Remove support for the deprecated torchtext legacy (#14375)

.azure/hpu-tests.yml

@@ -45,7 +45,7 @@ jobs:
         pip --version
         sudo pip uninstall -y lightning pytorch-lightning
         pip install fire
-        python .actions/assistant.py requirements-prune-pkgs torch,torchvision,torchtext
+        python .actions/assistant.py requirements-prune-pkgs torch,torchvision
         pip install ".[extra,test]"
         pip list
       env:

dockers/base-conda/Dockerfile

@@ -78,11 +78,11 @@ RUN \
     conda update -n base -c defaults conda && \
     CUDA_VERSION_MM=$(python -c "print('.'.join('$CUDA_VERSION'.split('.')[:2]))") && \
     conda create -y --name $CONDA_ENV \
-      python=${PYTHON_VERSION} pytorch=${PYTORCH_VERSION} torchvision torchtext cudatoolkit=${CUDA_VERSION_MM} \
+      python=${PYTHON_VERSION} pytorch=${PYTORCH_VERSION} torchvision cudatoolkit=${CUDA_VERSION_MM} \
       -c nvidia -c pytorch -c pytorch-test && \
     conda init bash && \
     # NOTE: this requires that the channel is presented in the yaml before packages \
-    printf "import re;\nfname = 'environment.yml';\nreq = open(fname).read();\nfor n in ['python', 'pytorch', 'torchtext', 'torchvision']:\n    req = re.sub(rf'- {n}[>=]+', f'# - {n}=', req);\nopen(fname, 'w').write(req)" > prune.py && \
+    printf "import re;\nfname = 'environment.yml';\nreq = open(fname).read();\nfor n in ['python', 'pytorch', 'torchvision']:\n    req = re.sub(rf'- {n}[>=]+', f'# - {n}=', req);\nopen(fname, 'w').write(req)" > prune.py && \
     python prune.py && \
     rm prune.py && \
     cat environment.yml && \
@@ -102,7 +102,7 @@ RUN \
     pip list | grep torch && \
     python -c "import torch; print(torch.__version__)" && \
     pip install -q fire && \
-    python assistant.py requirements_prune_pkgs torch,torchvision,torchtext && \
+    python assistant.py requirements_prune_pkgs torch,torchvision && \
     # Install remaining requirements
     pip install --no-cache-dir -r requirements/pytorch/base.txt \
         -r requirements/pytorch/extra.txt \

environment.yml

@@ -41,7 +41,6 @@ dependencies:
     - scikit-learn>=0.20.0
     - matplotlib>=3.1.1
     - omegaconf>=2.0.5
-    - torchtext>=0.10.*
 
     # Examples
     - torchvision>=0.10.*

requirements/pytorch/adjust-versions.py

@@ -5,14 +5,14 @@ from typing import Dict, Optional
 
 # IMPORTANT: this list needs to be sorted in reverse
 VERSIONS = [
-    dict(torch="1.12.1", torchvision="0.13.1", torchtext="0.13.1"),  # stable
-    dict(torch="1.12.0", torchvision="0.13.0", torchtext="0.13.0"),
-    dict(torch="1.11.0", torchvision="0.12.0", torchtext="0.12.0"),
-    dict(torch="1.10.2", torchvision="0.11.3", torchtext="0.11.2"),
-    dict(torch="1.10.1", torchvision="0.11.2", torchtext="0.11.1"),
-    dict(torch="1.10.0", torchvision="0.11.1", torchtext="0.11.0"),
-    dict(torch="1.9.1", torchvision="0.10.1", torchtext="0.10.1"),
-    dict(torch="1.9.0", torchvision="0.10.0", torchtext="0.10.0"),
+    dict(torch="1.12.1", torchvision="0.13.1"),  # stable
+    dict(torch="1.12.0", torchvision="0.13.0"),
+    dict(torch="1.11.0", torchvision="0.12.0"),
+    dict(torch="1.10.2", torchvision="0.11.3"),
+    dict(torch="1.10.1", torchvision="0.11.2"),
+    dict(torch="1.10.0", torchvision="0.11.1"),
+    dict(torch="1.9.1", torchvision="0.10.1"),
+    dict(torch="1.9.0", torchvision="0.10.0"),
 ]
 
