# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from collections.abc import Iterable
from itertools import islice

import torch
import torch.nn as nn

from vllm.attention.layer import Attention
from vllm.compilation.decorators import support_torch_compile
from vllm.config import CacheConfig, ModelConfig, VllmConfig, get_current_vllm_config
from vllm.distributed import (
    get_ep_group,
    get_pp_group,
    get_tensor_model_parallel_world_size,
)
from vllm.model_executor.layers.activation import SiluAndMul
from vllm.model_executor.layers.fused_moe import FusedMoE
from vllm.model_executor.layers.layernorm import RMSNorm
from vllm.model_executor.layers.linear import (
    MergedColumnParallelLinear,
    QKVParallelLinear,
    ReplicatedLinear,
    RowParallelLinear,
)
from vllm.model_executor.layers.logits_processor import LogitsProcessor
from vllm.model_executor.layers.mamba.mamba_utils import (
    MambaStateDtypeCalculator,
    MambaStateShapeCalculator,
)
from vllm.model_executor.layers.mamba.short_conv import ShortConv
from vllm.model_executor.layers.quantization import QuantizationConfig
from vllm.model_executor.layers.rotary_embedding import get_rope
from vllm.model_executor.layers.vocab_parallel_embedding import (
    ParallelLMHead,
    VocabParallelEmbedding,
)
from vllm.model_executor.model_loader.weight_utils import default_weight_loader
from vllm.sequence import IntermediateTensors
from vllm.transformers_utils.configs import Lfm2MoeConfig

from .interfaces import (
    HasInnerState,
    IsHybrid,
    MixtureOfExperts,
    SupportsLoRA,
    SupportsPP,
    SupportsQuant,
)
from .utils import (
    AutoWeightsLoader,
    PPMissingLayer,
    extract_layer_index,
    is_pp_missing_parameter,
    make_empty_intermediate_tensors_factory,
    make_layers,
    maybe_prefix,
)


class Lfm2MoeMlp(nn.Module):
    def __init__(
        self,
        dim: int,
        ff_dim: int,
        quant_config: QuantizationConfig | None = None,
        prefix: str = "",
    ):
        super().__init__()
        self.w1 = MergedColumnParallelLinear(
            input_size=dim,
            output_sizes=[ff_dim] * 2,
            bias=False,
            quant_config=quant_config,
            prefix=f"{prefix}.w1",
        )
        self.w2 = RowParallelLinear(
            input_size=ff_dim,
            output_size=dim,
            bias=False,
            quant_config=quant_config,
            prefix=f"{prefix}.w2",
        )
        self.act_fn = SiluAndMul()

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        gate_up, _ = self.w1(x)
        x = self.act_fn(gate_up)
        x, _ = self.w2(x)
        return x


class Lfm2MoeSparseMoeBlock(nn.Module):
    def __init__(
        self,
        config: Lfm2MoeConfig,
        quant_config: QuantizationConfig | None = None,
        prefix: str = "",
        enable_eplb: bool = False,
    ):
        super().__init__()
        self.tp_size = get_tensor_model_parallel_world_size()
        self.routed_scaling_factor = config.routed_scaling_factor

        self.ep_group = get_ep_group().device_group
        self.ep_rank = get_ep_group().rank_in_group
        self.ep_size = self.ep_group.size()
        self.n_routed_experts = config.num_experts

        if self.tp_size > self.n_routed_experts:
            raise ValueError(
                f"Tensor parallel size {self.tp_size} is greater than "
                f"the number of experts {self.n_routed_experts}."
            )

