Merge branch 'master' of github.com:EleutherAI/lm_evaluation_harness

lm_eval/models/gpt2.py

@@ -71,10 +71,8 @@ class GPT2LM(LM):
         loglikelihoods = []
         with torch.no_grad():
             for string, in tqdm(requests):
-                encoded = self.tokenizer.encode_plus(string)["input_ids"]
-
                 rolling_token_windows = list(map(utils.make_disjoint_window, utils.get_rolling_token_windows(
-                    token_list=encoded,
+                    token_list=self.tokenizer.encode(string),
                     prefix_token=self.EOT_TOKEN_ID,
                     max_seq_len=self.max_length,
                     context_len=1,

scripts/clean_training_data/archiver.py

+import os
+import zstandard
+import json
+import jsonlines
+import io
+import datetime
+
+def json_serial(obj):
+    """JSON serializer for objects not serializable by default json code"""
+
+    if isinstance(obj, (datetime.datetime,)):
+        return obj.isoformat()
+    raise TypeError ("Type %s not serializable" % type(obj))
+
+# Modified version of lm_dataformat Archive for single file.
+class Archive:
+    def __init__(self, file_path, compression_level=3):
+        self.file_path = file_path
+        dir_name = os.path.dirname(file_path)
+        if dir_name:
+            os.makedirs(dir_name, exist_ok=True)    
+        self.fh = open(self.file_path, 'wb')
+        self.cctx = zstandard.ZstdCompressor(level=compression_level)
+        self.compressor = self.cctx.stream_writer(self.fh)        
+    
+    def add_data(self, data, meta={}):
+        self.compressor.write(json.dumps({'text': data, 'meta': meta}, default=json_serial).encode('UTF-8') + b'\n')
+    
+    def commit(self):
+        self.compressor.flush(zstandard.FLUSH_FRAME)        
+        self.fh.flush()
+        self.fh.close()
+
+# Modified version of lm_dataformat Reader with self.fh set, allowing peeking for tqdm.
+class Reader:
+    def __init__(self):
+        pass
+
+    def read(self, file, get_meta=False, autojoin_paragraphs=True, para_joiner='\n\n'):
+        with open(file, 'rb') as fh:
+            self.fh = fh
+            cctx = zstandard.ZstdDecompressor()
+            reader = io.BufferedReader(cctx.stream_reader(fh))
+            rdr = jsonlines.Reader(reader)
+            for ob in rdr:
+                # naive jsonl where each object is just the string itself, with no meta. For legacy compatibility.
+                if isinstance(ob, str):
+                    assert not get_meta
+                    yield ob
+                    continue
+
+                text = ob['text']
+
+                if autojoin_paragraphs and isinstance(text, list):
+                    text = para_joiner.join(text)
+
+                if get_meta:
+                    yield text, (ob['meta'] if 'meta' in ob else {})
+                else:
+                    yield text
+
+# Simple text reader and writer with same interface as above
+class TextArchive:
+    def __init__(self, file_path, mode="ab"):
+        self.file_path = file_path
+        dir_name = os.path.dirname(file_path)
+        if dir_name:
+            os.makedirs(dir_name, exist_ok=True)    
+        self.fh = open(self.file_path, mode)      
+    
+    def add_data(self, data, meta={}):
+        self.fh.write(data.encode('UTF-8') + b'\n')
+    
+    def commit(self):
+        self.fh.flush()
+        self.fh.close()
+
+class TextReader:
+    def __init__(self, file_path):
+        self.file_path = file_path
+
+    def read(self):
+        with open(self.file_path, 'r', encoding="utf8") as fh:
+            self.fh = fh
+            while True:
+                line = self.fh.readline()
+                if line == -1 or line == "":
+                    break
+                else :
+                    yield line[:-1]
\ No newline at end of file

