add tests

llmfoundry/data/text_data.py

@@ -145,7 +145,9 @@ def __init__(
             )
         self.token_encoding_type = token_encoding_type
 
-        if local is not None and (remote is None or (local == remote)):
+        if local is not None and (
+            remote is None or (local == remote)
+        ) and split is not None:
             if os.path.isdir(local):
                 contents = set(os.listdir(local))
                 if split not in contents:

tests/data/test_text_data.py

@@ -0,0 +1,57 @@
+# Copyright 2022 MosaicML LLM Foundry authors
+# SPDX-License-Identifier: Apache-2.0
+import os
+import pathlib
+from streaming import MDSWriter
+from llmfoundry.data import StreamingTextDataset
+import pytest
+import numpy as np
+import torch
+
+@pytest.mark.parametrize('token_encoding_type', ['int16', 'int32', 'int64'])
+@pytest.mark.parametrize('samples', [10])
+@pytest.mark.parametrize('max_seq_len', [2048])
+@pytest.mark.parametrize('vocab_size', [10000])
+def test_encoding_types(tmp_path: pathlib.Path,
+                        token_encoding_type: str,
+                        samples: int,
+                        max_seq_len: int,
+                        vocab_size: int):
+    dataset_local_path = str(tmp_path)
+    encoding_dtype = getattr(np, token_encoding_type)
+
+    columns = {
+        'tokens': 'ndarray:'+token_encoding_type,
+    }
+
+    with MDSWriter(out=dataset_local_path, columns=columns) as writer:
+        for _ in range(samples):
+            tokens = np.random.randint(0, vocab_size, max_seq_len, dtype=encoding_dtype)
+            writer.write({'tokens': tokens})
+
+    print('Dataset local path:', dataset_local_path)
+    print(os.listdir(dataset_local_path))
+
+    dataset = StreamingTextDataset(
+        tokenizer=None,
+        token_encoding_type=token_encoding_type,
+        max_seq_len=max_seq_len,
+        local=dataset_local_path,
+        batch_size=1,
+    )
+
+    for _, sample in enumerate(dataset):
+        # StreamingTextDataset returns a torch Tensor, not numpy array
+        assert sample.dtype == getattr(torch, token_encoding_type)
+        assert sample.shape == (max_seq_len,)
+
+@pytest.mark.parametrize('token_encoding_type', ['int17', 'float32', 'complex', 'int8'])
+def test_unsupported_encoding_type(token_encoding_type: str):
+    with pytest.raises(ValueError, match='The token_encoding_type*'):
+        StreamingTextDataset(
+            tokenizer=None,
+            token_encoding_type=token_encoding_type,
+            max_seq_len=2048,
+            local='dataset/path',
+            batch_size=1,
+        )