# 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.factory import register_config
[docs]@register_config("roberta")
class RobertaConfig(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 (:obj:`int`, *optional*, defaults to 50265):
Vocabulary size of the RoBERTa model. Defines the number of different tokens that can be represented by
the :obj:`inputs_ids` passed when calling :class:`~easydel.modules.RobertaModel`.
hidden_size (:obj:`int`, *optional*, defaults to 768):
Dimensionality of the encoder layers and the pooler layer.
num_hidden_layers (:obj:`int`, *optional*, defaults to 12):
Number of hidden layers in the Transformer encoder.
num_attention_heads (:obj:`int`, *optional*, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
intermediate_size (:obj:`int`, *optional*, defaults to 3072):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
hidden_act (:obj:`str` or :obj:`function`, *optional*, defaults to :obj:`"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, :obj:`"gelu"`,
:obj:`"relu"`, :obj:`"swish"` and :obj:`"gelu_new"` are supported.
hidden_dropout_prob (:obj:`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob (:obj:`float`, *optional*, defaults to 0.1):
The dropout ratio for the attention probabilities.
max_position_embeddings (:obj:`int`, *optional*, defaults to 514):
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).
type_vocab_size (:obj:`int`, *optional*, defaults to 1):
The vocabulary size of the :obj:`token_type_ids` passed when calling
:class:`~easydel.modules.RobertaModel`.
initializer_range (:obj:`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (:obj:`float`, *optional*, defaults to 1e-5):
The epsilon used by the layer normalization layers.
position_embedding_type (:obj:`str`, *optional*, defaults to :obj:`"absolute"`):
Type of position embedding. Choose one of :obj:`"absolute"`, :obj:`"relative_key"`,
:obj:`"relative_key_query"`. For positional embeddings use :obj:`"absolute"`. For more information on
:obj:`"relative_key"`, please refer to [Self-Attention with Relative Position Representations (Shaw et
al.)](https://arxiv.org/abs/1803.02155). For more information on :obj:`"relative_key_query"`, please
refer to *Method 4* in [Improve Transformer Models with Better Relative Position Embeddings (Huang et
al.)](https://arxiv.org/abs/2009.13658).
use_cache (:obj:`bool`, *optional*, defaults to :obj:`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``.
classifier_dropout (:obj:`float`, *optional*):
The dropout ratio for the classification head.
gradient_checkpointing (:obj:`str`, *optional*, defaults to :obj:`"nothing_saveable"`):
What to save during gradient checkpointing. Choose one of :obj:`"nothing_saveable"`,
:obj:`"first_half_saveable"`, :obj:`"full_saveable"`.
"""
model_type: str = "roberta"
def __init__(
self,
vocab_size=50265,
hidden_size=768,
num_hidden_layers=12,
num_attention_heads=12,
intermediate_size=3072,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=514,
type_vocab_size=1,
initializer_range=0.02,
layer_norm_eps=1e-5,
pad_token_id=1,
bos_token_id=0,
eos_token_id=2,
position_embedding_type="absolute",
use_cache=True,
classifier_dropout=None,
gradient_checkpointing="nothing_saveable",
**kwargs,
):
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.hidden_act = hidden_act
self.intermediate_size = intermediate_size
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.initializer_range = initializer_range
self.layer_norm_eps = layer_norm_eps
self.position_embedding_type = position_embedding_type
self.use_cache = use_cache
self.classifier_dropout = classifier_dropout
self.gradient_checkpointing = gradient_checkpointing
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
**kwargs,
)
[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.
"""
return (
(
(
"embeddings/(position_embeddings|token_type_embeddings)/embedding",
PartitionSpec(),
),
("embeddings/word_embeddings/embedding", PartitionSpec()),
(
"attention/self/(key|query|value)/kernel",
PartitionSpec("fsdp", "tp"),
),
("attention/self/(key|query|value)/bias", PartitionSpec()),
("attention/output/dense/kernel", PartitionSpec("tp", "fsdp")),
("attention/output/dense/bias", PartitionSpec()),
("(LayerNorm|layer_norm)/(bias|scale)", PartitionSpec()),
("intermediate/dense/kernel", PartitionSpec("fsdp", "tp")),
("intermediate/dense/bias", PartitionSpec("tp")),
("output/dense/kernel", PartitionSpec("tp", "fsdp")),
("output/dense/bias", PartitionSpec()),
("lm_head/dense/kernel", PartitionSpec()),
("lm_head/dense/bias", PartitionSpec()),
("lm_head/decoder/kernel", PartitionSpec("fsdp", "tp")),
("lm_head/decoder/bias", PartitionSpec("tp")),
(".*", PartitionSpec()),
)
if not fully_sharded_data_parallel
else (
(
"embeddings/(position_embeddings|token_type_embeddings)/embedding",
PartitionSpec(),
),
("embeddings/word_embeddings/embedding", PartitionSpec()),
(
"attention/self/(key|query|value)/kernel",
PartitionSpec(("fsdp", "sp")),
),
("attention/self/(key|query|value)/bias", PartitionSpec()),
("attention/output/dense/kernel", PartitionSpec(("fsdp", "sp"))),
("attention/output/dense/bias", PartitionSpec()),
("(LayerNorm|layer_norm)/(bias|scale)", PartitionSpec()),
("intermediate/dense/kernel", PartitionSpec(("fsdp", "sp"))),
("intermediate/dense/bias", PartitionSpec("sp")),
("output/dense/kernel", PartitionSpec(("fsdp", "sp"))),
("output/dense/bias", PartitionSpec()),
("lm_head/dense/kernel", PartitionSpec()),
("lm_head/dense/bias", PartitionSpec()),
("lm_head/decoder/kernel", PartitionSpec(("fsdp", "sp"))),
("lm_head/decoder/bias", PartitionSpec("sp")),
(".*", PartitionSpec()),
)
)
[docs] def add_jax_args(self, gradient_checkpointing="nothing_saveable", **kwargs):
self.gradient_checkpointing = gradient_checkpointing
for k, v in kwargs.items():
setattr(self, k, v)