scripts/clean_training_data/generate_13_grams.py

+"""
+Outputs all 13-grams found in The Pile.
+
+Loops through all documents and uses the logic found in janitor.py to extract 13-grams. 
+We bucket each 13-gram by hash into separate file buckets to allow easy parallel processing in the 
+next stage. We also include the current pile document_id with each ngram instance to allow the 
+filtering to exclude 13-grams that match more then 10 unique documents (done further down the pipeline).
+
+We didn't use lm_dataformat to output as it increases time 4x (slow jsonify) and makes
+resuming hard (and we had the storage).
+
+Arguments
+---------
+--working_directory (-dir)
+    Directory containing the pile distribution. An "output" subdirectory will be created underneath
+    to store the bucketed 13-grams, checkpoint and done files. Default: current directory
+--n_value (-n)
+    n value in n-gram, added for later use if ever needed. Default: 13
+--bucket_count (-buckets)
+    Number of file buckets to use when generating 13grams. Default: 500
+"""
+
+import argparse
+import pickle
+import os
+from pathlib import Path
+import glob
+import signal
+from signal import SIGINT
+
+from tqdm import tqdm
+
+from scripts.clean_training_data.janitor import Janitor, word_ngrams
+from scripts.clean_training_data.archiver import TextArchive, Reader
+
+import logging
+from tqdm_multiprocess.logger import setup_logger_tqdm
+logger = logging.getLogger(__name__)
+
+pile_document_count = 210607728
+
+terminate = False
+def handler(signal_received, frame):
+    global terminate
+    terminate = True
+
+def get_pile(directory):
+    reader = Reader()
+    for file in glob.glob(os.path.join(directory, f"*.jsonl.zst*")):
+        for document in reader.read(file):
+            yield document
+
+def close_buckets(buckets):
+    for bucket in buckets:
+        bucket.commit()
+
+def do_ngrams_in_buckets(n_value, working_directory, bucket_count):
+
+    output_directory = os.path.join(working_directory, "output")
+    os.makedirs(output_directory, exist_ok=True)
+
+    logger.info(f"Generating {n_value}-grams and bucketing.")
+
+    # Done file
+    done_file = os.path.join(output_directory, f"ngram_buckets.done")
+    if os.path.exists(done_file):
+        logger.info("ngrams already generated and bucketed, skipping")
+        return
+
+    # Checkpoint
+    checkpoint_file = os.path.join(output_directory, f"ngram_buckets.ckpt")
+    if os.path.exists(checkpoint_file):
+        start_id = pickle.load(open(checkpoint_file,"rb"))
+    else:
+        start_id = 0
+
+    logger.info(f"Starting at pile document index {start_id}")
+    bucket_files = [os.path.join(output_directory, f"ngrams_{i}.bkt.txt") for i in range(bucket_count)]
+    buckets = list(map(TextArchive, bucket_files))
+
+    janitor = Janitor()
+    current_id = 0
+    batch_size = 1000
+    batch_counter = 0
+    with tqdm(total=pile_document_count, dynamic_ncols=True, unit="docs") as progress:
+        for document in get_pile(working_directory):
+            if current_id < start_id:
+                if terminate:
+                    close_buckets(buckets)
+                    return
+
+                current_id += 1
+                progress.update()
+                continue
+
+            # Save checkpoint every "batch_size", only allow terminate after checkpoint
+            if batch_counter == batch_size:
+                progress.update(batch_size)
+                batch_counter = 0
+                pickle.dump(current_id, open(checkpoint_file,"wb"))
+                if terminate:
+                    close_buckets(buckets)
+                    return
+
+            ngrams = word_ngrams(janitor.normalize_string(document), n_value)
+            for ngram in ngrams:
+                bucket = hash(ngram) % len(buckets)
+                buckets[bucket].add_data(f"{ngram} {current_id}")
+
+            batch_counter += 1
+            current_id += 1
+    
+    close_buckets(buckets)
+    Path(done_file).touch()
+
+
+parser = argparse.ArgumentParser(description='Generate 13 grams from Pile.')
+parser.add_argument("-dir", "--working_directory", default="")
+parser.add_argument("-n", "--n_value", type=int, default=13)
+parser.add_argument("-buckets", "--bucket_count", type=int, default=500)
+
+if __name__ == '__main__':
+
+    # Handle sigint (ctrl-c) cleanly
+    previous_signal_int = signal.signal(SIGINT, handler)
+
+    logfile_path = "ngrams.log"
+    setup_logger_tqdm(logfile_path)
+
+    args = parser.parse_args()
+    do_ngrams_in_buckets(args.n_value, args.working_directory, args.bucket_count)
\ No newline at end of file

