# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

# Adapted from:
# https://github.com/huggingface/transformers/blob/main/src/transformers/models/olmo_hybrid/modeling_olmo_hybrid.py
# Copyright 2026 The vLLM team.
#
# This code combines OLMo2/OLMo3 attention with Gated DeltaNet linear attention
# for the OLMo Hybrid architecture.
#
# 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 OLMo Hybrid model compatible with HuggingFace weights."""

from collections.abc import Iterable
from functools import partial
from itertools import islice

import torch
from torch import nn

from vllm.compilation.decorators import support_torch_compile
from vllm.config import (
    VllmConfig,
)
from vllm.distributed import (
    get_pp_group,
    get_tensor_model_parallel_rank,
    get_tensor_model_parallel_world_size,
    tensor_model_parallel_all_gather,
)
from vllm.distributed.utils import split_tensor_along_last_dim
from vllm.logger import init_logger
from vllm.model_executor.layers.activation import SiluAndMul
from vllm.model_executor.layers.attention import Attention
from vllm.model_executor.layers.layernorm import RMSNorm
from vllm.model_executor.layers.linear import (
    MergedColumnParallelLinear,
    QKVParallelLinear,
    RowParallelLinear,
)
from vllm.model_executor.layers.logits_processor import LogitsProcessor
from vllm.model_executor.layers.mamba.gdn.olmo_gdn_linear_attn import (
    OlmoHybridGatedDeltaNetAttention,
)
from vllm.model_executor.layers.mamba.mamba_utils import (
    MambaStateCopyFunc,
    MambaStateCopyFuncCalculator,
    MambaStateDtypeCalculator,
    MambaStateShapeCalculator,
)
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 .interfaces import HasInnerState, IsHybrid, SupportsLoRA, SupportsPP
from .utils import (
    AutoWeightsLoader,
    extract_layer_index,
    is_pp_missing_parameter,
    make_empty_intermediate_tensors_factory,
    make_layers,
    maybe_prefix,
)

logger = init_logger(__name__)


class OlmoHybridAttention(nn.Module):
    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
        super().__init__()
        self.config = vllm_config.model_config.hf_config

        hidden_size = self.config.hidden_size
        self.tp_size = get_tensor_model_parallel_world_size()
        self.total_num_heads = self.config.num_attention_heads

        assert hidden_size % self.total_num_heads == 0
        assert self.total_num_heads % self.tp_size == 0

        self.num_heads = self.total_num_heads // self.tp_size
        self.total_num_kv_heads = (
            self.config.num_key_value_heads or self.total_num_heads
        )
        if self.total_num_kv_heads >= self.tp_size:
            assert self.total_num_kv_heads % self.tp_size == 0
        else:
            assert self.tp_size % self.total_num_kv_heads == 0

        self.num_kv_heads = max(1, self.total_num_kv_heads // self.tp_size)
        self.head_dim = 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.max_position_embeddings = self.config.max_position_embeddings

        self.qkv_proj = QKVParallelLinear(
            hidden_size,
            self.head_dim,
            self.total_num_heads,
            self.total_num_kv_heads,
            bias=False,
            quant_config=vllm_config.quant_config,
            prefix=f"{prefix}.qkv_proj",
        )

        self.tp_rank = get_tensor_model_parallel_rank()

        self.k_norm = RMSNorm(
            self.total_num_kv_heads * self.head_dim,
            eps=self.config.rms_norm_eps,
        )
        self.q_norm = RMSNorm(
            self.config.hidden_size,
            eps=self.config.rms_norm_eps,
        )

        self.scaling = self.head_dim**-0.5

        self.attn = Attention(
            self.num_heads,
            self.head_dim,
            self.scaling,
            num_kv_heads=self.num_kv_heads,
            cache_config=vllm_config.cache_config,
            quant_config=vllm_config.quant_config,
            prefix=f"{prefix}.attn",
        )

        rope_parameters = getattr(self.config, "rope_parameters", None)
        self._use_rope = (rope_parameters is not None) and (
            rope_parameters["rope_theta"] is not None
        )

        if self._use_rope:
            self.rotary_emb = get_rope(
                self.head_dim,
                max_position=self.max_position_embeddings,
                rope_parameters=rope_parameters,
            )
        else:
            self.rotary_emb = None

        self.o_proj = RowParallelLinear(
            self.total_num_heads * self.head_dim,
            hidden_size,
            bias=False,
            quant_config=vllm_config.quant_config,
            prefix=f"{prefix}.o_proj",
        )

