from __future__ import annotations

import logging
from dataclasses import dataclass
from typing import TYPE_CHECKING, Optional

import torch
import triton

from sglang.kernels.ops.attention.metadata import get_num_kv_splits_triton
from sglang.kernels.ops.kvcache.kv_indices import (
    create_flashinfer_kv_indices_triton,
)
from sglang.srt.layers.attention.base_attn_backend import AttentionBackend
from sglang.srt.model_executor.forward_batch_info import ForwardBatch, ForwardMode
from sglang.srt.runtime_context import get_parallel
from sglang.srt.utils import get_bool_env_var, get_device_core_count

if TYPE_CHECKING:
    from sglang.srt.layers.radix_attention import RadixAttention
    from sglang.srt.model_executor.model_runner import ModelRunner
    from sglang.srt.speculative.spec_info import SpecInput

logger = logging.getLogger(__name__)


@dataclass
class ForwardMetadata:
    attn_logits: torch.Tensor
    attn_lse: torch.Tensor
    max_extend_len: int
    num_kv_splits: torch.Tensor
    kv_indptr: torch.Tensor
    kv_indices: torch.Tensor
    qo_indptr: torch.Tensor
    custom_mask: torch.Tensor
    mask_indptr: torch.Tensor


class WaveAttnBackend(AttentionBackend):
    def __init__(
        self,
        model_runner: ModelRunner,
        skip_prefill: bool = False,
        kv_indptr_buf: Optional[torch.Tensor] = None,
    ):
        # Lazy import to avoid the initialization of cuda context
        from sglang.srt.layers.attention.wave_ops.decode_attention import (
            decode_attention_fwd,
        )
        from sglang.srt.layers.attention.wave_ops.extend_attention import (
            extend_attention_wave,
        )

        super().__init__()

        # Set unique cache dir for each process to avoid cache write races
        import wave_lang.kernel.wave.cache as cache

        base_cache_dir = cache.CACHE_BASE_DIR
        new_dir = base_cache_dir / f"worker_{model_runner.tp_rank}"
        logger.info(f"Setting Wave cache dir: {new_dir}")
        cache.CACHE_BASE_DIR = new_dir

        self.decode_attention_fwd = decode_attention_fwd
        self.extend_attention_fwd = extend_attention_wave

        self.skip_prefill = skip_prefill

        # Pool refs — captured at construction so they survive deletion of the
        # corresponding ForwardBatch fields.
        self.req_to_token_pool = model_runner.req_to_token_pool
        self.token_to_kv_pool = model_runner.token_to_kv_pool

        max_bs = model_runner.req_to_token_pool.size

        if kv_indptr_buf is None:
            self.kv_indptr = torch.zeros(
                (max_bs + 1,), dtype=torch.int32, device=model_runner.device
            )
        else:
            self.kv_indptr = kv_indptr_buf

        self.req_to_token = model_runner.req_to_token_pool.req_to_token

        if not self.skip_prefill:
            self.qo_indptr = torch.zeros(
                (max_bs + 1,), dtype=torch.int32, device=model_runner.device
            )

            self.mask_indptr = torch.zeros(
                (max_bs + 1,), dtype=torch.int64, device=model_runner.device
            )

        self.num_draft_tokens = model_runner.server_args.speculative_num_draft_tokens

        self.num_head = (
            model_runner.model_config.num_attention_heads // get_parallel().attn_tp_size
        )
        self.num_kv_head = model_runner.model_config.get_num_kv_heads(
            get_parallel().attn_tp_size
        )

        self.static_kv_splits = get_bool_env_var(
            "SGLANG_TRITON_DECODE_ATTN_STATIC_KV_SPLITS", "false"
        )
        self.max_kv_splits = model_runner.server_args.triton_attention_num_kv_splits
        self.v_head_dim = model_runner.token_to_kv_pool.get_value_buffer(0).shape[-1]

        self.forward_metadata: ForwardMetadata = None

        self.max_context_len = model_runner.model_config.context_len

        self.device = model_runner.device
        self.device_core_count = get_device_core_count(model_runner.gpu_id)

    def get_num_kv_splits(
        self,
        num_kv_splits: torch.Tensor,
        seq_lens: torch.Tensor,
    ):
        num_token, num_seq = num_kv_splits.shape[0], seq_lens.shape[0]
        num_group = num_token // num_seq

        assert (
            num_group * num_seq == num_token
        ), f"num_seq({num_seq}), num_token({num_token}), something goes wrong!"

