# 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
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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("exaone")
class ExaoneConfig(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 102400):
Vocabulary size of the Exaone 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 2048):
Dimensionality of the encoder layers and the pooler layer.
intermediate_size (`int`, *optional*, defaults to 14336):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
head_dim (`int`, defaults to 128):
Dimensionality of the head for attention.
num_layers (`int`, *optional*, defaults to 32):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 32):
Number of attention heads for each attention layer in the Transformer encoder.
num_key_value_heads (`int`, *optional*, defaults to 8):
Number of key and value heads for each attention layer in the Transformer encoder.
activation_function (`str` or `function`, *optional*, defaults to `"silu"`):
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.
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_epsilon (`float`, *optional*, defaults to 1e-5):
The epsilon used by the rms 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`.
pad_token_id (`int`, *optional*):
The index of the padding token in the vocabulary.
bos_token_id (`int`, *optional*, defaults to 1):
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.
rope_theta (`float`, *optional*, defaults to 10000.0):
The theta value to use for rotary position embeddings.
rope_scaling (`tp.Dict[str, tp.Union[str, float]]`, *optional*):
The configuration for rope scaling.
gradient_checkpointing (`str`, *optional*, defaults to `"nothing_saveable"`):
The gradient checkpointing configuration.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
use_scan_mlp (`bool`, *optional*, defaults to `False`):
Whether to use the scan implementation for the MLP.
scan_mlp_chunk_size (`int`, *optional*, defaults to 1024):
The chunk size to use when scanning the MLP.
bits (`int`, *optional*):
The number of bits to quantize the model to.
attention_bias (`bool`, *optional*, defaults to `False`):
Whether to use bias in the attention layer.
"""
model_type: str = "exaone"
def __init__(
self,
vocab_size: int = 102400,
hidden_size: int = 2048,
intermediate_size: int = 14336,
num_layers: int = 32,
num_attention_heads: int = 32,
num_key_value_heads: int = 8,
activation_function="silu",
max_position_embeddings=2048,
initializer_range=0.02,
layer_norm_epsilon=1e-5,
use_cache=True,
embed_dropout: float = 0.0,
pad_token_id: tp.Optional[int] = None,
bos_token_id: int = 1,
eos_token_id: int = 2,
tie_word_embeddings=False,
rope_theta=10000.0,
rope_scaling: tp.Dict[str, tp.Union[str, float]] = None,
gradient_checkpointing: EasyDeLGradientCheckPointers = EasyDeLGradientCheckPointers.NONE,
attention_dropout: float = 0.0,
use_scan_mlp: bool = False,
scan_mlp_chunk_size: int = 1024,
bits: tp.Optional[int] = None,
**kwargs,
):
"""Initialize a new ExaoneConfig instance.
Args:
vocab_size (int, optional): Size of the vocabulary. Defaults to 102400.
hidden_size (int, optional): Dimensionality of the embeddings and hidden states. Defaults to 2048.
intermediate_size (int, optional): Dimensionality of the intermediate feed-forward layer. Defaults to 14336.
num_layers (int, optional): Number of hidden layers in the model. Defaults to 32.
num_attention_heads (int, optional): Number of attention heads. Defaults to 32.
num_key_value_heads (int, optional): Number of key/value heads (for GQA). Defaults to 8.
activation_function (str, optional): Activation function to use. Defaults to "silu".
max_position_embeddings (int, optional): Maximum sequence length. Defaults to 2048.
initializer_range (float, optional): Range for weight initialization. Defaults to 0.02.
layer_norm_epsilon (float, optional): Epsilon for layer normalization. Defaults to 1e-5.
use_cache (bool, optional): Whether to use KV cache for generation. Defaults to True.
embed_dropout (float, optional): Dropout probability for embeddings. Defaults to 0.0.
pad_token_id (Optional[int], optional): ID for padding token. Defaults to None.
bos_token_id (int, optional): ID for beginning of sequence token. Defaults to 1.
eos_token_id (int, optional): ID for end of sequence token. Defaults to 2.
tie_word_embeddings (bool, optional): Whether to tie input/output embeddings. Defaults to False.
rope_theta (float, optional): Base value for RoPE. Defaults to 10000.0.
rope_scaling (Dict[str, Union[str, float]], optional): RoPE scaling configuration. Defaults to None.
gradient_checkpointing (EasyDeLGradientCheckPointers, optional): Gradient checkpointing strategy. Defaults to EasyDeLGradientCheckPointers.NONE.
attention_dropout (float, optional): Dropout probability for attention. Defaults to 0.0.
use_scan_mlp (bool, optional): Whether to use scan for MLP computation. Defaults to False.
scan_mlp_chunk_size (int, optional): Chunk size for scan MLP. Defaults to 1024.
bits (Optional[int], optional): Quantization bits. Defaults to None.
