from dataclasses import dataclass
from typing import TYPE_CHECKING, TypeVar

import torch
import torch_npu
import vllm.envs as envs_vllm
from torch import nn
from vllm.config import VllmConfig, get_current_vllm_config
from vllm.distributed import get_tensor_model_parallel_world_size, get_tp_group
from vllm.forward_context import get_forward_context
from vllm.logger import logger
from vllm.model_executor.layers.attention.mla_attention import MLACommonMetadataBuilder
from vllm.model_executor.layers.linear import UnquantizedLinearMethod
from vllm.triton_utils import HAS_TRITON
from vllm.v1.attention.backend import AttentionBackend, AttentionCGSupport, MLAAttentionImpl  # type: ignore
from vllm.v1.kv_cache_interface import AttentionSpec

from vllm_ascend import envs
from vllm_ascend.ascend_config import get_ascend_config
from vllm_ascend.attention.attention_mask import AttentionMaskBuilder
from vllm_ascend.attention.attention_v1 import AscendAttentionState
from vllm_ascend.attention.context_parallel.common_cp import AscendPCPMetadata
from vllm_ascend.attention.mla_v1 import MAX_O_PROJ_PREFETCH_SIZE, MLAPO_MAX_SUPPORTED_TOKENS
from vllm_ascend.attention.utils import (
    AscendCommonAttentionMetadata,
    ascend_chunked_prefill_workspace_size,
    enable_cp,
    maybe_save_kv_layer_to_connector,
    trans_rope_weight,
    transdata,
    wait_for_kv_layer_from_connector,
)
from vllm_ascend.distributed.utils import all_gather_async
from vllm_ascend.ops.layer_shard_linear import (
    is_hidden_layer,
    post_process_after_loading_for_shard_weight_series,
    reach_layer_for_shard_weight_series,
    register_all_layers_to_shard_weight_series,
)
from vllm_ascend.ops.rotary_embedding import get_cos_and_sin_mla
from vllm_ascend.ops.triton.rope import rope_forward_triton
from vllm_ascend.quantization.methods import AscendW8A8LinearMethod
from vllm_ascend.utils import (
    ACL_FORMAT_FRACTAL_ND,
    _round_up,
    dispose_layer,
    enable_dsa_cp,
    enable_dsa_cp_with_layer_shard,
    get_weight_prefetch_method,
    maybe_trans_nz,
)
from vllm_ascend.worker.npu_input_batch import NPUInputBatch

if TYPE_CHECKING:
    from vllm.v1.core.sched.output import SchedulerOutput

# token count limits within bmm_transpose operator
BMM_TRANS_MAX_SUPPORTED_TOKENS = 1024


class AscendSFABackend(AttentionBackend):
    accept_output_buffer: bool = True

    @staticmethod
    def get_name() -> str:
        # HACK(Ronald1995): vllm `initialize_kv_cache` method in model runner v2 make
        # attention name assertion, we just set name to FLASH_ATTN to avoid assertion error.
        # rectify this when vllm disable the assertion.
        return "ASCEND_SFA" if not envs_vllm.VLLM_USE_V2_MODEL_RUNNER else "FLASH_ATTN"

    @staticmethod
    def get_builder_cls():
        if enable_cp():
            from vllm_ascend.attention.context_parallel.sfa_cp import AscendSFACPMetadataBuilder

            return AscendSFACPMetadataBuilder
        return AscendSFAMetadataBuilder

    @staticmethod
    def get_kv_cache_shape(num_blocks: int, block_size: int, num_kv_heads: int, head_size: int) -> tuple[int, ...]:
        return (num_blocks, block_size, num_kv_heads, head_size)

    @staticmethod
    def get_impl_cls() -> type["AscendSFAImpl"]:
        if enable_cp():
            from vllm_ascend.attention.context_parallel.sfa_cp import AscendSFACPImpl

            return AscendSFACPImpl
        return AscendSFAImpl

    @staticmethod
    def get_supported_kernel_block_sizes() -> list[int]:
        return [128]


@dataclass
class DSACPContext:
    num_tokens: int
    num_tokens_pad: int
    local_start: int
    local_end: int
    local_end_with_pad: int
    slot_mapping_cp: torch.Tensor
    actual_seq_lengths_query: torch.Tensor
    actual_seq_lengths_key: torch.Tensor


@dataclass
class AscendSFAMetadata:
    """Metadata for MLACommon.

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

    # NOTE(sang): Definition of context_len, query_len, and seq_len.
    # |---------- N-1 iteration --------|
    # |---------------- N iteration ---------------------|
    # |- tokenA -|......................|-- newTokens ---|
    # |---------- context_len ----------|
    # |-------------------- seq_len ---------------------|
    #                                   |-- query_len ---|
    num_actual_tokens: int  # Number of tokens excluding padding.
    slot_mapping: torch.Tensor
    seq_lens: torch.Tensor
    cum_query_lens: torch.Tensor
    block_table: torch.Tensor
    sin: torch.Tensor
    cos: torch.Tensor

    # For logging.
    num_input_tokens: int = 0  # Number of tokens including padding.
    # The dimension of the attention heads
    head_dim: int | None = None
    attn_mask: torch.Tensor = None
    # chunked prefill by default if no attn_states passed
    attn_state: AscendAttentionState = AscendAttentionState.ChunkedPrefill
    dsa_cp_context: DSACPContext | None = None
    reshape_cache_event: torch.npu.Event = None
    sfa_cp_metadata: AscendPCPMetadata | None = None
    num_decodes: int = 0
    num_decode_tokens: int = 0
    num_prefills: int = 0


