Merge branch 'master' into mauryaland-crf_score

.github/workflows/ci.yml

@@ -17,6 +17,8 @@ jobs:
         uses: actions/setup-python@v4
         with:
           python-version: 3.8
+      - name: Install Torch cpu
+        run: pip install torch --index-url https://download.pytorch.org/whl/cpu
       - name: Install Flair dependencies
         run: pip install -e .
       - name: Install unittest dependencies
@@ -31,4 +33,4 @@ jobs:
       - name: Run tests
         run: |
           python -c 'import flair'
-          pytest --runintegration --durations=0 -vv
+          pytest --runintegration -vv

.github/workflows/issues.yml

@@ -3,7 +3,7 @@ on: issue_comment
 jobs:
   issue_commented:
     name: Issue comment
-    if: ${{ !github.event.issue.pull_request && github.event.issue.author == github.even.issue_comment.author }}
+    if: ${{ github.event.issue.pull_request && github.event.issue.author == github.even.issue_comment.author }}
     runs-on: ubuntu-latest
     steps:
       - uses: actions-ecosystem/action-remove-labels@v1

.github/workflows/publish-docs.yml

@@ -18,6 +18,8 @@ jobs:
         uses: actions/setup-python@v4
         with:
           python-version: ${{ env.python-version }}
+      - name: Install Torch cpu
+        run: pip install torch --index-url https://download.pytorch.org/whl/cpu
       - name: Install Flair dependencies
         run: pip install -e .
       - name: Install unittest dependencies

flair/datasets/base.py

@@ -156,7 +156,6 @@ def __init__(
             log.warning('ATTENTION! The library "pymongo" is not installed!')
             log.warning('To use MongoDataset, please first install with "pip install pymongo"')
             log.warning("-" * 100)
-            pass
 
         self.in_memory = in_memory
         self.tokenizer = tokenizer

flair/datasets/biomedical.py

@@ -132,7 +132,6 @@ def filter_and_map_entities(
                 new_entities.append(new_entity)
             else:
                 logging.debug(f"Skip entity type {entity.type}")
-                pass
         mapped_entities_per_document[id] = new_entities
 
     return InternalBioNerDataset(documents=dataset.documents, entities_per_document=mapped_entities_per_document)

flair/embeddings/document.py

@@ -556,7 +556,6 @@ def __init__(
             log.warning('ATTENTION! The library "sentence-transformers" is not installed!')
             log.warning('To use Sentence Transformers, please first install with "pip install sentence-transformers"')
             log.warning("-" * 100)
-            pass
 
         self.model_name = model
         self.model = SentenceTransformer(

flair/embeddings/image.py

@@ -31,7 +31,7 @@ def embedding_type(self) -> str:
 
     def to_params(self) -> Dict[str, Any]:
         # legacy pickle-like saving for image embeddings, as implementation details are not obvious
-        return self.__getstate__()  # type: ignore[operator]
+        return self.__getstate__()
 
     @classmethod
     def from_params(cls, params: Dict[str, Any]) -> "Embeddings":
@@ -104,7 +104,6 @@ def __init__(self, name, pretrained=True, transforms=None) -> None:
             log.warning('ATTENTION! The library "torchvision" is not installed!')
             log.warning('To use convnets pretraned on ImageNet, please first install with "pip install torchvision"')
             log.warning("-" * 100)
-            pass
 
         model_info = {
             "resnet50": (torchvision.models.resnet50, lambda x: list(x)[:-1], 2048),

flair/embeddings/legacy.py

@@ -40,7 +40,6 @@ def __init__(
             log.warning('ATTENTION! The library "allennlp" is not installed!')
             log.warning('To use ELMoEmbeddings, please first install with "pip install allennlp==0.9.0"')
             log.warning("-" * 100)
-            pass
 
         assert embedding_mode in ["all", "top", "average"]
 

flair/optim.py

@@ -3,7 +3,7 @@
 import torch
 from torch.optim import Optimizer
 from torch.optim.lr_scheduler import LambdaLR, ReduceLROnPlateau, _LRScheduler
-from torch.optim.optimizer import required  # type: ignore[attr-defined]
+from torch.optim.optimizer import required
 
 log = logging.getLogger("flair")
 

flair/trainers/plugins/__init__.py

@@ -1,5 +1,4 @@
 from .base import BasePlugin, Pluggable, TrainerPlugin, TrainingInterrupt
-from .functional.amp import AmpPlugin
 from .functional.anneal_on_plateau import AnnealingPlugin
 from .functional.checkpoints import CheckpointPlugin
 from .functional.linear_scheduler import LinearSchedulerPlugin
@@ -11,7 +10,6 @@
 from .metric_records import MetricName, MetricRecord
 
