[Model] Add support for OLMo Hybrid (#32550)

docs/models/supported_models.md

@@ -448,6 +448,7 @@ th {
 | `OlmoForCausalLM` | OLMo | `allenai/OLMo-1B-hf`, `allenai/OLMo-7B-hf`, etc. | ✅︎ | ✅︎ |
 | `Olmo2ForCausalLM` | OLMo2 | `allenai/OLMo-2-0425-1B`, etc. | ✅︎ | ✅︎ |
 | `Olmo3ForCausalLM` | OLMo3 | `allenai/Olmo-3-7B-Instruct`, `allenai/Olmo-3-32B-Think`, etc. | ✅︎ | ✅︎ |
+| `OlmoHybridForCausalLM` | OLMo Hybrid | `allenai/Olmo-Hybrid-7B` | ✅︎ | ✅︎ |
 | `OlmoeForCausalLM` | OLMoE | `allenai/OLMoE-1B-7B-0924`, `allenai/OLMoE-1B-7B-0924-Instruct`, etc. | | ✅︎ |
 | `OPTForCausalLM` | OPT, OPT-IML | `facebook/opt-66b`, `facebook/opt-iml-max-30b`, etc. | ✅︎ | ✅︎ |
 | `OrionForCausalLM` | Orion | `OrionStarAI/Orion-14B-Base`, `OrionStarAI/Orion-14B-Chat`, etc. | | ✅︎ |

tests/models/registry.py

@@ -420,6 +420,7 @@ _TEXT_GENERATION_EXAMPLE_MODELS = {
     "OlmoForCausalLM": _HfExamplesInfo("allenai/OLMo-1B-hf"),
     "Olmo2ForCausalLM": _HfExamplesInfo("allenai/OLMo-2-0425-1B"),
     "Olmo3ForCausalLM": _HfExamplesInfo("allenai/Olmo-3-7B-Instruct"),
+    "OlmoHybridForCausalLM": _HfExamplesInfo("allenai/Olmo-Hybrid-7B"),
     "OlmoeForCausalLM": _HfExamplesInfo("allenai/OLMoE-1B-7B-0924-Instruct"),
     "OPTForCausalLM": _HfExamplesInfo(
         "facebook/opt-125m", {"1b": "facebook/opt-iml-max-1.3b"}

vllm/config/compilation.py

@@ -666,6 +666,7 @@ class CompilationConfig:
         "vllm::linear_attention",
         "vllm::plamo2_mamba_mixer",
         "vllm::gdn_attention_core",
+        "vllm::olmo_hybrid_gdn_full_forward",
         "vllm::kda_attention",
         "vllm::sparse_attn_indexer",
         "vllm::rocm_aiter_sparse_attn_indexer",

vllm/model_executor/layers/fla/ops/l2norm.py

@@ -76,16 +76,20 @@ def l2norm_fwd_kernel(
 
 
 @triton.jit
-def l2norm_fwd_kernel2(X, Y, eps, M, N: tl.constexpr, MBLOCK: tl.constexpr):
+def l2norm_fwd_kernel2(
+    X, Y, eps, M, N: tl.constexpr, BD: tl.constexpr, MBLOCK: tl.constexpr
+):
     xoffset = tl.program_id(0) * MBLOCK
     row_idx = xoffset + tl.arange(0, MBLOCK)[:, None]
     xmask = row_idx < M
-    rindex = tl.arange(0, N)[None, :]
-    xs = tl.load(X + (rindex + N * row_idx), xmask).to(tl.float32)
-    square = tl.broadcast_to(xs * xs, [MBLOCK, N])
+    rindex = tl.arange(0, BD)[None, :]
+    cmask = rindex < N
+    mask = xmask & cmask
+    xs = tl.load(X + (rindex + N * row_idx), mask, other=0.0).to(tl.float32)
+    square = tl.broadcast_to(xs * xs, [MBLOCK, BD])
     square_sum = tl.sum(tl.where(xmask, square, 0), 1)[:, None]
     rsqrt = tl.rsqrt(square_sum + eps)
-    tl.store(Y + (rindex + N * row_idx), xs * rsqrt, xmask)
+    tl.store(Y + (rindex + N * row_idx), xs * rsqrt, mask)
 
 
 def l2norm_fwd(
@@ -116,6 +120,7 @@ def l2norm_fwd(
             eps,
             T,
             D,
+            BD,
             MBLOCK,
         )
     else:

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

@@ -250,57 +250,55 @@ def layer_norm_fwd(
     return out, mean, rstd
 
 
-class LayerNormFn(torch.autograd.Function):
-    @input_guard
-    @staticmethod
-    def forward(
-        ctx,
+def _layer_norm_fn_impl(
+    x,
+    weight,
+    bias,
+    z=None,
+    eps=1e-6,
+    group_size=None,
+    norm_before_gate=True,
+    is_rms_norm=False,
+    activation: str = "swish",
+):
+    """Triton layer/RMS norm with optional gating.
+
+    If z is not None, computes norm(x) * silu(z) when norm_before_gate,
+    else norm(x * silu(z)).
+
+    This calls the triton kernel directly. The original code wrapped this
+    in a torch.autograd.Function (LayerNormFn) to save tensors for a
+    backward pass, but vLLM is inference-only so there is no backward pass.
+    The autograd wrapper also prevented torch.compile/dynamo from tracing
+    through the function due to its @staticmethod forward.
+    """
+    x_shape_og = x.shape
+    x = x.reshape(-1, x.shape[-1])
+    if x.stride(-1) != 1:
+        x = x.contiguous()
+    if z is not None:
+        assert z.shape == x_shape_og
+        z = z.reshape(-1, z.shape[-1])
+        if z.stride(-1) != 1:
+            z = z.contiguous()
+    weight = weight.contiguous()
+    if bias is not None:
+        bias = bias.contiguous()
+    y, _, _ = layer_norm_fwd(
         x,
         weight,
         bias,
-        z=None,
-        eps=1e-6,
-        group_size=None,
-        norm_before_gate=True,
-        is_rms_norm=False,
-        activation: str = "swish",
-    ):
-        """If z is not None, we do norm(x) * silu(z) if norm_before_gate, else norm(x * silu(z))"""
-
-        x_shape_og = x.shape
-        # reshape input data into 2D tensor
-        x = x.reshape(-1, x.shape[-1])
-        if x.stride(-1) != 1:
-            x = x.contiguous()
-        if z is not None:
-            assert z.shape == x_shape_og
-            z = z.reshape(-1, z.shape[-1])
-            if z.stride(-1) != 1:
-                z = z.contiguous()
-        weight = weight.contiguous()
-        if bias is not None:
-            bias = bias.contiguous()
-        y, mean, rstd = layer_norm_fwd(
-            x,
-            weight,
-            bias,
-            eps,
-            z=z,
-            group_size=group_size,
-            norm_before_gate=norm_before_gate,
-            is_rms_norm=is_rms_norm,
-            activation=activation,
-        )
-        ctx.save_for_backward(x, weight, bias, mean, rstd, z)
-        ctx.x_shape_og = x_shape_og
-        ctx.eps = eps
-        ctx.group_size = group_size
-        ctx.norm_before_gate = norm_before_gate
-        ctx.is_rms_norm = is_rms_norm
-        ctx.activation = activation
-        return y.reshape(x_shape_og)
+        eps,
+        z=z,
+        group_size=group_size,
+        norm_before_gate=norm_before_gate,
+        is_rms_norm=is_rms_norm,
+        activation=activation,
+    )
+    return y.reshape(x_shape_og)
 
 
+@input_guard
 def layernorm_fn(
     x,
     weight,
@@ -312,11 +310,12 @@ def layernorm_fn(
     is_rms_norm=False,
     activation: str = "swish",
 ):
-    return LayerNormFn.apply(
+    return _layer_norm_fn_impl(
         x, weight, bias, z, eps, group_size, norm_before_gate, is_rms_norm, activation
     )
 
