Fix tapas scatter (#20149)

* First draft

* Remove scatter dependency

* Add require_torch

* update vectorized sum test, add clone call

* remove artifacts

* fix style

* fix style v2

* remove "scatter" mentions from the code base

* fix isort error
Co-authored-by: Niels Rogge <nielsrogge@Nielss-MacBook-Pro.local>
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

docker/transformers-all-latest-gpu/Dockerfile

@@ -41,9 +41,6 @@ RUN python3 -m pip uninstall -y flax jax
 # TODO: remove this line once the conflict is resolved in these libraries.
 RUN python3 -m pip install --no-cache-dir git+https://github.com/onnx/tensorflow-onnx.git@ddca3a5eb2d912f20fe7e0568dd1a3013aee9fa3
 
-# Use installed torch version for `torch-scatter` to avid to deal with PYTORCH='pre'.
-# If torch is nightly version, the link is likely to be invalid, but the installation falls back to the latest torch-scatter
-RUN python3 -m pip install --no-cache-dir torch-scatter -f https://data.pyg.org/whl/torch-$(python3 -c "from torch import version; print(version.__version__.split('+')[0])")+$CUDA.html
 RUN python3 -m pip install --no-cache-dir intel_extension_for_pytorch==$INTEL_TORCH_EXT+cpu -f https://software.intel.com/ipex-whl-stable
 
 RUN python3 -m pip install --no-cache-dir git+https://github.com/facebookresearch/detectron2.git pytesseract

docker/transformers-doc-builder/Dockerfile

@@ -10,7 +10,6 @@ RUN apt-get -y update && apt-get install -y libsndfile1-dev && apt install -y te
 # Torch needs to be installed before deepspeed
 RUN python3 -m pip install --no-cache-dir ./transformers[deepspeed]
 
-RUN python3 -m pip install --no-cache-dir torch-scatter -f https://data.pyg.org/whl/torch-$(python -c "from torch import version; print(version.__version__.split('+')[0])")+cpu.html
 RUN python3 -m pip install --no-cache-dir torchvision git+https://github.com/facebookresearch/detectron2.git pytesseract
 RUN python3 -m pip install --no-cache-dir pytorch-quantization --extra-index-url https://pypi.ngc.nvidia.com
 RUN python3 -m pip install -U "itsdangerous<2.1.0"

docker/transformers-past-gpu/Dockerfile

@@ -34,10 +34,4 @@ RUN python3 ./transformers/utils/past_ci_versions.py --framework $FRAMEWORK --ve
 RUN echo "INSTALL_CMD = $INSTALL_CMD"
 RUN $INSTALL_CMD
 
-# Having installation problems for torch-scatter with torch <= 1.6. Disable so we have the same set of tests.
-# (This part will be removed once the logic of using `past_ci_versions.py` is used in other Dockerfile files.)
-# # Use installed torch version for `torch-scatter`.
-# # (The env. variable $CUDA is defined in `past_ci_versions.py`)
-# RUN [ "$FRAMEWORK" = "pytorch" ] && python3 -m pip install --no-cache-dir torch-scatter -f https://data.pyg.org/whl/torch-$(python3 -c "from torch import version; print(version.__version__.split('+')[0])")+$CUDA.html || echo "torch-scatter not to be installed"
-
 RUN python3 -m pip install -U "itsdangerous<2.1.0"

docs/source/en/model_doc/tapas.mdx

@@ -69,8 +69,7 @@ To summarize:
 