scripts/clean_training_data/janitor.py

@@ -41,6 +41,29 @@ def word_ngrams(s, n):
     ngram_seqs = form_ngrams(iter(tokens), n)
     return (" ".join(ngram) for ngram in ngram_seqs)
 
+# Does character sequences only - combined faster function to play around with later
+# def word_ngrams_indices_combined(sequence, n):
+#     current_word = ""
+#     history = []
+#     gap = False;
+#     start = 0
+#     end = 0
+#     for character in sequence:
+#         if character == " ":
+#             if not gap:
+#                 gap = True
+#                 history.append(current_word)
+#                 end += len(current_word) - 1
+#                 current_word = ""
+#                 if len(history) == n:
+#                     yield (tuple(history), start, end)
+#                     del history[0]
+#                     start = end + 1
+#                     end = start
+#         else:
+#             gap = False
+#             current_word += character
+
 
 # https://stackoverflow.com/questions/13734451/string-split-with-indices-in-python
 def split_indices(s):
@@ -140,8 +163,9 @@ class Janitor:
     def _split_chunks(self, dirty_string, dirty_parts):
         clean_chunks = []
         splice_idx = 0
+        end = -1
         for i, (ngram, start, end) in enumerate(dirty_parts):
-            if i > self.too_dirty_cutoff:
+            if i >= self.too_dirty_cutoff:
                 return []
             start = max(0, start - self.window_to_remove)
             end = min(len(dirty_string), end + self.window_to_remove)
@@ -150,6 +174,9 @@ class Janitor:
                 clean_chunks.append(dirty_string[splice_idx: start])
             splice_idx = end
 
+        if end < len(dirty_string) - self.minimum_slice_length:
+            clean_chunks.append(dirty_string[end+1:])
+
         return clean_chunks
 
     ##############
@@ -186,101 +213,101 @@ class Janitor:
 # Tests
 #################################################################
 
