common.py 82.1 KB
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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"""
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# MLA Common Components

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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:
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* Use a single latent vector to represent the per-token entry of the KV cache.
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* 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]
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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]
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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)
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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)
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// 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
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return spda_o @ W_O

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


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

Runtime
q_c      = h_t @ W_DQ
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q_nope   = (q_c @ W_UQ).view(-1, N, P)
ql_nope  = einsum("snh,lnh->snl", q, W_UK)
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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(
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    torch.cat([ql_nope, q_pe], dim=-1),
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    torch.cat([kv_c, k_pe], dim=-1),
    kv_c
)
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o = einsum("snl,lnv->snv", spda_o.reshape(-1, N, Lkv), W_UV)
return o.view(-1, N * V) @ self.num_heads @ W_O
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## Chunked Prefill

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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
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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)`

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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
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fixed workspace size.

The chunked prefill approach is as follows:

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MCC        Max chunk of context to process per iter, computed dynamically,
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           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)
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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)
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// 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
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// 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
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from dataclasses import dataclass, field
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from enum import Enum
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from typing import ClassVar, Generic, TypeVar
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import torch
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from tqdm import tqdm
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from vllm import _custom_ops as ops
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from vllm import envs
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from vllm._aiter_ops import rocm_aiter_ops
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from vllm.attention.backends.abstract import (
    AttentionBackend,
    AttentionLayer,
    MLAAttentionImpl,
)
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from vllm.attention.backends.utils import get_mla_dims
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from vllm.attention.ops.common import cp_lse_ag_out_rs
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from vllm.attention.ops.merge_attn_states import merge_attn_states
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from vllm.attention.utils.fa_utils import get_flash_attn_version
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from vllm.config import VllmConfig, get_current_vllm_config
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from vllm.distributed.parallel_state import get_dcp_group, is_global_first_rank
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from vllm.logger import init_logger
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from vllm.model_executor.layers.batch_invariant import (
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    vllm_is_batch_invariant,
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)
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from vllm.model_executor.layers.linear import (
    ColumnParallelLinear,
    LinearBase,
    UnquantizedLinearMethod,
)
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from vllm.platforms import current_platform
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from vllm.utils.flashinfer import has_nvidia_artifactory
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from vllm.utils.math_utils import cdiv, round_down
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from vllm.v1.attention.backends.utils import (
    AttentionMetadataBuilder,
    CommonAttentionMetadata,
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    get_dcp_local_seq_lens,
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    get_per_layer_parameters,
    infer_global_hyperparameters,
    split_decodes_and_prefills,
)
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from vllm.v1.kv_cache_interface import AttentionSpec
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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"


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try:
    from vllm.vllm_flash_attn import flash_attn_varlen_func
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    is_vllm_fa = True
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except ImportError:
    # For rocm use upstream flash attention
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    if current_platform.is_rocm():
        from flash_attn import flash_attn_varlen_func
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    is_vllm_fa = False
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try:
    from flashinfer import BatchPrefillWithRaggedKVCacheWrapper
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    from flashinfer.prefill import cudnn_batch_prefill_with_kv_cache  # noqa: F401

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    flashinfer_available = True
except ImportError:
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    BatchPrefillWithRaggedKVCacheWrapper = object

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    flashinfer_available = False

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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()
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logger = init_logger(__name__)

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CUDNN_WORKSPACE_SIZE = 12800

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class MLACommonBackend(AttentionBackend):
    accept_output_buffer: bool = True

    @staticmethod
    def get_name() -> str:
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        return "TRITON_MLA"
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    @staticmethod
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    def get_builder_cls() -> type["MLACommonMetadataBuilder"]:
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        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,
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        cache_dtype_str: str = "auto",
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    ) -> tuple[int, ...]:
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        return (num_blocks, block_size, head_size)

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    @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)

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

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    @classmethod
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    def is_mla(cls) -> bool:
        return True
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@dataclass
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class MLACommonPrefillMetadata:
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    """Prefill Specific Metadata"""
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    @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]
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        seq_lens: torch.Tensor
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        workspace: torch.Tensor
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        token_to_seq: torch.Tensor
        chunk_total_token: list[int]
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        # for mla DCP
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        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
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        cu_seq_lens_lst: list[list[int]] | None = None
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        chunk_size: int | None = None
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    block_table: torch.Tensor
    query_start_loc: torch.Tensor
    max_query_len: int
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    chunked_context: ChunkedContextMetadata | None = None
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    query_seq_lens: torch.Tensor | None = None
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    workspace_buffer: torch.Tensor | None = None
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    q_data_type: torch.dtype | None = None
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@dataclass
class FlashInferPrefillMetadata(MLACommonPrefillMetadata):
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    prefill_main: BatchPrefillWithRaggedKVCacheWrapper | None = None
    prefill_chunks: list[BatchPrefillWithRaggedKVCacheWrapper] = field(
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        default_factory=list
    )
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@dataclass
class CudnnPrefillMetadata(MLACommonPrefillMetadata):
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    class ChunkedContextMetadata(MLACommonPrefillMetadata.ChunkedContextMetadata):
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        seq_lens: torch.Tensor

