common.py

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
"""
# MLA Common Components

This file implements common components for MLA implementations.

First we define:

Sq      as Q sequence length
Skv     as KV sequence length

MLA has two possible ways of computing, a data-movement friendly approach and a
compute friendly approach, we generally want to use the compute friendly
approach for "prefill" (i.e. the ratio Sq / Skv is "small", is near 1)
and the data-movement friendly approach for "decode" (i.e. the ratio
Sq / Skv is "large").

NOTE what we deem small and large is currently determined by if its labelled
prefill or decode by the scheduler, but this is something we should probably
tune.

Main reference: DeepseekV2 paper, and FlashInfer Implementation
(https://arxiv.org/abs/2405.04434 and https://github.com/flashinfer-ai/flashinfer/pull/551).

Deepseek's MLA attention works the following way:
* Use a single latent vector to represent the per-token entry of the KV cache.
* For decode (i.e. the memory friendly approach) the attention "simulates" a
multi-head attention, while the compute is similar to multi-query attention.

Below is example of both paths assuming batchsize = 1

## More Extent Definitions:

C           Context length, `Skv - Sq`
H           hidden size
N           number of attention heads
Lq          latent dimension for Q              1536 in DSV3
Lkv         latent dimension for K/V            512 in DSV3
P           nope dimension, no rope.            128 in DSV3
R           rope dimension, goes through rope.  64 in DSV3
V           V head dim.                         128 in DSV3

## Vector/Matrix Definitions

h_t         hidden states (input to attention)  shape [Sq, H]
q_c         latent/compressed Q                 shape [Sq, Lq]
q_nope      uncompressed Q (no-rope)            shape [Sq, N, P]
q_pe        uncompressed Q (rope)               shape [Sq, N, R]
kv_c        latent/compressed KV                shape [Skv, Lkv]
k_pe        decoupled k position embeddings     shape [Skv, R]
new_kv_c    new kv_c from current iter          shape [Sq, Lkv]
new_k_pe    new k_pe from current iter          shape [Sq, R]
cache_kv_c  cached k_c from previous iters      shape [C, Lkv]
cache_k_pe  cached k_pe from previous iters     shape [C, R]
W_DQ        project h_t to q_c                  shape [H, Lq]
W_UQ        project q_c to q_nope               shape [Lq, N * P]
W_QR        project q_c to q_pe                 shape [Lq, N * R]
W_DKV       project h_t to kv_c                 shape [H, Lkv]
W_UK        project kv_c to k_nope              shape [Lkv, N, P]
W_KR        project h_t to k_pe                 shape [H, R]
W_UV        project kv_c to v                   shape [Lkv, N, V]
W_O         project v to h_t                    shape [N * V, H]


## Compute Friendly Approach (i.e. "_forward_prefill"):

q_c      = h_t @ W_DQ
q_nope   = (q_c @ W_UQ).view(Sq, N, P)
q_pe     = RoPE(q_c @ W_QR).view(Sq, N, R)
new_kv_c = h_t @ W_DKV
new_k_pe = RoPE(h_t @ W_KR)
kv_c     = torch.cat([new_kv_c, cache_kv_c], dim=0)
k_pe     = torch.cat([new_k_pe, cache_k_pe], dim=0)
k_nope   = (kv_c @ W_UK.view(Lkv, N * P)).view(Skv, N, P)
v        = (kv_c @ W_UV.view(Lkv, N * V)).view(Skv, N, V)

// MHA with QK headdim = P + R
//           V headdim = V
//      spda_o shape [Sq, N, V]
spda_o = scaled_dot_product_attention(
    torch.cat([q_nope, q_pe], dim=-1),
    torch.cat([k_nope, k_pe.unsqueeze(1).expand(-1, N, -1)], dim=-1),
    v
)
return spda_o @ W_O

NOTE: in the actual code,
    `kv_b_proj` is [W_UK; W_UV] concatenated per head
    `q_b_proj` is [W_UQ; W_QR] concatenated per head
    `out_proj` is W_O


## Data-Movement Friendly Approach (i.e. "_forward_decode"):

Runtime
q_c      = h_t @ W_DQ
q_nope   = (q_c @ W_UQ).view(-1, N, P)
ql_nope  = einsum("snh,lnh->snl", q, W_UK)
q_pe     = RoPE(q_c @ W_QR).view(Sq, N, R)
new_kv_c = h_t @ W_DKV
new_k_pe = RoPE(h_t @ W_KR)
kv_c     = torch.cat([new_kv_c, cache_kv_c], dim=0)
k_pe     = torch.cat([new_k_pe, cache_k_pe], dim=0)

// MQA with QK headdim = Lkv + R
//           V headdim = Lkv
//      spda_o shape [Sq, N, Lkv]
// NOTE: this is less compute-friendly since Lkv > P
//       but is more data-movement friendly since its MQA vs MHA
spda_o = scaled_dot_product_attention(
    torch.cat([ql_nope, q_pe], dim=-1),
    torch.cat([kv_c, k_pe], dim=-1),
    kv_c
)

o = einsum("snl,lnv->snv", spda_o.reshape(-1, N, Lkv), W_UV)
return o.view(-1, N * V) @ self.num_heads @ W_O


## Chunked Prefill

For chunked prefill we want to use the compute friendly algorithm. We are
assuming sufficiently large Sq / Skv ratio, in the future may want to switch to
the data-movement friendly approach if the chunk (i.e. `Sq`) is small.

However, the compute-friendly approach can potentially run out of memory if Skv
is large due to: `k_nope = (kv_c @ W_UK).view(Skv, N, P)`

To mitigate this, we chunk the computation of attention with respect to the
current context (i.e. `cache_kv_c` and `cache_k_pe`) so that we can used a
fixed workspace size.

The chunked prefill approach is as follows:

MCC        Max chunk of context to process per iter, computed dynamically,
           used to bound the memory usage

q_c        = h_t @ W_DQ
q_nope     = (q_c @ W_UQ).view(Sq, N, P)
q_pe       = RoPE(q_c @ W_QR).view(Sq, N, R)
new_kv_c   = h_t @ W_DKV
new_k_pe   = RoPE(h_t @ W_KR)
new_k_nope = (new_kv_c @ W_UK.view(Lkv, N * P)).view(Sq, N, P)
new_v      = (new_kv_c @ W_UV.view(Lkv, N * V)).view(Sq, N, V)

// MHA between queries and new KV
//     with QK headdim = P + R
//           V headdim = V
//    curr_o   shape [Sq, N, V]
//    curr_lse shape [N, Sq], this is just order FA returns
curr_o, curr_lse = scaled_dot_product_attention(
    torch.cat([q_nope, q_pe], dim=-1),
    torch.cat([new_k_nope, new_k_pe.unsqueeze(1).expand(-1, N, -1)], dim=-1),
    new_v,
    casual=True,
    return_softmax_lse=True
)

// Compute attention with the already existing context
for chunk_idx in range(cdiv(C, MCC)):
    chunk_start  = chunk_idx * MCC
    chunk_end    = min(chunk_start + MCC, C)
    Sc           = chunk_end - chunk_start
    cache_kv_c_chunk   = cache_kv_c[chunk_start:chunk_end]
    cache_k_pe_chunk   = cache_k_pe[chunk_start:chunk_end]
    cache_k_nope_chunk = (cache_kv_c_chunk @ W_UK).view(-1, N, P)
    cache_v_chunk      = (cache_kv_c_chunk @ W_UV).view(-1, N, V)

    chunk_o, chunk_lse = scaled_dot_product_attention(
        torch.cat([q_nope, q_pe], dim=-1),
        torch.cat([cache_k_nope_chunk,
                   cache_k_pe_chunk.unsqueeze(1).expand(-1, N, -1)],
                   dim=-1),
        cache_v_chunk,
        casual=False,
        return_softmax_lse=True
    )

    curr_o, curr_lse = merge_attn_states(
        suffix_output=curr_o,
        suffix_lse=curr_lse,
        prefix_output=chunk_o,
        prefix_lse=chunk_lse,
    )

return curr_o @ W_O
"""

import functools
from abc import abstractmethod
from dataclasses import dataclass, field
from enum import Enum
from typing import ClassVar, Generic, TypeVar

import torch
from tqdm import tqdm

from vllm import _custom_ops as ops
from vllm import envs
from vllm._aiter_ops import rocm_aiter_ops
from vllm.attention.backends.abstract import (
    AttentionBackend,
    AttentionLayer,
    MLAAttentionImpl,
)
from vllm.attention.backends.utils import get_mla_dims
from vllm.attention.ops.common import cp_lse_ag_out_rs
from vllm.attention.ops.merge_attn_states import merge_attn_states
from vllm.attention.utils.fa_utils import get_flash_attn_version
from vllm.config import VllmConfig, get_current_vllm_config
from vllm.distributed.parallel_state import get_dcp_group, is_global_first_rank
from vllm.logger import init_logger
from vllm.model_executor.layers.batch_invariant import (
    vllm_is_batch_invariant,
)
from vllm.model_executor.layers.linear import (
    ColumnParallelLinear,
    LinearBase,
    UnquantizedLinearMethod,
)
from vllm.platforms import current_platform
from vllm.utils.flashinfer import has_nvidia_artifactory
from vllm.utils.math_utils import cdiv, round_down
from vllm.v1.attention.backends.utils import (
    AttentionMetadataBuilder,
    CommonAttentionMetadata,
    get_dcp_local_seq_lens,
    get_per_layer_parameters,
    infer_global_hyperparameters,
    split_decodes_and_prefills,
)
from vllm.v1.kv_cache_interface import AttentionSpec


class QueryLenSupport(Enum):
    """Defines the level of query length support for an attention backend's
    decode pipeline.

