Jit support

CHANGELOG.md

@@ -8,6 +8,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ### Added
 
+- Added support for `torch.jit` to tasks where possible and documented task JIT compatibility ([#389](https://github.com/PyTorchLightning/lightning-flash/pull/389))
 
 ### Changed
 

docs/source/general/jit.rst

@@ -0,0 +1,59 @@
+#######################
+TorchScript JIT Support
+#######################
+
+.. _jit:
+
+We test all of our tasks for compatibility with :mod:`torch.jit`.
+This table gives a breakdown of the supported features.
+
+.. list-table::
+   :widths: 25 25 25 25
+   :header-rows: 1
+
+   * - Task
+     - :func:`torch.jit.script`
+     - :func:`torch.jit.trace`
+     - :func:`torch.jit.save`
+   * - :class:`~flash.image.classification.model.ImageClassifier`
+     - Yes
+     - Yes
+     - Yes
+   * - :class:`~flash.image.detection.model.ObjectDetector`
+     - Yes
+     - No
+     - Yes
+   * - :class:`~flash.image.embedding.model.ImageEmbedder`
+     - Yes
+     - Yes
+     - Yes
+   * - :class:`~flash.image.segmentation.model.SemanticSegmentation`
+     - Yes
+     - Yes
+     - Yes
+   * - :class:`~flash.image.style_transfer.model.StyleTransfer`
+     - No
+     - Yes
+     - Yes
+   * - :class:`~flash.tabular.classification.model.TabularClassifier`
+     - No
+     - Yes
+     - No
+   * - :class:`~flash.text.classification.model.TabularClassifier`
+     - No
+     - Yes :sup:`*`
+     - Yes
+   * - :class:`~flash.text.seq2seq.summarization.model.SummarizationTask`
+     - No
+     - Yes
+     - Yes
+   * - :class:`~flash.text.seq2seq.translation.model.TranslationTask`
+     - No
+     - Yes
+     - Yes
+   * - :class:`~flash.video.classification.model.VideoClassifier`
+     - No
+     - Yes
+     - Yes
+
+:sup:`*` Only with ``strict=False``.

docs/source/index.rst

@@ -49,6 +49,7 @@ Lightning Flash
    general/training
    general/finetuning
    general/predictions
+   general/jit
 
 
 .. toctree::

flash/core/data/process.py

@@ -49,7 +49,7 @@ def load_state_dict(cls, state_dict: Dict[str, Any], strict: bool = False):
         pass
 
 
-class Preprocess(BasePreprocess, Properties, Module):
+class Preprocess(BasePreprocess, Properties):
     """The :class:`~flash.core.data.process.Preprocess` encapsulates all the data processing logic that should run before
     the data is passed to the model. It is particularly useful when you want to provide an end to end implementation
     which works with 4 different stages: ``train``, ``validation``, ``test``,  and inference (``predict``).
@@ -454,7 +454,7 @@ def load_state_dict(cls, state_dict: Dict[str, Any], strict: bool):
         return cls(**state_dict)
 
 
-class Postprocess(Properties, Module):
+class Postprocess(Properties):
 
     def __init__(self, save_path: Optional[str] = None):
         super().__init__()

flash/core/model.py

@@ -263,12 +263,14 @@ def _resolve(
 
         return preprocess, postprocess, serializer
 
+    @torch.jit.unused
     @property
     def serializer(self) -> Optional[Serializer]:
         """The current :class:`.Serializer` associated with this model. If this property was set to a mapping
         (e.g. ``.serializer = {'output1': SerializerOne()}``) then this will be a :class:`.MappingSerializer`."""
         return self._serializer
 
+    @torch.jit.unused
     @serializer.setter
     def serializer(self, serializer: Union[Serializer, Mapping[str, Serializer]]):
         if isinstance(serializer, Mapping):
@@ -350,12 +352,14 @@ def build_data_pipeline(
         self._data_pipeline_state = data_pipeline.initialize(self._data_pipeline_state)
         return data_pipeline
 
+    @torch.jit.unused
     @property
     def data_pipeline(self) -> DataPipeline:
         """The current :class:`.DataPipeline`. If set, the new value will override the :class:`.Task` defaults. See
         :py:meth:`~build_data_pipeline` for more details on the resolution order."""
         return self.build_data_pipeline()
 
