# SPDX-License-Identifier: Apache-2.0

import os
from abc import abstractmethod
from enum import Enum
from typing import Callable, List, Optional, Tuple

import torch

from vllm.distributed import (get_tensor_model_parallel_rank,
                              get_tensor_model_parallel_world_size,
                              tensor_model_parallel_all_reduce)
from vllm.logger import init_logger
from vllm.model_executor.custom_op import CustomOp
from vllm.model_executor.layers.quantization.base_config import (
    QuantizationConfig, QuantizeMethodBase)
from vllm.model_executor.utils import set_weight_attrs
from vllm.platforms import current_platform
from vllm.platforms.interface import CpuArchEnum

if current_platform.is_cuda_alike():
    from .fused_moe import fused_experts
else:
    fused_experts = None  # type: ignore
if current_platform.is_tpu():
    # the iterative moe implementation is used until the moe_pallas is fixed
    from .moe_torch_iterative import fused_moe as fused_moe_pallas
else:
    fused_moe_pallas = None  # type: ignore
logger = init_logger(__name__)


class FusedMoeWeightScaleSupported(Enum):
    TENSOR = "tensor"
    CHANNEL = "channel"
    GROUP = "group"
    BLOCK = "block"


class FusedMoEMethodBase(QuantizeMethodBase):

    @abstractmethod
    def create_weights(self, layer: torch.nn.Module, num_experts: int,
                       hidden_size: int, intermediate_size_per_partition: int,
                       params_dtype: torch.dtype, **extra_weight_attrs):
        raise NotImplementedError

    @abstractmethod
    def apply(
        self,
        layer: torch.nn.Module,
        x: torch.Tensor,
        router_logits: torch.Tensor,
        top_k: int,
        renormalize: bool,
        use_grouped_topk: bool = False,
        topk_group: Optional[int] = None,
        num_expert_group: Optional[int] = None,
        custom_routing_function: Optional[Callable] = None,
        scoring_func: str = "softmax",
        e_score_correction_bias: Optional[torch.Tensor] = None,
    ) -> torch.Tensor:
        raise NotImplementedError


@CustomOp.register("unquantized_fused_moe")
class UnquantizedFusedMoEMethod(FusedMoEMethodBase, CustomOp):
    """MoE method without quantization."""

    def create_weights(self, layer: torch.nn.Module, num_experts: int,
                       hidden_size: int, intermediate_size_per_partition: int,
                       params_dtype: torch.dtype, use_nn_moe: bool,
                       **extra_weight_attrs):
        # Fused gate_up_proj (column parallel)
        if not use_nn_moe:
            w13_weight = torch.nn.Parameter(torch.empty(
            num_experts,
            2 * intermediate_size_per_partition,
            hidden_size,
            dtype=params_dtype),
                                        requires_grad=False)
        else:
            w13_weight = torch.nn.Parameter(torch.empty(
            num_experts,
            hidden_size,
            2 * intermediate_size_per_partition,
            dtype=params_dtype),
                                        requires_grad=False)
        layer.register_parameter("w13_weight", w13_weight)
        set_weight_attrs(w13_weight, extra_weight_attrs)

        # down_proj (row parallel)
        if not use_nn_moe:
            w2_weight = torch.nn.Parameter(torch.empty(
                num_experts,
                hidden_size,
                intermediate_size_per_partition,
                dtype=params_dtype),
                                        requires_grad=False)
        else:
            w2_weight = torch.nn.Parameter(torch.empty(
                num_experts,
                intermediate_size_per_partition,
                hidden_size,
                dtype=params_dtype),
                                        requires_grad=False)
        layer.register_parameter("w2_weight", w2_weight)
        set_weight_attrs(w2_weight, extra_weight_attrs)

    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
        super().process_weights_after_loading(layer)

        if current_platform.is_cpu():
            if current_platform.get_cpu_architecture() == CpuArchEnum.X86:
                import intel_extension_for_pytorch as ipex
                layer.ipex_fusion = ipex.llm.modules.GatedMLPMOE(
                    layer.w13_weight,
                    layer.w2_weight,
                    use_prepack=True,
                )
            else:
                raise NotImplementedError("CPU MOE only supports x86 arch.")

