vllm.distributed.eplb.rebalance_execute ¶

def _map_new_expert_indices_with_rank_mapping(
    new_global_expert_indices: torch.Tensor,
    rank_mapping: dict[int, int],
) -> torch.Tensor:
    num_layers, new_num_physical_experts = new_global_expert_indices.shape
    assert rank_mapping, "Rank mapping is required"

    # Get sizes from parameters and rank_mapping
    old_ep_size = len(rank_mapping)
    new_ep_size = sum(new_rank != -1 for new_rank in rank_mapping.values())
    num_local_physical_experts = new_num_physical_experts // new_ep_size
    old_num_physical_experts = old_ep_size * num_local_physical_experts

    mapped_expert_indices = torch.full(
        (num_layers, old_num_physical_experts),
        fill_value=-1,
        dtype=new_global_expert_indices.dtype,
        device=new_global_expert_indices.device,
    )

    for old_rank in range(old_ep_size):
        new_rank = rank_mapping[old_rank]
        if new_rank >= 0 and new_rank < new_ep_size:
            old_start_idx = old_rank * num_local_physical_experts
            old_end_idx = (old_rank + 1) * num_local_physical_experts
            new_start_idx = new_rank * num_local_physical_experts
            new_end_idx = (new_rank + 1) * num_local_physical_experts

            mapped_expert_indices[:, old_start_idx:old_end_idx] = (
                new_global_expert_indices[:, new_start_idx:new_end_idx]
            )

    return mapped_expert_indices

def _map_old_expert_indices_with_rank_mapping(
    old_global_expert_indices: torch.Tensor,
    rank_mapping: dict[int, int],
    new_ep_size: int,
) -> torch.Tensor:
    """
    Map the old global expert indices to the new global expert indices.

    Args:
        old_global_expert_indices:
            Shape (num_layers, old_ep_size * num_local_physical_experts).
        rank_mapping: Mapping from old rank to new rank.
        new_ep_size: New expert parallelism size.

    Returns:
        Mapped expert indices with shape
        (num_layers, new_ep_size * num_local_physical_experts).
    """
    num_layers, old_num_physical_experts = old_global_expert_indices.shape
    assert rank_mapping, "Rank mapping is required"

    # Get sizes from parameters and rank_mapping
    old_ep_size = len(rank_mapping)
    num_local_physical_experts = old_num_physical_experts // old_ep_size
    new_num_physical_experts = new_ep_size * num_local_physical_experts

    # Create mapped tensor with new shape, initialized to -1
    mapped_expert_indices = torch.full(
        (num_layers, new_num_physical_experts),
        fill_value=-1,
        dtype=old_global_expert_indices.dtype,
        device=old_global_expert_indices.device,
    )

    # Handle rank mapping (scale up/down with rank changes)
    for old_rank in range(old_ep_size):
        new_rank = rank_mapping.get(old_rank)
        if new_rank is not None and new_rank >= 0 and new_rank < new_ep_size:
            # This old rank exists in the new configuration
            old_start_idx = old_rank * num_local_physical_experts
            old_end_idx = (old_rank + 1) * num_local_physical_experts
            new_start_idx = new_rank * num_local_physical_experts
            new_end_idx = (new_rank + 1) * num_local_physical_experts

            mapped_expert_indices[:, new_start_idx:new_end_idx] = (
                old_global_expert_indices[:, old_start_idx:old_end_idx]
            )
        # If new_rank is None or >= new_ep_size, the experts remain -1
        # (scale down case)

    return mapped_expert_indices

def get_ep_ranks_with_experts_batch(
    expert_ids: np.ndarray,
    num_local_experts: int,
    old_indices: np.ndarray,
    new_indices: np.ndarray,
) -> tuple[dict[int, list[int]], dict[int, list[int]]]:
    """
    Get the ranks of the experts that need to be exchanged.

    Args:
        expert_ids: 1D array of expert indices to query.
        num_local_experts: The number of local experts.
        old_indices: The old indices of the experts.
        new_indices: The new indices of the experts.

