362 lines
14 KiB
Python
362 lines
14 KiB
Python
# coding=utf-8
|
|
# Copyright 2022 Meta 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.
|
|
""" ESM model configuration"""
|
|
|
|
from dataclasses import asdict, dataclass
|
|
from typing import Optional
|
|
|
|
from ...configuration_utils import PretrainedConfig
|
|
from ...utils import logging
|
|
|
|
|
|
logger = logging.get_logger(__name__)
|
|
|
|
# TODO Update this
|
|
|
|
from ..deprecated._archive_maps import ESM_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402
|
|
|
|
|
|
class EsmConfig(PretrainedConfig):
|
|
r"""
|
|
This is the configuration class to store the configuration of a [`ESMModel`]. It is used to instantiate a ESM model
|
|
according to the specified arguments, defining the model architecture. Instantiating a configuration with the
|
|
defaults will yield a similar configuration to that of the ESM
|
|
[facebook/esm-1b](https://huggingface.co/facebook/esm-1b) architecture.
|
|
|
|
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
|
|
documentation from [`PretrainedConfig`] for more information.
|
|
|
|
|
|
Args:
|
|
vocab_size (`int`, *optional*):
|
|
Vocabulary size of the ESM model. Defines the number of different tokens that can be represented by the
|
|
`inputs_ids` passed when calling [`ESMModel`].
|
|
mask_token_id (`int`, *optional*):
|
|
The index of the mask token in the vocabulary. This must be included in the config because of the
|
|
"mask-dropout" scaling trick, which will scale the inputs depending on the number of masked tokens.
|
|
pad_token_id (`int`, *optional*):
|
|
The index of the padding token in the vocabulary. This must be included in the config because certain parts
|
|
of the ESM code use this instead of the attention mask.
|
|
hidden_size (`int`, *optional*, defaults to 768):
|
|
Dimensionality of the encoder layers and the pooler layer.
|
|
num_hidden_layers (`int`, *optional*, defaults to 12):
|
|
Number of hidden layers in the Transformer encoder.
|
|
num_attention_heads (`int`, *optional*, defaults to 12):
|
|
Number of attention heads for each attention layer in the Transformer encoder.
|
|
intermediate_size (`int`, *optional*, defaults to 3072):
|
|
Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
|
|
hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
|
|
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
|
|
attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
|
|
The dropout ratio for the attention probabilities.
|
|
max_position_embeddings (`int`, *optional*, defaults to 1026):
|
|
The maximum sequence length that this model might ever be used with. Typically set this to something large
|
|
just in case (e.g., 512 or 1024 or 2048).
|
|
initializer_range (`float`, *optional*, defaults to 0.02):
|
|
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
|
|
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
|
|
The epsilon used by the layer normalization layers.
|
|
position_embedding_type (`str`, *optional*, defaults to `"absolute"`):
|
|
Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query", "rotary"`.
|
|
For positional embeddings use `"absolute"`. For more information on `"relative_key"`, please refer to
|
|
[Self-Attention with Relative Position Representations (Shaw et al.)](https://arxiv.org/abs/1803.02155).
|
|
For more information on `"relative_key_query"`, please refer to *Method 4* in [Improve Transformer Models
|
|
with Better Relative Position Embeddings (Huang et al.)](https://arxiv.org/abs/2009.13658).
|
|
is_decoder (`bool`, *optional*, defaults to `False`):
|
|
Whether the model is used as a decoder or not. If `False`, the model is used as an encoder.
|
|
use_cache (`bool`, *optional*, defaults to `True`):
|
|
Whether or not the model should return the last key/values attentions (not used by all models). Only
|
|
relevant if `config.is_decoder=True`.
|
|
emb_layer_norm_before (`bool`, *optional*):
|
|
Whether to apply layer normalization after embeddings but before the main stem of the network.
|
|
token_dropout (`bool`, defaults to `False`):
|
|
When this is enabled, masked tokens are treated as if they had been dropped out by input dropout.
