configuration_emo.py

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# coding=utf-8
# Copyright 2025 the HuggingFace 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
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#     http://www.apache.org/licenses/LICENSE-2.0
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from typing import Optional

from transformers.configuration_utils import PretrainedConfig
from transformers.modeling_rope_utils import rope_config_validation


class EmoConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`EmoModel`]. It is used to instantiate an Emo
    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 [allenai/Emo-7x7B-1T](https://huggingface.co/allenai/Emo-7x7B-1T).

    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 100352):
            Vocabulary size of the Emo model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`EmoModel`]
        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, check out [this
            paper](https://huggingface.co/papers/2305.13245). 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 4096):
            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*, defaults to 100277):
            Padding token id.
        bos_token_id (`int`, *optional*):
            Beginning of stream token id.
        eos_token_id (`int`, *optional*, defaults to 100257):
            End of stream token id.
        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
            Whether to tie weight embeddings
        rope_theta (`float`, *optional*, defaults to 500000.0):
            The base period of the RoPE embeddings.
        rope_scaling (`Dict`, *optional*):
            Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
            strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
            `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
            `max_position_embeddings` to the expected new maximum. See the following thread for more information on how
            these scaling strategies behave:
            https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
            experimental feature, subject to breaking API changes in future versions.
        attention_bias (`bool`, defaults to `False`, *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.
        num_experts_per_tok (`int`, *optional*, defaults to 5):
            Number of selected experts.
        num_experts (`int`, *optional*, defaults to 7):
            Number of routed experts.
        output_router_logits (`bool`, *optional*, defaults to `False`):
            Whether or not the router logits should be returned by the model. Enabling this will also
            allow the model to output the auxiliary loss, including load balancing loss and router z-loss.
        router_aux_loss_coef (`float`, *optional*, defaults to 0.01):
            The aux loss factor for the total loss.
        norm_topk_prob (`bool`, *optional*, defaults to `False`):
            Whether to normalize the topk probabilities.

    ```python
    >>> from transformers import EmoModel, EmoConfig

    >>> # Initializing a Emo style configuration
    >>> configuration = EmoConfig()

    >>> # Initializing a model from the Emo style configuration
    >>> model = EmoModel(configuration)

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

    model_type = "emo"
    keys_to_ignore_at_inference = ["past_key_values"]
    # Update base_model_tp_plan to remove the "rep" suffixes since no qk-norms
    base_model_tp_plan = {
        "layers.*.self_attn.q_proj": "colwise",  # No longer need rep
        "layers.*.self_attn.k_proj": "colwise",  # No longer need rep
        "layers.*.self_attn.v_proj": "colwise",
        "layers.*.self_attn.o_proj": "rowwise",  # No longer need rep
        "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"]),
    }

    def __init__(
        self,
        vocab_size=100352,
        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=4096,
        initializer_range=0.02,
        rms_norm_eps=1e-06,
        use_cache=True,
        pad_token_id=100277,
        bos_token_id=None,
        eos_token_id=100257,
        tie_word_embeddings=False,
        rope_theta=500000.0,
        rope_scaling=None,
        attention_bias=False,
        attention_dropout=0.0,
        num_experts_per_tok=5,
        num_experts=7,
        output_router_logits=False,
        router_aux_loss_coef=0.01,
        norm_topk_prob=False,
        num_shared_experts=0,
        num_experts_per_layer: Optional[list[int]] = None,
        num_shared_experts_per_layer: Optional[list[int]] = None,
        always_active_experts: Optional[list[int]] = None,
        always_active_experts_per_layer: Optional[list[list[int]]] = None,
        dense_intermediate_size: Optional[int] = None,
        dense_mlp_bias: bool = False,  # Some densefirst models were accidentally trained with bias=True on dense MLPs due to OLMo Core's FeedForwardConfig defaulting bias to True when not explicitly set
        **kwargs,
    ):
        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,
            **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.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.num_experts_per_tok = num_experts_per_tok
        self.num_experts = num_experts
        self.output_router_logits = output_router_logits
        self.router_aux_loss_coef = router_aux_loss_coef
        self.norm_topk_prob = norm_topk_prob
        # Validate the correctness of rotary position embeddings parameters
        # BC: if there is a 'type' field, move it to 'rope_type'.
        if self.rope_scaling is not None and "type" in self.rope_scaling:
            self.rope_scaling["rope_type"] = self.rope_scaling["type"]
        rope_config_validation(self)
        assert (
            num_shared_experts <= num_experts
        ), "num_shared_experts cannot be greater than num_experts"

        self.num_shared_experts = num_shared_experts  # note: we don't care about pruning here - pruning should be handled by the pruning script - the model should just assume that it will use all the experts available
        self.num_experts_per_layer = num_experts_per_layer
        self.num_shared_experts_per_layer = num_shared_experts_per_layer
        self.always_active_experts = always_active_experts
        self.always_active_experts_per_layer = always_active_experts_per_layer
        self.dense_intermediate_size = dense_intermediate_size
        self.dense_mlp_bias = dense_mlp_bias


__all__ = ["EmoConfig"]