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

import numpy as np
from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Generic, Optional, TypeVar, Union


import torch
import os

import vllm.envs as envs
from vllm import _custom_ops as ops
from vllm import envs
from vllm.attention.backends.abstract import (AttentionBackend, AttentionLayer,
                                              AttentionMetadata,
                                              MLAAttentionImpl)
from vllm.attention.backends.utils import get_mla_dims
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
from vllm.logger import init_logger
from vllm.model_executor.layers.linear import (ColumnParallelLinear,
                                               LinearBase,
                                               UnquantizedLinearMethod)
from vllm.platforms import current_platform
from vllm.utils import cdiv, round_down
from vllm.v1.attention.backends.utils import (
    AttentionMetadataBuilder, CommonAttentionMetadata,
    get_per_layer_parameters, infer_global_hyperparameters,
    reorder_batch_to_split_decodes_and_prefills, split_decodes_and_prefills)
from vllm.v1.kv_cache_interface import AttentionSpec
from vllm.v1.worker.block_table import BlockTable

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 (  # noqa: F401
        cudnn_batch_prefill_with_kv_cache)
    flashinfer_available = True
except ImportError:
    flashinfer_available = False

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

logger = init_logger(__name__)

CUDNN_WORKSPACE_SIZE = 12800


class MLACommonBackend(AttentionBackend):

    accept_output_buffer: bool = True

    @staticmethod
    def get_name() -> str:
        return "TRITON_MLA_VLLM_V1"

    @staticmethod
    def get_metadata_cls() -> type["AttentionMetadata"]:
        return MLACommonMetadata

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

    @classmethod
    def get_supported_dtypes(cls) -> list[torch.dtype]:
        return [torch.float16, torch.bfloat16]

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

    @classmethod
    def validate_head_size(cls, head_size: int) -> None:
        supported_head_sizes = cls.get_supported_head_sizes()
        if head_size not in supported_head_sizes:
            attn_type = cls.__name__.removesuffix("Backend")
            raise ValueError(
                f"Head size {head_size} is not supported by {attn_type}. "
                f"Supported head sizes are: {supported_head_sizes}. "
                "Set VLLM_ATTENTION_BACKEND=FLEX_ATTENTION to use "
                "FlexAttention backend which supports all head sizes.")


@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

    block_table: torch.Tensor
    query_start_loc: torch.Tensor
    max_query_len: int
    chunked_context: Optional[ChunkedContextMetadata] = None


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


@dataclass
class CudnnPrefillMetadata(MLACommonPrefillMetadata):

    class ChunkedContextMetadata(
            MLACommonPrefillMetadata.ChunkedContextMetadata):
        seq_lens: torch.Tensor

    query_seq_lens: Optional[torch.Tensor] = None
    cudnn_workspace: Optional[torch.Tensor] = None


@dataclass
class MLACommonDecodeMetadata:
    block_table: torch.Tensor
    seq_lens: torch.Tensor


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

    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: Optional[int] = None

    decode: Optional[D] = None
    prefill: Optional[Union[MLACommonPrefillMetadata,
                            FlashInferPrefillMetadata,
                            CudnnPrefillMetadata]] = None

    def __post_init__(self):
        if self.head_dim is not None:
            MLACommonBackend.validate_head_size(self.head_dim)


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


def use_flashinfer_prefill() -> bool:
    if flashinfer_available and not envs.VLLM_USE_CUDNN_PREFILL:
        # For blackwell default to flashinfer prefill if its available since
        #  its faster than FA2.
        return current_platform.has_device_capability(100)
    return False


def use_cudnn_prefill() -> bool:
    if flashinfer_available and envs.VLLM_USE_CUDNN_PREFILL:
        return current_platform.has_device_capability(100)
    return False


