from typing import TypeVar

import numpy as np
import torch
import torch_npu
from vllm.config import VllmConfig
from vllm.distributed import (
    get_dcp_group,
    get_decode_context_model_parallel_rank,
    get_decode_context_model_parallel_world_size,
    get_pcp_group,
)
from vllm.forward_context import ForwardContext, get_forward_context
from vllm.utils.math_utils import cdiv
from vllm.v1.attention.backend import AttentionCGSupport
from vllm.v1.kv_cache_interface import AttentionSpec, MLAAttentionSpec

from vllm_ascend.attention.attention_v1 import AscendAttentionState

# isort: off
from vllm_ascend.attention.mla_v1 import (
    AscendMLADecodeMetadata,
    AscendMLAImpl,
    AscendMLAMetadata,
    AscendMLAMetadataBuilder,
    AscendMLAPrefillMetadata,
    DecodeMLAPreprocessResult,
    PrefillMLAPreprocessResult,
    BUILD_METADATA_STEP_PREFILL,
)
# isort: on

from vllm_ascend.attention.context_parallel.common_cp import (
    AscendPCPMetadata,
    CPChunkedContextMetadata,
    _npu_attention_update,
    _process_attn_out_lse,
)
from vllm_ascend.attention.utils import AscendCommonAttentionMetadata
from vllm_ascend.compilation.acl_graph import get_draft_graph_params, get_graph_params, update_graph_params_workspaces
from vllm_ascend.utils import weak_ref_tensors

MAX_O_PROJ_PREFETCH_SIZE = 16 * 1024 * 1024

M = TypeVar("M", bound=AscendMLAMetadata)


class AscendMlaCPMetadataBuilder(AscendMLAMetadataBuilder):
    """
    NOTE: Please read the comment at the top of the file before trying to
    understand this class
    """

    def __init__(
        self,
        kv_cache_spec: MLAAttentionSpec,
        layer_names: list[str],
        vllm_config: VllmConfig,
        device: torch.device,
        metadata_cls: type[AscendMLAMetadata] | None = None,
        supports_dcp_with_varlen: bool = False,
    ):
        super().__init__(kv_cache_spec, layer_names, vllm_config, device, metadata_cls, supports_dcp_with_varlen)

        self.pcp_size = get_pcp_group().world_size
        self.pcp_rank = get_pcp_group().rank_in_group if self.pcp_size > 1 else 0
        self.dcp_size = get_decode_context_model_parallel_world_size()
        self.dcp_rank = get_decode_context_model_parallel_rank() if self.dcp_size > 1 else 0
        self.cp_local_block_size = vllm_config.parallel_config.cp_kv_cache_interleave_size
        self.cp_virtual_block_size = self.cp_local_block_size * self.dcp_size * self.pcp_size
        self.block_size = (self.block_size * self.cp_virtual_block_size) // np.gcd(
            self.block_size, self.cp_virtual_block_size
        )

    def build(
        self,
        common_prefix_len: int,
        common_attn_metadata: AscendCommonAttentionMetadata,
        fast_build: bool = False,
    ) -> AscendMLAMetadata:
        metadata_cls = super().build(common_prefix_len, common_attn_metadata)
        if self.pcp_size > 1:
            self.slot_mapping[: self.num_decode_tokens] = self.slot_mapping[
                : self.num_decode_tokens * self.pcp_size : self.pcp_size
            ]
            self.slot_mapping[self.num_decode_tokens : self.num_decode_tokens * self.pcp_size].fill_(-1)
        metadata_cls.slot_mapping = self.slot_mapping
        return metadata_cls

    @classmethod
    def get_cudagraph_support(
        cls: type["AscendMlaCPMetadataBuilder"],
        vllm_config: VllmConfig,
        kv_cache_spec: AttentionSpec,
    ) -> AttentionCGSupport:
        # Explicit override in case the underlying builder specialized this getter.
        # @override omitted only because of mypy limitation due to type variable.
        return AttentionCGSupport.UNIFORM_BATCH

    def set_num_actual_tokens(
        self,
        common_attn_metadata: AscendCommonAttentionMetadata,
    ):
        long_seq_metadata = common_attn_metadata.prefill_context_parallel_metadata
        if long_seq_metadata is None:
            raise AssertionError("long_seq_metadata should not be None.")

