[Model] Introduce Kimi Linear to vLLM (#27809)

Signed-off-by: lizhiyuan <lizhiyuan@moonshot.cn>
Signed-off-by: Zhiyuan Li <uniartisan2017@gmail.com>

docs/models/supported_models.md

@@ -382,6 +382,7 @@ th {
 | `InternLM3ForCausalLM` | InternLM3 | `internlm/internlm3-8b-instruct`, etc. | ✅︎ | ✅︎ |
 | `JAISLMHeadModel` | Jais | `inceptionai/jais-13b`, `inceptionai/jais-13b-chat`, `inceptionai/jais-30b-v3`, `inceptionai/jais-30b-chat-v3`, etc. | | ✅︎ |
 | `JambaForCausalLM` | Jamba | `ai21labs/AI21-Jamba-1.5-Large`, `ai21labs/AI21-Jamba-1.5-Mini`, `ai21labs/Jamba-v0.1`, etc. | ✅︎ | ✅︎ |
+| `KimiLinearForCausalLM` | Kimi-Linear-48B-A3B-Base, Kimi-Linear-48B-A3B-Instruct | `moonshotai/Kimi-Linear-48B-A3B-Base`, `moonshotai/Kimi-Linear-48B-A3B-Instruct` | | ✅︎ |
 | `Lfm2ForCausalLM`  | LFM2  | `LiquidAI/LFM2-1.2B`, `LiquidAI/LFM2-700M`, `LiquidAI/LFM2-350M`, etc. | ✅︎ | ✅︎ |
 | `Lfm2MoeForCausalLM`  | LFM2MoE  | `LiquidAI/LFM2-8B-A1B-preview`, etc. | ✅︎ | ✅︎ |
 | `LlamaForCausalLM` | Llama 3.1, Llama 3, Llama 2, LLaMA, Yi | `meta-llama/Meta-Llama-3.1-405B-Instruct`, `meta-llama/Meta-Llama-3.1-70B`, `meta-llama/Meta-Llama-3-70B-Instruct`, `meta-llama/Llama-2-70b-hf`, `01-ai/Yi-34B`, etc. | ✅︎ | ✅︎ |

tests/models/registry.py

@@ -296,6 +296,9 @@ _TEXT_GENERATION_EXAMPLE_MODELS = {
             "random": "ai21labs/Jamba-tiny-random",
         },
     ),
+    "KimiLinearForCausalLM": _HfExamplesInfo(
+        "moonshotai/Kimi-Linear-48B-A3B-Instruct", trust_remote_code=True
+    ),
     "Lfm2ForCausalLM": _HfExamplesInfo("LiquidAI/LFM2-1.2B"),
     "Lfm2MoeForCausalLM": _HfExamplesInfo(
         "LiquidAI/LFM2-8B-A1B", min_transformers_version="4.58"

vllm/config/compilation.py

@@ -453,6 +453,7 @@ class CompilationConfig:
         "vllm::linear_attention",
         "vllm::plamo2_mamba_mixer",
         "vllm::gdn_attention",
+        "vllm::kda_attention",
         "vllm::sparse_attn_indexer",
     ]
 

vllm/config/model.py

@@ -1236,6 +1236,7 @@ class ModelConfig:
             "deepseek_v32",
             "deepseek_mtp",
             "kimi_k2",
+            "kimi_linear",
             "longcat_flash",
         ):
             return self.hf_text_config.kv_lora_rank is not None

vllm/model_executor/layers/fla/ops/kda.py

@@ -1304,7 +1304,7 @@ def kda_gate_fwd_kernel(
     tl.store(y_ptr, b_y.to(y.dtype.element_ty), boundary_check=(0, 1))
 
 
-def kda_gate_fwd(
+def fused_kda_gate(
     g: torch.Tensor,
     A: torch.Tensor,
     head_k_dim: int,

