Fix streaming dataset setup for fine-tuning

Currently, when a StreamingFinetuningDataset is created using the
build_finetuning_dataloader method, a failure is returned as some
incorrect parameters are passed through to the constructor of
StreamingFinetuningDataset. This patch fixes the paramter mismatch and
adds test coverage for this case.

llmfoundry/data/finetuning/dataloader.py

@@ -125,17 +125,11 @@ def build_finetuning_dataloader(cfg: DictConfig,
             download_timeout=cfg.dataset.get('download_timeout', 60),
             validate_hash=cfg.dataset.get('validate_hash', None),
             keep_zip=cfg.dataset.get('keep_zip', False),
-            epoch_size=cfg.dataset.get('epoch_size', None),
             predownload=cfg.dataset.get('predownload', None),
-            cache_limit=cfg.dataset.get('cache_limit', None),
-            partition_algo=cfg.dataset.get('partition_algo', 'orig'),
             num_canonical_nodes=cfg.dataset.get('num_canonical_nodes', None),
             batch_size=device_batch_size,
             shuffle=cfg.dataset.get('shuffle', False),
-            shuffle_algo=cfg.dataset.get('shuffle_algo', 'py1b'),
             shuffle_seed=cfg.dataset.get('shuffle_seed', 9176),
-            shuffle_block_size=cfg.dataset.get('shuffle_block_size', 1 << 18),
-            sampling_method=cfg.dataset.get('sampling_method', 'balanced'),
         )
 
         collate_fn, dataloader_batch_size = _build_collate_fn(

tests/test_dataloader.py

@@ -13,6 +13,7 @@
 import torch
 from composer.utils import dist, using_torch_2
 from omegaconf import OmegaConf as om
+from streaming import MDSWriter
 
 from llmfoundry import (build_finetuning_dataloader,
                         build_text_denoising_dataloader)
@@ -42,6 +43,25 @@ def get_abs_data_path(data_local: str):
     return os.path.join(os.getcwd(), data_local)
 
 
+def build_mock_ft_streaming_dataset(data_path, split):
+    columns = {'prompt': 'str', 'response': 'str'}
+
+    dataset = [{
+        'prompt': 'This is just a test1',
+        'response': 'Hello World1'
+    }, {
+        'prompt': 'This is just a test2',
+        'response': 'Hello world2'
+    }]
+
+    output_path = os.path.join(data_path, split)
+
+    with MDSWriter(columns=columns, out=output_path,
+                   compression=None) as output_writer:
+        for sample in dataset:
+            output_writer.write(sample)
+
+
 @pytest.mark.parametrize('tokenizer_name', ['gpt2', 'facebook/opt-125m'])
 @pytest.mark.parametrize('pretokenize', [False, True])
 def test_correct_padding(tokenizer_name: str,
@@ -414,6 +434,45 @@ def test_finetuning_dataloader_custom_split_remote(
         _ = build_finetuning_dataloader(cfg, tokenizer, 4)
 
 
+def test_finetuning_dataloader_streaming(tmp_path: pathlib.Path):
+    tokenizer_name = 'gpt2'
+    max_seq_len = 2048
+
+    remote_path = os.path.join(tmp_path, 'remote')
+    local_path = os.path.join(tmp_path, 'local')
+
+    build_mock_ft_streaming_dataset(remote_path, 'train')
+
+    cfg = {
+        'name': 'finetuning',
+        'dataset': {
+            'remote': remote_path,
+            'local': local_path,
+            'split': 'train',
+            'max_seq_len': 2048,
+            'decoder_only_format': True,
+            'allow_pad_trimming': False,
+            'packing_ratio': None,
+            'shuffle': True,
+        },
+        'drop_last': False,
+        'num_workers': 4,
+        'pin_memory': False,
+        'prefetch_factor': 2,
+        'persistent_workers': False,
+        'timeout': 0
+    }
+
+    cfg = om.create(cfg)
+
+    tokenizer = build_tokenizer(
+        tokenizer_name='gpt2',
+        tokenizer_kwargs={'model_max_length': max_seq_len},
+    )
+
+    _ = build_finetuning_dataloader(cfg, tokenizer, 4)
+
+
 @pytest.mark.parametrize('add_bad_data_dropped', [True, False])
 @pytest.mark.parametrize('add_bad_data_error', [True, False])
 def test_malformed_data(