vllm.distributed.eplb.eplb_state ¶

@dataclass
class EplbModelState:
    """EPLB metrics."""

    physical_to_logical_map: torch.Tensor
    """
    Mapping from physical experts to logical experts.

    Shape: (num_moe_layers, num_physical_experts)

    # Example

    For a 2-layer MoE model with 6 physical experts and 4 logical experts on 3
    EP ranks, the mapping could look like this:

    ```
    [[0, 1, 2, 3, 0, 1],
     [0, 2, 0, 1, 0, 3]]
    ```
    """
    logical_to_physical_map: torch.Tensor
    """
    Mapping from logical experts to physical experts.

    This is a sparse matrix, where -1 indicates no mapping.

    Shape: (num_moe_layers, num_logical_experts, num_redundant_experts + 1)

    # Example

    For a 2-layer MoE model with 6 physical experts and 4 logical experts on 3
    EP ranks, the mapping could look like this:

    ```
    [[[0, 4, -1],
      [1, 5, -1],
      [2, -1, -1],
      [3, -1, -1]],
     [[0, 2, 4],
      [3, -1, -1],
      [1, -1, -1],
      [5, -1, -1]]]
    ```
    """
    logical_replica_count: torch.Tensor
    """
    Number of replicas for each logical expert.
    This is exactly the non-`-1` count in the `logical_to_physical_map`.

    Shape: (num_moe_layers, num_logical_experts)

    # Example
    For a 2-layer MoE model with 6 physical experts and 4 logical experts on 3
    EP ranks, the count could look like this:

    ```
    [[2, 2, 1, 1],
     [3, 1, 1, 1]]
    """

    expert_load_pass: torch.Tensor
    """
    Expert load during this forward pass. 
    We use the token count each expert processes as the load.

    Shape: (num_moe_layers, num_physical_experts)
    """
    expert_load_window: torch.Tensor
    """
    A sliding window of expert load.

    Shape: (window_size, num_moe_layers, num_physical_experts)

    NOTE: The expert_load_view now records load for all physical experts
    rather than just local experts. This ensures consistent load statistics
    across different dispatch methods (naive all-to-all, DeepEP, pplx-kernels).
    The recorded load will be multiplied by dp_size when using naive all-to-all
    due to each DP rank contributing the same token set to the calculation.
    See:
    https://github.com/vllm-project/vllm/pull/22167#pullrequestreview-3086143856
    """
    model_name: str
    model: MixtureOfExperts

class EplbState:
    """
    EplbState of each expert parallel model. Key is the model config hash.
    """

    def __init__(self, parallel_config: ParallelConfig, device: torch.device):
        self.parallel_config = parallel_config
        self.device = device
        self.model_states: dict[str, EplbModelState] = {}
        """
        Current step in the sliding window.

        Different from `expert_rearrangement_step`, 
        each EP rank may have its own `expert_load_window_step`.
        """
        self.expert_load_window_step: int = 0
        """
        Size of the expert load sliding window.
        This is a constant and is taken from the config.
        """
        self.expert_load_window_size: int = 0
        """
        Steps after last rearrangement.
        Will trigger a rearrangement if it exceeds the threshold.

        NOTE: Keep in mind that all EP ranks need to have the same
        `expert_rearrangement_step` value to ensure synchronization.
        Otherwise, the rearrangement will hang at collective
        communication calls.
        """
        self.expert_rearrangement_step: int = 0
        """
        Interval for expert rearrangement steps.
        This is a constant and is taken from the config.
        """
        self.expert_rearrangement_step_interval: int = 0

    @staticmethod
    def build_initial_global_physical_to_logical_map(
        num_routed_experts: int,
        num_redundant_experts: int,
    ) -> Sequence[int]:
        """
        Build an initial expert arrangement using the following structure:
        [original routed experts, redundant experts]

