From 08d54b8677946290ce9725592e6c22558eb43e55 Mon Sep 17 00:00:00 2001 From: Zhiyuan Chen Date: Mon, 8 Apr 2024 16:17:57 +0800 Subject: [PATCH] [WIP] Signed-off-by: Zhiyuan Chen --- multimolecule/models/rnafm/__init__.py | 93 ++ .../models/rnafm/configuration_rnafm.py | 120 ++ multimolecule/models/rnafm/modeling_rnafm.py | 1252 +++++++++++++++++ 3 files changed, 1465 insertions(+) create mode 100644 multimolecule/models/rnafm/__init__.py create mode 100644 multimolecule/models/rnafm/configuration_rnafm.py create mode 100644 multimolecule/models/rnafm/modeling_rnafm.py diff --git a/multimolecule/models/rnafm/__init__.py b/multimolecule/models/rnafm/__init__.py new file mode 100644 index 00000000..b5b5227c --- /dev/null +++ b/multimolecule/models/rnafm/__init__.py @@ -0,0 +1,93 @@ +# Copyright 2022 Facebook and The HuggingFace 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. +from typing import TYPE_CHECKING + +from transformers.utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available + +_import_structure = { + "configuration_rnafm": ["RNAFM_PRETRAINED_CONFIG_ARCHIVE_MAP", "RnaFmConfig"], + "tokenization_rnafm": ["RnaFmTokenizer"], +} + +try: + if not is_torch_available(): + raise OptionalDependencyNotAvailable() +except OptionalDependencyNotAvailable: + pass +else: + _import_structure["modeling_rnafm"] = [ + "RNAFM_PRETRAINED_MODEL_ARCHIVE_LIST", + "RnaFmForMaskedLM", + "RnaFmForSequenceClassification", + "RnaFmForTokenClassification", + "RnaFmModel", + "RnaFmPreTrainedModel", + ] + _import_structure["modeling_rnafmfold"] = ["RnaFmForProteinFolding", "RnaFmFoldPreTrainedModel"] + +try: + if not is_tf_available(): + raise OptionalDependencyNotAvailable() +except OptionalDependencyNotAvailable: + pass +else: + _import_structure["modeling_tf_rnafm"] = [ + "TF_RNAFM_PRETRAINED_MODEL_ARCHIVE_LIST", + "TFRnaFmForMaskedLM", + "TFRnaFmForSequenceClassification", + "TFRnaFmForTokenClassification", + "TFRnaFmModel", + "TFRnaFmPreTrainedModel", + ] + +if TYPE_CHECKING: + from .configuration_rnafm import RNAFM_PRETRAINED_CONFIG_ARCHIVE_MAP, RnaFmConfig + from .tokenization_rnafm import RnaFmTokenizer + + try: + if not is_torch_available(): + raise OptionalDependencyNotAvailable() + except OptionalDependencyNotAvailable: + pass + else: + from .modeling_rnafm import ( + RNAFM_PRETRAINED_MODEL_ARCHIVE_LIST, + RnaFmForMaskedLM, + RnaFmForSequenceClassification, + RnaFmForTokenClassification, + RnaFmModel, + RnaFmPreTrainedModel, + ) + from .modeling_rnafmfold import RnaFmFoldPreTrainedModel, RnaFmForProteinFolding + + try: + if not is_tf_available(): + raise OptionalDependencyNotAvailable() + except OptionalDependencyNotAvailable: + pass + else: + from .modeling_tf_rnafm import ( + TF_RNAFM_PRETRAINED_MODEL_ARCHIVE_LIST, + TFRnaFmForMaskedLM, + TFRnaFmForSequenceClassification, + TFRnaFmForTokenClassification, + TFRnaFmModel, + TFRnaFmPreTrainedModel, + ) + + +else: + import sys + + sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure) diff --git a/multimolecule/models/rnafm/configuration_rnafm.py b/multimolecule/models/rnafm/configuration_rnafm.py new file mode 100644 index 00000000..9c0e648a --- /dev/null +++ b/multimolecule/models/rnafm/configuration_rnafm.py @@ -0,0 +1,120 @@ +""" ESM model configuration""" + +from dataclasses import asdict, dataclass +from typing import Optional + +from transformers.configuration_utils import PretrainedConfig +from transformers.utils import logging + +logger = logging.get_logger(__name__) + + +class RnaFmConfig(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 + [ml4bio/RNA-FM](https://github.com/ml4bio/RNA-FM) 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 multimolecule import RnaFmModel, RnaFmConfig + + >>> # Initializing a RnaFm style configuration >>> configuration = RnaFmConfig() + + >>> # Initializing a model from the configuration >>> model = RnaFmModel(configuration) + + >>> # Accessing the model configuration >>> configuration = model.config + ```""" + + model_type = "rnafm" + + def __init__( + self, + vocab_size=25, + 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="rotary", + use_cache=True, + emb_layer_norm_before=None, + token_dropout=False, + is_folding_model=False, + **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 + self.hidden_act = "gelu" diff --git a/multimolecule/models/rnafm/modeling_rnafm.py b/multimolecule/models/rnafm/modeling_rnafm.py new file mode 100644 index 00000000..0429769d --- /dev/null +++ b/multimolecule/models/rnafm/modeling_rnafm.py @@ -0,0 +1,1252 @@ +""" PyTorch RNAFM model.""" + +from typing import List, Optional, Tuple, Union + +import torch +import torch.utils.checkpoint +from torch import nn +from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss +from transformers.activations import ACT2FN +from transformers.file_utils import ( + add_code_sample_docstrings, + add_start_docstrings, + add_start_docstrings_to_model_forward, +) +from transformers.modeling_outputs import ( + BaseModelOutputWithPastAndCrossAttentions, + BaseModelOutputWithPoolingAndCrossAttentions, + MaskedLMOutput, + SequenceClassifierOutput, + TokenClassifierOutput, +) +from transformers.modeling_utils import PreTrainedModel, find_pruneable_heads_and_indices, prune_linear_layer +from transformers.utils import logging + +from multimolecule.models.modeling_utils