Use allenai/c4 instead of c4 dataset (#1554)

Co-authored-by: Eitan Turok <[email protected]>

README.md

@@ -223,7 +223,7 @@ cd scripts
 
 # Convert C4 dataset to StreamingDataset format
 python data_prep/convert_dataset_hf.py \
-  --dataset c4 --data_subset en \
+  --dataset allenai/c4 --data_subset en \
   --out_root my-copy-c4 --splits train_small val_small \
   --concat_tokens 2048 --tokenizer EleutherAI/gpt-neox-20b --eos_text '<|endoftext|>'
 

TUTORIAL.md

@@ -216,7 +216,7 @@ Output the processed data to `./my-adaptation-data`. Note that we use smaller su
 <!--pytest.mark.skip-->
 ```bash
 python scripts/data_prep/convert_dataset_hf.py \
-  --dataset c4 --data_subset en \
+  --dataset allenai/c4 --data_subset en \
   --out_root my-adaptation-data --splits train_small val_small \
   --concat_tokens 4096 --tokenizer EleutherAI/gpt-neox-20b --eos_text '<|endoftext|>' \
   --compression zstd
@@ -248,7 +248,7 @@ The first step to training from scratch is to get your pretraining data prepared
 <!--pytest.mark.skip-->
 ```bash
 python scripts/data_prep/convert_dataset_hf.py \
-  --dataset c4 --data_subset en \
+  --dataset allenai/c4 --data_subset en \
   --out_root my-copy-c4 --splits train_small val_small \
   --concat_tokens 2048 --tokenizer gpt2 \
   --eos_text '<|endoftext|>' \

llmfoundry/command_utils/data_prep/convert_dataset_hf.py

@@ -158,7 +158,7 @@ def __init__(
     truncated_samples=100,
 )
 
-CONSTS = {'c4': c4constants, 'the_pile': pileconstants}
+CONSTS = {'allenai/c4': c4constants, 'the_pile': pileconstants}
 
 
 def build_hf_dataset(
@@ -335,7 +335,7 @@ def convert_dataset_hf(
         dataset_constants = CONSTS[dataset]
     except KeyError:
         raise ValueError(
-            f'Constants for dataset "{dataset}" not found. Currently only "the_pile" and "c4" are supported.',
+            f'Constants for dataset "{dataset}" not found. Currently only "the_pile" and "allenai/c4" are supported.',
         )
 
     if concat_tokens is not None and tokenizer is not None:

llmfoundry/command_utils/data_prep/convert_dataset_json.py

@@ -43,7 +43,7 @@ def build_hf_dataset(
         no_wrap (bool): if concatenating, whether to wrap text across `max_length` boundaries
         tokenizer (PreTrainedTokenizerBase): if mode is CONCAT_TOKENS, the tokenizer to use
         data_subset (str): Referred to as "name" in HuggingFace datasets.load_dataset.
-            Typically "all" (The Pile) or "en" (c4).
+            Typically "all" (The Pile) or "en" (allenai/c4).
 
     Returns:
         An IterableDataset.

mcli/mcli-1b-max-seq-len-8k.yaml

@@ -13,7 +13,7 @@ integrations:
 command: |
   cd llm-foundry/scripts
   python data_prep/convert_dataset_hf.py \
-    --dataset c4 --data_subset en \
+    --dataset allenai/c4 --data_subset en \
     --out_root ./my-copy-c4 --splits train_small val_small \
     --concat_tokens 8192 --tokenizer EleutherAI/gpt-neox-20b --eos_text '<|endoftext|>'
   composer train/train.py /mnt/config/parameters.yaml

mcli/mcli-1b.yaml

@@ -13,7 +13,7 @@ integrations:
 command: |
   cd llm-foundry/scripts
   python data_prep/convert_dataset_hf.py \
-    --dataset c4 --data_subset en \
+    --dataset allenai/c4 --data_subset en \
     --out_root ./my-copy-c4 --splits train_small val_small \
     --concat_tokens 2048 --tokenizer EleutherAI/gpt-neox-20b --eos_text '<|endoftext|>'
   composer train/train.py train/yamls/pretrain/mpt-1b.yaml \