        # In dcp only spec decode graph padding case,
        # num_actual_tokens_pcp_padded may be less than num_actual_tokens
        self.num_actual_tokens = max(
            long_seq_metadata.num_actual_tokens_pcp_padded, common_attn_metadata.num_actual_tokens
        )

    def build_cp_metadata(
        self,
        common_prefix_len: int,
        common_attn_metadata: AscendCommonAttentionMetadata,
    ) -> AscendPCPMetadata | None:
        common_long_seq_metadata = common_attn_metadata.prefill_context_parallel_metadata
        assert common_long_seq_metadata is not None
        return AscendPCPMetadata(
            q_head_idx=common_long_seq_metadata.q_head_idx_tensor,
            q_tail_idx=common_long_seq_metadata.q_tail_idx_tensor,
            kv_with_q_head_nomask_idx=common_long_seq_metadata.kv_with_q_head_nomask_idx_tensor,
            kv_with_q_head_mask_idx=common_long_seq_metadata.kv_with_q_head_mask_idx_tensor,
            kv_with_q_tail_nomask_idx=common_long_seq_metadata.kv_with_q_tail_nomask_idx_tensor,
            kv_with_q_tail_mask_idx=common_long_seq_metadata.kv_with_q_tail_mask_idx_tensor,
            attn_mask_seqlens=common_long_seq_metadata.attn_mask_seqlens,
            head_attn_nomask_seqlens=common_long_seq_metadata.head_attn_nomask_seqlens,
            tail_attn_nomask_seqlens=common_long_seq_metadata.tail_attn_nomask_seqlens,
            q_full_idx=common_long_seq_metadata.q_full_idx,
            pcp_allgather_restore_idx=common_long_seq_metadata.pcp_allgather_restore_idx,
        )

    def build_chunked_metadata(
        self,
        common_prefix_len: int,
        common_attn_metadata: AscendCommonAttentionMetadata,
    ):
        chunked_context_metadata = super().build_chunked_metadata(common_prefix_len, common_attn_metadata)
        if chunked_context_metadata is None:
            return None

        long_seq_metadata = common_attn_metadata.prefill_context_parallel_metadata
        assert long_seq_metadata is not None
        num_computed_tokens_of_pcp_dcp = long_seq_metadata.num_computed_tokens_of_pcp_dcp
        assert num_computed_tokens_of_pcp_dcp is not None
        local_context_lens_allranks = torch.tensor(num_computed_tokens_of_pcp_dcp[self.num_decodes_flatten :]).reshape(
            -1, self.dcp_size * self.pcp_size
        )
        # Note(qcs): The max local context lengths
        # padded to `cp_local_block_size`.
        padded_local_context_lens_cpu = (
            cdiv(
                self.context_lens_cpu,
                self.cp_virtual_block_size,
            )
            * self.cp_local_block_size
        )
        padded_local_max_context_chunk_across_ranks = (
            cdiv(
                self.max_context_chunk,
                self.cp_virtual_block_size,
            )
            * self.cp_local_block_size
        )
        local_chunk_starts = (
            torch.arange(self.num_chunks, dtype=torch.int32).unsqueeze(1).expand(-1, self.num_prefills)
            * padded_local_max_context_chunk_across_ranks
        )
        local_chunk_ends = torch.min(
            padded_local_context_lens_cpu.unsqueeze(0),
            local_chunk_starts + padded_local_max_context_chunk_across_ranks,
        )
        padded_local_chunk_seq_lens = (local_chunk_ends - local_chunk_starts).clamp(min=0)
        padded_local_cu_chunk_seq_lens_cpu = torch.zeros(
            self.num_chunks, self.num_prefills + 1, dtype=torch.int32, pin_memory=True
        )
        torch.cumsum(
            padded_local_chunk_seq_lens,
            dim=1,
            out=padded_local_cu_chunk_seq_lens_cpu[:, 1:],
            dtype=torch.int32,
        )
        chunked_metadata = CPChunkedContextMetadata(
            cu_seq_lens=chunked_context_metadata.cu_seq_lens,
            starts=local_chunk_starts.pin_memory().to(self.device, non_blocking=True),
            seq_tot=padded_local_chunk_seq_lens.sum(dim=1).tolist(),
            max_seq_lens=chunked_context_metadata.max_seq_lens,
            chunk_seq_lens=self.chunk_seq_lens,
            chunk_seq_lens_npu=chunked_context_metadata.chunk_seq_lens_npu,
            workspace=chunked_context_metadata.workspace,
            padded_chunk_seq_lens_npu=padded_local_chunk_seq_lens.npu(),
            padded_local_chunk_seq_lens=padded_local_chunk_seq_lens.tolist(),
            local_context_lens_allranks=local_context_lens_allranks.tolist(),
            padded_local_cu_seq_lens=padded_local_cu_chunk_seq_lens_cpu.pin_memory().to(self.device, non_blocking=True),
            cu_seq_lens_lst=self.cu_seq_lens_cpu.tolist(),
            chunk_size=padded_local_max_context_chunk_across_ranks,
        )
        return chunked_metadata

    def get_block_table_size(self, common_attn_metadata: AscendCommonAttentionMetadata, build_metadata_step: int):
        self.num_decodes_flatten = self.query_lens[: self.num_decodes].sum().item()
        if build_metadata_step == BUILD_METADATA_STEP_PREFILL:
            # For pcp + spec decode, we flatten seq_lens and block_table
            # to avoid irregular attn_mask shape
            return self.num_decodes_flatten + self.num_prefills
        else:
            return self.num_decodes_flatten

