modified to take advantage of multiple CPUs locally

README.md

@@ -626,6 +626,10 @@ configs:
 
 # Dataset Card for Wikipedia
 
+This repo is a fork of the original Hugging Face Wikipedia repo [here](https://huggingface.co/datasets/wikipedia).
+The difference is that this fork does away with the need for `apache-beam`, and this fork is also very fast if you have a lot of CPUs on your machine.
+It will also use all CPUs available to create a clean Wikipedia pretraining dataset. It takes less than an hour to process all of English wikipedia on a GCP n1-standard-96.
+
 ## Table of Contents
 - [Dataset Description](#dataset-description)
   - [Dataset Summary](#dataset-summary)
@@ -667,10 +671,10 @@ markdown and unwanted sections (references, etc.).
 
 The articles are parsed using the ``mwparserfromhell`` tool.
 
-To load this dataset you need to install Apache Beam and ``mwparserfromhell`` first:
+To load this dataset you need to install ``mwparserfromhell`` first:
 
 ```
-pip install apache_beam mwparserfromhell
+pip install mwparserfromhell
 ```
 
 Then, you can load any subset of Wikipedia per language and per date this way:
@@ -678,11 +682,8 @@ Then, you can load any subset of Wikipedia per language and per date this way:
 ```python
 from datasets import load_dataset
 
-load_dataset("wikipedia", language="sw", date="20220120", beam_runner=...)
+load_dataset("wikipedia", language="sw", date="20220120")
 ```
-where you can pass as `beam_runner` any Apache Beam supported runner for (distributed) data processing 
-(see [here](https://beam.apache.org/documentation/runners/capability-matrix/)).
-Pass "DirectRunner" to run it on your machine. 
 
 You can find the full list of languages and dates [here](https://dumps.wikimedia.org/backup-index.html).
 
@@ -856,4 +857,4 @@ the text.
 
 ### Contributions
 
-Thanks to [@lewtun](https://github.com/lewtun), [@mariamabarham](https://github.com/mariamabarham), [@thomwolf](https://github.com/thomwolf), [@lhoestq](https://github.com/lhoestq), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset.
+Thanks to [@lewtun](https://github.com/lewtun), [@mariamabarham](https://github.com/mariamabarham), [@thomwolf](https://github.com/thomwolf), [@lhoestq](https://github.com/lhoestq), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset.

wikipedia.py

@@ -23,6 +23,9 @@ import json
 import re
 import xml.etree.cElementTree as etree
 from urllib.parse import quote
+import mwparserfromhell
+from multiprocessing import Process, Manager
+from tqdm import tqdm
 
 import datasets
 
@@ -904,7 +907,7 @@ class WikipediaConfig(datasets.BuilderConfig):
 _DATE = "20220301"
 
 
-class Wikipedia(datasets.BeamBasedBuilder):
+class Wikipedia(datasets.GeneratorBasedBuilder):
     """Wikipedia dataset."""
 
     # Use mirror (your.org) to avoid download caps.
@@ -934,7 +937,7 @@ class Wikipedia(datasets.BeamBasedBuilder):
             citation=_CITATION,
         )
 
-    def _split_generators(self, dl_manager, pipeline):
+    def _split_generators(self, dl_manager):
         def _base_url(lang):
             return _BASE_URL_TMPL.format(lang=lang.replace("-", "_"), date=self.config.date)
 
@@ -963,9 +966,10 @@ class Wikipedia(datasets.BeamBasedBuilder):
             xml_urls.append(_base_url(lang) + fname)
 
             # Use dictionary since testing mock always returns the same result.
+
+        print("Dowloading Wikipedia dump")
         downloaded_files = dl_manager.download({"xml": xml_urls})
-        if not pipeline.is_local():
-            downloaded_files = dl_manager.ship_files_with_pipeline(downloaded_files, pipeline)
+        print("Finished downloading Wikipedia dump")
 
         return [
             datasets.SplitGenerator(  # pylint:disable=g-complex-comprehension
@@ -973,43 +977,40 @@ class Wikipedia(datasets.BeamBasedBuilder):
             )
         ]
 
