# 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("gemma2")
class Gemma2Config(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 256000):
Vocabulary size of the Gemma2 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 3072):
Dimensionality of the encoder layers and the pooler layer.
intermediate_size (`int`, *optional*, defaults to 24576):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
num_hidden_layers (`int`, *optional*, defaults to 28):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 16):
Number of attention heads for each attention layer in the Transformer encoder.
num_key_value_heads (`int`, *optional*, defaults to 16):
Number of key and value heads for each attention layer in the Transformer encoder.
head_dim (`int`, *optional*, defaults to 256):
Dimensionality of the attention head.
hidden_activation (`str` or `function`, *optional*, defaults to `"gelu_pytorch_tanh"`):
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 8192):
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.
rms_norm_eps (`float`, *optional*, defaults to 1e-6):
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*, defaults to 0):
The index of the padding token in the vocabulary.
eos_token_id (`int`, *optional*, defaults to 1):
The index of the end of sequence token in the vocabulary.
bos_token_id (`int`, *optional*, defaults to 2):
The index of the beginning of sequence token in the vocabulary.
tie_word_embeddings (`bool`, *optional*, defaults to `True`):
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.
attention_bias (`bool`, *optional*, defaults to `False`):
Whether to use attention bias.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
final_logit_softcapping (`float`, *optional*, defaults to 30.0):
The soft capping value for the final logits.
query_pre_attn_scalar (`int`, *optional*, defaults to 224):
The scalar value for the query pre-attention layer.
sliding_window (`int`, *optional*, defaults to 4096):
The sliding window size.
gradient_checkpointing (`str`, *optional*, defaults to `"nothing_saveable"`):
The gradient checkpointing configuration.
bits (`int`, *optional*):
The number of bits to quantize the model to.
scan_layers (`bool`, *optional*, defaults to `False`):
Whether to use the scan implementation of the layers.
"""
model_type: str = "gemma2"
def __init__(
self,
vocab_size=256000,
hidden_size=3072,
intermediate_size=24576,
num_hidden_layers=28,
num_attention_heads=16,
num_key_value_heads=16,
head_dim=256,
hidden_activation="gelu_pytorch_tanh",
max_position_embeddings=8192,
initializer_range=0.02,
rms_norm_eps=1e-6,
use_cache=True,
pad_token_id=0,
eos_token_id=1,
bos_token_id=2,
tie_word_embeddings=True,
rope_theta=10000.0,
attention_bias=False,
attention_dropout=0.0,
final_logit_softcapping=30.0,
query_pre_attn_scalar=224,
sliding_window=4096,
gradient_checkpointing: EasyDeLGradientCheckPointers = EasyDeLGradientCheckPointers.NONE,
bits: tp.Optional[int] = None,
scan_layers: bool = False,
attn_logit_softcapping: tp.Optional[bool] = None,
**kwargs,
):
"""The __init__ function is called when the class is instantiated.
It sets up the attributes of an object, which are sometimes called fields or properties.
The __init__ function can accept arguments, but self must be the first one.
"""
self.gradient_checkpointing = gradient_checkpointing
self.bits = bits
self.scan_layers = scan_layers
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.head_dim = head_dim
self.num_key_value_heads = num_key_value_heads
self.hidden_activation = hidden_activation
self.initializer_range = initializer_range
self.rms_norm_eps = rms_norm_eps
self.use_cache = use_cache
self.rope_theta = rope_theta
self.attention_bias = attention_bias
self.attention_dropout = attention_dropout
super().__init__(
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
pad_token_id=pad_token_id,
tie_word_embeddings=tie_word_embeddings,
bits=bits,
**kwargs,
)
self.final_logit_softcapping = final_logit_softcapping
self.query_pre_attn_scalar = query_pre_attn_scalar
self.sliding_window = sliding_window
self.cache_implementation = "hybrid"
self.attn_logit_softcapping = attn_logit_softcapping
[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(("fsdp", "sp"), "tp")),
("mlp/gate_proj/kernel", PartitionSpec(("fsdp", "sp"), "tp")),
("mlp/down_proj/kernel", PartitionSpec("tp", ("fsdp", "sp"))),
("mlp/up_proj/kernel", PartitionSpec(("fsdp", "sp"), "tp")),
("input_layernorm/kernel", PartitionSpec(None)),
("post_attention_layernorm/kernel", PartitionSpec(None)),
("pre_feedforward_layernorm/kernel", PartitionSpec(None)),
("post_feedforward_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,
bits: tp.Optional[int] = None,
**kwargs,
):
"""The attach_custom_arguments function adds the following arguments to the Transformer class:
Args:
self: Refer to the current object
gradient_checkpointing: str: Control the amount of memory
used by jax
bits: tp.Optional[int]: Determine the number of bits used in
the quantization
"""
self.gradient_checkpointing = gradient_checkpointing
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,
)