[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/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

 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

+# 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