# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# Copyright 2023-2025 SGLang Team
# 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.
# ==============================================================================
# Adapted from https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/models/nemotron_nas.py

"""Inference-only deci model compatible with HuggingFace weights."""

from typing import Iterable, Optional, Tuple, Type, Union

import torch
from torch import nn
from transformers import LlamaConfig

from sglang.srt.distributed import get_pp_group
from sglang.srt.layers.layernorm import RMSNorm
from sglang.srt.layers.logits_processor import LogitsProcessor, LogitsProcessorOutput
from sglang.srt.layers.pooler import Pooler, PoolingType
from sglang.srt.layers.quantization import QuantizationConfig
from sglang.srt.layers.utils import PPMissingLayer
from sglang.srt.layers.vocab_parallel_embedding import (
    DEFAULT_VOCAB_PADDING_SIZE,
    ParallelLMHead,
    VocabParallelEmbedding,
)
from sglang.srt.model_executor.forward_batch_info import ForwardBatch, PPProxyTensors
from sglang.srt.model_loader.weight_utils import (
    default_weight_loader,
    maybe_remap_kv_scale_name,
)
from sglang.srt.models.llama import LlamaAttention, LlamaMLP
from sglang.srt.utils import add_prefix, make_layers
from sglang.utils import logger


def _ffn_mult_to_intermediate_size(ffn_mult: float, n_embd: int) -> int:
    # DeciLM-specific code
    intermediate_size = int(2 * ffn_mult * n_embd / 3)
    return _find_multiple(intermediate_size, 256)


def _find_multiple(n: int, k: int) -> int:
    # DeciLM-specific code
    if n % k == 0:
        return n
    return n + k - (n % k)


class DeciLMDecoderLayer(nn.Module):

    def __init__(
        self,
        config: LlamaConfig,
        layer_idx: int,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
    ) -> None:
        super().__init__()
        block_config = config.block_configs[layer_idx]
        self._is_no_op_attention = block_config.attention.no_op
        self._is_no_op_ffn = block_config.ffn.no_op

        self.hidden_size = config.hidden_size
        rope_theta = config.rope_parameters["rope_theta"]
        rope_scaling = config.rope_parameters
        if rope_scaling is not None and getattr(
            config, "original_max_position_embeddings", None
        ):
            rope_scaling["original_max_position_embeddings"] = (
                config.original_max_position_embeddings
            )
        max_position_embeddings = getattr(config, "max_position_embeddings", 8192)
        # Support abacusai/Smaug-72B-v0.1 with attention_bias
        # Support internlm/internlm-7b with bias
        rope_is_neox_style = getattr(config, "rope_is_neox_style", True)
        attention_bias = getattr(config, "attention_bias", False) or getattr(
            config, "bias", False
        )
        # support internlm/internlm3-8b with qkv_bias
        if hasattr(config, "qkv_bias"):
            attention_bias = config.qkv_bias

        if not self._is_no_op_attention:
            num_kv_heads = (
                config.num_attention_heads // block_config.attention.n_heads_in_group
            )
            self.self_attn = LlamaAttention(
                config=config,
                hidden_size=self.hidden_size,
                num_heads=config.num_attention_heads,
                num_kv_heads=num_kv_heads,
                layer_id=layer_idx,
                rope_theta=rope_theta,
                rope_scaling=rope_scaling,
                rope_is_neox_style=rope_is_neox_style,
                max_position_embeddings=max_position_embeddings,
                quant_config=quant_config,
                prefix=add_prefix("self_attn", prefix),
                bias=attention_bias,
            )
            self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)

        if not self._is_no_op_ffn:
            ffn_mult = block_config.ffn.ffn_mult
            intermediate_size = _ffn_mult_to_intermediate_size(
                ffn_mult, config.hidden_size
            )
            self.mlp = LlamaMLP(
                hidden_size=self.hidden_size,
                intermediate_size=intermediate_size,
                hidden_act=config.hidden_act,
                quant_config=quant_config,
                prefix=add_prefix("mlp", prefix),
            )
            self.post_attention_layernorm = RMSNorm(
                config.hidden_size, eps=config.rms_norm_eps
            )

