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# Copyright 2024 The Qwen team, Alibaba Group and the HuggingFace Inc. team. All rights reserved.
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"""AceStep model configuration"""

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 AceStepConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`AceStepModel`]. It is used to instantiate an
    AceStep model according to the specified arguments, defining the model architecture.

    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 64003):
            Vocabulary size of the AceStep model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling the model.
        hidden_size (`int`, *optional*, defaults to 4096):
            Dimension of the hidden representations.
        intermediate_size (`int`, *optional*, defaults to 22016):
            Dimension of the MLP representations.
        num_hidden_layers (`int`, *optional*, defaults to 32):
            Number of hidden layers in the Transformer encoder.
        num_attention_heads (`int`, *optional*, defaults to 32):
            Number of attention heads for each attention layer in the Transformer encoder.
        num_key_value_heads (`int`, *optional*, defaults to 32):
            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 `False`):
            Whether the model's input and output word embeddings should be tied.
        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.
            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`.
        layer_types (`list`, *optional*):
            Attention pattern for each layer.
        attention_dropout (`float`, *optional*, defaults to 0.0):
            The dropout ratio for the attention probabilities.

    ```python
    >>> from acestep.models import AceStepConfig

    >>> # Initializing an AceStep configuration
    >>> configuration = AceStepConfig()

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

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

    model_type = "acestep"
    keys_to_ignore_at_inference = ["past_key_values"]
    
    # Default tensor parallel plan for the base model
    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=64003,
        fsq_dim=2048,
        fsq_input_levels=[8, 8, 8, 5, 5, 5],
        fsq_input_num_quantizers=1,
        hidden_size=2048,
        intermediate_size=6144,
        num_hidden_layers=24,
        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,
        rope_scaling=None,
        attention_bias=False,
        use_sliding_window=True,
        sliding_window=128,
        layer_types=None,
        attention_dropout=0.0,
        num_lyric_encoder_hidden_layers=8,
        audio_acoustic_hidden_dim=64,
        pool_window_size=5,
        text_hidden_dim=1024,
        in_channels=192,
        data_proportion=0.5,
        timestep_mu=-0.4,
        timestep_sigma=1.0,
        timbre_hidden_dim=64,
        num_timbre_encoder_hidden_layers=4,
        timbre_fix_frame=750,
        patch_size=2,
        num_attention_pooler_hidden_layers=2,
        num_audio_decoder_hidden_layers=24,
        lyric_alignment_layers_config=None,
        model_version="turbo",
        **kwargs,
    ):
        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
        
        # Text encoder configuration
        self.text_hidden_dim = text_hidden_dim

        # Lyric encoder configuration
        self.num_lyric_encoder_hidden_layers = num_lyric_encoder_hidden_layers
        self.patch_size = patch_size

        # Audio semantic token generation configuration
        self.audio_acoustic_hidden_dim = audio_acoustic_hidden_dim
        self.pool_window_size = pool_window_size
        self.in_channels = in_channels
        self.data_proportion = data_proportion
        self.timestep_mu = timestep_mu
        self.timestep_sigma = timestep_sigma
        
        # FSQ (Finite Scalar Quantization) configuration
        self.fsq_dim = fsq_dim
        self.fsq_input_levels = fsq_input_levels
        self.fsq_input_num_quantizers = fsq_input_num_quantizers
        
        # Timbre encoder configuration
        self.timbre_hidden_dim = timbre_hidden_dim
        self.num_timbre_encoder_hidden_layers = num_timbre_encoder_hidden_layers
        self.timbre_fix_frame = timbre_fix_frame
        self.num_attention_pooler_hidden_layers = num_attention_pooler_hidden_layers
        self.num_audio_decoder_hidden_layers = num_audio_decoder_hidden_layers
        self.lyric_alignment_layers_config = lyric_alignment_layers_config
        self.vocab_size = vocab_size

        # Backward compatibility: ensure num_key_value_heads is set
        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
        self.model_version = model_version
        
        # Validate rotary position embeddings parameters
        # Backward compatibility: 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

        # Set default layer types if not specified
        if self.layer_types is None:
            self.layer_types = [
                "sliding_attention" if bool((i + 1) % 2) else "full_attention" for i in range(self.num_hidden_layers)
            ]
        layer_type_validation(self.layer_types)

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


__all__ = ["AceStepConfig"]