+    @torch.jit.unused
     @data_pipeline.setter
     def data_pipeline(self, data_pipeline: Optional[DataPipeline]) -> None:
         self._preprocess, self._postprocess, self.serializer = Task._resolve(
@@ -366,14 +370,16 @@ def data_pipeline(self, data_pipeline: Optional[DataPipeline]) -> None:
             getattr(data_pipeline, '_postprocess_pipeline', None),
             getattr(data_pipeline, '_serializer', None),
         )
-        self._preprocess.state_dict()
+        # self._preprocess.state_dict()
         if getattr(self._preprocess, "_ddp_params_and_buffers_to_ignore", None):
             self._ddp_params_and_buffers_to_ignore = self._preprocess._ddp_params_and_buffers_to_ignore
 
+    @torch.jit.unused
     @property
     def preprocess(self) -> Preprocess:
         return getattr(self.data_pipeline, '_preprocess_pipeline', None)
 
+    @torch.jit.unused
     @property
     def postprocess(self) -> Postprocess:
         return getattr(self.data_pipeline, '_postprocess_pipeline', None)

flash/image/classification/model.py

@@ -14,14 +14,11 @@
 from types import FunctionType
 from typing import Any, Callable, Dict, List, Mapping, Optional, Sequence, Tuple, Type, Union
 
-import pytorch_lightning as pl
 import torch
 import torchmetrics
-from pytorch_lightning.callbacks.base import Callback
 from torch import nn
 from torch.optim.lr_scheduler import _LRScheduler
 
-import flash
 from flash.core.classification import ClassificationTask
 from flash.core.data.data_source import DefaultDataKeys
 from flash.core.data.process import Serializer

flash/image/detection/model.py

@@ -163,6 +163,9 @@ def get_model(
                 model = RetinaNet(backbone_model, num_classes=num_classes, anchor_generator=anchor_generator)
         return model
 
+    def forward(self, x: List[torch.Tensor]) -> Any:
+        return self.model(x)
+
     def training_step(self, batch, batch_idx) -> Any:
         """The training step. Overrides ``Task.training_step``
         """
@@ -178,7 +181,7 @@ def training_step(self, batch, batch_idx) -> Any:
     def validation_step(self, batch, batch_idx):
         images, targets = batch[DefaultDataKeys.INPUT], batch[DefaultDataKeys.TARGET]
         # fasterrcnn takes only images for eval() mode
-        outs = self.model(images)
+        outs = self(images)
         iou = torch.stack([_evaluate_iou(t, o) for t, o in zip(targets, outs)]).mean()
         self.log("val_iou", iou)
 
@@ -188,13 +191,13 @@ def on_validation_end(self) -> None:
     def test_step(self, batch, batch_idx):
         images, targets = batch[DefaultDataKeys.INPUT], batch[DefaultDataKeys.TARGET]
         # fasterrcnn takes only images for eval() mode
-        outs = self.model(images)
+        outs = self(images)
         iou = torch.stack([_evaluate_iou(t, o) for t, o in zip(targets, outs)]).mean()
         self.log("test_iou", iou)
 
     def predict_step(self, batch: Any, batch_idx: int, dataloader_idx: int = 0) -> Any:
         images = batch[DefaultDataKeys.INPUT]
-        return self.model(images)
+        return self(images)
 
     def configure_finetune_callback(self):
         return [ObjectDetectionFineTuning(train_bn=True)]

flash/image/embedding/model.py

@@ -11,7 +11,7 @@
 # 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.
-from typing import Any, Callable, Mapping, Optional, Sequence, Type, Union
+from typing import Any, Callable, Mapping, Optional, Sequence, Tuple, Type, Union
 
 import torch
 from pytorch_lightning.utilities.distributed import rank_zero_warn
@@ -89,13 +89,12 @@ def __init__(
             rank_zero_warn('embedding_dim. Remember to finetune first!')
 
     def apply_pool(self, x):
-        if self.pooling_fn == torch.max:
-            # torch.max also returns argmax
-            x = self.pooling_fn(x, dim=-1)[0]
-            x = self.pooling_fn(x, dim=-1)[0]
-        else:
-            x = self.pooling_fn(x, dim=-1)
-            x = self.pooling_fn(x, dim=-1)
+        x = self.pooling_fn(x, dim=-1)
+        if torch.jit.isinstance(x, Tuple[torch.Tensor, torch.Tensor]):
+            x = x[0]
+        x = self.pooling_fn(x, dim=-1)
+        if torch.jit.isinstance(x, Tuple[torch.Tensor, torch.Tensor]):
+            x = x[0]
         return x
 
     def forward(self, x) -> torch.Tensor:

flash/image/segmentation/model.py

@@ -136,13 +136,12 @@ def predict_step(self, batch: Any, batch_idx: int, dataloader_idx: int = 0) -> A
 
     def forward(self, x) -> torch.Tensor:
         # infer the image to the model
-        res: Union[torch.Tensor, Dict[str, torch.Tensor]] = self.backbone(x)
+        res = self.backbone(x)
 