-def print_cpp():
-    source = """   ,, I'm a very !dirty,, ,,  dirty boy. Clean me daddy. \n\nhe he he hehe heh.  lastword  """ * 2
-
-    for i in range(1, 10, 2):
-        pprint(janitor_util.clean_ngram(source, string.punctuation, i))
-        for ngram, start, end in \
-                janitor_util.clean_ngram_with_indices(source, string.punctuation, i):
-            print(ngram, "\t", start, end, source[start:end].replace("\n", "\\n"))
-
-
-def test_cpp():
-    source = """   ,, I'm a very !dirty,, ,,  dirty boy. Clean me daddy. \n\nhe he he hehe heh.  lastword  """ * 2
-    contaminant = "dirty boy. Clean he he"
-
-    jan_python = Janitor()
-    jan_cpp = Janitor()
-
-    jan_python.register_contaminant_python(contaminant)
-    jan_cpp.register_contaminant(contaminant)
-
-    assert jan_python.dirt_ngrams == jan_cpp.dirt_ngrams, (jan_python.dirt_ngrams, jan_cpp.dirt_ngrams)
-
-    assert jan_python.clean_python(source) == jan_cpp.clean(source), \
-        (jan_python.clean_python(source), jan_cpp.clean(source))
-
-    print("Passed test, python==cpp")
-
-
-def benchmark():
-    # Download and put in data folder: enwik8 (100 MB) from https://cs.fit.edu/~mmahoney/compression/textdata.html
-    setup = \
-        """
-        with open("data/enwik8", "r") as f:
-            data = f.read()
-        jan = Janitor(too_dirty_cutoff=1000)
-        jan.register_contaminant('''
-        theories is that there is a connection between &quot;geekdom&quot; and autism.  
-        This is hinted, for instance, by a ''Wired Magazine'' article in 2001 entitled &quot;
-        The [[Geek]] Syndrome&quot;, which is a point argued by many in the autism rights 
-        movement{{ref|Wired}}.  This article, many professionals assert, is just one example of 
-        the media's application of mental disease labels to what is actually variant normal behavior
-        &amp;mdash;they argue that shyness, lack of athletic ability or social skills, and intellectual
-        interests, even when they seem unusual to others, are not in themselves signs of autism or 
-        Asperger's syndrome. Others assert that it is actually the medical profession which is applying
-        mental disease labels to children who in the past would have simply been accepted as a little
-        different or even labeled 'gifted'. See [[clinomorphism]] for further discussion of this issue.
-        Due to the recent publicity surrounding autism and autis
-        ultan Al Nahyan]] granted [[Petroleum]] concessions, and oil was first found in 1958.  At first,
-        oil money had a marginal impact.  A few lowrise concete buildings were erected, and the first 
-        paved road was completed in 1961, but Sheikh Shakbut, uncertain whether the new oil royalties 
-        would last, took a cautious approach, prefering to save the revenue rather than investing it in 
-        development.  His brother, [[Zayed bin Sultan Al Nahayan]], saw that oil wealth had the potential 
-        to transform Abu Dhabi.  The ruling Al Nahayan family decided that Sheikh Zayed should replace his 
-        brother as Ruler and carry out his vision of developing the country.  On [[August 6]], [[1966]], 
-        with the assistance of the British, Sheikh Zayed became the new ruler.  See generally, Al-Fahim, M, 
-        ''From Rags to Riches: A Story of Abu Dhabi'', Chapter Six (London Centre of Arab Studies, 1995), 