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    cudnn_workspace: torch.Tensor | None = None
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@dataclass
class MLACommonDecodeMetadata:
    block_table: torch.Tensor
    seq_lens: torch.Tensor
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    dcp_tot_seq_lens: torch.Tensor | None
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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
    """
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    # NOTE(sang): Definition of context_len, query_len, and seq_len.
    # |---------- N-1 iteration --------|
    # |---------------- N iteration ---------------------|
    # |- tokenA -|......................|-- newTokens ---|
    # |---------- context_len ----------|
    # |-------------------- seq_len ---------------------|
    #                                   |-- query_len ---|

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

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

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    # The dimension of the attention heads
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    head_dim: int | None = None
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    decode: D | None = None
    prefill: (
        MLACommonPrefillMetadata
        | FlashInferPrefillMetadata
        | CudnnPrefillMetadata
        | None
    ) = None
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    def __post_init__(self):
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        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.")
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M = TypeVar("M", bound=MLACommonMetadata)
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A = TypeVar("A")
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def use_flashinfer_prefill() -> bool:
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    # For blackwell default to flashinfer prefill if it's available since
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    # it is faster than FA2.
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    from vllm.config import get_current_vllm_config

    vllm_config = get_current_vllm_config()
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    return (
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        not vllm_config.attention_config.disable_flashinfer_prefill
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        and flashinfer_available
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        and not vllm_config.attention_config.use_cudnn_prefill
        and not vllm_config.attention_config.use_trtllm_ragged_deepseek_prefill
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        and current_platform.is_device_capability_family(100)
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    )
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def use_cudnn_prefill() -> bool:
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    from vllm.config import get_current_vllm_config

    vllm_config = get_current_vllm_config()
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    return (
        flashinfer_available
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        and vllm_config.attention_config.use_cudnn_prefill
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        and current_platform.is_device_capability_family(100)
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        and has_nvidia_artifactory()
    )
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def use_trtllm_ragged_deepseek_prefill() -> bool:
    """Check if TRT-LLM ragged DeepSeek prefill should be used."""
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    from vllm.config import get_current_vllm_config

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


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class MLACommonMetadataBuilder(AttentionMetadataBuilder[M]):
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    """
    NOTE: Please read the comment at the top of the file before trying to
    understand this class
    """
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    # 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
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    # 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.
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    # Use `query_len_support` (above) to set this automatically
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    # when speculative decoding is enabled.
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    reorder_batch_threshold: int = 1
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    @staticmethod
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    def determine_chunked_prefill_workspace_size(vllm_config: VllmConfig) -> int:
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        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
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            max(
                8 * model_config.max_model_len,
                4 * scheduler_config.max_num_seqs * cache_config.block_size,
            ),
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            # 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)
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            64 * 1024,
        )
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        # Enforce that we enough for at least 1 page per request
        chunked_prefill_workspace_size = max(
            chunked_prefill_workspace_size,
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            scheduler_config.max_num_seqs * cache_config.block_size,
        )
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        return chunked_prefill_workspace_size

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

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        self.num_heads = self.model_config.get_num_attention_heads(parallel_config)
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        self.mla_dims = get_mla_dims(self.model_config)
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        self.aot_schedule = current_platform.is_cuda()
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        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
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        self.dcp_local_block_size = parallel_config.cp_kv_cache_interleave_size
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        self.dcp_virtual_block_size = self.dcp_local_block_size * self.dcp_world_size
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        self.cp_kv_cache_interleave_size = parallel_config.cp_kv_cache_interleave_size
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        # Don't try to access the runner on AMD
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        if self.aot_schedule:
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            self.page_size = self.kv_cache_spec.block_size
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        self.chunked_prefill_workspace_size = (
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            self.determine_chunked_prefill_workspace_size(vllm_config)
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        )
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        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.
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            assert self.chunked_prefill_workspace_size % self.dcp_world_size == 0
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            self.chunked_prefill_workspace = torch.empty(
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                (
                    self.chunked_prefill_workspace_size
                    + self.chunked_prefill_workspace_size // self.dcp_world_size,
                    self.model_config.get_head_size(),
                ),
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                dtype=self.model_config.dtype,
                device=device,
            )
        else:
            self.chunked_prefill_workspace = torch.empty(
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                (
                    self.chunked_prefill_workspace_size,
                    self.model_config.get_head_size(),
                ),
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                dtype=self.model_config.dtype,
                device=device,
            )
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        self._use_cudnn_prefill = use_cudnn_prefill()
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        self._use_fi_prefill = use_flashinfer_prefill()
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        self._use_trtllm_ragged_prefill = use_trtllm_ragged_deepseek_prefill()
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        self.prefill_metadata_cls = (
            FlashInferPrefillMetadata
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            if self._use_fi_prefill
            else CudnnPrefillMetadata
            if self._use_cudnn_prefill
            else MLACommonPrefillMetadata
        )
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        if self._use_fi_prefill:
            self._workspace_buffer = torch.empty(
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                envs.VLLM_FLASHINFER_WORKSPACE_BUFFER_SIZE,
                dtype=torch.uint8,
                device=device,
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            )
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            self._fi_prefill_main: BatchPrefillWithRaggedKVCacheWrapper | None = None
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            self._fi_prefill_chunks: list[BatchPrefillWithRaggedKVCacheWrapper] = []
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            self._global_hyperparameters = infer_global_hyperparameters(
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                get_per_layer_parameters(vllm_config, layer_names, MLACommonImpl)
            )
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        if self._use_trtllm_ragged_prefill:
            self._workspace_buffer = torch.empty(
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                envs.VLLM_FLASHINFER_WORKSPACE_BUFFER_SIZE,
                dtype=torch.uint8,
                device=device,
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            )

