# 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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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,
):
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,
):
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