mcli/mcli-pretokenize-oci-upload.yaml

@@ -24,7 +24,7 @@ command: |
 
   # Run the dataset conversion
   python convert_dataset_hf.py \
-    --dataset c4 --data_subset en \
+    --dataset allenai/c4 --data_subset en \
     --out_root ./my-copy-c4 \
     --splits val_small val train_small train \
     --concat_tokens 2048 --tokenizer EleutherAI/gpt-neox-20b --eos_text '<|endoftext|>'

scripts/data_prep/README.md

@@ -14,7 +14,7 @@ Currently supports `c4` and `The Pile`.
 ```bash
 # Convert C4 dataset to StreamingDataset format
 python convert_dataset_hf.py \
-  --dataset c4 --data_subset en \
+  --dataset allenai/c4 --data_subset en \
   --out_root my-copy-c4 --splits train_small val_small \
   --concat_tokens 2048 --tokenizer EleutherAI/gpt-neox-20b --eos_text '<|endoftext|>' \
   --compression zstd

scripts/train/README.md

@@ -27,7 +27,7 @@ If you haven't already, make sure to [install the requirements](../../README.md#
 
 To run pretraining, you'll need to make yourself a copy of a pretraining dataset and format it for efficient streaming. Check out the [`llm-foundry/data_prep`](../data_prep) folder for detailed instructions on how to convert your dataset to the MosaicML [StreamingDataset](https://github.com/mosaicml/streaming) format.
 
-As a quickstart, we elaborate on how to prepare the [C4 (Colossal, Cleaned, Common Crawl)](https://huggingface.co/datasets/c4) dataset here.
+As a quickstart, we elaborate on how to prepare the [C4 (Colossal, Cleaned, Common Crawl)](https://huggingface.co/datasets/allenai/c4) dataset here.
 
 We first convert the dataset from its native format (a collection of zipped JSONs)
 to MosaicML's StreamingDataset format, which is a collection of binary `.mds` files.
@@ -44,13 +44,13 @@ This will take 20-60 seconds depending on your internet bandwidth.
 You should see two folders once completed: `./my-copy-c4/train_small` and `./my-copy-c4/val_small` that are ~1.0GB total. Note that we are using the `--concat_tokens` option to pre tokenize our samples to be of the max sequence length without padding
 <!--pytest.mark.skip-->
 ```bash
-python ../data_prep/convert_dataset_hf.py --dataset c4 --data_subset en --out_root ./my-copy-c4 --splits train_small val_small --concat_tokens 2048 --tokenizer EleutherAI/gpt-neox-20b --eos_text '<|endoftext|>'
+python ../data_prep/convert_dataset_hf.py --dataset allenai/c4 --data_subset en --out_root ./my-copy-c4 --splits train_small val_small --concat_tokens 2048 --tokenizer EleutherAI/gpt-neox-20b --eos_text '<|endoftext|>'
 ```
 
 Alternatively, you can download the full `train` and `val` splits if you really want to train the model (i.e. not just profile the model). This will take 1-to-many hours depending on bandwidth, number of CPUs, etc. The final folder `./my-copy-c4/train` will be ~800GB so make sure you have space!
 <!--pytest.mark.skip-->
 ```bash
-python ../data_prep/convert_dataset_hf.py --dataset c4 --data_subset en --out_root ./my-copy-c4 --splits train val --concat_tokens 2048 --tokenizer EleutherAI/gpt-neox-20b --eos_text '<|endoftext|>'
+python ../data_prep/convert_dataset_hf.py --dataset allenai/c4 --data_subset en --out_root ./my-copy-c4 --splits train val --concat_tokens 2048 --tokenizer EleutherAI/gpt-neox-20b --eos_text '<|endoftext|>'
 ```
 
