`vllm.model_executor.layers.attention.mla_attention` ¶

MLA Common Components¶

This file implements common components for MLA implementations.

First we define:

Sq as Q sequence length Skv as KV sequence length

MLA has two possible ways of computing, a data-movement friendly approach and a compute friendly approach. We generally want to use the compute friendly approach for "prefill" (i.e. the ratio Sq / Skv is relatively large, often near 1) and the data-movement friendly approach for "decode" (i.e. the ratio Sq / Skv is small, often near 0).

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

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

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

Below is an example of both paths assuming batch size = 1

More Extent Definitions:¶

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

Vector/Matrix Definitions¶

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

Compute Friendly Approach (i.e. "forward_mha"):¶

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

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

in the actual code,

kv_b_proj is [W_UK; W_UV] concatenated per head q_b_proj is [W_UQ; W_QR] concatenated per head out_proj is W_O

Data-Movement Friendly Approach (i.e. "forward_mqa"):¶

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

// MQA with QK headdim = Lkv + R // V headdim = Lkv // sdpa_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 sdpa_o = scaled_dot_product_attention( torch.cat([ql_nope, q_pe], dim=-1), torch.cat([kv_c, k_pe], dim=-1), kv_c )

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

Chunked Prefill¶

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

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

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

The chunked prefill approach is as follows:

W Workspace rows, i.e. the context rows we may gather at once, used to bound the memory usage

The context is scheduled per request: requests are packed in order, splitting the next request on an aligned boundary when needed to fill W. So a chunk covers a contiguous run of prefills and only ever the tokens and context rows of those prefills — attention, up-projection and merging are charged only to the requests it covers, and no chunk contains an empty context span. See plan_mla_context_chunks.

Only a chunk's first request can be a continuation, so accumulation performs at most one request-slice merge and one bulk write per chunk.

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

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

// Compute attention with the already existing context. Shown for a single // request; with a batch, a chunk covers a run of requests and both q and the // gathered context below are sliced to that run. for chunk_idx in range(cdiv(C, W)): chunk_start = chunk_idx * W chunk_end = min(chunk_start + W, 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,
    causal=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

Classes:

MLAAttention –

Multi-Head Latent Attention layer.
MLACommonBackend –
MLACommonBaseImpl –

Shared MLA base providing dense-MHA prefill (via the selected
MLACommonImpl –

NOTE: Please read the comment at the top of the file before trying to
MLACommonMetadata –

Metadata for MLACommon.
MLACommonMetadataBuilder –

NOTE: Please read the comment at the top of the file before trying to
MLACommonPrefillMetadata –

Prefill Specific Metadata
QueryLenSupport –

Defines the level of query length support for an attention backend's

Functions:

accumulate_mla_context_chunk –

Fold one chunk's partial into the running context partial.
backend_supports_prefill_query_quantization –

Check if the selected MLA prefill backend supports query quantization.
build_mla_chunked_context_metadata –

Build chunked-context metadata for an MLA prefill.
init_mla_context_partial –

Allocate the running context partial over all prefill tokens.
neutralize_empty_context_partials –

Neutralize the partial of every prefill that no chunk covers.
plan_mla_context_chunks –

Pack per-request contexts into workspace-sized chunks.
reorg_kvcache –

reorg and unpad kvcache after cp local gather to tp layout for attn kernel.
unified_mla_kv_cache_update –

Returns a dummy that is passed to unified_attention to signal a side effect and

`MLAAttention` ¶

Bases: Module, AttentionLayerBase

Multi-Head Latent Attention layer.

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

This class takes query, and compressed key/value tensors as input. The class does the following:

Store the input key and value tensors in the KV cache.
Perform (multi-head/multi-query/grouped-query) attention.
Return the output tensor.

Source code in vllm/model_executor/layers/attention/mla_attention.py

class MLAAttention(nn.Module, AttentionLayerBase):
    """Multi-Head Latent Attention layer.

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

    This class takes query, and compressed key/value tensors as input.
    The class does the following:

    1. Store the input key and value tensors in the KV cache.
    2. Perform (multi-head/multi-query/grouped-query) attention.
    3. Return the output tensor.
    """

    supports_dense_mha_prefill: ClassVar[bool] = True

    def __init__(
        self,
        num_heads: int,
        scale: float,
        qk_nope_head_dim: int,
        qk_rope_head_dim: int,
        v_head_dim: int,
        q_lora_rank: int | None,
        kv_lora_rank: int,
        kv_b_proj: ColumnParallelLinear,
        dcp_q_replicate: bool = False,
        cache_config: CacheConfig | None = None,
        quant_config: QuantizationConfig | None = None,
        prefix: str = "",
        attn_backend: type[AttentionBackend] | None = None,
        use_sparse: bool = False,
        indexer: object | None = None,
        topk_indices_buffer: torch.Tensor | None = None,
        non_causal_multi_token_decode: bool = False,
        sliding_window: int | None = None,
        prefill_backend_cls: type[MLAPrefillBackend] | None = None,
        **extra_impl_args,
    ):
        super().__init__()
        self.num_heads = num_heads
        self.scale = scale
        self.qk_nope_head_dim = qk_nope_head_dim
        self.qk_rope_head_dim = qk_rope_head_dim
        self.v_head_dim = v_head_dim
        self.q_lora_rank = q_lora_rank
        self.kv_lora_rank = kv_lora_rank
        self.kv_b_proj = kv_b_proj
        self.dcp_q_replicate = dcp_q_replicate
        self.W_UK_T_dcp_qrep: torch.Tensor | None = None
        self.head_size = kv_lora_rank + qk_rope_head_dim
        self.layer_name = prefix
        self.indexer = indexer
        self.non_causal_multi_token_decode = non_causal_multi_token_decode
        self.sliding_window = sliding_window
        self.num_kv_heads = 1
        self.qk_head_dim = self.qk_nope_head_dim + self.qk_rope_head_dim

        if cache_config is not None:
            kv_cache_dtype: CacheDType = cache_config.cache_dtype
        else:
            kv_cache_dtype = "auto"
        self.quant_config = quant_config

        if cache_config is not None and cache_config.kv_cache_dtype_skip_layers:
            from vllm.model_executor.models.utils import extract_layer_index

            layer_idx = extract_layer_index(prefix)
            if str(layer_idx) in cache_config.kv_cache_dtype_skip_layers:
                kv_cache_dtype = "auto"
            logger.debug(
                "Layer %s: kv_cache_dtype=%s",
                prefix,
                kv_cache_dtype,
            )

        dtype = torch.get_default_dtype()
        if attn_backend is not None:
            assert attn_backend.is_mla(), (
                f"MLAAttention: attn_backend must be an MLA backend, "
                f"got {attn_backend.get_name()} instead"
            )
            self.attn_backend = attn_backend
        else:
            self.attn_backend = get_attn_backend(
                self.head_size,
                dtype,
                kv_cache_dtype,
                use_mla=True,
                use_sparse=use_sparse,
                num_heads=self.num_heads,
            )

        normalized_kv_cache_dtype = _canonicalize_sparse_mla_kv_cache_dtype(
            self.attn_backend, kv_cache_dtype
        )
        if normalized_kv_cache_dtype != kv_cache_dtype:
            if cache_config is not None:
                cache_config.cache_dtype = normalized_kv_cache_dtype
            kv_cache_dtype = normalized_kv_cache_dtype
            logger.info_once(
                "Using %s KV cache format for %s backend.",
                kv_cache_dtype,
                self.attn_backend.get_name(),
            )

        if (
            self.attn_backend.get_name() == "FLASHINFER_MLA_SPARSE"
            and kv_cache_dtype != "fp8_ds_mla"
            and is_quantized_kv_cache(kv_cache_dtype)
        ):
            logger.info_once(
                "Using standard fp8 KV cache format. To use DeepSeek's fp8_ds_mla "
                "KV cache format, please set `--attention-backend FLASHMLA_SPARSE`"
            )

        # Initialize KV cache quantization attributes
        self.kv_cache_dtype = kv_cache_dtype
        _init_kv_cache_quant(self, quant_config, prefix)

        if (
            cache_config is not None
            and cache_config.enable_prefix_caching
            and envs.VLLM_BATCH_INVARIANT
            and (
                self.attn_backend.get_name() == "TRITON_MLA"
                or self.attn_backend.get_name() == "FLASHINFER"
            )
        ):
            logger.warning_once(
                "Disabling prefix caching for TRITON_MLA / FLASHINFER "
                "with batch invariance, as it is not yet supported.",
            )
            cache_config.enable_prefix_caching = False

        # Sparse MLA reads top-k indices from a shared buffer. Pass it
        # explicitly so backbone "skip" layers (indexer=None) still find it.
        if use_sparse:
            extra_impl_args["topk_indices_buffer"] = topk_indices_buffer

        impl_cls = cast(type[MLAAttentionImpl], self.attn_backend.get_impl_cls())
        self.impl = impl_cls(  # type: ignore[assignment]  # impl_cls always returns an MLAAttentionImpl subclass
            num_heads=self.num_heads,
            head_size=self.head_size,
            scale=self.scale,
            num_kv_heads=1,
            alibi_slopes=None,
            sliding_window=sliding_window,
            kv_cache_dtype=self.kv_cache_dtype,
            logits_soft_cap=None,
            attn_type=AttentionType.DECODER,
            kv_sharing_target_layer_name=None,
            # MLA Args
            q_lora_rank=self.q_lora_rank,
            kv_lora_rank=self.kv_lora_rank,
            qk_nope_head_dim=self.qk_nope_head_dim,
            qk_rope_head_dim=self.qk_rope_head_dim,
            qk_head_dim=self.qk_nope_head_dim + self.qk_rope_head_dim,
            v_head_dim=self.v_head_dim,
            kv_b_proj=kv_b_proj,
            indexer=indexer,
            **extra_impl_args,
        )
        self.q_pad_num_heads = getattr(self.impl, "q_pad_num_heads", None)
        self.is_amx_bmm_enabled = getattr(self.impl, "uses_amx_bmm", False)
        # AMX reads kv_b_proj's weight directly and never calls it live; the
        # reference CPU MLA backend calls it but isn't perf-critical. Skip
        # the packed-kernel dispatch either way.
        kv_b_proj._cpu_skip_gemm_dispatch = True
        self.use_direct_call = not current_platform.opaque_attention_op()

        vllm_config = get_current_vllm_config()
        parallel_config = vllm_config.parallel_config
        self.use_pcp = parallel_config.prefill_context_parallel_size > 1
        compilation_config = vllm_config.compilation_config
        if prefix in compilation_config.static_forward_context:
            raise ValueError(f"Duplicate layer name: {prefix}")
        compilation_config.static_forward_context[prefix] = self

        self.prefill_backend: MLAPrefillBackend | None
        if self.impl.is_sparse and not (
            self.impl.supports_dense_mha_prefill and self.supports_dense_mha_prefill
        ):
            logger.warning_once(
                "Sparse MLA layer has no dense-MHA prefill path; using the top-k "
                "MQA path only."
            )
            self.prefill_backend = None
        else:
            try:
                prefill_backend_cls = prefill_backend_cls or get_mla_prefill_backend(
                    vllm_config
                )
            except ValueError:
                if (
                    not self.impl.is_sparse
                    or vllm_config.attention_config.mla_prefill_backend is not None
                ):
                    raise
                logger.warning_once(
                    "No MLA prefill backend supports this model; sparse MLA will "
                    "use the top-k MQA path only (no dense-MHA prefill)."
                )
                self.prefill_backend = None
            else:
                self.prefill_backend = prefill_backend_cls(
                    num_heads=self.num_heads,
                    scale=self.scale,
                    kv_lora_rank=self.kv_lora_rank,
                    qk_nope_head_dim=self.qk_nope_head_dim,
                    qk_rope_head_dim=self.qk_rope_head_dim,
                    v_head_dim=self.v_head_dim,
                    vllm_config=vllm_config,
                )

        self.kv_cache = torch.tensor([])

        self.use_sparse = use_sparse

        self.dcp_manager: MLADCPManager | None = None
        if self.impl.dcp_world_size > 1:
            query_dtype = (
                current_platform.fp8_dtype()
                if is_quantized_kv_cache(self.kv_cache_dtype)
                and self.kv_cache_dtype != "fp8_ds_mla"
                and self.impl.supports_quant_query_input
                else dtype
            )
            self.dcp_manager = MLADCPManager(
                vllm_config=vllm_config,
                device=next(kv_b_proj.parameters()).device,
                num_heads=self.num_heads,
                query_head_dim=self.kv_lora_rank + self.qk_rope_head_dim,
                output_head_dim=self.kv_lora_rank,
                query_dtype=query_dtype,
                output_dtype=dtype,
                padded_num_heads=self.q_pad_num_heads,
                is_lse_base_on_e=self.impl.lse_base_on_e,
                use_pcp=self.use_pcp,
            )

        self.is_aiter_triton_fp8_bmm_enabled = rocm_aiter_ops.is_fp8bmm_enabled()

