Gemma3n (Text-only) (#20134)

Signed-off-by: rshaw@neuralmagic.com <robertgshaw2@gmail.com>
Signed-off-by: Roger Wang <hey@rogerw.me>
Co-authored-by: Roger Wang <hey@rogerw.me>

docs/models/supported_models.md

@@ -336,6 +336,7 @@ Specified using `--task generate`.
 | `GemmaForCausalLM`                                | Gemma                                               | `google/gemma-2b`, `google/gemma-1.1-2b-it`, etc.                                                                                                                            | ✅︎                     | ✅︎                          | ✅︎                     |
 | `Gemma2ForCausalLM`                               | Gemma 2                                             | `google/gemma-2-9b`, `google/gemma-2-27b`, etc.                                                                                                                              | ✅︎                     | ✅︎                          | ✅︎                     |
 | `Gemma3ForCausalLM`                               | Gemma 3                                             | `google/gemma-3-1b-it`, etc.                                                                                                                                                 | ✅︎                     | ✅︎                          | ✅︎                     |
+| `Gemma3nForConditionalGeneration`                  | Gemma 3n                                             | `google/gemma-3n-E2B-it`, `google/gemma-3n-E4B-it`, etc.                                                                                                                                                 |                      |                           | ✅︎                     |
 | `GlmForCausalLM`                                  | GLM-4                                               | `THUDM/glm-4-9b-chat-hf`, etc.                                                                                                                                               | ✅︎                     | ✅︎                          | ✅︎                     |
 | `Glm4ForCausalLM`                                 | GLM-4-0414                                          | `THUDM/GLM-4-32B-0414`, etc.                                                                                                                                                 | ✅︎                     | ✅︎                          | ✅︎                     |
 | `GPT2LMHeadModel`                                 | GPT-2                                               | `gpt2`, `gpt2-xl`, etc.                                                                                                                                                      |                        | ✅︎                          | ✅︎                     |
@@ -392,6 +393,9 @@ Specified using `--task generate`.
 !!! note
     Currently, the ROCm version of vLLM supports Mistral and Mixtral only for context lengths up to 4096.
 
+!!! note
+    Only text inputs are currently supported for `Gemma3nForConditionalGeneration`. To use this model, please upgrade Hugging Face Transformers to version 4.53.0.
+
 ### Pooling Models
 
 See [this page](./pooling_models.md) for more information on how to use pooling models.

tests/models/registry.py

@@ -164,6 +164,8 @@ _TEXT_GENERATION_EXAMPLE_MODELS = {
     "GemmaForCausalLM": _HfExamplesInfo("google/gemma-1.1-2b-it"),
     "Gemma2ForCausalLM": _HfExamplesInfo("google/gemma-2-9b"),
     "Gemma3ForCausalLM": _HfExamplesInfo("google/gemma-3-1b-it"),
+    "Gemma3nForConditionalGeneration": _HfExamplesInfo("google/gemma-3n-E2B-it",    # noqa: E501
+                                          min_transformers_version="4.53"),
     "GlmForCausalLM": _HfExamplesInfo("THUDM/glm-4-9b-chat-hf"),
     "Glm4ForCausalLM": _HfExamplesInfo("THUDM/GLM-4-9B-0414"),
     "GPT2LMHeadModel": _HfExamplesInfo("openai-community/gpt2",

vllm/model_executor/layers/activation.py

@@ -135,6 +135,57 @@ class MulAndSilu(CustomOp):
     # def forward_xpu(self, x: torch.Tensor) -> torch.Tensor:
 
 
+@CustomOp.register("gelu_and_mul_sparse")
+class GeluAndMulSparse(CustomOp):
+    """An activation function for GeluAndMulSparse.
+    This activation function is used in Gemma3n. It computes:
+        up_proj = self.up_proj(x)
+        gate_proj = self.gate_proj(x)
+        gate_proj = self._gaussian_topk(gate_proj) # sparsity
+        activations = self.act_fn(gate_proj) # gelu
+        down_proj = self.down_proj(activations * up_proj)
+    Shapes:
+        x: (num_tokens, 2 * d) or (batch_size, seq_len, 2 * d)
+        return: (num_tokens, d) or (batch_size, seq_len, d)
+    """
+
+    def __init__(self, activation_sparsity: float, approximate: str = "none"):
+        super().__init__()
+        # Gelu.