    - SINGLE_ONLY: Decode pipeline only supports single-token queries
                   (query_len=1)
    - UNIFORM: Decode pipeline supports uniform multi-token queries
               (all requests must have same query_len > 1)
    - VARLEN: Decode pipeline supports variable-length queries
              (mixed query lengths in same batch)
    """

    SINGLE_ONLY = "single_only"
    UNIFORM = "uniform"
    VARLEN = "varlen"


try:
    from vllm.vllm_flash_attn import flash_attn_varlen_func

    is_vllm_fa = True
except ImportError:
    # For rocm use upstream flash attention
    if current_platform.is_rocm():
        from flash_attn import flash_attn_varlen_func
    is_vllm_fa = False

try:
    from flashinfer import BatchPrefillWithRaggedKVCacheWrapper
    from flashinfer.prefill import cudnn_batch_prefill_with_kv_cache  # noqa: F401

    flashinfer_available = True
except ImportError:
    BatchPrefillWithRaggedKVCacheWrapper = object

    flashinfer_available = False


def dynamic_per_batched_tensor_quant(
    x: torch.Tensor, dtype: torch.dtype = torch.float8_e4m3fn
):
    DTYPE_MAX = torch.finfo(dtype).max
    min_val, max_val = x.aminmax()
    amax = torch.maximum(min_val.abs(), max_val.abs()).clamp(min=1e-10)
    scale = DTYPE_MAX / amax
    x_scl_sat = (x * scale).clamp(min=-DTYPE_MAX, max=DTYPE_MAX)
    return x_scl_sat.to(dtype).contiguous(), scale.float().reciprocal()


logger = init_logger(__name__)

CUDNN_WORKSPACE_SIZE = 12800


class MLACommonBackend(AttentionBackend):
    accept_output_buffer: bool = True

    @staticmethod
    def get_name() -> str:
        return "TRITON_MLA"

    @staticmethod
    def get_builder_cls() -> type["MLACommonMetadataBuilder"]:
        return MLACommonMetadataBuilder

    @staticmethod
    def get_kv_cache_shape(
        num_blocks: int,
        block_size: int,
        num_kv_heads: int,  # assumed to be 1 for MLA
        head_size: int,
        cache_dtype_str: str = "auto",
    ) -> tuple[int, ...]:
        return (num_blocks, block_size, head_size)

    @staticmethod
    def get_kv_cache_stride_order(
        include_num_layers_dimension: bool = False,
    ) -> tuple[int, ...]:
        # `stride_order` indicates the permutation that gets
        # us from `get_kv_cache_shape` to the actual memory layout we want.
        # (num_blocks, num_layers, block_size, head_size)
        return (1, 0, 2, 3) if include_num_layers_dimension else (0, 1, 2)

    @classmethod
    def get_supported_head_sizes(cls) -> list[int]:
        return [576]

    @classmethod
    def is_mla(cls) -> bool:
        return True


@dataclass
class MLACommonPrefillMetadata:
    """Prefill Specific Metadata"""

    @dataclass
    class ChunkedContextMetadata:
        # New for MLA (compared to FlashAttention)
        # For handling chunked prefill
        cu_seq_lens: torch.Tensor
        starts: torch.Tensor
        seq_tot: list[int]
        max_seq_lens: list[int]
        seq_lens: torch.Tensor
        workspace: torch.Tensor
        token_to_seq: torch.Tensor
        chunk_total_token: list[int]

        # for mla DCP
        padded_local_chunk_seq_lens: list[list[int]] | None = None
        local_context_lens_allranks: list[list[int]] | None = None
        padded_local_cu_seq_lens: torch.Tensor | None = None
        cu_seq_lens_lst: list[list[int]] | None = None
        chunk_size: int | None = None

    block_table: torch.Tensor
    query_start_loc: torch.Tensor
    max_query_len: int
    chunked_context: ChunkedContextMetadata | None = None
    query_seq_lens: torch.Tensor | None = None
    workspace_buffer: torch.Tensor | None = None
    q_data_type: torch.dtype | None = None


@dataclass
class FlashInferPrefillMetadata(MLACommonPrefillMetadata):
    prefill_main: BatchPrefillWithRaggedKVCacheWrapper | None = None
    prefill_chunks: list[BatchPrefillWithRaggedKVCacheWrapper] = field(
        default_factory=list
    )


@dataclass
class CudnnPrefillMetadata(MLACommonPrefillMetadata):
    class ChunkedContextMetadata(MLACommonPrefillMetadata.ChunkedContextMetadata):
        seq_lens: torch.Tensor

    cudnn_workspace: torch.Tensor | None = None


@dataclass
class MLACommonDecodeMetadata:
    block_table: torch.Tensor
    seq_lens: torch.Tensor
    dcp_tot_seq_lens: torch.Tensor | None


D = TypeVar("D", bound=MLACommonDecodeMetadata)


@dataclass
class MLACommonMetadata(Generic[D]):
    """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_reqs: int
    max_query_len: int
    max_seq_len: int

    num_actual_tokens: int  # Number of tokens excluding padding.
    query_start_loc: torch.Tensor
    slot_mapping: torch.Tensor

    # New for MLA (compared to FlashAttention)
    # For handling prefill decode split
    num_decodes: int
    num_decode_tokens: int
    num_prefills: int

    # The dimension of the attention heads
    head_dim: int | None = None

    decode: D | None = None
    prefill: (
        MLACommonPrefillMetadata
        | FlashInferPrefillMetadata
        | CudnnPrefillMetadata
        | None
    ) = None

    def __post_init__(self):
        if self.head_dim is not None and not MLACommonBackend.supports_head_size(
            self.head_dim
        ):
            raise ValueError(f"Head dimension {self.head_dim} is not supported by MLA.")


M = TypeVar("M", bound=MLACommonMetadata)
A = TypeVar("A")


def use_flashinfer_prefill() -> bool:
    # For blackwell default to flashinfer prefill if it's available since
    # it is faster than FA2.
    from vllm.config import get_current_vllm_config

    vllm_config = get_current_vllm_config()
    return (
        not vllm_config.attention_config.disable_flashinfer_prefill
        and flashinfer_available
        and not vllm_config.attention_config.use_cudnn_prefill
        and not vllm_config.attention_config.use_trtllm_ragged_deepseek_prefill
        and current_platform.is_device_capability_family(100)
    )


def use_cudnn_prefill() -> bool:
    from vllm.config import get_current_vllm_config

    vllm_config = get_current_vllm_config()
    return (
        flashinfer_available
        and vllm_config.attention_config.use_cudnn_prefill
        and current_platform.is_device_capability_family(100)
        and has_nvidia_artifactory()
    )


def use_trtllm_ragged_deepseek_prefill() -> bool:
    """Check if TRT-LLM ragged DeepSeek prefill should be used."""
    from vllm.config import get_current_vllm_config

    vllm_config = get_current_vllm_config()
    return (
        flashinfer_available
        and vllm_config.attention_config.use_trtllm_ragged_deepseek_prefill
        and current_platform.is_device_capability_family(100)
    )


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

    # Defines the level of query length support for this backend.
    # - SINGLE_ONLY: Only single-token queries (no spec decode support)
    # - UNIFORM: Supports uniform multi-token queries (spec decode with uniform lengths)
    # - VARLEN: Supports variable-length queries (spec decode with mixed lengths)
    # If set to UNIFORM or VARLEN, this will increase `reorder_batch_threshold` when
    # speculative decoding is enabled.
    query_len_support: ClassVar[QueryLenSupport] = QueryLenSupport.SINGLE_ONLY

    # The threshold for reordering the batch into decode and prefill requests.
    # If > 1, the batch will be reordered such that requests with
    # query length <= threshold are classified as decode requests.
    # Use `query_len_support` (above) to set this automatically
    # when speculative decoding is enabled.
    reorder_batch_threshold: int = 1

    @staticmethod
    def determine_chunked_prefill_workspace_size(vllm_config: VllmConfig) -> int:
        scheduler_config = vllm_config.scheduler_config
        cache_config = vllm_config.cache_config
        model_config = vllm_config.model_config

        chunked_prefill_workspace_size = min(
            # Try for 8 full length request or at least 4 pages per-request
            max(
                8 * model_config.max_model_len,
                4 * scheduler_config.max_num_seqs * cache_config.block_size,
            ),
            # For long-context models try not to over-allocate limiting
            # kv-cache space, limiting it to 64k tokens,
            # which would result in the workspace being:
            #   2*(576)*(64*1024) = 144mb
            # (assuming 576 MLA head dim, and fp16)
            # which would result in up-projected context being
            #   2*(192*128)*(64*1024) = 3gb
            # (assuming 192 QK head dim, 128 heads, and fp16)
            64 * 1024,
        )