        # If kv_b_proj_weight is unquantized, quantize it to mxfp4 if supported
        self.is_aiter_triton_fp4_bmm_enabled = (
            rocm_aiter_ops.is_fp4bmm_enabled()
            and hasattr(self.kv_b_proj, "weight")
            and self.kv_b_proj.weight.dtype == torch.bfloat16
        )

        # Attributes for forward_impl method
        self._vllm_config = get_current_vllm_config()
        self._chunked_prefill_workspace_size: int | None = None
        self._decode_concat_quant_fp8_op = _DecodeConcatQuantFP8(
            static=True,
            group_shape=GroupShape.PER_TENSOR,
            compile_native=True,
        )
        self._quant_fp8_op = QuantFP8(
            static=True,
            group_shape=GroupShape.PER_TENSOR,
            compile_native=True,
        )

    def bind_kv_cache(self, kv_cache: torch.Tensor) -> None:
        # [B, H=1, N, C] -> [B, N, C]
        self.kv_cache = kv_cache.squeeze(1)

    @property
    def chunked_prefill_workspace_size(self) -> int:
        if self._chunked_prefill_workspace_size is None:
            self._chunked_prefill_workspace_size = (
                MLACommonMetadataBuilder.determine_chunked_prefill_workspace_size(
                    self._vllm_config
                )
            )
        return self._chunked_prefill_workspace_size

    def forward(
        self,
        q: torch.Tensor,
        kv_c_normed: torch.Tensor,
        k_pe: torch.Tensor,
        output_shape: torch.Size | None = None,
        q_dcp_replicated: torch.Tensor | None = None,
    ) -> torch.Tensor:
        if self.use_direct_call:
            forward_context: ForwardContext = get_forward_context()
            attn_metadata_raw = forward_context.attn_metadata
            attn_metadata: MLACommonMetadata
            if isinstance(attn_metadata_raw, dict):
                attn_metadata = attn_metadata_raw[self.layer_name]  # type: ignore[assignment]
            elif isinstance(attn_metadata_raw, list):
                # list[dict[str, AttentionMetadata]]: used in speculative decoding
                # where [0] is the base-model (non-speculative) metadata dict.
                attn_metadata = attn_metadata_raw[0][self.layer_name]  # type: ignore[assignment]
            else:
                attn_metadata = attn_metadata_raw
            self_kv_cache = self.kv_cache
            slot_mapping = forward_context.slot_mapping

            assert isinstance(slot_mapping, dict), (
                f"Expected slot_mapping to be a dict, got {type(slot_mapping)}. "
            )
            layer_slot_mapping = slot_mapping.get(self.layer_name)
            kv_for_cache, kpe_for_cache, layer_slot_mapping = (
                maybe_gather_mla_latent_cache_inputs(
                    kv_c_normed,
                    k_pe,
                    layer_slot_mapping,
                    attn_metadata.num_decode_tokens
                    if attn_metadata is not None
                    else None,
                    self.use_pcp,
                )
            )
            self.impl.do_kv_cache_update(  # type: ignore[attr-defined]
                kv_for_cache,
                kpe_for_cache,
                self_kv_cache,
                layer_slot_mapping,
                self.kv_cache_dtype,
                self._k_scale,
            )
            output = torch.empty(output_shape, dtype=q.dtype, device=q.device)
            self.forward_impl(
                q,
                kv_c_normed,
                k_pe,
                self_kv_cache,
                attn_metadata,
                output=output,
                q_dcp_replicated=q_dcp_replicated,
            )
            return output
        else:
            encoded = _encode_layer_name(self.layer_name)
            kv_cache_dummy_dep = torch.ops.vllm.unified_mla_kv_cache_update(
                kv_c_normed,
                k_pe,
                encoded,
                self.kv_cache_dtype,
                self._k_scale,
            )
            output = torch.empty(output_shape, dtype=q.dtype, device=q.device)
            torch.ops.vllm.unified_mla_attention_with_output(
                q,
                kv_c_normed,
                k_pe,
                output,
                encoded,
                kv_cache_dummy_dep=kv_cache_dummy_dep,
                q_dcp_replicated=q_dcp_replicated,
            )
            return output

    def forward_impl(
        self,
        q: torch.Tensor,
        k_c_normed: torch.Tensor,  # key in unified attn
        k_pe: torch.Tensor,  # value in unified attn
        kv_cache: torch.Tensor,
        attn_metadata: "MLACommonMetadata",
        output: torch.Tensor,
        output_scale: torch.Tensor | None = None,
        output_block_scale: torch.Tensor | None = None,
        quant_group_size: int | None = None,
        quant_scale_ue8m0: bool | None = None,
        quant_col_major: bool | None = None,
        quant_tma_aligned: bool | None = None,
        q_dcp_replicated: torch.Tensor | None = None,
    ) -> torch.Tensor:
        assert output is not None, "Output tensor must be provided."

        quant_key = _detect_output_quant_key(
            output, output_scale, output_block_scale, self.num_heads * self.v_head_dim
        )
        if quant_key is not None:
            # The fusion pass has allocated output with quantized dtype
            # (FP8 or uint8 for FP4). We can't write into it directly,
            # so we swap in a temp buffer for computation, then quantize
            # into the real output at the end.
            # NOTE(carlyou): this is temporary until kernels support fp8 output
            quant_output = output
            output = torch.empty(
                output.shape[0],
                self.num_heads * self.v_head_dim,
                dtype=q.dtype,
                device=output.device,
            )

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

            # The zero fill is required when used with DP + EP
            # to ensure all ranks within a DP group compute the
            # same expert outputs.
            if quant_key is not None:
                return quant_output.fill_(0)
            return output.fill_(0)

        fp8_attention = is_quantized_kv_cache(self.kv_cache_dtype)

        num_actual_toks = attn_metadata.num_actual_tokens
        if self.use_pcp and self.impl.dcp_world_size > 1 and quant_key is not None:
            raise NotImplementedError(
                "MRV2 MLA PCP+DCP does not support fused output quantization yet."
            )

        # Inputs and outputs may be padded for CUDA graphs
        output_padded = output
        output = output[:num_actual_toks, ...]
        q = q[:num_actual_toks, ...]
        if q_dcp_replicated is not None:
            q_dcp_replicated = q_dcp_replicated[:num_actual_toks, ...]
        k_c_normed = k_c_normed[:num_actual_toks, ...]
        k_pe = k_pe[:num_actual_toks, ...]

        if fp8_attention and self.kv_cache_dtype != "fp8_ds_mla":
            kv_cache = kv_cache.view(current_platform.fp8_dtype())

        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
        )
        num_mqa_tokens = attn_metadata.num_decode_tokens
        num_mha_tokens = q.size(0) - num_mqa_tokens

        if self.impl.is_sparse and num_mha_tokens > 0:
            prefill = getattr(attn_metadata, "prefill", None)
            use_dense_mha = getattr(prefill, "use_dense_mha", False)
            prefill_max_seq_len = attn_metadata.prefill_max_seq_len  # type: ignore[attr-defined]
            use_masked_mha = (
                self.prefill_backend is not None
                and self.impl.masked_mha_available  # type: ignore[attr-defined]
                and self.impl.dcp_world_size <= 1
                and prefill is not None
                and _use_masked_mha(
                    backend_name=self.attn_backend.get_name(),
                    tensor_parallel_size=self._vllm_config.parallel_config.tensor_parallel_size,
                    query_len=prefill.max_query_len,
                    seq_len=prefill_max_seq_len,
                )
                and self.impl.masked_mha_workspace_fits(prefill)  # type: ignore[attr-defined]
            )
            use_mha = (use_dense_mha or use_masked_mha) and not (
                self._vllm_config.attention_config.sparse_mla_force_mqa
            )
            if not use_mha:
                num_mqa_tokens = q.size(0)
                num_mha_tokens = 0

        mha_use_quant_output = (
            quant_key is not None
            and self.prefill_backend is not None
            and self.prefill_backend.supports_quant_output(quant_key)
            and (
                not self.impl.is_sparse
                or attn_metadata.prefill_max_seq_len  # type: ignore[attr-defined]
                <= attn_metadata.topk_tokens  # type: ignore[attr-defined]
            )
            and attn_metadata is not None
            and attn_metadata.prefill is not None
            and attn_metadata.prefill.chunked_context is None
            and self.impl.dcp_world_size <= 1
        )

        if num_mha_tokens > 0:
            if mha_use_quant_output:
                mha_output = quant_output
                mha_output_scale = output_scale
            else:
                mha_output = output
                mha_output_scale = None

            self.impl.forward_mha(  # type: ignore[attr-defined]
                q[num_mqa_tokens:],
                k_c_normed[num_mqa_tokens:],
                k_pe[num_mqa_tokens:],
                kv_cache,
                attn_metadata,
                self._k_scale,
                output=mha_output[num_mqa_tokens:num_actual_toks],
                output_scale=mha_output_scale,
            )

        if num_mqa_tokens > 0:
            if q_dcp_replicated is not None:
                mqa_q = q_dcp_replicated[:num_mqa_tokens]
                qrep_decode = True
            else:
                mqa_q = q[:num_mqa_tokens]
                qrep_decode = False
            mqa_output_slice = output[:num_mqa_tokens]

            mqa_q_nope, mqa_q_pe = mqa_q.split(
                [self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1
            )

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

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

            if self.is_aiter_triton_fp4_bmm_enabled:
                from aiter.ops.triton.batched_gemm_a16wfp4 import batched_gemm_a16wfp4

                mqa_ql_nope = batched_gemm_a16wfp4(
                    mqa_q_nope,
                    self.W_K,
                    self.W_K_scale,
                    transpose_bm=True,
                    prequant=True,
                    y_scale=self._q_scale if fp8_attention else None,
                )
            elif self.is_aiter_triton_fp8_bmm_enabled:
                # Multiply+Transpose (N, B, P)x(N, P, L)->(N, B, L)->(B, N, L)
                mqa_ql_nope = rocm_aiter_ops.triton_fp8_bmm(
                    mqa_q_nope,
                    self.W_K,
                    self.W_K_scale,
                    group_size=128,
                    transpose_bm=True,
                )
            elif self.is_amx_bmm_enabled:
                # bmm_cpu computes out[n] = mat1[n] @ mat2[n]^T against
                # AMXMLAImpl's own (N, L, P) packed W_UK -- same as prefill.
                N, B, P = mqa_q_nope.shape
                L = self.kv_lora_rank
                mqa_ql_nope = mqa_q_nope.new_empty((N, B, L))
                ops.bmm_cpu(
                    mqa_ql_nope,
                    mqa_q_nope,
                    self.impl._w_uk_packed,  # type: ignore[attr-defined]
                    True,
                    None,
                )
                mqa_ql_nope = mqa_ql_nope.transpose(0, 1)
            else:
                # Pads the head_dim if necessary (for the underlying kernel)
                N, B, P = mqa_q_nope.shape
                W_UK_T = self.W_UK_T_dcp_qrep if qrep_decode else self.W_UK_T
                assert W_UK_T is not None
                _, _, L = W_UK_T.shape

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

                # Multiply (N, B, P) x (N, P, L) -> (N, B, L)
                torch.bmm(mqa_q_nope, W_UK_T, out=mqa_ql_nope)

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

            if fp8_attention and self.impl.supports_quant_query_input:
                assert mqa_ql_nope.shape[0] == mqa_q_pe.shape[0]
                assert mqa_ql_nope.shape[1] == mqa_q_pe.shape[1]
                mqa_q = self._decode_concat_quant_fp8_op(
                    mqa_ql_nope, mqa_q_pe, self._q_scale
                )
            else:
                mqa_q = (mqa_ql_nope, mqa_q_pe)
            # concatenate nope + pe -> (B, N, L + P) (fp8 op above may have fused)
            if self.impl.dcp_world_size > 1:
                assert self.dcp_manager is not None
                if self.use_pcp:
                    if self.impl.dcp_world_size > self.impl.pcp_world_size:
                        if isinstance(mqa_q, tuple):
                            mqa_q = torch.cat(mqa_q, dim=-1)
                        mqa_q = get_tp_group().all_gather(mqa_q, dim=1)
                else:
                    if isinstance(mqa_q, tuple):
                        # concatenate mqa_ql_nope and mqa_q_pe -> (B, N, L + P)
                        mqa_q = torch.cat(mqa_q, dim=-1)
                    if not qrep_decode:
                        assert self.dcp_manager.query_gather is not None
                        mqa_q = self.dcp_manager.query_gather(mqa_q)

            # call decode attn
            if not self.impl.is_sparse:
                assert attn_metadata.decode is not None
            attn_out, lse = self.impl.forward_mqa(mqa_q, kv_cache, attn_metadata, self)  # type: ignore[attr-defined]

            # correct dcp attn_out with lse.
            if self.impl.dcp_world_size > 1:
                assert lse is not None
                assert self.dcp_manager is not None
                seq_lens = (
                    attn_metadata.decode.seq_lens
                    if attn_metadata.decode is not None
                    else cast(torch.Tensor, attn_metadata.seq_lens)[  # type: ignore[attr-defined]
                        : attn_metadata.num_decodes
                    ]
                )
                query_start_loc = attn_metadata.query_start_loc[
                    : attn_metadata.num_decodes + 1
                ]
                attn_out = self.dcp_manager.combine(
                    attn_out,
                    lse,
                    seq_lens=seq_lens,
                    query_start_loc=query_start_loc,
                )
                if self.use_pcp:
                    attn_out = finalize_mla_pcp_decode(attn_out, self.num_heads)