+        self.approximate = approximate
+        if approximate not in ("none", "tanh"):
+            raise ValueError(f"Unknown approximate mode: {approximate}")
+
+        # Sparsity.
+        if activation_sparsity == 0.0:
+            raise ValueError(
+                "activation_sparsity is 0.0. Please use GeluAndMul.")
+        target_sparsity_tensor = torch.tensor(activation_sparsity,
+                                              dtype=torch.float32)
+        normal_dist = torch.distributions.normal.Normal(0, 1)
+        self.std_multiplier = normal_dist.icdf(target_sparsity_tensor)
+
+    def _gaussian_topk(self, x: torch.Tensor) -> torch.Tensor:
+        """Get % sparse percentile of the Gaussian distribution."""
+        # NOTE(rob): for TP>1, we could all-gather to get the means/std.
+        # But we do not do this because in expectation they are the same
+        # and in practice the eval scores are good without gathering.
+        mean = torch.mean(x, dim=-1, keepdim=True)
+        std = torch.std(x, dim=-1, keepdim=True, unbiased=False)
+        cutoff_x = mean + std * self.std_multiplier
+        return nn.functional.relu(x - cutoff_x)
+
+    def forward_native(self, x: torch.Tensor) -> torch.Tensor:
+        """PyTorch-native implementation equivalent to forward()."""
+        d = x.shape[-1] // 2
+        out = self._gaussian_topk(x[..., :d])
+        out = F.gelu(out, approximate=self.approximate)
+        return out * x[..., d:]
+
+    def forward_cuda(self, x: torch.Tensor) -> torch.Tensor:
+        return self.forward_native(x)
+
+
 @CustomOp.register("gelu_and_mul")
 class GeluAndMul(CustomOp):
     """An activation function for GeGLU.

vllm/model_executor/models/gemma3n.py

@@ -0,0 +1,811 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# Copyright 2025 The vLLM team.
+# Copyright 2025 Google Inc. HuggingFace Inc. team. All rights reserved.
+#
+#
+# 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.
+from collections.abc import Iterable
+from typing import Optional, Union
+
+import torch
+from torch import nn
+from transformers.models.gemma3n.configuration_gemma3n import Gemma3nTextConfig
+
+from vllm.attention import Attention
+from vllm.compilation.decorators import support_torch_compile
+from vllm.config import CacheConfig, VllmConfig
+from vllm.distributed import get_tensor_model_parallel_world_size
+from vllm.logger import init_logger
+from vllm.model_executor.layers.activation import (_ACTIVATION_REGISTRY,
+                                                   GeluAndMul,
+                                                   GeluAndMulSparse)
+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.quantization import QuantizationConfig
+from vllm.model_executor.layers.rotary_embedding import get_rope
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    VocabParallelEmbedding)
+from vllm.model_executor.model_loader.weight_utils import (
+    default_weight_loader, maybe_remap_kv_scale_name)
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.sequence import IntermediateTensors
+
+from .utils import (AutoWeightsLoader, extract_layer_index,
+                    is_pp_missing_parameter, make_layers, maybe_prefix)
+
+logger = init_logger(__name__)
+
+
+class Gemma3nAltUp(nn.Module):
+    """Alternating updates (Altup)
+    The AltUp module wraps transformer layers. The `predict` step modifies the
+    input to the transformer layer, and the `correct` step propagates the output
+    of the transformer layer to the sparsely updated dimensions.
