[Model] Add huggingface skt/A.X-K1 model (#32407)

Signed-off-by: Sungwan(Alex) Kim <sw0726.kim@sktelecom.com>
Signed-off-by: fort726 <38447663+fort726@users.noreply.github.com>
Co-authored-by: Sungwan(Alex) Kim <sw0726.kim@sktelecom.com>
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
Co-authored-by: TJian <tunjian.tan@embeddedllm.com>

docs/models/supported_models.md

@@ -369,6 +369,7 @@ th {
 | `AquilaForCausalLM` | Aquila, Aquila2 | `BAAI/Aquila-7B`, `BAAI/AquilaChat-7B`, etc. | ✅︎ | ✅︎ |
 | `ArceeForCausalLM` | Arcee (AFM) | `arcee-ai/AFM-4.5B-Base`, etc. | ✅︎ | ✅︎ |
 | `ArcticForCausalLM` | Arctic | `Snowflake/snowflake-arctic-base`, `Snowflake/snowflake-arctic-instruct`, etc. | | ✅︎ |
+| `AXK1ForCausalLM` | A.X-K1 | `skt/A.X-K1`, etc. | | ✅︎ |
 | `BaiChuanForCausalLM` | Baichuan2, Baichuan | `baichuan-inc/Baichuan2-13B-Chat`, `baichuan-inc/Baichuan-7B`, etc. | ✅︎ | ✅︎ |
 | `BailingMoeForCausalLM` | Ling | `inclusionAI/Ling-lite-1.5`, `inclusionAI/Ling-plus`, etc. | ✅︎ | ✅︎ |
 | `BailingMoeV2ForCausalLM` | Ling | `inclusionAI/Ling-mini-2.0`, etc. | ✅︎ | ✅︎ |

tests/models/registry.py

@@ -194,6 +194,7 @@ _TEXT_GENERATION_EXAMPLE_MODELS = {
     "ArcticForCausalLM": _HfExamplesInfo(
         "Snowflake/snowflake-arctic-instruct", trust_remote_code=True
     ),
+    "AXK1ForCausalLM": _HfExamplesInfo("skt/A.X-K1", trust_remote_code=True),
     "BaiChuanForCausalLM": _HfExamplesInfo(
         "baichuan-inc/Baichuan-7B", trust_remote_code=True
     ),

vllm/model_executor/models/registry.py

@@ -75,6 +75,7 @@ _TEXT_GENERATION_MODELS = {
     "AquilaForCausalLM": ("llama", "LlamaForCausalLM"),  # AquilaChat2
     "ArceeForCausalLM": ("arcee", "ArceeForCausalLM"),
     "ArcticForCausalLM": ("arctic", "ArcticForCausalLM"),
+    "AXK1ForCausalLM": ("AXK1", "AXK1ForCausalLM"),
     # baichuan-7b, upper case 'C' in the class name
     "BaiChuanForCausalLM": ("baichuan", "BaiChuanForCausalLM"),
     # baichuan-13b, lower case 'c' in the class name