import MaskedLMHead, SequenceClassificationHead + +from .configuration_rnafm import RnaFmConfig + +logger = logging.get_logger(__name__) + +_CHECKPOINT_FOR_DOC = "ZhiyuanChen/rnafm" +_CONFIG_FOR_DOC = "RnaFmConfig" + + +RNAFM_START_DOCSTRING = r""" + + This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the + library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads + etc.) + + This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. + Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage + and behavior. + + Parameters: + config ([`RnaFmConfig`]): Model configuration class with all the parameters of the + model. Initializing with a config file does not load the weights associated with the model, only the + configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. +""" + +RNAFM_INPUTS_DOCSTRING = r""" + Args: + input_ids (`torch.LongTensor` of shape `({0})`): + Indices of input sequence tokens in the vocabulary. + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + position_ids (`torch.LongTensor` of shape `({0})`, *optional*): + Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, + config.max_position_embeddings - 1]`. + + [What are position IDs?](../glossary#position-ids) + head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): + Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: + + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + + inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*): + Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This + is useful if you want more control over how to convert `input_ids` indices into associated vectors than the + model's internal embedding lookup matrix. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple. +""" + + +class RnaFmPreTrainedModel(PreTrainedModel): + """ + An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained + models. + """ + + config_class = RnaFmConfig + base_model_prefix = "rnafm" + supports_gradient_checkpointing = True + _no_split_modules = ["RnaFmLayer", "RnaFmFoldTriangularSelfAttentionBlock", "RnaFmEmbeddings"] + + # Copied from transformers.models.bert.modeling_bert.BertPreTrainedModel._init_weights + def _init_weights(self, module): + """Initialize the weights""" + if isinstance(module, nn.Linear): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + elif isinstance(module, nn.LayerNorm): + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + +@add_start_docstrings( + "The bare RNAFM Model transformer outputting raw hidden-states without any specific head on top.", + RNAFM_START_DOCSTRING, +) +class RnaFmModel(RnaFmPreTrainedModel): + """ + + The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of + cross-attention is added between the self-attention layers, following the architecture described in [Attention is + all you need](https://arxiv.org/abs/1706.03762) by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, + Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin. + + To behave as an decoder the model needs to be initialized with the `is_decoder` argument of the configuration set + to `True`. To be used in a Seq2Seq model, the model needs to initialized with both `is_decoder` argument and + `add_cross_attention` set to `True`; an `encoder_hidden_states` is then expected as an input to the forward pass. + """ + + def __init__(self, config, add_pooling_layer=True): + super().__init__(config) + self.config = config + + self.embeddings = RnaFmEmbeddings(config) + self.encoder = RnaFmEncoder(config) + + self.pooler = RnaFmPooler(config) if add_pooling_layer else None + + self.contact_head = RnaFmContactPredictionHead( + in_features=config.num_hidden_layers * config.num_attention_heads, bias=True + ) + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.embeddings.word_embeddings + + def set_input_embeddings(self, value): + self.embeddings.word_embeddings = value + + def _prune_heads(self, heads_to_prune): + """ + Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base + class PreTrainedModel + """ + for layer, heads in heads_to_prune.items(): + self.encoder.layer[layer].attention.prune_heads(heads) + + @add_start_docstrings_to_model_forward(RNAFM_INPUTS_DOCSTRING.format("(batch_size, sequence_length)")) + @add_code_sample_docstrings( + checkpoint=_CHECKPOINT_FOR_DOC, + output_type=BaseModelOutputWithPoolingAndCrossAttentions, + config_class=_CONFIG_FOR_DOC, + ) + def forward( + self, + input_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.Tensor] = None, + head_mask: Optional[torch.Tensor] = None, + inputs_embeds: Optional[torch.Tensor] = None, + encoder_hidden_states: Optional[torch.Tensor] = None, + encoder_attention_mask: Optional[torch.Tensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]: + r""" + encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if + the model is configured as a decoder. + encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in + the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): + Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. + + If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that + don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all + `decoder_input_ids` of shape `(batch_size, sequence_length)`. + use_cache (`bool`, *optional*): + If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see + `past_key_values`). + """ + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if self.config.is_decoder: + use_cache = use_cache if use_cache is not None else self.config.use_cache + else: + use_cache = False + + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") + elif input_ids is not None: + self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask) + input_shape = input_ids.size() + elif inputs_embeds is not None: + input_shape = inputs_embeds.size()[:-1] + else: + raise ValueError("You have to specify either input_ids or inputs_embeds") + + batch_size, seq_length = input_shape + device = input_ids.device if input_ids is not None else inputs_embeds.device + + # past_key_values_length + past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0 + + if attention_mask is None: + attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device) + + # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] + # ourselves in which case we just need to make it broadcastable to all heads. + extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape) + + # If a 2D or 3D attention mask is provided for the cross-attention + # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] + if self.config.is_decoder and encoder_hidden_states is not None: + encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size() + encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) + if encoder_attention_mask is None: + encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device) + encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask) + else: + encoder_extended_attention_mask = None + + # Prepare head mask if needed + # 1.0 in head_mask indicate we keep the head + # attention_probs has shape bsz x n_heads x N x N + # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] + # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] + head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) + + embedding_output = self.embeddings( + input_ids=input_ids, + position_ids=position_ids, + attention_mask=attention_mask, + inputs_embeds=inputs_embeds, + past_key_values_length=past_key_values_length, + ) + encoder_outputs = self.encoder( + embedding_output, + attention_mask=extended_attention_mask, + head_mask=head_mask, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_extended_attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + sequence_output = encoder_outputs[0] + pooled_output = self.pooler(sequence_output) if self.pooler is not None else None + + if not return_dict: + return (sequence_output, pooled_output) + encoder_outputs[1:] + + return BaseModelOutputWithPoolingAndCrossAttentions( + last_hidden_state=sequence_output, + pooler_output=pooled_output, + past_key_values=encoder_outputs.past_key_values, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + cross_attentions=encoder_outputs.cross_attentions, + ) + + def predict_contacts(self, tokens, attention_mask): + attns = self(tokens, attention_mask=attention_mask, return_dict=True, output_attentions=True).attentions + attns = torch.stack(attns, dim=1) # Matches the original model layout + # In the original model, attentions for padding tokens are completely zeroed out. + # This makes no difference most of the time because the other tokens won't attend to them, + # but it does for the contact prediction task, which takes attentions as input, + # so we have to mimic that here. + attns *= attention_mask.unsqueeze(1).unsqueeze(2).unsqueeze(3) + attns *= attention_mask.unsqueeze(1).unsqueeze(2).unsqueeze(4) + return self.contact_head(tokens, attns) + + +@add_start_docstrings("""RNAFM Model with a `language modeling` head on top.""", RNAFM_START_DOCSTRING) +class RnaFmForMaskedLM(RnaFmPreTrainedModel): + _tied_weights_keys = ["lm_head.decoder.weight"] + + def __init__(self, config): + super().__init__(config) + + if config.is_decoder: + logger.warning( + "If you want to use `RnaFmForMaskedLM` make sure `config.is_decoder=False` for " + "bi-directional self-attention." + ) + + self.rnafm = RnaFmModel(config, add_pooling_layer=False) + self.lm_head = MaskedLMHead(config) + + self.init_weights() + + def get_output_embeddings(self): + return self.lm_head.decoder + + def set_output_embeddings(self, new_embeddings): + self.lm_head.decoder = new_embeddings + + @add_start_docstrings_to_model_forward(RNAFM_INPUTS_DOCSTRING.format("batch_size, sequence_length")) + @add_code_sample_docstrings( + checkpoint=_CHECKPOINT_FOR_DOC, + output_type=MaskedLMOutput, + config_class=_CONFIG_FOR_DOC, + mask="", + ) + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.Tensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.Tensor] = None, + labels: Optional[torch.LongTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, MaskedLMOutput]: + r""" + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., + config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the + loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` + kwargs (`Dict[str, any]`, optional, defaults to *{}*): + Used to hide legacy arguments that have been deprecated. + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + outputs = self.rnafm( + input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + sequence_output = outputs[0] + prediction_scores = self.lm_head(sequence_output) + + masked_lm_loss = None + if labels is not None: + loss_fct = CrossEntropyLoss() + + labels = labels.to(prediction_scores.device) + masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) + + if not return_dict: + output = (prediction_scores,) + outputs[2:] + return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output + + return MaskedLMOutput( + loss=masked_lm_loss, + logits=prediction_scores, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + def predict_contacts(self, tokens, attention_mask): + return self.rnafm.predict_contacts(tokens, attention_mask=attention_mask) + + +@add_start_docstrings( + """ + RNAFM Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled + output) e.g. for GLUE tasks. + """, + RNAFM_START_DOCSTRING, +) +class RnaFmForSequenceClassification(RnaFmPreTrainedModel): + def __init__(self, config): + super().