 For any of the above commands, you can also choose to compress the `.mds` files.

scripts/train/benchmarking/submit_benchmarks.py

@@ -479,7 +479,7 @@ def run_config(
     if args.data_remote is None:
         command += f"""
             cd llm-foundry/scripts
-            python data_prep/convert_dataset_hf.py --dataset c4 --data_subset en --out_root ./my-copy-c4 --splits train_small val_small --concat_tokens {max_seq_len} --eos_text '<|endoftext|>'
+            python data_prep/convert_dataset_hf.py --dataset allenai/c4 --data_subset en --out_root ./my-copy-c4 --splits train_small val_small --concat_tokens {max_seq_len} --eos_text '<|endoftext|>'
             composer train/train.py /mnt/config/parameters.yaml
             """
     else:

tests/a_scripts/data_prep/test_convert_dataset_hf.py

@@ -11,7 +11,7 @@ def test_download_script_from_api(tmp_path: Path):
     # test calling it directly
     path = os.path.join(tmp_path, 'my-copy-c4-1')
     convert_dataset_hf(
-        dataset='c4',
+        dataset='allenai/c4',
         data_subset='en',
         splits=['val_xsmall'],
         out_root=path,

tests/a_scripts/eval/test_eval.py

@@ -121,7 +121,7 @@ def test_loader_eval(
 
     # Set up multiple eval dataloaders
     first_eval_loader = test_cfg.eval_loader
-    first_eval_loader.label = 'c4'
+    first_eval_loader.label = 'allenai/c4'
     # Create second eval dataloader using the arxiv dataset.
     second_eval_loader = copy.deepcopy(first_eval_loader)
     second_eval_loader.label = 'arxiv'
@@ -157,16 +157,17 @@ def test_loader_eval(
     print(inmemorylogger.data.keys())
 
     # Checks for first eval dataloader
-    assert 'metrics/eval/c4/LanguageCrossEntropy' in inmemorylogger.data.keys()
+    assert 'metrics/eval/allenai/c4/LanguageCrossEntropy' in inmemorylogger.data.keys(
+    )
     assert isinstance(
-        inmemorylogger.data['metrics/eval/c4/LanguageCrossEntropy'],
+        inmemorylogger.data['metrics/eval/allenai/c4/LanguageCrossEntropy'],
         list,
     )
     assert len(
-        inmemorylogger.data['metrics/eval/c4/LanguageCrossEntropy'][-1],
+        inmemorylogger.data['metrics/eval/allenai/c4/LanguageCrossEntropy'][-1],
     ) > 0
     assert isinstance(
-        inmemorylogger.data['metrics/eval/c4/LanguageCrossEntropy'][-1],
+        inmemorylogger.data['metrics/eval/allenai/c4/LanguageCrossEntropy'][-1],
         tuple,
     )
 

tests/a_scripts/train/test_train.py

@@ -134,7 +134,7 @@ def test_train_multi_eval(tmp_path: pathlib.Path):
     test_cfg = gpt_tiny_cfg(c4_dataset_name, 'cpu')
     # Set up multiple eval dataloaders
     first_eval_loader = test_cfg.eval_loader
-    first_eval_loader.label = 'c4'
+    first_eval_loader.label = 'allenai/c4'
     # Create second eval dataloader using the arxiv dataset.
     second_eval_loader = copy.deepcopy(first_eval_loader)
     second_eval_loader.label = 'arxiv'
@@ -154,16 +154,17 @@ def test_train_multi_eval(tmp_path: pathlib.Path):
     assert isinstance(inmemorylogger, InMemoryLogger)
 
     # Checks for first eval dataloader
-    assert 'metrics/eval/c4/LanguageCrossEntropy' in inmemorylogger.data.keys()
+    assert 'metrics/eval/allenai/c4/LanguageCrossEntropy' in inmemorylogger.data.keys(
+    )
     assert isinstance(
-        inmemorylogger.data['metrics/eval/c4/LanguageCrossEntropy'],