 
+@input_guard
 def rmsnorm_fn(
     x,
     weight,
@@ -327,7 +326,7 @@ def rmsnorm_fn(
     norm_before_gate=True,
     activation: str = "swish",
 ):
-    return LayerNormFn.apply(
+    return _layer_norm_fn_impl(
         x, weight, bias, z, eps, group_size, norm_before_gate, True, activation
     )
 

vllm/model_executor/models/olmo_hybrid.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# Adapted from:
+# https://github.com/huggingface/transformers/blob/main/src/transformers/models/olmo_hybrid/modeling_olmo_hybrid.py
+# Copyright 2026 The vLLM team.
+#
+# This code combines OLMo2/OLMo3 attention with Gated DeltaNet linear attention
+# for the OLMo Hybrid architecture.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Inference-only OLMo Hybrid model compatible with HuggingFace weights."""
+
+from collections.abc import Iterable
+from functools import partial
+from itertools import islice
+
+import torch
+from einops import rearrange
+from torch import nn
+from transformers.activations import ACT2FN
+
+from vllm.compilation.decorators import support_torch_compile
+from vllm.config import (
+    CacheConfig,
+    ModelConfig,
+    SpeculativeConfig,
+    VllmConfig,
+    get_current_vllm_config,
+)
+from vllm.distributed import (
+    divide,
+    get_pp_group,
+    get_tensor_model_parallel_rank,
+    get_tensor_model_parallel_world_size,
+    tensor_model_parallel_all_gather,
+)
+from vllm.distributed.utils import split_tensor_along_last_dim
+from vllm.forward_context import ForwardContext, get_forward_context
+from vllm.logger import init_logger
+from vllm.model_executor.layers.activation import SiluAndMul
+from vllm.model_executor.layers.attention import Attention
+from vllm.model_executor.layers.fla.ops import (
+    chunk_gated_delta_rule,
+    fused_recurrent_gated_delta_rule,
+)
+from vllm.model_executor.layers.layernorm import RMSNorm, RMSNormGated
+from vllm.model_executor.layers.linear import (
+    ColumnParallelLinear,
+    MergedColumnParallelLinear,
+    QKVParallelLinear,
+    RowParallelLinear,
+)
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.mamba.abstract import MambaBase
+from vllm.model_executor.layers.mamba.mamba_utils import (
+    MambaStateCopyFunc,
+    MambaStateCopyFuncCalculator,
+    MambaStateDtypeCalculator,
+    MambaStateShapeCalculator,
+)
+from vllm.model_executor.layers.mamba.ops.causal_conv1d import (
+    causal_conv1d_fn,
+    causal_conv1d_update,
+)
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.rotary_embedding import get_rope
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    ParallelLMHead,
+    VocabParallelEmbedding,
+)
+from vllm.model_executor.model_loader.weight_utils import (
+    default_weight_loader,
+    sharded_weight_loader,
+)
+from vllm.model_executor.utils import set_weight_attrs
+from vllm.platforms import current_platform
+from vllm.sequence import IntermediateTensors
+from vllm.triton_utils import tl, triton
+from vllm.triton_utils.allocation import set_triton_allocator
+from vllm.utils.torch_utils import direct_register_custom_op
+from vllm.v1.attention.backend import AttentionMetadata
+from vllm.v1.attention.backends.gdn_attn import GDNAttentionMetadata
+
+from .interfaces import HasInnerState, IsHybrid, SupportsLoRA, SupportsPP
+from .utils import (
+    AutoWeightsLoader,
+    extract_layer_index,
+    is_pp_missing_parameter,
+    make_empty_intermediate_tensors_factory,
+    make_layers,
+    maybe_prefix,
+)
+
+logger = init_logger(__name__)
+
+
+def _make_fused_conv1d_weight_loader(dims, tp_size, tp_rank):
+    """Weight loader for loading separate HF conv weights into a fused conv1d.
+
+    dims: list of original (un-sharded) dims per section,
+          e.g. [key_dim, key_dim, value_dim]
+    """
+    sharded_dims = [d // tp_size for d in dims]
+
+    def weight_loader(param, loaded_weight, loaded_shard_id=None):
+        if loaded_weight.dim() == 2:
+            loaded_weight = loaded_weight.unsqueeze(1)
+        dim = dims[loaded_shard_id]
+        shard_size = dim // tp_size
+        tp_start = tp_rank * shard_size
+        sharded_weight = loaded_weight[tp_start : tp_start + shard_size]
+        offset = sum(sharded_dims[:loaded_shard_id])
+        param.data[offset : offset + shard_size].copy_(sharded_weight)
+
+    return weight_loader
+
+
+class OlmoHybridGatedDeltaNet(nn.Module, MambaBase):
+    """
+    Gated DeltaNet linear attention layer for OLMo Hybrid.
+
+    This implements the linear attention mechanism that replaces sliding window
+    attention in the hybrid architecture.
+    """
+
+    @property
+    def mamba_type(self) -> str:
+        return "gdn_attention"
+
+    def get_state_dtype(self) -> tuple[torch.dtype, torch.dtype]:
+        return MambaStateDtypeCalculator.gated_delta_net_state_dtype(
+            self.model_config.dtype,
+            self.cache_config.mamba_cache_dtype,
+            self.cache_config.mamba_ssm_cache_dtype,
+        )
+
+    def get_state_shape(self) -> tuple[tuple[int, ...], tuple[int, ...]]:
+        return MambaStateShapeCalculator.gated_delta_net_state_shape(
+            self.tp_size,
+            self.num_k_heads,
+            self.num_v_heads,
+            self.head_k_dim,
+            self.head_v_dim,
+            self.conv_kernel_size,
+            self.num_spec,
+        )
+
+    def __init__(
+        self,
+        config,
+        model_config: ModelConfig | None = None,
+        cache_config: CacheConfig | None = None,
+        quant_config: QuantizationConfig | None = None,
+        speculative_config: SpeculativeConfig | None = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.tp_size = get_tensor_model_parallel_world_size()
+        self.tp_rank = get_tensor_model_parallel_rank()
+        self.hidden_size = config.hidden_size
+        self.num_v_heads = config.linear_num_value_heads
+        self.num_k_heads = config.linear_num_key_heads
+        self.head_k_dim = config.linear_key_head_dim
+        self.head_v_dim = config.linear_value_head_dim
+        self.key_dim = self.head_k_dim * self.num_k_heads
+        self.value_dim = self.head_v_dim * self.num_v_heads
+
+        self.conv_kernel_size = config.linear_conv_kernel_dim
+        self.layer_idx = extract_layer_index(prefix)
+        self.activation = config.hidden_act
+        self.act = ACT2FN[config.hidden_act]
+        self.layer_norm_epsilon = config.rms_norm_eps
+        assert getattr(config, "linear_use_gate", True), (
+            "OlmoHybridGatedDeltaNet requires linear_use_gate=True"
+        )
+        self.allow_neg_eigval = getattr(config, "linear_allow_neg_eigval", False)
+        self.prefix = prefix
+
+        self.config = config
+        self.model_config = model_config
+        self.cache_config = cache_config
+        self.quant_config = quant_config
+        self.speculative_config = speculative_config
+        self.num_spec = (
+            self.speculative_config.num_speculative_tokens
+            if self.speculative_config
+            else 0
+        )
+
+        # Fused QKVG projection: 1 matmul instead of 4
+        self.in_proj_qkvg = MergedColumnParallelLinear(
+            input_size=self.hidden_size,
+            output_sizes=[self.key_dim, self.key_dim, self.value_dim, self.value_dim],
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.in_proj_qkvg",
+        )
+
+        # Separate B and A projections to preserve numerical precision.
+        # Fusing these into one matmul changes FP accumulation order for the
+        # gating scalars, which compounds through the GDN recurrent state.
+        self.b_proj = ColumnParallelLinear(
+            input_size=self.hidden_size,
+            output_size=self.num_v_heads,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.b_proj",
+        )
+        self.a_proj = ColumnParallelLinear(
+            input_size=self.hidden_size,
+            output_size=self.num_v_heads,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.a_proj",
+        )
+
+        # Fused conv1d: single parameter instead of 3
+        self.conv_dim = self.key_dim * 2 + self.value_dim
+        self.conv1d = ColumnParallelLinear(
+            input_size=self.conv_kernel_size,
+            output_size=self.conv_dim,
+            bias=False,
+            prefix=f"{prefix}.conv1d",
+        )
+        self.conv1d.weight.data = self.conv1d.weight.data.unsqueeze(1)
