Many more features added

tools/openwebtext/filter_ngrams.py

@@ -24,6 +24,7 @@ from functools import partial
 import json
 import multiprocessing
 import nltk
+import pickle
 import re
 import string
 import sys
@@ -61,11 +62,23 @@ def split_text(text, start_position, remove_char_each_side, seq):
     return text_first, text_second
 
 def check_and_clean_text(args, words, ngrams, text, start_position, \
-    text_buf_ngram_free, text_buf):
+    text_buf_ngram_free, text_buf, local_ngram):
 
     seq = " ".join(words)
     if seq in ngrams:
-        print(" [matched]: {}".format(seq), flush=True)
+        #print(" [matched]: {}".format(seq), flush=True)
+
+        if args.get_ngram_freq_only:
+            # increase freq of this seq and then only consider the later part
+            # of the text for further processing
+            if seq in local_ngram:
+                local_ngram[seq] += 1
+            else:
+                local_ngram[seq] = 1
+            #print(" [increased]: {} {}".format(seq, ngrams[seq]), flush=True)
+            if (start_position + len(seq) + 1) < len(text):
+                text_buf.append(text[start_position + len(seq) + 1:len(text)])
+            return False            
 
         # split the text
         text_first, text_second = split_text(text, start_position, \
@@ -84,6 +97,7 @@ def check_and_clean_text(args, words, ngrams, text, start_position, \
     # ngram free
     return True
 
+
 def free_ngram(line, args, key, ngrams, ngrams_freq_sorted):
     # remove all the ngrams
 
@@ -95,18 +109,19 @@ def free_ngram(line, args, key, ngrams, ngrams_freq_sorted):
         text_buf = []
 
     text_buf_ngram_free = []
+    local_ngram = {}
     while len(text_buf) > 0:
 
         # get the first one from the buffer
         text = text_buf.pop(0)
         words, positions = get_words(text)
-
+        
         ngram_free = True
         # find each max n-grams and check dictionary
-        for i in range(len(words) - args.ngram_size + 1):
+        for i in range(len(words) - args.max_ngram_size + 1):
             check_ngram_free = check_and_clean_text(args, words[i:\
-                i+args.ngram_size], ngrams, text, positions[i], \
-                text_buf_ngram_free, text_buf)
+                i+args.max_ngram_size], ngrams, text, positions[i], \
+                text_buf_ngram_free, text_buf, local_ngram)
 
             # the seq is ngram free? if yes, break
             if not check_ngram_free:
@@ -118,7 +133,7 @@ def free_ngram(line, args, key, ngrams, ngrams_freq_sorted):
             for ngram_len, _ in ngrams_freq_sorted:
                 check_ngram_free = check_and_clean_text(args, words[i:\
                     i+ngram_len], ngrams, text, positions[i], \
-                    text_buf_ngram_free, text_buf)
+                    text_buf_ngram_free, text_buf, local_ngram)
 
                 # same check as above
                 if not check_ngram_free:
@@ -130,16 +145,16 @@ def free_ngram(line, args, key, ngrams, ngrams_freq_sorted):
                 break
 
         # for the last max n-gram, check all the lower ngrams in it
-        if ngram_free and len(words) - args.ngram_size > 0:
+        if ngram_free and len(words) - args.max_ngram_size > 0:
             # get the last words of the lax max ngram
-            last_seq_words = words[(len(words) - args.ngram_size):len(words)]
-            last_seq_start_position = len(words) - args.ngram_size
+            last_seq_words = words[(len(words)-args.max_ngram_size):len(words)]
+            last_seq_start_position = len(words) - args.max_ngram_size
 
             # check all n-grams lower than the max
             for pos, (ngram_len, _) in enumerate(ngrams_freq_sorted):
 
                 # ignore the max ngram as has been considered already
-                if ngram_len == args.ngram_size:
+                if ngram_len == args.max_ngram_size:
                     continue
 
                 # find each ngram of ngram_len in max n-grams and check
@@ -147,7 +162,7 @@ def free_ngram(line, args, key, ngrams, ngrams_freq_sorted):
                     check_ngram_free = check_and_clean_text(args, \
                         last_seq_words[i:i+ngram_len], ngrams, text,\
                         positions[last_seq_start_position+i], \
-                        text_buf_ngram_free, text_buf)
+                        text_buf_ngram_free, text_buf, local_ngram)
 
                     if not check_ngram_free:
                         ngram_free = False
@@ -157,34 +172,35 @@ def free_ngram(line, args, key, ngrams, ngrams_freq_sorted):
                     break
 