+        inmemorylogger.data['metrics/eval/allenai/c4/LanguageCrossEntropy'],
         list,
     )
     assert len(
-        inmemorylogger.data['metrics/eval/c4/LanguageCrossEntropy'][-1],
+        inmemorylogger.data['metrics/eval/allenai/c4/LanguageCrossEntropy'][-1],
     ) > 0
     assert isinstance(
-        inmemorylogger.data['metrics/eval/c4/LanguageCrossEntropy'][-1],
+        inmemorylogger.data['metrics/eval/allenai/c4/LanguageCrossEntropy'][-1],
         tuple,
     )
 
@@ -212,7 +213,7 @@ def test_eval_metrics_with_no_train_metrics(tmp_path: pathlib.Path):
     c4_dataset_name = create_c4_dataset_xxsmall(tmp_path)
     test_cfg = gpt_tiny_cfg(c4_dataset_name, 'cpu')
     first_eval_loader = test_cfg.eval_loader
-    first_eval_loader.label = 'c4'
+    first_eval_loader.label = 'allenai/c4'
     test_cfg.eval_loader = om.create([first_eval_loader])
     test_cfg.eval_subset_num_batches = 1  # -1 to evaluate on all batches
     test_cfg.max_duration = '1ba'
@@ -226,15 +227,16 @@ def test_eval_metrics_with_no_train_metrics(tmp_path: pathlib.Path):
         0]  # pyright: ignore [reportGeneralTypeIssues]
     assert isinstance(inmemorylogger, InMemoryLogger)
 
-    assert 'metrics/eval/c4/LanguageCrossEntropy' in inmemorylogger.data.keys()
+    assert 'metrics/eval/allenai/c4/LanguageCrossEntropy' in inmemorylogger.data.keys(
+    )
     assert isinstance(
-        inmemorylogger.data['metrics/eval/c4/LanguageCrossEntropy'],
+        inmemorylogger.data['metrics/eval/allenai/c4/LanguageCrossEntropy'],
         list,
     )
     assert len(
-        inmemorylogger.data['metrics/eval/c4/LanguageCrossEntropy'][-1],
+        inmemorylogger.data['metrics/eval/allenai/c4/LanguageCrossEntropy'][-1],
     ) > 0
     assert isinstance(
-        inmemorylogger.data['metrics/eval/c4/LanguageCrossEntropy'][-1],
+        inmemorylogger.data['metrics/eval/allenai/c4/LanguageCrossEntropy'][-1],
         tuple,
     )

tests/data/test_dataloader.py

@@ -204,7 +204,7 @@ def test_correct_padding(
     shutil.rmtree(path, ignore_errors=True)
     if pretokenize:
         convert_dataset_hf(
-            dataset='c4',
+            dataset='allenai/c4',
             data_subset='en',
             splits=[split],
             out_root=path,
@@ -219,7 +219,7 @@ def test_correct_padding(
         )
     else:
         convert_dataset_hf(
-            dataset='c4',
+            dataset='allenai/c4',
             data_subset='en',
             splits=[split],
             out_root=path,
@@ -233,7 +233,7 @@ def test_correct_padding(
             num_workers=None,
         )
     if not os.path.isdir(path):
-        raise RuntimeError(f'c4 dataset at {path} not set up as expected')
+        raise RuntimeError(f'allenai/c4 dataset at {path} not set up as expected')
 
     test_cfg = get_config(
         conf_path='scripts/train/yamls/pretrain/mpt-125m.yaml',

tests/data_utils.py

@@ -231,7 +231,7 @@ def create_c4_dataset_xxsmall(path: Path) -> str:
 
     # Hyperparameters from https://github.com/mosaicml/llm-foundry/blob/340a56658560ebceb2a3aa69d6e37813e415acd0/README.md#L188
     convert_dataset_hf(
-        dataset='c4',
+        dataset='allenai/c4',
         data_subset='en',
         splits=[downloaded_split],
         out_root=c4_dir,