Add text embedder

CHANGELOG.md

@@ -7,6 +7,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 ## [Unreleased] - YYYY-DD-MM
 
 ### Added
+- Added `TextEmbedder` task ([#996](https://github.com/PyTorchLightning/lightning-flash/pull/996))
 
 - Added predict_kwargs in `ObjectDetector`, `InstanceSegmentation`, `KeypointDetector` ([#990](https://github.com/PyTorchLightning/lightning-flash/pull/990))
 

flash/core/utilities/imports.py

@@ -107,6 +107,7 @@ def _compare_version(package: str, op, version) -> bool:
 _ALBUMENTATIONS_AVAILABLE = _module_available("albumentations")
 _BAAL_AVAILABLE = _module_available("baal")
 _TORCH_OPTIMIZER_AVAILABLE = _module_available("torch_optimizer")
+_SENTENCE_TRANSFORMERS_AVAILABLE = _module_available("sentence_transformers")
 
 
 if _PIL_AVAILABLE:
@@ -130,6 +131,7 @@ class Image:
         _SENTENCEPIECE_AVAILABLE,
         _DATASETS_AVAILABLE,
         _TM_TEXT_AVAILABLE,
+        _SENTENCE_TRANSFORMERS_AVAILABLE,
     ]
 )
 _TABULAR_AVAILABLE = _TABNET_AVAILABLE and _PANDAS_AVAILABLE and _FORECASTING_AVAILABLE

flash/core/utilities/providers.py

@@ -42,6 +42,7 @@ def __str__(self):
 _LEARN2LEARN = Provider("learnables/learn2learn", "https://github.com/learnables/learn2learn")
 _PYSTICHE = Provider("pystiche/pystiche", "https://github.com/pystiche/pystiche")
 _HUGGINGFACE = Provider("Hugging Face/transformers", "https://github.com/huggingface/transformers")
+_SENTENCE_TRANSFORMERS = Provider("UKPLab/sentence-transformers", "https://github.com/UKPLab/sentence-transformers")
 _FAIRSEQ = Provider("PyTorch/fairseq", "https://github.com/pytorch/fairseq")
 _OPEN3D_ML = Provider("Intelligent Systems Lab Org/Open3D-ML", "https://github.com/isl-org/Open3D-ML")
 _PYTORCHVIDEO = Provider("Facebook Research/PyTorchVideo", "https://github.com/facebookresearch/pytorchvideo")

flash/text/__init__.py

@@ -1,4 +1,5 @@
 from flash.text.classification import TextClassificationData, TextClassifier  # noqa: F401
+from flash.text.embedding import TextEmbedder  # noqa: F401
 from flash.text.question_answering import QuestionAnsweringData, QuestionAnsweringTask  # noqa: F401
 from flash.text.seq2seq import (  # noqa: F401
     Seq2SeqData,

flash/text/embedding/__init__.py

@@ -0,0 +1 @@
+from flash.text.embedding.model import TextEmbedder  # noqa: F401

flash/text/embedding/backbones.py

@@ -0,0 +1,14 @@
+from flash.core.registry import ExternalRegistry, FlashRegistry
+from flash.core.utilities.imports import _TEXT_AVAILABLE
+from flash.core.utilities.providers import _HUGGINGFACE
+
+if _TEXT_AVAILABLE:
+    from transformers import AutoModel
+
+    HUGGINGFACE_BACKBONES = ExternalRegistry(
+        AutoModel.from_pretrained,
+        "backbones",
+        _HUGGINGFACE,
+    )
+else:
+    HUGGINGFACE_BACKBONES = FlashRegistry("backbones")