 

requirements/pytorch/check-avail-extras.py

@@ -4,4 +4,3 @@ import jsonargparse  # noqa: F401
 import matplotlib  # noqa: F401
 import omegaconf  # noqa: F401
 import rich  # noqa: F401
-import torchtext  # noqa: F401

requirements/pytorch/extra.txt

@@ -3,7 +3,6 @@
 
 # extended list of package dependencies to reach full functionality
 matplotlib>3.1, <3.5.3
-torchtext>=0.10.*, <0.14.0
 omegaconf>=2.0.5, <2.3.0
 hydra-core>=1.0.5, <1.3.0
 jsonargparse[signatures]>=4.12.0, <=4.12.0

src/pytorch_lightning/CHANGELOG.md

@@ -92,6 +92,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Removed the experimental `pytorch_lightning.utiltiies.meta` functions in favor of built-in https://github.com/pytorch/torchdistx support ([#13868](https://github.com/Lightning-AI/lightning/pull/13868))
 
 
+- Removed deprecated support for old torchtext versions ([#14375](https://github.com/Lightning-AI/lightning/pull/14375))
+
 ### Fixed
 
 - Fixed an assertion error when using a `ReduceOnPlateau` scheduler with the Horovod strategy ([#14215](https://github.com/Lightning-AI/lightning/pull/14215))

src/pytorch_lightning/core/hooks.py

@@ -624,7 +624,6 @@ class DataHooks:
         - :class:`list`
         - :class:`dict`
         - :class:`tuple`
-        - :class:`torchtext.data.batch.Batch`
 
         For anything else, you need to define how the data is moved to the target device (CPU, GPU, TPU, ...).
 

src/pytorch_lightning/utilities/__init__.py

@@ -43,7 +43,6 @@ from pytorch_lightning.utilities.imports import (  # noqa: F401
     _TORCH_GREATER_EQUAL_1_11,
     _TORCH_GREATER_EQUAL_1_12,
     _TORCH_QUANTIZE_AVAILABLE,
-    _TORCHTEXT_AVAILABLE,
     _TORCHVISION_AVAILABLE,
     _TPU_AVAILABLE,
     _XLA_AVAILABLE,

src/pytorch_lightning/utilities/apply_func.py

@@ -14,10 +14,9 @@
 """Utilities used for collections."""
 
 import dataclasses
-import operator
 from abc import ABC
 from collections import defaultdict, OrderedDict
-from copy import copy, deepcopy
+from copy import deepcopy
 from functools import partial
 from typing import Any, Callable, List, Mapping, Optional, Sequence, Tuple, Union
 
@@ -26,17 +25,6 @@ import torch
 from torch import Tensor
 
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
-from pytorch_lightning.utilities.imports import _compare_version, _TORCHTEXT_LEGACY
-from pytorch_lightning.utilities.rank_zero import rank_zero_deprecation
-
-if _TORCHTEXT_LEGACY:
-    if _compare_version("torchtext", operator.ge, "0.9.0"):
-        from torchtext.legacy.data import Batch
-    else:
-        from torchtext.data import Batch
-else:
-    Batch = type(None)
-
 
 _BLOCKING_DEVICE_TYPES = ("cpu", "mps")
 
@@ -326,23 +314,6 @@ def move_data_to_device(batch: Any, device: Union[str, torch.device]) -> Any:
         device = torch.device(device)
 
