[MODEL] add Exaone model support (#7819)

docs/source/models/supported_models.rst

@@ -51,6 +51,10 @@ Decoder-only Language Models
     - DeciLM
     - :code:`Deci/DeciLM-7B`, :code:`Deci/DeciLM-7B-instruct`, etc.
     -
+  * - :code:`ExaoneForCausalLM`
+    - EXAONE-3
+    - :code:`LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct`, etc.
+    - ✅︎
   * - :code:`FalconForCausalLM`
     - Falcon
     - :code:`tiiuae/falcon-7b`, :code:`tiiuae/falcon-40b`, :code:`tiiuae/falcon-rw-7b`, etc.

vllm/model_executor/models/__init__.py

@@ -22,6 +22,7 @@ _GENERATION_MODELS = {
     "DeciLMForCausalLM": ("decilm", "DeciLMForCausalLM"),
     "DeepseekForCausalLM": ("deepseek", "DeepseekForCausalLM"),
     "DeepseekV2ForCausalLM": ("deepseek_v2", "DeepseekV2ForCausalLM"),
+    "ExaoneForCausalLM": ("exaone", "ExaoneForCausalLM"),
     "FalconForCausalLM": ("falcon", "FalconForCausalLM"),
     "GemmaForCausalLM": ("gemma", "GemmaForCausalLM"),
     "Gemma2ForCausalLM": ("gemma2", "Gemma2ForCausalLM"),