        if self.static_kv_splits or self.device_core_count <= 0:
            num_kv_splits.fill_(self.max_kv_splits)
            return

        if num_seq < 256:
            SCHEDULE_SEQ = 256
        else:
            SCHEDULE_SEQ = triton.next_power_of_2(num_seq)

        get_num_kv_splits_triton[(1,)](
            num_kv_splits,
            seq_lens,
            num_seq,
            num_group,
            self.num_head,
            self.num_kv_head,
            self.max_kv_splits,
            self.device_core_count,
            MAX_NUM_SEQ=SCHEDULE_SEQ,
        )

    def init_forward_metadata_out_graph(
        self,
        forward_batch: ForwardBatch,
        in_capture: bool = False,
    ):
        bs = forward_batch.batch_size
        req_pool_indices = forward_batch.req_pool_indices
        seq_lens = forward_batch.seq_lens
        forward_mode = forward_batch.forward_mode
        spec_info = forward_batch.spec_info

        if in_capture:
            assert forward_batch.encoder_lens is None, "Not supported"
            # kv buffers come from spec_info rather than the cuda-graph pool.
            if forward_mode.is_decode_or_idle() and spec_info is not None:
                self.forward_metadata = ForwardMetadata(
                    attn_logits=self.cuda_graph_attn_logits,
                    attn_lse=self.cuda_graph_attn_lse,
                    max_extend_len=None,
                    num_kv_splits=self.cuda_graph_num_kv_splits,
                    kv_indptr=spec_info.kv_indptr,
                    kv_indices=spec_info.kv_indices,
                    qo_indptr=None,
                    custom_mask=None,
                    mask_indptr=None,
                )
                return

            self._apply_cuda_graph_metadata(
                bs=bs,
                req_pool_indices=req_pool_indices,
                seq_lens=seq_lens,
                forward_mode=forward_mode,
                spec_info=spec_info,
            )
            self.forward_metadata = self._build_cuda_graph_forward_metadata(
                bs, forward_mode, spec_info
            )
        else:
            self._apply_cuda_graph_metadata(
                bs=bs,
                req_pool_indices=req_pool_indices,
                seq_lens=seq_lens,
                forward_mode=forward_mode,
                spec_info=spec_info,
            )

    def init_forward_metadata(self, forward_batch: ForwardBatch):
        """Init auxiliary variables for wave attention backend."""

        bs = forward_batch.batch_size
        kv_indptr = self.kv_indptr
        spec_info = forward_batch.spec_info

        if forward_batch.forward_mode.is_decode_or_idle():
            if spec_info is None:
                kv_indptr[1 : bs + 1] = torch.cumsum(forward_batch.seq_lens, dim=0)
                kv_indptr = kv_indptr[: bs + 1]
                kv_indices = torch.empty(
                    forward_batch.seq_lens_sum, dtype=torch.int32, device=self.device
                )
                create_flashinfer_kv_indices_triton[(bs,)](
                    self.req_to_token,
                    forward_batch.req_pool_indices,
                    forward_batch.seq_lens,
                    kv_indptr,
                    None,
                    kv_indices,
                    self.req_to_token.stride(0),
                )
            else:
                kv_indptr, kv_indices = spec_info.kv_indptr, spec_info.kv_indices
                bs = kv_indptr.shape[0] - 1

            from sglang.srt.layers.attention.wave_ops.decode_attention import (
                decode_attention_intermediate_arrays_shapes,
            )