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

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

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        # 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}"
            )

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    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(
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                self._workspace_buffer, "NHD", backend="cutlass"
            )
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        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(
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                            self._workspace_buffer, "NHD", backend="cutlass"
                        )
                    )
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            assert num_chunks <= len(self._fi_prefill_chunks)

        # In MLA, the non-latent num_qo_heads == num_kv_heads
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        num_qo_heads = self.num_heads
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        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
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        head_dim_qk = self.mla_dims.qk_nope_head_dim + self.mla_dims.qk_rope_head_dim
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        head_dim_vo = self.mla_dims.v_head_dim

        # For main run, qo_indptr == kv_indptr
        kv_indptr = qo_indptr.clone()
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        # 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,
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            q_data_type=self.model_config.dtype,
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        )

        # 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,
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                    logits_soft_cap=self._global_hyperparameters.logits_soft_cap,
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                    q_data_type=self.model_config.dtype,
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                )

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

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

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    def build_for_cudagraph_capture(
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        self, common_attn_metadata: CommonAttentionMetadata
    ) -> M:
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        """
        This method builds the metadata for full cudagraph capture.
        Currently, only decode is supported for full cudagraphs with MLA.
        """
        m = common_attn_metadata
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        assert m.num_reqs <= (m.num_actual_tokens * self.reorder_batch_threshold), (
            "MLA only supports decode-only full CUDAGraph capture. "
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            "Make sure all cudagraph capture sizes <= max_num_seq."
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        )
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        assert m.max_query_len <= self.reorder_batch_threshold  # decode only
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        return self.build(0, m)

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    def build(
        self,
        common_prefix_len: int,
        common_attn_metadata: CommonAttentionMetadata,
        fast_build: bool = False,
    ) -> M:
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        num_reqs = common_attn_metadata.num_reqs
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        num_tokens = common_attn_metadata.num_actual_tokens
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        max_query_len = common_attn_metadata.max_query_len
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        max_seq_len = common_attn_metadata.max_seq_len
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        # 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.
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        device = self.device
        block_table_tensor = common_attn_metadata.block_table_tensor
        slot_mapping = common_attn_metadata.slot_mapping
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        query_start_loc = common_attn_metadata.query_start_loc
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        query_start_loc_cpu = common_attn_metadata.query_start_loc_cpu
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        seq_lens = common_attn_metadata.seq_lens
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        dcp_local_seq_lens = common_attn_metadata.dcp_local_seq_lens
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        num_decodes, num_prefills, num_decode_tokens, num_prefill_tokens = (
            split_decodes_and_prefills(
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                common_attn_metadata,
                decode_threshold=self.reorder_batch_threshold,
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                require_uniform=(self.query_len_support != QueryLenSupport.VARLEN),
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            )
        )
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        assert num_decodes + num_prefills == num_reqs
        assert num_decode_tokens + num_prefill_tokens == num_tokens

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

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            reqs_start = num_decodes  # prefill_start
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            context_lens_cpu = num_computed_tokens_cpu[reqs_start:num_reqs]
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            max_context_len_cpu = context_lens_cpu.max().item()
            num_prefills_with_context_cpu = (context_lens_cpu > 0).sum().item()
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            prefill_query_start_loc = (
                query_start_loc[reqs_start:] - query_start_loc[reqs_start]
            )
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            chunked_context_metadata = None
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            if max_context_len_cpu > 0:
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                # 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
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                max_context_chunk = (
                    self.chunked_prefill_workspace_size // num_prefills_with_context_cpu
                )
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                if self.aot_schedule:
                    # align max_context_chunk to page_size by rounding down,
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                    # currently the `gather_and_maybe_dequant_cache` kernel
                    # cannot handle `context_chunk_starts` that are not aligned
                    # to page_size
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                    max_context_chunk = round_down(max_context_chunk, self.page_size)
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                assert max_context_chunk > 0
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                num_chunks = cdiv(max_context_len_cpu, max_context_chunk)
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                # 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]]
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                # Note(simon): this is done in CPU because of downstream's
                # of `to_list`.
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                chunk_starts = (
                    torch.arange(num_chunks, dtype=torch.int32)
                    .unsqueeze(1)
                    .expand(-1, num_prefills)
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                    * max_context_chunk
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                )
                chunk_ends = torch.min(
                    context_lens_cpu.unsqueeze(0), chunk_starts + max_context_chunk
                )
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                chunk_seq_lens = (chunk_ends - chunk_starts).clamp(min=0)
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                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
                )
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                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
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                if self.dcp_world_size > 1:
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                    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
                    )
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                    # 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
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                    padded_local_max_context_chunk_across_ranks = (
                        cdiv(
                            max_context_chunk,
                            self.dcp_virtual_block_size,
                        )
                        * self.dcp_local_block_size
                    )
                    local_chunk_starts = (
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                        torch.arange(num_chunks, dtype=torch.int32)
                        .unsqueeze(1)
                        .expand(-1, num_prefills)
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                        * padded_local_max_context_chunk_across_ranks
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                    )
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                    local_chunk_ends = torch.min(
                        padded_local_context_lens_cpu.unsqueeze(0),
                        local_chunk_starts
                        + padded_local_max_context_chunk_across_ranks,
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                    )
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                    padded_local_chunk_seq_lens = (
                        local_chunk_ends - local_chunk_starts
                    ).clamp(min=0)
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                    padded_local_cu_chunk_seq_lens_cpu = torch.zeros(
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                        num_chunks, num_prefills + 1, dtype=torch.int32, pin_memory=True
                    )
                    torch.cumsum(
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                        padded_local_chunk_seq_lens,
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                        dim=1,
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                        out=padded_local_cu_chunk_seq_lens_cpu[:, 1:],
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                        dtype=torch.int32,
                    )

