vllm.models.kimi_k3.amd.specialized_decode.runtime.decode

The Kimi-K3 specialized decode executor (layer mode): one routing decision per step, then the layer kernels inside the stock layer loop.

Built by the model state once the weights are loaded (KimiK3ModelState): checks the loaded weights, builds the per-rank launch state (runner) and the layer kernels of every width, and compiles every build, before memory profiling and graph capture. prepare_kv binds it to every KV cache vLLM allocates. Each step plan returns a Step or None; Step.layer(i) is the kernel replacing decoder layer i (None for vLLM's layers). Weight reloads are not supported.

Classes:

• SpecializedDecode –

  The executor: builds the layer kernels, binds them to each KV cache and

• Step –

  A planned step: width rows of whole uniform decode requests.

Functions:

• agree –

  Raises on every TP rank if any rank has reasons: a rank that refused

• step_reason –

  None if a step's KDA and MLA metadata (identical on every TP rank) are a

SpecializedDecode

The executor: builds the layer kernels, binds them to each KV cache and plans every step.

Methods:

• after_step –

  Once per executed step, from compute_logits (eager on every rank, also

• plan –

  This step's plan, or None to run vLLM's layers.

• prepare_kv –

  Checks and binds the KDA states and MLA caches vLLM just bound (the

Source code in vllm/models/kimi_k3/amd/specialized_decode/runtime/decode.py

class SpecializedDecode:
    """The executor: builds the layer kernels, binds them to each KV cache and
    plans every step."""

    def __init__(self, backbone, config: DecodeConfig):
        tp, dcp = get_tp_group(), get_dcp_group()
        reasons = weight_reasons(backbone)
        if dcp.world_size > 1 and (
            dcp.world_size != DCP or dcp.rank_in_group != tp.rank_in_group
        ):
            reasons.append(
                f"DCP{dcp.world_size} rank {dcp.rank_in_group} on TP rank "
                f"{tp.rank_in_group} (the DCP group must be the TP group)"
            )
        agree(reasons, tp.cpu_group, "the loaded weights")
        self.backbone, self.config = backbone, config
        kinds = {
            i: kind
            for i, kind in layer_kinds(backbone.config).items()
            if backbone.start_layer <= i < backbone.end_layer
        }
        ffn = [i for i, kind in kinds.items() if kind == FFN]
        m1 = config.m1 and bool(ffn)
        if m1:
            why = mla_weight_reasons(backbone)
            m1 = tp_agree(not why, tp.cpu_group)
            if not m1 and dcp.world_size == 1 and config.q > 1:
                # Speculative DCP1 runs M1c only (``runtime.step_reasons``).
                # Ordinary q=1 falls back to vLLM's MLA ops, then K2.
                agree(why or ["another TP rank's MLA layers"], tp.cpu_group,
                      "DCP1 without M1")  # fmt: skip
            if not m1:
                logger.info(
                    "Kimi-K3 specialized decode: the MLA layers run vLLM's "
                    "attention ops, then K2 (M1 does not support: %s)",
                    "; ".join(why) if why else "another TP rank's MLA layers",
                )
        runner = self.runner = MonoRunner(next(backbone.parameters()).device, m1=m1)
        runner.num_spec = config.q - 1
        runner.interleave = config.interleave
        runner.first_layer = min(kinds)
        whole = [i for i, kind in kinds.items() if kind == WHOLE]
        runner.kda_prefix = backbone.layers[whole[0]].self_attn.prefix
        if m1:
            # the M1 layers' metadata: their MLAAttention's
            wrapper = backbone.layers[ffn[0]].self_attn.mla_attn
            runner.mla_prefix = wrapper.mla_attn.layer_name
        mla_kernel = FfnLayer if m1 else MlaOpsLayer
        self.kernels: dict[int, dict[int, Kernel]] = {
            w: {
                i: (WholeLayer if kind == WHOLE else mla_kernel)(
                    backbone.layers[i], runner, w
                )
                for i, kind in kinds.items()
            }
            for w in config.widths
        }
        self.kda: list[WholeLayer] = [
            k for k in self.kernels[config.widths[0]].values()
            if isinstance(k, WholeLayer)
        ]  # fmt: skip
        self.mla: list[FfnLayer] = [
            k for k in self.kernels[config.widths[0]].values()
            if isinstance(k, FfnLayer)
        ]  # fmt: skip
        builds = {
            b for by in self.kernels.values() for k in by.values() for b in k.builds()
        }
        for build in builds:
            compile_build(build)
        self.failstop = FailStop(
            runner.health[FAILED : FAILED + 1], runner.rank, tp.world_size, tp.cpu_group
        )
        # `_kv_key` of the KV cache prepare_kv bound
        self._kv: tuple | None = None
        m1c = [w for w in config.widths if m1c_at(runner, w)]
        mla = f"M1 (M1c in K2 at widths {m1c})" if m1c else "M1"
        logger.info(
            "Kimi-K3 specialized decode: %d layers (MLA: %s), widths %s, %d "
            "builds; other steps run vLLM's decode",
            len(kinds),
            mla if m1 else "vLLM's ops",
            list(config.widths),
            len(builds),
        )