-        ISBN 1 900404 00 1. With the announcement by Britain in 1968 that it would withdraw from the 
-        Gulf area by 1971, Sheikh Zayed became the main driving force behind the formation of the 
-        [[United Arab Emirates]]. After the Emirates gained independence in 1971, 
-        ''')
-        """
-
-    n = 1
-    print(f"Timing {n} run on 100 MB")
-    print("Register contaminant")
-    # print("\tPython", timeit.timeit("jan.register_contaminant_python(data)", setup=setup, globals=globals(), number=n))
-    print("\tCpp", timeit.timeit("jan.register_contaminant(data)", setup=setup, globals=globals(), number=n))
-
-    print("Clean")
-    # print("\tPython", timeit.timeit("jan.clean_python(data)", setup=setup, globals=globals(), number=n))
-    print("\tCpp", timeit.timeit("jan.clean(data)", setup=setup, globals=globals(), number=n))
-
-
-def test():
-    source = """   ,, I'm a very !dirty,, ,,  dirty boy. Clean me daddy. \n\nhe he he hehe heh.  lastword  """ * 2
-    contaminant = "dirty boy. Clean he he"
-
-    jan = Janitor(ngram_n=3)
-    jan.register_contaminant(contaminant)
-    cleaned = " ".join(jan.clean(source))
-    for contam in jan.dirt_ngrams:
-        assert contam not in cleaned, contam
-
-    filename = "data/saved_contam"
-    jan.save_contamination_ngrams(filename)
-
-    jan = Janitor(ngram_n=3)
-    jan.load_contamination_ngrams(filename)
-    cleaned = " ".join(jan.clean(source))
-    for contam in jan.dirt_ngrams:
-        assert contam not in cleaned, contam
-
-
-if __name__ == "__main__":
-    test()
-    # print_cpp()
-    # test_cpp()
-    # benchmark()
+# def print_cpp():
+#     source = """   ,, I'm a very !dirty,, ,,  dirty boy. Clean me daddy. \n\nhe he he hehe heh.  lastword  """ * 2
+
+#     for i in range(1, 10, 2):
+#         pprint(janitor_util.clean_ngram(source, string.punctuation, i))
+#         for ngram, start, end in \
+#                 janitor_util.clean_ngram_with_indices(source, string.punctuation, i):
+#             print(ngram, "\t", start, end, source[start:end].replace("\n", "\\n"))
+
+
+# def test_cpp():
+#     source = """   ,, I'm a very !dirty,, ,,  dirty boy. Clean me daddy. \n\nhe he he hehe heh.  lastword  """ * 2
+#     contaminant = "dirty boy. Clean he he"
+
+#     jan_python = Janitor()
+#     jan_cpp = Janitor()
+
+#     jan_python.register_contaminant_python(contaminant)
+#     jan_cpp.register_contaminant(contaminant)
+
+#     assert jan_python.dirt_ngrams == jan_cpp.dirt_ngrams, (jan_python.dirt_ngrams, jan_cpp.dirt_ngrams)
+
+#     assert jan_python.clean_python(source) == jan_cpp.clean(source), \
+#         (jan_python.clean_python(source), jan_cpp.clean(source))
+
+#     print("Passed test, python==cpp")
+
+
+# def benchmark():
+#     # Download and put in data folder: enwik8 (100 MB) from https://cs.fit.edu/~mmahoney/compression/textdata.html
+#     setup = \
+#         """
+#         with open("data/enwik8", "r") as f:
+#             data = f.read()
+#         jan = Janitor(too_dirty_cutoff=1000)
+#         jan.register_contaminant('''
+#         theories is that there is a connection between &quot;geekdom&quot; and autism.  
+#         This is hinted, for instance, by a ''Wired Magazine'' article in 2001 entitled &quot;
+#         The [[Geek]] Syndrome&quot;, which is a point argued by many in the autism rights 
+#         movement{{ref|Wired}}.  This article, many professionals assert, is just one example of 
