[Model] Add Olmo 3 model support (#11396)

docs/supported_models/generative_models.md

@@ -33,6 +33,7 @@ in the GitHub search bar.
 | **Gemma** (v1, v2, v3)              | `google/gemma-3-1b-it`                            | Google’s family of efficient multilingual models (1B–27B); Gemma 3 offers a 128K context window, and its larger (4B+) variants support vision input. |
 | **Phi** (Phi-1.5, Phi-2, Phi-3, Phi-4, Phi-MoE series) | `microsoft/Phi-4-multimodal-instruct`, `microsoft/Phi-3.5-MoE-instruct` | Microsoft’s Phi family of small models (1.3B–5.6B); Phi-4-multimodal (5.6B) processes text, images, and speech, Phi-4-mini is a high-accuracy text model and Phi-3.5-MoE is a mixture-of-experts model. |
 | **MiniCPM** (v3, 4B)               | `openbmb/MiniCPM3-4B`                            | OpenBMB’s series of compact LLMs for edge devices; MiniCPM 3 (4B) achieves GPT-3.5-level results in text tasks. |
+| **OLMo** (2, 3)               | `allenai/OLMo-2-1124-7B-Instruct`                       | Allen AI’s series of Open Language Models designed to enable the science of language models. |
 | **OLMoE** (Open MoE)               | `allenai/OLMoE-1B-7B-0924`                       | Allen AI’s open Mixture-of-Experts model (7B total, 1B active parameters) delivering state-of-the-art results with sparse expert activation. |
 | **StableLM** (3B, 7B)               | `stabilityai/stablelm-tuned-alpha-7b`            | StabilityAI’s early open-source LLM (3B & 7B) for general text generation; a demonstration model with basic instruction-following ability. |
 | **Command-R** (Cohere)              | `CohereForAI/c4ai-command-r-v01`                 | Cohere’s open conversational LLM (Command series) optimized for long context, retrieval-augmented generation, and tool use. |

python/sglang/srt/configs/__init__.py

@@ -10,6 +10,7 @@ from sglang.srt.configs.kimi_vl import KimiVLConfig
 from sglang.srt.configs.kimi_vl_moonvit import MoonViTConfig
 from sglang.srt.configs.longcat_flash import LongcatFlashConfig
 from sglang.srt.configs.nemotron_h import NemotronHConfig
+from sglang.srt.configs.olmo3 import Olmo3Config
 from sglang.srt.configs.qwen3_next import Qwen3NextConfig
 from sglang.srt.configs.step3_vl import (
     Step3TextConfig,
@@ -29,6 +30,7 @@ __all__ = [
     "Step3VLConfig",
     "Step3TextConfig",
     "Step3VisionEncoderConfig",
+    "Olmo3Config",
     "Qwen3NextConfig",
     "DotsVLMConfig",
     "DotsOCRConfig",

python/sglang/srt/configs/olmo3.py

@@ -0,0 +1,105 @@
+# coding=utf-8
+# Copyright 2024 The Qwen team, Alibaba Group and the 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.
+"""Olmo3 model configuration"""
+
+import enum
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.modeling_rope_utils import rope_config_validation
+from transformers.utils import logging
+
+logger = logging.get_logger(__name__)
+
+
+class Olmo3LayerType(enum.Enum):
+    full_attention = "full_attention"
+    sliding_attention = "sliding_attention"
+
+
+class Olmo3Config(PretrainedConfig):
+
+    model_type = "olmo3"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=50304,
+        hidden_size=4096,
+        intermediate_size=11008,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        num_key_value_heads=None,
+        hidden_act="silu",
+        max_position_embeddings=2048,
+        initializer_range=0.02,
+        use_cache=True,
+        pad_token_id=1,
+        bos_token_id=None,
+        eos_token_id=50279,
+        tie_word_embeddings=False,
+        rope_theta=10000.0,
+        rope_scaling=None,
+        attention_bias=False,
+        attention_dropout=0.0,
+        rms_norm_eps=1e-5,
+        sliding_window=4096,
+        layer_types=None,
+        **kwargs,
+    ):
+        # This model uses Olmo3ForCausalLM in transformers but Olmo2ForCausalLM
+        # in sglang.
+        if "architectures" not in kwargs:
+            kwargs["architectures"] = ["Olmo2ForCausalLM"]
+        elif "Olmo3ForCausalLM" in kwargs["architectures"]:
+            kwargs["architectures"].remove("Olmo3ForCausalLM")
+            kwargs["architectures"].append("Olmo2ForCausalLM")
+
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
+        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.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.rope_scaling = rope_scaling
+        rope_config_validation(self)
+        self.attention_bias = attention_bias
+        self.attention_dropout = attention_dropout
+
+        self.rms_norm_eps = rms_norm_eps
+
+        self.sliding_window = sliding_window
+        self.layer_types = layer_types
+        if self.layer_types is None:
+            self.layer_types = [
+                "sliding_attention" if (i + 1) % 4 != 0 else "full_attention"
+                for i in range(self.num_hidden_layers)
+            ]

