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

from typing import TYPE_CHECKING, NamedTuple, Optional

if TYPE_CHECKING:
    from vllm.attention.backends.abstract import AttentionBackend

import torch
from torch import nn
from torch.nn.parameter import Parameter

from vllm import envs
from vllm.attention.backends.abstract import AttentionMetadata
from vllm.config import CacheConfig, ModelConfig, get_current_vllm_config
from vllm.distributed.parallel_state import (
    get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size)
from vllm.forward_context import ForwardContext, get_forward_context
from vllm.model_executor.custom_op import CustomOp
from vllm.model_executor.layers.layernorm import RMSNorm
from vllm.model_executor.layers.linear import (ColumnParallelLinear,
                                               MergedColumnParallelLinear,
                                               RowParallelLinear)
from vllm.model_executor.layers.mamba.abstract import MambaBase
from vllm.model_executor.layers.mamba.mamba_utils import (
    MambaStateDtypeCalculator, MambaStateShapeCalculator)
from vllm.model_executor.layers.mamba.ops.causal_conv1d import (
    causal_conv1d_fn, causal_conv1d_update)
from vllm.model_executor.layers.mamba.ops.mamba_ssm import (
    selective_scan_fn, selective_state_update)
from vllm.model_executor.models.mamba_cache import MambaCacheParams
from vllm.model_executor.utils import set_weight_attrs
from vllm.platforms import current_platform
from vllm.utils import direct_register_custom_op
from vllm.v1.attention.backends.mamba1_attn import Mamba1AttentionMetadata


# Adapted from transformers.models.mamba.modeling_mamba.MambaMixer
@CustomOp.register("mamba_mixer")
class MambaMixer(MambaBase, CustomOp):
    """
    Compute ∆, A, B, C, and D the state space parameters and compute
    the `contextualized_states`. A, D are input independent
    (see Mamba paper [1] Section 3.5.2 "Interpretation of A"
    for why A isn't selective) ∆, B, C are input-dependent
    (this is a key difference between Mamba and the linear time
    invariant S4, and is why Mamba is called
    **selective** state spaces)
    """

    def __init__(self,
                 hidden_size: int,
                 ssm_state_size: int,
                 conv_kernel_size: int,
                 intermediate_size: int,
                 time_step_rank: int,
                 use_conv_bias: bool,
                 use_bias: bool,
                 use_rms_norm: bool,
                 rms_norm_has_weight: bool = True,
                 rms_norm_eps: float = 1e-5,
                 activation="silu",
                 is_lora_enabled: bool = False,
                 model_config: Optional[ModelConfig] = None,
                 cache_config: Optional[CacheConfig] = None,
                 prefix: str = ""):
        super().__init__()
        self.time_step_rank = time_step_rank
        self.ssm_state_size = ssm_state_size
        self.use_rms_norm = use_rms_norm
        self.activation = activation
        self.is_lora_enabled = is_lora_enabled
        self.conv_kernel_size = conv_kernel_size
        self.intermediate_size = intermediate_size

        self.conv1d = ColumnParallelLinear(
            input_size=conv_kernel_size,
            output_size=intermediate_size,
            bias=use_conv_bias,
        )
        # unsqueeze to fit conv1d weights shape into the linear weights shape.
        # Can't do this in `weight_loader` since it already exists in
        # `ColumnParallelLinear` and `set_weight_attrs`
        # doesn't allow to override it
        self.conv1d.weight.data = self.conv1d.weight.data.unsqueeze(1)

        self.in_proj = MergedColumnParallelLinear(hidden_size,
                                                  [intermediate_size] * 2,
                                                  bias=use_bias)

