diff --git a/deepspeed/runtime/pipe/module.py b/deepspeed/runtime/pipe/module.py
index 31fec30be788..9fbd91f750a9 100644
--- a/deepspeed/runtime/pipe/module.py
+++ b/deepspeed/runtime/pipe/module.py
@@ -662,3 +662,11 @@ def get_additional_losses(self):
          Return a dictionary of {"loss name": loss_value} or None if no additional losses.
         """
         return None
+
+    def compile(self, *args, **kwargs):
+        for idx, layer in enumerate(self.forward_funcs):
+            if isinstance(layer, nn.Module):
+                layer.compile(*args, **kwargs)
+            else:
+                new_layer = torch.compile(layer, *args, **kwargs)
+                self.forward_funcs[idx] = new_layer
diff --git a/tests/unit/pipe/test_pipe_module.py b/tests/unit/pipe/test_pipe_module.py
index 05c6a82ef55a..2a8a4b9b7d82 100644
--- a/tests/unit/pipe/test_pipe_module.py
+++ b/tests/unit/pipe/test_pipe_module.py
@@ -60,9 +60,12 @@ def batch_input():
 
 class TestPipeModuleSequential(DistributedTest):
     world_size = 2
+    # needs to be set for torch.compile: running torch.compile with daemonic process causes an error
+    non_daemonic_procs = True
 
     @pytest.mark.parametrize("activation_checkpoints", [False, True])
-    def test(self, sequential_model, simple_config, batch_input, activation_checkpoints):
+    @pytest.mark.parametrize("use_compile", [False, True])
+    def test(self, sequential_model, simple_config, batch_input, activation_checkpoints, use_compile):
         base_model = copy.deepcopy(sequential_model)
         base_input = batch_input.clone().detach()
         base_output = base_model(base_input)
@@ -71,7 +74,8 @@ def test(self, sequential_model, simple_config, batch_input, activation_checkpoi
 
         pipe_model = copy.deepcopy(sequential_model)
         pipe_model = PipelineModule(layers=pipe_model, num_stages=2)
-
+        if (use_compile):
+            pipe_model.compile()
         # Ensure all parameters are accounted for.
         my_params = sum(p.numel() for p in pipe_model.parameters())
         total_pipe_params = torch.LongTensor([my_params]).to(get_accelerator().device_name())