[Model] Add HyperCLOVAX-SEED-Think-14B language model support (#37107)

Signed-off-by: bigshanedogg <bigshane319@gmail.com>

docs/models/supported_models.md

@@ -418,6 +418,7 @@ th {
 | `Grok1ForCausalLM` | Grok2 | `xai-org/grok-2` | ✅︎ | ✅︎ |
 | `HunYuanDenseV1ForCausalLM` | Hunyuan Dense | `tencent/Hunyuan-7B-Instruct` | ✅︎ | ✅︎ |
 | `HunYuanMoEV1ForCausalLM` | Hunyuan-A13B | `tencent/Hunyuan-A13B-Instruct`, `tencent/Hunyuan-A13B-Pretrain`, `tencent/Hunyuan-A13B-Instruct-FP8`, etc. | ✅︎ | ✅︎ |
+| `HyperCLOVAXForCausalLM` | HyperCLOVAX-SEED-Think-14B | `naver-hyperclovax/HyperCLOVAX-SEED-Think-14B` | ✅︎ | ✅︎ |
 | `InternLMForCausalLM` | InternLM | `internlm/internlm-7b`, `internlm/internlm-chat-7b`, etc. | ✅︎ | ✅︎ |
 | `InternLM2ForCausalLM` | InternLM2 | `internlm/internlm2-7b`, `internlm/internlm2-chat-7b`, etc. | ✅︎ | ✅︎ |
 | `InternLM3ForCausalLM` | InternLM3 | `internlm/internlm3-8b-instruct`, etc. | ✅︎ | ✅︎ |

tests/models/language/generation/test_common.py

@@ -103,6 +103,10 @@ AITER_MODEL_LIST = [
             marks=[pytest.mark.core_model, pytest.mark.cpu_model],
         ),
         pytest.param("swiss-ai/Apertus-8B-Instruct-2509"),  # apertus
+        pytest.param(
+            "naver-hyperclovax/HyperCLOVAX-SEED-Think-14B",  # hyperclovax
+            marks=[large_gpu_mark(min_gb=32)],
+        ),
     ],
 )
 @pytest.mark.parametrize("max_tokens", [32])

tests/models/registry.py

@@ -320,7 +320,7 @@ _TEXT_GENERATION_EXAMPLE_MODELS = {
         "tencent/Hunyuan-A13B-Instruct", trust_remote_code=True
     ),
     "HyperCLOVAXForCausalLM": _HfExamplesInfo(
-        "naver-hyperclovax/HyperCLOVAX-SEED-Think-32B",
+        "naver-hyperclovax/HyperCLOVAX-SEED-Think-14B",
         trust_remote_code=True,
     ),
     "InternLMForCausalLM": _HfExamplesInfo(

