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"""A HuggingFace-style model configuration.""" |
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from typing import Dict, List, Optional, Union |
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from transformers import PretrainedConfig |
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attn_config_defaults: Dict = { |
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'attn_type': 'multihead_attention', |
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'attn_pdrop': 0.0, |
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'attn_impl': 'torch', |
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'qk_ln': False, |
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'clip_qkv': None, |
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'softmax_scale': None, |
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'prefix_lm': False, |
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'attn_uses_sequence_id': False, |
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'alibi': True, |
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'alibi_bias_max': 8, |
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"topk": 10, |
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'mask_by_sim':True, |
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'sim_threshold':0.25, |
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'use_active_externalism':True, |
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'memory_type':'manual' |
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} |
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init_config_defaults: Dict = { |
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'name': 'kaiming_normal_', |
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'fan_mode': 'fan_in', |
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'init_nonlinearity': 'relu', |
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'init_div_is_residual': True, |
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'emb_init_std': None, |
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'emb_init_uniform_lim': None, |
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'init_std': None, |
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'init_gain': 0.0, |
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} |
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class ExtendedMPTConfig(PretrainedConfig): |
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model_type = 'extended-mpt' |
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def __init__( |
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self, |
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d_model: int = 4096, |
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n_heads: int = 32, |
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n_layers: int = 32, |
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expansion_ratio: int = 4, |
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max_seq_len: int = 2048, |
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vocab_size: int = 50432, |
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resid_pdrop: float = 0.0, |
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emb_pdrop: float = 0.0, |
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learned_pos_emb: bool = True, |
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attn_config: Dict = attn_config_defaults, |
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init_device: str = 'cpu', |
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logit_scale: Optional[Union[float, str]] = None, |
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no_bias: bool = True, |
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verbose: int = 0, |
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embedding_fraction: float = 1.0, |
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norm_type: str = 'low_precision_layernorm', |
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use_cache: bool = False, |
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init_config: Dict = init_config_defaults, |
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use_active_externalism_by_layer: List[bool] = [True for _ in range(32)], |
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memory_device:str = 'cpu', |
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**kwargs, |
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): |
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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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self.use_active_externalism_by_layer = use_active_externalism_by_layer |
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self.memory_device = memory_device |
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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( |
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self.attn_config, |
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attn_config_defaults, |
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) |
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self.init_config = self._set_config_defaults( |
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self.init_config, |
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init_config_defaults, |
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) |
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if self.attn_config['memory_type']=='faiss' and self.attn_config['mask_by_sim'] is True: |
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raise ValueError( |
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'mask_by_sim is not supported for faiss memory type.' |
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) |
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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( |
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prob < 0 or prob > 1 for prob in |
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[self.attn_config['attn_pdrop'], self.resid_pdrop, self.emb_pdrop]): |
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raise ValueError( |
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"self.attn_config['attn_pdrop'], resid_pdrop, emb_pdrop are probabilities and must be between 0 and 1" |
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) |
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if self.attn_config['attn_impl'] not in ['torch', 'flash', 'triton']: |
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raise ValueError( |
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f"Unknown attn_impl={self.attn_config['attn_impl']}") |
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if self.attn_config['prefix_lm'] and self.attn_config[ |
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'attn_impl'] not in ['torch', 'triton']: |
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raise NotImplementedError( |
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'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 [ |
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'torch', 'triton' |
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]: |
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raise NotImplementedError( |
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'alibi only implemented with torch and triton attention.') |
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if self.attn_config['attn_uses_sequence_id'] and self.attn_config[ |
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'attn_impl'] not in ['torch', 'triton']: |
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raise NotImplementedError( |
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'attn_uses_sequence_id only implemented with torch and triton attention.' |
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) |
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if self.embedding_fraction > 1 or self.embedding_fraction <= 0: |
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raise ValueError( |
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'model.embedding_fraction must be between 0 (exclusive) and 1 (inclusive)!' |
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) |
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if isinstance(self.logit_scale, |
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str) and self.logit_scale != 'inv_sqrt_d_model': |
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raise ValueError( |
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f"{self.logit_scale=} is not recognized as an option; use numeric value or 'inv_sqrt_d_model'." |
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) |
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if self.init_config.get('name', None) is None: |
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raise ValueError(f"{self.init_config=} '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( |
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f'Positional information must be provided to the model using either learned_pos_emb or alibi.' |
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) |
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