diff --git a/CHANGELOG.md b/CHANGELOG.md
index aa3669c40ccf3..b39e4dab4a6f9 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -106,6 +106,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 - Added `on_exception` callback hook ([#9183](https://github.com/PyTorchLightning/pytorch-lightning/pull/9183))
 
+- Add a warning to deepspeed when inferring batch size ([#9221](https://github.com/PyTorchLightning/pytorch-lightning/pull/9221))
+
+
 ### Changed
 
 - Parsing of the `gpus` Trainer argument has changed: `gpus="n"` (str) no longer selects the GPU index n and instead selects the first n devices. ([#8770](https://github.com/PyTorchLightning/pytorch-lightning/pull/8770))
diff --git a/pytorch_lightning/plugins/training_type/deepspeed.py b/pytorch_lightning/plugins/training_type/deepspeed.py
index 36d487e3419c7..ca10b47bd9fd2 100644
--- a/pytorch_lightning/plugins/training_type/deepspeed.py
+++ b/pytorch_lightning/plugins/training_type/deepspeed.py
@@ -546,6 +546,11 @@ def _format_batch_size_and_grad_accum_config(self):
                 " as this will be set via accumulate_grad_batches=x argument passed via the Lightning Trainer."
             )
         if "train_micro_batch_size_per_gpu" not in self.config:
+            rank_zero_warn(
+                "Inferring the batch size for internal deepspeed logging from the `train_dataloader()`. "
+                "If you require skipping this, please pass "
+                "`Trainer(plugins=DeepSpeedPlugin(logging_batch_size_per_gpu=batch_size))`"
+            )
             batch_size = self._auto_select_batch_size()
             self.config["train_micro_batch_size_per_gpu"] = batch_size
         self.config["gradient_accumulation_steps"] = self.lightning_module.trainer.accumulate_grad_batches
diff --git a/tests/plugins/test_deepspeed_plugin.py b/tests/plugins/test_deepspeed_plugin.py
index 29086bd72a053..de4bb3ea987f9 100644
--- a/tests/plugins/test_deepspeed_plugin.py
+++ b/tests/plugins/test_deepspeed_plugin.py
@@ -1,6 +1,6 @@
 import json
 import os
-from typing import Any, Dict
+from typing import Any, Dict, Optional
 from unittest import mock
 
 import pytest
@@ -11,7 +11,7 @@
 from torch.utils.data import DataLoader
 from torchmetrics import Accuracy
 
-from pytorch_lightning import LightningModule, seed_everything, Trainer
+from pytorch_lightning import LightningDataModule, LightningModule, seed_everything, Trainer
 from pytorch_lightning.callbacks import Callback, LearningRateMonitor, ModelCheckpoint
 from pytorch_lightning.plugins import DeepSpeedPlugin, DeepSpeedPrecisionPlugin
 from pytorch_lightning.plugins.training_type.deepspeed import LightningDeepSpeedModule
@@ -808,3 +808,53 @@ def training_step(self, batch, batch_idx):
     trainer = Trainer(default_root_dir=tmpdir, plugins=[DeepSpeedPlugin()], gpus=1, fast_dev_run=True, precision=16)
     with pytest.raises(MisconfigurationException, match="returning `None` .* is not supported"):
         trainer.fit(model)
+
+
+@RunIf(min_gpus=1, deepspeed=True, special=True)
+def test_deepspeed_warn_train_dataloader_called(tmpdir):
+    """Test DeepSpeed warns when it calls ``lightning_module.train_dataloader`` internally for logging batch
+    size."""
+    model = BoringModel()
+    trainer = Trainer(
+        default_root_dir=tmpdir,
+        plugins=[DeepSpeedPlugin()],
+        gpus=1,
+        fast_dev_run=True,
+    )
+    with pytest.warns(UserWarning, match="Inferring the batch size for internal deepspeed logging"):
+        trainer.fit(model)
+
+
+@RunIf(min_gpus=1, deepspeed=True, special=True)
+def test_deepspeed_setup_train_dataloader(tmpdir):
+    """Test DeepSpeed works when setup is required to call, and the user passes the batch size manually."""
+
+    class TestSetupIsCalledDataModule(LightningDataModule):
+        def __init__(self):
+            super().__init__()
+            self._setup = False
+
+        def setup(self, stage: Optional[str] = None) -> None:
+            self._setup = True
+
+        def train_dataloader(self):
+            assert self._setup
+            return DataLoader(RandomDataset(32, 64), batch_size=2)
+
+        def val_dataloader(self):
+            assert self._setup
+            return DataLoader(RandomDataset(32, 64), batch_size=2)
+
+        def test_dataloader(self):
+            assert self._setup
+            return DataLoader(RandomDataset(32, 64), batch_size=2)
+
+    model = BoringModel()
+    trainer = Trainer(
+        default_root_dir=tmpdir,
+        plugins=[DeepSpeedPlugin(logging_batch_size_per_gpu=32)],
+        gpus=1,
+        fast_dev_run=True,
+    )
+    trainer.fit(model, datamodule=TestSetupIsCalledDataModule())
+    trainer.test(model)