 <frameworkcontent>
 <pt>
-Initializing a model with a pre-trained base and randomly initialized classification heads from the hub can be done as shown below. Be sure to have installed the
-[torch-scatter](https://github.com/rusty1s/pytorch_scatter) dependency:
+Initializing a model with a pre-trained base and randomly initialized classification heads from the hub can be done as shown below.
 
 ```py
 >>> from transformers import TapasConfig, TapasForQuestionAnswering

src/transformers/__init__.py

@@ -32,7 +32,6 @@ from .utils import (
     OptionalDependencyNotAvailable,
     _LazyModule,
     is_flax_available,
-    is_scatter_available,
     is_sentencepiece_available,
     is_speech_available,
     is_tensorflow_text_available,
@@ -784,28 +783,6 @@ else:
         ]
     )
 
-try:
-    if not is_scatter_available():
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    from .utils import dummy_scatter_objects
-
-    _import_structure["utils.dummy_scatter_objects"] = [
-        name for name in dir(dummy_scatter_objects) if not name.startswith("_")
-    ]
-else:
-    _import_structure["models.tapas"].extend(
-        [
-            "TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
-            "TapasForMaskedLM",
-            "TapasForQuestionAnswering",
-            "TapasForSequenceClassification",
-            "TapasModel",
-            "TapasPreTrainedModel",
-            "load_tf_weights_in_tapas",
-        ]
-    )
-
 
 # PyTorch-backed objects
 try:
@@ -2027,6 +2004,17 @@ else:
             "Swinv2PreTrainedModel",
         ]
     )
+    _import_structure["models.tapas"].extend(
+        [
+            "TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
+            "TapasForMaskedLM",
+            "TapasForQuestionAnswering",
+            "TapasForSequenceClassification",
+            "TapasModel",
+            "TapasPreTrainedModel",
+            "load_tf_weights_in_tapas",
+        ]
+    )
     _import_structure["models.t5"].extend(
         [
             "T5_PRETRAINED_MODEL_ARCHIVE_LIST",
@@ -3845,22 +3833,6 @@ if TYPE_CHECKING:
             TableTransformerPreTrainedModel,
         )
 
-    try:
-        if not is_scatter_available():
-            raise OptionalDependencyNotAvailable()
-    except OptionalDependencyNotAvailable:
-        from .utils.dummy_scatter_objects import *
-    else:
-        from .models.tapas import (
-            TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
-            TapasForMaskedLM,
-            TapasForQuestionAnswering,
-            TapasForSequenceClassification,
-            TapasModel,
-            TapasPreTrainedModel,
-            load_tf_weights_in_tapas,
-        )
-
     try:
         if not is_torch_available():
             raise OptionalDependencyNotAvailable()
@@ -4854,6 +4826,15 @@ if TYPE_CHECKING:
             T5PreTrainedModel,
             load_tf_weights_in_t5,
         )
+        from .models.tapas import (
+            TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
+            TapasForMaskedLM,
+            TapasForQuestionAnswering,
+            TapasForSequenceClassification,
+            TapasModel,
+            TapasPreTrainedModel,
+            load_tf_weights_in_tapas,
+        )
         from .models.time_series_transformer import (
             TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
             TimeSeriesTransformerForPrediction,

src/transformers/file_utils.py

@@ -102,7 +102,6 @@ from .utils import (
     is_rjieba_available,
     is_sagemaker_dp_enabled,
     is_sagemaker_mp_enabled,
-    is_scatter_available,
     is_scipy_available,
     is_sentencepiece_available,
     is_sklearn_available,

src/transformers/models/tapas/modeling_tapas.py

@@ -34,27 +34,14 @@ from ...utils import (
     ModelOutput,
     add_start_docstrings,
     add_start_docstrings_to_model_forward,
-    is_scatter_available,
     logging,
     replace_return_docstrings,
-    requires_backends,
 )
