[bugfix] Accumulated_gradient and TensoBoard

CHANGELOG.md

@@ -112,6 +112,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 - Change Metrics `persistent` default mode to `False` ([#4685](https://github.com/PyTorchLightning/pytorch-lightning/pull/4685))
 
+- LoggerConnector log_metrics will use `total_batch_idx` instead of `global_step` when logging on `training step` ([#4738](https://github.com/PyTorchLightning/pytorch-lightning/pull/4738))
+
 
 ### Fixed
 

pytorch_lightning/trainer/connectors/logger_connector/logger_connector.py

@@ -95,14 +95,14 @@ def cache_logged_metrics(self) -> Union[EpochResultStore, None]:
         if self._current_stage is not None:
             self._cached_results[self._current_stage].cache_result()
 
-    def on_trainer_init(self, logger, flush_logs_every_n_steps: int, log_every_n_steps: int, move_metrics_to_cpu: bool):
+    def on_trainer_init(self, logger, flush_logs_every_n_steps: int,
+                        log_every_n_steps: int, move_metrics_to_cpu: bool):
         # logging
         self.configure_logger(logger)
         # todo: IDE is complaining, these shall be initialized in the Trainer init at leas as placeholders
-        #  and assign here the desired value
+        # and assign here the desired value
         self.trainer.flush_logs_every_n_steps = flush_logs_every_n_steps
         self.trainer.log_every_n_steps = log_every_n_steps
-
         self.trainer.move_metrics_to_cpu = move_metrics_to_cpu
         self.trainer.split_idx = None
 
@@ -181,7 +181,7 @@ def cache_training_step_metrics(self, opt_closure_result):
         self.logged_metrics.update(logged_metrics_tmp)
         self.cached_results.legacy_batch_log_metrics.update(logged_metrics_tmp)
 
-    def log_metrics(self, metrics, grad_norm_dic, step=None):
+    def log_metrics(self, metrics, grad_norm_dic, step=None, log_train_step_metrics=False):
         """Logs the metric dict passed in.
         If `step` parameter is None and `step` key is presented is metrics,
         uses metrics["step"] as a step
@@ -190,6 +190,8 @@ def log_metrics(self, metrics, grad_norm_dic, step=None):
             metrics (dict): Metric values
             grad_norm_dic (dict): Gradient norms
             step (int): Step for which metrics should be logged. Default value corresponds to `self.global_step`
+            log_train_step_metrics (bool): Used to track if log_metrics function is being called in during training steps.
+                In training steps, we will log metrics on step: total_nb_idx (for accumulated gradients) and global_step for the rest.
         """
         # add gpu memory
         if self.trainer.on_gpu and self.trainer.log_gpu_memory:
@@ -207,8 +209,11 @@ def log_metrics(self, metrics, grad_norm_dic, step=None):
 
         elif step is None:
             # added metrics by Lightning for convenience
-            scalar_metrics['epoch'] = self.trainer.current_epoch
-            step = self.trainer.global_step
+            if log_train_step_metrics:
+                step = self.trainer.total_batch_idx
+            else:
+                scalar_metrics['epoch'] = self.trainer.current_epoch
+                step = self.trainer.global_step
 
         # log actual metrics
         if self.trainer.logger is not None:
@@ -619,5 +624,5 @@ def log_train_step_metrics(self, batch_output):
             metrics = self.cached_results.get_latest_batch_log_metrics()
             grad_norm_dic = batch_output.grad_norm_dic
             if len(metrics) > 0 or len(grad_norm_dic) > 0:
-                self.log_metrics(metrics, grad_norm_dic)
+                self.log_metrics(metrics, grad_norm_dic, log_train_step_metrics=True)
                 self.callback_metrics.update(metrics)