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


class AscendSFAMetadataBuilder(MLACommonMetadataBuilder[AscendSFAMetadata]):
    """
    NOTE: Please read the comment at the top of the file before trying to
    understand this class
    """

    def __init__(
        self,
        kv_cache_spec,
        layer_names: list[str],
        vllm_config: VllmConfig,
        device: torch.device,
        metadata_cls: type[AscendSFAMetadata] | None = None,
        supports_dcp_with_varlen: bool = False,
    ):
        super().__init__(
            kv_cache_spec,
            layer_names,
            vllm_config,
            device,
            metadata_cls if metadata_cls is not None else AscendSFAMetadata,
            supports_dcp_with_varlen,
        )

        self.block_size = vllm_config.cache_config.block_size
        self.max_blocks = (vllm_config.model_config.max_model_len + self.block_size - 1) // self.block_size

        self.speculative_config = vllm_config.speculative_config
        self.decode_threshold = 1
        if self.speculative_config:
            spec_token_num = self.speculative_config.num_speculative_tokens
            self.decode_threshold += spec_token_num
            assert self.decode_threshold <= 16, (
                f"decode_threshold exceeded \
                npu_fused_infer_attention_score TND layout's limit of 16, \
                got {self.decode_threshold}"
            )
        self.reorder_batch_threshold = self.decode_threshold
        self.attn_mask_builder = AttentionMaskBuilder(self.device)
        self.rope_dim = self.model_config.hf_text_config.qk_rope_head_dim
        self.enable_dsa_cp = enable_dsa_cp()

        max_num_reqs = vllm_config.scheduler_config.max_num_seqs
        self.actual_seq_lengths_query = torch.zeros(max_num_reqs + 1, dtype=torch.int32, device=device)
        self.actual_seq_lengths_key = torch.empty_like(self.actual_seq_lengths_query)

    @staticmethod
    def determine_chunked_prefill_workspace_size(vllm_config: VllmConfig) -> int:
        return ascend_chunked_prefill_workspace_size(vllm_config)

    @classmethod
    def get_cudagraph_support(
        cls: type["AscendSFAMetadataBuilder"],
        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 reorder_batch(self, input_batch: "NPUInputBatch", scheduler_output: "SchedulerOutput") -> bool:
        # No need to reorder for Ascend SFA
        return False

    def build(
        self,
        common_prefix_len: int,
        common_attn_metadata: AscendCommonAttentionMetadata,
        fast_build: bool = False,
    ) -> AscendSFAMetadata:
        num_reqs = common_attn_metadata.num_reqs
        num_actual_tokens = common_attn_metadata.num_actual_tokens
        num_input_tokens = common_attn_metadata.num_input_tokens

        block_table = common_attn_metadata.block_table_tensor[:num_reqs]
        slot_mapping = common_attn_metadata.slot_mapping[:num_input_tokens]
        input_positions = common_attn_metadata.positions[:num_input_tokens].long()

        cum_query_lens = common_attn_metadata.query_start_loc[1 : num_reqs + 1]
        seq_lens = common_attn_metadata.seq_lens[:num_reqs]

        cos, sin = get_cos_and_sin_mla(input_positions, True)

        dsa_cp_context = None
        if self.enable_dsa_cp:
            global_tp_size = get_tp_group().world_size
            num_tokens = num_input_tokens
            num_tokens_pad = _round_up(num_tokens, global_tp_size)
            num_tokens_per_device = num_tokens_pad // global_tp_size
            local_start = get_tp_group().rank_in_group * num_tokens_per_device
            local_end_with_pad = local_start + num_tokens_per_device
            local_end = min(local_end_with_pad, num_actual_tokens)

            pad_size = num_tokens_pad - cos.shape[0]
            assert cos.shape == sin.shape, f"cos.shape must be equal to sin.shape, got {cos.shape} and {sin.shape}"

            if pad_size > 0:
                cos = nn.functional.pad(cos, (0, 0, 0, 0, 0, 0, 0, pad_size))
                sin = nn.functional.pad(sin, (0, 0, 0, 0, 0, 0, 0, pad_size))

            pad_size_slot = num_tokens_pad - slot_mapping.shape[0]
            if pad_size_slot > 0:
                slot_mapping = nn.functional.pad(slot_mapping, (0, pad_size_slot), value=-1)
            else:
                slot_mapping = slot_mapping[:num_tokens_pad]
            slot_mapping_cp = slot_mapping[local_start:local_end_with_pad]

            cos = cos[local_start:local_end_with_pad]
            sin = sin[local_start:local_end_with_pad]

            assert cos.shape[0] == num_tokens_per_device, (
                f"cos.shape[0] must be equal to num_tokens_per_device, \
                    got {cos.shape[0]} and {num_tokens_per_device}"
            )
            assert slot_mapping_cp.shape[0] == num_tokens_per_device, (
                f"slot_mapping_cp.shape[0] must be equal to num_tokens_per_device, \
                    got {slot_mapping_cp.shape[0]} and {num_tokens_per_device}"
            )
            assert slot_mapping.shape[0] == num_tokens_pad, (
                f"slot_mapping.shape[0] must be equal to num_tokens_pad, \
                    got {slot_mapping.shape[0]} and {num_tokens_pad}"
            )

            actual_seq_lengths_query = self.actual_seq_lengths_query
            actual_seq_lengths_key = self.actual_seq_lengths_key

            num_segs = cum_query_lens.shape[0]
            last_token = 0
            cum = 0
            for i in range(0, num_segs):
                global_start = last_token
                global_end = cum_query_lens[i].item()
                last_token = global_end

                req_local_start = max(global_start, local_start)
                req_local_end = min(global_end, local_end_with_pad)
                num_local_tokens = req_local_end - req_local_start

                if num_local_tokens > 0:
                    cum += num_local_tokens
                    actual_seq_lengths_query[i] = cum

                    offset = global_end - req_local_end
                    actual_seq_lengths_key[i] = seq_lens[i].item() - offset
                else:
                    actual_seq_lengths_query[i] = cum
                    actual_seq_lengths_key[i] = 0

            actual_seq_lengths_query = actual_seq_lengths_query[:num_reqs]
            actual_seq_lengths_key = actual_seq_lengths_key[:num_reqs]

            dsa_cp_context = DSACPContext(
                num_tokens=num_tokens,
                num_tokens_pad=num_tokens_pad,
                local_start=local_start,
                local_end=local_end,
                local_end_with_pad=local_end_with_pad,
                slot_mapping_cp=slot_mapping_cp,
                actual_seq_lengths_query=actual_seq_lengths_query,
                actual_seq_lengths_key=actual_seq_lengths_key,
            )

        return self.metadata_cls(  # type: ignore
            num_input_tokens=common_attn_metadata.num_input_tokens,
            num_actual_tokens=num_actual_tokens,
            cum_query_lens=cum_query_lens,
            seq_lens=seq_lens,
            slot_mapping=slot_mapping,
            head_dim=self.model_config.get_head_size(),
            attn_mask=self.attn_mask_builder.get_attention_mask(self.model_config),
            attn_state=common_attn_metadata.attn_state,
            block_table=block_table,
            sin=sin[:num_input_tokens],
            cos=cos[:num_input_tokens],
            dsa_cp_context=dsa_cp_context,
        )

    def build_for_graph_capture(
        self,
        common_attn_metadata: AscendCommonAttentionMetadata,
        attn_state: AscendAttentionState = AscendAttentionState.DecodeOnly,
    ):
        if attn_state in {AscendAttentionState.DecodeOnly, AscendAttentionState.SpecDecoding}:
            attn_metadata = self.build(
                common_prefix_len=0,
                common_attn_metadata=common_attn_metadata,
            )
        else:
            raise NotImplementedError("Currently we only support building dummy metadata for DecodeOnly state")

        attn_metadata.attn_state = attn_state
        return attn_metadata


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

    # Supports forward using the all-gather o_proj weight for decode requests when Sharded CP is enabled.
    o_proj_full_pool: torch.Tensor | None = None

