# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
"""Inference-only ExaoneMoe MTP model."""

from collections.abc import Iterable

import torch
from torch import nn

from vllm.compilation.decorators import support_torch_compile
from vllm.config import VllmConfig
from vllm.distributed.parallel_state import get_pp_group
from vllm.logger import init_logger
from vllm.model_executor.layers.layernorm import RMSNorm
from vllm.model_executor.layers.linear import ColumnParallelLinear
from vllm.model_executor.layers.logits_processor import LogitsProcessor
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.model_executor.models.exaone_moe import ExaoneMoeDecoderLayer
from vllm.sequence import IntermediateTensors

from .utils import (
    AutoWeightsLoader,
    is_pp_missing_parameter,
    maybe_prefix,
)

logger = init_logger(__name__)

KVCache = tuple[torch.Tensor, torch.Tensor]


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

        model_config = vllm_config.model_config
        quant_config = vllm_config.quant_config
        lora_config = vllm_config.lora_config
        config = model_config.hf_config

        self.config = config
        lora_vocab = (
            (lora_config.lora_extra_vocab_size * (lora_config.max_loras or 1))
            if lora_config
            else 0
        )
        self.vocab_size = config.vocab_size + lora_vocab
        self.org_vocab_size = config.vocab_size

        self.mtp_start_layer_idx = config.num_hidden_layers
        self.num_mtp_layers = getattr(config, "num_nextn_predict_layers", 1)

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

        self.fc = ColumnParallelLinear(
            self.config.hidden_size * 2,
            self.config.hidden_size,
            gather_output=True,
            bias=False,
            return_bias=False,
            quant_config=quant_config,
            prefix=f"{prefix}.fc",
        )
        self.layers = nn.ModuleList(
            ExaoneMoeDecoderLayer(
                vllm_config.model_config.hf_config,
                quant_config=quant_config,
                prefix=f"{prefix}.layers.{idx}",
                mtp_layer=True,
            )
            for idx in range(self.num_mtp_layers)
        )

        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.pre_fc_norm_hidden = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.pre_fc_norm_embedding = RMSNorm(
            config.hidden_size, eps=config.rms_norm_eps
        )

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

    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
        intermediate_tensors: IntermediateTensors | None = None,
        inputs_embeds: torch.Tensor | None = None,
        spec_step_idx: int = 0,
    ) -> torch.Tensor:
        if get_pp_group().is_first_rank:
            if inputs_embeds is None:
                inputs_embeds = self.get_input_embeddings(input_ids)
            assert hidden_states.shape[-1] == inputs_embeds.shape[-1]
            inputs_embeds = self.pre_fc_norm_embedding(inputs_embeds)
            hidden_states = self.pre_fc_norm_hidden(hidden_states)
            hidden_states = torch.cat([inputs_embeds, hidden_states], dim=-1)
            hidden_states = self.fc(hidden_states)
            residual = None
        else:
            assert intermediate_tensors is not None
            hidden_states = intermediate_tensors["hidden_states"]
            residual = intermediate_tensors["residual"]

        current_step_idx = spec_step_idx % self.num_mtp_layers
        hidden_states, residual = self.layers[current_step_idx](
            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.norm(hidden_states, residual)
        return hidden_states

    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
        stacked_params_mapping = [
            # (param_name, shard_name, shard_id)
            ("qkv_proj", "q_proj", "q"),
            ("qkv_proj", "k_proj", "k"),
            ("qkv_proj", "v_proj", "v"),
            ("gate_up_proj", "gate_proj", 0),
            ("gate_up_proj", "up_proj", 1),
        ]

        # Params for weights, fp8 weight scales, fp8 activation scales
        # (param_name, weight_name, expert_id, shard_id)
        params_dict = dict(self.named_parameters())
        loaded_params: set[str] = set()
        for name, loaded_weight in weights:
            if "rotary_emb.inv_freq" in name:
                continue

            for param_name, weight_name, shard_id in stacked_params_mapping:
                if weight_name not in name:
                    continue

                if "mlp.experts" in name:
                    continue

                name = name.replace(weight_name, param_name)
                # Skip loading extra bias for GPTQ models.
                if name.endswith(".bias") and name not in params_dict:
                    continue
                # Skip layers on other devices.
                if is_pp_missing_parameter(name, self):
                    continue
                if name not in params_dict:
                    continue
                param = params_dict[name]
                weight_loader = param.weight_loader
                weight_loader(param, loaded_weight, shard_id)
                break
            else:
                # Skip loading extra bias for GPTQ models.
                if name.endswith(".bias") and name not in params_dict:
                    continue
                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


@support_torch_compile
class ExaoneMoeMTP(nn.Module):
    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
        config = vllm_config.model_config.hf_config
        self.vllm_config = vllm_config
        cache_config = vllm_config.cache_config
        assert not cache_config.enable_prefix_caching, (
            "ExaoneMoeMTP currently does not support prefix caching"
        )

        self.quant_config = vllm_config.quant_config

        super().__init__()
        self.config = config
        self.model = ExaoneMoeMultiTokenPredictor(
            vllm_config=vllm_config, prefix=maybe_prefix(prefix, "mtp")
        )
        self.unpadded_vocab_size = config.vocab_size
        self.lm_head = ParallelLMHead(
            self.unpadded_vocab_size,
            config.hidden_size,
            org_num_embeddings=config.vocab_size,
            # padding_size=DEFAULT_VOCAB_PADDING_SIZE,
            prefix=maybe_prefix(prefix, "lm_head"),
        )
        if config.tie_word_embeddings:
            self.lm_head.weight = self.model.embed_tokens.weight
        self.logits_processor = LogitsProcessor(
            self.unpadded_vocab_size, config.vocab_size
        )

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

    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
        intermediate_tensors: IntermediateTensors | None = None,
        inputs_embeds: torch.Tensor | None = None,
        spec_step_idx: int = 0,
        **kwargs: object,
    ):
        hidden_states = self.model(
            input_ids,
            positions,
            hidden_states,
            intermediate_tensors,
            inputs_embeds,
            spec_step_idx,
        )
        return hidden_states

    def compute_logits(
        self,
        hidden_states: torch.Tensor,
        spec_step_idx: int = 0,
    ) -> torch.Tensor | None:
        return self.logits_processor(self.lm_head, hidden_states)

    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
        shared_weight_names = ["embed_tokens", "lm_head"]

        def remap_weight_names(weights):
            for name, weight in weights:
                if name.startswith("mtp."):
                    name = name.replace("mtp.", "model.")
                elif not any(key in name for key in shared_weight_names):
                    continue
                yield name, weight

        loader = AutoWeightsLoader(self)
        return loader.load_weights(remap_weight_names(weights))