    def build_prefill_metadata(
        self,
        common_prefix_len: int,
        common_attn_metadata: AscendCommonAttentionMetadata,
    ) -> AscendMLAPrefillMetadata:
        prefill_metadata = super().build_prefill_metadata(common_prefix_len, common_attn_metadata)
        prefill_metadata.pcp_metadata = self.build_cp_metadata(common_prefix_len, common_attn_metadata)
        prefill_metadata.block_table = self.block_table[self.num_decodes_flatten :, ...]
        return prefill_metadata

    def build_decode_metadata(
        self,
        common_prefix_len: int,
        common_attn_metadata: AscendCommonAttentionMetadata,
    ) -> AscendMLADecodeMetadata:
        decode_metadata = super().build_decode_metadata(common_prefix_len, common_attn_metadata)

        long_seq_metadata = common_attn_metadata.prefill_context_parallel_metadata
        assert long_seq_metadata is not None
        num_computed_tokens_of_pcp_dcp = long_seq_metadata.num_computed_tokens_of_pcp_dcp
        assert num_computed_tokens_of_pcp_dcp is not None
        # [bs, pcp_size, dcp_size]
        num_computed_tokens_of_cp_dcp_array = np.array(num_computed_tokens_of_pcp_dcp)[: self.num_decodes_flatten]

        cp_seq_len = num_computed_tokens_of_cp_dcp_array[:, self.pcp_rank, self.dcp_rank]
        cp_seq_len = torch.tensor(cp_seq_len, dtype=torch.int32)
        decode_metadata.cp_seq_len = cp_seq_len.tolist()

        actual_seq_lengths_q = torch.arange(self.num_decodes_flatten) + 1
        decode_metadata.actual_seq_lengths_q = actual_seq_lengths_q

        return decode_metadata


class AscendMlaCPImpl(AscendMLAImpl):
    """
    NOTE: Please read the comment at the top of the file before trying to
    understand this class
    """

    def __init__(
        self,
        num_heads: int,
        head_size: int,
        scale: float,
        num_kv_heads: int,
        alibi_slopes: list[float] | None,
        sliding_window: int | None,
        kv_cache_dtype: str,
        logits_soft_cap: float | None,
        attn_type: str,
        kv_sharing_target_layer_name: str | None,
        **kwargs,
    ):
        super().__init__(
            num_heads,
            head_size,
            scale,
            num_kv_heads,
            alibi_slopes,
            sliding_window,
            kv_cache_dtype,
            logits_soft_cap,
            attn_type,
            kv_sharing_target_layer_name,
            **kwargs,
        )

        self.pcp_size = get_pcp_group().world_size
        self.pcp_rank = get_pcp_group().rank_in_group if self.pcp_size > 1 else 0
        self.pcp_group = get_pcp_group().device_group if self.pcp_size > 1 else None

        self.dcp_size = get_decode_context_model_parallel_world_size()
        self.dcp_rank = get_decode_context_model_parallel_rank() if self.dcp_size > 1 else 0
        self.dcp_group = get_dcp_group().device_group if self.dcp_size > 1 else None

    @staticmethod
    def update_graph_params(
        update_stream,
        forward_context,
        num_tokens,
        vllm_config=None,
        speculative_config=None,
        num_dcp_pcp_tokens=None,
        draft_attn_metadatas=None,
    ):
        if forward_context.is_draft_model:
            graph_params = get_draft_graph_params()
        else:
            graph_params = get_graph_params()
        # FIXME: Behold! We are using a temporary hack here to update the args
        # for each layer's attention op in the graph.
        with torch.npu.stream(update_stream):
            for key, param, handle, event in zip(
                forward_context.attn_metadata,
                graph_params.attn_params[num_tokens],
                graph_params.handles[num_tokens],
                graph_params.events[num_tokens],
            ):
                (
                    q_nope,
                    k_nope,
                    q_pe,
                    k_pe,
                    num_heads,
                    num_kv_heads,
                    input_layout,
                    spec_attn_mask,
                    sparse_mode,
                    scale,
                    block_table,
                    block_size,
                    actual_seq_lengths,
                    actual_seq_lengths_kv,
                    attn_output,
                    softmax_lse,
                ) = param

                decode_meta = forward_context.attn_metadata[key].decode
                seq_len = decode_meta.cp_seq_len
                if isinstance(seq_len, torch.Tensor):
                    seq_len = seq_len.tolist()
                actual_seq_lengths_kv = seq_len

                pad_length = num_tokens - len(actual_seq_lengths_kv)
                if pad_length > 0:
                    actual_seq_lengths_kv = actual_seq_lengths_kv + [0] * (num_tokens - len(actual_seq_lengths_kv))

                torch.npu.graph_task_update_begin(update_stream, handle)

