EXAONE 3.0 Model Support (#1258)

Co-authored-by: Yineng Zhang <[email protected]>

python/sglang/srt/configs/__init__.py

@@ -0,0 +1,5 @@
+from sglang.srt.configs.exaone import ExaoneConfig
+
+__all__ = [
+    "ExaoneConfig",
+]

python/sglang/srt/configs/exaone.py

@@ -0,0 +1,195 @@
+# coding=utf-8
+# Copyright 2024 The LG AI Research EXAONE Lab. All rights reserved.
+# Copyright 2024 The LG CNS AI Engineering Team.
+# Copyright 2023-2024 SGLang Team.
+#
+# 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 Any, Dict
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+logger = logging.get_logger(__name__)
+
+EXAONE_PRETRAINED_CONFIG_ARCHIVE_MAP: Dict[str, Any] = {}
+
+
+# ruff: noqa: E501
+class ExaoneConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a :class:`~transformers.ExaoneModel`. It is used to
+    instantiate a EXAONE 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 102400):
+            Vocabulary size of the EXAONE 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`.
+        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).
+        hidden_size (:obj:`int`, `optional`, defaults to 2048):
+            Dimensionality of the encoder layers and the pooler layer.
+        num_layers (:obj:`int`, `optional`, defaults to 32):
+            Number of hidden layers in the Transformer encoder.
+        num_attention_heads (:obj:`int`, `optional`, defaults to 32):
+            Number of attention heads for each attention layer in the Transformer decoder.
+        num_key_value_heads (:obj:`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`.
+        intermediate_size (:obj:`int`, `optional`, defaults to `hidden_size * 4`):
+            Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
+        activation_function (:obj:`str` or :obj:`function`, `optional`, defaults to :obj:`"silu"`):
+            The non-linear activation function (function or string) in the decoder.
+        rope_theta (:obj:`float`, `optional`, defaults to 10000.0):
+            The base period of the RoPE embeddings.
+        rope_scaling (:obj:`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` (:obj:`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` (:obj:`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` (:obj:`int`, `optional`):
+                    Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during
+                    pretraining.
+                `attention_factor` (:obj:`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` (:obj:`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` (:obj:`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` (:obj:`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` (:obj:`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` (:obj:`float`, `optional`):
+                    Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE
+                `high_freq_factor` (:obj:`float`, `optional`):
+                    Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE
+        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.
+        layer_norm_epsilon (:obj:`float`, `optional`, defaults to 1e-5):
+            The epsilon used by the layer normalization layers.
+        initializer_range (:obj:`float`, `optional`, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        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 ``configs.is_decoder=True``.
+        bos_token_id (:obj:`int`, `optional`, defaults to 0):
+            Beginning of stream token id.
+        eos_token_id (:obj:`int`, `optional`, defaults to 2):
+            End of stream token id.
+        tie_word_embeddings (:obj:`bool`, `optional`, defaults to :obj:`True`):
+            Whether to tie weight embeddings
+        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 EXAONEModel, ExaoneConfig
+
+            >>> # Initializing a EXAONE configuration
+            >>> configuration = ExaoneConfig()
+
+            >>> # Initializing a model from configuration
+            >>> model = EXAONEModel(configuration)
+
+            >>> # Accessing the model configuration
+            >>> configuration = model.configs
+    """
+
+    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",
+        rope_theta=10000.0,
+        rope_scaling=None,
+        embed_dropout=0.0,
+        attention_dropout=0.0,
+        layer_norm_epsilon=1e-5,
+        initializer_range=0.02,
+        use_cache=True,
+        bos_token_id=0,
+        eos_token_id=2,
+        tie_word_embeddings=True,
+        **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.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.rope_theta = rope_theta
+        self.rope_scaling = rope_scaling
+
+        self.bos_token_id = bos_token_id
+        self.eos_token_id = eos_token_id
+
+        super().__init__(
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs
+        )

python/sglang/srt/hf_transformers_utils.py

@@ -15,6 +15,7 @@
 
 """Utilities for Huggingface Transformers."""
 
+import contextlib
 import functools
 import json
 import os
@@ -34,14 +35,21 @@
 try:
     from vllm.transformers_utils.configs import ChatGLMConfig, DbrxConfig
 
+    from sglang.srt.configs import ExaoneConfig
+
     _CONFIG_REGISTRY: Dict[str, Type[PretrainedConfig]] = {
         ChatGLMConfig.model_type: ChatGLMConfig,
         DbrxConfig.model_type: DbrxConfig,
+        ExaoneConfig.model_type: ExaoneConfig,
     }
 except ImportError:
     # We want this file to run without vllm dependency
     _CONFIG_REGISTRY: Dict[str, Type[PretrainedConfig]] = {}
 
+for name, cls in _CONFIG_REGISTRY.items():
+    with contextlib.suppress(ValueError):
+        AutoConfig.register(name, cls)
+
 from sglang.srt.utils import is_multimodal_model
 
 
@@ -53,7 +61,7 @@ def download_from_hf(model_path: str):
 
 
 def get_config_json(model_path: str):
-    with open(os.path.join(model_path, "config.json")) as f:
+    with open(os.path.join(model_path, "configs.json")) as f:
         config = json.load(f)
     return config
 
@@ -89,7 +97,7 @@ def get_config(
 
 
 def get_context_length(config):
-    """Get the context length of a model from a huggingface model config."""
+    """Get the context length of a model from a huggingface model configs."""
     rope_scaling = getattr(config, "rope_scaling", None)
     if rope_scaling:
         rope_scaling_factor = config.rope_scaling["factor"]