[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/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