    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,
    ) -> None:
        self.num_heads = num_heads
        self.head_size = head_size
        self.scale = float(scale)
        self.num_kv_heads = num_kv_heads
        self.kv_cache_dtype = kv_cache_dtype

        # MLA Args
        self.q_lora_rank = kwargs["q_lora_rank"]
        self.kv_lora_rank = kwargs["kv_lora_rank"]
        self.qk_nope_head_dim = kwargs["qk_nope_head_dim"]
        self.qk_rope_head_dim = kwargs["qk_rope_head_dim"]
        self.qk_head_dim = kwargs["qk_head_dim"]
        self.v_head_dim = kwargs["v_head_dim"]
        self.rotary_emb = kwargs["rotary_emb"]
        self.q_proj = kwargs["q_proj"] if self.q_lora_rank is None else kwargs["q_b_proj"]
        self.fused_qkv_a_proj = kwargs.get("fused_qkv_a_proj")
        self.kv_b_proj = kwargs["kv_b_proj"]
        self.o_proj = kwargs["o_proj"]
        self.indexer = kwargs["indexer"]
        self.kv_a_proj_with_mqa = kwargs.get("kv_a_proj_with_mqa")
        self.kv_a_layernorm = kwargs.get("kv_a_layernorm")
        self.q_a_layernorm = kwargs.get("q_a_layernorm")
        self.num_queries_per_kv = self.num_heads // self.num_kv_heads
        self.tp_size = get_tensor_model_parallel_world_size()
        self.tp_rank = get_tp_group().rank_in_group
        self.q_b_proj = kwargs["q_b_proj"]

        ascend_config = get_ascend_config()
        self.enable_shared_expert_dp = ascend_config.enable_shared_expert_dp

        # In sfa, prefill and decode have the same calculation formula,
        # so do not distinguish between prefill and decode here.
        self.enable_mlapo = envs.VLLM_ASCEND_ENABLE_MLAPO

        assert self.indexer is not None, "Indexer is required for DSA."

        self.local_num_heads = self.num_heads
        self.vllm_config = get_current_vllm_config()
        self.is_kv_producer = (
            self.vllm_config.kv_transfer_config is not None and self.vllm_config.kv_transfer_config.is_kv_producer
        )

        # indexer param
        self.n_head: int = self.indexer.n_head  # 64
        self.head_dim: int = self.indexer.head_dim  # 128
        self.wq_b = self.indexer.wq_b
        self.wk = self.indexer.wk
        self.weights_proj = self.indexer.weights_proj
        self.k_norm = self.indexer.k_norm
        self.cp_size = 1
        self.is_rope_neox_style = True
        self.use_torch_npu_lightning_indexer = False
        if self.vllm_config.model_config.hf_config.model_type in ["glm_moe_dsa"]:
            self.is_rope_neox_style = False
            self.use_torch_npu_lightning_indexer = True

        self.enable_dsa_cp = enable_dsa_cp()
        self.enable_dsa_cp_prefill_only = enable_dsa_cp_with_layer_shard()
        if self.enable_dsa_cp:
            self.local_num_heads = self.num_heads * self.tp_size
        if self.enable_dsa_cp_prefill_only:
            self.layer_sharding_kwargs = []
            for layer_name in get_ascend_config().layer_sharding or []:
                if layer_name in kwargs:
                    self.layer_sharding_kwargs.append(kwargs[layer_name])
                else:
                    logger.warning_once(
                        f"[SFAImpl init] Layer '{layer_name}' not found in kwargs for layer sharding, "
                        "skipping sharding configuration"
                    )
            register_all_layers_to_shard_weight_series(self.layer_sharding_kwargs)

    def process_weights_after_loading(self, act_dtype: torch.dtype):
        # NOTE: We currently do not support quant kv_b_proj.
        assert isinstance(self.kv_b_proj.quant_method, UnquantizedLinearMethod)
        # NOTE: Weight will be reshaped next, we need to revert and transpose it.
        kv_b_proj_weight = torch_npu.npu_format_cast(self.kv_b_proj.weight.data, ACL_FORMAT_FRACTAL_ND).T
        assert kv_b_proj_weight.shape == (
            self.kv_lora_rank,
            self.local_num_heads * (self.qk_nope_head_dim + self.v_head_dim),
        ), (
            f"{kv_b_proj_weight.shape=}, "
            f"{self.kv_lora_rank=}, "
            f"{self.local_num_heads=}, "
            f"{self.qk_nope_head_dim=}, "
            f"{self.v_head_dim=}"
        )
        kv_b_proj_weight = kv_b_proj_weight.view(
            self.kv_lora_rank,
            self.local_num_heads,
            self.qk_nope_head_dim + self.v_head_dim,
        )

        W_UK, W_UV = kv_b_proj_weight.split([self.qk_nope_head_dim, self.v_head_dim], dim=-1)

        # Convert from (L, N, V) to (N, L, V)
        self.W_UV = W_UV.transpose(0, 1).contiguous()
        # Convert from (L, N, P) to (N, P, L)
        self.W_UK_T = W_UK.permute(1, 2, 0).contiguous()

        # TODO(zzzzwwjj): Currently, torch.ops._C_ascend.batch_matmul_transpose cannot support weight nz
        # self.W_UV = maybe_trans_nz(self.W_UV)