|
|
|
|
Examples:
|
|
|
|
```python
|
|
>>> from transformers import EsmModel, EsmConfig
|
|
|
|
>>> # Initializing a ESM facebook/esm-1b style configuration >>> configuration = EsmConfig()
|
|
|
|
>>> # Initializing a model from the configuration >>> model = ESMModel(configuration)
|
|
|
|
>>> # Accessing the model configuration >>> configuration = model.config
|
|
```"""
|
|
|
|
model_type = "esm"
|
|
|
|
def __init__(
|
|
self,
|
|
vocab_size=None,
|
|
mask_token_id=None,
|
|
pad_token_id=None,
|
|
hidden_size=768,
|
|
num_hidden_layers=12,
|
|
num_attention_heads=12,
|
|
intermediate_size=3072,
|
|
hidden_dropout_prob=0.1,
|
|
attention_probs_dropout_prob=0.1,
|
|
max_position_embeddings=1026,
|
|
initializer_range=0.02,
|
|
layer_norm_eps=1e-12,
|
|
position_embedding_type="absolute",
|
|
use_cache=True,
|
|
emb_layer_norm_before=None,
|
|
token_dropout=False,
|
|
is_folding_model=False,
|
|
esmfold_config=None,
|
|
vocab_list=None,
|
|
**kwargs,
|
|
):
|
|
super().__init__(pad_token_id=pad_token_id, mask_token_id=mask_token_id, **kwargs)
|
|
|
|
self.vocab_size = vocab_size
|
|
self.hidden_size = hidden_size
|
|
self.num_hidden_layers = num_hidden_layers
|
|
self.num_attention_heads = num_attention_heads
|
|
self.intermediate_size = intermediate_size
|
|
self.hidden_dropout_prob = hidden_dropout_prob
|
|
self.attention_probs_dropout_prob = attention_probs_dropout_prob
|
|
self.max_position_embeddings = max_position_embeddings
|
|
self.initializer_range = initializer_range
|
|
self.layer_norm_eps = layer_norm_eps
|
|
self.position_embedding_type = position_embedding_type
|
|
self.use_cache = use_cache
|
|
self.emb_layer_norm_before = emb_layer_norm_before
|
|
self.token_dropout = token_dropout
|
|
self.is_folding_model = is_folding_model
|
|
if is_folding_model:
|
|
if esmfold_config is None:
|
|
logger.info("No esmfold_config supplied for folding model, using default values.")
|
|
esmfold_config = EsmFoldConfig()
|
|
elif isinstance(esmfold_config, dict):
|
|
esmfold_config = EsmFoldConfig(**esmfold_config)
|
|
self.esmfold_config = esmfold_config
|
|
if vocab_list is None:
|
|
logger.warning("No vocab_list supplied for folding model, assuming the ESM-2 vocabulary!")
|
|
self.vocab_list = get_default_vocab_list()
|
|
else:
|
|
self.vocab_list = vocab_list
|
|
else:
|
|
self.esmfold_config = None
|
|
self.vocab_list = None
|
|
if self.esmfold_config is not None and getattr(self.esmfold_config, "use_esm_attn_map", False):
|
|
raise ValueError("The HuggingFace port of ESMFold does not support use_esm_attn_map at this time!")
|
|
|
|
def to_dict(self):
|
|
"""
|
|
Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`].
|
|
|
|
Returns:
|
|
`Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
|
|
"""
|
|
output = super().to_dict()
|
|
if isinstance(self.esmfold_config, EsmFoldConfig):
|
|
output["esmfold_config"] = self.esmfold_config.to_dict()
|
|
return output
|
|
|
|
|
|
@dataclass
|
|
class EsmFoldConfig:
|
|
esm_type: str = None
|
|
fp16_esm: bool = True
|
|
use_esm_attn_map: bool = False
|
|
esm_ablate_pairwise: bool = False
|
|
esm_ablate_sequence: bool = False
|
|
esm_input_dropout: float = 0
|
|
|
|
embed_aa: bool = True
|
|
bypass_lm: bool = False
|
|
|
|
lddt_head_hid_dim: int = 128
|
|
trunk: "TrunkConfig" = None
|
|
|
|
def __post_init__(self):
|
|
if self.trunk is None:
|
|
self.trunk = TrunkConfig()
|
|
elif isinstance(self.trunk, dict):
|
|
self.trunk = TrunkConfig(**self.trunk)
|
|
|
|
def to_dict(self):
|
|
"""
|
|
Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`].
|
|
|
|
Returns:
|
|
`Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
|
|
"""
|
|
output = asdict(self)
|
|
output["trunk"] = self.trunk.to_dict()
|
|
return output
|
|
|
|
|
|
@dataclass
|
|
class TrunkConfig:
|
|
num_blocks: int = 48
|
|
sequence_state_dim: int = 1024
|
|
pairwise_state_dim: int = 128
|
|
sequence_head_width: int = 32
|
|
pairwise_head_width: int = 32
|
|
position_bins: int = 32
|
|
dropout: float = 0
|
|
layer_drop: float = 0
|
|
cpu_grad_checkpoint: bool = False
|
|
max_recycles: int = 4
|
|
chunk_size: Optional[int] = 128
|
|
structure_module: "StructureModuleConfig" = None
|
|
|
|
def __post_init__(self):
|
|
if self.structure_module is None:
|
|
self.structure_module = StructureModuleConfig()
|
|
elif isinstance(self.structure_module, dict):
|
|
self.structure_module = StructureModuleConfig(**self.structure_module)
|
|
|
|
if self.max_recycles <= 0:
|
|
raise ValueError(f"`max_recycles` should be positive, got {self.max_recycles}.")