     def batch_to(data: Any) -> Any:
-        # try to move torchtext data first
-        if _TORCHTEXT_LEGACY and isinstance(data, Batch):
-            # TODO: also remove the torchtext dependency with Lightning 1.8
-            rank_zero_deprecation(
-                "The `torchtext.legacy.Batch` object is deprecated and Lightning will remove support for it in v1.8."
-                " We recommend you to migrate away from Batch by following the TorchText README:"
-                " https://github.com/pytorch/text#bc-breaking-legacy"
-            )
-            # Shallow copy because each Batch has a reference to Dataset which contains all examples
-            device_data = copy(data)
-            for field, field_value in data.dataset.fields.items():
-                if field_value is None:
-                    continue
-                device_field = move_data_to_device(getattr(data, field), device)
-                setattr(device_data, field, device_field)
-            return device_data
-
         kwargs = {}
         # Don't issue non-blocking transfers to CPU
         # Same with MPS due to a race condition bug: https://github.com/pytorch/pytorch/issues/83015
@@ -354,8 +325,7 @@ def move_data_to_device(batch: Any, device: Union[str, torch.device]) -> Any:
         # user wrongly implemented the `TransferableDataType` and forgot to return `self`.
         return data
 
-    dtype = (TransferableDataType, Batch) if _TORCHTEXT_LEGACY else TransferableDataType
-    return apply_to_collection(batch, dtype=dtype, function=batch_to)
+    return apply_to_collection(batch, dtype=TransferableDataType, function=batch_to)
 
 
 def convert_to_tensors(data: Any, device: Union[str, torch.device]) -> Any:

src/pytorch_lightning/utilities/data.py

@@ -124,9 +124,7 @@ def has_len(dataloader: Union[DataLoader, Iterable]) -> bool:
                 f"`{dataloader.__class__.__name__}` returned 0 length. Please make sure this was your intention."
             )
         has_len = True
-    except TypeError:
-        has_len = False
-    except NotImplementedError:  # e.g. raised by torchtext if a batch_size_fn is used
+    except (TypeError, NotImplementedError):
         has_len = False
 
     if has_len and has_iterable_dataset(dataloader):
@@ -170,9 +168,7 @@ def has_len_all_ranks(
         else:
             has_len = True
 
-    except TypeError:
-        has_len = False
-    except NotImplementedError:  # e.g. raised by torchtext if a batch_size_fn is used
+    except (TypeError, NotImplementedError):
         has_len = False
 
     if has_len and has_iterable_dataset(dataloader):

src/pytorch_lightning/utilities/imports.py

@@ -147,8 +147,6 @@ _POPTORCH_AVAILABLE = _package_available("poptorch")
 _PSUTIL_AVAILABLE = _package_available("psutil")
 _RICH_AVAILABLE = _package_available("rich") and _compare_version("rich", operator.ge, "10.2.2")
 _TORCH_QUANTIZE_AVAILABLE = bool([eg for eg in torch.backends.quantized.supported_engines if eg != "none"])
-_TORCHTEXT_AVAILABLE = _package_available("torchtext")
-_TORCHTEXT_LEGACY: bool = _TORCHTEXT_AVAILABLE and _compare_version("torchtext", operator.lt, "0.11.0")
 _TORCHVISION_AVAILABLE = _package_available("torchvision")
 _XLA_AVAILABLE: bool = _package_available("torch_xla")
 

tests/tests_pytorch/accelerators/test_mps.py

@@ -21,8 +21,6 @@ import tests_pytorch.helpers.pipelines as tpipes
 from pytorch_lightning import Trainer
 from pytorch_lightning.accelerators import MPSAccelerator
 from pytorch_lightning.demos.boring_classes import BoringModel
-from pytorch_lightning.utilities.imports import _TORCHTEXT_LEGACY
-from tests_pytorch.helpers.imports import Batch, Dataset, Example, Field, LabelField
 from tests_pytorch.helpers.runif import RunIf
 
 
@@ -135,30 +133,3 @@ def test_single_gpu_batch_parse():
 
     batch = trainer.strategy.batch_to_device(CustomBatchType(), torch.device("mps"))
     assert batch.a.type() == "torch.mps.FloatTensor"
-
-    # torchtext.data.Batch
-    if not _TORCHTEXT_LEGACY:
-        return
-
-    samples = [
-        {"text": "PyTorch Lightning is awesome!", "label": 0},
-        {"text": "Please make it work with torchtext", "label": 1},
-    ]
-
-    text_field = Field()
-    label_field = LabelField()
-    fields = {"text": ("text", text_field), "label": ("label", label_field)}
-
-    examples = [Example.fromdict(sample, fields) for sample in samples]
-    dataset = Dataset(examples=examples, fields=fields.values())
-    # Batch runs field.process() that numericalizes tokens, but it requires to build dictionary first
-    text_field.build_vocab(dataset)
-    label_field.build_vocab(dataset)
-
-    batch = Batch(data=examples, dataset=dataset)
-
-    with pytest.deprecated_call(match="The `torchtext.legacy.Batch` object is deprecated"):
-        batch = trainer.strategy.batch_to_device(batch, torch.device("mps"))
-
-    assert batch.text.type() == "torch.mps.LongTensor"
-    assert batch.label.type() == "torch.mps.LongTensor"

