# Copyright 2022 MosaicML Examples authors # SPDX-License-Identifier: Apache-2.0 """A HuggingFace-style model configuration.""" from typing import Optional, Tuple, Union from transformers import PretrainedConfig class MosaicGPTConfig(PretrainedConfig): model_type = 'mosaic_gpt' def __init__( self, d_model: int = 2048, n_heads: int = 16, n_layers: int = 24, mlp_ratio: int = 4, max_seq_len: int = 2048, vocab_size: int = 50368, attn_pdrop: float = 0.0, resid_pdrop: float = 0.0, emb_pdrop: float = 0.0, attn_impl: str = 'triton', attn_qk_ln: bool = False, attn_clip_qkv: Optional[float] = None, softmax_scale: Optional[float] = None, prefix_lm: Optional[bool] = False, attn_uses_sequence_id: Optional[bool] = False, alibi: bool = False, alibi_bias_max: int = 8, init_device: str = 'cpu', logit_scale: Optional[Union[float, str]] = None, no_bias: bool = False, verbose: int = 0, param_init_fn: str = 'kaiming_normal_', init_div_is_residual: Union[int, float, str, bool] = True, init_std: float = 0.02, emb_init_std: Optional[float] = None, emb_init_uniform_lim: Optional[Union[Tuple[float, float], float]] = None, init_gain: float = 0, fan_mode: str = 'fan_in', init_nonlinearity: str = 'relu', embedding_fraction: float = 1.0, low_precision_layernorm: bool = True, use_cache: bool = False, **kwargs, ): """The MosaicGPT 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. mlp_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. attn_pdrop (float): The dropout probability for the attention layers. 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. attn_impl (str): The attention implementation to use. One of 'torch', 'flash', or 'triton'. attn_qk_ln (bool): Whether to apply layer normalization to the queries and keys in the attention layer. attn_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. param_init_fn (str): The parameter initialization scheme to use. One of 'default_', 'baseline_', 'kaiming_uniform_', 'kaiming_normal_', 'neox_init_', 'small_init_', 'xavier_uniform_', or 'xavier_normal_'. init_div_is_residual (Union[int, float, str, bool]): Value to divide initial weights by if ``module._is_residual`` is True. init_std (float): The standard deviation of the normal distribution used to initialize the model, if using the baseline_ parameter initialization scheme. 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_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. embedding_fraction (float): The fraction to scale the gradients of the embedding layer by. low_precision_layernorm (bool): Whether to use low precision layer normalization. use_cache (bool): Whether or not the model should return the last key/values attentions """ self.d_model = d_model self.n_heads = n_heads self.n_layers = n_layers self.mlp_ratio = mlp_ratio self.max_seq_len = max_seq_len self.vocab_size = vocab_size self.attn_pdrop = attn_pdrop self.resid_pdrop = resid_pdrop self.emb_pdrop = emb_pdrop self.attn_impl = attn_impl self.attn_qk_ln = attn_qk_ln self.attn_clip_qkv = attn_clip_qkv self.softmax_scale = softmax_scale self.prefix_lm = prefix_lm self.attn_uses_sequence_id = attn_uses_sequence_id self.alibi = alibi self.alibi_bias_max = alibi_bias_max self.init_device = init_device self.logit_scale = logit_scale self.no_bias = no_bias self.verbose = verbose self.param_init_fn = param_init_fn self.init_div_is_residual = init_div_is_residual self.init_std = init_std self.emb_init_std = emb_init_std self.emb_init_uniform_lim = emb_init_uniform_lim self.init_std = init_std self.init_gain = init_gain self.fan_mode = fan_mode self.init_nonlinearity = init_nonlinearity self.embedding_fraction = embedding_fraction self.low_precision_layernorm = low_precision_layernorm self.use_cache = use_cache if 'name' in kwargs: del kwargs['name'] if 'loss_fn' in kwargs: del kwargs['loss_fn'] super().__init__(**kwargs) self._validate_config() def _validate_config(self): 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_pdrop, self.resid_pdrop, self.emb_pdrop]): raise ValueError( 'attn_pdrop, resid_pdrop, emb_pdrop are probabilities and must be between 0 and 1' ) if self.attn_impl not in ['torch', 'flash', 'triton']: raise ValueError(f'Unknown attn_impl={self.attn_impl}') if self.prefix_lm and self.attn_impl not in ['torch', 'triton']: raise NotImplementedError( 'prefix_lm only implemented with torch and triton attention.') if self.alibi and self.attn_impl not in ['torch', 'triton']: raise NotImplementedError( 'alibi only implemented with torch and triton attention.') if self.attn_uses_sequence_id and self.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=} is not recognized as an option; use numeric value or 'inv_sqrt_d_model'." )