                chunked_context_metadata_cls = (
                    CudnnPrefillMetadata.ChunkedContextMetadata
                    if self._use_cudnn_prefill
                    else MLACommonPrefillMetadata.ChunkedContextMetadata
                )
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                if self.dcp_world_size > 1:
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                    chunked_context_metadata = chunked_context_metadata_cls(
                        cu_seq_lens=cu_seq_lens_cpu.to(device, non_blocking=True),
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                        starts=local_chunk_starts.to(device, non_blocking=True),
                        seq_tot=padded_local_chunk_seq_lens.sum(dim=1).tolist(),
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                        max_seq_lens=chunk_seq_lens.max(dim=1).values.tolist(),
                        seq_lens=chunk_seq_lens,
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                        token_to_seq=token_to_seq_tensor_cpu.to(
                            device, non_blocking=True
                        ),
                        chunk_total_token=chunk_total_token.tolist(),
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                        workspace=self.chunked_prefill_workspace,
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                        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
                        ),
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                        cu_seq_lens_lst=cu_seq_lens_cpu.tolist(),
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                        chunk_size=padded_local_max_context_chunk_across_ranks,
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                    )
                else:
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                    chunked_context_metadata = chunked_context_metadata_cls(
                        cu_seq_lens=cu_seq_lens_cpu.to(device, non_blocking=True),
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                        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,
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                        token_to_seq=token_to_seq_tensor_cpu.to(
                            device, non_blocking=True
                        ),
                        chunk_total_token=chunk_total_token,
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                        workspace=self.chunked_prefill_workspace,
                    )
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                if self._use_cudnn_prefill:
                    chunked_context_metadata.seq_lens = chunk_seq_lens

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                assert (
                    max(chunked_context_metadata.max_seq_lens)
                    <= self.chunked_prefill_workspace_size
                )
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            prefill_metadata = self.prefill_metadata_cls(
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                block_table=block_table_tensor[reqs_start:, ...],
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                query_start_loc=prefill_query_start_loc,
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                max_query_len=max_query_len,
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                chunked_context=chunked_context_metadata,
            )

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

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            if self._use_trtllm_ragged_prefill:
                prefill_metadata.query_seq_lens = (
                    prefill_query_start_loc[1:] - prefill_query_start_loc[:-1]
                )
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                prefill_metadata.workspace_buffer = self._workspace_buffer
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        decode_metadata = None
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        if num_decodes > 0:
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            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

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                # 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

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

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        attn_metadata = self.metadata_cls(
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            num_reqs=common_attn_metadata.num_reqs,
            max_query_len=common_attn_metadata.max_query_len,
1017
            max_seq_len=max_seq_len,
1018
            num_actual_tokens=num_tokens,
1019
1020
            query_start_loc=query_start_loc,
            slot_mapping=slot_mapping,
1021
            head_dim=self.model_config.get_head_size(),
1022
            # MLACommonMetadata Chunk prefill specific
1023
1024
1025
            num_decodes=num_decodes,
            num_decode_tokens=num_decode_tokens,
            num_prefills=num_prefills,
1026
1027
            prefill=prefill_metadata,
            decode=decode_metadata,
1028
1029
        )

1030
        if self._use_fi_prefill and num_prefills > 0:
1031
1032
1033
1034
1035
            assert isinstance(attn_metadata.prefill, FlashInferPrefillMetadata)
            self._build_fi_prefill_wrappers(attn_metadata.prefill)

        return attn_metadata

1036

1037
1038
1039
def reorg_kvcache(
    allgatered_kv_c_normed: torch.Tensor,
    allgatered_k_pe: torch.Tensor,
1040
1041
    padded_local_chunk_seq_lens_lst: list[int],
    local_context_lens_allranks: list[list[int]],
1042
1043
    sum_seq_len: int,
    max_seq_len: int,
1044
1045
    chunk_size: int,
    chunk_idx: int,
1046
1047
1048
    toks: int,
) -> tuple[torch.Tensor, torch.Tensor]:
    """
1049
1050
1051
1052
1053
1054
    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, ...]
1055
    Args:
1056
1057
1058
        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.
1059
1060
        sum_seq_len: the sum of cp_chunk_seq_lens_lst.
        max_seq_len: the max value of cp_chunk_seq_lens_lst.
1061
1062
1063
        chunk_size: the local padded max context chunk from
            chunked_context_metadata building.
        chunk_idx: chunk idx of chunked_prefill.
1064
1065
1066
1067
1068
1069
        toks: the number of tokens for local gather cache.
    """
    kv_c_segments = []
    k_pe_segments = []
    src_token_idx = 0
    max_seq_len_check = 0
1070
1071
    for padded_local_chunk_seq_len, local_context_lens in zip(
        padded_local_chunk_seq_lens_lst, local_context_lens_allranks
1072
    ):
1073
        cur_seq_len = 0
1074
        for rank, local_context_len in enumerate(local_context_lens):
1075
1076
1077
1078
1079
1080
1081
1082
1083
1084
1085
1086
            # 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:
1087
1088
1089
                kv_c_segment = allgatered_kv_c_normed[
                    rank * toks + src_token_idx : rank * toks
                    + src_token_idx
1090
                    + local_chunk_len
1091
1092
1093
1094
                ]
                k_pe_segment = allgatered_k_pe[
                    rank * toks + src_token_idx : rank * toks
                    + src_token_idx
1095
                    + local_chunk_len
1096
                ]
1097
1098
                kv_c_segments.append(kv_c_segment)
                k_pe_segments.append(k_pe_segment)
1099
                cur_seq_len += local_chunk_len
1100
        max_seq_len_check = max(max_seq_len_check, cur_seq_len)
1101
        src_token_idx += padded_local_chunk_seq_len
1102
1103
1104
1105
1106
1107
1108
1109
    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


