# Copyright 2023 The EASYDEL Author @erfanzar (Erfan Zare Chavoshi).
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
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import typing as tp
from jax.sharding import PartitionSpec
from easydel.infra.base_module import EasyDeLBaseConfig
from easydel.infra.etils import EasyDeLGradientCheckPointers
from easydel.infra.factory import register_config
[docs]class MptAttentionConfig(EasyDeLBaseConfig):
"""
This is the configuration class to store the attention related configuration of a [`MptModel`].
Args:
attn_type (`str`, *optional*, defaults to `"multihead_attention"`):
The type of attention to use. Can be either `"multihead_attention"` or `"multiquery_attention"`.
attn_pdrop (`float`, *optional*, defaults to 0.0):
The dropout probability applied to the attention output.
attn_impl (`str`, *optional*, defaults to `"torch"`):
The implementation of the attention mechanism. Can be either `"torch"` or `"flash"`.
clip_qkv (`float`, *optional*):
The clip value applied to the query, key, and value tensors.
softmax_scale (`float`, *optional*):
The scale factor applied to the softmax function in the attention layer.
prefix_lm (`bool`, *optional*, defaults to `False`):
Whether to use a prefix LM.
qk_ln (`bool`, *optional*, defaults to `False`):
Whether to apply layer normalization to the query and key tensors.
attn_uses_sequence_id (`bool`, *optional*, defaults to `False`):
Whether the attention layer uses sequence IDs.
alibi (`bool`, *optional*, defaults to `True`):
Whether to use the ALiBi (Attention with Linear Biases) method.
alibi_bias_max (`int`, *optional*, defaults to 8):
The maximum value for the ALiBi bias.
"""
def __init__(
self,
attn_type="multihead_attention",
attn_pdrop=0,
attn_impl="torch",
clip_qkv=None,
softmax_scale=None,
prefix_lm=False,
qk_ln=False,
attn_uses_sequence_id=False,
alibi=True,
alibi_bias_max=8,
**kwargs,
):
"""Initializes an MptAttentionConfig object.
Args:
attn_type (str, optional): Type of attention mechanism. Defaults to "multihead_attention".
attn_pdrop (float, optional): Dropout probability for attention output. Defaults to 0.
attn_impl (str, optional): Implementation of attention ("torch" or "flash"). Defaults to "torch".
clip_qkv (float, optional): Clipping value for QKV projections. Defaults to None.
softmax_scale (float, optional): Scale factor for softmax. Defaults to None.
prefix_lm (bool, optional): Whether to use prefix language modeling. Defaults to False.
qk_ln (bool, optional): Whether to apply LayerNorm to Q and K. Defaults to False.
attn_uses_sequence_id (bool, optional): Whether attention uses sequence IDs. Defaults to False.
alibi (bool, optional): Whether to use ALiBi positional bias. Defaults to True.
alibi_bias_max (int, optional): Maximum ALiBi bias value. Defaults to 8.
**kwargs: Additional keyword arguments.
"""
super().__init__()
self.attn_type = attn_type
self.attn_pdrop = attn_pdrop
self.attn_impl = attn_impl
self.clip_qkv = 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.qk_ln = qk_ln
self.alibi_bias_max = alibi_bias_max
if attn_type not in ["multihead_attention", "multiquery_attention"]:
raise ValueError(
f"`attn_type` has to be either `multihead_attention` or `multiquery_attention`. Received: {attn_type}"
)
[docs] @classmethod
def from_pretrained(
cls, pretrained_model_name_or_path, **kwargs
) -> "EasyDeLBaseConfig":
"""Loads attention configuration from a pretrained model configuration file.
Args:
cls (type): The class itself.
pretrained_model_name_or_path (str): Path or identifier of the pretrained model.
**kwargs: Additional keyword arguments passed to `get_config_dict` and `from_dict`.
Returns:
EasyDeLBaseConfig: An instance of MptAttentionConfig loaded from the pretrained model.
"""
cls._set_token_in_kwargs(kwargs)
config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
if config_dict.get("model_type") == "mpt":
config_dict = config_dict["attn_config"]
return cls.from_dict(config_dict, **kwargs)
[docs]@register_config("mpt")
class MptConfig(EasyDeLBaseConfig):
"""
Configuration objects inherit from [`EasyDeLBaseConfig`] and can be used to control the model outputs. Read
the documentation from [`EasyDeLBaseConfig`] for more information.
