# 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]@register_config("gptj")
class GPTJConfig(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 50400):
Vocabulary size of the GPT-J 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 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).
n_embd (`int`, *optional*, defaults to 4096):
Dimensionality of the encoder layers and the pooler layer.
n_layer (`int`, *optional*, defaults to 28):
Number of hidden layers in the Transformer encoder.
n_head (`int`, *optional*, defaults to 16):
Number of attention heads for each attention layer in the Transformer encoder.
rotary_dim (`int`, *optional*, defaults to 64):
The dimension of the rotary position embedding.
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 0.0):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
embd_pdrop (`float`, *optional*, defaults to 0.0):
The dropout ratio for the embeddings.
attn_pdrop (`float`, *optional*, defaults to 0.0):
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.
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.
tie_word_embeddings (`bool`, *optional*, defaults to `False`):
Whether to tie the weights of the input embeddings and the output embeddings.
gradient_checkpointing (`str`, *optional*, defaults to `""`):
The gradient checkpointing configuration.
bits (`int`, *optional*):
The number of bits to quantize the model to.
"""
model_type: str = "gptj"
attribute_map = {
"max_position_embeddings": "n_positions",
"hidden_size": "n_embd",
"num_attention_heads": "n_head",
"num_hidden_layers": "n_layer",
}
def __init__(
self,
vocab_size: int = 50400,
n_positions: int = 2048,
n_embd: int = 4096,
n_layer: int = 28,
n_head: int = 16,
rotary_dim: int = 64,
n_inner: int = None,
activation_function: str = "gelu_new",
resid_pdrop: float = 0.0,
embd_pdrop: float = 0.0,
attn_pdrop: float = 0.0,
layer_norm_epsilon: float = 1e-5,
initializer_range: int = 0.02,
use_cache: int = True,
bos_token_id: int = 50256,
eos_token_id: int = 50256,
tie_word_embeddings: bool = False,
gradient_checkpointing: EasyDeLGradientCheckPointers = EasyDeLGradientCheckPointers.NONE,
bits: tp.Optional[int] = None,
**kwargs,
):
"""Initializes a GPTJConfig object.
Args:
vocab_size (int, optional): Vocabulary size. Defaults to 50400.
n_positions (int, optional): Maximum sequence length. Defaults to 2048.
n_embd (int, optional): Hidden size. Defaults to 4096.
n_layer (int, optional): Number of hidden layers. Defaults to 28.
n_head (int, optional): Number of attention heads. Defaults to 16.
rotary_dim (int, optional): Dimension for rotary position embeddings. Defaults to 64.
n_inner (int, optional): Inner dimension of FFN. Defaults to None.
activation_function (str, optional): Activation function. Defaults to "gelu_new".
resid_pdrop (float, optional): Residual dropout probability. Defaults to 0.0.
embd_pdrop (float, optional): Embedding dropout probability. Defaults to 0.0.
attn_pdrop (float, optional): Attention dropout probability. Defaults to 0.0.
layer_norm_epsilon (float, optional): Epsilon for layer normalization. Defaults to 1e-5.
initializer_range (int, optional): Initializer range. Defaults to 0.02.
use_cache (int, optional): Whether to use KV cache. Defaults to True.
bos_token_id (int, optional): Beginning-of-sequence token ID. Defaults to 50256.
eos_token_id (int, optional): End-of-sequence token ID. Defaults to 50256.
tie_word_embeddings (bool, optional): Whether to tie input/output embeddings. 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.
"""
self.bits = bits
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.rotary_dim = rotary_dim
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.use_cache = use_cache
self.bos_token_id = bos_token_id
self.eos_token_id = eos_token_id
self.from_pt = False
self.gradient_checkpointing = gradient_checkpointing
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 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/(k_proj|v_proj|q_proj)/kernel",
PartitionSpec("tp", ("fsdp", "sp")),
),
("attn/out_proj/kernel", PartitionSpec(("fsdp", "sp"), "tp")),
("mlp/fc_out/kernel", PartitionSpec(("fsdp", "sp"), "tp")),
("mlp/fc_out/bias", PartitionSpec("tp")),
("mlp/fc_in/kernel", PartitionSpec("tp", ("fsdp", "sp"))),
("mlp/fc_in/bias", PartitionSpec(("fsdp", "sp"))),
("lm_head/kernel", PartitionSpec(("fsdp", "sp"), "tp")),
("lm_head/bias", PartitionSpec(("fsdp", "sp"))),
(".*", PartitionSpec(("fsdp", "sp"))),
)
[docs] @staticmethod
def get_mesh_names():
"""Returns the mesh names used for model parallelism. For GPT-J, it returns mesh names for
data parallelism ('dp'), fully sharded data parallelism ('fsdp'), sequence parallelism ('sp'),
and tensor parallelism ('tp').