         # texts are ngram free
-        if ngram_free:
+        if ngram_free and not args.get_ngram_freq_only:
             text_buf_ngram_free.append(text)
 
     # check if the text has only been trimmed
     trimmed = 0
-    if len(text_buf_ngram_free) == 1 and len(text_buf_ngram_free[0]) < \
-        len(myjson[key]):
+    if not args.get_ngram_freq_only and len(text_buf_ngram_free) == 1 and \
+        len(text_buf_ngram_free[0]) < len(myjson[key]):
         trimmed = 1
 
-    return text_buf_ngram_free, trimmed
+    return text_buf_ngram_free, trimmed, local_ngram
 
 # insert word sequence into dictionary
 def insert_dict(words, ngrams, pos):
     seq = " ".join(words)
     if seq not in ngrams:
-        ngrams[seq] = pos
+        ngrams[seq] = 0
+        #ngrams[seq] = pos
 
 # insert each ngram from text into the ngrams dictionary
 def compute_ngrams_insert_dict(args, text, ngrams):
     words, positions = get_words(text)
-    if len(words) == 0:
+    if len(words) < args.min_ngram_size:
         return
 
-    if len(words) < args.ngram_size:
+    if len(words) < args.max_ngram_size:
         insert_dict(words, ngrams, positions[0])
 
-    for i in range(len(words) - args.ngram_size+1):
-        insert_dict(words[i:i+args.ngram_size], ngrams, positions[i])
+    for i in range(len(words) - args.max_ngram_size+1):
+        insert_dict(words[i:i+args.max_ngram_size], ngrams, positions[i])
 
 
 # Build ngrams for the lambada dataset
@@ -203,6 +219,7 @@ def process_task_lambda(args, task_file, ngrams):
 
 # Build ngrams for the dataset of the given task
 def process_task(args, task_name, ngrams):
+
     print(' reading from {} and computing ngrams'.format('import datasets'))
     print(" Current entities in ngrams {}".format(len(ngrams)), flush=True)
     # using validation/test data from datasets
@@ -253,39 +270,7 @@ def process_task(args, task_name, ngrams):
     print(" After task {} entities in ngrams {}, added {}".format(task_name, \
             len(ngrams), len(ngrams) - entities_in_ngrams), flush=True)
 
-if __name__ == '__main__':
-
-    # we use 13-grams, any text less than 200 characters got removed
-    # any text splitted more than 10 got removed as well
-
-    print('parsing the arguments ...')
-
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--tasks', nargs = '*', required=True, default=None, \
-                        help = 'Tasks to use for deduplication: currently '
-                        ' suuport [lambada, squad, natural_questions,'
-                        ' triviaqa, webqa, race, drop, coqa, and piqa]')
-    parser.add_argument('--lambada-path', type=str, default=None,
-                       help='Only Lambada task needs the path')
-    parser.add_argument('--dedup-dataset', nargs = '*', default=None,
-                       help='Dataset to deduplicate with the key to use'
-                        ' e.g. cc.json text')
-    parser.add_argument('--output', type=str, default=None,
-                       help='Output file name to save dedup dataset')
-    # Default dedup values
-    parser.add_argument('--ngram-size', type=int, default=13,
-                       help='Maximum size of ngram to use.')
-    parser.add_argument('--filter-text-char-len', type=int, default=200,
-                       help='Remove any text below this length.')
-    parser.add_argument('--splits-count', type=int, default=10,
-                       help='Remove any documents more than this many splits')
-    parser.add_argument('--remove-char-each-side', type=int, default=200,
-                       help='Maximum size of ngram to use.')
-
-    args = parser.parse_args()
-
-    # Build ngrams
-    ngrams = {}
+def compute_tasks_ngrams(args, ngrams):
     start_time = time.time()
     for _, task_name in enumerate(args.tasks):
         print('Task: {}'.format(task_name), flush=True)
@@ -294,10 +279,10 @@ if __name__ == '__main__':
             process_task_lambda(args, args.lambada_path, ngrams)
         else:
             process_task(args, task_name, ngrams)
+    print(" Taken time to compute ngrams {:.2f}".format(time.time() - \
+        start_time), flush=True)
 
