[Model] Add Afmoe architecture implementation (#28332)

Signed-off-by: Maziyar Panahi <maziyar.panahi@iscpif.fr>
Signed-off-by: Pranav <veldurthipranav@gmail.com>
Co-authored-by: Maziyar Panahi <maziyar.panahi@iscpif.fr>

docs/models/supported_models.md

@@ -351,6 +351,7 @@ th {
 
 | Architecture | Models | Example HF Models | [LoRA](../features/lora.md) | [PP](../serving/parallelism_scaling.md) |
 |--------------|--------|-------------------|----------------------|---------------------------|
+| `AfmoeForCausalLM` | Afmoe | TBA | ✅︎ | ✅︎ |
 | `ApertusForCausalLM` | Apertus | `swiss-ai/Apertus-8B-2509`, `swiss-ai/Apertus-70B-Instruct-2509`, etc. | ✅︎ | ✅︎ |
 | `AquilaForCausalLM` | Aquila, Aquila2 | `BAAI/Aquila-7B`, `BAAI/AquilaChat-7B`, etc. | ✅︎ | ✅︎ |
 | `ArceeForCausalLM` | Arcee (AFM) | `arcee-ai/AFM-4.5B-Base`, etc. | ✅︎ | ✅︎ |

tests/models/registry.py

@@ -173,6 +173,10 @@ class _HfExamplesInfo:
 
 _TEXT_GENERATION_EXAMPLE_MODELS = {
     # [Decoder-only]
+    "AfmoeForCausalLM": _HfExamplesInfo(
+        "arcee-ai/Trinity-Nano",
+        is_available_online=False,
+    ),
     "ApertusForCausalLM": _HfExamplesInfo("swiss-ai/Apertus-8B-Instruct-2509"),
     "AquilaModel": _HfExamplesInfo("BAAI/AquilaChat-7B", trust_remote_code=True),
     "AquilaForCausalLM": _HfExamplesInfo("BAAI/AquilaChat2-7B", trust_remote_code=True),

vllm/model_executor/models/registry.py

@@ -56,6 +56,7 @@ logger = init_logger(__name__)
 
 _TEXT_GENERATION_MODELS = {
     # [Decoder-only]
+    "AfmoeForCausalLM": ("afmoe", "AfmoeForCausalLM"),
     "ApertusForCausalLM": ("apertus", "ApertusForCausalLM"),
     "AquilaModel": ("llama", "LlamaForCausalLM"),
     "AquilaForCausalLM": ("llama", "LlamaForCausalLM"),  # AquilaChat2

vllm/transformers_utils/config.py

@@ -77,6 +77,7 @@ class LazyConfigDict(dict):
 
 
 _CONFIG_REGISTRY: dict[str, type[PretrainedConfig]] = LazyConfigDict(
+    afmoe="AfmoeConfig",
     chatglm="ChatGLMConfig",
     deepseek_vl_v2="DeepseekVLV2Config",
     deepseek_v32=DeepseekV3Config,

vllm/transformers_utils/configs/__init__.py

@@ -7,6 +7,7 @@ Model configs may be defined in this directory for the following reasons:
 - There is a need to override the existing config to support vLLM.
 """
 
+from vllm.transformers_utils.configs.afmoe import AfmoeConfig
 from vllm.transformers_utils.configs.chatglm import ChatGLMConfig
 from vllm.transformers_utils.configs.deepseek_vl2 import DeepseekVLV2Config
 from vllm.transformers_utils.configs.dotsocr import DotsOCRConfig
@@ -40,6 +41,7 @@ from vllm.transformers_utils.configs.step3_vl import (
 from vllm.transformers_utils.configs.ultravox import UltravoxConfig
 
 __all__ = [
+    "AfmoeConfig",
     "ChatGLMConfig",
     "DeepseekVLV2Config",
     "DotsOCRConfig",

vllm/transformers_utils/configs/afmoe.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from transformers.configuration_utils import PretrainedConfig
+
+
+class AfmoeConfig(PretrainedConfig):
+    model_type = "afmoe"
+
+    def __init__(
+        self,
+        vocab_size: int = 200_192,
+        hidden_size: int = 2048,
+        intermediate_size: int = 6144,
+        moe_intermediate_size: int = 1408,
+        num_hidden_layers: int = 32,
+        num_dense_layers: int = 1,
+        num_attention_heads: int = 16,
+        num_key_value_heads: int | None = None,
+        head_dim: int = 128,
+        hidden_act: str = "silu",
+        max_position_embeddings: int = 131072,
+        initializer_range: float = 0.02,
+        rms_norm_eps: float = 1e-5,
+        use_cache: bool = True,
+        tie_word_embeddings: bool = False,
+        rope_theta: float = 10000.0,
+        rope_scaling: dict | None = None,
+        num_experts: int = 64,
+        num_experts_per_tok: int = 6,
+        num_shared_experts: int = 2,
+        num_expert_groups: int = 1,
+        num_limited_groups: int = 1,
+        score_func: str = "sigmoid",
+        route_norm: bool = True,
+        route_scale: float = 1.0,
+        global_attn_every_n_layers: int = 4,
+        sliding_window: int = 2048,
+        layer_types: list[str] | None = None,
+        attention_dropout: float = 0.0,
+        mup_enabled: bool = False,
+        n_group: int = 1,
+        topk_group: int = 1,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_dense_layers = num_dense_layers
+        self.num_attention_heads = num_attention_heads
+        self.num_key_value_heads = num_key_value_heads or num_attention_heads
+        self.head_dim = head_dim
+        self.hidden_act = hidden_act
+        self.max_position_embeddings = max_position_embeddings
+        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.moe_intermediate_size = moe_intermediate_size
+        self.num_experts = num_experts
+        self.num_experts_per_tok = num_experts_per_tok
+        self.num_shared_experts = num_shared_experts
+        self.num_expert_groups = num_expert_groups
+        self.num_limited_groups = num_limited_groups
+        self.score_func = score_func
+        self.route_norm = route_norm
+        self.route_scale = route_scale
+
+        self.global_attn_every_n_layers = global_attn_every_n_layers
+        self.sliding_window = sliding_window
+        self.layer_types = layer_types
+        self.attention_dropout = attention_dropout
+
+        self.mup_enabled = mup_enabled
+        self.n_group = n_group
+        self.topk_group = topk_group
+
+        super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)
+
+
+__all__ = ["AfmoeConfig"]