+    See more in the research paper:
+    https://proceedings.neurips.cc/paper_files/paper/2023/file/f2059277ac6ce66e7e5543001afa8bb5-Paper-Conference.pdf
+    """
+
+    def __init__(
+        self,
+        hidden_size: int,
+        rms_norm_eps: float,
+        altup_num_inputs: int,
+        altup_coef_clip: float,
+        altup_active_idx: int,
+        prefix: str,
+    ):
+        super().__init__()
+
+        self.altup_num_inputs = altup_num_inputs
+        self.altup_active_idx = altup_active_idx
+        self.altup_coef_clip = altup_coef_clip
+
+        self.correction_coefs = ReplicatedLinear(
+            altup_num_inputs,
+            altup_num_inputs,
+            bias=False,
+            prefix=f"{prefix}.correction_coefs",
+            return_bias=False,
+        )
+        self.prediction_coefs = ReplicatedLinear(
+            altup_num_inputs,
+            altup_num_inputs**2,
+            bias=False,
+            prefix=f"{prefix}.prediction_coefs",
+            return_bias=False,
+        )
+        self.modality_router = ReplicatedLinear(
+            hidden_size,
+            altup_num_inputs,
+            bias=False,
+            prefix=f"{prefix}.modality_router",
+            return_bias=False,
+        )
+        self.router_norm = RMSNorm(
+            hidden_size=hidden_size,
+            eps=rms_norm_eps,
+        )
+        self.router_input_scale = torch.tensor(
+            hidden_size**-1.0, dtype=self.modality_router.weight.dtype)
+        self.correct_output_scale = nn.Parameter(
+            torch.zeros(hidden_size, dtype=torch.float32))
+
+    def _compute_router_modalities(self, x: torch.Tensor) -> torch.Tensor:
+        router_inputs = self.router_norm(x) * self.router_input_scale
+        routed = self.modality_router(router_inputs)
+        return torch.tanh(routed.float()).type_as(x)
+
+    def scale_corrected_output(self, corrected: torch.Tensor) -> torch.Tensor:
+        return (corrected.type_as(self.correct_output_scale) *
+                self.correct_output_scale).type_as(corrected)
+
+    def predict(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        # hidden:       [altup_num_inputs, num_tokens, hidden_size]
+        # modalities:   [num_tokens, num_altup_inputs]
+        # all_coefs:    [num_tokens, num_altup_inputs ** 2]
+        modalities = self._compute_router_modalities(
+            hidden_states[self.altup_active_idx])
+        all_coefs = self.prediction_coefs(modalities)
+
+        # Reshape and transpose the 2D matrix for the matmul.
+        # all_coefs_T:  [num_tokens, num_altup_inputs, num_altup_inputs]
+        all_coefs_T = all_coefs.reshape(
+            -1,
+            self.altup_num_inputs,
+            self.altup_num_inputs,
+        ).permute(0, 2, 1)
+
+        # hidden_states to [num_tokens, hidden_size, altup_num_inputs]
+        predictions = torch.matmul(hidden_states.permute(1, 2, 0), all_coefs_T)
+        # [altup_num_inputs, num_tokens, hidden_size]
+        predictions = predictions.permute(2, 0, 1)
+        predictions += hidden_states
+        return predictions.contiguous()
+
+    def correct(self, predictions: torch.Tensor,
+                activated: torch.Tensor) -> torch.Tensor:
+        # predictions:  [altup_num_inputs, num_tokens, hidden_size]
+        # activated:    [num_tokens, hidden_size]
+        # modalities:   [num_tokens, altup_num_inputs]
+        modalities = self._compute_router_modalities(activated)
+        # innovation:   [num_tokens, altup_num_inputs]
+        innovation = activated - predictions[self.altup_active_idx]
+        # innovation:   [altup_num_inputs, num_tokens, hidden_size]
+        innovation = innovation.repeat(self.altup_num_inputs, 1, 1)
+
+        # Permute to [altup_num_inputs, num_tokens] as the last dim
+        # is a scalar applied to each altup input and expand on
+        # num_tokens dim for broadcastability over hidden_size.
+        # all_coefs:    [num_tokens, altup_num_inputs]
+        all_coefs = self.correction_coefs(modalities) + 1.0
+        # all_coefs:    [altup_num_inputs, num_tokens, 1]
+        all_coefs = all_coefs.T.unsqueeze(-1)
+
+        # Elementwise (broadcast over hidden_size).