            # v_up projection
            self._v_up_proj(attn_out, out=mqa_output_slice)

        if quant_key is not None:
            quant_idx = num_mqa_tokens if mha_use_quant_output else num_actual_toks
            if quant_idx == 0:
                return quant_output
            actual = output[:quant_idx]
            if quant_key == kNvfp4Dynamic:
                # NVFP4: two FP4 values packed into one uint8
                assert output_block_scale is not None
                fp4_data, fp4_scales = ops.scaled_fp4_quant(actual, output_scale)
                quant_output[:quant_idx].copy_(fp4_data)
                output_block_scale[: fp4_scales.shape[0]].copy_(fp4_scales)
            elif quant_key in (kFp8Dynamic128Sym, kFp8Dynamic64Sym):
                # Per-group FP8
                assert output_block_scale is not None
                assert quant_group_size is not None, (
                    "Group FP8 output quant requested but "
                    "quant_group_size not passed through custom op"
                )
                finfo = torch.finfo(_FP8_DTYPE)
                torch.ops._C.per_token_group_fp8_quant(
                    actual,
                    quant_output[:quant_idx],
                    output_block_scale[:quant_idx],
                    quant_group_size,
                    1e-10,  # eps
                    finfo.min,
                    finfo.max,
                    quant_scale_ue8m0,
                    quant_col_major,
                    quant_tma_aligned,
                )
            elif quant_key == kFp8StaticTensorSym:
                # Static FP8 quantization
                fp8_data, _ = self._quant_fp8_op(actual, output_scale)
                quant_output[:quant_idx].copy_(fp8_data)
            else:
                raise ValueError(f"Unsupported quant_key: {quant_key}")
            return quant_output

        if self.use_pcp and output_padded.shape[0] > num_actual_toks:
            output_padded[num_actual_toks:].zero_()
        return output_padded

    def process_weights_after_loading(self, act_dtype: torch.dtype):
        # Let per-backend impls do their own weight packing first (no-op
        # unless overridden), mirroring Attention.process_weights_after_loading.
        self.impl.process_weights_after_loading(act_dtype)

        if self.is_amx_bmm_enabled:
            # AMXMLAImpl already packed its own W_UK/W_UV above, for both
            # prefill and decode. Release the now-unused raw weight.
            self.kv_b_proj.weight = torch.nn.Parameter(
                torch.empty(0), requires_grad=False
            )
            return

        # 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, out_dtype=act_dtype
        ).T

        if self.dcp_q_replicate:
            # qrep wired here: validate unsupported decode backends once.
            assert self.q_pad_num_heads in (None, self.num_heads), (
                "DCP query replication is unsupported on head-padding MLA "
                "backends (q_pad_num_heads)."
            )
            if (
                self.is_aiter_triton_fp4_bmm_enabled
                or self.is_aiter_triton_fp8_bmm_enabled
            ):
                raise NotImplementedError(
                    "DCP query replication is not implemented for the aiter "
                    "FP4/FP8 MLA BMM paths."
                )

        assert kv_b_proj_weight.shape == (
            self.kv_lora_rank,
            self.num_heads * (self.qk_nope_head_dim + self.v_head_dim),
        ), (
            f"{kv_b_proj_weight.shape=}, "
            f"{self.kv_lora_rank=}, "
            f"{self.num_heads=}, "
            f"{self.qk_nope_head_dim=}, "
            f"{self.v_head_dim=}"
        )
        kv_b_proj_weight = kv_b_proj_weight.view(
            self.kv_lora_rank,
            self.num_heads,
            self.qk_nope_head_dim + self.v_head_dim,
        )

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

        # If kv_b_proj_weight is unquantized, quantize it to mxfp4 if supported
        if self.is_aiter_triton_fp4_bmm_enabled:
            from vllm.model_executor.layers.quantization.quark.utils import (
                quark_quantize_weight_to_mxfp4,
            )

            self.W_K, self.W_K_scale = quark_quantize_weight_to_mxfp4(W_UK)
            # Convert from (L, N, P) to (N, L, P)
            self.W_K = self.W_K.transpose(0, 1)
            self.W_K_scale = self.W_K_scale.transpose(0, 1)

            self.W_V, self.W_V_scale = quark_quantize_weight_to_mxfp4(
                W_UV.permute(1, 2, 0)
            )
        elif self.is_aiter_triton_fp8_bmm_enabled:
            W_K = W_UK.transpose(0, 1)  # 16 512 128
            W_V = W_UV.permute(1, 2, 0)  # 16 128 512
            self.W_K, self.W_K_scale = dynamic_per_batched_tensor_quant(
                W_K, dtype=current_platform.fp8_dtype()
            )
            self.W_V, self.W_V_scale = dynamic_per_batched_tensor_quant(
                W_V, dtype=current_platform.fp8_dtype()
            )

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

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

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

        # If we should not load quant weights, we initialize the scales to 1.0
        # as the default value. See [Note: Register q/k/v/prob scales in state dict]
        # for more details.
        quant_method = (
            self.quant_config.get_quant_method(self, prefix=self.layer_name)
            if self.quant_config
            else None
        )
        if not should_load_quant_weights(quant_method):
            set_default_quant_scales(self, register_buffer=False)

    def get_attn_backend(self) -> type[AttentionBackend]:
        return self.attn_backend

    def get_kv_cache_spec(self, vllm_config: VllmConfig) -> KVCacheSpec:
        kv_cache_dtype = kv_cache_dtype_str_to_dtype(
            self.kv_cache_dtype, vllm_config.model_config
        )
        common_kwargs = dict(
            block_size=vllm_config.cache_config.block_size,
            num_kv_heads=1,
            head_size=self.head_size,
            dtype=kv_cache_dtype,
            cache_dtype_str=self.kv_cache_dtype,
            kv_quant_mode=get_kv_quant_mode(self.kv_cache_dtype),
            # fp8_ds_mla: 656-byte custom layout (kv_lora_rank=512 +
            # qk_rope_head_dim=64, head_size=576). See flashmla_sparse.py.
            state_content_bytes=656 if self.kv_cache_dtype == "fp8_ds_mla" else None,
        )
        if self.sliding_window is not None:
            return SlidingWindowMLASpec(
                **common_kwargs,
                sliding_window=self.sliding_window,
            )
        return MLAAttentionSpec(
            **common_kwargs,
            non_causal_multi_token_decode=self.non_causal_multi_token_decode,
        )

    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)
        out = out.view(-1, self.num_heads, self.v_head_dim)
        if self.is_aiter_triton_fp4_bmm_enabled:
            out = rocm_aiter_ops.batched_gemm_a16wfp4(
                x,
                self.W_V,
                self.W_V_scale,
                out,
                transpose_bm=True,
                prequant=True,
                y_scale=None,
            )
            x = out.view(-1, self.num_heads * self.v_head_dim)
        elif self.is_aiter_triton_fp8_bmm_enabled:
            # Multiply + Transpose (N, B, L) x (N, L, V)->(N, B, V)->(B, N, V)
            x = rocm_aiter_ops.triton_fp8_bmm(
                x, self.W_V, self.W_V_scale, group_size=128, transpose_bm=True, YQ=out
            )
        elif self.is_amx_bmm_enabled:
            # bmm_cpu computes out[n] = mat1[n] @ mat2[n]^T against
            # AMXMLAImpl's own (N, V, L) packed W_UV -- same as prefill.
            ops.bmm_cpu(
                out.transpose(0, 1),
                x,
                self.impl._w_uv_packed,  # type: ignore[attr-defined]
                True,
                None,
            )
        else:
            # Multiply + Transpose (N, B, L) x (N, L, V)->(N, B, V)->(B, N, V)
            torch.bmm(x, self.W_UV, out=out.transpose(0, 1))

`MLACommonBackend` ¶

Bases: AttentionBackend

Methods:

customize_spec –

Per-token-head modes pack an inline fp32 scale pair after the

Source code in vllm/model_executor/layers/attention/mla_attention.py

class MLACommonBackend(AttentionBackend):
    @classmethod
    def customize_spec(cls, spec: "AttentionSpec") -> "AttentionSpec":
        """Per-token-head modes pack an inline fp32 scale pair after the
        latent data (single-sided: ``head_size_v == 0`` for MLA)."""
        mode = spec.kv_quant_mode
        if spec.state_content_bytes is not None or not mode.is_per_token_head:
            return spec
        head_size = spec.head_size
        if mode == KVQuantMode.INT4_PER_TOKEN_HEAD:
            head_size //= 2
        scale_bytes = get_dtype_size(torch.float32)
        content = head_size * get_dtype_size(spec.dtype) + 2 * scale_bytes
        return replace(spec, state_content_bytes=content)

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

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

    @classmethod
    def get_supported_head_sizes(cls) -> list[int]:
        return [320, 576]

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

`customize_spec(spec)` `classmethod` ¶

Per-token-head modes pack an inline fp32 scale pair after the latent data (single-sided: head_size_v == 0 for MLA).

Source code in vllm/model_executor/layers/attention/mla_attention.py

@classmethod
def customize_spec(cls, spec: "AttentionSpec") -> "AttentionSpec":
    """Per-token-head modes pack an inline fp32 scale pair after the
    latent data (single-sided: ``head_size_v == 0`` for MLA)."""
    mode = spec.kv_quant_mode
    if spec.state_content_bytes is not None or not mode.is_per_token_head:
        return spec
    head_size = spec.head_size
    if mode == KVQuantMode.INT4_PER_TOKEN_HEAD:
        head_size //= 2
    scale_bytes = get_dtype_size(torch.float32)
    content = head_size * get_dtype_size(spec.dtype) + 2 * scale_bytes
    return replace(spec, state_content_bytes=content)

`MLACommonBaseImpl` ¶

Bases: MLAAttentionImpl[A], Generic[A]

Shared MLA base providing dense-MHA prefill (via the selected MLAPrefillBackend) for both dense and sparse impls; subclasses add decode (forward_mqa).

Source code in vllm/model_executor/layers/attention/mla_attention.py

class MLACommonBaseImpl(MLAAttentionImpl[A], Generic[A]):
    """
    Shared MLA base providing dense-MHA prefill (via the selected
    MLAPrefillBackend) for both dense and sparse impls; subclasses add decode
    (``forward_mqa``).
    """

    _use_flashinfer_concat_mla_k: bool

    def __init__(
        self,
        num_heads: int,
        head_size: int,
        scale: float,
        num_kv_heads: int,
        kv_cache_dtype: str,
        kv_lora_rank: int,
        qk_nope_head_dim: int,
        qk_rope_head_dim: int,
        qk_head_dim: int,
        v_head_dim: int,
        kv_b_proj: ColumnParallelLinear,
    ) -> None:
        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.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

    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,
        )

        if self._use_flashinfer_concat_mla_k:
            torch.ops.vllm.flashinfer_concat_mla_k(k, k_nope, k_pe)
        else:
            # Fallback: Direct copies with efficient broadcasting
            k[..., : k_nope.shape[-1]] = k_nope
            k[..., k_nope.shape[-1] :] = k_pe
        return k

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

        use_fp8_prefill = prefill_metadata.q_data_type == current_platform.fp8_dtype()
        kv_b_proj_input_dtype = _get_kv_b_proj_input_dtype(
            self.kv_b_proj, use_fp8_prefill
        )
        workspace = chunked_context.workspace

        if use_fp8_prefill:
            q = q.to(prefill_metadata.q_data_type)

        output = None
        output_lse = None
        for chunk in chunked_context.chunks:
            toks = chunk.num_context_tokens
            block_table = prefill_metadata.block_table[chunk.request_slice]
            if self.kv_cache_dtype == "fp8_ds_mla":
                ops.cp_gather_and_upconvert_fp8_kv_cache(
                    src_cache=kv_c_and_k_pe_cache,
                    dst=workspace[:toks],
                    block_table=block_table,
                    workspace_starts=chunk.cu_seq_lens,
                    batch_size=chunk.num_requests,
                    seq_starts=chunk.starts,
                )
            elif not use_fp8_prefill:
                ops.gather_and_maybe_dequant_cache(
                    src_cache=kv_c_and_k_pe_cache,
                    dst=workspace,
                    block_table=block_table,
                    cu_seq_lens=chunk.cu_seq_lens,
                    token_to_seq=chunk.token_to_seq,
                    num_tokens=toks,
                    kv_cache_dtype=self.kv_cache_dtype,
                    scale=k_scale,
                    seq_starts=chunk.starts,
                )
            else:
                # FP8 path: gather cache without dequantization
                ops.cp_gather_cache(
                    src_cache=kv_c_and_k_pe_cache,
                    dst=workspace[:toks],
                    block_table=block_table,
                    cu_seq_lens=chunk.cu_seq_lens,
                    batch_size=chunk.num_requests,
                    seq_starts=chunk.starts,
                )