    def forward(
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
        forward_batch: ForwardBatch,
        residual: Optional[torch.Tensor],
    ) -> Tuple[torch.Tensor, torch.Tensor]:
        # Self Attention

        if self._is_no_op_attention:
            pass
        else:
            if residual is None:
                residual = hidden_states
                hidden_states = self.input_layernorm(hidden_states)
            else:
                hidden_states, residual = self.input_layernorm(hidden_states, residual)
            hidden_states = self.self_attn(
                positions=positions,
                hidden_states=hidden_states,
                forward_batch=forward_batch,
            )

        # Fully Connected
        if not self._is_no_op_ffn:
            hidden_states, residual = self.post_attention_layernorm(
                hidden_states, residual
            )
            hidden_states = self.mlp(hidden_states)
        return hidden_states, residual


class DeciModel(nn.Module):
    def __init__(
        self,
        *,
        config: LlamaConfig,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
        layer_type: Type[DeciLMDecoderLayer] = DeciLMDecoderLayer,
    ):
        super().__init__()

        lora_config = None
        self.config = config
        self.quant_config = quant_config
        self.padding_idx = config.pad_token_id
        lora_vocab = (
            (lora_config.lora_extra_vocab_size * (lora_config.max_loras or 1))
            if lora_config
            else 0
        )
        vocab_size = config.vocab_size + lora_vocab
        if get_pp_group().is_first_rank:
            self.embed_tokens = VocabParallelEmbedding(
                vocab_size,
                config.hidden_size,
                org_num_embeddings=config.vocab_size,
                quant_config=quant_config,
            )
        else:
            self.embed_tokens = PPMissingLayer()

        def get_layer(idx: int, prefix: str):
            return layer_type(
                config,
                layer_idx=idx,
                quant_config=quant_config,
                prefix=prefix,
            )

        self.layers, self.start_layer, self.end_layer = make_layers(
            config.num_hidden_layers,
            get_layer,
            pp_rank=get_pp_group().rank_in_group,
            pp_size=get_pp_group().world_size,
            prefix=add_prefix("layers", prefix),
        )
        if get_pp_group().is_last_rank:
            self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        else:
            self.norm = PPMissingLayer(return_tuple=True)

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

    def forward(
        self,
        input_ids: Optional[torch.Tensor],
        positions: torch.Tensor,
        forward_batch: ForwardBatch,
        inputs_embeds: Optional[torch.Tensor] = None,
        pp_proxy_tensors: Optional[PPProxyTensors] = None,
    ) -> Union[torch.Tensor, PPProxyTensors]:
        if get_pp_group().is_first_rank:
            if inputs_embeds is not None:
                hidden_states = inputs_embeds
            else:
                hidden_states = self.get_input_embeddings(input_ids)
            residual = None
        else:
            assert pp_proxy_tensors is not None
            hidden_states = pp_proxy_tensors["hidden_states"]
            residual = pp_proxy_tensors["residual"]

        kv_cache_index = 0
        for i in range(self.start_layer, self.end_layer):
            layer = self.layers[i]
            if not layer._is_no_op_attention:
                hidden_states, residual = layer(
                    positions, hidden_states, forward_batch, residual
                )
                kv_cache_index += 1
            else:
                hidden_states, residual = layer(
                    positions, hidden_states, forward_batch, residual
                )

        if not get_pp_group().is_last_rank:
            return PPProxyTensors(
                {"hidden_states": hidden_states, "residual": residual}
            )

        hidden_states, _ = self.norm(hidden_states, residual)
        return hidden_states


class DeciLMForCausalLM(nn.Module):
    packed_modules_mapping = {
        "qkv_proj": ["q_proj", "k_proj", "v_proj"],
        "gate_up_proj": ["gate_proj", "up_proj"],
    }

    # LoRA specific attributes
    supported_lora_modules = [
        "qkv_proj",
        "o_proj",
        "gate_up_proj",
        "down_proj",
        "embed_tokens",
        "lm_head",
    ]
    embedding_modules = {
        "embed_tokens": "input_embeddings",
        "lm_head": "output_embeddings",
    }
    embedding_padding_modules = ["lm_head"]