-    print(" Taken time {:.2f}".format(time.time() - start_time), flush=True)
-
-    # get the range of the size of the ngrams
+def compute_ngram_freq_sorted(args, ngrams):
     ngrams_freq = {}
     for ngram_key in ngrams.keys():
         length = len(ngram_key.split())
@@ -309,33 +294,74 @@ if __name__ == '__main__':
     print(" Entities in ngrams {} min_ngram_size {} max_ngram_size {}".format(\
             len(ngrams), ngrams_freq_sorted[0][0], ngrams_freq_sorted[len(\
             ngrams_freq_sorted) -1 ][0]), flush=True)
+    return ngrams_freq_sorted
 
-    id_prefix = '-'.join(args.tasks[::2])
-
-    print('Reading file {} and deduping n-grams'.format(args.dedup_dataset))
+def get_ngrams_above_threshold(args, ngrams, ngrams_above_threshold, \
+    dedup_file, dedup_key, ngrams_freq_sorted):
 
+    start_time = time.time()
+    # get the ngrams frequency
+    args.get_ngram_freq_only = True
+ 
+    # Open the large file to process in parallel
+    num_workers = 40
+    pool = multiprocessing.Pool(num_workers)
+    fin = open(dedup_file, 'r', encoding='utf-8')
+    free_ngram_abt_partial=partial(free_ngram, args=args, key=dedup_key, \
+        ngrams=ngrams, ngrams_freq_sorted=ngrams_freq_sorted)
+    free_ngrams_abt = pool.imap(free_ngram_abt_partial, fin, 500)
+ 
     counter = 0
+    for _, _, local_ngram in free_ngrams_abt:
+        counter += 1
+        if counter % 1000 == 0:
+            print(' [compute_stat]> processed {} documents in {:.2f} seconds ...'.
+                    format(counter, time.time() - start_time), flush=True)
+        for local_key in local_ngram:
+            if local_key in ngrams:
+                ngrams[local_key] += 1
+        local_ngram = {}
+
+    print(' Time taken to compute statistics {:.2f} seconds'.format(time.time() - \
+        start_time), flush=True)
+    pool.close()
+    pool.join()
+
     start_time = time.time()
+    counter_threshold = 0
+    # Get ngram above theadhold
+    for local_key, local_val in ngrams.items():
+        if ngrams[local_key] > args.key_threshold:
+            print(" [threshold] {} {}".format(local_key, local_val), flush=True)
+            counter_threshold += 1
+            ngrams_above_threshold[local_key] = 1
+            
+    print(' Ngrams above threshold {}'.format(counter_threshold), flush=True)
+    fin.close()
 
-    if args.output is not None:
-        out_f = open(args.output, 'wb')
+def clean_ngrams_above_threshold(args, ngrams_above_threshold, dedup_file, \
+    dedup_key):
 
-    splitted, ignored, split_mt_thld, trimmed_count = 0, 0, 0, 0
+    start_time = time.time()
+    # Now actually filter the dataset
+    args.get_ngram_freq_only = False
+    id_prefix = '-'.join(args.tasks[::2])
 
-    assert len(args.dedup_dataset) == 2
-    dedup_file = args.dedup_dataset[0]
-    dedup_key = args.dedup_dataset[1]
+    # get the range of the size of the ngrams
+    ngrams_freq_sorted = compute_ngram_freq_sorted(args, ngrams_above_threshold)
 
-    # Setup multi-processing.
+    # Open the large file to process in parallel
     num_workers = 40
-    fin = open(dedup_file, 'r', encoding='utf-8')
     pool = multiprocessing.Pool(num_workers)
-    free_ngram_x=partial(free_ngram, args=args, key=dedup_key, ngrams=ngrams, \
-        ngrams_freq_sorted=ngrams_freq_sorted)
-
-    free_ngrams = pool.imap(free_ngram_x, fin, 25)
-
-    for text_buf_ngram_free, trimmed in free_ngrams:
+    fin = open(dedup_file, 'r', encoding='utf-8')
+    free_ngram_clean_partial=partial(free_ngram, args=args, key=dedup_key, \
+        ngrams=ngrams_above_threshold, ngrams_freq_sorted=ngrams_freq_sorted)
+    free_ngrams_clean = pool.imap(free_ngram_clean_partial, fin, 500)
+ 
+    out_f = open(args.output, 'wb')
+
+    counter = splitted = ignored = split_mt_thld = trimmed_count = 0
+    for text_buf_ngram_free, trimmed, _ in free_ngrams_clean:
         counter += 1
         try:
 