    def _apply_qk_norm(
        self, q: torch.Tensor, k: torch.Tensor
    ) -> tuple[torch.Tensor, torch.Tensor]:
        if self.tp_size > 1:
            q = tensor_model_parallel_all_gather(q.contiguous())
            k = tensor_model_parallel_all_gather(k.contiguous())
        q = self.q_norm(q)
        k = self.k_norm(k)
        if self.tp_size > 1:
            splitter = partial(split_tensor_along_last_dim, num_partitions=self.tp_size)
            q = splitter(q)[self.tp_rank]
            k = splitter(k)[self.tp_rank]
        return q, k

    def forward(
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
    ) -> torch.Tensor:
        qkv, _ = self.qkv_proj(hidden_states)
        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
        q, k = self._apply_qk_norm(q, k)
        if self._use_rope:
            q, k = self.rotary_emb(positions, q, k)
        attn_output = self.attn(q, k, v)
        output, _ = self.o_proj(attn_output)
        return output


class OlmoHybridMLP(nn.Module):
    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
        super().__init__()
        config = vllm_config.model_config.hf_config
        hidden_size = config.hidden_size
        intermediate_size = config.intermediate_size

        self.gate_up_proj = MergedColumnParallelLinear(
            hidden_size,
            [intermediate_size] * 2,
            bias=False,
            quant_config=vllm_config.quant_config,
            prefix=f"{prefix}.gate_up_proj",
        )

        self.act_fn = SiluAndMul()

        self.down_proj = RowParallelLinear(
            intermediate_size,
            hidden_size,
            bias=False,
            quant_config=vllm_config.quant_config,
            prefix=f"{prefix}.down_proj",
        )

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


class OlmoHybridDecoderLayer(nn.Module):
    def __init__(self, *, vllm_config: VllmConfig, prefix: str = "") -> None:
        super().__init__()
        config = vllm_config.model_config.hf_config

        layer_idx = extract_layer_index(prefix)
        self.layer_type = config.layer_types[layer_idx]
        self.layer_idx = layer_idx

        if self.layer_type == "linear_attention":
            self.linear_attn = OlmoHybridGatedDeltaNetAttention(
                config,
                vllm_config,
                prefix=f"{prefix}.linear_attn",
            )
            self.input_layernorm = RMSNorm(
                config.hidden_size,
                eps=config.rms_norm_eps,
            )
            self.post_attention_layernorm = RMSNorm(
                config.hidden_size,
                eps=config.rms_norm_eps,
            )
        else:
            self.self_attn = OlmoHybridAttention(
                vllm_config=vllm_config,
                prefix=f"{prefix}.self_attn",
            )
            # Attention layers use these norm names
            self.post_attention_layernorm = RMSNorm(
                config.hidden_size,
                eps=config.rms_norm_eps,
            )
            self.post_feedforward_layernorm = RMSNorm(
                config.hidden_size,
                eps=config.rms_norm_eps,
            )

        self.mlp = OlmoHybridMLP(
            vllm_config=vllm_config,
            prefix=f"{prefix}.mlp",
        )

    def forward(
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
    ) -> torch.Tensor:
        if self.layer_type == "linear_attention":
            residual = hidden_states
            hidden_states = self.input_layernorm(hidden_states)

            attn_output = torch.empty_like(hidden_states)
            self.linear_attn(
                hidden_states=hidden_states,
                output=attn_output,
            )
            hidden_states = residual + attn_output

            residual = hidden_states
            hidden_states = self.post_attention_layernorm(hidden_states)
            hidden_states = self.mlp(hidden_states)
            hidden_states = residual + hidden_states
        else:
            residual = hidden_states
            hidden_states = self.self_attn(positions, hidden_states)
            hidden_states = self.post_attention_layernorm(hidden_states)
            hidden_states = residual + hidden_states

            residual = hidden_states
            hidden_states = self.mlp(hidden_states)
            hidden_states = self.post_feedforward_layernorm(hidden_states)
            hidden_states = residual + hidden_states
        return hidden_states


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

        self.embed_tokens = VocabParallelEmbedding(
            self.config.vocab_size,
            self.config.hidden_size,
            prefix=f"{prefix}.embed_tokens",
        )

        self.start_layer, self.end_layer, self.layers = make_layers(
            self.config.num_hidden_layers,
            lambda prefix: OlmoHybridDecoderLayer(
                vllm_config=vllm_config, prefix=prefix
            ),
            prefix=f"{prefix}.layers",
        )

