configuration_doge.py

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# coding=utf-8
# Copyright 2024 Jingze Shi and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on the Wonderful Matrices paper implementation.
# The Doge family of small language models is trained by Jingze Shi.
#
# 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.
from transformers.configuration_utils import PretrainedConfig
from transformers.modeling_rope_utils import rope_config_validation


class DogeConfig(PretrainedConfig):
    r"""

    This is the configuration class to store the configuration of a [`DogeModel`]. It is used to instantiate an Doge

    model according to the specified arguments, defining the model architecture like [SmallDoge/Doge-20M](https://huggingface.co/SmallDoge/Doge-20M).



    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 32768):

            Vocabulary size of the Doge model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`DogeModel`]

        hidden_size (`int`, *optional*, defaults to 1024):

            Dimension of the hidden representations.

        intermediate_size (`int`, *optional*, defaults to 2048):

            Dimension of the MLP representations.

        num_hidden_layers (`int`, *optional*, defaults to 32):

            Number of hidden layers in the Transformer decoder.

        hidden_bias (`bool`, *optional*, defaults to `False`):

            Whether to use bias in the hidden layers.

        hidden_dropout (`float`, *optional*, defaults to 0.0):

            Dropout probability for each sequence transformation and state transformation module.

        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):

            The non-linear activation function (function or string) in the decoder.

        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`.

        bos_token_id (`int`, *optional*, defaults to 0):

            Beginning of stream token id.

        eos_token_id (`int`, *optional*, defaults to 1):

            End of stream token id.

        pad_token_id (`int`, *optional*, defaults to 2):

            Padding token id.

        tie_word_embeddings (`bool`, *optional*, defaults to `False`):

            Whether to tie weight embeddings

        max_position_embeddings (`int`, *optional*, defaults to 2048):

            The maximum sequence length that this model might ever be used with.

        rope_theta (`float`, *optional*, defaults to 10000.0):

            The base period of the RoPE embeddings.

        rope_scaling (`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.

            Doge family of small models use `{ 'rope_type': 'dynamic', 'factor': 4.0, 'original_max_position_embeddings': 2048 }` as the default value.

            Expected contents:

                `rope_type` (`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` (`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` (`int`, *optional*):

                    Used with 'dynamic', 'longrope' and 'llama3'.

                    The original max position embeddings used during pretraining.

                `attention_factor` (`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` (`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` (`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` (`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` (`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` (`float`, *optional*):

                    Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE

                `high_freq_factor` (`float`, *optional*):

                    Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE

        num_attention_heads (`int`, *optional*, defaults to 8):

            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`.

        attention_dropout (`float`, *optional*, defaults to 0.0):

            The dropout ratio for the attention probabilities.

        dynamic_mask_ratio (`float`, *optional*, defaults to 0.0):

            The ratio to control the proportion of the dynamic mask filled with the minimum value. For more details checkout [this paper](https://arxiv.org/pdf/2412.11834).

        is_moe (`bool`, *optional*, defaults to `False`):

            Whether to use the Cross Domain Mixture of Experts, if `True`, the MoE will inherit the MLP to initialize. For more details checkout [this paper](https://arxiv.org/pdf/2412.11834).

        num_cdmoe_experts (`int`, *optional*, defaults to 16348):

            Number of Experts for the Cross Domain Mixture of Experts.

        num_cdmoe_heads (`int`, *optional*, defaults to 4):

            Number of retrieval heads, used to mix multi-head experts.

        num_cdmoe_experts_per_head (`int`, *optional*, defaults to 8):

            Number of Experts per retrieval head, used to mix multi-head experts.

        expert_retrieval_size (`int`, *optional*, defaults to 64):

            Dimension of the Expert retrieval states for calculating the dot product of query and key to determine the expert index.



    ```python

    >>> from transformers import DogeConfig, DogeModel



    >>> # Initializing a Doge-320M style configuration

    >>> configuration = DogeConfig()



    >>> # Initializing a model from the Doge-320M style configuration

    >>> model = DogeModel(configuration)



    >>> # Accessing the model configuration

    >>> configuration = model.config

    ```"""

    model_type = "doge"
    keys_to_ignore_at_inference = ["past_key_values"]
    # Default tensor parallel plan for base model `DogeModel`
    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.dt_proj": "colwise",
        "layers.*.self_attn.o_proj": "rowwise",
        "layers.*.mlp.gate_proj": "colwise",
        "layers.*.mlp.up_proj": "colwise",
        "layers.*.mlp.down_proj": "rowwise",
    }

    def __init__(

        self,

        vocab_size=32768,

        hidden_size=1024,

        intermediate_size=2048,

        num_hidden_layers=32,

        hidden_bias=False,

        hidden_dropout=0.0,

        hidden_act="silu",

        initializer_range=0.02,

        rms_norm_eps=1e-06,

        use_cache=True,

        bos_token_id=0,

        eos_token_id=1,

        pad_token_id=2,

        tie_word_embeddings=False,

        max_position_embeddings=2048,

        rope_theta=10000.0,

        rope_scaling=None,

        num_attention_heads=8,

        num_key_value_heads=None,

        attention_dropout=0.0,

        dynamic_mask_ratio=0.0,

        is_moe=False,

        num_cdmoe_experts=16348,

        num_cdmoe_heads=4,

        num_cdmoe_experts_per_head=8,

        expert_retrieval_size=64,

        **kwargs,

    ):
        self.vocab_size = vocab_size
        self.hidden_size = hidden_size
        self.intermediate_size = intermediate_size
        self.num_hidden_layers = num_hidden_layers

        self.hidden_bias = hidden_bias
        self.hidden_dropout = hidden_dropout
        self.hidden_act = hidden_act
        self.initializer_range = initializer_range
        self.rms_norm_eps = rms_norm_eps
        self.use_cache = use_cache

        self.max_position_embeddings = max_position_embeddings
        self.rope_theta = rope_theta
        self.rope_scaling = rope_scaling
        self.num_attention_heads = num_attention_heads
        self.num_key_value_heads = num_key_value_heads
        self.attention_dropout = attention_dropout
        self.dynamic_mask_ratio = dynamic_mask_ratio
        self.is_moe = is_moe
        self.num_cdmoe_experts = num_cdmoe_experts
        self.num_cdmoe_heads = num_cdmoe_heads
        self.num_cdmoe_experts_per_head = num_cdmoe_experts_per_head
        self.expert_retrieval_size = expert_retrieval_size

        # Validate the correctness of rotary position embeddings parameters
        # BC: if there is a 'type' field, copy it 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)

        # for backward compatibility
        if num_key_value_heads is None:
            self.num_key_value_heads = num_attention_heads

        super().__init__(
            bos_token_id=bos_token_id,
            eos_token_id=eos_token_id,
            pad_token_id=pad_token_id,
            tie_word_embeddings=tie_word_embeddings,
            **kwargs,
        )


__all__ = ["DogeConfig"]