vllm/model_executor/models/hyperclovax.py

@@ -0,0 +1,551 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# SPDX-FileCopyrightText: Copyright 2025 NAVER Cloud HyperCLOVA team
+
+# Adapted from
+# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/llama/modeling_llama.py
+# Copyright 2025 NAVER Cloud HyperCLOVA team. All rights reserved.
+# Copyright 2023 The vLLM team.
+# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# 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.
+"""Inference-only HyperCLOVAX model compatible with HuggingFace weights."""
+
+from collections.abc import Iterable
+from itertools import islice
+
+import torch
+from torch import nn
+
+from vllm.compilation.decorators import support_torch_compile
+from vllm.config import CacheConfig, VllmConfig
+from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
+from vllm.model_executor.layers.activation import SiluAndMul
+from vllm.model_executor.layers.attention import Attention
+from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.model_executor.layers.linear import (
+    MergedColumnParallelLinear,
+    QKVParallelLinear,
+    RowParallelLinear,
+)
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.rotary_embedding import get_rope
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    ParallelLMHead,
+    VocabParallelEmbedding,
+)
+from vllm.model_executor.model_loader.weight_utils import (
+    default_weight_loader,
+    maybe_remap_kv_scale_name,
+)
+from vllm.sequence import IntermediateTensors
+from vllm.transformers_utils.configs.hyperclovax import HyperCLOVAXConfig
+
+from .interfaces import SupportsLoRA, SupportsPP
+from .utils import (
+    AutoWeightsLoader,
+    PPMissingLayer,
+    is_pp_missing_parameter,
+    make_empty_intermediate_tensors_factory,
+    make_layers,
+    maybe_prefix,
+)
+
+
+class HyperCLOVAXMLP(nn.Module):
+    def __init__(
+        self,
+        hidden_size: int,
+        intermediate_size: int,
+        hidden_act: str,
+        quant_config: QuantizationConfig | None = None,
+        bias: bool = False,
+        prefix: str = "",
+        reduce_results: bool = True,
+        disable_tp: bool = False,
+    ) -> None:
+        super().__init__()
+        self.gate_up_proj = MergedColumnParallelLinear(
+            input_size=hidden_size,
+            output_sizes=[intermediate_size] * 2,
+            bias=bias,
+            quant_config=quant_config,
+            disable_tp=disable_tp,
+            prefix=f"{prefix}.gate_up_proj",
+        )
+        self.down_proj = RowParallelLinear(
+            input_size=intermediate_size,
+            output_size=hidden_size,
+            bias=bias,
+            quant_config=quant_config,
+            reduce_results=reduce_results,
+            disable_tp=disable_tp,
+            prefix=f"{prefix}.down_proj",
+        )
+        if hidden_act != "silu":
+            raise ValueError(
+                f"Unsupported activation: {hidden_act}. Only silu is supported for now."
+            )
+        self.act_fn = SiluAndMul()
+
+    def forward(self, x):
+        x, _ = self.gate_up_proj(x)
+        x = self.act_fn(x)
+        x, _ = self.down_proj(x)
+        return x
+
+
+class HyperCLOVAXAttention(nn.Module):
+    def __init__(
+        self,
+        config: HyperCLOVAXConfig,
+        hidden_size: int,
+        num_heads: int,
+        num_kv_heads: int,
+        max_position_embeddings: int = 8192,
+        quant_config: QuantizationConfig | None = None,
+        bias: bool = False,
+        cache_config: CacheConfig | None = None,
+        prefix: str = "",
+        dual_chunk_attention_config: dict | None = None,
+    ) -> None:
+        super().__init__()
+        self.hidden_size = hidden_size
+        tp_size = get_tensor_model_parallel_world_size()
+        self.total_num_heads = num_heads
+        assert self.total_num_heads % tp_size == 0
+        self.num_heads = self.total_num_heads // tp_size
+        self.total_num_kv_heads = num_kv_heads
+        if self.total_num_kv_heads >= tp_size:
+            # Number of KV heads is greater than TP size, so we partition
+            # the KV heads across multiple tensor parallel GPUs.
+            assert self.total_num_kv_heads % tp_size == 0
+        else:
+            # Number of KV heads is less than TP size, so we replicate
+            # the KV heads across multiple tensor parallel GPUs.
+            assert tp_size % self.total_num_kv_heads == 0
+        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
+        self.head_dim = getattr(