                torch_npu.npu_fused_infer_attention_score.out(
                    q_nope,
                    k_nope,
                    k_nope,
                    query_rope=q_pe,
                    key_rope=k_pe,
                    num_heads=num_heads,
                    num_key_value_heads=num_kv_heads,
                    input_layout=input_layout,
                    atten_mask=spec_attn_mask,
                    sparse_mode=sparse_mode,
                    scale=scale,
                    antiquant_mode=0,
                    antiquant_scale=None,
                    softmax_lse_flag=True,
                    block_table=block_table,
                    block_size=block_size,
                    actual_seq_lengths_kv=actual_seq_lengths_kv,
                    actual_seq_lengths=actual_seq_lengths,
                    workspace=graph_params.workspaces.get(num_tokens),
                    out=[attn_output, softmax_lse],
                )
                torch.npu.graph_task_update_end(update_stream)

                event.record(update_stream)

    def get_num_actual_tokens(self, attn_metadata: M):
        if self.pcp_size > 1:
            return attn_metadata.num_actual_tokens_pcp_padded // self.pcp_size
        else:
            return attn_metadata.num_actual_tokens

    def _v_up_proj(self, x):
        # Convert from (B, N, L) to (N, B, L)
        x = x.view(-1, self.num_heads, self.kv_lora_rank).transpose(0, 1)
        # # Multiply (N, B, L) x (N, L, V) -> (N, B, V)
        x = torch.bmm(x, self.W_UV)
        # # Convert from (N, B, V) to (B, N * V)
        x = x.transpose(0, 1).reshape(-1, self.num_heads * self.v_head_dim)
        return x

    def mla_preprocess_prefill(self, q_c, kv_no_split, kv_cache, attn_metadata):
        if not self.pcp_size > 1:
            return super().mla_preprocess_prefill(q_c, kv_no_split, kv_cache, attn_metadata)
        num_decode_tokens = attn_metadata.num_decode_tokens
        num_actual_tokens = (
            attn_metadata.num_actual_tokens_pcp_padded - self.pcp_size * num_decode_tokens
        ) // self.pcp_size + num_decode_tokens
        prefill_q_c = q_c[num_decode_tokens:num_actual_tokens]
        prefill_q = self.q_proj(prefill_q_c)[0].view(-1, self.num_heads, self.qk_head_dim)
        prefill_q_pe = prefill_q[..., self.qk_nope_head_dim :]
        prefill_q_nope = prefill_q[..., : self.qk_nope_head_dim]
        cos = attn_metadata.prefill.cos[: num_actual_tokens - num_decode_tokens]
        sin = attn_metadata.prefill.sin[: num_actual_tokens - num_decode_tokens]
        prefill_q_pe = self.rope_single(prefill_q_pe, cos, sin)
        prefill_kv_no_split = kv_no_split[:num_actual_tokens]
        kv_c, k_pe = prefill_kv_no_split.split([self.kv_lora_rank, self.qk_rope_head_dim], dim=-1)
        kv_c_normed = self.kv_a_layernorm(kv_c.contiguous())  # type: ignore[misc]
        assert len(kv_cache) > 1, "the number of kv cache should be greater than 1, namely (nope_cache and rope_cache)"
        kv_c_normed = kv_c_normed.view([num_actual_tokens, self.num_kv_heads, -1])
        k_pe = k_pe.unsqueeze(1)
        prefill_k_pe = k_pe
        prefill_k_pe[num_decode_tokens:num_actual_tokens] = self.rope_single(
            prefill_k_pe[num_decode_tokens:num_actual_tokens], cos, sin
        )
        prefill_k_c_normed = kv_c_normed[:num_actual_tokens]
        prefill_kv_c_k_pe = torch.cat([prefill_k_c_normed, prefill_k_pe], dim=-1)
        prefill_kv_c_k_pe = get_pcp_group().all_gather(prefill_kv_c_k_pe, 0)
        prefill_kv_c_k_pe = torch.index_select(
            prefill_kv_c_k_pe, 0, attn_metadata.prefill.pcp_metadata.pcp_allgather_restore_idx
        )
        prefill_kv_c_k_pe = prefill_kv_c_k_pe[num_decode_tokens * self.pcp_size :]
        prefill_k_c_normed, prefill_k_pe = prefill_kv_c_k_pe.split([self.kv_lora_rank, self.qk_rope_head_dim], dim=-1)
        kv_c_normed, k_pe = prefill_k_c_normed, prefill_k_pe
        prefill_k_c_normed = prefill_k_c_normed.squeeze()
        slot_mapping = attn_metadata.slot_mapping[self.pcp_size * num_decode_tokens :]
        if self.is_kv_producer:
            attn_metadata.reshape_cache_event = torch.npu.Event()
        torch_npu._npu_reshape_and_cache(
            key=kv_c_normed, value=k_pe, key_cache=kv_cache[0], value_cache=kv_cache[1], slot_indices=slot_mapping
        )
        if self.is_kv_producer:
            attn_metadata.reshape_cache_event.record()
        prefill_k_nope, prefill_value = (
            self.kv_b_proj(prefill_k_c_normed)[0]
            .view(-1, self.num_heads, self.qk_nope_head_dim + self.v_head_dim)
            .split([self.qk_nope_head_dim, self.v_head_dim], dim=-1)
        )
        prefill_k_pe = prefill_k_pe.expand((*prefill_k_nope.shape[:-1], -1))
        return PrefillMLAPreprocessResult(prefill_q_nope, prefill_q_pe, prefill_k_nope, prefill_k_pe, prefill_value)

