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| # Copyright (c) Facebook, Inc. and its affiliates. | |
| # | |
| # This source code is licensed under the MIT license found in the | |
| # LICENSE file in the root directory of this source tree. | |
| import math | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| from ..modules import ( | |
| TransformerLayer, | |
| LearnedPositionalEmbedding, | |
| SinusoidalPositionalEmbedding, | |
| RobertaLMHead, | |
| ESM1bLayerNorm, | |
| ContactPredictionHead, | |
| ) | |
| class ProteinBertModel(nn.Module): | |
| def add_args(cls, parser): | |
| parser.add_argument( | |
| "--num_layers", default=36, type=int, metavar="N", help="number of layers" | |
| ) | |
| parser.add_argument( | |
| "--embed_dim", default=1280, type=int, metavar="N", help="embedding dimension" | |
| ) | |
| parser.add_argument( | |
| "--logit_bias", action="store_true", help="whether to apply bias to logits" | |
| ) | |
| parser.add_argument( | |
| "--ffn_embed_dim", | |
| default=5120, | |
| type=int, | |
| metavar="N", | |
| help="embedding dimension for FFN", | |
| ) | |
| parser.add_argument( | |
| "--attention_heads", | |
| default=20, | |
| type=int, | |
| metavar="N", | |
| help="number of attention heads", | |
| ) | |
| def __init__(self, args, alphabet): | |
| super().__init__() | |
| self.args = args | |
| self.alphabet_size = len(alphabet) | |
| self.padding_idx = alphabet.padding_idx | |
| self.mask_idx = alphabet.mask_idx | |
| self.cls_idx = alphabet.cls_idx | |
| self.eos_idx = alphabet.eos_idx | |
| self.prepend_bos = alphabet.prepend_bos | |
| self.append_eos = alphabet.append_eos | |
| self.emb_layer_norm_before = getattr(self.args, "emb_layer_norm_before", False) | |
| if self.args.arch == "roberta_large": | |
| self.model_version = "ESM-1b" | |
| self._init_submodules_esm1b() | |
| else: | |
| self.model_version = "ESM-1" | |
| self._init_submodules_esm1() | |
| def _init_submodules_common(self): | |
| self.embed_tokens = nn.Embedding( | |
| self.alphabet_size, self.args.embed_dim, padding_idx=self.padding_idx | |
| ) | |
| self.layers = nn.ModuleList( | |
| [ | |
| TransformerLayer( | |
| self.args.embed_dim, | |
| self.args.ffn_embed_dim, | |
| self.args.attention_heads, | |
| add_bias_kv=(self.model_version != "ESM-1b"), | |
| use_esm1b_layer_norm=(self.model_version == "ESM-1b"), | |
| ) | |
| for _ in range(self.args.layers) | |
| ] | |
| ) | |
| self.contact_head = ContactPredictionHead( | |
| self.args.layers * self.args.attention_heads, | |
| self.prepend_bos, | |
| self.append_eos, | |
| eos_idx=self.eos_idx, | |
| ) | |
| def _init_submodules_esm1b(self): | |
| self._init_submodules_common() | |
| self.embed_scale = 1 | |
| self.embed_positions = LearnedPositionalEmbedding( | |
| self.args.max_positions, self.args.embed_dim, self.padding_idx | |
| ) | |
| self.emb_layer_norm_before = ( | |
| ESM1bLayerNorm(self.args.embed_dim) if self.emb_layer_norm_before else None | |
| ) | |
| self.emb_layer_norm_after = ESM1bLayerNorm(self.args.embed_dim) | |
| self.lm_head = RobertaLMHead( | |
| embed_dim=self.args.embed_dim, | |
| output_dim=self.alphabet_size, | |
| weight=self.embed_tokens.weight, | |
| ) | |
| def _init_submodules_esm1(self): | |
| self._init_submodules_common() | |
| self.embed_scale = math.sqrt(self.args.embed_dim) | |