 __all__ = [
-    "AmpPlugin",
     "AnnealingPlugin",
     "CheckpointPlugin",
     "LinearSchedulerPlugin",

flair/trainers/plugins/base.py

@@ -4,6 +4,7 @@
 from itertools import count
 from queue import Queue
 from typing import (
+    Any,
     Callable,
     Dict,
     Iterator,
@@ -259,6 +260,9 @@ def pluggable(self) -> Optional[Pluggable]:
     def __str__(self) -> str:
         return self.__class__.__name__
 
+    def get_state(self) -> Dict[str, Any]:
+        return {"__cls__": f"{self.__module__}.{self.__class__.__name__}"}
+
 
 class TrainerPlugin(BasePlugin):
     @property

flair/trainers/plugins/functional/anneal_on_plateau.py

@@ -1,5 +1,6 @@
 import logging
 import os
+from typing import Any, Dict
 
 from flair.trainers.plugins.base import TrainerPlugin, TrainingInterrupt
 from flair.trainers.plugins.metric_records import MetricRecord
@@ -34,6 +35,7 @@ def __init__(
         self.anneal_factor = anneal_factor
         self.patience = patience
         self.initial_extra_patience = initial_extra_patience
+        self.scheduler: AnnealOnPlateau
 
     def store_learning_rate(self):
         optimizer = self.trainer.optimizer
@@ -106,3 +108,14 @@ def __str__(self) -> str:
             f"anneal_factor: '{self.anneal_factor}', "
             f"min_learning_rate: '{self.min_learning_rate}'"
         )
+
+    def get_state(self) -> Dict[str, Any]:
+        return {
+            **super().get_state(),
+            "base_path": str(self.base_path),
+            "min_learning_rate": self.min_learning_rate,
+            "anneal_factor": self.anneal_factor,
+            "patience": self.patience,
+            "initial_extra_patience": self.initial_extra_patience,
+            "anneal_with_restarts": self.anneal_with_restarts,
+        }

flair/trainers/plugins/functional/checkpoints.py

@@ -1,4 +1,5 @@
 import logging
+from typing import Any, Dict
 
 from flair.trainers.plugins.base import TrainerPlugin
 
@@ -27,3 +28,11 @@ def after_training_epoch(self, epoch, **kw):
             )
             model_name = "model_epoch_" + str(epoch) + ".pt"
             self.model.save(self.base_path / model_name, checkpoint=self.save_optimizer_state)
+
+    def get_state(self) -> Dict[str, Any]:
+        return {
+            **super().get_state(),
+            "base_path": str(self.base_path),
+            "save_model_each_k_epochs": self.save_model_each_k_epochs,
+            "save_optimizer_state": self.save_optimizer_state,
+        }

flair/trainers/plugins/functional/linear_scheduler.py

@@ -1,4 +1,5 @@
 import logging
+from typing import Any, Dict
 
 from flair.optim import LinearSchedulerWithWarmup
 from flair.trainers.plugins.base import TrainerPlugin
@@ -9,7 +10,7 @@
 class LinearSchedulerPlugin(TrainerPlugin):
     """Plugin for LinearSchedulerWithWarmup."""
 
-    def __init__(self, warmup_fraction: float, **kwargs) -> None:
+    def __init__(self, warmup_fraction: float) -> None:
         super().__init__()
 
         self.warmup_fraction = warmup_fraction
@@ -29,7 +30,7 @@ def after_setup(
         dataset_size,
         mini_batch_size,
         max_epochs,
-        **kw,
+        **kwargs,
     ):
         """Initialize different schedulers, including anneal target for AnnealOnPlateau, batch_growth_annealing, loading schedulers."""
         # calculate warmup steps
@@ -44,13 +45,13 @@ def after_setup(
         self.store_learning_rate()
 
     @TrainerPlugin.hook
-    def before_training_epoch(self, **kw):
+    def before_training_epoch(self, **kwargs):
         """Load state for anneal_with_restarts, batch_growth_annealing, logic for early stopping."""
         self.store_learning_rate()
         self.previous_learning_rate = self.current_learning_rate
 
     @TrainerPlugin.hook
-    def after_training_batch(self, optimizer_was_run: bool, **kw):
+    def after_training_batch(self, optimizer_was_run: bool, **kwargs):
         """Do the scheduler step if one-cycle or linear decay."""
         # skip if no optimization has happened.
         if not optimizer_was_run:
@@ -60,3 +61,9 @@ def after_training_batch(self, optimizer_was_run: bool, **kw):
 
     def __str__(self) -> str:
         return f"LinearScheduler | warmup_fraction: '{self.warmup_fraction}'"
+
+    def get_state(self) -> Dict[str, Any]:
+        return {
+            **super().get_state(),
+            "warmup_fraction": self.warmup_fraction,
+        }

flair/trainers/plugins/functional/weight_extractor.py

@@ -1,3 +1,5 @@
+from typing import Any, Dict
+
 from flair.trainers.plugins.base import TrainerPlugin
 from flair.training_utils import WeightExtractor
 