tests/tests_pytorch/deprecated_api/test_remove_1-8.py

@@ -35,20 +35,8 @@ from pytorch_lightning.strategies.ipu import LightningIPUModule
 from pytorch_lightning.trainer.configuration_validator import _check_datamodule_checkpoint_hooks
 from pytorch_lightning.trainer.states import RunningStage
 from pytorch_lightning.utilities import device_parser
-from pytorch_lightning.utilities.apply_func import move_data_to_device
-from pytorch_lightning.utilities.imports import _TORCHTEXT_LEGACY
 from pytorch_lightning.utilities.rank_zero import rank_zero_only, rank_zero_warn
 from tests_pytorch.helpers.runif import RunIf
-from tests_pytorch.helpers.torchtext_utils import get_dummy_torchtext_data_iterator
-
-
-@pytest.mark.skipif(not _TORCHTEXT_LEGACY, reason="torchtext.legacy is deprecated.")
-def test_v1_8_0_deprecated_torchtext_batch():
-
-    with pytest.deprecated_call(match="is deprecated and Lightning will remove support for it in v1.8"):
-        data_iterator, _ = get_dummy_torchtext_data_iterator(num_samples=3, batch_size=3)
-        batch = next(iter(data_iterator))
-        _ = move_data_to_device(batch=batch, device=torch.device("cpu"))
 
 
 def test_v1_8_0_on_init_start_end(tmpdir):

tests/tests_pytorch/helpers/imports.py

@@ -1,16 +0,0 @@
-import operator
-
-from pytorch_lightning.utilities.imports import _compare_version, _TORCHTEXT_LEGACY
-
-if _TORCHTEXT_LEGACY:
-    if _compare_version("torchtext", operator.ge, "0.9.0"):
-        from torchtext.legacy.data import Batch, Dataset, Example, Field, Iterator, LabelField
-    else:
-        from torchtext.data import Batch, Dataset, Example, Field, Iterator, LabelField
-else:
-    Batch = type(None)
-    Dataset = type(None)
-    Example = type(None)
-    Field = type(None)
-    Iterator = type(None)
-    LabelField = type(None)

tests/tests_pytorch/helpers/torchtext_utils.py

@@ -1,54 +0,0 @@
-# Copyright The PyTorch Lightning team.
-#
-# 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.
-import random
-import string
-
-from tests_pytorch.helpers.imports import Dataset, Example, Field, Iterator
-
-
-def _generate_random_string(length: int = 10):
-    return "".join(random.choices(string.ascii_letters, k=length))
-
-
-def get_dummy_torchtext_data_iterator(num_samples: int, batch_size: int, include_lengths: bool = False):
-    text_field = Field(
-        sequential=True,
-        pad_first=False,  # nosec
-        init_token="<s>",
-        eos_token="</s>",  # nosec
-        include_lengths=include_lengths,
-    )  # nosec
-
-    dataset = Dataset(
-        [
-            Example.fromdict({"text": _generate_random_string()}, {"text": ("text", text_field)})
-            for _ in range(num_samples)
-        ],
-        {"text": text_field},
-    )
-    text_field.build_vocab(dataset)
-
-    iterator = Iterator(
-        dataset,
-        batch_size=batch_size,
-        sort_key=None,
-        device=None,
-        batch_size_fn=None,
-        train=True,
-        repeat=False,
-        shuffle=None,
-        sort=None,
-        sort_within_batch=None,
-    )
-    return iterator, text_field