vllm/model_executor/models/exaone.py

@@ -0,0 +1,617 @@
+# coding=utf-8
+# Adapted from
+# https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct/blob/main/modeling_exaone.py
+# Copyright 2024 The LG U+ CTO AI Tech Lab.
+# Copyright 2021 The LG AI Research EXAONE Lab
+# Copyright (c) 2018, NVIDIA CORPORATION.  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 Exaone model compatible with HuggingFace weights."""
+
+from typing import Any, Dict, Iterable, List, Optional, Tuple, Union
+
+import torch
+from torch import nn
+
+from vllm.attention import Attention, AttentionMetadata
+from vllm.config import CacheConfig, LoRAConfig
+from vllm.distributed import (get_pp_group, get_tensor_model_parallel_rank,
+                              get_tensor_model_parallel_world_size)
+from vllm.model_executor.layers.activation import SiluAndMul
+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.base_config import (
+    QuantizationConfig)
+from vllm.model_executor.layers.quantization.compressed_tensors.utils import (
+    get_compressed_tensors_cache_scale)
+from vllm.model_executor.layers.rotary_embedding import get_rope
+from vllm.model_executor.layers.sampler import Sampler
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding)
+from vllm.model_executor.model_loader.weight_utils import (
+    default_weight_loader, kv_cache_scales_loader, maybe_remap_kv_scale_name)
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.sequence import IntermediateTensors, SamplerOutput
+from vllm.transformers_utils.configs.exaone import ExaoneConfig
+from vllm.utils import is_hip
+
+from .interfaces import SupportsLoRA
+from .utils import PPMissingLayer, is_pp_missing_parameter, make_layers
+
+
+class ExaoneGatedMLP(nn.Module):
+
+    def __init__(
+        self,
+        hidden_size: int,
+        intermediate_size: int,
+        hidden_act: str,
+        quant_config: Optional[QuantizationConfig] = None,
+        bias: bool = False,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.gate_up_proj = MergedColumnParallelLinear(
+            input_size=hidden_size,
+            output_sizes=[intermediate_size] * 2,
+            bias=bias,
+            quant_config=quant_config,
+            prefix=f"{prefix}.gate_up_proj",
+        )
+        self.c_proj = RowParallelLinear(
+            input_size=intermediate_size,
+            output_size=hidden_size,
+            bias=bias,
+            quant_config=quant_config,
+            prefix=f"{prefix}.c_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):
+        gate_up, _ = self.gate_up_proj(x)
+        x = self.act_fn(gate_up)
+        x, _ = self.c_proj(x)
+        return x
+
+
+class ExaoneAttention(nn.Module):
+
+    def __init__(
+        self,
+        config: ExaoneConfig,
+        hidden_size: int,
+        num_heads: int,
+        num_kv_heads: int,
+        rope_theta: float = 10000,
+        rope_scaling: Optional[Dict[str, Any]] = None,
+        max_position_embeddings: int = 8192,
+        quant_config: Optional[QuantizationConfig] = None,
+        bias: bool = False,
+        cache_config: Optional[CacheConfig] = None,
+        prefix: str = "",
+    ) -> 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)
+        # MistralConfig has an optional head_dim introduced by Mistral-Nemo
+        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 = self.head_dim**-0.5
+        self.rope_theta = rope_theta
+        self.max_position_embeddings = max_position_embeddings
+
+        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.out_proj = RowParallelLinear(
+            input_size=self.total_num_heads * self.head_dim,
+            output_size=hidden_size,
+            bias=bias,
+            quant_config=quant_config,
+            prefix=f"{prefix}.out_proj",
+        )
+
+        is_neox_style = True
+        if quant_config is not None and quant_config.get_name() == "gguf":
+            is_neox_style = False
+
+        self.rotary_emb = get_rope(
+            self.head_dim,
+            rotary_dim=self.head_dim,
+            max_position=max_position_embeddings,
+            base=rope_theta,
+            rope_scaling=rope_scaling,
+            is_neox_style=is_neox_style,
+        )
+        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,
+        )
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        kv_cache: torch.Tensor,
+        attn_metadata: AttentionMetadata,
+    ) -> 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, kv_cache, attn_metadata)
+        output, _ = self.out_proj(attn_output)
+        return output
+
+
+class ExaoneBlockAttention(nn.Module):
+
+    def __init__(
+        self,
+        config: ExaoneConfig,
+        hidden_size: int,
+        num_heads: int,
+        num_kv_heads: int,
+        rope_theta: float = 10000,
+        rope_scaling: Optional[Dict[str, Any]] = None,
+        max_position_embeddings: int = 8192,
+        quant_config: Optional[QuantizationConfig] = None,
+        bias: bool = False,
+        cache_config: Optional[CacheConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.attention = ExaoneAttention(
+            config=config,
+            hidden_size=hidden_size,
+            num_heads=num_heads,
+            num_kv_heads=num_kv_heads,
+            rope_theta=rope_theta,
+            rope_scaling=rope_scaling,
+            max_position_embeddings=max_position_embeddings,
+            quant_config=quant_config,
+            bias=bias,
+            cache_config=cache_config,
+            prefix=prefix,
+        )
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        kv_cache: torch.Tensor,
+        attn_metadata: AttentionMetadata,
+    ) -> torch.Tensor:
+        return self.attention(
+            positions=positions,
+            hidden_states=hidden_states,
+            kv_cache=kv_cache,
+            attn_metadata=attn_metadata,
+        )
+
+
+class ExaoneDecoderLayer(nn.Module):
+
+    def __init__(
+        self,
+        config: ExaoneConfig,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        rope_theta = getattr(config, "rope_theta", 10000)