+        delattr(self.conv1d.weight, "weight_loader")
+        set_weight_attrs(
+            self.conv1d.weight,
+            {
+                "weight_loader": _make_fused_conv1d_weight_loader(
+                    [self.key_dim, self.key_dim, self.value_dim],
+                    self.tp_size,
+                    self.tp_rank,
+                )
+            },
+        )
+
+        self.dt_bias = nn.Parameter(
+            torch.ones(self.num_v_heads // self.tp_size),
+        )
+        self.A_log = nn.Parameter(
+            torch.empty(
+                divide(self.num_v_heads, self.tp_size),
+            )
+        )
+
+        set_weight_attrs(self.A_log, {"weight_loader": sharded_weight_loader(0)})
+        set_weight_attrs(self.dt_bias, {"weight_loader": sharded_weight_loader(0)})
+
+        # use eps=1e-5 to match FLA's FusedRMSNormGated
+        self.o_norm = RMSNormGated(
+            self.head_v_dim,
+            eps=1e-5,
+            group_size=None,
+            norm_before_gate=True,
+            device=current_platform.current_device(),
+            dtype=config.torch_dtype if hasattr(config, "torch_dtype") else None,
+        )
+
+        self.o_proj = RowParallelLinear(
+            self.value_dim,
+            self.hidden_size,
+            bias=False,
+            input_is_parallel=True,
+            quant_config=quant_config,
+            prefix=f"{prefix}.o_proj",
+        )
+
+        # FLA triton kernels need a PyTorch-backed allocator for scratch
+        # memory (required by triton >= 3.x autotuner). Set once at init.
+        set_triton_allocator(current_platform.current_device())
+
+        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 rearrange_mixed_qkv(self, mixed_qkv):
+        if mixed_qkv is None:
+            return None, None, None
+        query, key, value = torch.split(
+            mixed_qkv,
+            [
+                self.key_dim // self.tp_size,
+                self.key_dim // self.tp_size,
+                self.value_dim // self.tp_size,
+            ],
+            dim=-1,
+        )
+
+        num_k_heads = self.num_k_heads // self.tp_size
+        num_v_heads = self.num_v_heads // self.tp_size
+
+        query = rearrange(query, "l (h d) -> 1 l h d", h=num_k_heads, d=self.head_k_dim)
+        key = rearrange(key, "l (h d) -> 1 l h d", h=num_k_heads, d=self.head_k_dim)
+        value = rearrange(value, "l (h d) -> 1 l h d", h=num_v_heads, d=self.head_v_dim)
+
+        # GQA expansion if needed
+        if num_v_heads > num_k_heads:
+            expand_ratio = num_v_heads // num_k_heads
+            query = query.unsqueeze(3).expand(-1, -1, -1, expand_ratio, -1)
+            query = query.reshape(1, query.shape[1], num_v_heads, self.head_k_dim)
+            key = key.unsqueeze(3).expand(-1, -1, -1, expand_ratio, -1)
+            key = key.reshape(1, key.shape[1], num_v_heads, self.head_k_dim)
+
+        return query.contiguous(), key.contiguous(), value.contiguous()
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        output: torch.Tensor,
+    ):
+        # NOTE: We wrap the ENTIRE linear attention forward (projections +
+        # core recurrence + output norm + output projection) in a single
+        # custom op, rather than just wrapping the recurrent core like
+        # other GDN models (e.g. Qwen3Next) do.
+        #
+        # Why: torch.compile with inductor generates fused kernels for
+        # matmuls and pointwise ops. These fused kernels can differ in
+        # floating-point accumulation order from eager-mode cuBLAS,
+        # introducing small numerical differences (~1e-7 per op). For
+        # standard transformer attention this is harmless because each
+        # position is computed independently. But for the GDN recurrent
+        # state, these tiny input differences compound at every timestep
+        # across the full sequence length, causing severe logprob
+        # divergence (e.g. ~15% top-1 agreement with eager baseline).
+        #
+        # By making the full forward opaque to inductor, the projections
+        # and output norm run with eager-mode kernels (cuBLAS, triton),
+        # preserving numerical consistency. The tradeoff is reduced
+        # compilation speedup (~1.5x vs ~3x), but logprob agreement
+        # improves from ~15% to ~83% top-1 vs eager.
+        #
+        # The remaining ~17% divergence comes from inductor compiling
+        # the MLP and transformer attention layers that are NOT wrapped
+        # in custom ops -- their small precision differences propagate
+        # as inputs to the GDN layers from outside.
+        torch.ops.vllm.olmo_hybrid_gdn_full_forward(
+            hidden_states,
+            output,
+            self.prefix,
+        )
+
+    def _full_forward(
+        self,
+        hidden_states: torch.Tensor,
+        output: torch.Tensor,
+    ):
+        num_tokens = hidden_states.size(0)
+
+        # ============================================================
+        # Part 1: Input Projection (2 fused matmuls instead of 6)
+        # ============================================================
+        projected_qkvg, _ = self.in_proj_qkvg(hidden_states)
+        conv_dim_sharded = (self.key_dim * 2 + self.value_dim) // self.tp_size
+        mixed_qkv = projected_qkvg[..., :conv_dim_sharded]
+        gate = projected_qkvg[..., conv_dim_sharded:]
+
+        b, _ = self.b_proj(hidden_states)
+        a, _ = self.a_proj(hidden_states)
+
+        # ============================================================
+        # Part 2: Core Attention
+        # ============================================================
+        core_attn_out = torch.zeros(
+            (num_tokens, self.num_v_heads // self.tp_size, self.head_v_dim),
+            dtype=hidden_states.dtype,
+            device=hidden_states.device,
+        )
+
+        self._forward_core(
+            mixed_qkv=mixed_qkv,
+            b=b,
+            a=a,
+            core_attn_out=core_attn_out,
+        )
+
+        # ============================================================
+        # Part 3: Output Projection
+        # ============================================================
+        gate = gate.view(num_tokens, self.num_v_heads // self.tp_size, self.head_v_dim)
+        core_attn_out_flat = core_attn_out.reshape(-1, core_attn_out.shape[-1])
+        gate_flat = gate.reshape(-1, gate.shape[-1])
+        core_attn_out_normed = self.o_norm(core_attn_out_flat, gate_flat)
+        core_attn_out = core_attn_out_normed.view(
+            num_tokens, self.num_v_heads // self.tp_size, self.head_v_dim
+        )
+
+        core_attn_out = rearrange(core_attn_out, "l h d -> l (h d)")
+        output[:num_tokens], _ = self.o_proj(core_attn_out)
+
+    def _forward_core(
+        self,
+        mixed_qkv: torch.Tensor,
+        b: torch.Tensor,
+        a: torch.Tensor,
+        core_attn_out: torch.Tensor,
+    ):
+        """
+        Core attention computation (called by custom op).
+        """
+        forward_context = get_forward_context()
+        attn_metadata: AttentionMetadata = forward_context.attn_metadata
+
+        if attn_metadata is None:
+            # V1 profile run
+            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
+        spec_query_start_loc = attn_metadata.spec_query_start_loc
+        non_spec_query_start_loc = attn_metadata.non_spec_query_start_loc
+        spec_sequence_masks = attn_metadata.spec_sequence_masks
+        spec_token_indx = attn_metadata.spec_token_indx
+        non_spec_token_indx = attn_metadata.non_spec_token_indx
+        spec_state_indices_tensor = attn_metadata.spec_state_indices_tensor
+        non_spec_state_indices_tensor = attn_metadata.non_spec_state_indices_tensor
+        self_kv_cache = self.kv_cache[forward_context.virtual_engine]
+        conv_state = self_kv_cache[0].transpose(-1, -2)
+        ssm_state = self_kv_cache[1]
+        num_actual_tokens = attn_metadata.num_actual_tokens
+        num_accepted_tokens = attn_metadata.num_accepted_tokens
+
+        mixed_qkv = mixed_qkv[:num_actual_tokens]
+        b = b[:num_actual_tokens]
+        a = a[:num_actual_tokens]
+
+        conv_weights = self.conv1d.weight.view(
+            self.conv1d.weight.size(0), self.conv1d.weight.size(2)
+        )
+