    def _kv_key(self) -> tuple | None:
        """The first KDA layer's recurrent state by all but its number of slots:
        an extensible KV cache re-binds its views at the same addresses with the
        final block count, which `cache_reasons` does not depend on."""
        state = self.kda[0].layer.self_attn.kv_cache
        if not isinstance(state, (list, tuple)) or len(state) != 2:
            return None
        s = state[1]
        if s.numel() == 0:
            return None
        return s.data_ptr(), s.dtype, s.shape[1:], s.stride()

    def prepare_kv(self) -> None:
        """Checks and binds the KDA states and MLA caches vLLM just bound (the
        CUDA-graph memory profiling cache, then the real one)."""
        self._kv = None
        if self._kv_key() is None:
            return
        caches = {k.layer.self_attn.prefix: k.caches() for k in self.kda}
        caches.update({k.wrapper.mla_attn.layer_name: k.caches() for k in self.mla})
        agree(cache_reasons(caches), get_tp_group().cpu_group, "the KV caches")
        self._kv = self._kv_key()

    def plan(self, positions: torch.Tensor, intermediate_tensors) -> Step | None:
        """This step's plan, or None to run vLLM's layers.

        Steps that run vLLM's layers:
        - pipeline-parallel stage inputs, and steps before a KV cache is bound
          (profiling, dummy runs);
        - PIECEWISE steps (captured without attention metadata);
        - widths without kernels (more than max_num_seqs requests);
        - steps that are not pure, uniform regular-decode or spec-verify steps
          (`step_reason`).

        Eager steps run the kernels too. Every predicate is the same on all TP
        ranks: the kernels' mailboxes need every rank on one path. Under FULL
        graphs this runs at capture only and the plan is baked in; a replay with
        fewer requests than its graph pads whole requests (NULL_BLOCK_ID state,
        one accepted token), which the kernels serve like real ones.
        """
        if intermediate_tensors is not None or self._kv is None:
            return None
        if not is_forward_context_available():
            return None
        context = get_forward_context()
        if context.cudagraph_runtime_mode == CUDAGraphMode.PIECEWISE:
            return None
        width = positions.size(-1)
        kernels = self.kernels.get(width)
        if kernels is None:
            return None
        md = context.attn_metadata
        runner = self.runner
        if (
            step_reason(
                md,
                runner.kda_prefix,
                width,
                self.config.q,
                runner.mla_prefix,
            )
            is not None
        ):
            return None
        assert isinstance(md, dict)
        if self._kv_key() != self._kv:
            raise RuntimeError(
                "Kimi-K3 specialized decode: the KV cache was re-bound without "
                "prepare_kv (ModelState.initialize_kv_cache)"
            )
        return Step(width, md, kernels)

    def after_step(self) -> None:
        """Once per executed step, from compute_logits (eager on every rank, also
        under graph replay). Raises on every rank once a rank gave up an in-kernel
        wait."""
        self.failstop.after_step()

_kv_key()

The first KDA layer's recurrent state by all but its number of slots: an extensible KV cache re-binds its views at the same addresses with the final block count, which cache_reasons does not depend on.