# Currently 394MB, this can be tuned based on GEMM sizes used.
# Chosen to be the same as sglang:
#  https://github.com/sgl-project/sglang/blob/766392c6bda2558b61ce6d1c1bfd8081a549e1f1/python/sglang/global_config.py#L37
FLASHINFER_WORKSPACE_BUFFER_SIZE = 394 * 1024 * 1024


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

    def __init__(self,
                 kv_cache_spec: AttentionSpec,
                 vllm_config: VllmConfig,
                 device: torch.device,
                 block_table: BlockTable,
                 metadata_cls: Optional[type[M]] = None):
        self.metadata_cls = metadata_cls \
            if metadata_cls is not None else MLACommonMetadata
        self.kv_cache_spec = kv_cache_spec
        self.device = device
        scheduler_config = vllm_config.scheduler_config
        self.model_config = vllm_config.model_config
        cache_config = vllm_config.cache_config
        parallel_config = vllm_config.parallel_config
        self.chunked_prefill_enabled = scheduler_config.chunked_prefill_enabled
        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()

        # Dont try to access the runner on AMD
        if self.aot_schedule:
            self.page_size = self.kv_cache_spec.block_size

        if self.chunked_prefill_enabled:
            self.chunked_prefill_workspace_size = min(
                # Max sure there is enough for 8 full length request or at least
                # 4 pages of cache per request
                max(
                    8 * self.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)
                128 * 1024)
            assert self.chunked_prefill_workspace_size >= \
                scheduler_config.max_num_seqs * cache_config.block_size
            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.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(
                FLASHINFER_WORKSPACE_BUFFER_SIZE,
                dtype=torch.uint8,
                device=device)

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

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

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

        self.block_table = block_table
        
        self.use_spec_decode = False
        self.num_scheduled_tokens_np = np.zeros(scheduler_config.max_num_seqs, dtype=np.int32)
        
        # support for cudagraph spec docoding
        self.spec_decode_block_table_tensor = None
        self.spec_decode_seq_lens = None
        
    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,
            kv_data_type=self.kv_cache_spec.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,
                    kv_data_type=self.kv_cache_spec.dtype,
                )

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


    def reorder_batch(self, input_batch: "InputBatch",
                      scheduler_output: "SchedulerOutput") -> bool:
        self.use_spec_decode = len(
            scheduler_output.scheduled_spec_decode_tokens) > 0
        return reorder_batch_to_split_decodes_and_prefills(input_batch,
                                                           scheduler_output,
                                                           decode_threshold=1,
                                                           num_scheduled_tokens_np=self.num_scheduled_tokens_np)

    def _build_decode(self, block_table_tensor: torch.Tensor,
                      seq_lens: torch.Tensor):
        return MLACommonDecodeMetadata(
            block_table=block_table_tensor,
            seq_lens=seq_lens,
        )

    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, \
        #     "MLA only supports decode-only full CUDAGraph capture. " \
        #     "Make sure all cudagraph capture sizes <= max_num_seq."

        # m.max_query_len = 1  # decode-only
        
        self.use_spec_decode = m.num_speculative_tokens > 0
        # support for cudagraph spec docoding
        if self.use_spec_decode:
            for i in range(m.num_reqs):
                self.num_scheduled_tokens_np[i] = m.num_actual_tokens // m.num_reqs

            if self.spec_decode_block_table_tensor is None:
                max_num_reqs = m.seq_lens.shape[0]
                block_table_tensor = self.block_table.get_device_tensor()
                tokens_per_seq = 1+m.num_speculative_tokens

                self.spec_decode_block_table_tensor = torch.zeros((block_table_tensor.shape[0]*tokens_per_seq,
                                                                   block_table_tensor.shape[1]),
                                     dtype=block_table_tensor.dtype,
                                     device=m.seq_lens.device)
                self.spec_decode_seq_lens = torch.zeros(max_num_reqs * tokens_per_seq,
                                     dtype=m.seq_lens.dtype,
                                     device=m.seq_lens.device)