+        if spec_sequence_masks is not None:
+            if attn_metadata.num_prefills == 0 and attn_metadata.num_decodes == 0:
+                mixed_qkv_spec = mixed_qkv
+                mixed_qkv_non_spec = None
+            else:
+                mixed_qkv_spec = mixed_qkv.index_select(0, spec_token_indx)
+                mixed_qkv_non_spec = mixed_qkv.index_select(0, non_spec_token_indx)
+        else:
+            mixed_qkv_spec = None
+            mixed_qkv_non_spec = mixed_qkv
+
+        if spec_sequence_masks is not None:
+            mixed_qkv_spec = causal_conv1d_update(
+                mixed_qkv_spec,
+                conv_state,
+                conv_weights,
+                None,  # no bias
+                self.activation,
+                conv_state_indices=spec_state_indices_tensor[:, 0][
+                    : attn_metadata.num_spec_decodes
+                ],
+                num_accepted_tokens=num_accepted_tokens,
+                query_start_loc=spec_query_start_loc,
+                max_query_len=spec_state_indices_tensor.size(-1),
+                validate_data=False,
+            )
+
+        if attn_metadata.num_prefills > 0:
+            mixed_qkv_non_spec_T = mixed_qkv_non_spec.transpose(0, 1)
+            mixed_qkv_non_spec = causal_conv1d_fn(
+                mixed_qkv_non_spec_T,
+                conv_weights,
+                None,
+                activation=self.activation,
+                conv_states=conv_state,
+                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)
+        elif attn_metadata.num_decodes > 0:
+            mixed_qkv_non_spec = causal_conv1d_update(
+                mixed_qkv_non_spec,
+                conv_state,
+                conv_weights,
+                None,
+                self.activation,
+                conv_state_indices=non_spec_state_indices_tensor[
+                    : attn_metadata.num_decodes
+                ],
+                validate_data=True,
+            )
+        else:
+            mixed_qkv_non_spec = None
+
+        query_spec, key_spec, value_spec = self.rearrange_mixed_qkv(mixed_qkv_spec)
+        query_non_spec, key_non_spec, value_non_spec = self.rearrange_mixed_qkv(
+            mixed_qkv_non_spec
+        )
+
+        g, beta = fused_olmo_hybrid_gdn_gating(
+            self.A_log, a, b, self.dt_bias, self.allow_neg_eigval
+        )
+
+        if spec_sequence_masks is not None:
+            if attn_metadata.num_prefills == 0 and attn_metadata.num_decodes == 0:
+                g_spec = g
+                beta_spec = beta
+                g_non_spec = None
+                beta_non_spec = None
+            else:
+                g_spec = g.index_select(1, spec_token_indx)
+                beta_spec = beta.index_select(1, spec_token_indx)
+                g_non_spec = g.index_select(1, non_spec_token_indx)
+                beta_non_spec = beta.index_select(1, non_spec_token_indx)
+        else:
+            g_spec = None
+            beta_spec = None
+            g_non_spec = g
+            beta_non_spec = beta
+
+        if spec_sequence_masks is not None:
+            core_attn_out_spec, last_recurrent_state = fused_recurrent_gated_delta_rule(
+                q=query_spec,
+                k=key_spec,
+                v=value_spec,
+                g=g_spec,
+                beta=beta_spec,
+                initial_state=ssm_state,
+                inplace_final_state=True,
+                cu_seqlens=spec_query_start_loc[: attn_metadata.num_spec_decodes + 1],
+                ssm_state_indices=spec_state_indices_tensor,
+                num_accepted_tokens=num_accepted_tokens,
+                use_qk_l2norm_in_kernel=True,
+            )
+        else:
+            core_attn_out_spec, last_recurrent_state = None, None
+
+        if attn_metadata.num_prefills > 0:
+            initial_state = ssm_state[non_spec_state_indices_tensor].contiguous()
+            initial_state[~has_initial_state, ...] = 0
+            (
+                core_attn_out_non_spec,
+                last_recurrent_state,
+            ) = chunk_gated_delta_rule(
+                q=query_non_spec,
+                k=key_non_spec,
+                v=value_non_spec,
+                g=g_non_spec,
+                beta=beta_non_spec,
+                initial_state=initial_state,
+                output_final_state=True,
+                cu_seqlens=non_spec_query_start_loc,
+                use_qk_l2norm_in_kernel=True,
+            )
+            ssm_state[non_spec_state_indices_tensor] = last_recurrent_state.to(
+                ssm_state.dtype
+            )
+        elif attn_metadata.num_decodes > 0:
+            core_attn_out_non_spec, last_recurrent_state = (
+                fused_recurrent_gated_delta_rule(
+                    q=query_non_spec,
+                    k=key_non_spec,
+                    v=value_non_spec,
+                    g=g_non_spec,
+                    beta=beta_non_spec,
+                    initial_state=ssm_state,
+                    inplace_final_state=True,
+                    cu_seqlens=non_spec_query_start_loc[
+                        : attn_metadata.num_decodes + 1
+                    ],
+                    ssm_state_indices=non_spec_state_indices_tensor,
+                    use_qk_l2norm_in_kernel=True,
+                )
+            )
+        else:
+            core_attn_out_non_spec, last_recurrent_state = None, None
+
+        if spec_sequence_masks is not None and core_attn_out_non_spec is not None:
+            merged_out = torch.empty(
+                (1, num_actual_tokens, *core_attn_out_spec.shape[2:]),
+                dtype=core_attn_out_non_spec.dtype,
+                device=core_attn_out_non_spec.device,
+            )
+            merged_out.index_copy_(1, spec_token_indx, core_attn_out_spec)
+            merged_out.index_copy_(1, non_spec_token_indx, core_attn_out_non_spec)
+            core_attn_out[:num_actual_tokens] = merged_out.squeeze(0)
+        elif spec_sequence_masks is not None:
+            core_attn_out[:num_actual_tokens] = core_attn_out_spec.squeeze(0)
+        else:
+            core_attn_out[:num_actual_tokens] = core_attn_out_non_spec.squeeze(0)
+
+
+class OlmoHybridAttention(nn.Module):
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        self.config = vllm_config.model_config.hf_config
+
+        hidden_size = self.config.hidden_size
+        self.tp_size = get_tensor_model_parallel_world_size()
+        self.total_num_heads = self.config.num_attention_heads
+
+        assert hidden_size % self.total_num_heads == 0
+        assert self.total_num_heads % self.tp_size == 0
+
+        self.num_heads = self.total_num_heads // self.tp_size
+        self.total_num_kv_heads = (
+            self.config.num_key_value_heads or self.total_num_heads
+        )
+        if self.total_num_kv_heads >= self.tp_size:
+            assert self.total_num_kv_heads % self.tp_size == 0
+        else:
+            assert self.tp_size % self.total_num_kv_heads == 0
+
+        self.num_kv_heads = max(1, self.total_num_kv_heads // self.tp_size)
+        self.head_dim = hidden_size // self.total_num_heads
+        self.q_size = self.num_heads * self.head_dim
+        self.kv_size = self.num_kv_heads * self.head_dim
+        self.max_position_embeddings = self.config.max_position_embeddings
+
+        self.qkv_proj = QKVParallelLinear(
+            hidden_size,
+            self.head_dim,
+            self.total_num_heads,
+            self.total_num_kv_heads,
+            bias=False,
+            quant_config=vllm_config.quant_config,
+            prefix=f"{prefix}.qkv_proj",
+        )
+
+        self.tp_rank = get_tensor_model_parallel_rank()
+
+        self.k_norm = RMSNorm(
+            self.total_num_kv_heads * self.head_dim,
+            eps=self.config.rms_norm_eps,
+        )
+        self.q_norm = RMSNorm(
+            self.config.hidden_size,
+            eps=self.config.rms_norm_eps,
+        )
+
+        self.scaling = self.head_dim**-0.5
+
+        self.attn = Attention(
+            self.num_heads,
+            self.head_dim,
+            self.scaling,
+            num_kv_heads=self.num_kv_heads,
+            cache_config=vllm_config.cache_config,
+            quant_config=vllm_config.quant_config,
+            prefix=f"{prefix}.attn",
+        )
+
