# Copyright 2025 The Baidu team.
# Copyright 2023 The vLLM team.
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# 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.
"""Inference-only Ernie-MTP model."""
from collections.abc import Iterable
from typing import Optional

import torch
import torch.nn as nn
from transformers import PretrainedConfig

from aphrodite.common.sequence import IntermediateTensors
from aphrodite.config import AphroditeConfig, CacheConfig, ModelConfig
from aphrodite.modeling.layers.layernorm import RMSNorm
from aphrodite.modeling.layers.logits_processor import LogitsProcessor
from aphrodite.modeling.layers.sampler import SamplerOutput, get_sampler
from aphrodite.modeling.layers.vocab_parallel_embedding import (
    ParallelLMHead, VocabParallelEmbedding)
from aphrodite.modeling.model_loader.weight_utils import default_weight_loader
from aphrodite.modeling.sampling_metadata import SamplingMetadata
from aphrodite.quantization import QuantizationConfig

from .interfaces import SupportsPP
from .llama import LlamaDecoderLayer
from .utils import is_pp_missing_parameter, maybe_prefix


class ErnieMultiTokenPredictorLayer(nn.Module):

    def __init__(
        self,
        config: PretrainedConfig,
        prefix: str,
        model_config: ModelConfig,
        cache_config: Optional[CacheConfig] = None,
        quant_config: Optional[QuantizationConfig] = None,
    ) -> None:
        super().__init__()

        self.mtp_emb_norm = RMSNorm(config.hidden_size,
                                    eps=config.rms_norm_eps)
        self.mtp_hidden_norm = RMSNorm(config.hidden_size,
                                       eps=config.rms_norm_eps)
        self.mtp_linear_proj = nn.Linear(config.hidden_size * 2,
                                         config.hidden_size,
                                         bias=False)
        self.mtp_block = LlamaDecoderLayer(config, cache_config, quant_config,
                                           prefix)

    def forward(
        self,
        inputs_embeds: torch.Tensor,
        positions: torch.Tensor,
        previous_hidden_states: torch.Tensor,
        spec_step_index: int = 0,
    ) -> torch.Tensor:
        assert inputs_embeds is not None
        # masking inputs at position 0, as not needed by MTP
        inputs_embeds[positions == 0] = 0

        inputs_embeds = self.mtp_emb_norm(inputs_embeds)
        previous_hidden_states = self.mtp_hidden_norm(previous_hidden_states)

        hidden_states = self.mtp_linear_proj(
            torch.cat([inputs_embeds, previous_hidden_states], dim=-1))

        hidden_states, residual = self.mtp_block(positions=positions,
                                                 hidden_states=hidden_states,
                                                 residual=None)
        hidden_states = residual + hidden_states

        return hidden_states


class ErnieMultiTokenPredictor(nn.Module):

    def __init__(self, *, aphrodite_config: AphroditeConfig, prefix: str = ""):
        super().__init__()

        config = aphrodite_config.model_config.hf_config
        self.mtp_start_layer_idx = config.num_hidden_layers
        self.num_mtp_layers = config.num_nextn_predict_layers
        # to map the exact layer index from weights
        self.layers = torch.nn.ModuleDict({
            str(idx):
            ErnieMultiTokenPredictorLayer(
                config,
                f"{prefix}.layers.{idx}",
                model_config=aphrodite_config.model_config,
                cache_config=aphrodite_config.cache_config,
            )
            for idx in range(self.mtp_start_layer_idx,
                             self.mtp_start_layer_idx + self.num_mtp_layers)
        })
        self.embed_tokens = VocabParallelEmbedding(
            config.vocab_size,
            config.hidden_size,
        )
        self.logits_processor = LogitsProcessor(config.vocab_size)

