# 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.
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# https://www.apache.org/licenses/LICENSE-2.0
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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
from ..siglip.configuration_siglip import SiglipVisionConfig
[docs]@register_config("gemma3_text")
class Gemma3TextConfig(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 262208):
Vocabulary size of the Gemma3Text model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`Gemma3TextModel`]
hidden_size (`int`, *optional*, defaults to 2304):
Dimension of the hidden representations.
intermediate_size (`int`, *optional*, defaults to 9216):
Dimension of the MLP representations.
num_hidden_layers (`int`, *optional*, defaults to 26):
Number of hidden layers in the Transformer decoder.
num_attention_heads (`int`, *optional*, defaults to 8):
Number of attention heads for each attention layer in the Transformer decoder.
num_key_value_heads (`int`, *optional*, defaults to 4):
This is the number of key_value heads that should be used to implement Grouped Query Attention. If
`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
`num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
by meanpooling all the original heads within that group. For more details checkout [this
paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
`num_attention_heads`.
head_dim (`int`, *optional*, defaults to 256):
The attention head dimension.
hidden_activation (`str` or `function`, *optional*, defaults to `"gelu_pytorch_tanh"`):
The non-linear activation function (function or string) in the decoder. Will default to `"gelu_pytorch_tanh"`
if not specified. `"gelu_pytorch_tanh"` uses an approximation of the `"gelu"` activation function.
max_position_embeddings (`int`, *optional*, defaults to 131072):
The maximum sequence length that this model might ever be used with.
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-06):
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):
Padding token id.
eos_token_id (`int`, *optional*, defaults to 1):
End of stream token id.
bos_token_id (`int`, *optional*, defaults to 2):
Beginning of stream token id.
tie_word_embeddings (`bool`, *optional*, defaults to `True`):
Whether to tie weight embeddings
rope_theta (`float`, *optional*, defaults to 1000000.0):
The base period of the RoPE embeddings.
attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
Whether to use a bias in the query, key, value and output projection layers during self-attention.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
query_pre_attn_scalar (`float`, *optional*, defaults to 256):
Scaling factor used on the attention scores
sliding_window (`int`, *optional*, defaults to 4096): in Gemma3Text, every other layer uses sliding window attention. This is the
size of the sliding window.
final_logit_softcapping (`float`, *optional*):
Scaling factor when applying tanh softcapping on the logits.
attn_logit_softcapping (`float`, *optional*):
Scaling factor when applying tanh softcapping on the attention scores.
cache_implementation (`str`, *optional*, defaults to `"hybrid"`): the cache type to be used with `generate`.
rope_scaling (`Dict`, *optional*):
Dictionary containing the scaling configuration for the RoPE embeddings used in gloabl attention. NOTE: if you apply new rope type
and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value
accordingly.
Expected contents:
`rope_type` (`str`):
The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
'llama3'], with 'default' being the original RoPE implementation.
`factor` (`float`, *optional*):
Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In
most scaling types, a `factor` of x will enable the model to handle sequences of length x *
original maximum pre-trained length.
`original_max_position_embeddings` (`int`, *optional*):
Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during
pretraining.
`attention_factor` (`float`, *optional*):
Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
computation. If unspecified, it defaults to value recommended by the implementation, using the
`factor` field to infer the suggested value.
`beta_fast` (`float`, *optional*):
Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear
ramp function. If unspecified, it defaults to 32.
`beta_slow` (`float`, *optional*):
Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear
ramp function. If unspecified, it defaults to 1.
`short_factor` (`List[float]`, *optional*):
Only used with 'longrope'. The scaling factor to be applied to short contexts (<
`original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
size divided by the number of attention heads divided by 2
`long_factor` (`List[float]`, *optional*):
Only used with 'longrope'. The scaling factor to be applied to long contexts (<
`original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
size divided by the number of attention heads divided by 2
`low_freq_factor` (`float`, *optional*):
Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE
`high_freq_factor` (`float`, *optional*):
Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE
rope_local_base_freq (float, *optional*, defaults to 10000.0):
The base period of the RoPE embeddings for local attention.
sliding_window_pattern (`int`, *optional*, defaults to 6):
Pattern for the sliding window attention.
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 = "gemma3_text"
def __init__(
self,
vocab_size=262_208,
hidden_size=2304,
intermediate_size=9216,
num_hidden_layers=26,
num_attention_heads=8,
num_key_value_heads=4,
head_dim=256,
hidden_activation="gelu_pytorch_tanh",
max_position_embeddings=131_072,
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=1_000_000.0,
attention_bias=False,
attention_dropout=0.0,
query_pre_attn_scalar=256,
sliding_window=4096,
final_logit_softcapping=None,
attn_logit_softcapping=None,
cache_implementation="hybrid",
rope_scaling=None,
rope_local_base_freq=10_000.0,
sliding_window_pattern=6,
gradient_checkpointing: EasyDeLGradientCheckPointers = EasyDeLGradientCheckPointers.NONE,
bits: tp.Optional[int] = None,
scan_layers: bool = False,
**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
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.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.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
self.query_pre_attn_scalar = query_pre_attn_scalar
self.sliding_window = sliding_window
self.final_logit_softcapping = final_logit_softcapping
self.attn_logit_softcapping = attn_logit_softcapping
self.cache_implementation = cache_implementation
self.rope_local_base_freq = rope_local_base_freq
# For configuring HybridCache to work with 5:1 attention pattern
self.sliding_window_pattern = sliding_window_pattern
self.rope_scaling = rope_scaling
[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.
