# 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("falcon")
class FalconConfig(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 65024):
Vocabulary size of the Falcon 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 4544):
Dimensionality of the encoder layers and the pooler layer.
num_hidden_layers (`int`, *optional*, defaults to 32):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 71):
Number of attention heads for each attention layer in the Transformer encoder.
num_ln_in_parallel_attn (`int`, *optional*):
The number of layer norms in the parallel attention layer.
layer_norm_epsilon (`float`, *optional*, defaults to 1e-5):
The epsilon used by 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`.
hidden_dropout (`float`, *optional*, defaults to 0.0):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
num_kv_heads (`int`, *optional*):
Number of key and value heads for each attention layer in the Transformer encoder. Will default to
`num_attention_heads` if not set.
alibi (`bool`, *optional*):
Whether to use alibi attention.
new_decoder_architecture (`bool`, *optional*):
Whether to use the new decoder architecture.
multi_query (`bool`, *optional*, defaults to `True`):
Whether to use multi-query attention.
parallel_attn (`bool`, *optional*, defaults to `True`):
Whether to use parallel attention.
bias (`bool`, *optional*, defaults to `False`):
Whether to use bias in the linear layers.
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).
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 rope scaling configuration.
bos_token_id (`int`, *optional*, defaults to 11):
The index of the beginning of sequence token in the vocabulary.
eos_token_id (`int`, *optional*, defaults to 11):
The index of the end of sequence token in the vocabulary.
ffn_hidden_size (`int`, *optional*):
Dimensionality of the hidden layer in the FFN
ff_factor (`int`, *optional*):
The scaling factor of the FFN
activation (`str`, *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.
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 = "falcon"
attribute_map = {
"num_hidden_layers": "num_hidden_layers",
"num_attention_heads": "num_attention_heads",
}
def __init__(
self,
vocab_size=65024,
hidden_size=4544,
num_hidden_layers=32,
num_attention_heads=71,
num_ln_in_parallel_attn=None,
layer_norm_epsilon=1e-5,
initializer_range=0.02,
use_cache=True,
hidden_dropout=0.0,
attention_dropout=0.0,
num_kv_heads=None,
alibi=False,
new_decoder_architecture=False,
multi_query=True,
parallel_attn=True,
bias=False,
max_position_embeddings=2048,
rope_theta=10000.0,
rope_scaling=None,
bos_token_id=11,
eos_token_id=11,
ffn_hidden_size=None,
ff_factor=None,
activation="gelu",
gradient_checkpointing: EasyDeLGradientCheckPointers = EasyDeLGradientCheckPointers.NONE,
bits: tp.Optional[int] = None,
**kwargs,
):
"""Initialize a new FalconConfig instance.
Args:
vocab_size (int, optional): Size of the vocabulary. Defaults to 65024.
hidden_size (int, optional): Dimensionality of hidden layers. Defaults to 4544.
num_hidden_layers (int, optional): Number of hidden layers. Defaults to 32.
num_attention_heads (int, optional): Number of attention heads. Defaults to 71.
num_ln_in_parallel_attn (int, optional): Number of layer norms in parallel attention. Defaults to None.
layer_norm_epsilon (float, optional): Epsilon for layer normalization. Defaults to 1e-5.
initializer_range (float, optional): Range for weight initialization. Defaults to 0.02.
use_cache (bool, optional): Whether to use KV cache. Defaults to True.
hidden_dropout (float, optional): Dropout probability for hidden layers. Defaults to 0.0.
attention_dropout (float, optional): Dropout probability for attention. Defaults to 0.0.
num_kv_heads (int, optional): Number of key/value heads. Defaults to None (same as num_attention_heads).
alibi (bool, optional): Whether to use alibi attention. Defaults to False.
new_decoder_architecture (bool, optional): Whether to use new decoder architecture. Defaults to False.
multi_query (bool, optional): Whether to use multi-query attention. Defaults to True.
parallel_attn (bool, optional): Whether to use parallel attention. Defaults to True.
bias (bool, optional): Whether to use bias in linear layers. Defaults to False.
max_position_embeddings (int, optional): Maximum sequence length. Defaults to 2048.
rope_theta (float, optional): Base value for RoPE. Defaults to 10000.0.
rope_scaling (dict, optional): RoPE scaling configuration. Defaults to None.
bos_token_id (int, optional): Beginning of sequence token ID. Defaults to 11.
eos_token_id (int, optional): End of sequence token ID. Defaults to 11.
ffn_hidden_size (int, optional): Size of feed-forward hidden layer. Defaults to None.
ff_factor (int, optional): Factor for feed-forward size. Defaults to None.
activation (str, optional): Activation function. Defaults to "gelu".
gradient_checkpointing (EasyDeLGradientCheckPointers, optional): Checkpointing strategy. Defaults to EasyDeLGradientCheckPointers.NONE.
bits (int, optional): Quantization bits. Defaults to None.