            # Extract kv_c_normed from workspace
            kv_c_normed = workspace[:toks][..., : self.kv_lora_rank]
            if kv_b_proj_input_dtype is not None:
                kv_c_normed = kv_c_normed.to(kv_b_proj_input_dtype)

            k_pe = workspace[:toks][..., self.kv_lora_rank :].unsqueeze(1)
            kv_nope = self.kv_b_proj(kv_c_normed)[0].view(
                -1, self.num_heads, self.qk_nope_head_dim + self.v_head_dim
            )

            # To Do: Use epilogue of kv_b_proj to generate fp8 kv_nope.
            if use_fp8_prefill:
                kv_nope = kv_nope.to(prefill_metadata.q_data_type)
                k_pe = k_pe.to(prefill_metadata.q_data_type)
            k_nope, v = kv_nope.split([self.qk_nope_head_dim, self.v_head_dim], dim=-1)

            k = self._concat_k_nope_k_pe(k_nope, k_pe)

            attn_output, attn_softmax_lse = (
                prefill_metadata.prefill_backend.run_prefill_context_chunk(
                    chunk=chunk,
                    q=q[chunk.token_slice],
                    k=k,
                    v=v,
                )
            )

            if output is None:
                if (
                    len(chunked_context.chunks) == 1
                    and not chunked_context.empty_token_slices
                ):
                    return attn_output, attn_softmax_lse
                output, output_lse = init_mla_context_partial(
                    chunked_context,
                    attn_output,
                    attn_softmax_lse,
                    num_tokens=q.shape[0],
                )
            accumulate_mla_context_chunk(
                chunk, attn_output, attn_softmax_lse, output, output_lse
            )

        return output, output_lse

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

        use_fp8_prefill = prefill_metadata.q_data_type == current_platform.fp8_dtype()
        kv_b_proj_input_dtype = _get_kv_b_proj_input_dtype(
            self.kv_b_proj, use_fp8_prefill
        )
        output = None
        output_lse = None
        workspace = chunked_context.workspace

        for chunk in chunked_context.chunks:
            assert chunk.padded_local_seq_lens is not None
            assert chunk.local_context_lens_allranks is not None
            assert chunk.padded_local_cu_seq_lens is not None
            assert chunk.padded_local_token_to_seq is not None
            assert chunk.local_starts is not None

            toks = chunk.num_local_context_tokens
            padded_local_cu_seq_lens = chunk.padded_local_cu_seq_lens
            block_table = prefill_metadata.block_table[chunk.request_slice]
            if self.kv_cache_dtype == "fp8_ds_mla":
                ops.cp_gather_and_upconvert_fp8_kv_cache(
                    src_cache=kv_c_and_k_pe_cache,
                    dst=workspace[:toks],
                    block_table=block_table,
                    workspace_starts=padded_local_cu_seq_lens,
                    batch_size=chunk.num_requests,
                    seq_starts=chunk.starts,
                )
            elif is_quantized_kv_cache(self.kv_cache_dtype):
                assert k_scale is not None
                ops.gather_and_maybe_dequant_cache(
                    src_cache=kv_c_and_k_pe_cache,
                    dst=workspace,
                    block_table=block_table,
                    cu_seq_lens=padded_local_cu_seq_lens,
                    token_to_seq=chunk.padded_local_token_to_seq,
                    num_tokens=toks,
                    kv_cache_dtype=self.kv_cache_dtype,
                    scale=k_scale,
                    seq_starts=chunk.starts,
                )
            else:
                ops.cp_gather_cache(
                    src_cache=kv_c_and_k_pe_cache,
                    dst=workspace[:toks],
                    block_table=block_table,
                    cu_seq_lens=padded_local_cu_seq_lens,
                    batch_size=chunk.num_requests,
                    seq_starts=chunk.starts,
                )
            # workspace
            # |------- N tokens --------|--------- N*dcp_size tokens ----------|
            # |<- use for local_gather ->|<--------- use for allgather -------->|
            allgather_offset = workspace.shape[0] // (dcp_world_size + 1)
            assert allgather_offset * (dcp_world_size + 1) == workspace.shape[0]
            assert toks <= allgather_offset
            local_gathered_kvcache = workspace[:toks]
            cur_allgather_workspace = workspace[
                allgather_offset : allgather_offset * (1 + dcp_world_size)
            ]
            assert toks * dcp_world_size <= cur_allgather_workspace.shape[0]
            cur_allgather_kvcache = cur_allgather_workspace[: toks * dcp_world_size]
            dcp_manager = cast(MLADCPManager, chunked_context.dcp_manager)
            dcp_manager.kv_gather(cur_allgather_kvcache, local_gathered_kvcache)
            assert (
                cur_allgather_kvcache.shape[-1]
                == self.kv_lora_rank + self.qk_rope_head_dim
            )
            allgatered_kv_c_normed, allgatered_k_pe = cur_allgather_kvcache.unsqueeze(
                1
            ).split([self.kv_lora_rank, self.qk_rope_head_dim], dim=-1)

            kv_c_normed, k_pe = reorg_kvcache(
                allgatered_kv_c_normed,
                allgatered_k_pe,
                padded_local_chunk_seq_lens_lst=chunk.padded_local_seq_lens,
                local_context_lens_allranks=chunk.local_context_lens_allranks,
                local_starts=chunk.local_starts,
                sum_seq_len=chunk.num_context_tokens,
                max_seq_len=chunk.max_seq_len,
                toks=toks,
            )
            if kv_b_proj_input_dtype is not None:
                kv_c_normed = kv_c_normed.to(kv_b_proj_input_dtype)

            kv_nope = self.kv_b_proj(kv_c_normed)[0].view(
                -1, self.num_heads, self.qk_nope_head_dim + self.v_head_dim
            )
            if use_fp8_prefill:
                kv_nope = kv_nope.to(prefill_metadata.q_data_type)
                k_pe = k_pe.to(prefill_metadata.q_data_type)
            k_nope, v = kv_nope.split([self.qk_nope_head_dim, self.v_head_dim], dim=-1)
            k = self._concat_k_nope_k_pe(k_nope, k_pe)

            attn_output, attn_softmax_lse = (
                prefill_metadata.prefill_backend.run_prefill_context_chunk(
                    chunk=chunk,
                    q=q[chunk.token_slice],
                    k=k,
                    v=v,
                )
            )

            if output is None:
                if (
                    len(chunked_context.chunks) == 1
                    and not chunked_context.empty_token_slices
                ):
                    return attn_output, attn_softmax_lse
                output, output_lse = init_mla_context_partial(
                    chunked_context,
                    attn_output,
                    attn_softmax_lse,
                    num_tokens=q.shape[0],
                )
            accumulate_mla_context_chunk(
                chunk, attn_output, attn_softmax_lse, output, output_lse
            )

        return output, output_lse

    def forward_mha(  # type: ignore[override]
        self,
        q: torch.Tensor,
        kv_c_normed: torch.Tensor,
        k_pe: torch.Tensor,
        kv_c_and_k_pe_cache: torch.Tensor,
        attn_metadata: MLACommonMetadata,
        k_scale: torch.Tensor,
        output: torch.Tensor,
        output_scale: torch.Tensor | None = None,
    ) -> None:
        assert attn_metadata.prefill is not None
        prefill_metadata = attn_metadata.prefill
        assert prefill_metadata.prefill_backend is not None
        use_fp8_prefill = prefill_metadata.q_data_type == current_platform.fp8_dtype()

        # Convert q to FP8 if FP8 prefill attention is enabled
        if use_fp8_prefill:
            q = q.to(prefill_metadata.q_data_type)

        has_context = prefill_metadata.chunked_context is not None
        assert output_scale is None or not has_context, (
            "Fused FP8 output is only wired for the non-chunked-context path"
        )

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

        if use_fp8_prefill:
            k = k.to(prefill_metadata.q_data_type)
            v = v.to(prefill_metadata.q_data_type)

        output_prefill = prefill_metadata.prefill_backend.run_prefill_new_tokens(
            q=q,
            k=k,
            v=v,
            return_softmax_lse=has_context,
            out=(
                output.view(-1, self.num_heads, self.v_head_dim)
                if output_scale is not None
                else None
            ),
            output_scale=output_scale,
        )

        if has_context:
            assert prefill_metadata.chunked_context is not None
            suffix_output, suffix_lse = output_prefill
            if self.dcp_world_size > 1:
                context_output, context_lse = (
                    self._context_parallel_compute_prefill_context(
                        q,
                        kv_c_and_k_pe_cache,
                        attn_metadata,
                        k_scale=k_scale,
                        dcp_world_size=self.dcp_world_size,
                    )
                )
            else:
                context_output, context_lse = self._compute_prefill_context(
                    q, kv_c_and_k_pe_cache, attn_metadata, k_scale
                )

            context_output = context_output[..., : self.v_head_dim]
            suffix_output = suffix_output[..., : self.v_head_dim]

            output = output.view(-1, self.num_heads, self.v_head_dim)
            merge_attn_states(
                output=output,
                prefix_output=context_output,
                prefix_lse=context_lse,
                suffix_output=suffix_output,
                suffix_lse=suffix_lse,
            )
        elif output_scale is None:
            # With output_scale set, backend already wrote into `output` in place.
            assert isinstance(output_prefill, torch.Tensor)
            output_prefill = output_prefill[..., : self.v_head_dim]
            output_prefill = output_prefill.flatten(start_dim=-2)
            output.copy_(output_prefill)

`_concat_k_nope_k_pe(k_nope, k_pe)` ¶

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.

Parameters:

k_nope ¶
(Tensor) –

Tensor of shape [..., nope_dim]
k_pe ¶
(Tensor) –

Tensor to broadcast and concatenate, typically shape [..., 1, pe_dim] or [..., pe_dim]

Returns:

Tensor –

Tensor of shape [..., nope_dim + pe_dim]

Source code in vllm/model_executor/layers/attention/mla_attention.py

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,
    )

    if self._use_flashinfer_concat_mla_k:
        torch.ops.vllm.flashinfer_concat_mla_k(k, k_nope, k_pe)
    else:
        # Fallback: Direct copies with efficient broadcasting
        k[..., : k_nope.shape[-1]] = k_nope
        k[..., k_nope.shape[-1] :] = k_pe
    return k

`MLACommonImpl` ¶

Bases: MLACommonBaseImpl[M], Generic[M]

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

Source code in vllm/model_executor/layers/attention/mla_attention.py

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

    def fused_output_quant_supported(self, quant_key):
        return quant_key in (
            kFp8StaticTensorSym,
            kNvfp4Dynamic,
            kFp8Dynamic128Sym,
            kFp8Dynamic64Sym,
        )

    def __init__(
        self,
        num_heads: int,
        head_size: int,
        scale: float,
        num_kv_heads: int,
        alibi_slopes: list[float] | None,
        sliding_window: int | None,
        kv_cache_dtype: str,
        logits_soft_cap: float | None,
        attn_type: str,
        kv_sharing_target_layer_name: str | None,
        # MLA Specific Arguments
        q_lora_rank: int | None,
        kv_lora_rank: int,
        qk_nope_head_dim: int,
        qk_rope_head_dim: int,
        qk_head_dim: int,
        v_head_dim: int,
        kv_b_proj: ColumnParallelLinear,
        # DSV3.2 MLA Specific Arguments
        indexer: object | None = None,
        q_pad_num_heads: int | None = None,
    ) -> None:
        if kv_sharing_target_layer_name is not None:
            raise NotImplementedError("KV sharing is not supported for MLA")

        super().__init__(
            num_heads,
            head_size,
            scale,
            num_kv_heads,
            kv_cache_dtype,
            kv_lora_rank,
            qk_nope_head_dim,
            qk_rope_head_dim,
            qk_head_dim,
            v_head_dim,
            kv_b_proj,
        )
        self.q_lora_rank = q_lora_rank
        self.indexer = indexer
        self.q_pad_num_heads = q_pad_num_heads
        self.supports_quant_query_input = True
        self.is_aiter_triton_fp8_bmm_enabled = rocm_aiter_ops.is_fp8bmm_enabled()

        # Use flashinfer's optimized concat_mla_k kernel when available.
        # The kernel is optimized for DeepSeek V3 dimensions:
        # num_heads=128, nope_dim=128, rope_dim=64
        self._use_flashinfer_concat_mla_k = (
            has_flashinfer()
            and (self.num_heads == 128)
            and (self.qk_nope_head_dim == 128)
            and (self.qk_rope_head_dim == 64)
        )

        parallel_config = get_current_vllm_config().parallel_config
        # Avoid requiring an initialized DCP group in tests.
        self.dcp_world_size: int = parallel_config.decode_context_parallel_size
        self.cp_kv_cache_interleave_size: int = (
            parallel_config.cp_kv_cache_interleave_size
        )

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

`MLACommonMetadata` `dataclass` ¶

Bases: AttentionMetadata, Generic[D]

Metadata for MLACommon.

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

Source code in vllm/model_executor/layers/attention/mla_attention.py

@dataclass
class MLACommonMetadata(AttentionMetadata, Generic[D]):
    """Metadata for MLACommon.

