"""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." )