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"""A HuggingFace-style model configuration."""
import warnings
from typing import Any, Dict, Optional, Union
from transformers import PretrainedConfig
from .attention import is_flash_v2_installed
from .blocks import attn_config_defaults
from .fc import FC_CLASS_REGISTRY
from .norm import LPLayerNorm
from .ffn import FFN_CLASS_REGISTRY
ffn_config_defaults: Dict = {'ffn_type': 'mptmlp'}
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, ffn_config: Dict=ffn_config_defaults, init_device: str='cpu', logit_scale: Optional[Union[float, str]]=None, no_bias: bool=False, embedding_fraction: float=1.0, norm_type: str='low_precision_layernorm', use_cache: bool=False, init_config: Dict=init_config_defaults, fc_type: str='torch', tie_word_embeddings: bool=True, verbose: Optional[int]=None, **kwargs: Any):
"""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 ffn.
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, grouped_query_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.
rope (bool): Whether to use rotary positional embeddings.
rope_theta (int): The base frequency for rope.
rope_impl (str): The implementation of rope to use. One of 'hf' (to use the implementation from https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/modeling_llama.py) or 'dail' (to use the implementation from https://github.com/Dao-AILab/flash-attention/blob/main/flash_attn/layers/rotary.py).
rope_dail_config (Dict): The configuration for the dail implementation of rope.
type (str): The type of rotary position embedding to use. Options: 'original' (for https://arxiv.org/pdf/2104.09864.pdf), 'xpos' (for https://arxiv.org/pdf/2212.10554.pdf).
pos_idx_in_fp32 (bool): If True, the position indices [0, ..., seqlen - 1] are in fp32, otherwise they might be in lower precision. A consequence could be, for example, that bf16 rounds position 1995 to 2000, which leads to them having the same positional embedding.
xpos_scale_base (float): The scale base for XPos (if using XPos).
rope_hf_config (Dict): A dictionary used to configure rope's scaling behavior (when scaling beyond the training length).
type (str): Can be one of 'no_scaling', 'linear', or 'dynamic'. 'no_scaling' uses the default implementation for rotary embeddings, 'linear' uses linear scaling as proposed by the Reddit user /u/kaiokendev, and 'dynamic' uses Dynamic NTK scaling as proposed by the Reddit users /u/bloc97 and /u/emozilla.
factor (float): Scaling factor to use if using 'linear' or 'dynamic' as rope_scaling.type.
kv_n_heads (Optional[int]): For grouped_query_attention only, allow user to specify number of kv heads.
ffn_config (Dict): A dictionary used to configure the model's ffn module:
ffn_type (str): type of ffn to use. Options: mptmlp, te_ln_mlp
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
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
fc_type (str): choose fc layer implementation. Options: torch and te. te layers support fp8 when using H100 GPUs.
tie_word_embeddings (bool): Whether to tie the input embedding and output layers.
"""
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.ffn_config = ffn_config
self.init_device = init_device
self.logit_scale = logit_scale
self.no_bias = no_bias
self.embedding_fraction = embedding_fraction
self.norm_type = norm_type
self.use_cache = use_cache
self.init_config = init_config
self.fc_type = fc_type
if verbose is not None:
warnings.warn(DeprecationWarning('verbose argument for MPTConfig is now ignored and will be removed. Use python_log_level instead.'))
if 'name' in kwargs:
del kwargs['name']
if 'loss_fn' in kwargs:
del kwargs['loss_fn']
if self.attn_config.get('alibi', False) or self.attn_config.get('rope', False):
self.learned_pos_emb = False
warnings.warn(f'alibi or rope is turned on, setting `learned_pos_emb` to `False.`')
super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)
self._validate_config()
def _set_config_defaults(self, config: Dict[str, Any], config_defaults: Dict[str, Any]) -> Dict[str, Any]:
for (k, v) in config_defaults.items():
if k not in config:
config[k] = v
elif isinstance(v, dict):
config[k] = self._set_config_defaults(config[k] if config[k] is not None else {}, v)
return config
def _validate_config(self) -> None:
self.attn_config = self._set_config_defaults(self.attn_config, attn_config_defaults)
self.ffn_config = self._set_config_defaults(self.ffn_config, ffn_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.attn_config['rope'] and self.attn_config['rope_impl'] not in ['dail', 'hf']:
raise ValueError('If rope is being used then rope_impl should be either "dail", or "hf".')
if self.attn_config['rope'] and self.attn_config['rope_impl'] == 'hf' and (self.attn_config['rope_hf_config']['type'] not in ['no_scaling', 'linear', 'dynamic']):
raise ValueError('If using hf implementation of rope, the type should be one of "no_scaling", "linear" or "dynamic".')
if self.attn_config['rope'] and self.attn_config['rope_impl'] == 'dail':
if self.attn_config['rope_dail_config']['type'] not in ['original', 'xpos']:
raise ValueError('If using the dail implementation of rope, the type should be one of "original" or "xpos".')
if not is_flash_v2_installed(v2_version='2.0.1'):
raise ImportError('If using the dail implementation of rope, the flash_attn library v2.0.1 or higher must be installed. Please check the instructions at https://github.com/mosaicml/llm-foundry/blob/main/TUTORIAL.md#what-kinds-of-positional-embeddings-does-llm-foundry-support')
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 or self.attn_config['alibi'] or self.attn_config['rope']):
warnings.warn(f'Positional information not being provided to the model using either learned_pos_emb or alibi or rope.')
if self.fc_type == 'te' or self.ffn_config['ffn_type'] == 'te_ln_mlp':
try:
import transformer_engine.pytorch as te
del te
except:
raise ImportError('TransformerEngine import fail. `fc_type: te` requires TransformerEngine be installed. ' + 'The required version of transformer_engine also requires FlashAttention v1.0.6 is installed:\n' + 'pip install flash-attn==1.0.6 --no-build-isolation \n' + 'pip install git+https://github.com/NVIDIA/TransformerEngine.git@144e4888b2cdd60bd52e706d5b7a79cb9c1a7156')
if self.ffn_config['ffn_type'] == 'mptmlp':
self.ffn_config['fc_type'] = self.fc_type
elif self.ffn_config['ffn_type'] == 'te_ln_mlp':
self.ffn_config['bias'] = not self.no_bias |