Handle long file names in convert text to mds

llmfoundry/command_utils/data_prep/convert_text_to_mds.py

@@ -240,41 +240,39 @@ def download_and_convert(
     object_store = maybe_create_object_store_from_uri(input_folder)
 
     # Download file_names
-    with tempfile.TemporaryDirectory() as tmp_dir:
-        log.info(f'Created temporary directory: {tmp_dir}')
-        downloading_iter = DownloadingIterable(
-            object_names=file_names,
-            output_folder=tmp_dir,
-            object_store=object_store,
-        )
-        log.info(f'Initializing tokenizer: {tokenizer_name}')
-        tokenizer = AutoTokenizer.from_pretrained(
-            tokenizer_name,
-            trust_remote_code=trust_remote_code,
-        )
-        tokenizer.model_max_length = 5000000000  # Hack to prevent warnings from HuggingFace
-
-        # Use the ConcatTokensDataset from LLM-foundry to concatenate sequences of tokens up
-        # to the maximum sequence length
-        dataset = ConcatTokensFromFilesDataset(
-            files=downloading_iter,
-            max_length=concat_tokens,
-            tokenizer=tokenizer,
-            eos_text=eos_text,
-            bos_text=bos_text,
-            no_wrap=no_wrap,
-        )
+    downloading_iter = DownloadingIterable(
+        object_names=file_names,
+        output_folder=None, # Downloads to temporary files.
+        object_store=object_store,
+    )
+    log.info(f'Initializing tokenizer: {tokenizer_name}')
+    tokenizer = AutoTokenizer.from_pretrained(
+        tokenizer_name,
+        trust_remote_code=trust_remote_code,
+    )
+    tokenizer.model_max_length = 5000000000  # Hack to prevent warnings from HuggingFace
+
+    # Use the ConcatTokensDataset from LLM-foundry to concatenate sequences of tokens up
+    # to the maximum sequence length
+    dataset = ConcatTokensFromFilesDataset(
+        files=downloading_iter,
+        max_length=concat_tokens,
+        tokenizer=tokenizer,
+        eos_text=eos_text,
+        bos_text=bos_text,
+        no_wrap=no_wrap,
+    )
 
-        columns = {'tokens': 'ndarray:int32'}
+    columns = {'tokens': 'ndarray:int32'}
 
-        log.info('Converting to MDS format...')
-        with MDSWriter(
-            out=output_folder,
-            columns=columns,
-            compression=compression,
-        ) as out:
-            for sample in tqdm(dataset):
-                out.write(sample)
+    log.info('Converting to MDS format...')
+    with MDSWriter(
+        out=output_folder,
+        columns=columns,
+        compression=compression,
+    ) as out:
+        for sample in tqdm(dataset):
+            out.write(sample)
 
     log.info(f'Completed download and conversion for {len(file_names)} files')
 

llmfoundry/utils/data_prep_utils.py

@@ -3,6 +3,7 @@
 
 import json
 import os
+import tempfile
 from glob import glob
 from typing import Optional
 
@@ -105,7 +106,7 @@ class DownloadingIterable:
     def __init__(
         self,
         object_names: list[str],
-        output_folder: str,
+        output_folder: Optional[str],
         object_store: Optional[ObjectStore],
     ):
         """Iterable that downloads files before yielding the local filename.
@@ -114,7 +115,7 @@ def __init__(
 
         Args:
             object_names (List[str]): Names of objects to download
-            output_folder (str): Local folder to write downloaded files to
+            output_folder (Optional[str]): Local folder to write downloaded files to. If none, uses a temporary folder.
             object_store (Optional[ObjectStore]): Object store to download from
         """
         self.object_names = object_names
@@ -131,7 +132,8 @@ def __iter__(self):
                 output_filename = os.path.join(
                     self.output_folder,
                     object_name.strip('/'),
-                )
+                ) if self.output_folder is not None else tempfile.NamedTemporaryFile(
+                ).name
 
                 download_file(
                     object_store=self.object_store,