configuration_moss_ttsd.py

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# coding=utf-8
# Copyright 2025 OpenMOSS and the 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
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#     http://www.apache.org/licenses/LICENSE-2.0
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from transformers.configuration_utils import PretrainedConfig, layer_type_validation
from transformers.modeling_rope_utils import rope_config_validation
from transformers.utils import logging


logger = logging.get_logger(__name__)


class MossTTSDConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`MossTTSDModel`]. It is used to instantiate a
    MOSS-TTSD 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 MOSS-TTSD
    [fnlp/MOSS-TTSD-v0.5](https://huggingface.co/fnlp/MOSS-TTSD-v0.5) architecture.

    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
    documentation from [`PretrainedConfig`] for more information.

    Example:

    ```python
    >>> from transformers import MossTTSDConfig, MossTTSDModel

    >>> # Initializing a MOSS-TTSD configuration
    >>> configuration = MossTTSDConfig()

    >>> # Initializing a model from the configuration
    >>> model = MossTTSDModel(configuration)

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

    Args:
            vocab_size (`int`, *optional*, defaults to 152697):
                Vocabulary size of the MOSS-TTSD model. Defines the number of different tokens that can be represented by the
                `inputs_ids` passed when calling [`MossTTSDModel`]
            hidden_size (`int`, *optional*, defaults to 2048):
                Dimension of the hidden representations.
            intermediate_size (`int`, *optional*, defaults to 6144):
                Dimension of the MLP representations.
            num_hidden_layers (`int`, *optional*, defaults to 28):
                Number of hidden layers in the Transformer encoder.
            num_attention_heads (`int`, *optional*, defaults to 16):
                Number of attention heads for each attention layer in the Transformer encoder.
            num_key_value_heads (`int`, *optional*, defaults to 8):
                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 `32`.
            head_dim (`int`, *optional*, defaults to 128):
                The attention head dimension.
            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 32768):
                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`.
            tie_word_embeddings (`bool`, *optional*, defaults to `True`):
                Whether the model's input and output word embeddings should be tied.
            rope_theta (`float`, *optional*, defaults to 1000000.0):
                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.
                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
            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.
            use_sliding_window (`bool`, *optional*, defaults to `False`):
                Whether to use sliding window attention.
            sliding_window (`int`, *optional*, defaults to 4096):
                Sliding window attention (SWA) window size. If not specified, will default to `4096`.
            max_window_layers (`int`, *optional*, defaults to 28):
                The number of layers using full attention. The first `max_window_layers` layers will use full attention, while any
                additional layer afterwards will use SWA (Sliding Window Attention).
            layer_types (`list`, *optional*):
                Attention pattern for each layer.
            attention_dropout (`float`, *optional*, defaults to 0.0):
                The dropout ratio for the attention probabilities.
            channels (`<fill_type>`, *optional*, defaults to 8): <fill_docstring>
            speech_vocab_size (`<fill_type>`, *optional*, defaults to 1025): <fill_docstring>
            speech_pad_token (`<fill_type>`, *optional*, defaults to 1024): <fill_docstring>
            speech_token_range (`<fill_type>`, *optional*, defaults to `(151665, 152689)`): <fill_docstring>
            speech_eos_token (`<fill_type>`, *optional*, defaults to 152694): <fill_docstring>

    ```python
    >>> from transformers import MossTTSDModel, MossTTSDConfig

    >>> # Initializing a Qwen3 style configuration
    >>> configuration = MossTTSDConfig()

    >>> # Initializing a model from the Qwen3-8B style configuration
    >>> model = MossTTSDModel(configuration)

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

    model_type = "moss_ttsd"
    keys_to_ignore_at_inference = ["past_key_values"]

    # Default tensor parallel plan for base model `MossTTSD`
    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.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"]),
    }

    def __init__(
        self,
        vocab_size=152697,
        hidden_size=2048,
        intermediate_size=6144,
        num_hidden_layers=28,
        num_attention_heads=16,
        num_key_value_heads=8,
        head_dim=128,
        hidden_act="silu",
        max_position_embeddings=32768,
        initializer_range=0.02,
        rms_norm_eps=1e-6,
        use_cache=True,
        tie_word_embeddings=True,
        rope_theta=1000000.0,
        rope_scaling=None,
        attention_bias=False,
        use_sliding_window=False,
        sliding_window=None,
        max_window_layers=28,
        layer_types=None,
        attention_dropout=0.0,
        channels=8,
        speech_vocab_size=1025,
        speech_pad_token=1024,
        speech_token_range=(151665, 152689),
        speech_eos_token=152694,
        **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
        self.use_sliding_window = use_sliding_window
        self.sliding_window = sliding_window if self.use_sliding_window else None
        self.max_window_layers = max_window_layers

        # 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.head_dim = head_dim
        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
        # 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)

        self.layer_types = layer_types
        if self.layer_types is None:
            self.layer_types = [
                "sliding_attention"
                if self.sliding_window is not None and i >= self.max_window_layers
                else "full_attention"
                for i in range(self.num_hidden_layers)
            ]
        layer_type_validation(self.layer_types)

        self.channels = channels
        self.speech_vocab_size = speech_vocab_size
        self.speech_pad_token = speech_pad_token
        self.speech_token_range = speech_token_range
        self.speech_eos_token = speech_eos_token

        super().__init__(
            tie_word_embeddings=tie_word_embeddings,
            **kwargs,
        )


__all__ = ["MossTTSDConfig"]