flash/text/embedding/model.py

@@ -0,0 +1,106 @@
+# 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 logging
+import os
+import warnings
+from typing import Any, Dict, List, Optional
+
+import torch
+from pytorch_lightning import Callback
+
+from flash.core.integrations.transformers.states import TransformersBackboneState
+from flash.core.model import Task
+from flash.core.registry import FlashRegistry, print_provider_info
+from flash.core.utilities.imports import _TEXT_AVAILABLE
+from flash.core.utilities.providers import _SENTENCE_TRANSFORMERS
+from flash.text.embedding.backbones import HUGGINGFACE_BACKBONES
+from flash.text.ort_callback import ORTCallback
+
+if _TEXT_AVAILABLE:
+    from sentence_transformers.models import Pooling
+
+    Pooling = print_provider_info("Pooling", _SENTENCE_TRANSFORMERS, Pooling)
+
+logger = logging.getLogger(__name__)
+
+
+class TextEmbedder(Task):
+    """The ``TextEmbedder`` is a :class:`~flash.Task` for generating sentence embeddings, training and validation.
+    For more details, see `embeddings`.
+
+    You can change the backbone to any question answering model from `UKPLab/sentence-transformers
+    <https://github.com/UKPLab/sentence-transformers>`_ using the ``backbone``
+    argument.
+
+    Args:
+        backbone: backbone model to use for the task.
+        enable_ort: Enable Torch ONNX Runtime Optimization: https://onnxruntime.ai/docs/#onnx-runtime-for-training
+    """
+
+    required_extras: str = "text"
+
+    backbones: FlashRegistry = HUGGINGFACE_BACKBONES
+
+    def __init__(
+        self,
+        backbone: str = "sentence-transformers/all-MiniLM-L6-v2",
+        tokenizer_backbone: Optional[str] = None,
+        tokenizer_kwargs: Optional[Dict[str, Any]] = None,
+        enable_ort: bool = False,
+    ):
+        os.environ["TOKENIZERS_PARALLELISM"] = "TRUE"
+        # disable HF thousand warnings
+        warnings.simplefilter("ignore")
+        # set os environ variable for multiprocesses
+        os.environ["PYTHONWARNINGS"] = "ignore"
+        super().__init__()
+
+        if tokenizer_backbone is None:
+            tokenizer_backbone = backbone
+        self.set_state(TransformersBackboneState(tokenizer_backbone, tokenizer_kwargs=tokenizer_kwargs))
+        self.model = self.backbones.get(backbone)()
+        self.pooling = Pooling(self.model.config.hidden_size)
+        self.enable_ort = enable_ort
+
+    def training_step(self, batch: Any, batch_idx: int) -> Any:
+        raise NotImplementedError("Training a `TextEmbedder` is not supported. Use a different text task instead.")
+
+    def validation_step(self, batch: Any, batch_idx: int) -> Any:
+        raise NotImplementedError("Validating a `TextEmbedder` is not supported. Use a different text task instead.")
+
+    def test_step(self, batch: Any, batch_idx: int) -> Any:
+        raise NotImplementedError("Testing a `TextEmbedder` is not supported. Use a different text task instead.")
+
+    def forward(self, batch: Dict[str, torch.Tensor]) -> torch.Tensor:
+        """Adapted from sentence-transformers:
+
+        https://github.com/UKPLab/sentence-transformers/blob/master/sentence_transformers/models/Transformer.py#L45
+        """
+
+        trans_features = {"input_ids": batch["input_ids"], "attention_mask": batch["attention_mask"]}
+        if "token_type_ids" in batch:
+            trans_features["token_type_ids"] = batch["token_type_ids"]
+
+        output_states = self.model(**trans_features, return_dict=False)
+        output_tokens = output_states[0]
+
+        batch.update({"token_embeddings": output_tokens, "attention_mask": batch["attention_mask"]})
+
+        return self.pooling(batch)["sentence_embedding"]
+
+    def configure_callbacks(self) -> List[Callback]:
+        callbacks = super().configure_callbacks() or []
+        if self.enable_ort:
+            callbacks.append(ORTCallback())
+        return callbacks

flash_examples/text_embedder.py

@@ -0,0 +1,34 @@
+# 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 torch
+
+import flash
+from flash.text import TextClassificationData, TextEmbedder
+
+# 1. Create the DataModule
+datamodule = TextClassificationData.from_lists(
+    predict_data=[
+        "Turgid dialogue, feeble characterization - Harvey Keitel a judge?.",
+        "The worst movie in the history of cinema.",
+        "I come from Bulgaria where it 's almost impossible to have a tornado.",
+    ]
+)
+
+# 2. Load a previously trained TextEmbedder
+model = TextEmbedder(backbone="sentence-transformers/all-MiniLM-L6-v2")
+
+# 3. Generate embeddings for the first 3 graphs
+trainer = flash.Trainer(gpus=torch.cuda.device_count())
+predictions = trainer.predict(model, datamodule=datamodule)
+print(predictions)

requirements/datatype_text.txt

@@ -3,3 +3,4 @@ filelock
 transformers>=4.5
 torchmetrics[text]>=0.5.1
 datasets>=1.8,<1.13
+sentence-transformers

tests/examples/test_scripts.py

@@ -80,6 +80,10 @@
             "text_classification.py",
             marks=pytest.mark.skipif(not _TEXT_TESTING, reason="text libraries aren't installed"),
         ),
+        pytest.param(
+            "text_embedder.py",
+            marks=pytest.mark.skipif(not _TEXT_TESTING, reason="text libraries aren't installed"),
+        ),
         # pytest.param(
         #     "text_classification_multi_label.py",
         #     marks=pytest.mark.skipif(not _TEXT_TESTING, reason="text libraries aren't installed")

tests/text/embedding/test_model.py

@@ -0,0 +1,43 @@
+# 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 os
+
+import pytest
+import torch
+
+import flash
+from flash.text import TextClassificationData, TextEmbedder
+from tests.helpers.utils import _TEXT_TESTING
+
+# ======== Mock data ========
+
+predict_data = [
+    "Turgid dialogue, feeble characterization - Harvey Keitel a judge?.",
+    "The worst movie in the history of cinema.",
+    "I come from Bulgaria where it 's almost impossible to have a tornado.",
+]
+# ==============================
+
+TEST_BACKBONE = "sentence-transformers/all-MiniLM-L6-v2"  # super small model for testing
+
+
+@pytest.mark.skipif(os.name == "nt", reason="Huggingface timing out on Windows")
+@pytest.mark.skipif(not _TEXT_TESTING, reason="text libraries aren't installed.")
+def test_predict(tmpdir):
+    datamodule = TextClassificationData.from_lists(predict_data=predict_data)
+    model = TextEmbedder(backbone=TEST_BACKBONE)
+
+    trainer = flash.Trainer(gpus=torch.cuda.device_count())
+    predictions = trainer.predict(model, datamodule=datamodule)
+    assert [t.size() for t in predictions[0]] == [torch.Size([384]), torch.Size([384]), torch.Size([384])]