        # Enforce that we enough for at least 1 page per request
        chunked_prefill_workspace_size = max(
            chunked_prefill_workspace_size,
            scheduler_config.max_num_seqs * cache_config.block_size,
        )

        return chunked_prefill_workspace_size

    def __init__(
        self,
        kv_cache_spec: AttentionSpec,
        layer_names: list[str],
        vllm_config: VllmConfig,
        device: torch.device,
        metadata_cls: type[M] | None = None,
        supports_dcp_with_varlen: bool = False,
    ):
        self.metadata_cls = (
            metadata_cls if metadata_cls is not None else MLACommonMetadata
        )
        self.kv_cache_spec = kv_cache_spec
        scheduler_config = vllm_config.scheduler_config
        self.model_config = vllm_config.model_config
        parallel_config = vllm_config.parallel_config
        self.compilation_config = vllm_config.compilation_config
        self.vllm_config = vllm_config
        self.device = device

        self.num_heads = self.model_config.get_num_attention_heads(parallel_config)
        self.mla_dims = get_mla_dims(self.model_config)
        self.aot_schedule = current_platform.is_cuda()
        try:
            self.dcp_world_size = get_dcp_group().world_size
            self.dcp_rank = get_dcp_group().rank_in_group
        except AssertionError:
            # DCP might not be initialized in testing
            self.dcp_world_size = 1
            self.dcp_rank = 0
        self.dcp_local_block_size = parallel_config.cp_kv_cache_interleave_size
        self.dcp_virtual_block_size = self.dcp_local_block_size * self.dcp_world_size
        self.cp_kv_cache_interleave_size = parallel_config.cp_kv_cache_interleave_size

        # Don't try to access the runner on AMD
        if self.aot_schedule:
            self.page_size = self.kv_cache_spec.block_size

        self.chunked_prefill_workspace_size = (
            self.determine_chunked_prefill_workspace_size(vllm_config)
        )

        if self.dcp_world_size > 1:
            # Note(hc): The local kvcache is incomplete when DCP is triggered,
            # an additional kvcache allgather across the DCP group is therefore
            # required, so the workspace has to be enlarged by 1/DCP relative
            # to the original TP allocation.
            assert self.chunked_prefill_workspace_size % self.dcp_world_size == 0
            self.chunked_prefill_workspace = torch.empty(
                (
                    self.chunked_prefill_workspace_size
                    + self.chunked_prefill_workspace_size // self.dcp_world_size,
                    self.model_config.get_head_size(),
                ),
                dtype=self.model_config.dtype,
                device=device,
            )
        else:
            self.chunked_prefill_workspace = torch.empty(
                (
                    self.chunked_prefill_workspace_size,
                    self.model_config.get_head_size(),
                ),
                dtype=self.model_config.dtype,
                device=device,
            )

        self._use_cudnn_prefill = use_cudnn_prefill()
        self._use_fi_prefill = use_flashinfer_prefill()
        self._use_trtllm_ragged_prefill = use_trtllm_ragged_deepseek_prefill()
        self.prefill_metadata_cls = (
            FlashInferPrefillMetadata
            if self._use_fi_prefill
            else CudnnPrefillMetadata
            if self._use_cudnn_prefill
            else MLACommonPrefillMetadata
        )

        if self._use_fi_prefill:
            self._workspace_buffer = torch.empty(
                envs.VLLM_FLASHINFER_WORKSPACE_BUFFER_SIZE,
                dtype=torch.uint8,
                device=device,
            )

            self._fi_prefill_main: BatchPrefillWithRaggedKVCacheWrapper | None = None
            self._fi_prefill_chunks: list[BatchPrefillWithRaggedKVCacheWrapper] = []

            self._global_hyperparameters = infer_global_hyperparameters(
                get_per_layer_parameters(vllm_config, layer_names, MLACommonImpl)
            )

        if self._use_trtllm_ragged_prefill:
            self._workspace_buffer = torch.empty(
                envs.VLLM_FLASHINFER_WORKSPACE_BUFFER_SIZE,
                dtype=torch.uint8,
                device=device,
            )

        if self._use_cudnn_prefill:
            self.cudnn_workspace = torch.empty(
                CUDNN_WORKSPACE_SIZE * scheduler_config.max_num_seqs,
                dtype=torch.int8,
                device=device,
            )

        supports_spec_decode = self.query_len_support != QueryLenSupport.SINGLE_ONLY
        self._init_reorder_batch_threshold(
            self.reorder_batch_threshold, supports_spec_decode, supports_dcp_with_varlen
        )

        # Validate consistency between query_len_support and reorder_batch_threshold
        if self.query_len_support == QueryLenSupport.SINGLE_ONLY:
            assert self.reorder_batch_threshold == 1, (
                f"reorder_batch_threshold must be 1 when query_len_support is "
                f"SINGLE_ONLY, got {self.reorder_batch_threshold}"
            )

    def _build_fi_prefill_wrappers(self, prefill: FlashInferPrefillMetadata):
        qo_indptr = prefill.query_start_loc

        has_context = False
        if prefill.chunked_context is not None:
            chunked_context = prefill.chunked_context
            has_context = True

        if self._fi_prefill_main is None:
            self._fi_prefill_main = BatchPrefillWithRaggedKVCacheWrapper(
                self._workspace_buffer, "NHD", backend="cutlass"
            )

        if has_context:
            num_chunks = chunked_context.cu_seq_lens.shape[0]
            # Allocate more prefill chunk wrappers if needed
            if len(self._fi_prefill_chunks) < num_chunks:
                for _ in range(len(self._fi_prefill_chunks), num_chunks):
                    self._fi_prefill_chunks.append(
                        BatchPrefillWithRaggedKVCacheWrapper(
                            self._workspace_buffer, "NHD", backend="cutlass"
                        )
                    )
            assert num_chunks <= len(self._fi_prefill_chunks)

        # In MLA, the non-latent num_qo_heads == num_kv_heads
        num_qo_heads = self.num_heads
        num_kv_heads = num_qo_heads

        # Sanity: Verify that num_kv_heads == 1 since it is latent space
        assert self.kv_cache_spec.num_kv_heads == 1

        # Get non-latent head_dim_qk and head_dim_vo
        head_dim_qk = self.mla_dims.qk_nope_head_dim + self.mla_dims.qk_rope_head_dim
        head_dim_vo = self.mla_dims.v_head_dim

        # For main run, qo_indptr == kv_indptr
        kv_indptr = qo_indptr.clone()

        # Prepare main prefill
        self._fi_prefill_main.plan(
            qo_indptr=qo_indptr,
            kv_indptr=kv_indptr,
            num_qo_heads=num_qo_heads,
            num_kv_heads=num_kv_heads,
            head_dim_qk=head_dim_qk,
            head_dim_vo=head_dim_vo,
            causal=True,  # This is main run
            sm_scale=self._global_hyperparameters.sm_scale,
            window_left=self._global_hyperparameters.window_left,
            logits_soft_cap=self._global_hyperparameters.logits_soft_cap,
            q_data_type=self.model_config.dtype,
        )

        # Prepare context prefills
        if has_context:
            for i in range(num_chunks):
                kv_indptr_chunk = chunked_context.cu_seq_lens[i]

                self._fi_prefill_chunks[i].plan(
                    qo_indptr=qo_indptr,
                    kv_indptr=kv_indptr_chunk,
                    num_qo_heads=num_qo_heads,
                    num_kv_heads=num_kv_heads,
                    head_dim_qk=head_dim_qk,
                    head_dim_vo=head_dim_vo,
                    causal=False,  # This is context run
                    sm_scale=self._global_hyperparameters.sm_scale,
                    window_left=self._global_hyperparameters.window_left,
                    logits_soft_cap=self._global_hyperparameters.logits_soft_cap,
                    q_data_type=self.model_config.dtype,
                )

        prefill.prefill_main = self._fi_prefill_main
        prefill.prefill_chunks = self._fi_prefill_chunks

    def _build_decode(
        self,
        block_table_tensor: torch.Tensor,
        seq_lens_device: torch.Tensor,
        max_seq_len: int,
        query_start_loc_cpu: torch.Tensor,
        query_start_loc_device: torch.Tensor,
        num_decode_tokens: int,
        dcp_tot_seq_lens_device: torch.Tensor | None,
    ) -> MLACommonDecodeMetadata:
        return MLACommonDecodeMetadata(
            block_table=block_table_tensor,
            seq_lens=seq_lens_device,
            dcp_tot_seq_lens=dcp_tot_seq_lens_device,
        )

    def build_for_cudagraph_capture(
        self, common_attn_metadata: CommonAttentionMetadata
    ) -> M:
        """
        This method builds the metadata for full cudagraph capture.
        Currently, only decode is supported for full cudagraphs with MLA.
        """
        m = common_attn_metadata
        assert m.num_reqs <= (m.num_actual_tokens * self.reorder_batch_threshold), (
            "MLA only supports decode-only full CUDAGraph capture. "
            "Make sure all cudagraph capture sizes <= max_num_seq."
        )

        assert m.max_query_len <= self.reorder_batch_threshold  # decode only

        return self.build(0, m)

    def build(
        self,
        common_prefix_len: int,
        common_attn_metadata: CommonAttentionMetadata,
        fast_build: bool = False,
    ) -> M:
        num_reqs = common_attn_metadata.num_reqs
        num_tokens = common_attn_metadata.num_actual_tokens
        max_query_len = common_attn_metadata.max_query_len
        max_seq_len = common_attn_metadata.max_seq_len

        # Note(simon): be careful about the CPU <> GPU memory movement in this
        # function. We should avoid GPU -> CPU sync as much as possible because
        # it blocks on all previous kernels.
        device = self.device
        block_table_tensor = common_attn_metadata.block_table_tensor
        slot_mapping = common_attn_metadata.slot_mapping

        query_start_loc = common_attn_metadata.query_start_loc
        query_start_loc_cpu = common_attn_metadata.query_start_loc_cpu
        seq_lens = common_attn_metadata.seq_lens
        dcp_local_seq_lens = common_attn_metadata.dcp_local_seq_lens

        num_decodes, num_prefills, num_decode_tokens, num_prefill_tokens = (
            split_decodes_and_prefills(
                common_attn_metadata,
                decode_threshold=self.reorder_batch_threshold,
                require_uniform=(self.query_len_support != QueryLenSupport.VARLEN),
            )
        )

        assert num_decodes + num_prefills == num_reqs
        assert num_decode_tokens + num_prefill_tokens == num_tokens

        prefill_metadata = None
        if num_prefills > 0:
            num_computed_tokens_cpu = common_attn_metadata.num_computed_tokens_cpu

            reqs_start = num_decodes  # prefill_start

            context_lens_cpu = num_computed_tokens_cpu[reqs_start:num_reqs]
            max_context_len_cpu = context_lens_cpu.max().item()
            num_prefills_with_context_cpu = (context_lens_cpu > 0).sum().item()
            prefill_query_start_loc = (
                query_start_loc[reqs_start:] - query_start_loc[reqs_start]
            )

            chunked_context_metadata = None
            if max_context_len_cpu > 0:
                # NOTE: it is recommend you read the `Chunked Prefill` section
                # in the comment at the top of the file before trying to
                # understand the following code