vllm/model_executor/layers/kda.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import torch
+from einops import rearrange
+from torch import nn
+
+from vllm.attention import AttentionBackend
+from vllm.attention.backends.abstract import AttentionMetadata
+from vllm.config import CacheConfig, ModelConfig, get_current_vllm_config
+from vllm.distributed import (
+    divide,
+    get_tensor_model_parallel_rank,
+    get_tensor_model_parallel_world_size,
+)
+from vllm.forward_context import ForwardContext, get_forward_context
+from vllm.logger import init_logger
+from vllm.model_executor.model_loader.weight_utils import sharded_weight_loader
+from vllm.model_executor.utils import set_weight_attrs
+from vllm.utils.torch_utils import direct_register_custom_op
+from vllm.v1.attention.backends.gdn_attn import GDNAttentionMetadata
+
+from .fla.ops.kda import (
+    FusedRMSNormGated,
+    chunk_kda,
+    fused_kda_gate,
+    fused_recurrent_kda,
+)
+from .linear import (
+    ColumnParallelLinear,
+    ReplicatedLinear,
+    RowParallelLinear,
+)
+from .mamba.abstract import MambaBase
+from .mamba.mamba_utils import MambaStateDtypeCalculator, MambaStateShapeCalculator
+from .mamba.ops.causal_conv1d import causal_conv1d_fn, causal_conv1d_update
+from .quantization.base_config import QuantizationConfig
+
+logger = init_logger(__name__)
+
+
+def kda_attention(
+    hidden_states: torch.Tensor,
+    output: torch.Tensor,
+    layer_name: str,
+) -> None:
+    forward_context: ForwardContext = get_forward_context()
+    self = forward_context.no_compile_layers[layer_name]
+    self._forward(hidden_states=hidden_states, output=output)
+
+
+def kda_attention_fake(
+    hidden_states: torch.Tensor,
+    output: torch.Tensor,
+    layer_name: str,
+) -> None:
+    return
+
+
+direct_register_custom_op(
+    op_name="kda_attention",
+    op_func=kda_attention,
+    mutates_args=["output"],
+    fake_impl=kda_attention_fake,
+)
+
+
+class KimiDeltaAttention(nn.Module, MambaBase):
+    @property
+    def mamba_type(self) -> str:
+        return "linear_attention"
+
+    def get_attn_backend(self) -> type["AttentionBackend"]:
+        from vllm.v1.attention.backends.gdn_attn import GDNAttentionBackend
+
+        return GDNAttentionBackend
+
+    def get_state_dtype(
+        self,
+    ) -> tuple[torch.dtype, torch.dtype, torch.dtype, torch.dtype]:
+        if self.model_config is None or self.cache_config is None:
+            raise ValueError("model_config and cache_config must be set")
+        return MambaStateDtypeCalculator.kda_state_dtype(
+            self.model_config.dtype, self.cache_config.mamba_cache_dtype
+        )
+
+    def get_state_shape(
+        self,
+    ) -> tuple[tuple[int, ...], tuple[int, ...], tuple[int, ...], tuple[int, ...]]:
+        return MambaStateShapeCalculator.kda_state_shape(
+            self.tp_size, self.num_heads, self.head_dim, conv_kernel_size=self.conv_size
+        )
+
+    def __init__(
+        self,
+        layer_idx: int,
+        hidden_size: int,
+        quant_config: QuantizationConfig | None = None,
+        cache_config: CacheConfig | None = None,
+        model_config: ModelConfig | None = None,
+        rms_norm_eps: float = 1e-5,
+        prefix: str = "",
+        **kwargs,
+    ) -> None:
+        super().__init__()
+        self.tp_size = get_tensor_model_parallel_world_size()
+        self.tp_rank = get_tensor_model_parallel_rank()
+        self.hidden_size = hidden_size
+        self.model_config = model_config
+        self.cache_config = cache_config
+        if model_config is None:
+            raise ValueError("model_config must be provided")
+        kda_config = model_config.linear_attn_config
+        self.head_dim = kda_config["head_dim"]
+        self.num_heads = kda_config["num_heads"]
+        self.layer_idx = layer_idx
+        self.prefix = prefix
+        assert self.num_heads % self.tp_size == 0
+        self.local_num_heads = divide(self.num_heads, self.tp_size)
+
+        projection_size = self.head_dim * self.num_heads
+        self.conv_size = kda_config["short_conv_kernel_size"]
+
+        self.q_proj = ColumnParallelLinear(
+            self.hidden_size,
+            projection_size,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.q_proj",
+        )
+        self.k_proj = ColumnParallelLinear(
+            self.hidden_size,
+            projection_size,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.k_proj",
+        )
+        self.v_proj = ColumnParallelLinear(
+            self.hidden_size,
+            projection_size,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.v_proj",
+        )
+
+        self.f_a_proj = ReplicatedLinear(
+            self.hidden_size,
+            self.head_dim,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.f_a_proj",
+        )
+
+        self.f_b_proj = ColumnParallelLinear(
+            self.head_dim,
+            projection_size,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.f_b_proj",
+        )
+        self.dt_bias = nn.Parameter(
+            torch.empty(divide(projection_size, self.tp_size), dtype=torch.float32)
+        )
+
+        set_weight_attrs(self.dt_bias, {"weight_loader": sharded_weight_loader(0)})
+
+        self.b_proj = ColumnParallelLinear(
+            self.hidden_size,
+            self.num_heads,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.b_proj",
+        )
+
+        self.q_conv1d = ColumnParallelLinear(
+            input_size=self.conv_size,
+            output_size=projection_size,
+            bias=False,
+            params_dtype=torch.float32,
+            prefix=f"{prefix}.q_conv1d",
+        )
+        self.k_conv1d = ColumnParallelLinear(
+            input_size=self.conv_size,
+            output_size=projection_size,
+            bias=False,
+            params_dtype=torch.float32,
+            prefix=f"{prefix}.k_conv1d",
+        )
+        self.v_conv1d = ColumnParallelLinear(
+            input_size=self.conv_size,
+            output_size=projection_size,
+            bias=False,
+            params_dtype=torch.float32,
+            prefix=f"{prefix}.v_conv1d",
+        )
+        # unsqueeze to fit conv1d weights shape into the linear weights shape.
+        # Can't do this in `weight_loader` since it already exists in
+        # `ColumnParallelLinear` and `set_weight_attrs`
+        # doesn't allow to override it
+        self.q_conv1d.weight.data = self.q_conv1d.weight.data.unsqueeze(1)
+        self.k_conv1d.weight.data = self.k_conv1d.weight.data.unsqueeze(1)
+        self.v_conv1d.weight.data = self.v_conv1d.weight.data.unsqueeze(1)
+
+        self.A_log = nn.Parameter(
+            torch.empty(1, 1, self.local_num_heads, 1, dtype=torch.float32)
+        )
+        set_weight_attrs(self.A_log, {"weight_loader": sharded_weight_loader(2)})
+
+        self.g_a_proj = ReplicatedLinear(
+            self.hidden_size,
+            self.head_dim,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.g_a_proj",
+        )
+        self.g_b_proj = ColumnParallelLinear(
+            self.head_dim,
+            projection_size,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.g_b_proj",
+        )
+        self.o_norm = FusedRMSNormGated(
+            self.head_dim, eps=rms_norm_eps, activation="sigmoid"
+        )
+        self.o_proj = RowParallelLinear(
+            projection_size,
+            self.hidden_size,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.o_proj",
+        )
+
+        compilation_config = get_current_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
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        positions: torch.Tensor,
+        output: torch.Tensor,
+    ) -> None:
+        return torch.ops.vllm.kda_attention(
+            hidden_states,
+            output,
+            self.prefix,
+        )
+
+    def _forward(
+        self,
+        hidden_states: torch.Tensor,
+        output: torch.Tensor,
+    ) -> None:
+        forward_context = get_forward_context()
+        attn_metadata: AttentionMetadata = forward_context.attn_metadata
+
+        if attn_metadata is None:
+            # V1 profile run
+            # Mimic the memory allocation in the real run
+            q = torch.empty_like(hidden_states)
+            k = torch.empty_like(hidden_states)
+            v = torch.empty_like(hidden_states)
+            g = hidden_states.new_empty(
+                hidden_states.size(0),
+                self.local_num_heads,
+                self.head_dim,
+                dtype=torch.float32,
+            )
+            beta = torch.empty(
+                hidden_states.size(0), self.local_num_heads, dtype=torch.float32
+            )
+            core_attn_out = torch.empty_like(hidden_states)
+            return
+
+        assert isinstance(attn_metadata, dict)
+        attn_metadata = attn_metadata[self.prefix]
+        assert isinstance(attn_metadata, GDNAttentionMetadata)
+        has_initial_state = attn_metadata.has_initial_state
+        non_spec_query_start_loc = attn_metadata.non_spec_query_start_loc
+        non_spec_state_indices_tensor = attn_metadata.non_spec_state_indices_tensor  # noqa: E501
+        constant_caches = self.kv_cache[forward_context.virtual_engine]
+
+        (conv_state_q, conv_state_k, conv_state_v, recurrent_state) = constant_caches
+        # deal with strides
+        conv_state_q = conv_state_q.transpose(-1, -2)
+        conv_state_k = conv_state_k.transpose(-1, -2)
+        conv_state_v = conv_state_v.transpose(-1, -2)
+
+        q_proj_states = self.q_proj(hidden_states)[0]
+        k_proj_states = self.k_proj(hidden_states)[0]
+        v_proj_states = self.v_proj(hidden_states)[0]
+
+        q_conv_weights = self.q_conv1d.weight.view(
+            self.q_conv1d.weight.size(0), self.q_conv1d.weight.size(2)
+        )
+        k_conv_weights = self.k_conv1d.weight.view(
+            self.k_conv1d.weight.size(0), self.k_conv1d.weight.size(2)
+        )
+        v_conv_weights = self.v_conv1d.weight.view(
+            self.v_conv1d.weight.size(0), self.v_conv1d.weight.size(2)
+        )
+        if attn_metadata.num_prefills > 0:
+            q_proj_states = q_proj_states.transpose(0, 1)
+            k_proj_states = k_proj_states.transpose(0, 1)
+            v_proj_states = v_proj_states.transpose(0, 1)
+            q = causal_conv1d_fn(
+                q_proj_states,
+                q_conv_weights,
+                self.q_conv1d.bias,
+                activation="silu",
+                conv_states=conv_state_q,
+                has_initial_state=has_initial_state,
+                cache_indices=non_spec_state_indices_tensor,
+                query_start_loc=non_spec_query_start_loc,
+                metadata=attn_metadata,
+            ).transpose(0, 1)
+            k = causal_conv1d_fn(
+                k_proj_states,
+                k_conv_weights,
+                self.k_conv1d.bias,
+                activation="silu",
+                conv_states=conv_state_k,
+                has_initial_state=has_initial_state,
+                cache_indices=non_spec_state_indices_tensor,
+                query_start_loc=non_spec_query_start_loc,
+                metadata=attn_metadata,
+            ).transpose(0, 1)
+            v = causal_conv1d_fn(
+                v_proj_states,
+                v_conv_weights,
+                self.v_conv1d.bias,
+                activation="silu",
+                conv_states=conv_state_v,
+                has_initial_state=has_initial_state,
+                cache_indices=non_spec_state_indices_tensor,
+                query_start_loc=non_spec_query_start_loc,
+                metadata=attn_metadata,
+            ).transpose(0, 1)
+        else:
+            decode_conv_indices = non_spec_state_indices_tensor[
+                : attn_metadata.num_decodes
+            ]
+            q = causal_conv1d_update(
+                q_proj_states,
+                conv_state_q,
+                q_conv_weights,
+                self.q_conv1d.bias,
+                activation="silu",
+                conv_state_indices=decode_conv_indices,
+                validate_data=True,
+            )
+            k = causal_conv1d_update(
+                k_proj_states,
+                conv_state_k,
+                k_conv_weights,
+                self.k_conv1d.bias,
+                activation="silu",
+                conv_state_indices=decode_conv_indices,
+                validate_data=True,
+            )
+            v = causal_conv1d_update(
+                v_proj_states,
+                conv_state_v,
+                v_conv_weights,
+                self.v_conv1d.bias,
+                activation="silu",
+                conv_state_indices=decode_conv_indices,
+                validate_data=True,
+            )
+
+        q, k, v = map(
+            lambda x: rearrange(x, "n (h d) -> 1 n h d", d=self.head_dim), (q, k, v)
+        )
+
+        beta = self.b_proj(hidden_states)[0].float().sigmoid()
+
+        g = self.f_b_proj(self.f_a_proj(hidden_states)[0])[0]
+        g = fused_kda_gate(g, self.A_log, self.head_dim, g_bias=self.dt_bias)
+
+        beta = beta.unsqueeze(0)
+        g = g.unsqueeze(0)
+
+        if attn_metadata.num_prefills > 0:
+            zero_idx = non_spec_state_indices_tensor[~has_initial_state]
+            recurrent_state[zero_idx] = 0
+            initial_state = recurrent_state[non_spec_state_indices_tensor].contiguous()
+            (
+                core_attn_out_non_spec,
+                last_recurrent_state,
+            ) = chunk_kda(
+                q=q,
+                k=k,
+                v=v,
+                g=g,
+                beta=beta,
+                initial_state=initial_state,
+                output_final_state=True,
+                use_qk_l2norm_in_kernel=True,
+                cu_seqlens=non_spec_query_start_loc,
+            )
+            # Init cache
+            recurrent_state[non_spec_state_indices_tensor] = last_recurrent_state
+        else:
+            (
+                core_attn_out_non_spec,
+                last_recurrent_state,
+            ) = fused_recurrent_kda(
+                q=q,
+                k=k,
+                v=v,
+                g=g,
+                beta=beta,
+                initial_state=recurrent_state,
+                use_qk_l2norm_in_kernel=True,
+                cu_seqlens=non_spec_query_start_loc,
+                ssm_state_indices=non_spec_state_indices_tensor,
+            )
+
+        g_proj_states = self.g_b_proj(self.g_a_proj(hidden_states)[0])[0]
+        g = rearrange(g_proj_states, "... (h d) -> ... h d", d=self.head_dim)
+        core_attn_out = self.o_norm(core_attn_out_non_spec, g)
+        core_attn_out = rearrange(core_attn_out, "1 n h d -> n (h d)")
+
+        output[:] = self.o_proj(core_attn_out)[0]