Source code in vllm/models/kimi_k3/amd/specialized_decode/runtime/decode.py

def _kv_key(self) -> tuple | None:
    """The first KDA layer's recurrent state by all but its number of slots:
    an extensible KV cache re-binds its views at the same addresses with the
    final block count, which `cache_reasons` does not depend on."""
    state = self.kda[0].layer.self_attn.kv_cache
    if not isinstance(state, (list, tuple)) or len(state) != 2:
        return None
    s = state[1]
    if s.numel() == 0:
        return None
    return s.data_ptr(), s.dtype, s.shape[1:], s.stride()

after_step()

Once per executed step, from compute_logits (eager on every rank, also under graph replay). Raises on every rank once a rank gave up an in-kernel wait.

Source code in vllm/models/kimi_k3/amd/specialized_decode/runtime/decode.py

def after_step(self) -> None:
    """Once per executed step, from compute_logits (eager on every rank, also
    under graph replay). Raises on every rank once a rank gave up an in-kernel
    wait."""
    self.failstop.after_step()

plan(positions, intermediate_tensors)

This step's plan, or None to run vLLM's layers.

Steps that run vLLM's layers: - pipeline-parallel stage inputs, and steps before a KV cache is bound (profiling, dummy runs); - PIECEWISE steps (captured without attention metadata); - widths without kernels (more than max_num_seqs requests); - steps that are not pure, uniform regular-decode or spec-verify steps (step_reason).

Eager steps run the kernels too. Every predicate is the same on all TP ranks: the kernels' mailboxes need every rank on one path. Under FULL graphs this runs at capture only and the plan is baked in; a replay with fewer requests than its graph pads whole requests (NULL_BLOCK_ID state, one accepted token), which the kernels serve like real ones.

Source code in vllm/models/kimi_k3/amd/specialized_decode/runtime/decode.py

def plan(self, positions: torch.Tensor, intermediate_tensors) -> Step | None:
    """This step's plan, or None to run vLLM's layers.

    Steps that run vLLM's layers:
    - pipeline-parallel stage inputs, and steps before a KV cache is bound
      (profiling, dummy runs);
    - PIECEWISE steps (captured without attention metadata);
    - widths without kernels (more than max_num_seqs requests);
    - steps that are not pure, uniform regular-decode or spec-verify steps
      (`step_reason`).

    Eager steps run the kernels too. Every predicate is the same on all TP
    ranks: the kernels' mailboxes need every rank on one path. Under FULL
    graphs this runs at capture only and the plan is baked in; a replay with
    fewer requests than its graph pads whole requests (NULL_BLOCK_ID state,
    one accepted token), which the kernels serve like real ones.
    """
    if intermediate_tensors is not None or self._kv is None:
        return None
    if not is_forward_context_available():
        return None
    context = get_forward_context()
    if context.cudagraph_runtime_mode == CUDAGraphMode.PIECEWISE:
        return None
    width = positions.size(-1)
    kernels = self.kernels.get(width)
    if kernels is None:
        return None
    md = context.attn_metadata
    runner = self.runner
    if (
        step_reason(
            md,
            runner.kda_prefix,
            width,
            self.config.q,
            runner.mla_prefix,
        )
        is not None
    ):
        return None
    assert isinstance(md, dict)
    if self._kv_key() != self._kv:
        raise RuntimeError(
            "Kimi-K3 specialized decode: the KV cache was re-bound without "
            "prepare_kv (ModelState.initialize_kv_cache)"
        )
    return Step(width, md, kernels)

prepare_kv()

Checks and binds the KDA states and MLA caches vLLM just bound (the CUDA-graph memory profiling cache, then the real one).

Source code in vllm/models/kimi_k3/amd/specialized_decode/runtime/decode.py

def prepare_kv(self) -> None:
    """Checks and binds the KDA states and MLA caches vLLM just bound (the
    CUDA-graph memory profiling cache, then the real one)."""
    self._kv = None
    if self._kv_key() is None:
        return
    caches = {k.layer.self_attn.prefix: k.caches() for k in self.kda}
    caches.update({k.wrapper.mla_attn.layer_name: k.caches() for k in self.mla})
    agree(cache_reasons(caches), get_tp_group().cpu_group, "the KV caches")
    self._kv = self._kv_key()

Step

Bases: NamedTuple

A planned step: width rows of whole uniform decode requests.