Returns:
tuple: A tuple containing the mesh names ("dp", "fsdp", "sp", "sp").
"""
return "dp", "fsdp", "sp", "sp"
[docs] def attach_custom_arguments(
self,
vocab_size: int = 50400,
n_positions: int = 2048,
n_embd: int = 4096,
n_layer: int = 28,
n_head: int = 16,
rotary_dim: int = 64,
n_inner: int = None,
activation_function: str = "gelu_new",
resid_pdrop: float = 0.0,
embd_pdrop: float = 0.0,
attn_pdrop: float = 0.0,
layer_norm_epsilon: float = 1e-5,
initializer_range: int = 0.02,
use_cache: int = True,
bos_token_id: int = 50256,
eos_token_id: int = 50256,
tie_word_embeddings: bool = False,
bits: tp.Optional[int] = None,
gradient_checkpointing: EasyDeLGradientCheckPointers = EasyDeLGradientCheckPointers.NONE,
**kwargs,
):
"""Attaches custom arguments to the configuration object.
This method allows adding or overriding configuration attributes dynamically.
It iterates through a dictionary of basic configuration parameters and sets them
as attributes of the configuration object if they don't already exist.
Args:
vocab_size (int, optional): Vocabulary size. Defaults to 50400.
n_positions (int, optional): Maximum sequence length. Defaults to 2048.
n_embd (int, optional): Hidden size. Defaults to 4096.
n_layer (int, optional): Number of hidden layers. Defaults to 28.
n_head (int, optional): Number of attention heads. Defaults to 16.
rotary_dim (int, optional): Dimension for rotary position embeddings. Defaults to 64.
n_inner (int, optional): Inner dimension of FFN. Defaults to None.
activation_function (str, optional): Activation function. Defaults to "gelu_new".
resid_pdrop (float, optional): Residual dropout probability. Defaults to 0.0.
embd_pdrop (float, optional): Embedding dropout probability. Defaults to 0.0.
attn_pdrop (float, optional): Attention dropout probability. Defaults to 0.0.
layer_norm_epsilon (float, optional): Epsilon for layer normalization. Defaults to 1e-5.
initializer_range (int, optional): Initializer range. Defaults to 0.02.
use_cache (int, optional): Whether to use KV cache. Defaults to True.
bos_token_id (int, optional): Beginning-of-sequence token ID. Defaults to 50256.
eos_token_id (int, optional): End-of-sequence token ID. Defaults to 50256.
tie_word_embeddings (bool, optional): Whether to tie input/output embeddings. Defaults to False.
bits (tp.Optional[int], optional): Quantization bits. Defaults to None.
gradient_checkpointing (EasyDeLGradientCheckPointers, optional): Gradient checkpointing strategy.
Defaults to EasyDeLGradientCheckPointers.NONE.
**kwargs: Additional keyword arguments.
Returns:
GPTJConfig: The configuration object itself (self).
"""
basics = dict(
bits=bits,
vocab_size=vocab_size,
n_positions=n_positions,
n_embd=n_embd,
n_layer=n_layer,
n_head=n_head,
rotary_dim=rotary_dim,
n_inner=n_inner,
activation_function=activation_function,
resid_pdrop=resid_pdrop,
embd_pdrop=embd_pdrop,
attn_pdrop=attn_pdrop,
layer_norm_epsilon=layer_norm_epsilon,
initializer_range=initializer_range,
use_cache=use_cache,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
tie_word_embeddings=tie_word_embeddings,
gradient_checkpointing=gradient_checkpointing,
)
for k, v in basics.items():
if not hasattr(self, k):
setattr(self, k, v)
self.from_pt = False
return self