llava/model/language_model/mpt/configuration_mpt.py

"""A HuggingFace-style model configuration."""
from typing import Dict, Optional, Union
from transformers import PretrainedConfig
attn_config_defaults: Dict = {'attn_type': 'multihead_attention', 'attn_pdrop': 0.0, 'attn_impl': 'triton', 'qk_ln': False, 'clip_qkv': None, 'softmax_scale': None, 'prefix_lm': False, 'attn_uses_sequence_id': False, 'alibi': False, 'alibi_bias_max': 8}
init_config_defaults: Dict = {'name': 'kaiming_normal_', 'fan_mode': 'fan_in', 'init_nonlinearity': 'relu', 'init_div_is_residual': True, 'emb_init_std': None, 'emb_init_uniform_lim': None, 'init_std': None, 'init_gain': 0.0}

class MPTConfig(PretrainedConfig):
    model_type = 'mpt'

    def __init__(self, d_model: int=2048, n_heads: int=16, n_layers: int=24, expansion_ratio: int=4, max_seq_len: int=2048, vocab_size: int=50368, resid_pdrop: float=0.0, emb_pdrop: float=0.0, learned_pos_emb: bool=True, attn_config: Dict=attn_config_defaults, init_device: str='cpu', logit_scale: Optional[Union[float, str]]=None, no_bias: bool=False, verbose: int=0, embedding_fraction: float=1.0, norm_type: str='low_precision_layernorm', use_cache: bool=False, init_config: Dict=init_config_defaults, **kwargs):
        """The MPT configuration class.



        Args:

            d_model (int): The size of the embedding dimension of the model.

            n_heads (int): The number of attention heads.

            n_layers (int): The number of layers in the model.

            expansion_ratio (int): The ratio of the up/down scale in the MLP.

            max_seq_len (int): The maximum sequence length of the model.

            vocab_size (int): The size of the vocabulary.

            resid_pdrop (float): The dropout probability applied to the attention output before combining with residual.

            emb_pdrop (float): The dropout probability for the embedding layer.

            learned_pos_emb (bool): Whether to use learned positional embeddings

            attn_config (Dict):  A dictionary used to configure the model's attention module:

                attn_type (str): type of attention to use. Options: multihead_attention, multiquery_attention

                attn_pdrop (float): The dropout probability for the attention layers.

                attn_impl (str): The attention implementation to use. One of 'torch', 'flash', or 'triton'.

                qk_ln (bool): Whether to apply layer normalization to the queries and keys in the attention layer.

                clip_qkv (Optional[float]): If not None, clip the queries, keys, and values in the attention layer to

                    this value.

                softmax_scale (Optional[float]): If not None, scale the softmax in the attention layer by this value. If None,

                    use the default scale of ``1/sqrt(d_keys)``.

                prefix_lm (Optional[bool]): Whether the model should operate as a Prefix LM. This requires passing an

                    extra `prefix_mask` argument which indicates which tokens belong to the prefix. Tokens in the prefix

                    can attend to one another bi-directionally. Tokens outside the prefix use causal attention.

                attn_uses_sequence_id (Optional[bool]): Whether to restrict attention to tokens that have the same sequence_id.

                    When the model is in `train` mode, this requires passing an extra `sequence_id` argument which indicates

                    which sub-sequence each token belongs to.

                    Defaults to ``False`` meaning any provided `sequence_id` will be ignored.

                alibi (bool): Whether to use the alibi bias instead of position embeddings.

                alibi_bias_max (int): The maximum value of the alibi bias.

            init_device (str): The device to use for parameter initialization.

            logit_scale (Optional[Union[float, str]]): If not None, scale the logits by this value.

            no_bias (bool): Whether to use bias in all layers.

            verbose (int): The verbosity level. 0 is silent.

            embedding_fraction (float): The fraction to scale the gradients of the embedding layer by.

            norm_type (str): choose type of norm to use

            multiquery_attention (bool): Whether to use multiquery attention implementation.

            use_cache (bool): Whether or not the model should return the last key/values attentions

            init_config (Dict): A dictionary used to configure the model initialization:

                init_config.name: The parameter initialization scheme to use. Options: 'default_', 'baseline_',