        Returns:
            physical_to_logical_map (Sequence[int]): A list of integers,
                where each integer is the index of the logical expert
                that the corresponding physical expert maps to.
        """
        global_physical_to_logical_map = list(range(num_routed_experts))
        global_physical_to_logical_map += [
            i % num_routed_experts for i in range(num_redundant_experts)
        ]
        return global_physical_to_logical_map

    def validate_ep_configuration(self, new_model: MixtureOfExperts):
        """
        Validate that the expert parallel configuration of
        the new model is the same as the existing models.
        """
        if len(self.model_states) > 0:
            model = next(iter(self.model_states.values())).model
            if (
                model.num_routed_experts != new_model.num_routed_experts
                or model.num_redundant_experts != new_model.num_redundant_experts
                or model.num_physical_experts != new_model.num_physical_experts
                or model.num_logical_experts != new_model.num_logical_experts
                or model.num_expert_groups != new_model.num_expert_groups
            ):
                raise RuntimeError(
                    "Model: {} "
                    "with config {} "
                    "{} {} {} {} "
                    "mismatch with new model {} "
                    "with config {} "
                    "{} {} {} {}".format(
                        type(model),
                        model.num_routed_experts,
                        model.num_redundant_experts,
                        model.num_physical_experts,
                        model.num_logical_experts,
                        model.num_expert_groups,
                        type(new_model),
                        new_model.num_routed_experts,
                        new_model.num_redundant_experts,
                        new_model.num_physical_experts,
                        new_model.num_logical_experts,
                        new_model.num_expert_groups,
                    )
                )

    def add_model(
        self,
        model: MixtureOfExperts,
        model_config: ModelConfig,
        global_expert_load: torch.Tensor | None = None,
        old_global_expert_indices: torch.Tensor | None = None,
        rank_mapping: dict[int, int] | None = None,
    ):
        """
        Build the initial EPLB state.
        """
        self.validate_ep_configuration(model)
        physical_to_logical_map_list = (
            EplbState.build_initial_global_physical_to_logical_map(
                model.num_routed_experts,
                model.num_redundant_experts,
            )
        )
        physical_to_logical_map = torch.tensor(
            physical_to_logical_map_list,
            device=self.device,
        )
        # Assuming 8 GPUs per node, this supports up to
        # (1023 + 1) / 8 = 128 nodes for now.
        # TODO(rui): make this configurable
        MAX_EXPERT_REDUNDANCY = 1023
        assert model.num_redundant_experts <= MAX_EXPERT_REDUNDANCY, (
            f"num_redundant_experts {model.num_redundant_experts} "
            f"must be less than or equal to {MAX_EXPERT_REDUNDANCY}"
        )
        max_slots_per_logical_expert = MAX_EXPERT_REDUNDANCY + 1
        logical_to_physical_map = torch.full(
            (model.num_logical_experts, max_slots_per_logical_expert),
            -1,
            device=self.device,
        )
        logical_replica_count = torch.zeros(
            (model.num_logical_experts,),
            device=self.device,
            dtype=torch.long,
        )

        for i in range(model.num_physical_experts):
            logical_idx = physical_to_logical_map[i]
            logical_to_physical_map[logical_idx, logical_replica_count[logical_idx]] = i
            logical_replica_count[logical_idx] += 1

        # Duplicate initial mapping for all layers
        physical_to_logical_map = (
            physical_to_logical_map.unsqueeze(0)
            .expand(
                model.num_moe_layers,
                -1,
            )
            .contiguous()
        )
        logical_to_physical_map = (
            logical_to_physical_map.unsqueeze(0)
            .expand(
                model.num_moe_layers,
                -1,
                -1,
            )
            .contiguous()
        )
        logical_replica_count = (
            logical_replica_count.unsqueeze(0)
            .expand(
                model.num_moe_layers,
                -1,
            )
            .contiguous()
        )

        expert_load_pass = torch.zeros(
            (model.num_moe_layers, model.num_physical_experts),
            dtype=torch.int32,
            device=self.device,
        )
        self.expert_load_window_size = self.parallel_config.eplb_config.window_size
        expert_load_window = torch.zeros(
            (
                self.expert_load_window_size,
                model.num_moe_layers,
                model.num_physical_experts,
            ),
            dtype=torch.int32,
            device=self.device,
        )