    def apply(
        self,
        layer: torch.nn.Module,
        x: torch.Tensor,
        router_logits: torch.Tensor,
        top_k: int,
        renormalize: bool,
        use_grouped_topk: bool = False,
        topk_group: Optional[int] = None,
        num_expert_group: Optional[int] = None,
        custom_routing_function: Optional[Callable] = None,
        scoring_func: str = "softmax",
        e_score_correction_bias: Optional[torch.Tensor] = None,
        use_nn_moe: Optional[bool] = False,
        moe_ep_size: Optional[int] = 1,
        start_expert: Optional[int] = -1,
        end_expert: Optional[int] = -1
    ) -> torch.Tensor:
        return self.forward(x=x,
                            layer=layer,
                            router_logits=router_logits,
                            top_k=top_k,
                            renormalize=renormalize,
                            use_grouped_topk=use_grouped_topk,
                            topk_group=topk_group,
                            num_expert_group=num_expert_group,
                            custom_routing_function=custom_routing_function,
                            scoring_func=scoring_func,
                            e_score_correction_bias=e_score_correction_bias,
                            use_nn_moe=use_nn_moe,
                            moe_ep_size=moe_ep_size,
                            start_expert=start_expert,
                            end_expert=end_expert)

    def forward_cuda(
        self,
        layer: torch.nn.Module,
        x: torch.Tensor,
        use_grouped_topk: bool,
        top_k: int,
        router_logits: torch.Tensor,
        renormalize: bool,
        topk_group: Optional[int] = None,
        num_expert_group: Optional[int] = None,
        custom_routing_function: Optional[Callable] = None,
        scoring_func: str = "softmax",
        e_score_correction_bias: Optional[torch.Tensor] = None,
        use_nn_moe: Optional[bool] = False,
        moe_ep_size: Optional[int] = 1,
        start_expert: Optional[int] = -1,
        end_expert: Optional[int] = -1
    ) -> torch.Tensor:
        topk_weights, topk_ids = FusedMoE.select_experts(
            hidden_states=x,
            router_logits=router_logits,
            use_grouped_topk=use_grouped_topk,
            top_k=top_k,
            renormalize=renormalize,
            topk_group=topk_group,
            num_expert_group=num_expert_group,
            custom_routing_function=custom_routing_function,
            scoring_func=scoring_func,
            e_score_correction_bias=e_score_correction_bias)

        return fused_experts(hidden_states=x,
                             w1=layer.w13_weight,
                             w2=layer.w2_weight,
                             topk_weights=topk_weights,
                             topk_ids=topk_ids,
                             inplace=True,
                             use_nn_moe=use_nn_moe,
                             moe_ep_size=moe_ep_size,
                             start_expert=start_expert,
                             end_expert=end_expert)

    def forward_cpu(
        self,
        layer: torch.nn.Module,
        x: torch.Tensor,
        use_grouped_topk: bool,
        top_k: int,
        router_logits: torch.Tensor,
        renormalize: bool,
        topk_group: Optional[int] = None,
        num_expert_group: Optional[int] = None,
        custom_routing_function: Optional[Callable] = None,
        use_nn_moe: Optional[bool] = False,
        **kwargs,
    ):
        assert custom_routing_function is None
        return layer.ipex_fusion(
            x,
            use_grouped_topk,
            top_k,
            router_logits,
            renormalize,
            topk_group,
            num_expert_group,
        )

    def forward_tpu(
        self,
        layer: torch.nn.Module,
        x: torch.Tensor,
        use_grouped_topk: bool,
        top_k: int,
        router_logits: torch.Tensor,
        renormalize: bool,
        topk_group: Optional[int] = None,
        num_expert_group: Optional[int] = None,
        custom_routing_function: Optional[Callable] = None,
        scoring_func: str = "softmax",
        e_score_correction_bias: Optional[torch.Tensor] = None,
		use_nn_moe: Optional[bool] = False, 
        moe_ep_size: Optional[int] = 1,
        start_expert: Optional[int] = -1,
        end_expert: Optional[int] = -1
    ) -> torch.Tensor:
        assert not use_grouped_topk
        assert num_expert_group is None
        assert topk_group is None
        assert custom_routing_function is None
        if scoring_func != "softmax":
            raise NotImplementedError(
                "Only softmax scoring function is supported for TPU.")
        if e_score_correction_bias is not None:
            raise NotImplementedError(
                "Expert score correction bias is not supported for TPU.")
        return fused_moe_pallas(hidden_states=x,
                                w1=layer.w13_weight,
                                w2=layer.w2_weight,
                                topk=top_k,
                                gating_output=router_logits,
                                renormalize=renormalize)