                    'kaiming_uniform_', 'kaiming_normal_', 'neox_init_', 'small_init_', 'xavier_uniform_', or

                    'xavier_normal_'. These mimic the parameter initialization methods in PyTorch.

                init_div_is_residual (Union[int, float, str, bool]): Value to divide initial weights by if ``module._is_residual`` is True.

                emb_init_std (Optional[float]): The standard deviation of the normal distribution used to initialize the embedding layer.

                emb_init_uniform_lim (Optional[Union[Tuple[float, float], float]]): The lower and upper limits of the uniform distribution

                    used to initialize the embedding layer. Mutually exclusive with ``emb_init_std``.

                init_std (float): The standard deviation of the normal distribution used to initialize the model,

                    if using the baseline_ parameter initialization scheme.

                init_gain (float): The gain to use for parameter initialization with kaiming or xavier initialization schemes.

                fan_mode (str): The fan mode to use for parameter initialization with kaiming initialization schemes.

                init_nonlinearity (str): The nonlinearity to use for parameter initialization with kaiming initialization schemes.

                ---

                See llmfoundry.models.utils.param_init_fns.py for info on other param init config options

        """
        self.d_model = d_model
        self.n_heads = n_heads
        self.n_layers = n_layers
        self.expansion_ratio = expansion_ratio
        self.max_seq_len = max_seq_len
        self.vocab_size = vocab_size
        self.resid_pdrop = resid_pdrop
        self.emb_pdrop = emb_pdrop
        self.learned_pos_emb = learned_pos_emb
        self.attn_config = attn_config
        self.init_device = init_device
        self.logit_scale = logit_scale
        self.no_bias = no_bias
        self.verbose = verbose
        self.embedding_fraction = embedding_fraction
        self.norm_type = norm_type
        self.use_cache = use_cache
        self.init_config = init_config
        if 'name' in kwargs:
            del kwargs['name']
        if 'loss_fn' in kwargs:
            del kwargs['loss_fn']
        super().__init__(**kwargs)
        self._validate_config()

    def _set_config_defaults(self, config, config_defaults):
        for (k, v) in config_defaults.items():
            if k not in config:
                config[k] = v
        return config

    def _validate_config(self):
        self.attn_config = self._set_config_defaults(self.attn_config, attn_config_defaults)
        self.init_config = self._set_config_defaults(self.init_config, init_config_defaults)
        if self.d_model % self.n_heads != 0:
            raise ValueError('d_model must be divisible by n_heads')
        if any((prob < 0 or prob > 1 for prob in [self.attn_config['attn_pdrop'], self.resid_pdrop, self.emb_pdrop])):
            raise ValueError("self.attn_config['attn_pdrop'], resid_pdrop, emb_pdrop are probabilities and must be between 0 and 1")
        if self.attn_config['attn_impl'] not in ['torch', 'flash', 'triton']:
            raise ValueError(f"Unknown attn_impl={self.attn_config['attn_impl']}")
        if self.attn_config['prefix_lm'] and self.attn_config['attn_impl'] not in ['torch', 'triton']:
            raise NotImplementedError('prefix_lm only implemented with torch and triton attention.')
        if self.attn_config['alibi'] and self.attn_config['attn_impl'] not in ['torch', 'triton']:
            raise NotImplementedError('alibi only implemented with torch and triton attention.')
        if self.attn_config['attn_uses_sequence_id'] and self.attn_config['attn_impl'] not in ['torch', 'triton']:
            raise NotImplementedError('attn_uses_sequence_id only implemented with torch and triton attention.')
        if self.embedding_fraction > 1 or self.embedding_fraction <= 0:
            raise ValueError('model.embedding_fraction must be between 0 (exclusive) and 1 (inclusive)!')
        if isinstance(self.logit_scale, str) and self.logit_scale != 'inv_sqrt_d_model':
            raise ValueError(f"self.logit_scale={self.logit_scale!r} is not recognized as an option; use numeric value or 'inv_sqrt_d_model'.")
        if self.init_config.get('name', None) is None:
            raise ValueError(f"self.init_config={self.init_config!r} 'name' needs to be set.")
        if not self.learned_pos_emb and (not self.attn_config['alibi']):
            raise ValueError(f'Positional information must be provided to the model using either learned_pos_emb or alibi.')