+#         the media's application of mental disease labels to what is actually variant normal behavior
+#         &amp;mdash;they argue that shyness, lack of athletic ability or social skills, and intellectual
+#         interests, even when they seem unusual to others, are not in themselves signs of autism or 
+#         Asperger's syndrome. Others assert that it is actually the medical profession which is applying
+#         mental disease labels to children who in the past would have simply been accepted as a little
+#         different or even labeled 'gifted'. See [[clinomorphism]] for further discussion of this issue.
+#         Due to the recent publicity surrounding autism and autis
+#         ultan Al Nahyan]] granted [[Petroleum]] concessions, and oil was first found in 1958.  At first,
+#         oil money had a marginal impact.  A few lowrise concete buildings were erected, and the first 
+#         paved road was completed in 1961, but Sheikh Shakbut, uncertain whether the new oil royalties 
+#         would last, took a cautious approach, prefering to save the revenue rather than investing it in 
+#         development.  His brother, [[Zayed bin Sultan Al Nahayan]], saw that oil wealth had the potential 
+#         to transform Abu Dhabi.  The ruling Al Nahayan family decided that Sheikh Zayed should replace his 
+#         brother as Ruler and carry out his vision of developing the country.  On [[August 6]], [[1966]], 
+#         with the assistance of the British, Sheikh Zayed became the new ruler.  See generally, Al-Fahim, M, 
+#         ''From Rags to Riches: A Story of Abu Dhabi'', Chapter Six (London Centre of Arab Studies, 1995), 
+#         ISBN 1 900404 00 1. With the announcement by Britain in 1968 that it would withdraw from the 
+#         Gulf area by 1971, Sheikh Zayed became the main driving force behind the formation of the 
+#         [[United Arab Emirates]]. After the Emirates gained independence in 1971, 
+#         ''')
+#         """
+
+#     n = 1
+#     print(f"Timing {n} run on 100 MB")
+#     print("Register contaminant")
+#     # print("\tPython", timeit.timeit("jan.register_contaminant_python(data)", setup=setup, globals=globals(), number=n))
+#     print("\tCpp", timeit.timeit("jan.register_contaminant(data)", setup=setup, globals=globals(), number=n))
+
+#     print("Clean")
+#     # print("\tPython", timeit.timeit("jan.clean_python(data)", setup=setup, globals=globals(), number=n))
+#     print("\tCpp", timeit.timeit("jan.clean(data)", setup=setup, globals=globals(), number=n))
+
+
+# def test_janitor_general():
+#     source = """   ,, I'm a very !dirty,, ,,  dirty boy. Clean me daddy. \n\nhe he he hehe heh.  lastword  """ * 2
+#     contaminant = "dirty boy. Clean he he"
+
+#     jan = Janitor(ngram_n=3)
+#     jan.register_contaminant(contaminant)
+#     cleaned = " ".join(jan.clean(source))
+#     for contam in jan.dirt_ngrams:
+#         assert contam not in cleaned, contam
+
+#     filename = "data/saved_contam"
+#     jan.save_contamination_ngrams(filename)
+
+#     jan = Janitor(ngram_n=3)
+#     jan.load_contamination_ngrams(filename)
+#     cleaned = " ".join(jan.clean(source))
+#     for contam in jan.dirt_ngrams:
+#         assert contam not in cleaned, contam
+
+
+# if __name__ == "__main__":
+#     test()
+#     # print_cpp()
+#     # test_cpp()
+#     # benchmark()