 from .configuration_tapas import TapasConfig
 
 
 logger = logging.get_logger(__name__)
 
-# soft dependency
-if is_scatter_available():
-    try:
-        from torch_scatter import scatter
-    except OSError:
-        logger.error(
-            "TAPAS models are not usable since `torch_scatter` can't be loaded. "
-            "It seems you have `torch_scatter` installed with the wrong CUDA version. "
-            "Please try to reinstall it following the instructions here: https://github.com/rusty1s/pytorch_scatter."
-        )
-
 _CONFIG_FOR_DOC = "TapasConfig"
 _TOKENIZER_FOR_DOC = "TapasTokenizer"
 _TOKENIZER_FOR_DOC = "google/tapas-base"
@@ -862,7 +849,6 @@ class TapasModel(TapasPreTrainedModel):
     """
 
     def __init__(self, config, add_pooling_layer=True):
-        requires_backends(self, "scatter")
         super().__init__(config)
         self.config = config
 
@@ -1798,12 +1784,9 @@ def _segment_reduce(values, index, segment_reduce_fn, name):
     # changed "view" by "reshape" in the following line
     flat_values = values.reshape(flattened_shape.tolist())
 
-    segment_means = scatter(
-        src=flat_values,
-        index=flat_index.indices.long(),
-        dim=0,
-        dim_size=int(flat_index.num_segments),
-        reduce=segment_reduce_fn,
+    out = torch.zeros(int(flat_index.num_segments), dtype=flat_values.dtype)
+    segment_means = out.scatter_reduce(
+        dim=0, index=flat_index.indices.long(), src=flat_values, reduce=segment_reduce_fn, include_self=False
     )
 
     # Unflatten the values.
@@ -1816,7 +1799,7 @@ def _segment_reduce(values, index, segment_reduce_fn, name):
         dim=0,
     )
 
-    output_values = segment_means.view(new_shape.tolist())
+    output_values = segment_means.clone().view(new_shape.tolist())
     output_index = range_index_map(index.batch_shape(), index.num_segments)
     return output_values, output_index
 
@@ -1901,7 +1884,7 @@ def reduce_max(values, index, name="segmented_reduce_max"):
         output_values (`torch.Tensor`of shape [B1, B2, ..., Bn, num_segments, V1, V2, ..]): Tensor containing the
         output values. output_index (`IndexMap`): IndexMap with shape [B1, B2, ..., Bn, num_segments].
     """
-    return _segment_reduce(values, index, "max", name)
+    return _segment_reduce(values, index, "amax", name)
 
 
 def reduce_min(values, index, name="segmented_reduce_min"):
@@ -1928,7 +1911,7 @@ def reduce_min(values, index, name="segmented_reduce_min"):
         output_values (`torch.Tensor`of shape [B1, B2, ..., Bn, num_segments, V1, V2, ..]): Tensor containing the
         output values. output_index (`IndexMap`): IndexMap with shape [B1, B2, ..., Bn, num_segments].
     """
-    return _segment_reduce(values, index, "min", name)
+    return _segment_reduce(values, index, "amin", name)
 
 
 # End of everything related to segmented tensors

src/transformers/testing_utils.py

@@ -65,7 +65,6 @@ from .utils import (
     is_pytorch_quantization_available,
     is_rjieba_available,
     is_safetensors_available,
-    is_scatter_available,
     is_scipy_available,
     is_sentencepiece_available,
     is_soundfile_availble,
@@ -319,16 +318,6 @@ def require_intel_extension_for_pytorch(test_case):
     )(test_case)
 
 
-def require_torch_scatter(test_case):
-    """
-    Decorator marking a test that requires PyTorch scatter.
-
-    These tests are skipped when PyTorch scatter isn't installed.
-
-    """
-    return unittest.skipUnless(is_scatter_available(), "test requires PyTorch scatter")(test_case)
-
-
 def require_tensorflow_probability(test_case):
     """
     Decorator marking a test that requires TensorFlow probability.
@@ -405,14 +394,6 @@ def require_pytesseract(test_case):
     return unittest.skipUnless(is_pytesseract_available(), "test requires PyTesseract")(test_case)
 
 
-def require_scatter(test_case):
-    """
-    Decorator marking a test that requires PyTorch Scatter. These tests are skipped when PyTorch Scatter isn't
-    installed.