        # Set the initial progress of rearrangement to 3/4
        eplb_step_interval = self.parallel_config.eplb_config.step_interval
        self.expert_rearrangement_step = max(
            0, eplb_step_interval - eplb_step_interval // 4
        )
        self.expert_rearrangement_step_interval = eplb_step_interval

        if global_expert_load is not None:
            ep_group = get_ep_group().device_group
            assert global_expert_load.shape == (
                model.num_moe_layers,
                model.num_logical_experts,
            )
            assert global_expert_load.dtype == torch.int64

            num_replicas = model.num_physical_experts
            num_groups = model.num_expert_groups
            num_nodes = get_node_count()
            num_gpus = ep_group.size()

            if num_gpus % num_nodes != 0:
                num_nodes = 1
                logger.warning_once(
                    f"num_gpus % num_nodes != 0, "
                    "not using hierarchical rearrangement algorithm.\n"
                    f"{num_gpus=}, {num_nodes=}"
                )

            # Get new expert mappings
            (
                new_physical_to_logical_map,
                new_logical_to_physical_map,
                new_logical_replica_count,
            ) = rebalance_experts(
                global_expert_load,
                num_replicas,
                num_groups,
                num_nodes,
                num_gpus,
            )

            max_physical_slots = new_logical_to_physical_map.shape[-1]
            assert max_physical_slots <= logical_to_physical_map.shape[-1]
            new_logical_to_physical_map = torch.nn.functional.pad(
                new_logical_to_physical_map,
                (0, logical_to_physical_map.shape[-1] - max_physical_slots),
                value=-1,
            )
            physical_to_logical_map = new_physical_to_logical_map.to(self.device)
            logical_to_physical_map.copy_(new_logical_to_physical_map)
            logical_replica_count.copy_(new_logical_replica_count)

        model.set_eplb_state(
            expert_load_pass,
            logical_to_physical_map,
            logical_replica_count,
        )
        if global_expert_load is not None:
            rearrange_expert_weights_inplace(
                old_global_expert_indices,
                new_physical_to_logical_map,
                model.expert_weights,
                ep_group,
                False,
                rank_mapping,
            )
            self.expert_rearrangement_step = 0

        self.model_states[model_config.compute_hash()] = EplbModelState(
            physical_to_logical_map,
            logical_to_physical_map,
            logical_replica_count,
            expert_load_pass,
            expert_load_window,
            model_config.model,
            model,
        )

    def step(
        self,
        is_dummy: bool = False,
        is_profile: bool = False,
        log_stats: bool = False,
    ) -> None:
        """
        Step the EPLB state.

        Args:
            is_dummy (bool): If `True`, this is a dummy step and the load
                metrics recorded in this forward pass will not count.
                Defaults to `False`.
            is_profile (bool): If `True`, perform a dummy rearrangement
                with maximum communication cost. This is used in
                `profile_run` to reserve enough memory
                for the communication buffer.
            log_stats (bool): If `True`, log the expert load metrics.

        # Stats
            The metrics are all summed up across layers.
            - `avg_tokens`: The average load across ranks.
            - `max_tokens`: The maximum load across ranks.
            - `balancedness`: The ratio of average load to maximum load.
        """

        if is_profile:
            self.rearrange(is_profile=True)
            return

        if is_dummy:
            # Do not record load metrics for dummy steps
            for eplb_model_state in self.model_states.values():
                eplb_model_state.expert_load_pass.zero_()

        if log_stats:
            # Sync the expert load pass for each model (main and drafter).
            # expert_load_pass: (num_moe_layers, num_physical_experts)
            expert_load_pass_list = self._sync_load_pass()
            ep_group = get_ep_group().device_group
            for expert_load_pass, eplb_model_state in zip(
                expert_load_pass_list, self.model_states.values()
            ):
                # num_tokens_per_rank: (num_moe_layers, num_ranks)
                num_tokens_per_rank = (
                    expert_load_pass.reshape(
                        expert_load_pass.shape[0], ep_group.size(), -1
                    )
                    .sum(dim=-1)
                    .float()
                )

                # Compute balancedness ratio:
                # for each layer:
                #   (mean load across ranks) / (max load across ranks)
                avg_tokens_tensor = num_tokens_per_rank.mean(dim=0).sum(dim=0)
                max_tokens_tensor = num_tokens_per_rank.max(dim=0).values.sum(dim=0)