+            config, "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.scaling = config.attention_multiplier
+
+        self.qkv_proj = QKVParallelLinear(
+            hidden_size=hidden_size,
+            head_size=self.head_dim,
+            total_num_heads=self.total_num_heads,
+            total_num_kv_heads=self.total_num_kv_heads,
+            bias=bias,
+            quant_config=quant_config,
+            prefix=f"{prefix}.qkv_proj",
+        )
+
+        self.o_proj = RowParallelLinear(
+            input_size=self.total_num_heads * self.head_dim,
+            output_size=hidden_size,
+            bias=bias,
+            quant_config=quant_config,
+            prefix=f"{prefix}.o_proj",
+        )
+
+        self.rotary_emb = get_rope(
+            self.head_dim,
+            max_position=max_position_embeddings,
+            is_neox_style=True,
+            rope_parameters=getattr(config, "rope_parameters", None),
+            dual_chunk_attention_config=dual_chunk_attention_config,
+        )
+
+        self.attn = Attention(
+            self.num_heads,
+            self.head_dim,
+            self.scaling,
+            num_kv_heads=self.num_kv_heads,
+            cache_config=cache_config,
+            quant_config=quant_config,
+            prefix=f"{prefix}.attn",
+        )
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor:
+        qkv, _ = self.qkv_proj(hidden_states)
+        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+        q, k = self.rotary_emb(positions, q, k)
+        attn_output = self.attn(q, k, v)
+        output, _ = self.o_proj(attn_output)
+        return output
+
+
+class HyperCLOVAXDecoderLayer(nn.Module):
+    def __init__(
+        self,
+        vllm_config: VllmConfig,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+
+        config = vllm_config.model_config.hf_config
+        cache_config = vllm_config.cache_config
+        quant_config = vllm_config.quant_config
+
+        self.hidden_size = config.hidden_size
+        self.residual_multiplier = config.residual_multiplier
+        max_position_embeddings = getattr(
+            config,
+            "max_position_embeddings",
+            8192,
+        )
+        dual_chunk_attention_config = getattr(
+            config,
+            "dual_chunk_attention_config",
+            None,
+        )
+        attention_bias = getattr(config, "attention_bias", False)
+
+        self.self_attn = HyperCLOVAXAttention(
+            config=config,
+            hidden_size=self.hidden_size,
+            num_heads=config.num_attention_heads,
+            num_kv_heads=getattr(
+                config, "num_key_value_heads", config.num_attention_heads
+            ),
+            max_position_embeddings=max_position_embeddings,
+            quant_config=quant_config,
+            bias=attention_bias,
+            cache_config=cache_config,
+            prefix=f"{prefix}.self_attn",
+            dual_chunk_attention_config=dual_chunk_attention_config,
+        )
+        self.mlp = HyperCLOVAXMLP(
+            hidden_size=self.hidden_size,
+            intermediate_size=config.intermediate_size,
+            hidden_act=config.hidden_act,
+            quant_config=quant_config,
+            bias=getattr(config, "mlp_bias", False),
+            prefix=f"{prefix}.mlp",
+        )
+        self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = RMSNorm(
+            config.hidden_size, eps=config.rms_norm_eps
+        )
+
+        # post-norm (dual-norm)
+        self.use_post_norm = config.use_post_norm
+        if self.use_post_norm:
+            self.post_norm1 = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+            self.post_norm2 = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        residual: torch.Tensor | None,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        # Unlike models that use a fused add-norm kernel (e.g. Llama), HyperCLOVAX
+        # applies the residual connection explicitly with a muP scaling factor
+        # (residual + hidden * residual_multiplier). As a result, each layer's
+        # hidden_states output already includes the residual addition, so the
+        # incoming residual is not needed and is reset at the start of each layer.
+        # The residual parameter is kept for interface consistency with other vllm
+        # decoder layers.
+
+        # Self Attention
+        residual = hidden_states
+
+        hidden_states = self.input_layernorm(hidden_states)
+
+        hidden_states = self.self_attn(positions=positions, hidden_states=hidden_states)