        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

        # 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 = self.block_table
        block_table_tensor = common_attn_metadata.block_table_tensor
        slot_mapping = common_attn_metadata.slot_mapping
        if slot_mapping is None:
            block_table.slot_mapping[:num_tokens].copy_(
                block_table.slot_mapping_cpu[:num_tokens],
                non_blocking=True)
            block_table.slot_mapping[num_tokens:].fill_(-1)
            slot_mapping = block_table.slot_mapping[:num_tokens]

        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

        query_seq_lens_cpu = query_start_loc_cpu[1:] - query_start_loc_cpu[:-1]

        num_computed_tokens_cpu = (common_attn_metadata.seq_lens_cpu -
                                   query_seq_lens_cpu)

        num_decodes, num_prefills, num_decode_tokens, num_prefill_tokens = \
            split_decodes_and_prefills(common_attn_metadata)

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

        prefill_metadata = None
        if num_prefills > 0:
            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 self.chunked_prefill_enabled and num_prefills > 0 \
                and 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_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)

                chunked_context_metadata_cls = \
                    CudnnPrefillMetadata.ChunkedContextMetadata \
                    if self._use_cudnn_prefill else \
                        MLACommonPrefillMetadata.ChunkedContextMetadata

                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,
                    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

        decode_metadata = None
        if num_decodes > 0:
            if self.use_spec_decode:
                query_lens = self.num_scheduled_tokens_np[:num_decodes]
                if common_attn_metadata.num_rejected_tokens is not None:
                    num_rejected_tokens = common_attn_metadata.num_rejected_tokens[:num_decodes]
                    query_lens = query_lens - np.array(num_rejected_tokens, dtype=np.int32)
                    self._num_decode_tokens -= sum(num_rejected_tokens)
                cu_num_blocks = np.cumsum(query_lens)
                virtual_batches = cu_num_blocks[-1]
                block_offsets = np.repeat(cu_num_blocks - query_lens, query_lens)
                arange = np.arange(virtual_batches, dtype=np.int32) - block_offsets
                rarange = np.repeat(query_lens, query_lens) - arange - 1

                repeats = torch.from_numpy(query_lens).pin_memory().to(
                    block_table_tensor.device, non_blocking=True).contiguous()
                decode_block_table_tensor = torch.repeat_interleave(
                    block_table_tensor[:num_decodes, ...],
                    repeats, dim=0).contiguous()
                decode_seq_lens = torch.repeat_interleave(seq_lens[:num_decodes], repeats, dim=0).contiguous()
                seq_lens_minus = torch.from_numpy(rarange).to(torch.int32).pin_memory().to(
                    seq_lens.device, non_blocking=True).contiguous()
                decode_seq_lens = decode_seq_lens - seq_lens_minus

                if self.spec_decode_block_table_tensor is not None:
                    self.spec_decode_block_table_tensor[:self._num_decode_tokens].copy_(decode_block_table_tensor)
                    self.spec_decode_seq_lens[:self._num_decode_tokens].copy_(decode_seq_lens)

                    decode_metadata = self._build_decode(
                        block_table_tensor=self.spec_decode_block_table_tensor[:self._num_decode_tokens, ...],
                        seq_lens=self.spec_decode_seq_lens[:self._num_decode_tokens],
                    )
                else:
                    decode_metadata = self._build_decode(
                        block_table_tensor=decode_block_table_tensor,
                        seq_lens=decode_seq_lens,
                    )
            else:
                decode_metadata = self._build_decode(
                    block_table_tensor=block_table_tensor[:num_decodes, ...],
                    seq_lens=seq_lens[:num_decodes],
                )

        attn_metadata = self.metadata_cls(
            num_reqs=common_attn_metadata.num_reqs,
            max_query_len=common_attn_metadata.max_query_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 can_run_in_cudagraph(
            self, common_attn_metadata: CommonAttentionMetadata) -> bool:
        # return common_attn_metadata.max_query_len == 1
        if not self.use_spec_decode:
            return common_attn_metadata.max_query_len == 1
        return self._num_prefills == 0