        # Load balancing settings.
        vllm_config = get_current_vllm_config()
        eplb_config = vllm_config.parallel_config.eplb_config
        self.enable_eplb = enable_eplb

        self.n_logical_experts = self.n_routed_experts
        self.n_redundant_experts = eplb_config.num_redundant_experts
        self.n_physical_experts = self.n_logical_experts + self.n_redundant_experts
        self.n_local_physical_experts = self.n_physical_experts // self.ep_size

        self.physical_expert_start = self.ep_rank * self.n_local_physical_experts
        self.physical_expert_end = (
            self.physical_expert_start + self.n_local_physical_experts
        )

        self.gate = ReplicatedLinear(
            config.hidden_size,
            config.num_experts,
            bias=False,
            quant_config=quant_config,
            prefix=f"{prefix}.gate",
        )
        if config.use_expert_bias:
            self.gate.e_score_correction_bias = nn.Parameter(
                torch.empty(self.n_routed_experts, dtype=torch.float32)
            )
        else:
            self.gate.e_score_correction_bias = None

        self.experts = FusedMoE(
            num_experts=self.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,  # needed for softmax score func
            num_expert_group=1,
            topk_group=1,
            prefix=f"{prefix}.experts",
            enable_eplb=self.enable_eplb,
            num_redundant_experts=self.n_redundant_experts,
            scoring_func="sigmoid",
            e_score_correction_bias=self.gate.e_score_correction_bias,
        )

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        orig_shape = hidden_states.shape
        hidden_dim = hidden_states.shape[-1]
        hidden_states = hidden_states.view(-1, hidden_dim)

        # router_logits: (num_tokens, n_experts)
        router_logits, _ = self.gate(hidden_states)
        final_hidden_states = (
            self.experts(hidden_states=hidden_states, router_logits=router_logits)
            * self.routed_scaling_factor
        )

        if self.tp_size > 1:
            final_hidden_states = self.experts.maybe_all_reduce_tensor_model_parallel(  # noqa E501
                final_hidden_states
            )

        return final_hidden_states.view(orig_shape)


class Lfm2MoeAttention(nn.Module):
    def __init__(
        self,
        config: Lfm2MoeConfig,
        layer_idx: int,
        hidden_size: int,
        num_heads: int,
        num_kv_heads: int,
        max_position_embeddings: int = 8192,
        cache_config: CacheConfig | None = None,
        quant_config: QuantizationConfig | None = None,
        prefix: str = "",
    ) -> None:
        super().__init__()
        self.layer_idx = layer_idx
        self.hidden_size = hidden_size
        self.num_kv_heads = num_kv_heads
        tp_size = get_tensor_model_parallel_world_size()
        self.total_num_heads = num_heads
        assert self.total_num_heads % tp_size == 0
        self.num_heads = self.total_num_heads // tp_size
        self.total_num_kv_heads = num_kv_heads
        if self.total_num_kv_heads >= tp_size:
            # Number of KV heads is greater than TP size, so we partition
            # the KV heads across multiple tensor parallel GPUs.
            assert self.total_num_kv_heads % tp_size == 0
        else:
            # Number of KV heads is less than TP size, so we replicate
            # the KV heads across multiple tensor parallel GPUs.
            assert tp_size % self.total_num_kv_heads == 0
        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
        self.head_dim = self.hidden_size // self.total_num_heads

        self.q_size = self.num_heads * self.head_dim
        self.kv_size = self.num_kv_heads * self.head_dim
        self.scaling = self.head_dim**-0.5
        self.max_position_embeddings = max_position_embeddings

        self.qkv_proj = QKVParallelLinear(
            hidden_size=self.hidden_size,
            head_size=self.head_dim,
            total_num_heads=self.total_num_heads,
            total_num_kv_heads=self.total_num_kv_heads,
            bias=False,
            quant_config=quant_config,
            prefix=f"{prefix}.qkv_proj",
        )
        self.out_proj = RowParallelLinear(
            input_size=self.total_num_heads * self.head_dim,
            output_size=self.hidden_size,
            bias=False,
            quant_config=quant_config,
            prefix=f"{prefix}.out_proj",
        )
        self.rotary_emb = get_rope(
            self.head_dim,
            max_position=self.max_position_embeddings,
            rope_parameters=config.rope_parameters,
            is_neox_style=True,
        )
        self.attn = Attention(
            self.num_heads,
            self.head_dim,
            self.scaling,
            num_kv_heads=self.num_kv_heads,
            cache_config=cache_config,
            prefix=f"{prefix}.attn",
        )
        self.q_layernorm = RMSNorm(self.head_dim, eps=config.norm_eps)
        self.k_layernorm = RMSNorm(self.head_dim, eps=config.norm_eps)

