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[Model] Add Olmo3 model implementation (#24534)

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Signed-off-by: Shane A <shanea@allenai.org>
Co-authored-by: Isotr0py <mozf@mail2.sysu.edu.cn>
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Shane A 2025-09-12 20:26:21 -07:00 committed by GitHub
parent 7f2ea7074e
commit 89e08d6d18
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7 changed files with 114 additions and 14 deletions
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1
docs/models/supported_models.md
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@ -389,6 +389,7 @@ th {
| `NemotronHForCausalLM` | Nemotron-H | `nvidia/Nemotron-H-8B-Base-8K`, `nvidia/Nemotron-H-47B-Base-8K`, `nvidia/Nemotron-H-56B-Base-8K`, etc. | ✅︎ | ✅︎ | ✅︎ |
| `OLMoForCausalLM` | OLMo | `allenai/OLMo-1B-hf`, `allenai/OLMo-7B-hf`, etc. | ✅︎ | ✅︎ | ✅︎ |
| `OLMo2ForCausalLM` | OLMo2 | `allenai/OLMo-2-0425-1B`, etc. | ✅︎ | ✅︎ | ✅︎ |
| `OLMo3ForCausalLM` | OLMo3 | TBA | ✅︎ | ✅︎ | ✅︎ |
| `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. | | ✅︎ | ✅︎ |

1
tests/models/registry.py
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@ -301,6 +301,7 @@ _TEXT_GENERATION_EXAMPLE_MODELS = {
trust_remote_code=True),
"OlmoForCausalLM": _HfExamplesInfo("allenai/OLMo-1B-hf"),
"Olmo2ForCausalLM": _HfExamplesInfo("allenai/OLMo-2-0425-1B"),
"Olmo3ForCausalLM": _HfExamplesInfo("shanearora/2025-sep-a-base-model"),
"OlmoeForCausalLM": _HfExamplesInfo("allenai/OLMoE-1B-7B-0924-Instruct"),
"OPTForCausalLM": _HfExamplesInfo("facebook/opt-125m",
{"1b": "facebook/opt-iml-max-1.3b"}),

42
vllm/model_executor/models/olmo2.py
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@ -52,10 +52,11 @@ from vllm.model_executor.layers.vocab_parallel_embedding import (
from vllm.model_executor.model_loader.weight_utils import default_weight_loader
from vllm.model_executor.models.interfaces import SupportsLoRA, SupportsPP
from vllm.model_executor.models.utils import (
AutoWeightsLoader, is_pp_missing_parameter,
AutoWeightsLoader, extract_layer_index, is_pp_missing_parameter,
make_empty_intermediate_tensors_factory, make_layers, maybe_prefix)
from vllm.model_executor.sampling_metadata import SamplingMetadata
from vllm.sequence import IntermediateTensors
from vllm.transformers_utils.configs import Olmo3Config
class Olmo2Attention(nn.Module):
@ -68,7 +69,7 @@ class Olmo2Attention(nn.Module):
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
super().__init__()
self.config = vllm_config.model_config.hf_config
assert isinstance(self.config, Olmo2Config)
assert isinstance(self.config, (Olmo2Config, Olmo3Config))
hidden_size = self.config.hidden_size
self.tp_size = get_tensor_model_parallel_world_size()
@ -111,14 +112,14 @@ class Olmo2Attention(nn.Module):
self.q_norm = RMSNorm(self.config.hidden_size,
eps=self.config.rms_norm_eps)
# Rotary embeddings.
self.rotary_emb = get_rope(
self.head_dim,
rotary_dim=self.head_dim,
max_position=self.max_position_embeddings,
base=self.rope_theta, # type: ignore
)
self.scaling = self.head_dim**-0.5
layer_idx = extract_layer_index(prefix)
sliding_window = None
if ((layer_types := getattr(self.config, "layer_types", None))
is not None and layer_types[layer_idx] == "sliding_attention"):
sliding_window = self.config.sliding_window
self.attn = Attention(
self.num_heads,
self.head_dim,
@ -126,7 +127,20 @@ class Olmo2Attention(nn.Module):
num_kv_heads=self.num_kv_heads,
cache_config=vllm_config.cache_config,
quant_config=vllm_config.quant_config,
prefix=prefix,
per_layer_sliding_window=sliding_window,
prefix=f"{prefix}.attn",
)
# Rotary embeddings. Rope scaling is only applied on full attention
# layers.
self.rope_scaling = (self.config.rope_scaling
if sliding_window is None else None)
self.rotary_emb = get_rope(
self.head_dim,
rotary_dim=self.head_dim,
max_position=self.max_position_embeddings,
base=self.rope_theta, # type: ignore
rope_scaling=self.rope_scaling,
)
# Attention output projection.
@ -176,7 +190,7 @@ class Olmo2MLP(nn.Module):
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
super().__init__()
config = vllm_config.model_config.hf_config
assert isinstance(config, Olmo2Config)
assert isinstance(config, (Olmo2Config, Olmo3Config))
hidden_size = config.hidden_size
intermediate_size = config.intermediate_size
@ -221,7 +235,7 @@ class Olmo2DecoderLayer(nn.Module):
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
super().__init__()
config = vllm_config.model_config.hf_config
assert isinstance(config, Olmo2Config)
assert isinstance(config, (Olmo2Config, Olmo3Config))
# Attention block.
self.self_attn = Olmo2Attention(vllm_config=vllm_config,
prefix=f"{prefix}.self_attn")
@ -261,7 +275,7 @@ class Olmo2Model(nn.Module):
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
super().__init__()
self.config = vllm_config.model_config.hf_config
assert isinstance(self.config, Olmo2Config)
assert isinstance(self.config, (Olmo2Config, Olmo3Config))
self.embed_tokens = VocabParallelEmbedding(
self.config.vocab_size,
@ -376,7 +390,7 @@ class Olmo2ForCausalLM(nn.Module, SupportsPP, SupportsLoRA):
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
super().__init__()
config = vllm_config.model_config.hf_config
assert isinstance(config, Olmo2Config)
assert isinstance(config, (Olmo2Config, Olmo3Config))
self.config = config
self.model = Olmo2Model(vllm_config=vllm_config,
prefix=maybe_prefix(prefix, "model"))