                # Just to make type checker happy
                tokens_tensors: list[float] = torch.stack(
                    [avg_tokens_tensor, max_tokens_tensor]
                ).tolist()
                avg_tokens, max_tokens = tokens_tensors
                balancedness = avg_tokens / max_tokens if max_tokens > 0 else 0.0

                if ep_group.rank() == 0:
                    logger.info(
                        "EPLB step: %d for model %s: avg_tokens=%.2f, "
                        "max_tokens=%d, balancedness=%.4f",
                        self.expert_rearrangement_step,
                        eplb_model_state.model_name,
                        avg_tokens,
                        max_tokens,
                        balancedness,
                    )

        # Update the expert load sliding window
        if not is_dummy:
            for eplb_model_state in self.model_states.values():
                eplb_model_state.expert_load_window[self.expert_load_window_step] = (
                    eplb_model_state.expert_load_pass.clone()
                )
                eplb_model_state.expert_load_pass.zero_()

            self.expert_load_window_step += 1
            if self.expert_load_window_step >= self.expert_load_window_size:
                self.expert_load_window_step = 0

        # Step the expert rearrangement step
        # Note that even if this is a dummy step, we still increment the
        # rearrangement step and perform rearrangement to ensure all ranks are
        # performing collective communication.
        self.expert_rearrangement_step += 1
        if self.expert_rearrangement_step >= self.expert_rearrangement_step_interval:
            self.expert_rearrangement_step = 0
            self.rearrange()

    def rearrange(
        self,
        is_profile: bool = False,
        execute_shuffle: bool = True,
        global_expert_loads: list[torch.Tensor] | None = None,
        rank_mapping: dict[int, int] | None = None,
    ) -> torch.Tensor | None:
        """
        Rearrange the experts according to the current load.

        Args:
            is_profile (bool): If `True`, perform a dummy rearrangement.
                This is used in `profile_run` to reserve enough memory,
                no memory movement will be performed. Default is False.
            execute_shuffle (bool): If `True`, execute the shuffle
                in elastic expert parallel (EEP). Default is True.
            global_expert_loads (list[torch.Tensor] | None): The global expert
                loads when scaling is done in EEP.
                List of expert loads for the main and drafter
                (when spec decode is used) models.
            rank_mapping (dict[int, int] | None): The rank mapping
                when scaling is done in EEP.
        """

        ep_group = get_ep_group().device_group
        ep_rank = ep_group.rank()

        time_start = None
        is_main_rank = ep_rank == 0
        if is_main_rank:
            torch.cuda.synchronize()
            time_start = time.perf_counter()
            logger.info("Rearranging experts %s...", "(profile)" if is_profile else "")

        if global_expert_loads is None:
            # Map the physical expert load to global logical experts
            global_expert_load_windows = []
            if not execute_shuffle:
                num_models = torch.tensor(
                    [len(self.model_states)], dtype=torch.int32, device="cpu"
                )
                torch.distributed.broadcast(
                    num_models, group=get_ep_group().cpu_group, group_src=0
                )

            for eplb_model_state in self.model_states.values():
                logical_expert_load_window = torch.zeros(
                    self.expert_load_window_size,
                    eplb_model_state.model.num_moe_layers,
                    eplb_model_state.model.num_logical_experts,
                    dtype=eplb_model_state.expert_load_window.dtype,
                    device=eplb_model_state.expert_load_window.device,
                )
                logical_expert_load_window.scatter_add_(
                    dim=-1,
                    index=eplb_model_state.physical_to_logical_map.unsqueeze(0)
                    .expand_as(eplb_model_state.expert_load_window)
                    .long(),
                    src=eplb_model_state.expert_load_window,
                )

                if not execute_shuffle:
                    metadata = torch.tensor(
                        [
                            eplb_model_state.model.num_moe_layers,
                            eplb_model_state.model.num_logical_experts,
                            eplb_model_state.physical_to_logical_map.shape[1],
                        ],
                        dtype=torch.int32,
                        device="cpu",
                    )
                    torch.distributed.broadcast(
                        metadata, group=get_ep_group().cpu_group, group_src=0
                    )