                # currently we allocate an equal amount of workspace for each
                # prefill in the batch, we could probably use a more advanced
                # algorithm here and allocate more workspace to prefills with
                # longer context lengths
                max_context_chunk = (
                    self.chunked_prefill_workspace_size // num_prefills_with_context_cpu
                )

                if self.aot_schedule:
                    # align max_context_chunk to page_size by rounding down,
                    # currently the `gather_and_maybe_dequant_cache` kernel
                    # cannot handle `context_chunk_starts` that are not aligned
                    # to page_size
                    max_context_chunk = round_down(max_context_chunk, self.page_size)

                assert max_context_chunk > 0
                num_chunks = cdiv(max_context_len_cpu, max_context_chunk)

                # if `max_context_chunk = 256`, `num_chunks = 3`, and
                #   `num_prefills_with_context = 4`, create a tensor that looks
                # like
                #  [[0, 0, 0, 0], [256, 256, 256, 256], [512, 512, 512, 512]]
                # Note(simon): this is done in CPU because of downstream's
                # of `to_list`.
                chunk_starts = (
                    torch.arange(num_chunks, dtype=torch.int32)
                    .unsqueeze(1)
                    .expand(-1, num_prefills)
                    * max_context_chunk
                )
                chunk_ends = torch.min(
                    context_lens_cpu.unsqueeze(0), chunk_starts + max_context_chunk
                )
                chunk_seq_lens = (chunk_ends - chunk_starts).clamp(min=0)

                cu_seq_lens_cpu = torch.zeros(
                    num_chunks, num_prefills + 1, dtype=torch.int32, pin_memory=True
                )
                torch.cumsum(
                    chunk_seq_lens, dim=1, out=cu_seq_lens_cpu[:, 1:], dtype=torch.int32
                )
                chunk_total_token = cu_seq_lens_cpu[:, -1]

                max_token_num_over_chunk = chunk_total_token.max().item()
                token_to_seq_tensor_cpu = torch.zeros(
                    [num_chunks, max_token_num_over_chunk], dtype=torch.int32
                )
                range_idx = torch.arange(num_prefills, dtype=torch.int32)
                for i in range(num_chunks):
                    chunk_token_to_seq_tensor = torch.repeat_interleave(
                        range_idx, chunk_seq_lens[i]
                    )
                    chunk_len = chunk_token_to_seq_tensor.shape[0]
                    token_to_seq_tensor_cpu[i, :chunk_len] = chunk_token_to_seq_tensor

                if self.dcp_world_size > 1:
                    local_context_lens_allranks = get_dcp_local_seq_lens(
                        context_lens_cpu,
                        self.dcp_world_size,
                        None,
                        self.dcp_local_block_size,
                    )
                    # Note(qcs): The max local context lengths
                    # padded to `dcp_local_block_size`.
                    padded_local_context_lens_cpu = (
                        cdiv(
                            context_lens_cpu,
                            self.dcp_virtual_block_size,
                        )
                        * self.dcp_local_block_size
                    )
                    # Note(hc): The above max_context_chunk already enforces
                    # block_size alignment, DCP just need the block_size can
                    # be divisible by dcp_world_size, because DCP use
                    # cp_gather_cache which not require `cp_chunk_starts`
                    # aligned to page_size.
                    assert max_context_chunk % self.dcp_world_size == 0
                    padded_local_max_context_chunk_across_ranks = (
                        cdiv(
                            max_context_chunk,
                            self.dcp_virtual_block_size,
                        )
                        * self.dcp_local_block_size
                    )
                    local_chunk_starts = (
                        torch.arange(num_chunks, dtype=torch.int32)
                        .unsqueeze(1)
                        .expand(-1, 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(
                        num_chunks, 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_context_metadata_cls = (
                    CudnnPrefillMetadata.ChunkedContextMetadata
                    if self._use_cudnn_prefill
                    else MLACommonPrefillMetadata.ChunkedContextMetadata
                )
                if self.dcp_world_size > 1:
                    chunked_context_metadata = chunked_context_metadata_cls(
                        cu_seq_lens=cu_seq_lens_cpu.to(device, non_blocking=True),
                        starts=local_chunk_starts.to(device, non_blocking=True),
                        seq_tot=padded_local_chunk_seq_lens.sum(dim=1).tolist(),
                        max_seq_lens=chunk_seq_lens.max(dim=1).values.tolist(),
                        seq_lens=chunk_seq_lens,
                        token_to_seq=token_to_seq_tensor_cpu.to(
                            device, non_blocking=True
                        ),
                        chunk_total_token=chunk_total_token.tolist(),
                        workspace=self.chunked_prefill_workspace,
                        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.to(
                            device, non_blocking=True
                        ),
                        cu_seq_lens_lst=cu_seq_lens_cpu.tolist(),
                        chunk_size=padded_local_max_context_chunk_across_ranks,
                    )
                else:
                    chunked_context_metadata = chunked_context_metadata_cls(
                        cu_seq_lens=cu_seq_lens_cpu.to(device, non_blocking=True),
                        starts=chunk_starts.to(device, non_blocking=True),
                        seq_tot=chunk_seq_lens.sum(dim=1).tolist(),
                        max_seq_lens=chunk_seq_lens.max(dim=1).values.tolist(),
                        seq_lens=chunk_seq_lens,
                        token_to_seq=token_to_seq_tensor_cpu.to(
                            device, non_blocking=True
                        ),
                        chunk_total_token=chunk_total_token,
                        workspace=self.chunked_prefill_workspace,
                    )

                if self._use_cudnn_prefill:
                    chunked_context_metadata.seq_lens = chunk_seq_lens

                assert (
                    max(chunked_context_metadata.max_seq_lens)
                    <= self.chunked_prefill_workspace_size
                )

            prefill_metadata = self.prefill_metadata_cls(
                block_table=block_table_tensor[reqs_start:, ...],
                query_start_loc=prefill_query_start_loc,
                max_query_len=max_query_len,
                chunked_context=chunked_context_metadata,
            )

            if self._use_cudnn_prefill:
                assert isinstance(prefill_metadata, CudnnPrefillMetadata)
                prefill_metadata.query_seq_lens = (
                    prefill_query_start_loc[1:] - prefill_query_start_loc[:-1]
                )
                prefill_metadata.cudnn_workspace = self.cudnn_workspace

            if self._use_trtllm_ragged_prefill:
                prefill_metadata.query_seq_lens = (
                    prefill_query_start_loc[1:] - prefill_query_start_loc[:-1]
                )
                prefill_metadata.workspace_buffer = self._workspace_buffer

        decode_metadata = None
        if num_decodes > 0:
            dcp_tot_seq_lens_device = None
            if self.dcp_world_size > 1:
                dcp_tot_seq_lens_device = seq_lens[:num_decodes]
                seq_lens = dcp_local_seq_lens

                # After DCP distribution, the maximum number of tokens for any rank is
                # ceil(L / (N * I)) * I, where L is max_seq_len, N is dcp_world_size,
                # and I is cp_kv_cache_interleave_size.
                # This eliminates GPU->CPU sync while minimizing workspace
                # over-allocation.
                num_partitions = self.dcp_world_size * self.cp_kv_cache_interleave_size
                max_seq_len = (
                    (max_seq_len + num_partitions - 1) // num_partitions
                ) * self.cp_kv_cache_interleave_size

            decode_metadata = self._build_decode(
                block_table_tensor=block_table_tensor[:num_decodes, ...],
                seq_lens_device=seq_lens[:num_decodes],
                max_seq_len=max_seq_len,
                query_start_loc_cpu=query_start_loc_cpu[: num_decodes + 1],
                query_start_loc_device=query_start_loc[: num_decodes + 1],
                num_decode_tokens=num_decode_tokens,
                dcp_tot_seq_lens_device=dcp_tot_seq_lens_device,
            )

        attn_metadata = self.metadata_cls(
            num_reqs=common_attn_metadata.num_reqs,
            max_query_len=common_attn_metadata.max_query_len,
            max_seq_len=max_seq_len,
            num_actual_tokens=num_tokens,
            query_start_loc=query_start_loc,
            slot_mapping=slot_mapping,
            head_dim=self.model_config.get_head_size(),
            # MLACommonMetadata Chunk prefill specific
            num_decodes=num_decodes,
            num_decode_tokens=num_decode_tokens,
            num_prefills=num_prefills,
            prefill=prefill_metadata,
            decode=decode_metadata,
        )

        if self._use_fi_prefill and num_prefills > 0:
            assert isinstance(attn_metadata.prefill, FlashInferPrefillMetadata)
            self._build_fi_prefill_wrappers(attn_metadata.prefill)

        return attn_metadata


def reorg_kvcache(
    allgatered_kv_c_normed: torch.Tensor,
    allgatered_k_pe: torch.Tensor,
    padded_local_chunk_seq_lens_lst: list[int],
    local_context_lens_allranks: list[list[int]],
    sum_seq_len: int,
    max_seq_len: int,
    chunk_size: int,
    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.
    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.
    """
    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