1110
1111
1112
# TODO(Lucas): rename MLACommonBaseImpl -> MLACommonImpl,
# and MLACommonImpl -> MLACommonDenseImpl or somthing like that
class MLACommonBaseImpl(MLAAttentionImpl[A], Generic[A]):
1113
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1115
1116
1117
1118
1119
1120
1121
1122
1123
    """
    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,
1124
1125
        alibi_slopes: list[float] | None,
        sliding_window: int | None,
1126
        kv_cache_dtype: str,
1127
        logits_soft_cap: float | None,
1128
        attn_type: str,
1129
        kv_sharing_target_layer_name: str | None,
1130
        # MLA Specific Arguments
1131
        q_lora_rank: int | None,
1132
1133
1134
1135
1136
1137
        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,
1138
        indexer=None,
1139
        q_pad_num_heads: int | None = None,
1140
    ) -> None:
1141
1142
1143
        if kv_sharing_target_layer_name is not None:
            raise NotImplementedError("KV sharing is not supported for MLA")

1144
1145
1146
1147
1148
1149
1150
1151
1152
1153
1154
1155
1156
        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
1157
        self.indexer = indexer
1158
        self.q_pad_num_heads = q_pad_num_heads
1159
        self.is_aiter_triton_fp8_bmm_enabled = rocm_aiter_ops.is_fp8bmm_enabled()
1160

1161
1162
1163
1164
1165
1166
1167
    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(
1168
1169
                f"Layer '{layer}' has no recognized weight attribute: {WEIGHT_NAMES}."
            )
1170
1171
1172
1173

        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)
1174
1175
1176
1177
1178
1179
                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)
1180
1181
1182
1183
1184
1185
1186
1187
1188
1189
1190
                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,
1191
1192
1193
1194
1195
1196
1197
1198
            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=}"
        )
1199
1200
1201
1202
1203
1204
1205
        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(
1206
1207
            [self.qk_nope_head_dim, self.v_head_dim], dim=-1
        )
1208

1209
        if self.is_aiter_triton_fp8_bmm_enabled:
1210
1211
1212
            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(
1213
1214
                W_K, dtype=current_platform.fp8_dtype()
            )
1215
            self.W_V, self.W_V_scale = dynamic_per_batched_tensor_quant(
1216
1217
                W_V, dtype=current_platform.fp8_dtype()
            )
1218
1219
1220
1221
1222
1223
1224
1225
1226
1227
1228
1229
1230
1231
1232

            # 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:
1233
1234
1235
1236
1237
                x = torch.empty(
                    (self.W_K.shape[0], m, self.W_K.shape[2]),
                    dtype=torch.bfloat16,
                    device=self.W_K.device,
                )
1238
                rocm_aiter_ops.triton_fp8_bmm(
1239
1240
1241
1242
1243
1244
1245
1246
                    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,
                )
1247
                rocm_aiter_ops.triton_fp8_bmm(
1248
1249
                    x, self.W_V, self.W_V_scale, group_size=128, transpose_bm=True
                )
1250
1251
1252
1253
1254
1255
1256
1257
1258
        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)
1259

1260
        if self.is_aiter_triton_fp8_bmm_enabled:
1261
            out = out.view(-1, self.num_heads, self.v_head_dim)
1262
            # Multiply + Transpose (N, B, L) x (N, L, V)->(N, B, V)->(B, N, V)
1263
            x = rocm_aiter_ops.triton_fp8_bmm(
1264
                x, self.W_V, self.W_V_scale, group_size=128, transpose_bm=True, YQ=out
1265
            )
1266
1267
1268
1269
1270
1271
1272
1273
        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)
1274
            out_new = out.transpose(0, 1).reshape(-1, self.num_heads * self.v_head_dim)
1275
1276
1277
1278
1279
1280
1281
1282
1283
1284
1285
1286
1287
1288
1289
1290

            # 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)

1291
1292
1293
1294
1295
        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
1296
1297
1298
1299
1300
1301
1302
        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
1303
1304
        elif use_cudnn_prefill():
            logger.debug_once("Using CUDNN prefill for MLA")
1305
            self._run_prefill_context_chunk = self._run_prefill_context_chunk_cudnn
1306
1307
            self._run_prefill_new_tokens = self._run_prefill_new_tokens_cudnn
            self._pad_v = False
1308
1309
1310
1311
1312
1313
1314
1315
1316
1317
1318
1319
        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:
1320
1321
1322
                self.flash_attn_varlen_func = functools.partial(
                    flash_attn_varlen_func, fa_version=self.vllm_flash_attn_version
                )
1323
1324
1325
1326
1327
1328
1329