+        corrected = torch.mul(innovation, all_coefs)
+        corrected += predictions
+
+        return corrected.contiguous()
+
+
+class Gemma3nLaurelBlock(nn.Module):
+    """Learned Augmented Residual Layer"""
+
+    def __init__(self, hidden_size: int, laurel_rank: int, rms_norm_eps: float,
+                 prefix: str):
+        super().__init__()
+
+        self.linear_left = ColumnParallelLinear(
+            hidden_size,
+            laurel_rank,
+            bias=False,
+            prefix=f"{prefix}.linear_left",
+            return_bias=False,
+        )
+        self.linear_right = RowParallelLinear(laurel_rank,
+                                              hidden_size,
+                                              bias=False,
+                                              prefix=f"{prefix}.linear_right",
+                                              return_bias=False)
+        self.post_laurel_norm = RMSNorm(
+            hidden_size=hidden_size,
+            eps=rms_norm_eps,
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        laurel_x = self.linear_left(x)
+        laurel_x = self.linear_right(laurel_x)
+        normed_laurel_x = self.post_laurel_norm(laurel_x)
+        return x + normed_laurel_x
+
+
+class Gemma3nMLP(nn.Module):
+
+    def __init__(
+        self,
+        hidden_size: int,
+        intermediate_size: int,
+        hidden_activation: str,
+        activation_sparsity: float = 0.0,
+        quant_config: Optional[QuantizationConfig] = None,
+        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,
+            prefix=f"{prefix}.down_proj",
+        )
+        if hidden_activation != "gelu_pytorch_tanh":
+            raise ValueError(
+                "Gemma3 uses `gelu_pytorch_tanh` as the hidden activation "
+                "function. Please set `hidden_act` and `hidden_activation` to "
+                "`gelu_pytorch_tanh`.")
+
+        self.act_fn = GeluAndMulSparse(
+            activation_sparsity=activation_sparsity,
+            approximate="tanh") if activation_sparsity > 0.0 else GeluAndMul(
+                approximate="tanh")
+
+    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 Gemma3nAttention(nn.Module):
+
+    def __init__(self,
+                 config: Gemma3nTextConfig,
+                 hidden_size: int,
+                 num_heads: int,
+                 num_kv_heads: int,
+                 head_dim: int,
+                 max_position_embeddings: int,
+                 cache_config: Optional[CacheConfig] = None,
+                 quant_config: Optional[QuantizationConfig] = None,
+                 prefix: str = "") -> None:
+        super().__init__()
+        self.config = config
+        self.hidden_size = hidden_size
+        tp_size = get_tensor_model_parallel_world_size()
+        self.total_num_heads = num_heads
+        assert self.total_num_heads % tp_size == 0
+        self.num_heads = self.total_num_heads // tp_size
+        self.total_num_kv_heads = num_kv_heads
+        if self.total_num_kv_heads >= tp_size:
+            # Number of KV heads is greater than TP size, so we partition
+            # the KV heads across multiple tensor parallel GPUs.
+            assert self.total_num_kv_heads % tp_size == 0
+        else:
+            # Number of KV heads is less than TP size, so we replicate
+            # the KV heads across multiple tensor parallel GPUs.
+            assert tp_size % self.total_num_kv_heads == 0
+        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
+        self.head_dim = head_dim
+        self.q_size = self.num_heads * self.head_dim
+        self.kv_size = self.num_kv_heads * self.head_dim
+
+        self.qkv_proj = QKVParallelLinear(
+            hidden_size,
+            self.head_dim,
+            self.total_num_heads,
+            self.total_num_kv_heads,
+            bias=config.attention_bias,
+            quant_config=quant_config,
+            prefix=f"{prefix}.qkv_proj",
+        )
+        self.o_proj = RowParallelLinear(
+            self.total_num_heads * self.head_dim,
+            hidden_size,
+            bias=config.attention_bias,
+            quant_config=quant_config,
+            prefix=f"{prefix}.o_proj",
+        )
+        self.q_norm = RMSNorm(hidden_size=self.head_dim,
+                              eps=config.rms_norm_eps)
+        self.k_norm = RMSNorm(hidden_size=self.head_dim,
+                              eps=config.rms_norm_eps)
+        self.v_norm = RMSNorm(hidden_size=self.head_dim,
+                              eps=config.rms_norm_eps,
+                              has_weight=False)
+
+        layer_idx = extract_layer_index(prefix)
+        if config.layer_types[layer_idx] == "sliding_attention":
+            self.sliding_window = config.sliding_window
+            rope_theta = config.rope_local_base_freq
+            rope_scaling = {"rope_type": "default"}