+        # Custom ln
+        if self.use_post_norm:
+            hidden_states = self.post_norm1(hidden_states)
+
+        # The residual is added outside the layernorm function to apply muP.
+        hidden_states = residual + hidden_states * self.residual_multiplier  # muP
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+
+        # Custom ln
+        if self.use_post_norm:
+            hidden_states = self.post_norm2(hidden_states)
+
+        # The residual is added outside the layernorm function to apply muP.
+        hidden_states = residual + hidden_states * self.residual_multiplier  # muP
+
+        return hidden_states, residual
+
+
+@support_torch_compile
+class HyperCLOVAXModel(nn.Module):
+    def __init__(
+        self,
+        *,
+        vllm_config: VllmConfig,
+        prefix: str = "",
+        layer_type: type[nn.Module] = HyperCLOVAXDecoderLayer,
+    ):
+        super().__init__()
+
+        config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+
+        self.config = config
+        self.quant_config = quant_config
+        self.vocab_size = config.vocab_size
+        self.embed_tokens: VocabParallelEmbedding | PPMissingLayer
+        if get_pp_group().is_first_rank or (
+            config.tie_word_embeddings and get_pp_group().is_last_rank
+        ):
+            self.embed_tokens = VocabParallelEmbedding(
+                self.vocab_size,
+                config.hidden_size,
+                quant_config=quant_config,
+            )
+        else:
+            self.embed_tokens = PPMissingLayer()
+        self.start_layer, self.end_layer, self.layers = make_layers(
+            config.num_hidden_layers,
+            lambda prefix: layer_type(vllm_config=vllm_config, prefix=prefix),
+            prefix=f"{prefix}.layers",
+        )
+        self.norm: RMSNorm | PPMissingLayer
+        if get_pp_group().is_last_rank:
+            self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        else:
+            self.norm = PPMissingLayer()
+
+        self.make_empty_intermediate_tensors = make_empty_intermediate_tensors_factory(
+            ["hidden_states", "residual"], config.hidden_size
+        )
+
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.embed_tokens(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor | None,
+        positions: torch.Tensor,
+        intermediate_tensors: IntermediateTensors | None,
+        inputs_embeds: torch.Tensor | None = None,
+    ) -> torch.Tensor | IntermediateTensors:
+        if get_pp_group().is_first_rank:
+            if inputs_embeds is not None:
+                hidden_states = inputs_embeds
+            else:
+                assert input_ids is not None
+                hidden_states = self.embed_input_ids(input_ids)
+            residual = None
+
+            hidden_states *= self.config.embedding_multiplier  # muP
+        else:
+            assert intermediate_tensors is not None
+            hidden_states = intermediate_tensors["hidden_states"]
+            residual = intermediate_tensors["residual"]
+
+        for layer in islice(self.layers, self.start_layer, self.end_layer):
+            hidden_states, residual = layer(positions, hidden_states, residual)
+
+        if not get_pp_group().is_last_rank:
+            assert residual is not None
+            return IntermediateTensors(
+                {"hidden_states": hidden_states, "residual": residual}
+            )
+
+        # The residual is added outside the layernorm function to apply muP.
+        hidden_states = self.norm(hidden_states)
+        return hidden_states
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            (".qkv_proj", ".q_proj", "q"),
+            (".qkv_proj", ".k_proj", "k"),
+            (".qkv_proj", ".v_proj", "v"),
+            (".gate_up_proj", ".gate_proj", 0),
+            (".gate_up_proj", ".up_proj", 1),
+        ]
+        params_dict = dict(self.named_parameters())
+        loaded_params: set[str] = set()
+        for name, loaded_weight in weights:
+            if "rotary_emb.inv_freq" in name:
+                continue
+            if "rotary_emb.cos_cached" in name or "rotary_emb.sin_cached" in name:
+                # Models trained using ColossalAI may include these tensors in
+                # the checkpoint. Skip them.
+                continue
+            if self.quant_config is not None and (
+                scale_name := self.quant_config.get_cache_scale(name)
+            ):
+                # Loading kv cache quantization scales