@@ -7,6 +9,7 @@ class WeightExtractorPlugin(TrainerPlugin):
 
     def __init__(self, base_path) -> None:
         super().__init__()
+        self.base_path = base_path
         self.weight_extractor = WeightExtractor(base_path)
 
     @TrainerPlugin.hook
@@ -17,3 +20,9 @@ def after_training_batch(self, batch_no, epoch, total_number_of_batches, **kw):
 
         if (iteration + 1) % modulo == 0:
             self.weight_extractor.extract_weights(self.model.state_dict(), iteration)
+
+    def get_state(self) -> Dict[str, Any]:
+        return {
+            **super().get_state(),
+            "base_path": str(self.base_path),
+        }

flair/trainers/plugins/loggers/log_file.py

@@ -1,5 +1,6 @@
 import logging
 from pathlib import Path
+from typing import Any, Dict
 
 from flair.trainers.plugins.base import TrainerPlugin
 from flair.training_utils import add_file_handler
@@ -12,10 +13,13 @@ class LogFilePlugin(TrainerPlugin):
 
     def __init__(self, base_path) -> None:
         super().__init__()
-
+        self.base_path = base_path
         self.log_handler = add_file_handler(log, Path(base_path) / "training.log")
 
     @TrainerPlugin.hook("_training_exception", "after_training")
     def close_file_handler(self, **kw):
         self.log_handler.close()
         log.removeHandler(self.log_handler)
+
+    def get_state(self) -> Dict[str, Any]:
+        return {**super().get_state(), "base_path": str(self.base_path)}

flair/trainers/plugins/loggers/loss_file.py

@@ -1,5 +1,5 @@
 from datetime import datetime
-from typing import Dict, Optional, Tuple, Union
+from typing import Any, Dict, Optional, Tuple, Union
 
 from flair.trainers.plugins.base import TrainerPlugin
 from flair.trainers.plugins.metric_records import MetricName
@@ -15,9 +15,9 @@ def __init__(
         super().__init__()
 
         self.first_epoch = epoch + 1
-
         # prepare loss logging file and set up header
         self.loss_txt = init_output_file(base_path, "loss.tsv")
+        self.base_path = base_path
 
         # set up all metrics to collect
         self.metrics_to_collect = metrics_to_collect
@@ -58,6 +58,13 @@ def __init__(
         # initialize the first log line
         self.current_row: Optional[Dict[MetricName, str]] = None
 
+    def get_state(self) -> Dict[str, Any]:
+        return {
+            **super().get_state(),
+            "base_path": str(self.base_path),
+            "metrics_to_collect": self.metrics_to_collect,
+        }
+
     @TrainerPlugin.hook
     def before_training_epoch(self, epoch, **kw):
         """Get the current epoch for loss file logging."""

flair/trainers/plugins/loggers/metric_history.py

@@ -1,5 +1,5 @@
 import logging
-from typing import Dict, Mapping
+from typing import Any, Dict, Mapping
 
 from flair.trainers.plugins.base import TrainerPlugin
 
@@ -32,3 +32,9 @@ def metric_recorded(self, record):
     def after_training(self, **kw):
         """Returns metric history."""
         self.trainer.return_values.update(self.metric_history)
+
+    def get_state(self) -> Dict[str, Any]:
+        return {
+            **super().get_state(),
+            "metrics_to_collect": dict(self.metrics_to_collect),
+        }

flair/trainers/plugins/loggers/tensorboard.py

@@ -1,5 +1,6 @@
 import logging
 import os
+from typing import Any, Dict
 
 from flair.trainers.plugins.base import TrainerPlugin
 from flair.training_utils import log_line
@@ -22,6 +23,7 @@ def __init__(self, log_dir=None, comment="", tracked_metrics=()) -> None:
         super().__init__()
         self.comment = comment
         self.tracked_metrics = tracked_metrics
+        self.log_dir = log_dir
 
         try:
             from torch.utils.tensorboard import SummaryWriter
@@ -56,3 +58,11 @@ def _training_finally(self, **kw):
         """Closes the writer."""
         assert self.writer is not None
         self.writer.close()
+
+    def get_state(self) -> Dict[str, Any]:
+        return {
+            **super().get_state(),
+            "log_dir": str(self.log_dir) if self.log_dir is not None else None,
+            "comment": self.comment,
+            "tracked_metrics": self.tracked_metrics,
+        }