ref: add .log to lightning module (1/n) (#3686)

pytorch_lightning/core/lightning.py

@@ -32,6 +32,7 @@
 from pytorch_lightning.overrides.data_parallel import LightningDistributedDataParallel
 from pytorch_lightning.utilities import rank_zero_warn
 from pytorch_lightning.utilities.device_dtype_mixin import DeviceDtypeModuleMixin
+from pytorch_lightning.core.step_result import Result
 from pytorch_lightning.utilities.parsing import (
     AttributeDict,
     collect_init_args,
@@ -102,6 +103,8 @@ def __init__(self, *args, **kwargs):
         # optionally can be set by user
         self._example_input_array = None
         self._datamodule = None
+        self._results = None
+        self._current_fx_name = ''
 
     @property
     def example_input_array(self) -> Any:
@@ -146,6 +149,78 @@ def forward(self, x):
         if self.trainer.is_global_zero:
             print(*args, **kwargs)
 
+    def log(
+        self,
+        name: str,
+        value: Any,
+        prog_bar: bool = False,
+        logger: bool = True,
+        on_step: bool = True,
+        on_epoch: bool = True,
+        reduce_fx: Callable = torch.mean,
+        tbptt_reduce_fx: Callable = torch.mean,
+        tbptt_pad_token: int = 0,
+        enable_graph: bool = False,
+        sync_dist: bool = False,
+        sync_dist_op: Union[Any, str] = 'mean',
+        sync_dist_group: Optional[Any] = None,
+    ):
+        """
+        Log a key, value
+
+        Example::
+
+            result.log('train_loss', loss)
+
+            # defaults used
+            result.log(
+                name,
+                value,
+                on_step=False,
+                on_epoch=False,
+                logger=True,
+                prog_bar=False,
+                reduce_fx=torch.mean,
+                enable_graph=False
+            )
+
+
+        Args:
+            name: key name
+            value: value name
+            prog_bar: if True logs to the progress base
+            logger: if True logs to the logger
+            on_step: if True logs the output of validation_step or test_step
+            on_epoch: if True, logs the output of the training loop aggregated
+            reduce_fx: Torch.mean by default
+            tbptt_reduce_fx: function to reduce on truncated back prop
+            tbptt_pad_token: token to use for padding
+            enable_graph: if True, will not auto detach the graph
+            sync_dist: if True, reduces the metric across GPUs/TPUs
+            sync_dist_op: the op to sync across
+            sync_dist_group: the ddp group
+        """
+        if self._results is not None:
+            # in any epoch end can't log step metrics (only epoch metric)
+            if 'epoch_end' in self._current_fx_name and on_step:
+                on_step = False
+
+            self._results.log(
+                name,
+                value,
+                prog_bar,
+                logger,
+                on_step,
+                on_epoch,
+                reduce_fx,
+                tbptt_reduce_fx,
+                tbptt_pad_token,
+                enable_graph,
+                sync_dist,
+                sync_dist_op,
+                sync_dist_group
+            )
+
     def forward(self, *args, **kwargs):
         r"""
         Same as :meth:`torch.nn.Module.forward()`, however in Lightning you want this to define