# TODO(Lucas): rename MLACommonBaseImpl -> MLACommonImpl,
# and MLACommonImpl -> MLACommonDenseImpl or somthing like that
class MLACommonBaseImpl(MLAAttentionImpl[A], Generic[A]):
    """
    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,
        # MLA Specific Arguments
        q_lora_rank: int | None,
        kv_lora_rank: int,
        qk_nope_head_dim: int,
        qk_rope_head_dim: int,
        qk_head_dim: int,
        v_head_dim: int,
        kv_b_proj: ColumnParallelLinear,
        indexer=None,
        q_pad_num_heads: int | None = None,
    ) -> None:
        if kv_sharing_target_layer_name is not None:
            raise NotImplementedError("KV sharing is not supported for MLA")

        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

        self.q_lora_rank = q_lora_rank
        self.kv_lora_rank = kv_lora_rank
        self.qk_nope_head_dim = qk_nope_head_dim
        self.qk_rope_head_dim = qk_rope_head_dim
        self.qk_head_dim = qk_head_dim
        self.v_head_dim = v_head_dim
        self.kv_b_proj = kv_b_proj
        self.indexer = indexer
        self.q_pad_num_heads = q_pad_num_heads
        self.is_aiter_triton_fp8_bmm_enabled = rocm_aiter_ops.is_fp8bmm_enabled()

    def process_weights_after_loading(self, act_dtype: torch.dtype):
        def get_layer_weight(layer):
            WEIGHT_NAMES = ("weight", "qweight", "weight_packed")
            for attr in WEIGHT_NAMES:
                if hasattr(layer, attr):
                    return getattr(layer, attr)
            raise AttributeError(
                f"Layer '{layer}' has no recognized weight attribute: {WEIGHT_NAMES}."
            )

        def get_and_maybe_dequant_weights(layer: LinearBase):
            if not isinstance(layer.quant_method, UnquantizedLinearMethod):
                # NOTE: This should only be used offline, since it's O(N^3)
                eye = torch.eye(
                    layer.input_size_per_partition,
                    dtype=act_dtype,
                    device=get_layer_weight(layer).device,
                )
                dequant_weights = layer.quant_method.apply(layer, eye, bias=None)
                del eye
                # standardize to (output, input)
                return dequant_weights.T
            return layer.weight

        # we currently do not have quantized bmm's which are needed for
        # `W_UV` and `W_UK_T`, we just store fp16/bf16 copies and perform
        # the bmm's in 16-bit, the extra memory overhead of this is fairly low
        kv_b_proj_weight = get_and_maybe_dequant_weights(self.kv_b_proj).T
        assert kv_b_proj_weight.shape == (
            self.kv_lora_rank,
            self.num_heads * (self.qk_nope_head_dim + self.v_head_dim),
        ), (
            f"{kv_b_proj_weight.shape=}, "
            f"{self.kv_lora_rank=}, "
            f"{self.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.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
        )

        if self.is_aiter_triton_fp8_bmm_enabled:
            W_K = W_UK.transpose(0, 1)  # 16 512 128
            W_V = W_UV.permute(1, 2, 0)  # 16 128 512
            self.W_K, self.W_K_scale = dynamic_per_batched_tensor_quant(
                W_K, dtype=current_platform.fp8_dtype()
            )
            self.W_V, self.W_V_scale = dynamic_per_batched_tensor_quant(
                W_V, dtype=current_platform.fp8_dtype()
            )

            # The kernel operates on non-padded inputs. Hence, pre-compiling
            # triton kernel to avoid runtime compilation for unseen batch sizes
            # Pre-compile for batch sizes 1 to 1024 to cover most use-cases.
            # On DS-R1, this step adds roughly 50s to the model loading time.
            max_batch_size = 1024  # [ToDo] Find the optimal upper limit
            pre_compilation_list = list(range(1, max_batch_size + 1))
            if is_global_first_rank():
                pre_compilation_list = tqdm(
                    pre_compilation_list,
                    desc="[Aiter Triton] Pre-compiling fp8 BMM kernel",
                    total=max_batch_size,
                )

            for m in pre_compilation_list:
                x = torch.empty(
                    (self.W_K.shape[0], m, self.W_K.shape[2]),
                    dtype=torch.bfloat16,
                    device=self.W_K.device,
                )
                rocm_aiter_ops.triton_fp8_bmm(
                    x, self.W_K, self.W_K_scale, group_size=128, transpose_bm=True
                )

                x = torch.empty(
                    (self.W_V.shape[0], m, self.W_V.shape[2]),
                    dtype=torch.bfloat16,
                    device=self.W_V.device,
                )
                rocm_aiter_ops.triton_fp8_bmm(
                    x, self.W_V, self.W_V_scale, group_size=128, transpose_bm=True
                )
        else:
            # Convert from (L, N, V) to (N, L, V)
            self.W_UV = W_UV.transpose(0, 1)
            # Convert from (L, N, P) to (N, P, L)
            self.W_UK_T = W_UK.permute(1, 2, 0)

    def _v_up_proj(self, x: torch.Tensor, out: torch.Tensor):
        # Convert from (B, N, L) to (N, B, L)
        x = x.view(-1, self.num_heads, self.kv_lora_rank).transpose(0, 1)

        if self.is_aiter_triton_fp8_bmm_enabled:
            out = out.view(-1, self.num_heads, self.v_head_dim)
            # Multiply + Transpose (N, B, L) x (N, L, V)->(N, B, V)->(B, N, V)
            x = rocm_aiter_ops.triton_fp8_bmm(
                x, self.W_V, self.W_V_scale, group_size=128, transpose_bm=True, YQ=out
            )
        else:
            # Convert from (B, N * V) to (N, B, V)
            out = out.view(-1, self.num_heads, self.v_head_dim).transpose(0, 1)

            # Multiply (N, B, L) x (N, L, V) -> (N, B, V)
            torch.bmm(x, self.W_UV, out=out)  # Reuse "out" to make it "hot"

            # Convert from (N, B, V) to (B, N * V)
            out_new = out.transpose(0, 1).reshape(-1, self.num_heads * self.v_head_dim)

            # Adjust output buffer shape back to the original (B, N * V)
            N, B, V = out.shape
            out.resize_((B, N * V))
            out.copy_(out_new)  # Copy result


class MLACommonImpl(MLACommonBaseImpl[M], Generic[M]):
    """
    NOTE: Please read the comment at the top of the file before trying to
    understand this class
    """

    def __init__(self, *args, **kwargs) -> None:
        super().__init__(*args, **kwargs)

        if use_flashinfer_prefill():
            logger.debug_once("Using FlashInfer prefill for MLA")
            self._run_prefill_context_chunk = self._run_prefill_context_chunk_fi
            self._run_prefill_new_tokens = self._run_prefill_new_tokens_fi
            self._pad_v = False
        elif use_trtllm_ragged_deepseek_prefill():
            logger.debug_once("Using TRT-LLM ragged DeepSeek prefill for MLA")
            self._run_prefill_context_chunk = (
                self._run_prefill_context_chunk_trtllm_ragged
            )
            self._run_prefill_new_tokens = self._run_prefill_new_tokens_trtllm_ragged
            self._pad_v = False
        elif use_cudnn_prefill():
            logger.debug_once("Using CUDNN prefill for MLA")
            self._run_prefill_context_chunk = self._run_prefill_context_chunk_cudnn
            self._run_prefill_new_tokens = self._run_prefill_new_tokens_cudnn
            self._pad_v = False
        else:  # Use FlashAttention
            logger.debug_once("Using FlashAttention prefill for MLA")
            self._run_prefill_context_chunk = self._run_prefill_context_chunk_fa
            self._run_prefill_new_tokens = self._run_prefill_new_tokens_fa

            # Handle the differences between the flash_attn_varlen from
            # flash_attn and the one from vllm_flash_attn. The former is used on
            # RoCM and the latter has an additional parameter to control
            # FA2 vs FA3
            self.flash_attn_varlen_func = flash_attn_varlen_func
            self.vllm_flash_attn_version = get_flash_attn_version()
            if self.vllm_flash_attn_version is not None:
                self.flash_attn_varlen_func = functools.partial(
                    flash_attn_varlen_func, fa_version=self.vllm_flash_attn_version
                )

            # For MLA the v head dim is smaller than qk head dim so we pad out
            # v with 0s to match the qk head dim for attention backends that do
            # not support different headdims
            # We don't need to pad V if we are on a hopper system with FA3
            self._pad_v = self.vllm_flash_attn_version is None or not (
                self.vllm_flash_attn_version == 3
                and current_platform.get_device_capability()[0] == 9
            )

        self.dcp_world_size: int | None = None

        self.chunked_prefill_workspace_size = (
            MLACommonMetadataBuilder.determine_chunked_prefill_workspace_size(
                get_current_vllm_config()
            )
        )
        self.cp_kv_cache_interleave_size: int = (
            get_current_vllm_config().parallel_config.cp_kv_cache_interleave_size
        )

    def _flash_attn_varlen_diff_headdims(
        self, q, k, v, return_softmax_lse=False, softmax_scale=None, **kwargs
    ):
        maybe_padded_v = v
        if self._pad_v:
            maybe_padded_v = torch.nn.functional.pad(
                v, [0, q.shape[-1] - v.shape[-1]], value=0
            )

        if is_vllm_fa:
            kwargs["return_softmax_lse"] = return_softmax_lse
        else:
            # ROCm leverages the upstream flash_attn, which takes a parameter
            # called "return_attn_probs" instead of return_softmax_lse
            kwargs["return_attn_probs"] = return_softmax_lse
        if vllm_is_batch_invariant():
            kwargs["num_splits"] = 1

        attn_out = self.flash_attn_varlen_func(
            q=q,
            k=k,
            v=maybe_padded_v,
            softmax_scale=softmax_scale,
            **kwargs,
        )