-    def _build_pcollection(self, pipeline, filepaths, language):
-        """Build PCollection of examples in the raw (text) form."""
-        import apache_beam as beam
-        import mwparserfromhell
+    def _generate_examples(self, filepaths, language, no_labels=False):
+
 
         def _extract_content(filepath):
             """Extracts article content from a single WikiMedia XML file."""
             logger.info("generating examples from = %s", filepath)
-            with beam.io.filesystems.FileSystems.open(filepath) as f:
-                f = bz2.BZ2File(filename=f)
-                # Workaround due to: https://github.com/tensorflow/tensorflow/issues/33563
-                utf_f = codecs.getreader("utf-8")(f)
-                context = etree.iterparse(utf_f, events=("end",))
-                for unused_event, elem in context:
-                    if not elem.tag.endswith("page"):
-                        continue
-                    namespace = elem.tag[:-4]
-                    title = elem.find(f"./{namespace}title").text
-                    ns = elem.find(f"./{namespace}ns").text
-                    id_ = elem.find(f"./{namespace}id").text
-                    red_ = elem.find(f"./{namespace}redirect")
-
-                    # Filter pages that are not in the "main" namespace.
-                    if ns != "0":
-                        elem.clear()
-                        continue
-
-                    raw_content = elem.find(f"./{namespace}revision/{namespace}text").text
+            content = []
+            f = bz2.BZ2File(filename=filepath)
+            # Workaround due to: https://github.com/tensorflow/tensorflow/issues/33563
+            utf_f = codecs.getreader("utf-8")(f)
+            context = etree.iterparse(utf_f, events=("end",))
+            for unused_event, elem in context:
+                if not elem.tag.endswith("page"):
+                    continue
+                namespace = elem.tag[:-4]
+                title = elem.find(f"./{namespace}title").text
+                ns = elem.find(f"./{namespace}ns").text
+                id_ = elem.find(f"./{namespace}id").text
+                red_ = elem.find(f"./{namespace}redirect")
+
+                # Filter pages that are not in the "main" namespace.
+                if ns != "0":
                     elem.clear()
+                    continue
+
+                raw_content = elem.find(f"./{namespace}revision/{namespace}text").text
+                elem.clear()
 
-                    # Filter redirects.
-                    if raw_content is None or red_ is not None:
-                        beam.metrics.Metrics.counter(language, "filtered-redirects").inc()
-                        continue
+                # Filter redirects.
+                if raw_content is None or red_ is not None:
+                    continue
 
-                    beam.metrics.Metrics.counter(language, "extracted-examples").inc()
-                    yield (id_, title, raw_content)
+                content.append((id_, title, raw_content))
+            return content
 
         def _clean_content(inputs, language):
             """Cleans raw wikicode to extract text."""
@@ -1017,28 +1018,40 @@ class Wikipedia(datasets.BeamBasedBuilder):
             try:
                 text = _parse_and_clean_wikicode(raw_content, parser=mwparserfromhell, language=language)
             except (mwparserfromhell.parser.ParserError) as e:
-                beam.metrics.Metrics.counter(language, "parser-error").inc()
                 logger.error("mwparserfromhell ParseError: %s", e)
                 return
 
             if not text:
-                beam.metrics.Metrics.counter(language, "empty-clean-examples").inc()
                 return
 
             url = _construct_url(title, language)
 
-            beam.metrics.Metrics.counter(language, "cleaned-examples").inc()
+            return id_, {"id": id_, "url": url, "title": title, "text": text}
 
-            yield id_, {"id": id_, "url": url, "title": title, "text": text}
+        print("Parsing and cleaning Wikipedia examples")
+        with Manager() as manager:
+            examples = manager.list()
+            processes = []
+            for filepath in filepaths:
+                def parse_and_clean(examples):
+                    content = _extract_content(filepath)
+                    for obj in tqdm(content):
+                        examples.append(_clean_content(obj, language=language))
+                p = Process(target=parse_and_clean, args=(examples,))
+                p.start()
+                processes.append(p)
 
-        return (
-            pipeline
-            | "Initialize" >> beam.Create(filepaths)
-            | "Extract content" >> beam.FlatMap(_extract_content)
-            | "Distribute" >> beam.transforms.Reshuffle()
-            | "Clean content" >> beam.FlatMap(_clean_content, language=language)
-        )
+            for p in processes:
+                p.join()
+
+            print("Parsed and cleaned Wikipedia examples")
+
+            for example in examples:
+                if example is not None:
+                    yield example
+        
 
+            
 
 def _parse_and_clean_wikicode(raw_content, parser, language):
     """Strips formatting and unwanted sections from raw page content."""