tests/tests_pytorch/models/test_gpu.py

@@ -28,9 +28,8 @@ from pytorch_lightning.demos.boring_classes import BoringModel
 from pytorch_lightning.plugins.environments import TorchElasticEnvironment
 from pytorch_lightning.utilities import device_parser
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
-from pytorch_lightning.utilities.imports import _compare_version, _TORCHTEXT_LEGACY
+from pytorch_lightning.utilities.imports import _compare_version
 from tests_pytorch.helpers.datamodules import ClassifDataModule
-from tests_pytorch.helpers.imports import Batch, Dataset, Example, Field, LabelField
 from tests_pytorch.helpers.runif import RunIf
 from tests_pytorch.helpers.simple_models import ClassificationModel
 
@@ -269,33 +268,6 @@ def test_single_gpu_batch_parse():
     batch = trainer.strategy.batch_to_device(CustomBatchType(), torch.device("cuda:0"))
     assert batch.a.type() == "torch.cuda.FloatTensor"
 
-    # torchtext.data.Batch
-    if not _TORCHTEXT_LEGACY:
-        return
-
-    samples = [
-        {"text": "PyTorch Lightning is awesome!", "label": 0},
-        {"text": "Please make it work with torchtext", "label": 1},
-    ]
-
-    text_field = Field()
-    label_field = LabelField()
-    fields = {"text": ("text", text_field), "label": ("label", label_field)}
-
-    examples = [Example.fromdict(sample, fields) for sample in samples]
-    dataset = Dataset(examples=examples, fields=fields.values())
-    # Batch runs field.process() that numericalizes tokens, but it requires to build dictionary first
-    text_field.build_vocab(dataset)
-    label_field.build_vocab(dataset)
-
-    batch = Batch(data=examples, dataset=dataset)
-
-    with pytest.deprecated_call(match="The `torchtext.legacy.Batch` object is deprecated"):
-        batch = trainer.strategy.batch_to_device(batch, torch.device("cuda:0"))
-
-    assert batch.text.type() == "torch.cuda.LongTensor"
-    assert batch.label.type() == "torch.cuda.LongTensor"
-
 
 @RunIf(min_cuda_gpus=1)
 def test_non_blocking():

tests/tests_pytorch/utilities/test_apply_func_torchtext.py

@@ -1,47 +0,0 @@
-# Copyright The PyTorch Lightning team.
-#
-# 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.
-import pytest
-import torch
-
-from pytorch_lightning.utilities.apply_func import move_data_to_device
-from pytorch_lightning.utilities.imports import _TORCHTEXT_LEGACY
-from tests_pytorch.helpers.runif import RunIf
-from tests_pytorch.helpers.torchtext_utils import get_dummy_torchtext_data_iterator
-
-
-@pytest.mark.parametrize("include_lengths", [False, True])
-@pytest.mark.parametrize("device", [torch.device("cuda", 0)])
-@pytest.mark.skipif(not _TORCHTEXT_LEGACY, reason="torchtext.legacy is deprecated.")
-@RunIf(min_cuda_gpus=1)
-def test_batch_move_data_to_device_torchtext_include_lengths(include_lengths, device):
-    data_iterator, _ = get_dummy_torchtext_data_iterator(num_samples=3, batch_size=3, include_lengths=include_lengths)
-    data_iter = iter(data_iterator)
-    batch = next(data_iter)
-
-    with pytest.deprecated_call(match="The `torchtext.legacy.Batch` object is deprecated"):
-        batch_on_device = move_data_to_device(batch, device)
-
-    if include_lengths:
-        # tensor with data
-        assert batch_on_device.text[0].device == device
-        # tensor with length of data
-        assert batch_on_device.text[1].device == device
-    else:
-        assert batch_on_device.text.device == device
-
-
-@pytest.mark.parametrize("include_lengths", [False, True])
-@pytest.mark.skipif(not _TORCHTEXT_LEGACY, reason="torchtext.legacy is deprecated.")
-def test_batch_move_data_to_device_torchtext_include_lengths_cpu(include_lengths):
-    test_batch_move_data_to_device_torchtext_include_lengths(include_lengths, torch.device("cpu"))