1
vllm/model_executor/models/registry.py
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@ -120,6 +120,7 @@ _TEXT_GENERATION_MODELS = {
"NemotronHForCausalLM": ("nemotron_h", "NemotronHForCausalLM"),
"OlmoForCausalLM": ("olmo", "OlmoForCausalLM"),
"Olmo2ForCausalLM": ("olmo2", "Olmo2ForCausalLM"),
"Olmo3ForCausalLM": ("olmo2", "Olmo2ForCausalLM"),
"OlmoeForCausalLM": ("olmoe", "OlmoeForCausalLM"),
"OPTForCausalLM": ("opt", "OPTForCausalLM"),
"OrionForCausalLM": ("orion", "OrionForCausalLM"),

1
vllm/transformers_utils/config.py
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@ -75,6 +75,7 @@ _CONFIG_REGISTRY: dict[str, type[PretrainedConfig]] = LazyConfigDict(
eagle="EAGLEConfig",
speculators="SpeculatorsConfig",
nemotron="NemotronConfig",
olmo3="Olmo3Config",
ovis="OvisConfig",
ultravox="UltravoxConfig",
step3_vl="Step3VLConfig",

2
vllm/transformers_utils/configs/__init__.py
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@ -23,6 +23,7 @@ from vllm.transformers_utils.configs.moonvit import MoonViTConfig
from vllm.transformers_utils.configs.nemotron import NemotronConfig
from vllm.transformers_utils.configs.nemotron_h import NemotronHConfig
from vllm.transformers_utils.configs.nemotron_vl import Nemotron_Nano_VL_Config
from vllm.transformers_utils.configs.olmo3 import Olmo3Config
from vllm.transformers_utils.configs.ovis import OvisConfig
from vllm.transformers_utils.configs.qwen3_next import Qwen3NextConfig
from vllm.transformers_utils.configs.speculators.base import SpeculatorsConfig
@ -45,6 +46,7 @@ __all__ = [
"NemotronConfig",
"NemotronHConfig",
"Nemotron_Nano_VL_Config",
"Olmo3Config",
"OvisConfig",
"SpeculatorsConfig",
"UltravoxConfig",

80
vllm/transformers_utils/configs/olmo3.py Normal file
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@ -0,0 +1,80 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from transformers.configuration_utils import PretrainedConfig
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 vLLM.
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
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)
]