279 lines
14 KiB
Python
279 lines
14 KiB
Python
# coding=utf-8
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# Copyright 2025 Westlake Representational Learning Lab (Fajie Yuan Lab) team and the HuggingFace Inc. team. All rights reserved.
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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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"""Evolla model configuration"""
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from ...configuration_utils import PretrainedConfig
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from ...modeling_rope_utils import rope_config_validation
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from ...utils import logging
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logger = logging.get_logger(__name__)
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class SaProtConfig(PretrainedConfig):
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r"""This is the configuration class to store the configuration of a [`EvollaSaProtProteinEncoder`]. It is used to instantiate a
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SaProt model according to the specified arguments, defining the model architecture.
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PretrainedConfig`] for more information.
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Args:
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vocab_size (`int`, *optional*, defaults to 446):
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Vocabulary size of the protein sequence model. Defines the number of different tokens that can be represented
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by the `inputs_ids` passed when calling [`EvollaModel`].
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mask_token_id (`int`, *optional*, defaults to 4):
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The id of the *mask* token in the protein sequence model.
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pad_token_id (`int`, *optional*, defaults to 1):
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The id of the *padding* token in the protein sequence model.
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hidden_size (`int`, *optional*, defaults to 1280):
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Dimensionality of the protein sequence model layers and the pooler layer.
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num_hidden_layers (`int`, *optional*, defaults to 33):
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Number of hidden layers in the protein sequence model.
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num_attention_heads (`int`, *optional*, defaults to 20):
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Number of attention heads for each attention layer in the protein sequence model.
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intermediate_size (`int`, *optional*, defaults to 5120):
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Dimensionality of the intermediate layers in the protein sequence model.
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hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
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The dropout ratio for the hidden layers in the protein sequence model.
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attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
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The dropout ratio for the attention probabilities in the protein sequence model.
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max_position_embeddings (`int`, *optional*, defaults to 1026):
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The maximum sequence length that the protein sequence model might ever be used with. Typically set this to
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something large just in case (e.g., 512 or 1024 or 2048).
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layer_norm_eps (`float`, *optional*, defaults to 1e-05):
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The epsilon value for the layer normalization layer in the protein sequence model.
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position_embedding_type (`str`, *optional*, defaults to `"rotary"`):
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The type of position embedding to use in the protein sequence model. Currently only `"rotary"` is supported.
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emb_layer_norm_before (`bool`, *optional*, defaults to `False`):
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Whether to apply layer normalization before the position embedding in the protein sequence model.
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token_dropout (`bool`, *optional*, defaults to `True`):
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Whether to apply dropout to the tokens in the protein sequence model."""
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def __init__(
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self,
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vocab_size=446,
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mask_token_id=4,
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pad_token_id=1,
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hidden_size=1280,
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num_hidden_layers=33,
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num_attention_heads=20,
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intermediate_size=5120,
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hidden_dropout_prob=0.1,
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attention_probs_dropout_prob=0.1,
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max_position_embeddings=1026,
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initializer_range=0.02,
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layer_norm_eps=1e-05,
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position_embedding_type="rotary",
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use_cache=True,
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emb_layer_norm_before=False,
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token_dropout=True,
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**kwargs,
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):
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super().__init__(pad_token_id=pad_token_id, mask_token_id=mask_token_id, **kwargs)
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self.vocab_size = vocab_size
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self.hidden_size = hidden_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.intermediate_size = intermediate_size
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self.hidden_dropout_prob = hidden_dropout_prob
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self.attention_probs_dropout_prob = attention_probs_dropout_prob
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self.max_position_embeddings = max_position_embeddings
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self.initializer_range = initializer_range
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self.layer_norm_eps = layer_norm_eps
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self.position_embedding_type = position_embedding_type
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self.use_cache = use_cache
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self.emb_layer_norm_before = emb_layer_norm_before
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self.token_dropout = token_dropout
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class EvollaConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`EvollaModel`]. It is used to instantiate an
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Evolla model according to the specified arguments, defining the model architecture. Instantiating a configuration
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with the defaults will yield a similar configuration to that of the Evolla-10B.
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e.g. [westlake-repl/Evolla-10B-hf](https://huggingface.co/westlake-repl/Evolla-10B-hf)
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PretrainedConfig`] for more information.
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Args:
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protein_encoder_config (`dict`, *optional*):
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Dictionary of configuration options used to initialize [`SaProtConfig`].
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vocab_size (`int`, *optional*, defaults to 128256):
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Vocabulary size of the Evolla llama model. Defines the number of different tokens that can be represented by the
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`inputs_ids` passed when calling [`EvollaModel`].
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hidden_size (`int`, *optional*, defaults to 4096):
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Dimensionality of the llama layers and the pooler layer.
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intermediate_size (`int`, *optional*, defaults to 14336):
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Dimensionality of the intermediate layers in the llama model.
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num_hidden_layers (`int`, *optional*, defaults to 32):
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Number of hidden layers in the llama model.
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num_attention_heads (`int`, *optional*, defaults to 32):
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Number of attention heads for each attention layer in the llama model.
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num_key_value_heads (`int`, *optional*, defaults to 8):
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Number of key-value pairs for each attention layer in the llama model.
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hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
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The non-linear activation function (function or string) in the llama model. If string, `"gelu"`, `"relu"`,
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`"selu"` and `"silu"` are supported.
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max_position_embeddings (`int`, *optional*, defaults to 8192):
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The maximum sequence length that this model might ever be used with. Typically set this to something large
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just in case (e.g., 512 or 1024 or 2048).