    def forward(
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
    ) -> torch.Tensor:
        n_tokens, _ = hidden_states.shape
        qkv, _ = self.qkv_proj(hidden_states)
        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
        q = q.view(n_tokens, self.num_heads, self.head_dim).contiguous()
        k = k.view(n_tokens, self.num_kv_heads, self.head_dim).contiguous()
        q = self.q_layernorm(q)
        k = self.k_layernorm(k)
        q, k = self.rotary_emb(positions, q, k)
        q = q.view(n_tokens, self.num_heads * self.head_dim)
        k = k.view(n_tokens, self.num_kv_heads * self.head_dim)
        attn_output = self.attn(q, k, v)
        output, _ = self.out_proj(attn_output)
        return output


class Lfm2MoeAttentionDecoderLayer(nn.Module):
    def __init__(
        self,
        config: Lfm2MoeConfig,
        layer_idx: int,
        model_config: ModelConfig | None = None,
        cache_config: CacheConfig | None = None,
        quant_config: QuantizationConfig | None = None,
        prefix: str = "",
        enable_eplb: bool = False,
    ) -> None:
        super().__init__()
        self.prefix = prefix
        self.config = config
        self.layer_idx = layer_idx

        max_position_embeddings = getattr(config, "max_position_embeddings", 8192)

        self.self_attn = Lfm2MoeAttention(
            config=config,
            layer_idx=layer_idx,
            hidden_size=config.hidden_size,
            num_heads=config.num_attention_heads,
            num_kv_heads=config.num_key_value_heads,
            max_position_embeddings=max_position_embeddings,
            cache_config=cache_config,
            quant_config=quant_config,
            prefix=f"{prefix}.self_attn",
        )

        if layer_idx < config.num_dense_layers:
            self.feed_forward = Lfm2MoeMlp(
                dim=config.hidden_size,
                ff_dim=config.intermediate_size,
                quant_config=quant_config,
                prefix=f"{prefix}.feed_forward",
            )
        else:
            self.feed_forward = Lfm2MoeSparseMoeBlock(
                config=config,
                quant_config=quant_config,
                prefix=f"{prefix}.feed_forward",
                enable_eplb=enable_eplb,
            )

        self.operator_norm = RMSNorm(config.hidden_size, eps=config.norm_eps)
        self.ffn_norm = RMSNorm(config.hidden_size, eps=config.norm_eps)

    def forward(
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
        residual: torch.Tensor | None,
        **kwargs,
    ) -> tuple[torch.Tensor, torch.Tensor]:
        if residual is None:
            residual = hidden_states
            hidden_states = self.operator_norm(hidden_states)
        else:
            hidden_states, residual = self.operator_norm(hidden_states, residual)
        hidden_states = self.self_attn(positions=positions, hidden_states=hidden_states)
        hidden_states, residual = self.ffn_norm(hidden_states, residual)
        return self.feed_forward(hidden_states), residual


class Lfm2MoeShortConvDecoderLayer(nn.Module):
    def __init__(
        self,
        config: Lfm2MoeConfig,
        layer_idx: int,
        model_config: ModelConfig | None = None,
        cache_config: CacheConfig | None = None,
        quant_config: QuantizationConfig | None = None,
        prefix: str = "",
        enable_eplb: bool = False,
    ) -> None:
        super().__init__()
        self.layer_idx = layer_idx
        self.conv = ShortConv(
            config=config,
            dim=config.hidden_size,
            layer_idx=layer_idx,
            model_config=model_config,
            cache_config=cache_config,
            prefix=f"{prefix}.conv",
        )

