[Model] Add FlexOlmo model implementation (#24923)

Signed-off-by: Shane A <shanea@allenai.org>

vllm/transformers_utils/config.py

@@ -74,6 +74,7 @@ _CONFIG_REGISTRY: dict[str, type[PretrainedConfig]] = LazyConfigDict(
     deepseek_vl_v2="DeepseekVLV2Config",
     deepseek_v3="DeepseekV3Config",
     deepseek_v32="DeepseekV3Config",
+    flex_olmo="FlexOlmoConfig",
     kimi_vl="KimiVLConfig",
     Llama_Nemotron_Nano_VL="Nemotron_Nano_VL_Config",
     RefinedWeb="RWConfig",  # For tiiuae/falcon-40b(-instruct)

vllm/transformers_utils/configs/__init__.py

@@ -17,6 +17,7 @@ from vllm.transformers_utils.configs.eagle import EAGLEConfig
 # tiiuae/falcon-7b(-instruct) models. Newer Falcon models will use the
 # `FalconConfig` class from the official HuggingFace transformers library.
 from vllm.transformers_utils.configs.falcon import RWConfig
+from vllm.transformers_utils.configs.flex_olmo import FlexOlmoConfig
 from vllm.transformers_utils.configs.jais import JAISConfig
 from vllm.transformers_utils.configs.kimi_vl import KimiVLConfig
 from vllm.transformers_utils.configs.lfm2_moe import Lfm2MoeConfig
@@ -45,6 +46,7 @@ __all__ = [
     "DeepseekV3Config",
     "DotsOCRConfig",
     "EAGLEConfig",
+    "FlexOlmoConfig",
     "RWConfig",
     "JAISConfig",
     "Lfm2MoeConfig",

vllm/transformers_utils/configs/flex_olmo.py

@@ -0,0 +1,77 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from transformers.configuration_utils import PretrainedConfig
+
+
+class FlexOlmoConfig(PretrainedConfig):
+    model_type = "flex_olmo"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=100352,
+        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=4096,
+        initializer_range=0.02,
+        rms_norm_eps=1e-06,
+        use_cache=True,
+        pad_token_id=100277,
+        bos_token_id=None,
+        eos_token_id=100257,
+        tie_word_embeddings=False,
+        rope_theta=500000.0,
+        rope_scaling=None,
+        attention_bias=False,
+        attention_dropout=0.0,
+        num_experts_per_tok=5,
+        num_experts=7,
+        output_router_logits=False,
+        router_aux_loss_coef=0.01,
+        norm_topk_prob=False,
+        **kwargs,
+    ):
+        if "architectures" not in kwargs:
+            kwargs["architectures"] = ["FlexOlmoForCausalLM"]
+
+        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.rms_norm_eps = rms_norm_eps
+        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.num_experts_per_tok = num_experts_per_tok
+        self.num_experts = num_experts
+        self.output_router_logits = output_router_logits
+        self.router_aux_loss_coef = router_aux_loss_coef
+        self.norm_topk_prob = norm_topk_prob
+        # Validate the correctness of rotary position embeddings parameters
+        # BC: if there is a 'type' field, move it to 'rope_type'.
+        if self.rope_scaling is not None and "type" in self.rope_scaling:
+            self.rope_scaling["rope_type"] = self.rope_scaling["type"]