vllm/model_executor/layers/mamba/mamba_utils.py

@@ -80,6 +80,15 @@ class MambaStateDtypeCalculator:
         state_dtype = get_kv_cache_torch_dtype(mamba_cache_dtype, model_dtype)
         return (state_dtype, state_dtype)
 
+    @classmethod
+    def kda_state_dtype(
+        cls,
+        model_dtype: ModelDType | torch.dtype,
+        mamba_cache_dtype: MambaDType,
+    ):
+        state_dtype = get_kv_cache_torch_dtype(mamba_cache_dtype, model_dtype)
+        return (state_dtype, state_dtype, state_dtype, torch.float32)
+
 
 class MambaStateShapeCalculator:
     @classmethod
@@ -182,3 +191,35 @@ class MambaStateShapeCalculator:
             head_v_dim,
         )
         return conv_state_shape, temporal_state_shape
+
+    @classmethod
+    def kda_state_shape(
+        cls,
+        tp_world_size: int,
+        num_heads: int,
+        head_dim: int,
+        num_k_heads: int | None = None,
+        head_k_dim: int | None = None,
+        conv_kernel_size: int = 4,
+        num_spec: int = 0,
+    ) -> tuple[tuple[int, int], tuple[int, int], tuple[int, int], tuple[int, int, int]]:
+        if num_k_heads is None:
+            num_k_heads = num_heads
+        if head_k_dim is None:
+            head_k_dim = head_dim
+
+        proj_size = num_heads * head_dim
+        proj_k_size = num_k_heads * head_k_dim
+
+        conv_state_shape = (divide(proj_size, tp_world_size), conv_kernel_size - 1)
+        conv_state_k_shape = (divide(proj_k_size, tp_world_size), conv_kernel_size - 1)
+        recurrent_state_shape = (divide(num_heads, tp_world_size), head_dim, head_dim)
+
+        conv_state_shape = conv_state_shape[1], conv_state_shape[0]
+        conv_state_k_shape = conv_state_k_shape[1], conv_state_k_shape[0]
+        return (
+            conv_state_shape,
+            conv_state_k_shape,
+            conv_state_k_shape,
+            recurrent_state_shape,
+        )

vllm/model_executor/layers/mla.py

@@ -147,9 +147,10 @@ class MultiHeadLatentAttentionWrapper(CustomOp):
         # Add head dim of 1 to k_pe
         k_pe = k_pe.unsqueeze(1)
 