+        rope_scaling = getattr(config, "rope_scaling", None)
+        if rope_scaling is not None and getattr(
+                config, "original_max_position_embeddings", None):
+            rope_scaling["original_max_position_embeddings"] = (
+                config.original_max_position_embeddings)
+        max_position_embeddings = getattr(config, "max_position_embeddings",
+                                          8192)
+        # Support abacusai/Smaug-72B-v0.1 with attention_bias
+        # Support internlm/internlm-7b with bias
+        attention_bias = getattr(config, "attention_bias", False) or getattr(
+            config, "bias", False)
+        self.attn = ExaoneBlockAttention(
+            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),
+            rope_theta=rope_theta,
+            rope_scaling=rope_scaling,
+            max_position_embeddings=max_position_embeddings,
+            quant_config=quant_config,
+            bias=attention_bias,
+            cache_config=cache_config,
+            prefix=f"{prefix}.attn",
+        )
+        self.mlp = ExaoneGatedMLP(
+            hidden_size=self.hidden_size,
+            intermediate_size=config.intermediate_size,
+            hidden_act=config.activation_function,
+            quant_config=quant_config,
+            bias=getattr(config, "mlp_bias", False),
+            prefix=f"{prefix}.mlp",
+        )
+        self.ln_1 = RMSNorm(config.hidden_size, eps=config.layer_norm_epsilon)
+        self.ln_2 = RMSNorm(config.hidden_size, eps=config.layer_norm_epsilon)
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        kv_cache: torch.Tensor,
+        attn_metadata: AttentionMetadata,
+        residual: Optional[torch.Tensor],
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        # Self Attention
+        if residual is None:
+            residual = hidden_states
+            hidden_states = self.ln_1(hidden_states)
+        else:
+            hidden_states, residual = self.ln_1(hidden_states, residual)
+        hidden_states = self.attn(
+            positions=positions,
+            hidden_states=hidden_states,
+            kv_cache=kv_cache,
+            attn_metadata=attn_metadata,
+        )
+
+        # Fully Connected
+        hidden_states, residual = self.ln_2(hidden_states, residual)
+        hidden_states = self.mlp(hidden_states)
+        return hidden_states, residual
+
+
+class ExaoneModel(nn.Module):
+
+    def __init__(
+        self,
+        config: ExaoneConfig,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        lora_config: Optional[LoRAConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.config = config
+        self.padding_idx = config.pad_token_id
+        lora_vocab = ((lora_config.lora_extra_vocab_size *
+                       (lora_config.max_loras or 1)) if lora_config else 0)
+        self.vocab_size = config.vocab_size + lora_vocab
+        self.wte = config.vocab_size
+        if get_pp_group().is_first_rank or (config.tie_word_embeddings
+                                            and get_pp_group().is_last_rank):
+            self.wte = VocabParallelEmbedding(
+                self.vocab_size,
+                config.hidden_size,
+                org_num_embeddings=config.vocab_size,
+                quant_config=quant_config,
+            )
+        else:
+            self.wte = PPMissingLayer()
+        self.start_layer, self.end_layer, self.h = make_layers(
+            config.num_hidden_layers,
+            lambda prefix: ExaoneDecoderLayer(
+                config=config,
+                cache_config=cache_config,
+                quant_config=quant_config,
+                prefix=prefix,
+            ),
+            prefix=f"{prefix}.h",
+        )
+        if get_pp_group().is_last_rank:
+            self.ln_f = RMSNorm(config.hidden_size,
+                                eps=config.layer_norm_epsilon)
+        else:
+            self.ln_f = PPMissingLayer()
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.wte(input_ids)
+
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor],
+        positions: torch.Tensor,
+        kv_caches: List[torch.Tensor],
+        attn_metadata: AttentionMetadata,
+        intermediate_tensors: Optional[IntermediateTensors],
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        if get_pp_group().is_first_rank:
+            if inputs_embeds is not None:
+                hidden_states = inputs_embeds
+            else:
+                hidden_states = self.get_input_embeddings(input_ids)
+            residual = None
+        else:
+            assert intermediate_tensors is not None
+            hidden_states = intermediate_tensors["hidden_states"]
+            residual = intermediate_tensors["residual"]
+
+        for i in range(self.start_layer, self.end_layer):
+            layer = self.h[i]
+            hidden_states, residual = layer(
+                positions,
+                hidden_states,
+                kv_caches[i - self.start_layer],
+                attn_metadata,
+                residual,
+            )
+
+        if not get_pp_group().is_last_rank:
+            return IntermediateTensors({
+                "hidden_states": hidden_states,
+                "residual": residual
+            })
+
+        hidden_states, _ = self.ln_f(hidden_states, residual)
+        return hidden_states
+
+
+class ExaoneForCausalLM(nn.Module, SupportsLoRA):
+    packed_modules_mapping = {
+        "qkv_proj": [
+            "q_proj",
+            "k_proj",
+            "v_proj",
+        ],
+        "gate_up_proj": [
+            "c_fc_0",
+            "c_fc_1",
+        ],
+    }
+
+    # LoRA specific attributes
+    supported_lora_modules = [
+        "qkv_proj",
+        "out_proj",
+        "gate_up_proj",
+        "c_proj",
+        "wte",
+        "lm_head",
+    ]
+    embedding_modules = {
+        "wte": "input_embeddings",
+        "lm_head": "output_embeddings",
+    }
+    embedding_padding_modules = ["lm_head"]
+    bitsandbytes_stacked_params_mapping = {