            # 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
1330
1331
                and current_platform.get_device_capability()[0] == 9
            )
1332

1333
        self.dcp_world_size: int | None = None
1334

1335
        self.chunked_prefill_workspace_size = (
1336
            MLACommonMetadataBuilder.determine_chunked_prefill_workspace_size(
1337
1338
1339
                get_current_vllm_config()
            )
        )
1340
1341
        self.cp_kv_cache_interleave_size: int = (
            get_current_vllm_config().parallel_config.cp_kv_cache_interleave_size
1342
        )
1343
1344
1345
1346

    def _flash_attn_varlen_diff_headdims(
        self, q, k, v, return_softmax_lse=False, softmax_scale=None, **kwargs
    ):
1347
1348
1349
        maybe_padded_v = v
        if self._pad_v:
            maybe_padded_v = torch.nn.functional.pad(
1350
1351
                v, [0, q.shape[-1] - v.shape[-1]], value=0
            )
1352

1353
1354
1355
1356
1357
1358
        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
1359
        if vllm_is_batch_invariant():
1360
            kwargs["num_splits"] = 1
1361

1362
1363
1364
1365
1366
1367
1368
1369
1370
1371
1372
1373
1374
1375
1376
1377
1378
1379
1380
        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

1381
    def _run_prefill_new_tokens_fa(
1382
        self, prefill: MLACommonPrefillMetadata, q, k, v, return_softmax_lse
1383
    ):
1384
1385
1386
1387
1388
1389
1390
1391
1392
1393
1394
1395
1396
        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,
        )

1397
    def _run_prefill_new_tokens_fi(
1398
        self, prefill: MLACommonPrefillMetadata, q, k, v, return_softmax_lse
1399
    ):
1400
1401
        assert isinstance(prefill, FlashInferPrefillMetadata)
        assert prefill.prefill_main is not None
1402

1403
        ret = prefill.prefill_main.run(
1404
1405
1406
1407
1408
1409
            q=q,
            k=k,
            v=v,
            return_lse=return_softmax_lse,
        )

1410
1411
1412
1413
        if isinstance(ret, tuple):
            return ret[0], ret[1].transpose(0, 1).contiguous()
        return ret

1414
    def _run_prefill_new_tokens_cudnn(
1415
        self, prefill: MLACommonPrefillMetadata, q, k, v, return_softmax_lse
1416
    ):
1417
1418
1419
1420
1421
1422
1423
1424
1425
1426
1427
1428
1429
        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,
1430
1431
1432
1433
            # Do not support False for now
            return_lse=True,
            # Indicates actual_seq_lens are on GPU or CPU.
            is_cuda_graph_compatible=True,
1434
1435
1436
1437
1438
        )
        if return_softmax_lse:
            return output, lse
        return output

1439
    def _run_prefill_context_chunk_fa(
1440
        self, prefill: MLACommonPrefillMetadata, chunk_idx: int, q, k, v
1441
    ):
1442
1443
1444
1445
1446
1447
1448
1449
1450
1451
1452
1453
1454
1455
        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,
        )

1456
    def _run_prefill_context_chunk_fi(
1457
        self, prefill: MLACommonPrefillMetadata, chunk_idx: int, q, k, v
1458
    ):
1459
        assert isinstance(prefill, FlashInferPrefillMetadata)
1460

1461
        attn_out, lse = prefill.prefill_chunks[chunk_idx].run(
1462
1463
1464
1465
1466
            q=q,
            k=k,
            v=v,
            return_lse=True,
        )
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        # Convert from (q_len, num_heads) to (num_heads, q_len)
        return attn_out, lse.transpose(0, 1).contiguous()
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    def _run_prefill_context_chunk_cudnn(
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        self, prefill: MLACommonPrefillMetadata, chunk_idx: int, q, k, v
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    ):
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        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),
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            actual_seq_lens_kv=prefill.chunked_context.seq_lens[chunk_idx].view(
                -1, 1, 1, 1
            ),
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            causal=False,
            return_lse=True,
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            # Indicates actual_seq_lens are on GPU or CPU.
            is_cuda_graph_compatible=True,
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        )

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

        assert prefill.query_seq_lens is not None
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        assert prefill.workspace_buffer is not None
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        ret = trtllm_ragged_attention_deepseek(
            query=q,
            key=k,
            value=v,
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            workspace_buffer=prefill.workspace_buffer,
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            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(
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        self, prefill: MLACommonPrefillMetadata, chunk_idx: int, q, k, v
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    ):
        """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
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        assert prefill.workspace_buffer is not None
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        out = torch.zeros(
            q.shape[0],
            q.shape[1],
            v.shape[2],
            device=q.device,
            dtype=q.dtype,
        )
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        prefill.workspace_buffer.fill_(0)
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        attn_out, lse = trtllm_ragged_attention_deepseek(
            query=q,
            key=k,
            value=v,
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            workspace_buffer=prefill.workspace_buffer,
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            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()