        # selective projection used to make dt, B and C input dependent
        self.x_proj = RowParallelLinear(
            intermediate_size,
            time_step_rank + ssm_state_size * 2,
            bias=False,
        )
        # time step projection (discretization) -
        # In the forward we need to apply dt_proj without the bias,
        # as the bias is added in the selective scan kernel.
        self.dt_proj = ColumnParallelLinear(time_step_rank,
                                            intermediate_size,
                                            bias=True,
                                            skip_bias_add=True)

        def weight_loader(param: Parameter, loaded_weight: torch.Tensor):
            tp_rank = get_tensor_model_parallel_rank()
            tp_size = get_tensor_model_parallel_world_size()
            param.data.copy_(
                loaded_weight.data.split(loaded_weight.shape[0] // tp_size,
                                         dim=0)[tp_rank])

        def A_weight_loader(param: Parameter, loaded_weight: torch.Tensor):
            weight_loader(param, -torch.exp(loaded_weight.float()))

        tp_size = get_tensor_model_parallel_world_size()
        self.A = nn.Parameter(
            torch.empty(
                intermediate_size // tp_size,
                ssm_state_size,
                dtype=torch.float32,
            ))
        self.D = nn.Parameter(torch.ones(intermediate_size // tp_size))

        set_weight_attrs(self.D, {"weight_loader": weight_loader})
        set_weight_attrs(self.A, {"weight_loader": A_weight_loader})

        self.out_proj = RowParallelLinear(
            intermediate_size,
            hidden_size,
            bias=use_bias,
            input_is_parallel=True,
        )

        self.dt_layernorm = RMSNorm(
            time_step_rank,
            eps=rms_norm_eps,
            has_weight=rms_norm_has_weight,
        ) if use_rms_norm else None

        self.b_layernorm = RMSNorm(
            ssm_state_size,
            eps=rms_norm_eps,
            has_weight=rms_norm_has_weight,
        ) if use_rms_norm else None

        self.c_layernorm = RMSNorm(
            ssm_state_size,
            eps=rms_norm_eps,
            has_weight=rms_norm_has_weight,
        ) if use_rms_norm else None

        if envs.VLLM_USE_V1:
            compilation_config = get_current_vllm_config().compilation_config
            if prefix in compilation_config.static_forward_context:
                raise ValueError(f"Duplicate layer name: {prefix}")
            compilation_config.static_forward_context[prefix] = self
            # The outer list is for v0 PP virtual engine. Though this code path
            # only runs for v1, we have to do this to unify with the interface
            # of Attention + v0 PP.
            # The inner tuple is (conv_state, ssm_state)
            self.kv_cache = [(torch.tensor([]), torch.tensor([]))]

        self.model_config = model_config
        self.cache_config = cache_config
        self.prefix = prefix

    def _ssm_transform(
            self, x: torch.Tensor
    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
        if self.is_lora_enabled:
            #  Lora kernel requires contiguous tensor.
            ssm_params = self.x_proj(x.contiguous())[0]
        else:
            ssm_params = self.x_proj(x)[0]
        time_step, B, C = torch.split(
            ssm_params,
            [self.time_step_rank, self.ssm_state_size, self.ssm_state_size],
            dim=-1)
        if self.use_rms_norm:
            assert self.dt_layernorm is not None
            assert self.b_layernorm is not None
            assert self.c_layernorm is not None
            time_step = self.dt_layernorm(time_step.contiguous())
            B = self.b_layernorm(B.contiguous())
            C = self.c_layernorm(C.contiguous())
        discrete_time_step = self.dt_proj(time_step)[0].transpose(-2, -1)
        return discrete_time_step, B, C

    def forward(self,
                hidden_states: torch.Tensor,
                output: torch.Tensor,
                mamba_cache_params: Optional[MambaCacheParams] = None):
        if not envs.VLLM_USE_V1:
            CustomOp.forward(self, hidden_states, output, mamba_cache_params)
        else:
            torch.ops.vllm.mamba_mixer(
                hidden_states,
                output,
                self.prefix,
            )

    def forward_native(self,
                       hidden_states: torch.Tensor,
                       output: torch.Tensor,
                       mamba_cache_params: Optional[MambaCacheParams] = None):
        pass

    def forward_cuda(self,
                     hidden_states: torch.Tensor,
                     output: torch.Tensor,
                     mamba_cache_params: Optional[MambaCacheParams] = None):
        """
        Run the Mamba-1 SSM pipeline.

