Support torchtext on a single GPU (#2379)

* Handle torchtext.data.Batch on GPU

* Update CHANGELOG.md

* Apply code review requests

* Correct the docs

* Change requirements

CHANGELOG.md

@@ -8,6 +8,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ### Added
 
+- Added TorchText support for moving data to GPU ([#2379](https://github.com/PyTorchLightning/pytorch-lightning/pull/2379))
+
 ### Changed
 
 - Changed epoch indexing from 0 instead of 1 ([#2289](https://github.com/PyTorchLightning/pytorch-lightning/pull/2289))

pytorch_lightning/core/hooks.py

@@ -208,7 +208,7 @@ def transfer_batch_to_device(self, batch: Any, device: torch.device) -> Any:
         - :class:`list`
         - :class:`dict`
         - :class:`tuple`
-        - ``torchtext.data.Batch`` (COMING SOON)
+        - :class:`torchtext.data.batch.Batch`
 
         For anything else, you need to define how the data is moved to the target device (CPU, GPU, TPU, ...).
 

pytorch_lightning/utilities/apply_func.py

@@ -3,6 +3,8 @@
 from typing import Any, Callable, Union
 
 import torch
+from torchtext.data import Batch
+from copy import copy
 
 
 def apply_to_collection(data: Any, dtype: Union[type, tuple], function: Callable, *args, **kwargs) -> Any:
@@ -84,6 +86,16 @@ def move_data_to_device(batch: Any, device: torch.device):
         - :meth:`torch.Tensor.to`
         - :class:`torch.device`
     """
-    def to(data):
+
+    def batch_to(data):
+        if isinstance(data, Batch):
+            # Shallow copy because each Batch has a reference to Dataset which contains all examples
+            device_data = copy(data)
+            for field in data.fields:
+                # Batch contains output of Field.process(...) which is tensor hence .to(...) exists
+                device_field = getattr(data, field).to(device, non_blocking=True)
+                setattr(device_data, field, device_field)
+            return device_data
+
         return data.to(device, non_blocking=True)
-    return apply_to_collection(batch, dtype=TransferableDataType, function=to)
+    return apply_to_collection(batch, dtype=(TransferableDataType, Batch), function=batch_to)

requirements/base.txt

@@ -7,3 +7,4 @@ tensorboard>=1.14
 future>=0.17.1  # required for builtins in setup.py
 # pyyaml>=3.13
 PyYAML>=5.1  # OmegaConf requirement
+torchtext>=0.3.1

requirements/extra.txt

@@ -10,4 +10,4 @@ matplotlib>=3.1.1
 horovod>=0.19.1
 omegaconf>=2.0.0
 # scipy>=0.13.3
-scikit-learn>=0.20.0
+scikit-learn>=0.20.0

tests/models/test_gpu.py

@@ -10,6 +10,7 @@
 from pytorch_lightning.trainer.distrib_parts import _parse_gpu_ids, determine_root_gpu_device
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from tests.base import EvalModelTemplate
+from torchtext.data import Batch, Dataset, Example, Field, LabelField
 
 PRETEND_N_OF_GPUS = 16
 
@@ -301,3 +302,32 @@ def to(self, *args, **kwargs):
 
     batch = trainer.transfer_batch_to_gpu(CustomBatchType())
     assert batch.a.type() == 'torch.cuda.FloatTensor'
+
+    # torchtext.data.Batch
+    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)
+    batch = trainer.transfer_batch_to_gpu(batch, 0)
+
+    assert batch.text.type() == 'torch.cuda.LongTensor'
+    assert batch.label.type() == 'torch.cuda.LongTensor'