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    def process_weights_after_loading(self, act_dtype: torch.dtype):
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        def get_layer_weight(layer):
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            WEIGHT_NAMES = ("weight", "qweight", "weight_packed")
            for attr in WEIGHT_NAMES:
                if hasattr(layer, attr):
                    return getattr(layer, attr)
            raise AttributeError(
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                f"Layer '{layer}' has no recognized weight attribute: {WEIGHT_NAMES}."
            )
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        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)
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                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)
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                del eye
                # standardize to (output, input)
                return dequant_weights.T
            return layer.weight

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        # we currently do not have quantized bmm's which are needed for
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        # `W_UV` and `W_UK_T`, we just store fp16/bf16 copies and perform
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        # the bmm's in 16-bit, the extra memory overhead of this is fairly low
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        kv_b_proj_weight = get_and_maybe_dequant_weights(self.kv_b_proj).T
        assert kv_b_proj_weight.shape == (
            self.kv_lora_rank,
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            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=}"
        )
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        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(
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            [self.qk_nope_head_dim, self.v_head_dim], dim=-1
        )
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        if self.is_aiter_triton_fp8_bmm_enabled:
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            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(
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                W_K, dtype=current_platform.fp8_dtype()
            )
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            self.W_V, self.W_V_scale = dynamic_per_batched_tensor_quant(
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                W_V, dtype=current_platform.fp8_dtype()
            )
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            # 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:
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                x = torch.empty(
                    (self.W_K.shape[0], m, self.W_K.shape[2]),
                    dtype=torch.bfloat16,
                    device=self.W_K.device,
                )
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                rocm_aiter_ops.triton_fp8_bmm(
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                    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,
                )
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                rocm_aiter_ops.triton_fp8_bmm(
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                    x, self.W_V, self.W_V_scale, group_size=128, transpose_bm=True
                )
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        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)
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    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

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    def _compute_prefill_context(
        self,
        q: torch.Tensor,
        kv_c_and_k_pe_cache: torch.Tensor,
        attn_metadata: MLACommonMetadata,
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        k_scale: torch.Tensor,
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    ):
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        assert attn_metadata.prefill is not None
        prefill_metadata = attn_metadata.prefill
        assert prefill_metadata.chunked_context is not None
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        output = None
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        iters = len(prefill_metadata.chunked_context.seq_tot)
        workspace = prefill_metadata.chunked_context.workspace
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        for i in range(iters):
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            toks = prefill_metadata.chunked_context.seq_tot[i]
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            ops.gather_and_maybe_dequant_cache(
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                src_cache=kv_c_and_k_pe_cache,
                dst=workspace,
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                block_table=prefill_metadata.block_table,
                cu_seq_lens=prefill_metadata.chunked_context.cu_seq_lens[i],
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                token_to_seq=prefill_metadata.chunked_context.token_to_seq[i],
                num_tokens=prefill_metadata.chunked_context.chunk_total_token[i],
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                kv_cache_dtype=self.kv_cache_dtype,
                scale=k_scale,
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                seq_starts=prefill_metadata.chunked_context.starts[i],
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            )

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            kv_c_normed = workspace[:toks][..., : self.kv_lora_rank]
            k_pe = workspace[:toks][..., self.kv_lora_rank :].unsqueeze(1)
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            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)
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            k = self._concat_k_nope_k_pe(k_nope, k_pe)
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            attn_output, attn_softmax_lse = self._run_prefill_context_chunk(
                prefill=prefill_metadata,
                chunk_idx=i,
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                q=q,
                k=k,
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                v=v,
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            )

            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

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    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,
    ):
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        assert k_scale is None, "DCP not support scaled kvcache now."
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        assert attn_metadata.prefill is not None
        prefill_metadata = attn_metadata.prefill
        assert prefill_metadata.chunked_context is not None
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        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
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        assert prefill_metadata.chunked_context.cu_seq_lens_lst is not None
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        assert prefill_metadata.chunked_context.chunk_size is not None
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        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,
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                cu_seq_lens=prefill_metadata.chunked_context.padded_local_cu_seq_lens[
                    i
                ],
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                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)
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            assert allgather_offset * (dcp_world_size + 1) == workspace.shape[0]
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            assert toks <= allgather_offset
            local_gathered_kvcache = workspace[:toks]
            cur_allgather_workspace = workspace[
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                allgather_offset : allgather_offset * (1 + dcp_world_size)
            ]
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            assert toks * dcp_world_size <= cur_allgather_workspace.shape[0]
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            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)
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            kv_c_normed, k_pe = reorg_kvcache(
                allgatered_kv_c_normed,
                allgatered_k_pe,
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                padded_local_chunk_seq_lens_lst=prefill_metadata.chunked_context.padded_local_chunk_seq_lens[
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                    i
                ],
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                local_context_lens_allranks=prefill_metadata.chunked_context.local_context_lens_allranks,
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                sum_seq_len=prefill_metadata.chunked_context.cu_seq_lens_lst[i][-1],
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                max_seq_len=prefill_metadata.chunked_context.max_seq_lens[i],
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                chunk_size=prefill_metadata.chunked_context.chunk_size,
                chunk_idx=i,
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                toks=toks,
            )
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            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)
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            k = self._concat_k_nope_k_pe(k_nope, k_pe)
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            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