+        rope_parameters = getattr(self.config, "rope_parameters", None)
+        self._use_rope = (rope_parameters is not None) and (
+            rope_parameters["rope_theta"] is not None
+        )
+
+        if self._use_rope:
+            self.rotary_emb = get_rope(
+                self.head_dim,
+                max_position=self.max_position_embeddings,
+                rope_parameters=rope_parameters,
+            )
+        else:
+            self.rotary_emb = None
+
+        self.o_proj = RowParallelLinear(
+            self.total_num_heads * self.head_dim,
+            hidden_size,
+            bias=False,
+            quant_config=vllm_config.quant_config,
+            prefix=f"{prefix}.o_proj",
+        )
+
+    def _apply_qk_norm(
+        self, q: torch.Tensor, k: torch.Tensor
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        if self.tp_size > 1:
+            q = tensor_model_parallel_all_gather(q.contiguous())
+            k = tensor_model_parallel_all_gather(k.contiguous())
+        q = self.q_norm(q)
+        k = self.k_norm(k)
+        if self.tp_size > 1:
+            splitter = partial(split_tensor_along_last_dim, num_partitions=self.tp_size)
+            q = splitter(q)[self.tp_rank]
+            k = splitter(k)[self.tp_rank]
+        return q, k
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor:
+        qkv, _ = self.qkv_proj(hidden_states)
+        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+        q, k = self._apply_qk_norm(q, k)
+        if self._use_rope:
+            q, k = self.rotary_emb(positions, q, k)
+        attn_output = self.attn(q, k, v)
+        output, _ = self.o_proj(attn_output)
+        return output
+
+
+class OlmoHybridMLP(nn.Module):
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        hidden_size = config.hidden_size
+        intermediate_size = config.intermediate_size
+
+        self.gate_up_proj = MergedColumnParallelLinear(
+            hidden_size,
+            [intermediate_size] * 2,
+            bias=False,
+            quant_config=vllm_config.quant_config,
+            prefix=f"{prefix}.gate_up_proj",
+        )
+
+        self.act_fn = SiluAndMul()
+
+        self.down_proj = RowParallelLinear(
+            intermediate_size,
+            hidden_size,
+            bias=False,
+            quant_config=vllm_config.quant_config,
+            prefix=f"{prefix}.down_proj",
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        gate_up, _ = self.gate_up_proj(x)
+        x = self.act_fn(gate_up)
+        x, _ = self.down_proj(x)
+        return x
+
+
+class OlmoHybridDecoderLayer(nn.Module):
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = "") -> None:
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        model_config = vllm_config.model_config
+        cache_config = vllm_config.cache_config
+        quant_config = vllm_config.quant_config
+        speculative_config = vllm_config.speculative_config
+
+        layer_idx = extract_layer_index(prefix)
+        self.layer_type = config.layer_types[layer_idx]
+        self.layer_idx = layer_idx
+
+        if self.layer_type == "linear_attention":
+            self.linear_attn = OlmoHybridGatedDeltaNet(
+                config,
+                model_config=model_config,
+                cache_config=cache_config,
+                quant_config=quant_config,
+                speculative_config=speculative_config,
+                prefix=f"{prefix}.linear_attn",
+            )
+            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,
+            )
+        else:
+            self.self_attn = OlmoHybridAttention(
+                vllm_config=vllm_config,
+                prefix=f"{prefix}.self_attn",
+            )
+            # Attention layers use these norm names
+            self.post_attention_layernorm = RMSNorm(
+                config.hidden_size,
+                eps=config.rms_norm_eps,
+            )
+            self.post_feedforward_layernorm = RMSNorm(
+                config.hidden_size,
+                eps=config.rms_norm_eps,
+            )
+
+        self.mlp = OlmoHybridMLP(
+            vllm_config=vllm_config,
+            prefix=f"{prefix}.mlp",
+        )
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor:
+        if self.layer_type == "linear_attention":
+            residual = hidden_states
+            hidden_states = self.input_layernorm(hidden_states)
+
+            attn_output = torch.empty_like(hidden_states)
+            self.linear_attn(
+                hidden_states=hidden_states,
+                output=attn_output,
+            )
+            hidden_states = residual + attn_output
+
+            residual = hidden_states
+            hidden_states = self.post_attention_layernorm(hidden_states)
+            hidden_states = self.mlp(hidden_states)
+            hidden_states = residual + hidden_states
+        else:
+            residual = hidden_states
+            hidden_states = self.self_attn(positions, hidden_states)
+            hidden_states = self.post_attention_layernorm(hidden_states)
+            hidden_states = residual + hidden_states
+
+            residual = hidden_states
+            hidden_states = self.mlp(hidden_states)
+            hidden_states = self.post_feedforward_layernorm(hidden_states)
+            hidden_states = residual + hidden_states
+        return hidden_states
+
+
+@support_torch_compile
+class OlmoHybridModel(nn.Module):
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        self.config = vllm_config.model_config.hf_config
+
+        self.embed_tokens = VocabParallelEmbedding(
+            self.config.vocab_size,
+            self.config.hidden_size,
+            prefix=f"{prefix}.embed_tokens",
+        )
+
+        self.start_layer, self.end_layer, self.layers = make_layers(
+            self.config.num_hidden_layers,
+            lambda prefix: OlmoHybridDecoderLayer(
+                vllm_config=vllm_config, prefix=prefix
+            ),
+            prefix=f"{prefix}.layers",
+        )
+
+        self.norm = RMSNorm(
+            self.config.hidden_size,
+            eps=self.config.rms_norm_eps,
+        )
+
+        self.make_empty_intermediate_tensors = make_empty_intermediate_tensors_factory(
+            ["hidden_states"], self.config.hidden_size
+        )
+
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.embed_tokens(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: IntermediateTensors | None,
+        inputs_embeds: torch.Tensor | None = None,
+    ) -> torch.Tensor | IntermediateTensors:
+        if get_pp_group().is_first_rank:
+            if inputs_embeds is not None:
+                hidden_states = inputs_embeds
+            else:
+                hidden_states = self.embed_tokens(input_ids)
+        else:
+            assert intermediate_tensors is not None
+            hidden_states = intermediate_tensors["hidden_states"]
+            assert isinstance(hidden_states, torch.Tensor)
+
+        for layer in islice(self.layers, self.start_layer, self.end_layer):
+            hidden_states = layer(positions, hidden_states)
+
+        if not get_pp_group().is_last_rank:
+            return IntermediateTensors({"hidden_states": hidden_states})
+
+        hidden_states = self.norm(hidden_states)
+        return hidden_states
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
+        stacked_params_mapping = [
+            ("qkv_proj", "q_proj", "q"),
+            ("qkv_proj", "k_proj", "k"),
+            ("qkv_proj", "v_proj", "v"),
+            ("gate_up_proj", "gate_proj", 0),
+            ("gate_up_proj", "up_proj", 1),
+        ]
+
+        linear_attn_stacked_params_mapping = [
+            ("in_proj_qkvg", "q_proj", 0),
+            ("in_proj_qkvg", "k_proj", 1),
+            ("in_proj_qkvg", "v_proj", 2),
+            ("in_proj_qkvg", "g_proj", 3),
+            ("conv1d", "q_conv1d", 0),
+            ("conv1d", "k_conv1d", 1),
+            ("conv1d", "v_conv1d", 2),
+        ]
+
+        params_dict = dict(self.named_parameters(remove_duplicate=False))
+        loaded_params: set[str] = set()
+
+        for name, loaded_weight in weights:
+            if is_pp_missing_parameter(name, self):
+                continue
+
+            handled = False
+
+            if "linear_attn" in name:
+                for (
+                    param_name,
