# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.

from abc import ABC, abstractmethod

import torch

from megatron.core import parallel_state
from megatron.core.transformer.mlp import MLPSubmodules
from megatron.core.transformer.module import MegatronModule
from megatron.core.transformer.moe.experts import GroupedMLP, SequentialMLP
from megatron.core.transformer.moe.router import TopKRouter
from megatron.core.transformer.moe.token_dispatcher import MoEDroplessTokenDispatcher
from megatron.core.transformer.transformer_config import TransformerConfig


class BaseMoELayer(MegatronModule, ABC):
    """Base class for a mixture of experts layer.

    Args:
        config (TransformerConfig): Configuration object for the transformer model.
    """

    def __init__(self, config: TransformerConfig):
        super(BaseMoELayer, self).__init__(config)
        self.config = config
        self.expert_parallel_size = parallel_state.get_expert_model_parallel_world_size()
        assert self.config.num_moe_experts % self.expert_parallel_size == 0
        self.num_local_experts = self.config.num_moe_experts // self.expert_parallel_size
        local_expert_indices_offset = (
            parallel_state.get_expert_model_parallel_rank() * self.num_local_experts
        )
        self.local_expert_indices = [
            local_expert_indices_offset + i for i in range(self.num_local_experts)
        ]
        self.router = None
        self.experts = None
        self.token_dispatcher = None

    @abstractmethod
    def forward(self, hidden_states):
        pass


class MoELayer(BaseMoELayer):
    """Mixture of experts Layer **currently only supports no token dropping**.

    Args:
        BaseMoELayer (MegatronModule): Base class for MoE layers
    """

    def __init__(self, config: TransformerConfig, submodules: MLPSubmodules = None):
        self.submodules = submodules
        super(MoELayer, self).__init__(config=config)
        self.router = TopKRouter(
            self.num_local_experts, self.local_expert_indices, config=self.config
        )
        if self.config.moe_grouped_gemm:
            self.experts = GroupedMLP(self.num_local_experts, self.config)
        else:
            assert isinstance(self.submodules, MLPSubmodules)
            self.experts = SequentialMLP(self.num_local_experts, self.config, self.submodules)
        self.token_dispatcher = MoEDroplessTokenDispatcher(
            self.num_local_experts, self.local_expert_indices, config=self.config
        )

    def forward(self, hidden_states: torch.Tensor):
        # process MoE
        scores, indices = self.router(hidden_states)
        (
            dispatched_input,
            tokens_per_expert,
            scores,
            indices,
            global_local_map,
        ) = self.token_dispatcher.token_permutation(hidden_states, scores, indices)
        expert_output, mlp_bias = self.experts(dispatched_input, tokens_per_expert)
        output, mlp_bias = self.token_dispatcher.token_unpermutation(
            expert_output, scores, indices, global_local_map, mlp_bias
        )
        return output, mlp_bias