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

    # NOTE(sang): Definition of context_len, query_len, and seq_len.
    # |---------- N-1 iteration --------|
    # |---------------- N iteration ---------------------|
    # |- tokenA -|......................|-- newTokens ---|
    # |---------- context_len ----------|
    # |-------------------- seq_len ---------------------|
    #                                   |-- query_len ---|

    num_reqs: int
    max_query_len: int
    max_seq_len: int

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

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

    causal: bool = True

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

    prefill: MLACommonPrefillMetadata | None = None
    decode: D | None = None

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

`MLACommonMetadataBuilder` ¶

Bases: AttentionMetadataBuilder[M]

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

Methods:

build_for_cudagraph_capture –

This method builds the metadata for full cudagraph capture.
determine_prefill_query_data_type –

Determine the query data type for prefill queries.

Source code in vllm/model_executor/layers/attention/mla_attention.py

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

    kv_cache_spec: AttentionSpec

    # 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

    # Whether this builder can flatten a non-causal query block into decode rows.
    supports_non_causal_multi_token_decode: ClassVar[bool] = False

    # Whether can support non-causal multi-token decode with DCP KV cache.
    supports_non_causal_multi_token_dcp: ClassVar[bool] = False

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

    def _validate_dspark_dcp_support(self, supports_dcp_with_varlen: bool) -> None:
        speculative_config = getattr(self.vllm_config, "speculative_config", None)
        parallel_config = self.vllm_config.parallel_config
        if (
            speculative_config is None
            or getattr(speculative_config, "method", None) != "dspark"
            or parallel_config.decode_context_parallel_size <= 1
        ):
            return

        if self.non_causal_multi_token_decode:
            supported = self.supports_non_causal_multi_token_dcp
            query_mode = "non-causal draft"
        else:
            supported = supports_dcp_with_varlen
            query_mode = "causal multi-token"

        if not supported:
            raise ValueError(
                f"{type(self).__name__} does not support {query_mode} MLA "
                "attention for DSpark with decode context parallelism. Select "
                "a backend with explicit DSpark DCP support or set "
                "decode_context_parallel_size=1."
            )

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

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

        return align_mla_chunked_context_workspace_size(
            vllm_config,
            chunked_prefill_workspace_size,
        )

    @staticmethod
    def determine_prefill_query_data_type(
        vllm_config: VllmConfig,
        model_dtype: torch.dtype,
    ) -> torch.dtype:
        """
        Determine the query data type for prefill queries.
        Return FP8 dtype if cache is FP8 and prefill query quantization
        is enabled, else model dtype.
        """
        use_fp8 = (
            is_quantized_kv_cache(vllm_config.cache_config.cache_dtype)
            and vllm_config.attention_config.use_prefill_query_quantization
            and backend_supports_prefill_query_quantization()
        )

        if use_fp8:
            fp8_dtype = current_platform.fp8_dtype()
            logger.info_once("FP8 prefill attention enabled: query data type is FP8")
            return fp8_dtype
        elif vllm_config.attention_config.use_prefill_query_quantization:
            logger.info_once(
                "Unable to perform FP8 prefill attention when"
                " use_prefill_query_quantization is enabled. Please"
                " ensure that --kv-cache-dtype is set to fp8 and your prefill"
                " backend is compatible with FP8 attention.",
            )
            return model_dtype
        elif (
            is_quantized_kv_cache(vllm_config.cache_config.cache_dtype)
            and backend_supports_prefill_query_quantization()
        ):
            logger.warning_once(
                "FP8 KV cache is enabled but prefill queries are not "
                "quantized to FP8. For long-context workloads (ISL >= 4K), "
                "enabling FP8 prefill attention can significantly optimize "
                "prefill latency. To enable, add: "
                '--attention-config \'{"use_prefill_query_quantization"'
                ": true}'",
            )

        return model_dtype

    def __init__(
        self,
        kv_cache_spec: AttentionSpec,
        layer_names: list[str],
        vllm_config: VllmConfig,
        device: torch.device,
        metadata_cls: type[M] | None = None,
        supports_dcp_with_varlen: bool = False,
    ):
        self.metadata_cls = (
            metadata_cls if metadata_cls is not None else MLACommonMetadata
        )
        self.kv_cache_spec = kv_cache_spec
        self.model_config = vllm_config.model_config
        parallel_config = vllm_config.parallel_config
        self.compilation_config = vllm_config.compilation_config
        self.vllm_config = vllm_config
        self.device = device
        self.use_pcp = parallel_config.prefill_context_parallel_size > 1
        self.non_causal_multi_token_decode = getattr(
            kv_cache_spec, "non_causal_multi_token_decode", False
        )
        self._validate_dspark_dcp_support(supports_dcp_with_varlen)

        # A draft cache group can have a different head count from the target.
        self.num_heads = get_num_attention_heads_from_layers(
            vllm_config, layer_names
        ) or self.model_config.get_num_attention_heads(parallel_config)
        # Hybrid MLA models may use different latent dimensions per KV group.
        layer = self.compilation_config.static_forward_context[layer_names[0]]
        self.mla_dims = MLADims(
            q_lora_rank=layer.q_lora_rank,
            kv_lora_rank=layer.kv_lora_rank,
            qk_nope_head_dim=layer.qk_nope_head_dim,
            qk_rope_head_dim=layer.qk_rope_head_dim,
            v_head_dim=layer.v_head_dim,
        )
        self.aot_schedule = current_platform.is_cuda()

        self.kv_cache_spec = kv_cache_spec
        self.q_data_type = self.determine_prefill_query_data_type(
            vllm_config, self.model_config.dtype
        )
        attention_layer = self.compilation_config.static_forward_context[layer_names[0]]

        try:
            self.dcp_world_size = get_dcp_group().world_size
        except AssertionError:
            # DCP might not be initialized in testing
            self.dcp_world_size = 1
        self.dcp_local_block_size = parallel_config.cp_kv_cache_interleave_size
        self.dcp_virtual_block_size = self.dcp_local_block_size * self.dcp_world_size
        self.cp_kv_cache_interleave_size = parallel_config.cp_kv_cache_interleave_size

        self.page_size = self.kv_cache_spec.block_size

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

        use_packed_fp8_cache = vllm_config.cache_config.cache_dtype == "fp8_ds_mla"
        self.dcp_manager: MLADCPManager | None = None
        if self.dcp_world_size > 1:
            # Note(hc): The local kvcache is incomplete when DCP is triggered,
            # an additional kvcache allgather across the DCP group is therefore
            # required, so the workspace has to be enlarged by 1/DCP relative
            # to the original TP allocation.
            assert self.chunked_prefill_workspace_size % self.dcp_world_size == 0
            self.chunked_prefill_workspace = torch.empty(
                (
                    self.chunked_prefill_workspace_size
                    + self.chunked_prefill_workspace_size // self.dcp_world_size,
                    self.mla_dims.kv_lora_rank + self.mla_dims.qk_rope_head_dim,
                ),
                dtype=torch.bfloat16
                if use_packed_fp8_cache
                else self.model_config.dtype,
                device=device,
            )
            self.dcp_manager = getattr(attention_layer, "dcp_manager", None)
            assert isinstance(self.dcp_manager, MLADCPManager)
            self.dcp_manager.init_kv_gather(
                self.chunked_prefill_workspace,
                self.chunked_prefill_workspace_size,
            )
        else:
            self.chunked_prefill_workspace = torch.empty(
                (
                    self.chunked_prefill_workspace_size,
                    self.mla_dims.kv_lora_rank + self.mla_dims.qk_rope_head_dim,
                ),
                dtype=torch.bfloat16 if use_packed_fp8_cache else self.q_data_type,
                device=device,
            )

        # Metadata builders are created per ubatch when DBO is enabled. MLA
        # prefill backends keep the prepared metadata on the backend object, so
        # each builder needs its own backend instance to avoid cross-ubatch races.
        self._prefill_backend = attention_layer.prefill_backend.clone()

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

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

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

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

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

        return self.build(0, m)

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

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

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

        non_causal_decode = common_attn_metadata.causal is False
        if non_causal_decode:
            if not (
                self.supports_non_causal_multi_token_decode
                and self.non_causal_multi_token_decode
            ):
                raise ValueError(
                    "Non-causal multi-token MLA requires an explicitly supported "
                    "attention group."
                )
            query_lens = query_start_loc_cpu[1:] - query_start_loc_cpu[:-1]
            num_active_reqs = int(torch.count_nonzero(query_lens > 0))
            uniform_active_queries = num_active_reqs > 0 and bool(
                torch.all(query_lens[:num_active_reqs] == query_lens[0])
            )
            trailing_graph_padding = bool(torch.all(query_lens[num_active_reqs:] == 0))
            if not (uniform_active_queries and trailing_graph_padding):
                raise ValueError(
                    "Non-causal MLA requires a uniform query block; got query "
                    f"lengths {query_lens.tolist()}."
                )
            # Use exact GPU sequence lengths instead of the prefill path's CPU
            # context-length upper bounds.
            num_decodes = num_reqs
            num_prefills = 0
            num_decode_tokens = num_tokens
            num_prefill_tokens = 0
        else:
            num_decodes, num_prefills, num_decode_tokens, num_prefill_tokens = (
                split_decodes_and_prefills(
                    common_attn_metadata,
                    decode_threshold=self.reorder_batch_threshold,
                    require_uniform=(self.query_len_support != QueryLenSupport.VARLEN),
                    treat_short_extends_as_decodes=not self.use_pcp,
                )
            )

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

        prefill_metadata = None
        if num_prefills > 0:
            reqs_start = num_decodes  # prefill_start

            # Upper bound is exact for prefill rows (no D2H sync).
            seq_lens_cpu = common_attn_metadata.seq_lens_cpu_upper_bound
            assert seq_lens_cpu is not None
            prefill_query_lens_cpu = (
                query_start_loc_cpu[reqs_start + 1 : num_reqs + 1]
                - query_start_loc_cpu[reqs_start:num_reqs]
            )
            context_lens_cpu = (
                seq_lens_cpu[reqs_start:num_reqs] - prefill_query_lens_cpu
            )
            prefill_query_start_loc = (
                query_start_loc[reqs_start:] - query_start_loc[reqs_start]
            )
            prefill_query_start_loc_cpu = (
                query_start_loc_cpu[reqs_start:] - query_start_loc_cpu[reqs_start]
            )

            chunked_context_metadata = build_mla_chunked_context_metadata(
                context_lens_cpu=context_lens_cpu,
                prefill_query_start_loc_cpu=prefill_query_start_loc_cpu,
                chunked_prefill_workspace=self.chunked_prefill_workspace,
                chunked_prefill_workspace_size=self.chunked_prefill_workspace_size,
                block_size=self.page_size,
                align_chunk_to_block=True,
                device=device,
                dcp_world_size=self.dcp_world_size,
                dcp_local_block_size=self.dcp_local_block_size,
                dcp_virtual_block_size=self.dcp_virtual_block_size,
                dcp_manager=self.dcp_manager,
            )

            prefill_metadata = MLACommonPrefillMetadata(
                block_table=block_table_tensor[reqs_start:, ...],
                query_start_loc=prefill_query_start_loc,
                max_query_len=max_query_len,
                chunked_context=chunked_context_metadata,
                output_dtype=self.model_config.dtype,
                q_data_type=self.q_data_type,
                prefill_backend=self._prefill_backend,
            )

            self._prefill_backend.prepare_metadata(prefill_metadata)

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

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

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

        attn_metadata = self.metadata_cls(
            num_reqs=common_attn_metadata.num_reqs,
            max_query_len=common_attn_metadata.max_query_len,
            max_seq_len=max_seq_len,
            num_actual_tokens=num_tokens,
            query_start_loc=query_start_loc,
            slot_mapping=slot_mapping,
            head_dim=self.model_config.get_head_size(),
            # MLACommonMetadata Chunk prefill specific
            num_decodes=num_decodes,
            num_decode_tokens=num_decode_tokens,
            num_prefills=num_prefills,
            causal=not non_causal_decode,
            prefill=prefill_metadata,
            decode=decode_metadata,
        )

        return attn_metadata  # type: ignore[return-value]

`build_for_cudagraph_capture(common_attn_metadata)` ¶

This method builds the metadata for full cudagraph capture. Currently, only decode is supported for full cudagraphs with MLA.

Source code in vllm/model_executor/layers/attention/mla_attention.py

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

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

    return self.build(0, m)

`determine_prefill_query_data_type(vllm_config, model_dtype)` `staticmethod` ¶

Determine the query data type for prefill queries. Return FP8 dtype if cache is FP8 and prefill query quantization is enabled, else model dtype.