+        else:
+            self.sliding_window = None
+            rope_theta = config.rope_theta
+            rope_scaling = config.rope_scaling
+
+        first_kv_shared_layer_idx = (config.num_hidden_layers -
+                                     config.num_kv_shared_layers)
+        self.is_kv_shared = layer_idx >= first_kv_shared_layer_idx
+
+        if self.is_kv_shared:
+            # Last full attention layer is 1 before sharing
+            # Last sliding attention layer is 2 before sharing
+            offset = 2 if self.sliding_window is not None else 1
+            kv_shared_layer_index = first_kv_shared_layer_idx - offset
+            kv_sharing_target_layer_name = f"model.language_model.layers.{kv_shared_layer_index}.self_attn.attn"  # noqa: E501
+        else:
+            kv_sharing_target_layer_name = None
+
+        self.rotary_emb = get_rope(
+            self.head_dim,
+            rotary_dim=self.head_dim,
+            max_position=max_position_embeddings,
+            base=rope_theta,
+            is_neox_style=True,
+            rope_scaling=rope_scaling,
+        )
+
+        self.attn = Attention(
+            num_heads=self.num_heads,
+            head_size=self.head_dim,
+            scale=1.0,
+            num_kv_heads=self.num_kv_heads,
+            cache_config=cache_config,
+            quant_config=quant_config,
+            per_layer_sliding_window=self.sliding_window,
+            kv_sharing_target_layer_name=kv_sharing_target_layer_name,
+            prefix=f"{prefix}.attn")
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        **kwargs,
+    ) -> 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 = q.unflatten(-1, (self.num_heads, self.head_dim))
+        q = self.q_norm(q)
+        q = q.flatten(-2, -1)
+        k = k.unflatten(-1, (self.num_kv_heads, self.head_dim))
+        k = self.k_norm(k)
+        k = k.flatten(-2, -1)
+        v = v.unflatten(-1, (self.num_kv_heads, self.head_dim))
+        v = self.v_norm(v)
+        v = v.flatten(-2, -1)
+
+        q, k = self.rotary_emb(positions, q, k)
+        attn_output = self.attn(q, k, v)
+
+        output, _ = self.o_proj(attn_output)
+        return output
+
+
+class Gemma3nDecoderLayer(nn.Module):
+
+    def __init__(
+        self,
+        config: Gemma3nTextConfig,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.altup_active_idx = config.altup_active_idx
+        assert config.altup_correct_scale
+
+        self.altup = Gemma3nAltUp(
+            hidden_size=config.hidden_size,
+            rms_norm_eps=config.rms_norm_eps,
+            altup_num_inputs=config.altup_num_inputs,
+            altup_coef_clip=config.altup_coef_clip,
+            altup_active_idx=config.altup_active_idx,
+            prefix=f"{prefix}.altup",
+        )
+        self.self_attn = Gemma3nAttention(
+            config=config,
+            hidden_size=config.hidden_size,
+            num_heads=config.num_attention_heads,
+            num_kv_heads=config.num_key_value_heads,
+            head_dim=config.head_dim,
+            max_position_embeddings=config.max_position_embeddings,
+            cache_config=cache_config,
+            quant_config=quant_config,
+            prefix=f"{prefix}.self_attn",
+        )
+        self.mlp = Gemma3nMLP(
+            hidden_size=config.hidden_size,
+            # NOTE: Matformer https://github.com/huggingface/transformers/blob/a52478253bbe522a420e88ea3940d4d98a935300/src/transformers/models/gemma3n/modular_gemma3n.py#L258 # noqa: E501
+            intermediate_size=config.intermediate_size[extract_layer_index(
+                prefix)],
+            hidden_activation=config.hidden_activation,
+            quant_config=quant_config,
+            activation_sparsity=config.activation_sparsity_pattern[
+                extract_layer_index(prefix)],
+            prefix=f"{prefix}.mlp",
+        )
+        self.laurel = Gemma3nLaurelBlock(
+            hidden_size=config.hidden_size,
+            laurel_rank=config.laurel_rank,
+            rms_norm_eps=config.rms_norm_eps,
+            prefix=f"{prefix}.laurel",
+        )
+
+        # NOTE(rob): should be ColumnParallelLinear and RowParallelLinear
+        # But, we need to add per_layer_input_gate(x) to per_layer_input.
+        # per_layer_input cannot be sharded, so we replicate for now.
+        self.per_layer_input_gate = ReplicatedLinear(
+            config.hidden_size,
+            config.hidden_size_per_layer_input,
+            bias=False,
+            prefix=f"{prefix}.per_layer_input_gate",
+            return_bias=False,
+        )
+        self.per_layer_projection = ReplicatedLinear(
+            config.hidden_size_per_layer_input,
+            config.hidden_size,
+            bias=False,
+            prefix=f"{prefix}.per_layer_projection",
+            return_bias=False,
+        )
+
+        # LayerNorms.