__init__(config) + self.num_labels = config.num_labels + self.config = config + + self.rnafm = RnaFmModel(config, add_pooling_layer=False) + self.classifier = RnaFmClassificationHead(config) + + self.init_weights() + + @add_start_docstrings_to_model_forward(RNAFM_INPUTS_DOCSTRING.format("batch_size, sequence_length")) + @add_code_sample_docstrings( + checkpoint=_CHECKPOINT_FOR_DOC, + output_type=SequenceClassifierOutput, + config_class=_CONFIG_FOR_DOC, + ) + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.Tensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, SequenceClassifierOutput]: + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + outputs = self.rnafm( + input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + sequence_output = outputs[0] + logits = self.classifier(sequence_output) + + loss = None + if labels is not None: + labels = labels.to(logits.device) + + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(logits, labels) + + if not return_dict: + output = (logits,) + outputs[2:] + return ((loss,) + output) if loss is not None else output + + return SequenceClassifierOutput( + loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +@add_start_docstrings( + """ + RNAFM Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for + Named-Entity-Recognition (NER) tasks. + """, + RNAFM_START_DOCSTRING, +) +class RnaFmForTokenClassification(RnaFmPreTrainedModel): + def __init__(self, config): + super().__init__(config) + self.num_labels = config.num_labels + + self.rnafm = RnaFmModel(config, add_pooling_layer=False) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + self.classifier = nn.Linear(config.hidden_size, config.num_labels) + + self.init_weights() + + @add_start_docstrings_to_model_forward(RNAFM_INPUTS_DOCSTRING.format("batch_size, sequence_length")) + @add_code_sample_docstrings( + checkpoint=_CHECKPOINT_FOR_DOC, + output_type=TokenClassifierOutput, + config_class=_CONFIG_FOR_DOC, + ) + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.Tensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, TokenClassifierOutput]: + r""" + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`. + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + outputs = self.rnafm( + input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + sequence_output = outputs[0] + + sequence_output = self.dropout(sequence_output) + logits = self.classifier(sequence_output) + + loss = None + if labels is not None: + loss_fct = CrossEntropyLoss() + + labels = labels.to(logits.device) + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + + if not return_dict: + output = (logits,) + outputs[2:] + return ((loss,) + output) if loss is not None else output + + return TokenClassifierOutput( + loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +class RnaFmEncoder(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.layer = nn.ModuleList([RnaFmLayer(config) for _ in range(config.num_hidden_layers)]) + self.emb_layer_norm_after = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.gradient_checkpointing = False + + def forward( + self, + hidden_states, + attention_mask=None, + head_mask=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + past_key_values=None, + use_cache=None, + output_attentions=False, + output_hidden_states=False, + return_dict=True, + ): + if self.gradient_checkpointing and self.training: + if use_cache: + logger.warning_once( + "`use_cache=True` is incompatible with `config.gradient_checkpointing=True`. Setting " + "`use_cache=False`..." + ) + use_cache = False + all_hidden_states = () if output_hidden_states else None + all_self_attentions = () if output_attentions else None + all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None + + next_decoder_cache = () if use_cache else None + for i, layer_module in enumerate(self.layer): + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + layer_head_mask = head_mask[i] if head_mask is not None else None + past_key_value = past_key_values[i] if past_key_values is not None else None + + if self.gradient_checkpointing and self.training: + layer_outputs = self._gradient_checkpointing_func( + layer_module.