Disable batch_size extraction for torchmetric instances

CHANGELOG.md

@@ -196,8 +196,10 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Fixed a consolidation error in Lite when attempting to save the state dict of a sharded optimizer ([#10746](https://github.com/PyTorchLightning/pytorch-lightning/pull/10746))
 
 
-- Fixed the default logging level for batch hooks associated with training from `on_step=False, on_epoch=True` to `on_step=True, on_epoch=False` ([#10756](https://github.com/PyTorchLightning/pytorch-lightning/pull/10756))
+-  Disabled batch_size extraction for torchmetric instances because they accumulate the metrics internally ([#10815](https://github.com/PyTorchLightning/pytorch-lightning/pull/10815))
+
 
+- Fixed the default logging level for batch hooks associated with training from `on_step=False, on_epoch=True` to `on_step=True, on_epoch=False` ([#10756](https://github.com/PyTorchLightning/pytorch-lightning/pull/10756))
 
 
 -

pytorch_lightning/trainer/connectors/logger_connector/result.py

@@ -338,12 +338,13 @@ class ResultMetricCollection(dict):
     with the same metadata.
     """
 
-    def __init__(self, *args: Any) -> None:
-        super().__init__(*args)
-
     @property
     def meta(self) -> _Metadata:
-        return list(self.values())[0].meta
+        return next(iter(self.values())).meta
+
+    @property
+    def has_tensor(self) -> bool:
+        return any(v.is_tensor for v in self.values())
 
     def __getstate__(self, drop_value: bool = False) -> dict:
         def getstate(item: ResultMetric) -> dict:
@@ -403,7 +404,7 @@ def append_fn(v: ResultMetric) -> None:
         apply_to_collection(list(self.values()), ResultMetric, append_fn)
         return o
 
-    def _extract_batch_size(self, batch_size: Optional[int], meta: _Metadata) -> int:
+    def _extract_batch_size(self, value: _METRIC_COLLECTION, batch_size: Optional[int], meta: _Metadata) -> int:
         # check if we have extracted the batch size already
         if batch_size is None:
             batch_size = self.batch_size
@@ -412,7 +413,8 @@ def _extract_batch_size(self, batch_size: Optional[int], meta: _Metadata) -> int
             return batch_size
 
         batch_size = 1
-        if self.batch is not None and meta.on_epoch and meta.is_mean_reduction:
+        is_tensor = value.is_tensor if isinstance(value, ResultMetric) else value.has_tensor
+        if self.batch is not None and is_tensor and meta.on_epoch and meta.is_mean_reduction:
             batch_size = extract_batch_size(self.batch)
             self.batch_size = batch_size
 
@@ -477,7 +479,7 @@ def log(
                 f"You called `self.log({name}, ...)` twice in `{fx}` with different arguments. This is not allowed"
             )
 
-        batch_size = self._extract_batch_size(batch_size, meta)
+        batch_size = self._extract_batch_size(self[key], batch_size, meta)
         self.update_metrics(key, value, batch_size)
 
     def register_key(self, key: str, meta: _Metadata, value: _METRIC_COLLECTION) -> None:

tests/trainer/logging_/test_logger_connector.py

@@ -17,7 +17,7 @@
 import pytest
 import torch
 from torch.utils.data import DataLoader
-from torchmetrics import Accuracy, AveragePrecision
+from torchmetrics import Accuracy, AveragePrecision, MeanAbsoluteError, MeanSquaredError
 
 from pytorch_lightning import LightningModule
 from pytorch_lightning.callbacks.base import Callback
@@ -640,3 +640,51 @@ def training_step(self, batch, batch_idx):
 