    Returns:
        A tuple of two dictionaries mapping expert_id to:
        - ranks_to_send: The ranks that have this expert and need to send.
        - ranks_to_recv: The ranks that need to receive this expert.
    """
    ranks_to_send_map: dict[int, list[int]] = {}
    ranks_to_recv_map: dict[int, list[int]] = {}

    # Fast path: if no experts, return empty dicts
    if expert_ids.size == 0:
        return ranks_to_send_map, ranks_to_recv_map

    unique_experts = np.unique(expert_ids)
    num_positions = len(old_indices)
    position_indices = np.arange(num_positions, dtype=np.int32)

    # Vectorized approach: find all positions matching any query expert in one pass
    # Use np.isin to get boolean masks for all relevant positions at once
    old_relevant_mask = np.isin(old_indices, unique_experts)
    new_relevant_mask = np.isin(new_indices, unique_experts)

    # Process old_indices (send ranks)
    if np.any(old_relevant_mask):
        old_relevant_positions = position_indices[old_relevant_mask]
        old_relevant_experts = old_indices[old_relevant_mask]
        old_relevant_ranks = old_relevant_positions // num_local_experts

        # Sort by expert first, then by position (to maintain first-appearance order)
        sort_order = np.lexsort((old_relevant_positions, old_relevant_experts))
        sorted_experts = old_relevant_experts[sort_order]
        sorted_ranks = old_relevant_ranks[sort_order]

        # Find boundaries where expert changes
        expert_boundaries = np.concatenate(
            [[0], np.where(np.diff(sorted_experts) != 0)[0] + 1, [len(sorted_experts)]]
        )

        # For each expert, extract unique ranks in order of first appearance
        for i in range(len(expert_boundaries) - 1):
            start, end = expert_boundaries[i], expert_boundaries[i + 1]
            expert = int(sorted_experts[start])
            expert_ranks = sorted_ranks[start:end]

            # Get unique ranks preserving order
            _, unique_idx = np.unique(expert_ranks, return_index=True)
            unique_ranks = expert_ranks[np.sort(unique_idx)]
            ranks_to_send_map[expert] = unique_ranks.tolist()

    # Process new_indices (recv ranks)
    if np.any(new_relevant_mask):
        new_relevant_positions = position_indices[new_relevant_mask]
        new_relevant_experts = new_indices[new_relevant_mask]
        new_relevant_ranks = new_relevant_positions // num_local_experts

        # Sort by expert first, then by position
        sort_order = np.lexsort((new_relevant_positions, new_relevant_experts))
        sorted_experts = new_relevant_experts[sort_order]
        sorted_ranks = new_relevant_ranks[sort_order]

        # Find boundaries where expert changes
        expert_boundaries = np.concatenate(
            [[0], np.where(np.diff(sorted_experts) != 0)[0] + 1, [len(sorted_experts)]]
        )

        # For each expert, extract unique ranks and exclude local copies
        for i in range(len(expert_boundaries) - 1):
            start, end = expert_boundaries[i], expert_boundaries[i + 1]
            expert = int(sorted_experts[start])
            expert_ranks = sorted_ranks[start:end]

            # Get unique ranks preserving order
            _, unique_idx = np.unique(expert_ranks, return_index=True)
            unique_ranks = expert_ranks[np.sort(unique_idx)]

            # Remove ranks that have local copies (in send map)
            send_ranks_set = set(ranks_to_send_map.get(expert, []))
            recv_ranks_actual = [
                int(r) for r in unique_ranks if r not in send_ranks_set
            ]
            ranks_to_recv_map[expert] = recv_ranks_actual

    # Handle experts that only appear in old (send only) or new (recv only)
    for expert in unique_experts:
        expert = int(expert)
        if expert not in ranks_to_send_map:
            ranks_to_send_map[expert] = []
        if expert not in ranks_to_recv_map:
            ranks_to_recv_map[expert] = []

    return ranks_to_send_map, ranks_to_recv_map

def move_from_buffer(
    expert_weights: Iterable[torch.Tensor],
    expert_weights_buffers: list[torch.Tensor],
    is_unchanged: np.ndarray,
    is_received_locally: np.ndarray,
    recv_metadata: RecvMetadata,
    new_indices: np.ndarray,
    ep_rank: int,
) -> None:
    """
    Copies expert weights from communication buffers back to the target weight tensors
    after EPLB rebalancing.