Methods:

• layer –

  The kernel replacing decoder layer idx in this step, or None (vLLM's).

Source code in vllm/models/kimi_k3/amd/specialized_decode/runtime/decode.py

class Step(NamedTuple):
    """A planned step: `width` rows of whole uniform decode requests."""

    width: int
    attn_metadata: Mapping[str, Any]  # by layer name
    kernels: Mapping[int, Kernel]  # by decoder layer, at this width

    def layer(self, idx: int) -> Callable | None:
        """The kernel replacing decoder layer idx in this step, or None (vLLM's)."""
        kernel = self.kernels.get(idx)
        return None if kernel is None else partial(kernel.launch, self)

layer(idx)

The kernel replacing decoder layer idx in this step, or None (vLLM's).

Source code in vllm/models/kimi_k3/amd/specialized_decode/runtime/decode.py

def layer(self, idx: int) -> Callable | None:
    """The kernel replacing decoder layer idx in this step, or None (vLLM's)."""
    kernel = self.kernels.get(idx)
    return None if kernel is None else partial(kernel.launch, self)

agree(reasons, group, what)

Raises on every TP rank if any rank has reasons: a rank that refused alone would leave the others spinning in-kernel.

Source code in vllm/models/kimi_k3/amd/specialized_decode/runtime/decode.py

def agree(reasons: list[str], group, what: str) -> None:
    """Raises on every TP rank if any rank has `reasons`: a rank that refused
    alone would leave the others spinning in-kernel."""
    if not tp_agree(not reasons, group):
        why = "; ".join(reasons) if reasons else "another TP rank refused"
        raise ValueError(
            f"Kimi-K3 specialized decode does not support {what}: {why} "
            "(decode_backend=auto runs vLLM's decode)"
        )

step_reason(attn_metadata, kda_prefix, width, q, mla_prefix='')

None if a step's KDA and MLA metadata (identical on every TP rank) are a pure, uniform regular-decode or spec-verify step filling width rows, else why not. q is the deployment's configured rows per request.

Source code in vllm/models/kimi_k3/amd/specialized_decode/runtime/decode.py

def step_reason(
    attn_metadata, kda_prefix: str, width: int, q: int, mla_prefix: str = ""
) -> str | None:
    """None if a step's KDA and MLA metadata (identical on every TP rank) are a
    pure, uniform regular-decode or spec-verify step filling `width` rows, else
    why not. `q` is the deployment's configured rows per request."""
    if not isinstance(attn_metadata, dict):
        return "no metadata"
    m: Any = attn_metadata.get(kda_prefix)
    if m is None:
        return "no metadata"
    if m.num_prefills:
        return "not pure decode"
    if m.num_spec_decodes:
        if m.num_decodes:
            return "not pure decode"
        if m.spec_sequence_masks is None:
            return "not spec-verify"
        actual_q = m.spec_state_indices_tensor.size(-1)
        if (
            actual_q != q
            or m.uniform_spec_sequence_length != actual_q
            or m.num_spec_decodes * actual_q != width
        ):
            return "not uniform"
    else:
        if q != 1:
            return "not spec-verify"
        if m.num_decodes < 1:
            return "not pure decode"
        state_indices = m.non_spec_state_indices_tensor
        if (
            state_indices is None
            or state_indices.ndim != 1
            or m.num_decodes != width
            or state_indices.size(0) != width
        ):
            return "not uniform"
    if mla_prefix:
        a: Any = attn_metadata.get(mla_prefix)
        if a is None or a.decode is None:
            return "no MLA decode metadata"
        if a.num_prefills or a.num_decode_tokens != width:
            return "MLA: not pure decode"
        if a.decode.max_qo_len != q:
            return "MLA: not uniform"
        # Gluon serves bf16 caches only; M1a hands AITER an fp8 query
        if getattr(a.decode, "use_gluon_decode", False) or getattr(
            a.decode, "use_gluon_verify", False
        ):
            return "MLA: Gluon decode"
    return None