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    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,
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        k_scale: torch.Tensor,
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        output: torch.Tensor,
    ) -> None:
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        # TODO (zyongye): Prefill function here
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        assert attn_metadata.prefill is not None
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        assert self.dcp_world_size is not None
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        has_context = attn_metadata.prefill.chunked_context is not None
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        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)
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        k = self._concat_k_nope_k_pe(k_nope, k_pe)
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        output_prefill = self._run_prefill_new_tokens(
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            prefill=attn_metadata.prefill,
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            q=q,
            k=k,
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            v=v,
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            return_softmax_lse=has_context,
        )

        if has_context:
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            suffix_output, suffix_lse = output_prefill
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            if self.dcp_world_size > 1:
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                context_output, context_lse = (
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                    self._context_parallel_compute_prefill_context(
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                        q,
                        kv_c_and_k_pe_cache,
                        attn_metadata,
                        k_scale=None,
                        dcp_world_size=self.dcp_world_size,
                    )
                )
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            else:
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                context_output, context_lse = self._compute_prefill_context(
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                    q, kv_c_and_k_pe_cache, attn_metadata, k_scale
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                )
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            # 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)
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            merge_attn_states(
                output=output,
                prefix_output=context_output,
                prefix_lse=context_lse,
                suffix_output=suffix_output,
                suffix_lse=suffix_lse,
            )
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        else:
            output_prefill = output_prefill[..., : v.shape[-1]].flatten(start_dim=-2)
            output.copy_(output_prefill)
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    @abstractmethod
    def _forward_decode(
        self,
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        q: torch.Tensor | tuple[torch.Tensor, torch.Tensor],
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        kv_c_and_k_pe_cache: torch.Tensor,
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        attn_metadata: M,
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        layer: AttentionLayer,
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    ) -> tuple[torch.Tensor, torch.Tensor | None]:
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        raise NotImplementedError

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

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        if output_scale is not None or output_block_scale is not None:
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            raise NotImplementedError(
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                "fused output quantization is not yet supported for MLACommonImpl"
            )
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        if attn_metadata is None:
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            # 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(
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                (
                    self.chunked_prefill_workspace_size,
                    self.num_heads,
                    self.qk_nope_head_dim + self.v_head_dim,
                ),
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                device=k_c_normed.device,
                dtype=k_c_normed.dtype,
            )

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

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

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        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, ...]
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        q = q[:num_actual_toks, ...]
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        k_c_normed = k_c_normed[:num_actual_toks, ...]
        k_pe = k_pe[:num_actual_toks, ...]

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

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        decode_q = q[:num_decode_tokens]
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        prefill_q = q[num_decode_tokens:]
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        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,
            )

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

2020
        if has_prefill:
2021
            self._forward_prefill(
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                prefill_q,
                prefill_k_c_normed,
                prefill_k_pe,
                kv_cache,
                attn_metadata,
                layer._k_scale,
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                output=output[num_decode_tokens:],
2029
            )
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        if has_decode:
2032
            assert attn_metadata.decode is not None
2033

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            decode_q_nope, decode_q_pe = decode_q.split(
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                [self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1
            )
2037

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            # Convert from (B, N, P) to (N, B, P)
            decode_q_nope = decode_q_nope.transpose(0, 1)
2040

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            if self.q_pad_num_heads is not None:
                B, N, L = decode_q_pe.shape
2043
                decode_pe_padded = decode_q_pe.new_empty((B, self.q_pad_num_heads, L))
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                decode_pe_padded.resize_((B, N, L))
                decode_pe_padded.copy_(decode_q_pe)
                decode_q_pe = decode_pe_padded

2048
            if self.is_aiter_triton_fp8_bmm_enabled:
2049
                # Multiply+Transpose (N, B, P)x(N, P, L)->(N, B, L)->(B, N, L)
2050
                decode_ql_nope = rocm_aiter_ops.triton_fp8_bmm(
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                    decode_q_nope,
                    self.W_K,
                    self.W_K_scale,
                    group_size=128,
                    transpose_bm=True,
                )
2057
            else:
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                # Pads the head_dim if necessary (for the underlying kernel)
                N, B, P = decode_q_nope.shape
                _, _, L = self.W_UK_T.shape
2061

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

2070
                # Multiply (N, B, P) x (N, P, L) -> (N, B, L)
2071
                torch.bmm(decode_q_nope, self.W_UK_T, out=decode_ql_nope)
2072

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                # Convert from (N, B, L) to (B, N, L)
                decode_ql_nope = decode_ql_nope.transpose(0, 1)
2075

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            if fp8_attention:
                ql_nope_shape = decode_ql_nope.shape
                q_pe_shape = decode_q_pe.shape
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                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,
2091
                )
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                decode_q0[..., : ql_nope_shape[2]].copy_(decode_ql_nope)
                decode_q0[..., ql_nope_shape[2] :].copy_(decode_q_pe)
2094

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                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)
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            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
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            attn_out, lse = self._forward_decode(
                decode_q, kv_cache, attn_metadata, layer
            )
2113

2114
            # correct dcp attn_out with lse.
2115
            if self.dcp_world_size > 1:
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                attn_out = cp_lse_ag_out_rs(
                    attn_out,
                    lse,
                    get_dcp_group(),
2120
                    is_lse_base_on_e=not getattr(self, "_use_fi_prefill", False),
2121
                )
2122
2123

            # v_up projection
2124
            self._v_up_proj(attn_out, out=output[:num_decode_tokens])
2125
        return output_padded