support separate datasets for train, valid and test

megatron/arguments.py

@@ -839,6 +839,21 @@ def _add_data_args(parser):
                        '1) a single data path, 2) multiple datasets in the'
                        'form: dataset1-weight dataset1-path dataset2-weight '
                        'dataset2-path ...')
+    group.add_argument('--train-data-path', nargs='*', default=None,
+                       help='Path to the training dataset. Accepted format:'
+                       '1) a single data path, 2) multiple datasets in the'
+                       'form: dataset1-weight dataset1-path dataset2-weight '
+                       'dataset2-path ...')
+    group.add_argument('--valid-data-path', nargs='*', default=None,
+                       help='Path to the validation dataset. Accepted format:'
+                       '1) a single data path, 2) multiple datasets in the'
+                       'form: dataset1-weight dataset1-path dataset2-weight '
+                       'dataset2-path ...')
+    group.add_argument('--test-data-path', nargs='*', default=None,
+                       help='Path to the test dataset. Accepted format:'
+                       '1) a single data path, 2) multiple datasets in the'
+                       'form: dataset1-weight dataset1-path dataset2-weight '
+                       'dataset2-path ...')
     group.add_argument('--split', type=str, default='969, 30, 1',
                        help='Comma-separated list of proportions for training,'
                        ' validation, and test split. For example the split '

megatron/data/dataset_utils.py

@@ -63,12 +63,16 @@ def get_datasets_weights_and_num_samples(data_prefix,
     # Add 0.5% (the 1.005 factor) so in case the bleding dataset does
     # not uniformly distribute the number of samples, we still have
     # samples left to feed to the network.
+    if isinstance(train_valid_test_num_samples, list):
         datasets_train_valid_test_num_samples = []
         for weight in weights:
             datasets_train_valid_test_num_samples.append(
                 [int(math.ceil(val * weight * 1.005))
                 for val in train_valid_test_num_samples])
-
+    else:
+        datasets_train_valid_test_num_samples = [
+            int(math.ceil(train_valid_test_num_samples * weight * 1.005))
+            for weight in weights]
 
     return prefixes, weights, datasets_train_valid_test_num_samples
 

megatron/data/gpt_dataset.py

@@ -28,11 +28,15 @@ from megatron.data.dataset_utils import get_train_valid_test_split_
 from megatron.data.indexed_dataset import make_dataset as make_indexed_dataset
 
 
-def build_train_valid_test_datasets(data_prefix, data_impl, splits_string,
+def build_train_valid_test_datasets(data_prefix, train_data_prefix, 
+                                    valid_data_prefix, test_data_prefix, 
+                                    data_impl, splits_string,
                                     train_valid_test_num_samples,
                                     seq_length, seed, skip_warmup):
     """Build train, valid, and test datasets."""
 
+    if data_prefix:
+        print_rank_0("Single data path provided for train, valid & test")
         # Single dataset.
         if len(data_prefix) == 1:
             return _build_train_valid_test_datasets(data_prefix[0],
@@ -75,6 +79,82 @@ def build_train_valid_test_datasets(data_prefix, data_impl, splits_string,
 
         return (blending_train_dataset, blending_valid_dataset,
                 blending_test_dataset)
+    else:
+        print_rank_0("Separate data paths provided for train, valid & test. Split string will be ignored.")
+        assert (train_data_prefix is not None)
+        train_dataset, valid_dataset, test_dataset = None, None, None
+        # Single dataset.
+        train_dataset = build_dataset("train", train_data_prefix, data_impl,
+                                    train_valid_test_num_samples[0], seq_length, seed,
+                                    skip_warmup)
+
+        if valid_data_prefix is not None:
+            valid_dataset = build_dataset("valid", valid_data_prefix, data_impl,
+                                    train_valid_test_num_samples[1], seq_length, seed,
+                                    False)
+
+        if test_data_prefix is not None:
+            test_dataset = build_dataset("test", test_data_prefix, data_impl,
+                                    train_valid_test_num_samples[2], seq_length, seed,
+                                    False)
+
+        return (train_dataset, valid_dataset, test_dataset)
+
+
+def build_dataset(dataset_name, data_prefix, data_impl, num_samples, seq_length, seed, skip_warmup):
+    dataset = None
+    if len(data_prefix) == 1:
+        dataset = _build_dataset(dataset_name,
+                        data_prefix[0], data_impl,
+                        num_samples, seq_length,
+                        seed, skip_warmup)
+    else:
+        # Blending dataset.
+        # Parse the values.
+        output = get_datasets_weights_and_num_samples(data_prefix, num_samples)
+        prefixes, weights, dataset_num_samples = output
+
+        # Build individual datasets.
+        datasets = []
+        for i in range(len(prefixes)):
+            ds = _build_dataset(dataset_name, prefixes[i],
+                            data_impl, dataset_num_samples[i],
+                            seq_length, seed, skip_warmup)
+            if ds:
+                datasets.append(ds)
+
+        if datasets:
+            dataset = BlendableDataset(datasets, weights)
+
+    return dataset
+
+
+def _build_dataset(dataset_name, data_prefix, data_impl,
+                num_samples, seq_length, seed, skip_warmup):
+    """
+    Build dataset. This method is called when individual
+    train, valid, test datasets are provided
+    """
+
+    # Indexed dataset.
+    indexed_dataset = get_indexed_dataset_(data_prefix,
+                                           data_impl,
+                                           skip_warmup)
+
+    total_num_of_documents = indexed_dataset.sizes.shape[0]
+
+    print_rank_0('    {}:'.format(dataset_name))
+    print_rank_0('     document indices in [0, {}) total of {} '
+                 'documents'.format(total_num_of_documents, total_num_of_documents))
+
+    documents = np.arange(start=0, stop=total_num_of_documents,
+                        step=1, dtype=np.int32)
+
+    dataset = GPTDataset(dataset_name, data_prefix,
+                        documents, indexed_dataset,
+                        num_samples, seq_length, seed)
+
+    return dataset
 
 
 def _build_train_valid_test_datasets(data_prefix, data_impl, splits_string,

pretrain_gpt.py

@@ -108,6 +108,9 @@ def train_valid_test_datasets_provider(train_val_test_num_samples):
                  'for GPT ...')
     train_ds, valid_ds, test_ds = build_train_valid_test_datasets(
         data_prefix=args.data_path,
+        train_data_prefix=args.train_data_path,
+        valid_data_prefix=args.valid_data_path,
+        test_data_prefix=args.test_data_path,
         data_impl=args.data_impl,
         splits_string=args.split,
         train_valid_test_num_samples=train_val_test_num_samples,