    def mla_preprocess_decode(self, q_c, kv_no_split, kv_cache, attn_metadata):
        num_decode_tokens = attn_metadata.num_decode_tokens
        decode_q_c = q_c[:num_decode_tokens]
        cos = attn_metadata.decode.cos
        sin = attn_metadata.decode.sin
        decode_ql_nope, decode_q_pe = self._q_proj_and_k_up_proj(decode_q_c)
        decode_ql_nope, decode_q_pe = self.reorg_decode_q(decode_ql_nope, decode_q_pe)
        decode_q_pe = self.rope_single(decode_q_pe, cos, sin)
        decode_slots = attn_metadata.slot_mapping[:num_decode_tokens]
        decode_kv_no_split = kv_no_split[:num_decode_tokens]
        decode_k_pe, decode_k_nope = self.exec_kv_decode(decode_kv_no_split, cos, sin, kv_cache, decode_slots)
        return DecodeMLAPreprocessResult(decode_ql_nope, decode_q_pe, decode_k_nope, decode_k_pe)

    def get_context_seq_len_npu(self, index: int, attn_metadata: AscendMLAMetadata):
        prefill_metadata = attn_metadata.prefill
        assert prefill_metadata is not None
        assert prefill_metadata.chunked_context is not None
        assert isinstance(prefill_metadata.chunked_context, CPChunkedContextMetadata)
        assert prefill_metadata.chunked_context.padded_chunk_seq_lens_npu is not None
        iters = len(prefill_metadata.chunked_context.seq_tot)
        assert 0 <= index < iters
        return prefill_metadata.chunked_context.padded_chunk_seq_lens_npu[index]

    def reorg_decode_q(self, decode_q_nope, decode_q_pe):
        if self.dcp_size > 1:
            decode_q_no_split = torch.cat([decode_q_nope, decode_q_pe], dim=-1)
            decode_q_no_split = get_dcp_group().all_gather(decode_q_no_split, 1)
            decode_q_nope, decode_q_pe = decode_q_no_split.split([self.kv_lora_rank, self.qk_rope_head_dim], dim=-1)
        return decode_q_nope, decode_q_pe

    def _forward_prefill(
        self,
        q_nope: torch.Tensor,
        q_pe: torch.Tensor,
        k_nope: torch.Tensor,
        k_pe: torch.Tensor,
        value: torch.Tensor,
        kv_c_and_k_pe_cache: tuple[torch.Tensor],
        attn_metadata: AscendMLAMetadata,
    ) -> torch.Tensor:
        if not self.pcp_size > 1:
            return super()._forward_prefill(q_nope, q_pe, k_nope, k_pe, value, kv_c_and_k_pe_cache, attn_metadata)
        assert attn_metadata.prefill is not None
        assert attn_metadata.prefill.pcp_metadata is not None
        num_tokens = q_nope.size(0)
        # Use precomputed indices from the metadata (already converted to tensors and on device)
        q_head_idx = attn_metadata.prefill.pcp_metadata.q_head_idx
        q_tail_idx = attn_metadata.prefill.pcp_metadata.q_tail_idx
        kv_with_q_head_nomask_idx = attn_metadata.prefill.pcp_metadata.kv_with_q_head_nomask_idx
        kv_with_q_head_mask_idx = attn_metadata.prefill.pcp_metadata.kv_with_q_head_mask_idx
        kv_with_q_tail_nomask_idx = attn_metadata.prefill.pcp_metadata.kv_with_q_tail_nomask_idx
        kv_with_q_tail_mask_idx = attn_metadata.prefill.pcp_metadata.kv_with_q_tail_mask_idx
        attn_mask_seqlens = attn_metadata.prefill.pcp_metadata.attn_mask_seqlens
        head_attn_nomask_seqlens = attn_metadata.prefill.pcp_metadata.head_attn_nomask_seqlens
        tail_attn_nomask_seqlens = attn_metadata.prefill.pcp_metadata.tail_attn_nomask_seqlens
        output_head, lse_head = self._attention_with_mask_and_nomask(
            q_nope=torch.index_select(q_nope, 0, q_head_idx),
            q_pe=torch.index_select(q_pe, 0, q_head_idx),
            k_nope=k_nope,
            k_pe=k_pe,
            value=value,
            kv_mask_idx=kv_with_q_head_mask_idx,
            kv_nomask_idx=kv_with_q_head_nomask_idx,
            attn_mask_seqlens=attn_mask_seqlens,
            attn_nomask_seqlens=head_attn_nomask_seqlens,
            mask=attn_metadata.attn_mask,
        )