pytorch_lightning/trainer/trainer.py

@@ -21,48 +21,48 @@
 import torch
 from torch.utils.data import DataLoader
 
+from pytorch_lightning import _logger as log
+from pytorch_lightning.accelerators.accelerator import Accelerator
+from pytorch_lightning.accelerators.accelerator_connector import AcceleratorConnector
+from pytorch_lightning.accelerators.cpu_accelerator import CPUAccelerator
 from pytorch_lightning.callbacks import Callback, ModelCheckpoint
 from pytorch_lightning.core.datamodule import LightningDataModule
 from pytorch_lightning.core.lightning import LightningModule
 from pytorch_lightning.core.memory import ModelSummary
-from pytorch_lightning.core.step_result import Result, EvalResult
+from pytorch_lightning.core.step_result import EvalResult, Result
 from pytorch_lightning.loggers import LightningLoggerBase
+from pytorch_lightning.plugins.plugin_connector import PluginConnector
 from pytorch_lightning.profiler import BaseProfiler
 from pytorch_lightning.trainer.callback_hook import TrainerCallbackHookMixin
 from pytorch_lightning.trainer.configuration_validator import ConfigValidator
+from pytorch_lightning.trainer.connectors.callback_connector import CallbackConnector
+from pytorch_lightning.trainer.connectors.checkpoint_connector import CheckpointConnector
+from pytorch_lightning.trainer.connectors.data_connector import DataConnector
+from pytorch_lightning.trainer.connectors.debugging_connector import DebuggingConnector
 from pytorch_lightning.trainer.connectors.env_vars_connector import overwrite_by_env_vars
+from pytorch_lightning.trainer.connectors.logger_connector import LoggerConnector
+from pytorch_lightning.trainer.connectors.model_connector import ModelConnector
+from pytorch_lightning.trainer.connectors.optimizer_connector import OptimizerConnector
+from pytorch_lightning.trainer.connectors.precision_connector import PrecisionConnector
+from pytorch_lightning.trainer.connectors.profiler_connector import ProfilerConnector
+from pytorch_lightning.trainer.connectors.slurm_connector import SLURMConnector
+from pytorch_lightning.trainer.connectors.training_trick_connector import TrainingTricksConnector
 from pytorch_lightning.trainer.data_loading import TrainerDataLoadingMixin
+from pytorch_lightning.trainer.evaluation_loop import EvaluationLoop
 from pytorch_lightning.trainer.logging import TrainerLoggingMixin
 from pytorch_lightning.trainer.model_hooks import TrainerModelHooksMixin
 from pytorch_lightning.trainer.optimizers import TrainerOptimizersMixin
+from pytorch_lightning.trainer.properties import TrainerProperties
 from pytorch_lightning.trainer.states import TrainerState, trainer_state
+from pytorch_lightning.trainer.training_loop import TrainLoop
 from pytorch_lightning.trainer.training_tricks import TrainerTrainingTricksMixin
+from pytorch_lightning.tuner.tuning import Tuner
 from pytorch_lightning.utilities import rank_zero_warn
+from pytorch_lightning.utilities.cloud_io import load as pl_load
 from pytorch_lightning.utilities.debugging import InternalDebugger
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
-from pytorch_lightning.trainer.evaluation_loop import EvaluationLoop
-from pytorch_lightning.trainer.training_loop import TrainLoop
-from pytorch_lightning.accelerators.accelerator_connector import AcceleratorConnector
-from pytorch_lightning.trainer.connectors.logger_connector import LoggerConnector
-from pytorch_lightning.trainer.connectors.optimizer_connector import OptimizerConnector
-from pytorch_lightning.trainer.connectors.training_trick_connector import TrainingTricksConnector
-from pytorch_lightning.trainer.connectors.callback_connector import CallbackConnector
-from pytorch_lightning.trainer.connectors.model_connector import ModelConnector
-from pytorch_lightning.trainer.connectors.debugging_connector import DebuggingConnector
-from pytorch_lightning.trainer.connectors.checkpoint_connector import CheckpointConnector
-from pytorch_lightning.trainer.connectors.slurm_connector import SLURMConnector
-from pytorch_lightning import _logger as log
-from pytorch_lightning.tuner.tuning import Tuner
-from pytorch_lightning.trainer.connectors.precision_connector import PrecisionConnector
-from pytorch_lightning.trainer.connectors.profiler_connector import ProfilerConnector
-from pytorch_lightning.trainer.connectors.data_connector import DataConnector
-from pytorch_lightning.utilities.cloud_io import load as pl_load
-from pytorch_lightning.utilities.model_utils import is_overridden
-from pytorch_lightning.trainer.properties import TrainerProperties
-from pytorch_lightning.plugins.plugin_connector import PluginConnector
-from pytorch_lightning.accelerators.accelerator import Accelerator
-from pytorch_lightning.accelerators.cpu_accelerator import CPUAccelerator
 from pytorch_lightning.utilities.memory import recursive_detach
+from pytorch_lightning.utilities.model_utils import is_overridden
 
 # warnings to ignore in trainer
 warnings.filterwarnings(
@@ -385,7 +385,7 @@ def __init__(
             logger,
             flush_logs_every_n_steps,
             log_every_n_steps,
-            move_metrics_to_cpu
+            move_metrics_to_cpu,
         )
 
