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models/LLaVA/build/lib/llava/model/mpt/configuration_mpt.py
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models/LLaVA/build/lib/llava/model/mpt/configuration_mpt.py
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"""A HuggingFace-style model configuration."""
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from typing import Dict, Optional, Union
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from transformers import PretrainedConfig
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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}
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init_config_defaults: Dict = {'name': 'kaiming_normal_', 'fan_mode': 'fan_in', 'init_nonlinearity': 'relu'}
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class MPTConfig(PretrainedConfig):
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model_type = 'mpt'
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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):
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"""The MPT configuration class.
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Args:
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d_model (int): The size of the embedding dimension of the model.
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n_heads (int): The number of attention heads.
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n_layers (int): The number of layers in the model.
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expansion_ratio (int): The ratio of the up/down scale in the MLP.
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max_seq_len (int): The maximum sequence length of the model.
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vocab_size (int): The size of the vocabulary.
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resid_pdrop (float): The dropout probability applied to the attention output before combining with residual.
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emb_pdrop (float): The dropout probability for the embedding layer.
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learned_pos_emb (bool): Whether to use learned positional embeddings
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attn_config (Dict): A dictionary used to configure the model's attention module:
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attn_type (str): type of attention to use. Options: multihead_attention, multiquery_attention
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attn_pdrop (float): The dropout probability for the attention layers.
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attn_impl (str): The attention implementation to use. One of 'torch', 'flash', or 'triton'.
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qk_ln (bool): Whether to apply layer normalization to the queries and keys in the attention layer.
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clip_qkv (Optional[float]): If not None, clip the queries, keys, and values in the attention layer to
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this value.
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softmax_scale (Optional[float]): If not None, scale the softmax in the attention layer by this value. If None,
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use the default scale of ``1/sqrt(d_keys)``.
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prefix_lm (Optional[bool]): Whether the model should operate as a Prefix LM. This requires passing an
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extra `prefix_mask` argument which indicates which tokens belong to the prefix. Tokens in the prefix
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can attend to one another bi-directionally. Tokens outside the prefix use causal attention.
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attn_uses_sequence_id (Optional[bool]): Whether to restrict attention to tokens that have the same sequence_id.
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When the model is in `train` mode, this requires passing an extra `sequence_id` argument which indicates
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which sub-sequence each token belongs to.
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Defaults to ``False`` meaning any provided `sequence_id` will be ignored.
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alibi (bool): Whether to use the alibi bias instead of position embeddings.
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alibi_bias_max (int): The maximum value of the alibi bias.
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init_device (str): The device to use for parameter initialization.
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logit_scale (Optional[Union[float, str]]): If not None, scale the logits by this value.
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no_bias (bool): Whether to use bias in all layers.
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verbose (int): The verbosity level. 0 is silent.
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embedding_fraction (float): The fraction to scale the gradients of the embedding layer by.
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norm_type (str): choose type of norm to use
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multiquery_attention (bool): Whether to use multiquery attention implementation.
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use_cache (bool): Whether or not the model should return the last key/values attentions
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init_config (Dict): A dictionary used to configure the model initialization:
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init_config.name: The parameter initialization scheme to use. Options: 'default_', 'baseline_',
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'kaiming_uniform_', 'kaiming_normal_', 'neox_init_', 'small_init_', 'xavier_uniform_', or
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'xavier_normal_'. These mimic the parameter initialization methods in PyTorch.
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init_div_is_residual (Union[int, float, str, bool]): Value to divide initial weights by if ``module._is_residual`` is True.
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emb_init_std (Optional[float]): The standard deviation of the normal distribution used to initialize the embedding layer.
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emb_init_uniform_lim (Optional[Union[Tuple[float, float], float]]): The lower and upper limits of the uniform distribution
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used to initialize the embedding layer. Mutually exclusive with ``emb_init_std``.
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init_std (float): The standard deviation of the normal distribution used to initialize the model,
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if using the baseline_ parameter initialization scheme.
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init_gain (float): The gain to use for parameter initialization with kaiming or xavier initialization schemes.
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fan_mode (str): The fan mode to use for parameter initialization with kaiming initialization schemes.
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init_nonlinearity (str): The nonlinearity to use for parameter initialization with kaiming initialization schemes.
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---
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See llmfoundry.models.utils.param_init_fns.py for info on other param init config options
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"""
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self.d_model = d_model
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self.n_heads = n_heads
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self.n_layers = n_layers
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self.expansion_ratio = expansion_ratio
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self.max_seq_len = max_seq_len
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self.vocab_size = vocab_size
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self.resid_pdrop = resid_pdrop
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self.emb_pdrop = emb_pdrop
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self.learned_pos_emb = learned_pos_emb
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self.attn_config = attn_config
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self.init_device = init_device
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self.logit_scale = logit_scale
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self.no_bias = no_bias
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self.verbose = verbose
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self.embedding_fraction = embedding_fraction
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self.norm_type = norm_type
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self.use_cache = use_cache
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self.init_config = init_config
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if 'name' in kwargs:
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del kwargs['name']
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if 'loss_fn' in kwargs:
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del kwargs['loss_fn']
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super().__init__(**kwargs)
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self._validate_config()
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def _set_config_defaults(self, config, config_defaults):
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for (k, v) in config_defaults.items():
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if k not in config:
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config[k] = v
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return config
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def _validate_config(self):
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self.attn_config = self._set_config_defaults(self.attn_config, attn_config_defaults)
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self.init_config = self._set_config_defaults(self.init_config, init_config_defaults)
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if self.d_model % self.n_heads != 0:
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raise ValueError('d_model must be divisible by n_heads')
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if any((prob < 0 or prob > 1 for prob in [self.attn_config['attn_pdrop'], self.resid_pdrop, self.emb_pdrop])):
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raise ValueError("self.attn_config['attn_pdrop'], resid_pdrop, emb_pdrop are probabilities and must be between 0 and 1")
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if self.attn_config['attn_impl'] not in ['torch', 'flash', 'triton']:
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raise ValueError(f"Unknown attn_impl={self.attn_config['attn_impl']}")
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if self.attn_config['prefix_lm'] and self.attn_config['attn_impl'] not in ['torch', 'triton']:
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raise NotImplementedError('prefix_lm only implemented with torch and triton attention.')
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if self.attn_config['alibi'] and self.attn_config['attn_impl'] not in ['torch', 'triton']:
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raise NotImplementedError('alibi only implemented with torch and triton attention.')
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if self.attn_config['attn_uses_sequence_id'] and self.attn_config['attn_impl'] not in ['torch', 'triton']:
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raise NotImplementedError('attn_uses_sequence_id only implemented with torch and triton attention.')
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if self.embedding_fraction > 1 or self.embedding_fraction <= 0:
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raise ValueError('model.embedding_fraction must be between 0 (exclusive) and 1 (inclusive)!')
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if isinstance(self.logit_scale, str) and self.logit_scale != 'inv_sqrt_d_model':
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raise ValueError(f"self.logit_scale={self.logit_scale!r} is not recognized as an option; use numeric value or 'inv_sqrt_d_model'.")
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if self.init_config.get('name', None) is None:
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raise ValueError(f"self.init_config={self.init_config!r} 'name' needs to be set.")
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if not self.learned_pos_emb and (not self.attn_config['alibi']):
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raise ValueError(f'Positional information must be provided to the model using either learned_pos_emb or alibi.')
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