Merge remote-tracking branch 'upstream/numpy' into bart

README.md

@@ -19,12 +19,17 @@ This is a work-in-progress.
 First of all, install the latest MXNet. You may use the following commands:
 
 ```bash
+# Install the version with CUDA 10.0
+pip install -U --pre "mxnet-cu100>=2.0.0b20200716" -f https://dist.mxnet.io/python
 
 # Install the version with CUDA 10.1
-pip install -U --pre mxnet-cu101>=2.0.0b20200716 -f https://dist.mxnet.io/python
+pip install -U --pre "mxnet-cu101>=2.0.0b20200716" -f https://dist.mxnet.io/python
+
+# Install the version with CUDA 10.2
+pip install -U --pre "mxnet-cu102>=2.0.0b20200716" -f https://dist.mxnet.io/python
 
 # Install the cpu-only version
-pip install -U --pre mxnet>=2.0.0b20200716 -f https://dist.mxnet.io/python
+pip install -U --pre "mxnet>=2.0.0b20200716" -f https://dist.mxnet.io/python
 ```
 
 

scripts/datasets/general_nlp_benchmark/prepare_glue.py

@@ -68,7 +68,23 @@ def read_tsv_glue(tsv_file, num_skip=1, keep_column_names=False):
                 nrows = len(elements)
             else:
                 assert nrows == len(elements)
-    return pd.DataFrame(out, columns=column_names)
+    df = pd.DataFrame(out, columns=column_names)
+    series_l = []
+    for col_name in df.columns:
+        idx = df[col_name].first_valid_index()
+        val = df[col_name][idx]
+        if isinstance(val, str):
+            try:
+                dat = pd.to_numeric(df[col_name])
+                series_l.append(dat)
+                continue
+            except ValueError:
+                pass
+            finally:
+                pass
+        series_l.append(df[col_name])
+    new_df = pd.DataFrame({name: series for name, series in zip(df.columns, series_l)})
+    return new_df
 
 
 def read_jsonl_superglue(jsonl_file):
@@ -157,6 +173,13 @@ def read_sts(dir_path):
         else:
             df = df[[7, 8, 1, 9]]
             df.columns = ['sentence1', 'sentence2', 'genre', 'score']
+        genre_l = []
+        for ele in df['genre'].tolist():
+            if ele == 'main-forum':
+                genre_l.append('main-forums')
+            else:
+                genre_l.append(ele)
+        df['genre'] = pd.Series(genre_l)
         df_dict[fold] = df
     return df_dict, None
 
@@ -320,8 +343,8 @@ def read_rte_superglue(dir_path):
 def read_wic(dir_path):
     df_dict = dict()
     meta_data = dict()
-    meta_data['entities1'] = {'type': 'entity', 'parent': 'sentence1'}
-    meta_data['entities2'] = {'type': 'entity', 'parent': 'sentence2'}
+    meta_data['entities1'] = {'type': 'entity', 'attrs': {'parent': 'sentence1'}}
+    meta_data['entities2'] = {'type': 'entity', 'attrs': {'parent': 'sentence2'}}
 