scripts/clean_training_data/process_sorted_buckets.py

+"""
+Processes each sorted bucket, creating a new file listing all ngrams that matched more then 10
+unique documents with their unique document counts. Uses multiprocessing and very little memory
+as we stream from presorted buckets. Will use a lot of disk though.
+
+Arguments
+---------
+--working_directory (-dir)
+    Directory containing the sorted buckets, processed files will be deposited here. Default: current directory
+--move_dir (-move)
+    Directory to move processed 13grams too. Default: Do nothing
+--process_count (-procs)
+    Number of processes to use. Default: 4
+"""
+
+import argparse
+import glob
+import os
+from pathlib import Path
+import re
+import shutil
+
+from tqdm import tqdm
+from tqdm_multiprocess import TqdmMultiProcessPool
+
+from scripts.clean_training_data.archiver import TextReader, TextArchive
+
+import logging
+from tqdm_multiprocess.logger import setup_logger_tqdm
+logger = logging.getLogger(__name__)
+
+# Multiprocessed
+def process_bucket(bucket_file_path, processed_directory, move_dir, tqdm_func, global_tqdm):  
+
+    bucket_id = re.sub("\D", "", os.path.basename(bucket_file_path))
+    done_file = os.path.join(processed_directory, f"ngram_bucket_processing_{bucket_id}.done")
+    if os.path.exists(done_file):
+        logger.info(f"bucket {bucket_id} already processed, skipping")
+        return
+
+    # For managing tqdm
+    file_size = os.path.getsize(bucket_file_path)
+    bucket_progress = tqdm_func(total=file_size, dynamic_ncols=True, unit="byte", unit_scale=1)
+    current_file_position = 0
+    update_frequency = 100 * 1000000 # 100mb
+    update_counter = 0
+
+    # Iterate through and output ngrams which occur in more then 10 documents 
+    bucket = TextReader(bucket_file_path)
+
+    output_file_path = bucket_file_path + ".processed"
+    output_archive = TextArchive(output_file_path, mode="wb")
+
+    current_ngram = ""
+    current_ngram_document_ids = set()
+    for line in bucket.read():
+        [ngram, document_id] = line.rsplit(" ", 1)
+
+        # Write ngram if more then 10 unique document occurences
+        if ngram != current_ngram:
+            if len(current_ngram_document_ids) > 10:
+                output_archive.add_data(f"{current_ngram} {len(current_ngram_document_ids)}")
+            current_ngram = ngram
+            current_ngram_document_ids = set()
+
+        current_ngram_document_ids.add(document_id)
+
+        # Update tqdm
+        update_counter += bucket.fh.tell() - current_file_position
+        current_file_position = bucket.fh.tell()
+        if update_counter > update_frequency:
+            bucket_progress.update(update_counter)
+            update_counter = 0
+
+    # Remainder
+    if len(current_ngram_document_ids) > 10:
+        output_archive.add_data(f"{current_ngram} {len(current_ngram_document_ids)}")
+
+    output_archive.commit()
+    Path(done_file).touch()
+
+    if move_dir:
+        shutil.move(output_file_path, move_dir)
+
+    global_tqdm.update()
+
+def process_sorted_buckets(working_directory, move_dir, process_count):
+    bucket_file_paths = glob.glob(os.path.join(working_directory, f"*.bkt.txt.sorted"))
+    processed_directory = os.path.join(working_directory, "processed")
+    os.makedirs(processed_directory, exist_ok=True)
+
+    pool = TqdmMultiProcessPool(process_count) 
+    tasks = [(process_bucket, (bucket_file, processed_directory, move_dir)) for bucket_file in bucket_file_paths]
+
+    global_tqdm = tqdm(total=len(bucket_file_paths), dynamic_ncols=True, unit="bucket")
+    on_done = lambda _ : None
+    on_error = lambda _ : None
+    _ = pool.map(global_tqdm, tasks, on_error, on_done)
+
+parser = argparse.ArgumentParser(description='Process 13 grams from sorted buckets.')
+parser.add_argument("-dir", "--working_directory", default="")
+parser.add_argument("-move", "--move_dir", default="")
+parser.add_argument("-procs", "--process_count", type=int, default=4)
+
+if __name__ == '__main__':
+
+    logfile_path = "process13grams.log"
+    setup_logger_tqdm(logfile_path)
+
+    args = parser.parse_args()
+    process_sorted_buckets(args.working_directory, args.move_dir, args.process_count)
\ No newline at end of file

scripts/clean_training_data/sort_13_gram_buckets.py

+"""
+Iteratively runs gnu sort on each bucket, gnu handles the multiprocessing.
+
+Arguments
+---------
+--working_directory (-dir)
+    Directory containing the bucketed 13-grams. Sorted buckets will be deposited in the same
+    directory and the unsorted buckets are removed after.
+"""
+
+import glob
+import argparse
+import os
+from pathlib import Path
+import signal
+from signal import SIGINT
+import re
+import subprocess
+
+from tqdm import tqdm
+
+import logging
+from tqdm_multiprocess.logger import setup_logger_tqdm
+logger = logging.getLogger(__name__)
+
+terminate = False
+def handler(signal_received, frame):
+    global terminate
+    terminate = True
+
+def sort_13_gram_buckets(working_directory):
+    bucket_file_paths = glob.glob(os.path.join(working_directory, f"*.bkt.txt")) 
+
+    for bucket_file_path in tqdm(bucket_file_paths, dynamic_ncols=True):
+        bucket_id = re.sub("\D", "", os.path.basename(bucket_file_path))
+        done_file = os.path.join(working_directory, f"ngram_bucket_sorting_{bucket_id}.done")
+        if os.path.exists(done_file):
+            logger.info(f"bucket {bucket_id} already processed, skipping")
+            return
+
+        sorted_file_path = bucket_file_path + ".sorted"
+        command = f"sort {bucket_file_path} > {sorted_file_path}"
+        logger.info(command)    
+        subprocess.call(command, shell=True)
+
+        if terminate:
+            return
+
+        Path(done_file).touch()
+        os.remove(bucket_file_path)
+
+parser = argparse.ArgumentParser(description='sort 13gram buckets')
+parser.add_argument("-dir", "--working_directory", default="")
+
+if __name__ == '__main__':
+
+    # Handle sigint (ctrl-c) cleanly
+    previous_signal_int = signal.signal(SIGINT, handler)
+
+    logfile_path = "sort13grambuckets.log"
+    setup_logger_tqdm(logfile_path)
+
+    args = parser.parse_args()
+    sort_13_gram_buckets(args.working_directory)
\ No newline at end of file