        Steps
        -----
        1. Apply the gated-MLP linear projection to the raw input.
        2. Pass the projected sequence through the convolutional mixing layer.
        3. Feed the result into the State-Space Model (SSM) blocks.
        4. Perform the recurrence y ← SSM(A, B, C, Δ)(x)
           to produce contextual representations.
        5. Project the contextualised sequence back
           to the output embedding dimension.

        Batch handling
        --------------
        Prefill and decode tokens are processed by dedicated CUDA
        kernels for both the convolutional (conv1d) and SSM stages.
        In the case of a mixed batch (containing both prefill and
        decode tokens), both sets of kernels are executed independently
        and their outputs are concatenated before the final output projection.
        """

        forward_context: ForwardContext = get_forward_context()
        attn_metadata = forward_context.attn_metadata

        if envs.VLLM_USE_V1:
            if attn_metadata is not None:
                assert isinstance(attn_metadata, dict)
                attn_metadata = attn_metadata[self.prefix]
                mamba1_metadata = attn_metadata
                assert isinstance(mamba1_metadata, Mamba1AttentionMetadata)
                query_start_loc = mamba1_metadata.query_start_loc
                state_indices_tensor = mamba1_metadata.state_indices_tensor
                self_kv_cache = self.kv_cache[forward_context.virtual_engine]
                conv_state = self_kv_cache[0].transpose(-1, -2)
                ssm_state = self_kv_cache[1]
                has_initial_states = mamba1_metadata.has_initial_states
                num_padded_decodes = mamba1_metadata.num_padded_decodes
        else:
            assert isinstance(attn_metadata, AttentionMetadata)
            assert mamba_cache_params is not None
            conv_state = mamba_cache_params.conv_state
            ssm_state = mamba_cache_params.ssm_state
            state_indices_tensor = mamba_cache_params.state_indices_tensor
            query_start_loc = attn_metadata.query_start_loc
            context_lens_tensor = attn_metadata.context_lens_tensor
            has_initial_states = None
            if context_lens_tensor is not None:
                has_initial_states = context_lens_tensor > 0
            num_padded_decodes = attn_metadata.num_decode_tokens

        # 1. Gated MLP's linear projection
        projected_states = self.in_proj(hidden_states)[0].transpose(-2, -1)
        hidden_states_BC, gate = projected_states.chunk(2, dim=-2)

        conv_weights = self.conv1d.weight.view(self.conv1d.weight.size(0),
                                               self.conv1d.weight.size(2))

        if envs.VLLM_USE_V1 and attn_metadata is None:
            # V1 profile run
            hidden_states_BC = hidden_states_BC.contiguous()
            return self.out_proj(hidden_states_BC.transpose(-2, -1))[0]

        num_prefill_tokens = attn_metadata.num_prefill_tokens  # token count
        num_decode_tokens = attn_metadata.num_decode_tokens
        num_prefills = attn_metadata.num_prefills  # request count
        num_decodes = attn_metadata.num_decode_tokens  # token count (=request)
        has_prefill = num_prefill_tokens > 0
        has_decode = num_decode_tokens > 0
        num_actual_tokens = num_prefill_tokens + num_decode_tokens

        prefill_decode_split = split_batch_to_prefill_and_decode(
            hidden_states_BC,
            gate,
            state_indices_tensor,
            query_start_loc,
            has_initial_states,
            num_prefill_tokens,
            num_decode_tokens,
            num_prefills,
            num_decodes,
            num_padded_decodes,
        )
        hidden_states_BC_p = prefill_decode_split.hidden_states_BC_p
        hidden_states_BC_d = prefill_decode_split.hidden_states_BC_d
        gate_p = prefill_decode_split.gate_p
        gate_d = prefill_decode_split.gate_d
        state_indices_tensor_p = prefill_decode_split.state_indices_tensor_p
        state_indices_tensor_d = prefill_decode_split.state_indices_tensor_d
        query_start_loc_p = prefill_decode_split.query_start_loc_p
        has_initial_states_p = prefill_decode_split.has_initial_states_p

        ssm_outputs = []

        if has_prefill:
            # 2. Convolution sequence transformation
            conv_out_p = causal_conv1d_fn(
                hidden_states_BC_p,
                conv_weights,
                self.conv1d.bias,
                activation=self.activation,
                conv_states=conv_state,
                has_initial_state=has_initial_states_p,
                cache_indices=state_indices_tensor_p,
                query_start_loc=query_start_loc_p)
            # 3. State Space Model sequence transformations.
            discrete_time_step_p, B_p, C_p = self._ssm_transform(
                conv_out_p.transpose(-2, -1))
            time_proj_bias = self._time_proj_bias()