Source code in vllm/model_executor/layers/attention/mla_attention.py

@staticmethod
def determine_prefill_query_data_type(
    vllm_config: VllmConfig,
    model_dtype: torch.dtype,
) -> torch.dtype:
    """
    Determine the query data type for prefill queries.
    Return FP8 dtype if cache is FP8 and prefill query quantization
    is enabled, else model dtype.
    """
    use_fp8 = (
        is_quantized_kv_cache(vllm_config.cache_config.cache_dtype)
        and vllm_config.attention_config.use_prefill_query_quantization
        and backend_supports_prefill_query_quantization()
    )

    if use_fp8:
        fp8_dtype = current_platform.fp8_dtype()
        logger.info_once("FP8 prefill attention enabled: query data type is FP8")
        return fp8_dtype
    elif vllm_config.attention_config.use_prefill_query_quantization:
        logger.info_once(
            "Unable to perform FP8 prefill attention when"
            " use_prefill_query_quantization is enabled. Please"
            " ensure that --kv-cache-dtype is set to fp8 and your prefill"
            " backend is compatible with FP8 attention.",
        )
        return model_dtype
    elif (
        is_quantized_kv_cache(vllm_config.cache_config.cache_dtype)
        and backend_supports_prefill_query_quantization()
    ):
        logger.warning_once(
            "FP8 KV cache is enabled but prefill queries are not "
            "quantized to FP8. For long-context workloads (ISL >= 4K), "
            "enabling FP8 prefill attention can significantly optimize "
            "prefill latency. To enable, add: "
            '--attention-config \'{"use_prefill_query_quantization"'
            ": true}'",
        )

    return model_dtype

`MLACommonPrefillMetadata` `dataclass` ¶

Prefill Specific Metadata

Classes:

ContextChunk –

One workspace-sized slice of paged context for a run of prefills.

Source code in vllm/model_executor/layers/attention/mla_attention.py

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

    @dataclass
    class ContextChunk:
        """One workspace-sized slice of paged context for a run of prefills."""

        index: int
        request_slice: slice
        token_slice: slice
        continuation_token_end: int
        is_continuation: bool
        num_context_tokens: int
        query_start_loc: torch.Tensor
        max_query_len: int
        cu_seq_lens: torch.Tensor
        starts: torch.Tensor
        max_seq_len: int
        seq_lens: torch.Tensor
        token_to_seq: torch.Tensor

        # for mla DCP
        padded_local_seq_lens: list[int] | None = None
        local_context_lens_allranks: list[list[int]] | None = None
        padded_local_cu_seq_lens: torch.Tensor | None = None
        padded_local_token_to_seq: torch.Tensor | None = None
        num_local_context_tokens: int = 0
        local_starts: list[int] | None = None

        @property
        def num_requests(self) -> int:
            return self.request_slice.stop - self.request_slice.start

    @dataclass
    class ChunkedContextMetadata:
        context_lens: torch.Tensor
        workspace: torch.Tensor
        chunks: "list[MLACommonPrefillMetadata.ContextChunk]"
        context_lens_list: list[int]
        empty_token_slices: list[slice]
        dcp_manager: MLADCPManager | None = None

    block_table: torch.Tensor
    query_start_loc: torch.Tensor
    max_query_len: int
    chunked_context: ChunkedContextMetadata | None = None
    q_data_type: torch.dtype | None = None
    output_dtype: torch.dtype | None = None
    prefill_backend: MLAPrefillBackend | None = None
    query_lens_cpu: torch.Tensor | None = None
    use_dense_mha: bool = False
    topk_mask_workspace: torch.Tensor | None = None

`ContextChunk` `dataclass` ¶

One workspace-sized slice of paged context for a run of prefills.

Source code in vllm/model_executor/layers/attention/mla_attention.py

@dataclass
class ContextChunk:
    """One workspace-sized slice of paged context for a run of prefills."""

    index: int
    request_slice: slice
    token_slice: slice
    continuation_token_end: int
    is_continuation: bool
    num_context_tokens: int
    query_start_loc: torch.Tensor
    max_query_len: int
    cu_seq_lens: torch.Tensor
    starts: torch.Tensor
    max_seq_len: int
    seq_lens: torch.Tensor
    token_to_seq: torch.Tensor

    # for mla DCP
    padded_local_seq_lens: list[int] | None = None
    local_context_lens_allranks: list[list[int]] | None = None
    padded_local_cu_seq_lens: torch.Tensor | None = None
    padded_local_token_to_seq: torch.Tensor | None = None
    num_local_context_tokens: int = 0
    local_starts: list[int] | None = None

    @property
    def num_requests(self) -> int:
        return self.request_slice.stop - self.request_slice.start

`QueryLenSupport` ¶

Bases: 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)

Source code in vllm/model_executor/layers/attention/mla_attention.py

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"

`_ContextChunkPlan` `dataclass` ¶

Request-space layout of one context chunk, before tensors are built.

Source code in vllm/model_executor/layers/attention/mla_attention.py

@dataclass
class _ContextChunkPlan:
    """Request-space layout of one context chunk, before tensors are built."""

    request_start: int
    request_end: int
    # Per-request context offset and row count, in request order.
    starts: list[int]
    seq_lens: list[int]
    is_continuation: bool

`_DecodeConcatQuantFP8` ¶

Bases: QuantFP8

QuantFP8 variant that concatenates decode_ql_nope and decode_q_pe before quantization. When disabled, forward_native is compiled via torch.compile, fusing cat/reshape/quant/view together.

Source code in vllm/model_executor/layers/attention/mla_attention.py

@CustomOp.register(
    "mla_decode_concat_quant_fp8",
    dynamic_arg_dims={"decode_ql_nope": 0, "decode_q_pe": 0},
)
class _DecodeConcatQuantFP8(QuantFP8):
    """
    QuantFP8 variant that concatenates decode_ql_nope and decode_q_pe before
    quantization. When disabled, forward_native is compiled via torch.compile,
    fusing cat/reshape/quant/view together.
    """

    def _make_forward(quant_fn):  # noqa: N805
        """Factory to create forward methods that concat before quantization."""

        def forward(
            self,
            decode_ql_nope: torch.Tensor,
            decode_q_pe: torch.Tensor,
            scale: torch.Tensor,
            scale_ub: torch.Tensor | None = None,
        ) -> torch.Tensor:
            decode_q0 = torch.cat((decode_ql_nope, decode_q_pe), dim=-1)
            decode_q_flat = decode_q0.reshape(decode_q0.shape[0], -1)
            decode_q, _ = quant_fn(self, decode_q_flat, scale, scale_ub)
            return decode_q.view(decode_q0.shape)

        return forward

    forward_native = _make_forward(QuantFP8.forward_native)  # type: ignore[arg-type]
    forward_cuda = _make_forward(QuantFP8.forward_cuda)  # type: ignore[arg-type]
    forward_hip = _make_forward(QuantFP8.forward_hip)  # type: ignore[arg-type]

`_make_forward(quant_fn)` ¶

Factory to create forward methods that concat before quantization.

Source code in vllm/model_executor/layers/attention/mla_attention.py

def _make_forward(quant_fn):  # noqa: N805
    """Factory to create forward methods that concat before quantization."""

    def forward(
        self,
        decode_ql_nope: torch.Tensor,
        decode_q_pe: torch.Tensor,
        scale: torch.Tensor,
        scale_ub: torch.Tensor | None = None,
    ) -> torch.Tensor:
        decode_q0 = torch.cat((decode_ql_nope, decode_q_pe), dim=-1)
        decode_q_flat = decode_q0.reshape(decode_q0.shape[0], -1)
        decode_q, _ = quant_fn(self, decode_q_flat, scale, scale_ub)
        return decode_q.view(decode_q0.shape)

    return forward

`_detect_output_quant_key(output, output_scale, output_block_scale, output_dim)` ¶

Detect the output quantization key from fusion pass parameters.

Returns the appropriate QuantKey, or None if no quantization is needed. Detection is based on output dtype and which scale tensors are present.

Source code in vllm/model_executor/layers/attention/mla_attention.py

def _detect_output_quant_key(
    output: torch.Tensor,
    output_scale: torch.Tensor | None,
    output_block_scale: torch.Tensor | None,
    output_dim: int,
) -> QuantKey | None:
    """Detect the output quantization key from fusion pass parameters.

    Returns the appropriate QuantKey, or None if no quantization is needed.
    Detection is based on output dtype and which scale tensors are present.
    """
    if output_scale is None and output_block_scale is None:
        return None
    if output_block_scale is not None:
        if output.dtype == _FP8_DTYPE:
            # Per-group FP8 uses block scales only, not a separate output_scale
            assert output_scale is None
            # Infer group size from scale shape
            num_groups = output_block_scale.shape[-1]
            group_size = output_dim // num_groups
            if group_size == 128:
                return kFp8Dynamic128Sym
            elif group_size == 64:
                return kFp8Dynamic64Sym
            else:
                raise ValueError(
                    f"Unsupported group FP8 group_size={group_size} "
                    f"(output_dim={output_dim}, num_groups={num_groups}). "
                    f"Only group_size 128 and 64 are supported."
                )
        # output_scale None implies MXFP4, not supported
        assert output_scale is not None
        return kNvfp4Dynamic
    return kFp8StaticTensorSym

`_flat_int32(values)` ¶

Pinned int32 CPU tensor backing one concatenated per-chunk field.

Source code in vllm/model_executor/layers/attention/mla_attention.py

def _flat_int32(values: list[int] | np.ndarray) -> torch.Tensor:
    """Pinned int32 CPU tensor backing one concatenated per-chunk field."""
    return np_to_pinned_tensor(np.asarray(values, dtype=np.int32))

`accumulate_mla_context_chunk(chunk, attn_output, attn_softmax_lse, output, output_lse, output_written=False)` ¶

Fold one chunk's partial into the running context partial.

Only the first request may be a continuation; its tokens are merged and the remaining token range is initialized.

Parameters:

output_written ¶
(bool, default: False ) –

The chunk's attention output already landed in output[chunk.token_slice] because the backend was handed it as out, leaving only the lse to fold. Invalid for a continuation chunk, whose leading tokens must be merged rather than overwritten.

Source code in vllm/model_executor/layers/attention/mla_attention.py

def accumulate_mla_context_chunk(
    chunk: "MLACommonPrefillMetadata.ContextChunk",
    attn_output: torch.Tensor,
    attn_softmax_lse: torch.Tensor,
    output: torch.Tensor,
    output_lse: torch.Tensor,
    output_written: bool = False,
) -> None:
    """Fold one chunk's partial into the running context partial.

    Only the first request may be a continuation; its tokens are merged and the
    remaining token range is initialized.

    Args:
        output_written: The chunk's attention output already landed in
            ``output[chunk.token_slice]`` because the backend was handed it as
            ``out``, leaving only the lse to fold. Invalid for a continuation
            chunk, whose leading tokens must be merged rather than overwritten.
    """
    token_start = chunk.token_slice.start
    token_end = chunk.token_slice.stop
    init_start = token_start
    if chunk.is_continuation:
        assert not output_written, (
            "a continuation chunk must not write over the partial it merges with"
        )
        init_start = chunk.continuation_token_end
        num_merged = init_start - token_start
        merge_attn_states(
            output=output[token_start:init_start],
            output_lse=output_lse[:, token_start:init_start],
            prefix_output=output[token_start:init_start],
            prefix_lse=output_lse[:, token_start:init_start],
            suffix_output=attn_output[:num_merged],
            suffix_lse=attn_softmax_lse[:, :num_merged],
        )
    if init_start < token_end:
        written = init_start - token_start
        if not output_written:
            output[init_start:token_end].copy_(attn_output[written:])
        output_lse[:, init_start:token_end].copy_(attn_softmax_lse[:, written:])

`backend_supports_prefill_query_quantization()` `cached` ¶

Check if the selected MLA prefill backend supports query quantization.

Currently supported backends: - FlashInfer - TRT-LLM Ragged

Not supported: - FlashAttention (FA3/FA4) - Non-GB200 devices (FP8 prefill requires device capability 100)

Source code in vllm/model_executor/layers/attention/mla_attention.py

@functools.cache
def backend_supports_prefill_query_quantization() -> bool:
    """Check if the selected MLA prefill backend supports query quantization.

    Currently supported backends:
    - FlashInfer
    - TRT-LLM Ragged

    Not supported:
    - FlashAttention (FA3/FA4)
    - Non-GB200 devices (FP8 prefill requires device capability 100)
    """
    # FP8 prefill query quantization requires GB200 (device capability 100)
    # for the necessary FP8 kernels at the moment.
    if not current_platform.is_device_capability_family(100):
        return False

    from vllm.config import get_current_vllm_config
    from vllm.v1.attention.backends.mla.prefill import get_mla_prefill_backend

    vllm_config = get_current_vllm_config()
    backend_cls = get_mla_prefill_backend(vllm_config)
    return backend_cls.get_name() in (
        "FLASHINFER",
        "TRTLLM_RAGGED",
        "TOKENSPEED_MLA",
    )

`build_mla_chunked_context_metadata(*, context_lens_cpu, prefill_query_start_loc_cpu, chunked_prefill_workspace, chunked_prefill_workspace_size, block_size, align_chunk_to_block, device, dcp_world_size, dcp_local_block_size, dcp_virtual_block_size, dcp_manager=None)` ¶

Build chunked-context metadata for an MLA prefill.

Shared by dense and sparse builders. Packs the prefill contexts into a flat list of workspace-sized per-request chunks and, under DCP, plans the per-rank interleaved local chunks the all-gather reduction consumes.