| self.embed_positions = SinusoidalPositionalEmbedding(self.args.embed_dim, self.padding_idx) | |
| self.embed_out = nn.Parameter(torch.zeros((self.alphabet_size, self.args.embed_dim))) | |
| self.embed_out_bias = None | |
| if self.args.final_bias: | |
| self.embed_out_bias = nn.Parameter(torch.zeros(self.alphabet_size)) | |
| def forward(self, tokens, repr_layers=[], need_head_weights=False, return_contacts=False, return_representation=False): | |
| if return_contacts: | |
| need_head_weights = True | |
| assert tokens.ndim == 2 | |
| padding_mask = tokens.eq(self.padding_idx) # B, T | |
| x = self.embed_scale * self.embed_tokens(tokens) | |
| if getattr(self.args, "token_dropout", False): | |
| x.masked_fill_((tokens == self.mask_idx).unsqueeze(-1), 0.0) | |
| # x: B x T x C | |
| mask_ratio_train = 0.15 * 0.8 | |
| src_lengths = (~padding_mask).sum(-1) | |
| mask_ratio_observed = (tokens == self.mask_idx).sum(-1).float() / src_lengths | |
| x = x * (1 - mask_ratio_train) / (1 - mask_ratio_observed)[:, None, None] | |
| x = x + self.embed_positions(tokens) | |
| if self.model_version == "ESM-1b": | |
| if self.emb_layer_norm_before: | |
| x = self.emb_layer_norm_before(x) | |
| if padding_mask is not None: | |
| x = x * (1 - padding_mask.unsqueeze(-1).type_as(x)) | |
| repr_layers = set(repr_layers) | |
| hidden_representations = {} | |
| if 0 in repr_layers: | |
| hidden_representations[0] = x | |
| if need_head_weights: | |
| attn_weights = [] | |
| # (B, T, E) => (T, B, E) | |
| x = x.transpose(0, 1) | |
| if not padding_mask.any(): | |
| padding_mask = None | |
| for layer_idx, layer in enumerate(self.layers): | |
| x, attn = layer( | |
| x, self_attn_padding_mask=padding_mask, need_head_weights=need_head_weights | |
| ) | |
| if (layer_idx + 1) in repr_layers: | |
| hidden_representations[layer_idx + 1] = x.transpose(0, 1) | |
| if need_head_weights: | |
| # (H, B, T, T) => (B, H, T, T) | |
| attn_weights.append(attn.transpose(1, 0)) | |
| if self.model_version == "ESM-1b": | |
| x = self.emb_layer_norm_after(x) | |
| x = x.transpose(0, 1) # (T, B, E) => (B, T, E) | |
| # last hidden representation should have layer norm applied | |
| if (layer_idx + 1) in repr_layers: | |
| hidden_representations[layer_idx + 1] = x | |
| x = self.lm_head(x) | |
| else: | |
| x = F.linear(x, self.embed_out, bias=self.embed_out_bias) | |
| x = x.transpose(0, 1) # (T, B, E) => (B, T, E) | |
| if return_representation: | |
| result = {"logits": x, "representations": hidden_representations} | |
| else: | |
| result = {"logits": x} | |
| if need_head_weights: | |
| # attentions: B x L x H x T x T | |
| attentions = torch.stack(attn_weights, 1) | |
| if self.model_version == "ESM-1": | |
| # ESM-1 models have an additional null-token for attention, which we remove | |
| attentions = attentions[..., :-1] | |
| if padding_mask is not None: | |
| attention_mask = 1 - padding_mask.type_as(attentions) | |
| attention_mask = attention_mask.unsqueeze(1) * attention_mask.unsqueeze(2) | |
| attentions = attentions * attention_mask[:, None, None, :, :] | |
| result["attentions"] = attentions | |
| if return_contacts: | |
| contacts = self.contact_head(tokens, attentions) | |
| result["contacts"] = contacts | |
| return result | |
| def predict_contacts(self, tokens): | |
| return self(tokens, return_contacts=True)["contacts"] | |
| def num_layers(self): | |
| return self.args.layers |