vllm/transformers_utils/configs/AXK1.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from typing import Any
+
+from transformers import PretrainedConfig
+
+
+class AXK1Config(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`AXK1Model`].
+    It is used to instantiate an A.X model according to the specified arguments,
+    defining the model architecture. Instantiating a configuration with the defaults
+    will yield a similar configuration to that of the A.X K1.
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control
+    the model outputs. Read the documentation from [`PretrainedConfig`] for more
+    information.
+    Args:
+        vocab_size (`int`, *optional*, defaults to 163840):
+            Vocabulary size of the A.X K1 model. Defines the number of different
+            tokens that can be represented by the `inputs_ids` passed when calling
+            [`AXK1Model`]
+        hidden_size (`int`, *optional*, defaults to 7168):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 18432):
+            Dimension of the MLP representations.
+        moe_intermediate_size (`int`, *optional*, defaults to 2048):
+            Dimension of the MoE representations.
+        num_hidden_layers (`int`, *optional*, defaults to 61):
+            Number of hidden layers in the Transformer decoder.
+        num_nextn_predict_layers (`int`, *optional*, defaults to 1):
+            Number of nextn predict layers in the AXK1 Model.
+        num_attention_heads (`int`, *optional*, defaults to 64):
+            Number of attention heads for each attention layer in the Transformer
+            decoder.
+        n_shared_experts (`int`, *optional*, defaults to 1):
+            Number of shared experts, None means dense model.
+        n_routed_experts (`int`, *optional*, defaults to 192):
+            Number of routed experts, None means dense model.
+        routed_scaling_factor (`float`, *optional*, defaults to 2.5):
+            Scaling factor or routed experts.
+        topk_method (`str`, *optional*, defaults to `noaux_tc`):
+            Topk method used in routed gate.
+        n_group (`int`, *optional*, defaults to 8):
+            Number of groups for routed experts.
+        topk_group (`int`, *optional*, defaults to 4):
+            Number of selected groups for each token(for each token, ensuring the
+            selected experts is only within `topk_group` groups).
+        num_experts_per_tok (`int`, *optional*, defaults to 8):
+            Number of selected experts, None means dense model.
+        moe_layer_freq (`int`, *optional*, defaults to 1):
+            The frequency of the MoE layer: one expert layer for every
+            `moe_layer_freq - 1` dense layers.
+        first_k_dense_replace (`int`, *optional*, defaults to 1):
+            Number of dense layers in shallow layers
+            (embed->dense->dense->...->dense->moe->moe...->lm_head).
+                      \--k dense layers--/
+        norm_topk_prob (`bool`, *optional*, defaults to True):
+            Whether to normalize the weights of the routed experts.
+        scoring_func (`str`, *optional*, defaults to 'sigmoid'):
+            Method of computing expert weights.
+        aux_loss_alpha (`float`, *optional*, defaults to 0.0001):
+            Auxiliary loss weight coefficient.
+        seq_aux = (`bool`, *optional*, defaults to True):
+            Whether to compute the auxiliary loss for each individual sample.
+        num_key_value_heads (`int`, *optional*):
+            This is the number of key_value heads that should be used to implement
+            Grouped Query Attention. If `num_key_value_heads=num_attention_heads`,
+            the model will use Multi Head Attention (MHA), if `num_key_value_heads=1
+            the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+            converting a multi-head checkpoint to a GQA checkpoint, each group key and
+            value head should be constructed by meanpooling all the original heads
+            within that group. For more details checkout
+            [this paper](https://arxiv.org/pdf/2305.13245.pdf).
+            If it is not specified, will default to `num_attention_heads`.
+        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+            The non-linear activation function (function or string) in the decoder.
+        max_position_embeddings (`int`, *optional*, defaults to 131072):
+            The maximum sequence length that this model might ever be used with.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for
+            initializing all weight matrices.
+        rms_norm_eps (`float`, *optional*, defaults to 1e-06):
+            The epsilon used by the rms normalization layers.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions
+            (not used by all models). Only relevant if `config.is_decoder=True`.
+        pad_token_id (`int`, *optional*):
+            Padding token id.
+        bos_token_id (`int`, *optional*, defaults to 163691):
+            Beginning of stream token id.
+        eos_token_id (`int`, *optional*, defaults to 163691):
+            End of stream token id.
+        pretraining_tp (`int`, *optional*, defaults to 1):
+            Experimental feature. Tensor parallelism rank used during pretraining.
+            Please refer to
+            [this document](https://huggingface.co/docs/transformers/parallelism)
+            to understand more about it. This value is necessary to ensure exact
+            reproducibility of the pretraining results. Please refer to
+            [this issue](https://github.com/pytorch/pytorch/issues/76232).
+        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+            Whether to tie weight embeddings
+        rope_theta (`float`, *optional*, defaults to 10000.0):
+            The base period of the RoPE embeddings.
+        rope_scaling (`Dict`, *optional*):
+            Dictionary containing the scaling configuration for the RoPE embeddings.
+            Currently supports two scaling strategies: linear and dynamic.
+            Their scaling factor must be a float greater than 1. The expected format
+            is  `{"type": strategy name, "factor": scaling factor}`. When using this
+            flag, don't update `max_position_embeddings` to the expected new maximum.
+        attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
+            Whether to use a bias in the query, key, value and output projection
+            layers during self-attention.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+    """
+
+    model_type = "AXK1"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size: int = 163840,
+        hidden_size: int = 7168,
+        intermediate_size: int = 18432,
+        moe_intermediate_size: int = 2048,
+        num_hidden_layers: int = 61,
+        num_nextn_predict_layers: int | None = 1,
+        num_attention_heads: int = 64,
+        num_key_value_heads: int = 64,
+        n_shared_experts: int | None = 1,
+        n_routed_experts: int | None = 192,
+        ep_size: int | None = 8,  ## Ignored - Expert parallel size
+        routed_scaling_factor: float | None = 2.5,
+        kv_lora_rank: int | None = 512,
+        q_lora_rank: int | None = 1536,
+        qk_rope_head_dim: int | None = 64,
+        v_head_dim: int | None = 128,
+        qk_nope_head_dim: int | None = 128,
+        topk_method: str | None = "noaux_tc",
+        n_group: int | None = 8,
+        topk_group: int | None = 4,
+        num_experts_per_tok: int | None = 8,
+        moe_layer_freq: int | None = 1,
+        first_k_dense_replace: int = 1,
+        norm_topk_prob: bool = True,
+        scoring_func: str | None = "sigmoid",
+        aux_loss_alpha: float | None = 0.0001,
+        seq_aux: float | None = True,
+        hidden_act: str | None = "silu",
+        max_position_embeddings: int | None = 131072,
+        initializer_range: float | None = 0.02,
+        rms_norm_eps: float = 1e-6,
+        use_cache: bool | None = True,
+        pad_token_id: int | None = None,
+        bos_token_id: int | None = 163691,
+        eos_token_id: int | None = 163691,
+        pretraining_tp: int | None = 1,
+        tie_word_embeddings: bool | None = False,
+        rope_theta: float | None = 10000.0,
+        rope_scaling: dict[str, Any] | None = None,
+        rope_parameters: dict[str, Any] | None = None,
+        attention_bias: bool | None = False,
+        attention_dropout: float | None = 0.0,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.moe_intermediate_size = moe_intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_nextn_predict_layers = num_nextn_predict_layers
+        self.num_attention_heads = num_attention_heads
+        self.n_shared_experts = n_shared_experts
+        self.n_routed_experts = n_routed_experts
+        self.ep_size = ep_size
+        self.routed_scaling_factor = routed_scaling_factor
+        self.kv_lora_rank = kv_lora_rank
+        self.q_lora_rank = q_lora_rank
+        self.qk_rope_head_dim = qk_rope_head_dim
+        self.v_head_dim = v_head_dim
+        self.qk_nope_head_dim = qk_nope_head_dim
+        self.topk_method = topk_method
+        self.n_group = n_group
+        self.topk_group = topk_group
+        self.num_experts_per_tok = num_experts_per_tok
+        self.moe_layer_freq = moe_layer_freq
+        self.first_k_dense_replace = first_k_dense_replace
+        self.norm_topk_prob = norm_topk_prob
+        self.scoring_func = scoring_func
+        self.aux_loss_alpha = aux_loss_alpha
+        self.seq_aux = seq_aux
+        # 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.pretraining_tp = pretraining_tp
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.rope_scaling = rope_scaling
+        self.rope_parameters = rope_parameters
+        self.attention_bias = attention_bias
+        self.attention_dropout = attention_dropout
+
+        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,
+        )