__call__, + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + ) + else: + layer_outputs = layer_module( + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + ) + + hidden_states = layer_outputs[0] + if use_cache: + next_decoder_cache = next_decoder_cache + (layer_outputs[-1],) + if output_attentions: + all_self_attentions = all_self_attentions + (layer_outputs[1],) + if self.config.add_cross_attention: + all_cross_attentions = all_cross_attentions + (layer_outputs[2],) + + if self.emb_layer_norm_after: + hidden_states = self.emb_layer_norm_after(hidden_states) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if not return_dict: + return tuple( + v + for v in [ + hidden_states, + next_decoder_cache, + all_hidden_states, + all_self_attentions, + all_cross_attentions, + ] + if v is not None + ) + return BaseModelOutputWithPastAndCrossAttentions( + last_hidden_state=hidden_states, + past_key_values=next_decoder_cache, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + cross_attentions=all_cross_attentions, + ) + + +class RnaFmLayer(nn.Module): + def __init__(self, config): + super().__init__() + self.chunk_size_feed_forward = config.chunk_size_feed_forward + self.seq_len_dim = 1 + self.attention = RnaFmAttention(config) + self.is_decoder = config.is_decoder + self.add_cross_attention = config.add_cross_attention + if self.add_cross_attention: + if not self.is_decoder: + raise RuntimeError(f"{self} should be used as a decoder model if cross attention is added") + self.crossattention = RnaFmAttention(config) + self.intermediate = RnaFmIntermediate(config) + self.output = RnaFmOutput(config) + self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + + def forward( + self, + hidden_states, + attention_mask=None, + head_mask=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + past_key_value=None, + output_attentions=False, + ): + # decoder uni-directional self-attention cached key/values tuple is at positions 1,2 + self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None + self_attention_outputs = self.attention( + hidden_states, + attention_mask, + head_mask, + output_attentions=output_attentions, + past_key_value=self_attn_past_key_value, + ) + attention_output = self_attention_outputs[0] + + # if decoder, the last output is tuple of self-attn cache + if self.is_decoder: + outputs = self_attention_outputs[1:-1] + present_key_value = self_attention_outputs[-1] + else: + outputs = self_attention_outputs[1:] # add self attentions if we output attention weights + + cross_attn_present_key_value = None + if self.is_decoder and encoder_hidden_states is not None: + if not hasattr(self, "crossattention"): + raise AttributeError( + f"If `encoder_hidden_states` are passed, {self} has to be instantiated" + " with cross-attention layers by setting `config.add_cross_attention=True`" + ) + + # cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple + cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None + cross_attention_outputs = self.crossattention( + attention_output, + attention_mask, + head_mask, + encoder_hidden_states, + encoder_attention_mask, + cross_attn_past_key_value, + output_attentions, + ) + attention_output = cross_attention_outputs[0] + outputs = outputs + cross_attention_outputs[1:-1] # add cross attentions if we output attention weights + + # add cross-attn cache to positions 3,4 of present_key_value tuple + cross_attn_present_key_value = cross_attention_outputs[-1] + present_key_value = present_key_value + cross_attn_present_key_value + + layer_output = self.feed_forward_chunk(attention_output) + + outputs = (layer_output,) + outputs + + # if decoder, return the attn key/values as the last output + if self.is_decoder: + outputs = outputs + (present_key_value,) + return outputs + + def feed_forward_chunk(self, attention_output): + attention_output_ln = self.layer_norm(attention_output) + intermediate_output = self.intermediate(attention_output_ln) + layer_output = self.output(intermediate_output, attention_output) + return layer_output + + +class RnaFmAttention(nn.Module): + def __init__(self, config): + super().__init__() + self.self = RnaFmSelfAttention(config) + self.output = RnaFmSelfOutput(config) + self.pruned_heads = set() + self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + + def prune_heads(self, heads): + if len(heads) == 0: + return + heads, index = find_pruneable_heads_and_indices( + heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads + ) + + # Prune linear layers + self.self.query = prune_linear_layer(self.self.query, index) + self.self.key = prune_linear_layer(self.self.key, index) + self.self.value = prune_linear_layer(self.self.value, index) + self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) + + # Update hyper params and store pruned heads + self.self.num_attention_heads = self.self.num_attention_heads - len(heads) + self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads + self.pruned_heads = self.pruned_heads.union(heads) + + def forward( + self, + hidden_states, + attention_mask=None, + head_mask=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + past_key_value=None, + output_attentions=False, + ): + hidden_states_ln = self.layer_norm(hidden_states) + self_outputs = self.self( + hidden_states_ln, + attention_mask, + head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + ) + attention_output = self.output(self_outputs[0], hidden_states) + outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them + return outputs + + +class RnaFmSelfAttention(nn.Module): + def __init__(self, config, position_embedding_type=None): + super().