         # some frameworks like torchvision return a dict.
         # In particular, torchvision segmentation models return the output logits
         # in the key `out`.
-        out: torch.Tensor
-        if isinstance(res, dict):
+        if torch.jit.isinstance(res, Dict[str, torch.Tensor]):
             out = res['out']
         elif torch.is_tensor(res):
             out = res

flash/tabular/classification/model.py

@@ -83,7 +83,11 @@ def __init__(
 
     def forward(self, x_in) -> torch.Tensor:
         # TabNet takes single input, x_in is composed of (categorical, numerical)
-        x = torch.cat([x for x in x_in if x.numel()], dim=1)
+        xs = []
+        for x in x_in:
+            if x.numel():
+                xs.append(x)
+        x = torch.cat(xs, dim=1)
         return self.model(x)[0]
 
     def training_step(self, batch: Any, batch_idx: int) -> Any:

flash/text/classification/model.py

@@ -76,12 +76,35 @@ def backbone(self):
         # see huggingface's BertForSequenceClassification
         return self.model.bert
 
-    def forward(self, batch_dict):
-        return self.model(**batch_dict)
+    def forward(
+        self,
+        input_ids=None,
+        attention_mask=None,
+        token_type_ids=None,
+        position_ids=None,
+        head_mask=None,
+        inputs_embeds=None,
+        labels=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None
+    ):
+        return self.model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            labels=labels,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict
+        )
 
     def step(self, batch, batch_idx) -> dict:
         output = {}
-        out = self.forward(batch)
+        out = self.forward(**batch)
         loss, logits = out[:2]
         output["loss"] = loss
         output["y_hat"] = logits
@@ -91,6 +114,9 @@ def step(self, batch, batch_idx) -> dict:
         output["logs"] = {name: metric(probs, batch["labels"]) for name, metric in self.metrics.items()}
         return output
 
+    def predict_step(self, batch: Any, batch_idx: int, dataloader_idx: int = 0) -> Any:
+        return self(**batch)
+
     def _ci_benchmark_fn(self, history: List[Dict[str, Any]]):
         """
         This function is used only for debugging usage with CI

tests/image/classification/test_model.py

@@ -11,6 +11,8 @@
 # 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 os
+
 import pytest
 import torch
 
@@ -108,3 +110,21 @@ def test_multilabel(tmpdir):
     assert (torch.tensor(predictions) < 0).sum() == 0
     assert len(predictions[0]) == num_classes == len(label)
     assert len(torch.unique(label)) <= 2
+
+
+@pytest.mark.skipif(not _IMAGE_AVAILABLE, reason="image libraries aren't installed.")
+@pytest.mark.parametrize("jitter, args", [(torch.jit.script, ()), (torch.jit.trace, (torch.rand(1, 3, 32, 32), ))])
+def test_jit(tmpdir, jitter, args):
+    path = os.path.join(tmpdir, "test.pt")
+
+    model = ImageClassifier(2)
+    model.eval()
+
+    model = jitter(model, *args)
+
+    torch.jit.save(model, path)
+    model = torch.jit.load(path)
+
+    out = model(torch.rand(1, 3, 32, 32))
+    assert isinstance(out, torch.Tensor)
+    assert out.shape == torch.Size([1, 2])

tests/image/detection/test_model.py

@@ -11,6 +11,8 @@
 # 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 os
+
 import pytest
 import torch
 from pytorch_lightning import Trainer
@@ -75,3 +77,23 @@ def test_training(tmpdir, model):
     dl = DataLoader(ds, collate_fn=collate_fn)
     trainer = Trainer(default_root_dir=tmpdir, fast_dev_run=True)
     trainer.fit(model, dl)
+
+
+@pytest.mark.skipif(not _IMAGE_AVAILABLE, reason="image libraries aren't installed.")
+def test_jit(tmpdir):
+    path = os.path.join(tmpdir, "test.pt")
+
+    model = ObjectDetector(2)
+    model.eval()
+
+    model = torch.jit.script(model)  # torch.jit.trace doesn't work with torchvision RCNN
+
+    torch.jit.save(model, path)
+    model = torch.jit.load(path)
+
+    out = model([torch.rand(3, 32, 32)])
+
+    # torchvision RCNN always returns a (Losses, Detections) tuple in scripting
+    out = out[1]
+
+    assert {"boxes", "labels", "scores"} <= out[0].keys()