**kwargs: Additional keyword arguments.
"""
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.num_layers = num_layers
self.num_attention_heads = num_attention_heads
self.num_hidden_layers = num_layers
if num_key_value_heads is None:
num_key_value_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
if intermediate_size:
self.intermediate_size = intermediate_size
else:
self.intermediate_size = hidden_size * 4
self.activation_function = activation_function
self.embed_dropout = embed_dropout
self.attention_dropout = attention_dropout
self.layer_norm_epsilon = layer_norm_epsilon
self.initializer_range = initializer_range
self.use_cache = use_cache
self.rope_theta = rope_theta
self.rope_scaling = rope_scaling
self.bos_token_id = bos_token_id
self.eos_token_id = eos_token_id
self.gradient_checkpointing = gradient_checkpointing
self.use_scan_mlp = use_scan_mlp
self.scan_mlp_chunk_size = scan_mlp_chunk_size
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
tie_word_embeddings=tie_word_embeddings,
use_scan_mlp=use_scan_mlp,
scan_mlp_chunk_size=scan_mlp_chunk_size,
bits=bits,
**kwargs,
)
[docs] def get_partition_rules(self, *args, **kwargs):
"""
Get the partition rules for the model.
Returns:
`tp.Tuple[tp.Tuple[str, PartitionSpec]]`: The partition rules.
"""
return (
("embed_tokens/embedding", PartitionSpec(("fsdp", "sp"), "tp")),
("self_attn/q_proj/kernel", PartitionSpec("tp", ("fsdp", "sp"))),
("self_attn/k_proj/kernel", PartitionSpec("tp", ("fsdp", "sp"))),
("self_attn/v_proj/kernel", PartitionSpec("tp", ("fsdp", "sp"))),
("self_attn/o_proj/kernel", PartitionSpec(("sp", "fsdp"), "tp")),
("mlp/c_fc_1/kernel", PartitionSpec(("fsdp", "sp"), "tp")),
("mlp/c_proj/kernel", PartitionSpec("tp", ("fsdp", "sp"))),
("mlp/c_fc_0/kernel", PartitionSpec(("fsdp", "sp"), "tp")),
("input_layernorm/kernel", PartitionSpec(None)),
("post_attention_layernorm/kernel", PartitionSpec(None)),
("model/norm/kernel", PartitionSpec(None)),
("lm_head/kernel", PartitionSpec(("fsdp", "sp"), "tp")),
(".*", PartitionSpec(None)),
)
[docs] def attach_custom_arguments(
self,
gradient_checkpointing: EasyDeLGradientCheckPointers = EasyDeLGradientCheckPointers.NONE,
use_scan_mlp: bool = False,
scan_mlp_chunk_size: int = 1024,
bits: tp.Optional[int] = None,
attention_dropout: float = 0.0,
rope_scaling: tp.Dict[str, tp.Union[str, float]] = None,
attention_bias: bool = False,
**kwargs,
):
"""The attach_custom_arguments function adds the following arguments to the model:
Args:
gradient_checkpointing (str): Determine whether to use
gradient checkpointing
use_scan_mlp (bool): Determine whether to use the scan_mlp
function or notn
scan_mlp_chunk_size (int): Chunk the input to the mlp
bits (tp.Optional[int]): Specify the number of bits to use for
quantization
attention_dropout (float): Set the dropout rate for the
attention layer
attention_bias (bool): when ever to use attention_bias
rope_scaling (tp.Dict[str, tp.Union[str, float]]): rope_scaling for
rope
Returns:
A tuple of the following:
"""
self.attention_bias = attention_bias
self.rope_scaling = rope_scaling
self.gradient_checkpointing = gradient_checkpointing
self.use_scan_mlp = use_scan_mlp
self.scan_mlp_chunk_size = scan_mlp_chunk_size
self.attention_dropout = attention_dropout
self.bits = bits
[docs] @staticmethod
def get_weight_decay_exclusions():
"""Returns a tuple of parameter names for which weight decay should be excluded.
Returns:
tuple: An empty tuple, indicating no weight decay exclusions.
"""
return tuple()
[docs] @staticmethod
def rng_keys():
"""Returns the names of the random number generator keys used by the model.
Returns:
tuple: A tuple containing "params", "dropout", and "fcm" as the RNG keys.
"""
return "params", "dropout", "fcm"
@property
def granted_freq_max_position_embedding(self) -> int:
"""Returns the maximum position embedding size for frequency-based position embeddings.
Returns:
int: The maximum position embedding size, falling back to max_position_embeddings if not explicitly set.
"""
return getattr(
self,
"freq_max_position_embeddings",
self.max_position_embeddings,
)
@property
def granted_mask_max_position_embedding(self) -> int:
"""Returns the maximum position embedding size for mask-based position embeddings.
Returns:
int: The maximum position embedding size, falling back to max_position_embeddings if not explicitly set.
"""
return getattr(
self,
"mask_max_position_embeddings",
self.max_position_embeddings,
)