[Dev] fix(combined-1f1b): release loss-node input storage after combined backward

[Wohox]

What does this PR do ?

Release the loss_node input tensors (notably the fp32 loss tensor) as soon as the combined model backward has consumed the loss gradient, so they don't linger in GPU memory through the rest of the combined 1F1B step.

In combined_forward_backward_step() (combined 1F1B schedule), loss_node retains references to its input tensors after the loss backward has materialized the gradient. Those inputs — most notably the fp32 loss tensor — stay live in GPU memory through the remainder of the combined model backward, holding on to roughly O(seq_len) bytes of fp32 storage per micro-batch.

This PR adds a small _release_tensor_storage() helper and uses it to:

1. Capture loss_node.inputs after the loss backward.
2. Drop the autograd state immediately via loss_node._release_state().
3. Free the input tensor storage with untyped_storage().resize_(0) once the combined model backward has consumed the loss gradient.

record_stream(torch.cuda.current_stream()) guards against any lingering async users on the current stream before the storage is resized.

Diff: +16 / -1 in megatron/core/pipeline_parallel/combined_1f1b.py.

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Linked issue: Related to #4909 (main-branch port of the same fix).

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[Wohox]

/ok to test f802e0e

[lhb8125]

/claude review

[claude]

Nit: record_stream() will raise on CPU tensors. Adding a .is_cuda guard is cheap insurance:

Suggested change

	if tensors is None:
	return
	for tensor in tensors:
	if isinstance(tensor, torch.Tensor):
	tensor.record_stream(torch.cuda.current_stream())
	tensor.untyped_storage().resize_(0)
	def _release_tensor_storage(tensors):
	"""Release tensor storage after all backward users are done."""
	if tensors is None:
	return
	for tensor in tensors:
	if isinstance(tensor, torch.Tensor) and tensor.is_cuda:

[claude]

Minor: the record_stream + untyped_storage().resize_(0) two-liner already exists in ScheduleNode._forward (utils.py:229-231) and in fine_grained_activation_offload.py:1062-1064. Consider promoting this helper to megatron/core/pipeline_parallel/utils.py and importing it at all three sites to avoid drift between copies.

[lhb8125]

I guess the reason why the input is saved additionally is to release loss_node as early as possible, but do we really need it, considering the loss tensor is small?

[Wohox]

/ok to test 25ff5cc

[copy-pr-bot]

/ok to test 25ff5cc

@Wohox, there was an error processing your request: E2

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[yaox12]

/ok to test b9466b1

[svcnvidia-nemo-ci]

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You can track the progress here: https://github.com/NVIDIA/Megatron-LM/actions/runs/26554827671