diff --git a/stable_diffusion/mlperf_logging_utils.py b/stable_diffusion/mlperf_logging_utils.py
index b49c8d959..4f4c84d6c 100644
--- a/stable_diffusion/mlperf_logging_utils.py
+++ b/stable_diffusion/mlperf_logging_utils.py
@@ -105,15 +105,15 @@ def on_train_epoch_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModu
     def on_train_batch_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule",
                              batch: Any, batch_idx: int) -> None:
         if trainer.global_step % self.train_log_interval == 0:
-            self.logger.start(key=mllog_constants.BLOCK_START, value="training_step", metadata={mllog_constants.STEP_NUM: trainer.global_step})
+            self.logger.start(key=mllog_constants.BLOCK_START, value="training_step", metadata={mllog_constants.EPOCH_NUM: self._samples_count(trainer)})
 
     def on_train_batch_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule",
                            outputs: STEP_OUTPUT, batch: Any, batch_idx: int) -> None:
         if trainer.global_step % self.train_log_interval == 0:
             logs = trainer.callback_metrics
-            self.logger.event(key="loss", value=logs["train/loss"].item(), metadata={mllog_constants.STEP_NUM: trainer.global_step})
-            self.logger.event(key="lr_abs", value=logs["lr_abs"].item(), metadata={mllog_constants.STEP_NUM: trainer.global_step})
-            self.logger.end(key=mllog_constants.BLOCK_STOP, value="training_step", metadata={mllog_constants.STEP_NUM: trainer.global_step})
+            self.logger.event(key="loss", value=logs["train/loss"].item(), metadata={mllog_constants.EPOCH_NUM: self._samples_count(trainer)})
+            self.logger.event(key="lr_abs", value=logs["lr_abs"].item(), metadata={mllog_constants.EPOCH_NUM: self._samples_count(trainer)})
+            self.logger.end(key=mllog_constants.BLOCK_STOP, value="training_step", metadata={mllog_constants.EPOCH_NUM: self._samples_count(trainer)})
 
     def on_validation_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
         self.logger.start(key=mllog_constants.EVAL_START, value=trainer.global_step)
@@ -123,11 +123,11 @@ def on_validation_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModul
         if "validation/fid" in logs:
             self.logger.event(key=mllog_constants.EVAL_ACCURACY,
                               value=logs["validation/fid"].item(),
-                              metadata={mllog_constants.STEP_NUM: trainer.global_step, "metric": "FID"})
+                              metadata={mllog_constants.EPOCH_NUM: self._samples_count(trainer), "metric": "FID"})
         if "validation/clip" in logs:
             self.logger.event(key=mllog_constants.EVAL_ACCURACY,
                               value=logs["validation/clip"].item(),
-                              metadata={mllog_constants.STEP_NUM: trainer.global_step, "metric": "CLIP"})
+                              metadata={mllog_constants.EPOCH_NUM: self._samples_count(trainer), "metric": "CLIP"})
         self.logger.end(key=mllog_constants.EVAL_STOP, value=trainer.global_step)
 
     def on_validation_epoch_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
@@ -146,5 +146,11 @@ def on_validation_batch_end(self, trainer: "pl.Trainer", pl_module: "pl.Lightnin
         if batch_idx % self.validation_log_interval == 0:
             self.logger.end(key=mllog_constants.BLOCK_STOP, value="validation_step", metadata={mllog_constants.STEP_NUM: batch_idx})
 
+    def _samples_count(self, trainer: "pl.Trainer") -> int:
+        batch_size_per_gpu = trainer.train_dataloader.batch_size
+        num_gpus = trainer.num_gpus if trainer.num_gpus else 1
+        global_batch_size = batch_size_per_gpu * num_gpus
+
+        return global_batch_size * trainer.global_step
 
 mllogger = SDLogger()