    Args:
        expert_weights: List of the actual MoE layer weights used in the execution.
        expert_weights_buffers: Intermediate buffers containing the experts weights
            after the transfer is completed.
        is_unchanged: (num_local_experts,), True where an expert row is unchanged.
        is_received_locally: (num_local_experts,), True where a row is updated locally.
        recv_metadata: RecvMetadata containing remote receive metadata.
        new_indices: (num_experts_total,) mapping from local rows to desired
            (possibly global) expert id, after rebalance.
        ep_rank: Rank of the process in the expert parallel group.
    """
    recv_primary_mask = recv_metadata.recv_primary_mask
    recv_count = recv_metadata.recv_count
    recv_expert_ids = recv_metadata.recv_expert_ids
    recv_dst_rows = recv_metadata.recv_dst_rows
    num_local_experts = is_unchanged.shape[0]

    # Mask for rows to copy back from buffers:
    # copy if locally received OR remote primary recv
    copy_mask = np.logical_or(is_received_locally, recv_primary_mask)
    dest_mask_np = np.logical_and(~is_unchanged, copy_mask)
    if bool(dest_mask_np.any()):
        dest_indices = np.nonzero(dest_mask_np)[0].tolist()
        for dst in dest_indices:
            for w, b in zip(expert_weights, expert_weights_buffers):
                w[dst].copy_(b[dst], non_blocking=True)

    if recv_count == 0:
        return

    # Duplicate remote received rows to non-primary duplicate dsts
    base = ep_rank * num_local_experts
    local_experts = new_indices[base + np.arange(num_local_experts, dtype=np.int32)]
    duplicate_mask = np.logical_and(
        np.logical_and(~is_unchanged, ~is_received_locally),
        np.logical_and(~recv_primary_mask, local_experts != -1),
    )
    # All received experts are unique in the destination, so no need to copy duplicates
    if not bool(duplicate_mask.any()):
        return

    dup_dst_rows = np.nonzero(duplicate_mask)[0]
    dup_experts = local_experts[dup_dst_rows]

    prim_experts = recv_expert_ids[:recv_count]
    prim_dsts = recv_dst_rows[:recv_count]
    order = np.argsort(prim_experts, kind="stable")
    prim_experts_sorted = prim_experts[order]
    prim_dsts_sorted = prim_dsts[order]
    pos = np.searchsorted(prim_experts_sorted, dup_experts)
    valid = np.logical_and(
        pos < prim_experts_sorted.shape[0],
        prim_experts_sorted[np.minimum(pos, prim_experts_sorted.shape[0] - 1)]
        == dup_experts,
    )
    if not bool(valid.any()):
        return

    matched_dst_rows = dup_dst_rows[valid]
    matched_src_rows = prim_dsts_sorted[pos[valid]]

    for dst, src in zip(matched_dst_rows.tolist(), matched_src_rows.tolist()):
        for w in expert_weights:
            w[dst].copy_(w[src], non_blocking=True)

def move_to_buffer(
    num_local_experts: int,
    old_indices: np.ndarray,
    new_indices: np.ndarray,
    expert_weights: Iterable[torch.Tensor],
    expert_weights_buffers: Sequence[torch.Tensor],
    cuda_stream: torch.cuda.Stream | None,
    ep_group: ProcessGroup,
) -> MoveToBufferResult:
    """
    Rearranges expert weights during EPLB rebalancing.

    Args:
        num_local_experts: Number of local experts.
        old_indices: (num_experts_total,) ndarray of current (old)
            global-to-local expert assignments.
        new_indices: (num_experts_total,) ndarray of desired (new)
            global-to-local assignments after rebalance.
        expert_weights: Original expert weights for the layer.
        expert_weights_buffers: Intermediate buffers (one per tensor).
        cuda_stream: CUDA stream for async copies (can be None for sync mode).
        ep_group: Distributed process group for expert parallel comms.

    Returns:
        is_unchanged (np.ndarray): (num_local_experts,), True where an expert row
            is unchanged after rebalance.
        is_received_locally (np.ndarray): (num_local_experts,), True where a row
            can be updated from local data.
        RecvMetadata: Metadata needed for completing remote weight transfers.
    """
    assert old_indices.shape == new_indices.shape
    ep_rank = ep_group.rank()

    recv_primary_mask = np.zeros((num_local_experts,), dtype=np.bool_)
    send_expert_ids = np.full((num_local_experts,), -1, dtype=np.int64)
    send_src_rows = np.full((num_local_experts,), -1, dtype=np.int32)
    recv_expert_ids = np.full((num_local_experts,), -1, dtype=np.int64)
    recv_dst_rows = np.full((num_local_experts,), -1, dtype=np.int32)

    base = ep_rank * num_local_experts
    local_rows = np.arange(num_local_experts, dtype=np.int32)
    local_global = base + local_rows

    old_local_expert_ids = old_indices[local_global]
    new_local_expert_ids = new_indices[local_global]