        # Unpack the output if there is multiple results
        lse = None
        if isinstance(attn_out, tuple):
            attn_out, lse = attn_out[0], attn_out[1]

        # Remain consistent with old `flash_attn_varlen_func` where there
        # is only one output tensor if `return_softmax_lse` is False.
        if return_softmax_lse:
            return attn_out, lse
        return attn_out

    def _run_prefill_new_tokens_fa(
        self, prefill: MLACommonPrefillMetadata, q, k, v, return_softmax_lse
    ):
        return self._flash_attn_varlen_diff_headdims(
            q=q,
            k=k,
            v=v,
            cu_seqlens_q=prefill.query_start_loc,
            cu_seqlens_k=prefill.query_start_loc,
            max_seqlen_q=prefill.max_query_len,
            max_seqlen_k=prefill.max_query_len,
            softmax_scale=self.scale,
            causal=True,
            return_softmax_lse=return_softmax_lse,
        )

    def _run_prefill_new_tokens_fi(
        self, prefill: MLACommonPrefillMetadata, q, k, v, return_softmax_lse
    ):
        assert isinstance(prefill, FlashInferPrefillMetadata)
        assert prefill.prefill_main is not None

        ret = prefill.prefill_main.run(
            q=q,
            k=k,
            v=v,
            return_lse=return_softmax_lse,
        )

        if isinstance(ret, tuple):
            return ret[0], ret[1].transpose(0, 1).contiguous()
        return ret

    def _run_prefill_new_tokens_cudnn(
        self, prefill: MLACommonPrefillMetadata, q, k, v, return_softmax_lse
    ):
        assert isinstance(prefill, CudnnPrefillMetadata)
        assert prefill.query_seq_lens is not None
        output, lse = cudnn_batch_prefill_with_kv_cache(
            q=q,
            k_cache=k,
            v_cache=v,
            scale=self.scale,
            workspace_buffer=prefill.cudnn_workspace,
            max_token_per_sequence=prefill.max_query_len,
            max_sequence_kv=prefill.max_query_len,
            actual_seq_lens_q=prefill.query_seq_lens.view(-1, 1, 1, 1),
            actual_seq_lens_kv=prefill.query_seq_lens.view(-1, 1, 1, 1),
            causal=True,
            # Do not support False for now
            return_lse=True,
            # Indicates actual_seq_lens are on GPU or CPU.
            is_cuda_graph_compatible=True,
        )
        if return_softmax_lse:
            return output, lse
        return output

    def _run_prefill_context_chunk_fa(
        self, prefill: MLACommonPrefillMetadata, chunk_idx: int, q, k, v
    ):
        assert prefill.chunked_context is not None
        return self._flash_attn_varlen_diff_headdims(
            q=q,
            k=k,
            v=v,
            cu_seqlens_q=prefill.query_start_loc,
            cu_seqlens_k=prefill.chunked_context.cu_seq_lens[chunk_idx],
            max_seqlen_q=prefill.max_query_len,
            max_seqlen_k=prefill.chunked_context.max_seq_lens[chunk_idx],
            softmax_scale=self.scale,
            causal=False,  # Context is unmasked
            return_softmax_lse=True,
        )

    def _run_prefill_context_chunk_fi(
        self, prefill: MLACommonPrefillMetadata, chunk_idx: int, q, k, v
    ):
        assert isinstance(prefill, FlashInferPrefillMetadata)

        attn_out, lse = prefill.prefill_chunks[chunk_idx].run(
            q=q,
            k=k,
            v=v,
            return_lse=True,
        )

        # Convert from (q_len, num_heads) to (num_heads, q_len)
        return attn_out, lse.transpose(0, 1).contiguous()

    def _run_prefill_context_chunk_cudnn(
        self, prefill: MLACommonPrefillMetadata, chunk_idx: int, q, k, v
    ):
        assert isinstance(prefill, CudnnPrefillMetadata)
        assert prefill.chunked_context is not None
        assert prefill.chunked_context.seq_lens[chunk_idx] is not None
        assert prefill.query_seq_lens is not None
        return cudnn_batch_prefill_with_kv_cache(
            q=q,
            k_cache=k,
            v_cache=v,
            scale=self.scale,
            workspace_buffer=prefill.cudnn_workspace,
            max_token_per_sequence=prefill.max_query_len,
            max_sequence_kv=prefill.chunked_context.max_seq_lens[chunk_idx],
            actual_seq_lens_q=prefill.query_seq_lens.view(-1, 1, 1, 1),
            actual_seq_lens_kv=prefill.chunked_context.seq_lens[chunk_idx].view(
                -1, 1, 1, 1
            ),
            causal=False,
            return_lse=True,
            # Indicates actual_seq_lens are on GPU or CPU.
            is_cuda_graph_compatible=True,
        )

    def _run_prefill_new_tokens_trtllm_ragged(
        self, prefill: MLACommonPrefillMetadata, q, k, v, return_softmax_lse
    ):
        """TRT-LLM ragged attention for new tokens (causal)."""
        from flashinfer.prefill import trtllm_ragged_attention_deepseek

        assert prefill.query_seq_lens is not None
        assert prefill.workspace_buffer is not None

        ret = trtllm_ragged_attention_deepseek(
            query=q,
            key=k,
            value=v,
            workspace_buffer=prefill.workspace_buffer,
            seq_lens=prefill.query_seq_lens,
            max_q_len=prefill.max_query_len,
            max_kv_len=prefill.max_query_len,
            bmm1_scale=self.scale,
            bmm2_scale=1.0,
            o_sf_scale=1.0,
            batch_size=prefill.query_seq_lens.shape[0],
            window_left=-1,
            cum_seq_lens_q=prefill.query_start_loc,
            cum_seq_lens_kv=prefill.query_start_loc,
            enable_pdl=False,
            is_causal=True,
            return_lse=return_softmax_lse,
        )

        if isinstance(ret, tuple):
            # Convert from (q_len, num_heads) to (num_heads, q_len)
            return ret[0], ret[1].transpose(0, 1).contiguous()
        return ret

    def _run_prefill_context_chunk_trtllm_ragged(
        self, prefill: MLACommonPrefillMetadata, chunk_idx: int, q, k, v
    ):
        """TRT-LLM ragged attention for context chunks (non-causal)."""
        from flashinfer.prefill import trtllm_ragged_attention_deepseek

        assert prefill.chunked_context is not None
        assert prefill.chunked_context.seq_lens[chunk_idx] is not None
        assert prefill.workspace_buffer is not None

        out = torch.zeros(
            q.shape[0],
            q.shape[1],
            v.shape[2],
            device=q.device,
            dtype=q.dtype,
        )
        prefill.workspace_buffer.fill_(0)

        attn_out, lse = trtllm_ragged_attention_deepseek(
            query=q,
            key=k,
            value=v,
            workspace_buffer=prefill.workspace_buffer,
            seq_lens=prefill.chunked_context.seq_lens[chunk_idx],
            max_q_len=prefill.max_query_len,
            max_kv_len=prefill.chunked_context.max_seq_lens[chunk_idx],
            bmm1_scale=self.scale,
            bmm2_scale=1.0,
            o_sf_scale=1.0,
            batch_size=prefill.chunked_context.seq_lens[chunk_idx].shape[0],
            window_left=-1,
            cum_seq_lens_q=prefill.query_start_loc,
            cum_seq_lens_kv=prefill.chunked_context.cu_seq_lens[chunk_idx],
            enable_pdl=False,
            is_causal=False,
            return_lse=True,
            out=out,
        )

        # Convert from (q_len, num_heads) to (num_heads, q_len)
        return attn_out, lse.transpose(0, 1).contiguous()

    def process_weights_after_loading(self, act_dtype: torch.dtype):
        def get_layer_weight(layer):
            WEIGHT_NAMES = ("weight", "qweight", "weight_packed")
            for attr in WEIGHT_NAMES:
                if hasattr(layer, attr):
                    return getattr(layer, attr)
            raise AttributeError(
                f"Layer '{layer}' has no recognized weight attribute: {WEIGHT_NAMES}."
            )

        def get_and_maybe_dequant_weights(layer: LinearBase):
            if not isinstance(layer.quant_method, UnquantizedLinearMethod):
                # NOTE: This should only be used offline, since it's O(N^3)
                eye = torch.eye(
                    layer.input_size_per_partition,
                    dtype=act_dtype,
                    device=get_layer_weight(layer).device,
                )
                dequant_weights = layer.quant_method.apply(layer, eye, bias=None)
                del eye
                # standardize to (output, input)
                return dequant_weights.T
            return layer.weight

        # we currently do not have quantized bmm's which are needed for
        # `W_UV` and `W_UK_T`, we just store fp16/bf16 copies and perform
        # the bmm's in 16-bit, the extra memory overhead of this is fairly low
        kv_b_proj_weight = get_and_maybe_dequant_weights(self.kv_b_proj).T
        assert kv_b_proj_weight.shape == (
            self.kv_lora_rank,
            self.num_heads * (self.qk_nope_head_dim + self.v_head_dim),
        ), (
            f"{kv_b_proj_weight.shape=}, "
            f"{self.kv_lora_rank=}, "
            f"{self.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.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
        )

        if self.is_aiter_triton_fp8_bmm_enabled:
            W_K = W_UK.transpose(0, 1)  # 16 512 128
            W_V = W_UV.permute(1, 2, 0)  # 16 128 512
            self.W_K, self.W_K_scale = dynamic_per_batched_tensor_quant(
                W_K, dtype=current_platform.fp8_dtype()
            )
            self.W_V, self.W_V_scale = dynamic_per_batched_tensor_quant(
                W_V, dtype=current_platform.fp8_dtype()
            )