        # Dispose kv_b_proj since it is replaced by W_UV and W_UK_T to save memory
        dispose_layer(self.kv_b_proj)
        if self.enable_dsa_cp:
            if self.enable_dsa_cp_prefill_only:
                for layer in self.layer_sharding_kwargs or []:
                    if is_hidden_layer(layer):
                        post_process_after_loading_for_shard_weight_series(layer)
            else:
                self._init_o_proj_tp_full_params()

        if self.enable_mlapo:
            quant_method = getattr(
                getattr(self.fused_qkv_a_proj, "quant_method", None),
                "quant_method",
                None,
            )
            reasons = []
            if self.fused_qkv_a_proj is None or not isinstance(quant_method, AscendW8A8LinearMethod):
                reasons.append(
                    "Currently mlapo only supports W8A8 quantization in SFA scenario."
                    "Some layers in your model are not quantized with W8A8,"
                    "thus mlapo is disabled for these layers."
                )
            if self.enable_dsa_cp:
                reasons.append("Currently mlapo does not support SFA with CP,thus mlapo is disabled for these layers.")
            if reasons:
                self.enable_mlapo = False
                for msg in reasons:
                    logger.warning_once(msg)
            else:
                self._process_weights_for_fused_mlapo(act_dtype)
        if not self.enable_mlapo:
            # if mlapo, W_UK_T can't trans nz
            self.W_UK_T = maybe_trans_nz(self.W_UK_T)

    def _v_up_proj(self, x):
        num_input_tokens, _, _ = x.shape
        if (
            x.dtype in [torch.float16, torch.bfloat16]
            and hasattr(torch.ops._C_ascend, "batch_matmul_transpose")
            and num_input_tokens <= BMM_TRANS_MAX_SUPPORTED_TOKENS
        ):
            x = x.view(-1, self.local_num_heads, self.kv_lora_rank)
            res = torch.empty((num_input_tokens, self.local_num_heads, self.v_head_dim), dtype=x.dtype, device=x.device)
            torch.ops._C_ascend.batch_matmul_transpose(x, self.W_UV, res)
            x = res.reshape(-1, self.local_num_heads * self.v_head_dim)
        else:
            # Convert from (B, N, L) to (N, B, L)
            x = x.view(-1, self.local_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.local_num_heads * self.v_head_dim)
        return x

    # Return `ql_nope`, `q_pe`
    def _q_proj_and_k_up_proj(self, x):
        q_nope, q_pe = (
            self.q_proj(x)[0]
            .view(-1, self.local_num_heads, self.qk_head_dim)
            .split([self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1)
        )

        # Convert from (B, N, P) to (N, B, P)
        q_nope = q_nope.transpose(0, 1)
        # Multiply (N, B, P) x (N, P, L) -> (N, B, L)
        ql_nope = torch.bmm(q_nope, self.W_UK_T)
        # Convert from (N, B, L) to (B, N, L)
        return ql_nope.transpose(0, 1), q_pe

    def _get_full_kv(self, k, attn_metadata):
        return k

    def exec_kv(
        self,
        kv_no_split: torch.Tensor,
        cos: torch.Tensor,
        sin: torch.Tensor,
        kv_cache: tuple,
        slots: torch.Tensor,
        attn_metadata: M,
    ):
        B = kv_no_split.shape[0]
        N = self.num_kv_heads
        S = 1
        # npu_kv_rmsnorm_rope_cache needs [B, N, S, D]
        kv_no_split = kv_no_split.view(B, N, S, self.kv_lora_rank + self.qk_rope_head_dim)
        cache_mode = "PA"

        if self.enable_dsa_cp:
            _, _, k_pe, k_nope = torch_npu.npu_kv_rmsnorm_rope_cache(
                kv_no_split,
                self.kv_a_layernorm.weight,  # type: ignore[union-attr]
                cos,
                sin,
                slots.to(torch.int64),
                kv_cache[1],
                kv_cache[0],
                epsilon=self.kv_a_layernorm.variance_epsilon,  # type: ignore[union-attr]
                cache_mode=cache_mode,
                is_output_kv=True,
            )
            return k_pe, k_nope
        else:
            torch_npu.npu_kv_rmsnorm_rope_cache(
                kv_no_split,
                self.kv_a_layernorm.weight,  # type: ignore[union-attr]
                cos,
                sin,
                slots.to(torch.int64),
                kv_cache[1],
                kv_cache[0],
                epsilon=self.kv_a_layernorm.variance_epsilon,  # type: ignore[union-attr]
                cache_mode=cache_mode,
            )
            return None, None

    def rope_single(
        self,
        x: torch.Tensor,
        cos: torch.Tensor,
        sin: torch.Tensor,
    ) -> torch.Tensor:
        B, N, D = x.shape
        S = 1
        x = x.view(B, N, S, D)
        x = torch_npu.npu_interleave_rope(x, cos, sin)
        return x.view(B, N, D)

    # Processing the input parameters for MLAPO by reordering and transposing
    # QKV(and part of Q) weight, applying RoPE-related dimension transformations,
    # and handling quantization parameters.
    def _process_weights_for_fused_mlapo(self, act_dtype: torch.dtype):
        assert self.kv_a_proj_with_mqa is None
        assert self.fused_qkv_a_proj is not None

        kv_a_proj_wt = self.fused_qkv_a_proj.weight.data[..., self.q_lora_rank :].contiguous()
        q_a_proj_wt = self.fused_qkv_a_proj.weight.data[..., : self.q_lora_rank].contiguous()

        kv_a_proj_wt = kv_a_proj_wt.t().contiguous()
        kv_a_proj_wt = trans_rope_weight(kv_a_proj_wt, self.qk_rope_head_dim)
        kv_a_proj_wt = kv_a_proj_wt.t().contiguous()
        wd_qkv = torch.cat((kv_a_proj_wt, q_a_proj_wt), dim=-1)
        wd_qkv = wd_qkv.t().contiguous()
        wd_qkv = transdata(wd_qkv, block_size=(16, 32)).unsqueeze(0).contiguous()
        self.wd_qkv = torch_npu.npu_format_cast(wd_qkv, 29)