+                param = params_dict[scale_name]
+                weight_loader = getattr(param, "weight_loader", default_weight_loader)
+                loaded_weight = (
+                    loaded_weight if loaded_weight.dim() == 0 else loaded_weight[0]
+                )
+                weight_loader(param, loaded_weight)
+                loaded_params.add(scale_name)
+                continue
+            if "scale" in name or "zero_point" in name:
+                # Remapping the name of FP8 kv-scale or zero point.
+                remapped_name = maybe_remap_kv_scale_name(name, params_dict)
+                if remapped_name is None:
+                    continue
+                name = remapped_name
+            for param_name, weight_name, shard_id in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+
+                if is_pp_missing_parameter(name, self):
+                    continue
+
+                param = params_dict[name]
+                weight_loader = param.weight_loader  # type: ignore[attr-defined]
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+
+                if is_pp_missing_parameter(name, self):
+                    continue
+
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader", default_weight_loader)
+                weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+        return loaded_params
+
+
+class HyperCLOVAXForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
+    packed_modules_mapping = {
+        "qkv_proj": [
+            "q_proj",
+            "k_proj",
+            "v_proj",
+        ],
+        "gate_up_proj": [
+            "gate_proj",
+            "up_proj",
+        ],
+    }
+
+    # LoRA specific attributes
+    embedding_modules = {
+        "embed_tokens": "input_embeddings",
+        "lm_head": "output_embeddings",
+    }
+
+    def __init__(
+        self,
+        *,
+        vllm_config: VllmConfig,
+        prefix: str = "",
+        layer_type: type[nn.Module] = HyperCLOVAXDecoderLayer,
+    ):
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        self.config = config
+
+        self.model = self._init_model(
+            vllm_config=vllm_config,
+            prefix=maybe_prefix(prefix, "model"),
+            layer_type=layer_type,
+        )
+
+        self.lm_head: ParallelLMHead | PPMissingLayer
+        if get_pp_group().is_last_rank:
+            self.lm_head = ParallelLMHead(
+                config.vocab_size,
+                config.hidden_size,
+                quant_config=quant_config,
+                prefix=maybe_prefix(prefix, "lm_head"),
+            )
+            if config.tie_word_embeddings:
+                self.lm_head = self.lm_head.tie_weights(self.model.embed_tokens)
+
+            logit_scale = getattr(config, "logit_scale", 1.0)
+            if hasattr(config, "logits_scaling"):
+                logit_scale *= config.logits_scaling  # muP
+            self.logits_processor = LogitsProcessor(
+                config.vocab_size,
+                scale=logit_scale,
+            )
+        else:
+            self.lm_head = PPMissingLayer()
+
+        self.make_empty_intermediate_tensors = (  # type: ignore[method-assign]
+            self.model.make_empty_intermediate_tensors
+        )
+
+    def _init_model(
+        self,
+        vllm_config: VllmConfig,
+        prefix: str = "",
+        layer_type: type[nn.Module] = HyperCLOVAXDecoderLayer,
+    ):
+        return HyperCLOVAXModel(
+            vllm_config=vllm_config,
+            prefix=prefix,
+            layer_type=layer_type,
+        )
+
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.model.embed_tokens(input_ids)
+
+    def forward(  # type: ignore[override]
+        self,
+        input_ids: torch.Tensor | None,
+        positions: torch.Tensor,
+        *,
+        intermediate_tensors: IntermediateTensors | None,
+        inputs_embeds: torch.Tensor | None = None,
+    ) -> torch.Tensor | IntermediateTensors:
+        model_output = self.model(
+            input_ids, positions, intermediate_tensors, inputs_embeds
+        )
+        return model_output
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor | None:
+        logits = self.logits_processor(self.lm_head, hidden_states)
+        return logits
+
+    def load_weights(
+        self,
+        weights: Iterable[tuple[str, torch.Tensor]],
+    ) -> set[str]:
+        loader = AutoWeightsLoader(
+            self,
+            skip_prefixes=["lm_head."] if self.config.tie_word_embeddings else None,
+        )
+        return loader.load_weights(weights)