__init__() + if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): + raise ValueError( + f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention " + f"heads ({config.num_attention_heads})" + ) + + self.num_attention_heads = config.num_attention_heads + self.attention_head_size = int(config.hidden_size / config.num_attention_heads) + self.all_head_size = self.num_attention_heads * self.attention_head_size + + self.query = nn.Linear(config.hidden_size, self.all_head_size) + self.key = nn.Linear(config.hidden_size, self.all_head_size) + self.value = nn.Linear(config.hidden_size, self.all_head_size) + + self.dropout = nn.Dropout(config.attention_probs_dropout_prob) + self.position_embedding_type = position_embedding_type or getattr(config, "position_embedding_type", "absolute") + self.rotary_embeddings = None + if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query": + self.max_position_embeddings = config.max_position_embeddings + self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size) + elif self.position_embedding_type == "rotary": + self.rotary_embeddings = RotaryEmbedding(dim=self.attention_head_size) + + self.is_decoder = config.is_decoder + + def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor: + new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) + x = x.view(new_x_shape) + return x.permute(0, 2, 1, 3) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + output_attentions: Optional[bool] = False, + ) -> Tuple[torch.Tensor]: + mixed_query_layer = self.query(hidden_states) + + # If this is instantiated as a cross-attention module, the keys + # and values come from an encoder; the attention mask needs to be + # such that the encoder's padding tokens are not attended to. + is_cross_attention = encoder_hidden_states is not None + + if is_cross_attention and past_key_value is not None: + # reuse k,v, cross_attentions + key_layer = past_key_value[0] + value_layer = past_key_value[1] + attention_mask = encoder_attention_mask + elif is_cross_attention: + key_layer = self.transpose_for_scores(self.key(encoder_hidden_states)) + value_layer = self.transpose_for_scores(self.value(encoder_hidden_states)) + attention_mask = encoder_attention_mask + elif past_key_value is not None: + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + key_layer = torch.cat([past_key_value[0], key_layer], dim=2) + value_layer = torch.cat([past_key_value[1], value_layer], dim=2) + else: + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + + query_layer = self.transpose_for_scores(mixed_query_layer) + + # Matt: Our BERT model (which this code was derived from) scales attention logits down by sqrt(head_dim). + # RNAFM scales the query down by the same factor instead. Modulo numerical stability these are equivalent, + # but not when rotary embeddings get involved. Therefore, we scale the query here to match the original + # RNAFM code and fix rotary embeddings. + query_layer = query_layer * self.attention_head_size**-0.5 + + if self.is_decoder: + # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states. + # Further calls to cross_attention layer can then reuse all cross-attention + # key/value_states (first "if" case) + # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of + # all previous decoder key/value_states. Further calls to uni-directional self-attention + # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case) + # if encoder bi-directional self-attention `past_key_value` is always `None` + past_key_value = (key_layer, value_layer) + + if self.position_embedding_type == "rotary": + query_layer, key_layer = self.rotary_embeddings(query_layer, key_layer) + + # Take the dot product between "query" and "key" to get the raw attention scores. + attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) + + if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query": + seq_length = hidden_states.size()[1] + position_ids_l = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(-1, 1) + position_ids_r = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(1, -1) + distance = position_ids_l - position_ids_r + positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1) + positional_embedding = positional_embedding.to(dtype=query_layer.dtype) # fp16 compatibility + + if self.position_embedding_type == "relative_key": + relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding) + attention_scores = attention_scores + relative_position_scores + elif self.position_embedding_type == "relative_key_query": + relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding) + relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding) + attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key + + if attention_mask is not None: + # Apply the attention mask is (precomputed for all layers in RnaFmModel forward() function) + attention_scores = attention_scores + attention_mask + + # Normalize the attention scores to probabilities. + attention_probs = nn.functional.softmax(attention_scores, dim=-1) + + # This is actually dropping out entire tokens to attend to, which might + # seem a bit unusual, but is taken from the original Transformer paper. + attention_probs = self.dropout(attention_probs) + + # Mask heads if we want to + if head_mask is not None: + attention_probs = attention_probs * head_mask + + context_layer = torch.matmul(attention_probs.to(value_layer.dtype), value_layer) + + context_layer = context_layer.permute(0, 2, 1, 