-    """
-    return unittest.skipUnless(is_scatter_available(), "test requires PyTorch Scatter")(test_case)
-
-
 def require_pytorch_quantization(test_case):
     """
     Decorator marking a test that requires PyTorch Quantization Toolkit. These tests are skipped when PyTorch

src/transformers/utils/__init__.py

@@ -127,7 +127,6 @@ from .import_utils import (
     is_safetensors_available,
     is_sagemaker_dp_enabled,
     is_sagemaker_mp_enabled,
-    is_scatter_available,
     is_scipy_available,
     is_sentencepiece_available,
     is_sklearn_available,

src/transformers/utils/dummy_pt_objects.py

@@ -5099,6 +5099,48 @@ def load_tf_weights_in_t5(*args, **kwargs):
     requires_backends(load_tf_weights_in_t5, ["torch"])
 
 
+TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST = None
+
+
+class TapasForMaskedLM(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
+class TapasForQuestionAnswering(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
+class TapasForSequenceClassification(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
+class TapasModel(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
+class TapasPreTrainedModel(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
+def load_tf_weights_in_tapas(*args, **kwargs):
+    requires_backends(load_tf_weights_in_tapas, ["torch"])
+
+
 TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST = None
 
 

src/transformers/utils/dummy_scatter_objects.py

-# This file is autogenerated by the command `make fix-copies`, do not edit.
-# flake8: noqa
-from ..utils import DummyObject, requires_backends
-
-
-TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST = None
-
-
-class TapasForMaskedLM(metaclass=DummyObject):
-    _backends = ["scatter"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["scatter"])
-
-
-class TapasForQuestionAnswering(metaclass=DummyObject):
-    _backends = ["scatter"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["scatter"])
-
-
-class TapasForSequenceClassification(metaclass=DummyObject):
-    _backends = ["scatter"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["scatter"])
-
-
-class TapasModel(metaclass=DummyObject):
-    _backends = ["scatter"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["scatter"])
-
-
-class TapasPreTrainedModel(metaclass=DummyObject):
-    _backends = ["scatter"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["scatter"])
-
-
-def load_tf_weights_in_tapas(*args, **kwargs):
-    requires_backends(load_tf_weights_in_tapas, ["scatter"])

src/transformers/utils/import_utils.py

@@ -145,6 +145,7 @@ except importlib_metadata.PackageNotFoundError:
     except importlib_metadata.PackageNotFoundError:
         _faiss_available = False
 
+
 _ftfy_available = importlib.util.find_spec("ftfy") is not None
 try:
     _ftfy_version = importlib_metadata.version("ftfy")
@@ -176,6 +177,7 @@ try:
 except importlib_metadata.PackageNotFoundError:
     _tf2onnx_available = False
 
+
 _onnx_available = importlib.util.find_spec("onnxruntime") is not None
 try:
     _onxx_version = importlib_metadata.version("onnx")
@@ -184,14 +186,6 @@ except importlib_metadata.PackageNotFoundError:
     _onnx_available = False
 
 
-_scatter_available = importlib.util.find_spec("torch_scatter") is not None
-try:
-    _scatter_version = importlib_metadata.version("torch_scatter")
-    logger.debug(f"Successfully imported torch-scatter version {_scatter_version}")
-except importlib_metadata.PackageNotFoundError:
-    _scatter_available = False
-
-
 _pytorch_quantization_available = importlib.util.find_spec("pytorch_quantization") is not None
 try:
     _pytorch_quantization_version = importlib_metadata.version("pytorch_quantization")
@@ -584,10 +578,6 @@ def is_in_notebook():
         return False
 
 
-def is_scatter_available():
-    return _scatter_available
-
-
 def is_pytorch_quantization_available():
     return _pytorch_quantization_available
 
@@ -826,13 +816,6 @@ installation section: https://github.com/rspeer/python-ftfy/tree/master#installi