                global_expert_load_window = logical_expert_load_window.sum(dim=0)
                global_expert_load_windows.append(global_expert_load_window)
            # Perform all-reduce to get the expert load across all ranks for each model
            global_expert_load_windows = self._allreduce_list(
                global_expert_load_windows
            )
            if not execute_shuffle:
                for eplb_model_state, global_expert_load_window in zip(
                    self.model_states.values(), global_expert_load_windows
                ):
                    # (num_moe_layers, old_num_physical_experts)
                    old_global_expert_indices = eplb_model_state.physical_to_logical_map
                    torch.distributed.broadcast(
                        old_global_expert_indices, group=ep_group, group_src=0
                    )
            if not execute_shuffle:
                return global_expert_load_windows
        else:
            assert execute_shuffle
            global_expert_load_windows = global_expert_loads

        # TODO(bowen): Treat differently for prefill and decode nodes
        eplb_model_state = next(iter(self.model_states.values()))
        model = eplb_model_state.model
        num_replicas = model.num_physical_experts
        num_groups = model.num_expert_groups
        if rank_mapping is not None and len(rank_mapping) == ep_group.size():
            # NOTE(yongji): scale down, we need to rebalance the experts on
            # remaining GPUs, transfer the experts while we haven't shutdown
            # the GPUs to be released.
            cpu_group = get_ep_group().cpu_group
            num_nodes = _node_count_with_rank_mapping(cpu_group, rank_mapping)
            num_gpus = sum(new_rank != -1 for new_rank in rank_mapping.values())
            num_replicas = (
                num_replicas // ep_group.size() * num_gpus
            )  # handle num replicas change
        else:
            num_nodes = get_node_count()
            num_gpus = ep_group.size()

        if num_gpus % num_nodes != 0:
            self.num_nodes = 1
            logger.warning_once(
                f"num_gpus % num_nodes != 0, "
                "not using hierarchical rearrangement algorithm.\n"
                f"{num_gpus=}, {num_nodes=}"
            )

        for eplb_model_state, global_expert_load_window in zip(
            self.model_states.values(), global_expert_load_windows
        ):
            # Get new expert mappings for the model
            (
                new_physical_to_logical_map,
                new_logical_to_physical_map,
                new_logical_replica_count,
            ) = rebalance_experts(
                global_expert_load_window,
                num_replicas,
                num_groups,
                num_nodes,
                num_gpus,
            )

            # Update expert weights
            rearrange_expert_weights_inplace(
                eplb_model_state.physical_to_logical_map,
                new_physical_to_logical_map,
                eplb_model_state.model.expert_weights,
                ep_group,
                is_profile,
                rank_mapping,
            )

            if not is_profile:
                if (
                    eplb_model_state.physical_to_logical_map.shape[1]
                    != new_physical_to_logical_map.shape[1]
                ):
                    eplb_model_state.physical_to_logical_map = (
                        new_physical_to_logical_map.to(
                            eplb_model_state.physical_to_logical_map.device
                        )
                    )
                else:
                    eplb_model_state.physical_to_logical_map.copy_(
                        new_physical_to_logical_map
                    )
                max_physical_slots = new_logical_to_physical_map.shape[-1]
                assert (
                    max_physical_slots
                    <= eplb_model_state.logical_to_physical_map.shape[-1]
                )
                new_logical_to_physical_map = torch.nn.functional.pad(
                    new_logical_to_physical_map,
                    (
                        0,
                        eplb_model_state.logical_to_physical_map.shape[-1]
                        - max_physical_slots,
                    ),
                    value=-1,
                )
                eplb_model_state.logical_to_physical_map.copy_(
                    new_logical_to_physical_map
                )
                eplb_model_state.logical_replica_count.copy_(new_logical_replica_count)

        if is_main_rank:
            assert time_start is not None
            torch.cuda.synchronize()
            time_end = time.perf_counter()
            logger.info(
                "Rearranged experts%sin %.2f seconds.",
                " (profile) " if is_profile else " ",
                time_end - time_start,
            )
        return None

