image-match/python/py/Lib/site-packages/transformers/models/exaone4/configuration_exaone4.py

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# Copyright 2025 The LG AI Research and HuggingFace Inc. 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
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# Unless required by applicable law or agreed to in writing, software
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from huggingface_hub.dataclasses import strict

from ...configuration_utils import PreTrainedConfig
from ...modeling_rope_utils import RopeParameters
from ...utils import auto_docstring


@auto_docstring(checkpoint="LGAI-EXAONE/EXAONE-4.0-32B")
@strict
class Exaone4Config(PreTrainedConfig):
    r"""
    sliding_window_pattern (`str`, *optional*):
        The pattern to use for sliding window attention. Can be one of:
            - `None`: No sliding window attention is used
            - `int`: Every `sliding_window` layers, use global attention, else use local attention.
            - `str`: A sequence of "L" (local attention) and "G" (global attention) characters that defines the
              attention pattern. The pattern starts from layer 0 and repeats every `sliding_window` layers. The
              final layer always uses global attention regardless of the pattern.
        For instance, sliding_window_pattern="LLLG" same as sliding_window=4, which means:
            - Layer 0, 1, 2: local attention,
            - Layer 3: global attention,
            ...(repeated)

    Example:

    ```python
    >>> from transformers import Exaone4Model, Exaone4Config

    >>> # Initializing a EXAONE configuration
    >>> configuration = Exaone4Config()

    >>> # Initializing a model from configuration
    >>> model = Exaone4Model(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""

    model_type = "exaone4"
    keys_to_ignore_at_inference = ["past_key_values"]
    # Default tensor parallel plan for base model `LlamaModel`
    base_model_tp_plan = {
        "layers.*.self_attn.q_proj": "colwise",
        "layers.*.self_attn.k_proj": "colwise",
        "layers.*.self_attn.v_proj": "colwise",
        "layers.*.self_attn.q_norm": "replicated_with_grad_allreduce",
        "layers.*.self_attn.k_norm": "replicated_with_grad_allreduce",
        "layers.*.self_attn.o_proj": "rowwise",
        "layers.*.mlp.gate_proj": "colwise",
        "layers.*.mlp.up_proj": "colwise",
        "layers.*.mlp.down_proj": "rowwise",
    }
    base_model_pp_plan = {
        "embed_tokens": (["input_ids"], ["inputs_embeds"]),
        "layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
        "norm": (["hidden_states"], ["hidden_states"]),
    }

    vocab_size: int = 102400
    hidden_size: int = 4096
    intermediate_size: int = 16384
    num_hidden_layers: int = 32
    num_attention_heads: int = 32
    num_key_value_heads: int = 32
    hidden_act: str = "silu"
    max_position_embeddings: int = 2048
    initializer_range: float = 0.02
    rms_norm_eps: float = 1e-5
    use_cache: bool = True
    bos_token_id: int | None = 0
    eos_token_id: int | list[int] | None = 2
    pad_token_id: int | None = None
    tie_word_embeddings: bool = False
    rope_parameters: RopeParameters | dict | None = None
    attention_dropout: float | int = 0.0
    sliding_window: int | None = 4096
    sliding_window_pattern: str | int | None = 4
    layer_types: list[str] | None = None

    def __post_init__(self, **kwargs):
        if self.sliding_window is None:
            self.sliding_window_pattern = 0
        if self.layer_types is None:
            self.layer_types = [
                "sliding_attention"
                if ((i + 1) % (self.sliding_window_pattern) != 0 and i < self.num_hidden_layers)
                else "full_attention"
                for i in range(self.num_hidden_layers)
            ]

        super().__post_init__(**kwargs)


__all__ = ["Exaone4Config"]