Update pytorch-ie to 0.23

configs/dataset/_convert_documents.yaml

@@ -1,2 +1,3 @@
 convert_documents:
-  _processor_: pytorch_ie.DatasetDict.map
+  _processor_: pytorch_ie.DatasetDict.to_document_type
+  document_type: ???

requirements.txt

@@ -1,5 +1,5 @@
 # --------- pytorch-ie --------- #
-pytorch-ie>=0.19.0,<1.0.0
+pytorch-ie>=0.23.0,<1.0.0
 # pie-utils provides some useful helper methods for pytorch-ie,
 # e.g. document processors or span utils (convert span annotations
 # to sequence encodings such as BIO, IO or BIOUL, and back).

src/train.py

@@ -41,7 +41,10 @@
 from omegaconf import DictConfig
 from pytorch_ie import DatasetDict
 from pytorch_ie.core import PyTorchIEModel, TaskModule
-from pytorch_ie.models import TransformerTokenClassificationModel
+from pytorch_ie.models import *  # noqa: F403
+from pytorch_ie.models.interface import RequiresModelNameOrPath, RequiresNumClasses
+from pytorch_ie.taskmodules import *  # noqa: F403
+from pytorch_ie.taskmodules.interface import ChangesTokenizerVocabSize
 from pytorch_lightning import Callback, Trainer
 from pytorch_lightning.loggers import Logger
 
@@ -92,49 +95,61 @@ def train(cfg: DictConfig) -> Tuple[dict, dict]:
     if cfg.get("seed"):
         pl.seed_everything(cfg.seed, workers=True)
 
-    # Init pytorch-ie dataset
-    log.info(f"Instantiating dataset <{cfg.dataset._target_}>")
-    dataset: DatasetDict = hydra.utils.instantiate(cfg.dataset, _convert_="partial")
-
     # Init pytorch-ie taskmodule
     log.info(f"Instantiating taskmodule <{cfg.taskmodule._target_}>")
     taskmodule: TaskModule = hydra.utils.instantiate(cfg.taskmodule, _convert_="partial")
 
+    # Init pytorch-ie dataset
+    log.info(f"Instantiating dataset <{cfg.dataset._target_}>")
+    dataset: DatasetDict = hydra.utils.instantiate(
+        cfg.dataset,
+        _convert_="partial",
+    )
+
+    # auto-convert the dataset if the taskmodule specifies a document type
+    if taskmodule.document_type is not None:
+        if issubclass(dataset.document_type, taskmodule.document_type):
+            log.info(
+                f"the dataset is already of the document type that is specified by the taskmodule: "
+                f"{taskmodule.document_type}"
+            )
+        else:
+            log.info(
+                f"convert the dataset to the document type that is specified by the taskmodule: "
+                f"{taskmodule.document_type}"
+            )
+            dataset = dataset.to_document_type(taskmodule.document_type)
+    else:
+        log.warning(
+            "The taskmodule does not specify a document type. The dataset can not be automatically converted "
+            "to a document type."
+        )
+
     # Init pytorch-ie datamodule
     log.info(f"Instantiating datamodule <{cfg.datamodule._target_}>")
     datamodule: PieDataModule = hydra.utils.instantiate(
         cfg.datamodule, dataset=dataset, taskmodule=taskmodule, _convert_="partial"
     )
     # Use the train dataset split to prepare the taskmodule
-    taskmodule.prepare(dataset["train"])
+    taskmodule.prepare(dataset[datamodule.train_split])
 
     # Init the pytorch-ie model
     log.info(f"Instantiating model <{cfg.model._target_}>")
     # get additional model arguments
     additional_model_kwargs: Dict[str, Any] = {}
     model_cls = get_class(cfg.model["_target_"])
-    # NOTE: DEFINE THE additional_model_kwargs IF YOU WANT TO USE ANOTHER MODEL! SEE EXAMPLES BELOW.
-    if model_cls == TransformerTokenClassificationModel:
+    # NOTE: MODIFY THE additional_model_kwargs IF YOUR MODEL REQUIRES ANY MORE PARAMETERS FROM THE TASKMODULE!
+    # SEE EXAMPLES BELOW.
+    if issubclass(model_cls, RequiresNumClasses):
         additional_model_kwargs["num_classes"] = len(taskmodule.label_to_id)
-    # elif model_cls == pytorch_ie.models.TransformerSpanClassificationModel:
-    #     additional_model_kwargs["num_classes"] = len(taskmodule.label_to_id)
-    #     max_train_steps = cfg["trainer"]["max_epochs"] * datamodule.num_train
-    #     additional_model_kwargs["t_total"] = int(
-    #         max_train_steps / float(cfg["datamodule"]["batch_size"])
-    #     )
-    # elif model_cls == pytorch_ie.models.TransformerTextClassificationModel:
-    #    additional_model_kwargs["num_classes"] = len(taskmodule.label_to_id)
-    #    max_train_steps = cfg["trainer"]["max_epochs"] * datamodule.num_train
-    #    additional_model_kwargs["t_total"] = int(
-    #        max_train_steps / float(cfg["datamodule"]["batch_size"])
-    #    )
-    # elif model_cls == pytorch_ie.models.TransformerSeq2SeqModel:
-    #    pass
-    else:
-        raise Exception(
-            f"unknown model class: {model_cls.__name__}. Please adjust the train.py script for that class, i.e. "
-            f"define how to set additional_model_kwargs for your model."
-        )
+    if issubclass(model_cls, RequiresModelNameOrPath):
+        if "model_name_or_path" not in cfg.model:
+            raise Exception(
+                f"Please specify model_name_or_path in the model config for {model_cls.__name__}."
+            )
+    if isinstance(taskmodule, ChangesTokenizerVocabSize):
+        additional_model_kwargs["tokenizer_vocab_size"] = len(taskmodule.tokenizer)
+
     # initialize the model
     model: PyTorchIEModel = hydra.utils.instantiate(
         cfg.model, _convert_="partial", **additional_model_kwargs

tests/helpers/package_available.py

@@ -1,7 +1,7 @@
 import platform
 
 import pkg_resources
-from pytorch_lightning.utilities.xla_device import XLADeviceUtils
+from lightning_fabric.accelerators import TPUAccelerator
 
 
 def _package_available(package_name: str) -> bool:
@@ -12,7 +12,7 @@ def _package_available(package_name: str) -> bool:
         return False
 
 
-_TPU_AVAILABLE = XLADeviceUtils.tpu_device_exists()
+_TPU_AVAILABLE = TPUAccelerator.is_available()
 
 _IS_WINDOWS = platform.system() == "Windows"