    @staticmethod
    def recv_state() -> tuple[list[torch.Tensor], list[torch.Tensor]]:
        """
        Receive the expert load and old placement from the master rank.
        """
        ep_group = get_ep_group()
        num_models = torch.empty(1, dtype=torch.int32, device="cpu")
        torch.distributed.broadcast(num_models, group=ep_group.cpu_group, group_src=0)
        num_models = num_models.item()
        global_expert_loads = []
        old_global_expert_indices_per_model = []
        for _ in range(num_models):
            metadata = torch.empty(3, dtype=torch.int32, device="cpu")
            torch.distributed.broadcast(metadata, group=ep_group.cpu_group, group_src=0)
            num_moe_layers, num_logical_experts, num_old_physical_experts = (
                metadata.tolist()
            )
            global_expert_load = torch.zeros(
                (num_moe_layers, num_logical_experts),
                dtype=torch.int64,
                device=ep_group.device,
            )
            all_reduce(global_expert_load, group=ep_group.device_group)
            old_global_expert_indices = torch.empty(
                (num_moe_layers, num_old_physical_experts),
                dtype=torch.int64,
                device=ep_group.device,
            )
            torch.distributed.broadcast(
                old_global_expert_indices,
                group=ep_group.device_group,
                group_src=0,
            )
            global_expert_loads.append(global_expert_load)
            old_global_expert_indices_per_model.append(old_global_expert_indices)
        return global_expert_loads, old_global_expert_indices_per_model

    @classmethod
    def get_eep_state(
        cls, parallel_config: ParallelConfig
    ) -> tuple[
        list[torch.Tensor] | None,
        list[torch.Tensor] | None,
        dict[int, int] | None,
    ]:
        num_local_physical_experts = torch.empty(1, dtype=torch.int32, device="cpu")
        torch.distributed.broadcast(
            num_local_physical_experts,
            group=get_ep_group().cpu_group,
            group_src=0,
        )
        num_local_physical_experts = int(num_local_physical_experts.item())
        new_ep_size = get_ep_group().world_size
        global_expert_loads, old_global_expert_indices_per_model = (
            EplbState.recv_state()
        )

        # EP configuration for all models has to be the same so as eplb config
        num_logical_experts = global_expert_loads[0].shape[1]
        parallel_config.eplb_config.num_redundant_experts = (
            num_local_physical_experts * new_ep_size - num_logical_experts
        )
        assert (
            old_global_expert_indices_per_model[0].shape[1] % num_local_physical_experts
            == 0
        )
        old_ep_size = (
            old_global_expert_indices_per_model[0].shape[1]
            // num_local_physical_experts
        )
        rank_mapping = {old_ep_rank: old_ep_rank for old_ep_rank in range(old_ep_size)}
        return (
            global_expert_loads,
            old_global_expert_indices_per_model,
            rank_mapping,
        )

    def _allreduce_list(self, tensor_list: list[torch.Tensor]) -> list[torch.Tensor]:
        """
        All-reduce a list of tensors.
        """
        if len(tensor_list) == 1:
            all_reduce(tensor_list[0], group=get_ep_group().device_group)
            return tensor_list
        assert all(t.dim() == 2 for t in tensor_list), "All tensors must be 2D."
        assert all(t.shape[1] == tensor_list[0].shape[1] for t in tensor_list), (
            "All tensors must have the same shape[1]."
        )
        # Concatenate, all_reduce, then unpack to original shapes.
        # We assume all tensors are 2D and shape[1] (num_physical_experts)
        # is the same across all models.
        shapes = [t.shape for t in tensor_list]
        concat_tensor = torch.cat(tensor_list, dim=0)

        ep_group = get_ep_group().device_group
        all_reduce(concat_tensor, group=ep_group)

        all_reduce_list = []
        offset = 0
        for shape in shapes:
            all_reduce_list.append(concat_tensor[offset : offset + shape[0], :])
            offset += shape[0]
        return all_reduce_list

    def _sync_load_pass(self) -> list[torch.Tensor]:
        """
        Sync the expert load pass across all ranks for log stats.
        Doesn't update the expert load pass in eplb_model_state.
        """
        load_pass_list = []
        for eplb_model_state in self.model_states.values():
            load_pass_list.append(eplb_model_state.expert_load_pass.clone())
        return self._allreduce_list(load_pass_list)