 that match your environment. Please note that you may need to restart your runtime after installation.
 """
 
-
-# docstyle-ignore
-SCATTER_IMPORT_ERROR = """
-{0} requires the torch-scatter library but it was not found in your environment. You can install it with pip as
-explained here: https://github.com/rusty1s/pytorch_scatter. Please note that you may need to restart your runtime after installation.
-"""
-
 # docstyle-ignore
 PYTORCH_QUANTIZATION_IMPORT_ERROR = """
 {0} requires the pytorch-quantization library but it was not found in your environment. You can install it with pip:
@@ -941,7 +924,6 @@ BACKENDS_MAPPING = OrderedDict(
         ("pyctcdecode", (is_pyctcdecode_available, PYCTCDECODE_IMPORT_ERROR)),
         ("pytesseract", (is_pytesseract_available, PYTESSERACT_IMPORT_ERROR)),
         ("sacremoses", (is_sacremoses_available, SACREMOSES_IMPORT_ERROR)),
-        ("scatter", (is_scatter_available, SCATTER_IMPORT_ERROR)),
         ("pytorch_quantization", (is_pytorch_quantization_available, PYTORCH_QUANTIZATION_IMPORT_ERROR)),
         ("sentencepiece", (is_sentencepiece_available, SENTENCEPIECE_IMPORT_ERROR)),
         ("sklearn", (is_sklearn_available, SKLEARN_IMPORT_ERROR)),

tests/deepspeed/test_model_zoo.py

@@ -130,8 +130,7 @@ SPEECH_TO_TEXT_TINY = "hf-internal-testing/tiny-random-speech_to_text"
 # models with low usage, unstable API, things about to change - do nothing about the following until someone runs into a problem
 TAPAS_TINY = "hf-internal-testing/tiny-random-tapas"
 # additional notes on tapas
-# 1. requires torch_scatter - skip if it's not installed?
-# 2. "Table must be of type pd.DataFrame" failure
+# 1. "Table must be of type pd.DataFrame" failure
 
 
 # TODO: new models to add:

tests/models/auto/test_modeling_auto.py

@@ -28,7 +28,6 @@ from transformers.testing_utils import (
     DUMMY_UNKNOWN_IDENTIFIER,
     SMALL_MODEL_IDENTIFIER,
     RequestCounter,
-    require_scatter,
     require_torch,
     slow,
 )
@@ -197,7 +196,6 @@ class AutoModelTest(unittest.TestCase):
             self.assertIsInstance(model, BertForQuestionAnswering)
 
     @slow
-    @require_scatter
     def test_table_question_answering_model_from_pretrained(self):
         for model_name in TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST[5:6]:
             config = AutoConfig.from_pretrained(model_name)

tests/models/layoutxlm/test_tokenization_layoutxlm.py

@@ -32,7 +32,6 @@ from transformers.testing_utils import (
     get_tests_dir,
     is_pt_tf_cross_test,
     require_pandas,
-    require_scatter,
     require_sentencepiece,
     require_tokenizers,
     require_torch,
@@ -1176,7 +1175,6 @@ class LayoutXLMTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
 
     @require_torch
     @slow
-    @require_scatter
     def test_torch_encode_plus_sent_to_model(self):
         import torch
 

tests/models/tapas/test_modeling_tapas.py

@@ -32,13 +32,7 @@ from transformers import (
     is_torch_available,
 )
 from transformers.models.auto import get_values
-from transformers.testing_utils import (
-    require_scatter,
-    require_tensorflow_probability,
-    require_torch,
-    slow,
-    torch_device,
-)
+from transformers.testing_utils import require_tensorflow_probability, require_torch, slow, torch_device
 from transformers.utils import cached_property
 
 from ...test_configuration_common import ConfigTester
@@ -414,7 +408,6 @@ class TapasModelTester:
 
 
 @require_torch
-@require_scatter
 class TapasModelTest(ModelTesterMixin, unittest.TestCase):
 
     all_model_classes = (
@@ -553,7 +546,6 @@ def prepare_tapas_batch_inputs_for_training():
 
 
 @require_torch
-@require_scatter
 class TapasModelIntegrationTest(unittest.TestCase):
     @cached_property
     def default_tokenizer(self):
@@ -907,7 +899,7 @@ class TapasModelIntegrationTest(unittest.TestCase):