|
|
if self.sequence_state_dim % self.sequence_state_dim != 0:
|
|
raise ValueError(
|
|
"`sequence_state_dim` should be a round multiple of `sequence_state_dim`, got"
|
|
f" {self.sequence_state_dim} and {self.sequence_state_dim}."
|
|
)
|
|
if self.pairwise_state_dim % self.pairwise_state_dim != 0:
|
|
raise ValueError(
|
|
"`pairwise_state_dim` should be a round multiple of `pairwise_state_dim`, got"
|
|
f" {self.pairwise_state_dim} and {self.pairwise_state_dim}."
|
|
)
|
|
|
|
sequence_num_heads = self.sequence_state_dim // self.sequence_head_width
|
|
pairwise_num_heads = self.pairwise_state_dim // self.pairwise_head_width
|
|
|
|
if self.sequence_state_dim != sequence_num_heads * self.sequence_head_width:
|
|
raise ValueError(
|
|
"`sequence_state_dim` should be equal to `sequence_num_heads * sequence_head_width, got"
|
|
f" {self.sequence_state_dim} != {sequence_num_heads} * {self.sequence_head_width}."
|
|
)
|
|
if self.pairwise_state_dim != pairwise_num_heads * self.pairwise_head_width:
|
|
raise ValueError(
|
|
"`pairwise_state_dim` should be equal to `pairwise_num_heads * pairwise_head_width, got"
|
|
f" {self.pairwise_state_dim} != {pairwise_num_heads} * {self.pairwise_head_width}."
|
|
)
|
|
if self.pairwise_state_dim % 2 != 0:
|
|
raise ValueError(f"`pairwise_state_dim` should be even, got {self.pairwise_state_dim}.")
|
|
|
|
if self.dropout >= 0.4:
|
|
raise ValueError(f"`dropout` should not be greater than 0.4, got {self.dropout}.")
|
|
|
|
def to_dict(self):
|
|
"""
|
|
Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`].
|
|
|
|
Returns:
|
|
`Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
|
|
"""
|
|
output = asdict(self)
|
|
output["structure_module"] = self.structure_module.to_dict()
|
|
return output
|
|
|
|
|
|
@dataclass
|
|
class StructureModuleConfig:
|
|
"""
|
|
Args:
|
|
sequence_dim:
|
|
Single representation channel dimension
|
|
pairwise_dim:
|
|
Pair representation channel dimension
|
|
ipa_dim:
|
|
IPA hidden channel dimension
|
|
resnet_dim:
|
|
Angle resnet (Alg. 23 lines 11-14) hidden channel dimension
|
|
num_heads_ipa:
|
|
Number of IPA heads
|
|
num_qk_points:
|
|
Number of query/key points to generate during IPA
|
|
num_v_points:
|
|
Number of value points to generate during IPA
|
|
dropout_rate:
|
|
Dropout rate used throughout the layer
|
|
num_blocks:
|
|
Number of structure module blocks
|
|
num_transition_layers:
|
|
Number of layers in the single representation transition (Alg. 23 lines 8-9)
|
|
num_resnet_blocks:
|
|
Number of blocks in the angle resnet
|
|
num_angles:
|
|
Number of angles to generate in the angle resnet
|
|
trans_scale_factor:
|
|
Scale of single representation transition hidden dimension
|
|
epsilon:
|
|
Small number used in angle resnet normalization
|
|
inf:
|
|
Large number used for attention masking
|
|
"""
|
|
|
|
sequence_dim: int = 384
|
|
pairwise_dim: int = 128
|
|
ipa_dim: int = 16
|
|
resnet_dim: int = 128
|
|
num_heads_ipa: int = 12
|
|
num_qk_points: int = 4
|
|
num_v_points: int = 8
|
|
dropout_rate: float = 0.1
|
|
num_blocks: int = 8
|
|
num_transition_layers: int = 1
|
|
num_resnet_blocks: int = 2
|
|
num_angles: int = 7
|
|
trans_scale_factor: int = 10
|
|
epsilon: float = 1e-8
|
|
inf: float = 1e5
|
|
|
|
def to_dict(self):
|
|
return asdict(self)
|
|
|
|
|
|
def get_default_vocab_list():
|
|
return (
|
|
"<cls>",
|
|
"<pad>",
|
|
"<eos>",
|
|
"<unk>",
|
|
"L",
|
|
"A",
|
|
"G",
|
|
"V",
|
|
"S",
|
|
"E",
|
|
"R",
|
|
"T",
|
|
"I",
|
|
"D",
|
|
"P",
|
|
"K",
|
|
"Q",
|
|
"N",
|
|
"F",
|
|
"Y",
|
|
"M",
|
|
"H",
|
|
"W",
|
|
"C",
|
|
"X",
|
|
"B",
|
|
"U",
|
|
"Z",
|
|
"O",
|
|
".",
|
|
"-",
|
|
"<null_1>",
|
|
"<mask>",
|
|
)
|