play with datasets

run.sh

@@ -1 +1 @@
-python3 $HOME/transformers/examples/pytorch/xla_spawn.py --num_cores ${TPU_NUM_DEVICES} wtpsplit/train/train.py $1
+python3 ~/wtpsplit/xla_spawn.py --num_cores ${TPU_NUM_DEVICES} wtpsplit/train/train.py $1

wtpsplit/train/train.py

@@ -253,9 +253,9 @@ def main():
     elif args.from_scratch:
         backbone = LACanineForTokenClassification(config)
     else:
-        backbone = LACanineForTokenClassification.from_pretrained(args.model_name_or_path, 
-                                                                  ignore_mismatched_sizes=True, 
-                                                                  config=config)
+        backbone = LACanineForTokenClassification.from_pretrained(
+            args.model_name_or_path, ignore_mismatched_sizes=True, config=config
+        )
 
     model = Model(
         backbone,
@@ -270,8 +270,15 @@ def prepare_dataset(
         num_workers=1,
         include_languages=None,
         shuffle=False,
+        split="train",
     ):
-        dataset = load_dataset("parquet", data_files=path, split="train")
+        from datasets.download import DownloadConfig
+
+        dlconf = DownloadConfig(cache_dir="/home/Markus/.cache/huggingface/datasets")
+        dataset = load_dataset("markus583/mC4-TEST", split=split, download_config=dlconf)
+        # optional: delete downloaded dataset, it is stored in /dev/shm/cache now 
+        # os.system("rm -rf /home/Markus/.cache/huggingface/datasets")
+
         if include_languages is not None:
             include_languages = set(include_languages)
 
@@ -443,6 +450,14 @@ def maybe_pad(text):
                     # a bit hacky but oh well, only drop if sentence
                     remove_columns=["ends_with_punctuation"] if args.text_column == "text" else [],
                 )
+
+        # used dataset is in cache now
+        # recursively remove the files in cache_dir starting with m_c4
+        for root, dirs, files in os.walk(os.environ.get("HF_DATASETS_CACHE")):
+            for file in files:
+                if file.startswith("m_c4"):
+                    print(f"Removing {os.path.join(root, file)}")
+                    os.remove(os.path.join(root, file))
 
         return dataset
 
@@ -451,12 +466,14 @@ def maybe_pad(text):
         num_workers=args.preprocessing_num_workers,
         include_languages=args.include_languages,
         shuffle=args.shuffle,
+        split="train",
     )
     valid_dataset = prepare_dataset(
         args.valid_text_path,
         num_workers=args.preprocessing_num_workers,
         include_languages=args.include_languages,
         shuffle=False,
+        split="valid",
     )
 
     eval_data = torch.load(

xla_spawn.py

@@ -0,0 +1,83 @@
+# Copyright 2020 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+A simple launcher script for TPU training
+
+Inspired by https://github.com/pytorch/pytorch/blob/master/torch/distributed/launch.py
+
+::
+    >>> python xla_spawn.py --num_cores=NUM_CORES_YOU_HAVE
+               YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 and all other
+               arguments of your training script)
+
+"""
+
+
+import importlib
+import sys
+from argparse import REMAINDER, ArgumentParser
+from pathlib import Path
+
+import torch_xla.distributed.xla_multiprocessing as xmp
+
+
+def parse_args():
+    """
+    Helper function parsing the command line options
+    @retval ArgumentParser
+    """
+    parser = ArgumentParser(
+        description=(
+            "PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes"
+        )
+    )
+
+    # Optional arguments for the launch helper
+    parser.add_argument("--num_cores", type=int, default=1, help="Number of TPU cores to use (1 or 8).")
+
+    # positional
+    parser.add_argument(
+        "training_script",
+        type=str,
+        help=(
+            "The full path to the single TPU training "
+            "program/script to be launched in parallel, "
+            "followed by all the arguments for the "
+            "training script"
+        ),
+    )
+
+    # rest from the training program
+    parser.add_argument("training_script_args", nargs=REMAINDER)
+
+    return parser.parse_args()
+
+
+def main():
+    args = parse_args()
+
+    # Import training_script as a module.
+    script_fpath = Path(args.training_script)
+    sys.path.append(str(script_fpath.parent.resolve()))
+    mod_name = script_fpath.stem
+    mod = importlib.import_module(mod_name)
+
+    # Patch sys.argv
+    sys.argv = [args.training_script] + args.training_script_args + ["--tpu_num_cores", str(args.num_cores)]
+
+    xmp.spawn(mod._mp_fn, args=(), nprocs=args.num_cores)
+
+
+if __name__ == "__main__":
+    main()