python/sglang/srt/models/olmo2.py

@@ -48,6 +48,12 @@ from sglang.srt.model_loader.weight_utils import default_weight_loader
 from sglang.srt.utils import add_prefix, make_layers
 
 
+# Aligned with HF's implementation, using sliding window inclusive with the last token
+# SGLang assumes exclusive
+def get_attention_sliding_window_size(config):
+    return config.sliding_window - 1 if hasattr(config, "sliding_window") else None
+
+
 class Olmo2Attention(nn.Module):
     """
     This is the attention block where the output is computed as
@@ -85,6 +91,8 @@ class Olmo2Attention(nn.Module):
         self.num_kv_heads = max(1, self.total_num_kv_heads // self.tp_size)
 
         self.head_dim = self.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 = config.max_position_embeddings
         self.rope_theta = config.rope_theta
 
@@ -104,12 +112,26 @@ class Olmo2Attention(nn.Module):
             eps=self.config.rms_norm_eps,
         )
         self.q_norm = RMSNorm(self.config.hidden_size, eps=self.config.rms_norm_eps)
-        # Rotary embeddings.
+
+        sliding_window = None
+        if (
+            layer_types := getattr(self.config, "layer_types", None)
+        ) is not None and layer_types[layer_id] == "sliding_attention":
+            sliding_window = get_attention_sliding_window_size(self.config)
+
+        # Rotary embeddings. Rope scaling is only applied on full attention
+        # layers.
+        self.rope_scaling = (
+            self.config.rope_scaling
+            if sliding_window is None
+            else {"rope_type": "default"}
+        )
         self.rotary_emb = get_rope(
             self.head_dim,
             rotary_dim=self.head_dim,
             max_position=self.max_position_embeddings,
             base=self.rope_theta,
+            rope_scaling=self.rope_scaling,
         )
         self.scaling = self.head_dim**-0.5
         self.attn = RadixAttention(
@@ -118,6 +140,7 @@ class Olmo2Attention(nn.Module):
             self.scaling,
             num_kv_heads=self.num_kv_heads,
             layer_id=layer_id,
+            sliding_window_size=sliding_window,
             quant_config=quant_config,
             prefix=add_prefix("attn", prefix),
         )
@@ -152,7 +175,7 @@ class Olmo2Attention(nn.Module):
         forward_batch: ForwardBatch,
     ) -> torch.Tensor:
         qkv, _ = self.qkv_proj(hidden_states)
-        q, k, v = qkv.chunk(chunks=3, dim=-1)
+        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
         q, k = self._apply_qk_norm(q, k)
         q, k = self.rotary_emb(positions, q, k)
         attn_output = self.attn(q, k, v, forward_batch)
@@ -224,6 +247,7 @@ class Olmo2DecoderLayer(nn.Module):
         prefix: str = "",
     ):
         super().__init__()
+        self.layer_id = layer_id
         # Attention block.
         self.self_attn = Olmo2Attention(
             config, layer_id, quant_config, prefix=add_prefix("self_attn", prefix)
@@ -280,8 +304,8 @@ class Olmo2Model(nn.Module):
         self.layers = make_layers(
             config.num_hidden_layers,
             lambda idx, prefix: Olmo2DecoderLayer(
-                layer_id=idx,
                 config=config,
+                layer_id=idx,
                 quant_config=quant_config,
                 prefix=prefix,
             ),
@@ -294,7 +318,7 @@ class Olmo2Model(nn.Module):
         input_ids: torch.Tensor,
         positions: torch.Tensor,
         forward_batch: ForwardBatch,
-        input_embeds: torch.Tensor = None,
+        input_embeds: Optional[torch.Tensor] = None,
     ) -> torch.Tensor:
         """
         :param input_ids: A tensor of shape `(batch_size, seq_len)`.
@@ -351,6 +375,9 @@ class Olmo2ForCausalLM(nn.Module):
             )
         self.logits_processor = LogitsProcessor(config)
 