+        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,
+        )
+        self.pre_feedforward_layernorm = RMSNorm(
+            config.hidden_size,
+            eps=config.rms_norm_eps,
+        )
+        self.post_feedforward_layernorm = RMSNorm(
+            config.hidden_size,
+            eps=config.rms_norm_eps,
+        )
+        self.post_per_layer_input_norm = RMSNorm(
+            config.hidden_size,
+            eps=config.rms_norm_eps,
+        )
+
+        self.act_fn = _ACTIVATION_REGISTRY[config.hidden_activation]
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        per_layer_input: torch.Tensor,
+        **kwargs,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+
+        # ActUp (predict).
+        predictions = self.altup.predict(hidden_states)
+        active_prediction = predictions[self.altup_active_idx]
+        active_prediction_normed = self.input_layernorm(active_prediction)
+        laurel_output = self.laurel(active_prediction_normed)
+
+        # Attention.
+        attn = self.self_attn(
+            positions=positions,
+            hidden_states=active_prediction_normed,
+            **kwargs,
+        )
+        attn = self.post_attention_layernorm(attn)
+        attn_gated = attn + active_prediction
+        attn_laurel = (attn_gated + laurel_output) / torch.sqrt(
+            torch.tensor(2.0))
+
+        # MLP.
+        attn_norm = self.pre_feedforward_layernorm(attn_laurel)
+        attn_ffw = self.mlp(attn_norm)
+        attn_ffw_norm = self.post_feedforward_layernorm(attn_ffw)
+        attn_ffw_laurel_gated = attn_laurel + attn_ffw_norm
+
+        # ActUp (connect).
+        corrected_predictions = self.altup.correct(predictions,
+                                                   attn_ffw_laurel_gated)
+        first_prediction = corrected_predictions[self.altup_active_idx]
+        first_prediction = self.altup.scale_corrected_output(first_prediction)
+
+        # per_layer_input_gate adapted from jax.numpy.einsum("btd,dp->btp", ...)
+        first_prediction = self.per_layer_input_gate(first_prediction)
+        first_prediction = self.act_fn(first_prediction)
+        first_prediction = torch.mul(first_prediction, per_layer_input)
+
+        # per_layer_projection adapted from jax.numpy.einsum("btp,pd->btd", ...)
+        first_prediction = self.per_layer_projection(first_prediction)
+        first_prediction = self.post_per_layer_input_norm(first_prediction)
+        corrected_predictions[1:] += first_prediction
+
+        return corrected_predictions
+
+
+@support_torch_compile
+class Gemma3nTextModel(nn.Module):
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config = vllm_config.model_config.hf_config.text_config
+        cache_config = vllm_config.cache_config
+        quant_config = vllm_config.quant_config
+        self.config = config
+        self.quant_config = quant_config
+
+        self.embed_tokens = VocabParallelEmbedding(
+            config.vocab_size,
+            config.hidden_size,
+            prefix=f"{prefix}.embed_tokens",
+        )
+        self.embed_scale = torch.tensor(
+            config.hidden_size**0.5,
+            dtype=self.embed_tokens.weight.dtype,
+        )
+        self.embed_tokens_per_layer = VocabParallelEmbedding(
+            config.vocab_size_per_layer_input,
+            config.num_hidden_layers * config.hidden_size_per_layer_input,
+            prefix=f"{prefix}.per_layer_embed_tokens",
+        )
+        self.embed_scale_per_layer = torch.tensor(
+            config.hidden_size_per_layer_input**0.5,
+            dtype=self.embed_tokens.weight.dtype,
+        )
+        self.per_layer_model_projection = ColumnParallelLinear(
+            config.hidden_size,
+            config.num_hidden_layers * config.hidden_size_per_layer_input,
+            bias=False,
+            gather_output=True,
+            return_bias=False,
+            prefix=f"{prefix}.per_layer_model_projection",
+        )
+        self.per_layer_projection_norm = RMSNorm(
+            hidden_size=config.hidden_size_per_layer_input,
+            eps=config.rms_norm_eps,
+        )
+        self.per_layer_input_scale = torch.rsqrt(torch.tensor(2.0)).to(
+            self.embed_tokens.weight.dtype)
+        self.per_layer_projection_scale = torch.tensor(
+            config.hidden_size**0.5,
+            dtype=self.embed_tokens.weight.dtype,
+        )
+        self.altup_projections = nn.ModuleList([
+            ColumnParallelLinear(
+                config.hidden_size,
+                config.hidden_size,
+                bias=False,
+                gather_output=True,
+                return_bias=False,
+                prefix=f"{prefix}.{idx-1}.altup_projections",
+            ) for idx in range(1, self.config.altup_num_inputs)
+        ])
+        self.altup_unembed_projections = nn.ModuleList([
+            ColumnParallelLinear(
+                config.hidden_size,
+                config.hidden_size,
+                bias=False,
+                gather_output=True,
+                return_bias=False,
+                prefix=f"{prefix}.{idx-1}.altup_unembed_projections",
+            ) for idx in range(1, self.config.altup_num_inputs)
+        ])
+
+        # Transformer blocks.