     for fold in ['train', 'val', 'test']:
         if fold != 'test':
@@ -340,13 +363,13 @@ def read_wic(dir_path):
             end2 = row['end2']
             if fold == 'test':
                 out.append([sentence1, sentence2,
-                            (start1, end1),
-                            (start2, end2)])
+                            {'start': start1, 'end': end1},
+                            {'start': start2, 'end': end2}])
             else:
                 label = row['label']
                 out.append([sentence1, sentence2,
-                            (start1, end1),
-                            (start2, end2),
+                            {'start': start1, 'end': end1},
+                            {'start': start2, 'end': end2},
                             label])
         df = pd.DataFrame(out, columns=columns)
         df_dict[fold] = df
@@ -357,8 +380,8 @@ def read_wsc(dir_path):
     df_dict = dict()
     tokenizer = WhitespaceTokenizer()
     meta_data = dict()
-    meta_data['noun'] = {'type': 'entity', 'parent': 'text'}
-    meta_data['pronoun'] = {'type': 'entity', 'parent': 'text'}
+    meta_data['noun'] = {'type': 'entity', 'attrs': {'parent': 'text'}}
+    meta_data['pronoun'] = {'type': 'entity', 'attrs': {'parent': 'text'}}
     for fold in ['train', 'val', 'test']:
         jsonl_path = os.path.join(dir_path, '{}.jsonl'.format(fold))
         df = read_jsonl_superglue(jsonl_path)
@@ -374,20 +397,20 @@ def read_wsc(dir_path):
             span2_text = target['span2_text']
             # Build entity
             # list of entities
-            # 'entity': {'start': 0, 'end': 100}
+            # 'entities': {'start': 0, 'end': 100}
             tokens, offsets = tokenizer.encode_with_offsets(text, str)
             pos_start1 = offsets[span1_index][0]
             pos_end1 = pos_start1 + len(span1_text)
             pos_start2 = offsets[span2_index][0]
             pos_end2 = pos_start2 + len(span2_text)
             if fold == 'test':
                 samples.append({'text': text,
-                                'noun': (pos_start1, pos_end1),
-                                'pronoun': (pos_start2, pos_end2)})
+                                'noun': {'start': pos_start1, 'end': pos_end1},
+                                'pronoun': {'start': pos_start2, 'end': pos_end2}})
             else:
                 samples.append({'text': text,
-                                'noun': (pos_start1, pos_end1),
-                                'pronoun': (pos_start2, pos_end2),
+                                'noun': {'start': pos_start1, 'end': pos_end1},
+                                'pronoun': {'start': pos_start2, 'end': pos_end2},
                                 'label': label})
         df = pd.DataFrame(samples)
         df_dict[fold] = df
@@ -406,8 +429,8 @@ def read_boolq(dir_path):
 def read_record(dir_path):
     df_dict = dict()
     meta_data = dict()
-    meta_data['entities'] = {'type': 'entity', 'parent': 'text'}
-    meta_data['answers'] = {'type': 'entity', 'parent': 'text'}
+    meta_data['entities'] = {'type': 'entity', 'attrs': {'parent': 'text'}}
+    meta_data['answers'] = {'type': 'entity', 'attrs': {'parent': 'text'}}
     for fold in ['train', 'val', 'test']:
         if fold != 'test':
             columns = ['source', 'text', 'entities', 'query', 'answers']
@@ -422,15 +445,11 @@ def read_record(dir_path):
             passage = row['passage']
             text = passage['text']
             entities = passage['entities']
-            entities = [(ele['start'], ele['end']) for ele in entities]
+            entities = [{'start': ele['start'], 'end': ele['end']} for ele in entities]
             for qas in row['qas']:
                 query = qas['query']
                 if fold != 'test':
-                    answer_entities = []
-                    for answer in qas['answers']:
-                        start = answer['start']
-                        end = answer['end']
-                        answer_entities.append((start, end))
+                    answer_entities = qas['answers']
                     out.append((source, text, entities, query, answer_entities))
                 else:
                     out.append((source, text, entities, query))
@@ -518,11 +537,15 @@ def format_mrpc(data_dir):
     os.makedirs(mrpc_dir, exist_ok=True)
     mrpc_train_file = os.path.join(mrpc_dir, "msr_paraphrase_train.txt")
     mrpc_test_file = os.path.join(mrpc_dir, "msr_paraphrase_test.txt")
-    download(GLUE_TASK2PATH["mrpc"]['train'], mrpc_train_file)
-    download(GLUE_TASK2PATH["mrpc"]['test'], mrpc_test_file)
+    download(GLUE_TASK2PATH["mrpc"]['train'], mrpc_train_file,
+             sha1_hash=_URL_FILE_STATS[GLUE_TASK2PATH["mrpc"]['train']])
+    download(GLUE_TASK2PATH["mrpc"]['test'], mrpc_test_file,
+             sha1_hash=_URL_FILE_STATS[GLUE_TASK2PATH["mrpc"]['test']])
     assert os.path.isfile(mrpc_train_file), "Train data not found at %s" % mrpc_train_file
     assert os.path.isfile(mrpc_test_file), "Test data not found at %s" % mrpc_test_file
-    download(GLUE_TASK2PATH["mrpc"]['dev'], os.path.join(mrpc_dir, "dev_ids.tsv"))
+    download(GLUE_TASK2PATH["mrpc"]['dev'],
+             os.path.join(mrpc_dir, "dev_ids.tsv"),
+             sha1_hash=_URL_FILE_STATS[GLUE_TASK2PATH["mrpc"]['dev']])
 