+    def get_attention_sliding_window_size(self):
+        return get_attention_sliding_window_size(self.config)
+
     def forward(
         self,
         input_ids: torch.Tensor,

python/sglang/srt/server_args.py

@@ -36,6 +36,7 @@ from sglang.srt.utils import (
     configure_ipv6,
     get_device,
     get_device_memory_capacity,
+    get_device_sm,
     is_cuda,
     is_flashinfer_available,
     is_hip,
@@ -942,6 +943,31 @@ class ServerArgs:
                 f"Disable hybrid SWA memory for {model_arch} as it is not yet supported."
             )
             self.disable_hybrid_swa_memory = True
+        elif model_arch in ["Olmo2ForCausalLM"]:
+            # FIXME: https://github.com/sgl-project/sglang/pull/7367 is not compatible with Olmo3 model.
+            logger.warning(
+                f"Disabling hybrid SWA memory for {model_arch} as it is not yet supported."
+            )
+            self.disable_hybrid_swa_memory = True
+
+            if self.attention_backend is None:
+                if is_cuda() and is_sm100_supported():
+                    self.attention_backend = "trtllm_mha"
+                elif is_cuda() and get_device_sm() >= 80:
+                    self.attention_backend = "fa3"
+                else:
+                    self.attention_backend = "triton"
+
+            # Flashinfer appears to degrade performance when sliding window attention
+            # is used for the Olmo2 architecture. Olmo2 does not use sliding window attention
+            # but Olmo3 does.
+            assert (
+                self.attention_backend != "flashinfer"
+            ), "FlashInfer backend can significantly degrade the performance of Olmo3 models."
+
+            logger.info(
+                f"Using {self.attention_backend} as attention backend for {model_arch}."
+            )
 
         if is_deepseek_nsa(hf_config):
             if (

python/sglang/srt/utils/common.py

@@ -2530,6 +2530,7 @@ def is_fa3_default_architecture(hf_config):
         "Qwen2ForCausalLM",
         "Llama4ForConditionalGeneration",
         "LlamaForCausalLM",
+        "Olmo2ForCausalLM",
         "Gemma2ForCausalLM",
         "Gemma3ForConditionalGeneration",
         "Qwen3ForCausalLM",

python/sglang/srt/utils/hf_transformers_utils.py

@@ -47,6 +47,7 @@ from sglang.srt.configs import (
     LongcatFlashConfig,
     MultiModalityConfig,
     NemotronHConfig,
+    Olmo3Config,
     Qwen3NextConfig,
     Step3VLConfig,
 )
@@ -64,6 +65,7 @@ _CONFIG_REGISTRY: Dict[str, Type[PretrainedConfig]] = {
     InternVLChatConfig.model_type: InternVLChatConfig,
     Step3VLConfig.model_type: Step3VLConfig,
     LongcatFlashConfig.model_type: LongcatFlashConfig,
+    Olmo3Config.model_type: Olmo3Config,
     Qwen3NextConfig.model_type: Qwen3NextConfig,
     FalconH1Config.model_type: FalconH1Config,
     DotsVLMConfig.model_type: DotsVLMConfig,

test/srt/models/test_generation_models.py

@@ -61,6 +61,7 @@ ALL_MODELS = [
     ModelCase("Qwen/Qwen2.5-14B-Instruct"),
     ModelCase("HuggingFaceTB/SmolLM-135M-Instruct", skip_long_prompt=True),
     ModelCase("allenai/OLMo-1B-0724-hf", decode_tolerance=8e-2, skip_long_prompt=True),
+    ModelCase("shanearora/2025-sep-a-base-model"),
     ModelCase(
         "THUDM/glm-4-9b-chat", tp_size=2, trust_remote_code=True, skip_long_prompt=True
     ),