+        self.start_layer, self.end_layer, self.layers = make_layers(
+            config.num_hidden_layers,
+            lambda prefix: Gemma3nDecoderLayer(
+                config, cache_config, quant_config, prefix=prefix),
+            prefix=f"{prefix}.layers")
+        self.norm = RMSNorm(
+            config.hidden_size,
+            eps=config.rms_norm_eps,
+        )
+        self.eps = torch.tensor(torch.finfo().min)
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.embed_tokens(input_ids) * self.embed_scale
+
+    def get_per_layer_input_embeddings(
+            self, input_ids: torch.Tensor) -> torch.Tensor:
+        # Deal with the fact that vocab_size_per_layer_input < vocab_size
+        # which causes us to have some out of vocab tokens by setting
+        # those token ids to 0. This matches the HF implementation.
+        per_layer_inputs_mask = torch.logical_and(
+            input_ids >= 0, input_ids < self.config.vocab_size_per_layer_input)
+        per_layer_inputs_tokens = torch.where(per_layer_inputs_mask, input_ids,
+                                              torch.zeros_like(input_ids))
+        return self.embed_tokens_per_layer(
+            per_layer_inputs_tokens) * self.embed_scale_per_layer
+
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor],
+        positions: torch.Tensor,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        **kwargs,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        if inputs_embeds is not None:
+            hidden_states_0 = inputs_embeds
+        else:
+            hidden_states_0 = self.get_input_embeddings(input_ids)
+
+        # Per layer inputs.
+        if input_ids is None:
+            raise ValueError("Passing None for input ids is not supported.")
+        per_layer_inputs = self.get_per_layer_input_embeddings(input_ids)
+        per_layer_inputs = per_layer_inputs.reshape(
+            -1, self.config.num_hidden_layers,
+            self.config.hidden_size_per_layer_input)
+        per_layer_projection = self.per_layer_model_projection(hidden_states_0)
+        per_layer_projection = per_layer_projection.reshape(
+            *hidden_states_0.shape[:-1],
+            self.config.num_hidden_layers,
+            self.config.hidden_size_per_layer_input,
+        )
+        per_layer_projection = self.per_layer_projection_norm(
+            per_layer_projection)
+        per_layer_inputs = per_layer_projection + per_layer_inputs
+        per_layer_inputs *= self.per_layer_input_scale
+
+        # Altup embed.
+        hidden_states = [hidden_states_0] * self.config.altup_num_inputs
+        target_magnitude = torch.mean(hidden_states_0**2, dim=-1,
+                                      keepdim=True)**0.5
+        for i in range(1, self.config.altup_num_inputs):
+            hidden_states[i] = self.altup_projections[i - 1](hidden_states[i])
+            new_magnitude = torch.mean(hidden_states[i]**2,
+                                       dim=-1,
+                                       keepdim=True)**0.5
+            hidden_states[i] *= target_magnitude / torch.maximum(
+                new_magnitude, self.eps)
+        hidden_states = torch.stack(hidden_states, dim=0)
+
+        # Transformer blocks.
+        for layer_idx, layer in enumerate(self.layers):
+            # [altup_num_inputs, num_tokens, hidden_size]
+            hidden_states = layer(
+                positions=positions,
+                hidden_states=hidden_states,
+                per_layer_input=per_layer_inputs[:, layer_idx, :],
+                **kwargs,
+            )
+
+        # Altup unembed.
