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# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
#
# 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.
from transformers.configuration_utils import PretrainedConfig
from transformers.modeling_rope_utils import rope_config_validation
from transformers.configuration_utils import layer_type_validation
from transformers.utils import logging
logger = logging.get_logger(__name__)
class AfmoeConfig(PretrainedConfig):
"""
n_group (`int`, *optional*, defaults to 1):
Number of groups for routed experts.
topk_group (`int`, *optional*, defaults to 1):
Number of selected groups for each token(for each token, ensuring the selected experts is only within `topk_group` groups).
"""
model_type = "afmoe"
base_model_pp_plan = {
"embed_tokens": (["input_ids"], ["inputs_embeds"]),
"layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
"norm": (["hidden_states"], ["hidden_states"]),
}
def __init__(
self,
num_hidden_layers: int = 32,
vocab_size: int = 200192,
hidden_size: int = 2048,
intermediate_size: int = 6144,
moe_intermediate_size=1408,
num_dense_layers=1,
num_attention_heads=16,
num_key_value_heads=None,
head_dim=128,
hidden_act="silu",
max_position_embeddings=16384,
initializer_range=0.02,
rms_norm_eps=1e-5,
use_cache=True,
tie_word_embeddings=False,
rope_theta=10000.0,
rope_scaling=None,
num_experts=64,
num_experts_per_tok=6,
num_shared_experts=2,
num_expert_groups=1,
num_limited_groups=1,
score_func="sigmoid",
route_norm=True,
route_scale=1.0,
global_attn_every_n_layers=4,
sliding_window=1024,
mup_enabled=False,
layer_types=None,
attention_dropout: float = 0.0,
n_group: int = 1,
topk_group: int = 1,
**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_dense_layers = num_dense_layers
self.num_attention_heads = num_attention_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.rope_scaling = rope_scaling
# MoE specific
self.moe_intermediate_size = moe_intermediate_size
self.num_experts_per_tok = num_experts_per_tok
self.n_group = n_group
self.topk_group = topk_group
self.num_experts = num_experts
self.num_shared_experts = num_shared_experts
self.num_expert_groups = num_expert_groups
self.num_limited_groups = num_limited_groups
self.score_func = score_func
self.route_norm = route_norm
self.route_scale = route_scale
# Attention specific
self.attention_dropout = attention_dropout
self.global_attn_every_n_layers = global_attn_every_n_layers
self.sliding_window = sliding_window
self.layer_types = layer_types
if self.layer_types is None:
self.layer_types = [
"sliding_attention" if bool((i + 1) % global_attn_every_n_layers) else "full_attention" for i in range(self.num_hidden_layers)
]
layer_type_validation(self.layer_types)
# muP specific
self.mup_enabled = mup_enabled
if num_key_value_heads is None:
num_key_value_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
# Validate rope configs
if self.rope_scaling is not None and "type" in self.rope_scaling:
self.rope_scaling["rope_type"] = self.rope_scaling["type"]
rope_config_validation(self)
super().__init__(
tie_word_embeddings=tie_word_embeddings,
**kwargs,
)
__all__ = ["AfmoeConfig"]
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