|
|
|
|
|
|
|
"""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'." |
|
) |
|
|