            attn_logits_shape, attn_logits_max_shape = (
                decode_attention_intermediate_arrays_shapes(
                    bs, self.v_head_dim, self.num_head, self.max_kv_splits
                )
            )
            attn_logits = torch.empty(
                attn_logits_shape,
                dtype=torch.float32,
                device=self.device,
            )
            attn_lse = torch.empty(
                attn_logits_max_shape,
                dtype=torch.float32,
                device=self.device,
            )
            num_kv_splits = torch.empty((bs,), dtype=torch.int32, device=self.device)

            self.get_num_kv_splits(num_kv_splits, forward_batch.seq_lens)

            qo_indptr = None
            custom_mask = None
            mask_indptr = None
            max_extend_len = None
        elif forward_batch.forward_mode.is_target_verify():
            bs = len(forward_batch.req_pool_indices)
            qo_indptr = torch.arange(
                0,
                (1 + bs) * self.num_draft_tokens,
                step=self.num_draft_tokens,
                dtype=torch.int32,
                device=self.device,
            )
            # Different with flashinfer kv_indptr and kv_indices construction
            kv_indptr[1 : bs + 1] = torch.cumsum(forward_batch.seq_lens, dim=0)
            kv_indptr = kv_indptr[: bs + 1]
            kv_indices = torch.empty(
                kv_indptr[-1], dtype=torch.int32, device=self.device
            )
            create_flashinfer_kv_indices_triton[(bs,)](
                self.req_to_token,
                forward_batch.req_pool_indices,
                forward_batch.seq_lens,
                kv_indptr,
                None,
                kv_indices,
                self.req_to_token.stride(0),
            )

            custom_mask = spec_info.custom_mask
            seq_mask_len = self.num_draft_tokens * (
                forward_batch.seq_lens + self.num_draft_tokens
            )
            mask_indptr = self.mask_indptr
            mask_indptr[1 : bs + 1] = torch.cumsum(seq_mask_len[:bs], dim=0)
            mask_indptr = mask_indptr[: bs + 1]
            max_extend_len = self.num_draft_tokens
            num_kv_splits = None
            attn_logits = None
            attn_lse = None
        else:
            kv_indptr[1 : bs + 1] = torch.cumsum(
                forward_batch.extend_prefix_lens, dim=0
            )
            kv_indptr = kv_indptr[: bs + 1]
            kv_indices = torch.empty(
                forward_batch.extend_prefix_lens.sum().item(),
                dtype=torch.int32,
                device=self.device,
            )
            create_flashinfer_kv_indices_triton[(bs,)](
                self.req_to_token,
                forward_batch.req_pool_indices,
                forward_batch.extend_prefix_lens,
                kv_indptr,
                None,
                kv_indices,
                self.req_to_token.stride(0),
            )

            qo_indptr = self.qo_indptr
            qo_indptr[1 : bs + 1] = torch.cumsum(forward_batch.extend_seq_lens, dim=0)
            qo_indptr = qo_indptr[: bs + 1]
            custom_mask = None
            mask_indptr = None
            attn_logits = None
            attn_lse = None
            max_extend_len = torch.max(forward_batch.extend_seq_lens).item()
            num_kv_splits = None

        self.forward_metadata = ForwardMetadata(
            attn_logits,
            attn_lse,
            max_extend_len,
            num_kv_splits,
            kv_indptr,
            kv_indices,
            qo_indptr,
            custom_mask,
            mask_indptr,
        )

    def init_cuda_graph_state(
        self,
        max_bs: int,
        max_num_tokens: int,
        kv_indices_buf: Optional[torch.Tensor] = None,
    ):
        from sglang.srt.layers.attention.wave_ops.decode_attention import (
            decode_attention_intermediate_arrays_shapes,
        )

        attn_logits_shape, attn_logits_max_shape = (
            decode_attention_intermediate_arrays_shapes(
                max_bs, self.v_head_dim, self.num_head, self.max_kv_splits
            )
        )
        self.cuda_graph_attn_logits = torch.zeros(
            attn_logits_shape,
            dtype=torch.float32,
            device=self.device,
        )
        self.cuda_graph_attn_lse = torch.zeros(
            attn_logits_max_shape,
            dtype=torch.float32,
            device=self.device,
        )
        self.cuda_graph_num_kv_splits = torch.full(
            (max_bs,), self.max_kv_splits, dtype=torch.int32, device=self.device
        )
        if kv_indices_buf is None:
            self.cuda_graph_kv_indices = torch.zeros(
                (max_bs * self.max_context_len),
                dtype=torch.int32,
                device=self.device,
            )
        else:
            self.cuda_graph_kv_indices = kv_indices_buf