    forward_native = forward_cuda


class FusedMoE(torch.nn.Module):
    """FusedMoE layer for MoE models.

    This layer contains both MergedColumnParallel weights (gate_up_proj /
    w13) and RowParallelLinear weights (down_proj/ w2).

    Note: Mixtral uses w1, w2, and w3 for gate, up, and down_proj. We
    copy that naming convention here and handle any remapping in the
    load_weights function in each model implementation.

    Args:
        num_experts: Number of experts in the model
        top_k: Number of experts selected for each token
        hidden_size: Input hidden state size of the transformer
        intermediate_size: Intermediate size of the experts
        params_dtype: Data type for the parameters.
        reduce_results: Whether to all all_reduce on the output of the layer
        renomalize: Whether to renormalize the logits in the fused_moe kernel
        quant_config: Quantization configure.
    """

    def __init__(
        self,
        num_experts: int,
        top_k: int,
        hidden_size: int,
        intermediate_size: int,
        params_dtype: Optional[torch.dtype] = None,
        reduce_results: bool = False,
        renormalize: bool = True,
        use_grouped_topk: bool = False,
        num_expert_group: Optional[int] = None,
        topk_group: Optional[int] = None,
        quant_config: Optional[QuantizationConfig] = None,
        tp_size: Optional[int] = None,
        prefix: str = "",
        custom_routing_function: Optional[Callable] = None,
        scoring_func: str = "softmax",
        e_score_correction_bias: Optional[torch.Tensor] = None,
        moe_ep_size: Optional[int] = 1,
    ):
        super().__init__()

        if params_dtype is None:
            params_dtype = torch.get_default_dtype()

        self.tp_size = (tp_size if tp_size is not None else
                        get_tensor_model_parallel_world_size())
        self.top_k = top_k
        self.num_experts = num_experts
        assert intermediate_size % self.tp_size == 0
        self.intermediate_size_per_partition = intermediate_size // self.tp_size
        self.reduce_results = reduce_results
        self.renormalize = renormalize
        self.use_grouped_topk = use_grouped_topk
        if self.use_grouped_topk:
            assert num_expert_group is not None and topk_group is not None
        self.num_expert_group = num_expert_group
        self.topk_group = topk_group
        self.custom_routing_function = custom_routing_function
        self.scoring_func = scoring_func
        self.e_score_correction_bias = e_score_correction_bias

        self.tp_rank = get_tensor_model_parallel_rank()
        self.moe_ep_size = moe_ep_size
        self.moe_tp_rank = self.tp_rank // self.moe_ep_size
        self.moe_tp_size = self.tp_size // self.moe_ep_size
        if self.moe_ep_size > 1:
            self.intermediate_size_per_partition = intermediate_size // self.moe_tp_size
        self.moe_ep_rank = self.tp_rank % self.moe_ep_size
        num_experts_per_node = num_experts // self.moe_ep_size
        self.start_expert = num_experts_per_node * self.moe_ep_rank
        self.end_expert = self.start_expert + num_experts_per_node

        if self.scoring_func != "softmax" and not self.use_grouped_topk:
            raise ValueError("Only softmax scoring function is supported for "
                             "non-grouped topk.")