3).contiguous() + new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) + context_layer = context_layer.view(new_context_layer_shape) + + outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) + + if self.is_decoder: + outputs = outputs + (past_key_value,) + return outputs + + +class RnaFmSelfOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward(self, hidden_states, input_tensor): + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = hidden_states + input_tensor + return hidden_states + + +class RnaFmIntermediate(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.intermediate_size) + if isinstance(config.hidden_act, str): + self.intermediate_act_fn = ACT2FN[config.hidden_act] + else: + self.intermediate_act_fn = config.hidden_act + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.intermediate_act_fn(hidden_states) + return hidden_states + + +class RnaFmOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.intermediate_size, config.hidden_size) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward(self, hidden_states, input_tensor): + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = hidden_states + input_tensor + return hidden_states + + +class RnaFmEmbeddings(nn.Module): + """ + Same as BertEmbeddings with a tiny tweak for positional embeddings indexing. + """ + + def __init__(self, config): + super().__init__() + self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id) + + if config.emb_layer_norm_before: + self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + else: + self.layer_norm = None + self.dropout = nn.Dropout(config.hidden_dropout_prob) + # position_ids (1, len position emb) is contiguous in memory and exported when serialized + self.position_embedding_type = getattr(config, "position_embedding_type", "absolute") + self.register_buffer( + "position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False + ) + + self.padding_idx = config.pad_token_id + self.position_embeddings = nn.Embedding( + config.max_position_embeddings, config.hidden_size, padding_idx=self.padding_idx + ) + self.token_dropout = config.token_dropout + self.mask_token_id = config.mask_token_id + + def forward( + self, input_ids=None, attention_mask=None, position_ids=None, inputs_embeds=None, past_key_values_length=0 + ): + if position_ids is None: + if input_ids is not None: + # Create the position ids from the input token ids. Any padded tokens remain padded. + position_ids = create_position_ids_from_input_ids(input_ids, self.padding_idx, past_key_values_length) + else: + position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds) + + if inputs_embeds is None: + inputs_embeds = self.word_embeddings(input_ids) + + # Note that if we want to support RNAFM-1 (not 1b!) in future then we need to support an + # embedding_scale factor here. + embeddings = inputs_embeds + + # Matt: RNAFM has the option to handle masking in MLM in a slightly unusual way. If the token_dropout + # flag is False then it is handled in the same was as BERT/RoBERTa. If it is set to True, however, + # masked tokens are treated as if they were selected for input dropout and zeroed out. + # This "mask-dropout" is compensated for when masked tokens are not present, by scaling embeddings by + # a factor of (fraction of unmasked tokens during training) / (fraction of unmasked tokens in sample). + # This is analogous to the way that dropout layers scale down outputs during evaluation when not + # actually dropping out values (or, equivalently, scale up their un-dropped outputs in training). + if self.token_dropout: + embeddings = embeddings.masked_fill((input_ids == self.mask_token_id).unsqueeze(-1), 0.0) + mask_ratio_train = 0.15 * 0.8 # Hardcoded as the ratio used in all RNAFM model training runs + src_lengths = attention_mask.sum(-1) + mask_ratio_observed = (input_ids == self.mask_token_id).sum(-1).float() / src_lengths + embeddings = (embeddings * (1 - mask_ratio_train) / (1 - mask_ratio_observed)[:, None, None]).to( + embeddings.dtype + ) + + if self.position_embedding_type == "absolute": + position_embeddings = self.position_embeddings(position_ids) + embeddings = embeddings + position_embeddings + + if self.layer_norm is not None: + embeddings = self.layer_norm(embeddings) + if attention_mask is not None: + embeddings = (embeddings * attention_mask.unsqueeze(-1)).to(embeddings.dtype) + # Matt: I think this line was copied incorrectly from BERT, disabling it for now. + # embeddings = self.dropout(embeddings) + return embeddings + + def create_position_ids_from_inputs_embeds(self, inputs_embeds): + """ + We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids. + + Args: + inputs_embeds: torch.Tensor + + Returns: torch.Tensor + """ + input_shape = inputs_embeds.size()[:-1] + sequence_length = input_shape[1] + + position_ids = torch.arange( + self.padding_idx + 1, sequence_length + self.padding_idx + 1, dtype=torch.long, device=inputs_embeds.device + ) + return position_ids.unsqueeze(0).expand(input_shape) + + +class RotaryEmbedding(torch.nn.Module): + """ + Rotary position embeddings based on those in + [RoFormer](https://huggingface.co/docs/transformers/model_doc/roformer). Query and keys are transformed by rotation + matrices which depend on their relative positions. + """ + + def __init__(self, dim: int): + super().