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vllm.compilation.passes.utility.split_coalescing

Coalesce duplicate split_with_sizes nodes that operate on the same input tensor with the same split sizes.

On certain hardware/dtype combinations (e.g. B200 + FP8) the Inductor graph may contain multiple split_with_sizes calls on the same tensor that CSE fails to merge. This pass detects and replaces the duplicates so that downstream pattern-matching passes (e.g. QK-Norm+RoPE fusion) see a single split node with all users attached.

See also
  • vLLM #33295 (original issue)
  • PyTorch #174472 (upstream CSE gap)

Classes:

SplitCoalescingPass

Bases: VllmInductorPass

Replace duplicate split_with_sizes nodes with a single canonical node when they share the same input tensor and split sizes.

Source code in vllm/compilation/passes/utility/split_coalescing.py 
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class SplitCoalescingPass(VllmInductorPass):
    """Replace duplicate ``split_with_sizes`` nodes with a single canonical
    node when they share the same input tensor and split sizes."""

    @VllmInductorPass.time_and_log
    def __call__(self, graph: fx.Graph) -> None:
        count = 0

        # Map from input tensor node -> list of split nodes seen so far.
        split_nodes: dict[fx.Node, list[fx.Node]] = {}

        for node in graph.nodes:
            if not is_func(node, torch.ops.aten.split_with_sizes.default):
                continue
            if not all(is_func(user, operator.getitem) for user in node.users):
                continue

            arg_node, split_sizes = node.args[:2]

            if arg_node not in split_nodes:
                split_nodes[arg_node] = [node]
                continue

            # Find existing node with same split_sizes
            canonical = next(
                (
                    n
                    for n in split_nodes[arg_node]
                    if list(n.args[1]) == list(split_sizes)
                ),
                None,
            )
            if canonical is not None:
                node.replace_all_uses_with(canonical)
                graph.erase_node(node)
                count += 1
            else:
                split_nodes[arg_node].append(node)

        logger.debug("Coalesced %d duplicate split_with_sizes nodes", count)