        if layer_idx < config.num_dense_layers:
            self.feed_forward = Lfm2MoeMlp(
                dim=config.hidden_size,
                ff_dim=config.intermediate_size,
                quant_config=quant_config,
                prefix=f"{prefix}.feed_forward",
            )
        else:
            self.feed_forward = Lfm2MoeSparseMoeBlock(
                config=config,
                quant_config=quant_config,
                prefix=f"{prefix}.feed_forward",
                enable_eplb=enable_eplb,
            )

        self.operator_norm = RMSNorm(config.hidden_size, eps=config.norm_eps)
        self.ffn_norm = RMSNorm(config.hidden_size, eps=config.norm_eps)

    def forward(
        self,
        hidden_states: torch.Tensor,
        residual: torch.Tensor | None,
        **kwargs,
    ):
        if residual is None:
            residual = hidden_states
            hidden_states = self.operator_norm(hidden_states)
        else:
            hidden_states, residual = self.operator_norm(hidden_states, residual)
        output = torch.empty_like(hidden_states)
        self.conv(
            hidden_states,
            output,
        )
        hidden_states, residual = self.ffn_norm(output, residual)
        hidden_states = self.feed_forward(hidden_states)
        return hidden_states, residual


@support_torch_compile
class Lfm2MoeModel(nn.Module):
    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
        super().__init__()

        config = vllm_config.model_config.hf_config
        model_config = vllm_config.model_config
        cache_config = vllm_config.cache_config
        quant_config = vllm_config.quant_config

        parallel_config = vllm_config.parallel_config
        enable_eplb = parallel_config.enable_eplb
        eplb_config = parallel_config.eplb_config
        self.num_redundant_experts = eplb_config.num_redundant_experts

        self.config = config

        self.vocab_size = config.vocab_size

        self.embed_tokens = VocabParallelEmbedding(
            self.vocab_size, config.hidden_size, org_num_embeddings=config.vocab_size
        )

        def get_layer(prefix: str):
            layer_idx = extract_layer_index(prefix)
            is_attn = self.config.layer_types[layer_idx] == "full_attention"
            layer_class = (
                Lfm2MoeAttentionDecoderLayer
                if is_attn
                else Lfm2MoeShortConvDecoderLayer
            )
            return layer_class(
                config,
                layer_idx,
                model_config,
                cache_config,
                quant_config=quant_config,
                prefix=prefix,
                enable_eplb=enable_eplb,
            )

        self.start_layer, self.end_layer, self.layers = make_layers(
            config.num_hidden_layers, get_layer, prefix=f"{prefix}.layers"
        )
        self.make_empty_intermediate_tensors = make_empty_intermediate_tensors_factory(
            ["hidden_states", "residual"], config.hidden_size
        )

        if get_pp_group().is_last_rank:
            self.embedding_norm = RMSNorm(config.hidden_size, eps=config.norm_eps)
        else:
            self.embedding_norm = PPMissingLayer()

    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
        return self.embed_tokens(input_ids)

    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        intermediate_tensors: IntermediateTensors | None = None,
        inputs_embeds: torch.Tensor | None = None,
    ) -> torch.Tensor:
        if get_pp_group().is_first_rank:
            if inputs_embeds is not None:
                hidden_states = inputs_embeds
            else:
                hidden_states = self.embed_input_ids(input_ids)
            residual = None
        else:
            assert intermediate_tensors is not None
            hidden_states = intermediate_tensors["hidden_states"]
            residual = intermediate_tensors["residual"]

        for layer in islice(self.layers, self.start_layer, self.end_layer):
            hidden_states, residual = layer(
                positions=positions,
                hidden_states=hidden_states,
                residual=residual,
            )
        if not get_pp_group().is_last_rank:
            return IntermediateTensors(
                {"hidden_states": hidden_states, "residual": residual}
            )
        hidden_states, _ = self.embedding_norm(hidden_states, residual)
        return hidden_states

    def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
        return FusedMoE.make_expert_params_mapping(
            ckpt_gate_proj_name="w1",
            ckpt_down_proj_name="w2",
            ckpt_up_proj_name="w3",
            num_experts=self.config.num_experts,
            num_redundant_experts=self.num_redundant_experts,
        )