        output_tail, lse_tail = self._attention_with_mask_and_nomask(
            q_nope=torch.index_select(q_nope, 0, q_tail_idx),
            q_pe=torch.index_select(q_pe, 0, q_tail_idx),
            k_nope=k_nope,
            k_pe=k_pe,
            value=value,
            kv_mask_idx=kv_with_q_tail_mask_idx,
            kv_nomask_idx=kv_with_q_tail_nomask_idx,
            attn_mask_seqlens=attn_mask_seqlens,
            attn_nomask_seqlens=tail_attn_nomask_seqlens,
            mask=attn_metadata.attn_mask,
        )

        q_full_idx = attn_metadata.prefill.pcp_metadata.q_full_idx
        attn_output = torch.index_select(torch.cat([output_head, output_tail], dim=0), 0, q_full_idx)
        attn_lse = torch.index_select(torch.cat([lse_head, lse_tail], dim=1), 1, q_full_idx)

        output, _ = self._compute_prefill_context(
            q_nope, q_pe, kv_c_and_k_pe_cache, self.qk_rope_head_dim, attn_metadata, attn_output, attn_lse
        )

        output = output.reshape([num_tokens, self.num_heads * self.v_head_dim])

        return output

    def _attention_with_mask_and_nomask(
        self,
        q_nope: torch.Tensor,
        q_pe: torch.Tensor,
        k_nope: torch.Tensor,
        k_pe: torch.Tensor,
        value: torch.Tensor,
        kv_mask_idx: torch.Tensor,
        kv_nomask_idx: list[torch.Tensor],
        attn_mask_seqlens: torch.Tensor,
        attn_nomask_seqlens: list[torch.Tensor],
        mask: torch.Tensor,
    ):
        attn_output = torch.empty(
            q_nope.shape[0], self.num_heads, self.v_head_dim, dtype=k_pe.dtype, device=k_pe.device
        )
        attn_lse = torch.empty(self.num_heads, q_pe.shape[0], dtype=torch.float32, device=k_pe.device)
        # mask
        k_nope_mask = torch.index_select(k_nope, 0, kv_mask_idx)
        value_mask = torch.index_select(value, 0, kv_mask_idx)
        k_pe_mask = torch.index_select(k_pe, 0, kv_mask_idx)
        torch_npu.atb.npu_ring_mla(
            q_nope=q_nope,
            q_rope=q_pe,
            k_nope=k_nope_mask,
            k_rope=k_pe_mask,
            value=value_mask,
            mask=mask,
            seqlen=attn_mask_seqlens,
            head_num=self.num_heads,
            kv_head_num=self.num_heads,
            pre_out=None,
            prev_lse=None,
            qk_scale=self.scale,
            kernel_type="kernel_type_high_precision",
            mask_type="mask_type_triu",
            input_layout="type_bsnd",
            calc_type="calc_type_first_ring",
            output=attn_output,
            softmax_lse=attn_lse,
        )

        # nomask
        if not kv_nomask_idx or len(kv_nomask_idx[0]) == 0:
            return attn_output, attn_lse

        for kv_nomask_idx_split, attn_nomask_seqlens_split in zip(kv_nomask_idx, attn_nomask_seqlens):
            k_nope_nomask = torch.index_select(k_nope, 0, kv_nomask_idx_split)
            value_nomask = torch.index_select(value, 0, kv_nomask_idx_split)
            k_pe_nomask = torch.index_select(k_pe, 0, kv_nomask_idx_split)
            torch_npu.atb.npu_ring_mla(
                q_nope=q_nope,
                q_rope=q_pe,
                k_nope=k_nope_nomask,
                k_rope=k_pe_nomask,
                value=value_nomask,
                mask=mask,
                seqlen=attn_nomask_seqlens_split,
                head_num=self.num_heads,
                kv_head_num=self.num_heads,
                pre_out=attn_output,
                prev_lse=attn_lse,
                qk_scale=self.scale,
                kernel_type="kernel_type_high_precision",
                mask_type="no_mask",
                input_layout="type_bsnd",
                calc_type="calc_type_default",
                output=attn_output,
                softmax_lse=attn_lse,
            )
        return attn_output, attn_lse

    def _forward_decode(
        self,
        q_nope: torch.Tensor,
        q_pe: torch.Tensor,
        k_nope: torch.Tensor,
        k_pe: torch.Tensor,
        block_size: int,
        attn_metadata: AscendMLAMetadata,
    ) -> torch.Tensor:
        decode_meta = attn_metadata.decode
        assert decode_meta is not None
        num_tokens = q_nope.size(0)
        # shape of knope/k_pe for npu graph mode should be:
        # [num_blocks, num_kv_heads, block_size, self.kv_lora_rank/self.qk_rope_head_dim]
        if self.dcp_size > 1:
            num_heads = self.num_heads * self.dcp_size
        else:
            num_heads = self.num_heads
        # use pcp & dcp split computed token nums from scheduler to compute actual seq_len and seq_mask
        k_nope = k_nope.view(-1, self.num_kv_heads, block_size, self.kv_lora_rank)
        k_pe = k_pe.view(-1, self.num_kv_heads, block_size, self.qk_rope_head_dim)

