Fix tapas scatter

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 @@
     OptionalDependencyNotAvailable,
     _LazyModule,
     is_flax_available,
-    is_scatter_available,
     is_sentencepiece_available,
     is_speech_available,
     is_tensorflow_text_available,
@@ -757,28 +756,6 @@
         ]
     )
 
-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:
@@ -1952,6 +1929,17 @@
             "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",
@@ -3736,22 +3724,6 @@
             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()
@@ -4709,6 +4681,15 @@
             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 @@
     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 @@
     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
 
@@ -1797,12 +1783,9 @@ 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.
@@ -1815,7 +1798,7 @@ 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
 
@@ -1900,7 +1883,7 @@ 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"):
@@ -1927,7 +1910,7 @@ 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 @@
     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 @@
     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

@@ -4911,6 +4911,48 @@ 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