            # 4. Perform the recurrence y ← SSM(A, B, C, Δ)(x)
            scan_out_p = selective_scan_fn(
                conv_out_p,
                ssm_state,
                discrete_time_step_p,
                self.A,
                B_p.transpose(-2, -1),
                C_p.transpose(-2, -1),
                self.D.float(),
                gate_p,
                time_proj_bias,
                delta_softplus=True,
                cache_indices=state_indices_tensor_p,
                has_initial_state=has_initial_states_p,
                query_start_loc=query_start_loc_p)
            ssm_outputs.append(scan_out_p)

        if has_decode:
            # 2. Convolution sequence transformation
            conv_out_d = causal_conv1d_update(
                hidden_states_BC_d.transpose(0, 1),
                conv_state,
                conv_weights,
                self.conv1d.bias,
                self.activation,
                conv_state_indices=state_indices_tensor_d).transpose(0, 1)

            # 3. State Space Model sequence transformation.
            discrete_time_step_d, B_d, C_d = self._ssm_transform(
                conv_out_d.transpose(-2, -1))
            time_proj_bias = self._time_proj_bias()

            # 4. Perform the recurrence y ← SSM(A, B, C, Δ)(x)
            scan_outputs_d = torch.empty_like(
                hidden_states_BC_d.transpose(0, 1))
            selective_state_update(ssm_state,
                                   conv_out_d.transpose(0, 1),
                                   discrete_time_step_d.transpose(0, 1),
                                   self.A,
                                   B_d,
                                   C_d,
                                   self.D,
                                   gate_d.transpose(0, 1),
                                   time_proj_bias,
                                   dt_softplus=True,
                                   state_batch_indices=state_indices_tensor_d,
                                   out=scan_outputs_d)
            scan_outputs_d = scan_outputs_d.transpose(0, 1)

            if envs.VLLM_USE_V1:
                ssm_outputs.insert(0, scan_outputs_d)
            else:
                ssm_outputs.append(scan_outputs_d)

        scan_outputs_combined = ssm_outputs[0] if len(
            ssm_outputs) == 1 else torch.cat(ssm_outputs, dim=-1)

        # 5. Final output projection
        if self.is_lora_enabled:  # Lora kernel requires contiguous tensor.
            scan_outputs_combined = scan_outputs_combined.transpose(
                -2, -1).contiguous()
            out = self.out_proj(scan_outputs_combined)[0]
        else:
            out = self.out_proj(scan_outputs_combined.transpose(-2, -1))[0]

        output[:num_actual_tokens] = out

    def get_state_dtype(self) -> tuple[torch.dtype]:
        assert self.model_config is not None
        assert self.cache_config is not None
        return MambaStateDtypeCalculator.mamba1_state_dtype(
            self.model_config.dtype,
            self.cache_config.mamba_cache_dtype,
            self.cache_config.mamba_ssm_cache_dtype,
        )

    def get_state_shape(self) -> tuple[tuple[int, ...], tuple[int, ...]]:
        return MambaStateShapeCalculator.mamba1_state_shape(
            tp_world_size=get_tensor_model_parallel_world_size(),
            intermediate_size=self.intermediate_size,
            state_size=self.ssm_state_size,
            conv_kernel=self.conv_kernel_size,
        )

