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# coding=utf-8
# Copyright 2025 The HuggingFace 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.
"""openai model configuration"""
from transformers.configuration_utils import PretrainedConfig, layer_type_validation
from transformers.modeling_rope_utils import rope_config_validation
class GptOssConfig(PretrainedConfig):
r"""
This will yield a configuration to that of the BERT
[google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) architecture.
"""
model_type = "gpt_oss"
base_model_pp_plan = {
"embed_tokens": (["input_ids"], ["inputs_embeds"]),
"layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
"norm": (["hidden_states"], ["hidden_states"]),
}
base_model_tp_plan = {
"layers.*.self_attn.q_proj": "colwise",
"layers.*.self_attn.k_proj": "colwise",
"layers.*.self_attn.v_proj": "colwise",
"layers.*.self_attn.o_proj": "rowwise",
"layers.*.self_attn.sinks": "local_rowwise",
"layers.*.mlp.experts": "gather",
"layers.*.mlp.router": "ep_router",
"layers.*.mlp.experts.gate_up_proj": "grouped_gemm",
"layers.*.mlp.experts.gate_up_proj_bias": "grouped_gemm",
"layers.*.mlp.experts.down_proj": "grouped_gemm",
"layers.*.mlp.experts.down_proj_bias": "grouped_gemm",
}
def __init__(
self,
num_hidden_layers: int = 36,
num_local_experts: int = 128,
vocab_size: int = 201088,
hidden_size: int = 2880,
intermediate_size: int = 2880,
head_dim: int = 64,
num_attention_heads: int = 64,
num_key_value_heads: int = 8,
sliding_window: int = 128,
rope_theta: float = 150000.0,
tie_word_embeddings=False,
hidden_act: str = "silu",
initializer_range: float = 0.02,
max_position_embeddings=131072,
rms_norm_eps: float = 1e-5,
rope_scaling={
"rope_type": "yarn",
"factor": 32.0,
"beta_fast": 32.0,
"beta_slow": 1.0,
"truncate": False,
"original_max_position_embeddings": 4096,
},
attention_dropout: float = 0.0,
num_experts_per_tok=4,
router_aux_loss_coef: float = 0.9,
output_router_logits=False,
use_cache=True,
layer_types=None,
**kwargs,
):
self.vocab_size = vocab_size
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.num_local_experts = num_local_experts
self.sliding_window = sliding_window
self.num_experts_per_tok = num_experts_per_tok
# for backward compatibility
if num_key_value_heads is None:
num_key_value_heads = num_attention_heads
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.rope_theta = rope_theta
self.rope_scaling = rope_scaling
self.attention_dropout = attention_dropout
self.head_dim = head_dim if head_dim is not None else self.hidden_size // self.num_attention_heads
self.layer_types = layer_types
if self.layer_types is None:
self.layer_types = [
"sliding_attention" if bool((i + 1) % 2) else "full_attention" for i in range(self.num_hidden_layers)
]
layer_type_validation(self.layer_types)
self.attention_bias = True
self.max_position_embeddings = max_position_embeddings
self.router_aux_loss_coef = router_aux_loss_coef
self.output_router_logits = output_router_logits
self.use_cache = use_cache
# Validate the correctness of rotary position embeddings parameters
# BC: if there is a 'type' field, copy it it to 'rope_type'.
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__ = ["GptOssConfig"]
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