configuration_gigarembed.py

import warnings

from typing import Literal
from transformers import AutoConfig
from transformers.models.auto import CONFIG_MAPPING
from transformers.configuration_utils import PretrainedConfig
from transformers.modeling_rope_utils import rope_config_validation

GIGAREMBED_TYPE = "gigarembed"
LATENT_ATTENTION_TYPE = "latent_attention"


class GigarConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`GigarModel`]. It is used to instantiate an Gigar
    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 Gigar-7B.

    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 32000):
            Vocabulary size of the Gigar model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`GigarModel`]
        hidden_size (`int`, *optional*, defaults to 4096):
            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 32):
            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`.
        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 2048):
            The maximum sequence length that this model might ever be used with. Gigar 1 supports up to 2048 tokens,
            Gigar 2 up to 4096, CodeLlama up to 16384.
        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`.
        pad_token_id (`int`, *optional*):
            Padding token id.
        bos_token_id (`int`, *optional*, defaults to 1):
            Beginning of stream token id.
        eos_token_id (`int`, *optional*, defaults to 2):
            End of stream token id.
        pretraining_tp (`int`, *optional*, defaults to 1):
            Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
            document](https://huggingface.co/docs/transformers/main/perf_train_gpu_many#tensor-parallelism) to
            understand more about it. This value is necessary to ensure exact reproducibility of the pretraining
            results. Please refer to [this issue](https://github.com/pytorch/pytorch/issues/76232).
        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
            Whether to tie weight embeddings
        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',
                    'gigar3'], 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 'gigar3'. 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 'gigar3'. Scaling factor applied to low frequency components of the RoPE
                `high_freq_factor` (`float`, *optional*):
                    Only used with 'gigar3'. Scaling factor applied to high frequency components of the 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.
        mlp_bias (`bool`, *optional*, defaults to `False`):
            Whether to use a bias in up_proj, down_proj and gate_proj layers in the MLP layers.
        head_dim (`int`, *optional*):
            The attention head dimension. If None, it will default to hidden_size // num_attention_heads

    ```python
    >>> from transformers import GigarModel, GigarConfig

    >>> # Initializing a Gigar gigar-7b style configuration
    >>> configuration = GigarConfig()

    >>> # Initializing a model from the gigar-7b style configuration
    >>> model = GigarModel(configuration)

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

    model_type = "gigar"
    keys_to_ignore_at_inference = ["past_key_values"]
    # Default tensor parallel plan for base model `GigarModel`
    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=32000,
        hidden_size=4096,
        intermediate_size=11008,
        num_hidden_layers=32,
        num_attention_heads=32,
        num_key_value_heads=None,
        hidden_act="silu",
        max_position_embeddings=2048,
        initializer_range=0.02,
        rms_norm_eps=1e-6,
        use_cache=True,
        pad_token_id=None,
        bos_token_id=1,
        eos_token_id=2,
        pretraining_tp=1,
        tie_word_embeddings=False,
        rope_theta=10000.0,
        rope_scaling=None,
        attention_bias=False,
        attention_dropout=0.0,
        mlp_bias=False,
        head_dim=None,
        apply_qk_norm=False,
        mla_config=None,
        **kwargs,
    ):
        super().__init__(
            pad_token_id=pad_token_id,
            bos_token_id=bos_token_id,
            eos_token_id=eos_token_id,
            tie_word_embeddings=tie_word_embeddings,
            **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

        # 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.pretraining_tp = pretraining_tp
        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.mlp_bias = mlp_bias
        self.head_dim = head_dim if head_dim is not None else self.hidden_size // self.num_attention_heads
        # 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)

        self.apply_qk_norm = apply_qk_norm
        self.mla_config = mla_config

        self._validate_mla_config()

    def _validate_mla_config(self):
        if self.mla_config is None:
            warnings.warn("MLA config is None!")
            return

        EXPECTED_KEYS = [
            "qk_nope_head_dim",
            "qk_rope_head_dim",
            "v_head_dim",
            "kv_lora_rank",
            "q_lora_rank",
        ]
        if not all((key in self.mla_config for key in EXPECTED_KEYS)):
            raise ValueError(
                f"MLA config is expected to have the following keys {EXPECTED_KEYS} but got {self.mla_config.keys()}."
            )

        if self.mla_config["qk_nope_head_dim"] + self.mla_config["qk_rope_head_dim"] != self.mla_config["v_head_dim"]:
            err_msg = (
                f"QK and V head dims do not match! Got {self.mla_config['qk_nope_head_dim']} + {self.mla_config['qk_rope_head_dim']} "
                f"= {self.mla_config['qk_rope_head_dim'] + self.mla_config['qk_nope_head_dim']} and {self.mla_config['v_head_dim']}."
            )
            raise ValueError(err_msg)


class GigarEmbedConfig(PretrainedConfig):
    model_type = "gigarembed"
    is_composition = False

    def __init__(
        self,
        latent_attention_config=None,
        text_config=None,
        padding_side: Literal["right", "left"]="right",
        add_pad_token: bool=True,
        is_mask_instruction: bool = True,
        add_eos: bool=True,
        mask_type: str="b",
        **kwargs,
    ):
        if isinstance(latent_attention_config, dict):
            latent_attention_config["model_type"] = (
                latent_attention_config["model_type"] if "model_type" in latent_attention_config else LATENT_ATTENTION_TYPE
            )
            latent_attention_config = CONFIG_MAPPING[latent_attention_config["model_type"]](**latent_attention_config)

        self.latent_attention_config = latent_attention_config

        if isinstance(text_config, dict):
            text_config = GigarConfig(**text_config)
        elif text_config is None:
            text_config = None

        self.text_config = text_config
        self.padding_side = padding_side
        self.is_mask_instruction = is_mask_instruction
        self.add_pad_token = add_pad_token
        self.add_eos = add_eos
        self.mask_type = mask_type
        if "hidden_size" in kwargs:
            self.hidden_size = kwargs["hidden_size"]

        super().__init__(**kwargs)


class LatentAttentionConfig(PretrainedConfig):
    model_type = LATENT_ATTENTION_TYPE
    is_composition = False
    _name_or_path = "latent_attention"

    def __init__(
        self,
        num_latents_value: int,
        num_cross_heads: int,
        hidden_dim: int,
        latent_dim: int,
        cross_dim_head: int,
        mult: int,
        **kwargs,
    ):
        self.num_latents_value = num_latents_value
        self.num_cross_heads = num_cross_heads
        self.hidden_dim = hidden_dim
        self.latent_dim = latent_dim
        self.cross_dim_head = cross_dim_head
        self.mult = mult

        super().__init__(**kwargs)


AutoConfig.register(GIGAREMBED_TYPE, GigarEmbedConfig)
AutoConfig.register(LATENT_ATTENTION_TYPE, LatentAttentionConfig)

GigarEmbedConfig.register_for_auto_class()
LatentAttentionConfig.register_for_auto_class()