        kv_a_proj_deq_scl = self.fused_qkv_a_proj.deq_scale[self.q_lora_rank :].contiguous()
        q_a_proj_deq_scl = self.fused_qkv_a_proj.deq_scale[: self.q_lora_rank].contiguous()
        kv_a_proj_deq_scl = kv_a_proj_deq_scl.reshape(self.kv_lora_rank + self.qk_rope_head_dim, -1).contiguous()
        kv_a_proj_deq_scl = trans_rope_weight(kv_a_proj_deq_scl, self.qk_rope_head_dim)
        kv_a_proj_deq_scl = kv_a_proj_deq_scl.view(self.kv_lora_rank + self.qk_rope_head_dim).contiguous()
        self.deq_scale_qkv = torch.cat((kv_a_proj_deq_scl, q_a_proj_deq_scl), dim=-1).contiguous()

        kv_a_proj_qt_bias = self.fused_qkv_a_proj.quant_bias[self.q_lora_rank :].contiguous()
        q_a_proj_qt_bias = self.fused_qkv_a_proj.quant_bias[: self.q_lora_rank].contiguous()

        kv_a_proj_qt_bias = kv_a_proj_qt_bias.reshape(self.kv_lora_rank + self.qk_rope_head_dim, -1).contiguous()
        kv_a_proj_qt_bias = trans_rope_weight(kv_a_proj_qt_bias, self.qk_rope_head_dim)
        kv_a_proj_qt_bias = kv_a_proj_qt_bias.view(self.kv_lora_rank + self.qk_rope_head_dim).contiguous()
        self.quant_bias_qkv = torch.cat((kv_a_proj_qt_bias, q_a_proj_qt_bias), dim=-1).contiguous()

        wu_q = self.q_proj.weight.data
        wu_q = wu_q.t().reshape(self.num_heads, self.qk_nope_head_dim + self.qk_rope_head_dim, -1)
        wu_q = trans_rope_weight(wu_q, self.qk_rope_head_dim)
        wu_q = wu_q.reshape(self.num_heads * (self.qk_nope_head_dim + self.qk_rope_head_dim), -1)
        wu_q = transdata(wu_q, block_size=(16, 32)).unsqueeze(0).contiguous()
        self.wu_q = torch_npu.npu_format_cast(wu_q, 29)

        qb_deq_scl = self.q_proj.deq_scale.data
        qb_deq_scl = qb_deq_scl.reshape(self.num_heads, self.qk_nope_head_dim + self.qk_rope_head_dim, -1)
        qb_deq_scl = trans_rope_weight(qb_deq_scl, self.qk_rope_head_dim)
        self.qb_deq_scl = qb_deq_scl.reshape(self.num_heads * (self.qk_nope_head_dim + self.qk_rope_head_dim))

        qb_qt_bias = self.q_proj.quant_bias.data
        qb_qt_bias = qb_qt_bias.reshape(self.num_heads, self.qk_nope_head_dim + self.qk_rope_head_dim, -1)
        qb_qt_bias = trans_rope_weight(qb_qt_bias, self.qk_rope_head_dim)
        self.qb_qt_bias = qb_qt_bias.reshape(self.num_heads * (self.qk_nope_head_dim + self.qk_rope_head_dim))

        device = self.q_proj.weight.device
        self.gamma1 = self.q_a_layernorm.weight.data  # type: ignore[union-attr]
        self.beta1 = self.q_a_layernorm.bias.data  # type: ignore[union-attr]
        self.gamma2 = self.kv_a_layernorm.weight.data  # type: ignore[union-attr]
        self.quant_scale0 = self.fused_qkv_a_proj.input_scale.data
        self.quant_offset0 = self.fused_qkv_a_proj.input_offset.data
        self.quant_scale1 = self.q_proj.input_scale.data
        self.quant_offset1 = self.q_proj.input_offset.data
        self.ctkv_scale = torch.tensor([1], dtype=act_dtype, device=device)
        self.q_nope_scale = torch.tensor([1], dtype=act_dtype, device=device)

        # On KV consumers (decode-only) MLAPO uses the transformed weights built above;
        # the original fused_qkv_a_proj/q_proj weights and quant params are no longer
        # referenced, so drop them to save memory.
        if (
            self.vllm_config.kv_transfer_config is not None
            and self.vllm_config.kv_transfer_config.is_kv_consumer
            and self.vllm_config.scheduler_config.max_num_batched_tokens <= MLAPO_MAX_SUPPORTED_TOKENS
        ):
            self.fused_qkv_a_proj.weight = None
            self.fused_qkv_a_proj.deq_scale = None
            self.fused_qkv_a_proj.quant_bias = None
            self.q_proj.weight = None
            self.q_proj.deq_scale = None
            self.q_proj.quant_bias = None
            torch.npu.empty_cache()

    def _sfa_preprocess_decode(
        self,
        hidden_states: torch.Tensor,
        kv_cache: tuple[torch.Tensor, torch.Tensor, torch.Tensor],
        attn_metadata: M,
        need_gather_q_kv: bool,
        num_input_tokens: int,
    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
        hidden_states = torch.ops.vllm.maybe_all_gather_and_maybe_unpad(hidden_states.contiguous(), need_gather_q_kv)
        k_nope, k_pe = kv_cache[0], kv_cache[1]
        ql_nope = torch.empty(
            (num_input_tokens, self.W_UK_T.shape[0], k_nope.shape[-1]),
            dtype=hidden_states.dtype,
            device=hidden_states.device,
        )
        q_pe = torch.empty(
            (num_input_tokens, self.W_UK_T.shape[0], k_pe.shape[-1]),
            dtype=hidden_states.dtype,
            device=hidden_states.device,
        )
        q_c = torch.empty(
            (num_input_tokens, self.q_lora_rank),
            dtype=hidden_states.dtype,
            device=hidden_states.device,
        )
        torch.ops._C_ascend.mla_preprocess(
            hidden_states,
            self.wd_qkv,
            self.deq_scale_qkv,
            self.gamma1,
            self.beta1,
            self.wu_q,
            self.qb_deq_scl,
            self.gamma2,
            attn_metadata.cos,
            attn_metadata.sin,
            self.W_UK_T,
            k_nope,
            k_pe,
            attn_metadata.slot_mapping,
            quant_scale0=self.quant_scale0,
            quant_offset0=self.quant_offset0,
            bias0=self.quant_bias_qkv,
            quant_scale1=self.quant_scale1,
            quant_offset1=self.quant_offset1,
            bias1=self.qb_qt_bias,
            ctkv_scale=self.ctkv_scale,
            q_nope_scale=self.q_nope_scale,
            cache_mode="krope_ctkv",
            quant_mode="per_tensor_quant_asymm",
            enable_inner_out=True,
            q_out0=ql_nope,
            kv_cache_out0=k_nope,
            q_out1=q_pe,
            kv_cache_out1=k_pe,
            inner_out=q_c,
        )
        return hidden_states, ql_nope, q_pe, q_c