Args:
d_model (`int`, *optional*, defaults to 2048):
Dimensionality of the encoder layers and the pooler layer.
n_heads (`int`, *optional*, defaults to 16):
Number of attention heads for each attention layer in the Transformer encoder.
n_layers (`int`, *optional*, defaults to 24):
Number of hidden layers in the Transformer encoder.
expansion_ratio (`int`, *optional*, defaults to 4):
Expansion ratio of the feed-forward layer.
max_seq_len (`int`, *optional*, defaults to 2048):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 2048 or 4096).
vocab_size (`int`, *optional*, defaults to 50368):
Vocabulary size of the MPT model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed to the forward method.
resid_prob_drop (`float`, *optional*, defaults to 0.0):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
layer_norm_epsilon (`float`, *optional*, defaults to 1e-5):
The epsilon used by the layer normalization layers.
emb_prob_drop (`float`, *optional*, defaults to 0.0):
The dropout ratio for the embeddings.
learned_pos_emb (`bool`, *optional*, defaults to `True`):
Whether to learn positional embeddings.
attn_config ([`MptAttentionConfig`], *optional*):
The configuration of the attention layer.
init_device (`str`, *optional*, defaults to `"cpu"`):
The device to initialize the model on.
logit_scale (`float` or `str`, *optional*):
The logit scale. If set to `"inv_sqrt_d_model"`, the logit scale is calculated as `1 / math.sqrt(d_model)`.
no_bias (`bool`, *optional*, defaults to `True`):
Whether to use bias in the linear layers.
verbose (`int`, *optional*, defaults to 0):
The verbosity level.
embedding_fraction (`float`, *optional*, defaults to 1.0):
The fraction of the embedding matrix to use.
norm_type (`str`, *optional*, defaults to `"low_precision_layernorm"`):
The type of layer normalization to use.
use_cache (`bool`, *optional*, defaults to `False`):
Whether or not the model should return the last key/values attentions (not used by all models). Only
relevant if `config.is_decoder=True`.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
alibi (`bool`, *optional*, defaults to `True`):
Whether to use ALiBi (Attention with Linear Biases) method.
use_bias (`bool`, *optional*, defaults to `False`):
Whether to use bias in the linear layers.
act_fn (`str`, *optional*, defaults to `"gelu"`):
The activation function to use.
qk_ln (`bool`, *optional*, defaults to `False`):
Whether to apply layer normalization to the query and key tensors.
use_lm_head (`bool`, *optional*, defaults to `False`):
Whether to use a language modeling head.
use_norm_bias (`bool`, *optional*, defaults to `False`):
Whether to use bias in the layer normalization layers.
gradient_checkpointing (`str`, *optional*, defaults to `"nothing_saveable"`):
The gradient checkpointing configuration.
bits (`int`, *optional*):
The number of bits to quantize the model to.
"""
model_type = "mpt"
attribute_map = {
"num_attention_heads": "n_heads",
"hidden_size": "d_model",
"num_hidden_layers": "n_layers",
"max_position_embeddings": "max_seq_len",
"tie_word_embeddings": "use_lm_head",
}
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_prob_drop: float = 0.0,
layer_norm_epsilon: float = 1e-5,
emb_prob_drop: float = 0.0,
learned_pos_emb: bool = True,
attn_config: tp.Optional[MptAttentionConfig] = None,
init_device: str = "cpu", # Ignored by EasyDeL.
logit_scale: tp.Optional[tp.Union[float, str]] = None,
no_bias: bool = True,
verbose: int = 0,
embedding_fraction: float = 1.0,
norm_type: str = "low_precision_layernorm",
use_cache: bool = False,
initializer_range=0.02,
alibi: bool = True,
use_bias: bool = False,
act_fn: str = "gelu",
qk_ln: bool = False,
use_lm_head: bool = False,
use_norm_bias: bool = False,
gradient_checkpointing: EasyDeLGradientCheckPointers = EasyDeLGradientCheckPointers.NONE,
bits: tp.Optional[int] = None,
**kwargs,
):
"""Initializes an MptConfig object.
Args:
d_model (int, optional): Hidden size. Defaults to 2048.
n_heads (int, optional): Number of attention heads. Defaults to 16.
n_layers (int, optional): Number of hidden layers. Defaults to 24.
expansion_ratio (int, optional): Expansion ratio for MLP. Defaults to 4.
max_seq_len (int, optional): Maximum sequence length. Defaults to 2048.
vocab_size (int, optional): Vocabulary size. Defaults to 50368.
resid_prob_drop (float, optional): Residual dropout probability. Defaults to 0.0.
layer_norm_epsilon (float, optional): Epsilon for layer normalization. Defaults to 1e-5.
emb_prob_drop (float, optional): Embedding dropout probability. Defaults to 0.0.
learned_pos_emb (bool, optional): Whether to learn positional embeddings. Defaults to True.
attn_config (MptAttentionConfig, optional): Attention configuration. Defaults to None.
init_device (str, optional): Device for initialization (ignored). Defaults to "cpu".
logit_scale (float | str, optional): Logit scaling factor. Defaults to None.
no_bias (bool, optional): Whether to disable bias in linear layers. Defaults to True.
verbose (int, optional): Verbosity level (ignored). Defaults to 0.
embedding_fraction (float, optional): Embedding fraction (ignored). Defaults to 1.0.
norm_type (str, optional): Type of layer normalization. Defaults to "low_precision_layernorm".
use_cache (bool, optional): Whether to use KV cache. Defaults to False.
initializer_range (float, optional): Initializer range. Defaults to 0.02.
alibi (bool, optional): Whether to use ALiBi positional bias. Defaults to True.
use_bias (bool, optional): Whether to use bias in linear layers. Defaults to False.
act_fn (str, optional): Activation function. Defaults to "gelu".
qk_ln (bool, optional): Whether to apply LayerNorm to Q and K. Defaults to False.
use_lm_head (bool, optional): Whether to use a language modeling head (tied embeddings). Defaults to False.
use_norm_bias (bool, optional): Whether to use bias in layer normalization. Defaults to False.
gradient_checkpointing (EasyDeLGradientCheckPointers, optional): Gradient checkpointing strategy.