        if not self.skip_prefill:
            self.cuda_graph_custom_mask = torch.zeros(
                (max_bs * self.max_context_len),
                dtype=torch.uint8,
                device=self.device,
            )

    def _build_cuda_graph_forward_metadata(
        self,
        bs: int,
        forward_mode: ForwardMode,
        spec_info: Optional[SpecInput],
    ) -> ForwardMetadata:
        if forward_mode.is_decode_or_idle():
            return ForwardMetadata(
                attn_logits=self.cuda_graph_attn_logits,
                attn_lse=self.cuda_graph_attn_lse,
                max_extend_len=None,
                num_kv_splits=self.cuda_graph_num_kv_splits,
                kv_indptr=self.kv_indptr[: bs + 1],
                kv_indices=self.cuda_graph_kv_indices,
                qo_indptr=None,
                custom_mask=None,
                mask_indptr=None,
            )
        elif forward_mode.is_target_verify():
            return ForwardMetadata(
                attn_logits=None,
                attn_lse=None,
                max_extend_len=self.num_draft_tokens,
                num_kv_splits=None,
                kv_indptr=self.kv_indptr[: bs + 1],
                kv_indices=self.cuda_graph_kv_indices,
                qo_indptr=self.qo_indptr[: bs + 1],
                custom_mask=self.cuda_graph_custom_mask,
                mask_indptr=self.mask_indptr[: bs + 1],
            )
        else:
            raise ValueError(f"Invalid forward mode: {forward_mode=} for CUDA Graph.")

    def _apply_cuda_graph_metadata(
        self,
        bs: int,
        req_pool_indices: torch.Tensor,
        seq_lens: torch.Tensor,
        forward_mode: ForwardMode,
        spec_info: Optional[SpecInput],
    ):
        """Shared capture+replay body for the cuda-graph init path.

        Public entry: :py:meth:`init_forward_metadata_out_graph`.
        """
        if forward_mode.is_decode_or_idle():
            kv_indptr = self.kv_indptr
            kv_indices = self.cuda_graph_kv_indices
            num_kv_splits = self.cuda_graph_num_kv_splits
            if spec_info is None:
                kv_indptr[1 : bs + 1] = torch.cumsum(seq_lens[:bs], dim=0)
                kv_indptr = kv_indptr[: bs + 1]
                create_flashinfer_kv_indices_triton[(bs,)](
                    self.req_to_token,
                    req_pool_indices[:bs],
                    seq_lens[:bs],
                    kv_indptr,
                    None,
                    kv_indices,
                    self.req_to_token.stride(0),
                )
                num_token = bs
            else:
                kv_indptr[: spec_info.kv_indptr.shape[0]] = spec_info.kv_indptr
                kv_indices[: spec_info.kv_indices.shape[0]] = spec_info.kv_indices
                num_token = spec_info.kv_indptr.shape[0] - 1
            self.get_num_kv_splits(num_kv_splits[:num_token], seq_lens[:bs])
        elif forward_mode.is_target_verify():
            bs = len(req_pool_indices)
            qo_indptr = self.qo_indptr[: bs + 1]
            qo_indptr[: bs + 1] = torch.arange(
                0,
                (1 + bs) * self.num_draft_tokens,
                step=self.num_draft_tokens,
                dtype=torch.int32,
                device=self.device,
            )
            kv_indptr = self.kv_indptr[: bs + 1]
            kv_indptr[1 : bs + 1] = torch.cumsum(seq_lens, dim=0)
            kv_indices = self.cuda_graph_kv_indices
            create_flashinfer_kv_indices_triton[(bs,)](
                self.req_to_token,
                req_pool_indices,
                seq_lens,
                kv_indptr,
                None,
                kv_indices,
                self.req_to_token.stride(0),
            )
            custom_mask = self.cuda_graph_custom_mask
            custom_mask[: spec_info.custom_mask.shape[0]] = spec_info.custom_mask
            seq_mask_len = self.num_draft_tokens * (seq_lens + self.num_draft_tokens)
            mask_indptr = self.mask_indptr[: bs + 1]
            mask_indptr[1 : bs + 1] = torch.cumsum(seq_mask_len, dim=0)
        else:
            raise ValueError(
                f"Invalid forward mode: {forward_mode=} for CUDA Graph replay."
            )