+                    weight_name,
+                    shard_id,
+                ) in linear_attn_stacked_params_mapping:
+                    if weight_name not in name:
+                        continue
+                    mapped_name = name.replace(weight_name, param_name)
+                    if mapped_name.endswith(".bias") and (
+                        mapped_name not in params_dict
+                    ):
+                        continue
+                    if mapped_name not in params_dict:
+                        continue
+                    param = params_dict[mapped_name]
+                    weight_loader = param.weight_loader
+                    weight_loader(param, loaded_weight, shard_id)
+                    name = mapped_name
+                    handled = True
+                    break
+            else:
+                for param_name, weight_name, shard_id in stacked_params_mapping:
+                    if weight_name not in name:
+                        continue
+                    name = name.replace(weight_name, param_name)
+                    if name.endswith(".bias") and name not in params_dict:
+                        continue
+                    if name not in params_dict:
+                        continue
+                    param = params_dict[name]
+                    weight_loader = param.weight_loader
+                    weight_loader(param, loaded_weight, shard_id)
+                    handled = True
+                    break
+
+            if not handled:
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                if name not in params_dict:
+                    continue
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader", default_weight_loader)
+                weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+        return loaded_params
+
+
+class OlmoHybridForCausalLM(
+    nn.Module, HasInnerState, SupportsPP, SupportsLoRA, IsHybrid
+):
+    packed_modules_mapping = {
+        "qkv_proj": ["q_proj", "k_proj", "v_proj"],
+        "gate_up_proj": ["gate_proj", "up_proj"],
+        "in_proj_qkvg": ["q_proj", "k_proj", "v_proj", "g_proj"],
+    }
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        self.config = config
+        self.vllm_config = vllm_config
+        self.model_config = vllm_config.model_config
+
+        self.model = OlmoHybridModel(
+            vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")
+        )
+
+        if config.tie_word_embeddings:
+            self.lm_head = self.model.embed_tokens
+        else:
+            self.lm_head = ParallelLMHead(
+                config.vocab_size,
+                config.hidden_size,
+                quant_config=vllm_config.quant_config,
+                prefix=maybe_prefix(prefix, "lm_head"),
+            )
+
+        self.logits_processor = LogitsProcessor(config.vocab_size)
+        self.make_empty_intermediate_tensors = (
+            self.model.make_empty_intermediate_tensors
+        )
+
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.model.embed_input_ids(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: IntermediateTensors | None = None,
+        inputs_embeds: torch.Tensor | None = None,
+    ) -> torch.Tensor | IntermediateTensors:
+        hidden_states = self.model(
+            input_ids=input_ids,
+            positions=positions,
+            intermediate_tensors=intermediate_tensors,
+            inputs_embeds=inputs_embeds,
+        )
+        return hidden_states
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor | None:
+        logits = self.logits_processor(self.lm_head, hidden_states)
+        return logits
+
+    @classmethod
+    def get_mamba_state_dtype_from_config(
+        cls,
+        vllm_config: "VllmConfig",
+    ) -> tuple[torch.dtype, torch.dtype]:
+        return MambaStateDtypeCalculator.gated_delta_net_state_dtype(
+            vllm_config.model_config.dtype,
+            vllm_config.cache_config.mamba_cache_dtype,
+            vllm_config.cache_config.mamba_ssm_cache_dtype,
+        )
+
+    @classmethod
+    def get_mamba_state_shape_from_config(
+        cls, vllm_config: "VllmConfig"
+    ) -> tuple[tuple[int, int], tuple[int, 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.gated_delta_net_state_shape(
+            tp_size,
+            hf_config.linear_num_key_heads,
+            hf_config.linear_num_value_heads,
+            hf_config.linear_key_head_dim,
+            hf_config.linear_value_head_dim,
+            hf_config.linear_conv_kernel_dim,
+            num_spec,
+        )
+
+    @classmethod
+    def get_mamba_state_copy_func(cls) -> tuple[MambaStateCopyFunc, MambaStateCopyFunc]:
+        return MambaStateCopyFuncCalculator.gated_delta_net_state_copy_func()
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
+        loader = AutoWeightsLoader(
+            self,
+            skip_prefixes=(
+                ["lm_head.weight"] if self.config.tie_word_embeddings else None
+            ),
+        )
+        return loader.load_weights(weights)
+
+
+def olmo_hybrid_gdn_full_forward(
+    hidden_states: torch.Tensor,
+    output: torch.Tensor,
+    layer_name: str,
+) -> None:
+    """Full linear attention forward wrapped as a custom op.
+
+    Prevents inductor from compiling the projections around the GDN core,
+    which would introduce numerical divergence that compounds through
+    the recurrent state.
+    """
+    forward_context: ForwardContext = get_forward_context()
+    self = forward_context.no_compile_layers[layer_name]
+    self._full_forward(
+        hidden_states=hidden_states,
+        output=output,
+    )
+
+
+def olmo_hybrid_gdn_full_forward_fake(
+    hidden_states: torch.Tensor,
+    output: torch.Tensor,
+    layer_name: str,
+) -> None:
+    """Fake implementation for torch.compile."""
+    return
+
+
+direct_register_custom_op(
+    op_name="olmo_hybrid_gdn_full_forward",
+    op_func=olmo_hybrid_gdn_full_forward,
+    mutates_args=["output"],
+    fake_impl=olmo_hybrid_gdn_full_forward_fake,
+)
+
+
+@triton.jit
+def fused_olmo_hybrid_gdn_gating_kernel(
+    g,
+    beta_output,
+    A_log,
+    a,
+    b,
+    dt_bias,
+    seq_len,
+    allow_neg_eigval: tl.constexpr,
+    NUM_HEADS: tl.constexpr,
+    beta: tl.constexpr,
+    threshold: tl.constexpr,
+    BLK_HEADS: tl.constexpr,
+):
+    i_b, i_s, i_d = tl.program_id(0), tl.program_id(1), tl.program_id(2)
+    head_off = i_d * BLK_HEADS + tl.arange(0, BLK_HEADS)
+    off = i_b * seq_len * NUM_HEADS + i_s * NUM_HEADS + head_off
+    mask = head_off < NUM_HEADS
+    blk_A_log = tl.load(A_log + head_off, mask=mask)
+    blk_a = tl.load(a + off, mask=mask)
+    blk_b = tl.load(b + off, mask=mask)
+    blk_bias = tl.load(dt_bias + head_off, mask=mask)
+
+    # g = -self.A_log.float().exp() * F.softplus(a.float() + self.dt_bias)
+    x = blk_a.to(tl.float32) + blk_bias.to(tl.float32)
+    softplus_x = tl.where(
+        beta * x <= threshold, (1 / beta) * tl.log(1 + tl.exp(beta * x)), x
+    )
+    blk_g = -tl.exp(blk_A_log.to(tl.float32)) * softplus_x
+    tl.store(g + off, blk_g.to(g.dtype.element_ty), mask=mask)
+
+    # beta = self.b_proj(hidden_states).sigmoid()
+    # if self.allow_neg_eigval: beta = beta * 2.0
+    blk_beta_output = tl.sigmoid(blk_b.to(tl.float32))
+    if allow_neg_eigval:
+        blk_beta_output = blk_beta_output * 2.0
+    tl.store(
+        beta_output + off, blk_beta_output.to(beta_output.dtype.element_ty), mask=mask
+    )
+
+
+def fused_olmo_hybrid_gdn_gating(
+    A_log: torch.Tensor,
+    a: torch.Tensor,
+    b: torch.Tensor,
+    dt_bias: torch.Tensor,
+    allow_neg_eigval: bool = False,
+    beta: float = 1.0,
+    threshold: float = 20.0,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    batch, num_heads = a.shape
+    seq_len = 1
+    grid = (batch, seq_len, triton.cdiv(num_heads, 8))
+    g = torch.empty(1, batch, num_heads, dtype=torch.float32, device=a.device)
+    beta_output = torch.empty(1, batch, num_heads, dtype=torch.float32, device=b.device)
+    fused_olmo_hybrid_gdn_gating_kernel[grid](
+        g,
+        beta_output,
+        A_log,
+        a,
+        b,
+        dt_bias,
+        seq_len,
+        allow_neg_eigval,
+        num_heads,
+        beta,
+        threshold,
+        8,
+        num_warps=1,
+    )
+    return g, beta_output