        if quant_config is None:
            self.quant_method: Optional[QuantizeMethodBase] = (
                UnquantizedFusedMoEMethod())
        else:
            self.quant_method = quant_config.get_quant_method(self, prefix)
        assert self.quant_method is not None

        if quant_config is None:
            # Not considering quant for now, temporarily
            self.use_nn_moe = int(os.environ.get('MOE_NN', 1)) == 1
        else:
            self.use_nn_moe = False
            
        moe_quant_params = {
            "num_experts": num_experts if self.moe_ep_size == 1 else num_experts_per_node,
            "hidden_size": hidden_size,
            "intermediate_size_per_partition":
            self.intermediate_size_per_partition,
            "params_dtype": params_dtype,
            "weight_loader": self.weight_loader,
            "use_nn_moe": self.use_nn_moe,
        }
        # need full intermediate size pre-sharding for WNA16 act order
        if (self.quant_method.__class__.__name__ ==
                "CompressedTensorsWNA16MoEMethod"):
            moe_quant_params["intermediate_size_full"] = intermediate_size
        
        if (self.quant_method.__class__.__name__ in ("BlockInt8MoEMethod")):
            moe_quant_params["intermediate_size"] = self.intermediate_size_per_partition

        self.quant_method.create_weights(layer=self, **moe_quant_params)

    def _load_per_tensor_weight_scale(self, shard_id: str,
                                      param: torch.nn.Parameter,
                                      loaded_weight: torch.Tensor,
                                      expert_id: int):
        param_data = param.data
        # for per tensor weight quantization
        if shard_id in ("w1", "w3"):
            # We have to keep the weight scales of w1 and w3 because
            # we need to re-quantize w1/w3 weights after weight loading.
            idx = 0 if shard_id == "w1" else 1
            param_data[expert_id][idx] = loaded_weight
        # If we are in the row parallel case (down_proj)
        elif shard_id == "w2":
            param_data[expert_id] = loaded_weight

    def _load_model_weight_or_group_weight_scale(self,
                                                 shard_dim: int,
                                                 expert_data: torch.Tensor,
                                                 shard_id: str,
                                                 loaded_weight: torch.Tensor,
                                                 tp_rank: int,
                                                 load_full_w2: bool = False):
        """
        Load grouped weight scales for group quantization or model weights
            :param shard_dim: dimension to shard
            :param expert_data: parameter for a particular expert
            :param shard_id: either w1, w2, or w3
            :param loaded_weight: checkpoint weight to load into the param
            :param tp_rank: tensor parallel rank
            :param load_full_w2: whether or not the w2 loaded should be sharded.
        """
        if shard_id == "w2":
            # In the case where we have actorder/g_idx, we do not partition the
            # w2 scales, as indicated by `load_full` argument, for all tp cases
            self._load_w2(shard_dim=shard_dim,
                          loaded_weight=loaded_weight,
                          expert_data=expert_data,
                          tp_rank=tp_rank,
                          load_full=load_full_w2)
        elif shard_id in ("w1", "w3"):
            self._load_w13(shard_id=shard_id,
                           shard_dim=shard_dim,
                           loaded_weight=loaded_weight,
                           expert_data=expert_data,
                           tp_rank=tp_rank)

    def _load_per_channel_weight_scale(self, expert_data: torch.Tensor,
                                       shard_dim: int, shard_id: str,
                                       loaded_weight: torch.Tensor,
                                       tp_rank: int):
        # for per channel weight quantization
        if shard_id == "w2":
            expert_data.copy_(loaded_weight)
        elif shard_id in ("w1", "w3"):
            self._load_w13(shard_id=shard_id,
                           shard_dim=shard_dim,
                           loaded_weight=loaded_weight,
                           expert_data=expert_data,
                           tp_rank=tp_rank)

    def _load_w13(self, expert_data: torch.Tensor, shard_dim: int,
                  shard_id: str, loaded_weight: torch.Tensor, tp_rank: int):

