Fill-Mask
Transformers
PyTorch
Joblib
Safetensors
DNA
biology
genomics
custom_code
Inference Endpoints
File size: 14,876 Bytes
e9599ba
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
# 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 transformers import PretrainedConfig, logging

logger = logging.get_logger(__name__)

# TODO Update this
ESM_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "facebook/esm-1b": "https://huggingface.co/facebook/esm-1b/resolve/main/config.json",
    # See all ESM models at https://huggingface.co/models?filter=esm
}


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,
        add_bias_fnn=True,
        **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
        # Arguments needed for Dalmatian
        self.add_bias_fnn = add_bias_fnn
        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>",
    )