    def forward(
        self,
        layer_name,
        hidden_states: torch.Tensor,  # query in unified attn
        kv_cache: tuple[torch.Tensor, torch.Tensor, torch.Tensor],
        attn_metadata: M,
        need_gather_q_kv: bool = False,
        output: torch.Tensor | None = None,
    ) -> torch.Tensor:
        assert output is not None, "Output tensor must be provided."
        forward_context = get_forward_context()
        if attn_metadata is None:
            # Profiling run.
            if self.enable_dsa_cp_prefill_only and not forward_context.in_profile_run:
                for layer in self.layer_sharding_kwargs or []:
                    if is_hidden_layer(layer):
                        reach_layer_for_shard_weight_series(layer)
            return output.fill_(0)

        cos = attn_metadata.cos
        sin = attn_metadata.sin
        actual_seq_lengths_query = attn_metadata.cum_query_lens
        actual_seq_lengths_key = attn_metadata.seq_lens
        if self.enable_dsa_cp:
            need_gather_q_kv = False
        # Inputs and outputs may be padded for CUDA graphs
        num_input_tokens = attn_metadata.num_input_tokens
        output_padded = output

        # all-gather o_proj weight for prefill stage of PD mix node
        o_proj_full_handle = None
        # if is PD mix stage, using original TP o_proj weight, and also need to full gather for o_proj
        # weight for prefill stage.
        should_shard_weight = self.enable_dsa_cp_prefill_only or attn_metadata.attn_state not in {
            AscendAttentionState.DecodeOnly,
            AscendAttentionState.SpecDecoding,
        }

        if self.enable_mlapo and num_input_tokens <= MLAPO_MAX_SUPPORTED_TOKENS:
            hidden_states, ql_nope, q_pe, q_c = self._sfa_preprocess_decode(
                hidden_states=hidden_states,
                kv_cache=kv_cache,
                attn_metadata=attn_metadata,
                need_gather_q_kv=need_gather_q_kv,
                num_input_tokens=num_input_tokens,
            )
            q, k = self.indexer_select_pre_process(
                x=hidden_states, qr=q_c, cos=cos, sin=sin, need_gather_q_kv=need_gather_q_kv
            )
        else:
            assert self.fused_qkv_a_proj is not None, "q lora is required for DSA."
            weight_prefetch_method = get_weight_prefetch_method()
            weight_prefetch_method.maybe_prefetch_mla_or_sla_weight_in_current_stream(
                inputs=self.fused_qkv_a_proj.weight, dependency=hidden_states
            )
            qkv_lora = self.fused_qkv_a_proj(hidden_states)[0]
            q_c, kv_no_split = qkv_lora.split(
                [self.q_lora_rank, self.kv_lora_rank + self.qk_rope_head_dim],
                dim=-1,
            )
            assert self.q_a_layernorm is not None, "q_a_layernorm must be initialized"
            q_c = self.q_a_layernorm(q_c)
            # Process for Flash Comm V1
            if need_gather_q_kv:
                q_c = torch.ops.vllm.maybe_all_gather_and_maybe_unpad(q_c.contiguous(), need_gather_q_kv)
                kv_no_split = torch.ops.vllm.maybe_all_gather_and_maybe_unpad(
                    kv_no_split.contiguous(), need_gather_q_kv
                )

            q, k = self.indexer_select_pre_process(
                x=hidden_states, qr=q_c, cos=cos, sin=sin, need_gather_q_kv=need_gather_q_kv
            )

            wait_for_kv_layer_from_connector(layer_name)

            slot_mapping = attn_metadata.slot_mapping
            if self.enable_dsa_cp:
                assert attn_metadata.dsa_cp_context is not None
                slot_mapping = attn_metadata.dsa_cp_context.slot_mapping_cp
                actual_seq_lengths_query = attn_metadata.dsa_cp_context.actual_seq_lengths_query
                actual_seq_lengths_key = attn_metadata.dsa_cp_context.actual_seq_lengths_key

            k_pe, k_nope = self.exec_kv(kv_no_split, cos, sin, kv_cache, slot_mapping, attn_metadata)

            if self.enable_dsa_cp:
                assert k_pe is not None
                assert k_nope is not None
                # support all_gather kv async for communication calculation overlap
                fused_kv_no_split, kv_ag_handle = all_gather_async(
                    torch.cat(
                        [k_pe.view(-1, k_pe.shape[-1]), k_nope.view(-1, k_nope.shape[-1]), k.view(-1, k.shape[-1])],
                        dim=1,
                    ),
                    get_tp_group(),
                    async_op=should_shard_weight,
                )

            ql_nope, q_pe = self._q_proj_and_k_up_proj(q_c)
            q_pe = self.rope_single(q_pe, cos, sin)

            if self.enable_dsa_cp:
                if kv_ag_handle is not None:
                    kv_ag_handle.wait()

                if self.enable_dsa_cp_prefill_only:
                    for layer in self.layer_sharding_kwargs or []:
                        if is_hidden_layer(layer):
                            reach_layer_for_shard_weight_series(layer)
                elif should_shard_weight:
                    _, o_proj_full_handle = all_gather_async(
                        self.o_proj_tp_weight, get_tp_group(), output=AscendSFAImpl.o_proj_full_pool
                    )

                if kv_cache is not None:
                    assert fused_kv_no_split is not None
                    k_pe, k_nope, k = fused_kv_no_split.split(
                        [self.qk_rope_head_dim, self.kv_lora_rank, self.head_dim], dim=-1
                    )
                    slot_mapping = attn_metadata.slot_mapping.view(-1, 1)
                    torch_npu.npu_scatter_nd_update_(kv_cache[0].view(-1, k_nope.shape[-1]), slot_mapping, k_nope)
                    torch_npu.npu_scatter_nd_update_(kv_cache[1].view(-1, k_pe.shape[-1]), slot_mapping, k_pe)

            k = self._get_full_kv(k, attn_metadata)
            if kv_cache is not None:
                torch_npu.npu_scatter_nd_update_(
                    kv_cache[2].view(-1, k.shape[-1]), attn_metadata.slot_mapping.view(-1, 1), k.view(-1, k.shape[-1])
                )  # b, s, n, d