        actual_seq_lengths = None
        input_layout = "BNSD"

        if (
            attn_metadata.attn_state
            in [
                AscendAttentionState.SpecDecoding,
                AscendAttentionState.ChunkedPrefill,
                AscendAttentionState.DecodeOnly,
            ]
            and self.speculative_config is not None
        ):
            input_layout = "TND"
            # TODO: If the driver is upgraded later, the contiguous function can be deleted.
            q_nope = q_nope.view(num_tokens, num_heads, -1).contiguous()
            q_pe = q_pe.view(num_tokens, num_heads, -1)
            sparse_mode = 3
            spec_attn_mask = attn_metadata.decode.attn_mask  # type:ignore
            actual_seq_lengths = decode_meta.actual_seq_lengths_q
        else:
            q_nope = q_nope.view(num_tokens, num_heads, 1, -1).contiguous()
            q_pe = q_pe.view(num_tokens, num_heads, 1, -1)
            sparse_mode = 0
            spec_attn_mask = None

        common_kwargs = {
            "query_rope": q_pe,
            "key_rope": k_pe,
            "num_heads": num_heads,
            "num_key_value_heads": self.num_kv_heads,
            "input_layout": input_layout,
            "atten_mask": spec_attn_mask,
            "sparse_mode": sparse_mode,
            "scale": self.scale,
            "antiquant_mode": 0,
            "antiquant_scale": None,
            "block_table": decode_meta.block_table,
            "block_size": block_size,
            "actual_seq_lengths": actual_seq_lengths,
            "actual_seq_lengths_kv": decode_meta.cp_seq_len,
            "softmax_lse_flag": True,
        }

        forward_context: ForwardContext = get_forward_context()
        if forward_context.is_draft_model:
            graph_params = get_draft_graph_params()
        else:
            graph_params = get_graph_params()
        if forward_context.capturing:
            stream = torch_npu.npu.current_stream()
            event = torch.npu.ExternalEvent()
            event.wait(stream)
            event.reset(stream)
            graph_params.events[num_tokens].append(event)
            workspace = graph_params.workspaces.get(num_tokens)
            if workspace is None:
                workspace = torch_npu._npu_fused_infer_attention_score_get_max_workspace(
                    q_nope,
                    k_nope,
                    k_nope,
                    **common_kwargs,
                )
                update_graph_params_workspaces(num_tokens, workspace)
            attn_output = torch.empty_like(q_nope)
            if input_layout == "BNSD":
                softmax_lse = torch.empty((num_tokens, num_heads, 1, 1), dtype=torch.float, device=q_nope.device)
            else:
                softmax_lse = torch.empty((num_tokens, num_heads, 1), dtype=torch.float, device=q_nope.device)

            graph_params.attn_params[num_tokens].append(
                (
                    weak_ref_tensors(q_nope),
                    weak_ref_tensors(k_nope),
                    weak_ref_tensors(q_pe),
                    weak_ref_tensors(k_pe),
                    num_heads,
                    self.num_kv_heads,
                    input_layout,
                    weak_ref_tensors(spec_attn_mask) if spec_attn_mask is not None else None,
                    sparse_mode,
                    self.scale,
                    weak_ref_tensors(decode_meta.block_table),
                    block_size,
                    actual_seq_lengths,
                    decode_meta.cp_seq_len,
                    weak_ref_tensors(attn_output),
                    weak_ref_tensors(softmax_lse),
                )
            )
            torch.npu.graph_task_group_begin(stream)
            torch_npu.npu_fused_infer_attention_score.out(
                q_nope, k_nope, k_nope, **common_kwargs, workspace=workspace, out=[attn_output, softmax_lse]
            )
            handle = torch.npu.graph_task_group_end(stream)
            graph_params.handles[num_tokens].append(handle)
        else:
            attn_output, softmax_lse = torch_npu.npu_fused_infer_attention_score(
                q_nope,
                k_nope,
                k_nope,
                **common_kwargs,
            )
        if input_layout == "BNSD":
            B_attn, N_attn, S, D = attn_output.shape
            B_lse, N_lse, Q_S, _ = softmax_lse.shape

            attn_output = attn_output.permute(0, 2, 1, 3).reshape(B_attn * S, N_attn, D)
            softmax_lse = softmax_lse.permute(0, 2, 1, 3).reshape(B_lse * Q_S, N_lse, 1)