            # The kernel operates on non-padded inputs. Hence, pre-compiling
            # triton kernel to avoid runtime compilation for unseen batch sizes
            # Pre-compile for batch sizes 1 to 1024 to cover most use-cases.
            # On DS-R1, this step adds roughly 50s to the model loading time.
            max_batch_size = 1024  # [ToDo] Find the optimal upper limit
            pre_compilation_list = list(range(1, max_batch_size + 1))
            if is_global_first_rank():
                pre_compilation_list = tqdm(
                    pre_compilation_list,
                    desc="[Aiter Triton] Pre-compiling fp8 BMM kernel",
                    total=max_batch_size,
                )

            for m in pre_compilation_list:
                x = torch.empty(
                    (self.W_K.shape[0], m, self.W_K.shape[2]),
                    dtype=torch.bfloat16,
                    device=self.W_K.device,
                )
                rocm_aiter_ops.triton_fp8_bmm(
                    x, self.W_K, self.W_K_scale, group_size=128, transpose_bm=True
                )

                x = torch.empty(
                    (self.W_V.shape[0], m, self.W_V.shape[2]),
                    dtype=torch.bfloat16,
                    device=self.W_V.device,
                )
                rocm_aiter_ops.triton_fp8_bmm(
                    x, self.W_V, self.W_V_scale, group_size=128, transpose_bm=True
                )
        else:
            # Convert from (L, N, V) to (N, L, V)
            self.W_UV = W_UV.transpose(0, 1)
            # Convert from (L, N, P) to (N, P, L)
            self.W_UK_T = W_UK.permute(1, 2, 0)

    def _concat_k_nope_k_pe(
        self, k_nope: torch.Tensor, k_pe: torch.Tensor
    ) -> torch.Tensor:
        """
        Efficiently concatenate k_nope and k_pe tensors along the last dimension.

        This function avoids the performance penalty of torch.cat with expanded
        non-contiguous tensors by pre-allocating the output and using direct copies.

        Args:
            k_nope: Tensor of shape [..., nope_dim]
            k_pe: Tensor to broadcast and concatenate, typically shape [..., 1, pe_dim]
                or [..., pe_dim]

        Returns:
            Tensor of shape [..., nope_dim + pe_dim]
        """
        k = torch.empty(
            (*k_nope.shape[:-1], k_nope.shape[-1] + k_pe.shape[-1]),
            dtype=k_nope.dtype,
            device=k_nope.device,
        )
        # Direct copies with efficient broadcasting
        k[..., : k_nope.shape[-1]] = k_nope
        k[..., k_nope.shape[-1] :] = k_pe
        return k

    def _compute_prefill_context(
        self,
        q: torch.Tensor,
        kv_c_and_k_pe_cache: torch.Tensor,
        attn_metadata: MLACommonMetadata,
        k_scale: torch.Tensor,
    ):
        assert attn_metadata.prefill is not None
        prefill_metadata = attn_metadata.prefill
        assert prefill_metadata.chunked_context is not None

        output = None
        iters = len(prefill_metadata.chunked_context.seq_tot)
        workspace = prefill_metadata.chunked_context.workspace
        for i in range(iters):
            toks = prefill_metadata.chunked_context.seq_tot[i]
            ops.gather_and_maybe_dequant_cache(
                src_cache=kv_c_and_k_pe_cache,
                dst=workspace,
                block_table=prefill_metadata.block_table,
                cu_seq_lens=prefill_metadata.chunked_context.cu_seq_lens[i],
                token_to_seq=prefill_metadata.chunked_context.token_to_seq[i],
                num_tokens=prefill_metadata.chunked_context.chunk_total_token[i],
                kv_cache_dtype=self.kv_cache_dtype,
                scale=k_scale,
                seq_starts=prefill_metadata.chunked_context.starts[i],
            )

            kv_c_normed = workspace[:toks][..., : self.kv_lora_rank]
            k_pe = workspace[:toks][..., self.kv_lora_rank :].unsqueeze(1)

            kv_nope = self.kv_b_proj(kv_c_normed)[0].view(
                -1, self.num_heads, self.qk_nope_head_dim + self.v_head_dim
            )
            k_nope, v = kv_nope.split([self.qk_nope_head_dim, self.v_head_dim], dim=-1)

            k = self._concat_k_nope_k_pe(k_nope, k_pe)

            attn_output, attn_softmax_lse = self._run_prefill_context_chunk(
                prefill=prefill_metadata,
                chunk_idx=i,
                q=q,
                k=k,
                v=v,
            )

            if output is None:
                output = attn_output
                output_lse = attn_softmax_lse
            else:
                output_tmp = torch.empty_like(output)
                output_lse_tmp = torch.empty_like(output_lse)
                merge_attn_states(
                    output=output_tmp,
                    output_lse=output_lse_tmp,
                    prefix_output=output,
                    prefix_lse=output_lse,
                    suffix_output=attn_output,
                    suffix_lse=attn_softmax_lse,
                )
                output = output_tmp
                output_lse = output_lse_tmp

        return output, output_lse

    def _context_parallel_compute_prefill_context(
        self,
        q: torch.Tensor,
        kv_c_and_k_pe_cache: torch.Tensor,
        attn_metadata: MLACommonMetadata,
        k_scale: torch.Tensor,
        dcp_world_size: int,
    ):
        assert k_scale is None, "DCP not support scaled kvcache now."
        assert attn_metadata.prefill is not None
        prefill_metadata = attn_metadata.prefill
        assert prefill_metadata.chunked_context is not None
        assert prefill_metadata.chunked_context.padded_local_chunk_seq_lens is not None
        assert prefill_metadata.chunked_context.local_context_lens_allranks is not None
        assert prefill_metadata.chunked_context.padded_local_cu_seq_lens is not None
        assert prefill_metadata.chunked_context.cu_seq_lens_lst is not None
        assert prefill_metadata.chunked_context.chunk_size is not None

        output = None
        iters = len(prefill_metadata.chunked_context.seq_tot)
        workspace = prefill_metadata.chunked_context.workspace

        for i in range(iters):
            toks = prefill_metadata.chunked_context.seq_tot[i]
            ops.cp_gather_cache(
                src_cache=kv_c_and_k_pe_cache,
                dst=workspace,
                block_table=prefill_metadata.block_table,
                cu_seq_lens=prefill_metadata.chunked_context.padded_local_cu_seq_lens[
                    i
                ],
                batch_size=attn_metadata.num_prefills,
                seq_starts=prefill_metadata.chunked_context.starts[i],
            )
            # workspace
            # |------- N tokens --------|--------- N*dcp_size tokens ----------|
            # |<- use for loca_gather ->|<--------- use for allgather -------->|
            allgather_offset = workspace.shape[0] // (dcp_world_size + 1)
            assert allgather_offset * (dcp_world_size + 1) == workspace.shape[0]
            assert toks <= allgather_offset
            local_gathered_kvcache = workspace[:toks]
            cur_allgather_workspace = workspace[
                allgather_offset : allgather_offset * (1 + dcp_world_size)
            ]
            assert toks * dcp_world_size <= cur_allgather_workspace.shape[0]
            cur_allgather_kvcache = cur_allgather_workspace[: toks * dcp_world_size]
            cur_allgather_kvcache.copy_(
                get_dcp_group().all_gather(local_gathered_kvcache, dim=0)
            )
            assert (
                cur_allgather_kvcache.shape[-1]
                == self.kv_lora_rank + self.qk_rope_head_dim
            )
            allgatered_kv_c_normed, allgatered_k_pe = cur_allgather_kvcache.unsqueeze(
                1
            ).split([self.kv_lora_rank, self.qk_rope_head_dim], dim=-1)

            kv_c_normed, k_pe = reorg_kvcache(
                allgatered_kv_c_normed,
                allgatered_k_pe,
                padded_local_chunk_seq_lens_lst=prefill_metadata.chunked_context.padded_local_chunk_seq_lens[
                    i
                ],
                local_context_lens_allranks=prefill_metadata.chunked_context.local_context_lens_allranks,
                sum_seq_len=prefill_metadata.chunked_context.cu_seq_lens_lst[i][-1],
                max_seq_len=prefill_metadata.chunked_context.max_seq_lens[i],
                chunk_size=prefill_metadata.chunked_context.chunk_size,
                chunk_idx=i,
                toks=toks,
            )

            kv_nope = self.kv_b_proj(kv_c_normed)[0].view(
                -1, self.num_heads, self.qk_nope_head_dim + self.v_head_dim
            )
            k_nope, v = kv_nope.split([self.qk_nope_head_dim, self.v_head_dim], dim=-1)
            k = self._concat_k_nope_k_pe(k_nope, k_pe)

            attn_output, attn_softmax_lse = self._run_prefill_context_chunk(
                prefill=prefill_metadata,
                chunk_idx=i,
                q=q,
                k=k,
                v=v,
            )