vllm/model_executor/models/registry.py

@@ -133,7 +133,7 @@ _TEXT_GENERATION_MODELS = {
     "HunYuanDenseV1ForCausalLM": ("hunyuan_v1", "HunYuanDenseV1ForCausalLM"),
     "HCXVisionForCausalLM": ("hyperclovax_vision", "HCXVisionForCausalLM"),
     "HCXVisionV2ForCausalLM": ("hyperclovax_vision_v2", "HCXVisionV2ForCausalLM"),
-    "HyperCLOVAXForCausalLM": ("llama", "LlamaForCausalLM"),
+    "HyperCLOVAXForCausalLM": ("hyperclovax", "HyperCLOVAXForCausalLM"),
     "InternLMForCausalLM": ("llama", "LlamaForCausalLM"),
     "InternLM2ForCausalLM": ("internlm2", "InternLM2ForCausalLM"),
     "InternLM2VEForCausalLM": ("internlm2_ve", "InternLM2VEForCausalLM"),

vllm/transformers_utils/configs/__init__.py

@@ -33,6 +33,7 @@ _CLASS_TO_MODULE: dict[str, str] = {
     "HunYuanVLConfig": "vllm.transformers_utils.configs.hunyuan_vl",
     "HunYuanVLTextConfig": "vllm.transformers_utils.configs.hunyuan_vl",
     "HunYuanVLVisionConfig": "vllm.transformers_utils.configs.hunyuan_vl",
+    "HyperCLOVAXConfig": "vllm.transformers_utils.configs.hyperclovax",
     "IsaacConfig": "vllm.transformers_utils.configs.isaac",
     # RWConfig is for the original tiiuae/falcon-40b(-instruct) and
     # tiiuae/falcon-7b(-instruct) models. Newer Falcon models will use the
@@ -91,6 +92,7 @@ __all__ = [
     "HunYuanVLConfig",
     "HunYuanVLTextConfig",
     "HunYuanVLVisionConfig",
+    "HyperCLOVAXConfig",
     "IsaacConfig",
     "RWConfig",
     "JAISConfig",