vllm/model_executor/models/registry.py

@@ -171,6 +171,7 @@ _TEXT_GENERATION_MODELS = {
     "OlmoForCausalLM": ("olmo", "OlmoForCausalLM"),
     "Olmo2ForCausalLM": ("olmo2", "Olmo2ForCausalLM"),
     "Olmo3ForCausalLM": ("olmo2", "Olmo2ForCausalLM"),
+    "OlmoHybridForCausalLM": ("olmo_hybrid", "OlmoHybridForCausalLM"),
     "OlmoeForCausalLM": ("olmoe", "OlmoeForCausalLM"),
     "OPTForCausalLM": ("opt", "OPTForCausalLM"),
     "OrionForCausalLM": ("orion", "OrionForCausalLM"),

vllm/transformers_utils/config.py

@@ -97,6 +97,7 @@ _CONFIG_REGISTRY: dict[str, type[PretrainedConfig]] = LazyConfigDict(
     speculators="SpeculatorsConfig",
     nemotron="NemotronConfig",
     olmo3="Olmo3Config",
+    olmo_hybrid="OlmoHybridConfig",
     ovis="OvisConfig",
     ultravox="UltravoxConfig",
     step3_vl="Step3VLConfig",

vllm/transformers_utils/configs/__init__.py

@@ -49,6 +49,7 @@ _CLASS_TO_MODULE: dict[str, str] = {
     "NemotronConfig": "vllm.transformers_utils.configs.nemotron",
     "NemotronHConfig": "vllm.transformers_utils.configs.nemotron_h",
     "Olmo3Config": "vllm.transformers_utils.configs.olmo3",
+    "OlmoHybridConfig": "vllm.transformers_utils.configs.olmo_hybrid",
     "OvisConfig": "vllm.transformers_utils.configs.ovis",
     "PixelShuffleSiglip2VisionConfig": "vllm.transformers_utils.configs.isaac",
     "RadioConfig": "vllm.transformers_utils.configs.radio",
@@ -102,6 +103,7 @@ __all__ = [
     "NemotronConfig",
     "NemotronHConfig",
     "Olmo3Config",
+    "OlmoHybridConfig",
     "OvisConfig",
     "PixelShuffleSiglip2VisionConfig",
     "RadioConfig",