            if output is None:
                output = attn_output
                output_lse = attn_softmax_lse
            else:
                output_tmp = torch.empty_like(output)
                output_lse_tmp = torch.empty_like(output_lse)
                merge_attn_states(
                    output=output_tmp,
                    output_lse=output_lse_tmp,
                    prefix_output=output,
                    prefix_lse=output_lse,
                    suffix_output=attn_output,
                    suffix_lse=attn_softmax_lse,
                )
                output = output_tmp
                output_lse = output_lse_tmp

        return output, output_lse

    def _forward_prefill(
        self,
        q: torch.Tensor,
        kv_c_normed: torch.Tensor,
        k_pe: torch.Tensor,
        kv_c_and_k_pe_cache: torch.Tensor,
        attn_metadata: MLACommonMetadata,
        k_scale: torch.Tensor,
        output: torch.Tensor,
    ) -> None:
        # TODO (zyongye): Prefill function here
        assert attn_metadata.prefill is not None
        assert self.dcp_world_size is not None

        has_context = attn_metadata.prefill.chunked_context is not None
        kv_nope = self.kv_b_proj(kv_c_normed)[0].view(
            -1, self.num_heads, self.qk_nope_head_dim + self.v_head_dim
        )
        k_nope, v = kv_nope.split([self.qk_nope_head_dim, self.v_head_dim], dim=-1)

        k = self._concat_k_nope_k_pe(k_nope, k_pe)

        output_prefill = self._run_prefill_new_tokens(
            prefill=attn_metadata.prefill,
            q=q,
            k=k,
            v=v,
            return_softmax_lse=has_context,
        )

        if has_context:
            suffix_output, suffix_lse = output_prefill
            if self.dcp_world_size > 1:
                context_output, context_lse = (
                    self._context_parallel_compute_prefill_context(
                        q,
                        kv_c_and_k_pe_cache,
                        attn_metadata,
                        k_scale=None,
                        dcp_world_size=self.dcp_world_size,
                    )
                )
            else:
                context_output, context_lse = self._compute_prefill_context(
                    q, kv_c_and_k_pe_cache, attn_metadata, k_scale
                )

            # unpad if necessary
            if self._pad_v:
                context_output = context_output[..., : v.shape[-1]]
                suffix_output = suffix_output[..., : v.shape[-1]]

            output = output.view(-1, self.num_heads, self.v_head_dim)
            merge_attn_states(
                output=output,
                prefix_output=context_output,
                prefix_lse=context_lse,
                suffix_output=suffix_output,
                suffix_lse=suffix_lse,
            )
        else:
            output_prefill = output_prefill[..., : v.shape[-1]].flatten(start_dim=-2)
            output.copy_(output_prefill)

    @abstractmethod
    def _forward_decode(
        self,
        q: torch.Tensor | tuple[torch.Tensor, torch.Tensor],
        kv_c_and_k_pe_cache: torch.Tensor,
        attn_metadata: M,
        layer: AttentionLayer,
    ) -> tuple[torch.Tensor, torch.Tensor | None]:
        raise NotImplementedError

    def forward(
        self,
        layer: AttentionLayer,
        q: torch.Tensor,
        k_c_normed: torch.Tensor,  # key in unified attn
        k_pe: torch.Tensor,  # value in unified attn
        kv_cache: torch.Tensor,
        attn_metadata: M,
        output: torch.Tensor | None = None,
        output_scale: torch.Tensor | None = None,
        output_block_scale: torch.Tensor | None = None,
    ) -> torch.Tensor:
        assert output is not None, "Output tensor must be provided."

        if output_scale is not None or output_block_scale is not None:
            raise NotImplementedError(
                "fused output quantization is not yet supported for MLACommonImpl"
            )

        if attn_metadata is None:
            # During the profile run try to simulate to worse case output size
            # for `self.kv_b_proj(kv_c_normed)` in `_compute_prefill_context`
            # since this can be large
            _ = torch.empty(
                (
                    self.chunked_prefill_workspace_size,
                    self.num_heads,
                    self.qk_nope_head_dim + self.v_head_dim,
                ),
                device=k_c_normed.device,
                dtype=k_c_normed.dtype,
            )

            # The zero fill is required when used with DP + EP
            # to ensure all ranks within a DP group compute the
            # same expert outputs.
            return output.fill_(0)

        if self.dcp_world_size is None:
            self.dcp_world_size = get_dcp_group().world_size

        fp8_attention = self.kv_cache_dtype.startswith("fp8")

        num_actual_toks = attn_metadata.num_actual_tokens

        # Inputs and outputs may be padded for CUDA graphs
        output_padded = output
        output = output[:num_actual_toks, ...]
        q = q[:num_actual_toks, ...]
        k_c_normed = k_c_normed[:num_actual_toks, ...]
        k_pe = k_pe[:num_actual_toks, ...]

        assert (
            attn_metadata.num_decodes is not None
            and attn_metadata.num_prefills is not None
            and attn_metadata.num_decode_tokens is not None
        )

        has_decode = attn_metadata.num_decodes > 0
        has_prefill = attn_metadata.num_prefills > 0
        num_decode_tokens = attn_metadata.num_decode_tokens

        decode_q = q[:num_decode_tokens]

        prefill_q = q[num_decode_tokens:]
        prefill_k_pe = k_pe[num_decode_tokens:]
        prefill_k_c_normed = k_c_normed[num_decode_tokens:]

        # write the latent and rope to kv cache
        if kv_cache.numel() > 0:
            ops.concat_and_cache_mla(
                k_c_normed,
                k_pe.squeeze(1),
                kv_cache,
                attn_metadata.slot_mapping.flatten(),
                kv_cache_dtype=self.kv_cache_dtype,
                scale=layer._k_scale,
            )

        if fp8_attention:
            kv_cache = kv_cache.view(current_platform.fp8_dtype())

        if has_prefill:
            self._forward_prefill(
                prefill_q,
                prefill_k_c_normed,
                prefill_k_pe,
                kv_cache,
                attn_metadata,
                layer._k_scale,
                output=output[num_decode_tokens:],
            )

        if has_decode:
            assert attn_metadata.decode is not None

            decode_q_nope, decode_q_pe = decode_q.split(
                [self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1
            )

            # Convert from (B, N, P) to (N, B, P)
            decode_q_nope = decode_q_nope.transpose(0, 1)

            if self.q_pad_num_heads is not None:
                B, N, L = decode_q_pe.shape
                decode_pe_padded = decode_q_pe.new_empty((B, self.q_pad_num_heads, L))
                decode_pe_padded.resize_((B, N, L))
                decode_pe_padded.copy_(decode_q_pe)
                decode_q_pe = decode_pe_padded

            if self.is_aiter_triton_fp8_bmm_enabled:
                # Multiply+Transpose (N, B, P)x(N, P, L)->(N, B, L)->(B, N, L)
                decode_ql_nope = rocm_aiter_ops.triton_fp8_bmm(
                    decode_q_nope,
                    self.W_K,
                    self.W_K_scale,
                    group_size=128,
                    transpose_bm=True,
                )
            else:
                # Pads the head_dim if necessary (for the underlying kernel)
                N, B, P = decode_q_nope.shape
                _, _, L = self.W_UK_T.shape

                if self.q_pad_num_heads is not None:
                    decode_ql_nope = decode_q_nope.new_empty(
                        (self.q_pad_num_heads, B, L)
                    )
                    decode_ql_nope.resize_((N, B, L))
                else:
                    decode_ql_nope = decode_q_nope.new_empty((N, B, L))

                # Multiply (N, B, P) x (N, P, L) -> (N, B, L)
                torch.bmm(decode_q_nope, self.W_UK_T, out=decode_ql_nope)

                # Convert from (N, B, L) to (B, N, L)
                decode_ql_nope = decode_ql_nope.transpose(0, 1)

            if fp8_attention:
                ql_nope_shape = decode_ql_nope.shape
                q_pe_shape = decode_q_pe.shape
                assert decode_ql_nope.shape[0] == decode_q_pe.shape[0]
                assert decode_ql_nope.shape[1] == decode_q_pe.shape[1]
                decode_q_shape = (
                    ql_nope_shape[0],
                    ql_nope_shape[1],
                    ql_nope_shape[2] + q_pe_shape[2],
                )
                # Using empty and copy since torch.cat introduces significant overhead.
                decode_q0 = torch.empty(
                    decode_q_shape,
                    device=decode_ql_nope.device,
                    dtype=decode_ql_nope.dtype,
                )
                decode_q0[..., : ql_nope_shape[2]].copy_(decode_ql_nope)
                decode_q0[..., ql_nope_shape[2] :].copy_(decode_q_pe)

                decode_q, _ = ops.scaled_fp8_quant(
                    decode_q0.view(decode_q_shape[0], -1),
                    layer._q_scale,
                )
                decode_q = decode_q.view(decode_q_shape)
            else:
                decode_q = (decode_ql_nope, decode_q_pe)
            if self.dcp_world_size > 1:
                assert not fp8_attention, "DCP not support fp8 kvcache now."
                # concatenate decode_ql_nope and decode_q_pe -> (B, N, L + P)
                decode_q = torch.cat(decode_q, dim=-1)
                # decode_q do allgather in head dim.
                decode_q = get_dcp_group().all_gather(decode_q, dim=1)

            # call decode attn
            attn_out, lse = self._forward_decode(
                decode_q, kv_cache, attn_metadata, layer
            )

            # correct dcp attn_out with lse.
            if self.dcp_world_size > 1:
                attn_out = cp_lse_ag_out_rs(
                    attn_out,
                    lse,
                    get_dcp_group(),
                    is_lse_base_on_e=not getattr(self, "_use_fi_prefill", False),
                )

            # v_up projection
            self._v_up_proj(attn_out, out=output[:num_decode_tokens])
        return output_padded