**kwargs: Additional arguments.
"""
self.vocab_size = vocab_size
n_embed = kwargs.pop("n_embed", None)
self.hidden_size = hidden_size if n_embed is None else n_embed
self.num_hidden_layers = num_hidden_layers
if num_ln_in_parallel_attn is None:
num_ln_in_parallel_attn = 0
self.num_ln_in_parallel_attn = num_ln_in_parallel_attn
self.num_attention_heads = num_attention_heads
self.layer_norm_epsilon = layer_norm_epsilon
self.initializer_range = initializer_range
self.rope_theta = rope_theta
self.rope_scaling = rope_scaling
self.max_position_embeddings = max_position_embeddings
self.use_cache = use_cache
self.hidden_dropout = hidden_dropout
self.attention_dropout = attention_dropout
self.bos_token_id = bos_token_id
self.activation = activation
self.eos_token_id = eos_token_id
self.multi_query = multi_query
self.alibi = alibi
self.bias = bias
self.gradient_checkpointing = gradient_checkpointing
self.parallel_attn = parallel_attn
if num_kv_heads is None:
num_kv_heads = num_attention_heads
self.num_kv_heads = num_kv_heads
self.new_decoder_architecture = new_decoder_architecture
self.bits = bits
self.from_pt = False
self.head_dim = self.hidden_size // self.num_attention_heads
if ffn_hidden_size is None:
ffn_hidden_size = hidden_size * 4
self.ffn_hidden_size = ffn_hidden_size
if ff_factor is None:
ff_factor = ffn_hidden_size // hidden_size
self.ff_factor = ff_factor
super().__init__(
bos_token_id=bos_token_id, eos_token_id=eos_token_id, bits=bits, **kwargs
)
@property
def rotary(self):
return not self.alibi
[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 (
("word_embeddings/embedding", PartitionSpec(("fsdp", "sp"), "tp")),
(
"self_attention/query_key_value/kernel",
PartitionSpec("tp", ("fsdp", "sp")),
),
("self_attention/dense/(kernel)", PartitionSpec(("fsdp", "sp"), "tp")),
("mlp/dense_4h_to_h/(kernel)", PartitionSpec("tp", ("fsdp", "sp"))),
("mlp/dense_h_to_4h/(kernel)", PartitionSpec(("fsdp", "sp"), "tp")),
("lm_head/kernel", PartitionSpec("tp", ("fsdp", "sp"))),
("transformer/ln_f/bias", PartitionSpec(("fsdp", "sp"))),
("transformer/ln_f/scale", PartitionSpec(("fsdp", "sp"))),
(
"transformer/post_attention_layernorm/scale",
PartitionSpec(("fsdp", "sp")),
),
(
"transformer/post_attention_layernorm/bias",
PartitionSpec(("fsdp", "sp")),
),
("lm_head/kernel", PartitionSpec(("fsdp", "sp"), "tp")),
(".*", PartitionSpec(None)),
)
[docs] @staticmethod
def get_mesh_names():
"""Returns the mesh names used for model parallelism.
Returns:
tuple: A tuple containing "dp", "fsdp", and "tp" as the mesh names.
"""
return "dp", "fsdp", "tp", "sp"
[docs] def attach_custom_arguments(
self,
gradient_checkpointing: EasyDeLGradientCheckPointers = EasyDeLGradientCheckPointers.NONE,
bits: tp.Optional[int] = None,
**kwargs,
):
"""Attach custom arguments to the configuration.
Args:
gradient_checkpointing (EasyDeLGradientCheckPointers, optional): Gradient checkpointing strategy. Defaults to EasyDeLGradientCheckPointers.NONE.
bits (int, optional): Quantization bits. Defaults to None.
**kwargs: Additional keyword arguments.
Returns:
FalconConfig: The updated configuration instance.
"""
basics = dict(bits=bits, gradient_checkpointing=gradient_checkpointing, **kwargs)
for key_states, value_states in basics.items():
if not hasattr(self, key_states):
setattr(self, key_states, value_states)
self.from_pt = False
@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,
)