    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        previous_hidden_states: torch.Tensor,
        inputs_embeds: Optional[torch.Tensor] = None,
        spec_step_idx: int = 0,
    ) -> torch.Tensor:
        if inputs_embeds is None:
            inputs_embeds = self.embed_tokens(input_ids)
        return self.layers[str(self.mtp_start_layer_idx + spec_step_idx)](
            inputs_embeds,
            positions,
            previous_hidden_states,
            spec_step_idx,
        )

    def compute_logits(
        self,
        hidden_states: torch.Tensor,
        lm_head: ParallelLMHead,
        sampling_metadata: SamplingMetadata,
        spec_step_idx: int = 0,
    ) -> torch.Tensor:
        self.layers[str(self.mtp_start_layer_idx + spec_step_idx)]
        logits = self.logits_processor(lm_head, hidden_states,
                                       sampling_metadata)
        return logits


class ErnieMTP(nn.Module, SupportsPP):

    def __init__(self, *, aphrodite_config: AphroditeConfig, prefix: str = ""):
        super().__init__()

        self.config = aphrodite_config.model_config.hf_config
        self.model = ErnieMultiTokenPredictor(aphrodite_config=aphrodite_config,
                                              prefix=maybe_prefix(
                                                  prefix, "model"))
        self.lm_head = ParallelLMHead(self.config.vocab_size,
                                      self.config.hidden_size)
        self.sampler = get_sampler()

        if self.config.tie_word_embeddings:
            self.lm_head.weight = self.model.embed_tokens.weight

    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
        intermediate_tensors: Optional[IntermediateTensors] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
        spec_step_idx: int = 0,
    ) -> torch.Tensor:
        assert spec_step_idx == 0, "ernie_mtp only support predict one token"
        hidden_states = self.model(input_ids, positions, hidden_states,
                                   inputs_embeds, spec_step_idx)
        return hidden_states

    def compute_logits(
        self,
        hidden_states: torch.Tensor,
        sampling_metadata: SamplingMetadata,
        spec_step_idx: int = 0,
    ) -> Optional[torch.Tensor]:
        return self.model.compute_logits(hidden_states, self.lm_head,
                                         sampling_metadata, spec_step_idx)

    def sample(
        self,
        logits: torch.Tensor,
        sampling_metadata: SamplingMetadata,
    ) -> Optional[SamplerOutput]:
        next_tokens = self.sampler(logits, sampling_metadata)
        return next_tokens

    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"),
            ("gate_up_proj", "gate_proj", 0),
            ("gate_up_proj", "up_proj", 1),
        ]

        params_dict = dict(self.named_parameters())
        loaded_params: set[str] = set()
        for name, loaded_weight in weights:

            if self.config.tie_word_embeddings and name.endswith(
                    "lm_head.weight"):
                continue
            if "rotary_emb.inv_freq" in name:
                continue
            if "mtp" in name:
                name = self._rewrite_spec_layer_name(self.config, name)

            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 "mtp" not in name:
                    continue
                # We have mlp.experts[0].gate_proj in the checkpoint.
                # Since we handle the experts below in expert_params_mapping,
                # we need to skip here BEFORE we update the name, otherwise
                # name will be updated to mlp.experts[0].gate_up_proj, which
                # will then be updated below in expert_params_mapping
                # for mlp.experts[0].gate_gate_up_proj, which breaks load.
                if (("mlp.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:
                # 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

                # According to DeepSeek-V3 Technical Report, MTP modules
                # shares embedding layer. We only load the first weights.
                if "mtp_" not in name and ("embed_tokens" not in name
                                           and "lm_head" not in name):
                    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

    def _rewrite_spec_layer_name(self, config: PretrainedConfig,
                                 name: str) -> str:
        """
        Rewrite the weight name to match the format of the original model.
        """
        spec_layer_weight_names = [
            "embed_tokens", "mtp_emb_norm", "mtp_hidden_norm",
            "mtp_linear_proj"
        ]
        layer_idx = config.num_hidden_layers
        for weight_name in spec_layer_weight_names:
            if weight_name in name:
                name = name.replace(
                    f"model.{weight_name}.0.",
                    f"model.layers.{layer_idx}.{weight_name}.")
                return name
        name = name.replace("model.mtp_block.0.",
                            f"model.layers.{layer_idx}.mtp_block.")
        return name