vllm/transformers_utils/configs/hyperclovax.py

@@ -0,0 +1,277 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# SPDX-FileCopyrightText: Copyright 2025 NAVER Cloud HyperCLOVA team
+#
+# Copyright 2025 NAVER Cloud HyperCLOVA 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.
+"""HyperCLOVA X model configuration."""
+
+from transformers.configuration_utils import PretrainedConfig
+
+
+class HyperCLOVAXConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a
+    [`HyperCLOVAXModel`]. It is used to instantiate a HyperCLOVAX model
+    according to the specified arguments, defining the model architecture.
+    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 32000):
+            Vocabulary size of the HyperCLOVAX model. Defines the number of
+            different tokens that can be represented by the `input_ids`
+            passed when calling [`HyperCLOVAXModel`]
+        hidden_size (`int`, *optional*, defaults to 4096):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 11008):
+            Dimension of the MLP representations.
+        num_hidden_layers (`int`, *optional*, defaults to 32):
+            Number of hidden layers in the Transformer decoder.
+        num_attention_heads (`int`, *optional*, defaults to 32):
+            Number of attention heads for each attention layer in the
+            Transformer decoder.
+        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 2048):
+            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 1):
+            Beginning of stream token id.
+        eos_token_id (`int`, *optional*, defaults to 2):
+            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/main/perf_train_gpu_many#tensor-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. NOTE: if you apply new rope type and you expect the
+            model to work on longer `max_position_embeddings`, we recommend
+            you to update this value accordingly.
+            Expected contents:
+                `rope_type` (`str`):
+                    The sub-variant of RoPE to use. Can be one of ['default',
+                    'linear', 'dynamic', 'yarn', 'longrope', 'llama3'], with
+                    'default' being the original RoPE implementation.
+                `factor` (`float`, *optional*):
+                    Used with all rope types except 'default'. The scaling
+                    factor to apply to the RoPE embeddings. In most scaling
+                    types, a `factor` of x will enable the model to handle
+                    sequences of length x * original maximum pre-trained
+                    length.
+                `original_max_position_embeddings` (`int`, *optional*):
+                    Used with 'dynamic', 'longrope' and 'llama3'. The
+                    original max position embeddings used during pretraining.
+                `attention_factor` (`float`, *optional*):
+                    Used with 'yarn' and 'longrope'. The scaling factor to be
+                    applied on the attention computation. If unspecified, it
+                    defaults to value recommended by the implementation, using
+                    the `factor` field to infer the suggested value.
+                `beta_fast` (`float`, *optional*):
+                    Only used with 'yarn'. Parameter to set the boundary for
+                    extrapolation (only) in the linear ramp function. If
+                    unspecified, it defaults to 32.
+                `beta_slow` (`float`, *optional*):
+                    Only used with 'yarn'. Parameter to set the boundary for
+                    interpolation (only) in the linear ramp function. If
+                    unspecified, it defaults to 1.
+                `short_factor` (`List[float]`, *optional*):
+                    Only used with 'longrope'. The scaling factor to be
+                    applied to short contexts (<
+                    `original_max_position_embeddings`). Must be a list of
+                    numbers with the same length as the hidden size divided
+                    by the number of attention heads divided by 2
+                `long_factor` (`List[float]`, *optional*):
+                    Only used with 'longrope'. The scaling factor to be
+                    applied to long contexts (<
+                    `original_max_position_embeddings`). Must be a list of
+                    numbers with the same length as the hidden size divided
+                    by the number of attention heads divided by 2
+                `low_freq_factor` (`float`, *optional*):
+                    Only used with 'llama3'. Scaling factor applied to low
+                    frequency components of the RoPE
+                `high_freq_factor` (`float`, *optional*):
+                    Only used with 'llama3'. Scaling factor applied to high
+                    frequency components of the RoPE
+        attention_bias (`bool`, *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.
+        mlp_bias (`bool`, *optional*, defaults to `False`):
+            Whether to use a bias in up_proj, down_proj and gate_proj layers
+            in the MLP layers.
+        head_dim (`int`, *optional*):
+            The attention head dimension. If None, it will default to
+            hidden_size // num_heads
+        embedding_multiplier (`float`, *optional*, defaults to `None`):
+            Multiplier applied to the embedding weights. If `None`, it is
+            equivalent to `1.0`.
+        logits_scaling (`float`, *optional*, defaults to `None`):
+            Scaling factor for logits. If `None`, it is equivalent to `1.0`.
+        attention_multiplier (`float`, *optional*, defaults to `None`):
+            Multiplier applied to the attention weights. If `None`, it is
+            equivalent to `self.head_dim ** -0.5`.
+        residual_multiplier (`float`, *optional*, defaults to `None`):
+            Scaling factor for residual connections. If `None`, it is
+            equivalent to `1.0`.
+        use_post_norm (`bool`, *optional*, defaults to `True`):
+            Determines whether to apply Peri-Layer Normalization. Set to
+            False to disable this feature.
+        rope_parameters (`dict`, *optional*):
+            Dictionary containing the RoPE parameters used by vLLM's
+            `get_rope`. When provided, takes precedence over `rope_theta`
+            and `rope_scaling`. If `None`, it is derived from `rope_theta`
+            and `rope_scaling` automatically.
+    """
+
+    model_type = "hyperclovax"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=32000,
+        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,
+        rms_norm_eps=1e-6,
+        use_cache=True,
+        pad_token_id=None,
+        bos_token_id=1,
+        eos_token_id=2,
+        pretraining_tp=1,
+        tie_word_embeddings=False,
+        rope_theta=10000.0,
+        rope_scaling=None,
+        attention_bias=False,
+        attention_dropout=0.0,
+        mlp_bias=False,
+        head_dim=None,
+        embedding_multiplier=None,  # mup
+        logits_scaling=None,  # mup
+        attention_multiplier=None,  # mup
+        residual_multiplier=None,  # mup
+        use_post_norm=True,  # post-norm(peri-LN)
+        rope_parameters=None,
+        auto_map=None,
+        **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.pretraining_tp = pretraining_tp
+        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.mlp_bias = mlp_bias
+        self.head_dim = (
+            head_dim
+            if head_dim is not None
+            else self.hidden_size // self.num_attention_heads
+        )
+        # Derive rope_parameters for vLLM's get_rope() from rope_theta /
+        # rope_scaling, unless the caller already provided rope_parameters.
+        if rope_parameters is None:
+            if rope_scaling is not None:
+                # Shallow-copy to avoid mutating the caller's dict.
+                rope_parameters = dict(rope_scaling)
+                # BC: 'type' field -> 'rope_type', remove stale key.
+                if "type" in rope_parameters:
+                    rope_parameters.setdefault("rope_type", rope_parameters.pop("type"))
+            else:
+                rope_parameters = {"rope_type": "default"}
+            if "rope_theta" not in rope_parameters:
+                rope_parameters["rope_theta"] = rope_theta
+        self.rope_parameters = rope_parameters
+
+        # BC: keep self.rope_scaling consistent for HF serialization.
+        if self.rope_scaling is not None and "type" in self.rope_scaling:
+            self.rope_scaling["rope_type"] = self.rope_scaling["type"]
+
+        # mup
+        self.embedding_multiplier = (
+            embedding_multiplier if embedding_multiplier is not None else 1.0
+        )
+        self.logits_scaling = logits_scaling if logits_scaling is not None else 1.0
+        self.attention_multiplier = (
+            attention_multiplier
+            if attention_multiplier is not None
+            else self.head_dim**-0.5
+        )
+        self.residual_multiplier = (
+            residual_multiplier if residual_multiplier is not None else 1.0
+        )
+
+        # post-norm (Peri-LN)
+        self.use_post_norm = use_post_norm
+
+        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,
+            auto_map=auto_map,
+            **kwargs,
+        )