import os from typing import Union from transformers.configuration_utils import PretrainedConfig from transformers.models.auto import CONFIG_MAPPING from transformers.modeling_rope_utils import rope_config_validation from transformers.utils import logging logger = logging.get_logger(__name__) class HithinkOmniVisionConfig(PretrainedConfig): model_type = "hithink_omni" base_config_key = "vision_config" def __init__( self, depth=32, hidden_size=3584, hidden_act="silu", intermediate_size=3420, num_heads=16, in_channels=3, patch_size=14, spatial_merge_size=2, temporal_patch_size=2, tokens_per_second=4, window_size=112, out_hidden_size=3584, fullatt_block_indexes=[7, 15, 23, 31], **kwargs, ): super().__init__(**kwargs) self.depth = depth self.hidden_size = hidden_size self.hidden_act = hidden_act self.intermediate_size = intermediate_size self.num_heads = num_heads self.in_channels = in_channels self.patch_size = patch_size self.spatial_merge_size = spatial_merge_size self.temporal_patch_size = temporal_patch_size self.tokens_per_second = tokens_per_second self.window_size = window_size self.fullatt_block_indexes = fullatt_block_indexes self.out_hidden_size = out_hidden_size class HithinkAudioEncoderConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`HithinkAudioEncoder`]. It is used to instantiate a HithinkAudio audio encoder according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the audio encoder of the HithinkAudio architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: num_mel_bins (`int`, *optional*, defaults to 128): Number of mel features used per input features. Should correspond to the value used in the `HithinkOmniProcessor` class. encoder_layers (`int`, *optional*, defaults to 32): Number of encoder layers. encoder_attention_heads (`int`, *optional*, defaults to 20): Number of attention heads for each attention layer in the Transformer encoder. encoder_ffn_dim (`int`, *optional*, defaults to 5120): Dimensionality of the "intermediate" (often named feed-forward) layer in encoder. encoder_layerdrop (`float`, *optional*, defaults to 0.0): The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556) for more details. d_model (`int`, *optional*, defaults to 1280): Dimensionality of the layers. dropout (`float`, *optional*, defaults to 0.0): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. activation_function (`str`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"silu"` and `"gelu_new"` are supported. activation_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for activations inside the fully connected layer. scale_embedding (`bool`, *optional*, defaults to `False`): Scale embeddings by diving by sqrt(d_model). init_std (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. max_source_positions (`int`, *optional*, defaults to 1500): The maximum sequence length of log-mel filter-bank features that this model might ever be used with. Example: ```python >>> from transformers import HithinkAudioEncoderConfig, HithinkAudioEncoder >>> # Initializing a HithinkAudioEncoderConfig >>> configuration = HithinkAudioEncoderConfig() >>> # Initializing a HithinkAudioEncoder (with random weights) >>> model = HithinkAudioEncoder(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "hithink_audio_encoder" def __init__( self, num_mel_bins=128, encoder_layers=32, encoder_attention_heads=20, encoder_ffn_dim=5120, encoder_layerdrop=0.0, d_model=1280, dropout=0.0, attention_dropout=0.0, activation_function="gelu", activation_dropout=0.0, scale_embedding=False, init_std=0.02, max_source_positions=1500, **kwargs, ): super().__init__(**kwargs) self.num_mel_bins = num_mel_bins self.d_model = d_model self.encoder_layers = encoder_layers self.encoder_attention_heads = encoder_attention_heads self.encoder_ffn_dim = encoder_ffn_dim self.dropout = dropout self.attention_dropout = attention_dropout self.activation_function = activation_function self.activation_dropout = activation_dropout self.encoder_layerdrop = encoder_layerdrop self.num_hidden_layers = encoder_layers self.init_std = init_std self.scale_embedding = scale_embedding # scale factor will be sqrt(d_model) if True self.max_source_positions = max_source_positions class HithinkAudioDecoderConfig(PretrainedConfig): model_type = "hithink_omni_audio_decoder" def __init__( self, num_hidden_layers=6, codebook_size=1024, num_codebooks=8, **kwargs, ): super().__init__(**kwargs) self.num_hidden_layers = num_hidden_layers self.codebook_size = codebook_size self.num_codebooks = num_codebooks class HithinkOmniConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`HithinkOmniModel`]. It is used to instantiate a HithinkOmni 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 152064): Vocabulary size of the HithinkOmni model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`HithinkOmniModel`] hidden_size (`int`, *optional*, defaults to 8192): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 29568): Dimension of the MLP representations. num_hidden_layers (`int`, *optional*, defaults to 80): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 64): 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 checkout [this paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `32`. 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-05): 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 1000000.0): The base period of the RoPE embeddings. 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 80): The number of layers that use SWA (Sliding Window Attention). The bottom layers use SWA while the top use full attention. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. vision_config (`Dict`, *optional*): The config for the visual encoder initialization. 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 ```python >>> from transformers import HithinkOmniForConditionalGeneration, HithinkOmniConfig >>> # Initializing a HithinkOmni style configuration >>> configuration = HithinkOmniConfig() >>> # Initializing a model from the HithinkOmni-7B style configuration >>> model = HithinkOmniForConditionalGeneration(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "hithink_omni" sub_configs = {"vision_config": HithinkOmniVisionConfig} keys_to_ignore_at_inference = ["past_key_values"] # Default tensor parallel plan for base model `HithinkOmni` 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", } def __init__( self, vocab_size=152064, vocab_size_ext=None, hidden_size=8192, intermediate_size=29568, num_hidden_layers=80, num_attention_heads=64, num_key_value_heads=8, hidden_act="silu", max_position_embeddings=32768, initializer_range=0.02, rms_norm_eps=1e-05, use_cache=True, tie_word_embeddings=False, rope_theta=1000000.0, use_sliding_window=False, sliding_window=4096, max_window_layers=80, attention_dropout=0.0, vision_config=None, rope_scaling=None, audio_config=None, audio_token_index=151665, audio_decoder_config=None, **kwargs, ): if isinstance(vision_config, dict): self.vision_config = self.sub_configs["vision_config"](**vision_config) elif vision_config is None: self.vision_config = self.sub_configs["vision_config"]() self.vocab_size = vocab_size self.vocab_size_ext = vocab_size_ext 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 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.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.attention_dropout = attention_dropout self.rope_scaling = rope_scaling # define audio config self.audio_token_index = audio_token_index self.ignore_index = -100 if isinstance(audio_config, dict): audio_config = HithinkAudioEncoderConfig(**audio_config) elif audio_config is None: audio_config = HithinkAudioEncoderConfig( d_model=1280, encoder_attention_heads=20, encoder_ffn_dim=5120, encoder_layerdrop=0.0, encoder_layers=32, num_mel_bins=128, max_source_positions=1500, scale_embedding=False, activation_function="gelu", ) self.audio_config = audio_config if isinstance(audio_decoder_config, dict): self.audio_decoder_config = HithinkAudioDecoderConfig(**audio_decoder_config) else: self.audio_decoder_config = None # Validate the correctness of rotary position embeddings parameters # BC: if there is a 'type' field, move it to 'rope_type'. # and change type from 'mrope' to 'default' because `mrope` does defeault RoPE calculations # one can set it to "linear"/"dynamic" etc. to have scaled RoPE # TODO: @raushan update config in the hub if self.rope_scaling is not None and "type" in self.rope_scaling: if self.rope_scaling["type"] == "mrope": self.rope_scaling["type"] = "default" self.rope_scaling["rope_type"] = self.rope_scaling["type"] rope_config_validation(self, ignore_keys={"mrope_section"}) super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)