# Copyright 2023 The EASYDEL Author @erfanzar (Erfan Zare Chavoshi).
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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# https://www.apache.org/licenses/LICENSE-2.0
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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("gpt_neox")
class GPTNeoXConfig(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 50432):
Vocabulary size of the GPT NeoX 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 6144):
Dimensionality of the encoder layers and the pooler layer.
num_hidden_layers (`int`, *optional*, defaults to 44):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 64):
Number of attention heads for each attention layer in the Transformer encoder.
intermediate_size (`int`, *optional*, defaults to 24576):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
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.
rotary_pct (`float`, *optional*, defaults to 0.25):
The percentage of hidden dimensions to allocate to rotary embeddings.
rotary_emb_base (`int`, *optional*, defaults to 10000):
The base for the rotary position embedding.
classifier_dropout (`float`, *optional*, defaults to 0.1):
The dropout ratio for the classifier layer.
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., 2048 or 4096).
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-5):
The epsilon used by the layer normalization layers.
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 0):
The id of the *beginning-of-sequence* token.
eos_token_id (`int`, *optional*, defaults to 2):
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 `"everything_saveable"`):
The gradient checkpointing configuration.
use_parallel_residual (`bool`, *optional*, defaults to `True`):
Whether to use a parallel residual connection in the attention layer.
"""
model_type: str = "gpt_neox"
def __init__(
self,
vocab_size=50432,
hidden_size=6144,
num_hidden_layers=44,
num_attention_heads=64,
intermediate_size=24576,
hidden_act="gelu",
rotary_pct=0.25,
rotary_emb_base=10000,
attention_dropout=0.0,
hidden_dropout=0.0,
classifier_dropout=0.1,
max_position_embeddings=2048,
initializer_range=0.02,
layer_norm_eps=1e-5,
use_cache=True,
bos_token_id=0,
eos_token_id=2,
tie_word_embeddings=False,
use_parallel_residual=True,
rope_scaling=None,
attention_bias=True,
gradient_checkpointing=EasyDeLGradientCheckPointers.NONE,
**kwargs,
):
"""Initializes a GPTNeoXConfig object.
Args:
vocab_size (int, optional): Vocabulary size. Defaults to 50432.
hidden_size (int, optional): Hidden size. Defaults to 6144.
num_hidden_layers (int, optional): Number of hidden layers. Defaults to 44.
num_attention_heads (int, optional): Number of attention heads. Defaults to 64.
intermediate_size (int, optional): Intermediate size. Defaults to 24576.
hidden_act (str, optional): Activation function. Defaults to "gelu".
rotary_pct (float, optional): Percentage of hidden dimensions for rotary embeddings. Defaults to 0.25.
rotary_emb_base (int, optional): Base for rotary embeddings. Defaults to 10000.
attention_dropout (float, optional): Attention dropout rate. Defaults to 0.0.
hidden_dropout (float, optional): Hidden dropout rate. Defaults to 0.0.
classifier_dropout (float, optional): Classifier dropout rate. Defaults to 0.1.
max_position_embeddings (int, optional): Maximum position embeddings. Defaults to 2048.
initializer_range (float, optional): Initializer range. Defaults to 0.02.
layer_norm_eps (float, optional): Layer normalization epsilon. Defaults to 1e-5.
use_cache (bool, optional): Whether to use KV cache. Defaults to True.
bos_token_id (int, optional): Beginning-of-sequence token ID. Defaults to 0.
eos_token_id (int, optional): End-of-sequence token ID. Defaults to 2.
tie_word_embeddings (bool, optional): Whether to tie word embeddings. Defaults to False.
use_parallel_residual (bool, optional): Whether to use parallel residual connections. Defaults to True.
rope_scaling (dict, optional): RoPE scaling configuration. Defaults to None.
attention_bias (bool, optional): Whether to use attention bias. Defaults to True.
gradient_checkpointing (EasyDeLGradientCheckPointers, optional): Gradient checkpointing strategy.
Defaults to EasyDeLGradientCheckPointers.NONE.
**kwargs: Additional keyword arguments.
"""
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.rotary_pct = rotary_pct
self.rotary_emb_base = rotary_emb_base
self.rope_theta = rotary_emb_base
self.classifier_dropout = classifier_dropout
self.initializer_range = initializer_range
self.layer_norm_eps = layer_norm_eps
self.use_cache = use_cache
self.tie_word_embeddings = tie_word_embeddings
self.hidden_dropout = hidden_dropout
self.gradient_checkpointing = gradient_checkpointing
self.attention_dropout = attention_dropout
self.use_parallel_residual = use_parallel_residual
self.rope_scaling = rope_scaling
self.attention_bias = attention_bias
self.from_pt = False
super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **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")),
("attention/w_qkv/kernel", PartitionSpec(("fsdp", "sp"), "tp")),
("attention/w_qkv/bias", PartitionSpec(("fsdp", "sp"))), # 1D for bias
("attention/wo/kernel", PartitionSpec("tp", ("fsdp", "sp"))),
("attention/wo/bias", PartitionSpec(("fsdp", "sp"))), # 1D for bias
("mlp/dense_h_to_4h/kernel", PartitionSpec(("fsdp", "sp"), "tp")),
("mlp/dense_h_to_4h/bias", PartitionSpec(("fsdp", "sp"))), # 1D for bias
("mlp/dense_4h_to_h/kernel", PartitionSpec("tp", ("fsdp", "sp"))),
("mlp/dense_4h_to_h/bias", PartitionSpec(("fsdp", "sp"))), # 1D for bias
("post_attention_layernorm/(bias|scale)", PartitionSpec(None)),
("input_layernorm/(bias|scale)", PartitionSpec(None)),
("transformer/final_layer_norm/(scale|bias)", PartitionSpec(None)),
("lm_head/kernel", PartitionSpec(("fsdp", "sp"), "tp")),
(".*", PartitionSpec(None)),
)
[docs] @staticmethod
def get_mesh_names():
"""Returns the mesh names used for model parallelism. For GPT-NeoX, it returns mesh names for
data parallelism ('dp'), fully sharded data parallelism ('fsdp'), tensor parallelism ('tp'),
and sequence parallelism ('sp').
Returns:
tuple: A tuple containing the mesh names ("dp", "fsdp", "tp", "sp").
"""
return "dp", "fsdp", "tp", "sp"
[docs] def attach_custom_arguments(
self,
**kwargs,
):
"""Attaches custom arguments to the configuration object.
This method allows adding or overriding configuration attributes dynamically.
It primarily sets the `from_pt` attribute to False and ignores other keyword arguments.
Args:
**kwargs: Additional keyword arguments (ignored).
"""
self.from_pt = False