Parameters:

context_lens_cpu ¶
(Tensor) –

Per-prefill context length (seq_len - query_len).
prefill_query_start_loc_cpu ¶
(Tensor) –

Prefill query cumulative offsets (0-based).
chunked_prefill_workspace ¶
(Tensor) –

Scratch buffer the context gather writes to.
chunked_prefill_workspace_size ¶
(int) –

Row capacity of the workspace.
block_size ¶
(int) –

KV cache page size for chunk-start alignment.
align_chunk_to_block ¶
(bool) –

Round the chunk size down to block_size.
device ¶
(device) –

Target device for the returned tensors.
dcp_world_size ¶
(int) –

Decode-context-parallel world size (1 if disabled).
dcp_local_block_size ¶
(int) –

Per-rank interleave block size for DCP.
dcp_virtual_block_size ¶
(int) –

dcp_local_block_size * dcp_world_size.
dcp_manager ¶
(MLADCPManager | None, default: None ) –

Shared MLA DCP collective manager.

Returns:

ChunkedContextMetadata | None –

The chunked-context metadata, or None when no prefill has any context.

Source code in vllm/model_executor/layers/attention/mla_attention.py

def build_mla_chunked_context_metadata(
    *,
    context_lens_cpu: torch.Tensor,
    prefill_query_start_loc_cpu: torch.Tensor,
    chunked_prefill_workspace: torch.Tensor,
    chunked_prefill_workspace_size: int,
    block_size: int,
    align_chunk_to_block: bool,
    device: torch.device,
    dcp_world_size: int,
    dcp_local_block_size: int,
    dcp_virtual_block_size: int,
    dcp_manager: MLADCPManager | None = None,
) -> "MLACommonPrefillMetadata.ChunkedContextMetadata | None":
    """Build chunked-context metadata for an MLA prefill.

    Shared by dense and sparse builders. Packs the prefill contexts into a flat
    list of workspace-sized per-request chunks and, under DCP, plans the
    per-rank interleaved local chunks the all-gather reduction consumes.

    Args:
        context_lens_cpu: Per-prefill context length (seq_len - query_len).
        prefill_query_start_loc_cpu: Prefill query cumulative offsets (0-based).
        chunked_prefill_workspace: Scratch buffer the context gather writes to.
        chunked_prefill_workspace_size: Row capacity of the workspace.
        block_size: KV cache page size for chunk-start alignment.
        align_chunk_to_block: Round the chunk size down to ``block_size``.
        device: Target device for the returned tensors.
        dcp_world_size: Decode-context-parallel world size (1 if disabled).
        dcp_local_block_size: Per-rank interleave block size for DCP.
        dcp_virtual_block_size: ``dcp_local_block_size * dcp_world_size``.
        dcp_manager: Shared MLA DCP collective manager.

    Returns:
        The chunked-context metadata, or None when no prefill has any context.
    """
    # NOTE: it is recommended you read the `Chunked Prefill` section in the
    # comment at the top of the file before trying to understand this code.
    context_lens = context_lens_cpu.tolist()
    if max(context_lens, default=0) <= 0:
        return None

    # The `gather_and_maybe_dequant_cache` kernel cannot handle chunk starts
    # that are not aligned to block_size, so a split request advances in
    # block-aligned steps.
    chunk_alignment = block_size if align_chunk_to_block else 1
    if dcp_world_size > 1:
        # Each rank gathers only its own shard, so a chunk's workspace cost is
        # 1/dcp_world_size of its context rows, rounded up to the interleave
        # block size. Sub-chunks are additionally aligned to the virtual block
        # size so that every rank's local start stays interleave-aligned.
        row_budget = chunked_prefill_workspace_size // dcp_world_size
        chunk_alignment = math.lcm(chunk_alignment, dcp_virtual_block_size)

        def padded_rows(rows: int) -> int:
            return cdiv(rows, dcp_virtual_block_size) * dcp_local_block_size
    else:
        row_budget = chunked_prefill_workspace_size

        def padded_rows(rows: int) -> int:
            return rows

    max_context_chunk = round_down(chunked_prefill_workspace_size, chunk_alignment)
    assert max_context_chunk > 0, (
        f"chunked prefill workspace ({chunked_prefill_workspace_size} rows) is "
        f"smaller than the context chunk alignment ({chunk_alignment} rows)"
    )

    plans = plan_mla_context_chunks(
        context_lens,
        row_budget,
        max_context_chunk,
        chunk_alignment,
        padded_rows,
    )

    query_start_loc = prefill_query_start_loc_cpu.tolist()
    empty_token_slices = [
        slice(query_start_loc[i], query_start_loc[i + 1])
        for i, context_len in enumerate(context_lens)
        if context_len == 0
    ]

    use_dcp = dcp_world_size > 1
    local_context_lens_allranks = (
        get_dcp_local_seq_lens(
            context_lens_cpu, dcp_world_size, None, dcp_local_block_size
        ).tolist()
        if use_dcp
        else None
    )

    # Concatenate the per-chunk fields so each one costs a single
    # host-to-device transfer, and remember where every chunk's slice lands.
    starts_flat: list[int] = []
    seq_lens_flat: list[int] = []
    cu_seq_lens_flat: list[int] = []
    cu_seqlens_q_flat: list[int] = []
    token_to_seq_parts: list[np.ndarray] = []
    padded_local_cu_seq_lens_flat: list[int] = []
    padded_local_token_to_seq_parts: list[np.ndarray] = []
    local_starts_per_chunk: list[list[int]] = []
    local_seq_lens_per_chunk: list[list[int]] = []
    layouts: list[tuple[slice, slice, slice, slice]] = []

    request_offset = boundary_offset = token_offset = local_token_offset = 0
    for plan in plans:
        num_requests = len(plan.seq_lens)
        num_tokens = sum(plan.seq_lens)
        num_local_tokens = num_tokens

        seq_lens_flat.extend(plan.seq_lens)
        cu_seq_lens_flat.extend(itertools.accumulate(plan.seq_lens, initial=0))
        query_base = query_start_loc[plan.request_start]
        cu_seqlens_q_flat.extend(
            query_start_loc[request] - query_base
            for request in range(plan.request_start, plan.request_end + 1)
        )
        token_to_seq_parts.append(
            np.repeat(np.arange(num_requests, dtype=np.int32), plan.seq_lens)
        )

        if use_dcp:
            # A request's local rows are its context rows sharded across ranks
            # and rounded up to the interleave block size, so the local start of
            # a continuation is the padded row count of the context before it.
            local_starts = [padded_rows(start) for start in plan.starts]
            local_seq_lens = [
                padded_rows(start + length) - local_start
                for start, length, local_start in zip(
                    plan.starts, plan.seq_lens, local_starts
                )
            ]
            local_starts_per_chunk.append(local_starts)
            local_seq_lens_per_chunk.append(local_seq_lens)
            padded_local_cu_seq_lens_flat.extend(
                itertools.accumulate(local_seq_lens, initial=0)
            )
            padded_local_token_to_seq_parts.append(
                np.repeat(np.arange(num_requests, dtype=np.int32), local_seq_lens)
            )
            num_local_tokens = sum(local_seq_lens)
            # The gather takes per-rank local offsets under DCP.
            starts_flat.extend(local_starts)
        else:
            starts_flat.extend(plan.starts)
        assert num_local_tokens <= row_budget

        layouts.append(
            (
                slice(request_offset, request_offset + num_requests),
                slice(boundary_offset, boundary_offset + num_requests + 1),
                slice(token_offset, token_offset + num_tokens),
                slice(local_token_offset, local_token_offset + num_local_tokens),
            )
        )
        request_offset += num_requests
        boundary_offset += num_requests + 1
        token_offset += num_tokens
        local_token_offset += num_local_tokens

    seq_lens_cpu = _flat_int32(seq_lens_flat)
    starts = _flat_int32(starts_flat).to(device, non_blocking=True)
    cu_seq_lens = _flat_int32(cu_seq_lens_flat).to(device, non_blocking=True)
    cu_seqlens_q = _flat_int32(cu_seqlens_q_flat).to(device, non_blocking=True)
    token_to_seq = _flat_int32(np.concatenate(token_to_seq_parts)).to(
        device, non_blocking=True
    )
    if use_dcp:
        padded_local_cu_seq_lens = _flat_int32(padded_local_cu_seq_lens_flat).to(
            device, non_blocking=True
        )
        padded_local_token_to_seq = _flat_int32(
            np.concatenate(padded_local_token_to_seq_parts)
        ).to(device, non_blocking=True)

    chunks: list[MLACommonPrefillMetadata.ContextChunk] = []
    for index, (plan, layout) in enumerate(zip(plans, layouts)):
        request_slice, boundary_slice, token_slice, local_token_slice = layout
        query_lens = [
            query_start_loc[request + 1] - query_start_loc[request]
            for request in range(plan.request_start, plan.request_end)
        ]
        chunk = MLACommonPrefillMetadata.ContextChunk(
            index=index,
            request_slice=slice(plan.request_start, plan.request_end),
            token_slice=slice(
                query_start_loc[plan.request_start], query_start_loc[plan.request_end]
            ),
            continuation_token_end=query_start_loc[plan.request_start + 1],
            is_continuation=plan.is_continuation,
            num_context_tokens=token_slice.stop - token_slice.start,
            query_start_loc=cu_seqlens_q[boundary_slice],
            max_query_len=max(query_lens),
            cu_seq_lens=cu_seq_lens[boundary_slice],
            starts=starts[request_slice],
            max_seq_len=max(plan.seq_lens),
            seq_lens=seq_lens_cpu[request_slice],
            token_to_seq=token_to_seq[token_slice],
            num_local_context_tokens=local_token_slice.stop - local_token_slice.start,
        )
        if use_dcp:
            assert local_context_lens_allranks is not None
            chunk.padded_local_seq_lens = local_seq_lens_per_chunk[index]
            chunk.local_context_lens_allranks = local_context_lens_allranks[
                plan.request_start : plan.request_end
            ]
            chunk.padded_local_cu_seq_lens = padded_local_cu_seq_lens[boundary_slice]
            chunk.padded_local_token_to_seq = padded_local_token_to_seq[
                local_token_slice
            ]
            chunk.local_starts = local_starts_per_chunk[index]
        chunks.append(chunk)

    return MLACommonPrefillMetadata.ChunkedContextMetadata(
        context_lens=context_lens_cpu.to(device, non_blocking=True),
        workspace=chunked_prefill_workspace,
        chunks=chunks,
        context_lens_list=context_lens,
        empty_token_slices=empty_token_slices,
        dcp_manager=dcp_manager,
    )

`init_mla_context_partial(chunked_context, attn_output, attn_softmax_lse, num_tokens)` ¶

Allocate the running context partial over all prefill tokens.

Laid out like the chunk partials so the final whole-batch merge against the suffix partial sees matching head strides. Callers whose backend honors an out tensor already know that layout and can allocate directly, pairing it with neutralize_empty_context_partials.

Source code in vllm/model_executor/layers/attention/mla_attention.py

def init_mla_context_partial(
    chunked_context: "MLACommonPrefillMetadata.ChunkedContextMetadata",
    attn_output: torch.Tensor,
    attn_softmax_lse: torch.Tensor,
    num_tokens: int,
) -> tuple[torch.Tensor, torch.Tensor]:
    """Allocate the running context partial over all prefill tokens.

    Laid out like the chunk partials so the final whole-batch merge against the
    suffix partial sees matching head strides. Callers whose backend honors an
    ``out`` tensor already know that layout and can allocate directly, pairing it
    with ``neutralize_empty_context_partials``.
    """
    output = torch.empty(
        (num_tokens, *attn_output.shape[1:]),
        dtype=attn_output.dtype,
        device=attn_output.device,
    )
    output_lse = torch.empty(
        (attn_softmax_lse.shape[0], num_tokens),
        dtype=attn_softmax_lse.dtype,
        device=attn_softmax_lse.device,
    )
    neutralize_empty_context_partials(chunked_context, output, output_lse)
    return output, output_lse

`neutralize_empty_context_partials(chunked_context, output, output_lse)` ¶

Neutralize the partial of every prefill that no chunk covers.

A prefill without context is never gathered, so nothing would write its rows; a zero output with an -inf lse carries no weight into the final merge against the suffix partial.

Source code in vllm/model_executor/layers/attention/mla_attention.py

def neutralize_empty_context_partials(
    chunked_context: "MLACommonPrefillMetadata.ChunkedContextMetadata",
    output: torch.Tensor,
    output_lse: torch.Tensor,
) -> None:
    """Neutralize the partial of every prefill that no chunk covers.

    A prefill without context is never gathered, so nothing would write its rows;
    a zero output with an ``-inf`` lse carries no weight into the final merge
    against the suffix partial.
    """
    for token_slice in chunked_context.empty_token_slices:
        output[token_slice].zero_()
        output_lse[:, token_slice].fill_(float("-inf"))

`plan_mla_context_chunks(context_lens, row_budget, max_context_chunk, split_alignment, padded_rows)` ¶

Pack per-request contexts into workspace-sized chunks.