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rms_norm_eps (`float`, *optional*, defaults to 1e-05):
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The epsilon value for the RMS-norm layer in the llama model.
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rope_theta (`float`, *optional*, defaults to 500000.0):
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The threshold value for the RoPE layer in the llama model.
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rope_scaling (`float`, *optional*):
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The scaling factor for the RoPE layer in the llama model.
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attention_bias (`bool`, *optional*, defaults to `False`):
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Whether to use bias in the attention layer.
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attention_dropout (`float`, *optional*, defaults to 0.0):
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The dropout ratio for the attention layer.
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mlp_bias (`bool`, *optional*, defaults to `False`):
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Whether to use bias in the MLP layer.
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aligner_ffn_mult (`int`, *optional*, defaults to 4):
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The FFN multiplier for the aligner layer.
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aligner_enable_bias (`bool`, *optional*, defaults to `True`):
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Whether to use bias in the aligner layer.
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aligner_attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
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The dropout ratio for the attention probabilities in the aligner layer.
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aligner_num_add_layers (`int`, *optional*, defaults to 8):
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The number of additional layers for the aligner layer.
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resampler_depth (`int`, *optional*, defaults to 6):
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The depth of the resampler layer in the llama model.
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resampler_dim_head (`int`, *optional*, defaults to 64):
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The dimension of the heads in the resampler layer in the llama model.
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resampler_heads (`int`, *optional*, defaults to 8):
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The number of heads in the resampler layer in the llama model.
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resampler_num_latents (`int`, *optional*, defaults to 64):
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The number of latents in the resampler layer in the llama model.
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resampler_ff_mult (`int`, *optional*, defaults to 4):
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The FFN multiplier for the resampler layer.
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initializer_range (`float`, *optional*, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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pad_token_id (`int`, *optional*):
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The id of the *padding* token.
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bos_token_id (`int`, *optional*, defaults to 128000):
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The id of the *beginning-of-sequence* token.
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eos_token_id (`int`, *optional*, defaults to 128009):
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The id of the *end-of-sequence* token.
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use_cache (`bool`, *optional*, defaults to `False`):
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Whether or not the model should return the last key/values attentions (not used by all models).
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tie_word_embeddings (`bool`, *optional*, defaults to `False`):
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Whether or not to tie the input and output word embeddings.
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Example:
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```python
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>>> from transformers import EvollaModel, EvollaConfig
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>>> # Initializing a Evolla evolla-10b style configuration
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>>> configuration = EvollaConfig()
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>>> # Initializing a model from the evolla-10b style configuration
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>>> model = EvollaModel(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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model_type = "EvollaModel"
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sub_configs = {"protein_encoder_config": SaProtConfig}
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def __init__(
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self,
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protein_encoder_config=None,
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vocab_size=128256, # llama vocab size
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hidden_size=4096, # llama hidden size
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intermediate_size=14336, # llama intermediate size
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num_hidden_layers=32, # llama num layers
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num_attention_heads=32, # llama num heads
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num_key_value_heads=8, # llama num key-value heads
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hidden_act="silu", # llama activation function
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max_position_embeddings=8192, # llama rope max length
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rms_norm_eps=1e-05,
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rope_theta=500000.0,
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rope_scaling=None,
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attention_bias=False,
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attention_dropout=0.0,
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mlp_bias=False,
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aligner_ffn_mult=4,
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aligner_enable_bias=True,
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aligner_attention_probs_dropout_prob=0.1,
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aligner_num_add_layers=8,
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resampler_depth=6,
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resampler_dim_head=64,
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resampler_heads=8,
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resampler_num_latents=64,
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resampler_ff_mult=4,
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initializer_range=0.02,
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pad_token_id=None,
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bos_token_id=128000,
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eos_token_id=128009,
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use_cache=False,
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tie_word_embeddings=False,
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**kwargs,
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):
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self.vocab_size = vocab_size
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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
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self.hidden_act = hidden_act
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self.max_position_embeddings = max_position_embeddings
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self.rms_norm_eps = rms_norm_eps
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self.tie_word_embeddings = tie_word_embeddings
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self.attention_bias = attention_bias
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self.attention_dropout = attention_dropout
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self.mlp_bias = mlp_bias
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self.aligner_ffn_mult = aligner_ffn_mult
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self.aligner_enable_bias = aligner_enable_bias
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self.aligner_attention_probs_dropout_prob = aligner_attention_probs_dropout_prob
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self.aligner_num_add_layers = aligner_num_add_layers
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self.use_cache = use_cache
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self.initializer_range = initializer_range
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self.resampler_depth = resampler_depth
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self.resampler_dim_head = resampler_dim_head
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self.resampler_heads = resampler_heads
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self.resampler_num_latents = resampler_num_latents
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self.resampler_ff_mult = resampler_ff_mult
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self.rope_theta = rope_theta
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self.rope_scaling = rope_scaling
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# Validate the correctness of rotary position embeddings parameters
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# BC: if there is a 'type' field, copy it it to 'rope_type'.
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if self.rope_scaling is not None and "type" in self.rope_scaling:
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self.rope_scaling["rope_type"] = self.rope_scaling["type"]
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rope_config_validation(self)
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# Subconfig
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if protein_encoder_config is None:
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protein_encoder_config = {}
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logger.info("`protein_encoder_config` is `None`. Initializing the `SaProtConfig` with default values.")
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self.protein_encoder_config = SaProtConfig(**protein_encoder_config)
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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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__all__ = ["EvollaConfig"]
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