# Copyright 2025 The EasyDeL Author @erfanzar (Erfan Zare Chavoshi).
#
# Licensed under the Apache License, Version 2.0 (the "License");
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# https://www.apache.org/licenses/LICENSE-2.0
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from eformer.common_types import ColumnWise, Replicated, RowWise
from easydel.infra.base_module import EasyDeLBaseConfig
from easydel.infra.etils import EasyDeLGradientCheckPointers
from easydel.infra.factory import register_config
[docs]@register_config("gemma")
class GemmaConfig(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 Gemma 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_act (`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.
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.
hidden_activation (`str`, *optional*):
The hidden activation function to use.
"""
model_type: str = "gemma"
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_act="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,
gradient_checkpointing: EasyDeLGradientCheckPointers = EasyDeLGradientCheckPointers.NONE,
bits: int | None = None,
scan_layers: bool = False,
hidden_activation="gelu_pytorch_tanh",
**kwargs,
):
"""Initialize a new GemmaConfig instance.
Args:
vocab_size (int, optional): Size of the vocabulary. Defaults to 256000.
hidden_size (int, optional): Dimensionality of the embeddings and hidden states. Defaults to 3072.
intermediate_size (int, optional): Dimensionality of the feed-forward layer. Defaults to 24576.
num_hidden_layers (int, optional): Number of hidden layers. Defaults to 28.
num_attention_heads (int, optional): Number of attention heads. Defaults to 16.
num_key_value_heads (int, optional): Number of key/value heads (for GQA). Defaults to 16.
head_dim (int, optional): Dimension of each attention head. Defaults to 256.
hidden_act (str, optional): Activation function for hidden layers. Defaults to "gelu_pytorch_tanh".
max_position_embeddings (int, optional): Maximum sequence length. Defaults to 8192.
initializer_range (float, optional): Range for weight initialization. Defaults to 0.02.
rms_norm_eps (float, optional): Epsilon for RMS normalization. Defaults to 1e-6.
use_cache (bool, optional): Whether to use KV cache for generation. Defaults to True.
pad_token_id (int, optional): ID for padding token. Defaults to 0.
eos_token_id (int, optional): ID for end of sequence token. Defaults to 1.
bos_token_id (int, optional): ID for beginning of sequence token. Defaults to 2.
tie_word_embeddings (bool, optional): Whether to tie input/output embeddings. Defaults to True.
rope_theta (float, optional): Base value for RoPE. Defaults to 10000.0.
attention_bias (bool, optional): Whether to use bias in attention. Defaults to False.
attention_dropout (float, optional): Dropout probability for attention. Defaults to 0.0.
gradient_checkpointing (EasyDeLGradientCheckPointers, optional):
Checkpointing strategy. Defaults to EasyDeLGradientCheckPointers.NONE.
bits (Optional[int], optional): Quantization bits. Defaults to None.
scan_layers (bool, optional): Whether to scan layers. Defaults to False.
hidden_activation (str, optional): Activation for hidden layers. Defaults to "gelu_pytorch_tanh".
**kwargs: Additional arguments.
"""
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_act = hidden_act
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
self.hidden_activation = hidden_activation
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,
)
[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.
"""
pmag = self.partition_manager
return (
(r"embed_tokens/embedding", pmag.resolve(ColumnWise)),
(r"self_attn/(q_proj|k_proj|v_proj)/kernel", pmag.resolve(ColumnWise)),
(r"self_attn/o_proj/kernel", pmag.resolve(RowWise)),
(r"self_attn/.*proj/bias", pmag.resolve(Replicated)),
(r"mlp/(gate_proj|up_proj)/kernel", pmag.resolve(ColumnWise)),
(r"mlp/down_proj/kernel", pmag.resolve(RowWise)),
(r"mlp/.*proj/bias", pmag.resolve(Replicated)),
(r".*(input_layernorm|post_attention_layernorm|norm)/kernel", pmag.resolve(Replicated)),
(r"lm_head/kernel", pmag.resolve(ColumnWise)),
(r"score/kernel", pmag.resolve(RowWise)),
(r".*bias", pmag.resolve(Replicated)),
(r".*", pmag.resolve(Replicated)),
)