vllm.transformers_utils.configs.olmo_hybrid ¶

class OlmoHybridConfig(PretrainedConfig):
    r"""
        Configuration class for [`OlmoHybridModel`]. It is used to
        instantiate an OLMo Hybrid 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/Olmo-Hybrid-7B](https://huggingface.co/allenai/Olmo-Hybrid-7B)
        model.

        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 OlmoHybrid model. Defines
                the number of different tokens that can be
                represented by the `inputs_ids` passed when
                calling [`OlmoHybridModel`].
            hidden_size (`int`, *optional*, defaults to 3840):
                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 30):
                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 65536):
                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.
            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_parameters (`RopeParameters`, *optional*):
                Dictionary containing the configuration
                parameters for the RoPE embeddings. Can be
                `None` to disable RoPE.
            attention_bias (`bool`, *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.
            rms_norm_eps (`float`, *optional*,
                defaults to 1e-06):
                The epsilon used by the rms normalization
                layers.
            layer_types (`list`, *optional*):
                Attention pattern for each layer. Can contain
                `"full_attention"` or `"linear_attention"`.
                Defaults to linear attention for most layers
                with full attention for every 4th layer.
            linear_num_key_heads (`int`, *optional*):
                Number of key heads for the linear attention
                layers. Defaults to `num_attention_heads`.
            linear_num_value_heads (`int`, *optional*):
                Number of value heads for the linear attention
                layers. Defaults to `num_attention_heads`.
            linear_key_head_dim (`int`, *optional*):
                Dimension of each key head in linear attention
                layers. Defaults to
                `0.75 * hidden_size / linear_num_key_heads`.
            linear_value_head_dim (`int`, *optional*):
                Dimension of each value head in linear
                attention layers. Defaults to
                `2 * linear_key_head_dim`.
            linear_a_log_min (`float`, *optional*,
                defaults to 0.0):
                Minimum value for uniform initialization of
                A_log in GatedDeltaNet layers.
            linear_a_log_max (`float`, *optional*,
                defaults to 16.0):
                Maximum value for uniform initialization of
                A_log in GatedDeltaNet layers.
            linear_dt_min (`float`, *optional*,
                defaults to 0.001):
                Minimum value for dt initialization in
                GatedDeltaNet layers.
            linear_dt_max (`float`, *optional*,
                defaults to 0.1):
                Maximum value for dt initialization in
                GatedDeltaNet layers.
            linear_dt_init_floor (`float`, *optional*,
                defaults to 0.0001):
                Floor value for clamping dt during
                initialization in GatedDeltaNet layers.
            linear_conv_kernel_dim (`int`, *optional*,
                defaults to 4):
                Kernel size for the short convolution applied
                to queries, keys, and values in linear
                attention layers.
            linear_allow_neg_eigval (`bool`, *optional*,
                defaults to `True`):
                Whether to allow negative eigenvalues in the
                GatedDeltaNet recurrence. When `True`, the
                beta parameter is scaled by 2.0 to allow
                values in range [0, 2] instead of [0, 1].
    ```python
        >>> from transformers import (
        ...     OlmoHybridModel,
        ...     OlmoHybridConfig,
        ... )

        >>> configuration = OlmoHybridConfig()
        >>> model = OlmoHybridModel(configuration)
        >>> configuration = model.config
    ```
    """

    model_type = "olmo_hybrid"
    keys_to_ignore_at_inference = ["past_key_values"]
    base_model_tp_plan = {
        "layers.*.self_attn.q_proj": "colwise_gather_output",
        "layers.*.self_attn.k_proj": "colwise_gather_output",
        "layers.*.self_attn.v_proj": "colwise_gather_output",
        "layers.*.self_attn.o_proj": "rowwise_split_input",
        "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: int | None = 100352,
        hidden_size: int | None = 3840,
        intermediate_size: int | None = 11008,
        num_hidden_layers: int | None = 32,
        num_attention_heads: int | None = 30,
        num_key_value_heads: int | None = None,
        hidden_act: str | None = "silu",
        max_position_embeddings: int | None = 65536,
        initializer_range: float | None = 0.02,
        use_cache: bool | None = True,
        pad_token_id: int | None = 100277,
        bos_token_id: int | None = None,
        eos_token_id: int | None = 100257,
        tie_word_embeddings: bool | None = False,
        rope_parameters=None,
        attention_bias: bool | None = False,
        attention_dropout: float | None = 0.0,
        rms_norm_eps: float | None = 1e-06,
        layer_types: list[str] | None = None,
        linear_num_key_heads: int | None = None,
        linear_num_value_heads: int | None = None,
        linear_key_head_dim: int | None = None,
        linear_value_head_dim: int | None = None,
        linear_a_log_min: float = 0.0,
        linear_a_log_max: float = 16.0,
        linear_dt_min: float = 0.001,
        linear_dt_max: float = 0.1,
        linear_dt_init_floor: float = 1e-4,
        linear_conv_kernel_dim: int = 4,
        linear_allow_neg_eigval: bool = True,
        **kwargs,
    ):
        super().__init__(**kwargs)

        assert num_hidden_layers is not None
        assert hidden_size is not None
        assert num_attention_heads is not None

        if layer_types is None:
            # Default: linear attention for most layers, full attention every 4th layer
            layer_types = ["linear_attention"] * int(num_hidden_layers)
            for i in range(int(num_hidden_layers)):
                if i % 4 == 3:
                    layer_types[i] = "full_attention"
            # Ensure at least one full attention layer for small num_hidden_layers
            if "full_attention" not in layer_types:
                layer_types[-1] = "full_attention"

        layer_type_validation(layer_types, num_hidden_layers)
        if "linear_attention" not in layer_types:
            raise ValueError(
                "OLMoHybrid expects at least one 'linear_attention' layer."
            )
        if all(t == "linear_attention" for t in layer_types):
            raise ValueError("OLMoHybrid expects at least one attention layer.")

        self.layer_types = layer_types

        if linear_num_key_heads is None:
            linear_num_key_heads = num_attention_heads
        if linear_num_value_heads is None:
            linear_num_value_heads = num_attention_heads
        if linear_key_head_dim is None:
            linear_key_head_dim = int(0.75 * hidden_size / linear_num_key_heads)
        if linear_value_head_dim is None:
            linear_value_head_dim = 2 * linear_key_head_dim

        self.linear_num_key_heads = linear_num_key_heads
        self.linear_num_value_heads = linear_num_value_heads
        self.linear_key_head_dim = linear_key_head_dim
        self.linear_value_head_dim = linear_value_head_dim
        self.linear_a_log_min = linear_a_log_min
        self.linear_a_log_max = linear_a_log_max
        self.linear_dt_min = linear_dt_min
        self.linear_dt_max = linear_dt_max
        self.linear_dt_init_floor = linear_dt_init_floor
        self.linear_conv_kernel_dim = linear_conv_kernel_dim
        self.linear_allow_neg_eigval = linear_allow_neg_eigval
        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.attention_bias = attention_bias
        self.attention_dropout = attention_dropout
        self.rope_parameters = rope_parameters

        self.tie_word_embeddings = tie_word_embeddings
        self.pad_token_id = pad_token_id
        self.bos_token_id = bos_token_id
        self.eos_token_id = eos_token_id