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# Copyright 2023 Baichuan Inc. All Rights Reserved.
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# 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.utils import logging
from transformers import WhisperConfig
from transformers import CLIPVisionConfig
logger = logging.get_logger(__name__)
class OmniConfig(PretrainedConfig):
model_type = "omni"
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
vocab_size=125696,
hidden_size=4096,
intermediate_size=11008,
num_hidden_layers=32,
num_attention_heads=32,
num_key_value_heads=None,
sparse_attention_heads=None,
sparse_attention_layers=[],
head_dim=None,
attention_qkv_pack=True,
attention_qkv_bias=False,
use_norm_head=True,
hidden_act="silu",
max_position_embeddings=4096,
position_embedding_type="rope",
initializer_range=0.02,
rms_norm_eps=1e-6,
use_cache=True,
pad_token_id=0,
bos_token_id=1,
eos_token_id=2,
tie_word_embeddings=False,
audio_config=None,
visual_config=None,
video_config=None,
vocoder_config=None,
flow_matching_config=None,
**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.num_key_value_heads = num_key_value_heads or self.num_attention_heads
self.sparse_attention_heads = sparse_attention_heads
self.sparse_attention_layers = sparse_attention_layers
self.head_dim = head_dim or self.hidden_size // self.num_attention_heads
self.attention_qkv_pack = attention_qkv_pack
self.attention_qkv_bias = attention_qkv_bias
self.use_norm_head = use_norm_head
self.hidden_act = hidden_act
self.position_embedding_type = position_embedding_type
self.initializer_range = initializer_range
self.rms_norm_eps = rms_norm_eps
self.use_cache = use_cache
assert self.position_embedding_type.lower() in ("rope", "alibi")
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,
)
if audio_config is not None:
self.audio_config = WhisperConfig(**audio_config)
if self.audio_config.vq_config is not None:
self.audio_config.vq_config = PretrainedConfig(**self.audio_config.vq_config)
if vocoder_config is not None:
self.vocoder_config = WhisperConfig(**vocoder_config)
if flow_matching_config is not None:
self.flow_matching_config = PretrainedConfig(**flow_matching_config)
self.flow_matching_config.cfm_params = PretrainedConfig(**self.flow_matching_config.cfm_params)
if visual_config is not None:
self.visual_config = CLIPVisionConfig(**visual_config)
if video_config is not None:
self.video_config = CLIPVisionConfig(**video_config)
def to_diff_dict(self):
data = super().to_diff_dict()
data["model_type"] = self.model_type
return data
def get_rotary_base(self):
if hasattr(self, "rotary_emb_base"):
return self.rotary_emb_base
else:
return self.rope_theta
if __name__ == '__main__':
from transformers import AutoConfig
config = AutoConfig.from_pretrained("./", trust_remote_code=True)
print(config) |