Add similarity transformer body (#159)

* Add similarity transformer body

* Update pipeline to Write a single CSV file

* Fix lint errors

* Use CSV writer to handle formatting rows

* Use direct transformer encoding methods with variable scopes

* Complete end-to-end training with new model and problem

* Read from mutliple csv files

code_search/README.md

@@ -63,10 +63,9 @@ Results are saved back into a BigQuery table.
 
 * Execute the `Dataflow` job
 ```
-$ python preprocess/scripts/process_github_archive.py -i files/select_github_archive.sql \
-         -o code_search:function_docstrings -p kubeflow-dev -j process-github-archive \
-         --storage-bucket gs://kubeflow-dev --machine-type n1-highcpu-32 --num-workers 16 \
-         --max-num-workers 16
+$ python preprocess/scripts/process_github_archive.py -p kubeflow-dev -j process-github-archive \
+  --storage-bucket gs://kubeflow-examples/t2t-code-search -o code_search:function_docstrings \
+  --machine-type n1-highcpu-32 --num-workers 16 --max-num-workers 16
 ```
 
 ## 2. Model Training
@@ -102,23 +101,13 @@ See [GCR Pushing and Pulling Images](https://cloud.google.com/container-registry
 
 #### 2.2.1 Function Summarizer
 
-This part generates a model to summarize functions into docstrings using the data generated in previous
-step. It uses `tensor2tensor`.
-
-* Generate `TFRecords` for training
-```
-$ export MOUNT_DATA_DIR=/path/to/data/folder
-$ docker run --rm -it -v ${MOUNT_DATA_DIR}:/data ${BUILD_IMAGE_TAG} \
-    t2t-datagen --problem=github_function_summarizer --data_dir=/data
-```
-
 * Train transduction model using `Tranformer Networks` and a base hyper-parameters set
 ```
 $ export MOUNT_DATA_DIR=/path/to/data/folder
 $ export MOUNT_OUTPUT_DIR=/path/to/output/folder
 $ docker run --rm -it -v ${MOUNT_DATA_DIR}:/data -v ${MOUNT_OUTPUT_DIR}:/output ${BUILD_IMAGE_TAG} \
-    t2t-trainer --problem=github_function_summarizer --data_dir=/data --output_dir=/output \
-                --model=transformer --hparams_set=transformer_base
+    --generate_data --problem=github_function_docstring --data_dir=/data --output_dir=/output \
+    --model=similarity_transformer --hparams_set=transformer_tiny
 ```
 
 ### 2.2 Train on Kubeflow

code_search/app/code_search/t2t/__init__.py

@@ -1 +1,2 @@
-import code_search.t2t.similarity_transformer
+from . import function_docstring
+from . import similarity_transformer

code_search/app/code_search/t2t/function_docstring.py

@@ -0,0 +1,40 @@
+"""Github function/text similatrity problems."""
+import csv
+import glob
+import os
+from tensor2tensor.data_generators import text_problems
+from tensor2tensor.utils import metrics
+from tensor2tensor.utils import registry
+
+
+@registry.register_problem
+class GithubFunctionDocstring(text_problems.Text2TextProblem):
+  # pylint: disable=abstract-method
+
+  """This class defines the problem of finding similarity between Python
+  function and docstring"""
+
+  @property
+  def is_generate_per_split(self):
+    return False
+
+  @property
+  def approx_vocab_size(self):
+    return 2**13
+
+  def generate_samples(self, data_dir, tmp_dir, dataset_split):  # pylint: disable=no-self-use,unused-argument
+    """Returns a generator to return {"inputs": [text], "targets": [text]}."""
+
+    # TODO(sanyamkapoor): separate train/eval data set.
+    pair_files_glob = os.path.join(data_dir, 'pairs-*.csv')
+    for pairs_file_path in glob.glob(pair_files_glob):
+      with open(pairs_file_path, 'r') as csv_file:
+        pairs_reader = csv.reader(csv_file)
+        for row in pairs_reader:
+          function_tokens, docstring_tokens = row[-2:]
+          yield {'inputs': docstring_tokens, 'targets': function_tokens}
+
+  def eval_metrics(self):  # pylint: disable=no-self-use
+    return [
+        metrics.Metrics.ACC
+    ]

code_search/app/code_search/t2t/similarity_transformer.py

@@ -1,7 +1,10 @@
-import os
-from tensor2tensor.utils import t2t_model
+"""Using Transformer Networks for String similarities."""
+from tensor2tensor.data_generators import problem
+from tensor2tensor.layers import common_layers
+from tensor2tensor.models import transformer
 from tensor2tensor.utils import registry
-from tensor2tensor.data_generators import text_problems
+from tensor2tensor.utils import t2t_model
+import tensorflow as tf
 
 
 @registry.register_model
@@ -9,31 +12,56 @@ class SimilarityTransformer(t2t_model.T2TModel):
   # pylint: disable=abstract-method
 
   """
-  This class defines the model to compute similarity scores between functions and
-  docstrings
+  This class defines the model to compute similarity scores between functions
+  and docstrings
   """
 
   def body(self, features):
-    # TODO: need to fill this with Transformer encoder/decoder
-    # and loss calculation
-    raise NotImplementedError
+    """Body of the Similarity Transformer Network."""
 