class MLACommonImpl(MLAAttentionImpl[M], Generic[M]):
    """
    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: Optional[list[float]],
        sliding_window: Optional[int],
        kv_cache_dtype: str,
        logits_soft_cap: Optional[float],
        attn_type: str,
        kv_sharing_target_layer_name: Optional[str],
        # MLA Specific Arguments
        q_lora_rank: Optional[int],
        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,
    ) -> 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.use_llama_nn = os.environ.get('LLAMA_NN') == '1'

        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_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
                and torch.cuda.get_device_properties(torch.cuda.current_device()).multi_processor_count == 120)
        

    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)
            maybe_padded_v = torch.nn.functional.pad(
                    v, [0, q.shape[-1] - v.shape[-1]- 32], value=0)
            maybe_padded_v = maybe_padded_v[..., :-32].reshape(v.shape[0], v.shape[1],v.shape[2])

        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

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

    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,
            return_lse=True,  # do not support False for now
            is_cuda_graph_compatible=
            True,  #Indicates actual_seq_lens are on GPU or CPU.
        )
        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)
        return prefill.prefill_chunks[chunk_idx].run(
            q=q,
            k=k,
            v=v,
            return_lse=True,
        )

    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,
            is_cuda_graph_compatible=
            True,  #Indicates actual_seq_lens are on GPU or CPU.
        )

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

    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:"
                f" {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 if not envs.VLLM_USE_NN else layer.weight.T

        # we currently do not have quantized bmm's which are needed for
        # `W_UV` and `W_UK_T`, we we just store fp16/bf16 copies and perform
        # the bmm's in 16-bit, the extra memory overhead of this is fairly low
        if self.use_llama_nn and isinstance(self.kv_b_proj.quant_method, UnquantizedLinearMethod):
            kv_b_proj_weight = get_and_maybe_dequant_weights(self.kv_b_proj)
        else:
            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)

        # 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 _compute_prefill_context(
        self,
        q: torch.Tensor,
        kv_c_and_k_pe_cache: torch.Tensor,
        attn_metadata: MLACommonMetadata,
    ):
        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_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],
                batch_size=attn_metadata.num_prefills,
                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 = torch.cat((k_nope, k_pe.expand((*k_nope.shape[:-1], -1))),
                          dim=-1)

            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,
    ) -> torch.Tensor:
        assert attn_metadata.prefill is not None

        if envs.VLLM_HAS_CONTEXT_DEFAULT:
            has_context = attn_metadata.prefill.chunked_context is not None
        else:
            has_context = False
        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 = torch.cat((k_nope, k_pe.expand((*k_nope.shape[:-1], -1))), dim=-1)

        output = 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
            context_output, context_lse = self._compute_prefill_context( \
                q, kv_c_and_k_pe_cache, attn_metadata)

            output = torch.empty_like(suffix_output)
            merge_attn_states(
                output=output,
                prefix_output=context_output,
                prefix_lse=context_lse,
                suffix_output=suffix_output,
                suffix_lse=suffix_lse,
            )

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

        return output.flatten(start_dim=-2)

    @abstractmethod
    def _forward_decode(
        self,
        ql_nope: torch.Tensor,
        q_pe: torch.Tensor,
        kv_c_and_k_pe_cache: torch.Tensor,
        attn_metadata: M,
    ) -> torch.Tensor:
        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: Optional[torch.Tensor] = None,
        output_scale: Optional[torch.Tensor] = None,
    ) -> torch.Tensor:
        assert output is not None, "Output tensor must be provided."

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

        if attn_metadata is None:
            # 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)

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

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

            output[:num_decode_tokens] = self._forward_decode(
                decode_ql_nope, decode_q_pe, kv_cache, attn_metadata)

        return output_padded