+        target_magnitude = torch.mean(hidden_states[0]**2,
+                                      dim=-1,
+                                      keepdim=True)**0.5
+        for i in range(1, self.config.altup_num_inputs):
+            hidden_states[i] = self.altup_unembed_projections[i - 1](
+                hidden_states[i])
+            new_magnitude = torch.mean(hidden_states[i]**2,
+                                       dim=-1,
+                                       keepdim=True)**0.5
+            hidden_states[i] *= target_magnitude / torch.maximum(
+                new_magnitude, self.eps)
+        # [altup_num_inputs,num_tokens,hidden_size] -> [num_tokens,hidden_size]
+        hidden_states = torch.mean(hidden_states, dim=0)
+
+        return self.norm(hidden_states)
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("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),
+        ]
+        params_dict = dict(self.named_parameters())
+        loaded_params: set[str] = set()
+        for name, loaded_weight in weights:
+            if (self.quant_config is not None and
+                (scale_name := self.quant_config.get_cache_scale(name))):
+                # Loading kv cache scales for compressed-tensors quantization
+                param = params_dict[scale_name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                loaded_weight = loaded_weight[0]
+                weight_loader(param, loaded_weight)
+                loaded_params.add(scale_name)
+                continue
+            for (param_name, shard_name, shard_id) in stacked_params_mapping:
+                if shard_name not in name:
+                    continue
+                # Avoid spurious match with ".up_proj".
+                if "altup_projections" in name:
+                    continue
+                name = name.replace(shard_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:
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    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)
+            loaded_params.add(name)
+
+        return loaded_params
+
+
+class Gemma3nModel(nn.Module):
+
+    def __init__(self, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        self.language_model = Gemma3nTextModel(vllm_config=vllm_config,
+                                               prefix=maybe_prefix(
+                                                   prefix, "language_model"))
+
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor],
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        **kwargs,
+    ) -> torch.Tensor:
+        return self.language_model(input_ids=input_ids,
+                                   positions=positions,
+                                   inputs_embeds=inputs_embeds,
+                                   **kwargs)
+
+
+class Gemma3nForConditionalGeneration(nn.Module):
+    packed_modules_mapping = {
+        "qkv_proj": [
+            "q_proj",
+            "k_proj",
+            "v_proj",
+        ],
+        "gate_up_proj": [
+            "gate_proj",
+            "up_proj",
+        ],
+    }
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        config = vllm_config.model_config.hf_config
+        lora_config = vllm_config.lora_config
+        del lora_config  # Unused.
+        super().__init__()
+        self.config = config
+        self.model = Gemma3nModel(vllm_config=vllm_config,
+                                  prefix=maybe_prefix(prefix, "model"))
+        self.logits_processor = LogitsProcessor(
+            config.text_config.vocab_size,
+            soft_cap=config.text_config.final_logit_softcapping)
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.model.language_model.get_input_embeddings(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        **kwargs,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        hidden_states = self.model(input_ids, positions, intermediate_tensors,
+                                   inputs_embeds, **kwargs)
+        return hidden_states
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: Optional[SamplingMetadata],
+    ) -> Optional[torch.Tensor]:
+        logits = self.logits_processor(self.model.language_model.embed_tokens,
+                                       hidden_states, sampling_metadata)
+        return logits
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        loader = AutoWeightsLoader(self,
+                                   skip_substrs=([
+                                       "embed_audio.", "embed_vision.",
+                                       "audio_tower.", "vision_tower."
+                                   ]))
+        return loader.load_weights(weights)

vllm/model_executor/models/registry.py

@@ -58,6 +58,8 @@ _TEXT_GENERATION_MODELS = {
     "GemmaForCausalLM": ("gemma", "GemmaForCausalLM"),
     "Gemma2ForCausalLM": ("gemma2", "Gemma2ForCausalLM"),
     "Gemma3ForCausalLM": ("gemma3", "Gemma3ForCausalLM"),
+    #TODO(ywang96): Support multimodal gemma3n
+    "Gemma3nForConditionalGeneration": ("gemma3n", "Gemma3nForConditionalGeneration"),    # noqa: E501
     "GlmForCausalLM": ("glm", "GlmForCausalLM"),
     "Glm4ForCausalLM": ("glm4", "Glm4ForCausalLM"),
     "GPT2LMHeadModel": ("gpt2", "GPT2LMHeadModel"),