshape(new int[]{0, -3});
+        // Get Probability distribution
+        float[] startProb = NDArray.softmax(
+                NDArray.new softmaxParam(startLogits))[0].toArray();
+        float[] endProb = NDArray.softmax(
+                NDArray.new softmaxParam(endLogits))[0].toArray();
+        int startIdx = argmax(startProb);
+        int endIdx = argmax(endProb);
+        return tokens.subList(startIdx, endIdx + 1);
+    }
+
+    public static void main(String[] args) throws Exception{
+        BertQA inst = new BertQA();
+        CmdLineParser parser = new CmdLineParser(inst);
+        parser.parseArgument(args);
+        BertDataParser util = new BertDataParser();
+        Context context = Context.cpu();
+        if (System.getenv().containsKey("SCALA_TEST_ON_GPU") &&
+                Integer.valueOf(System.getenv("SCALA_TEST_ON_GPU")) == 1) {
+            context = Context.gpu();
+        }
+        // pre-processing - tokenize sentence
+        List<String> tokenQ = util.tokenizer(inst.inputQ.toLowerCase());
+        List<String> tokenA = util.tokenizer(inst.inputA.toLowerCase());
+        int validLength = tokenQ.size() + tokenA.size();
+        logger.info("Valid length: " + validLength);
+        // generate token types [0000...1111....0000]
+        List<Float> QAEmbedded = new ArrayList<>();
+        util.pad(QAEmbedded, 0f, tokenQ.size()).addAll(
+                util.pad(new ArrayList<Float>(), 1f, tokenA.size())
+        );
+        List<Float> tokenTypes = util.pad(QAEmbedded, 0f, inst.seqLength);
+        // make BERT pre-processing standard
+        tokenQ.add("[SEP]");
+        tokenQ.add(0, "[CLS]");
+        tokenA.add("[SEP]");
+        tokenQ.addAll(tokenA);
+        List<String> tokens = util.pad(tokenQ, "[PAD]", inst.seqLength);
+        logger.info("Pre-processed tokens: " + Arrays.toString(tokenQ.toArray()));
+        // pre-processing - token to index translation
+        util.parseJSON(inst.modelVocab);
+        List<Integer> indexes = util.token2idx(tokens);
+        List<Float> indexesFloat = new ArrayList<>();
+        for (int integer : indexes) {
+            indexesFloat.add((float) integer);
+        }
+        // Preparing the input data
+        List<NDArray> inputBatch = Arrays.asList(
+                new NDArray(indexesFloat,
+                        new Shape(new int[]{1, inst.seqLength}), context),
+                new NDArray(tokenTypes,
+                        new Shape(new int[]{1, inst.seqLength}), context),
+                new NDArray(new float[] { validLength },
+                        new Shape(new int[]{1}), context)
+        );
+        // Build the model
+        List<Context> contexts = new ArrayList<>();
+        contexts.add(context);
+        List<DataDesc> inputDescs = Arrays.asList(
+                new DataDesc("data0",
+                        new Shape(new int[]{1, inst.seqLength}), DType.Float32(), Layout.NT()),
+                new DataDesc("data1",
+                        new Shape(new int[]{1, inst.seqLength}), DType.Float32(), Layout.NT()),
+                new DataDesc("data2",
+                        new Shape(new int[]{1}), DType.Float32(), Layout.N())
+        );
+        Predictor bertQA = new Predictor(inst.modelPathPrefix, inputDescs, contexts, inst.epoch);
+        // Start prediction
+        NDArray result = bertQA.predictWithNDArray(inputBatch).get(0);
+        List<String> answer = postProcessing(result, tokens);
+        logger.info("Question: " + inst.inputQ);
+        logger.info("Answer paragraph: " + inst.inputA);
+        logger.info("Answer: " + Arrays.toString(answer.toArray()));
+    }
+}