-        q[..., self.qk_nope_head_dim :], k_pe = self.rotary_emb(
-            positions, q[..., self.qk_nope_head_dim :], k_pe
-        )
+        if self.rotary_emb is not None:
+            q[..., self.qk_nope_head_dim :], k_pe = self.rotary_emb(
+                positions, q[..., self.qk_nope_head_dim :], k_pe
+            )
 
         if self.indexer and self.is_sparse:
             _topk_indices = self.indexer(hidden_states, q_c, positions, self.rotary_emb)

vllm/model_executor/models/config.py

 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 from copy import deepcopy
+from math import lcm
 from typing import TYPE_CHECKING
 
 import vllm.envs as envs
@@ -8,7 +9,7 @@ from vllm.logger import init_logger
 from vllm.model_executor.models import ModelRegistry
 from vllm.utils.math_utils import cdiv, round_up
 from vllm.utils.torch_utils import STR_DTYPE_TO_TORCH_DTYPE
-from vllm.v1.kv_cache_interface import FullAttentionSpec, MambaSpec
+from vllm.v1.kv_cache_interface import FullAttentionSpec, MambaSpec, MLAAttentionSpec
 
 if TYPE_CHECKING:
     from vllm.config import VllmConfig
@@ -347,12 +348,28 @@ class HybridAttentionMambaModelConfig(VerifyAndUpdateConfig):
             kv_cache_dtype = STR_DTYPE_TO_TORCH_DTYPE[cache_config.cache_dtype]
 
         # get attention page size (for 1 token)
-        attn_page_size_1_token = FullAttentionSpec(
-            block_size=1,
-            num_kv_heads=model_config.get_num_kv_heads(parallel_config),
-            head_size=model_config.get_head_size(),
-            dtype=kv_cache_dtype,
-        ).page_size_bytes
+        # Attention backend constraints:
+        # - FlashAttention (FA) requires block size to be multiple of 16
+        # - MLA (Multi-head Latent Attention) requires larger alignment:
+        #   * CUTLASS_MLA backend: kernel_block_size 128 alignment
+        #   * Other MLA backends: kernel_block_size 64 alignment
+        if model_config.use_mla:
+            use_cutlass_mla = envs.VLLM_ATTENTION_BACKEND == "CUTLASS_MLA"
+            kernel_block_alignment_size = 128 if use_cutlass_mla else 64
+            attn_page_size_1_token = MLAAttentionSpec(
+                block_size=1,
+                num_kv_heads=model_config.get_num_kv_heads(parallel_config),
+                head_size=model_config.get_head_size(),
+                dtype=kv_cache_dtype,
+            ).page_size_bytes
+        else:
+            kernel_block_alignment_size = 16
+            attn_page_size_1_token = FullAttentionSpec(
+                block_size=1,
+                num_kv_heads=model_config.get_num_kv_heads(parallel_config),
+                head_size=model_config.get_head_size(),
+                dtype=kv_cache_dtype,
+            ).page_size_bytes
 
         model_cls, _ = ModelRegistry.resolve_model_cls(
             model_config.architecture,
@@ -372,17 +389,6 @@ class HybridAttentionMambaModelConfig(VerifyAndUpdateConfig):
         if mamba_page_size == 0:
             return
 
-        # Attention backend constraints:
-        # - FlashAttention (FA) requires block size to be multiple of 16
-        # - MLA (Multi-head Latent Attention) requires larger alignment:
-        #   * CUTLASS_MLA backend: 128-byte alignment
-        #   * Other MLA backends: 64-byte alignment
-        if model_config.use_mla:
-            use_cutlass_mla = envs.VLLM_ATTENTION_BACKEND == "CUTLASS_MLA"
-            kernel_block_alignment_size = 128 if use_cutlass_mla else 64
-        else:
-            kernel_block_alignment_size = 16
-
         if cache_config.enable_prefix_caching:
             # With prefix caching, select attention block size to
             # optimize for mamba kernel performance
@@ -400,15 +406,8 @@ class HybridAttentionMambaModelConfig(VerifyAndUpdateConfig):
             # easily by changing the way we layout chunks in the
             # mamba2 kernels.
 
-            from math import gcd
-
-            def lcm(a, b):
-                return a * b // gcd(a, b)
-
-            base_chunk_size = mamba_block_size or model_config.get_mamba_chunk_size()
-
+            base_chunk_size = model_config.get_mamba_chunk_size()
             attn_tokens_per_mamba_state = cdiv(mamba_page_size, attn_page_size_1_token)
-
             chunk_size = lcm(base_chunk_size, kernel_block_alignment_size)
             attn_block_size = chunk_size * cdiv(attn_tokens_per_mamba_state, chunk_size)
             cache_config.mamba_block_size = attn_block_size