         # init debugging flags

tests/loggers/test_all.py

@@ -21,13 +21,13 @@
 import pytest
 
 import tests.base.develop_utils as tutils
-from pytorch_lightning import Trainer, Callback
+from pytorch_lightning import Callback, Trainer
 from pytorch_lightning.loggers import (
-    TensorBoardLogger,
+    CometLogger,
     MLFlowLogger,
     NeptuneLogger,
+    TensorBoardLogger,
     TestTubeLogger,
-    CometLogger,
     WandbLogger,
 )
 from pytorch_lightning.loggers.base import DummyExperiment
@@ -124,15 +124,15 @@ def log_metrics(self, metrics, step):
     if logger_class == TensorBoardLogger:
         expected = [
             (0, ['hp_metric']),
-            (0, ['epoch', 'train_some_val']),
+            (0, ['train_some_val']),
             (0, ['early_stop_on', 'epoch', 'val_acc']),
             (0, ['hp_metric']),
             (1, ['epoch', 'test_acc', 'test_loss'])
         ]
         assert log_metric_names == expected
     else:
         expected = [
-            (0, ['epoch', 'train_some_val']),
+            (0, ['train_some_val']),
             (0, ['early_stop_on', 'epoch', 'val_acc']),
             (1, ['epoch', 'test_acc', 'test_loss'])
         ]

tests/loggers/test_tensorboard.py

@@ -14,6 +14,7 @@
 import os
 from argparse import Namespace
 from distutils.version import LooseVersion
+from unittest import mock
 
 import pytest
 import torch
@@ -23,7 +24,7 @@
 
 from pytorch_lightning import Trainer, seed_everything
 from pytorch_lightning.loggers import TensorBoardLogger
-from tests.base import EvalModelTemplate, BoringModel
+from tests.base import BoringModel, EvalModelTemplate
 
 
 @pytest.mark.skipif(
@@ -201,3 +202,63 @@ def test_tensorboard_log_graph_warning_no_example_input_array(tmpdir):
             ' attribute is not set or `input_array` was not given'
     ):
         logger.log_graph(model)
+
+
+@mock.patch('pytorch_lightning.loggers.TensorBoardLogger.log_metrics')
+@pytest.mark.parametrize('expected', [
+    ([5, 11, 17]),
+])
+def test_tensorboard_with_accummulated_gradients(mock_log_metrics, expected, tmpdir):
+    """
+    Tests to ensure that tensorboard log properly when accumulated_gradients > 1
+    """
+    class TestModel(BoringModel):
+        _count = 0
+        _indexes = []
+
+        def training_step(self, batch, batch_idx):
+            output = self.layer(batch)
+            loss = self.loss(batch, output)
+            self.log('count', self._count, on_step=True, on_epoch=True)
+            self.log('loss', loss, on_step=True, on_epoch=True)
+
+            if self.trainer.logger_connector.should_update_logs:
+                self._indexes.append(self._count)
+
+            self._count += 1
+            return loss
+
+        def validation_step(self, batch, batch_idx):
+            output = self.layer(batch)
+            loss = self.loss(batch, output)
+            self.log('val_loss', loss, on_step=True, on_epoch=True)
+            return loss
+
+        def configure_optimizers(self):
+            optimizer = torch.optim.SGD(self.layer.parameters(), lr=.001)
+            lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=1)
+            return [optimizer], [lr_scheduler]
+
+    model = TestModel()
+    model.training_epoch_end = None
+    model.validation_epoch_end = None
+
+    logger_0 = TensorBoardLogger(tmpdir, default_hp_metric=False)
+
+    accumulate_grad_batches = 2
+    trainer = Trainer(
+        default_root_dir=tmpdir,
+        limit_train_batches=12,
+        limit_val_batches=12,
+        max_epochs=3,
+        gpus=0,
+        accumulate_grad_batches=accumulate_grad_batches,
+        logger=[logger_0],
+        log_every_n_steps=3,
+    )
+    trainer.fit(model)
+
+    mock_count_epochs = [m[2]["step"] for m in mock_log_metrics.mock_calls if "count_epoch" in m[2]["metrics"]]
+    assert mock_count_epochs == expected
+    mock_count_steps = [m[2]["step"] for m in mock_log_metrics.mock_calls if "count_step" in m[2]["metrics"]]
+    assert model._indexes == mock_count_steps