+        # shard_name, weight_name, index
+        "q_proj": ("qkv_proj", 0),
+        "k_proj": ("qkv_proj", 1),
+        "v_proj": ("qkv_proj", 2),
+        "c_fc_0": ("gate_up_proj", 0),
+        "c_fc_1": ("gate_up_proj", 1),
+    }
+
+    def __init__(
+        self,
+        config: ExaoneConfig,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        lora_config: Optional[LoRAConfig] = None,
+    ) -> None:
+        super().__init__()
+
+        self.config = config
+        self.lora_config = lora_config
+
+        self.transformer = ExaoneModel(
+            config,
+            cache_config,
+            quant_config,
+            lora_config=lora_config,
+            prefix="model",
+        )
+        if get_pp_group().is_last_rank:
+            self.unpadded_vocab_size = config.vocab_size
+            if lora_config:
+                self.unpadded_vocab_size += lora_config.lora_extra_vocab_size
+            self.lm_head = ParallelLMHead(
+                self.unpadded_vocab_size,
+                config.hidden_size,
+                org_num_embeddings=config.vocab_size,
+                padding_size=DEFAULT_VOCAB_PADDING_SIZE
+                # We need bigger padding if using lora for kernel
+                # compatibility
+                if not lora_config else lora_config.lora_vocab_padding_size,
+                quant_config=quant_config,
+            )
+            if config.tie_word_embeddings:
+                self.lm_head.weight = self.transformer.wte.weight
+
+            logit_scale = getattr(config, "logit_scale", 1.0)
+            self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
+                                                    config.vocab_size,
+                                                    logit_scale)
+            self.sampler = Sampler()
+        else:
+            self.lm_head = PPMissingLayer()
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        kv_caches: List[torch.Tensor],
+        attn_metadata: AttentionMetadata,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        model_output = self.transformer(input_ids, positions, kv_caches,
+                                        attn_metadata, intermediate_tensors)
+        return model_output
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[torch.Tensor]:
+        logits = self.logits_processor(self.lm_head, hidden_states,
+                                       sampling_metadata)
+        return logits
+
+    def sample(
+        self,
+        logits: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[SamplerOutput]:
+        next_tokens = self.sampler(logits, sampling_metadata)
+        return next_tokens
+
+    def make_empty_intermediate_tensors(
+            self, batch_size: int, dtype: torch.dtype,
+            device: torch.device) -> IntermediateTensors:
+        return IntermediateTensors({
+            "hidden_states":
+            torch.zeros(
+                (batch_size, self.config.hidden_size),
+                dtype=dtype,
+                device=device,
+            ),
+            "residual":
+            torch.zeros(
+                (batch_size, self.config.hidden_size),
+                dtype=dtype,
+                device=device,
+            ),
+        })
+
+    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
+        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", ".c_fc_0", 0),
+            (".gate_up_proj", ".c_fc_1", 1),
+        ]
+        params_dict = dict(self.named_parameters())
+        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
+            # With tie_word_embeddings, we can skip lm_head.weight
+            # The weight might appear unnecessarily in the files if the model is
+            # processed with quantization, LoRA, fine-tuning, etc.
+            if self.config.tie_word_embeddings and "lm_head.weight" in name:
+                continue
+            if scale_name := get_compressed_tensors_cache_scale(name):
+                # Loading kv cache scales for compressed-tensors quantization
+                param = params_dict[scale_name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                loaded_weight = loaded_weight[0]
+                weight_loader(param, loaded_weight)
+                continue
+            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
+                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
+                # Remapping the name of FP8 kv-scale.
+                name = maybe_remap_kv_scale_name(name, params_dict)
+                if name is None:
+                    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)
+
+    # If this function is called, it should always initialize KV cache scale
+    # factors (or else raise an exception). Thus, handled exceptions should
+    # make sure to leave KV cache scale factors in a known good (dummy) state
+    def load_kv_cache_scales(self, quantization_param_path: str) -> None:
+        tp_size = get_tensor_model_parallel_world_size()
+        tp_rank = get_tensor_model_parallel_rank()
+        for layer_idx, scaling_factor in kv_cache_scales_loader(
+                quantization_param_path,
+                tp_rank,
+                tp_size,
+                self.config.num_hidden_layers,
+                self.config.__class__.model_type,
+        ):
+            if not isinstance(self.transformer.h[layer_idx], nn.Identity):
+                layer_self_attn = self.transformer.h[layer_idx].attn
+
+            if is_hip():
+                # The scaling factor convention we are assuming is
+                # quantized_value * scaling_factor ~= true_value
+                # which is consistent with the practice of setting
+                # scaling_factor = tensor_amax / FPtype_max
+                scaling_factor *= 2
+            if hasattr(layer_self_attn, "kv_scale"):
+                layer_self_attn.attn._kv_scale = scaling_factor
+            else:
+                raise RuntimeError("Self attention has no KV cache scaling "
+                                   "factor attribute!")