+    with tf.variable_scope('string_embedding'):
+      string_embedding = self.encode(features, 'inputs')
 
-@registry.register_problem
-class GithubFunctionDocstring(text_problems.Text2TextProblem):
-  # pylint: disable=abstract-method
+    loss = None
+    if 'targets' in features:
+      with tf.variable_scope('code_embedding'):
+        code_embedding = self.encode(features, 'targets')
 
-  """This class defines the problem of finding similarity between Python function
-   and docstring"""
+      cosine_dist = tf.losses.cosine_distance(
+          tf.nn.l2_normalize(string_embedding, axis=1),
+          tf.nn.l2_normalize(code_embedding, axis=1),
+          axis=1, reduction=tf.losses.Reduction.NONE)
 
-  @property
-  def is_generate_per_split(self):
-    return False
+      # TODO(sanyamkapoor): need negative sampling, won't be all ones anymore.
+      labels = tf.one_hot(tf.ones(
+          tf.shape(features['targets'])[0], tf.int32), 2)
+      logits = tf.concat([cosine_dist, 1 - cosine_dist], axis=1)
 
-  def generate_samples(self, data_dir, _tmp_dir, dataset_split):  #pylint: disable=no-self-use
-    """This method returns the generator to return {"inputs": [text], "targets": [text]} dict"""
+      loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=labels,
+                                                     logits=logits)
 
-    functions_file_path = os.path.join(data_dir, '{}.function'.format(dataset_split))
-    docstrings_file_path = os.path.join(data_dir, '{}.docstring'.format(dataset_split))
+    if loss is not None:
+      return string_embedding, loss
 
-    return text_problems.text2text_txt_iterator(functions_file_path, docstrings_file_path)
+    return string_embedding
+
+  def encode(self, features, input_key):
+    hparams = self._hparams
+    inputs = common_layers.flatten4d3d(features[input_key])
+
+    (encoder_input, encoder_self_attention_bias, _) = (
+        transformer.transformer_prepare_encoder(inputs, problem.SpaceID.EN_TOK,
+                                                self._hparams))
+
+    encoder_input = tf.nn.dropout(encoder_input,
+                                  1.0 - hparams.layer_prepostprocess_dropout)
+    encoder_output = transformer.transformer_encoder(
+        encoder_input,
+        encoder_self_attention_bias,
+        self._hparams,
+        nonpadding=transformer.features_to_nonpadding(features, input_key))
+    encoder_output = tf.expand_dims(encoder_output, 2)
+
+    encoder_output = tf.reduce_mean(tf.squeeze(encoder_output, axis=2), axis=1)
+
+    return encoder_output

code_search/preprocess/preprocess/pipeline.py

@@ -1,8 +1,9 @@
 import os
 import logging
 import time
+import csv
+import io
 import apache_beam as beam
-import apache_beam.io as io
 from apache_beam import pvalue
 from apache_beam.metrics import Metrics
 from apache_beam.options.pipeline_options import StandardOptions, PipelineOptions, \
@@ -117,11 +118,11 @@ class ProcessGithubFiles(beam.PTransform):
                          'function_tokens', 'docstring_tokens']
     self.data_types = ['STRING', 'STRING', 'STRING', 'INTEGER', 'STRING', 'STRING', 'STRING']
 
-    self.num_shards = 1
+    self.num_shards = 10
 
   def expand(self, input_or_inputs):
     tokenize_result = (input_or_inputs
-      | "Read Github Dataset" >> io.Read(io.BigQuerySource(query=self.query_string,
+      | "Read Github Dataset" >> beam.io.Read(beam.io.BigQuerySource(query=self.query_string,
                                                           use_standard_sql=True))
       | "Split 'repo_path'" >> beam.ParDo(SplitRepoPath())
       | "Tokenize Code/Docstring Pairs" >> beam.ParDo(TokenizeCodeDocstring())
@@ -130,7 +131,7 @@ class ProcessGithubFiles(beam.PTransform):
 