vllm/transformers_utils/configs/olmo_hybrid.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+
+from transformers.configuration_utils import PretrainedConfig, layer_type_validation
+
+
+class OlmoHybridConfig(PretrainedConfig):
+    r"""
+        Configuration class for [`OlmoHybridModel`]. It is used to
+        instantiate an OLMo Hybrid model according to the specified
+        arguments, defining the model architecture. Instantiating a
+        configuration with the defaults will yield a similar
+        configuration to that of the
+        [allenai/Olmo-Hybrid-7B](https://huggingface.co/allenai/Olmo-Hybrid-7B)
+        model.
+
+        Configuration objects inherit from [`PreTrainedConfig`] and
+        can be used to control the model outputs. Read the
+        documentation from [`PreTrainedConfig`] for more information.
+
+        Args:
+            vocab_size (`int`, *optional*, defaults to 100352):
+                Vocabulary size of the OlmoHybrid model. Defines
+                the number of different tokens that can be
+                represented by the `inputs_ids` passed when
+                calling [`OlmoHybridModel`].
+            hidden_size (`int`, *optional*, defaults to 3840):
+                Dimension of the hidden representations.
+            intermediate_size (`int`, *optional*,
+                defaults to 11008):
+                Dimension of the MLP representations.
+            num_hidden_layers (`int`, *optional*,
+                defaults to 32):
+                Number of hidden layers in the Transformer
+                decoder.
+            num_attention_heads (`int`, *optional*,
+                defaults to 30):
+                Number of attention heads for each attention
+                layer in the Transformer decoder.
+            num_key_value_heads (`int`, *optional*):
+                This is the number of key_value heads that
+                should be used to implement Grouped Query
+                Attention. If
+                `num_key_value_heads=num_attention_heads`,
+                the model will use Multi Head Attention (MHA),
+                if `num_key_value_heads=1` the model will use
+                Multi Query Attention (MQA) otherwise GQA is
+                used. When converting a multi-head checkpoint
+                to a GQA checkpoint, each group key and value
+                head should be constructed by meanpooling all
+                the original heads within that group. For more
+                details, check out
+                [this paper](https://huggingface.co/papers/2305.13245).
+                If it is not specified, will default to
+                `num_attention_heads`.
+            hidden_act (`str` or `function`, *optional*,
+                defaults to `"silu"`):
+                The non-linear activation function (function
+                or string) in the decoder.
+            max_position_embeddings (`int`, *optional*,
+                defaults to 65536):
+                The maximum sequence length that this model
+                might ever be used with.
+            initializer_range (`float`, *optional*,
+                defaults to 0.02):
+                The standard deviation of the
+                truncated_normal_initializer for initializing
+                all weight matrices.
+            use_cache (`bool`, *optional*, defaults to `True`):
+                Whether or not the model should return the last
+                key/values attentions (not used by all models).
+                Only relevant if `config.is_decoder=True`.
+            pad_token_id (`int`, *optional*,
+                defaults to 100277):
+                Padding token id.
+            bos_token_id (`int`, *optional*):
+                Beginning of stream token id.
+            eos_token_id (`int`, *optional*,
+                defaults to 100257):
+                End of stream token id.
+            tie_word_embeddings (`bool`, *optional*,
+                defaults to `False`):
+                Whether to tie weight embeddings.
+            rope_parameters (`RopeParameters`, *optional*):
+                Dictionary containing the configuration
+                parameters for the RoPE embeddings. Can be
+                `None` to disable RoPE.
+            attention_bias (`bool`, *optional*,
+                defaults to `False`):
+                Whether to use a bias in the query, key, value
+                and output projection layers during
+                self-attention.
+            attention_dropout (`float`, *optional*,
+                defaults to 0.0):
+                The dropout ratio for the attention
+                probabilities.
+            rms_norm_eps (`float`, *optional*,
+                defaults to 1e-06):
+                The epsilon used by the rms normalization
+                layers.
+            layer_types (`list`, *optional*):
+                Attention pattern for each layer. Can contain
+                `"full_attention"` or `"linear_attention"`.
+                Defaults to linear attention for most layers
+                with full attention for every 4th layer.
+            linear_num_key_heads (`int`, *optional*):
+                Number of key heads for the linear attention
+                layers. Defaults to `num_attention_heads`.
+            linear_num_value_heads (`int`, *optional*):
+                Number of value heads for the linear attention
+                layers. Defaults to `num_attention_heads`.
+            linear_key_head_dim (`int`, *optional*):
+                Dimension of each key head in linear attention
+                layers. Defaults to
+                `0.75 * hidden_size / linear_num_key_heads`.
+            linear_value_head_dim (`int`, *optional*):
+                Dimension of each value head in linear
+                attention layers. Defaults to
+                `2 * linear_key_head_dim`.
+            linear_a_log_min (`float`, *optional*,
+                defaults to 0.0):
+                Minimum value for uniform initialization of
+                A_log in GatedDeltaNet layers.
+            linear_a_log_max (`float`, *optional*,
+                defaults to 16.0):
+                Maximum value for uniform initialization of
+                A_log in GatedDeltaNet layers.
+            linear_dt_min (`float`, *optional*,
+                defaults to 0.001):
+                Minimum value for dt initialization in
+                GatedDeltaNet layers.
+            linear_dt_max (`float`, *optional*,
+                defaults to 0.1):
+                Maximum value for dt initialization in
+                GatedDeltaNet layers.
+            linear_dt_init_floor (`float`, *optional*,
+                defaults to 0.0001):
+                Floor value for clamping dt during
+                initialization in GatedDeltaNet layers.
+            linear_conv_kernel_dim (`int`, *optional*,
+                defaults to 4):
+                Kernel size for the short convolution applied
+                to queries, keys, and values in linear
+                attention layers.
+            linear_allow_neg_eigval (`bool`, *optional*,
+                defaults to `True`):
+                Whether to allow negative eigenvalues in the
+                GatedDeltaNet recurrence. When `True`, the
+                beta parameter is scaled by 2.0 to allow
+                values in range [0, 2] instead of [0, 1].
+    ```python
+        >>> from transformers import (
+        ...     OlmoHybridModel,
+        ...     OlmoHybridConfig,
+        ... )
+
+        >>> configuration = OlmoHybridConfig()
+        >>> model = OlmoHybridModel(configuration)
+        >>> configuration = model.config
+    ```
+    """
+
+    model_type = "olmo_hybrid"
+    keys_to_ignore_at_inference = ["past_key_values"]
+    base_model_tp_plan = {
+        "layers.*.self_attn.q_proj": "colwise_gather_output",
+        "layers.*.self_attn.k_proj": "colwise_gather_output",
+        "layers.*.self_attn.v_proj": "colwise_gather_output",
+        "layers.*.self_attn.o_proj": "rowwise_split_input",
+        "layers.*.mlp.gate_proj": "colwise",
+        "layers.*.mlp.up_proj": "colwise",
+        "layers.*.mlp.down_proj": "rowwise",
+    }
+    base_model_pp_plan = {
+        "embed_tokens": (["input_ids"], ["inputs_embeds"]),
+        "layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
+        "norm": (["hidden_states"], ["hidden_states"]),
+    }
+
+    def __init__(
+        self,
+        vocab_size: int | None = 100352,
+        hidden_size: int | None = 3840,
+        intermediate_size: int | None = 11008,
+        num_hidden_layers: int | None = 32,
+        num_attention_heads: int | None = 30,
+        num_key_value_heads: int | None = None,
+        hidden_act: str | None = "silu",
+        max_position_embeddings: int | None = 65536,
+        initializer_range: float | None = 0.02,
+        use_cache: bool | None = True,
+        pad_token_id: int | None = 100277,
+        bos_token_id: int | None = None,
+        eos_token_id: int | None = 100257,
+        tie_word_embeddings: bool | None = False,
+        rope_parameters=None,
+        attention_bias: bool | None = False,
+        attention_dropout: float | None = 0.0,
+        rms_norm_eps: float | None = 1e-06,
+        layer_types: list[str] | None = None,
+        linear_num_key_heads: int | None = None,
+        linear_num_value_heads: int | None = None,
+        linear_key_head_dim: int | None = None,
+        linear_value_head_dim: int | None = None,
+        linear_a_log_min: float = 0.0,
+        linear_a_log_max: float = 16.0,
+        linear_dt_min: float = 0.001,
+        linear_dt_max: float = 0.1,
+        linear_dt_init_floor: float = 1e-4,
+        linear_conv_kernel_dim: int = 4,
+        linear_allow_neg_eigval: bool = True,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+
+        assert num_hidden_layers is not None
+        assert hidden_size is not None
+        assert num_attention_heads is not None
+
+        if layer_types is None:
+            # Default: linear attention for most layers, full attention every 4th layer
+            layer_types = ["linear_attention"] * int(num_hidden_layers)
+            for i in range(int(num_hidden_layers)):
+                if i % 4 == 3:
+                    layer_types[i] = "full_attention"
+            # Ensure at least one full attention layer for small num_hidden_layers
+            if "full_attention" not in layer_types:
+                layer_types[-1] = "full_attention"
+
+        layer_type_validation(layer_types, num_hidden_layers)
+        if "linear_attention" not in layer_types:
+            raise ValueError(
+                "OLMoHybrid expects at least one 'linear_attention' layer."
+            )
+        if all(t == "linear_attention" for t in layer_types):
+            raise ValueError("OLMoHybrid expects at least one attention layer.")
+
+        self.layer_types = layer_types
+
+        if linear_num_key_heads is None:
+            linear_num_key_heads = num_attention_heads
+        if linear_num_value_heads is None:
+            linear_num_value_heads = num_attention_heads
+        if linear_key_head_dim is None:
+            linear_key_head_dim = int(0.75 * hidden_size / linear_num_key_heads)
+        if linear_value_head_dim is None:
+            linear_value_head_dim = 2 * linear_key_head_dim
+
+        self.linear_num_key_heads = linear_num_key_heads
+        self.linear_num_value_heads = linear_num_value_heads
+        self.linear_key_head_dim = linear_key_head_dim
+        self.linear_value_head_dim = linear_value_head_dim
+        self.linear_a_log_min = linear_a_log_min
+        self.linear_a_log_max = linear_a_log_max
+        self.linear_dt_min = linear_dt_min
+        self.linear_dt_max = linear_dt_max
+        self.linear_dt_init_floor = linear_dt_init_floor
+        self.linear_conv_kernel_dim = linear_conv_kernel_dim
+        self.linear_allow_neg_eigval = linear_allow_neg_eigval
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        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.attention_bias = attention_bias
+        self.attention_dropout = attention_dropout
+        self.rope_parameters = rope_parameters
+
+        self.tie_word_embeddings = tie_word_embeddings
+        self.pad_token_id = pad_token_id
+        self.bos_token_id = bos_token_id
+        self.eos_token_id = eos_token_id