        # Index the loaded weight for tp sharding.
        # gate_up_proj: "MergedColumnParallel", so tp sharding on output_dim
        shard_size = expert_data.shape[shard_dim] // 2
        loaded_weight = loaded_weight.narrow(shard_dim if not self.use_nn_moe else ~shard_dim,
                                             shard_size * tp_rank,
                                             shard_size)
        # Narrow parameter and load.
        # w1, gate_proj: Load into first logical weight of w13.
        if shard_id == "w1":
            expert_data = expert_data.narrow(shard_dim, 0, shard_size)
        # w3, up_proj: Load into second logical weight of w13.
        else:
            assert shard_id == "w3"
            expert_data = expert_data.narrow(shard_dim, shard_size, shard_size)
        if not self.use_nn_moe:
            expert_data.copy_(loaded_weight)
        else:
            expert_data.copy_(loaded_weight.T)

    def _load_w2(self,
                 expert_data: torch.Tensor,
                 shard_dim: int,
                 loaded_weight: torch.Tensor,
                 tp_rank: int,
                 load_full: bool = False):

        # Index the loaded weight for tp sharding.
        # down_proj: "RowParallel" so tp sharding on input_dim
        # Narrow parameter and load.
        shard_size = expert_data.shape[shard_dim]
        if not load_full:
            loaded_weight = loaded_weight.narrow(shard_dim if not self.use_nn_moe else ~shard_dim,
                                                 shard_size * tp_rank,
                                                 shard_size)
        # w2, down_proj: Load into only logical weight of w2.
        if not self.use_nn_moe:
            expert_data.copy_(loaded_weight)
        else:
            expert_data.copy_(loaded_weight.T)

    def _load_single_value(self, param: torch.nn.Parameter,
                           loaded_weight: torch.Tensor, expert_id: int):
        param_data = param.data

        # Input scales can be loaded directly and should be equal.
        if not self.use_nn_moe:
            param_data[expert_id] = loaded_weight
        else:
            param_data[expert_id] = loaded_weight.T

    def _load_g_idx(self, shard_id: str, expert_data: torch.Tensor,
                    shard_dim: int, loaded_weight: torch.Tensor, tp_rank: int):

        if shard_id == "w2":
            self._load_w2(shard_dim=shard_dim,
                          loaded_weight=loaded_weight,
                          expert_data=expert_data,
                          tp_rank=tp_rank)
        else:
            assert shard_id in ("w1", "w3")
            if not self.use_nn_moe:
                expert_data.copy_(loaded_weight)
            else:
                expert_data.copy_(loaded_weight.T)

    def weight_loader(self, param: torch.nn.Parameter,
                      loaded_weight: torch.Tensor, weight_name: str,
                      shard_id: str, expert_id: int) -> None:

        # compressed-tensors checkpoints with packed weights are stored flipped
        # TODO (mgoin): check self.quant_method.quant_config.quant_format
        # against known CompressionFormat enum values that have this quality
        loaded_weight = loaded_weight.t().contiguous() if (
            self.quant_method.__class__.__name__
            == "CompressedTensorsWNA16MoEMethod") else loaded_weight

        if shard_id not in ("w1", "w2", "w3"):
            raise ValueError(f"shard_id must be ['w1','w2','w3'] but "
                             f"got {shard_id}.")

        WEIGHT_SCALE_SUPPORTED = [
            e.value for e in FusedMoeWeightScaleSupported
        ]
        # Fetch the dim to shard the parameter/loaded weight
        # based on the shard id. This will be whatever
        # dimension intermediate_size_per_partition is used.
        SHARD_ID_TO_SHARDED_DIM = {"w1": 0, "w2": 1, "w3": 0}

        expert_id = expert_id - self.start_expert
        expert_data = param.data[expert_id]
        tp_rank = get_tensor_model_parallel_rank()
        tp_rank = tp_rank // self.moe_ep_size

        # is_transposed: if the dim to shard the weight
        # should be flipped. Required by GPTQ, compressed-tensors
        # should be whatever dimension intermediate_size_per_partition is
        is_transposed = getattr(param, "is_transposed", False) or self.use_nn_moe
        shard_dim = SHARD_ID_TO_SHARDED_DIM[shard_id]
        if is_transposed:
            shard_dim = int(not shard_dim)