"""
fsdpsp_over_tp = PartitionSpec(("fsdp", "sp"), "tp")
tp_over_fsdpsp = PartitionSpec("tp", ("fsdp", "sp"))
return (
("model/embed_tokens/embedding", tp_over_fsdpsp),
("self_attn/(q_proj|k_proj|v_proj)/kernel", fsdpsp_over_tp),
("self_attn/o_proj/kernel", tp_over_fsdpsp),
("mlp/gate_proj/kernel", fsdpsp_over_tp),
("mlp/down_proj/kernel", tp_over_fsdpsp),
("mlp/up_proj/kernel", fsdpsp_over_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", fsdpsp_over_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():
return tuple()
[docs] @staticmethod
def rng_keys():
return "params", "dropout", "fcm"
@property
def granted_freq_max_position_embedding(self) -> int:
return getattr(
self,
"freq_max_position_embeddings",
self.max_position_embeddings,
)
@property
def granted_mask_max_position_embedding(self) -> int:
return getattr(
self,
"mask_max_position_embeddings",
self.max_position_embeddings,
)
[docs]@register_config("gemma3")
class Gemma3Config(EasyDeLBaseConfig):
r"""
Args:
text_config (`Union[Gemma3TextConfig, dict]`, *optional*):
The config object of the text backbone.
vision_config (`Union[AutoConfig, dict]`, *optional*):
Custom vision config or dict.
mm_tokens_per_image (`int`, *optional*, defaults to 256):
The number of tokens per image embedding.
boi_token_index (`int`, *optional*, defaults to 255999):
The begin-of-image token index to wrap the image prompt.
eoi_token_index (`int`, *optional*, defaults to 256000):
The end-of-image token index to wrap the image prompt.
image_token_index (`int`, *optional*, defaults to 262144):
The image token index to encode the image prompt.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
Example:
```python
>>> from transformers import Gemma3ForConditionalGeneration, Gemma3Config, SiglipVisionConfig, Gemma3TextConfig
>>> # Initializing a Siglip-like vision config
>>> vision_config = SiglipVisionConfig()
>>> # Initializing a Gemma3 Text config
>>> text_config = Gemma3TextConfig()
>>> # Initializing a Gemma3 gemma-3-4b style configuration
>>> configuration = Gemma3Config(vision_config, text_config)
>>> # Initializing a model from the gemma-3-4b style configuration
>>> model = Gemma3TextConfig(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "gemma3"
sub_configs = {
"text_config": Gemma3TextConfig,
"vision_config": SiglipVisionConfig,
}
def __init__(
self,
text_config: tp.Optional[Gemma3TextConfig] = None,
vision_config: tp.Optional[SiglipVisionConfig] = None,
mm_tokens_per_image: int = 256,
boi_token_index: int = 255_999,
eoi_token_index: int = 256_000,
image_token_index: int = 262_144,
initializer_range: float = 0.02,
**kwargs,
):
if text_config is None:
text_config = Gemma3TextConfig()
elif isinstance(text_config, dict):
text_config = Gemma3TextConfig(**text_config)
if isinstance(vision_config, dict):
vision_config = SiglipVisionConfig(**vision_config)
else:
vision_config = SiglipVisionConfig()
self.text_config = text_config
self.vision_config = vision_config
self.mm_tokens_per_image = mm_tokens_per_image
self.boi_token_index = boi_token_index
self.eoi_token_index = eoi_token_index
self.image_token_index = image_token_index
self.initializer_range = initializer_range
super().__init__(**kwargs)
[docs] def get_partition_rules(self, *args, **kwargs):
fsdpsp_over_tp = PartitionSpec(("fsdp", "sp"), "tp")
tp_over_fsdpsp = PartitionSpec("tp", ("fsdp", "sp"))
return (
("language_model/model/embed_tokens/embedding", tp_over_fsdpsp),
("self_attn/(q_proj|k_proj|v_proj)/kernel", fsdpsp_over_tp),
("self_attn/o_proj/kernel", tp_over_fsdpsp),
("mlp/gate_proj/kernel", fsdpsp_over_tp),
("mlp/down_proj/kernel", tp_over_fsdpsp),
("mlp/up_proj/kernel", fsdpsp_over_tp),
("input_layernorm/kernel", PartitionSpec(None)),
("post_attention_layernorm/kernel", PartitionSpec(None)),
("pre_feedforward_layernorm/kernel", PartitionSpec(None)),
("post_feedforward_layernorm/kernel", PartitionSpec(None)),
("language_model/model/norm/kernel", PartitionSpec(None)),
("language_model/lm_head/kernel", fsdpsp_over_tp),
(".*", PartitionSpec(None)),
)