vllm/transformers_utils/config.py

@@ -11,11 +11,11 @@ from transformers.models.auto.modeling_auto import (
 from vllm.envs import VLLM_USE_MODELSCOPE
 from vllm.logger import init_logger
 from vllm.transformers_utils.configs import (ChatGLMConfig, DbrxConfig,
-                                             EAGLEConfig, InternVLChatConfig,
-                                             JAISConfig, MedusaConfig,
-                                             MLPSpeculatorConfig, MPTConfig,
-                                             NemotronConfig, RWConfig,
-                                             UltravoxConfig)
+                                             EAGLEConfig, ExaoneConfig,
+                                             InternVLChatConfig, JAISConfig,
+                                             MedusaConfig, MLPSpeculatorConfig,
+                                             MPTConfig, NemotronConfig,
+                                             RWConfig, UltravoxConfig)
 
 if VLLM_USE_MODELSCOPE:
     from modelscope import AutoConfig
@@ -34,6 +34,7 @@ _CONFIG_REGISTRY: Dict[str, Type[PretrainedConfig]] = {
     "mlp_speculator": MLPSpeculatorConfig,
     "medusa": MedusaConfig,
     "eagle": EAGLEConfig,
+    "exaone": ExaoneConfig,
     "internvl_chat": InternVLChatConfig,
     "nemotron": NemotronConfig,
     "ultravox": UltravoxConfig,

vllm/transformers_utils/configs/__init__.py

@@ -1,6 +1,7 @@
 from vllm.transformers_utils.configs.chatglm import ChatGLMConfig
 from vllm.transformers_utils.configs.dbrx import DbrxConfig
 from vllm.transformers_utils.configs.eagle import EAGLEConfig
+from vllm.transformers_utils.configs.exaone import ExaoneConfig
 # RWConfig is for the original tiiuae/falcon-40b(-instruct) and
 # tiiuae/falcon-7b(-instruct) models. Newer Falcon models will use the
 # `FalconConfig` class from the official HuggingFace transformers library.
@@ -22,6 +23,7 @@ __all__ = [
     "JAISConfig",
     "MedusaConfig",
     "EAGLEConfig",
+    "ExaoneConfig",
     "MLPSpeculatorConfig",
     "NemotronConfig",
     "UltravoxConfig",

