# 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,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
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]@register_config("opt")
class OPTConfig(EasyDeLBaseConfig):
"""
Configuration objects inherit from [`EasyDeLBaseConfig`] and can be used to control the model outputs. Read
the documentation from [`EasyDeLBaseConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 50272):
Vocabulary size of the OPT model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed to the forward method.
hidden_size (`int`, *optional*, defaults to 768):
Dimensionality of the encoder layers and the pooler layer.
num_hidden_layers (`int`, *optional*, defaults to 12):
Number of hidden layers in the Transformer encoder.
ffn_dim (`int`, *optional*, defaults to 3072):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
max_position_embeddings (`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., 512 or 1024 or 2048).
do_layer_norm_before (`bool`, *optional*, defaults to `True`):
Whether to perform layer normalization before the attention block.
_remove_final_layer_norm (`bool`, *optional*, defaults to `False`):
Whether to remove the final layer norm.
word_embed_proj_dim (`int`, *optional*):
The dimension of the word embedding projection. If not provided, it will default to `hidden_size`.
dropout (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
num_attention_heads (`int`, *optional*, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
activation_function (`str` or `function`, *optional*, defaults to `"relu"`):
The non-linear activation function (function or string) to use in the encoder and pooler. If string,
`"gelu"`, `"relu"`, `"swish"` and `"gelu_new"` are supported.
layerdrop (`float`, *optional*, defaults to 0.0):
The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
for more details.
init_std (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models). Only
relevant if `config.is_decoder=True`.
pad_token_id (`int`, *optional*, defaults to 1):
The index of the padding token in the vocabulary.
bos_token_id (`int`, *optional*, defaults to 2):
The id of the *beginning-of-sequence* token.
eos_token_id (`int`, *optional*, defaults to 2):
The id of the *end-of-sequence* token.
enable_bias (`bool`, *optional*, defaults to `True`):
Whether to use bias in the linear layers.
layer_norm_elementwise_affine (`bool`, *optional*, defaults to `True`):
Whether to use elementwise affine in the layer normalization layers.
gradient_checkpointing (`str`, *optional*, defaults to `"nothing_saveable"`):
The gradient checkpointing configuration.
"""
model_type: str = "opt"
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
vocab_size: int = 50272,
hidden_size: int = 768,
num_hidden_layers: int = 12,
ffn_dim: int = 3072,
max_position_embeddings: int = 2048,
do_layer_norm_before: bool = True,
_remove_final_layer_norm: bool = False,
word_embed_proj_dim: int = None,
dropout: float = 0.1,
attention_dropout: float = 0.0,
num_attention_heads: int = 12,
activation_function: str = "relu",
layerdrop: float = 0.0,
init_std: float = 0.02,
use_cache: bool = True,
pad_token_id: int = 1,
bos_token_id: int = 2,
eos_token_id: int = 2,
enable_bias: bool = True,
layer_norm_elementwise_affine: bool = True,
gradient_checkpointing: EasyDeLGradientCheckPointers = EasyDeLGradientCheckPointers.NONE,
**kwargs,
):
"""Initializes the OPTConfig object.
Args:
vocab_size (int, optional): Vocabulary size. Defaults to 50272.
hidden_size (int, optional): Dimensionality of the encoder layers. Defaults to 768.
num_hidden_layers (int, optional): Number of hidden layers. Defaults to 12.
ffn_dim (int, optional): Dimensionality of the feed-forward layer. Defaults to 3072.
max_position_embeddings (int, optional): Maximum sequence length. Defaults to 2048.
do_layer_norm_before (bool, optional): Whether to apply layer norm before attention. Defaults to True.
_remove_final_layer_norm (bool, optional): Whether to remove the final layer norm. Defaults to False.
word_embed_proj_dim (int, optional): Dimension of the word embedding projection. Defaults to `hidden_size`.
dropout (float, optional): Dropout probability. Defaults to 0.1.
attention_dropout (float, optional): Attention dropout probability. Defaults to 0.0.
num_attention_heads (int, optional): Number of attention heads. Defaults to 12.
activation_function (str, optional): Activation function name. Defaults to "relu".
layerdrop (float, optional): LayerDrop probability. Defaults to 0.0.
init_std (float, optional): Initialization standard deviation. Defaults to 0.02.
use_cache (bool, optional): Whether to use key/value cache. Defaults to True.
pad_token_id (int, optional): Padding token ID. Defaults to 1.
bos_token_id (int, optional): Beginning-of-sequence token ID. Defaults to 2.
eos_token_id (int, optional): End-of-sequence token ID. Defaults to 2.
enable_bias (bool, optional): Whether to use bias in linear layers. Defaults to True.
layer_norm_elementwise_affine (bool, optional): Whether layer norm uses elementwise affine parameters.
Defaults to True.
gradient_checkpointing (EasyDeLGradientCheckPointers, optional): Gradient checkpointing strategy.
Defaults to EasyDeLGradientCheckPointers.NONE.
