[AutoMM] Support customizing use_fast for AutoTokenizer (open-mmlab#3379

)

multimodal/src/autogluon/multimodal/configs/model/fusion_mlp_image_text_tabular.yaml

@@ -62,6 +62,7 @@ model:
     data_types:
       - "text"
     tokenizer_name: "hf_auto"
+    use_fast: True  # Use a fast Rust-based tokenizer if it is supported for a given model. If a fast tokenizer is not available for a given model, a normal Python-based tokenizer is returned instead.
     max_text_len: 512  # If None or <=0, then use the max length of pretrained models.
     insert_sep: True
     low_cpu_mem_usage: False

multimodal/src/autogluon/multimodal/data/process_text.py

@@ -95,6 +95,7 @@ def __init__(
         train_augment_types: Optional[List[str]] = None,
         template_config: Optional[DictConfig] = None,
         normalize_text: Optional[bool] = False,
+        use_fast: Optional[bool] = True,
     ):
         """
         Parameters
@@ -125,6 +126,11 @@ def __init__(
             Whether to normalize text to resolve encoding problems.
             Examples of normalized texts can be found at
             https://github.com/autogluon/autogluon/tree/master/examples/automm/kaggle_feedback_prize#15-a-few-examples-of-normalized-texts
+        use_fast
+            Use a fast Rust-based tokenizer if it is supported for a given model.
+            If a fast tokenizer is not available for a given model, a normal Python-based tokenizer is returned instead.
+            See: https://huggingface.co/docs/transformers/model_doc/auto#transformers.AutoTokenizer.from_pretrained.use_fast
+
         """
         self.prefix = model.prefix
         self.tokenizer_name = tokenizer_name
@@ -136,6 +142,7 @@ def __init__(
             self.tokenizer = self.get_pretrained_tokenizer(
                 tokenizer_name=tokenizer_name,
                 checkpoint_name=model.checkpoint_name,
+                use_fast=use_fast,
             )
         if hasattr(self.tokenizer, "deprecation_warnings"):
             # Disable the warning "Token indices sequence length is longer than the specified maximum sequence..."
@@ -410,6 +417,7 @@ def get_special_tokens(tokenizer):
     def get_pretrained_tokenizer(
         tokenizer_name: str,
         checkpoint_name: str,
+        use_fast: Optional[bool] = True,
     ):
         """
         Load the tokenizer for a pre-trained huggingface checkpoint.
@@ -420,14 +428,18 @@ def get_pretrained_tokenizer(
             The tokenizer type, e.g., "bert", "clip", "electra", and "hf_auto".
         checkpoint_name
             Name of a pre-trained checkpoint.
+        use_fast
+            Use a fast Rust-based tokenizer if it is supported for a given model.
+            If a fast tokenizer is not available for a given model, a normal Python-based tokenizer is returned instead.
+            See: https://huggingface.co/docs/transformers/model_doc/auto#transformers.AutoTokenizer.from_pretrained.use_fast
 
         Returns
         -------
         A tokenizer instance.
         """
         try:
             tokenizer_class = ALL_TOKENIZERS[tokenizer_name]
-            return tokenizer_class.from_pretrained(checkpoint_name)
+            return tokenizer_class.from_pretrained(checkpoint_name, use_fast=use_fast)
         except TypeError as e:
             try:
                 tokenizer_class = ALL_TOKENIZERS["bert"]

multimodal/src/autogluon/multimodal/utils/data.py

@@ -157,6 +157,7 @@ def create_data_processor(
             train_augment_types=OmegaConf.select(model_config, "text_train_augment_types"),
             template_config=getattr(config.data, "templates", OmegaConf.create({"turn_on": False})),
             normalize_text=getattr(config.data.text, "normalize_text", False),
+            use_fast=OmegaConf.select(model_config, "use_fast", default=True),
         )
     elif data_type == CATEGORICAL:
         data_processor = CategoricalProcessor(

multimodal/tests/hf_model_list.yaml

@@ -40,6 +40,7 @@ others_2:
 - t5-small
 - microsoft/layoutlmv3-base
 - microsoft/layoutlmv2-base-uncased
+- albert-base-v2
 predictor:
 - CLTL/MedRoBERTa.nl
 - google/electra-small-discriminator

multimodal/tests/unittests/others_2/test_data_processors.py

@@ -0,0 +1,66 @@
+import os
+import shutil
+import tempfile
+
+import pytest
+from transformers import AlbertTokenizer, AlbertTokenizerFast
+
+from autogluon.multimodal import MultiModalPredictor
+from autogluon.multimodal.constants import TEXT
+
+from ..utils.unittest_datasets import AEDataset, HatefulMeMesDataset, IDChangeDetectionDataset, PetFinderDataset
+
+ALL_DATASETS = {
+    "petfinder": PetFinderDataset,
+    "hateful_memes": HatefulMeMesDataset,
+    "ae": AEDataset,
+}
+
+
+@pytest.mark.parametrize(
+    "checkpoint_name,use_fast,tokenizer_type",
+    [
+        (
+            "albert-base-v2",
+            None,
+            AlbertTokenizerFast,
+        ),
+        (
+            "albert-base-v2",
+            True,
+            AlbertTokenizerFast,
+        ),
+        (
+            "albert-base-v2",
+            False,
+            AlbertTokenizer,
+        ),
+    ],
+)
+def test_tokenizer_use_fast(checkpoint_name, use_fast, tokenizer_type):
+    dataset = ALL_DATASETS["ae"]()
+    metric_name = dataset.metric
+
+    predictor = MultiModalPredictor(
+        label=dataset.label_columns[0],
+        problem_type=dataset.problem_type,
+        eval_metric=metric_name,
+    )
+    hyperparameters = {
+        "data.categorical.convert_to_text": True,
+        "data.numerical.convert_to_text": True,
+        "model.hf_text.checkpoint_name": checkpoint_name,
+    }
+    if use_fast is not None:
+        hyperparameters["model.hf_text.use_fast"] = use_fast
+
+    with tempfile.TemporaryDirectory() as save_path:
+        if os.path.isdir(save_path):
+            shutil.rmtree(save_path)
+        predictor.fit(
+            train_data=dataset.train_df,
+            time_limit=5,
+            save_path=save_path,
+            hyperparameters=hyperparameters,
+        )
+    assert isinstance(predictor._data_processors[TEXT][0].tokenizer, tokenizer_type)