__init__() + # Generate and save the inverse frequency buffer (non trainable) + inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2, dtype=torch.int64).float() / dim)) + inv_freq = inv_freq + self.register_buffer("inv_freq", inv_freq) + + self._seq_len_cached = None + self._cos_cached = None + self._sin_cached = None + + def _update_cos_sin_tables(self, x, seq_dimension=2): + seq_len = x.shape[seq_dimension] + + # Reset the tables if the sequence length has changed, + # or if we're on a new device (possibly due to tracing for instance) + if seq_len != self._seq_len_cached or self._cos_cached.device != x.device: + self._seq_len_cached = seq_len + t = torch.arange(x.shape[seq_dimension], device=x.device).type_as(self.inv_freq) + freqs = torch.outer(t, self.inv_freq) + emb = torch.cat((freqs, freqs), dim=-1).to(x.device) + + self._cos_cached = emb.cos()[None, None, :, :] + self._sin_cached = emb.sin()[None, None, :, :] + + return self._cos_cached, self._sin_cached + + def forward(self, q: torch.Tensor, k: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: + self._cos_cached, self._sin_cached = self._update_cos_sin_tables(k, seq_dimension=-2) + + return ( + apply_rotary_pos_emb(q, self._cos_cached, self._sin_cached), + apply_rotary_pos_emb(k, self._cos_cached, self._sin_cached), + ) + + +# Copied from transformers.models.bert.modeling_bert.BertPooler +class RnaFmPooler(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.activation = nn.Tanh() + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + # We "pool" the model by simply taking the hidden state corresponding + # to the first token. + first_token_tensor = hidden_states[:, 0] + pooled_output = self.dense(first_token_tensor) + pooled_output = self.activation(pooled_output) + return pooled_output + + +class RnaFmLMHead(nn.Module): + """RNAFM Head for masked language modeling.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + if isinstance(config.hidden_act, str): + self.transform_act_fn = ACT2FN[config.hidden_act] + else: + self.transform_act_fn = config.hidden_act + self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + + self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + self.bias = nn.Parameter(torch.zeros(config.vocab_size)) + + def forward(self, features, **kwargs): + x = self.dense(features) + x = self.transform_act_fn(x) + x = self.layer_norm(x) + + # project back to size of vocabulary with bias + x = self.decoder(x) + self.bias + return x + + +class RnaFmContactPredictionHead(nn.Module): + """Performs symmetrization, apc, and computes a logistic regression on the output features""" + + def __init__( + self, + in_features: int, + bias=True, + eos_idx: int = 2, + ): + super().__init__() + self.in_features = in_features + self.eos_idx = eos_idx + self.regression = nn.Linear(in_features, 1, bias) + self.activation = nn.Sigmoid() + + def forward(self, tokens, attentions): + # remove eos token attentions + eos_mask = tokens.ne(self.eos_idx).to(attentions) + eos_mask = eos_mask.unsqueeze(1) * eos_mask.unsqueeze(2) + attentions = attentions * eos_mask[:, None, None, :, :] + attentions = attentions[..., :-1, :-1] + # remove cls token attentions + attentions = attentions[..., 1:, 1:] + batch_size, layers, heads, seqlen, _ = attentions.size() + attentions = attentions.view(batch_size, layers * heads, seqlen, seqlen) + + # features: batch x channels x tokens x tokens (symmetric) + attentions = attentions.to( + self.regression.weight.device + ) # attentions always float32, may need to convert to float16 + attentions = average_product_correct(symmetrize(attentions)) + attentions = attentions.permute(0, 2, 3, 1) + return self.activation(self.regression(attentions).squeeze(3)) + + +class RnaFmClassificationHead(nn.Module): + """Head for sentence-level classification tasks.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + self.out_proj = nn.Linear(config.hidden_size, config.num_labels) + + def forward(self, features, **kwargs): + x = features[:, 0, :] # take token (equiv. to [CLS]) + x = self.dropout(x) + x = self.dense(x) + x = torch.tanh(x) + x = self.dropout(x) + x = self.out_proj(x) + return x + + +def rotate_half(x): + x1, x2 = x.chunk(2, dim=-1) + return torch.cat((-x2, x1), dim=-1) + + +def apply_rotary_pos_emb(x, cos, sin): + cos = cos[:, :, : x.shape[-2], :] + sin = sin[:, :, : x.shape[-2], :] + + return (x * cos) + (rotate_half(x) * sin) + + +def symmetrize(x): + "Make layer symmetric in final two dimensions, used for contact prediction." + return x + x.transpose(-1, -2) + + +def average_product_correct(x): + "Perform average product correct, used for contact prediction." + a1 = x.sum(-1, keepdims=True) + a2 = x.sum(-2, keepdims=True) + a12 = x.sum((-1, -2), keepdims=True) + + avg = a1 * a2 + avg.div_(a12) # in-place to reduce memory + normalized = x - avg + return normalized + + +def create_position_ids_from_input_ids(input_ids, padding_idx, past_key_values_length=0): + """ + Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols + are ignored. This is modified from fairseq's `utils.make_positions`. + + Args: + x: torch.Tensor x: + + Returns: torch.Tensor + """ + # The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA. + mask = input_ids.ne(padding_idx).int() + incremental_indices = (torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length) * mask + return incremental_indices.long() + padding_idx