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A10G
"""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.') |