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

        self.make_empty_intermediate_tensors = make_empty_intermediate_tensors_factory(
            ["hidden_states"], self.config.hidden_size
        )

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

        for layer in islice(self.layers, self.start_layer, self.end_layer):
            hidden_states = layer(positions, hidden_states)

        if not get_pp_group().is_last_rank:
            return IntermediateTensors({"hidden_states": hidden_states})

        hidden_states = self.norm(hidden_states)
        return hidden_states

    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),
        ]

        linear_attn_stacked_params_mapping = [
            ("in_proj_qkvg", "q_proj", 0),
            ("in_proj_qkvg", "k_proj", 1),
            ("in_proj_qkvg", "v_proj", 2),
            ("in_proj_qkvg", "g_proj", 3),
            ("conv1d", "q_conv1d", 0),
            ("conv1d", "k_conv1d", 1),
            ("conv1d", "v_conv1d", 2),
        ]

        params_dict = dict(self.named_parameters(remove_duplicate=False))
        loaded_params: set[str] = set()

        for name, loaded_weight in weights:
            if is_pp_missing_parameter(name, self):
                continue

            handled = False

            if "linear_attn" in name:
                for (
                    param_name,
                    weight_name,
                    shard_id,
                ) in linear_attn_stacked_params_mapping:
                    if weight_name not in name:
                        continue
                    mapped_name = name.replace(weight_name, param_name)
                    if mapped_name.endswith(".bias") and (
                        mapped_name not in params_dict
                    ):
                        continue
                    if mapped_name not in params_dict:
                        continue
                    param = params_dict[mapped_name]
                    weight_loader = param.weight_loader
                    weight_loader(param, loaded_weight, shard_id)
                    name = mapped_name
                    handled = True
                    break
            else:
                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)
                    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)
                    handled = True
                    break

            if not handled:
                if name.endswith(".bias") and name not in params_dict:
                    continue
                if name not in params_dict:
                    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 OlmoHybridForCausalLM(
    nn.Module, HasInnerState, SupportsPP, SupportsLoRA, IsHybrid
):
    packed_modules_mapping = {
        "qkv_proj": ["q_proj", "k_proj", "v_proj"],
        "gate_up_proj": ["gate_proj", "up_proj"],
        "in_proj_qkvg": ["q_proj", "k_proj", "v_proj", "g_proj"],
    }

    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
        super().__init__()
        config = vllm_config.model_config.hf_config
        self.config = config
        self.vllm_config = vllm_config
        self.model_config = vllm_config.model_config

        self.model = OlmoHybridModel(
            vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")
        )

        if config.tie_word_embeddings:
            self.lm_head = self.model.embed_tokens
        else:
            self.lm_head = ParallelLMHead(
                config.vocab_size,
                config.hidden_size,
                quant_config=vllm_config.quant_config,
                prefix=maybe_prefix(prefix, "lm_head"),
            )

        self.logits_processor = LogitsProcessor(config.vocab_size)
        self.make_empty_intermediate_tensors = (
            self.model.make_empty_intermediate_tensors
        )

    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
        return self.model.embed_input_ids(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 | IntermediateTensors:
        hidden_states = self.model(
            input_ids=input_ids,
            positions=positions,
            intermediate_tensors=intermediate_tensors,
            inputs_embeds=inputs_embeds,
        )
        return hidden_states

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

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

    @classmethod
    def get_mamba_state_shape_from_config(
        cls, vllm_config: "VllmConfig"
    ) -> tuple[tuple[int, int], tuple[int, int]]:
        parallel_config = vllm_config.parallel_config
        hf_config = vllm_config.model_config.hf_config
        tp_size = parallel_config.tensor_parallel_size
        num_spec = (
            vllm_config.speculative_config.num_speculative_tokens
            if vllm_config.speculative_config
            else 0
        )
        return MambaStateShapeCalculator.gated_delta_net_state_shape(
            tp_size,
            hf_config.linear_num_key_heads,
            hf_config.linear_num_value_heads,
            hf_config.linear_key_head_dim,
            hf_config.linear_value_head_dim,
            hf_config.linear_conv_kernel_dim,
            num_spec,
        )

    @classmethod
    def get_mamba_state_copy_func(cls) -> tuple[MambaStateCopyFunc, MambaStateCopyFunc]:
        return MambaStateCopyFuncCalculator.gated_delta_net_state_copy_func()

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