        topk_indices = self.indexer_select_post_process(
            x=hidden_states,
            qr=q_c,
            q=q,
            k=k,
            kv_cache=kv_cache,
            attn_metadata=attn_metadata,
            cos=cos,
            sin=sin,
            actual_seq_lengths_query=actual_seq_lengths_query,
            actual_seq_lengths_key=actual_seq_lengths_key,
            need_gather_q_kv=need_gather_q_kv,
        )

        attn_output = self._execute_sparse_flash_attention_process(
            ql_nope, q_pe, kv_cache, topk_indices, attn_metadata, actual_seq_lengths_query, actual_seq_lengths_key
        )

        attn_output = self._v_up_proj(attn_output)
        weight_prefetch_method = get_weight_prefetch_method()
        weight_prefetch_method.maybe_prefetch_mla_or_sla_weight_in_current_stream(
            inputs=self.o_proj.weight,
            dependency=attn_output,
            max_size=MAX_O_PROJ_PREFETCH_SIZE,
            linear_layer=self.o_proj,
        )

        if self.enable_dsa_cp and not self.enable_dsa_cp_prefill_only:
            # When using SFA-CP with pd mixed, o_proj has two cases:
            # 1. prefill: o_proj is a TP weight, we need to all-gather o_proj weight to switch TP=1.
            # 2. decode: all-to-all the hidden_state before the o_proj forward.
            result, require_o_proj_forward = self._handle_o_proj_weight_switch_and_forward(
                attn_output=attn_output,
                output=output,
                o_proj_full_handle=o_proj_full_handle,
                should_shard_weight=should_shard_weight,
            )
            if not require_o_proj_forward:
                return result
            attn_output = result

        output[...] = self.o_proj(attn_output)[0]

        maybe_save_kv_layer_to_connector(layer_name, list(kv_cache))

        return output_padded

    def _execute_sparse_flash_attention_process(
        self, ql_nope, q_pe, kv_cache, topk_indices, attn_metadata, actual_seq_lengths_query, actual_seq_lengths_key
    ):
        block_table = attn_metadata.block_table
        kv = kv_cache[0]
        key_rope = kv_cache[1]

        attn_output = torch.ops._C_ascend.npu_sparse_flash_attention(
            query=ql_nope,
            key=kv,
            value=kv,
            sparse_indices=topk_indices,
            scale_value=self.scale,
            sparse_block_size=1,
            block_table=block_table,
            actual_seq_lengths_query=actual_seq_lengths_query,
            actual_seq_lengths_kv=actual_seq_lengths_key,
            query_rope=q_pe,
            key_rope=key_rope,
            layout_query="TND",
            layout_kv="PA_BSND",
            sparse_mode=3,
        )
        return attn_output

    def indexer_select_pre_process(
        self,
        x: torch.Tensor,
        qr: torch.Tensor,
        cos: torch.Tensor,
        sin: torch.Tensor,
        need_gather_q_kv: bool = False,
    ):
        k_proj, _ = self.wk(x)  # [b,s,7168] @ [7168,128] = [b,s,128]
        k_proj = torch.ops.vllm.maybe_all_gather_and_maybe_unpad(k_proj, need_gather_q_kv)
        k = self.k_norm(k_proj).unsqueeze(1)
        k = k.view(-1, 1, self.head_dim)

        if HAS_TRITON:
            q, _ = self.wq_b(qr)  # [b,s,1536] @ [1536,64*128] = [b,s,64*128]
            q = q.view(-1, self.n_head, self.head_dim)  # [n_toks,64,128]

            cos = cos.view(-1, self.qk_rope_head_dim)
            sin = sin.view(-1, self.qk_rope_head_dim)
            q, k = rope_forward_triton(
                q, k, cos, sin, rope_dim=self.qk_rope_head_dim, is_neox_style=self.is_rope_neox_style
            )
        else:
            k_pe, k_nope = torch.split(k, [self.qk_rope_head_dim, self.head_dim - self.qk_rope_head_dim], dim=-1)

            cos = cos.view(-1, 1, 1, self.qk_rope_head_dim)
            sin = sin.view(-1, 1, 1, self.qk_rope_head_dim)

            k_pe = k_pe.unsqueeze(2)
            k_pe = torch_npu.npu_interleave_rope(k_pe, cos, sin)
            k_pe = k_pe.squeeze(2)

            k = torch.cat([k_pe, k_nope], dim=-1)  # [b*s,128]
            q = None

        return q, k

    def indexer_select_post_process(
        self,
        x: torch.Tensor,
        qr: torch.Tensor,
        q: torch.Tensor | None,
        k: torch.Tensor,
        kv_cache: tuple[torch.Tensor, torch.Tensor, torch.Tensor],
        attn_metadata: M,
        cos: torch.Tensor,
        sin: torch.Tensor,
        actual_seq_lengths_query: torch.Tensor,
        actual_seq_lengths_key: torch.Tensor,
        need_gather_q_kv: bool = False,
    ):
        if q is None:
            q, _ = self.wq_b(qr)  # [b,s,1536] @ [1536,64*128] = [b,s,64*128]
            q = q.view(-1, self.n_head, self.head_dim)  # [n_toks,64,128]
            cos_q, sin_q = cos, sin

            q_pe, q_nope = torch.split(
                q, [self.qk_rope_head_dim, self.head_dim - self.qk_rope_head_dim], dim=-1
            )  # [b,s,64,64+64]

            q_pe = q_pe.unsqueeze(2)
            q_pe = torch_npu.npu_rotary_mul(q_pe, cos_q, sin_q)
            q_pe = q_pe.squeeze(2)
            q = torch.cat([q_pe, q_nope], dim=-1)  # [b*s,64,128]

        if kv_cache is not None:
            if self.is_kv_producer:
                attn_metadata.reshape_cache_event = torch.npu.Event()
            torch_npu.npu_scatter_nd_update_(
                kv_cache[2].view(-1, k.shape[-1]), attn_metadata.slot_mapping.view(-1, 1), k.view(-1, k.shape[-1])
            )  # b, s, n, d
            if self.is_kv_producer:
                attn_metadata.reshape_cache_event.record()

        weights, _ = self.weights_proj(x)
        weights = torch.ops.vllm.maybe_all_gather_and_maybe_unpad(weights, need_gather_q_kv)