     # should not get overridden if logged manually
     assert trainer.logged_metrics == {"epoch": -1}
+
+
+def test_result_collection_batch_size_extraction():
+    fx_name = "training_step"
+    log_val = torch.tensor(7.0)
+
+    results = ResultCollection(training=True, device="cpu")
+    results.batch = torch.randn(1, 4)
+    train_mse = MeanSquaredError()
+    train_mse(torch.randn(4, 5), torch.randn(4, 5))
+    results.log(fx_name, "train_logs", {"mse": train_mse, "log_val": log_val}, on_step=False, on_epoch=True)
+    assert results.batch_size == 1
+    assert isinstance(results["training_step.train_logs"]["mse"].value, MeanSquaredError)
+    assert results["training_step.train_logs"]["log_val"].value == log_val
+
+    results = ResultCollection(training=True, device="cpu")
+    results.batch = torch.randn(1, 4)
+    results.log(fx_name, "train_log", log_val, on_step=False, on_epoch=True)
+    assert results.batch_size == 1
+    assert results["training_step.train_log"].value == log_val
+    assert results["training_step.train_log"].cumulated_batch_size == 1
+
+
+def test_result_collection_no_batch_size_extraction():
+    results = ResultCollection(training=True, device="cpu")
+    results.batch = torch.randn(1, 4)
+    fx_name = "training_step"
+    batch_size = 10
+    log_val = torch.tensor(7.0)
+
+    train_mae = MeanAbsoluteError()
+    train_mae(torch.randn(4, 5), torch.randn(4, 5))
+    train_mse = MeanSquaredError()
+    train_mse(torch.randn(4, 5), torch.randn(4, 5))
+    results.log(fx_name, "step_log_val", log_val, on_step=True, on_epoch=False)
+    results.log(fx_name, "epoch_log_val", log_val, on_step=False, on_epoch=True, batch_size=batch_size)
+    results.log(fx_name, "epoch_sum_log_val", log_val, on_step=True, on_epoch=True, reduce_fx="sum")
+    results.log(fx_name, "train_mae", train_mae, on_step=True, on_epoch=False)
+    results.log(fx_name, "train_mse", {"mse": train_mse}, on_step=True, on_epoch=False)
+
+    assert results.batch_size is None
+    assert isinstance(results["training_step.train_mse"]["mse"].value, MeanSquaredError)
+    assert isinstance(results["training_step.train_mae"].value, MeanAbsoluteError)
+    assert results["training_step.step_log_val"].value == log_val
+    assert results["training_step.step_log_val"].cumulated_batch_size == 0
+    assert results["training_step.epoch_log_val"].value == log_val * batch_size
+    assert results["training_step.epoch_log_val"].cumulated_batch_size == batch_size
+    assert results["training_step.epoch_sum_log_val"].value == log_val

tests/trainer/logging_/test_train_loop_logging.py

@@ -27,7 +27,6 @@
 from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint, TQDMProgressBar
 from pytorch_lightning.core.lightning import LightningModule
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
-from tests.deprecated_api import no_warning_call
 from tests.helpers.boring_model import BoringModel, RandomDataset, RandomDictDataset
 from tests.helpers.runif import RunIf
 
@@ -746,36 +745,3 @@ def validation_epoch_end(self, *_) -> None:
     train_data = DataLoader(RandomDataset(32, 64), batch_size=2)
     val_data = DataLoader(RandomDataset(32, 64), batch_size=2)
     trainer.fit(model, train_dataloaders=train_data, val_dataloaders=val_data)
-
-
-def test_no_batch_size_extraction_with_specifying_explictly(tmpdir):
-    batch_size = BoringModel().train_dataloader().batch_size + 1
-    fast_dev_run = 2
-    log_val = 7
-
-    class CustomBoringModel(BoringModel):
-        def on_before_batch_transfer(self, batch, *args, **kwargs):
-            # This is an ambiguous batch which have multiple potential batch sizes
-            if self.trainer.training:
-                batch = {"batch1": torch.randn(batch_size, 10), "batch2": batch}
-            return batch
-
-        def training_step(self, batch, batch_idx):
-            self.log("step_log_val", log_val, on_epoch=False)
-            self.log("epoch_log_val", log_val, batch_size=batch_size, on_step=False, on_epoch=True)
-            self.log("epoch_sum_log_val", log_val, on_epoch=True, reduce_fx="sum")
-            return super().training_step(batch["batch2"], batch_idx)
-
-        def on_train_epoch_end(self, *args, **kwargs):
-            results = self.trainer._results
-            assert results["training_step.step_log_val"].value == log_val
-            assert results["training_step.step_log_val"].cumulated_batch_size == 0
-            assert results["training_step.epoch_log_val"].value == log_val * batch_size * fast_dev_run
-            assert results["training_step.epoch_log_val"].cumulated_batch_size == batch_size * fast_dev_run
-            assert results["training_step.epoch_sum_log_val"].value == log_val * fast_dev_run
-
-    model = CustomBoringModel()
-    trainer = Trainer(default_root_dir=tmpdir, fast_dev_run=fast_dev_run)
-
-    with no_warning_call(match="Trying to infer the `batch_size`"):
-        trainer.fit(model)