# 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
import jax
from easydel.infra.base_module import EasyDeLBaseConfig
from easydel.infra.etils import EasyDeLGradientCheckPointers
from easydel.infra.factory import register_config
[docs]@register_config("gpt2")
class GPT2Config(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 50257):
Vocabulary size of the GPT-2 model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed to the forward method.
n_positions (`int`, *optional*, defaults to 1024):
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).
n_embd (`int`, *optional*, defaults to 768):
Dimensionality of the encoder layers and the pooler layer.
n_layer (`int`, *optional*, defaults to 12):
Number of hidden layers in the Transformer encoder.
n_head (`int`, *optional*, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
n_inner (`int`, *optional*):
Dimensionality of the inner feed-forward layers.
activation_function (`str`, *optional*, defaults to `"gelu_new"`):
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.
resid_pdrop (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
embd_pdrop (`float`, *optional*, defaults to 0.1):
The dropout ratio for the embeddings.
attn_pdrop (`float`, *optional*, defaults to 0.1):
The dropout ratio for the attention probabilities.
layer_norm_epsilon (`float`, *optional*, defaults to 1e-5):
The epsilon to use in the layer normalization layers.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
summary_type (`str`, *optional*, defaults to `"cls_index"`):
The summary type to use. Possible values are `"cls_index"` (equivalent to the output of the last token
of the first sentence in a sequence) and `"last"` (equivalent to the output of the last token in
the sequence).
summary_use_proj (`bool`, *optional*, defaults to `True`):
Whether to use a projection after the vector extraction.
summary_activation (`str`, *optional*):
The activation to use for the summary.
summary_proj_to_labels (`bool`, *optional*, defaults to `True`):
Whether to project the summary to the labels.
summary_first_dropout (`float`, *optional*, defaults to 0.1):
The dropout ratio to be used after the projection and activation.
scale_attn_weights (`bool`, *optional*, defaults to `True`):
Scale attention weights by dividing by sqrt(hidden_size).
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`.
bos_token_id (`int`, *optional*, defaults to 50256):
The id of the *beginning-of-sequence* token.
eos_token_id (`int`, *optional*, defaults to 50256):
The id of the *end-of-sequence* token.
scale_attn_by_inverse_layer_idx (`bool`, *optional*, defaults to `False`):
Whether to scale attention weights by `1 / layer_idx + 1`.
reorder_and_upcast_attn (`bool`, *optional*, defaults to `False`):
Whether to reorder and upcast attention.
gradient_checkpointing (`str`, *optional*, defaults to `"nothing_saveable"`):
The gradient checkpointing configuration.
tie_word_embeddings (`bool`, *optional*, defaults to `False`):
Whether to tie the weights of the input embeddings and the output embeddings.
bits (`int`, *optional*):
The number of bits to quantize the model to.
"""
model_type: str = "gpt2"
keys_to_ignore_at_inference = ["past_key_values"]
attribute_map = {
"hidden_size": "n_embd",
"max_position_embeddings": "n_positions",
"num_attention_heads": "n_head",
"num_hidden_layers": "n_layer",
}
def __init__(
self,
vocab_size=50257,
n_positions=1024,
n_embd=768,
n_layer=12,
n_head=12,
n_inner=None,
activation_function="gelu_new",
resid_pdrop=0.1,
embd_pdrop=0.1,
attn_pdrop=0.1,
layer_norm_epsilon=1e-5,
initializer_range=0.02,
summary_type="cls_index",
summary_use_proj=True,
summary_activation=None,
summary_proj_to_labels=True,
summary_first_dropout=0.1,
scale_attn_weights=True,
use_cache=True,
bos_token_id=50256,
eos_token_id=50256,
scale_attn_by_inverse_layer_idx=False,
reorder_and_upcast_attn=False,
gradient_checkpointing: EasyDeLGradientCheckPointers = EasyDeLGradientCheckPointers.NONE,
tie_word_embeddings: bool = False,
bits: tp.Optional[int] = None,
**kwargs,
):
self.vocab_size = vocab_size
self.n_positions = n_positions
self.n_embd = n_embd
self.n_layer = n_layer
self.n_head = n_head
self.n_inner = n_inner
self.activation_function = activation_function
self.resid_pdrop = resid_pdrop
self.embd_pdrop = embd_pdrop
self.attn_pdrop = attn_pdrop
self.layer_norm_epsilon = layer_norm_epsilon
self.initializer_range = initializer_range
self.summary_type = summary_type
self.summary_use_proj = summary_use_proj
self.summary_activation = summary_activation
self.summary_first_dropout = summary_first_dropout
self.summary_proj_to_labels = summary_proj_to_labels
self.scale_attn_weights = scale_attn_weights
self.use_cache = use_cache
self.scale_attn_by_inverse_layer_idx = scale_attn_by_inverse_layer_idx
self.reorder_and_upcast_attn = reorder_and_upcast_attn
self.bos_token_id = bos_token_id
self.eos_token_id = eos_token_id
self.gradient_checkpointing = gradient_checkpointing
self.bits = bits
super().__init__(
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
tie_word_embeddings=tie_word_embeddings,
bits=bits,
**kwargs,
)
[docs] def attach_custom_arguments(
self,
gradient_checkpointing: EasyDeLGradientCheckPointers = EasyDeLGradientCheckPointers.NONE,
bits: tp.Optional[int] = None,
**kwargs,
):
args = dict(gradient_checkpointing=gradient_checkpointing, bits=bits, **kwargs)
for k, v in args.items():
if not hasattr(self, k):
setattr(self, k, v)
[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 (
(
"transformer/wte/embedding",
jax.sharding.PartitionSpec("tp", ("fsdp", "sp")),
),
("transformer/lm_head", jax.sharding.PartitionSpec(("fsdp", "sp"), "tp")),
(".*", jax.sharding.PartitionSpec(("fsdp", "sp"))),
)