Avoid materializing unused Megatron forward outputs

skyrl/backends/skyrl_train/workers/megatron/megatron_worker.py

@@ -988,6 +988,9 @@ def forward(
             # micro-batching (when `max_tokens_per_microbatch > 0`) is handled inside
             # `_forward_logprobs`, which also reorders back to the original sample order.
             log_probs = self._forward_logprobs(data)
+            # All ranks must finish the forward collectives; only collection ranks return token arrays.
+            if not self.mesh_rank.is_collection_dp_rank():
+                return WorkerOutput()
             loss_fn_outputs = [{"logprobs": log_probs[i].tolist()} for i in range(log_probs.shape[0])]
             return WorkerOutput(loss_fn_outputs=loss_fn_outputs, metrics={})
 
@@ -1866,6 +1869,8 @@ def forward(self, data: TrainingInputBatch) -> WorkerOutput:
         ``max_tokens_per_microbatch > 0``) is handled inside ``_forward_logprobs``.
         """
         log_probs = self._forward_logprobs(data)
+        if not self.mesh_rank.is_collection_dp_rank():
+            return WorkerOutput()
         loss_fn_outputs = [{"logprobs": log_probs[i].tolist()} for i in range(log_probs.shape[0])]
         return WorkerOutput(loss_fn_outputs=loss_fn_outputs, metrics={})
 

tests/backends/skyrl_train/workers/megatron/test_forward_collection_outputs.py

@@ -0,0 +1,32 @@
+"""Only collection ranks materialize token outputs after the distributed forward."""
+
+from unittest.mock import Mock
+
+import pytest
+import torch
+
+pytest.importorskip("megatron.core")
+
+from skyrl.backends.skyrl_train.distributed.dispatch import MeshRank
+from skyrl.backends.skyrl_train.workers.megatron.megatron_worker import (
+    MegatronPolicyWorkerBase,
+    MegatronRefWorkerBase,
+)
+
+
+@pytest.mark.parametrize("worker_cls", [MegatronPolicyWorkerBase, MegatronRefWorkerBase])
+@pytest.mark.parametrize("sp,tp,pp", [(0, 0, 1), (1, 0, 1), (0, 1, 1), (0, 0, 0)])
+def test_forward_keeps_computation_but_only_collects_selected_rank(worker_cls, sp, tp, pp):
+    worker = worker_cls.__new__(worker_cls)
+    worker.mesh_rank = MeshRank(dp=0, sp=sp, tp=tp, pp=pp, world_size=8, dp_size=1, pp_size=2)
+    collects = worker.mesh_rank.is_collection_dp_rank()
+    # A non-collection rank must not even inspect/materialize its returned tensor.
+    values = torch.tensor([[-0.5, -1.0]]) if collects else object()
+    worker._forward_logprobs = Mock(return_value=values)
+    data = object()
+
+    output = worker.forward(data)
+
+    worker._forward_logprobs.assert_called_once_with(data)
+    assert output.loss_fn_outputs == ([{"logprobs": [-0.5, -1.0]}] if collects else [])
+    assert output.metrics == {}