     #pylint: disable=expression-not-assigned
     (tokenize_result.err_rows
-     | "Failed Row Tokenization" >> io.WriteToBigQuery(project=self.project,
+     | "Failed Row Tokenization" >> beam.io.WriteToBigQuery(project=self.project,
                                                         dataset=self.output_dataset,
                                                         table=self.output_table + '_failed',
                                                         schema=self.create_failed_output_schema())
@@ -145,7 +146,7 @@ class ProcessGithubFiles(beam.PTransform):
 
     #pylint: disable=expression-not-assigned
     (info_result.err_rows
-     | "Failed Function Info" >> io.WriteToBigQuery(project=self.project,
+     | "Failed Function Info" >> beam.io.WriteToBigQuery(project=self.project,
                                                         dataset=self.output_dataset,
                                                         table=self.output_table + '_failed',
                                                         schema=self.create_failed_output_schema())
@@ -156,24 +157,43 @@ class ProcessGithubFiles(beam.PTransform):
 
     # pylint: disable=expression-not-assigned
     (processed_rows
-     | "Filter Function tokens" >> beam.Map(lambda x: x['function_tokens'])
-     | "Write Function tokens" >> io.WriteToText('{}/raw_data/data'.format(self.storage_bucket),
-                                                 file_name_suffix='.function',
-                                                 num_shards=self.num_shards))
-    (processed_rows
-     | "Filter Docstring tokens" >> beam.Map(lambda x: x['docstring_tokens'])
-     | "Write Docstring tokens" >> io.WriteToText('{}/raw_data/data'.format(self.storage_bucket),
-                                                  file_name_suffix='.docstring',
-                                                  num_shards=self.num_shards))
+     | "Filter Tiny Docstrings" >> beam.Filter(
+        lambda row: len(row['docstring_tokens'].split(' ')) > 5)
+     | "Format For Write" >> beam.Map(self.format_for_write)
+     | "Write To File" >> beam.io.WriteToText('{}/data/pairs'.format(self.storage_bucket),
+                                         file_name_suffix='.csv',
+                                         num_shards=self.num_shards))
     # pylint: enable=expression-not-assigned
 
     return (processed_rows
-      | "Save Tokens" >> io.WriteToBigQuery(project=self.project,
+      | "Save Tokens" >> beam.io.WriteToBigQuery(project=self.project,
                                                   dataset=self.output_dataset,
                                                   table=self.output_table,
                                                   schema=self.create_output_schema())
     )
 
+  def format_for_write(self, row):
+    """This method filters keys that we don't need in the
+    final CSV. It must ensure that there are no multi-line
+    column fields. For instance, 'original_function' is a
+    multi-line string and makes CSV parsing hard for any
+    derived Dataflow steps. This uses the CSV Writer
+    to handle all edge cases like quote escaping."""
+
+    filter_keys = [
+        'original_function',
+        'lineno',
+    ]
+    target_keys = [col for col in self.data_columns if col not in filter_keys]
+    target_values = [row[key].encode('utf-8') for key in target_keys]
+
+    with io.BytesIO() as fs:
+      cw = csv.writer(fs)
+      cw.writerow(target_values)
+      result_str = fs.getvalue().strip('\r\n')
+
+    return result_str
+
   def create_output_schema(self):
     table_schema = bigquery.TableSchema()
 

code_search/preprocess/requirements.txt

@@ -1,4 +1,4 @@
 astor~=0.6.0
-apache-beam[gcp]~=2.4.0
+apache-beam[gcp]~=2.5.0
 nltk~=3.3.0
 spacy~=2.0.0

code_search/preprocess/scripts/process_github_archive.py

@@ -1,5 +1,6 @@
 from __future__ import print_function
 import argparse
+import os
 import apache_beam as beam
 
 from preprocess.pipeline import create_pipeline_opts, ProcessGithubFiles
@@ -7,7 +8,10 @@ from preprocess.pipeline import create_pipeline_opts, ProcessGithubFiles
 
 def parse_arguments(args):
   parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
-  parser.add_argument('-i', '--input', metavar='', type=str, help='Path to BigQuery SQL script')
+
+  default_script_file = os.path.abspath('{}/../../files/select_github_archive.sql'.format(__file__))
+  parser.add_argument('-i', '--input', metavar='', type=str, default=default_script_file,
+                      help='Path to BigQuery SQL script')
   parser.add_argument('-o', '--output', metavar='', type=str,
                       help='Output string of the format <dataset>:<table>')
   parser.add_argument('-p', '--project', metavar='', type=str, default='Project', help='Project ID')