        # Case input scale: input_scale loading is only supported for fp8
        if "input_scale" in weight_name:
            # this is needed for compressed-tensors only
            loaded_weight = loaded_weight.to(param.data.device)

            if param.data[expert_id] != 1 and (param.data[expert_id] -
                                               loaded_weight).abs() > 1e-5:
                raise ValueError(
                    "input_scales of w1 and w3 of a layer "
                    f"must be equal. But got {param.data[expert_id]} "
                    f"vs. {loaded_weight}")

            self._load_single_value(param=param,
                                    loaded_weight=loaded_weight,
                                    expert_id=expert_id)
            return

        # Case g_idx
        if "g_idx" in weight_name:
            self._load_g_idx(shard_dim=0,
                             shard_id=shard_id,
                             loaded_weight=loaded_weight,
                             expert_data=expert_data,
                             tp_rank=tp_rank)
            return

        # Case weight scales and zero_points
        if ("scale" in weight_name or "zero" in weight_name):
            # load the weight scales and zp based on the quantization scheme
            # supported weight scales/zp can be found in
            # FusedMoeWeightScaleSupported
            # TODO @dsikka: once hardened, refactor to use vLLM Parameters
            # specific to each case
            quant_method = getattr(param, "quant_method", None)
            if quant_method == FusedMoeWeightScaleSupported.CHANNEL.value:
                self._load_per_channel_weight_scale(
                    shard_id=shard_id,
                    shard_dim=shard_dim,
                    loaded_weight=loaded_weight,
                    expert_data=expert_data,
                    tp_rank=tp_rank)
            elif quant_method in [
                    FusedMoeWeightScaleSupported.GROUP.value,
                    FusedMoeWeightScaleSupported.BLOCK.value,
            ]:
                self._load_model_weight_or_group_weight_scale(
                    shard_id=shard_id,
                    shard_dim=shard_dim,
                    loaded_weight=loaded_weight,
                    expert_data=expert_data,
                    tp_rank=tp_rank,
                    load_full_w2=getattr(param, "load_full_w2", False))
            elif quant_method == FusedMoeWeightScaleSupported.TENSOR.value:
                self._load_per_tensor_weight_scale(shard_id=shard_id,
                                                   param=param,
                                                   loaded_weight=loaded_weight,
                                                   expert_id=expert_id)
            else:
                raise ValueError(
                    f"quant method must be one of {WEIGHT_SCALE_SUPPORTED}")
            return

        # Case weight_shape
        if "weight_shape" in weight_name:
            # only required by compressed-tensors
            self._load_single_value(param=param,
                                    loaded_weight=loaded_weight,
                                    expert_id=expert_id)
            return

        # Case model weights
        if "weight" in weight_name:
            self._load_model_weight_or_group_weight_scale(
                shard_id=shard_id,
                shard_dim=shard_dim,
                loaded_weight=loaded_weight,
                expert_data=expert_data,
                tp_rank=tp_rank)
            return

    @staticmethod
    def select_experts(hidden_states: torch.Tensor,
                       router_logits: torch.Tensor,
                       top_k: int,
                       use_grouped_topk: bool,
                       renormalize: bool,
                       topk_group: Optional[int] = None,
                       num_expert_group: Optional[int] = None,
                       custom_routing_function: Optional[Callable] = None,
                       scoring_func: str = "softmax",
                       e_score_correction_bias: Optional[torch.Tensor] = None):
        from vllm.model_executor.layers.fused_moe.fused_moe import (
            fused_topk, grouped_topk)

        # DeekSeekv2 uses grouped_top_k
        if use_grouped_topk:
            assert topk_group is not None
            assert num_expert_group is not None
            topk_weights, topk_ids = grouped_topk(
                hidden_states=hidden_states,
                gating_output=router_logits,
                topk=top_k,
                renormalize=renormalize,
                num_expert_group=num_expert_group,
                topk_group=topk_group,
                scoring_func=scoring_func,
                e_score_correction_bias=e_score_correction_bias)
        elif custom_routing_function is None:
            topk_weights, topk_ids = fused_topk(hidden_states=hidden_states,
                                                gating_output=router_logits,
                                                topk=top_k,
                                                renormalize=renormalize)
        else:
            topk_weights, topk_ids = custom_routing_function(
                hidden_states=hidden_states,
                gating_output=router_logits,
                topk=top_k,
                renormalize=renormalize)

        return topk_weights, topk_ids

    def forward(self, hidden_states: torch.Tensor,
                router_logits: torch.Tensor):
        assert self.quant_method is not None