    # Unchanged mask
    is_unchanged = old_local_expert_ids == new_local_expert_ids

    # Local receive eligibility
    new_valid = new_local_expert_ids != -1
    can_recv_local = np.isin(
        new_local_expert_ids, old_local_expert_ids, assume_unique=False
    )
    is_received_locally = np.logical_or(
        is_unchanged, np.logical_and(new_valid, can_recv_local)
    )

    # Send map: first src row per unique expert present locally in old mapping
    send_count = 0
    valid_old = old_local_expert_ids != -1
    if np.any(valid_old):
        uniq_experts, first_idx = np.unique(
            old_local_expert_ids[valid_old], return_index=True
        )
        filtered_rows = local_rows[valid_old]
        src_rows = filtered_rows[first_idx]
        send_count = int(uniq_experts.shape[0])
        send_expert_ids[:send_count] = uniq_experts
        send_src_rows[:send_count] = src_rows

    # Recv map: primary dst per unique expert needed remotely
    recv_count = 0
    need_recv_mask = np.logical_and(~is_received_locally, new_valid)
    if np.any(need_recv_mask):
        desired_experts = new_local_expert_ids[need_recv_mask]
        desired_dsts = local_rows[need_recv_mask]
        uniq_recv_experts, uniq_indices = np.unique(desired_experts, return_index=True)
        dst_rows = desired_dsts[uniq_indices]
        recv_count = int(uniq_recv_experts.shape[0])
        recv_expert_ids[:recv_count] = uniq_recv_experts
        recv_dst_rows[:recv_count] = dst_rows
        recv_primary_mask[dst_rows] = True

    eligible_local_buffer_mask = np.logical_and(~is_unchanged, is_received_locally)

    # 1. Local moves into tmp buffers
    if bool(eligible_local_buffer_mask.any()) and send_count > 0:
        dest_indices = np.nonzero(eligible_local_buffer_mask)[0].tolist()
        expert_to_src_map = dict(
            zip(send_expert_ids[:send_count], send_src_rows[:send_count])
        )
        for dst in dest_indices:
            expert = new_local_expert_ids[dst]
            src_local = expert_to_src_map.get(expert, -1)
            if src_local != -1:
                for w, b in zip(expert_weights, expert_weights_buffers):
                    b[dst].copy_(w[src_local], non_blocking=True)

    p2p_ops: list[P2POp] = []

    # Pre-compute global ranks mapping
    ep_size = ep_group.size()
    rank_to_global = {rank: get_global_rank(ep_group, rank) for rank in range(ep_size)}

    # 2. Post sends
    if send_count > 0:
        experts = send_expert_ids[:send_count]
        srcs = send_src_rows[:send_count]
        order = np.argsort(experts, kind="stable")
        experts = experts[order]
        srcs = srcs[order]

        send_map, recv_map = get_ep_ranks_with_experts_batch(
            experts,
            num_local_experts,
            old_indices,
            new_indices,
        )

        for expert, src in zip(experts.tolist(), srcs.tolist()):
            ranks_to_send = send_map[expert]
            ranks_to_recv = recv_map[expert]
            if not ranks_to_send or not ranks_to_recv:
                continue
            num_dst_per_sender = len(ranks_to_recv) // len(ranks_to_send)
            sender_pos = ranks_to_send.index(ep_rank)
            recv_begin = sender_pos * num_dst_per_sender
            recv_end = recv_begin + num_dst_per_sender
            recv_ranks = ranks_to_recv[recv_begin:recv_end]
            remainder_start = len(ranks_to_send) * num_dst_per_sender
            recver_pos = remainder_start + sender_pos
            if recver_pos < len(ranks_to_recv):
                recv_ranks.append(ranks_to_recv[recver_pos])
            for dst in recv_ranks:
                dst_global = rank_to_global[dst]
                p2p_ops += [
                    P2POp(
                        torch.distributed.isend,
                        w[src],
                        dst_global,
                    )
                    for w in expert_weights
                ]