    # Mistral/Llama models can also be loaded with --load-format mistral
    # from consolidated.safetensors checkpoints
    mistral_mapping = {
        "layers": "model.layers",
        "attention": "self_attn",
        "wq": "q_proj",
        "wk": "k_proj",
        "wv": "v_proj",
        "wo": "o_proj",
        "attention_norm": "input_layernorm",
        "feed_forward": "mlp",
        "w1": "gate_proj",
        "w2": "down_proj",
        "w3": "up_proj",
        "ffn_norm": "post_attention_layernorm",
        "tok_embeddings": "model.embed_tokens",
        "output": "lm_head",
        "norm": "model.norm",
    }

    def __init__(
        self,
        *,
        config: LlamaConfig,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
    ):
        super().__init__()
        lora_config = None
        self.config = config
        self.lora_config = lora_config

        self.model = self._init_model(
            config=config, quant_config=quant_config, prefix=add_prefix("model", prefix)
        )
        if self.config.tie_word_embeddings:
            self.lm_head = self.model.embed_tokens
        else:
            self.unpadded_vocab_size = config.vocab_size
            if lora_config:
                self.unpadded_vocab_size += lora_config.lora_extra_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
                    # We need bigger padding if using lora for kernel
                    # compatibility
                    if not lora_config
                    else lora_config.lora_vocab_padding_size
                ),
                quant_config=quant_config,
                prefix=add_prefix("lm_head", prefix),
            )
        self.logits_processor = LogitsProcessor(config)
        self.pooler = Pooler(pooling_type=PoolingType.LAST, normalize=True)

    def _init_model(
        self,
        config: LlamaConfig,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
    ):
        return DeciModel(config=config, quant_config=quant_config, prefix=prefix)

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

    @torch.no_grad()
    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        forward_batch: ForwardBatch,
        inputs_embeds: Optional[torch.Tensor] = None,
        get_embedding: bool = False,
        pp_proxy_tensors: Optional[PPProxyTensors] = None,
    ) -> LogitsProcessorOutput:
        hidden_states = self.model(
            input_ids,
            positions,
            forward_batch,
            inputs_embeds,
            pp_proxy_tensors=pp_proxy_tensors,
        )
        if get_pp_group().is_last_rank:
            if not get_embedding:
                return self.logits_processor(
                    input_ids, hidden_states, self.lm_head, forward_batch
                )
            else:
                return self.pooler(hidden_states, forward_batch)
        else:
            return hidden_states

    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]) -> None:
        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_dict = dict(self.named_parameters())

        for name, loaded_weight in weights:
            if "rotary_emb.inv_freq" in name:
                continue
            if "rotary_emb.cos_cached" in name or "rotary_emb.sin_cached" in name:
                # Models trained using ColossalAI may include these tensors in
                # the checkpoint. Skip them.
                continue
            if self.config.tie_word_embeddings and "lm_head.weight" in name:
                continue
            if self.model.quant_config is not None and (
                scale_name := self.model.quant_config.get_cache_scale(name)
            ):
                # Loading kv cache quantization scales
                param = params_dict[scale_name]
                weight_loader = getattr(param, "weight_loader", default_weight_loader)
                loaded_weight = (
                    loaded_weight if loaded_weight.dim() == 0 else loaded_weight[0]
                )
                weight_loader(param, loaded_weight)
                continue
            if "scale" in name:
                name = maybe_remap_kv_scale_name(name, params_dict)
                if name is None:
                    continue

            for param_name, weight_name, shard_id in stacked_params_mapping:
                if weight_name not 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
                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 name in params_dict.keys():
                    param = params_dict[name]
                    weight_loader = getattr(
                        param, "weight_loader", default_weight_loader
                    )
                    weight_loader(param, loaded_weight)
                else:
                    logger.warning(f"Parameter {name} not found in params_dict")


EntryClass = [DeciLMForCausalLM]
