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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. | |
| from typing import Union | |
| import torch | |
| import torch.nn as nn | |
| import esm | |
| from esm.modules import ContactPredictionHead, ESM1bLayerNorm, RobertaLMHead, TransformerLayer | |
| # ```该代码定义了一个名为 ESM2 的 PyTorch 模型，继承自 nn.Module。在 __init__ 方法中，定义了一些超参数，例如 num_layers、embed_dim、attention_heads 等等。同时，它还初始化了一些子模块，例如 Embedding 层 embed_tokens、一系列 Transformer 层 layers、预测接触的 ContactPredictionHead 层 contact_head，以及一些线性层 lm_head、supervised_linear、structure_linear 等。该模型的前向传播在 forward 方法中定义，接收一个表示序列的 token 序列 tokens，返回预测的标签和其他附加信息。``` | |
| class ESM2(nn.Module): | |
| def __init__( | |
| self, | |
| num_layers: int = 33, | |
| embed_dim: int = 1280, | |
| attention_heads: int = 20, | |
| alphabet: Union[esm.data.Alphabet, str] = "ESM-1b", | |
| token_dropout: bool = True, | |
| ): | |
| super().__init__() | |
| self.num_layers = num_layers | |
| self.embed_dim = embed_dim | |
| self.attention_heads = attention_heads | |
| if not isinstance(alphabet, esm.data.Alphabet): | |
| alphabet = esm.data.Alphabet.from_architecture(alphabet) | |
| self.alphabet = alphabet | |
| 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.token_dropout = token_dropout | |
| self._init_submodules() | |
| def _init_submodules(self): | |
| self.embed_scale = 1 | |
| self.embed_tokens = nn.Embedding( | |
| self.alphabet_size, | |
| self.embed_dim, | |
| padding_idx=self.padding_idx, | |
| ) | |
| self.layers = nn.ModuleList( | |
| [ | |
| TransformerLayer( | |
| self.embed_dim, | |
| 4 * self.embed_dim, | |
| self.attention_heads, | |
| add_bias_kv=False, | |
| use_esm1b_layer_norm=True, | |
| use_rotary_embeddings=True, | |
| ) | |
| for _ in range(self.num_layers) | |
| ] | |
| ) | |
| self.contact_head = ContactPredictionHead( | |
| self.num_layers * self.attention_heads, | |
| self.prepend_bos, | |
| self.append_eos, | |
| eos_idx=self.eos_idx, | |
| ) | |
| self.emb_layer_norm_after = ESM1bLayerNorm(self.embed_dim) | |
| self.lm_head = RobertaLMHead( | |
| embed_dim=self.embed_dim, | |
| output_dim=self.alphabet_size, | |
| weight=self.embed_tokens.weight, | |
| ) | |
| self.supervised_linear = nn.Linear(self.embed_dim, 1) | |
| self.structure_linear = nn.Linear(self.embed_dim, 3) | |
| def forward(self, tokens, repr_layers=[], need_head_weights=True, return_contacts=True, return_representation=True, return_attentions_symm = False, return_attentions = 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 self.token_dropout: | |
| x.masked_fill_((tokens == self.mask_idx).unsqueeze(-1), 0.0) | |
| #print(f'tokens = {tokens}') | |
| #print(f'self.mask_idx = {self.mask_idx}') | |
| #print('x.shape = ', x.shape) | |
| # x: B x T x C | |
| mask_ratio_train = 0.15 * 0.8 | |
| src_lengths = (~padding_mask).sum(-1) | |
| #print(f'mask_ratio_train = {mask_ratio_train}') | |
| #print(f'padding_mask = {padding_mask}') | |
| #print(f'src_lengths = {src_lengths}') | |
| mask_ratio_observed = (tokens == self.mask_idx).sum(-1).to(x.dtype) / src_lengths | |
| #print('mask_ratio_observed = ',mask_ratio_observed) | |
| x = x * (1 - mask_ratio_train) / (1 - mask_ratio_observed)[:, None, None] | |
| #print(f'x.shape = {x.shape}:\n', x) | |
| if padding_mask is not None: | |
| x = x * (1 - padding_mask.unsqueeze(-1).type_as(x)) | |
| #print(f'x.shape = {x.shape}:\n', 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)) | |
| # print(x.shape) # 73, 2, 1280 | |
| 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_supervised = self.supervised_linear(x[:,0,:]) | |
| x_structure = self.structure_linear(x) | |
| x = self.lm_head(x) | |
| if return_representation: | |
| result = {"logits": x, "logits_supervised": x_supervised, "logits_structure": x_structure, "representations": hidden_representations} | |
| else: | |
| result = {"logits": x, "logits_supervised": x_supervised, "logits_structure": x_structure} | |
| if need_head_weights: | |
| # attentions: B x L x H x T x T | |
| attentions = torch.stack(attn_weights, 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, :, :] | |
| if return_attentions: result["attentions"] = attentions | |
| if return_contacts: | |
| attentions_symm, contacts = self.contact_head(tokens, attentions) | |
| result["contacts"] = contacts | |
| if return_attentions_symm: result["attentions_symm"] = attentions_symm | |
| return result | |
| def predict_contacts(self, tokens): | |
| return self(tokens, return_contacts=True)["contacts"] | |
| def predict_symmetric_attentions(self, tokens): | |
| return self(tokens, return_contacts=True)["attentions_symm"] | |
| def predict_attentions(self, tokens): | |
| return self(tokens, need_head_weights=True)["attentions"] | |
| def predict_representations(self, tokens): | |
| return self(tokens, return_representation=True)['representations'] | |
| def predict_logits(self, tokens): | |
| return self(tokens)['logits'] | |
| def predict_logits_supervised(self, tokens): | |
| return self(tokens)['logits_supervised'] | |
| def predict_logits_structure(self, tokens): | |
| return self(tokens)['logits_structure'] | |