Defaults to EasyDeLGradientCheckPointers.NONE.
bits (tp.Optional[int], optional): Quantization bits. Defaults to None.
**kwargs: Additional keyword arguments.
"""
if attn_config is None:
self.attn_config = MptAttentionConfig(**kwargs)
elif isinstance(attn_config, dict):
self.attn_config = MptAttentionConfig(**attn_config | kwargs)
else:
self.attn_config = attn_config
self.d_model = d_model
self.use_norm_bias = use_norm_bias
self.use_lm_head = use_lm_head
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_prob_drop = resid_prob_drop
self.use_bias = use_bias
self.emb_prob_drop = emb_prob_drop
self.gradient_checkpointing = gradient_checkpointing
self.norm_type = norm_type
self.learned_pos_emb = learned_pos_emb
self.act_fn = act_fn
self.logit_scale = logit_scale
self.no_bias = no_bias
self.qk_ln = qk_ln
self.alibi = alibi
self.verbose = verbose
self.initializer_range = initializer_range
self.embedding_fraction = embedding_fraction
self.init_device = init_device
self.use_cache = use_cache
self.bits = bits
self.layer_norm_epsilon = layer_norm_epsilon
self.from_pt = False
super().__init__(bits=bits, **kwargs)
@staticmethod
def _set_config_defaults(config, config_defaults):
"""Sets default values for missing configuration parameters.
Args:
config (dict): The configuration dictionary to update.
config_defaults (dict): A dictionary containing default values.
Returns:
dict: The updated configuration dictionary.
"""
for k, v in config_defaults.items():
if k not in config:
config[k] = v
return config
[docs] def get_partition_rules(self, *args, **kwargs):
"""
Get the partition rules for the model. This method defines how the model's parameters are
partitioned across devices for distributed training and inference.
Args:
*args: Additional positional arguments (unused).
**kwargs: Additional keyword arguments (unused).
Returns:
`tp.Tuple[tp.Tuple[str, PartitionSpec]]`: A tuple of partition rules, where each rule is a tuple
containing a regex pattern for parameter names and the corresponding `PartitionSpec`.
"""
return (
("wte/embedding", PartitionSpec(("fsdp", "sp"), "tp")),
("attn/Wqkv/kernel", PartitionSpec(("fsdp", "sp"), "tp")),
("attn/out_proj/kernel", PartitionSpec("tp", ("fsdp", "sp"))),
("ffn/down_proj/kernel", PartitionSpec("tp", ("fsdp", "sp"))),
("ffn/up_proj/kernel", PartitionSpec(("fsdp", "sp"), "tp")),
("transformer/norm_1/scale", PartitionSpec(None)),
("transformer/norm_2/scale", PartitionSpec(None)),
("transformer/norm_f/scale", PartitionSpec(None)),
("lm_head/kernel", PartitionSpec(("fsdp", "sp"), "tp")),
(".*", PartitionSpec(None)),
)
[docs] def attach_custom_arguments(
self,
gradient_checkpointing: EasyDeLGradientCheckPointers = EasyDeLGradientCheckPointers.NONE,
bits: tp.Optional[int] = None,
**kwargs,
):
"""Attaches custom arguments to the configuration object.
This method allows adding or overriding configuration attributes dynamically.
It primarily sets attributes related to gradient checkpointing and quantization bits.
Args:
gradient_checkpointing (EasyDeLGradientCheckPointers, optional): Gradient checkpointing strategy.
Defaults to EasyDeLGradientCheckPointers.NONE.
bits (tp.Optional[int], optional): Quantization bits. Defaults to None.
**kwargs: Additional keyword arguments (ignored).
"""
if hasattr(self, "attn_config"):
for k, v in self.attn_config.__dict__.items():
if not hasattr(self, k):
setattr(self, k, v)
basics = dict(bits=bits, gradient_checkpointing=gradient_checkpointing, **kwargs)
for k, v in basics.items():
if not hasattr(self, k):
setattr(self, k, v)
self.from_pt = False
@property
def granted_freq_max_position_embedding(self) -> int:
"""Returns the maximum position embedding size specifically for frequency-based position embeddings.
If `freq_max_position_embeddings` is set, it returns that value. Otherwise, it falls back to
`max_seq_len`.
Returns:
int: The granted maximum position embedding size for frequency encoding.
"""
return getattr(
self,
"freq_max_position_embeddings",
self.max_position_embeddings,
)
@property
def granted_mask_max_position_embedding(self) -> int:
"""Returns the maximum position embedding size specifically for mask-based position embeddings.
If `mask_max_position_embeddings` is set, it returns that value. Otherwise, it falls back to
`max_seq_len`.
Returns:
int: The granted maximum position embedding size for mask encoding.
"""
return getattr(
self,
"mask_max_position_embeddings",
self.max_position_embeddings,
)