vllm/model_executor/models/kimi_linear.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from collections.abc import Iterable
+from typing import Any
+
+import torch
+from torch import nn
+
+from vllm.compilation.decorators import support_torch_compile
+from vllm.config import CacheConfig, ModelConfig, ParallelConfig, VllmConfig
+from vllm.distributed import (
+    get_pp_group,
+    get_tensor_model_parallel_world_size,
+    tensor_model_parallel_all_reduce,
+)
+from vllm.logger import init_logger
+from vllm.model_executor.layers.activation import SiluAndMul
+from vllm.model_executor.layers.fused_moe import FusedMoE
+from vllm.model_executor.layers.kda import KimiDeltaAttention
+from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.model_executor.layers.linear import (
+    ColumnParallelLinear,
+    MergedColumnParallelLinear,
+    QKVParallelLinear,
+    ReplicatedLinear,
+    RowParallelLinear,
+)
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.mamba.mamba_utils import (
+    MambaStateDtypeCalculator,
+    MambaStateShapeCalculator,
+)
+from vllm.model_executor.layers.mla import MLAModules, MultiHeadLatentAttentionWrapper
+from vllm.model_executor.layers.quantization.base_config import QuantizationConfig
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    ParallelLMHead,
+    VocabParallelEmbedding,
+)
+from vllm.model_executor.model_loader.weight_utils import (
+    default_weight_loader,
+    maybe_remap_kv_scale_name,
+)
+from vllm.sequence import IntermediateTensors
+from vllm.transformers_utils.configs.kimi_linear import KimiLinearConfig
+
+from .interfaces import HasInnerState, IsHybrid, MixtureOfExperts, SupportsPP
+from .utils import (
+    PPMissingLayer,
+    is_pp_missing_parameter,
+    make_layers,
+    maybe_prefix,
+)
+
+logger = init_logger(__name__)
+
+
+class KimiMLP(nn.Module):
+    def __init__(
+        self,
+        hidden_size: int,
+        intermediate_size: int,
+        hidden_act: str,
+        quant_config: QKVParallelLinear | None = None,
+        reduce_results: bool = True,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+
+        self.gate_up_proj = MergedColumnParallelLinear(
+            hidden_size,
+            [intermediate_size] * 2,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.gate_up_proj",
+        )
+        self.down_proj = RowParallelLinear(
+            intermediate_size,
+            hidden_size,
+            bias=False,
+            quant_config=quant_config,
+            reduce_results=reduce_results,
+            prefix=f"{prefix}.down_proj",
+        )
+        if hidden_act != "silu":
+            raise ValueError(
+                f"Unsupported activation: {hidden_act}. Only silu is supported for now."
+            )
+        self.act_fn = SiluAndMul()
+
+    def forward(self, x):
+        gate_up, _ = self.gate_up_proj(x)
+        x = self.act_fn(gate_up)
+        x, _ = self.down_proj(x)
+        return x
+
+
+class KimiMoE(nn.Module):
+    def __init__(
+        self,
+        config: KimiLinearConfig,
+        quant_config: QuantizationConfig | None = None,
+        prefix: str = "",
+        layer_idx: int = 0,
+    ):
+        super().__init__()
+        hidden_size = config.hidden_size
+        intermediate_size = config.intermediate_size
+        moe_intermediate_size = config.moe_intermediate_size
+        num_experts = config.num_experts
+        moe_renormalize = config.moe_renormalize
+        self.tp_size = get_tensor_model_parallel_world_size()
+        self.routed_scaling_factor = config.routed_scaling_factor
+        self.num_shared_experts = config.num_shared_experts
+        self.layer_idx = layer_idx
+
+        if config.hidden_act != "silu":
+            raise ValueError(
+                f"Unsupported activation: {config.hidden_act}. "
+                "Only silu is supported for now."
+            )
+
+        # Gate always runs at half / full precision for now.
+        self.gate = ReplicatedLinear(
+            hidden_size,
+            num_experts,
+            bias=False,
+            quant_config=None,
+            prefix=f"{prefix}.gate",
+        )
+
+        self.gate.e_score_correction_bias = nn.Parameter(torch.empty(num_experts))
+
+        self.experts = FusedMoE(
+            num_experts=num_experts,
+            top_k=config.num_experts_per_token,
+            hidden_size=hidden_size,
+            intermediate_size=moe_intermediate_size,
+            reduce_results=False,
+            renormalize=moe_renormalize,
+            quant_config=quant_config,
+            use_grouped_topk=config.use_grouped_topk,
+            num_expert_group=config.num_expert_group,
+            topk_group=config.topk_group,
+            prefix=f"{prefix}.experts",
+            scoring_func=config.moe_router_activation_func,
+            e_score_correction_bias=self.gate.e_score_correction_bias,
+        )
+
+        if self.num_shared_experts is not None:
+            intermediate_size = moe_intermediate_size * self.num_shared_experts
+            self.shared_experts = KimiMLP(
+                hidden_size=config.hidden_size,
+                intermediate_size=intermediate_size,
+                hidden_act=config.hidden_act,
+                quant_config=quant_config,
+                reduce_results=False,
+            )
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        num_tokens, hidden_size = hidden_states.shape
+        hidden_states = hidden_states.view(-1, hidden_size)
+        if self.num_shared_experts is not None:
+            shared_output = self.shared_experts(hidden_states)
+        router_logits, _ = self.gate(hidden_states)
+        final_hidden_states = (
+            self.experts(hidden_states=hidden_states, router_logits=router_logits)
+            * self.routed_scaling_factor
+        )
+        if shared_output is not None:
+            final_hidden_states = final_hidden_states + shared_output
+
+        if self.tp_size > 1:
+            final_hidden_states = tensor_model_parallel_all_reduce(final_hidden_states)
+        return final_hidden_states.view(num_tokens, hidden_size)
+
+
+class KimiMLAAttention(nn.Module):
+    """
+    Main reference: DeepseekV2 vllm Implementation
+    """
+
+    def __init__(
+        self,
+        config: KimiLinearConfig,
+        hidden_size: int,
+        num_heads: int,
+        qk_nope_head_dim: int,
+        qk_rope_head_dim: int,
+        v_head_dim: int,
+        q_lora_rank: int | None,
+        kv_lora_rank: int,
+        rope_theta: float = 10000,
+        use_nope: bool = False,
+        rope_scaling: dict[str, Any] | None = None,
+        cache_config: CacheConfig | None = None,
+        quant_config: QuantizationConfig | None = None,
+        prefix: str = "",
+        **kwargs,
+    ) -> None:
+        super().__init__()
+        self.hidden_size = hidden_size
+        self.qk_nope_head_dim = qk_nope_head_dim
+        self.qk_rope_head_dim = qk_rope_head_dim
+        self.qk_head_dim = qk_nope_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.num_heads = num_heads
+        tp_size = get_tensor_model_parallel_world_size()
+        self.num_local_heads = num_heads // tp_size
+        self.scaling = self.qk_head_dim**-0.5
+        self.rope_theta = rope_theta
+        self.use_nope = use_nope
+        assert self.use_nope is True
+        assert self.q_lora_rank is None
+        assert rope_scaling is None
+        assert num_heads % tp_size == 0
+        self.kv_a_proj_with_mqa = ReplicatedLinear(
+            self.hidden_size,
+            self.kv_lora_rank + self.qk_rope_head_dim,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.kv_a_proj_with_mqa",
+        )
+        self.q_proj = ColumnParallelLinear(
+            self.hidden_size,
+            self.num_heads * self.qk_head_dim,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.q_proj",
+        )
+        self.kv_a_layernorm = RMSNorm(
+            self.kv_lora_rank,
+            eps=config.rms_norm_eps,
+        )
+        self.kv_b_proj = ColumnParallelLinear(
+            self.kv_lora_rank,
+            self.num_heads * (self.qk_nope_head_dim + self.v_head_dim),
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.kv_b_proj",
+        )
+        self.o_proj = RowParallelLinear(
+            self.num_heads * self.v_head_dim,
+            self.hidden_size,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.o_proj",
+        )
+
+        mla_modules = MLAModules(
+            kv_a_layernorm=self.kv_a_layernorm,
+            kv_b_proj=self.kv_b_proj,
+            rotary_emb=None,
+            o_proj=self.o_proj,
+            fused_qkv_a_proj=None,
+            kv_a_proj_with_mqa=self.kv_a_proj_with_mqa,
+            q_a_layernorm=None,
+            q_b_proj=None,
+            q_proj=self.q_proj,
+            indexer=None,
+            is_sparse=False,
+            topk_indices_buffer=None,
+        )
+        self.mla_attn = MultiHeadLatentAttentionWrapper(
+            self.hidden_size,
+            self.num_local_heads,
+            self.scaling,
+            self.qk_nope_head_dim,
+            self.qk_rope_head_dim,
+            self.v_head_dim,
+            self.q_lora_rank,
+            self.kv_lora_rank,
+            mla_modules,
+            cache_config,
+            quant_config,
+            prefix,
+        )
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        output: torch.Tensor,
+    ) -> None:
+        output[:] = self.mla_attn(positions, hidden_states)
+
+
+class KimiDecoderLayer(nn.Module):
+    def __init__(
+        self,
+        config: KimiLinearConfig,
+        layer_idx: int,
+        cache_config: CacheConfig | None = None,
+        quant_config: QuantizationConfig | None = None,
+        parallel_config: ParallelConfig | None = None,
+        model_config: ModelConfig | None = None,
+        prefix: str = "",
+        **kwargs,
+    ) -> None:
+        super().__init__()
+        self.hidden_size = config.hidden_size
+
+        self.is_moe = config.is_moe
+
+        if config.is_kda_layer(layer_idx):
+            self.self_attn = KimiDeltaAttention(
+                layer_idx=layer_idx,
+                hidden_size=config.hidden_size,
+                quant_config=quant_config,
+                cache_config=cache_config,
+                model_config=config,
+                prefix=f"{prefix}.self_attn",
+            )
+        else:
+            self.self_attn = KimiMLAAttention(
+                layer_idx=layer_idx,
+                hidden_size=self.hidden_size,
+                num_heads=config.num_attention_heads,
+                quant_config=quant_config,
+                cache_config=cache_config,
+                model_config=model_config,
+                prefix=f"{prefix}.self_attn",
+                config=config,
+                qk_nope_head_dim=config.qk_nope_head_dim,
+                qk_rope_head_dim=config.qk_rope_head_dim,
+                v_head_dim=config.v_head_dim,
+                q_lora_rank=config.q_lora_rank,
+                kv_lora_rank=config.kv_lora_rank,
+                use_nope=config.mla_use_nope,
+            )
+
+        if (
+            self.is_moe
+            and config.num_experts is not None
+            and layer_idx >= config.first_k_dense_replace
+            and layer_idx % config.moe_layer_freq == 0
+        ):
+            self.block_sparse_moe = KimiMoE(
+                config=config,
+                quant_config=quant_config,
+                prefix=f"{prefix}.mlp",
+            )
+            self.mlp = self.block_sparse_moe
+        else:
+            self.mlp = KimiMLP(
+                hidden_size=self.hidden_size,
+                intermediate_size=config.intermediate_size,
+                hidden_act=config.hidden_act,
+                quant_config=quant_config,
+                prefix=f"{prefix}.mlp",
+            )
+        self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = RMSNorm(
+            config.hidden_size, eps=config.rms_norm_eps
+        )
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        residual: torch.Tensor | None,
+        **kwargs,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        # Self Attention
+        if residual is None:
+            residual = hidden_states
+            hidden_states = self.input_layernorm(hidden_states)
+        else:
+            hidden_states, residual = self.input_layernorm(hidden_states, residual)
+
+        attn_output = torch.empty_like(hidden_states)
+        self.self_attn(
+            hidden_states=hidden_states,
+            positions=positions,
+            output=attn_output,
+        )
+        hidden_states = attn_output
+
+        # Fully Connected
+        hidden_states, residual = self.post_attention_layernorm(hidden_states, residual)
+        hidden_states = self.mlp(hidden_states)
+        return hidden_states, residual
+
+
+@support_torch_compile
+class KimiLinearModel(nn.Module):
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+
+        config = vllm_config.model_config.hf_text_config
+        model_config = vllm_config.model_config
+        cache_config = vllm_config.cache_config
+        quant_config = vllm_config.quant_config
+        parallel_config = vllm_config.parallel_config
+        self.config = config
+
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+
+        if get_pp_group().is_first_rank:
+            self.embed_tokens = VocabParallelEmbedding(
+                config.vocab_size,
+                config.hidden_size,
+                prefix=f"{prefix}.embed_tokens",
+            )
+        else:
+            self.embed_tokens = PPMissingLayer()
+
+        extra_kwargs = {}
+
+        def get_layer(prefix: str):
+            layer_idx = int(prefix.rsplit(".", 1)[1])
+            return KimiDecoderLayer(
+                config,
+                layer_idx,
+                cache_config,
+                quant_config,
+                parallel_config,
+                model_config,
+                prefix,
+                **extra_kwargs,
+            )
+
+        self.start_layer, self.end_layer, self.layers = make_layers(
+            config.num_hidden_layers,
+            get_layer,
+            prefix=f"{prefix}.layers",
+        )
+
+        if get_pp_group().is_last_rank:
+            self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        else:
+            self.norm = PPMissingLayer()
+
+        world_size = get_tensor_model_parallel_world_size()
+        assert config.num_attention_heads % world_size == 0, (
+            "num_attention_heads must be divisible by world_size"
+        )
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.embed_tokens(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor | None,
+        positions: torch.Tensor,
+        intermediate_tensors: IntermediateTensors | None,
+        inputs_embeds: torch.Tensor | None = None,
+        **kwargs,
+    ) -> torch.Tensor:
+        if get_pp_group().is_first_rank:
+            if inputs_embeds is not None:
+                hidden_states = inputs_embeds
+            else:
+                hidden_states = self.get_input_embeddings(input_ids)
+            residual = None
+        else:
+            assert intermediate_tensors is not None
+            hidden_states = intermediate_tensors["hidden_states"]
+            residual = intermediate_tensors["residual"]
+
+        for _, layer in enumerate(self.layers[self.start_layer : self.end_layer]):
+            hidden_states, residual = layer(
+                positions=positions,
+                hidden_states=hidden_states,
+                residual=residual,
+            )
+
+        if not get_pp_group().is_last_rank:
+            return IntermediateTensors(
+                {"hidden_states": hidden_states, "residual": residual}
+            )
+
+        hidden_states, _ = self.norm(hidden_states, residual)
+        return hidden_states
+
+
+class KimiLinearForCausalLM(
+    nn.Module, HasInnerState, SupportsPP, MixtureOfExperts, IsHybrid
+):
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        self.model_config = vllm_config.model_config
+        self.vllm_config = vllm_config
+        self.config = self.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        self.quant_config = quant_config
+        self.model = KimiLinearModel(
+            vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")
+        )
+        if get_pp_group().is_last_rank:
+            self.lm_head = ParallelLMHead(
+                self.config.vocab_size,
+                self.config.hidden_size,
+                quant_config=quant_config,
+                prefix=maybe_prefix(prefix, "lm_head"),
+            )
+        else:
+            self.lm_head = PPMissingLayer()
+        logit_scale = getattr(self.config, "logit_scale", 1.0)
+        self.logits_processor = LogitsProcessor(
+            self.config.vocab_size, scale=logit_scale
+        )
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.model.get_input_embeddings(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: IntermediateTensors | None = None,
+        inputs_embeds: torch.Tensor | None = None,
+        **kwargs,
+    ) -> torch.Tensor | IntermediateTensors:
+        hidden_states = self.model(
+            input_ids, positions, intermediate_tensors, inputs_embeds, **kwargs
+        )
+        return hidden_states
+
+    @classmethod
+    def get_mamba_state_dtype_from_config(
+        cls,
+        vllm_config: "VllmConfig",
+    ) -> tuple[torch.dtype, torch.dtype, torch.dtype, torch.dtype]:
+        return MambaStateDtypeCalculator.kda_state_dtype(
+            vllm_config.model_config.dtype, vllm_config.cache_config.mamba_cache_dtype
+        )
+
+    @classmethod
+    def get_mamba_state_shape_from_config(
+        cls, vllm_config: "VllmConfig"
+    ) -> tuple[tuple[int, ...], tuple[int, ...], tuple[int, ...], tuple[int, ...]]:
+        parallel_config = vllm_config.parallel_config
+        hf_config = vllm_config.model_config.hf_config
+        tp_size = parallel_config.tensor_parallel_size
+        num_spec = (
+            vllm_config.speculative_config.num_speculative_tokens
+            if vllm_config.speculative_config
+            else 0
+        )
+        return MambaStateShapeCalculator.kda_state_shape(
+            tp_size,
+            hf_config.linear_attn_config["num_heads"],
+            hf_config.linear_attn_config["head_dim"],
+            conv_kernel_size=hf_config.linear_attn_config["short_conv_kernel_size"],
+            num_spec=num_spec,
+        )
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor | None:
+        return self.logits_processor(self.lm_head, hidden_states)
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            (".gate_up_proj", ".gate_proj", 0),
+            (".gate_up_proj", ".up_proj", 1),
+        ]
+        if self.config.is_moe:
+            # Params for weights, fp8 weight scales, fp8 activation scales
+            # (param_name, weight_name, expert_id, shard_id)
+            expert_params_mapping = FusedMoE.make_expert_params_mapping(
+                ckpt_gate_proj_name="w1",
+                ckpt_down_proj_name="w2",
+                ckpt_up_proj_name="w3",
+                num_experts=self.config.num_experts,
+            )
+        else:
+            expert_params_mapping = []
+        params_dict = dict(self.named_parameters())
+        loaded_params: set[str] = set()
+        for args in weights:
+            name, loaded_weight = args[:2]
+            kwargs = args[2] if len(args) > 2 else {}
+            if "rotary_emb.inv_freq" in name:
+                continue
+
+            spec_layer = get_spec_layer_idx_from_weight_name(self.config, name)
+            if spec_layer is not None:
+                continue  # skip spec decode layers for main model
+            if "rotary_emb.cos_cached" in name or "rotary_emb.sin_cached" in name:
+                # Models trained using ColossalAI may include these tensors in
+                # the checkpoint. Skip them.
+                continue
+            for param_name, weight_name, shard_id in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                # We have mlp.experts[0].gate_proj in the checkpoint.
+                # Since we handle the experts below in expert_params_mapping,
+                # we need to skip here BEFORE we update the name, otherwise
+                # name will be updated to mlp.experts[0].gate_up_proj, which
+                # will then be updated below in expert_params_mapping
+                # for mlp.experts[0].gate_gate_up_proj, which breaks load.
+                if ("mlp.experts." in name) and name not in params_dict:
+                    continue
+                name = name.replace(weight_name, param_name)
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                if is_pp_missing_parameter(name, self):
+                    continue
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                for idx, (param_name, weight_name, expert_id, shard_id) in enumerate(
+                    expert_params_mapping
+                ):
+                    if weight_name not in name:
+                        continue
+                    name = name.replace(weight_name, param_name)
+                    if is_pp_missing_parameter(name, self):
+                        continue
+                    param = params_dict[name]
+                    weight_loader = param.weight_loader
+                    weight_loader(
+                        param,
+                        loaded_weight,
+                        name,
+                        expert_id=expert_id,
+                        shard_id=shard_id,
+                    )
+                    break
+                else:
+                    # Skip loading extra bias for GPTQ models.
+                    if (
+                        name.endswith(".bias")
+                        and name not in params_dict
+                        and not self.config.is_linear_attn
+                    ):  # noqa: E501
+                        continue
+                    # Remapping the name of FP8 kv-scale.
+                    name = maybe_remap_kv_scale_name(name, params_dict)
+                    if name is None:
+                        continue
+                    if is_pp_missing_parameter(name, self):
+                        continue
+
+                    param = params_dict[name]
+                    weight_loader = getattr(
+                        param, "weight_loader", default_weight_loader
+                    )
+                    weight_loader(param, loaded_weight, **kwargs)
+            loaded_params.add(name)
+
+
+def get_spec_layer_idx_from_weight_name(
+    config: KimiLinearConfig, weight_name: str
+) -> int | None:
+    if hasattr(config, "num_nextn_predict_layers") and (
+        config.num_nextn_predict_layers > 0
+    ):
+        layer_idx = config.num_hidden_layers
+        for i in range(config.num_nextn_predict_layers):
+            if weight_name.startswith(f"model.layers.{layer_idx + i}."):
+                return layer_idx + i
+    return None