        # Matrix multiply.
        final_hidden_states = self.quant_method.apply(
            layer=self,
            x=hidden_states,
            router_logits=router_logits,
            top_k=self.top_k,
            renormalize=self.renormalize,
            use_grouped_topk=self.use_grouped_topk,
            topk_group=self.topk_group,
            num_expert_group=self.num_expert_group,
            custom_routing_function=self.custom_routing_function,
            scoring_func=self.scoring_func,
            e_score_correction_bias=self.e_score_correction_bias,
            use_nn_moe=self.use_nn_moe,
            moe_ep_size=self.moe_ep_size,
            start_expert=self.start_expert,
            end_expert=self.end_expert)

        if self.reduce_results and self.tp_size > 1:
            final_hidden_states = tensor_model_parallel_all_reduce(
                final_hidden_states)

        return final_hidden_states

    @classmethod
    def make_expert_params_mapping(
            cls, ckpt_gate_proj_name: str, ckpt_down_proj_name: str,
            ckpt_up_proj_name: str,
            num_experts: int) -> List[Tuple[str, str, int, str]]:

        return [
            # (param_name, weight_name, expert_id, shard_id)
            ("experts.w13_" if weight_name
             in [ckpt_gate_proj_name, ckpt_up_proj_name] else "experts.w2_",
             f"experts.{expert_id}.{weight_name}.", expert_id, shard_id)
            for expert_id in range(num_experts) for shard_id, weight_name in [
                ("w1", ckpt_gate_proj_name),
                ("w2", ckpt_down_proj_name),
                ("w3", ckpt_up_proj_name),
            ]
        ]
    
    @classmethod
    def make_expert_params_mapping_ep(
            cls, ckpt_gate_proj_name: str, ckpt_down_proj_name: str,
            ckpt_up_proj_name: str,
            num_experts: int,
            moe_ep_size) -> List[Tuple[str, str, int, str]]:

        # tp_size = get_tensor_model_parallel_world_size()
        tp_rank = get_tensor_model_parallel_rank()
        # moe_tp_rank = tp_rank // moe_ep_size
        moe_ep_rank = tp_rank % moe_ep_size
        experts_per_rank = num_experts // moe_ep_size
        experts_range = range(moe_ep_rank * experts_per_rank,
                              (moe_ep_rank + 1) * experts_per_rank)

        return [
            # (param_name, weight_name, expert_id, shard_id)
            ("experts.w13_" if weight_name
             in [ckpt_gate_proj_name, ckpt_up_proj_name] else "experts.w2_",
             f"experts.{expert_id}.{weight_name}.", expert_id, shard_id)
            for expert_id in experts_range for shard_id, weight_name in [
                ("w1", ckpt_gate_proj_name),
                ("w2", ckpt_down_proj_name),
                ("w3", ckpt_up_proj_name),
            ]
        ]

    def _load_fp8_scale(self, param: torch.nn.Parameter,
                        loaded_weight: torch.Tensor, weight_name: str,
                        shard_id: str, expert_id: int) -> None:
        param_data = param.data

        # Input scales can be loaded directly and should be equal.
        if "input_scale" in weight_name:
            if param_data[expert_id] != 1 and (param_data[expert_id] -
                                               loaded_weight).abs() > 1e-5:
                raise ValueError(
                    "input_scales of w1 and w3 of a layer "
                    f"must be equal. But got {param_data[expert_id]} "
                    f"vs. {loaded_weight}")
            param_data[expert_id] = loaded_weight
        # Weight scales
        elif "weight_scale" in weight_name:
            # If we are in merged column case (gate_up_proj)
            if shard_id in ("w1", "w3"):
                # We have to keep the weight scales of w1 and w3 because
                # we need to re-quantize w1/w3 weights after weight loading.
                idx = 0 if shard_id == "w1" else 1
                param_data[expert_id][idx] = loaded_weight
            # If we are in the row parallel case (down_proj)
            else:
                param_data[expert_id] = loaded_weight
