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# coding=utf-8
# Copyright 2024 The HuggingFace Inc. team and Gemma2MoE Contributors.
#
# 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
#
# http://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,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Gemma2MoE model configuration"""
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging
logger = logging.get_logger(__name__)
class Gemma2MoeConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`Gemma2MoeModel`]. It is used to instantiate an Gemma2MoE
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configuration to that of the Gemma-2-9b but with MoE capabilities.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
"""
model_type = "gemma2moe"
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
vocab_size=256000,
hidden_size=3584,
intermediate_size=14336,
num_hidden_layers=42,
num_attention_heads=16,
num_key_value_heads=8,
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,
# Gemma 2 Specific Args
query_pre_attn_scalar=224, # 1/sqrt(head_dim) yerine Gemma2'ye özel scaling (genelde hidden_size temelli)
sliding_window=4096, # Sliding Window Attention window size
logit_soft_capping=30.0, # Final logit soft capping
attn_logit_soft_capping=50.0, # Attention scores soft capping
# MoE Arguments
num_experts_per_tok=2,
num_local_experts=8,
router_aux_loss_coef=0.001,
output_router_logits=False,
router_jitter_noise=0.0, # Opsiyonel: Router stabilitesi için jitter
**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
# Grouped Query Attention (GQA) kontrolü
if num_key_value_heads is None:
num_key_value_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
self.head_dim = head_dim
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
# Gemma 2 Specifics
self.query_pre_attn_scalar = query_pre_attn_scalar
self.sliding_window = sliding_window
self.logit_soft_capping = logit_soft_capping
self.attn_logit_soft_capping = attn_logit_soft_capping
# MoE Specifics
self.num_experts_per_tok = num_experts_per_tok
self.num_local_experts = num_local_experts
self.router_aux_loss_coef = router_aux_loss_coef
self.output_router_logits = output_router_logits
self.router_jitter_noise = router_jitter_noise
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,
**kwargs,
) |