    def get_cuda_graph_seq_len_fill_value(self):
        return 1

    def forward_extend(
        self,
        q: torch.Tensor,
        k: torch.Tensor,
        v: torch.Tensor,
        layer: RadixAttention,
        forward_batch: ForwardBatch,
        save_kv_cache=True,
    ):
        # TODO: reuse the buffer across layers
        if layer.qk_head_dim != layer.v_head_dim:
            o = q.new_empty((q.shape[0], layer.tp_q_head_num * layer.v_head_dim))
        else:
            o = torch.empty_like(q)

        if save_kv_cache:
            self.token_to_kv_pool.set_kv_buffer(
                layer, forward_batch.out_cache_loc, k, v
            )

        max_extend_len = self.forward_metadata.max_extend_len
        computed_max_ext_seq_len = torch.max(forward_batch.extend_seq_lens)
        if computed_max_ext_seq_len != max_extend_len:
            assert len(forward_batch.extend_seq_lens) == 1
            forward_batch.extend_seq_lens[0] = max_extend_len
            forward_batch.seq_lens = max_extend_len

        self.extend_attention_fwd(
            q.view(-1, layer.tp_q_head_num, layer.qk_head_dim),
            k.contiguous(),
            v.contiguous(),
            self.token_to_kv_pool.get_key_buffer(layer.layer_id),
            self.token_to_kv_pool.get_value_buffer(layer.layer_id),
            self.forward_metadata.qo_indptr,
            self.forward_metadata.kv_indptr,
            self.forward_metadata.kv_indices,
            self.forward_metadata.custom_mask,
            self.forward_metadata.mask_indptr,
            self.forward_metadata.max_extend_len,
            o.view(-1, layer.tp_q_head_num, layer.v_head_dim),
            is_causal=True,
            layer_scaling=layer.scaling,
            logit_cap=layer.logit_cap,
        )
        return o

    def forward_decode(
        self,
        q: torch.Tensor,
        k: torch.Tensor,
        v: torch.Tensor,
        layer: RadixAttention,
        forward_batch: ForwardBatch,
        save_kv_cache=True,
    ):
        # During torch.compile, there is a bug in rotary_emb that causes the
        # output value to have a 3D tensor shape. This reshapes the output correctly.
        q = q.reshape(-1, layer.tp_q_head_num * layer.qk_head_dim)

        # TODO: reuse the buffer across layers
        if layer.qk_head_dim != layer.v_head_dim:
            o = q.new_empty((q.shape[0], layer.tp_q_head_num * layer.v_head_dim))
        else:
            o = torch.empty_like(q)

        if save_kv_cache:
            self.token_to_kv_pool.set_kv_buffer(
                layer, forward_batch.out_cache_loc, k, v
            )

        self.decode_attention_fwd(
            q.view(-1, layer.tp_q_head_num, layer.qk_head_dim),
            self.token_to_kv_pool.get_key_buffer(layer.layer_id),
            self.token_to_kv_pool.get_value_buffer(layer.layer_id),
            o.view(-1, layer.tp_q_head_num, layer.v_head_dim),
            self.forward_metadata.kv_indptr,
            self.forward_metadata.kv_indices,
            self.forward_metadata.attn_logits,
            self.forward_metadata.attn_lse,
            self.forward_metadata.num_kv_splits,
            self.max_kv_splits,
            layer.scaling,
            layer.logit_cap,
        )
        return o