vllm/model_executor/models/registry.py

@@ -118,6 +118,7 @@ _TEXT_GENERATION_MODELS = {
     "InternLM3ForCausalLM": ("llama", "LlamaForCausalLM"),
     "JAISLMHeadModel": ("jais", "JAISLMHeadModel"),
     "JambaForCausalLM": ("jamba", "JambaForCausalLM"),
+    "KimiLinearForCausalLM": ("kimi_linear", "KimiLinearForCausalLM"),  # noqa: E501
     "Lfm2ForCausalLM": ("lfm2", "Lfm2ForCausalLM"),
     "Lfm2MoeForCausalLM": ("lfm2_moe", "Lfm2MoeForCausalLM"),
     "LlamaForCausalLM": ("llama", "LlamaForCausalLM"),

vllm/transformers_utils/config.py

@@ -79,6 +79,7 @@ _CONFIG_REGISTRY: dict[str, type[PretrainedConfig]] = LazyConfigDict(
     deepseek_v3="DeepseekV3Config",
     deepseek_v32="DeepseekV3Config",
     flex_olmo="FlexOlmoConfig",
+    kimi_linear="KimiLinearConfig",
     kimi_vl="KimiVLConfig",
     Llama_Nemotron_Nano_VL="Nemotron_Nano_VL_Config",
     RefinedWeb="RWConfig",  # For tiiuae/falcon-40b(-instruct)

