try fix

pytorch_lightning/plugins/training_type/ipu.py

@@ -96,6 +96,7 @@ def __init__(
         self.autoreport = autoreport
         self.autoreport_dir = autoreport_dir
         self.poptorch_models = {}
+        self._original_accumulate_grad_batches: Optional[int] = None
         self._training_opts = training_opts
         self._inference_opts = inference_opts
 
@@ -149,7 +150,7 @@ def _create_opts(self, training: bool) -> "poptorch.Options":
         opts = poptorch.Options()
         opts.deviceIterations(self.device_iterations)
         opts.replicationFactor(self.replication_factor)
-        gradient_accumulation = self.lightning_module.trainer.accumulate_grad_batches if training else 1
+        gradient_accumulation = self.accumulate_grad_batches if training else 1
         opts.Training.gradientAccumulation(gradient_accumulation)
 
         if os.environ.get("PL_GLOBAL_SEED"):
@@ -184,22 +185,27 @@ def _convert_to_poptorch_loader(
         dataloader = poptorch.DataLoader(**dl_kwargs, options=opts)
         return dataloader
 
+    @property
+    def accumulate_grad_batches(self) -> int:
+        return self._original_accumulate_grad_batches
+
     def _handle_gradient_accumulation_steps(self) -> None:
         """Override the trainer.accumulation_scheduler to act as ``accumulate_grad_batches=1`` if gradient
         accumulation has been set.
 
         ``optimizer_step`` will be called on every batch, and the IPU will handle grad accumulation internally.
         """
-        accumulation_scheduler = self.lightning_module.trainer.accumulation_scheduler
-
-        if accumulation_scheduler.epochs != [0]:
+        accumulate_grad_batches = self.lightning_module.trainer.accumulate_grad_batches
+        if not isinstance(accumulate_grad_batches, int):
             raise MisconfigurationException(
-                "IPUs currently does not support different `accumulate_grad_batches` at different epoch."
+                "IPUs currently only support `Trainer.accumulate_grad_batches` being an integer."
+                f" Received {accumulate_grad_batches}"
             )
-
-        # TODO(@tchaton): Add support for accumulate_grad_batches being a dictionary
-        self.lightning_module.trainer.accumulation_scheduler = GradientAccumulationScheduler({0: 1})
-        # accumulation_scheduler.scheduling.update({0: 1})
+        # save the original value which will be used to update the global step progress
+        self._original_accumulate_grad_batches = accumulate_grad_batches
+        if accumulate_grad_batches > 1:
+            # TODO(@tchaton): Add support for accumulate_grad_batches being a dictionary
+            self.lightning_module.trainer.accumulation_scheduler = GradientAccumulationScheduler({0: 1})
 
     @property
     def _n_replicate(self):