    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
        stacked_params_mapping = [
            (".qkv_proj", ".q_proj", "q"),
            (".qkv_proj", ".k_proj", "k"),
            (".qkv_proj", ".v_proj", "v"),
            (".w1", ".w1", 0),
            (".w1", ".w3", 1),
        ]
        params_dict = dict(self.named_parameters())
        loaded_params: set[str] = set()
        expert_params_mapping = self.get_expert_mapping()
        for name, loaded_weight in weights:
            if "expert_bias" in name:
                name = name.replace("expert_bias", "gate.e_score_correction_bias")

            for param_name, weight_name, shard_id in stacked_params_mapping:
                # Skip non-stacked layers and experts (experts handled below).
                if weight_name not in name:
                    continue

                if ("feed_forward.experts." in name) and name not in params_dict:
                    continue
                name = name.replace(weight_name, param_name)
                # Skip loading extra bias for GPTQ models.
                if (
                    name.endswith(".bias") or name.endswith("_bias")
                ) and name not in params_dict:
                    continue
                # Skip layers on other devices.
                if is_pp_missing_parameter(name, self):
                    continue

                param = params_dict[name]
                weight_loader = param.weight_loader
                weight_loader(param, loaded_weight, shard_id)
                break
            else:
                for mapping in expert_params_mapping:
                    param_name, weight_name, expert_id, shard_id = mapping

                    if weight_name not in name:
                        continue

                    name = name.replace(weight_name, param_name)
                    # Skip layers on other devices.
                    if is_pp_missing_parameter(name, self):
                        continue

                    # Skip loading extra bias for GPTQ models.
                    if (
                        name.endswith(".bias") or name.endswith("_bias")
                    ) and name not in params_dict:
                        continue
                    param = params_dict[name]

                    weight_loader = param.weight_loader
                    weight_loader(
                        param,
                        loaded_weight,
                        name,
                        shard_id=shard_id,
                        expert_id=expert_id,
                    )
                    break
                else:
                    # Skip loading extra bias for GPTQ models.
                    if (
                        name.endswith(".bias") or name.endswith("_bias")
                    ) and name not in params_dict:
                        continue
                    # Skip layers on other devices.
                    if is_pp_missing_parameter(name, self):
                        continue
                    param = params_dict[name]
                    weight_loader = getattr(
                        param, "weight_loader", default_weight_loader
                    )
                    weight_loader(param, loaded_weight)
            loaded_params.add(name)
        return loaded_params


class Lfm2MoeForCausalLM(
    nn.Module,
    HasInnerState,
    SupportsLoRA,
    SupportsPP,
    IsHybrid,
    SupportsQuant,
    MixtureOfExperts,
):
    packed_modules_mapping = {
        "qkv_proj": [
            "q_proj",
            "k_proj",
            "v_proj",
        ],
        "w1": [
            "w1",
            "w3",
        ],
    }

    # LoRA specific attributes
    embedding_modules = {
        "embed_tokens": "input_embeddings",
        "lm_head": "output_embeddings",
    }

    @classmethod
    def get_mamba_state_dtype_from_config(
        cls,
        vllm_config: "VllmConfig",
    ) -> tuple[torch.dtype, ...]:
        return MambaStateDtypeCalculator.short_conv_state_dtype(
            vllm_config.model_config.dtype,
            vllm_config.cache_config.mamba_cache_dtype,
        )

    @classmethod
    def get_mamba_state_shape_from_config(
        cls,
        vllm_config: "VllmConfig",
    ) -> tuple[tuple[int, int]]:
        """Calculate shapes for LFM2's convolutional cache.