        # Update out&lse
        attn_out_lse = _process_attn_out_lse(attn_output, softmax_lse)
        attn_output = _npu_attention_update(self.kv_lora_rank, attn_out_lse)
        return self._v_up_proj(attn_output)

    def _out_lse_reshape(self, attn_out: torch.Tensor, attn_lse: torch.Tensor) -> torch.Tensor:
        attn_out = attn_out.contiguous().view(attn_out.shape[0] * attn_out.shape[1], attn_out.shape[2])
        attn_lse = attn_lse.contiguous().view(attn_lse.shape[0] * attn_lse.shape[1] * attn_lse.shape[2])
        return attn_out, attn_lse

    def _reorg_kvcache(
        self,
        kv_c_normed: torch.Tensor,
        k_pe: torch.Tensor,
        chunked_context: CPChunkedContextMetadata,
        chunk_idx: int,
        toks: int,
    ) -> tuple[torch.Tensor, torch.Tensor]:
        """
        reorg and unpad kvcache after cp local gather to tp layout for attn kernel.
        e.g.
        kv_c_normed in rank0 = [T0_0, T0_1, T0_2, T0_3, T1_0, T1_1, ...]
        kv_c_normed in rank1 = [T0_4, T0_5, pad, pad, T1_2, pad, ...]
        allgatered_kv_c_normed = [T0_0, T0_1, T0_2, T0_3, T1_0, T1_1, ...,
                                T0_4, T0_5, pad, pad, T1_2, pad, ...]
        -> reorganized_kv_c_normed = [T0_0, T0_1, T0_2, T0_3, T0_4, T0_5,
                                    T1_0, T1_1, T1_2, ...]
        Args:
            padded_local_chunk_seq_lens_lst: local chunk context lengths
                under current CP rank.
            local_context_lens_allranks: local context lengths on each CP rank.
            sum_seq_len: the sum of cp_chunk_seq_lens_lst.
            max_seq_len: the max value of cp_chunk_seq_lens_lst.
            chunk_size: the local padded max context chunk from
                chunked_context_metadata building.
            chunk_idx: chunk idx of chunked_prefill.
            toks: the number of tokens for local gather cache.
        """
        assert chunked_context is not None
        assert chunked_context.padded_local_chunk_seq_lens is not None
        assert chunked_context.local_context_lens_allranks is not None
        assert chunked_context.cu_seq_lens_lst is not None
        assert chunked_context.max_seq_lens is not None
        assert chunked_context.chunk_size is not None

        padded_local_chunk_seq_lens_lst = chunked_context.padded_local_chunk_seq_lens[chunk_idx]
        local_context_lens_allranks = chunked_context.local_context_lens_allranks
        sum_seq_len = chunked_context.cu_seq_lens_lst[chunk_idx][-1]
        max_seq_len = chunked_context.max_seq_lens[chunk_idx]
        chunk_size: int = chunked_context.chunk_size
        cache_kv_c_k_pe = torch.cat([kv_c_normed, k_pe], dim=-1)
        if self.dcp_size > 1:
            cache_kv_c_k_pe = get_dcp_group().all_gather(cache_kv_c_k_pe, 0)

        if self.pcp_size > 1:
            cache_kv_c_k_pe = get_pcp_group().all_gather(cache_kv_c_k_pe, 0)

        allgatered_kv_c_normed, allgatered_k_pe = cache_kv_c_k_pe.split(
            [self.kv_lora_rank, self.qk_rope_head_dim], dim=-1
        )

        kv_c_segments = []
        k_pe_segments = []
        src_token_idx = 0
        max_seq_len_check = 0
        for padded_local_chunk_seq_len, local_context_lens in zip(
            padded_local_chunk_seq_lens_lst, local_context_lens_allranks
        ):
            cur_seq_len = 0
            for rank, local_context_len in enumerate(local_context_lens):
                # Note(qcs): We split the context into multiple chunks,
                # depending on the size of the workspace.
                # local_context in dcp0:   |-----------------|
                # local_context in dcp1:   |--------------|
                # n*padded_local_chunk:    |-----|-----|-----|
                # local_chunk_len in dcp1: |-----|-----|--|
                # so we need update the last chunk length in dcp1.
                local_chunk_len = min(
                    max(0, local_context_len - chunk_idx * chunk_size),
                    padded_local_chunk_seq_len,
                )
                if local_chunk_len != 0:
                    kv_c_segment = allgatered_kv_c_normed[
                        rank * toks + src_token_idx : rank * toks + src_token_idx + local_chunk_len
                    ]
                    k_pe_segment = allgatered_k_pe[
                        rank * toks + src_token_idx : rank * toks + src_token_idx + local_chunk_len
                    ]
                    kv_c_segments.append(kv_c_segment)
                    k_pe_segments.append(k_pe_segment)
                    cur_seq_len += local_chunk_len
            max_seq_len_check = max(max_seq_len_check, cur_seq_len)
            src_token_idx += padded_local_chunk_seq_len
        reorganized_kv_c_normed = torch.cat(kv_c_segments, dim=0)
        reorganized_k_pe = torch.cat(k_pe_segments, dim=0)
        assert reorganized_kv_c_normed.shape[0] == sum_seq_len
        assert reorganized_k_pe.shape[0] == sum_seq_len
        assert max_seq_len_check == max_seq_len
        return reorganized_kv_c_normed, reorganized_k_pe