@@ -361,18 +387,95 @@ if __name__ == '__main__':
                     out_f.write('\n'.encode('utf-8'))
 
             if counter % 1000 == 0:
-                print(' [search]> processed {} documents in {:.2f} seconds ...'.
+                print(' [final]> processed {} documents in {:.2f} seconds ...'.
                     format(counter, time.time() - start_time), flush=True)
         except Exception as e:
             print('Error:', e)
 
-    if args.output is not None:
-        out_f.close()
+    print(' [final]> processed {} documents in {:.2f} seconds ...'.
+        format(counter, time.time() - start_time), flush=True)
+    
+    print(' Total docs {} splitted {} ignored {} splits > theshold {} trimmed'\
+        ' {}'.format(counter, splitted, ignored, split_mt_thld, trimmed_count)\
+        , flush=True)
 
+    pool.close()
+    pool.join()
+
+    out_f.close()
     fin.close()
 
-    print("Deduped file written to: {}".format(args.output), flush=True)
-    print("Total docs {} splitted {} ignored {} docs with many splits {}"\
-        " trimmed {}".format(counter, splitted, ignored, split_mt_thld, \
-        trimmed_count), flush=True)
+if __name__ == '__main__':
+
+    # we use 13-grams, any text less than 200 characters got removed
+    # any text splitted more than 10 got removed as well
+
+    print('parsing the arguments ...')
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--tasks', nargs = '*', required=True, default=None, \
+                        help = 'Tasks to use for deduplication: currently '
+                        ' suuport [lambada, squad, natural_questions,'
+                        ' triviaqa, webqa, race, drop, coqa, and piqa]')
+    parser.add_argument('--lambada-path', type=str, default=None,
+                       help='Only Lambada task needs the path')
+    parser.add_argument('--dedup-dataset', nargs = '*', default=None,
+                       help='Dataset to deduplicate with the key to use'
+                        ' e.g. cc.json text')
+    parser.add_argument('--output', type=str, default=None,
+                       help='Output file name to save dedup dataset')
+    # Default dedup values
+    parser.add_argument('--max-ngram-size', type=int, default=13,
+                       help='Maximum size of ngram to use.')
+    parser.add_argument('--min-ngram-size', type=int, default=8,
+                       help='Minimum size of ngram to use.')
+    parser.add_argument('--filter-text-char-len', type=int, default=200,
+                       help='Remove any text below this length.')
+    parser.add_argument('--key-threshold', type=int, default=10,
+                       help='Number of keys to consider as threshold')
+    parser.add_argument('--save-dictionary', type=str, default=None,
+                       help='Save the dictionary')
+    parser.add_argument('--load-dictionary', type=str, default=None,
+                       help='Load the dictionary')
+    parser.add_argument('--splits-count', type=int, default=10,
+                       help='Remove any documents more than this many splits')
+    parser.add_argument('--remove-char-each-side', type=int, default=200,
+                       help='Maximum size of ngram to use.')
+
+    args = parser.parse_args()
+
+    assert len(args.dedup_dataset) == 2
+    dedup_file = args.dedup_dataset[0]
+    dedup_key = args.dedup_dataset[1]
+
+    # Setup multi-processing
+    num_workers = 40
+    if args.load_dictionary is None:
+
+        # Build ngrams
+        ngrams = {}
+        compute_tasks_ngrams(args, ngrams)
+
+        # get the range of the size of the ngrams
+        ngrams_freq_sorted = compute_ngram_freq_sorted(args, ngrams)
+
+        # get ngram freq from large file in parallel
+        # get ngrams above threshold
+        ngrams_above_threshold = {}
+        get_ngrams_above_threshold(args, ngrams, ngrams_above_threshold, \
+            dedup_file, dedup_key, ngrams_freq_sorted)
+
+        # save the dictionary if needed
+        if args.save_dictionary is not None:
+            with open(args.save_dictionary, 'wb') as save_dict_handle:
+                pickle.dump(ngrams_above_threshold, save_dict_handle)
+    else:
+        with open(args.load_dictionary, 'rb') as load_dict_handle:
+            ngrams_above_threshold = pickle.load(load_dict_handle)
+
+    # filter the large file
+    if args.output is not None:
+        clean_ngrams_above_threshold(args, ngrams_above_threshold, \
+            dedup_file, dedup_key)
+
     print('done :-)')