 # Below: tests for Tapas utilities which are defined in modeling_tapas.py.
 # These are based on segmented_tensor_test.py of the original implementation.
 # URL: https://github.com/google-research/tapas/blob/master/tapas/models/segmented_tensor_test.py
-@require_scatter
+@require_torch
 class TapasUtilitiesTest(unittest.TestCase):
     def _prepare_tables(self):
         """Prepares two tables, both with three distinct rows.
@@ -1064,11 +1056,11 @@ class TapasUtilitiesTest(unittest.TestCase):
 
     def test_reduce_sum_vectorized(self):
         values = torch.as_tensor([[1.0, 2.0, 3.0], [2.0, 3.0, 4.0], [3.0, 4.0, 5.0]])
-        index = IndexMap(indices=torch.as_tensor([0, 0, 1]), num_segments=2, batch_dims=0)
+        index = IndexMap(indices=torch.as_tensor([[0, 0, 1]]), num_segments=2, batch_dims=0)
         sums, new_index = reduce_sum(values, index)
 
         # We use np.testing.assert_allclose rather than Tensorflow's assertAllClose
-        np.testing.assert_allclose(sums.numpy(), [[3.0, 5.0, 7.0], [3.0, 4.0, 5.0]])
+        np.testing.assert_allclose(sums.numpy(), [3.0, 3.0])
         # We use np.testing.assert_array_equal rather than Tensorflow's assertAllEqual
         np.testing.assert_array_equal(new_index.indices.numpy(), [0, 1])
         np.testing.assert_array_equal(new_index.num_segments.numpy(), 2)

tests/models/tapas/test_tokenization_tapas.py

@@ -35,7 +35,6 @@ from transformers.models.tapas.tokenization_tapas import (
 from transformers.testing_utils import (
     is_pt_tf_cross_test,
     require_pandas,
-    require_scatter,
     require_tensorflow_probability,
     require_tokenizers,
     require_torch,
@@ -1031,7 +1030,6 @@ class TapasTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
 
     @require_torch
     @slow
-    @require_scatter
     def test_torch_encode_plus_sent_to_model(self):
         import torch
 

tests/pipelines/test_pipelines_common.py

@@ -50,7 +50,6 @@ from transformers.testing_utils import (
     RequestCounter,
     is_staging_test,
     nested_simplify,
-    require_scatter,
     require_tensorflow_probability,
     require_tf,
     require_torch,
@@ -749,7 +748,6 @@ class PipelineUtilsTest(unittest.TestCase):
 
     @slow
     @require_torch
-    @require_scatter
     def test_load_default_pipelines_pt_table_qa(self):
         import torch
 

tests/pipelines/test_pipelines_table_question_answering.py

@@ -22,14 +22,7 @@ from transformers import (
     TFAutoModelForTableQuestionAnswering,
     pipeline,
 )
-from transformers.testing_utils import (
-    require_pandas,
-    require_tensorflow_probability,
-    require_tf,
-    require_torch,
-    require_torch_scatter,
-    slow,
-)
+from transformers.testing_utils import require_pandas, require_tensorflow_probability, require_tf, require_torch, slow
 
 from .test_pipelines_common import PipelineTestCaseMeta
 
@@ -145,7 +138,6 @@ class TQAPipelineTests(unittest.TestCase, metaclass=PipelineTestCaseMeta):
             )
 
     @require_torch
-    @require_torch_scatter
     def test_small_model_pt(self):
         model_id = "lysandre/tiny-tapas-random-wtq"
         model = AutoModelForTableQuestionAnswering.from_pretrained(model_id)
@@ -248,7 +240,6 @@ class TQAPipelineTests(unittest.TestCase, metaclass=PipelineTestCaseMeta):
             )
 
     @require_torch
-    @require_torch_scatter
     def test_slow_tokenizer_sqa_pt(self):
         model_id = "lysandre/tiny-tapas-random-sqa"
         model = AutoModelForTableQuestionAnswering.from_pretrained(model_id)
@@ -490,7 +481,6 @@ class TQAPipelineTests(unittest.TestCase, metaclass=PipelineTestCaseMeta):
             )
 
     @slow
-    @require_torch_scatter
     def test_integration_wtq_pt(self):
         table_querier = pipeline("table-question-answering")
 
@@ -584,7 +574,6 @@ class TQAPipelineTests(unittest.TestCase, metaclass=PipelineTestCaseMeta):
         self.assertListEqual(results, expected_results)
 
     @slow
-    @require_torch_scatter
     def test_integration_sqa_pt(self):
         table_querier = pipeline(
             "table-question-answering",