        key = kv_cache[2]
        block_table = attn_metadata.block_table

        # DSV3.2 currently has graph compilation issues when using torch_npu.npu.lightning_indexer.
        # So two branches are maintained temporarily.
        # TODO: torch.ops._C_ascend.npu_lightning_indexer needs to be removed.
        if self.use_torch_npu_lightning_indexer:
            topk_indices, _ = torch_npu.npu_lightning_indexer(
                query=q,
                key=key,
                weights=weights,
                actual_seq_lengths_query=actual_seq_lengths_query,
                actual_seq_lengths_key=actual_seq_lengths_key,
                block_table=block_table,
                layout_query="TND",
                layout_key="PA_BSND",
                sparse_count=2048,
                sparse_mode=3,
            )
        else:
            topk_indices = torch.ops._C_ascend.npu_lightning_indexer(
                query=q,
                key=key,
                weights=weights,
                actual_seq_lengths_query=actual_seq_lengths_query,
                actual_seq_lengths_key=actual_seq_lengths_key,
                block_table=block_table,
                layout_query="TND",
                layout_key="PA_BSND",
                sparse_count=2048,
                sparse_mode=3,
            )
        return topk_indices

    def _init_o_proj_tp_full_params(self):
        """
        Initialize TP-mode and Full-mode parameters for o_proj weight,
        preparing for weight switching in PD mix stage.

        For PD mix stage:
        - Use original TP o_proj weight for decode phase
        - Need full-gather o_proj weight from all TP ranks for prefill phase
        """
        if AscendSFAImpl.o_proj_full_pool is None:
            sample = self.o_proj.weight
            AscendSFAImpl.o_proj_full_pool = torch.empty(
                (sample.shape[0] * self.tp_size, sample.shape[1]), dtype=sample.dtype, device=sample.device
            )

        # Save TP-mode parameters (original sharded weights)
        self.o_proj_tp_weight = self.o_proj.weight.clone().detach()
        self.o_proj_tp_aclnn_input_scale = self.o_proj.aclnn_input_scale.clone().detach()
        self.o_proj_tp_aclnn_input_scale_reciprocal = self.o_proj.aclnn_input_scale_reciprocal.clone().detach()
        self.o_proj_tp_aclnn_input_offset = self.o_proj.aclnn_input_offset.clone().detach()

        # Initially switch to TP mode for graph capture
        self.o_proj.weight.set_(self.o_proj_tp_weight)
        self.o_proj.aclnn_input_scale.set_(self.o_proj_tp_aclnn_input_scale)
        self.o_proj.aclnn_input_scale_reciprocal.set_(self.o_proj_tp_aclnn_input_scale_reciprocal)
        self.o_proj.aclnn_input_offset.set_(self.o_proj_tp_aclnn_input_offset)

        # Precompute Full-mode quantization parameters by repeating TP parameters across all TP ranks
        self.o_proj_full_aclnn_input_scale = self.o_proj.aclnn_input_scale.repeat(self.tp_size)
        self.o_proj_full_aclnn_input_scale_reciprocal = self.o_proj.aclnn_input_scale_reciprocal.repeat(self.tp_size)
        self.o_proj_full_aclnn_input_offset = self.o_proj.aclnn_input_offset.repeat(self.tp_size)

    def _handle_o_proj_weight_switch_and_forward(
        self,
        attn_output: torch.Tensor,
        output: torch.Tensor,
        o_proj_full_handle: torch.distributed.Work | None,
        should_shard_weight: bool,
    ) -> tuple[torch.Tensor, bool]:
        """
        Handle o_proj weight switching between TP-mode and Full-mode, and execute forward computation.
        """
        # Gather o_proj weight from all TP ranks for Full-mode computation
        if should_shard_weight:
            # Wait for the completion of o_proj weight all-gather operation
            if o_proj_full_handle is not None:
                o_proj_full_handle.wait()

            # Switch o_proj to Full-mode (gathered weight from all TP ranks)
            self.o_proj.weight.set_(AscendSFAImpl.o_proj_full_pool)
            self.o_proj.aclnn_input_scale.set_(self.o_proj_full_aclnn_input_scale)
            self.o_proj.aclnn_input_scale_reciprocal.set_(self.o_proj_full_aclnn_input_scale_reciprocal)
            self.o_proj.aclnn_input_offset.set_(self.o_proj_full_aclnn_input_offset)

            # Apply quantization method and execute forward computation
            output[...] = self.o_proj.quant_method.quant_method.apply(self.o_proj, attn_output)

            # Switch o_proj back to TP-mode for subsequent decode operations
            self.o_proj.weight.set_(self.o_proj_tp_weight)
            self.o_proj.aclnn_input_scale.set_(self.o_proj_tp_aclnn_input_scale)
            self.o_proj.aclnn_input_scale_reciprocal.set_(self.o_proj_tp_aclnn_input_scale_reciprocal)
            self.o_proj.aclnn_input_offset.set_(self.o_proj_tp_aclnn_input_offset)

            return output, False
        else:
            # For decode scenario: perform all-to-all communication on o_proj input activations
            # Reshape for all-to-all: [batch * seq, tp_size, head_dim] -> [tp_size, batch * seq, head_dim]
            send = (
                attn_output.view(-1, self.tp_size, self.num_heads * self.v_head_dim)
                .permute(1, 0, 2)
                .reshape(-1, self.num_heads * self.v_head_dim)
            )

            attn_output = torch.empty_like(send)
            torch.distributed.all_to_all_single(attn_output, send, group=get_tp_group().device_group)

            return attn_output, True

    def forward_mha(
        self,
        q: torch.Tensor,
        kv_c_normed: torch.Tensor,
        k_pe: torch.Tensor,
        kv_c_and_k_pe_cache: torch.Tensor,
        attn_metadata: M,
        k_scale: torch.Tensor,
        output: torch.Tensor,
    ) -> None:
        raise NotImplementedError("forward_mha is not supported for SFA attention. Use forward() instead.")

    def forward_mqa(
        self,
        q: torch.Tensor | tuple[torch.Tensor, torch.Tensor],
        kv_c_and_k_pe_cache: torch.Tensor,
        attn_metadata: M,
        layer,
    ) -> tuple[torch.Tensor, torch.Tensor | None]:
        raise NotImplementedError("forward_mqa is not supported for SFA attention. Use forward() instead.")