vllm/transformers_utils/configs/__init__.py

@@ -19,6 +19,7 @@ from vllm.transformers_utils.configs.eagle import EAGLEConfig
 from vllm.transformers_utils.configs.falcon import RWConfig
 from vllm.transformers_utils.configs.flex_olmo import FlexOlmoConfig
 from vllm.transformers_utils.configs.jais import JAISConfig
+from vllm.transformers_utils.configs.kimi_linear import KimiLinearConfig
 from vllm.transformers_utils.configs.kimi_vl import KimiVLConfig
 from vllm.transformers_utils.configs.lfm2_moe import Lfm2MoeConfig
 from vllm.transformers_utils.configs.medusa import MedusaConfig
@@ -54,6 +55,7 @@ __all__ = [
     "MiDashengLMConfig",
     "MLPSpeculatorConfig",
     "MoonViTConfig",
+    "KimiLinearConfig",
     "KimiVLConfig",
     "NemotronConfig",
     "NemotronHConfig",

vllm/transformers_utils/configs/kimi_linear.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from transformers.configuration_utils import PretrainedConfig
+
+from vllm.logger import init_logger
+
+logger = init_logger(__name__)
+
+
+class KimiLinearConfig(PretrainedConfig):
+    model_type = "kimi_linear"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        model_type="kimi_linear",
+        vocab_size=163840,
+        hidden_size=4096,
+        head_dim=None,
+        intermediate_size=11008,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        num_key_value_heads=None,
+        hidden_act="silu",
+        initializer_range=0.02,
+        rms_norm_eps=1e-6,
+        use_cache=True,
+        pad_token_id=0,
+        bos_token_id=1,
+        eos_token_id=2,
+        rope_theta=10000.0,
+        rope_scaling=None,
+        tie_word_embeddings=False,
+        moe_intermediate_size: int | None = None,
+        moe_renormalize: bool = True,
+        moe_router_activation_func: str = "sigmoid",
+        num_experts: int | None = None,
+        num_experts_per_token: int | None = None,
+        num_shared_experts: int = 0,
+        routed_scaling_factor: float = 1.0,
+        first_k_dense_replace: int = 0,
+        moe_layer_freq: int = 1,
+        use_grouped_topk: bool = True,
+        num_expert_group: int = 1,
+        topk_group: int = 1,
+        q_lora_rank: int | None = None,
+        kv_lora_rank: int | None = None,
+        qk_nope_head_dim: int | None = None,
+        qk_rope_head_dim: int | None = None,
+        v_head_dim: int | None = None,
+        mla_use_nope: bool | None = False,
+        num_nextn_predict_layers: int = 0,
+        linear_attn_config: dict | None = None,
+        **kwargs,
+    ):
+        self.model_type = model_type
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.head_dim = (
+            head_dim if head_dim is not None else hidden_size // num_attention_heads
+        )
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+
+        # for backward compatibility
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+
+        self.num_key_value_heads = num_key_value_heads
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.rope_scaling = rope_scaling
+
+        self.q_lora_rank = q_lora_rank
+        self.kv_lora_rank = kv_lora_rank
+        self.qk_nope_head_dim = qk_nope_head_dim
+        self.qk_rope_head_dim = qk_rope_head_dim
+        self.v_head_dim = v_head_dim
+        self.mla_use_nope = mla_use_nope
+        # moe config
+        self.num_experts = num_experts
+        self.num_experts_per_token = num_experts_per_token
+        self.moe_renormalize = moe_renormalize
+        self.num_shared_experts = num_shared_experts
+        self.routed_scaling_factor = routed_scaling_factor
+        self.moe_router_activation_func = moe_router_activation_func
+        assert self.moe_router_activation_func in ("softmax", "sigmoid")
+        self.moe_intermediate_size = moe_intermediate_size
+        self.first_k_dense_replace = first_k_dense_replace
+        self.moe_layer_freq = moe_layer_freq
+        self.use_grouped_topk = use_grouped_topk
+        self.num_expert_group = num_expert_group
+        self.topk_group = topk_group
+        self.num_nextn_predict_layers = num_nextn_predict_layers
+
+        if linear_attn_config is not None:
+            assert linear_attn_config["kda_layers"] is not None
+            assert linear_attn_config["full_attn_layers"] is not None
+        self.linear_attn_config = linear_attn_config
+
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
+
+    @property
+    def is_mla(self):
+        return (
+            self.q_lora_rank is not None
+            or self.kv_lora_rank is not None
+            or self.qk_nope_head_dim is not None
+            or self.qk_rope_head_dim is not None
+            or self.v_head_dim is not None
+            or self.mla_use_nope is True
+        )
+
+    @property
+    def is_moe(self):
+        return self.num_experts is not None
+
+    @property
+    def is_linear_attn(self) -> bool:
+        return not (
+            self.linear_attn_config is None
+            or (
+                isinstance(self.linear_attn_config, dict)
+                and self.linear_attn_config["kda_layers"] is not None
+                and len(self.linear_attn_config["kda_layers"]) == 0
+            )
+        )
+
+    def is_kda_layer(self, layer_idx: int):
+        return (
+            self.linear_attn_config is not None
+            and (layer_idx + 1) in self.linear_attn_config["kda_layers"]
+        )

vllm/v1/worker/gpu_model_runner.py

@@ -8,6 +8,7 @@ from collections import defaultdict
 from collections.abc import Iterator
 from contextlib import contextmanager
 from copy import deepcopy
+from functools import reduce
 from itertools import product
 from typing import TYPE_CHECKING, Any, NamedTuple, TypeAlias, cast
 
@@ -4134,26 +4135,18 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
 
     def calculate_reorder_batch_threshold(self) -> None:
         """
-        Check that if any backends reorder batches; that the reordering
-        is compatible (e.g., decode threshold is the same)
+        Choose the minimum reorder batch threshold from all attention groups.
+        Backends should be able to support lower threshold then what they request
+        just may have a performance penalty due to that backend treating decodes
+        as prefills.
         """
-        for group in self._attn_group_iterator():
-            attn_metadata_builder_i = group.get_metadata_builder()
-
-            # check that if any backends reorder batches; that the reordering
-            # is compatible (e.g., decode threshold is the same)
-            reorder_batch_threshold_i = attn_metadata_builder_i.reorder_batch_threshold
-            if reorder_batch_threshold_i is not None:
-                if self.reorder_batch_threshold is not None:
-                    if reorder_batch_threshold_i != self.reorder_batch_threshold:
-                        raise ValueError(
-                            f"Attention backend reorders decodes with "
-                            f"threshold {reorder_batch_threshold_i} but other "
-                            f"backend uses threshold "
-                            f"{self.reorder_batch_threshold}"
-                        )
-                else:
-                    self.reorder_batch_threshold = reorder_batch_threshold_i
+        min_none_high = lambda a, b: a if b is None else b if a is None else min(a, b)
+
+        reorder_batch_thresholds = [
+            group.get_metadata_builder().reorder_batch_threshold
+            for group in self._attn_group_iterator()
+        ]
+        self.reorder_batch_threshold = reduce(min_none_high, reorder_batch_thresholds)
 
     def _find_compatible_block_sizes(
         self,