    # 3. Post recvs
    if recv_count > 0:
        experts = recv_expert_ids[:recv_count]
        dsts = recv_dst_rows[:recv_count]
        order = np.argsort(experts, kind="stable")
        experts = experts[order]
        dsts = dsts[order]

        send_map, recv_map = get_ep_ranks_with_experts_batch(
            experts,
            num_local_experts,
            old_indices,
            new_indices,
        )

        for expert, dst in zip(experts.tolist(), dsts.tolist()):
            ranks_to_send = send_map[expert]
            ranks_to_recv = recv_map[expert]
            if not ranks_to_send or not ranks_to_recv:
                continue
            num_dst_per_sender = len(ranks_to_recv) // len(ranks_to_send)
            recver_pos = ranks_to_recv.index(ep_rank)
            remainder_start = len(ranks_to_send) * num_dst_per_sender
            if recver_pos < remainder_start:
                src = ranks_to_send[recver_pos // num_dst_per_sender]
            else:
                src = ranks_to_send[recver_pos - remainder_start]
            src_global = rank_to_global[src]
            p2p_ops += [
                P2POp(
                    torch.distributed.irecv,
                    b[dst],
                    src_global,
                )
                for b in expert_weights_buffers
            ]

    # 4. Execute the P2P operations. The real communication happens here.
    if p2p_ops and cuda_stream is not None:
        with torch.cuda.stream(cuda_stream):
            reqs = batch_isend_irecv(p2p_ops)
            for req in reqs:
                req.wait()
    elif p2p_ops:
        reqs = batch_isend_irecv(p2p_ops)
        for req in reqs:
            req.wait()
    # wait for the communication to finish
    return (
        is_unchanged,
        is_received_locally,
        RecvMetadata(
            recv_primary_mask=recv_primary_mask,
            recv_count=recv_count,
            recv_expert_ids=recv_expert_ids,
            recv_dst_rows=recv_dst_rows,
        ),
    )

def rearrange_expert_weights_inplace(
    old_global_expert_indices: torch.Tensor,
    new_global_expert_indices: torch.Tensor,
    expert_weights: Sequence[Iterable[torch.Tensor]],
    ep_group: ProcessGroup,
    is_profile: bool = False,
    rank_mapping: dict[int, int] | None = None,
) -> None:
    """
    Rearranges the expert weights in place according to the new expert indices.

    The value of the indices arguments are logical indices of the experts,
    while keys are physical.

    Args:
        old_global_expert_indices: Shape (num_moe_layers, num_physical_experts).
        new_global_expert_indices: Shape (num_moe_layers, num_physical_experts).
        expert_weights: A sequence of shape (num_moe_layers)(weight_count)
            of tensors of shape (num_local_physical_experts, hidden_size_i).
            For example, a linear layer may have up and down projection,
            so weight_count = 2. Each weight's hidden size can be different.
        ep_group: The device process group for expert parallelism.
        is_profile (bool): If `True`, do not perform any actual weight copy.
            This is used during profile run, where we only perform dummy
            communications to reserve enough memory for the buffers.
        rank_mapping: A dictionary mapping old rank to new rank.
    """
    if rank_mapping is not None:
        if len(rank_mapping) == ep_group.size():
            # scale down
            new_global_expert_indices = _map_new_expert_indices_with_rank_mapping(
                new_global_expert_indices,
                rank_mapping,
            )
        else:
            # scale up
            old_global_expert_indices = _map_old_expert_indices_with_rank_mapping(
                old_global_expert_indices,
                rank_mapping,
                ep_group.size(),
            )

    assert old_global_expert_indices.shape[1] == new_global_expert_indices.shape[1]

    num_moe_layers, num_physical_experts = old_global_expert_indices.shape
    assert len(expert_weights) == num_moe_layers

    num_local_physical_experts = next(iter(expert_weights[0])).shape[0]
    assert new_global_expert_indices.shape == (num_moe_layers, num_physical_experts)

    ep_size = ep_group.size()
    assert num_physical_experts == ep_size * num_local_physical_experts

    first_layer_weights = list(expert_weights[0])
    # Buffers to hold the expert weights during the exchange.
    # NOTE: Currently we assume the same weights across different layers
    # have the same shape.
    weights_buffer: list[torch.Tensor] = [
        torch.empty_like(w) for w in first_layer_weights
    ]
    if is_profile:
        # Reserve communication buffers via a minimal dummy all_gather on first layer
        for weight, buffer in zip(expert_weights[0], weights_buffer):
            dummy_recv_buffer = [buffer for _ in range(ep_size)]
            torch.distributed.barrier()
            all_gather(
                dummy_recv_buffer,
                weight,
                group=ep_group,
            )
        return