**kwargs: Additional keyword arguments passed to the parent class.
"""
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
**kwargs,
)
self.vocab_size = vocab_size
self.gradient_checkpointing = gradient_checkpointing
self.max_position_embeddings = max_position_embeddings
self.num_attention_heads = num_attention_heads
self.word_embed_proj_dim = (
word_embed_proj_dim if word_embed_proj_dim is not None else hidden_size
)
self.ffn_dim = ffn_dim
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.dropout = dropout
self.attention_dropout = attention_dropout
self.activation_function = activation_function
self.init_std = init_std
self.layerdrop = layerdrop
self.use_cache = use_cache
self.do_layer_norm_before = do_layer_norm_before
self.enable_bias = enable_bias
self.layer_norm_elementwise_affine = layer_norm_elementwise_affine
self._remove_final_layer_norm = _remove_final_layer_norm
self.from_pt = False
[docs] def get_partition_rules(self, fully_sharded_data_parallel: bool = True):
"""
Get the partition rules for the model.
Args:
fully_sharded_data_parallel (`bool`, *optional*, defaults to `True`):
Whether to use fully sharded data parallelism.
Returns:
`tp.Tuple[tp.Tuple[str, PartitionSpec]]`: The partition rules.
"""
if not fully_sharded_data_parallel:
raise NotImplementedError
else:
return (".*", PartitionSpec(("fsdp", "sp")))
[docs] def attach_custom_arguments(
self,
vocab_size: int = 50272,
hidden_size: int = 768,
num_hidden_layers: int = 12,
ffn_dim: int = 3072,
max_position_embeddings: int = 2048,
do_layer_norm_before: bool = True,
_remove_final_layer_norm: bool = False,
word_embed_proj_dim: int = None,
dropout: float = 0.1,
attention_dropout: float = 0.0,
num_attention_heads: int = 12,
activation_function: str = "relu",
layerdrop: float = 0.0,
init_std: float = 0.02,
use_cache: bool = True,
pad_token_id: int = 1,
bos_token_id: int = 2,
eos_token_id: int = 2,
enable_bias: bool = True,
layer_norm_elementwise_affine: bool = True,
gradient_checkpointing: EasyDeLGradientCheckPointers = EasyDeLGradientCheckPointers.NONE,
**kwargs,
):
"""Attaches custom arguments to the configuration object.
This method allows dynamically adding or overriding configuration attributes.
It iterates through the provided arguments and sets them as attributes
of the configuration object if they don't already exist.
Args:
vocab_size (int, optional): Vocabulary size. Defaults to 50272.
hidden_size (int, optional): Dimensionality of the encoder layers. Defaults to 768.
num_hidden_layers (int, optional): Number of hidden layers. Defaults to 12.
ffn_dim (int, optional): Dimensionality of the feed-forward layer. Defaults to 3072.
max_position_embeddings (int, optional): Maximum sequence length. Defaults to 2048.
do_layer_norm_before (bool, optional): Whether to apply layer norm before attention. Defaults to True.
_remove_final_layer_norm (bool, optional): Whether to remove the final layer norm. Defaults to False.
word_embed_proj_dim (int, optional): Dimension of the word embedding projection. Defaults to `hidden_size`.
dropout (float, optional): Dropout probability. Defaults to 0.1.
attention_dropout (float, optional): Attention dropout probability. Defaults to 0.0.
num_attention_heads (int, optional): Number of attention heads. Defaults to 12.
activation_function (str, optional): Activation function name. Defaults to "relu".
layerdrop (float, optional): LayerDrop probability. Defaults to 0.0.
init_std (float, optional): Initialization standard deviation. Defaults to 0.02.
use_cache (bool, optional): Whether to use key/value cache. Defaults to True.
pad_token_id (int, optional): Padding token ID. Defaults to 1.
bos_token_id (int, optional): Beginning-of-sequence token ID. Defaults to 2.
eos_token_id (int, optional): End-of-sequence token ID. Defaults to 2.
enable_bias (bool, optional): Whether to use bias in linear layers. Defaults to True.
layer_norm_elementwise_affine (bool, optional): Whether layer norm uses elementwise affine parameters.
Defaults to True.
gradient_checkpointing (EasyDeLGradientCheckPointers, optional): Gradient checkpointing strategy.
Defaults to EasyDeLGradientCheckPointers.NONE.
**kwargs: Additional keyword arguments to attach.
"""
basics = dict(
vocab_size=vocab_size,
hidden_size=hidden_size,
num_hidden_layers=num_hidden_layers,
ffn_dim=ffn_dim,
max_position_embeddings=max_position_embeddings,
do_layer_norm_before=do_layer_norm_before,
_remove_final_layer_norm=_remove_final_layer_norm,
word_embed_proj_dim=word_embed_proj_dim,
dropout=dropout,
attention_dropout=attention_dropout,
num_attention_heads=num_attention_heads,
activation_function=activation_function,
layerdrop=layerdrop,
init_std=init_std,
use_cache=use_cache,
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
enable_bias=enable_bias,
layer_norm_elementwise_affine=layer_norm_elementwise_affine,
gradient_checkpointing=gradient_checkpointing,
**kwargs,
)
for k, v in basics.items():
if not hasattr(self, k):
setattr(self, k, v)
self.from_pt = False