vllm/transformers_utils/configs/exaone.py

@@ -0,0 +1,190 @@
+# coding=utf-8
+# Copied from
+# https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct/blob/main/configuration_exaone.py
+# Copyright 2021 The LG AI Research EXAONE Lab. 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.
+"""Exaone model configuration"""
+
+from typing import Dict
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+logger = logging.get_logger(__name__)
+
+EXAONE_PRETRAINED_CONFIG_ARCHIVE_MAP: Dict[str, str] = {}
+
+
+class ExaoneConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a :class:
+    `~transformers.ExaoneModel`. It is used to instantiate a GPT Lingvo 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 Exaone
+
+    Configuration objects inherit from :class:`~transformers.PretrainedConfig`
+    and can be used to control the model outputs. Read the documentation from :
+    class:`~transformers.PretrainedConfig` for more information.
+
+    Args:
+        vocab_size (:obj:`int`, `optional`, defaults to 50257):
+            Vocabulary size of the GPT Lingvo model. Defines the number of
+            different tokens that can be represented by the :obj:`inputs_ids`
+            passed when calling :class:`~transformers.ExaoneModel`. Vocabulary
+            size of the model.
+            Defines the different tokens that can be represented by the
+            `inputs_ids` passed to the forward method of :class:
+            `~transformers.EXAONEModel`.
+        hidden_size (:obj:`int`, `optional`, defaults to 2048):
+            Dimensionality of the encoder layers and the pooler layer.
+        num_layers (:obj:`int`, `optional`, defaults to 24):
+            Number of hidden layers in the Transformer encoder.
+        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`.
+        rotary_pct (`float`, *optional*, defaults to 0.25):
+            percentage of hidden dimensions to allocate to rotary embeddings
+        intermediate_size (:obj:`int`, `optional`, defaults to 8192):
+            Dimensionality of the "intermediate" (i.e., feed-forward) layer in
+            the Transformer encoder.
+        activation_function (:obj:`str` or :obj:`function`, `optional`,
+        defaults to :obj:`"gelu_new"`):
+            The non-linear activation function (function or string) in the
+            encoder and pooler. If string, :obj:`"gelu"`, :obj:`"relu"`,
+            :obj:`"selu"` and :obj:`"gelu_new"` are supported.
+        embed_dropout (:obj:`float`, `optional`, defaults to 0.0):
+            The dropout probabilitiy for all fully connected layers in the
+            embeddings, encoder, and pooler.
+        attention_dropout (:obj:`float`, `optional`, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+        max_position_embeddings (:obj:`int`, `optional`, defaults to 2048):
+            The maximum sequence length that this model might ever be used with.
+            Typically set this to something large just in case
+            (e.g., 512 or 1024 or 2048).
+        type_vocab_size (:obj:`int`, `optional`, defaults to 2):
+            The vocabulary size of the :obj:`token_type_ids` passed when calling
+            :class:`~transformers.EXAONEModel`.
+        initializer_range (:obj:`float`, `optional`, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for
+            initializing all weight matrices.
+        layer_norm_epsilon (:obj:`float`, `optional`, defaults to 1e-5):
+            The epsilon used by the layer normalization layers.
+        use_cache (:obj:`bool`, `optional`, defaults to :obj:`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``.
+        gradient_checkpointing (:obj:`bool`, `optional`,
+        defaults to :obj:`False`):
+            If True, use gradient checkpointing to save memory at the expense
+            of slower backward pass.
+        Example::
+
+            >>> from transformers import ExoneModel, ExaoneConfig
+
+            >>> # Initializing a EXAONE configuration
+            >>> configuration = ExaoneConfig()
+
+            >>> # Initializing a model from configuration
+            >>> model = ExoneModel(configuration)
+
+            >>> # Accessing the model configuration
+            >>> configuration = model.config
+    """
+
+    model_type = "exaone"
+    keys_to_ignore_at_inference = ["past_key_values"]
+    attribute_map = {"num_hidden_layers": "num_layers"}
+
+    def __init__(
+        self,
+        vocab_size=102400,
+        max_position_embeddings=2048,
+        hidden_size=2048,
+        num_layers=32,
+        num_attention_heads=32,
+        num_key_value_heads=None,
+        intermediate_size=None,
+        activation_function="silu",
+        rotary_pct=0.25,
+        resid_dropout=0.0,
+        embed_dropout=0.0,
+        attention_dropout=0.0,
+        layer_norm_epsilon=1e-6,
+        initializer_range=0.02,
+        use_cache=True,
+        bos_token_id=0,
+        eos_token_id=2,
+        tie_word_embeddings=True,
+        **kwargs,
+    ):
+        super().__init__(
+            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.num_layers = num_layers
+        self.num_attention_heads = num_attention_heads
+        self.num_hidden_layers = num_layers
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+        self.num_key_value_heads = num_key_value_heads
+        if intermediate_size:
+            self.intermediate_size = intermediate_size
+        else:
+            self.intermediate_size = hidden_size * 4
+        self.activation_function = activation_function
+        self.resid_dropout = resid_dropout
+        self.embed_dropout = embed_dropout
+        self.attention_dropout = attention_dropout
+        self.layer_norm_epsilon = layer_norm_epsilon
+        self.initializer_range = initializer_range
+        self.use_cache = use_cache
+        self.rotary_pct = rotary_pct
+
+        self.bos_token_id = bos_token_id
+        self.eos_token_id = eos_token_id
+
+        self.use_logit_cap = kwargs.pop("use_logit_cap", False)
+        self.ln_no_scale = kwargs.pop("ln_no_scale", False)
+        self.use_gated = kwargs.pop("use_gated", False)
+        self.use_emb_norm = kwargs.pop("use_emb_norm", False)
+        self.use_rotary_pos = kwargs.pop("use_rotary_pos", False)
+        self.rotary_type = kwargs.pop("rotary_type", None)
+        self.scaling_factor = kwargs.pop("scaling_factor", 1)
+        self.use_absolute_pos = kwargs.pop("use_absolute_pos", True)
+        self.use_extra_logit = kwargs.pop("use_extra_logit", True)
+        self.rotary_expand_length = kwargs.pop("rotary_expand_length", None)
+        self.rotary_base = kwargs.pop("rotary_base", 10000.0)
+        self.use_qkv_fuse = kwargs.pop("use_qkv_fuse", False)
+        self.rescale_before_lm_head = kwargs.pop("rescale_before_lm_head",
+                                                 (rotary_pct == 0.25))
+        if self.use_rotary_pos:
+            self.use_absolute_pos = False