    # NOTE(bowen): We need this synchronize to run, but I don't know why.
    # If you figure out the reason, please let me know -- thank you!
    torch.cuda.synchronize()

    old_global_expert_indices_cpu = old_global_expert_indices.cpu().numpy()
    new_global_expert_indices_cpu = new_global_expert_indices.cpu().numpy()

    for layer_idx in range(num_moe_layers):
        is_unchanged, is_received_locally, recv_metadata = move_to_buffer(
            num_local_experts=num_local_physical_experts,
            old_indices=old_global_expert_indices_cpu[layer_idx],
            new_indices=new_global_expert_indices_cpu[layer_idx],
            expert_weights=expert_weights[layer_idx],
            expert_weights_buffers=weights_buffer,
            cuda_stream=None,
            ep_group=ep_group,
        )

        move_from_buffer(
            expert_weights=expert_weights[layer_idx],
            expert_weights_buffers=weights_buffer,
            is_unchanged=is_unchanged,
            is_received_locally=is_received_locally,
            recv_metadata=recv_metadata,
            new_indices=new_global_expert_indices_cpu[layer_idx],
            ep_rank=ep_group.rank(),
        )

async def transfer_layer(
    old_global_expert_indices: torch.Tensor,
    new_global_expert_indices: torch.Tensor,
    expert_weights: Sequence[Iterable[torch.Tensor]],
    expert_weights_buffer: Sequence[torch.Tensor],
    ep_group: ProcessGroup,
    is_profile: bool = False,
    layer: int = 0,
    cuda_stream: torch.cuda.Stream | None = None,
    rank_mapping: dict[int, int] | None = None,
) -> MoveToBufferResult:
    """
    Rearranges the expert weights in place according to the new expert indices.

    The value of the indices arguments are logical indices of the experts,
    while keys are physical.

    Args:
        old_global_expert_indices: Shape (num_moe_layers, num_physical_experts).
        new_global_expert_indices: Shape (num_moe_layers, num_physical_experts).
        expert_weights: A sequence of shape (num_moe_layers)(weight_count)
            of tensors of shape (num_local_physical_experts, hidden_size_i).
            For example, a linear layer may have up and down projection,
            so weight_count = 2. Each weight's hidden size can be different.
        ep_group: The device process group for expert parallelism.
        is_profile (bool): If `True`, do not perform any actual weight copy.
            This is used during profile run, where we only perform dummy
            communications to reserve enough memory for the buffers.

    Returns:
        is_unchanged (np.ndarray): (1, num_local_experts), True where expert
            is left unchanged.
        is_received_locally (np.ndarray): (1, num_local_experts), True where expert
            can be received locally.
        RecvMetadata: Metadata needed for completing remote weight transfers.
    """
    ep_size = ep_group.size()
    if rank_mapping is not None:
        if len(rank_mapping) == ep_group.size():
            # scale down
            new_global_expert_indices = _map_new_expert_indices_with_rank_mapping(
                new_global_expert_indices,
                rank_mapping,
            )
        else:
            # scale up
            old_global_expert_indices = _map_old_expert_indices_with_rank_mapping(
                old_global_expert_indices,
                rank_mapping,
                ep_group.size(),
            )

    assert old_global_expert_indices.shape[1] == new_global_expert_indices.shape[1]
    num_moe_layers, num_physical_experts = old_global_expert_indices.shape
    assert len(expert_weights) == num_moe_layers
    num_local_physical_experts = next(iter(expert_weights[0])).shape[0]
    assert new_global_expert_indices.shape == (num_moe_layers, num_physical_experts)
    assert num_physical_experts == ep_size * num_local_physical_experts

    old_global_expert_indices_np = old_global_expert_indices.cpu().numpy()
    new_global_expert_indices_np = new_global_expert_indices.cpu().numpy()

    is_unchanged, is_received_locally, recv_metadata = move_to_buffer(
        num_local_experts=num_local_physical_experts,
        old_indices=old_global_expert_indices_np[layer],
        new_indices=new_global_expert_indices_np[layer],
        expert_weights=expert_weights[layer],
        expert_weights_buffers=expert_weights_buffer,
        cuda_stream=cuda_stream,
        ep_group=ep_group,
    )
    return is_unchanged, is_received_locally, recv_metadata