vllm/transformers_utils/configs/__init__.py

@@ -16,6 +16,7 @@ import importlib
 
 _CLASS_TO_MODULE: dict[str, str] = {
     "AfmoeConfig": "vllm.transformers_utils.configs.afmoe",
+    "AXK1Config": "vllm.transformers_utils.configs.AXK1",
     "BagelConfig": "vllm.transformers_utils.configs.bagel",
     "ChatGLMConfig": "vllm.transformers_utils.configs.chatglm",
     "ColModernVBertConfig": "vllm.transformers_utils.configs.colmodernvbert",
@@ -70,6 +71,7 @@ _CLASS_TO_MODULE: dict[str, str] = {
 
 __all__ = [
     "AfmoeConfig",
+    "AXK1Config",
     "BagelConfig",
     "ChatGLMConfig",
     "ColModernVBertConfig",

vllm/transformers_utils/model_arch_config_convertor.py

@@ -233,6 +233,7 @@ class ModelArchConfigConvertorBase:
         if not hasattr(self.hf_text_config, "model_type"):
             return False
         elif self.hf_text_config.model_type in (
+            "AXK1",
             "deepseek_v2",
             "deepseek_v3",
             "deepseek_v32",
@@ -253,7 +254,13 @@ class ModelArchConfigConvertorBase:
             # underlying architecture
             return (
                 self.hf_text_config.model.model_type
-                in ("deepseek_v2", "deepseek_v3", "deepseek_v32", "deepseek_mtp")
+                in (
+                    "AXK1",
+                    "deepseek_v2",
+                    "deepseek_v3",
+                    "deepseek_v32",
+                    "deepseek_mtp",
+                )
                 and self.hf_text_config.kv_lora_rank is not None
             )
         return False