    @property
    def mamba_type(self) -> str:
        return "mamba1"

    def get_attn_backend(self) -> type["AttentionBackend"]:
        from vllm.v1.attention.backends.mamba1_attn import (
            Mamba1AttentionBackend)
        return Mamba1AttentionBackend

    def _time_proj_bias(self) -> Optional[torch.Tensor]:
        if hasattr(self.dt_proj, "bias") and self.dt_proj.bias is not None:
            return self.dt_proj.bias.float()
        return None


class PrefillDecodeSplit(NamedTuple):
    hidden_states_BC_p: torch.Tensor
    hidden_states_BC_d: torch.Tensor
    gate_p: torch.Tensor
    gate_d: torch.Tensor
    state_indices_tensor_p: torch.Tensor
    state_indices_tensor_d: torch.Tensor
    query_start_loc_p: Optional[torch.Tensor]
    has_initial_states_p: Optional[torch.Tensor]


def split_batch_to_prefill_and_decode(
    hidden_states_BC: torch.Tensor,
    gate: torch.Tensor,
    state_indices_tensor: torch.Tensor,
    query_start_loc: torch.Tensor,
    has_initial_states: Optional[torch.Tensor],
    num_prefill_tokens: int,
    num_decode_tokens: int,
    num_prefills: int,
    num_decodes: int,
    num_padded_decodes: int,
) -> PrefillDecodeSplit:
    num_actual_tokens = num_prefill_tokens + num_padded_decodes

    if envs.VLLM_USE_V1:
        # In v1, decode tokens come first, then prefill tokens.
        hidden_states_BC_d, hidden_states_BC_p = torch.split(
            hidden_states_BC[..., :num_actual_tokens],
            [num_padded_decodes, num_prefill_tokens],
            dim=-1)
        gate_d, gate_p = torch.split(gate[..., :num_actual_tokens],
                                     [num_padded_decodes, num_prefill_tokens],
                                     dim=-1)

        # num_padded_decodes accounts for CUDA graph padding when applicable
        state_indices_tensor_d, state_indices_tensor_p = torch.split(
            state_indices_tensor[:num_padded_decodes + num_prefills],
            [num_padded_decodes, num_prefills],
            dim=0)
        query_start_loc_p = (query_start_loc[-num_prefills - 1:] -
                             num_padded_decodes if num_prefills > 0 else None)
        has_initial_states_p = has_initial_states[-num_prefills:] if (
            has_initial_states is not None and num_prefills > 0) else None
    else:
        # In v0, prefill tokens come first, then decode tokens.
        hidden_states_BC_p, hidden_states_BC_d = torch.split(
            hidden_states_BC, [num_prefill_tokens, num_decode_tokens], dim=-1)
        gate_p, gate_d = torch.split(gate,
                                     [num_prefill_tokens, num_decode_tokens],
                                     dim=-1)
        state_indices_tensor_p, state_indices_tensor_d = torch.split(
            state_indices_tensor, [num_prefills, num_decodes], dim=0)
        query_start_loc_p = (query_start_loc[:num_prefills +
                                             1] if num_prefills > 0 else None)
        has_initial_states_p = has_initial_states[:num_prefills] if (
            has_initial_states is not None and num_prefills > 0) else None

    return PrefillDecodeSplit(
        hidden_states_BC_p=hidden_states_BC_p,
        hidden_states_BC_d=hidden_states_BC_d,
        gate_p=gate_p,
        gate_d=gate_d,
        state_indices_tensor_p=state_indices_tensor_p,
        state_indices_tensor_d=state_indices_tensor_d,
        query_start_loc_p=query_start_loc_p,
        has_initial_states_p=has_initial_states_p,
    )


def mamba_mixer(
    hidden_states: torch.Tensor,
    output: torch.Tensor,
    layer_name: str,
) -> None:
    forward_context: ForwardContext = get_forward_context()
    self = forward_context.no_compile_layers[layer_name]
    self.forward_cuda(hidden_states=hidden_states,
                      output=output,
                      mamba_cache_params=None)


def mamba_mixer_fake(
    hidden_states: torch.Tensor,
    output: torch.Tensor,
    layer_name: str,
) -> None:
    return


direct_register_custom_op(
    op_name="mamba_mixer",
    op_func=mamba_mixer,
    mutates_args=["output"],
    fake_impl=mamba_mixer_fake,
    dispatch_key=current_platform.dispatch_key,
)
