Akoumparouli/nemo ux validate dataset asset accessibility (#10309)

* Add validate_dataset_asset_accessibility

Signed-off-by: Alexandros Koumparoulis <akoumparouli@nvidia.com>

* Add CI tests for validate_dataset_asset_accessibility

Signed-off-by: Alexandros Koumparoulis <akoumparouli@nvidia.com>

* Apply isort and black reformatting

Signed-off-by: akoumpa <akoumpa@users.noreply.github.com>

* fix

Signed-off-by: Alexandros Koumparoulis <akoumparouli@nvidia.com>

* fix for zipped lists

Signed-off-by: Alexandros Koumparoulis <akoumparouli@nvidia.com>

* Apply isort and black reformatting

Signed-off-by: akoumpa <akoumpa@users.noreply.github.com>

* fix

Signed-off-by: Alexandros Koumparoulis <akoumparouli@nvidia.com>

---------

Signed-off-by: Alexandros Koumparoulis <akoumparouli@nvidia.com>
Signed-off-by: akoumpa <akoumpa@users.noreply.github.com>
Co-authored-by: akoumpa <akoumpa@users.noreply.github.com>

nemo/collections/llm/gpt/data/pre_training.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 
 import logging
+import os
 import warnings
 from pathlib import Path
 from typing import TYPE_CHECKING, Any, Dict, List, Optional
@@ -34,6 +35,66 @@
     from nemo.collections.common.tokenizers.tokenizer_spec import TokenizerSpec
 
 
+def is_number_tryexcept(s):
+    """Returns True if string is a number."""
+    if s is None:
+        return False
+    try:
+        float(s)
+        return True
+    except ValueError:
+        return False
+
+
+def is_zipped_list(paths):
+    # ["30", "path/to/dataset_1_prefix", "70", "path/to/dataset_2_prefix"]
+    even = paths[::2]
+    if len(even) == 0:
+        return False
+    is_num = list(map(is_number_tryexcept, even))
+    if any(is_num):
+        assert all(is_num), "Got malformatted zipped list"
+    return is_num[0]
+
+
+def validate_dataset_asset_accessibility(paths):
+    if paths is None:
+        raise ValueError("Expected path to have a value.")
+
+    if isinstance(paths, tuple) or isinstance(paths, list):
+        if is_zipped_list(paths):
+            # remove weights from paths.
+            paths = paths[1::2]
+        for p in paths:
+            validate_dataset_asset_accessibility(p)
+        return
+    elif isinstance(paths, dict):
+        for p in paths.values():
+            validate_dataset_asset_accessibility(p)
+        return
+
+    if not isinstance(paths, str) and not isisntance(paths, Path):
+        raise ValueError("Expected path to be of string or Path type.")
+
+    path = Path(paths)
+    suffices = ('.bin', '.idx')
+    if path.is_dir():
+        if not os.access(path, os.R_OK):
+            raise PermissionError(f"Expected {str(path)} to be readable.")
+        # Will let the downstream class confirm contents are ok.
+        return
+    if path.exists():
+        if not os.access(path, os.R_OK):
+            raise PermissionError(f"Expected {str(path)} to be readable.")
+        return
+    for suffix in suffices:
+        file_path = Path(str(path) + suffix)
+        if not file_path.exists():
+            raise FileNotFoundError(f"Expected {str(file_path)} to exist.")
+        if not os.access(file_path, os.R_OK):
+            raise PermissionError(f"Expected {str(file_path)} to be readable.")
+
+
 class PreTrainingDataModule(pl.LightningDataModule, IOMixin):
     """PyTorch Lightning-compatible data module for pre-training
        GPT-style models.
@@ -100,6 +161,8 @@ def __init__(
 
         from megatron.core.datasets.utils import get_blend_from_list
 
+        validate_dataset_asset_accessibility(paths)
+
         build_kwargs = {}
         if isinstance(paths, dict):
             if split is not None:

tests/collections/llm/gpt/data/test_pre_training_data.py

@@ -78,3 +78,37 @@ def test_multiple_data_distributions(tokenizer, trainer):
 
     ## this should succeed
     data.setup(stage="dummy")
+
+
+def test_validate_dataset_asset_accessibility_file_does_not_exist(tokenizer, trainer):
+    raised_exception = False
+    try:
+        data = PreTrainingDataModule(
+            paths=["/this/path/should/not/exist/"],
+            seq_length=512,
+            micro_batch_size=2,
+            global_batch_size=2,
+            tokenizer=tokenizer,
+        )
+        data.trainer = trainer
+    except FileNotFoundError:
+        raised_exception = True
+
+    assert raised_exception == True, "Expected to raise a FileNotFoundError"
+
+
+def test_validate_dataset_asset_accessibility_file_is_none(tokenizer, trainer):
+    raised_exception = False
+    try:
+        data = PreTrainingDataModule(
+            paths=None,
+            seq_length=512,
+            micro_batch_size=2,
+            global_batch_size=2,
+            tokenizer=tokenizer,
+        )
+        data.trainer = trainer
+    except ValueError:
+        raised_exception = True
+
+    assert raised_exception == True, "Expected to raise a ValueError"