setup.py

@@ -33,5 +33,10 @@ setuptools.setup(
         "pycountry==20.7.3",
         "numexpr==2.7.2",
         "lm_dataformat==0.0.19",
+        "pytest==6.2.3",
+        "pybind11==2.6.2",
+        "tqdm-multiprocess==0.0.11",
+        "zstandard==0.15.2",
+        "jsonlines==2.0.0"
     ]
 )

tests/test_generate_13_grams.py

+import os
+from collections import Counter
+import shutil
+import glob
+
+from scripts.clean_training_data.janitor import *
+from scripts.clean_training_data.generate_13_grams import do_ngrams_in_buckets
+from scripts.clean_training_data.archiver import Archive, TextReader
+
+
+def test_generate_13_grams_1():
+    data = """A goose (plural geese) is a bird of any of several waterfowl species in the family Anatidae. 
+    This group comprises the genera Anser (the grey geese and white geese) and Branta (the black geese). 
+    Some other birds, mostly related to the shelducks, have "goose" as part of their names. 
+    More distantly related members of the family Anatidae are swans, most of which are larger 
+    than true geese, and ducks, which are smaller. The term "goose" may refer to either a male 
+    or female bird, but when paired with "gander", refers specifically to a female one (the latter referring 
+    to a male). Young birds before fledging are called goslings. The collective noun for a group of 
+    geese on the ground is a gaggle; when in flight, they are called a skein, a team, or a wedge; when 
+    flying close together, they are called a plump."""
+
+    data = data + data
+
+    # Simple Generation
+    n = 13
+    janitor = Janitor()    
+    ngrams = word_ngrams(janitor.normalize_string(data), n)
+    comparison = list(ngrams)
+    comparison_counter = Counter(comparison)
+    print(len(comparison))
+    # print(comparison)
+
+    # Generating into buckets
+    test_working_directory = "test_generate_13_grams"
+    output_directory = os.path.join(test_working_directory, "output")        
+    try:
+        shutil.rmtree(output_directory)
+    except FileNotFoundError:
+        pass
+    os.makedirs(test_working_directory, exist_ok=True)
+    archive = Archive(os.path.join(test_working_directory, "test.jsonl.zst"))
+    archive.add_data(data)
+    archive.commit()
+    bucket_count = 4
+    do_ngrams_in_buckets(n, test_working_directory, bucket_count)
+
+    # Rebuild from buckets
+    rebuilt_ngrams = []
+
+    bucket_file_paths = glob.glob(os.path.join(test_working_directory, "output", f"*.bkt.txt")) 
+    for bucket_file_path in bucket_file_paths:
+        reader = TextReader(bucket_file_path)
+        
+        for line in reader.read():
+            [ngram, document_id] = line.rsplit(" ", 1)
+            rebuilt_ngrams.append(ngram)
+
+    # Compare
+    result_counter = Counter(rebuilt_ngrams)
+    # print(len(result_counter))
+    # print(len(comparison_counter))
+    assert(len(result_counter) == len(comparison_counter))
+    # print(result_counter)
+    # print(comparison_counter)    
+    assert(comparison_counter == result_counter)
\ No newline at end of file