     dev_ids = []
     with open(os.path.join(mrpc_dir, "dev_ids.tsv"), encoding="utf8") as ids_fh:
@@ -575,7 +598,7 @@ def get_tasks(benchmark, task_names):
 @DATA_PARSER_REGISTRY.register('prepare_glue')
 def get_parser():
     parser = argparse.ArgumentParser()
-    parser.add_argument("--benchmark", choices=['glue', 'superglue', 'sts'],
+    parser.add_argument("--benchmark", choices=['glue', 'superglue'],
                         default='glue', type=str)
     parser.add_argument("-d", "--data_dir", help="directory to save data to", type=str,
                         default=None)
@@ -618,39 +641,44 @@ def main(args):
                 base_dir = os.path.join(args.data_dir, 'rte_diagnostic')
                 os.makedirs(base_dir, exist_ok=True)
                 download(TASK2PATH['diagnostic'][0],
-                         path=os.path.join(base_dir, 'diagnostic.tsv'))
+                         path=os.path.join(base_dir, 'diagnostic.tsv'),
+                         sha1_hash=_URL_FILE_STATS[TASK2PATH['diagnostic'][0]])
                 download(TASK2PATH['diagnostic'][1],
-                         path=os.path.join(base_dir, 'diagnostic-full.tsv'))
+                         path=os.path.join(base_dir, 'diagnostic-full.tsv'),
+                         sha1_hash=_URL_FILE_STATS[TASK2PATH['diagnostic'][1]])
                 df = reader(base_dir)
-                df.to_pickle(os.path.join(base_dir, 'diagnostic-full.pd.pkl'))
+                df.to_parquet(os.path.join(base_dir, 'diagnostic-full.parquet'))
             else:
                 for key, name in [('broadcoverage-diagnostic', 'AX-b'),
                                   ('winogender-diagnostic', 'AX-g')]:
                     data_file = os.path.join(args.cache_path, "{}.zip".format(key))
                     url = TASK2PATH[key]
                     reader = TASK2READER[key]
-                    download(url, data_file)
+                    download(url, data_file, sha1_hash=_URL_FILE_STATS[url])
                     with zipfile.ZipFile(data_file) as zipdata:
                         zipdata.extractall(args.data_dir)
                     df = reader(os.path.join(args.data_dir, name))
-                    df.to_pickle(os.path.join(args.data_dir, name, '{}.pd.pkl'.format(name)))
+                    df.to_parquet(os.path.join(args.data_dir, name, '{}.parquet'.format(name)))
         elif task == 'mrpc':
             reader = TASK2READER[task]
             format_mrpc(args.data_dir)
             df_dict, meta_data = reader(os.path.join(args.data_dir, 'mrpc'))
             for key, df in df_dict.items():
                 if key == 'val':
                     key = 'dev'
-                df.to_pickle(os.path.join(args.data_dir, 'mrpc', '{}.pd.pkl'.format(key)))
+                df.to_parquet(os.path.join(args.data_dir, 'mrpc', '{}.parquet'.format(key)))
             with open(os.path.join(args.data_dir, 'mrpc', 'metadata.json'), 'w') as f:
                 json.dump(meta_data, f)
         else:
             # Download data
             data_file = os.path.join(args.cache_path, "{}.zip".format(task))
             url = TASK2PATH[task]
             reader = TASK2READER[task]
-            download(url, data_file)
+            download(url, data_file, sha1_hash=_URL_FILE_STATS[url])
             base_dir = os.path.join(args.data_dir, task)
+            if os.path.exists(base_dir):
+                print('Found!')
+                continue
             zip_dir_name = None
             with zipfile.ZipFile(data_file) as zipdata:
                 if zip_dir_name is None:
@@ -662,7 +690,7 @@ def main(args):
             for key, df in df_dict.items():
                 if key == 'val':
                     key = 'dev'
-                df.to_pickle(os.path.join(base_dir, '{}.pd.pkl'.format(key)))
+                df.to_parquet(os.path.join(base_dir, '{}.parquet'.format(key)))
             if meta_data is not None:
                 with open(os.path.join(base_dir, 'metadata.json'), 'w') as f:
                     json.dump(meta_data, f)

scripts/question_answering/run_squad.py

@@ -563,8 +563,8 @@ def train(args):
                 segment_ids = sample.segment_ids.as_in_ctx(ctx) if use_segmentation else None
                 valid_length = sample.valid_length.as_in_ctx(ctx)
                 p_mask = sample.masks.as_in_ctx(ctx)
-                gt_start = sample.gt_start.as_in_ctx(ctx)
-                gt_end = sample.gt_end.as_in_ctx(ctx)
+                gt_start = sample.gt_start.as_in_ctx(ctx).astype(np.int32)
+                gt_end = sample.gt_end.as_in_ctx(ctx).astype(np.int32)
                 is_impossible = sample.is_impossible.as_in_ctx(ctx).astype(np.int32)
                 batch_idx = mx.np.arange(tokens.shape[0], dtype=np.int32, ctx=ctx)
                 p_mask = 1 - p_mask  # In the network, we use 1 --> no_mask, 0 --> mask

setup.py

@@ -55,6 +55,8 @@ def find_version(*file_paths):
         'scripts',
     )),
     package_dir={"": "src"},
+    package_data={'': [os.path.join('models', 'model_zoo_checksums', '*.txt'),
+                       os.path.join('cli', 'data', 'url_checksums', '*.txt')]},
     zip_safe=True,
     include_package_data=True,
     install_requires=requirements,

src/gluonnlp/__init__.py

@@ -12,3 +12,4 @@
 from . import optimizer
 from . import registry
 from . import sequence_sampler
+from . import embedding