################################################################################
# Copyright(c)2020-2025 Shanghai Biren Technology Co., Ltd. All rights reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
################################################################################

from typing import Optional

import torch
from torch import nn
from transformers import PretrainedConfig

from vllm.distributed import (get_tensor_model_parallel_world_size,
                              tensor_model_parallel_all_reduce)
from vllm.model_executor.layers.fused_moe import FusedMoE
from vllm.model_executor.layers.linear import ReplicatedLinear
from vllm.model_executor.layers.quantization import QuantizationConfig
from vllm.model_executor.models.deepseek_v2 import (DeepseekV2MLP,
                                                    ParallelConfig)
from vllm_br import envs
from vllm_br.utils import get_grandparent_pid


class DeepseekV2MoE(nn.Module):

    def __init__(
        self,
        config: PretrainedConfig,
        parallel_config: ParallelConfig,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
    ):
        super().__init__()
        self.tp_size = get_tensor_model_parallel_world_size()
        self.routed_scaling_factor = config.routed_scaling_factor
        self.n_shared_experts = config.n_shared_experts
        self.static_moe_decoder_max_len = 512

        self.is_sequence_parallel = parallel_config.use_sequence_parallel_moe

        if config.hidden_act != "silu":
            raise ValueError(f"Unsupported activation: {config.hidden_act}. "
                             "Only silu is supported for now.")

        self.gate = ReplicatedLinear(config.hidden_size,
                                     config.n_routed_experts,
                                     bias=False,
                                     quant_config=None,
                                     prefix=f"{prefix}.gate")
        if config.topk_method == "noaux_tc":
            self.gate.e_score_correction_bias = nn.Parameter(
                torch.empty(config.n_routed_experts, device="cpu"))
        else:
            self.gate.e_score_correction_bias = None

        self.experts = FusedMoE(
            num_experts=config.n_routed_experts,
            top_k=config.num_experts_per_tok,
            hidden_size=config.hidden_size,
            intermediate_size=config.moe_intermediate_size,
            reduce_results=False,
            renormalize=config.norm_topk_prob,
            quant_config=quant_config,
            use_grouped_topk=True,
            num_expert_group=config.n_group,
            topk_group=config.topk_group,
            prefix=f"{prefix}.experts",
            scoring_func=config.scoring_func,
            e_score_correction_bias=self.gate.e_score_correction_bias)

        if config.n_shared_experts is not None:
            intermediate_size = (config.moe_intermediate_size *
                                 config.n_shared_experts)
            self.shared_experts = DeepseekV2MLP(
                hidden_size=config.hidden_size,
                intermediate_size=intermediate_size,
                hidden_act=config.hidden_act,
                quant_config=quant_config,
                reduce_results=False,
                prefix=f"{prefix}.shared_experts",
            )

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        if envs.VLLM_BR_USE_CPU_ALL_REDUCE != 0 and not hasattr(
                self, "grandparent_pid"):
            self.grandparent_pid = get_grandparent_pid()
        orig_shape = hidden_states.shape
        assert self.n_shared_experts is not None, 'n_shared_experts must be set'
        # NOTE: gate has been fused with shared_experts, no more single gate call
        # and we packed router weights, shared_experts weights and down weights in a tuple
        tuple_router_shared_expert_weight = (
            self.gate.weight, self.shared_experts.gate_up_proj.weight,
            self.shared_experts.down_proj.weight)
        hidden_states = hidden_states.view(-1, orig_shape[-1])

        final_hidden_states = self.experts(
            hidden_states=hidden_states,
            router_logits=tuple_router_shared_expert_weight)

        if hasattr(final_hidden_states, 'all_reduced'):
            # NOTE: this flag indicates that the final_hidden_states has been reduced in fused_moe
            delattr(final_hidden_states, 'all_reduced')
        elif self.tp_size > 1:
            final_hidden_states = tensor_model_parallel_all_reduce(
                final_hidden_states)
        return final_hidden_states.view(orig_shape)