120 lines
4.6 KiB
Python
120 lines
4.6 KiB
Python
# Copyright 2023 Baichuan Inc. All Rights Reserved.
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# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
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#
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# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
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# and OPT implementations in this library. It has been modified from its
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# original forms to accommodate minor architectural differences compared
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# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from transformers.configuration_utils import PretrainedConfig
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from transformers.utils import logging
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from transformers import WhisperConfig
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from transformers import CLIPVisionConfig
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logger = logging.get_logger(__name__)
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class OmniConfig(PretrainedConfig):
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model_type = "omni"
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keys_to_ignore_at_inference = ["past_key_values"]
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def __init__(
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self,
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vocab_size=125696,
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hidden_size=4096,
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intermediate_size=11008,
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num_hidden_layers=32,
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num_attention_heads=32,
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num_key_value_heads=None,
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sparse_attention_heads=None,
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sparse_attention_layers=[],
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head_dim=None,
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attention_qkv_pack=True,
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attention_qkv_bias=False,
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use_norm_head=True,
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hidden_act="silu",
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max_position_embeddings=4096,
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position_embedding_type="rope",
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initializer_range=0.02,
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rms_norm_eps=1e-6,
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use_cache=True,
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pad_token_id=0,
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bos_token_id=1,
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eos_token_id=2,
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tie_word_embeddings=False,
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audio_config=None,
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visual_config=None,
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video_config=None,
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vocoder_config=None,
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flow_matching_config=None,
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**kwargs,
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):
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self.vocab_size = vocab_size
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self.max_position_embeddings = max_position_embeddings
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self.hidden_size = hidden_size
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self.intermediate_size = intermediate_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.num_key_value_heads = num_key_value_heads or self.num_attention_heads
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self.sparse_attention_heads = sparse_attention_heads
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self.sparse_attention_layers = sparse_attention_layers
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self.head_dim = head_dim or self.hidden_size // self.num_attention_heads
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self.attention_qkv_pack = attention_qkv_pack
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self.attention_qkv_bias = attention_qkv_bias
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self.use_norm_head = use_norm_head
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self.hidden_act = hidden_act
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self.position_embedding_type = position_embedding_type
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self.initializer_range = initializer_range
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self.rms_norm_eps = rms_norm_eps
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self.use_cache = use_cache
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assert self.position_embedding_type.lower() in ("rope", "alibi")
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super().__init__(
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pad_token_id=pad_token_id,
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bos_token_id=bos_token_id,
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eos_token_id=eos_token_id,
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tie_word_embeddings=tie_word_embeddings,
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**kwargs,
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)
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if audio_config is not None:
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self.audio_config = WhisperConfig(**audio_config)
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if self.audio_config.vq_config is not None:
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self.audio_config.vq_config = PretrainedConfig(**self.audio_config.vq_config)
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if vocoder_config is not None:
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self.vocoder_config = WhisperConfig(**vocoder_config)
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if flow_matching_config is not None:
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self.flow_matching_config = PretrainedConfig(**flow_matching_config)
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self.flow_matching_config.cfm_params = PretrainedConfig(**self.flow_matching_config.cfm_params)
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if visual_config is not None:
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self.visual_config = CLIPVisionConfig(**visual_config)
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if video_config is not None:
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self.video_config = CLIPVisionConfig(**video_config)
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def to_diff_dict(self):
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data = super().to_diff_dict()
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data["model_type"] = self.model_type
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return data
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def get_rotary_base(self):
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if hasattr(self, "rotary_emb_base"):
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return self.rotary_emb_base
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else:
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return self.rope_theta
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if __name__ == '__main__':
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from transformers import AutoConfig
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config = AutoConfig.from_pretrained("./", trust_remote_code=True)
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print(config) |