        Args:
            vllm_config: vLLM config

        Returns:
            Tuple containing:
            - conv_state_shape: Shape for convolutional state cache
        """
        parallel_config = vllm_config.parallel_config
        hf_config = vllm_config.model_config.hf_config

        return MambaStateShapeCalculator.short_conv_state_shape(
            tp_world_size=parallel_config.tensor_parallel_size,
            intermediate_size=hf_config.hidden_size,
            conv_kernel=hf_config.conv_L_cache,
        )

    def __init__(self, *, vllm_config: VllmConfig, prefix: str = "") -> None:
        config = vllm_config.model_config.hf_config
        quant_config = vllm_config.quant_config
        cache_config = vllm_config.cache_config

        assert not cache_config.enable_prefix_caching, (
            "Lfm2Moe currently does not support prefix caching"
        )

        super().__init__()
        self.config = config
        self.model = Lfm2MoeModel(
            vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")
        )

        if get_pp_group().is_last_rank:
            self.lm_head = ParallelLMHead(
                config.vocab_size,
                config.hidden_size,
                quant_config=quant_config,
                prefix=maybe_prefix(prefix, "lm_head"),
            )
            self.lm_head = self.lm_head.tie_weights(self.model.embed_tokens)
        else:
            self.lm_head = PPMissingLayer()

        self.logits_processor = LogitsProcessor(config.vocab_size)

        self.make_empty_intermediate_tensors = (
            self.model.make_empty_intermediate_tensors
        )

        # Set MoE hyperparameters
        self.expert_weights = []

        self.moe_layers = []
        example_layer = None
        for layer in self.model.layers:
            if isinstance(layer, PPMissingLayer):
                continue

            assert isinstance(
                layer, (Lfm2MoeAttentionDecoderLayer, Lfm2MoeShortConvDecoderLayer)
            )
            if isinstance(layer.feed_forward, Lfm2MoeSparseMoeBlock):
                example_layer = layer.feed_forward
                self.moe_layers.append(layer.feed_forward.experts)

        if example_layer is None:
            raise RuntimeError(
                "No Lfm2MoeSparseMoeBlock layer found in the model.layers."
            )

        self.num_moe_layers = len(self.moe_layers)
        self.num_expert_groups = 1
        self.num_shared_experts = 0
        self.num_logical_experts = example_layer.n_logical_experts
        self.num_physical_experts = example_layer.n_physical_experts
        self.num_local_physical_experts = example_layer.n_local_physical_experts
        self.num_routed_experts = example_layer.n_routed_experts
        self.num_redundant_experts = example_layer.n_redundant_experts

    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
        return self.model.embed_input_ids(input_ids)

    def update_physical_experts_metadata(
        self,
        num_physical_experts: int,
        num_local_physical_experts: int,
    ) -> None:
        assert self.num_local_physical_experts == num_local_physical_experts
        self.num_physical_experts = num_physical_experts
        self.num_local_physical_experts = num_local_physical_experts
        self.num_redundant_experts = num_physical_experts - self.num_logical_experts
        for layer in self.model.layers:
            if isinstance(layer.feed_forward, Lfm2MoeSparseMoeBlock):
                moe = layer.feed_forward
                moe.n_local_physical_experts = num_local_physical_experts
                moe.n_physical_experts = num_physical_experts
                moe.n_redundant_experts = self.num_redundant_experts
                moe.experts.update_expert_map()

    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        intermediate_tensors: IntermediateTensors | None = None,
        inputs_embeds: torch.Tensor | None = None,
        **kwargs,
    ) -> torch.Tensor:
        hidden_states = self.model(
            input_ids, positions, intermediate_tensors, inputs_embeds
        )
        return hidden_states

    def compute_logits(self, hidden_states: torch.Tensor) -> torch.Tensor:
        logits = self.logits_processor(self.lm_head, hidden_states)
        return logits

    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
        loader = AutoWeightsLoader(
            self,
            skip_prefixes=(["lm_head."] if self.config.tie_word_embeddings else None),
        )
        return loader.load_weights(weights)

    def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
        return self.model.get_expert_mapping()