Source code in vllm/model_executor/layers/attention/mla_attention.py

def plan_mla_context_chunks(
    context_lens: list[int],
    row_budget: int,
    max_context_chunk: int,
    split_alignment: int,
    padded_rows: Callable[[int], int],
) -> list[_ContextChunkPlan]:
    """Pack per-request contexts into workspace-sized chunks."""
    assert max_context_chunk > 0
    assert split_alignment > 0

    def aligned_split_len(start: int, remaining: int, available: int) -> int:
        max_split = min(
            max_context_chunk,
            round_down(remaining - 1, split_alignment),
        )
        low, high = 0, max_split // split_alignment
        while low < high:
            mid = (low + high + 1) // 2
            length = mid * split_alignment
            rows = padded_rows(start + length) - padded_rows(start)
            if rows <= available:
                low = mid
            else:
                high = mid - 1
        return low * split_alignment

    plans: list[_ContextChunkPlan] = []
    num_requests = len(context_lens)
    request = 0
    start = 0
    while request < num_requests:
        context_len = context_lens[request]
        if context_len == 0:
            assert start == 0
            request += 1
            continue

        request_start = request
        starts: list[int] = []
        seq_lens: list[int] = []
        rows = 0
        while request < num_requests and context_lens[request] > 0:
            remaining = context_lens[request] - start
            request_rows = padded_rows(start + remaining) - padded_rows(start)
            if rows + request_rows > row_budget:
                split_len = aligned_split_len(start, remaining, row_budget - rows)
                if split_len > 0:
                    starts.append(start)
                    seq_lens.append(split_len)
                    rows += padded_rows(start + split_len) - padded_rows(start)
                    start += split_len
                break
            rows += request_rows
            starts.append(start)
            seq_lens.append(remaining)
            request += 1
            start = 0
        assert seq_lens
        plans.append(
            _ContextChunkPlan(
                request_start=request_start,
                request_end=request_start + len(seq_lens),
                starts=starts,
                seq_lens=seq_lens,
                is_continuation=starts[0] > 0,
            )
        )
    return plans

`reorg_kvcache(allgatered_kv_c_normed, allgatered_k_pe, padded_local_chunk_seq_lens_lst, local_context_lens_allranks, local_starts, sum_seq_len, max_seq_len, toks)` ¶

reorg and unpad kvcache after cp local gather to tp layout for attn kernel. e.g. allgatered_kv_c_normed = [T0_0, T0_1, T0_2, T0_3, T1_0, T1_1, ..., T0_4, T0_5, pad, pad, T1_2, pad, ...] -> reorganized_kv_c_normed = [T0_0, T0_1, T0_2, T0_3, T0_4, T0_5, T1_0, T1_1, T1_2, ...] Args: padded_local_chunk_seq_lens_lst: local chunk context lengths under current CP rank. local_context_lens_allranks: local context lengths on each CP rank. local_starts: per-request local offset into the context this chunk starts at. sum_seq_len: the sum of cp_chunk_seq_lens_lst. max_seq_len: the max value of cp_chunk_seq_lens_lst. toks: the number of tokens for local gather cache.

Source code in vllm/model_executor/layers/attention/mla_attention.py

def reorg_kvcache(
    allgatered_kv_c_normed: torch.Tensor,
    allgatered_k_pe: torch.Tensor,
    padded_local_chunk_seq_lens_lst: list[int],
    local_context_lens_allranks: list[list[int]],
    local_starts: list[int],
    sum_seq_len: int,
    max_seq_len: int,
    toks: int,
) -> tuple[torch.Tensor, torch.Tensor]:
    """
    reorg and unpad kvcache after cp local gather to tp layout for attn kernel.
    e.g.
    allgatered_kv_c_normed = [T0_0, T0_1, T0_2, T0_3, T1_0, T1_1, ...,
                              T0_4, T0_5, pad, pad, T1_2, pad, ...]
    -> reorganized_kv_c_normed = [T0_0, T0_1, T0_2, T0_3, T0_4, T0_5,
                                  T1_0, T1_1, T1_2, ...]
    Args:
        padded_local_chunk_seq_lens_lst: local chunk context lengths
            under current CP rank.
        local_context_lens_allranks: local context lengths on each CP rank.
        local_starts: per-request local offset into the context this chunk
            starts at.
        sum_seq_len: the sum of cp_chunk_seq_lens_lst.
        max_seq_len: the max value of cp_chunk_seq_lens_lst.
        toks: the number of tokens for local gather cache.
    """
    kv_c_segments = []
    k_pe_segments = []
    src_token_idx = 0
    max_seq_len_check = 0
    for padded_local_chunk_seq_len, local_context_lens, local_start in zip(
        padded_local_chunk_seq_lens_lst, local_context_lens_allranks, local_starts
    ):
        cur_seq_len = 0
        for rank, local_context_len in enumerate(local_context_lens):
            # Note(qcs): We split the context into multiple chunks,
            # depending on the size of the workspace.
            # local_context in dcp0:   |-----------------|
            # local_context in dcp1:   |--------------|
            # n*padded_local_chunk:    |-----|-----|-----|
            # local_chunk_len in dcp1: |-----|-----|--|
            # so we need update the last chunk length in dcp1.
            local_chunk_len = min(
                max(0, local_context_len - local_start),
                padded_local_chunk_seq_len,
            )
            if local_chunk_len != 0:
                kv_c_segment = allgatered_kv_c_normed[
                    rank * toks + src_token_idx : rank * toks
                    + src_token_idx
                    + local_chunk_len
                ]
                k_pe_segment = allgatered_k_pe[
                    rank * toks + src_token_idx : rank * toks
                    + src_token_idx
                    + local_chunk_len
                ]
                kv_c_segments.append(kv_c_segment)
                k_pe_segments.append(k_pe_segment)
                cur_seq_len += local_chunk_len
        max_seq_len_check = max(max_seq_len_check, cur_seq_len)
        src_token_idx += padded_local_chunk_seq_len
    reorganized_kv_c_normed = torch.cat(kv_c_segments, dim=0)
    reorganized_k_pe = torch.cat(k_pe_segments, dim=0)
    assert reorganized_kv_c_normed.shape[0] == sum_seq_len
    assert reorganized_k_pe.shape[0] == sum_seq_len
    assert max_seq_len_check == max_seq_len
    return reorganized_kv_c_normed, reorganized_k_pe

`unified_mla_kv_cache_update(kv_c_normed, k_pe, layer_name, kv_cache_dtype, k_scale)` ¶

Returns a dummy that is passed to unified_attention to signal a side effect and the data dependency between them to ensure torch.compile preserves ordering.

Source code in vllm/model_executor/layers/attention/mla_attention.py

def unified_mla_kv_cache_update(
    kv_c_normed: torch.Tensor,
    k_pe: torch.Tensor,
    layer_name: LayerNameType,
    kv_cache_dtype: str,
    k_scale: torch.Tensor,
) -> torch.Tensor:
    """
    Returns a dummy that is passed to unified_attention to signal a side effect and
    the data dependency between them to ensure torch.compile preserves ordering.
    """
    layer_name = _resolve_layer_name(layer_name)
    attn_metadata, attn_layer, kv_cache, layer_slot_mapping = get_attention_context(
        layer_name
    )
    if layer_slot_mapping is not None:
        kv_c_normed, k_pe, layer_slot_mapping = maybe_gather_mla_latent_cache_inputs(
            kv_c_normed,
            k_pe,
            layer_slot_mapping,
            attn_metadata.num_decode_tokens if attn_metadata is not None else None,
            attn_layer.use_pcp,
        )
        attn_layer.impl.do_kv_cache_update(  # type: ignore[attr-defined]
            kv_c_normed,
            k_pe,
            kv_cache,
            layer_slot_mapping,
            kv_cache_dtype,
            k_scale,
        )

    return torch.empty(0, device=kv_c_normed.device, dtype=kv_c_normed.dtype)

vllm.model_executor.layers.attention.mla_attention ¶

MLA Common Components¶

More Extent Definitions:¶

Vector/Matrix Definitions¶

Compute Friendly Approach (i.e. "forward_mha"):¶

Data-Movement Friendly Approach (i.e. "forward_mqa"):¶

Chunked Prefill¶

MLAAttention ¶

MLACommonBackend ¶

customize_spec(spec) classmethod ¶

MLACommonBaseImpl ¶

_concat_k_nope_k_pe(k_nope, k_pe) ¶

k_nope ¶

k_pe ¶

MLACommonImpl ¶

MLACommonMetadata dataclass ¶

MLACommonMetadataBuilder ¶

build_for_cudagraph_capture(common_attn_metadata) ¶

determine_prefill_query_data_type(vllm_config, model_dtype) staticmethod ¶

MLACommonPrefillMetadata dataclass ¶

ContextChunk dataclass ¶

QueryLenSupport ¶

_ContextChunkPlan dataclass ¶

_DecodeConcatQuantFP8 ¶

_make_forward(quant_fn) ¶

_detect_output_quant_key(output, output_scale, output_block_scale, output_dim) ¶

_flat_int32(values) ¶

accumulate_mla_context_chunk(chunk, attn_output, attn_softmax_lse, output, output_lse, output_written=False) ¶

output_written ¶

backend_supports_prefill_query_quantization() cached ¶

build_mla_chunked_context_metadata(*, context_lens_cpu, prefill_query_start_loc_cpu, chunked_prefill_workspace, chunked_prefill_workspace_size, block_size, align_chunk_to_block, device, dcp_world_size, dcp_local_block_size, dcp_virtual_block_size, dcp_manager=None) ¶

context_lens_cpu ¶

prefill_query_start_loc_cpu ¶

chunked_prefill_workspace ¶

chunked_prefill_workspace_size ¶

block_size ¶

align_chunk_to_block ¶

device ¶

dcp_world_size ¶

dcp_local_block_size ¶

dcp_virtual_block_size ¶

dcp_manager ¶

init_mla_context_partial(chunked_context, attn_output, attn_softmax_lse, num_tokens) ¶

neutralize_empty_context_partials(chunked_context, output, output_lse) ¶

plan_mla_context_chunks(context_lens, row_budget, max_context_chunk, split_alignment, padded_rows) ¶

reorg_kvcache(allgatered_kv_c_normed, allgatered_k_pe, padded_local_chunk_seq_lens_lst, local_context_lens_allranks, local_starts, sum_seq_len, max_seq_len, toks) ¶

unified_mla_kv_cache_update(kv_c_normed, k_pe, layer_name, kv_cache_dtype, k_scale) ¶

`vllm.model_executor.layers.attention.mla_attention` ¶

`MLAAttention` ¶

`MLACommonBackend` ¶

`customize_spec(spec)` `classmethod` ¶

`MLACommonBaseImpl` ¶

`_concat_k_nope_k_pe(k_nope, k_pe)` ¶

`k_nope` ¶

`k_pe` ¶

`MLACommonImpl` ¶

`MLACommonMetadata` `dataclass` ¶

`MLACommonMetadataBuilder` ¶

`build_for_cudagraph_capture(common_attn_metadata)` ¶

`determine_prefill_query_data_type(vllm_config, model_dtype)` `staticmethod` ¶

`MLACommonPrefillMetadata` `dataclass` ¶

`ContextChunk` `dataclass` ¶

`QueryLenSupport` ¶

`_ContextChunkPlan` `dataclass` ¶

`_DecodeConcatQuantFP8` ¶

`_make_forward(quant_fn)` ¶

`_detect_output_quant_key(output, output_scale, output_block_scale, output_dim)` ¶

`_flat_int32(values)` ¶

`accumulate_mla_context_chunk(chunk, attn_output, attn_softmax_lse, output, output_lse, output_written=False)` ¶

`output_written` ¶

`backend_supports_prefill_query_quantization()` `cached` ¶

`build_mla_chunked_context_metadata(*, context_lens_cpu, prefill_query_start_loc_cpu, chunked_prefill_workspace, chunked_prefill_workspace_size, block_size, align_chunk_to_block, device, dcp_world_size, dcp_local_block_size, dcp_virtual_block_size, dcp_manager=None)` ¶

`context_lens_cpu` ¶

`prefill_query_start_loc_cpu` ¶

`chunked_prefill_workspace` ¶

`chunked_prefill_workspace_size` ¶

`block_size` ¶

`align_chunk_to_block` ¶

`device` ¶

`dcp_world_size` ¶

`dcp_local_block_size` ¶

`dcp_virtual_block_size` ¶

`dcp_manager` ¶

`init_mla_context_partial(chunked_context, attn_output, attn_softmax_lse, num_tokens)` ¶

`neutralize_empty_context_partials(chunked_context, output, output_lse)` ¶

`plan_mla_context_chunks(context_lens, row_budget, max_context_chunk, split_alignment, padded_rows)` ¶

`reorg_kvcache(allgatered_kv_c_normed, allgatered_k_pe, padded_local_chunk_seq_lens_lst, local_context_lens_allranks, local_starts, sum_seq_len, max_seq_len, toks)` ¶

`unified_mla_kv_cache_update(kv_c_normed, k_pe, layer_name, kv_cache_dtype, k_scale)` ¶